l\CC- \9^
THE FEDERAL DEMOCRATIC R EPUR LIC OF ETHIOPIA
MINISTRY OF WATER RESOURCES
Arjo-Dedessa Irrigation Project
Feasibility Study Report
VOLUME - III
Water Resources
VOLUME - III (a)
Annexure - 7 Meteorological And Hydrological Studies
Annexure - 8 Hydrogeological Studies
May. 2007
Addis Ababa
WATER WORKS DESIGN & SUPERVISION ENTERPRISE
IN ASSOCIATION WITH
1MTERCQNHNENTAL CONSULTANTS AND TECHNOCRATS INDIA PVT LTD.
P.O Box 2561 Addis Ababa Ethiopia
Tcl:(251 >1 614501/631X90 Fax.(1251)1 6l5371E-inail. w w d s c tHclecoin.ncl.clARJO-DEDESSA IRRIGATION PROJECT
FEASIBILITY STUDY REPORT
CONTENTS OF THE REPORT
SERIAL NO.
VOLUME NO.
PARTICULARS
1
2
3
EXECUTIVE SUMMARY
MAIN REPORT
Part I - Report
Part II - Maps & Drawings
ANNEXURES
VOLUME-I
SURVEY AND INVETIGATION (ANNEXURES - 1 to 3)
4
Volume -1 (a)
Topographic Survey, Geomorphological Studies &
Geological & Geotechnical Investigation
Part I - Report
5
6
Volume -1 (b)
Part II - Appendices
VOLUME -II
NATURAL RESOURCES (ANNEXURES - 4 to 6)
Forestry, Energy & Catchment Development Plan
VOLUME - III
WATER RESOURCES (ANNEXURES - 7 to 11)
7
8
9
10
11
12
13
14
15
16
Volume - III (a)
Meteorological & Hydrological & Hydrogeological Studies
Dam & Appurtenant Works
Part I - Report
Volume - III (b)
Part II - Drawings
Irrigation & Drainage
Volume-III (c)
Part I - Report
Part II - Drawings
Hydraulic Structures
Volume - III (d)
Part I - Report
Part II - Drawings
VOLUME IV
AGRICUTLRUE (ANNEXURES - 12 to 18)
Volume - IV (a)
Soil Survey & Land Evaluation
Volume - IV (b)
Agricultural Planning
Volume - IV (c)
Livestock, Fisheries, Agricultural Mechanization &
Agricultural Marketing
VOLUME-V
ENVIRONMENTAL AND SOCIO - ECONOMIC ASPECTS
(ANNEXURES-19 to 25)
17
18
Volume - v (a)
Environment, Health & Socio-economic Aspects
Volume - v (b)
Organization & Management, Physical Infrastructure
Resettlement, Financial & Economic AnalysisAnnexure - 7
METEOROLOGICAL AND
HYDROLOGICAL STUDIESArjo-Dedessa Irrigation Project Meteorological and Hydrological Aspects
May 2007
Table of Contents
TABLE
LISTOFTABLES-HI
LIST OF ANNEXUREIV
OF
CONTENTS
LIST OF FIGURES...................................................................................................................... V
1. INTRODUCTION
1.1
The Nile Basin
1.2
abbay Basin
i .3
The Dedessa Catchments
2. REVIEW OF PREVIOUS STUDIES
2. l        Lahmayer 1962 Study
2.2         Land        and        Water        Resources        of        the        Blue        Nile        Basin        Ethiopia 2.3         Country wide Master Plan studies - EVDSA/WAPCOS (1988-90)
.8
2.4         Study by BCEOM - in association with ISL and BRG 1999 ..................................................................... 9
2 5       Study by Ministry of Water resources for Lower Dedessa (2001)
2.6       Utility of Earlier Studies
3. DATA BASE
3.1         Climatological Data
15
16
17
3.2.        Rainfall................................................................................................................................................ 17
3.3.        River Discharge17
3.4 SEDMENTDATA'8
4. ESTIMATION OF REFERENCE EVAPOTRANSPIRATION19
4.1         INTRODUCTION19
4.2         Meteorological Data
4.2.1      Meteorological Observation Stations 
4.2.2      Air temperature 21
4.2.3      Air humidity22
4.2.4      Wind speed23
4.2.5      Solar radiation 23
4.3         Penman-Monteith Equation
Arjo Didesa Project Area Arjo Dedessa Project Area
5. RAINFALL ANALYSIS
5.1         Introduction
5.2         Rainfall Internal and External Consistency
5.3         Estimation of Rainfall Reliability Level
5.4         Estimation of Maximum Rainfall Frequencies
20
20
24
30
6. MONTHLY STREAMFLOW ANALYSIS........................................................................................................................ •
6.1         Hydrological Observation Stations•
6               2               Extension of records2
6.3        Synthetic flowst
7         ESTIMATION OF FLOODS FOR UNGAUGED CATCHMENTS!
7.1         Rational Method
7.2         SCS Method:
7.3         Transferring Gauged Data ..................................................
7.4         Estimation of weighted average
7.5         Synder Methodof Constructing Synthetic Hydrograph
8         FLOOD FREQUENCY ANALYSIS
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8 1       Introduction59
8.2         Estimation of Probability weighted Moments60
8.3         Generalized Extreme Value Distribution61
8.4         Log-Logistic distribution61
8.5         regionally averaged quantiles62
8.6         Choice of Distribution63
May 2007
9. 
PROBABLE
MAXIMUM
FLOOD65
10 SEDIMENT ANALYSIS69
10.
l                Estimation of Sediment yield69
10.2     Distribution of sediment in the reservoir70
11.     WATER QUALITY73
12.      DESIGN FLOOD.......................................................................................................................................................75
12.1      Synthetic Hydrograph75
12.2      Flood Routing Through the Reservoir75
12.3      Routed Design Flood at the Coffer Dam78
13.      RESERVOIR SIMULATION87
14.      DEDESSA SUB-BASIN MODELING91
14.1. Modeling for arjo Dedessa dam for irrigation 14.2The sub basin modeling
14.2.1       Power Generation in Dedessa Dam93
J 4.2.2       Optima! Power Generation 94
14.2.3         Reduction In flows at Dedessa Sub-basin 96
14.2.4         Conclusions96
15.      RECOMMENDATIONS ON TRAINING NEEDS101
91
92
REFERENCES
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May 2QQ7
LIST OF TABLES
Table l.l:
Break up of Abbay Basin Area4
Table 4.1:
Table Details of Meteorological Observation Stations located in
and around the
PROJECT AREA
Table 4.2:
Types of Climatic Data available at the various Meteorological O
bservation
Stations....................................................................................................
................................
26
Table 4.3:
Summary of Meteorological Characteristics at Project Area
Table 4.4:
Summary of Estimated Mean ETo of Arjo Dedessa Project Area.
..... 28
Table 4.5:
Magnitude of ETo at given non-exeedance probability level...............
.................................. 29
Table 5.1:
Cross Correlation Matrix of Annual Rainfall36
Table 5.2:
Mean Monthly Rainfall36
Table 5.3:
Parameter Estimates of Weibull Distribution36
Table 5 4:
Monthly Rainfalls Corresponding to Different Reliability Levels
37
Table 5.5:
Parameter Estimates of Half-Monthly Rainfall
37
Table 5.6:
Half-Monthly Rainfall Magnitudes at Different reliability Levels
.38
Table 5.7:          Half-Monthly Rainfall Magnitudes in Percent of the Mean Corresponding to
Different
Reliability
Levels39
Table 5.8:
Maximum Annual 24-hr Rainfall40
Table 5.9:
Maximum Rainfall Magnitudes and Frequencies40
Table 5.10:
Maximum Rainfall Magnitudes for Higher Durations41
Table 6.1:
Availability of Streamflow Data in the Region
48
Table 6.2:
3
Mean Monthly Flow (in Mm /sec)
.48
Table 6.3:
Coefficient of Variation of Monthly Discharges
48
Table 6.4:
Summary of Regional monthly flow characteristics 49
Table 6.5:
Table Statistics of monthly flows at Arjo Dedessa dam site 49
Table 6.6:
Reliability Level Vs Monthly Streamflows at Dam Site49
Table 6.7:
Characteristics of Random Numbers Used for Flow Generation 50
Table 8 1:
Estimated Flood Quantiles Based on GEV Distribution
64
Table 8.2:
Estimated Flood Quantiles Based on LLG Distribution 64
Table 9.1:
Table Cumulative Volume of Probable Maximum Flood67
Table 9.2:
Design Flood Hydrograph Derived from Estimated PMF 68
Table 10.1:
Relationships between water discharge and sediment load 70
Table 10.2:
Monthly Total Sediment Load at the Reservoir Site 71
Table 10.3:
Accumulated Total Sediment Load at the Reservoir Site
..
.
71
Table 10.4:        Distribution of Sediment in the Arjo Dedessa Reservoir72
Table 11.1:        Water quality results for sites on the Dedessa river 74
Table 12.1:        Catchment Characteristics80
Table 12.2:        Routed    Design Flood at FRL Corresponding to 1352 m...
Table 12.3:        Routed    Design Flood at FRL Corresponding to 1555 m
Table 12.4:        Routed    Design Flood at FRL Corresponding to 1356m
Table 12.5: Routed Design Flood at Coffer Dam of Arjo Didessa Reservoir
Table 13.1:        Irrigation Water Requirements for Various Scenarios 88
Table13.2:        Reservoir Characteristics at Dead Storage Level
Table 13.3:        Storage Reservoir Characteristicsand Reliability Levels
.80
81
82
83
89
.90
Table  14.1:  Total   Monthly   Release   (Irrigation   plus   Power)  options   for   various  FRL  values
CONSIDERED
Table 14.2:        Annual Energy generations at Dedessa dam for the various options 97
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LISTOF ANNEXURE
Annex A:
RESULTS OBTAINED FROM THE METEOROLOGICAL ANALYSIS-..............................................
A1:
A2:
A3:
A4:
A5:
A6:
A7:
A8:
A9:
Estimated Mean Temperature at Arjo Dedessa Project Area (in C)q
Estimated Minimum Temperature at Arjo Dedessa Project Area (in C)
Estimated Maximum Temperature at Arjo Dedessa Project Area (in C)
Estimated Relative Humidity at Arjo Dedessa Project Area (in percent).
Estimated Mean Daily Sunshine Duration at Arjo Dedessa Project Area
Estimated Mean Daily ETo of Arjo Dedessa Project Area
Annex B:
B1:
B2:
B3:
B4:
B5
B6:
B7:
B8:
Estimated Mean Monthly ETo of Arjo Dedessa Project Area
Estimated Mean Monthly Rainfall at Arjo Dedessa Project Area
Estimated Mean Half-Monthly Rainfal at the Project Area (in mm/hr)
Results obtained from the Hydrological Analysis
Estimated Monthly Flows at Arjo Dedessa Dam Site
Regional monthly coefficient of variations (CV)
Regional monthly coefficient of skewness
Annex C:
16 Regional monthly coefficient of correlations (R)
C1:
C2:
C3:
C4:
C5:
C6:
Standardized probability weighted moments
Mean Monthly Total (Suspended and Bed) Sediment Load
Eleveation-Area-Capacity Relationships of Arjo Dedessa Reservoir
Sample Simulation Results of Arjo Dedessa Reservoir
Standards
Commonly used values of runoff coefficients
Annex D:
Runoff coefficient for pervious surfaces by selected hydrologic soil
D1:
D2:
D3:
D4:
D5:
D6:
SCS Curve Numbers for Various Conditions1
Reservoir Flood Standards
Ratios of the basic dimensionless hydrograph of the SCS
Guidelines for Interpretations of Water Quality for Irrigation
Meteorological Data
Details of Meteorological Observation Stations
D7:
D8:
D9:
Annex E:
E1:
E2:
E3:
E4:
E5:
Types    of    Climatic    Data    available    at    the    various    Meteorological
Mean Monthly Rainfall at Bedele Station
Mean Monthly Rainfall at Jimma Station
Mean Monthly Rainfall at Dedessa Station
Mean Temperature at Bedele Station .......
Relative Humidity at Bedele Station (in%)
Wind Speed at Jimma Station
Sunshine Duration at Bedele Station .................................................................
Hydrological
List of Selected Hydrological Observation Stations
Mean Monthly Streamflow at Dedessan near Arjo Station Mean Monthly Stream flow at Dedessan near Dembi Station Annual Maximum Floods
Measured Sediment Concentration at Gumara Gauging Station ..
Data
IV
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LIST OF FIGURES
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Figure 4.1:        mean Monthly Temperature at Figure 4.2: Mean Monthly Relative Humidity at............ 30
Figure 4.3:        Mean Monthly Wind Speed at Figure 4.4: Mean Monthly Sunshine Hours at................ 30
Figure 4.5         Mean Monthly ETo at Arjo Dedessa Project Area............................................................ 31
Figure 5.1(a):   Trend Analysis of Annual Rainfall at Jimma Station......................................................... 42
Figure 5.1(b):   Trend Analysis of Annual Rainfall at Bedelle Station.................................................... 42
Figure 5.1(c):   Trend Analysis of Annual Rainfall at Dedessa Station.................................................... 43
Figure 5.2(a):   Single Mass Curve of Jimma Rainfall................................................................................. ...43
Figure 5.2(b):    Single Mass Curve of Bedelle Rainfall................................................................................ 44
Figure 5.2(c):   Single Mass Curve of Dedessa Rainfall............................................................................... 44
Figure 5.3:         Mean Monthly Rainfall at Arjo Dedessa Projetct Area.................................................. 45
Figure 12.1:       Elevation-Area-Capacity Curves of Arjo Dedessa Reservoir
84
Figure 12.2:       Inflow and Outflow Flood Hydrographs at Arjo Dedessa Reservoir.......................... 84
Figure 12.3:       Inflow and Outflow Flood Hydrographs at Arjo Dedessa Reservoir ......................... 85
Figure 12.4:       Average Hydrograph of Maximum flows at Arjo Dedessa Dam Site............................... 86
Figure 14.1:       Power Duration Curves............................................................................................................. 98
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1. INTRODUCTION
1.1    The Nile Basin
May 2007
Having a length of 6.825 km., the Nile River takes the rank of number one with respect to
length, in the world. It drains a total drainage area of 2.96 M.Km , with its annual total flow
2
of about 84 Bm . As one could appreciate,
3
the discharge per Km2 is small when compared
to other large rivers of the world. From the Lake Victoria, to the Mediterranean Sea, the river crosses very many different areas associated with different climate, relief and geology. The main sources of the river are the equatorial lake plateau and the Ethiopian Highlands. The three major tributaries which emanate from the Ethiopian Highlands are, from south to the north, the Baro Akobo, the Blue Nile, familiarly known as Abbay and the Tekeze -Atbara System.
1.2    Abbay Basin
The Abbay Basin is one of the most important basims, in Ethiopia. It accounts for about 17.5% of Ethiopian land area, 25% of its population and 50% of its annual average surface water resources. In the Lake Tana, it has the country’s largest fresh water lake, covering an
extent of
3000 km . The Abbay has an average annual
2
run off of about 50 B m3. The
rivers of Abbay contribute on an average, 62% of Nile river flows in Aswan dam.
The climate of the Abbay Basin is dominated by two features, namely, it’s near equatorial location and in altitude range between 590 m.amsl. to 4000 m. amsl. These influences result in rich variety of local climates, ranging from hot/desert like, along the Sudan border to the temperate type on the high plateau with depiction of cold in the high mountains. The Basin can g enerally be d escribed a s t emperate a t h igher e levation a nd tropical a t lower elevations. However due to the distinctive aspects of the highland climate, it is perhaps better to describe it using the local climatic zones, that have bean established with elevation (and resultant temperature) as controlling factors.
The Qolla zone lies below 1800 meters and has annual temperature range from 20°C to 28°C. The Woina Dega zone lies between 1800 meters and 2400 maters and has annual average temperature ranging from 16°C to 20°C. The Dega zone, above 2400 meters has average annual temperature ranging from 10°C to 16°C. The major portion of the population inhabit in the more climatically pleasant and healthier upper two zones, which leaves the lower Qolla zone sparsely populated.
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There are mainly 3 rainy seasons. The main one (kiremt) lasts generally from June to September, during which, the south west winds bring rains from the Atlantic Ocean. About 70% to even up to 90% of the rainfall occurs during this season. This season obviously is associated with minimum levels of sunshine, low variations in daily temperature and high relative humidity. A dry season (Bega) lasts from October to January, during which clear skies are associated with maximum sunshine hours, high daily temperature variation and low relative humidity. F inally the m inor rainy season (Belg) lasts from February to May, during which south east winds  bring the small rains  from the Indian Ocean. The temperature range is very high in this season. The precise timing and duration of these seasons vary considerably depending upon location within Abbay Basin, and to some lesser extent, year to year as well.
Generally speaking, the low altitude depicts the pattern of 11.5 to even 12.5 hours of sunshine and temperature varying only for a small range from 3°C to 7°C through out the year.
The mean annual rainfall over the entire basin could be reckoned at 1400 millimeters; where as the mean evapotranspiration is about 1 300 millimeter. Broadly, based on the rainfall pattern, 4 different areas have been identified by the earlier Master Plan study (Abbay River Basin Integrated Master Plan Project - BCEOM in association with BRGM, ad ISL consulting Engineers - 1998). These are:
i)    Southern area covering East and West Wellega, Jimma and lllubabor Zones with relatively high rainfall (1400-2200 millimeters) and a long wet season.
ii)   A central area covering most of Western Gojam and parts of neighboring zones as well, characterized by relatively high rainfall (1400 - 2200 millimeters). But there is a more pronounced seasonal pattern associated with short wet and longer dry seasons.
iii)  An area covering most of the eastern part of the basin including North and South Wello, Eastern part of East Gojam and South Gondar and much of North and North West Shewa region, excluding small proportion of mountain areas. This area is characterized by relatively low rainfall (less than 1200 millimeter) distributed in both the rainy seasons, depicting bimodality.
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iv)         The West and North West area covering North Gondar region. The rainfall is about 1200 millimeters, falling predominantly, in the main rainy season associated with high average rates of evapotranspiration.
With respect to the hydrographic scenario, the Basin is dominated by Abbay River which rises in the center of the catchment and develops its course in a clock wise spinal. It collects tributaries all along its 992 km. journey up to Sudan border. As said earlier, the Abbay Basin catchment area of 199,812 km2 is encompassing the break up as below:
As Gilgel Abbay, the main river flows north from its source near Sekela into the wide and shallow Lake Tana. The lake also receives other tributaries from a catchment of 15,054 km2 including tributaries Megech, Ribb and Gumara. Shortly after leaving the lake, the river reaches the celebrated Tis Abbay falls, thereafter flowing in a deep and rugged gorge on its way to Sudan Border. The break-up of Abay basin catchment area is available in Table 1.1.
1.3    The Dedessa Catchments
The Dedessa River is the largest tributary of the Abbay in terms of the volume of water contribution to the total flow of Abbay at the Sudan border. Draining nearly an area of 34,000 square kilo meters, the Dedessa river originates in the Mt.Vennio and Mt.Wache ranges, flowing in an easterly direction for about 75 kilo meters, then turning rather sharply to the north until it reaches the Abbay River. The major tributaries of the Dedessa river are the Wama, entering from the east, the Dabana from the west, and the Angar from the east.
Dedessa Catchment is situated in the south-west part of Abbay Basin. The catchment area at a gauging station near Arjo town is 9,981 km . The climate of the Dedessa Catchment results  from its  location and elevation (1220 to 3012 meters). The catchment is characterized by mountainous, highly rugged and dissected topography with deep slopes. The lowest part of the catchment is characterized by valley floor with flat to gentle slopes.
Most of the rainfall in the Dedessa Catchment is concentrated in the June to September period with virtual drought from November through February. Annual totals, average from less than 150 centimeters to more than 200 centimeters. The Dedessa catchment, besides reflecting a marked rainfall increase with higher elevation, also receives heavier annual quantities than most of the catchments in the Abbay basin. Examination of the rainfall records from this area shows that this is due to a longer (May through October) rainy season rather than heavier maximum monthly quantities
2
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The mean annual flow of Dedessa river at Arjo station is about 3,800 million m3 having its maximum flow in August and September (52 percent of the annual) and minimum flow in February and March (less than 1.5 percent of the annual).
Further  vivid  description  of  the  climatic  factors,  rainfall  and  hydrological  parameters  are made in the ensuing respective sections of this report.
Table 1.1:       Break up of Abbay Basin Area
System Tributary
Joining
From
Catchment
Area (Km )
2
Lake Tana — 15,054
North Gojam Right side 14,389
Beshilo
Weleka
Left side                 13,242
u
Jemma
u
South Gojam                   Right side
Muger
Guder
Fincha
Dedessa
Angar
Wonbera
Dabus
Beles
Dinder
Left side
M
it
U
M
Right side
Left side
Right side
—
Rahad                                    —                        8,269
6,415
15,782
16,762
8,188
7,011
4,089
19,630
7,901
12,957
21,032
14,200
14,891
Total
199,812
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2. REVIEW OF PREVIOUS STUDIES
May 2007
Because of the importance of the Abbay, very many studies had been carried out in the past, concerning the basin. Though, there was  not any  specific  study  for the development of Dedessa sub basin exclusively, all the studies under taken, pertaining to Abbay or the country as  a whole, spoke of Dedessa development. Hence, though such a review has  to be more exhaustive, it is  necessary  to undertake such a review, before attempting Dedessa sub basin development especially  the aspects  connected with hydrology  in this  sectoral study. Such a review is presented in the following sub paragraphs.
2.1 Lahmayer 1962 Study
This  was  mainly  concerned with Gilgel Abbay  Scheme. Although, it was  not having adequate data base, the study  provided interesting information about Gilgel Abbay  and its  tributaries, which could be considered as a data base for review and up dating in the light of the raw data.
2.2 Land and Water Resources of the Blue Nile Basin Ethiopia
The study  was  under taken by  the United States  Department of the Interior Bureau of Reclamation during 1958 - 1964. This  detailed study  on land and water resources development of the Abbay  basin is  worth mentioning important study. Even at a time, when there was a little or no data, very exhaustive analysis of the available data had been carried out to establish hydrological studies. In fact, the pioneering works undertaken during that time, has made Abbay  basin in Ethiopia, to be proud of a possessing a good net work  of hydrometric stations  for making reliable estimates. The hydrological aspects  have been covered in Appendix  III of the report. In 4 different sections  of this  Appendix, the various  facets  of hydrological analysis have been put forth as below:
Section I:       A  general  description  on  climate,  availability  of  water,  sedimentation  rates and possible irrigation requirements.
Section II:      A presentation of the status of historical stream flow and their elaborations.
Section III:     A presentation on flood flows and the analysis attempted Section IV:    A presentation on water use.
s
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In the Section I, on giving an account of the synoptic and climatic situations, the 22 stations with long data have been analyzed for precipitation and temperature. The basin isotherm and precipitation maps  have been prepared based on above analysis. The water availability aspects  have been also addressed in this  Section I. The samples  analyzed at 5 stations indicated a I ow sodium h azard; m ost of them s howed I ow t o m edium s alinity h azard, some samples  contained from none to a maximum of 0.2 ppm of Boron, which was  found to be tolerable by  most sensitive crops. The samples  showing highest salinity  were from Mugger and Dinder. With respect to sedimentation, ratings  were established at 4 stations  and based on extrapolation; the sediment rate was computed at all the identified potential dam sites. The pattern of sediment distribution in reservoirs, expected after 50 years of useful life was adopted uniformly  i n a water balance type of simulation, for establishing the success  of each of the identified projects. In the section, 3 regions  have been identified for development with respective crop projections, cropping calendar and crop water requirement. The water requirements varied from 782 millimeters in Gilgel Abbay to 1375 millimeters in Dinder area.
The section II has addressed the stream flow development for locations of the identified dam
sites by possible best set of analysis available at the time. In the study period 59 gauging
stations were established, including 14 stations with automatic stage records. By the various 
analysis including graphical rainfall runoff correlations, the flow series were built up at all dam locations and at salient locations. The annual mean flows at Sudan border was estimated at
50 Bm3.
For the Dedessa at Arjo, the correlated flows were established for 8 years (1911 to 1917 and 1932). The mean annual flow estimate was  4332.82 Mm3 (catchment area 9486 Square kilo meters)
For the Dabana at Abasina, the mean annual flow was established at 1757.26 Mm3  (catchment area 3080 Square kilometers)
For  the  Angar  near  Nekemte,  the  flow  estimate  at  annual  mean  level  was  2523  33  Mm3 (Catchment area 4350 square kilometers)
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3
For the proposed Dedessa dam, with a catchment area of 3360 square kilo meters, the flow was estimated to be 1533.75 Mm . Similarly, for the dam site at Dabana, with a catchment area of 2654 kilo meters, the annual flow at mean level was  estimated at 1515.63 Mm . For the Angar, 2 dam sites had been identified, one at a location with a catchment area of 1780 square kilo m eters (AG-2 D am) a nd a nother a 11 ocation with a c atchment a rea o f 4 523 s quare kilo meters  (AG-6 Dam). The mean annual flows  estimated respectively  at these two locations were, 983.125 and 2251.26 Mm3.
The section III, addresses the causative factors of floods in Abbay. The design rainfall for the basin was  estimated based on 6 representative station’s  data. These are presented below,
which are forming a data base, worth for review and up dating for adoption.
1 day design rainfall
=
87 Milli meters
2 day design rainfall
=
126   “
5 day design rainfall
=
185   “
10 day design rainfall
=
251   “
15 day design rainfall
=
321   “
3
3
The design flood was  estimated by  physical approach using unit hydrograph, storm analysis and design flood hydrograph convolution for 2 1 dam sites. For the others, flood frequency analysis based statistical return period floods were estimated.
For the Dedessa DD-2 reservoir, the PMF was computed as 5390 m /sec with a base period of 99 hours. For the Dabana storage reservoir, the PMF estimate was at 4860 m /sec with a base period of 91 hours. For the Angar-2 reservoir with a catchment area of 1780 square Kilometers, the PMF and the duration of the hydrograph were 3970 m /sec and 80 hours respectively. For the Angar-6 reservoir, the above respective figures were 6180 m /sec and 111 hours.
For the Dedessa storage dam, the 5, 10, 25, 50,100, year floods were estimated to be 1700. 1900, 2100, 2300 and 2400 m /sec respectively. These formed a good data base for review in the present study.
3
3
3
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The section IV, as  indicated in the beginning deals  with the water use studies. The 1960 water use status  was  reviewed along with such water uses  in villages. Then, the run off estimates have been adopted considering the envisaged irrigation and power developments  in respective simulation study. Due to Dedessa dam USBR study, estimated a reduction of about 139 M m3 in Dedessa flows to the Abbay..
On the whole, this USBR study is a useful one for the basin as a whole, though with paucity of data, because of its systematic treatment of hydrological and simulation aspects.
2.3    Country wide Master Plan studies — EVDSA/WAPCOS (1988-90)
These studies  were carried out during 1988-90 for country  wide water and land resources development, covering all river basins. These were primarily  desk  studies  for identifying potential irrigation and hydropower sites  based on country  wide data collection and analysis, coupled with detailed map studies. The study  was  addressing the Abbay  basin also, along with other basins. The USBR studies  were reviewed in detail for the Abbay  basin, and subsequently  this  study  came out with modifications  in the USBR sites  in some cases  and proposed many  other sites  as  well. Though desk  study, the data base available to this  study, on hydrological and hydro meteorological aspects  were fairly  adequate, because of the earlier pioneering works by USBR in establishing a good net work of such data observation stations.
In this  study, the rainfall as  well as  run off data for the vicinity  of the identified projects  were collected and analyzed even though elaborate consistency  checks  were not under taken. The data gaps  filling and extrapolation of data have been done using statistical regression analysis, using mostly  bi-variate linear as  well non linear correlations. In certain major dams, synthetic data generation by single site multi season stochastic models as well has been under taken.
With respect to floods, the flood frequency  analysis  of the annual maximum flood series  has been carried out for the observation stations, in the vicinity  of all identified potential dam sites The EV1 distribution with the method of maximum likelihood fitting has  been followed in the above flood frequency  analysis. On deciding the appropriate design criteria for the inflow design flood for each identified dam site, the flood peaks  of the nearest gauging station have been transferred to the dam site by  empirical formula. For deriving the shape of the complete design inflow hydrograph at the dam site, the procedure as indicated below has been followed
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For the nearest gauging station, the observed maximum flood hydrograph were isolated. For these a relationship between time and Q/QP ratio were established (t against Q/QP) where, t represents  time, Q represents  the discharge at't’ and QP is  the peak  discharge. Using this pattern of relationship the dam site hydrograph has  been developed using the peak  as estimated by flood frequency analysis.
Simulation studies  have been systematically  carried out to judge the success  of a scheme at criteria based reliability  levels. Climatic  data other than rainfall have also been documented, though without detailed processing or elaborations. The sediment rates  as  quoted by  various previous  studies  of different basins  have been adopted and used in simulation studies, considering life expectancy of projects as 50 years.
The data base on the geometry  of identified studies  could be considered to be only approximate, as  were based on purely  topographic  maps. The hydrological and hydro meteorological data were also not put into rigorous  analysis, which is  called for design of identified projects. However, this study along with USBR study back up gave a very good data base/ inventory  of all possible exploitable dam sites  in Abbay  basin. This  EVDSA/WAPCOS study has carried out such exercise in all the other river basins of the country as well.
The dams  identified in the Dedessa sub basin by  this  study  is  the Arjo Dedessa irrigation project. This  proposal envisaged a dam, a chute spillway, an irrigation outlet with in take structure and a canal taking off on the right side. The project envisaged irrigation over 139,000 hectares  with 160% irrigation intensity. The inflow in a 75% reliable year was  estimated to be 776.80 Mm3. The 10,000 year flood with a peak  of 890 cubic  meters  per second was  getting moderated to an out flow hydrograph of peak  of 642 cubic  meters  per second. The dead storage was fixed as 93 Mm , on the basis of annual rate of around 1.9 Mm .
2.4    Study by BCEOM - in association with ISL and BRG 1999
The  study  entitled  “Abbay  River  Basin  integrated  development  Master  Plan  Project"  has  been carried out with the following water resources oriented objectives.
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3
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•     Preparation of the
river basin development
m
aster plan that will guid
e the development
of  the  resources  of
the  basin  potentially  wit
h
respect  to  occurrence,
distribution,  quality 
and quantity of water resources for the coming 30 to 50 years.
•      To   prepare   water   allocation   and   utilization   plans   under   alternative   development scenarios  and  to  generate  data,  information  and  knowledge  that  will  contribute  to  the future water allocation negotiations with downstream countries.
The hydrological and hydro meteorological studies are more relevant for review in this section. These aspects have been covered in Section II, volume III of the Master Plan Study report.
The basic  climatic  features and the climatological data of the Abbay river basin are discussed first. The rainfall data procured by the study was for 173 stations; the data length varied from 3 to 40 years, with associated monthly  gaps. The data for other climatic  factors  were available for 108 stations. The field verification of these stations  and equipments  has  also been under taken. The data is  said to have been subjected to analysis  and some errors  connected with observations  or processing were identified. The study  has  perhaps  not attempted to improve the quality  of such data on the plea that it was  outside the consultant's  capacity  or responsibility. As  such, the data base after screening and omitting such erroneous  data is stated to be good.
With  respect  to  Hydrology  in  the  part  2  of  the  above  volume  III  the  following  aspects  have been covered systematically.
•    Basic Hydrographic Features
•    The Hydrometric net work
•    Review of previous studies
•    Data collection and Analysis
•    Sectoral methodology
In the basic hydrographic aspects, a good work of preparing a map of the basin has been presented dividing the basin in to reasonably homogeneous areas, named Basin Units representing each of the catchment of one tributary or of several minor ones., with similar behaviors. With respect to the hydrometric net work, it has been considered to be adequate as 
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per WMO norms, taking in to consideration 116 effective stations. The list of station, their locations, and drainage areas  controlled have been well documented in T  ables, appendices and maps. The master plan has found that the spatial distribution of the stations is not even or equitable. The parts  of Beshilo, Weleka and Jemma were not adequately  represented. Based on visit to all accessible observation stations, the master plan could identify.
•     In many cases, those stations which have been abandoned are not worth reconstruction either  they  had  not  been  installed  at  optimal  locations  or  the  catchment  area  controlled by them was too small.
•    The numbers of automatic water level recorders still operational at the study time were
low,  though  most  of  the  stations  had  been  equipped  with  such  recorders  at  the  time  of their installations.
•    In many gauges stations the cables of bank operated cable way were missing.
•     On  the  other  hand  most  staff  gauges  were  in good  condition  and  water  level  data  was considered to be quite reasonable.
There  upon  recommendations  have  been  spelt  out  to  be  executed  as  and  when  budget, equipment and man power are made available.
In the first observation, it is felt that what is meant as optimum could have been indicated. As far as  hydrological net works  are concerned, optimality  varies  depending upon the purpose of the net work. It could be a general purpose net work or could be a specific purpose net work. It is  not clear that what is  the master plan ascribed optimality. Further, since because a gauging station controls  a small catchment, it seems  they  have been ignored by  the master plan study.
Measurement of discharges have a multipurpose oriented utilities like, ascertaining the
sensitive or non sensitive areas, flood rate estimation or for a specific purpose including
community based water harvesting schemes etc. Hence, the approach of ignoring stations 
with small catchments does not seem to be based on analysis of vivid concepts.
Subsequently,   the   Master   Plan   has   made   a   very   brief   review   of   hydrological   aspects   of previous studies. Again it is felt that a Master Plan of the magnitude could have reviewed in a
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more elaborate manner. The review could have brought out each aspect of hydrological and hydro meteorological and simulation studies  attempted by  the earlier studies. Only, such detailed review would hare formed the datum to appreciate the further studies taken up by the Master Plan.
Next, the data collection and analysis part is discussed in the report. The data sets processed were mean daily  levels, discharge measurements  and suspended sediment concentration data. With respect to data processing, the major study  carried out seems  to be review and reestablishing rating curves. The details  on very  many  hydrological analysis, which should have been carried out before final use of he data in basin simulation are not finding a place in the hydrology volume (not explicitly described).
With respect to rating curve analysis, detailed exploration of the discharge data of 121 stations, have been made and equation of the form
Q = a (H-H )
o
b
have been fitted, which is common practice in Hydrology. It is a good thing that Master Plan
has used both analytical and graphical methods in parallel in establishing such rating curves.
For all the stations analyzed, a total of 253 equations have been arrived with good correlation coefficients of 0.90 and above in 90% of the pairs of data.
The  discharges  have  been  estimated  using  these  equations.  Then  Master  Plan  study  states that on checking homogeneity, the monthly values wee extracted.
It  would  have  been  better  to  appreciate  if  the  nomenclature  of  Homogeneity  with  justifications had been indicated in the report.
It  is  also  stated  further  in  the  Master  Plan  that  the  discharges  were  compared  with  earlier studies and the results did not have major variations.
Again it is felt that better appreciation could have been made, if the concept of major variation had been spelt out.
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3
Subsequently, water Balance model of the basin has  been formulated taking only representative s tations so a s t o m ake t he m odel comprehensive without o ver loading. T he selection criteria used for identification and inclusion of hydrometric stations in the model seem to be appropriate. The input hydrological discharges  were established on daily  time resolution level for the period of 33 years (1960-1992). The gap filling techniques seem to have in built subjective inferences.
With respect to assessment of water availability, a map has  been prepared on a scale of 1:1000, 000 to estimate the global water availability  to be applied to all potential water resources development sites in Abbay basin.
This map could be of use just for a preliminary estimate of annual flows at pre-feasibility level. The water availability  has  been further estimated by  detailed water balance model. In this, in the first run, the present scenario has  been in built and for 18 net work  stations, and water availability has been estimated. The salient findings of this run are as below which are in order
•    The annual average volume flowing across the border from Abbay River is 50.30 Bm .
•     About  83%  of  the  average  annual  volume  at  border  is  flowing  in  4  months  (July  - October),  while  the  4  months  from  February  to  May  account  for  less  than  4%  of  the mean annual flow.
•     The  average  annual  flow  at  the  out  let  from  Tana  represents  about  7%  of  the  above annual flows at border.
Floods:  The  highest  flood  hydrographs  recorded  at  each  of  the  water  balance  net  work stations have been put to analyses to establish the relation between time and Q/Qm ratios.
Then flood frequency  analysis  of the annual maximum discharges  has  been carried out by Gumbel's EV1 distribution to estimate the return period flood peaks. (It is not clear whether the annual maximum observed peaks  were converted in to instantaneous  peaks). Further another relationship connecting catchment size to flood peak  has  been derived for 2 groups  of catchment sizes; one for catchment groups  having catchment area less  than 10,000 km2 and another one for catchment sizes  more than 10,000 km2. It has  been concluded that the formulae derived so can be used for pre-feasibility  level studies  only. Master Plan has 
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Ma y 200            —
attempted to link slope of different catchments with flood discharge rates, which also did not exhibit any correlation. It has been opined in the Master Plan that rainfall runoff correlations were not applicable.
No data or study results on flood side will be of any use to the present project specific study. The flood studies adopted by the Master Plan might be useful for preliminary estimates level only.
Sediment  transportation:  For  96  stations,  sediment  rating  curves  have  been  analyzed  with available data, on the basis of following type of relationships.
QL = a (H-Ha)b linking water level with flow
QS = aQl? linking sediment
discharge QS with liquid discharge QL with a and |3 as parameters.
The total sedimentation transported is accounted as the sum of suspended sediment load and bed load. For bed load estimation, 3 different options  are indicated, with a sort of recommendation on Meyer Peter equation. However, at the end of sediment deliberations, Master Plan study  has  felt that a global estimate of bed load transport for the whole Abbay basin or for any of the large sub basins has no meaning; Hence the study has further stated that at Master Plan level, considering the amount of bed load transport definitely  being small compared to suspended load and considering the size of the Abbay  basin, it was  not necessary to proceed with data collection for evaluation of bed load estimation.
The   above  statement  of  Master   Plan  is  not  able  to  be  appreciated  without  elucidation Subsequently, the Master Plan has prepared maps related to erosion
The other studies  connected with water balance and water resources  modeling seem to be in order. Software WATBAL has been used in the modeling for various scenarios of development, after initial calibration for the present scenario.
These have been done for
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• present situation
• Future situation encompassing
o  Scenario  N°  1
(identified projects 1)
o  Scenario  N°  2
o  Scenario  N°  3
o Scenario N° 4
(identified projects 2 including Tana Beles) (identified projects in main stream)
(Full development)
The  results  could  be  utilized  for  comparing  the  results  of  specific  sub  basin  oriented  modeling studies, like the one under taken in the present study.
2.5 Study by Ministry of Water resources for Lower Dedessa (2001)
This  is  a reconnaissance level study  for the Lower Dedessa Medium hydro power project. This gives a vivid analysis of the data and the Hydrological features of the Dedessa sub basin. The dam site controlling a catchment area of 18,200 square Kilo meters, has  the following water resources potential according to this study.
Mean annual flow
=7290 Mm3
Annual Flow in 75% exceedance year=6300 Mm3
Annual Flow in 80% exceedance year=6100 Mm3
Annual Flow in 90% exceedance year=5230 Mm3
Annual Flow in 95% exceedance year=4600 Mm3
The following are the frequency floods estimates
10 Year return period flood
20 Year return period flood 100 Year return period flood 1000 Year return period flood 10000 Year return period flood
= 1977 cubic meter per second =2210 cubic  meter per second =2735 cubic  meter per second =3480 cubic  meter per second =4224 cubic meter per second
A sediment rate of 500tons  per square kilo meter per year has  been adopted. This  gives annual sediment load of 9.1 million tons  per year. The 50 year sediment volume of 329 Mm3 had been accounted for reservoir sizing.
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The study  had analyzed the energy  generation with a net head of 133 meters  and with the assumption of regulation of 70% of the mean annual flow. The energy that could be generated so, was estimated to be 1580 GWh per year, with an installed capacity of 299 MW, on adopting a plant factor of 0.6. This  is  very  relevant to Dedessa sub basin and the present study. The useful data were taken in to account.
2.6 Utility of Earlier Studies
All the earlier studies reviewed have some useful data base. All the useful information (data of all the above studies  have been reviewed and utilized as  per need and appropriateness). However, the present study  is  specific  sub basin (Dedessa) development study, and as  such, systematic  hydrological and modeling studies  are warranted with modern tools  of analysis. This sectoral report narrates these specific studies.Arjo Dedessa Irrigation Project
Meteorological and Hydrologi^cal Aspects
3. DATA BASE
May 2007
For the study towards the development of the Dedessa sub basin, the data on climate, river flows and sedimentation become relevant with respect to Hydrological Modeling and W ater Resources Planning.
3.1    Climatological Data
The climatological data like sunshine duration, relative humidity, wind speeds  temperature in the resolution levels of monthly means of daily means, monthly mean of daily maximum and monthly  mean of daily  minimum, become very  important parameters  with respect to the proposed command area development. These, in association with command area rainfall form the basic  inputs  for the estimation of the crop water requirement exercise. A vivid analysis follows on these important parameters in the section 4 of this report.
3.2.   Rain fall
The rainfall requires  critical analysis  for the dam site and the upstream for appreciating and developing floods; either as  a full hydrograph or in the form of flood peak  rate, expected at different Exceedance probability levels (return periods). The rainfall data needs to be analyzed in its various forms, like maximum of observed 1 day/2 day or 3 to 15 days values, or as a time series  of observed annual maximum 1 day  rainfall or as  monthly  / fortnightly  totals  as  per analysis at different stages. The analysis has to under take the gambit of short duration rainfall intensities as well, in connection with design of drainages, and return period rainfall peaks for the design of flood control works, in the downstream of the dam, in and in the vicinity of the proposed command area.
3.3.  River Discharge
Likewise, the river flows  data time series, preferably  on monthly  or fortnightly  time resolution levels, forms  the fore runner for establishing water resources  availability, for performing simulation whether as  basic  historical time series  or indirectly  through synthetically  generated time series  (mostly  generated by  a stochastic  generation scheme); where as, the same flow data has  to be analyzed in a different form as  discharge (flow rates  instead of flow volumes) with respect to flood analysis.
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3.4.  Sedment Data
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The sediment data is obviously required to plan the size of the storages, for apportioning the active and inactive storage spaces  and for determining the revised geometry  of the storage after certain design life, (revised elevation - area - capacity  relationship); as  this  revised relation is used in the simulation modeling. The water quality is another aspect, which needs to be a ddressed i n H ydrological s tudy along with I ow f low a nalysis f or m andatory d ownstream releases, closely associated with the concerns of environmental and ecological aspects.
With such appreciation of the frame work of hydrological studies and modeling, the data base was  established, as  elaborately  discussed in the Interim report. However in the following sections and in the annexure enclosed with this report, the data base on climatology, hydrology including sedimentation, floods are indicated. All the data available in a regional sense, up to date (2004) were brought to analysis desk and appropriately included.
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4.
ESTIMATION OF REFERENCE EVAPOTRANSPIRATION
4.1   Introduction
The climate of the Arjo Didessa meters).
catchment results from its location and elevation (1300 to 3012
The list of stations  for observations  of meteorological data along with length of records  and availability of data types is given in Table 4.1. All relevant types of data have been recorded at Jimma (located very close to the head of the Didessa catchment) stations since 1953. Rainfall and temperature have been observed at Didessa and Bedele. Rainfall and temperature have been also recorded at Dembi and Agara stations.
Climatic data for the project area such as temperature, relative humidity, sunshine hours, wind speed and rainfall have been collected and reviewed. Consistency tests, data rectification and other adjustments  have been performed as  necessary, data gaps  have been filled using interpolation and correlations methods as appropriate.
The FAO Penman-Monteith method is  maintained as  the sole standard method for the computation of crop reference evapotranspiration (ET0) from meteorological data. This section describes  how the monthly  ET0 is  determined from temperature, humidity, wind-speed and sunshine hours data collected at Bahir Dar station. The ET0 calculation has been carried out by means of a computer.
The FAO Penman-Monteith equation determines the evapotranspiration from the hypothetical grass reference surface and provides a standard to which evapotranspiration for various crops growing in the Arjo-Dedessa project command area can be related
The methods  for calculating evapotranspiration from meteorological data require various climatological and physical parameters. Some of the data are measured directly in Bahir Dar meteorological station. Other parameters are related to commonly measured data and can be derived with the help of a direct or empirical relationship. This section discusses the source and computation of all data required for the calculation of the reference evapotranspiration by means of the FAO Penman-Monteith method.
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The meteorological factors  determining evapotranspiration are weather parameters  which provide energy  for vaporization and remove water vapour from the evaporating surface. The principal weather parameters  to consider are presented below. The data obtained from the Bedele, Dedessa and Jimma stations  are assumed directly  applicable to the Arjo Dedessa Irrigation Project with application of appropriate information transfer mechanisms.
4.2    Meteorological Data
4.2.1    Meteorological Observation Stations
Five meteorological observation stations  are located in and around the Arjo-Dedessa Irrigation Project area. These meteorological data are obtained from the National Meteorological Services  Agency  (NMSA). Out of these Jimma is  Class  1 station that include observations  of rainfall, temperature, relative humidity, sunshine duration, wind speed, and evaporation. Bedele and Dedessa are also Class  1 stations  but do not include sunshine duration. In addition, Agaro and Dembi are Class  3 stations  that include observations  of rainfall and temperature. The longest record that covers  50 years  of meteorological data is  available at Jimma station that includes rainfall and temperature data since 1952.
Agaro and Dembi stations  are located within the cachment area of the proposed dam site Jimma station is  located at approximately  at 10 km from the divide of Dedessa catchment. Bedelle station is  located close to the proposed irrigation command area of the project. Dedessa station is  located further downstream of the command area. The details  like location altitude, year of establishment along with their class are available in Table 4.1
Meteorological data available at the five observation stations  located in and around the project area have been collected. The types  of meteorological data available at these stations  along with the length of data are given in Table 4.2.
Jimma Airport meteorological station is taken to be the most reliable Class I station from which meteorological information relevant to the project area has  been derived. The data from Jimma station has  been used for both the catchment study  and estimation of meteorological variables at the irrigation command area. The necessary  adjustment for the differences  in elevation has been made in transferring the data. Meteorological information from Agaro and Dembi stations
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which are located within the dam site catchment have been used for catchment study, in addition to Jimma station. Similarly, meteorological data of Bedelle and Dedessa have been used in addition to the data obtained from Jimma station for estimating rainfall and evapotranspiration at the proposed irrigation command.
In summary, climatologicall data obtained from Bedele, Dedessa and Jimma stations  were found to be the most reliable and relevant for use in the analysis of rainfall for the Arjo Dedessa irrigation project. Bedele is  geographically  the closest station to the command area. Dedessa station and the command area are located at the same elevation (i.e, both 1330 m asl). Jimma station (which is Class I) has the longest record of all the stations within the neighbourhood of the command area so as  to be index  station in extrapolating and interpolating missing and short term of climatic data relevant to the Arjo Dedessa catchment and command area.
4.2.2    Air temperature
In the project farm area, the mean monthly  temperature variations  throughout the year are minor, being 20.0°C in December to 25.4°C in March. The mean monthly  temperatures  of the command area is given in Table 4.3 and graphically illustrated by Figure 4.1. Temperature data have been used in estimating evapotranspiration rates from reference crops.
Agrometeorology  is  concerned with the air temperature near the level of the crop canopy. In traditional and modern automatic  weather stations  the air temperature is  measured inside shelters  (Stevenson screens  or ventilated radiation shields) placed in line with World Meteorological Organization (WMO) standards  at 2 m above the ground. Minimum and maximum thermometers  record the minimum and maximum air temperature over a 24-hour period.
Due to the non-linearity  of humidity  data required in the FAO Penman-Monteith equation, the vapour pressure for a certain period should be computed as  the mean between the vapour pressure at the daily  maximum and minimum air temperatures  of that period. The daily maximum air temperature (Tmax) and daily minimum air temperature (Tmin) are, respectively, the maximum and minimum air temperature observed during the 24-hour period. The mean daily air temperature (Tmean) is only employed in the FAO Penman-Monteith equation to calculate the
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slope  of  the  saturation  vapour  pressure  curves  (A),  estimating  saturation  vapour  pressure (e°(T) and oTK4 (Stefan-Boltzmann law).
The solar radiation absorbed by the atmosphere and the heat emitted by the earth increase the air temperature. The sensible heat of the surrounding air transfers  energy  to the crop and exerts  as  such a controlling influence on the rate of evapotranspiration. In sunny, warm weather the loss of water by evapotranspiration is greater than in cloudy and cool weather.
4.2.3    Air humidity
Mean humidity  values  vary  between 56.63 percent in March to 88.63 percent in September. The mean monthly  humidity  of the command area is  given in Table 4.3 and graphically illustrated by  Figure 4.2. Data of humidity  have been used in estimating evapotranspiration rates from reference crops.
The relative humidity (RH) expresses the degree of saturation of the air as a ratio of the actual (e ) to the saturation (e°(T)) vapour pressure at the same temperature (T). Relative humidity is 
a
the ratio between the amount of water the ambient air actually  holds  and the amount it could hold at the same temperature. It is  dimensionless  and is  commonly  given as  a percentage. Although the actual vapour pressure might be relatively  constant throughout the day, the relative humidity  fluctuates  between a maximum near sunrise and a minimum around early afternoon. The variation of the relative humidity  is  the result of the fact that the saturation vapour pressure is  determined by  the air temperature. As  the temperature changes  during the day, the relative humidity also changes substantially.
While the energy  supply  from the sun and surrounding air is  the main driving force for the vaporization of water, the difference between the water vapour pressure at the evapotranspiring surface and the surrounding air is  the determining factor for the vapour removal. Well-watered fields in hot dry arid areas consume large amounts of water due to the abundance of energy  and the desiccating power of the atmosphere. In humid tropical regions, notwithstanding the high energy  input, the high humidity  of the air will reduce the evapotranspiration demand. In such an environment, the air is  already  close to saturation, so that less additional water can be stored and hence the evapotranspiration rate is lower than in arid regions.
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4.2.4    Wind speed
Mean wind speed values vary between 0.48 meter per second in November to 1.08 meter per second in April. The mean monthly wind speeds of the command area are given in Table 4.3 and graphically  illustrated by  Figure 4.3. Average wind speed recorded at Jimma station in meters  per second (m/s) is  taken as  wind speed information relevant for the Arjo-Dedessa command area as there is no reliable data at other climatic stations nearby. Time series aspect of the wind data is not considered as it exhibits unacceptable erratic variation within the record period. Using the mean values  against the time series  data has  been tested for Bahir Dar station and resulted in equal result for monthly  and annual evapotranspriation estimates. However, the mean wind values  produced an estimated coefficient of variation (CV) of 2.2% instead of 2.4% estimated from the time series.
Wind is  characterized by  its  direction and velocity. Wind direction refers  to the direction from which the wind is  blowing. For the computation of evapotranspiration, wind speed is  the relevant variable. Wind speed is  measured with anemometers. The anemometers  commonly used in weather stations are composed of cups or propellers which are turned by the force of the wind. By  counting the number of revolutions  over a given time period, the average wind speed over the measuring period is computed.
The process  of vapour removal depends  to a large extent on wind and air turbulence which transfers  large quantities  of air over the evaporating surface. When vaporizing water, the air above the evaporating surface becomes gradually saturated with water vapour. If this air is not continuously  replaced with drier air, the driving force for water vapour removal and the evapotranspiration rate decreases.
4.2.5    Solar radiation
Mean sunshine duration is 8.3 hours in December and is reduced to 3.7 hours during July. The mean monthly sunshine durations of the command area are given in Table 4.3 and graphically illustrated by  Figure 4.4. Data of sunshine hours  have been used in estimating evapotranspiration rates from reference crops.
The relative sunshine duration (n/N) is  another ratio that expresses  the cloudiness  of the atmosphere. It is  the ratio of the actual duration of sunshine, n, to the maximum possible
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duration of sunshine or daylight hours N. In the absence of any clouds, the actual duration of
sunshine is equal to the daylight hours (n = N) and the ratio is one, while on cloudy days n and
consequently the ratio may be zero.
In the absence of a direct measurement of R ,
s
the relative
sunshine duration, n/N, is often used to derive solar radiation from extraterrestrial radiation. As with extraterrestrial radiation, the day length N depends on the position of the sun and is hence a function of latitude and date.
The evapotranspiration process  is  determined by  the amount of energy  available to vaporize water. Solar radiation is  the largest energy  source and is  able to change large quantities  of liquid water into water vapour. The potential amount of radiation that can reach the evaporating surface is determined by its location and time of the year. Due to differences in the position of the sun, the potential radiation differs  at various  latitudes  and in different seasons. The actual solar radiation reaching the evaporating surface depends  on the turbidity  of the atmosphere and the presence of clouds  which reflect and absorb major parts  of the radiation. When assessing the effect of solar radiation on evapotranspiration, one should also bear in mind that not all available energy is used to vaporize water. Part of the solar energy is used to heat up the atmosphere and the soil profile.
4.3 Penman-Monteith Equation
From   the   original   Penman-Monteith   equation   and   the   equations   of   the   aerodynamic   and canopy resistance, the FAO Penman-Monteith equation can be expressed as follows:
ETo = 0408 A (PJ1 Tj + y [900/(T + 273)1               - ej A+ y (1+0.34 uj
where
ET0 = reference evapotranspiration [mm day' ],
(2.1)
1
R  = net radiation at the crop surface [MJ m'
n
2
day' ],
1
G = soil heat flux density [MJ m'2
day' ],
1
1
T = air temperature at 2 m height [°C], u 2 = wind speed at 2 m height [m s' ], e  = saturation vapour pressure [kPa], e  = actual vapour pressure [kPa] = e  RH
s
a
s         mean.
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a
e s - e  = saturation vapour pressure deficit [kPa],
A   =   slope   vapour   pressure   curve   [kPa   °C'1], y = psychrometric constant [kPa °C ].
RH = relative humidity divided by 100 Rn      ~ Rns " Rnl
R
ns         = 0.77 Rs
Rs          = (0.25 + 0.50 n/N) Ra
n          = ctTk4(0.34         -0.14 ea05) (0.135 Rs/Ro - 0.35) R,
Rs0 = [0.75 + 2 (Altitude)/100000] Ra
The calculation procedure consists of the following steps:
(2.4)
(2.5)
(2.6)
1.   Derivation of s ome c limatic p arameters from t he d aily m aximum (Tmax) a nd minimum (Tmin) air temperature, altitude (z) and mean wind speed (u ).
2
2.   Calculation  of  the  vapour  pressure  deficit  (es   -  e ).  The  saturation  vapour  pressure  (es)
a
is derived from Tmax  and Tmin, while the actual
vapour pressure (e ) can be derived from
a
the  dewpoint  temperature  (Tdew),  from  maximum  (RHmax)  and  minimum  (RHmin)  relative humidity, from the maximum (RHmax), or from mean relative humidity (RHmean).
3. Determination of the net radiation (R„) as  the difference between the net shortwave
radiation (Rns) and the net
longwave radiation (R ,).
n
In the calculation sheet, the effect of
soil  heat  flux  (G)  is  ignored  for  daily  calculations  as  the  magnitude  of  the  flux  in  this 
case  is  relatively  small.  The  net  radiation,  expressed  in  MJ  m'2   day ,
1
is  converted  to
mm/day  (equivalent  evaporation)  in  the  FAO  Penman-Monteith  equation  by  using  0.408 as the conversion factor within the equation.
4.  ET0 is obtained by combining the results of the previous steps.
The following values were selected from FAO 1998 (Crop Evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56).
o Psychometric constant (y) for different altitudes (z),
________________________________________ ______________________________________ 25
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o Slope of vapour pressure curve (A) for different temperatures (T),
o Saturation vapour pressure [(e°(T)] for different temperatures (T),
o Daily extraterrestrial radiation (R ) for different latitudes,
a
o Mean maximum possible daylight hours (N) for different latitudes,
o oTk    (Stefan-Boltzmann law) at different temperatures (T)
4
The
estimated  mean  daily  and  monthly  reference  crop  evapotranspiration  magnitudes  (in  mm)
from
1991  to  2004,  based on  Penman-Monteith  Equation,  are  presented  by Tables 4.3 and  4.4,
respectively and graphically illustrated by Figure 4.5
The results of the meteorological analysis including the rainfall are provided in the annexture A. Table 4.1:        Table Details of Meteorological Observation Stations located in and around the project area.
S.No
Station Name
Latitude
North
Longitude
East
Deg.   | Min.
Deg.    Min.
Altitude Period
(m)
1       Agaro
2       Bedelle
3 Dedessa
4       Dembi
5       Jimma
07 51
08 27
09 33
08 04
07 40
36 36
36 20
36        06
36        37
36        50
1700
2005
1300
1950
1725
1980-2004
1967-2004
1971-2004
1954-2004
1952-2004
No
Station name
Yrs
RF | Tm
RH WS SD | 1 hr |l day Rday
30
28
20
21
50
Class
X         X         x                 X XXX                 X
XX
XX
X         X                         XXXXXX
1
1
3
3
1
Table 4.2:         Types of Climatic Data available at the various Meteorological Observation Stations
1      Didessa
2      Bedele
3      Dembi
4      Agaro
5      Jimma
Yrs      refer to rainfall series
RF      monthly rainfall
Tm
SD       sunshine duration
average temperature
1 hr
RH
relative humidity
maximum 1-hour rainfall
1 day
WS
wind speed
maximum 1-day rainfall
Rday
number of rain days
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Table 4.3:
Summary of Meteorological Characteristics at Project Area
Jan Feb March April May June July Aug Sept Oct Nov Dec Monthly Mean Temperature in oC
Average
CV
Skew
Min
Max
21.9
0.048
0.071
19.5
24.6
23.15
0.044
-0.58
20.44
24.9
25.4
0.036
0.067
23.63
27.37
25.05 24.03    22.09 21.32
21.19 21.83 22.35 22.144 19.99
0.065
-3.628
16.26
27.72
0.071
-3.23
15.89
26.19
0.029 0.028
0.117 0.275
20.96
23.41
19.99
22.72
0.031
0.007
19.88
22.34
0.027
0.93
21.06
23.2
0.038 0.0544 0.066 0.353 -0.118 -0.02 20.19 19.081   16.92 24.25 25.051  22.88
Relative Humidity (%)
Average  64.77  58.97  56.63  67.97  75.72  81.07  87.46   88.52   88.63   84.55 79.366 72.44 CV      0.099  0.127  0.084  0.073  0.053  0.047   0.061   0.048    0.03    0.053 0.0705 0.089
Skew    -0.27   -0.27   -0.37   0.474    -0.2    -0.87   -2.92      -2      0.249    0.19    0.287   -0.13 Min      51.5    43.2   45.09   57.42  65.23    65.8   61.77   72.23   82.49   74.24 67.667 55.37 Max      77.3   71.68  64.08  82.17  86.18    90.3    98.1    97.53   95.67   94.73 93.187 85.13
Wind Speed (m/s)
Average   0.62     0.8      1.0     1.08    1.04    0.86     0.62    0.51      0.5      0.51     0.48    0.51 Sunshine Hours (hrs/day)
Average  8.155  7.638  7.452  7.287  7.624  6.061   3.703  4.059   6.164   7.855 8.3245 8.306 CV     0.125  0.166  0.145  0.165  0.157  0.189  0.243    0.23     0.22    0.139 0.2139 0 104
Skew -0.47 -0.36 -0.27 -1.12 0.141 -0.36 0.248 -0.42 -0.27 0.138 -3.525 0.971 Min 6.05 4.905 4.592 3.304 5.428 3.364 2.277 1.6 2.622 5.778 0.1121 6.634 Max 10 9.919 9.744 9.44 10.15 8.12 5.445 6.2 9.12 10.08 10.201 11.02
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Table 4.4:
Summary of Estimated Mean ETo of Arjo Dedessa Project Area
Jan      Feb      March April       May     June    July      Aug      Sept     Oct     Nov     Dec
Daily: Estimated ETo (mm/day)
Average    3.7     4.11     4.62    4.51     4.33    3.58    2.96    3.11     3.81    4.03   3.79    3.44 CM      0.05    0.06    0.05    0.07     0.08    0.08    0.08     0.08     0.09    0.06   0.12    0.05
Skew -0.47 -0.77 -0 -0.95 -0.65 -0.19 0.33 -0.51 -0.18 -0.27 -3.11 0.88 Min 3.23 3.38 3.99 3.44 3.35 2.97 2.58 2.44 2.88 3.5 1.95 3.11 Max 4.03 4.49 5.24 5.13 5.06 4.15 3.46 3.59 4.49 4.47 4.24 3.96
Monthly: Estimated ETo
(mm/month)
Average    115     115      143     135     134     107     91.8     96.5     114     125    114     107 CM      0.05    0.06    0.05    0.07     0.08    0.08     0.08     0.08     0.09    0.06   0.12    0.05
Skew -0.47 -0.77 -0 -0.95 -0.65 -0.19 0.33 -0.51 -0.18 -0.27 -3.11 0.88 Min 100 94.8 124 103 104 89 80 75.7 86.5 109 58.6 96.4 Max 125 126 162 154 157 125 107 111 135 139 127 123
Estimated Eto (mm/day) (form average values of data)
Average   3.69    4.08    4.58    4.48     4.28    3.57     2.95     3.10     3.75    4.00   3.79     3.44 '
Estimated Eto (mm/month)
(form average values of data)
Average 114        114     142      134     133     107      92        96       113     124^114        107
Note: Total annual ETo = 1397 mm
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Table 4.5:_ Magnitude of ETo at given non-exeedance probability level
May 2007
Non-Exd
Probalility Jan Feb Mar April
(mm/day)
May June July Aug Sept Oct Nov Dec
(%)
55      3.72  4.14  4.65   4.56   4.37  3 61  2.99   3.14   3.85   4.06   3.84  3.46
60      3.75  4.18  4.68   4.60   4.42  3.65  3.02   3.18   3.89   4.09   3.90  3.49
65      3.77  4.21  4.71    4.64   4.47  3.68  3.05  3.21    3.94   4.12   3.96  3.51
70      3.80  4.25  4.75   4.69   4.52   3.72  3.09   3.24   3.99   4.15   4.02  3.53
75      3.83  4.29  4.79   4.74   4.57  3.77  3.12   3.28   4.04   4.19   4.09  3.56
80      3.86  4.33  4.83   4.80   4.63  3.81  3.16   3.32   4.09   4.23   4.16  3.59
85      3.90  4.38  4.88   4.86   4.70  3.87  3.21   3.37   4.16   4.28   4.25  3.63
90      3.95  4.45  4.94   4.95   4.79  3.93  3.27   3.44   4.24   4.34   4.35  3.67
95      4.02  4.54  5.03   5.07   4.93  4.04  3.35   3.53   4.36   4.43   4.51   3.74
Avg      3.70  4.11   4.62   4.51   4.33  3.58  2.96  3.11    3.81   4.03   3.79   3.44
Non-Exd Probalility Jan
/O/ \
(mm/month)
Feb Mar April May JuneJuly Aug Sept Oct Nov Dec Annual
55       115    116   144     137     136   108   93    97.4    116   126    115    107    1405
60       116   117   145     138     137   109   94    98.4    117   127    117    108    1412
65       117   118   146     139     138   111    95    99.5    118   128    119    109    1420
70       118    119   147    141     140   112   96    101     120   129    121    110    1428
75       119    120   148     142     142   113   97     102    121   130    123    110    1436
80       120   121    150     144     144   114   98     103    123   131    125    111    1446
85       121    123   151     146     146   116   99     105    125   133    127    112    1457
90       122    125   153     148     149   118  101    107    127   135    131    114   1471
95       125   127   156     152     153   121   104    109    131   137    135    116    1492
Avg 115 115 143 135 134 107 92 96.5 114 125 114 107 1397
Non-Exd
Growth factor (Xp)
Probalility Jan      Feb    Mar    April      May    June July       Aug Sept   Oct       Nov  Dec   Annual (%)
55       1.01  1.01  1.01    1.01    1.01  1.01   1.01   1.01  1.01   1.01   1.01 1.01   1.005
60      1.01   1.02  1.01    1.02    1.02  1.02   1.02   1.02  1.02    1.02    1 03 1.01    1.01
65       1.02   1.02  1.02    1.03    1.03  1.03   1.03   1.03  1.03    1.02    1.04 1.02   1.016
70       1.03  1.03  1.03    1.04    1.04  1.04   1.04   1.04  1.05    1.03    1.06 1.03   1.022
75       1.04   1.04  1.04    1.05    1.06  1.05   1.05   1.06  1.06    1.04    1.08 1.04   1.028
80       1.04   1.05  1.05    1.06    1.07  1.07   1.07   1.07  1.07    1.05    1.1   1.04   1.035
85       1.05   1.07  1.06    1.08    1.09  1.08   1.08   1 08  1.09    1.06    1.12 1 05   1.043
90       1.07   1.08  1.07     1.1     1.11   1.1     1.1     1.1   1.11    1.08    1.15 1.07   1.053
95       1.09   1.1   1.09    1.12    1.14  1.13   1.13   1.13  1.14    1.1      1.19 1.09   1.068
Avg        1        1       1         1         1       1        1       1       1         1        1       1         1
---------------------------------------------------------- ---------------------------------------------------- 29
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Temperature (oC)
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Figure 4.1:
Mean Monthly Temperature at
Figure 4.2: Mean Monthly Relative Humidity at
Arjo Dedessa Project Area 
Arjo Dedessa Project Area
i
Figure 4.3: Mean Monthly Wind Speed at
Arjo Didesa Project Area
Figure 4.4:
Mean Monthly Sunshine Hours at
Arjo Dedessa Project Area
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30ETo (mm)
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Figure 4.5:
Mean Monthly ETo at Arjo Dedessa Project Area
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5. RAINFALL ANALYSIS
5.1
Introduction
The
rainfall  data  obtained  f
rom
Be
de
le,
Dedessa
and
Jimma
sta
tions  were  found  t
o  be  the
most
reliable  and  relevant
for
use
in
th
e  analysis
of
rainfall
for
the  Arjo  Dedessa
irrigation
project. Bedele is geographically the closest station to the command area. Dedessa station and the c ommand a rea a re I ocated a 11 he s ame e levation (i.e, b oth 1 330 m a si). J imma s tation (which is  Class  I) has  the longest record of all the stations  within the neighbourhood of the command area so as  to be index  station in extrapolating and interpolating missing and shortage of rainfall data relevant to the Arjo Dedessa catchment and command area.
The rainfall in the Arjo Didessa Catchment as well as in its surrounding is uni-modal type. Most of the rainfall is  concentrated in the May  to September covering with virtual drought from November through March. The five wettest months cover 63 percent of the total annual rainfall. The dry season, being from November to February (four months) has a total rainfall of about 7% of the mean annual rainfall.
The mean monthly rainfall for stations in the project area is given in Table 5.1 and their pattern is  graphically  illustrated in Figure 5.3. The length of rainfall data collected from Jimma is relatively  more reliable compared to the data set collected from other stations  because of its long record length and shorter time interval of observation. Didessa and Bedele stations  also provide more relevant rainfall data that can be adopted for the command area since Bedele is close to the project area and Didessa station is located within the same climatic zone as that of the project area.
5.2    Rainfall Internal and External Consistency
Though a good data base has  been established for rainfall, considering the data of the three stations, namely, Dedessa. Jimma and Bedele, it is  customary  in Hydro-meteorological studies to firm up the consistency with statistical analysis.
The trend in the annual rainfall pattern is a relevant aspect with respect to the behavior of the rainfall and to compare such behavior with those of other relevant stations involved in the study with a view to firm up external consistency. Accordingly, a 3 year moving average analysis
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was taken up for each of the 3 stations. The results are shown in the figures 5.1 (a), (b) and (c). These show similar trends.
Similarly, an analysis with respect to the cross correlation structure among the 3 stations was carried out. The cross correlation matrix depicts the following status (Table 5.2) at annual time resolution level, which is not abnormal.
Then, the single mass curve analysis was taken up for each of these 3 stations. (Figures 5.2.
(a), (b) and (c).
These also do not indicate any abnormality to reject this data set.
As  such,  these  rainfall  data  were  used  in  its  different  forms  for  various  spectrums  of  the analysis.
5.3 Estimation of Rainfall Reliability Level
Using the rainfall and irrigation water available for the command area requires the magnitude and reliability  level of rainfall corresponding to various  rainfall time intervals. Annual, monthly and half monthly intervals are chosen durations for the Arjo-Dedessa irrigation project.
Rainfall reliability level and magnitude can be analyzed by making use of theoretical distributions. In this study the Extreme Value Type III (minimum) distribution was utilized. The distribution is also known as the Weibull distribution or as Gumbel's Limited distribution of the Smallest Value. Naturally rainfalls are bound by zero or other higher values on the left. The Weibull (or EV3) distribution can be expressed as:
(5.1)
where
Xp = E + (U-E)[-ln(1-p)]1/A
Xp - magnitude of rainfall corresponding to probability level of p,
The moment estimates of the parameters E (the lower limit), U and a fi.e., E, U and A) can be obtained from the following equation:
U = Xm + Cv.Xm Y(a)
E = U -Cv.Xm.Z(a)
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(5.2)
(5.3)
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where
Xm = mean value,
Cv = coefficient of variation = standard deviation divided by the mean, Y(a) = (2-Cs)/6
Z(a) = [8+(3-Cs) ]/9
and
A =3/(1+Cs)
Cs = skew coefficient calculated from data
May 2007
3
The estimated monthly and half-monthly parameters of Weibull Distribution are given in Table
5.3 and 5.5, respectively.
The following algorithm were used in the derivation of annual, monthly, and half-month 55 to 95 percent reliability levels.
1.  Averages, standard deviations, standard deviation, skew coefficient of annual, monthly 
and half-monthly averages were calculated.
2.  Weibull Distribution parameters (namely U, E and A) corresponding to annual, monthly 
and half-monthly rainfalls were calculated from the statistics.
3.  Magnitudes of rainfalls corresponding to 55 to 95 percent reliability levels were
estimated.
The results so obtained are given in Tables 5.4 and 5.6 for monthly and half-monthly time intervals, respectively.
The  half  monthly  rainfall  magnitudes  as  a  percentage  of  mean,  corresponding  to  various reliability levels are shown in Table 5.7.
5.4 Estimation of Maximum Rainfall Frequencies
For internal drains  and smaller control works, the need for various  return period rainfall and with different durations  were found to be necessary. In general, maximum rainfall magnitudes 
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of
intensity corresponding to various return periods (or
probability levels) can be estimated by 
applying the following form.
It = Im [1+CV.K(T)] where
(5.4)
l T = rainfall intensity corresponding to return period of T years (mm), Im = mean of annual maximum intensity of rainfall (mm/hr),
CV = coefficient of variation of annual intensity of rainfall, K(T) = frequency factor
For Extreme Value Type I (or Gumbel) Distribution,
K(T) = -0.779 {0.577 + ln.ln[T/{(T -1}]}
The maximum annual 24hr rainfall service is given in Table 5 8.
(5.5)
The results of the maximum 24-hr and 1-hr rainfall frequencies are presented in Table 5.9 The 1-hr rainfall intensities were estimated by the formula derived for Ethiopia expressed as
l h = lM.(0 523 + 0 15 In D)
(3.6)
where
l h = maximum rainfall intensity corresponding to duration D (mm/hr).
In = natural logarithm
D = duration of lh (hr),
I24 = maximum rainfall intensity corresponding to duration 24 hours (mm/hr)
The rainfall magnitudes for higher durations, 2 days & 3 days have also been analysed using the EVI frequency distribution. The results for various return periods are shown in Table 5 10
More details of the analysis are available in the annexure A 'Results obtained from meteorological Analysis".
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The annual rainfall available at different years are given below
May 2007
Near year
=
1453 mm
75 % year
=
1148 mm
80 % year
=
1090 mm
95 % year
=
853 mm
Table 5.1: Mean Monthly Rainfall
Description         Jan    Feb    March   April May       June   July     Aug     Sep     Oct       Nov   Dec    Total
Didessa             3      6       26      49    158   274    312    277    209    104    28      8      1454
Bedele              18    23      65     105  239   291     310    303    302    156   41     12     1864
Dembi               25    44     101    126  223   303    307    367   290    145    52     29     2013
Agaro 24 36 88 93 149 232 234 231 174 127 54 30 1471 Jimma 33 49 88 133 172 219 208 210 182 103 68 36 1502
Table 5.2: Cross Correlation Matrix of Annual Rainfall
Dedessa
Dedessa                                  1 Jimma
Bedele
—
Jimma
Bedele
0.11
1
0.24
0 48
1
Table 5.3: Parameter Estimates of Weibull Distribution
Jan Feb Mar AprilMay JuneJuly Aug Sept Oct Nov Dec Annual
0.5     0.8     0.4     0.4     0.5   1.1   0.8 0.3    0.1     0.3    0.3     0.2   -0.1  0.1 2.9     1.3    3.7     3.4     2.4    0.9   1.3
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Table 5.4 Monthly Rainfalls Corresponding to Different Reliability Levels
Reliability
Level Jan Feb Mar April May June July Aug Sept Oct Nov Dec Annual
(%)
55    10.8 12.2 40.9 58.1    150    213    219    221    212  91.65 18.6 7.58      1353 60     8.84 9.79 35.8 51.7   137    198    211    210    203  81.66 16.36.12       1304 65       6.92 7.4 30.8 45.3   124    184    204    200    194   71.76 14.24.74       1253 70          5 5.03 25.8 38.6   111    169    197    190    184  61.82 12.2 3.43      1202
75     3.04 2.63 20.7 31.5  97.9    154    190    178    174  51.68 10.4 2.16      1148
May 2007
80
0.99 0.16 15.5 23.8
84.2    138    183    166    164  41.13 8.83 0.92
1090
85
0      0 9.97 15.2
69.4    120    177    152    152  29.86 7.36 0
1025
90
0      0 3.87 4.84
52.9   99.7    170    135    138   17.29 6.02 0
951
95
0000
32.7   73.7    163    113    119   1.974 4.82 0
853
A vg 15^318.453.6 69.5 180.8243.0245.9238.3228.6115.531.412.7 1453
Table 5.5:       Parameter Estimates of Half-Monthly Rainfall
Month
Parameters of Weibull distribution
1/a
y( )
a
z(a)
UE
1.1
1.2
2.1
2.2
3.1
3.2
4.1
4.2
5.1
5.2
6.1
6.2
7.1
7.2
8.1
8.2
9.1
9.2
10.1
10.2
11.1
11.2
12.1
12.2
0.8          0.1          1.5         8.6           -4.3 0.9          0.0          1.1         8.1           -2.6
1.2         -0.1         0.9          7.7           -4.0
0.8          0.1          1.5         11.0         -6.1
0.6          0.2          1.8        24 3         -14.8
0.6          0.2          2.0        37.7         -117
0.6          0.2          1.9        26.5         -13.5 0.5          0.2          2.4         54.5         -25.6
0.6          0.2          1.9        87.6         -11.2
0.6          0.2          2.0        112.7         7.5
0.5          0.3          2.8        134.3       -19.8
0.5          0.2          2.5         135          10 4
0.7          0.2          1.7         130          40.7
0.9         0.1          1.2         126          80.9
0.5          0.3          2.9         128           5.2
0.5          0.3          2.7         131          37.4
0.6          0.2          2.2         128          49.2
0.4          0.3          3.6        119 6         -6.2
0.5          0.2          2.4         83.5         -17.7
0.7         0.1          1.6        47.1         -11.7
0.8         0.1          1.3        21.5          -3.2
1.2         -0.1         0.9         10.3          -3.0
1.1          0.0          1.0         5.8           -3.3
1.3         -0.1         0.9         5.1           -3.7
Annual
0.4          0.3          3.2       1541.2      575.1
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Table 5.6: Half-Monthly Rainfall Magnitudes at Different Reliability Levels
May 2007
Month
Avg
Reliability level
55% 60% 65% 70% 75% 80% 85% 90% 95%
1.1
1.2
2.1
2.2
3.1
3.2
4.1
4.2
5.1
5.2
6.1
6.2
7.1
7.2
8.1
8.2
9.1
9.2
10.1
10.2
11.1
11.2
12.1
12.2
7.5
7.8
8.8
9.6
20.6
33.0
22.7
46.7
78.2
102.6
119.8
123.2
122
123.9
116.5
121.8
120
108.5
73.7
41.8
19.7
11.8
6.1
6.6
1453
Annual
4.41      3.43      2.49     1.58    0.69       0          0          0           0
4         3.09      2.24     1.45     0.7        0          0          0           0
2.46      1.38      0.41        0          0          0          0          0           0
5.52      4.22      2.98     1.77     0.6        0          0          0           0
13.2      10.5      7.87     5.25     2.64       0          0          0           0
24.4      21.1      17.9     14.6     11.4     8.08    4.61     0.82        0
15.2      12.4      9.67     6.99     4.32     1.61       0          0           0
34.9        30       25.1     20.1     15.1     9.86     4.25       0           0
59.6      52.8      46.1      39.5      33      26.3     19.4     11.9      3.32
84.27 77.2 70.28 63.42 56.51 49.43 42.02 33.96 24.5 101 92.5 83.9 75.1 65.9 56.3 45.7 33.6 18.3 106 98.2 90.7 83.2 75.5 67.4 58.7 48.9 36 8
103.9    97.46    91.26   85.17   79.13   73.04   66.78   60.14     52.6 110       106       102      99.2      96        93      90.1     87.2      84.2 102      95.7      88.9       82      74.9     67.3      59      49.3       37 110       104        99       93.6     87.9      82      75.6     68.2     59.1 108       103      97.6     92.7     87.6     82.4     76.9     70.8      63.5 97.9      92.1      86.1       80      73.4     66.3     58.4     48.9      36.1
58.7      52.5      46.2       40      33.6      27       19.9      12       2.37
29.2      24.9      20.8     16.8     12.8     8.77     4.68     0.38        0
13.1      11.1       9.3      7.54     5.83     4.16      2.5      0.82        0
4.15      2.92      1.83     0.85       0          0          0          o           o 2.02      1.21      0.46        0          0          0          0          o           n 0.83         0           0          0         0          0          0          0           0
1353    1304    1253   1202  1148   1090   1025  950.5    853
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Table 5.7:           Half-Monthly Rainfall Magnitudes in Percent of the Mean Corresponding to Different
39
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Meteorological and Hydrological Aspects Table 5.8 Maximum Annual 24-hr Rainfall
May 2007
Year (mm/d) Year (mm/d) Year (mm/d)
1967      47.1               1980      45.6                 1993       50.1
1968      47.0               1981
1994       57.8
1969      64.0               1983      50.0                 1995       61.0
1970      57.0               1984      51.7                 1996       60.1
1971      70.0               1985      62.2                 1997      104.5
1972      45.2               1986      64.6                 1998       63.5
1973 35.9 1987 58.4 1999 63.0
1974 51.8 1988 53.2 2000 47.9
1975 36.4 1989 46.8 2001 69.0
1976 47.1 1990 49.5 2002 57.0
1978
1991       87.8                2003
1979      45.0               1992      51.6                2004       39.1
Average 54.14 CV      0.306
Skew 2E-04
Table 5.9 Maximum Rainfall Magnitudes and Frequencies
Return
Peiod (T)
Frequency
Factor (K)
Rainfall Magnitude (mm)
24-hr
1-hr
2
5
10
15
20
25
30
40
50
-0.16
0.72
1.30
1.64
1.86
2.04
2.20
2.40
2.61
51                       27
66                       35
76                       40
82                       43
85                       44
88                       46
91                       48
94                       49
98                      51
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Table 5.10 Maximum Rainfall Magnitudes for Higher Durations
May 2007
Return
Period, T
(Years)
EVI Factor
K(T)
Max 2day
Rainfall (mm)
Max. 3day
Rainfall (mm)
1day Avg. of Max.
3 day Rainfall
(mm)
2
5
10
15
20
25
50
100
-0.164
0.719
1.304
1.634
1.865
2.043
2.591
3.135
76.9
94.6
106.3
112.9
117.5
121.0
132.0
142.9
92.7
112.7
125.9
133.4
138.6
142.6
155.0
167.3
29.8
37.8
43.2
46.2
48.3
49.9
54.9
59.9
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413-Year Rolling Mean
Ji
3-Year Rolling Mea
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Year
5.1(a ): Trend Analysis of Annual Rainfall at Jimma Station
Figure 5.1(b): Trend Analysis of Annual Rainfall at Bedelle Station
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Year
Figure 5.1(c): Trend Analysis of Annual Rainfall at Dedessa Station
Figure 5.2(a): Single Mass Curve of Jimma Rainfall
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Cummulative Annual Rainfall (mm)
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Years
Figure 5.2(b): Single Mass Curve of Bedelle Rainfall
Years
Figure 5.2(c): Single Mass Curve of Dedessa Rainfall
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6.     MONTHLY STREAMFLOW ANALYSIS 6.1   Hydrological Observation Stations
May 2007
Hydrometric stations on the Dedessa river and the neighbouring rivers within the Abbay basin the Omo-Ghibe basin have also been identified. The hydrological data are obtained from the Hydrology Department of the Ministry of Water Resources (MoWR).
There are two hydrological observation stations  located on the Dedessa River. These are Dedessa near Arjo town (Station No. 114001) and Dedessa near Dembi town (Station No. 114014). Station 114001 with catchment area of 9981 km2 is located about 44 km downstream of the proposed dam site and has  become operational since 1960 where as  Station 114014 with a catchment area of 1806 km2 is located about 137 km upstream of the proposed dam site and has become operational since 1985.
Besides  the two hydrological observation stations  located on the main Dedessa river, several hydrological observation stations  are located on its  tributaries. In addition, hydrological observation s tations o n t he n eighboring Dabana n ear A basina (Station N o. 1 14005), (GilgeI Ghibe near Assendabo (Station No. 091008), and Gojeb near Shebe (Station No. 091012) have been found to be relevant to the study.
The  list  of  hydrological  observation  stations  for  which  data  have  been  collected  along  with other details like their location and 'catchment area is available in Table 6.1.
6.2   Extension of records
Streamflow gauging stations have been installed on the Dedessa River near Arjo town (Station 14001) since 1960 and near Dembi town (Station 14014) since 1985. Before the streamflow analysis, data obtained from Station 114001 (Didessa near Arjo) and Station 114014 (Didessa near Dembi) were checked for temporal and spatial homogeneity. The investigation of homogeneity was concentrated mainly on the outliers of the monthly data series of the record. Extremely high or low values of each series, that has occurred at time T(i) in each month M(i), was checked against the records of the month M(i-1) and M(i+1) that has occurred at the same year T(i) so as to judge, whether the relation is still more or less the same as that of the other years. Whenever the outliers were rejected by this test, again another test was performed with
46
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other data series of Station 114005 (Dabana near Abasina), Station 91008 (Gilgel Ghibe near Assendabo) and Station 91012 (Gojeb near Shebe) for the same period T(i).
The data were checked for its consistency before use for different analysis. The investigation will concentrated mainly on the outliers of the monthly and daily data series. Extremely high or low values that occurred at a given time were checked against records of other near by rivers (such as  Dabana). If the outliers  are found to be inconsistent with others, then they  were replaced by  new values  that were generated regionally  from data set obtained from other stations.
6.3   Synthetic flows
In this section, analysis of monthly flows is made with the intension of improving information required fro reservoir storage design. Reservoir storage design on the basis  of generated (synthetic) flows is preassumed.
It was  realized that the specific  sequence of historic  events  was  a random occurrence that would certainly  never occur again in the same way. To get some idea about other possible sequence within the population, in order to improve reservoir design, generating a long streamflow series is a highly accepted procedure by hydrologists. However, it has been pointed out synthetic (generated) flows do not improve poor records but merely improve the quality of designs made with whatever records are available.
Mean monthly  discharges, coefficient of variation of monthly  flows, summary  of regional monthly flow characteristics, statistics of monthly flows at Arjo Dedessa Dam Site are shown by Tables 6.2, 6.3, 6.4, 6.5 respectively. The monthly stream flows at different reliability level are available in Table 6 6.
In order to maintain the historical skewness in the generated flows, the Weibull distribution has been fitted to the standardized residuals  of the monthly  series  Accordingly, the estimated parameters  of the random generator that were used in the Thomas-Fierring model are presented in Table 6.7.
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Mone   details   on   various   results   are   shown   in   the   annexure   B   “Results   obtained   from hydrological analysis."
The flows at the dam site at annual level are as below.
Mear year
=
2328 Mm3
75% year
=
1729 Mm3
80% year
=
1625 “
90% year
=
1384 “
95% year
=
1224 “
Table 6.1:           Availability of Streamflow Data in the Region
SI No
St.
No
Station name and Location
Lat
North
Long
East
Area
(Km2)
Flow
(Mm /yr)
3
Runoff
(mm)
1     114001      Dedessa near Arjo                     8.41       36.25      9981       3826          383
2      114014     Upper Dedessa near Dembi       8.02       36.28       1806       1221          676
3      114005     Dabana near Abasina Gilgel Gibhe near
4      091008     Asendabo
9.02 36.03 2281 1870 820
7.45      37.11       2966       1211          408
5     091012      Gojeb near Shebe                     7.25       36.23      3494       1803         516
Table 6.2:      Mean Monthly Flow (in Mm3/sec)
St. No
Jan Feb March April May June July                               Aug Sep               Oct         Nov        Dec
114001
114014
114005
091008
901012
41.9 26.7 26 33.5 63.6 216 646 1050 902 526 148 80.7
12.2    9.5    11.5 16.9 36.4 112.4 228.9 306.9 262.8 154.3                    48.4     20 5
35.4 19.8     16.3 17.0 35.1 107.0 258.8 433.0 473.4 313.9 103.3                       56 6
14.7 13.5     12.2 15.9 33.9 100.3 216.5 319.2 277.8                   137    60.2     24 5 31.3 22.1     22.1 31.3 86.4 1692 323.7 415.6 400.9 202.3                      82.8     46.5
Annual 3761
1221 1870 1211 1803
Table 6.3:      Coefficient of Variation of Monthly Discharges
St. No
Jan Feb March April May June July Aug Sep                                             Oct         Nov        Dec
114001
114014
114005
091008
901012
0.51 0.57 0.66 0.68 0.62 0.45 0.35 0.3 0.33 0.67 0 62 0 5Q
0.47 0.5 0.48 0.56 0.69 0.53 0.29 0.27 0.30 0.56 0 81 0 53
0.28 0.42 0.33 0.43 0 60 0.47 0.20 0.16 0.25 0 44 0 30 0 34
0.46 0.58 0.78 0.45 0.43 0.36 0.22 0.28 0.27 0.61 0 79 0 SR
0.79 0.36 0.34 0.43 0.63 0.44 0.31 0.26 0.34 0.49 0.54 0 70
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Table 6.4 Summary of Regional monthly flow characteristics
May 2007
Param
Year  Jan
Feb
Mar
April
May
June
July   Aug    Sep
Oct
Nov
Dec
cv
Skew
R
194
151
0.51
1.80
194    0.38
B      194    0.34
Table 6.5 Table Statistics of monthly flows at Arjo  Dedessa dam site
(in Million m )
0.68 0.595 0.58 0.61 0.47 0.33 0.29 0.32 0.58 0.66 0.57
1.66 1.503 0.86 1.65 1.05 0.12 0.7 0.59 0.94 2.19 2.02
0.57 0.527 0.45 0.49 0.66 0.53 0.56 0.51 0.5 0.56 0.51
0.76 0.46 0.44 0.52 0.51 0.37 0.5 0.56 0.91 0.64 0.44
3
Param
Jan Feb Mar [Apr May Jun Jjul ]Aug Sep |oct Nov Dec
Annual
Average
Stdev
Skew
Max
Min
R
B
25.00 16.97 17.86 24.21 48.26 157.2 412.8 633 540.6 299.8 105.9 46.11
12.81 9.72 11.82 16.41 29.69 70.67 143.2 189.6 179.2 180 5 89.35 27.17
0.626 0.743 1.067 0.937 1.629 0.891 0.126 0.037 0.254 0.585 1.866 0.96
59.09 40.3 46.01 68.61 144.9 366.1 751.5 1043 905.1 661 400.8 120.1 6.806 3.462 1.775 4.586 9.685 38.71 143.8 296.2 195.6 49.32 12.69 4.808 0.446 0.828 0.889 0 59 0.298 0.785 0.45 0.529 0.436 0.431 0.796 0.704
0.21 0.628 1.081 0.819 0.54 1.868 0.912 0.701 0.412 0.434 0.394 0.214
2328
619.3
0.498
4089
1124
Table 6.6:      Reliability Level Vs Monthly Streamflows at Dam Site
Reliability Level
Jan
Feb
Mar
Apr
May_
Jun
Jul
Aug
Sep
Oct
Nov Dec Annual
20.2 12.7 14.1 19.3 37.5 131.4 371.1 568.8 480.4 239.0 75.8 35.5 19.0 11.6 13.0 17.4 34.7 122.3 348.4 542.6 454.6 216 6 69 9 33 2
17.9 10.6 12.0 15.6 32.0 113.5 325.3 516.7 429.0 194.7 64 5 31 1
16.9   9.6 11.0 13.8 29.5 104.9 301.3 490.8 403.3 173.1 59.6 29^2
16.0   8.7 10.1 11.9 27.1   96.4 276.0 464.5 376.9 151.6 55.0 27.4
15.0   7.8   9.2 10.1 24.9   87.8 248.5 437.3 349.4 129.7 50.9 25.7
14.2   6.9   8.3   8.1 22.6   78.9 217.8 408.3 319.8 107.0 47.0 24.1
13.3 6.1 7.4 6.0 20.4
12.5 5.2 6.5 3.5 18.2
69.5 181.2 376.4 286.7 82.5 43.5 22.6
59.0 132.1 337.9 246.0 54.4 40.2 21.2
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Table 6.7 Characteristics of Random Numbers Used for Flow Generation
May 2007
Param
Jan
Feb
Mar
Apr
May
Jun           ul
Aug        Sep        Oct
Nov
Dec
Average  0.00   0.00   0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00 CV     0.51   0.68   0.60     0.58    0.61     0.47     0.33     0.29     0.32     0.58     0.66     0.57
Skew    1.80   1.66   1.50     0.86     1.65     1.05    0.12     0.70     0.59     0.94     2.19     2.02
0.88
1/a       0.933      7   0.834   0.620   0.883   0.683  0.373   0.567   0.530  0.647   1.063   1.007 0.05
y(a)      0.033      7   0.083   0.190   0.058   0.158  0.313   0.217   0.235  0.177  -0.032  -0.003
1.15
z(a)      1.081      6   1.262   1.978   1.162   1.713  3.543   2.241    2.444   1.860   0.948   0.993
Weibul Distribution
X = E + (U-E)*(-ln(1-F)<   >
1/a
u--: Xm +Cv*Xm*y(a)
E = U - Cv'Xm*z(a)
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7 ESTIMATION OF FLOODS FOR UNGAUGED CATCHMENTS
May 2007
In the hydrologic  analysis  for dams, weirs, bridges  and drainage structures, it must be recognized that there are many  variable factors  that affect floods. Some of the factors  that need be recognized and considered on an individual site by site basis are.
o rainfall amount and storm distribution;
o catchment area size, shape and orientation;
o ground cover;
o type of soil;
o slopes of terrain and stream(S);
o antecedent moisture condition;
o storage potential (overbank, ponds, wetlands, reservoirs, channel, etc.); and
o catchment area.
In  general,  three  types  of  estimation  floods  magnitudes  (namely:  the  Rational  Method,  SCS method and Transferring Gauged Data method) can be applied for the project area.
7.1 Rational Method
The Rational Method can be applied to small catchments if they do not exceed 12.8 km2 (or 5 square mile) at the most (Gray, 1971). The consequences of applying the Rational Method to larger catchments is to produce an over estimate of discharge and a conservative design. The method is  nevertheless  frequently  used in standard or modified form for much larger catchments. This  is  because of its  relatively  simplicity. The vast majority  of catchments producing floods  imposed on the command drainage system lie within the validity  of the Rational Method and it has been used as the principal method of estimating design discharges of dykes, culverts, drainage channels, etc.
The Rational Method is based on the following formula:
Qm = 0.2778 C.I.A.Fr
(7.1)
where
Qm = peak flow corresponding to return period of T years in m /sec;
3
C = a 'runoff' coefficient expressing the ratio of rate of runoff to rate of rainfall (see Annex C1 and C2);
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2
I = average maximum intensity of rainfall in mm/hr, for a duration equal to the time of concentration;
A = drainage area in km ;
Fr = is the areal reduction factor (this factors improves the catchment limitations 
imposed on the use of rational method).
Coefficient of runoff C for the formula are given by many soil and water conservation texts (see Annex C.1). Information on rainfall intensity I in a time of concentration (time period required for flow to reach the outlet from the most remote point in the catchment) is required and can be estimated by  the following formula. The selection of the correct value of 'C ’ presents  some difficulty. It represents  a parameter that can influence runoff including: soils  type, antecedent soil conditions, land use, vegetation and seasonal growth. Therefore, the value of 'C ’ can vary from one moment to another according to changes, especially soil moisture conditions.
(7.2)
where
Tc = (1/3080)xL1 155.K0385 (Kirpich equation)
Tc = time of concentration (in hours),
L = maximum length of main stream (in meters),
H = elevation difference of upper and outlet of catchment, (in meters).
The area reduction factor (Fr) is introduced to account for the spatial variability of point rainfall over the catchment. This is not significant for small catchments but becomes so as catchment size increases. The relationship adopted for 'Fr' is based on that developed for the East African condition (Fiddes, 1997). The relationship can be expressed as:
/7
where
Fr = 1 - 0.02 D'033 A 050
D = duration in hours;
A = drainage area in km ;
2
This equation applies for storms of up to 8 hours duration. For longer catchments the value of D can be taken as 8 for use in the above formula.
durations on large
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7.2   SCS Method
May 2007
A relationship between accumulated rainfall and accumulated runoff was  derived by  SCS (Soil Conservation Service). The SCS runoff equation is  therefore a method of estimating direct runoff from 24-houror 1-day storm rainfall. The equation is:
2
Q = (P - la) /(P-la) + S
(7.4)
where
Q = accumulated direct runoff, mm
P = accumulated rainfall (potential maximum runoff), mm
la = initial abstraction including surface storage, inception, and infiltration prior to
runoff, mm
S = potential maximum retention, mm
The relationship between la and S was  developed from experimental catchment area data. It removes  the necessity  for estimating la for common usage. The empirical relationship used in the SCS runoff equation is:
la = 0.2xS
(7 5)
Substituting 0.2xS for la in equation 7.4, the SCS rainfall-runoff equation becomes.
2
Q = (P - 0.2S) /(P+0.8S)
(7.6)
S  is  related  to  soil  and  cover  conditions  of  the  catchment  area  through  CN.  CN  has  a  range  of 0 to 100 (can be obtained from Annex C3 and ERA 2002), and S is related to CN by:
S = 254x[(100/CN) -1]
(7.7)
Conversion   from   average   antecedent   moisture   conditions   to   wet   conditions   can   be   done   by multiplying the average CN values by Cf [where Cf = (CN/1OO)’04]
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7.3   Transferring Gauged Data
May 3007
Gauged data may  be transferred to an ungauged site of interest provided such data are nearby (i e within the same hydrologic  region, and there are no major tributaries  or diversions between the gage and the site of interest). These procedures  make use of the constants obtained in developing the regression equations. These procedures  are adopted from the work of Admasu (1989) as follows
(7.8)
where
Qu = Qg (Au/Ag)°,0
Qu = mean annual daily maximum flow al ungauged site (m ’/s).
2
3
Qg = mean annual daily maximum flow al nearby gauged site (m /s).
Au = ungauged site catchment area (km ).
Ag = gauged site catchment area (km ).
The   estimate   daily   (or   the   24-hr)   annual   maximum   flood   could   be   converted   into   a   momentarv peak as
2
Qp = Cf. Qu
(7 9)
Here. Cf is factor estimated as Cf = 1 > 0.5/Tc (where. Tc is time of concentration)
7.4    Estimation of Weighted Average
In general, the Rational method. SCS method and Transferring Gauged Data a^e recommended for relatively small, medium and large catchments  respectively  However it is very  important to establish a smooth pattern of transition from small to medium and from medium to large catchments
Accordingly, the recommended weighted average estimate of peak flood can be given as
Qw = Wj.Q, ♦ W .Q  + W).Qj
22
(7 10)
>4
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where:
May 2007
Qi,  Q2,  and  Q3    are  mean  annual  momentary  peaks  estimated  by  Rational,  SCS  and Transferring Gauged Data methods, respectively
1t     2    and   W3   are   weighing   factors   of   mean   momentary   peak   estimates   by   Rational, WW
SCS and Transferring Gauged Data methods, respectively
= 12.5/(12.5+Au)
(7.11)
= (Au/Ag)070
(7.12)
= 1 - (W, + W3)
(7.13)
Where Ag and Au are gauged and ungauged site catchment areas, respectively.
7.5   Synder Method of Constructing Synthetic Hydrograph
The   Synder   method   has   been   used   extensively   to   provide   a   means   of   generating   a   synthetic unit hydrograph. The following steps are used:
Step 1: Determine the lag time, TL, of the unit hydrograph. The lag time is  the time from the centroid of the excess  rainfall to the to the hydrograph peak. Synder derived the following empirical equation for lag time:
Tl = Ct (L.Lea)0 3                                                                                                                      (7.14)
where,
Tl = lag time (in hrs),
L = length along the main channel from outlet to divide (in km),
Lea  =  length  along  the  main  channel  from  the  outlet  to  the  point  opposite  the  catchment area centroid in km, and
Ct   =   an   empirical   coefficent   ranging   from   1.36   to   1.66.   For   Ethiopian   condition,   Ct   can be estimated as: Ct = 1 + 0.33x S °1
S = slope of the main stream.
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Step 2: Determination of hydrograph duration. The relationship developed by Synder for the duration of the excess rainfall, Tr in hours, is a function of the lag time computed above.
May 2007
T  =T /5.5
rl
(5.15)
This  results  in an initial value for Tr of TL/5.5. However, a relationship has  been developed to adjust the computed lag time for other durations. This  is  necessary  because the equation above results  in inconvenient values  of unit hydrograph duration. The adjustment relationship is:
T (adj) = T  + 0.25(T
L
l
rn
-T )
r
(7.16)
where T (adj) is the adjusted lag time for the new duration, T
L
RN .
(7.17)
32
The unit peak discharge can be determined from the following equation: Up = (1/2940)x(0.94 - la/P)x(6.08 - In Tc)3 5
where,
Up = unit peak discharge (in m /s/km /mm),
Tc = time of concentration (in hrs),
The peak discharge Qp is computed from:
Qp = Up.A. (P - 0.2S) /(P+0.8S)
and the time from rise of the hydrograph to the peak, Tp, is compured from: Tp = 0.5 Trn + T (adj)
2
(7.18)
L
(5.19)
Finally, the hydrograph can be constructed using T/Tp and Q/Qp values presented in Table C5
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May 2007
Tables  5.1  to  5.3  show  estimated  mean  floods  at  seven  relatively  large  catchments  crossing  the command area from the left and right bank directions.
Table 5.1 Location of Proposed Cross Drainages and Catchment Characteristics
Cachm- ent
Area        Stream Length
Max. Elev Min.
Elev.
Stream
Slope
C :(for
Rational
Method)
CN
scs Method
(km )
2         km) (m asl)
(m asl)
(%)
1      CDR-16     27.15       15.50      2460          1336       0.0725          0.24               71
2      CDR-19        40.25     13.50      2360          1339        0.0756          0.24               71
3      CDR-28        33.00     17.40      2480          1338        0.0656          0.23               71
4       CDL-3          29.60       9.00      1780          1340       0.0489          0.22               71
5      CDL-25       300.00     44 00      2480          1335        0.0260          0.21               70
6      CDL-28         61.00     27.00      1900          1335        0.0209          0.21               70
7      CDL-33         70.00     17.50      1990          1330       0.0377          0.22               71
Table 5.2 Characteristics of Flood Hydrographs
Cach in cut
Base
Flow
Ct
Tc (time
of cone.
Tr
TL(adj)
Tp (peak
time)
Tp(adj)
T
(km)
(m7s)
(hrs)
b
(hrs)
(hrs)
(hrs)
(hrs)
(hrs)
1     CDR-16      0.935       1.43         1.50        0.27        6.32          6.15             6.76            90.95
2     CDR-19      1.387       1.43         1.33        0.24        5.80          5.65             6.19            89.39
3     CDR-28      1.137       1.43        1.71        0.31        6.81           6.62             7.32            92.43
4       CDL-3       1 020       1.45         1.15        0.21        4.63           4.49             4.97            85.88
5     CDL-25       10.34       1.48        4.98        0.90       12.62        12.06           14.09          109.87
6     CDL-28       2.102       1.49        3.72        0.68        9.48           9.06            10.58          100.44
7     CDL-33       2.412       1.46        2.12        0.39        7.03           6.79             7.66            93.09
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rV
I
I
I
I
I 1
I
I I
I 1 I fl
I I I I I
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Table 5.3 Estimated Average Floods
May 2007
Cachment
Q(l)
Rational
Method
Q(2)
scs
Method
Q(3)
Admasu’s
Method
Qavg
(Weighted
Average )
Unit
Runoff
(km)
(hr)
(mm/lir))
2
(lt/s/km )
1          CDR-16             34.94              18.98             10.35       19.10
2 CDR-19 55.45 30.37 12.32 29.74
3         CDR-28             37.95              21.15              10.72               21.02
4           CDL-3              43.25              23.20              9.93                23.11
5          CDL-25            127.43             90.45              50.25                79.28
6          CDL-28             35.63              22.24              16.48               21.93
7          CDL-33             62.30              37.56              18.14               35.94
703
739
637
781
264
359
513
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8 FLOOD FREQUENCY ANALYSIS
8.1   Introduction
May 2007
In flood frequency  analysis, the objective is  to estimate a flood magnitude corresponding to any required return period of occurrence. The resulting relationship between magnitude and return period is  referred as  the Q-T relationship. Return period, T, may  be defined as  the time-interval (on the average) for which a particular flood having magnitude Qt (also known as  qunatile) is expected to be exceeded.
A  reliable  estimate  of  the  entire  Q-T  relationship  cannot  be  obtained  from  small  samples  of  at- site   data.   The   benefits   of   regionalization   in   flood   frequency   analysis   have   been   recognized   at least   since   the   work   of   Delrymple   (1960).   Nowadays,   the   regionalization   approach   to   flood frequency analysis (particularly the index-flood method) is becoming more popular.
An essential prerequisite for the index-flood method is  the standardization of the flood data from sites  with different flood magnitudes. The most common practice, used also in this  study, is to standardize data by division by an estimate of the at-site mean, thus:
Xi = Qi/Qm
(8.1)
Where  Xi  is  the  ith   standardized  flow,  Qi  is  the  i,h    annual  maximum  flow,  Qm  is  the  average value of at-site annual maximum flow series.
Then the quantile QT is estimated as
Qt -
Qm.XT
(8.2)
Thus,  the  mean  annual  flood  is  the  index-flood.  The  parameters  of  the  distribution  of  X  are obtained from the combined set of regional data.
In   this   study,   two   distributions   have   been   used   for   flood   frequency   analysis.   These   distributions are:
(a)        Generalized Extreme Value (GEV) distribution (Jenkinson, 1969)
(b)        Log-Logistic (LLG) distribution (Ahmed et al., 1988)
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8.2 Estimation of Probability Weighted Moments
May 2007
Probability  weighted moments  (PWM) are useful for estimating the parameters  of distributions whose inverse forms  can explicitly  be defined such as  GEV and LLG (Greenwood, et. Al., 1979; and Hosking, 1986). Accordingly, probability  weighted moments  (PWMs) for unbiased sample estimates can be calculated as:
N
m
10k = . l/N)SXi.(1-F|)k i=1
(8.3)
where
Xi = flood having ranked order of /
Fi = (i - 0.35)/N = plotting position of the /th ranked flood
N = record length
The regionally averaged probability weighted moments were computed as follows:
1. The standardized PWMs were computed for each site as:
Xi = Qi/Qm
where Xi is the ith standardized flow, Qi is the i'h ranked flow and Qm is the average of the data set
2.      For each annual maximum flood record the first two probability weighted moments, PWMs, i.e. m10k, k = 1 and 2) were computed using Equation (1).
3.      For each k, weighted average values of PWMs over M sites included in the region were calculated as follows:
M
Xi.Nj.(m10k))
M,ok -        i£L
M
E X,.Nj j=1
(8.4)
I
I
These M10k values were then treated exactly as sample estimates of PWMs for the regional standardized annual maximum flood population. LLG and GEV were fitted to the standardized regional PWMs.
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PWMs          M1OO= 1.0000
M1O1 = 0.411964
M102= 0.250375
8.3 Generalized Extreme Value Distribution
May 2007
Jenkison (1969) introduced the Generalized Extreme Value (GEV) distribution for modeling
floods.  It  has  become  more  important  in  hydrology  since  it  was  recommended  by  NERC  (1975) for modeling annual maximum streamflows of British rivers.
The GEV distribution is defined as:
k
X = u + (a/k) {1 - (-In F) }
The shape parameter k, can be estimated as:
k = 7.859 C + 2.9554 C2
where
C = (Mioo — .M101). - In 2
1   0          1                102) In 3
(8.6) (8.7)
(8.8)
(2.M q -6.M oi+3.M
the scale parameter
a — k-(Miop 2.Mmy)
(1-2*).r(1+k)
and the location parameter u = M100 - (a/k).{1 -T(1+k)}
GEV Parameters             C =     -0.00061 Gama =       1.0023
u = 0.879428 a = 0.253011
k =       -0.00483
8.4 Log-Logistic Distribution
(8.9)
(8.10)
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Log-Logistic (LLG) distribution was introduced for flood frequency analysis by Ahmed et al. (1988).
The LLG distribution is defined as:
May 2007
X= a + b.{F/(1-F)}c
The parameters c, b, and a can be estimated as follows.
The shape parameter:
c =       M1Q0-6. M101+6. M io? M100-2.M101
the scale parameter
b =              Mioo"2.Mioi
c.G
and the location parameter
a = M100 — b. G
where
G= . 7T.c)/sinn.c)
LLG Paameters G= 1.051033
a=    -0.01767
b=    0.968257
c= 0.173014
8.5   Regionally averaged quantiles
(8.11)
(8.12)
(8.13)
(8.14)
(8.15)
Generally, the differences  between estimates  of standardized quantiles  by  GEV and LLG distributions  increases  with increase in return periods. In all cases  clear differences  are observed for the large return periods  where LLG distribution gives  larger quantiles  compared to GEV distribution. Estimated flood quantiles  based on GEV and LLG distributions  are presented in Tables 8.1 and 8.2, respectively.
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May 2007
Log-Logistic  (LLG)  distribution  was  introduced  for  flood  frequency  analysis  by  Ahmed  et  al. (1988).
The LLG distribution is defined as:
X = a + b.{F/(1-F)}c
The  parameters  c,  b,  and  a  can  be  estimated  as  follows.
The shape parameter:
c =       Mioo 6. M-ioi +6.M io?
_
M1Oo-2.Mioi
the scale parameter
b =              Mioo-2.Mioi
c.G
and the location parameter
a  =  M100—   b.  G
where
G= . 7t.c)/sinjr.c)
LLG Paameters G= 1.051033
a = -0.01767
b= 0.968257
c= 0.173014
8.5   Regionally averaged quantiles
(8.11)
(8.12)
(8.13)
(8.14)
(8.15)
Generally, lhe differences  between estimates  of standardized quantiles  by  GEV and LLG distributions  increases  with increase in return penods. In all cases  clear dtfferences  are observed for (he large return penods  where LLG distribution gives  larger quant,les  compared to GEV distribution. Estimated flood quantiles  based on GEV and LLG distributions  are presented in Tables 8.1 and 8.2. respectively.
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Meteorological and Hydrological Aspects 8.6   Choice of Distribution
May 2007
Robustness  as  a criteria has  received widespread attention for choice of distribution for an annual maximum flood (AMF). Robustness  is  assumed to test whether a distribution and method of parameter estimation, considered jointly, are insensitive to departures  from assumptions  made in their use. In this  regard, LLG/PWM has  been found to be more robust than GEV/PWM (Admasu, 1989).
Often, a compromise has  to be reached between flexibility  and sensitivity  to outliers. One may need to compromise on some kind of mid-way  solution. The compromise could be to use a three-parameter distribution such as  GEV and LLG. However, for this  purpose, LLG has  been found to be more attractive than GEV.
In a ddition, L LG i s I ess s ensitive (compared 10 G EV) t o c hanges i n t he s mall v alues 0 f t he annual maximum floods. Therefore, LLG/PWM is  better than GEV/PWM for modeling AM samples that display a dog-leg shape when plotted on probability paper.
In general, LLG/PWM has  been found to be more appropriate for modeling annual maximum floods  (AMF) compared to other distributions. Therefore, LLG/PWM has  been applied for estimating design floods required for the project.
Thus, based on probability  weighted moments  method and using LLW distribution on a regional flood frequency  approach, the return period floods  have been arrived at for the proposed dam site. The monetary  peaks  discharges  at various  levels  of probability  of exceedance (return periods) are summarized below.
10 yr return period flood = 575 m /sec
20 yr return period flood = 654 m /sec
50 yr return period flood = 771m /sec
100 yr return period flood = 871m /sec 500 yr return period flood = 1155 m /sec 1000 yr return period flood = 1303 m /sec 10000 yr return period flood = 1948m /sec
3
3
3
3
3
3
3
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Table 8.1 Estimated Flood Quantiles Based on GEV Distribution
May 2007
T
Return
period
Xt
Growth
factor
Mean Max. Daily Discharge
Momentary Peatc
Gauging
Site (1)
Gauging Wama at
Site (2) Junction
Dam
site
Gauging Gauging
Site(1) Site (2)
Wama at
Junction
Dam site
(years)
(ratio)
3
(m /s)
(m3/s)       (m3/s)     (m3/s)
33
(m /s) (m /s)
3
(m /s)
3
(m /s)
2           0.95
5            1.23
10          1.42
20           1 60
50           1.83
100         2.01
200         2.19
500         2.43
1000        2.61
2000        2.79
5000        3.03 10000       3.22
670          214          238         354
869          278          309        460
1001         320          356        530
1129         361          402         597
1296         414          459         686
1422         455          505         752
1549         495          550        819
1716         549          610        908
1844         589          655         975
1972         631          700        1043
2143         685          761        1134
2273         727          808        1203
788           272          264         392
1022         353          342         509
1178         406           395         586
1329         458          445         661
1526         526          509         759
1674          577          559         833
1822         629          609         907
2020          697          676        1005
2170          749          726        1080
2321          801           776         1155
2522         870          842        1255
2675          923          895        1331
Average       1         707        226        251       374       832         287         278         414
/vote, me analysis is based on regional flood frequency analysis based on General Extreme Value (GEV) distribution with parameters estimated by probability weighted moments method
Table 8.2 Estimated Flood Quantiles Based on LLG Distribution
T
Return
period
xt
Growth
factor
Mean
Max. Daily Discharqe
Gauging Site cp
Gauging
Site (2)
Wama at
Junction
Dam
site
Momentary Peak
Gauging Gauging Wama at
Site (1)
(years)
(ratio)
3
(m /s)
3
(m /s)
Site (2)      Junction
3
(m /s)
3
(m /s)
264
336
386
439
517
584
662
776
876
987
Dam
site
394
500
575
654
771
871
984
1155
1303
1471
Average         1             707             226           251         374
2             0.95             672               215             238          356
5             1.21             854               273             304          452
10            1.39             982               314             349          519
20            1.58            1118              357             397          591
50            1.86            1317             421              467          697
100            2.1             1488              476             527          787
200           2.38            1680              537             597          889
500           2.79            1972              630             700         1043
1000          3.15            2225             711             791          1177
2000          3.55           2511              803             891          1329
5000          4.17            2947              942            1047        1559
10000         4.71            3326             1063           1182        1760
791
1005
1156
1315
1550
1751
1977
2320
2619
2956
3468
3915
273
347
399
454
535
604
682
800
903
1020
1196
1350
1159 1726
1309 1948
lyoie. i ne analysis is  aseo on regional flood frequency analysis based on Loa-Lo^^TTi
332                 287          278         414
with parameters estimated by probability weighted moments method.
D
9 L 3 <LLG) dlstnbution
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9. PROBABLE MAXIMUM FLOOD
The probable maximum flood is defined as the most severe flood considered reasonably
May 2007
possible to occur. It is customarily obtained by using unit hydrographs and rainfall information.
Annex  C4 gives  the recommended reservoir flood standards  for three main categories  of reservoirs. Category  A has  the most stringent standards, since for such a reservoir a breach in the dam would endanger the lives  of people congregated in a town or village downstream. The worst conditions  are envisaged, the spillway  already  taking the average daily  inflow when the probable maximum flood (PMF) arrives  and the wave surcharge allowance must be for heights greater than 0.6 m. Categories B and C have decreasing standard requirements.
In Ethiopia, the study  of design floods  on the basis  of PMF requires  further investigation. The challenging problem is  particularly  that of storm rainfall and the varying temporal pattern of intensities  throughout a storm’s  duration; the occurrence of the peak  intensity  is  a variable to be considered for further study  at national level. The initial state of the catchment is  also of great significance in the generation of an extreme flood. A thorough knowledge of a catchment and of its  varying responses  during adverse conditions  is  essential for an engineering hydrologist to evaluate design floods  and such situation need to be investigated at an institute level.
However, in such a situation where there is  maximum rainfall data, Hersfield (1961) suggests the use of following equation to estimate 24-hr PMP in region so as  to superimpose it on design unit hydrograph to give the PMF
PMP24 = Pm +K.Sn = Pm (1+K.CV)
Where
PMP24 - 24-hr probable maximum precipitation
Pm = mean of the 24-hr annual maximum over the period of record Sn = standard deviation of the 24-hr annual maximum
CV = Pm/Sn = coefficient of variation K = a constant equal to 15
(9.1)
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The mean annual maximum 24-hr rainfall for Bedele and Jimma are 55.5 mm and 51.3 mm, respectively; and also the coefficient of variation for Bedele and Jimma are 0.198 and 0.201, respectively. For the Arjo Dedessa dam catchment:
Pm = (55.5+51.3)/2 = 53.4 mm
CV = (0.198+0.201 )/2 = 0.20
Sn = 0.20x53.4= 10.68
K = 15
Then
PMP24 = Pm +K.Sn
= Pm(1+K.CV)
= 4.01’Pm
= 4.01x53.4 = 214 mm/day
Total volume of daily rainfall
Vp= A. PMP24 = 5310*214 = 1129.5 Mm3
May 2007
3
3
Assuming runoff coefficient C that corresponds to the occurrence of continuous rainfall to be 0.68, the estimated rainfall depth that produces probable maximum flood becomes:
Vpe= C.Vp = 0.68*1129.5 = 768.5 Mm3
Using the catchment characteristics given in Table 12.1 and applying the dimensionless hydrograph given by Table C4, the flood peak component due to the probable maximum rainfall becomes 3502 m /s. Assuming the a base flow of 188 m /s the total peak flood becomes:
Qp = base flow + peak due to storm = 188 + 3502 = 3690 m /s
Cumulative volumes of Probable Maximum Floods (PMF) derived from probable maximum 24- hr rainfall is given by Table 9.1 while design flood hydrograph derived from estimated PMF is given by 9.2.
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Table 9.1: Table Cumulative Volume of Probable Maximum Flood
May 2007
Time
(hrs)
Cum. Volume
Cum. Volume
(Mm3)
(%)
Time
(hrs)
(Mm3)
(%)
Time
(hrsj
Cum. Volume
(Mm3) (%)
0.00        0.000         0.00
4.50        0.440         0.06
9.00        2.936         0.38
13.50 9.389 1.22
18.00 21.87 2.85
22.50 42.15 5.48
27.00 71.51 9.31
31.50 110.5 14.4
36.00 157.6 20.5
40.50 210.4 27.4
45.00 266.4 34.7
49.50       322.6         42.0
54.00       376.4         49.0
58.50       426.4         55.5
63.00 471.6 61.4
67.50 511.8 66.6
72.00 546.4 71.1
76.50 576.0 75.0
81.00 601.7 78.3
85.50       623.9         81.2
90.00      643.4         83.7
94.50      660.4         85.9
99.00      675.1         87.9
103.50 687.8 89.5
108.00       698.7         90.9
112.50 708.3 92.2
117.00 716.4 93.2
121.50 723.2 94.1
126.00 729.1 94.9
130.50 734.4 95.6
135.00 739.0 96.2
139.50 743.0 96.7
144.00       746.5        97.1
148.50       749.3         97.5
153.00       751.7         97.8
157.50       753.6        98.1
162.00       755.5         98.3
166.50       757.2         98.5
171.00       758.6         98.7
175.50       759.9         98.9
180.00       761.0        99.0
184.50       762.0        99.2
189.00       762.9        99.3
193.50      763.6        99.4
198.00      764.3        99.4
202.50       764.8        99.5
207.00         765.3           99.6
211.50        765.8           99.6
216.00        766.1           99.7
220.50         766.5           99.7
225.00        766.8           99.8
229.50        767.1           99.8
234.00        767.3           99.8
238.50       767.5           99.9
243.00        767.7           99.9
247.50       767.9           99.9
252.00       768.1           99.9
256.50       768.2          100.0
261.00       768.3          100.0
265.50       768.4          100.0
270.00        768.5          100.0
274.50        768.5         100.0
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Table 9.2 Design Flood Hydrograph Derived from Estimated PMF
May 2007
Ti
Qi
Ti
Qi
Ti
Qi
Ti
Qi
Ti
Qi
(hr)
(m3/s)
(hr)
(m3/s)
(hr)
(m3/s)
(hr)
(m3/s)
(hr)
(m3/s)
0.00         188
2.25         216
4.50         241
6.75         339
9.00         451
11.25        573
13.50        748
15.75        958
18.00       1169
20.25       1449
22.50       1694
24.75       2009
27.00       2289
29.25       2604
31.50       2885
33.75       3095
36.00       3305
38.25       3445
40.50       3585
42.75       3655
45.00 3690
47.25 3655
49.50 3620
51.75 3515
54.00 3410
56.25 3270
58.50 3130
60.75 2990
63.00 2815
65.25 2674
67.50 2499
69.75 2324
72.00 2149
74.25 2009
76.50 1904
78.75 1764
81.00 1659 83.25 1554 85.50 1484 87.75 1379
90.00 1309 92.25 1239 94.50 1169 96.75 1099 99.00 1028 101.25 958 103.50 923 105.75 853 108.00 818 110.25 783 112.50 748 114.75 678 117.00 643 119.25 608 121.50 573 123.75 552 126.00 531 128.25 514 130.50 496 132.75 472
135.00 451
137.25 433
139.50 419
141.75 402
144.00 384
146.25 367
148.50 349
150.75 332
153.00 314
155.25 297
157.50 314
159.75 307
162.00 300
164.25 290
166.50 283
168.75 279 171.00 272 173.25 269 175.50 262 177.75 258
180.00       251 182.25        248 184.50         244 186.75        241 189.00         237 191.25         234 193.50         230 195.75         230 198.00         227 200.25         223 202.50         220 204.75         220 207.00         216 209 25         216 211.50         213 213.75         213
216.00        209
218.25        209
220.50        209
222.75 206
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Meteorological and Hydrological Aspects 10 SEDIMENT ANALYSIS
10.1 Estimation of Sediment yield
May 2007
Suspended    sediment    data    from    Stations    14001,    14014    and    14005    were    obtained.    These discharges are given in Annex E5. Suspended concentration rates varying between 45 mg/lt to
1670 mg/lt were observed from Didessa river..
Relationships  between water discharge and sediment loads  at various  sites  in the region are presented in Table 10.1. At-site and regional approach has  been used to to estimate sediment rates  from the estimated streamflows  at the dam site. Accordingly, the following relationship has been derived:
Gs = 0.078Q15
(10.1)
Where
Gs = sediment load (in gm)
2
Q = monthly mean discharge (in lt/s/km ).
Careful assessment regarding reservoir sedimentation will be required in the site as  the life of the storage created by  a dam is  dependant of the sedimentation situation. Accordingly, reservoir p lanning will i nclude a ssessment o f the p robable r ate o f s  edimentation i n order t o predict the useful life of the proposed reservoir.
In estimating reservoir sedimentation, materials  originating as  bed load will be assumed to have a density  of 1.4gm/cc  and material carried in suspension will be assumed to have a density  of 1.2 gm/cc. The bed load at the dam sites  shall be assumed to be 15 percent of the suspended load
Monthly   total   sediment   load   (suspended   and   bed   load)   and   accumulated   total   sediment   load   at the reservoir site are given by Table 10.2 and 10.3, respectively.
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10.2 Distribution of sediment in the reservoir
May 2007
So far the sediment load likely  to deposit in the reservoir has  been estimated. But estimating the sediment inflow to the reservoir is  not enough as  how high the sediment will accumulate in the reservoir is  also important. Accordingly, Area-Increment method has  been used for this purpose. The method is expressed as:
(10.2)
Where
Vs = Ao(H - Ho) + Vo
Vs = sediment volume to be distributed
Vo = sediment below the new zero elevation
H = original reservoir depth from the stream bed level
Ho height to which the reservoir is completely filled with sediment, i.e., = height of the new zero elevation
Ao surface areas of the reservoir at the new zero elevation and is the = area correction factor.
The results so obtained from the analysis of distribution of sediment in the Arjo-Dedessa Reservoir are given in Table 10.4.
Table 10.1 Relationships between water discharge and sediment load
No
Station
Area
(km )
2
Module Loss
32
10 t/yr |
t/km /y
3005 Guder at Guder 524 77 90
4007 Little Angar near Gutin 1975 176 89
4005 Dabana near Abasina 2881 453 157
4002 Anagar near Meqemte
6002 Abbay at Sudan Border
4674 702 150
172254 335170 1946
Vales of C in:
Qs =C.(Qw)1 5
0.025
0 096
0 059
0 061
0165
Arjo Dedessa Dam site
5278        776         147        0.078
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Table 10.2: Monthly Total Sediment Load at the Reservoir Site
Table 10.3 Accumulated Total Sediment Load at the Reservoir Site
(in Mm )
3
Jan Feb ,March April May June |july Auq Sep                       Oct Nov Dec
Annual
Years
25
50
75
100
125
150
175
200
0.06 0.03         0.03  0.04  0.11   0.68    3.28       6.2       5.29  3.00     0.48     0.17
0.12 0.07         0.06  0.09  0.23   1.36      6.6     12.4       10.6  6.0       0.96     0.34
0.19 0.10         0.09  0.13  0.34   2.04      9.8     18.6       15.9  9.0       1.45     0 52
0.25 0.13         0.11  0.17  0.45   2.72    13.1     24.8       21.1  12.0     1.93     0.69
0.31 0.17         0.14  0.21  0.56   3.40    16.4     31.1       26.4  15.0     2.41     0.86
0.37 0.20         0.17  0.26  0.68   4.08    19.7     37.3       31.7  18 0     2 89     1 03
0.43 0.23         0.20  0.30  0.79   4.76    22.9     43.5       37.0  21 0     3 38     1 20
0.50 0.27 0.23 0.34 0.90 5.44 26.2 49.7 42.3 24.0 3.86 1.37
19.39
38.78
58.17
77.56
96.95
116.3
135.7
155.1
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Table 10.4: Distribution of Sediment in the Arjo Dedessa Reservoir
May 2007
50 years sediment load Ho = 2.03 m
Ao = 1.52 Mm ____
2
100 years sediment load
Ho = 3.42 m
Ao = 3.15 Mm2
r
Original
Original capacit
Depth
Sedime\
nt
volume
Revised
Revised
capacit
Original
Original
capaci1ty Depth J Mm ) (m)
3: 4
Sediment
volume
_ (Mm ) _ 1
Revise
d area
3
56
Revised
capacity
(Mm3)
7
1346 56.76
1343 48.13
1340 39.92
1337 32.17
629.8 27 38.7 55.24
466.4 24 34.2 46.61
331.8 21 29.6 38.41
224 18 25.1 30.66
591.1 1346 56.76 629.8    27       77.4    53.61    552.4
432.2 1343 48.13 466.4
68
44.98
398.4
302.2 1340 39.92 331.8
24
21
18
15
12
9
6
58 6
36.78
273.3
198.9 1337 32 17 224
49.1
29.03
174.8
1334 24.93
140.7
15
23.41
120.2 1334 24.93 140 7
39.7
21.78
101
1331 18.24
79.64
12
20.5
16
11.4
6.88
2.33
16.72
63.67 1331 18.24 79.64
30.2
15.09
49.4
1328 12.19
38.24
9
1325 6.911
13.6
6
10.68
5.395
26.82 1328 12.19 38.24
20.8
9.046
17.44
6.724 1325 6.911 13.6
11.4
3.765
2.239
1322 2.619
2.322
3
1322 2.619 2.322       3
1.92
150 years sediment load
Ho = 4.69 m
Ao = 4.90 Mm2
200 years sediment load Ho = 5.89 m
A o = 6.73 Mm2
Elev (m asl)
Original area
2
(Mm )
r-
Original capacit
y
1
(Mm )
Depth (m)
Sedime nt
volume
1
(Mm )
Revised area
Revised capacit
y            —•»»•
D riginal Original
Elev
area
capacity Depth
S edime nt
volume
Revised Revised
(Mm ) (Mm1) (m asl)(Mm2) (Mm1)
67
area
1
(Mm )
2
(Mm )
capacity
1
(Mm )
1
2
3
4
5
3
1346 56.76 629.8
1343 48.13 466.4
1340 39.92 331.8
1337 32.17 224
27     116   51.86  513.3 1346 56.76 629 8
24 102 43.24 364.6
21 87.1 35.03 244.7
18 72.4 27.28 151.5
4
27
4
5
155
6
50.02
1334 24.93 140.7    15    57.7   20.03
1331 18.24 79.64 1328 12.19 38.24 1325 6.911 13.6
12 43 13.34 9 28.4 7.297 6 13.7
82.96
36.6
9.887
1343 48.13 466.4
1340 39.92 331.8
1337 32.17 224
1334 24.93 140.7 1331 18.24 79.64 1328 12.19 38.24 1325 6.911 13.6
2
21
18
15
135
115
94.5
74.3
41.4
33.19
25.44
18.19
12
54.1
11.5
9
33.9
5.457
7
474.7
331.5
217.1
129.4
66.37
25.52
4.326
6
13.7
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rological and Hydrological Aspects
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11.
WATER QUALITY
The water quality status of the Dedessa river is evaluated using the data collected from MoWR database, Abbay  River Basin Master Plan Study  Report (1998). The samples  were taken as part of the supporting measurement for the aquatic  study  and appear to represent the only available data on water quality  within the catchment. Table 11.1 shows  water quality measurements  taken for Dedessa at two sites. The samples  were taken as  part of the supporting measurement for the aquatic study and appear to represent the only available data on water quality within the catchment. The samples shown in Table 11.1 were taken after the start of the rainy  season and presumably  all tributaries  of the river exhibited adequate water quality at the time of sampling.
Total Dissolved Solids (TDS) and Electrical Conductivity (EC)
Total dissolved solids characterized mainly by  major anions and cations  are directly related to the electrical conductivity of the water. The low Electrical Conductivity (EC) and TDS value in general shows  that the water is  soft in nature and has  low salinity. Moreover, the low conductivity is sign of low fertility of the water with regard to aquatic life. Electrical conductivity (EC) measurements  were very  low at all the sampled sites. EC is  the ability  of the water to conduct electric current and directly related to the amount of cations and anions in the water.
pH
The pH value is the measure of the concentration of hydrogen (H+) and hydroxyl (OH-) ions in the water It is to determine the acidity or alkalinity of the substance.
Sodium and Potassium
The Na and K reading expressed in terms  of Sodium Adsorption Ratio (SAR) is  the useful parameter for the evaluation of the water body  for irrigation purpose. For irrigation water it is important to measure the sodium adsorption ratio as follows:
SAR = ----------------------------- - (9.1)
;
l(Ca++ + Mg-)/]05
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The higher the SAR value of the water the less  suitable will be for irrigation purposes. The maximum computed value among the readings  is  0.29 which is  less  than 10. This  illustrates that the water is very much suitable for irrigation purpose.
Chloride
The chloride concentration of the rivers stipulated in Tables 11.1 is very low (1.0 to 2.0 mg/l), at times  of sampling. Hence the parameter was  in conformity  with the standard set by  EPA (250mg/l for aquatic species).
Table 11.1 Water quality results for sites on the Dedessa river
Sampling
at Bedele bridge
at Gimbi bridge
Comment
WQ
Sample date Elevation (m asl)
TDS (gm/l)
EC (Ds/cm)
pH
Na"
K*
Ca** (mg/l)
Mg" (mg/l)
Cl’ (mg/l)
SAR
lilt fr>r KAainr Qiisnrc /DCC/CAt
13/08/96
1300
20
60
6.74
3.3
1.9
6.4
2.43
1.0
0.29
14/08/96
1200
20
50
6.97
3.0
2.5
9.6
1.4
2.0
0.25
N
N
N
N
N
N
N
N
N
' I
River basin study, 1998)
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12.
DESIGN FLOOD
12.1 Synthetic Hydrograph
Among several known methods  of unit hydrographs, the one developed by  Synder (1938) is most commonly  used. The method is  derived from drainage basins  in the Appalachian Mountain regions  in the United States  ranging in areas  from 25 km to 25,000 km The relations  of unit hydrograph characteristics  and catchment characteristics  are expressed as follows:
Tp = Ct((L.Lc)03
(12.1)
Where
Tp = lag time from the mid point of the rainfall-excess duration Tr to the
peak of a unit hydrograph (hr),
Ct = coefficient depending on basin characteristics
Lc = river distance from the station to the point of interest nearest the
centroid of the drainage area (km)
L = river distance from the station to the upstream limits of the drainage
area (km)
The  catchment  characteristics  (such  as  L.  Lc,  Area,  Slope)  of  Desessa  at  the  Arjo-Dedessa Dam Site is given by Table 12 1
The U S Soil Conservation Service (SCS) developed a dimensionless hydrograph that has its ordinate values expressed as the dimensionless ratio, Ti/Tp Qi is the discharge at any time Ti The shape of the unit hydrograph dimensionless  hydrograph, and Tp is  the period corresponding to peak as shown in Annex Table C5 For a given catchment, once the value of Tp is defined using Equations 12 1, the unit hydrograph can be constructed
12.2 Flood Routing Through the Reservoir
The dam is located where failure may probably be confined to the irrigation command and the dam itself No excess.ve loss  of human life would be expected For such condition, a des.gn flood corresponding to a10.000-year return period has been proposed The volume of flow m
1000-year return period flood hydrograph is about 400Mm ’
75
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The data used to complete surcharge storage due to the design flood and routing calculations were: inflow hydrograph for all selected design storms, elevation-storage relations for proposed storage facility, and stage-discharge relations for all outlet control structures.
The step-by-step method of reservoir routing method has been used The data required for the method to construct the outflow hydrograph is: (a) the inflow hydrograph, (b) the elevation capacity  relation curve of the reservoir, and (c) the outflow-elevation curve or the outflow equation.
Using these data a design procedure is  used to route the inflow hydrograph through the storage facility  with different surcharge storage and spillway  crest (outlet) geometry  until the desired outflow hydrograph has been achieved
A spillway with ogee shaped crested weir has been assumed The equation generally used for such broad-crested weir is
where:
J
Q =CLH'5
Q    = discharge, in m /sec
C    = broad-crested weir coefficient L     = broad-crested weir length, m H     - head over weir crest, m
(122)
If the upstream edge of a weir is so rounded as to prevent concentration and ,f the slope of the crest is as great as the loss of head due to friction, flow will pass through critical depth at the weir crest; thus gives an average C value of 2 2
The following procedures were used to perform routing through the reservoir
StefU;   Developing   an   inflow   hydrograph,   stage-d.scha.ge   curve   for   the  proposed   storage facility.
Steai  Selecting  a  routing  time  penod.  Dt.  to  provide  at  .east  five  pomts  on  the  rising  |lmb  of the inflow hydrograph
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Step 3: Using the storage-discharge data from Step 1 to develop storage characteristics curves that provide value S (+/-) (Q/2).Dt versus stage.
Step 4: For a given time interval, 11 and I2 are known. Give the depth of storage or stage, H1, at the beginning of that time interval, S1 - (Q1/2).Dt can be determined from the appropriate storage characteristics curve.
Step 6: Determining the value of S2 + (Q2/2).Dt from the following equation:
3
S2 + (Q2/2).Dt = [S1 - (Q1/2).DtJ + [11 + l2)/2.Dt] where
S2 = storage volume at time 2, m /sec Q2 = outflow rate at time 2, m3/sec Dt = routing time period, sec
S1 = storage volume at time 2, m3 Q1 = outflow rate at time 1, m3/sec 11  = inflow rate at time 1, m3/sec 12  = inflow rate at time 2, m3/sec
(12.3)
Step  6.  Enter  the  storage  characteristics  curve  at  the  calculated  value  of  S2  +  (Q2/2)  Dt determined in Step 5 and read off a new depth of water, H2.
SteP 7;  Determine the value of Q2, which corresponds to a stage of H2 determined in Step 6 using the stage-discharge curve.
Repeal Step 1 through 7 by setting values of 11. Q1, S1. and H1 equal to the previous 12. 02. S2 and H2. and using a new 12 value. This process is continued until the entire inflow hydrograph has been routed through the storage basin.
Since  Equation  (12.3)  contains  two  unknowns,  stepwise  trial  and  error  procedure  is  used Results   of   routed   design   floods   based   on   LLG   distribution.   half-PMF   and   full-PMF   are presented  by  Table  12.2,  12.3  and  12.4,  respectively.  The  elevation  -  area  capacity curves  Z the  reservoir  are  shown  in  Figure  12.1.  The  10000yr  return  period  design  inflow  and  outflow hydrographs are shown In Figure 12.2. Such inflow and outflow hydrographs for smaller return
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periods  like  10,  20,  50  and  100  year  return  periods  are  shown
in
Figure  12.3.  The  average
hydrograph of maximum flow at the dam site is available in Figure 12.4.
12.3 Routed Design Flood at the Coffer Dam
Velocity Formula
The velocity equation through the pipe outlet is given by
V = (2gH )
L
05
(10.4)
where
A = cross-sectional area of barrel (or pipe), V = velocity through the barrel,
Hl = total head loss through the barrel, H  = (1.5 + f L/D) V /2g
2
l
(12.5)
L = length of the barrel/pipe
D/4 = R = hydraulic mean radius of the barrel
D = diameter of barrel/pipe
V = velocity through the barrel/pipe
f = coefficient of friction
Manning's Formula
It is one of the most common formula, which is commonly used for the analysis of problems of flow through channels, but often used for the analysis of pipe flow problems too. According to this formula the discharge Q through pipes is given by:
V = (F/nJ.D273 S '
12
(12.6)
Where
V = velocity through the pipe,
n = Manning's roughness coefficient (a value of 0.065 has been selected for old concrete pipe),
D = diameter of the pipe,
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S = slope of the hydraulic gradient or energy slope = h|/L
F = is constant equal to 0 39685
L = length of the pipe (m).
h t = head loss through the pipe (m)
Equation 12 5 can be arranged to express the relations for head los
s through a pipe
h, = L ((n/F) V/D  ]
20 2
(12 7)
Combinod Formula
Combining Equation 12 5 and 12 7 the above two expressions for v
elocity reduced to
• (1 5/2g* L [(n/F) D  ’)| } V
2/    2      2
(128)
and
V     (Hu/(1 5/2g * L [(n/F)D 'Y))
2        u5
(12 9)
also
Q     A (H,/{1 5/2g * L ((n/F) D2,3]2})0i
(nD /4) (H,/{1 5/2g * L [(n/F) D "Y})
2
1
(12 10)
Where A is cross sectional area of pipe in m'
Substituting n 0 0165. g 9 61 in Equation 12 8. can be expressed as H, (0 076453* L (0 041577 D "*] } V
22
V (H /{0 076453* I [0 041577 D ^))
t
2/
U5
Q (0 7857 D ) (H /{0 076453* L |0 0415/7 D
2
t
2 Y}) '
Results ol routed design flood at Coffer Darn of Ago Didessa Reservoir are presented m Table 12 5 These results are available lor 10, 20, 50 and 100 year return penod floods According
to the criteria the appropriate results could be adopted tor designs
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Table 12.1 Catchment Characteristics
May 2007
Arjo Dedessa Catchment Characteristics
Area (km2)
Length (km)
Centroid distance (km) Max elevation (m asl) Minimum elevation (m asl)
Elevation difference (m) S (m/m) Ct
Tc (hrs) Tl (hrs) Tr (hrs) T  adj) (hrs)
L(
TP (hrs)
Dimension
5278
199
141
3012
1315
1697
0.0085 1.53
24.00
33.00
4.40
38.00 45.00
Table 12.2 Routed Design Flood at FRL Corresponding to 1352 m
(a)     Based on LLG distribution
L(m)
Qout (m /s)
3
____
Max Surface
Area (Km )
2
Surcharge
Storage (Mm )
3
50 871 3.97
75 1031 3.39
100                   1154
3.02
125 1253 2.75
150 1333 2.54
175 1399 2.36
87.06
85.20
84.01
83.14
82.47
81.91
1340.1
1288.0
1255.0
1231.4
1213.1
1198.3
Peak inflow = 1950 m3/s
(b) Based on Half-PMF
L(m)
Qout (m /s)
3
H (m)
Max Surface
Area (Km )
2
Surcharge
Storage (Mm )
3
50 798
75 941
3.75
3.19
100                   1051                      2.84
125                   1140
2.58
150                   1212                     2.38
175                   1273
2.22
86.34
84.56
83.43
82.61
81.97
81.45
Peak inflow = 1780 m3/s
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1319.8
1270.3
1239.1
1216.9
1199.7
1186.0
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(c) Based on PMF
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L(m)
Qout (m /s)
3
H(m)
Max Surface
Area (Km )
2
Surcharge
Storage (Mm )
3
50                    1668                     6.13
75                    2008                     5.29
100                   2265                     4.73
125 2466 4.32
150 2626 3.99
175 2756 3.71
93.95
91.28
89.50
88.16
87.10
86.24
1539.8
1461.3
1409.5
1371.3
1341.2
1316.8
Peak inflow = 3690 m3/s
Table 12.3 Routed Design Flood at FRL Corresponding to 1555 m
(a) Based on LLG distribution
L(m)
Qout (m /s)
3
H (m)
Max Surface
Area (Km )
2
Surcharge
Storage (Mm )
3
50 812
75 964
3.79
3.24
100 1083 2.89
125 1180 2.64
150                   1261
2.44
175                   1329                     2.28
96.26
94.57
93.48
92.70
92.09
91.59
Peak inflow = 1950 m3/s
1601.8
1546.7
1511.9
1487.1
1468 0
1452 6
(b) Based on Half-PMF
L(m)
Qout (m /s)
3
H (m)
Max Surface
Area (Km )
2
Surcharge Storaop
50
75
100
125
150
175
746 3.58
881 3.06
988 2.72
1075 2.48
1148 2.30
1208 2.14
------------ ------- -
95.61
93.98
92.95
92.20
91.63
91.16
1580.6
1527.9
1495.0
1471.6
1453.7
1439.2
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L(m)
Qout (m /s)
3
H(m)
Max Surface
Area (Km )
2
Surcharge
Storage (Mm )
3
50 1540 5.81
75 1867 5.04
100 2118 4.53
125 2320 4.14
150 2483 3.84
175 2618 3.59
102.52
100.13
98.54
97.36
96.41
95.64
1811.9
1730.7
1677.1
1637.8
1606.8
1581.3
Peak inflow = 3690 m3/s
Table 12.4 Routed Design Flood at FRL Corresponding to 1356m
(a) Based on LLG distribution
L (m)
Qout (m /s)
3
H (m)
Max Surface
Area (Km )
2
Surcharge
Storaqe (Mm )
3
50 800 3.75
75 951 3.21
100 1069 2.87
125 1165 2.62
150 1246 2.42
175 1314 2.27
99.11
97.49
96.45
95.71
95.12
94.65
1693.0
1637.1
1602.0
1577.0
1557 7
1542 n
Peak inflow = 1950 m3/s
(b) Based on Half-PMF
LL )
m
Qout (m /s)
3
-
Max Surface
Area (Km )
2
Surcharge
Storaae (Mm !
3
50                     735                       3.55
75                     869
3.03
100 975 2.70
125 1061 2.46
150 1134
175 1195
2.28
2.13
98.49
96.93
95.94
95.23
94.68
94.23
Peak inflow = 1780 m3/s
1671 5
1618 2
1584 9
1561 2
1543.1
1528 5
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L (m)
Qout (m /s)
3
H(m)
Max Surface
Area (Km )
2
Surcharge
Storage (Mm )
3
50 1515 5.75
75 1838 4.99
100 2088 4.48
125 2289 4.11
150 2453 3.81
175 2588 3.56
105.09
102.81
1905.2
1823.3
101.30 1769.3
100.17 1729.8
99.28 1698.5
98.53 1672.9
Peak inflow = 3690 m3/s
Table 12.5: Routed Design Flood at Coffer Dam of Arjo Didessa Reservoir
3
T = 10 yrs, Peak = 654 m /s
3
T = 20 yrs, Peak = 654 m /s
D
(m)
Q
(m3/s)
Elev
(m asl)
D
(mJ
Q
(m3/s)
Elev
(m asl)
5.0            210              1337.98 5.5            223              1333.01
6.0            239              1330.00
6.5            258              1328.06
2x4.0          220              1334.18
2x4.5          243              1329.86
5.0            211                1338.22
5.5             225               1333.29
6.0             243               1330.32
6.5            263               1328.40
2x4.0 222
2x4 5 247
1334.45
1330.19
3
T = 50 yrs, Peak = 771 m /s
D
Q
(m3/s)
Elev
(m asl)
3
T= 100 yrs, Peak = 871 m /s
D
(m)
Q
(m3/s)
Elev
(m asl)
5.0            213              1338.59
5.5            229              1333.70
6.0            248              1330.79
6.5            271              1328.90
2x4.0 225 1334.86 2x4.5 253 1330.69
5.0 215 1338.89
5.5            232               1334.05
6.0            253               1331.19
6.5            277               1329.32
2x4.0 228
2x4.5 257
1335.20
1331.10
Note : Length of pipe, L = 400 m
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Elevation-Are
a-Capacity Curve
for Arjo Didessa Ro
servolr
Area (Sq. K
m)
125
100
75
50
25
0
Figure 12.1: Elevation-Area-Capacity Curves of Arjo Dedessa Reservoir
Inflow and Outflow Hydrographs at Ar)o Otdossa Reservoir
Figure 12.2: Inflow and Outflow Flood Hydrographs at Arjo Dedessa Reservoir
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Elevation (m)Discharege (m3/s)
Discharege (m3/s)
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Inflow and Outflow Hydrographs at Arjo Didessa Coffer Oam, T
■ 10 years
Inflow and Outflow Hydrographs at Arjo Odessa Coffer Dam, T
.JO
cn
E
Time (hn|
Inflow and Outflow Hydrographs at Arjo Dldessa
Coffer Dam, T = 50 years
Time ( hrs)
Figure 12.3: Inflow and Outflow Flood Hydrographs at Arjo Dedessa Reservoir
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Average Hydrograph of Maximum Flows at Arjo Didessa Dam site
Figure 12.4: Average Hydrograph of Maximum flows at Arjo Dedessa Dam Site
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13.   RESERVOIR SIMULATION
May 2007
Determining design capacity of Arjo Dedessa reservoir required reservoir simulation study. The factors  that has  been considered in the analysis  include the following. This  particular section deals with the dam/ reservoir simulation for establishing the viability of contemplated irrigation
o Useable flow between reservoir and diversion,
□ Spill past diversion weir,
o Flow into the reservoir,
o Evaporation from the reservoir,
o Rainfall on the reservoir,
o Total irrigation demand, and
o Irrigation water release from the reservoir.
The  simulation  sample  is  provided  in  annexure  B  -  8.  the  description  of  various  column, referred to there in are as below:
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Column
Abbrev
Unit
Description
Col [1]
Year
Year
Col [2]
Month
Month
Col [3]
Inflow
Mm3
Natural inflow to the reservoir as simulated by Thomas-
Feiring model (monthly flows for 1000 years)
Col [4]
DF
Mm3
Natural flow (corresponding to 75% reliability level)
between the dam site and diversion site
Col [5]
Eo-Rf
Mm
Evaporation minus rainfall over the reservoir. Eo is 
evaporation at 75% exceedance level as estimated from
ETo and Rf is monthly rainfall at 75% reliability level
Col [6]
IWD
Mm3
Irrigation water requirement (given by Table 13.1)
Col [7]
(Eo-Rf)
Mm3
Col [5] converted to volume covering the entire
command area
Col [8]
SL
Mm3
Seepage loss (assumed to be 1% percent of the
existing storage in the reservoir)
Col [9]
IWR
Mm3
Irrigation water release from the reservoir (Col[6]-Col[4J)
Col [10]
CS
Mm3
Change in storage between the ends of the previous
and current months
Col [11]
Storage
Mm3
Reservoir storage at the end of month
Col [12]
Area
Km2
Reservoir average surface area between beginning and
end of month
Col [13]
Level
m. asl
Reservoir storage at the end of month
Col [14]
Spill
Mm3
Spill from the reservoir between beginning and end of
month
Col [15]    STRQ       Mm3         Total annual storage requirement
Col [16]    T.Spill       Mm3         Annual spill from the reservoir (sum of Col[14] from
month 1 to 12)
The details of simulation inputs are provided in the Tables 13.1 and 13.2. The abstract of t he detailed irrigation targeted simulation runs are provided in Table 13.3. With these parameters the contemplated irrigation (Table 13.1) are successful at various reliabilities shown in Table 13 3
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Table 13.1: Irrigation Water Requirements for Various Scenarios 
3
(for 13700 ha in Mm )
May 2007
Phase I
Phase II
Roti              Rot2               Roti               Rot2
Sep          8.38
Oct         10.41
Nov          2.89
Dec 11.04
Jan         19.00
Feb         23 25
Mar         18.49
Apr          4.98
May         2.01
Jun         21.64
Jul           5.97
Aug 6 89
Total 134.93
8.43 12.43 12.45
10.69 13.19 13.72
2.90 3.16 3.29
12.49         14.02          16.07
22.18         24.64          28.61
27.87         29.98          34.92
22.15 22.99 26.41
5.21 5.09 5.30
2.01           2.01            2.01
21.64         32.42          32.42
5.97           8.95            8.95
6.89 10.33 10.33
148.43 179.22 194.49
Table13.2 Reservoir Characteristics at Dead
Description
l
Uni)
---------------———L----------
Qunatity
Minimum drawdown level Minimum river bed level Area at dead storage level Dead storage
m.a.s.l
m.a.s.l
km2
Mm3
1350.0
1320.0
67.88
874.7
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Table 13.3 Storage Reservoir Characteristics and Reliability Levels
Reliability level
May 2007
Description
Unit
75%
80
%
85%
90%
95%
Phase I, Rot 1
Dam heightfat. spillway
c.l)
Spillway crest level
Area at normal water level Live storage
Total storage
Spill over the spillway Spill to live storage ratio Phase I, Rot 2
Dam heightfat spillway c.l) Spillway crest level
Area at normal water level Live storage
Total storage
Spill over the spillway Spill to live storage ratio Phase II, Rot 1
m
m.a.s.l
km2
Mm3
Mm3
Mm3
m
m.a.s.l
km2
Mm3
Mm3
Mm3
30.89    30.98    31.08     31.24     31.42
1350.89 1350.98 1351.08    1351.24 1351.42
70.797    71.094   71.424    71.952    72.546
67.3        74.4        82.3        95.1       109.4
942.0 949.1 957.0 969.7 984.1
1800.0 1799.0 1754.0 1703.0 1640.0
26.74 24.17 21.30 17.92 14.98
31.05    31.14    31.24    31.41     31.59
1351.05  1351.14  1351.24 1351.41   1351.59
71.325   71.622    71.952    72.513    73.107
80.0       87.1        95.1       108.6      123.1
954.6 961.8 969.7 983.3 997.8
1786.0 1785.0 1740.0 1689.0 1626 0
22.34 20.49 18.31 15.55 13.21
Dam heightfat spillway
c.l)
Spillway crest level
Area at normal water level Live storage
Total storage
Spill over the spillway Spill to live storage ratio Phase II, Rot 2
m
m.a.s.l
km2
Mm3
Mm3
Mm3
Dam heightfat spillway
c.l)
Spillway crest level
Area at normal water level Live storage
Total storage
Spill over the spillway Spill to live storage ratio
m
m.a.s.l
km2
Mm3
Mm3
Mm3
31.14    31.22    31.33      31.5      31.68
1351.14 1351.22 1351.33    1351.50 1351.68
71.622   71.886    72.249    72.810    73.404
87.1        93.5       102.2      115.9      130.4
961 8     968 1      976.9      990.5     1005.0
1755.0    1754.0    1709.0    1658.0
1595.0
20.15      18.77      16.72      14.31      12.24
31.32    31.39    31.52     31.69     31.87
1351.32 1351.39 1351.52   1351.69  1351 87
72.216 72.447 72.876 73.437 74.031
101.4      107.0      117.5      131.2      145 7
1739.0   1738.0    1693.0    1642.0
976.1      981.7     992.1     1005.8
1020 4
17.14      16.24     14.41      12.52      10.84
1579 0
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14.  DEDESSA SUB-BASIN MODELING
14.1. Modeling for Arjo Dedessa dam for irriga
ti
on
The  simmlations  discussed  in  previous  sections  is  for
a
comprehensive
analysis  of  Dedessa
dam, which is the main focus of the TOR. A detailed Ms-Excel based model, using 1000 years of monthly  data of inflows, generated synthetically, were incorporated in the various  runs, to obtain the results, as indicated in the Table 13.3. As can be seen from the water requirement available at Table 13.1, for the ultimate development phase II, of the Dedessa dam, the optimal water abstraction is for RoT 2, scenario cropping pattern. Based on various combinations of the simulation runs, which are very  exhaustive, it could be stated that there could be two net phases of development. The designs however need to be made for the phase II development. The firmed up geometry of the reservoir at Dedessa dam for these two phases could as below. This is for the success of irrigation at 80% reliability. The following abstracts are based on the results of simulation considering only contemplated irrigation.
□ For phase I development of Dedessa dam
•    Irrigation command
•    Full reservoir level
•    Storage at full reservoir
•    Dead storage level
•    Volume at Dead stage level
•    Live storage
•    River bed level
•    Area at Dead storage level
■   Area at full reservoirs level
■   Irrigation Demand
□ For phase II development of Dedessa dam
•    Irrigation command
■   Full reservoir level
•    Storage at full reservoir level
■   Dead storage level
■   Volumes at dead storage level
13,700 ha 1351.14 m 961.8 Mm3 1350.00 m 874.67 Mm3 87.10Mm3 1320.00m
67.88 km2
71.14km2
148.43Mm3
13,700 ha 1351.39 m 981.7 Mm3 1350.00m 874.67 Mm3
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Live storage
107.00 Mm
River bed level
1320.00 m
Area at dead storage level
67.88 Km2
Area at full reservoir level
72.07 km2
Irrigation Demand
194.49Mm3
Thus both the above phases satisfy contemplated irrigation with 80% reliability. In the phase I and phase II, the cropping patterns  envisaged are different. However, for the final design of Deddesa dam, it has to be based on phase II development with the above dam features if the development is for irrigation only.
14.2 The sub basin modeling
When the aspect of the development of Dedessa sub basin as a whole, including the present proposal of Dedessa dam, is taken for considerations, the need for the simulation of the sub basin becomes  relevant. The details  and components  of such sub basin simulation modeling could be for the following objectives.
•     To  explore  the  possibility  of  power  generation  at  Dedessa  dam.  Since  only  a  small fraction  of  the  total  inflow  is  to  be  used  for  irrigation,  looking  at  power  development options at the site by raising the dam height is an important consideration
•     In such a scenario as above, that is, full scale upper optimal development of Dedessa dam,  it  becomes  a  necessity  to  check  that  this  optimal  water  abstraction  does  not jeopardize  future  developments  in  the  whole  of  the  Dedessa  sub-basin.  This  requires basin modelling at a larger scale considering contemplated developments upstream and downstream.
In this  report, simulation exercise has  been carried out to address  the first objective A tailor made sub basin modeling was  carried out by  developing a Fortran source code named "Arjosim" to quickly undertake simulation exercise of various components with alternatives
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14.2.1
Power Generation in 
Dedessa Dam
The  D
edessa  dam  with  its 
phase  II  configuration,  could  irrigate
13
,700ha  at
80%  reliability.
Since,
left  and  right  bank 
main  canals  take  off  from  the  dam,
no
incidental
power  can  be
generated as such. Hence, power development options are sought by raising the dam height to have additional storage for separate power releases. The FRL for optimal phase II irrigation development is 1351.39 m. Two scenarios for Higher FRL values are considered with FRL of 1353 m, and 1356 m. For each FRL considered, four different power release options are taken up for the simulation as depicted in Table 14.1. It should be noted that the given releases are total monthly releases for irrigation and hydropower. That is, the given total releases less the monthly  irrigation requirement shall be used for power generation. There will be a separate outlet for power at the same level as that of irrigation outlet, that is, at 1350.00m.
The reservoir characteristics at the two FRLs considered for the simulation are: Full Reservoir Level = 1353 m
■ Storage at full reservoir level Live storage
Dead storage level
Volume at dead storage level Area at full reservoir level
Full Reservoir Level = 1356 m
*    Storage at full reservoir level *    Live storage
*    Dead storage level
■ Volume at dead storage level « Area at full reservoir level
1092.51 Mm3 217.33 Mm3
1350.00m 874.67 Mm3
77.88 km2
1341.16 Mm3 466.49 Mm3
1350.00m 874.67 Mm3
87.85 km2
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The effective turbine center was  fixed at 1322.00 m elevation. This  of course, needs  to be finalized as  per ground condition and dam head/or otherwise, other location based powerhouse. Also, the plant factor adopted in the basin simulation runs  is  unity, and with unrestricted installed capacity. B ased on actual budget and power requirement analysis  by Hydropower and Dam design engineer, these installed capacities  could be changed or power generation could be restricted. The results  presented in the Hydrological simulation are with these assumptions on turbine center level and plant factor. However, at detailed design stage, with these firmed up figures, the sub basin models could be re-run in short time.
The power duration curves  for the various  runs  are shown in Figures  14.1 (a1) to 14.1 (b4). These figures  are useful to reckon the monthly  power generation at various  exceedance probability  levels  at proposed FRLs  of 1353 and 1356. With respect to annual energy production the results are presented in the Table 14.2 for Dedessa dam. This piture of energy generation is  after satisfying irrigation needs  fully  at 80% reliability. However the quantum of energy and power are of not very attractive type.
14.2.2 Optimal Power Generation
With  FRL  upto  1956,  as  seen  above,  though  irrigation  is  successful,  the  power  generation scenario  is  not  attractive.  Hence  the  simulation  model  'Anjosim'  was,  slightly  recoded  for making  simulations  for  increased  FRL  considerations  and  exploring  the  possibility  of  power generation at a higher level, though the exercises are beyond the scope of the present TOR A large  number  of  runs  of  'ArjosinV  were  undertaken  under  3  different  domains  of  preposition These are:
Domain 1: k eeping t he p ower o utlet a t a I ower I evel o f 1 346 m a nd i ncreasing t he F RL i n stages, with various monthly power release patterns.
Domain 2: keeping the power outlet at 1346 m and increasing the FRL in stages as above but with model in built decision support based power releases.
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ay 2007
Domain 3: keeping the power outlet at the sam
e
level as that of irrigation outlet
(of course, a
separate  power  outlet)  and  increasing  the  FRL
in
stages,  with  decision  support
based  power
releases by the model.
Domain 1 Simulations
A set of 12 different runs were attempted within the FRL ranges from 1370 to 1376 m, and with different monthly power release pattern. Some of the optical ones are:
i. With FRL 1370, the 97% power is 11.22 MW
ii.  With FRL 1372, the 97% power is 13.30MW
iii.  With FRL 1374, the 97% power is 13.62MW
Beyond FRL 1374, there were no encouraging improvements in power generation.
Domain 2 Simulations
These runs totaling to 10 were made with decision support based power releases, in built in the model. This  obviously  increases  the power generation for the set of FRLs. The optimal runs indicated the following situation.
i.    With FRL at 1372, the 97% power is 16.49MW
ii.   With FRL at 1374, the firm power is 17.20MW
in. With FRL at 1376, the firm power is 17.90MW
iv.  With FRL at 1378, the firm power is 19.50 MW
Domain 3 Simulations
Here, in addition to decision support power releases, the outlet was best at 1350W instead of 1346M adopted in the previous  domains. This  improved the situation. Some of the salient model runs yielded the following scenarios.
i.   At FRL 1370, the firm power at 97% is 17MW ii.   At FRL 1372m, the 97% power is 18.1 MW
iii.  At FRL 1374m, the 97% power is 18.95MW iv.  At FRL 1376m, the 97% power is 20MW
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14.2.3
Reduction In flows at Dedessa Sub-basin
In  either
of  the  cases,  covering  8  runs  of  simulation,  there
is 
no  detr
imental  eff
ect
on  the
downstre
am  flows  of  Dedessa  sub  basin.  The  maximum  co
nsu
mptive
utilization
is 
only  for
irrigation, which is only 194.49Mm   annually, as the additional
3
releases made for power are not
3
for consumptive uses. The power releases  flows  downstream. Thus, a net reduction in the sub basin flows will be up to a datum of 195mm , even without accounting for any regeneration from irrigation. The sub basin of Dedessa contributes at its end annually to Abbay a auuatum of 14,033mm3 (BCEOM Master Plan study). This will get reduced to 13,838mm3 because of the proposed arjo Dedessa project, which is  only  a negligible reduction of 1.39%. As  such, the Arjo Dedessa Dam proposal is  not going to jeopardise the overall developments  of the sub basin considering various identified projects of the sub basin.
14.2.4     Conclusions
The simulation runs could be made for further different scenario At this stage, it could be said that the optimal power generation would be with FRL at 1376 m and with power outlet at 1350 m. The tail race is assumed to be at 1322 m in all the runs. With a plant factor of 0.6, the installed  capacity  could  be  around  33MW.  Hydrologically,  the  simulations  can  be  performed further  without  any  constraints,  provided  that  there  are  no  geological,  geothechnical,  design environmental,   social   and   other   constraints.   The   power   generation   at   different   reliability percentages are shown in the Figure 14.2 for the last run.
However, to form up power generation, even at higher FRLs. a prefeasibility study is required for exploring the possibilities  of increasing the height of dam and establishing the technical feasibility and economic viability for generation of hydro-power
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Table 14.1: Total Monthly Release (Irrigation plus Power) options for various FRL values
considered
Options               FRL = 1353 m
FRL = 1356 m
Option I
75 Mm3 for all months
Twice of monthly 
irrigation releases
Option II
75 Mm  - May to
3
September & 50 Mm  -
3
Three times monthly irrigation releases
Oct. to April
Option III
75 Mm  - May to
3
September & 40 Mm  -
3
100 Mm3 for all
months
Oct. to April
Option IV
150 Mm  - May to August
3
75  Mm   -  September
3
3
& 40 Mm3 - Oct. to March, 75 Mm  in Sept & April
to  April  &  100MCM  - May. to August
Table 14.2: Annual Energy generations at Dedessa dam for the various options
FRL Scenario
Release
Options
Annual Energy Production (GWh) at Exceedance
Probability Levels of
Mean
Level
50%
75%
90%
95%
97%
1353 m
I
II
III
IV                43.6          44.7          43.3          40.1           36.3           28.3
32.0 32.3 29.8 27.3 25.2 22 8
28.0 28.9 26.5 24.3 22.7 20 5
25.3 25.9 25.5 22.9 21.3 19 6
1356 m
I
II
III
IV                47.6          48.5          47.5          43.8           40.9          38.1
11.9 12.0 11.9 11.8 11.7 11 7
24.5 24.5 24.3 24.0 23.8 23 6
54.0 55.2 50.6 45.9 42.9 39 2
_____I
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Figuro 14 1 Power Duration Curve*
i 1
Percent Power Is Equalled or Exceeded. %
(a. 1) Power duration curve for FRL = 1353m - Option I
(a.2) Power duration curve for FRL = 1353m - Option II
(a.3) Power duration curve for FRL = 1353m - Option III
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(a.4) Power duration curve for FRL = 1353m - Option IV
(b. 1) Power duration curve for FRL = 1356m - Option I
Percent Power is Equalled or Exceeded. %
(b.2) Power duration curve for FRL = 1356m - Option II
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(b.3) Power duration curve for FRL = 1356m - Option III
Percont Power i« Equalled or Excoeded. %
(b.4) Power duration curve for FRL = 1356m - Option IV
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07
15. RECOMMENDATIONS ON TRAINING NEEDS
It  is  very  heartening  development  that  the  water  resources  exploitation
f
or
optimal
utilization
towards  economic  stability  is  getting  the  major  thrust  in  Ethiopia.  Wit
h
p
hase  of
activities,
soon, the rich river basins will be impounded in different storages associated with a number of runoff the river schemes either for irrigation or power or in a multi objective sense. As such, in the present scenario, the hydrological planning and design are in the usage. However, when the basins, in the near future, become stuffed with many projects, then, the real time operation of those projects  will be the dire necessity. Judicial operation of a multi reservoir /project system has a lot of benefits in combating floods, tiding over drought, reduction in water losses and others. However, such real time operation would require a complete knowledge (in the hydrological sense) of the basin system, and hydrology  and simulation techniques  (simulation coupled with optimization models). Such a scenario of real time operation will have to be backed up by  flood forecasting networks, advance prediction of seasonal rainfall, adequate telemetry  (for transfer of data), dedicated computers, telecommunication soft ware in addition to subject matter (hydrology/simulation) software. The personnel heading such operations  in a basin, and those working in such a team need to be trained in the above mentioned aspect so as t o u ndertake o peration which has i mmerse a dvantages. H ence, i t i s r ecommended t hat experts  on Hydrology  and Hydrological modeling should be identified for undergoing such trainings  in Institutions/universities  in appropriate countries. Though there could be many such institutes, the consultant feels for such multi system operation, adequate expertise is available
in countries like U.S.A and India, where many institutions, Universities, River basin authorities and research stations  could be identified for offering such trainings. Such trained personnel with expertise in hydrology will be able to head future river basin authorities for optimal real
time operation of the systems.
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REFERENCES
May 2007
Admasu Gebeyehu, 1988: Regional Analysis on Some Aspects of Streamflow Characteristics in Ethiopia. (Unpublished Draft Report). August 1988.
Admasu Gebeyehu, 1989: Regional flood Frequency Analysis. PhD thesis, Royal Institute of Technology, Bulletin No TRITA-VBI-148, Stockholm, Sweden.
BCEOM, 1999. Abbay River Bain Integrated Development Master Plan Project. Phase 2: Data Collection, Site Investigation Survey and Analysis. Section II: Sectoral Studies Volume III: Water Resources: Part I - Climatology and Part II - Hydrology. BCEOM - French Engineering Consultants - in association with ISL and BRGM.
ERA (Ethiopian Roads Authority), 2002: Drainage Design Manual, Hydrology.
Fiddes D, 1977: Flood estimation for small East African rural catchments, Proceeding Institution of Civil Engineers, Part 2, 63, 21-34 (1977)
Gray, D.M. Editor in Chief, 1971: Handbook on the Principles of Hydrology.
Haan, C.T, 1977: Statistical Methods in Hydrology. The Iowa State University Press, Ames. Hersfield, D M., 1961: Estimating the probable maximum precipitation; ASCE, J. Hyd. Div., 87,
No. Hy 5, 99-116 .
Matalas, N.C., 1963: Probability Distribution of Low Flows. Statistical Studies in Hydrology.
Geol. Survey Prof. Paper 434-A
Shaw, Elizabeth M„ 1988: Hydrology in Practice. International Van Nostrand Reinhold. USBR (United States Department of the Interior Bureau of Reclamation), 1964 Land and
Water Studies of the Blue Nile Basin. Ethiopia. Appendix III - Hydrology.
WAPCOS (Water and Power Consultancy Services (India) Ltd.), 1990. Preliminary Water
Resources Development Master Plan for Ethiopia. Final Report. Volume III. Annex A: Hydrology & Hydrogeology. Addis Ababa.
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ANNEXES
Annex A: Results obtained from the Meteorological Analysis
A1:
May 2007
Year
Jan        Feb   (Marchj
1961 22.2 21.8 24.7
1962 20.0 21 8 24.9
1963    21.7      23.2      24.3
1964    21.7      22 9      25.4
1965    21.5      21.8      24.8
1966 22 7 23.6 25 3
1967 19.5 22.5 25.1
1968 19.9 22.4 23 6
1969 22.4 23.2 25.1
1970 21.5 22.9 25.0
1971 21.6 20.4 23.9
1972 21.5 23.1 24.2
1973 22.7 22.6 25.8
1974 21.2 23.3 25.3
1975 21.0 23.6 25.9
1976 20.6 23 9 24.8
1977 23.0 23.5 24.9
1978 21.1 22.8 26.1
1979 22 2 23.5 24.7
1980     22.3      24.1     26.1
1981    22 4      22.6      26 2
1982     22.7      22.3      23 8
1983 20.3 23.6 26.1
1984 20 6 20.6 254
1985 21.5 22.9 25.7
1986 21.5 24 7 23.9
1987 21.8 23.0 25.4
1988     22.4      24.5     25.1
1989     21.3      22.3      24.6
1990     21.0      21.9      24.8
1991    23.0      23.6     25.1
1992    22.1      24.1      25.2
1993     22.6      23.5      24.3
1994     21.6      24.2      26.8
1995 22.3 24.5 26.4
1996 22.6 24.1 26.3
1997 23.4 22.4 26.9
1998 24.6 24 9 27.4
1999 21.9 22 8 26.2
2000 21.6 22 9 26.6
2001 23.0 24.3 26.0
2002 23.2 23.6 26.9
2003 21.9 24 5 26.4
2004 24.2 24.2 26 2
April  May  i  June  |  July 24 0     23.2    21.5   20.7
Aug I Sept Oct Nov Dec
20.2 21.6 22.0 21.9 19 7
22.5     23 2    21.0   20.0      200      21.3     22.1     20.2     19.0
16.3     15.9    21.6  21.1       20.8     21.4     21.7     24.6     21.3
24.6     23 7    21.5   20.4      20 4     21.4     22.0     21.5     20 8
24.8 23.5 21.6 21.3 20.8 21.5 22.4 23.2 20.5
24.8 24.1 21.7 21.4 20.8 21.5 22.3 22.0 18.8
24.8 23.7 21.2 20.7 20 5 21.3 21.1 23.0 18.7
24.5 24 1 21.6 21.6 21.1 21.2 21 4 21.2 19.6
24 6 24.0 22.0 20.7 20.7 21.5 21.7 21 2 16.9
24 9     24.3    22 0   21.4      20.8     21.6     22.8     19.8     18.2
24.7     23.7    21.1   21.0       19.9    21.1     22 4     21.7     18.2
24.9     23.2    21.0   21.8      21.3     21.6     22.3     22.8     20 2
26 8     24.1    21.8   21.3      20.8     21.5     21.7     21.7     17.4
23.9 23.9 21.8 20.8 21.3 21.3 21.2 19.1 19.0
24 8 23.7 21.7 20.5 20.2 21.3 21 9 20.4 18.9
24.3 23.1 21.0 21.0 20 8 21.6 22.4 22.5 19.7
25.3 24.4 21 4 21.0 21.3 21.9 23.5 23.0 20.9
25 6 23.8 21.9 20.9 21.2 21.1 22.3 20.9 20.4
24.2 24.2 21.9 21.3 21.6 22.0 22.3 21.6 20.9
25.8     24 4    22.3    21.1
25.1     24 7    22.3   20.6      21.1     21.5     22.0     21.6     19.2
20.8     22.0     22.0     22.0     19.5
24.9     23.5    21.9   21.2
25.6     25.0    22.8   22.3
26.2     24.5    21 9   20.9
25.1     24.0    21.7   20.6 24 6     24.6    22.0   21.3
25.1     24.2    22.6   22.2
25 8     25.3    22.5   21.3
24 5     23.3    21.9   21.3
25 1     18.6    22.4   21.4
25.1     24.6    22.6   21.3 25.2     24.6    22.3  21.1
25.6     24.4    22.7  21.4
24.9     24.3    22.3  21.1
26.5     25.3    23.1   21.9 25.9     24.7    21.9   21.7
25.5     24 4    23.1   22.3
26.3     24.9    22.8    21 6
20 7     21.6     21.8     23.0     20.6
21 8     21.7     22.4     22.3     18.6
20.7    21.1     20.2     23.4     20.6
20 4     21.5     21.5     22.1     19.7
21.1 21.2 21.7 22.0 20.4
21.6 21.9 23.1 22.7 20.2
21.6 21 9 22.5 20.6 18.8
21.6 21.7 22.0 21 9 21.6
21.4 21.9 22.1 22.4 20.9
20.8     22.0     21.4     21.4     18.2
21.3     21.8     22.7     21.8    21.3
21.4     21.8     22.9     21.7     19.61
21.5 21.8 22.0 23.3 20.0
22.1 22.3 23.0 23.3 22.2
21 8
27.7     26.2    234   22.4      22.3    23.1     24.2     22.0     19 1
22.3     23.2     24.0    25.1     22.9
22.4     22.4     22.0    20.2
26.6 25.5 22.6 21.9 21.8 23.0 24.0 23.5 20 8
26.4 25.5 23.0 22.0 22 2 23.0 24.2 23 6 21 6
25.9 25.8 22.8 22.7 22.2 22.8 23 3 23.1 22.7
21.3     22 9     23.5     21.0     19.8
25.7     25.8    232   22.0
_26.525.6          22.8    21,5
22.2 22.7 23.0 23.5 20 6
22.1 22.5 22.6 23 2 21.8
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A2:      Estimated Minimum Temperature at Arjo Dedessa rrojeci Mrea yn___ /        —
Year
Jan      Feb iMarch, April          May    June | July | Aug         Sept |    Oct       Nov      Dec
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
9.7 10.5 13.8 14.7 14.9 14.6 14.7 14.3 14.5 13.0 10.3 8.0
8.8 10.5 13.9 13.8 15.0 14.3 14.2 14.2 14.4 13.0 9.5 7.7
9.5 11.2 13.6 9.9 10.3 14.7 15.0 14.7 14.4 12.8 11.6 8.6
9.5 11.1 14.2 15.0 15.3 14.6 14.5 14.4 14.4 13.0 10.1 8.5
9.4      10.6     13.9      15.1    15.1    14.7    15.1    14.7    14.5     13.2      10.9       8.3
9.9      11.4     14.2      15.1     15.5    14.8    15.2    14.7     14.5     13.2      10.3       7.6
8.5      10.9     14.1      15.1     15.3     144     14.7    14.5    14.3     12.5      10.8       7.6
8.7 10.8 13.2 15.0 15.5 14.7 15.3 14.9 14.3 12.6 10.0 8.0
9.8 11.2 14.0 15.0 15.5 14.9 14.7 14.6 14.5 12.8 10.0 6.9
9.4 11.1 14.0 152 15.7 14.9 15.2 14.7 14.5 13.5 9.3 7.4
9.5 9.9 13.4 15.1 15.3 14.3 14.9 14.1 14 2 13.2 10.2 7.4
9.4 11.2 13.5 15.2 14.9 14.3 15.4 15.1 14.6 13.2 10.8 8.2
9.9      10.9     14.4      16.3     15.5    14.8    15.1    14.7    14.5     12 8      10.2       7.1
9.3      11.3     14.2      14.6     15.4    14.8    14.8    15.1     14.3     12.6       9.0        7.7
9.2      11.4     14.5     15.1     15.3    14.8    14.6    14.3    14.4     12.9       9.6        7.7
9.0 11.6 13.9 14 8 14.9 14.3 14.9 14.7 14.5 13.2 10.6 8.0
10.1 11.4 14.0 15.4 15.8 14.6 14.9 15 1 14.8 13.9 10.8 8.5
9.2      110      14.6      15.6     15.3    14.9    14.8    15.0    14.2     13.2       9.8        8.3
9.7      11.4     13.8      14 8    15.6    14.9    15.1    15.3    14 8     13.2      10.2       8.5
9.8      11.7     14.6      15.7     15.8    15.1    15.0    14.7    14.8     13.0      10.3       7.9
9.8 10.9 14.7 15.3 16.0 15.1 14.6 14.9 14.5 13.0 10.2 7.8
9.9 10.8 133 15.2 15.1 14.9 15.1 14.7 14.5 12.9 10.8 8.4
8.9 11.4 14.6 15.6 16.1 15.5 15.8 15.5 14.6 13.2 10 5 7.6
9.0 10.0 14.2 16.0 15.8 14.9 14 8 14.6 14.2 11.9 11.0 8.4
9.4 11.1 14.4 15.3 15.5 14.8 14.6 14.4 14.5 12.7 10.4 8.0
9.4 12.0 13.4 15.0 15.8 15.0 15.1 14 9 14.3 12.8 10.3 8.3
9.5      11.1     14.2      15.3     15.6    15.4    15.8    15.3    14.8     13.6      10.7       8.2
9.8      11.9     14.0      15.7     16.3    15.3    15.1    15.3    14.8     13.3       9.7        7.6
9.3      10.8     13.8     14.9     15.0    14 9    15.1    15.3    14.6     13 0      10.3       8.8
9.2 10.6 13.9 15.3 12.0 15.2 15.2 15.2 14.8 13.0 10.6 8.5
10.1      114      14.0      15.3     15.9    15.4    15.1    14.7    14.8     12.6      10.1       7.4
9.7      11.6     14.1      15.4     15.8    15.1    15.0    15.1     14.7     13.4      10.3       8 6
9.9      11.4     13.6      15.6     15.7    15.4    15.2    15.2    14.7     13.5      10.2       7.9
9.5 11.7 15.0 15.2 15.7 15.2 15.0 15.2 14.7 13.0 11.0 8.1
9.8      11.9     14.8     16.2     16.3    15.7    15.5    15.6    15.0     13 6      11.0       9 0
9.9      11.6     14.7     15.8     16.0    14.9    15.4    15.4    15.1     13.3      10.4       8 2
10.2     10.8     15.1      15.6     15.8    15.7    15.8    15.8    15.6     14.2      11.8       9 3
10 8     12.1     15.3     16.9     16.9    15.9    15.9    15.8    15.6     14.3      10.3       7.8
9.6      11.0     14.7     16.0     16.1    15.5    15.4    15 1    15.4     13 9       9.9        8 0
10.1 11.8 14.6 16.1 16.5 15.6 15.6 15.7 15.5 14.3 11.1 8 8
10.2     11.4     15.0     15.8     16 6    15.5    16.1    15.7    15.4      138      10.9       9 2
9.6      11.8     14.8     15.7     16.7    15.8    15.6    15.7    15.3      136       11.0       8 4
10.6     11.7     14.7     16.2     16.5    15.5    15.3    15.7    15.2     13.4      10.9       8.8
9.5      11.1     14.9     16.2     16.4    15.4    15.6    15.4    15.5     14 2      11 1
84
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A3: Estimated Maximum Temperature at Arjo Dedessa Project Area (in °C)
May 2007
Year
1961
1962
1963
1964
1965
1966
1967
Jan Feb (March April May June July Aug j Sept Oct
34.7 33.0 35.6 33.6 31.7 28 6 26.7 26 1 24.4 30.9
31.2 33.0 35.8 31 6 31.8 27.9 25.8 25 8 24.2 31.1
33.9 352 34.9 22.8 21.7 28 8 27.2 26.9 24 2 30.6
33.9 34.7 36.6 34.4 32.4 28.6 26.3 26.4 24.2 31.0
33.6 33.1 35.7 34.7 32.1 28.8 27.5 26.9 24.3 31.5
35.4 35 8 36.5 34.7 32 9 28.9 27.6 26.9 24.4 31.4
30.4 34.1 36.2 34.7 32.4 28.2 26.7 26.5 24.1 29.6
1968
31.1     33.9     34 0     34.3     32.9    28.8    27 8    27.3    24.0     30.1
1969
35.0     35.2     36.1      344     32.8    29.2    26.7    26.7    24.3     30 6
1970
33 6     34.6     36.0     34.8     33.3    29.2    27.6    26.9    24.4     32.1
1971
33.7     31.0     34.5     34.6     32.4    28.0    27.0    25.7    23.9     31.5
1972
336     35 0     34.8     34.8     31.7    27 9    28.1    27 5    24.5     31.4
1973
35.4     34 2     37.1     37.5     32.9    29.0    27.5    26.9    24.4     30.5
1974
33.1     35.3     36.5     33.5     32.7    28.9    26.8    27 6    24.1     29 9
1975
32.8     35.7     37.3     34.8     32.4    28.9    26.5    26.1    24.2     30 8
1976
1977
32.2     36.2     35.7     34.1     31.5    27.9    27.0    26.9    24.4     31.5
35.9     35.6     35.9     35.4     33.4    28.5    27.1    27.6    24 8     33.0
1978
32.9     34.5     37.5     35.9     32.5    29.2    27.0    27.4    23.9     31.4
1979
34.7     35.5     35.6     33.9     33.1    29.1    27.5    27 9    24.9     31.4
1980
1981
34.8     36.5     37.5     36.1     33.4    29.6    27.3    26.9    24.9     30 9
1982
35.0     34.2     37.8     35.1     33.8    29.6    26.6    27.2    24.4     30 9
1983
35.4     33.7     34.3     34 8     32.1    29.2    27.4    26 8    24.4     30.7
1984
31.7     35.7     37.6     35.8     34.2    30.4    28.8    28.2    24.6     31.5
1985
32.2     31.2     36.5     36.7     33.5    29.1    27 0    26.7     239      28 4
1986
33.6     34.6     37.0     35.2     32.8    28.9    26 5    26.4    24.3     30.3
1987
33.6     37.5     34.5     34.4     33.6    29.3    27.5    27.3    24.0     30 5
1988
34.0     34.8     36.5     35.1     33.1    30.1    28.6    27.9    24.8     32 5
1989
35.0     37.2     36.1     36.1     34 6    29.9    27.5    27.9    24.8     31.7
1990
33.2     33.8     35.4     34.2     31 9    29.1    27.5    27 9    24.6     30.9
1991
32.8     33.2     35.7     35.2     25.4    29.8    276     27.7    24.8     31 1
1992
1993
35.9 35.8 36.1 35.2 33.7 30.1 27.5 26.9 24.9 30.1
34.5 36.5 36.3 35.3 33.6 29 6 27.2 27 5 24.7 32 0
1994
35.3     35.5     35.0     35.9     33.4    30.1    27.6    27.7    24.7     32 2
1995
1996
33.7 36.7 38.6 34.8 333 29.7 27 3 27.8 24.7
34 8 37.2 38.0 37.2 34 6 30.8 282 28.5 25.3
35.3 36.5 37.9 36.3 33.8 29.2 28.0 28.2 254
36.5 33.9 38.8 35.7 33.4 30.8 28.7 28 9 26.3
31 0
32 3
1997
1998
38.4 37.7 39.4 38.8 35.8 31.1 28.9 28.9 26.1
34.2 34.5 37.8 36.8 34.1 30.4 27.9 27 6 25.9
31 5
33 8
1999
34 1
2000
33.7     34.7     38.2     37.3     34.8    30.1    28.3    28.2    26 0
33 0
2001
35.9     36.8     37.4     37.0     34.9    30.5    28.4    28.7    26.0
33 7
2002
36.2     35.7     38.7     36.2     35.2    30.3   29.3    28.7    25 8
34 1
2003
32 R
2004
34.2     37.1     38.0     36.0     35.3    30.8   28.4    28.7    25.7
37.8     36.6     37.8     37.2     35.0    30.4    27.8    28.6    25.5
32 3
31.8
Dec
31.4
30.4
34.0
33.3
32.8
30.1
29.9
31.3
27.0
29.1
29.1
32.3
27.8
30.3
30.2
31.5
33.4
32.6
33.4
31.2
30.6
32.9
29.8
33.0
31.4
32.6
32.3
30.1
34.5
33.4
29.1
3 34 1. .30
31.9
35.5
32.3
36.6
30.5
31.6
33.2
34 6
36.2
33.0
34 8
Water Works Design & Supervision Enterprise- - - - - - - - - - -
In Association with Intercontinental Consultants and Technocrats Pvt Ltdlr|t> (tatwuui frr1<«rlun Prti|*u
1
ind Hyitrn’otf .’il l<pert<
W
A4 *'limited Wwlativw Hum Kilty it Ar|<>
Project Area (in percent)
/ear
Jar
Feb
Mmh
April
ft)
May
7!
lurr
*8
July
87
Aug
Sept
Ort
Nov
D«x-
196! *
47
32
16
87
82
82
68
ro
••
t962
••
40
44
71
•18
85
34
M
M
15
HM
44
!961
65
•>
*<
HI
42
14
86
19
91
19
HI
47
85
1964
f»
M
57
43
75
•12
IM
HM
>2
44
71
75
1965
<7
S2
50
18
49
78
ai
85
84
42
96
*4
1966
45
66
58
71
71
78
•u;
X)
15
44
40
69
196'
45
50
57
48
76
81
91
80
)2
83
49
73
1968
55
46
•»4
18
75
at
HM
85
49
HI
78
72
1969
7*2
48
44
69
75
82
HM
89
IM
IO
77
65
19/0
:\
42
44
49
74
79
•in
12
80
HM
72
66
1971
40
48
4)
64
78
HI
87
X)
1/
87
42
78
1972
64
45
56
70
74
79
89
85
86
80
HI
75
45
59
57
48
78
HI
62
HM
19
HI
77
63
1974
4
49
53
48
78
HI
)O
X)
93
H.3
72
51
1975
52
59
51
70
74
H1
HM
)3
)O
44
73
66
1976
55
55
57
48
79
HI
92
91
91
91
1977
xr»
93
66
82
58
57
86
81
H9
HM
87
86
1978
82
•)d
74
6!
58
'14
75
77
87
46
H4
1979
84
68
75
65
61
55
64
7f)
77
84
85
86
79
I960
50
55
69
53
48
76
67
HI
HM
87
85
79
1981
54
46
•41
75
63
72
65
76
HM
HM
83
1962
67
62
55
42
77
65
75
84
HO
HM
X)
1963
n6
90
47
63
61
70
79
H6
78
79
86
1964
SO
59
47
89
49
61
46
76
H2
80
HM
HM
72
1965
66
50
51
87
86
74
78
60
82
90
75
HO
1966
56
60
57
87
69
73
81
77
85
87
37
70
1987
50
56
63
46
69
79
82
81
73
83
89
• *.
1988
72
64
49
HM
62
75
85
82
79
90
91
75
1989
63
61
60
75
93
73
87
79
91
73
4d
86
1990
67
70
62
68
77
91
84
61
90
82
JO
83
1991
66
69
60
80
91
74
83
Ho
90
30
91
71
1992
68
67
54
69
79
89
84
42
90
34
75
75
69
63
56
75
80
HM
85
89
89
44
69
’9
1994
57
53
56
67
78
90
86
46
86
32
JM
49
86
1995
60
58
57
72
79
90
75
98
80
w2
1996
72
43
63
73
90
30
92
83
44
89
31
88
87
*9
1997
73
53
53
72
76
81
81
86
49
89
87
*0
19s8
74
66
62
64
79
66
74
16
39
SC
1999
61
48
53
65
74
76
93
80
90
J2
39
*8
86
67
2000
55
48
45
67
77
St
8U
92
’3
19
86
2001
67
57
58
69
8»
90
91
93
34
92
68
’4
2002
71
56
62
64
71
11
6.5
36
32
’5
2003
71
40
6u
□6
3M
66
67
78
So
34
30
•8
JO
34
•»
*9
2004
66
58
55
70
3
81
87
60
39
'»J
91
•8
*8
* iwt WurL> 0»,ixu 4 '•up«nuit!0 Kattrsnu u.«uu.. .«
tMWMO
lOOArjo Dedessa Irrigation Project
Meteorological and Hydrological Aspects
May 2007
Year
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
Jan      Feb   Marchj April    May   June   July   Aug   Sept |  Oct |
1975 94
1976 90
8.3 7.3 7.1 6.8 5.4 6.5 4.3 6.2 9.1 9.2
6.7 6.9 8.2 7.8 9.0 6.3 2.9 2.0 6.3 6.5
7.2        6.2       8.4       6.8       7.3      4.4      2.8      3.8      5.8      8.0
9.6        8.3       7.7       8.0       8.7      5.8      3.0      3.0      6.8      9.1
9.1        8.3       8.6       6.5       83       3.4      4.3      5.5      7.9       7.4
7.8        8.2       7.1       8.5       7.8      5.3      4.2      3.8      6.0      9.2
7.9        9.6       8.4       7.2       9.1      8.1      4.9      5.2      8.3      8.5
8.1        8.0       7.7       8.1       6.8      6.8      2.3      3.2      5.7      6.5
8.2        7.6       7.5       7.3       7.6      6.1       3.7      4.1       6.2       7.9
10.0       9.5       9.7       7.8       9.4      8.0      48       4.7      7.5      7.9
8.2        7.6       7.5       7.3       7.6      6.1      3.7      4.1       6.2       7.9
9.2 8.8 7.4 9.4 8.3 6.4 5.4 4 8 8.0 8.8
8.5 8.9 8.5 8.4 10.1 7.3 3.2 4.1 7.2 9.5
7.9 8.1 7.3 6.7 9.1 5.9 4.8 4.8 5.8 8.6
1978 9.4
1979 6.3
1980 6.1
1981 8.5
1982 7.4
1983 80
1984     8.1
1985     8.6
1986     6.9
1987     8.1
1988     8.4
1989     8.6
1991 8.3
1992 6.7
1993 7.2
1994 9.6
1995     9.1
1996     7.8
1997     7.9
1998     8.1
1999     8.2
2001     8.2
2002     9.2
2003     8.5
2004     7.9
1977     6.2        6.3       6.6       7.2       7.4      5.8      3.7      4.1      6.3      6.9      8.2      8 3
8.3 7.5 6.4 7.1 5.0 3.1 1.6 4.1 7.5 8.4 9.2
8.7 7.8 8.7 6.8 7.1 3.7 4.1 5.2 6.6 6.7 8.1
8.9 7.4
7.6 7.5
9.1 8.3
90       10.0
8.4      8.1
9.8      8.3
7.6      9.0
10.1     8.3
8.3      8.3
8.3      8.3
8.3      8.3
9.9      7.7
9.5 9.0
0.1 8.3
5.8       7.5       7.6       7.8      6.1      2.3      3.8      5.5      6.8      8.9      7.1
6.6       6.0       7.0
5.3       5.8
65
6.7      5.1      2.4      3.8      4.2      5.8      7.8
75
8.6 4.6 5.9 7.9 5.8 3.7 4.5 2.6 6.5 9.6 7.9
6.0 8.3 6 5 7.1 6.8 3.9 3.7 6.7 7.5 6.6 7.3
7.6 7.1 8.0 6.6 7.8 5.4 3.6 5.6 6.6 8.4 8 2
9.9 8.1 8.9 6.6 6.0 4.7 4.3 7.0 10.1 8.6 8.1
7.4      5.8      3.6      3.9      5.9      7.9       7.8      8.6
7.6 7.5 6.8 6.4 5.9 2.8 3.1
6.2 6.5 5.3 7.2 3.7 3.3 4.0
58
7.6      8.7      7.8
7.8       6.2       8.3       5.8      5.6      4.9      4.2      7.4      7.6      8.3      11.0
4.1       7.4      9.4      8.2
7.6       9.5       8.4       8.3      7.0      2.7      4.9       5.5      8.5      10.2     9.1
1990     9.2        4.9       6.3       3.3       5.4      4.6      3.7      3.9      5.9      9.5      8.9      9.2
8.4       7.3       7.2       9.6      7.3      3.7      5.1      6.3      8.3      8.2      66
7.3       7.1       6.8       5.4      6.5      4.3      6.2      9.1      9.2      8.9      7.4
6.9       8.2       7.8       9.0      6.3      2.9      2.0      6.3      6.5       7.6      7.5
6.2       8.4       6.8       7.3      4.4      2.8      3.8      5.8      8.0      9.1      8 3
8.3       7.7       8.0       8.7      5.8      3.0      3.0      6.8      9.1      9.0      10.0
8.3       8.6       6.5       8.3      3.4      4.3      5.5      7.9      7.4      8.4      8.1
8.2       7.1       8.5       7.8      5.3      4.2      3.8      6.0
9.6       8.4       7.2       9.1      8.1      4.9      5.2      8.3
92
9.8      8.3
8.0       7.7       8.1       6.8      6.8      2.3      3.2      5.7      6.5      10.1     8 3
8.5      7.6      9.0
76
2000    10.0      9.5       9.7       7.8       9.4      8.0      4.8      4.7      7.5      7.9      8.3      8.3
7.5       7.3       7.6      6.1      3.7      4.1      6.2      7.9      8.3      8.3
7.6       7.5       7.3       7.6      6.1      3.7      4.1      6.2      7.9      8.3      8.3
8.8       7.4       9.4       8.3      6.4      5.4      4.8      8.0      8.8      9.9      7.7
8.9       8.5       8.4      10.1     7.3      3.2      4.1      7.2      9.5      9.5      90
8.1       7.3       6.7       9.1      5.9
4 8      4.8      58       8.6      0.1      8.3
Water Works Design & Supervision Enterorise - - - - - - - - - - -
In Association wile Intercontinental Consultants and Technocrats Pet. ttd.Arjo-Dedessa Irrigation Project
Meteorological and Hydrological Aspects
A6: Estimated Mean Daily ETo of Arjo Dedessa Project Area
May 2007
Year
1961
Jan | Feb , March! April May fjune , July r Aug ^Sept | Oct Nov/
Dec
3.73      3.93      4.49      4.26     3.73     3.64     3.04     3.54     4.43     4.27     3 86     3.24
1962 3.23 3.75 4.66 4.32 4.43 3 49 2.63 2.5 3.72 3.63 3.48 3.19
1963 3.48 3.72 4.66 3 44 3.36 3.11 2.71 3 3.63 3.98 4.22 3.56
1964    3 95      4.18      4.65      4.66     4.52     3.45    2.72     2 79     3.87     4.25
1965    3.84      4.13      4.83      4.25     4.43     2.97      3.1      3.43     4.13
3 88
3 86 3.82
3.92 3.45
1966    3.68      4.22      4.47      4.73      4.35      3.4      3 07       3
3.7      4.31     4.09     3.35
1967    3.45      4.44      4.77      4.45      4.6      3.95     3.15     3.32     4.22     4.03     3.72     3.46
1968    3.53      4.06       4.5       4.64      4.1      3.71     2.63      2.9
3 59     3.59     4.06      3.4
1969    3.71      4.06       4.5       4.43      4.28     3.56     2.91     3.06     3.72     3 94      3.7      3.19
1970    4 01      4.45      5.06      4.58     4.76    4.01     3 21     3.22     4.06     4.04
1971    3.66       3.9      4.49      4.49     4.24     349     2.93     3.01     3.69
4
3.58 3.28
3.75 3.29
1972    3.86      4.34       4.5       5.03     4.34     3.55     3.39     3.28     4.17     4.23      4.2      3.34
1973    3 81       4.3       4.87      4.92     4.91     3.84    2.81     3.09     3.97     4.32
4 01
3 34
1974 3.58 4 18 4.53 4.24 4.61 3.51 3.3 3.29 3.62 4.07 1.95 3.35
1975 3.82 4.27 4.66 4.34 4.09 3.3 2.73 2.44 3.2 3.87 3.64 3.49
1976 3.73 4.38 4.65 4 75 4.02 3.68 2.93 3.06 3.5 3.69 3 48 3 38
1977
1978    3.85      3.62     4.64       4.7      4.39     3.57     2 58     3.04     3.53     3.74     3.79     3.23
3.85     4.34      4.49     4.24    3.46      2.9       3.1      3.78
3 87
3.86     3 52
1979
1980
1981
1982
3.36      3.89      4.18      4.37     4.08    3.38     2.64     3.07      3.3       3.5      3.64     3.34
3.29 3.63 4.21 4.39 4.32 3.56 2.93 3 05 3.7 3.98 3.66
3.78 4 25 3.99 4.13 4.42 3.53 2.91 3.2 2.88 3.64 4.01
1983    3.54      4.14      4.55      4.76     4.15     4 06    3.43     3.03     3.61      3.7      3.82     3.32
3.6       3.74      4.63      4.32     4.13     3.77    2.98     2 98     3.87     3.86     3.49      3.3
3 44
3.3
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
3.58      4 31      4.73      5.02     4.08     3 55    3.14    3.11      3.87      4.3      3.98     3.46
3 86
3 82
4
3.72      4.18      4.42      4.39     4.64     3.83     2.97     3.38     3.77     4.06     3 74     3 24
4 17    4.02     3 48
3 42       3.94     4.71      4.62     4.65     3.63     2.73     2.59     3.78      3.7      3.61     3 38
4 37
4 22
1999    3.68       4.07      4.67      4.57     4.36     3.63     2.98    3.11     3.83    4.1 1     3.7      3.42
3.68       4 29      4.57      4.88     4.37     3.35     3.09     3.03     3.77     4.31     4.09     3.46
3.77       4 43      4.95      4.46     4.65     4.07     3.23     3.46     4.41     4.32      3.9      3.85
3.91       4.33       4.8       4.93     4.24     3.85     2.66     2.96     3.74     3.83     4.12     3.36
3.73 4.04 4.59 4.36 3.93 3.53 2.71 2.81 3.63
3.43 3.94 4.31 4.03 4 22 3.04 2.85 3.08 3.22
3.67      4.14     4.31      4.74     3.83      3.5     3.27     3.16     4.06
3.76       4.2       5 04      4.82     4 58     3.78     2.71     3.34     3.59
3.82       3.38     4.31      3.57     3.35     3.25    2.92     3.07     3.69
3.79      4.05      4.43     4.31      3.82    3.72     3.06     3.59     4.49
3.54       3.75      4.68      4.29     4.25    3.21     2.72     3.04     3.67
3.94       4.3       4.79      4.65     4.55      3.5      2.75     2.83     3.91
3.92 4 34 4.96 4.34 4.53 2.98 3.11 3.52 4.21
3 88     3.32
4 01     3.46
3 83     3 96
3.92 3 67
3.84 3.11
4 09
4 26
3 94
3.91 3 4
4.06 3 75
3.92 3 59
2001    3.78      4.21      4.66      4.64      4.42     3.65     2 98     3.16     3.84     4 19    3.92     3.57
2000    4.03       4.45      5.24      4.79     4.86     4.15    3.31     3.35     4.19     4.16     3.92      3.5
2002       4         4 39      4.75      5.13     4.59    3.71     3.46      3.4      4.29     4.34     4.24     3 54
2003     3.75      4.49      4.92      4.82      5.06    3.88     2.85     3.16     4.08     4.47     4.19     3.63
2004    3.84      4.28      4.58     4.51      4.83      3.6      3 22     3.35     3.72
Water Works Design & Supervision Enterprise
108
Association with totereoottoental Consultants and Technocrats Pvt. Ltd.Arjo Dedessa Irrigation Project
Meteorological and Hydrological Aspects
A7:     Estimated Mean Monthly ETo of Arjo Dedessa Project Area (in mm/month)
Year
May 2007
Jan  ] Feb March| April     May  June  July  Auq  Sept  Oct   Nov   Dec ||  Annual
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973    118     120    151      148     152    115   87.2   95.9    119    134    120     104         1464
116 110 139 128 116 109 94.2 110 133 132 116 100 1403
100     105    144      130      137    105    81.6   77.4    112    112    104     99          1308
108 104 145 103 104 93 2 84 92.9 109 124 127 110 1303
122     117    144      140      140    104    84.3   86.6    116    132    116     119         1420
119 116 150 128 137 89 96.2 106 124 120 118 107 1410
114     118    138      142     135    102   95.1   93.1    111    134    123     104         1408
107 124 148 133 143 118 97 7 103 127 125 111 107 1444
109     114    140      139     127    111    81.4   89.9    108    111    122     105         1358
115     114    140      133      133    107   90.1   94.8    112    122    111     99          1369
124     124    157      137     148    120    99.4   99.7    122    125    107     102         1466
113     109    139      135     132    105    90.8   93.2    111    124    113     102         1366
120 122 140 151 135 106 105 102 125 131 126 104 1466
1974    111     117    140     127     143    105   102    102     109    126   58.6     104
1975    118     120    144      130     127     99    84.7   75.7
1976    116     123    144     143     125    111   90 8   94.8    105    115    104     105
96
120    109     108
1977 105 108 135 135 132 104 90 96 113 120 116 109
1978 119 101 144 141 136 107 80 94.2 106 116 114 100
1979 104 109 130 131 126 101 81.9 95.3 98.9 109 109 104
1980    102     102    130      132     134    107   90.9   94.5
1981    117     119    124      124     137    106   90.1   99.2   86.5
1982    112     105    144     129     128    113   92 3   92.5    116    120    105     102
1983    110     116    141     143     129    122    106     94      108    115    115     103
1984    111     121    146      151     127    107   97.4    966    116    133    120     107
111
123
113
1985    116     113    142      131     122    106   84.1  87.1
1986    106     110    134      121     131    91 3   88.2   95.6   96.5
1987 114 116 134 142 119 105 101 98 122 124 115 123
1988 117 118 156 145 142 113 84.2 103 108 129 121 108
1989 115 117 137 132 144 115 92 105 113 126 112 100
109
120
118
110 107
120     102
116     103
120     107
1990    118     94.8    133      107     104    97.4   90.6
1991    117     113    137      129     118    112   94.8   111     135    131    115    96.4
95
111    136    117     114
1992    106     110    146      139     144    109   84.5   80.3
1993 110 105 145 129 132 96.4 84.4 94.3
1994 122 120 149 139 141 105 85.2 87 8
113
110
1995    122     121    154      130     140    89.4   96.4
1996    114     120    142      147     135    101   95.8    94      113    134    123     107
109
117 132 122 116
126 122 118 111
115 108 105
127 117 105
1997    117     124    153      134     144    122    100    107     132    134    117     119
1998    121      121    149      148     132    115   82.6   91.9    112    119    124     104
2000    125     124    162      144     151    125   103    104    126    129    118     109
1999    114     114    145      137     135    109   92.4   96.3
115
127    111     106
2001    117     118    144      139     137    110   92.4    98      115    130    118     111
2002    124     123    147      154     142    111    107    106    129    135    127     110
2003    116     126    153      145     157    117   88 2     98      122    139    126     112
2004    119     120    142      135     150    108   100    104     112    130   61.7    112
1346
1332
1375
1362
1359
1299
1343
1338
1358
1401
1432
1349
1320
1412
1443
1408
1318
1411
1360
1355
1437
1439
1425
1505
1419
1402
1518
1429
1515
1498
1393
Water Works Design & Supervision Enternrise
Association with Intercontinental Consultants and TeeLcrats IM ltd.
109Arjo Dedessa Irrigation Project
Meteorological and Hydrological Aspects
A8: Estimated Mean Monthly Rainfall at Arjo Dedessa Project Area
May 2007
Year
in mm)
Jan  Feb 1March   April   May   June   July    Aug    Sept     Oct     Nov   Dec
Annual
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1978
1979
1980
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
15.6    0      96.3     21.7    172     172     140     220     220    111.5    151    0.12 0    57 6   0.75     36.4    93.8     169     186     207     192   90.97   21.7   14.5
37.7  35 9   53.7     140     121     144     159     212     165   56.39   7.39      0 29 2   30     51.3     21.2     149     154     249     162     256    107.2   4.44   4.04
11.3  0.32   20.7     30.8    142     193     180     135     262    188.2   32.3   14.1
15.6 11.1     15      89.1      53      166     186     157     170   49.48     69     9.24
0    9.97    5.66     294     236     155     186     233     143   58.82    12.2   5.77
38.7  7.98   31.8     5.14    287     205     242     168     212    72.78   4.62    167
23 5 55.1   21.1     60.4    116     124     189     159     141    131.3   11.5   12.5 23.7  16 2   90.3     55.8    154     154     192     215     273    88.66     36     5.37
3     3.5    66.5     103     122     154     206     206      181   122.6   0.58   12.5 15.6  8.89     17      25.6    97.5    202     235     159     154   64.94   31.2   12.5
15.6  17.8   57.3     118     164     100     136     176     246    49.87   28.7      0
15.6  8 23   27.6     35.6    149     213     296     270     177    113.9   57.2      0
15.6    0      31.6     69.8    234     249     443     198     230    8.716   26.8   5.77
2.79    0      12.3     105     257     341     242     212     315     48.2    32.5   37.7 0    39.2   46.9     56.4    37 5    315     274     244     191    103.8   35.9   0.46
2.89  17.2   88.6     79.9    123     282     488     328     183   116.4   42.3   34.3 14.1  42.7   74.3     15.3    335     325     177    297     343    259.9    15.4   3.87
25 6  26 7   76.3     108     245     284     316     332     297    189.3   61.3   47 1
8.77  37 8   50.4     83 3    90.6    438     228     319     241    73.89   33 4   3.12
26.4 7.91    68.2     74.4    139     106     364     309     222    98.59     35      13
24.8  32.8    106      105     222     225     267     291     250    104 7   53.6   15.8
1.04   31     69.5     100     213     313     217     347     166    149.4   2 03      0
11.2  1.15   20.1     53.9    127     227     255     228     279    8.467    13.7     0
0    22.9   75.5     109     222     195     229     310     279    86.46    11.2   22.3
45.5 19 1    162     71.7    338     225     245     197     215    74.57   44.6   7.56
39.6  0.97   56.6     139     293     314     205     291     234     213     40.4   2.54 13    84     81.1     15.2    162     280     264     272     239    235.9   67 2   1.04
31.7  2.91     15      92.3    393     427     257     239     309    197.3   8 89   42.1 0      0      3.69     105     195     345     222     260     274     255     34.4   7.39
0     32     65.5     60.3    327     346     298     292     312    206 3    19.9   24.1
17.7  2.19   39.7     42 2    106     252     274     153     224   59.11    1.84   53.1
5.84  53.5    117     121     58.9    358     232     300     155   51.59   11.5   1 86
4 96  2.1     61.2     51.5    150     353     326     243     249    193.9   40.7   13 1
1320
1070
1132
1217
1210
990.6
1075
1292
1045
1303
1181
1024
1110
1362
1514
1607
1345
1785
1903
2008
1607
1464
1697
1609
1223
1563
1645
1830
1638
2014
1702
1983
1226
1466
1 689
Average
CV
Skew
Max
Min
75.3  18.4   53.6     69.5   180.8 243.0  245.9  238.3  228.6   115.5   31.4   12.7
0.85  0.96   0.68     0.55    0.49    0.37    0.31    0.25    0.24   0.591    0.9    1.13
0.62  0.78   0. 74     0.1     0.62    0.38    1.44   0.07    0.17   0.635   2.31    1.48
45.5  57.6  161.7   139.9  392.7  437.6  487.6  346.6  343.1  259.9   150.7  53.1
0.0   0.0     0.8       5.1     37.5   100.3  136.2  135.3  141.3    8.5      0.6     0 0
.I      .
Water Works Design & Supervision Enterprise          —~
1452 9
0 209
0.26
990 6
1 10
In Association with Intercontinental Consultants and Technocrats Pvt LtdArjo-Dedessa Irrigation Project
Meteorological and Hydrological Aspects
A9:_ Estimated Mean Half-Monthly Rainfal at the Project Area (in mm/hr)
May 2007
3.1
3.2    4.1
4.2  ! 5.1
5.2
6.1 I 6.2
7.1
7.2
9.1
8.2
9.1
9.2
10
10
11.1
11
12.1
12
Total
Year
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976 1978 1979 1980 1983 1984 1985
1986 1
1987
1988
1989
1990 ' 1991 ! 1992
Ll
1.1  I   I 2.1     2.2
7.8   7.79     0       0 0       0     57.3  0.29
26.1   11.6  14.7  21.2 2.1   27.1   7.27  22.7
11    0.27  0.32     0
7.8   7.79  4.31  6.84 0       0     9.97     0
30.6  8 12  2.26  5.72 0     23.5  47.7   7.4
23.7     0     10.3  5.89
3       0      3.5      0
7.8   7.79  8.89     0
7.8   7.79  8.89  8.89
7.8   7.79  7.98  0.25
7.8   7.79     0       0
0.3   2.49     0       0
0       0     2.75  36.4
0     2.89     0     17.2
9.4   4.73  2.66    40
18.6  7.03    26     0.7 43
0     8.77  13.1   24.7 2 14 2  12.2     0     7.91 3
0     24 8   17.7   15 2 2
.
22 4  73.9     0     21.7  75.2  96.97   112  60.8   68.4   71.7   110   110   132   87.7  110   1.92 75.7    75   0.12    0 0     0.75  1.27 35 1    31     62.8   81.4  87.4   82.5   104   121   86.1 76.5   115  76.8  14 2    0     21.7  10.3 4.25
37.6 16.1   6.75   133    18    103.1  72.3 71.9   55.2   103   106  106  83.6  81.7   36   20.4 3.46  3.92    0       0
23.6  27.7  1.73  19 5  34.3   114.3  49.4  104   127.4  122   81.7  79.8 94.4   162    50    57.3 4.44     0       0    4.04
4.62    16   30.4  0.46  42.1   99.86  62.2  131    63.4    116   61.4  73.9  125   137   104  84.3  26.8  5.48  3.17   11
0      15   36.5  52.6  25.5  27.53  78.1  87.9   87.2     99    89.7  67.2 94.2  75.8  25.7  23.8  61.3  7.69    0    9 24
0     5.66  25.4  4.04   140   96.39  96.2  58.9   91.6   94 2   108   125  78.8  64.1  57.1  1.73 12.2     0       0    5.77
19.9  11.9  0.29  4 85   188   98.87   102   103   119.5  123   101   67.5  102   110  44.4  28.3    0     4.62  4.62  12.1
0.69  20.4  5.14  55.2  67.5  48.89  80.8  43.3   75.2    113   62.1  96.9  92.5  48.8  86.9  44.4  11.5     0     6.06  6.46
57.9  32.4  32.2  23.5  78.8  74.81    72   81.8   102.9 89.1   51.9  163  198   74.2  40.1  48.6   32    3.98  2.89  2 48
56.4  10.1   12.4  90.9  49.1   73.14  87.8   66     79.7    127   71.2  134  84.8  96.5   72    50.5  0 58     0     6.06  6.46 0.46  16.5   11.6   14   57.2  40.35  99.6  102    145   90.3  85.5  736   97.2    57   40.4  24.5  15.6  15.6  6.06  6.46
27.7  29.6  28.6  89.8  64.2   100.3  41.6  58.7   65.6   70.6    99    76.5  138   109  26.3  23.5  11.9  16.9    0       0
18.7  8.89  20.7  14.9  54.3  94.53  89.7   123   168.1   128   134   136  89.9  86.7  78.7  35.2  44.2    13      0       0
4 73  26.9  17.3  52.5  53.9  180.3   138   111   232.6  211   51.8   147  66 7   163  7.16  1.56 15.2  11.6  4.96  0.81
1.5   10.8  57.5  47.8   121   136.6   168   173   94.1    148   82.6   130  160   155  37.2   11   24.9  7.58  36.9  0.81
15.2  31.7  28.1  28 3  1.24  36.22   195   119   147 6  127   110   134  114   77.1   85    18.9 28.2  7.68  0.23  0.23
48.1  40.5  6.93  72.9  19.2  104.3   158   124   224.5   263   215   112  116   66.8  81.4   35   31.2   11    34.3    0 57    17.3  0.98   14.3  145   190.2   172   154    75.1    102    175   122   179   164   155   104   154     0       0    3.87
.9 32.5       65.3 42.7     148   96.74   182   102   208.4   107    186   145  194   104   91.5  97.8  29.5  31.8  19.8  27.3 1.6 28.8        0     83.3  60.3   30.3    253   185   123.1   105   108   211  98.1   143   53.4  20.4  32.4  1.03  3.12    0 7.6 30.6      1.27 73.2    89.9 49.04     13.3  93.1  195.1   169   179   131  144   77.9  97.3  1.27  10.5  24.5  12.7  0.29
5.2 80.6     3 34   102   152 70.12 82.1       143   161.3   106   159   131   102   148   81.4  23.3   29    24.6   1.15 14.7
1320
1070 1132 1217 1210 990.6 1075 1292
1045 1303 1181 1024 1110 1362 1514 1607 1345 1785 1903 2008 1607 1464 1697
111Arjo Dedessa Irrigation Project
Meteorological and Hydrological Aspects
May 2007
(in mm/hr)
1.1
1.2
2.1
2.2
3.1
3.2 4.1
4.2
5.1
5.2
6.1
6.2  I 7.1
7.2
8.1
8.2
9.1
9.2
10
10
11.1
11
12.1
12
Total
Year
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
0      1.04  6.64   24.4 3.06 66.4 63.6            36.6     93.7    119.7     86.2     227     79.7      137     149     197      104     61.9     83.9     65.5   1 39 0.65       0       0
11.2      0        0     1.15     1.3    18.8 29.2       24.7       55     71.64      105       122    109 7     145     116     111      166      112     7 24     1.23   9.52 4.16       0       0
0        0      5.6    17.3      0     75.5 0.52        109       116    105.9     63.4      131     123 9     105    177     133      125      154     40.8     45.7   5.95    5.3   1.96 20.4
9.2   36.3 4.09        15    78.8 82.9 26.6         45.1      146    192.4      159      66.1     84.7      160     100    96 6     93.2      122     58.2     16.4   8.48 36.1    0.69 6.87
2.1     37.5     0     0.97 28.1      28.5 72.8       66 6      71.7   221.5      152      161      78.6      127     165     126      148     85.8      119     94.2   25.3 15 1    0.06 2.48
11.2    1.84  1.94   6.46 24.6 56.5 8.89            6.33     69.7    92.18      142       138    141.7     122     131     142      118      121      92.9      143    62.3 4 92       0     1.04
28.3 3.35         0     2.91      0       15      19      73.3      227       166      209       217     99.2      158     141    98 2      146      162      148     48.9   8 89     0    26.6   15.5
0        0        0        0     2.31   1.39 28.4       76.2      76.9    118.6      215       129     99.4      123     149     112      139      135      168     87.1   25.2 9 15 2.89       4.5
0        0     23.7     8.3    1.24 64.3 36.9         23.3      96.3     230.8     122       224    118.2     180     126     165      187      124     96.4      110    7.27 12.7 14.4 9.75
12.5 5.19        0     2.19 8.37 31.3 37 7            4.49      19.8     86 64     139       114     156.4     118    51.7     102      954      129     35.3     23.8      0     1 84 3.29 49.8
2     3.84   17 8 35.7 39.2 77.6 68.8               52.3     20.3 38.67          165       193     127.5    105     133     166     85.9     69.1     23.7     27.9   6 64 4.91      1.4   0.46
0.2 4.76        1.4     0.7 10.6 50.6 9.7              41.8      28.5 121 9          148       204    256.7    69.6 91-1        152      132      117      168     25.7    11.3 29.4 9.24 3.88
1609
1223
1563
1645
1830
1638
2014
1702
1983
1226
1466
1689
Average
Skew
7.5    7.8    8.8    9.6 20.6 33.0 22.7
46.7    78 2   102.6
119.8  123.2  122.0
CV        1.18  1.28  1.48  1.21  1.03  0.74  0.95  0.72    0.68   0.511   0.46    0.41   0.425
123.9 117 121 8 120.0 108.5 73.7 41.8 19.7 11.8 6.1 6.6 1452.9
0.31 0.36 0.29 0.3 0.32 0.58 0.86 0.94 1.26 1.57 1.5 0.209
1.27 1.82 2.47 1.27 0.95 0.83 0.95
0.63    0.94
0.84    0.41    0.57
1.039
1.64   0.36  0.47    0.72     0.1     0.63    1.11  1.42 2.61  2.15  2.9
0.26
Max
Min
30.6  37.5  57.3  40.0  78.8  82.9  72.8  133.1  226.7 230.8 252.9 226.8 256.7 263.1
216  210 9  198.4  164.1  168.3  143.0 75.7 75.0 36.9 49.8    2014.5
0.5      1.2     27.5 13.3 43.3 55.2 69.6
990.6
0 0 0.0 0.0 0.0 0.0 0.9 0.0
51.7  67.2    66.7    48.8     7.2      1.2 0.0 0.0 0.0 0.0
H2Arjo-Dedessa Irrigation Project
Meteorological and Hydrological Aspects
Annex B: Results obtained from the Hydrological Analysis
B1:     Estimated Monthly Flows at Arjo Dedessa Dam Site
May 2007
Year  Jan    iFeb    iMar     Apr     jMay      |Jun    ]jul Aug            (Sep     Oct      Nov         Dec I
Annual
1961
1962
1963
1964
1965
1966
1967 1969 1970 1971 1972 1973 1974 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
30.74  28 02   25.73   68.61   49.78   207.7   751.5   972.5     900      661      291.8    101.4       4089
34.68   18 98   19.47   5.073   21.39   157.2   397.6    560.6   700.8    512.2    64.92    31.95       2525
22 72    12.3    12.28   30.86   111.6   235.3   429.5    826.3   622.8    223.9     111.8    97.23       2736
7642   24.32   17.86   24.21   41.69     167      670     784.4   705.4    621.4      307     42.03       3413
33.05 16.1 11.28 34.56 21.07 122.1 532.7 633.6 410.3 573.5 194.6 90.12 2673 42.41 40.3 39 61 53.61 47.41 134.3 547.5 654.6 726.3 49.32 25.38 11.62 2372 6.806 5.364 15.57 14 73 19.24 82.14 452.6 764.7 719.5 634.8 400.8 120.1 3236
30.4   29.71   46.01   28.69   55.55   253.9     607      857     565.3    174.2    53.81    26.41       2728 20.38  5.351   9.245   24.21   34.36   209.3   593.4   635.7   673.2    121.1    110.2    35.46        2472
22.1    10.44   7.454   9.019    40.6    175.5   525.6   708.6   568.4    450.3    184.8    60.15       2763
31.3    18.92   13.43   25.78   42.79   8561    512.9   746.2   430.7    125.4    71.09    29.98      2134 15.44  7.121   3.221   4.586   82.96    181.5   156.2     633     839.1    354.5    90.45       37          2405 21.79  10.31   9.887   6.561   71.15     190    409.5    770.6    195.6   83.73    72.32     27.9        1869 10 27   7.805   6.815   16.56   35.18   94.19   340.6   497.5     371      157.1    57.07    25.13        1619 10.73   9.737   6 839   49.56    144.9   366.1   584.3     777       745     235.5    62.71     52.8        3045
47.75   27.57   29 73   25.58   40.74   38.71   412.8     633     540.6    315.2    19.85    7.138        2139
35.71    19.2    19.63   13.95   34.68   180 3   451.6    571.2   414.6    455.6    107.4    53.96        2358
25.9    20.13    234      16.88    39.8    111.5   361.4     687     668.2    617.6    282.5    69.26        2924
23.01 10.61 8.146 6.854 21.69 134.7 449.2 401.2 311.8 78.36 12.69 4.808 1463 7.152 3.462 1.775 8.54 47.33 123.1 280 559 504.1 155.7 48.31 25.72 1764
10.49   6.052   11.02   9.993   9.685   86.35   288.9    296.2   438.4     132.9   50.52     28.15      1369
7.756   3.775   8.501     12.5    27.82    157.2   412.8     633     402.9    231.4    97.47      38.7        2034
24.16   17.49   14.79   5.553   28.08   203.6   354.6   689.1    905.1    315.2     114.9   46.11        2719
25      14.84   13.97   24.24   20.58   87.63    174.3   301.1   651.3    315.2    65.15    70.31        1764
31.93   19.11   23.19   44 03   31.91    114.4   219.3     1043    669.7    280.5    46.23    23.01        2546
16.3    12.32   10.51   18.94   44.17    113.8   517.4   964.2    486 9    310.9    29.61      21.7        2547
14.62   15.23   9.435    14.96   42.85    116.8   235.1   497.2    338.1    482.9    99.78      41.9         1909
25.4    17.72   16.74   39.56   79.86   226.9   383.1   698.7    377 8    243.8     107 6     36.17       2253 23.32   12.45   10.62   11.34   45.32    160.1   341.3    736 3     514     93.66    41.38     21.09       2011
12.16   7.787   10.15   14.06   29.04   60.34    143.8   343.1     291     93.56     36.44     82.8         1124
1996
2001
2002
2003
29.25 18.44 26.36 21.96 107.7 271.3 462.2 515.2 358 225.1 71.87 49 76 2157
50 8 37.86 45.32 41.35 97.36 291.4 473.1 630.3 602.2 403.8 130.5 63.78 286R
59.09 36.38 37.91 45.85 35.1 127 292.1 356.4 346 126.2 70.05 47.88 1580
33.66 23.05 38.53 53.75 36.41 87.39 347.2 393.2 540.6 315.2 90.27 46.11
31.2 25.55 20.76 20.73 49 47 146 8 336.3 385.2 384.9 322.3 85 26 46.11
2005
2004
1855
-
Water Works Design & Supervision Enterprise
Id Association with Intercontinental Consultants and Technocrats
113
Pvt. Ltd.Arjo-Dedessa Irrigation Project
Meteorological and Hydrological Aspects
B2: Regional monthly coefficient of variations (CV)
May 2007
Station
Year | Jan   j Feb    Mar  April\Mav]June\ July | Aug] Sep I      Oct   Nov    Dec
11400
114014
114005
114004
114003
91008
91012
1  35 20
23
22
21
38
35
0.51 0.57 0.66 0.68 0.62 0.45 0 35 0.3 0.33 0.67 0 62 0.59
0.47 0.46 0.48 0.56 0.69 0.53 0.27 0.33 0.28 0 56 0.81 0.53
0.28 0.41 0.33 0.43 0.6 0.47 0.2 0.16 0.25 0.44 0.3 0.34
0.33      0.42      0.45    0.55   0.56    0.43    0.57      0.38       0.43     0.64    0.35      0.33
0.51       0.83      0.57    0.71   0.66   0.58    0.35      0 25       0.36     0.56    0.46      0.55
0.68        1.3        0.7      0.6    0.61   0.46    0.29      0.32       0.28     0.64       1        0.86
0.61 0.49 0.76 0.49 0.57 042 0.3 0.29 0.32 0.52 0.8 0.59
B3:     Regional monthly coefficient of skewness
Station
Year
Jan     Feb     Mar  April\May\june\ July       >4ug    Sep      Oct Nov     Dec
114001
114014
114005
91008
91012
35
20
23
38
35
0.63 0.74 1.Q7 0.94 1.63 0.89 0.13 0.04 0.25 0.99 1.37 0.96
1.35 0.31 0.47 0.81 0.85 1.03 -0.5 0.93 0.89 0.67 2.6 1 98
0.33 1.48 0.59 -0.1 2.18 1.54 0.07 0.45 0.81 0.42 0.52 1.09
2.73 0.29 3.26 0.77 1.14 0.81 0.17 1.14 1.13 0.77 3.14 2.85
3.17 4.94 1.22 1.5 2.34 1.15 0.43 0.94 0.05 1.6 2.86 2.79
B4:     16 Regional monthjy coefficient of correlations (R)
Station
Year
114001
114014
35
20     0.36        0.5        077       0.56 0.62 0.81       0.46      0.54       0.28     -0.04    0.65      0.78
Jan     Feb     Mar  April May\june     July\   Aug      Sep     Oct  Nov
Dec
-0.1 0.83 0.89 0.59 0.3 0.78 0.45 0.53 0.44 0.45 0.74 0.78
114005
23
114004
114003
91008
91012
22
21
38
35
0.84 0.5 0.35 0.3 0.64 0.38 0.48 0.68 0.8 0.83 0.25 0.16
0.8 0.52 0.21 0.52 0.6 0.58 0.58 0.69 0.61 0.77 0.37 0 14
0.84        0.3       0.18        0.52 0.45 0.73       0.68      0 66       0.58      0.82   0.56       0 1
0.27       0.33      0.37        0.27 0.53 0.59       0.54      0.55       0.46      0.45   0.77      0 62
0.12       0.83      0.72       0.48 0.41 0.72       0.53      0.41        0.48      0.31    0.44      0.65
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B5: Standardized probability weighted moments
May 2007
Station
Name of Station                  Location Deg N. Deg E.
Area
(km2)
Record
Length,N
PWMs
mi
10
mjo2
1  114001    Dedessa near Arjo Upper Dedessa near
2   114014    Dembi
3   114005    Dabana near Abasina
4    91008     Gilgel Ghibe
5   91012     Gojeb near Shebe Angar Gutin near
6   114007    Neqemte
7   114004    Wama near Neqemte
8   114003    Siffa near Neqemte
9   115008    Aleltu at Nejo
10  101001    Sor at Metu
11  91013     Ghibe-Limu near Limu
12 91001 Great Ghibe near Abelti
8.41    36.25   9981
8.02 36.28     1806
9.02 36.03     2881
7.45 37.11     2966
7.25 36.23     3494
1975
8.53 36.47      844 8.52 35.45      951
9.30     35.3      155 8.19 35.36     1622 8.35 37.13      663 8.14 37.15    15460
29
15
18
37
34
20
21
20
7
17
10
19
0.40739     0.243171
0.420504 0.262951 0.414042 0.253945
0.400025    0.240847
0.408619     0.248091
0.421145    0.254033
0.419805 0.254029 0.416527 0.251324 0.400261 0.247582 0.401323 0.244658 0.425296 0.261749
0.424754 0.260656
Weighted Average
255
0.411964
0.250375
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B6: Mea n Mor thly To tai (Suspend ed and Bed) Sediment Load
at Arjo De dessa Dam Sit e (in 1000 m J)
May 2007
Year    Jan     Feb     March        April    May     June      July     Aug    Sep     Oct      Nov      Dec
Annual
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1979
1980
1982
1983
1984
1985
1986
1987
1988
1989
1990
1992
1993
1994
1995
1996
2001
2002
2003
2004
2.11 1.75 1.27 5.73 3.11 33.4 315 523 476 372 32.3 15.3
2.56 0 94 0.81 1.07 3.18 22.4 114 216 319 189 6.92 2.41
1.3 0.47 0.39 1.6 11.3 40.8 129 403 264 50.4 16.5 14.3
1.78 1.32 0.71 1.07 2.34 23.6 262 370 322 387 19.7 3.74
2.37 0.72 0.34 1.91 0.79 14.3 182 263 135 227 40.1 12.7
3.54 3.13 2.54 3.86 2.88 22.4 190 277 338 102 40.8 9.14
0.64 0.57 0.57 0.49 3.18 22.4 140 356 333 352 71.4 20.1
5.73 2.22 0.52 0.22 2.26 2.27 123 265 299 102 10.4 2.97
2.07 1.92 3 22 1.42 3.71 46.1 224 427 205 102 5.13 1 78
1.09 0.75 0.4 0.23 1.72 33.8 216 246 205 102 16.1 2.85
1.25 0.36 0.18 0.22 2.25 25.5 178 315 228 154 36.9 6.64
2.17 0 88 0.45 1.2 2.44 8.09 171 342 146 19.9 8 2.18
0.7 0.2 0.05 0 08 7.04 26.9 123 246 425 105 11.8 3.05
1.22 0.35 0.28 0.13 5.51 29 119 360 205 102 8 23 1.94
1.91 1.29 0.65 0.59 1.79 9.43 88.9 179 115 28.6 5 63 1.64
0.39 0.3 0.15 3.41 17.2 82.8 211 365 352 54.6 6.55 5.39
2.69 0.96 0.83 0.45 1.74 26.7 140 223 138 157 15.5 5 58
1.61 1.03 1.09 0.61 2.18 12.3 97.7 300 296 339 29.6 8 32
1.33 0.35 0.2 0.14 0.82 16.7 138 127 87.3 9 39 15.5 5.24
0.2 0.06 0.02 0.2 2.87 14.5 65 216 188 28.2 4.31 1.71
0.38    0.15       0.33        0.26     0.23     8.21      68.3      78      151      21.9     15.5     5.24
0.23    0.07       0.22        0.38     1.23     22.4     91.7     301      132     53.1     13.3     3.28
1.44    0.78       0.52        0.1      1.24     32.4     94.8     246      205      102     17.3     1.31
2.38    0.63       0 48        1.08     0.76      8.4       30.4    80.1     284      102     6.96     8.52
2.24    0 95       1.08        2.82     1.53     12.9       44      246      205      102     15.5     5.24
0.64    0.62       0.26         0.5      2.45     13.3     49.1     179     99.4     172     13.8     3.72
1.56    0.84       0.64        2 37     6.63     38.5      107     308      119     57.7     15.5     2.94
1.36 0.48 0.31 0.32 2.68 22 89.2 335 194 12.5 3.37 1.24
0.48    0 23       0.29        0.45     1.31     4.63     22.4     98.7     78.2     12.5    2.75
1 78    0.87       0.71        0.93     10.7     51.2      145     189      109      102
5 24
15.5      4.9
4.72    2.83       3.15        2.55      9.1       57.5      150     261      250      129     21.1
7 2Q 6.01    2.66       2.37        3 01      1.78     15.2     69.5     105      103     20.1     7.82     4.61 2.44    1.28       2.43        3.88     1 89     8.37      123     123      205      102     15.5     5.24 2.16    1.43        0.9         0.84     3.08      19.2     87.1     119      122     90.2     10.7     5.24
1781
878.9
932.5
1397
880.6
995.4
1300
815.6
1024
826.7
948.3
704.9
949.6
833.7
434.5
1098
711.7
1089
402.1
520.8
349
618.6
703.4
525.4
639.8
534 7
660 6
662 4
297
631 5
oyg.3
341.1
594 5
461.9
Water Works Design & SupendsionEnterpri^------------------"
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Meteorological and Hydrological Aspects
May 2007
■jo Dedessa Reservoir
Elevetion Area
(m a si) (km }-
Volume
Elevetion
Area          Volume
2               (Mm3)
2                 (Mm3±_
(m asl)        (km )
1320           0.54            0.0
1321            0.99            0.8
1322           2.39            2.5
1323           3.78            5.5
1324           5.11           10.0
1325           6 64           15.9
1326           8.03           23.2
1327           9.46           31.9
1328           10.87          42.1
1329           12.67          53.9
1330           14.40          67.4
1331           15.89          82.5
1332           17.74          99.4
1333           19.46         118.0
1334           23.59         139.5
1335          26.66         164.6
1336 29 05 192.5
1337 31.73 222.9
1338 34.24 255.8
1339 36.62 291.3
1340 39.45 329.3
1341          41.97          370.0
1342          44.25         413.1
1343          47.05         458.8
1344 49.95 507.3
1345 53.11 558.8
1346 56.31 613.5
1347 59.15 671.2
1348 61.56 738.8
1349          63.97         808.7
1350          67.88         874.7
1351           70.62         943.9
1352 74.35 1016.4
1353 77.88 1092.5
1354 81.28 1172.1
1355 84.51 1255.0
1356           87.85          1341.2
1357           90.75          1430.5
1358           94.18          1522.9
1359 97.23 1618.6
1360          100.54         1717.5
1361          103.64         1819.6
1362          106.39         1924.6
1363 109.00 2032.3
1364 111.91 2142.8
1365          115.05         2256.3
1366          117.68         2372.6
1367          120.20         2491.6
1368          122.76         2613.0
1369          125.28         2737.1
1370          127.69         2863.5
1371          130.25         2992.5
1372 133.03 3124.2
1373 135.45 3258.4
1374 138.08 3395.2
1375 142.38 3535.4
1376 145.27 3679.2
1377 148.00 3825.8
1378 150.75 3975.2
1379 153.73 4127.5
1380 156.02 4282.3
1381 159.09 4439.9
1382 162.13 4600.5
1383          166.96         4765.0
1384          173 01         4935.0
1385          178.58         5110.8
1386          183.38         5291 8
1387 188.10 5477 5
1388 193.28 5668 2
1389 197.36 5863 6
1390 201.96 6063 2
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Meteorological and Hydrological Aspects
May 2007
B8:        Sample Simulation Results of Arjo Dedessa Reservoir
I-------------- I---------     1 ....       Month
L
-------------------
Inflow
(Mm3)
3
-------------l1— ----------- 1 ------------ 1------------ - DF        Eo-Rf       IWD     (Eo-Rf)
(m3/s)      (mm)      (Mm3)    (Mm2) 4             5             6     L1
-------------
SL
(Mm3)
8
—
IWR (Mm3)
9
Reservoir Status
cs
(Mm3)
10
Storage f Area
(Mm3) (km 2)
11              12
Level
(m asl)
13
Spill
(Mm3)
14
STRQ
(Mm3)
15
T. Spill
(Mm3)
16
404         Sep
404          Oct
404         Nov 404         Dec 404         Jan 404         Feb 404         Mar 404          Apr 404         May
404         Jun 404          Jul
577.4
333.3
102.7
47.7
22.5
12.3
16.3
16.8
40.1
160.9
400.3
0.0              0              0.0                          0.0         1.0
0.0            111            0.0                          8.2         1.0
0.0            143          46.2                         10.6        1.0
0.0            135          60.0                         10.0        1.0
0.0            146          74.0                         10.7        1.0
0.0            147          85.0                         10 4        0.9
0.0            164          50.4                         10.9        0.8
0.0            146            7.4                          9.4         0.8
0.0             80             0.0                          5.1         0.8
0.0              0              0.0                           0.0         0.8
0.0 0 0.0 0.0 1.0
0.0
0.0
46.2
60.0
74.0
85.0
50.4
7.4
0.0
00
0.0
576.4          1006.3         74.1
324.1           1006.3         74.1
44.9           1006.3         74.1
-23.4            982.9           73.1
-63.2            919.7           70.4
-84.0            835.8           66.7
-45.9 789.9 64.6 -0.9 789.0 64.6
34.1             823.1           66.1
160.1            983.2           73.1
399.3           1006.3          74.1
1351.9      576.4 1351 9       324 1 1351.9        44.9
1351.5         0.0
1350.7         0.0
1349.4         0.0
1348.7         0.0
1348.7         0.0
1349.2         0.0
1351.5         0.0
1351.9       376.3
404 Aug
405 Sep
405 Oct
647.4
717.9
777.8
0.0 0 0.0 0.0 1.0
0.0 0 0.0 0.0 1.0
0.0            111            0.0
8 2          1.0
0.0
0.0
0.0
646.4           1006.3           74.1
716.9           1006.3           74.1
768.6           1006.3          74.1
1351.9      646.4             789 1351.9      716.9
1351.9      768.6
Water Works Design & Supervision EnternrkpArjo-Dedessa Irrigation Project Meteorological and Hydrological Aspects
B8:      (....... . continued)
May 2007
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
405          Nov
405         Dec
405          Jan 405          Feb
405         Mar 405          Apr 405         May 405          Jun 405          Jul
405.1
193.8
70.5
48.7
46.9
31.3
43.5
149.4
466.8
0.0            143           46.2                       10.6        1.0
0.0            135           60.0                       10 0        1.0
0.0            146           74.0                       10.8        1.0
0.0            147           85.0                       10.8        1.0
0.0            164          50.4                        11.7        0.9
0.0            146           7.4                         10.3        0.9
0.0             80             0.0                          5.7         0.9
0.0 0 0.0
0.0 0 0.0
0.0 1.0
0.0 1.0
46.2
60.0
74.0
85.0
50.4
7.4
0.0
0.0
0.0
347.3           1006.3          74.1
122.8           1006.3          74.1
-15.3            990.9           73.5
-48.1 942.8 71.4
-16.2 926.6 70.7 12.6 939.2 71.3 36.9 976.1 72.8 148.4 1006 3 74.1 465.8 1006.3 74.1
1351.9 347.3
1351.9 122.8
1351.7          0.0
1351.0          0.0
1350.8          0.0
1350.9          0.0
1351.4          0.0
1351.9        118.3
1351.9        465.8
405 Aug 406 Sep 406 Oct 406 Nov 406 Dec 406 Jan 406 Feb 406 Mar 406 Apr
640.7 516.8 402.5
166.2
71.5
31.7
24.8
17.0
13.5
0.0              0              0.0                          0.0          1.0
0.0              0              0.0                          0.0         1.0
0.0            111            0.0                          8.2          1.0
0.0            143          46.2
10.6        1.0
0.0 135 60.0 10.0 1.0
0.0 146 74.0 108 1.0
0.0            147           85.0
10.6         1.0
0.0            164           50.4                        11.3        0.9
0.0            146           74
9.7          0.8
0.0
0.0
0.0
46.2
60.0
74.0
85.0
50.4
7.4
639.7            1006.3         74.1
515.8            1006.3         74.1
393.3            1006.3         74.1
108.4            1006.3         74.1
0.5              1006.3          74.1
-54.1             952.1            71.8
-71.7 880.5 68.7 -45.6 834.9 66.7
-4.5                8304           66.5
1351.9         639.7         926.6 1351.9        515.8
1351.9      393.3
1351.9      108.4
1351.9        0.5
1351  1        0.0
1350.1         0.0
1349.4        0.0
1349.4        0.0
119
Water Works Design & Sunervkinn iwor™™Arjo Dedessa Irrigation Project
Meteorological and Hydrological Aspects 88:       (..^....continued)
May 2007
1u
—
4
5
6
7
8
9
10
11
12
13
14
15
16
406
May
406         Jun 406          Jul 406         Aug 407         Sep 407         Oct 407         Nov 407         Dec 407         Jan
I 407            Feb 407         Mar 407         Apr 407         May
: 407 Jun 407 Jul 407 Aug
3 45.8
135.6 403.5
668.9
564.2
337.5
160.8
73.1
32.4
23.5
23.1
24.7
44.2
130.6
359.7
565.6
_______
0.0 80 0.0 5.3 0.8
0.0 0 0.0 0.0 0.9
0.0 0 0.0 0.0 1.0
0.0 0 0.0 0.0 1.0
0.0             0             0.0
0.0         1.0
0.0           111           0.0                          8.2         1.0
0.0            143           46.2                       10.6        1.0
0.0            135           60.0                       10.0        1.0
0.0            146          74.0                         10.8        1.0
0.0            147           85.0                        10.6        1.0
0.0 164 50.4
0.0 146 7.4
11.3 0.9
9.8 0.8
0.0             80            0.0                           5.4         0.8
0.0              0             0.0
0.0              0             0.0
0.0              0             0.0
0.0 0.9
0.0 1.0
0.0 1.0
0.0
0.0
0.0
0.0
0.0
0.0
46.2
60.0
74.0
85.0
50.4
7.4
0.0
0.0
0.0
0.0
39.6             870.0            68.2
134.7           1004.7           74.0
402.5           1006.3          74.1
667.9           1006.3          74.1
563.2           1006.3          74.1
378.3           1006.3          74.1
103.0           1006 3          74.1
2.1             1006.3          74.1
-53.5            952.8            71.8
-73.0 879.8 68.7 -39.5 840.3 66.9
6.7              847.0            67.2 38.0             885.1            68.9
129.7            1006.3          74.1
358.7             1006.3          74.1
564.6             1006.3          74.1
1349.9          0.0
1351 8          0.0
1351.9        401.0
1351.9         667.9           830.4
1351.9        563.2
1351.9        378.3
1351.9        103.0
1351 9           2.1
1351.1           0.0
1350.1           0.0
1349.5           0.0
1349.6           0.0
1350.2           0.0
1351.9           8.5
1351.9        358.7
1351 9        564.6              840.3
2087
1978
Wnrlrc?
o n--------      . -Arjo Dedessa Irrigation Project
Meteorological and Hydrological Aspects
May 2007
Annex C: Standards
C1:      Commonly used values of runoff coefficients____________________ Runoff
Class         Description of catchment
percentage
A           Flat, cultivated and blck cotton soils
B           Flat, partly cultivated stiff soils
C           Average catchment
D           Hills and plains with little cultivation
E Very hilly and steep, with little or no cultivation
10
15
20
35
45
C2: Runoff coefficient for pervious surfaces by selected hydrologic soil
groupings and slope ranges 
Terrain Type
Soil Type
A
B
CD
Flat, <2%
0.04 - 0.09           0.07 - 0.12
Rolling, 2 - 6%                   0.09-0.14             0.12-0.17
Mountain, 6-15%               0.13-0.18             0.18-0.24
Escarpment, >15%           0.18-0.22            0.24 - 0.30
0.11 -0.16            0.15 - 0.20
0.16-0.21              0.20-0.25
0.23 - 0.31            0.28 - 0.38
0.30 - 0.40           0.38 - 0.48
Note: Where A, B, C, and D are defined as shown in C3:
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C3:
SCS Curve Numbers for Various Conditions1
Cover Description
Curve numbers for
hydrologic soil group
Cover type
Hydrologi
c
condition
A
8
C
D
Fallow Bare soil
Crop residue
cover (CR)
Pasture, grassland, or range- continuous forage for grazing2
Meadow-continuous grass, protected from grazing
Brush-weed-grass mixture with brush the major element3
Woods-grass combination5
Woods6
Farms—buildings, lanes, driveways, and surrounding lots_____________ Arid and semi-arid rangelandse
-
Poor
Good
Poor
Fair
Good
-
Poor
Fair
Good
Poor
Fair
Good
Poor
Fair
Good
77           86           91           94
76           85           90           93
74           83           88           90 68           79           86           89 49           69           79           84 39           61           74           80 35           59           72           79
48           67           77           83 35           56           70           77 304               48           65           73 57           73           82           86 43           65           76           82 32           58           72           79 45           66           77           83 36           60           73           79 304                55           70           77
Mixture of grass, weeds, and low- growing brush, with brush the minor element
Mountain brush mixture of small trees and brush
Small trees with grass understory Poor
Brush with grass understory Desert shrub brush
Hyd cond7
Poor Fair Good Poor Fair Good
Fair Good Poor Fair Good Poor Fair Good
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Meteorological and Hydrological Aspects
May 2007
Average runoff condition, and la = 0.2S
2  Poor: < 50% ground cover or heavily grazed with no mulch
Fair: 50 to 75% ground cover and not heavily grazed
Good: > 75% ground cover and lightly or only occasionally grazed
3 Poor: < 50% ground cover
Fair: 50 to 75% ground cover
Good: > 75% ground cover
4  Actual curve number is less than 30; use CN = 30 for runoff computations.
5      CNs   shown   were   computed   for   areas   with   50%   grass   (pasture)   cover.   Other   combinations   of conditions may be computed from CNs for woods and pasture.
6   Poor For.eFstolriettst er,litsmtera, lsml treaells,treaens,d barnudshbraurshe daerst e rdoeyestdrobyey dhebay vy  hegarvy  azignrgazi orngreogrulraergular burning.
Fair Woods grazed but not burned, and some forest litter covers the soil. Good . Woods protected from grazing, litter and brush adequately cover soil.
7 Poor: < 30 % ground cover (litter, grass, and brush overstory)
Fair: 30 to 70 % ground cover
Good: > 70 % ground cover
Soil Groups
Group A: Sand, loamy sand or sandy loam. Soils having a low runoff potential due to high infiltration rates. These soils  primarily  consist of deep, well-drained sands  and gravels.
GrouP B' Silt loam’ or loam- Soils having a moderately  low runoff potential due to moderate infiltration rates. These soils  primarily  consist of moderately  deeo to deen moderately well to well drained soils with moderately fine to moderately coarse textures
Group_C: Sandy clay loam Soils having a moderately high runoff potential due to slow infiltration rates. These soils  primarily  consist of soils  in which a layer exists  nlar thA
fine textire31 ’"P^5
m °Vement of wa,er °r soi|s with moderately fine to
Group_D: Clay loam, silty clay loam, sandy clay, silty clav or clav ctniie h, •
runoff potential due to very slow infiltration rates. These soils primarily consktV ?'9h with high swelling potential, soils with permanently-high watenables  sn?k 
V            f CayS
=a c,a> layer at or near the sortace. and shatti
5 ls
’
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123Arjo-Dedessa Irrigation Project
Meteorological and Hydrological Aspects
May 2007
Dam design flood inflow
Category of reservoir
A: Breach endangers in
Initial
condition
General
Min. standard,
Rare
overtopping
Wind speed,
Min. wave
surcharge
Spilling long term av. Daily
Larger of 0.5 PMF or 10,000
Average annual max.
lives in commentary     inflow        PMF                year flood               hourly wind
B: Breach may endanger lives not in a community,
Larger of
0.5 PMF or Larger of 0.3
Wave surcharge
10,000 year    PMF or 1,000          allowance > 0.6
extensive drainage       Full           flood
year flood               m
C: Breach with negligible risk to life and causing limited damage
Larger of
0.3 PMF or Larger of 0.2
Av. Annual max.  hourly wind.  Wave surcharge
1,000 year      PMF or 150            allowance > 0.4
Full           flood
year flood               m
purpose of PMF, the ordinates of the computed PMF hydrograph are multiplied by the proportion indicated.
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Meteorological and Hydrological Aspects
May 2007
C5:         Ratios of the basjcdirnensionless hydrograph of theSCS
T/Tp               Qi/Qp
Ti/Tp            Qi/Qp
Ti/Tp              Qi/Qp
00
0.05          0.008
0.1           0.015
0.15           0.043
0.2           0.075
0.25           0.11
0.3             0.16
0.35            0.22
0.4             0.28
0.45            0.36
0.5             0.43
0.55            0.52
0.6              0.6
0.65            0.69
0.7             0.77
0.75            0.83
0.8             0.89
0.85            0.93
0.9             0.97
0.95            0.99
11
r 1.05                 0.99
1.1             0.98 1.15            0.95
1.2             0.92 1.25            0.88
1.3             0.84 1.35            0.80
1.4             0.75 1.45            0.71 1.50            0.66 1.55            0.61
1.6             0.56 1.7             0.49 1.8             0.42 1.9             0.37
2.0             0.32
2.1              0.28
2.2             0.24
2.3             0.21
2.4             0.18
2.5             0.16
2.6 0.13
2.7 0.11
2.8 0.098
2.9 0.088
3 0.075
3.2 0.056
3.5 0.036
3.7 0.027
4 0.018
4.2 0.014
4.5 0.009
4.7 0.007
5 0.004
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125Arjo-Dedessa Irrigation Project
Meteorological and Hydrological AspectsMay_2QQ/_
C6: Guidelines for Interpretations of Water Quality for Irrigation
Water parameter SALINITY
Salt Content
Symbol Unit1 Usual range in irrigation water
Electrical Conductivity
ECW
dS/m
0-3
dS/m
(or)
Total Dissolved Solids
TDS
mg/l
0 - 2000
mg/l
Cations and Anions
Calcium
Ca~
me/l
0-20
me/l
Magnesium
Mg++
me/l
0-5
me/l
Sodium
Na+
me/l
0-40
me/l
Chloride
cr
me/l
0-30
me/l
NUTRIENTS-
Potassium
K+
mg/l
0-2
mg/l
MISCELLANEOUS
Acid/Basicity
pH
1-14
6.0-8.5
Sodium Adsorption Ratio3
SAR
12
(me/l) ,
0-15
Source: FAO, 1994: Water Quality for Agriculture. Irrigation and Drainage Paper. No Rev 1, Reprinted, 1994
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Arjo-Dedessa Irrigation Project
Meteorological and Hydrological Aspects
Ma y 2007
Annex D:
D1:
Meteorological Data
tfeteoroloqical Observation Stations
Station Name
Latitude
North
Longitude
East
S.No.
Deg.    | Min.
Deg.     Min.
Altitude
(m)
Class
Period
1             Agaro                        07         51         36         36            2030          1980-2004             1
2             Arjo
08         45         36         30            2565          1972-2004             3
3             Bedelle                     08         27        36         20            2030          1967-2004             1
4             Dedessa                   09         23         36         06            1200          1971-2004             1
5             Dembi                       08         04        36         27            1950          1954-2004             3
6             Gimbi                        09         10        35         47            1970          1978-2004             1
7             Jimma                       07         40         36         50            1725          1952-2004             1
8             Kone                         08         41         36         47            2000          1979-2004             4
9             Meko
08         41         36         02            2000          1980-1989             4
10           Nekemte                   09         05         36         28            2080          1971-2004             1
11           Wama
08         59        36         40            1450          1980-1987             3
D2:        Types of Climatic Data available at the various Meteorological Observation Stations
S.No.
Station Name
Altitude _ (m)
Yrs        RF
1        Agaro                         2030          25           X
2        Arjo                            2565          33           X
3        Bedelle                      2030          38           X
4        Dedessa                    1200          34           X
5        Dembi                        1950          51            X
6        Gimbi                         1970          27           X
7        Jimma                        1725          53           X
8        Kone                          2000          26           X
9        Meko                         2000          10           X
10       Nekemte                    2080          34           X
11       Wama                        1450           8            X Yrs refer to rainfall series
TM
x x X
X
X
X
X
RH WS ; SD
XXx x                    X x x
x           x        X x           x        x
EP X
X X X
RF       monthly rainfall
Tm       average temperature RH       relative humidity WS      wind speed
Data
SD 1 hr 1 day
Rd ay
data
sunshine duration maximum 1-hour rainfall maximum 1-day rainfall number of rain days
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127Arjo-Dedessa Irrigation Project
Meteorological and Hydrological Aspects
D3: Mean Monthly Rainfall at Bedele Station
May 2007
fin mm]
------------------------- ----------- ------------ -------------
Year
Jan      Feb    IlVIarchlADril iMav Dune
|ju!y          Aug   |Sept      Oct         Nov      Dec
Annual
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1978
1979
1980
1983
1984
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
0          0         152        33        181        221        212       381       380        167        253           0
0         100         1          55        163         0          323        380       324        137         35           22
67        59         81        198       210        250        275      368        286         98           7             0
54        50         89        258       258        266        432      280        444        186          0             7
20         1          41         53        246        334       311       234        455        326         56           25
0         20         42        178       116        263        337      287        287        110        122          26
0         19         10        51        337        269        322        403       248        102         21            10
68        13         55          9         497        355       351        292       368        126          8            29
44       101        37        105       202        215        327        276       245        228         20            0
41         29        156        97          0          267        333        373       472        154         62            9
5          7         115       179       204        267       310      303     281        204         41          12
0          0          29         44        169       350        408        276       267        113          0             0
0          0           0         208       285        174       236        304       363         86          50            0
0          8          48         62        237        369       499        468       306        197         99            0
0          0          45         66        291        275       469       211       242          9           25            5
2         34         80        141       243        222       218      357        200        183          1             0
17         2          33        106       225       281      250      258        276         21           17            0
0         17        102       130       257        292        257      318        353         77          10           24
48        34        208        83        317        232        290      203        128         70          45           13
53         2          50        142       340        307       221       304        232        309         38            4
13        15         87         57        203        332        314      327       291        245         59            1
28         3          13        147       431        441        319      242        324        328          8            39
0          0           4         123       256        420        186      273        286        242         29            8
0         33         87         72        322        390       297      329        370        211          29           14
16         4          63         38        169       298        296      202        253         63           3            46
7         66         84        131        37         320       278      258        197         52          13            3 6          3          46         81        248        447        299      268        273        168         62           15
1980
1539
1897
2322
2101
1788
1790
2171
1796
1993
1926
1655
1706
2293
1637
1680
1484
1837
1670
2001
1942
2321
1827
2155
1450
144 A
1917
Average
CV
Skew
Max
Min
1.28 1 28 0.77 0.58 0.434 0.312 0.242 0.21 0.266 0.563 1.256
1.05 1.6 1.09 0.68 0.098 -0.91 0.837 0.56 0.345 0.375 2.913
68 101 208 258 497 447 499 468 472 328 253
18       23        65        105     239      291      310     303        302      156       41          12
0         0         0         9         0          0        186     202      128        9           0           n
1.114
1.172
46
1 QCA
I oo4
0 14
0 18
2322
1 AACt
I
Water Works Design & Supervision Enternrisp “
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128ArjoDedessa Irrigation Project
Meteorological and Hydrological Aspects
D4: Mean Monthly Rainfall at Jimma Station
May 2007
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Ltd.
:ra ;
:Arjo Dedessa Irrigation Project
Meteorological and Hydrological Aspects
May 2007
D4:     (... continued)
-------------- r------------- 1               n              .
Year |Jan |Feb March (April |May
June July Aug Sept
Oct |Nov Dec
Annual
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
9 81 110 237 237 225 189 263 164
33 28 87 153 213 274 255 155 177
8 27 74 193 115 163 181 216 141
35 22 135 203 175 183 231 91 245
65 49 69 178 274 237 122 276 148
17            3             0
11           18          11
54           30           96
24 93 40
337 243 36
103       22        97         93
174      223      248       307         200        201          47            1
30 1 84 72 214 575 136 102 131
0 1 39 194 238 154 266 159 255
16 13 86 117 341 299 312 161 183
69 5 11 90
29 61 87 111
137 242 150 235 165
12 272 187 151 239
51         28         46        131       162       128      216       219         210
198           1             2
244          47           25
163          76            4 80            8           138 92           30           15 133          67           89
1534
1415
1298
1476
2034
1713
1546
1622
1772
1330
1285
1482
Avg
CV
Skew
Max
Min
33 49 88 133 172 219 208 210 182 103 68 36
0.86    0.71     0.48     0.43    0.39    0.34    0.24     0.26      0.26      0.67      0.96      1.06
0 95    0.68     0.55    0.41    0.41    2.53    0.11     0.12      0.04      1.10      1.32      1.74
105      140      199     301     341     575     312      344       299       337       243       170
0         1          7          16       12      114     96        91         58
11          1           0
1502
0.13
0.83
2034
1169
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130Arjo Dedessa Irrigation Project
Meteorological and Hydrological Aspects
D5: Mean Monthly Rainfall at Dedessa Station
(in mm)
May 2007
Annual
Year
1971
1972
1973 1976
40
65
0
14
11
0
6
0
5
3
90
41
187        283        497
181
146
152
123
266
273        203
219
289
00
217         196
1977
1978
1979
1980
1981
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000 2001 2002 2003 2004
0            0             0            83          80
0            0             0             0            0          191        245         197
0            0             2            13         228        285        272         240
184          121            60              5
49             20              0
289          193            14              0
130           96              7               4
39             68              0
196          107            32              0
123            0               8              14
158          146            27             23
221            94              5               1 215           97             54              1 380          241             3               0 305          120            22             76 152           53             19              0 132            5               1              10 171           183            65              6 170          132            15              0
177           89             10              5
370            0              12              0
147            0              60              0
254          191            29              0
351          141            17             11
300          192            22              0 254          128            40             29 102           17              0               8 193           67             36              6 140          147            36              0
1679
1222
784
1128
1278
0           48           39           88         150
0 0 35 11 244 207
205
00
334        625         401
0            0             9            20         221        306        297         309 1            0            47           89         219        316        373         296
0            5            12           73          90         184        339         259
4            3            25           29         141        399        428         296
1            9            32            8          275        341        370         442
0            0           160          38         243        289        337         289 4            10           39            2           11         280        402         245 7           17           33           74         101        215        355         304 4            0             0             0           84         197        153         207 0            8            26           95         141        224        307         264
0            0             1            79
0            0            34           19          81         112        265         241
21            4            79           60         239        311        380         380
13           0            13          154        184        203        277         247 0            0            13           14         251        399        196         371 18           0             0            47         178        306        151         274 0            0             0           154        241        448        136         347 0           11           27           31          143        214        196         224
6            15           30           31           77         203        252         126
0           35           50            0           73         366        417         302
0             5             6            35         106        252        427         318
3             6            26           49         158        274        312         277
1.91        1.87        1.28        0.88       0.51      0.29       0.37        0 25
2.18       2.78        2 75        0.94      -0.25      0.25       0 64        0.35
21           48          160         154        275        448        625         442
0            0             0             0            0          112        136         126
1695
1306
1695
1284
1691
2101
1880
1216
1251
1069
1382
1032
1856
1299
1717
1494
1842
1296
865
1544
1470
Average
CV
Skew
Max
Min
209
0.38
0.87
380
102
104
0.64 -0.04 241
0
28
0.76 0.59
68
0
8
2.05 3.66
76
0
1426
0.23
0.05
2101
784
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D6:     Mean Temperature at Bedele Station
r (i  °9),
May 2007
n
Year
1971 179 19.9 21 2 20 8 21.5 20.4 18.2 16.7 16.6 15.5
1972                 19.5    20.9     18.4    17.9    19.8      18.3
15.4 17 2
158 15.9
1973      179     18.7    21.5    21.5    19.4    17.9      14.3      16.4      18.0     17.3      17.4     24.9
1974     17.7     198     19.3     20.2    18.7    18.2      16.9      15.7     172       17.1      17.9      17.0
1975     17.3     19.3    19.7     198     19.4     17.7      16.7      16.2      17.7      18.1      17.2
1976      179     18.1    19.1     18.8                17.8      17.6      17.2     17.1      17.7      17.0      16.7
1977     18.2     19.0
1978 17.3 18.4 19.3 18.9 18.5 17.5
16 8 16.0 16.1 19.7
16 0      16.5
1979                 21.0    20.5     19.7    19.5     7.9       16.6      17.3      17.3      17.5
1980
19.2    19.1     17.6      17.2      16.9      174       170       17.6      174
1981      17.3     17.5    17.1                 170
1982
1983                  7.5     19.1     20.1     17.6     17.4      17.8      183      18.7      190       19.4      20.8
1984                  21.4    21.2     22.0    19.7     18.0      16.9      17.3      17.4      18.6      18.9      19.6 ' 1985     20.5     20.5    21.4     20.5    18.6     18.0      17.2      17.1      17.3      17.7      18.8      18.9
1986      199     20.6    20.8     209     21.4    18.1      17.4      17.8      17.7      18.2      18.7      18.4
1987     19.4     20.8    20.4
18.1      182       18.3      19.0
2000
1988     19.7     20.3    20.8     21.6    19.6    18.1      170       17.3      17.5      18.3      18.2      17.9
1989     17.8     18.7   19.1      193     19.0     17.8      15.0      17.3      17.7      17.5      18.0      17.8
1990     18.1     18.7    19.5     20.2    19.3     18.5      17.4      17.4      17.8      18.2      18.9      19.1
1991      19.3     20.6    20.7     21.0    20.4     18.3                   17.4      18.2      18.4      18.7      20.0
1992     19.1     20.1    21.3     20.6     207     19.5      18.4      180      19.6      19.1      18.8      20.6
1993
1994     20.9     20.9     220     21.3     19.5                  17.8      177      182       19.0      19.6      19.5
1995     20.5     20.8    21.3     21.4     19.9     19.4      17.6      18.1      18.5      18.8      18.9      19.5
1996     19.2     21.0    20.6     20.7    19.1     18.4      17.8      18.0      18.3      18.3      18.5      18.7
1997     19.2     19.9    21.5     19.9    19.3     18.6      17.8      18.0      188
1998     13.9     21.2     21.2     23.2    21.0     19.3      18.1      18.3      18.9      19.2      18.6      19 2
1999     19.6     21.7     21.7     21.8    19.1     18.7      17.3      17.7      18.6      17.9      18.4      19.2
2001      19.1     20.9     20.7     21.2     19.9     18.0      17.5      17.7      18.6      19.4      18.7      19 2
2002      19.2     21.1     21.6     21.8    21.2     18.5      18.3      17.5      18.1      18.1      18 6      18 9
2003      20.0     21.5     21.1     21.2     21.9     18.7      17.6      18.1      18.6      19.0      19 6     1Q 6
2004      20.8     21.0     22.1     21.5    20.8     18.3      17.8      18.1      18.6      18.7      19 1    1 Q «
17.7      17.9      18.5      18.9      18.6      18 9
Average
CV
Skew
Min
Max
0.08     0.13     0.05     0.05     0.06
i y .o 18.7     19.7    20.6     20.6     19.6     18.0      17.3      17.4      17.9      18.0                  TP O
0.12      0.05      0.04      0.05      0.05       0.06      0.09
18 3     1 o.y
-1.21
-3.96    -1.19    -0.08    0.00
-4.24
-2.06
-0.86
13.9      7.5      17.1     18.4     17.0      7.9       14.3      15.7      16.0      15.5      15.4      15.9
-0.58
-0.94
-1I.JJ
i 44
20.9     21.7     22.1     23.2     21.9     20.4      18.4      18.3      19.6      19.4      19.7      24 9
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JArjo Dedessa Irrigation Project Meteorological and Hydrological Aspects
May 2007
D7:      Relative Humidity at Bedele Station (in%)_______________ _ .----------------------------------------
7
lYear
Jan      Feb      Mar      Apr     May    Jun |      Jul    ]_Aug         Sep,   Oct       Nov      Dec
1987
80         80         69         58
1988       63        80        58        52        70        78         89         84         83         76         63         50
1989      51        53        67        62        68        80         83         85         84         72         64         75
1990       59        62        56        60        70        79         82         80         80         70         69         60
1991       55        46        56        67        78        80         82         84         77         71         65         65
1992       68        65        58        62        66        75         77         81         74         72         69         64
1993
1994       53        48        48        57        78        83         85         72         81         61         66         61
1995       46        50        51        59       72        75         85         83         78         69         69         67
1996       59        54        63        62        75        82         83         84         80         72         68         64
1997       63
1998       65        55
64
1999       59        50        49        55        76        76         84         84         80         78         64         63
2000       58
82         84         82         80         74         70
2001       62        62        65        65        73        83         84         84         83         80         74         67
2002       69        60        66        65        67        81         85         86         83
2003       62        55        63        60        57        81         88         86         84         73         69         61
2004       61        54        56        62        66        81         85         84         82         74         70         69
Average
60        57        58        61        70        78         84         83         81         69         63         60
Water Works Design & Supervision Enterprise
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D8:     Wind Speed at Jimma Station
May 2007
Water Works Design & Supervision Enterorise
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134ArjoDedessa Irrigation Project Meteorological and Hydrological Aspects
D9:     Sunshine Duration at Bedele Station
May 2007
—
Year      i Jan       Feb     Mar     Apr     May     Jun        Jul    I Aug        Sep       Oct       Nov      Dec
in hrs/dav)
___ _________ ._____ _______ i----------1--------
1987      7.9       7.2       6.9      8.5      5.6        6         6.3        5.7        8.4         8         8.3        8.3
1988      9.3      7.1       6.5      8.2      7.9       6.4        2.6        4.2        5.1
9.7        9.5
1989      8.5       8.2       7.9      7.9      7.4        6         3.8        5.1        6.5        7.9        8.8        6.4
1990 8.9 5.6 7.7 6.4 8.2 6.4 3.8 4.8 6 9.6 8.7 9
1991 79 8.1 6.8 63 7.1 4.5 2.7 3.4 5.9 8 8.4 7
1992                  5.7       7.7      7.1       8.3      6.1        3.4        2.2        6.7        6.2        6.2        8.3
1993      7.6       6.6       7.5       5.5
1994                  7.7       7.5      7.7      7.6
1995      8.8       6.6       7.7       6.6        •          »           •            *           •            *            *
*         2.4        3.4        5.8        9.5        8.4        9.3
1996
*
*
7.9        •          •          *         3.7        4.3        6.7         9          7.9        7.8
1997      7.2       9.2       7.5       6.8        7        6.1        4.1         4.6        8.4        7.6        6.7        8.3
1998      7.5       7.7       6.4       8.7      6.4       6.7        2.9        3.3        4.7        5.4        8.4        9.2
1999      8.1       9.1       8.8       76       7.6       6.6        3.5        4.6        6.6        5.8        9.8        8.8
2000        9        8.8        9        6.1       7.8        8
2001
♦
*
*
5.5        6.4
*
*
2002
*         8.2       7.5      9.1       8.4       6.1        5.6        3.1        6.9        8 8        8.3        7.6
2003      7.9       86       7.3       8.8      9.6       5.7        2.9        3.6        6.1        9.5        8.7        8.6
2004      7.5       7.9        6        5.7      7.4       4.3        4.2        4.5        3.5        8.2        8.3        7.7
Average
8.2       7.6       7.5       7.3      7.6      6.1        3.7        4.1        6.2        7.9        8.3        8.3
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135Arjo Dedessa Irrigation Project Meteorological and Hydrological Aspects
Annex E:         Hydrological Data
I           Llbl Ul oeitruLeu riyuiuiuyiuai Station
May 2007
—
s.
No
Latitude, N
Longitude, E
Period
No.
Station Name
Deg.
Min.
Deg.
Min.
2
Catch.
Area
(km )
1
114001
Dedessa near Arjo
08
41
36
25
9981
1960-2004
2
114002
Angar near Nekemt
09
30
36
35
3742
1994-2004
3
114005
Dabana near Abasina
09
02
36
03
2881
1962-1984
4
114007
Angar near Gutin
29
1999-2003
5
114008
Yebu at Yebu
07
48
36
42
47
1979-2004
6
114009
Urgessa near Gembe
07
50
36
39
19
1979-2004
7
114013
Dabana near Bunno Bedelle
08
24
36
17
47
1984-2003
8
114014
Dedessa near Dembi (Toba)
08
03
36
27
1806
1985-2003
9
114016
Loko near Nekemt
09
22
36
36
375
1997-2004
10
114019
Temssa near Agaro
07
51
36
35
47.5
1989-2004
11
113004
Melke near Guder
08
51
37
44
38
1998-2004
12
113038
Indris near Guder
08
56
37
45
111
1986-2004
13
101006 ---- 1
Uka at Uka
08
10
35
22
52.5
1980-2004
14
091008
Gilgelghibe near Asendabo
07
45
37
11
2966
1967-2004
15
091012
Gojeb near Shebe
07
25
36
23
3577
1970-2004
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Meteorological and Hydrological Aspects
Annex E2: Mean Monthly Streamflow at Dedessan near Arjo Station
May 2007
Year
Jan      Feb |  Mar I  Apr     May
Jun
Jul
Aug
Sep
Oct
Nov^   Dec
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1054       1124
51.5 44.0 37.5 94.9 65.6 286 1176 1613 1503 1304 280 177.5
58.1   29.8    28.4-
-                  622        930       1170       855      107          55.9
38.1   19.3    179    42.7 147 1      324        672      1370       1040       374      184        170.3
-              38.2-          -            54.9     230      1049     1301       1178     1337      205          73 6
55.4   25.3    16.4   47.8    278      168        834     1051         685       957      320        157.8
71.0   63.4    57.7   74.1    62.5 -                  857      1086       1212 -            -
- 22.7 20.4 708 1268 1201 1260 459 210.4
96.0   52 9    21.4-
----
138          63.7
50 9 46.7 67.1 39.7 73.2 349 950 1421 944 291 89 46.2
34.1-          -         -            45.3     288        929 -             -             -                  181          62.1
37.0   16.4    10.9   12 5   53.5     241        823      1175         949       752      304        105.3
52 4   29.8    19.6   35.7   56.4      118        803      1237         719        209      117          52.5
25.9   11.2     4 7      63 109.3      250 -             -                  1401        592      149          64 8
36.5   16.2    14.4     9.1    93.7     261        641      1278
24.5   21.9    11.5-
48.3   37.8    24.7-
-             25.9   18.1   12.7
55.5    20.1-             12.4   80.1
--
-
-
-
119 48.9
151 89 5
324 128 7
- - 22.9 46.4 130 533 825 619 262 94 44 0 18.0 15.3 10.0 68.6 190 9 504 914 1288 1244 393 103 92.5 80.0 43.4 43.3 35.4 53.7 53 -
598    30.2    28.6   19.3   45.7     248        707        947         692       761       177         Q4 S
43.4   31.7   34.1   23.3   52.4      153        566      1139       1116     1231       265 38.5   16.7    11.9     9.5   28.6      185        703        665        521        131
121.3
13.0     5.9    12.4   17.3   36.7
-
40.5   27.5    21.6     7.7   37.0     280        555
12.0     5.4      2.6   11.8   62.4      169        438        927         842        260         79          4S n 17 6     9.5   16.1   13.8    128      119        452        491         732        222
673        386      160          67.8
23 3    20.4   33.5   27.1     121         273        499       1087 - 53.5   30.0    338    60.9   42.0     157        343
189-
107 123.1
-
--
-
543      1143
24.5    23.9    13.8   20.7   56.5     161        368        824         564        806      164
42.5    27.9    24.4   54 7 105.2     312
600      1159
39.1    19.6    15.5   15.7   59.7      220        534      1221        858
631
407      177
156
68
38.1
734
63.3
36 9
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E2:      (.... Continued)
Jul         Aug
May 2007
Year
Jan
Feb
Mar
Apr
May
Jun
Sep
Oct
Nov
Dec
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
20.4    12.2    14.8   19.4   38 3        83        225        569         486       156        60
-          -         -             30.4 141.8      373        723        854         598              -
87.2
41.4    19.0 -          -         -         -           -             -             -
---- ----
--                     -
-
-
--
--
--
242        135.7
85.1    59.5   66.1    57.2 128.3     401         740      1045       1005       674      215         1117 99.0    57.2    55.3   63.4   46.2      175        457       591         578       211       115          83.9 56.4    36 2    56.2   74.3   48.0      120                      652                              -           -
52.3    40.2    30.3   28.7   65.2      202        526        639         643        538      140 -
Avg
CV
Skew Max
Min
46.2 28.7 26.0 33.2 66.5 223 654 1007 889 581 177 90.9
0.47 0.52 0.66 0.71 0.59 0.46 0.34 0.30 0.32 0.68 0.51 0.50
0.66 0.65 1.14 0.94 1.54 0.74 0.22 -0.16 0.43 0.73 1.26 1.00
99.0 63.4 67.1 94.9 190.9 503.5 1176.1 1612.8 1502.5 1337.4 459.2 210.4
12.0      5.4      2.6      6.3 12.8       53.2     225.0     491.1     485.9 Mean annual flow = 3821 Mm3)
130.8     59.9          36.9
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138Arjo-Dedessa Irrigation Project
Meteorological and Hydrological Aspects
E3: 41 Mean Monthly Stream flow at Dedessan near Dembi Station
May 2007
Million m3)
—
Year
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
6.4     3.7      4.2      78    26.8        92          83        165        302        272     70.2        21.9 8.5      5.5      9.7     8.4     9.4        58        262        208         299          67     22.9        11.5
5.9      4 5      9.7     9.2    18.6      105        243        342         218        104     44.1        27.1
17.9   15 7    12.5     6.4    23.9      153        217        392         489        142     52.0        18.8
11.0     7.6      4.5    11.5     8.2       31        167        253         272        142     24 8        13.8
18.3      5.8      5.6   22.7     9.9        52        252        564         362        126     20.9          9.4 7.2      6.3      6.6    12.5    29 5        76        305        522         263        140     13.4          8.9 7.8   15.3    12.9     8.8    36.6        78        138        269         183        218     45 2        17.1
11.2   17.2    16.6    376    80 8      252        334        378         204        110     48.7        14.7
12.3   13.5    16.8    22.5   37.8      112        274        381         282          59     26.6        16.4
12.9   12.1    16.5   23.8    48.7        87        190        272        331        101      32.5        33 7
12.9      94    16.5   23.8    48.6        87        190        272        331        101      32.5        33.7
21.2    13.8    23.2   30 1    55.2      142        219        343         204        262      188        56.1
28.1    13.6    20.3   12.7   31.1      110        272        415         299        329     88.8        21.1
13.0     6.5      8.3     9.5    54.9      148        242        245         214        302      484        18.3
10.2     5.6      4.5     9.7    41.2      119        290        309         242        209     82 0        21.9
12.7     9.6    11.2    14.5    55.5      226        330        322         296        212      59.6        22.5 6.5      6.9      6.6     9.0     6.5        63        192        255         195          68     27.7        14.9 7.4      4.0      9.2   24.0     6.7        59       241         223         324          82      20.3        10.0
130    13.0    13.7   33.0    98.5      198        208        208         145          39     18.8        18 8
Average CV
Skew Max
Min
12.2   9.47    11.5    16.9   36.4   112.4     232.4     316.9         273     154.3     48.4        20.5
0.47    0.46    0 48   0.56    0.69  0.526        0.27     0.326        0.28    0.561     0.81        0.53
1.35   0.31    0.47   0.81    0.85   1.028      -0.47     0.927        0.89     0.668        2.6        1.98
28.1    17.2    23.2   37.6    98.5      252        334        564         489        329    187 9        56.1 5.9      3.7      4.2     6.4      6.5       31          83        165         145          39     13.4          8.9
Mean annual flow = 1255 Mm3)
Water Works Design & Supervision Enterprise- - - - - - - - - - - - - - - In Association with Intercontinental Consultants and Technocrats Pvt Ltd
139Arjo-Dedessa Irrigation Project Meteorological and Hydrological Aspects
May 2007
Annual Maximum Floods
(in m3/s) _ _
Year
__
Dedessa near Arjo
________
Upper
Dedessa
near Dembi
_____
Dabana
near Abasina
Gilgel Ghibe near
Asendabo
Gojeb ne arShebe
Angar near
neqemte
Angar near
Gutin
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
633
1173
893
789
850
951
814
1165
781
634
896
721
490
645
554
1083
369
480
502
574
771
750
646
804
353
525
525
744
312
601
213
206
212
243
354
228
200
151
189
262
164
175
226
175
211
244
271
300
370
267
467
264
434
449
247
196
257
180
314
341
238
202
248
151           132 198           272 130           176
330           127 151           231 167           110 205           116
168
129
148
151
223
135
149
119
92
96
127
143
85
121
247
86
106
192
237
159
255
100
174
339         188 185        258 186        241 284        217
214
140
249
177
288
138
297
314
184
150
203
226
132
245
366
164
164
261
164
150
282
132
193
199
439
236
335
361
209
247
256
157
271
252
128
141
73
55
124
107
134
105
125
142
136
142
149
178
172
109
106
79
181
128
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140Meteorological and Hydrological Aspects
May 2007
Measured Sediment Concentration at Gumara
Station
Field f
No. sample' Lab.No. River / Stream Station Date & Time
---- 5------ I       '
G/height      Flow        Depth
I No
I
I
i of Sampling
Time
(Sec)
3
(m) (m /s)
(m)
Width
(m)
Sediment Concen
(mg/l)
Remarks AV
484         1                        Dedessa         Toba
15-Mar-90
0.43
4.16          0.26               6.9          51.56
485
2
Dedessa
Toba
15-Mar-90
0.43
4.16
0.70
13.8
40.00
486
3
Dedessa
Toba
15-Mar-90
0.43
4.16
0.36
20.7
42.81
664
1
Dedessa
Toba
21-Mar-90
0.50
5.41
0.54
7.0
59.69
665
2
Dedessa
Toba
21-Mar-90
0.50
5.41
0.80
14.0
666
3
173.13
Dedessa
Toba
21- Mar-90
0.50
5.41
0.39
6.1
1057      1
Dedessa         Toba
90.63
1058 2 Dedessa Toba
1059 3 Dedessa Toba
1190      1
Dedessa         Toba
1191       2                        Dedessa          Toba
2-Jul-90
2-Jul-90
2-Jul-90
22- Dec-90
22-Dec-90
9.14          8.40               6.6        215.31
50
50
1192       3                        Dedessa          Toba       22-Dec-90        50
1223      1
Dedessa          Toba
1224 2 Dedessa Toba
1225 3 Dedessa Toba
2698      1
Dedessa          Toba
2699       2                        Dedessa          Toba
2700 3
2704 1
2705 2
Dedessa Toba
Dedessa Toba
Dedessa Toba
18-Sep-90
18-Sep-90
18-Sep-90
16-May-93
16-May-93
16-May-93
12-Apr-93
12-Apr-93
12-,
45
45
40
| 2706       3           _ _____ Dedessa          Toba
1.81
1.81
1.81
0.40
0.40
0.40
2.46
2 46
2.46
0.46
0 46
0.46
0.64
0.64
0.64
9.14 8.00 13.3
9.14 7.00 19.9
4.37          0.43               7.0
4.37          0.74             14.0
4.37          0.46             21.0
98.50
98.50
98.50
2.89               0.66        19. !
2.89              0.61           6. 2.89              0.71         13.
135.62 190.00
90.94 99.37
63.13
141.56
187.82
124.69 5         87.90 5       108.63
8.01
8.01
I 8.01
_
0         8140 73.05 61.59 59.20
64.61
_
In
--- Wat     - • -  er Works Design & Sinm-wE5:       (... continued)
J
Field
No. sample
No
Lab.No.
River /
Stream
Station
______
541         1        179/04       Dedessa           Arjo         10-Aug-04       30        2.45
542         2                          Dedessa           Arjo         10-Aug-04       26        2.45
543         3                          Dedessa           Arjo         10-Aug-04       28        2.45
544         1        180/04       Dedessa           Arjo         11-Aug-04       32        2.56
545         2                          Dedessa           Arjo         11-Aug-04       28        2.56
546         3                          Dedessa           Arjo         11-Aug-04       27        2.56
559         1        185/04       Dedessa           Arjo          1-Sep-04        29        3.61
560         2                          Dedessa           Arjo          1-Sep-04        28        3.61
Date  &  Time of Sampling
Time
(Sec)
G/height (m)
Flow
(m /s)
3
Depth
(m)
Width
(m)
Sediment Concen
(mg/l)
Remarks
AV
561          3
Dedessa           Arjo          1-Sep-04        29        3.61
143.12 9.05ft 17.5
143.12 9.1ft 35.0
143.12 9.5ft 52.5
196.11       11ft        18.0
196.11       9.6ft        36.0
196.11       9 6ft        54.0
611.23      137ft       18.0
611.23      14.3ft       37.0
611.23      1.46ft       55.0
1417.50
1739.74
1740.74
1652.86
1718.12
1639.31
1360.22
1328.81
1438.44
142
Water Works Design & Supervision Enterprise
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HYDROGEOLOGICAL
STUDIESArjo Dedessa Irrigation Project
Hydrogeological investigations
May 2007
TABLE OF CONTENTS
TABLE
LIST OF TABLES-1
LIST OF FIGURES............... -1
1. INTRODUCTION~
1.1 General
1.2 Location
1.3 Physiography■
1.4 Methodology and approaches
2. PHYSICAL HYDROGEOLOGY
OF
CONTENTS-
2.1 Hydrogeological setup........................................................................................................................................ 6
2.2 Hydrogeologic units7
2.2.1   Fractured and/or weathered volcanic rocks8
2.2.2 Alluvial sediments8
2.2.3  Delluvial sediments
2.3 Inventory and well data8
2.4 Hydrodynamic parameters of aquifers9
3. GROUNDWATER POTENTIAL
3.1   Groundwater recharge estimation
3.1.1  Base flow analysis
3.1.2  Water balance method..................
4. WATER QUALITY................................................... ~
4.1 Water samples
4.2         Sampling techniques and analysis
4.3         Pictorial representations of water quality analysis
4.3.1  Piper diagram
4.3.2  Schoeller diagrams
4.3.3  Stiff diagram
4.4         Agricultural/irrigation water quality.............................
5.  PIEZOMETERS OBSERVATION WELLS AND WELL FILED 5.1 Distribution of Piezometers/Observation Wells............... 5.2 Future well field...
6.  WATER LOGGING AND DRAINAGE
6.1 Topographic features and soil characteristics
6.2 Existing groundwater table
.8
11
27
6.3
7.
Future groundwater table and recharge from irrigation
SALINITY AND SODICITY
8. EXSITING AND FUTURE PROSPECT OF GROUNDWATER
8.1 Existing groundwater development
8.2 Future groundwater prospect
16
.16 18
...18 
..18
19
19
. 23 27 
.28
30
.30
.30 
.31
39
41
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Hydr
ogeolo
gical investigations
May 2007
LIST OF TABLES
Table
2.1
Details of boreholes in the study area
Table
3.1
Monthly total flow, and Minimum flow of Dedessa river near Arjo1
Table
4.1
SAR and EC values in terms of irrigation water suitability23
Table
4.2
SAR   and   EC   values   for   surface   and   groundwater   in   Arjo-Dedessa
area   24
Table              5.1              Locations              of
the              Proposed              Piezometers27
Table     6.1     Annual     gross     crop     water     requirement     at     primary     canal     head31
Table     6.2     Estimation     of     wetted     area     for     Arjo-     Dedessa     irrigation     canal33
Table 6.3 Seepage loss from unlined Arjo- Dedessa irrigation canal33
LIST OF FIGURES
Figure 1.1 Figure 1.2
Figure 2.1
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 4-6a
Figure 5.1
Location map of Arjo- Dedessa irrigation project............................................................ 3
A map showing 3- D view of Dedessa river Catchment.................................................. 5
A     map     showing     hydrogeologic     units     of     Dedessa     river     cathment
Location Piper Schoeller
of
water
sample
points 
Trilinear             Diagram             for             Water             Samples 
diagram
for
water
samples 
Stiff diagram for water samples
Electrical conductivity  (EC) in gs/cm and Total Dissolved Solids  (TDS) in
10
17
20
21
22
mg/L
Map
Showing
Contour
for
Proposed Piezometers/Observation wells 
for24 TDS26
29
Figure 6.1
Profiles along different lines in the command area
37
Figure 6.2
A map showing lines of profiles in the command area
35
Figure 8.1
A map showing groundwater potential areas 
42
Water Works Design & Supervision Enterprise
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Hydrogeological investigations
1. INTRODUCTION 1.1 General
May 2007
Hydrogeology  refers  to the occurrence, movement, chemistry  (quality), etc  of groundwater in general. It has  a diversified application in developing, utilizing and managing groundwater. Furthermore, different aspects  of groundwater such as  recharge-discharge condition, aquifer type and set up, its  interaction with surface water, etc  are treated under this  discipline. The nature of groundwater and how it behaves, when subjected to nature or man-made activities  is  studied for different purposes  such as  for water supply  (domestic, agriculture and industry) and construction of surface and sub surface engineering structures (e.g., dam, tunnels, drainages, etc). In order to develop and manage groundwater, one has to know its potential, quality and movement.
Ethiopia has  diversified hydrogeological features  that are attributed to its  geology, structure, hydrometeorology, topography, etc. The hydrogeology  of the country  has  not been studied thoroughly  so far. With the exception of few areas such as  Rift valley that has been mapped at scale of 1:250,000, the country  has  only  small scale (1:2,000,000) hydrogeological map. Hydrogeology  is  usually  applied for locating borehole sites  in light of water supply. Groundwater has  been used without detail understanding of the resource. In spite of ample groundwater resources  of the country, very  little been studied and used so far. Currently, however, not only  groundwater development and/or utilization but also its  management is getting an attention.
Since any development relies generally on proper utilization of natural resources the government has also given toeus on water resource development bolh for domestic water supply and irrigation. As part of this waler resource development Inleg,aled Master Plan Study of various River basins has been undertaken in the country since some years Th Abay River basin is one of them. Arjo-Oedessa irrigation project has been identified as o of the projects during the Abay River Basin Master Plan study.
Hydrogeological investigation and study that mainly focuses on g roundwater occurrence movement, chemistry, etc has been included as one of the components of the feasibility study and design of the project. Accordingly, this study at Feasibility Study level has been undertaken.
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1.2 Location
May 2007
The project area is  located within Dedessa River sub-basin which is  in turn located in Abay River basin. It is  within the Western Oromia National Regional State; particularly  at tri
junction of East Wollega, llubabor and Jima zones. The sub basin of the irrigation project is bordered byOmo-Gibe River basin on eastern side and Baro Akobo River basin on SW side. The total area of the catchment at the proposed dam is  about 5,632.64 km2. The project location is shown in Fig. 1.1.
The project area can be reached from Addis  Ababa through two alternative high ways  that take either to Bedele or Nekemt. The all- weather gravel road that takes  from Nekemt to Bedele passes  through the project area. Hence, in general the project area is  about 480km from Addis Ababa through Jima and Bedele.
2Fig. 1-1 Location map of Arjo-Dedessa Irrigation project
100000
I'rfBOO
moow
^O30«
locox.
350000
’10000'.’
Diga Leka
1CSOCOC
— ■ Vt Nunu Kumba v 
... ,„</•— r
7/
Wama Boneya
\
__ A
I•
Llmu
♦                P-cp<red               p, ,
Rtvef/Stre»m
Setema \
Command »r»a Wo’«d» Boundary
Goma j-
JJ
»wxoArjo-Dedessa Irrigation Project
Hydrogeological investigations
May 2007
1.3 Physiography
The basin drains a part of Jima high lands including Goma (Agaro), Setema, Sigimo. L.mu Saka, and part of llubabor including Borecha, Dedessa. Gachi and Bedelle Woredas above the proposed dam. Arjo, Nunu Kumba, Sibu Sire and Wama Bonaya woredas are also within the catchment. The command area is drained by river Dedessa and other tributaries such as Wama River. The general slope of the basin/or catchment is toward NE, E and NW directions. The area has  generally  a rugged topography  with the highest and lowest elevation is about 2 890 a nd 1 030 m amsl respectively located at S igimo-Gera area and Dedessa river valley. The river basin has mainly a dendritic drainage pattern. 3- D view of Dedessa river catchment is shown in Fig. 1.2.
1.4 Methodology and approaches
In order to undertake the hydrogeolgoical investigation of Arjo-Dedessa irrigation project area, the following methodology and approaches have been used:
•    Reviewing of the previous works and data available with different institutions such as MoWR,  Institute  of  Geological  Survey.  Regional  Water  Resources  Bureau  and Offices, Regional Waterworks Construction Enterprises.
•       Field    survey    to    collect    primary    information    on    geology,    hydrogeology, geomorphology, and other physical features of the area.
. Preparations of inventory for water supply schemes and fixing their location with
GPS; measurement of water level for wells; estimation of discharge for springs;
water sampling from springs, wells and streams/rivers for physicochemical analysis 
. Interpretation of aerial photos, satellite images and Topo maps (1:50,000 & 1-
250,000); applications of various GIS and remote sensing software to identify and delineate lithology and geological structures, and finally to analyze, and compile the spatial data; and to incorporate them as maps for various themes
. In-situ measurement of physical parameters of water quality indicators by using field water quality test kits (Ph, EC, TDS and Temperature meter).
. Finally, preparations of hydrogeological maps with different thematic layers such as hydrogeologic  units,  depth  to  groundwater  table,  water  quality,  groundwater  yield based on all the collected and analyzed data.
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2. PHYSICAL HYDROGEOLOGY 2.1 Hydrogeological setup
May 2007
The basin comprises volcanic lava flows mainly basalts and ignimbrite as well as pyroclastic falls on Jima side; where as on llubabor and Wollega sides, the outcrop is mainly basaltic lava flows  except within the Dedessa River Valley, where the outcrop is  crystalline basement of various granites, gneisses and pegmatite. The volcanic rocks on Jima side are referred to as  Lower part of Jima volcanics  comprising flood basalt with minor salics  on the western, southwestern, eastern and southeastern part of the river catchment; especially on the high land. However, at the lowland part of the catchment along the Dedessa River, the outcrop is  referred to as  Alghe group comprising biotite and horn blende gneisses, granulite and migmatite with minor Meta-sedimentary gneisses.
The volcanics  are of Late Eocene- Late Oligocene age; where as  the gneisses  are of Archean age. The volcanic  rocks  (basaltic  lava flow) in Bedele area is  referred to as Makonnen basalt comprising flood basalts. It directly overlies the crystalline basement that shows  the unconformable relation for the two outcrops. It is  Oligocene-Miocene age. Similarly, Arjo area is comprised by such flood basalts of the so-called Makonnen basalt.
Based on the investigations made in the area, various aquifer set up exist. The main aquifers are weathered and /or fractured basaltic lava flows and other acidic volcanic rocks such as rhyolite and ignimbrite. The aquifers of volcanic origin have both confined and unconfined character; for example the volcanic rocks in Agaro area show both characters The confining layer in this particular area is highly weathered volcanic ash of clay size revealed from existing well drilling data. There are aquifers that are attributed to sediments especially alluvial sediments along river valley and streams, and colluvial sediments nea mountains and escarpment. In addition to these, there are also minor aquifers attributed
weathered basement rocks (granites and granitic pegmatatites). This last aquifer
little significance in the area due to their limited areal extent and limited aquifer' roT The presence of thermal springs, cold groundwater and saline springs in the are^ UCt'V'ty'
°
diversification of the hydrogeological set up of the river catchment tu
Sh °WS
'" 1 ne aquifers of spriimn
and weathered basement rocks origin are un confined. In addition t th •
10 t^e lithological
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diversification,   geological   structures   mainly   faults   play   significant   role   on   groundwater dynamics and quality of the area.
Within Dedessa River valley, deep penetration of groundwater may  be hindered by  the underlying massive basement crystalline rocks  except where they  are controlled by geological structures as revealed by the presence of thermal springs in the river valley.
Thevolcanic  ashand clay  form the confining layer for water-bearing formation (aquifer). Aquifer in Agaro area can be mentioned as an example. The volcanic lava flows are highly weathered (fractured) enhancing the infiltration of precipitation (mainly  rainfall) for groundwater recharge. Groundwater recharge in the area is  very  maximal due to different reasons; among them are the high precipitation, dense vegetation cover, and highly weathered and/or fractured volcanic lava flows.
The mam ground water recharge of the area is the SE, SW and the N E part of the river
catchment. The pyroclastic falls of mainly volcanic ash composition is found at most parts of
the river catchment such as in Setema and Atnago area. The intercalations of the volcanic 
ash with fractured and/or weathered volcanic lava flows cause the emergency of many 
springs in the river catchment of the area.
However, the presences  of thermal springs  with in the Dedessa river valley  are attributed mainly  to geological structures, faults. The physicochemical characteristics  of the thermal springs  highly  vary  from upstream to down stream along Dedessa River within the project area Although it needs  further investigation to verify  the physico-chemical variation, the longer residence time of rock-water interaction while groundwater flows  from upstream to down stream could be one possible reason.
The  topography  of  the  area  along  with  the  prevailing  hydro-meteorological  conditions  favors the existence of well demarcated groundwater recharge and discharge area
2.2 Hydrogeologic units
unconsolidated) forming the frame work for groundwater occurrence
. movement and quality
7Arjo Dedessa Irrigation Project
Hydrogeological investigations
May 2007
The hydrogeologic  units  in Dedessa river catchment have been categorized into the following units. They  have been shown on map available at Fig. 2.1 and have been discussed here under in brief.
2.2 .1 Fractured and/or weathered volcanic rocks
This  comprises  basaltic  lava flows, acidic  lava flows  and falls  such as  ignmbrites  and pyroclastic  falls. In this  category  of hydrogeologic  unit, secondary  porosities  such as fractures  produced due to weathering and geological structures play immeasurable role as compared to the primary  porosity/permeability. The areal coverage of this  hydrogeologic 
2
unit in Dedessa river catchment is 8,189.35 Km  (79.56%).
2.2.2  Alluvial sediments
As t he n ame implies t hese are c oncentrated a long r ivers a nd s treams; a nd it c omprises various sediments ranging in size from clay through sand and gravel to boulders. The areal
2
coverage of this hydrogeologic unit in Dedessa river catchment is 1651 05 Km  (16 04%).
2.2.3 Delluvial sediments
These sediments  exist at the transition zone from the mountains/or plateaus  to the river valley  They  are derived from down slope moving earth materials  from mountains  mainly due to gravitational force From hydrogeological point of view, there are no as  such demarcated properties  between alluvial sediments  and delluvials. However for the convenience of description, this  hydrogeolog.c  unit has  been described separately  Delluvial
2
sediments with in the project area have coverage of 452.91 Km  (4 40 %)
2.3 Inventory and well data
In order to undertake detailed investigation of the groundwater of the area, water supply 
schemes no
have been inventoried and their geographical locations have been identified using
GPS Borehole data have been collected for detailed analysis  of hydrodynamic  parameters though complete well data are not available in the area. Some of the measurements  that have been undertaken for water supply  sources  during the field survey  are physical parameters of water quality by using field test kits such as TDS, PH and temperature meter
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In addition to these primary  data, secondary  data have also been collected on both water quality a nd other Well d ata. S atellite images a nd a erial p hotos h ave b een i nterpreted t o identify  geological structures  and other lithological boundary  demarcation that have been verified by field work.
2.4  Hydrodynamic parameters of aquifers
As  it has  been mentioned previously, the main aquifers  in Arjo-Dedessa project area are fractured and/or weathered volcanic  lava flows  (basaltic  and acidic  lava flows) and sediments  (alluvial and colluvial).The boreholes  in the fractured volcanic  rocks  and sediments  are either with partial well data or totally  without well data. Table 2.1 shows  the boreholes drilled previously in the study area and their depths:
Table 2. i               Details of boreholes in the study area
No
Well No
PA/village
Depth (m)
SWL
1
Well 1
Metta
55.75
16
2
Well 2
Metta
43.4
12
3
Well 3
Metta
43.5
17
4
HT-2
Qollo Siri
27
5
HT-6
Chilalo Bildima
72
6
HT-7
Chafe Anani
57
7
Well 1
Chuli
52
1.3
8
Well 2
Chuli
41
1.7
9
Well 3
Chuli
26
2.1
10
Well 4
Chuli
30
2.1
11
Well 5
Chuli
25.5
1.5
12
Well 6
Chuli
42.5
2.2
13
Mada Talila
Bakalcha Biftu
52
14
Iftu Biftu
Bakalcha Biftu
35.55
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9Pig- 2-1 A map showing hydrogeologic units of Arjo-Dedessa river catchment
q
90000 Meters
IttiUCXT
10SCCCX)
iocoou
SSOt'-X?
eojoccArjo Dedessa Irrigation Project
Hydrogeological investigations
3. GROUNDWATER POTENTIAL 3.1 Groundwater recharge estimation
May 2007
In order to develop groundwater of an area, its  potential including its  annual replenishment, which is  usually  referred to as  groundwater recharge, has  to be known. Groundwater recharge of an area can be estimated based on different approaches. One of the best approaches  is  through water balance method. In order to employ  this  method of groundwater potential estimation, the necessary  data have been acquired for analysis. In supplementary  to this  method, base flow analysis  for River/stream flow (Dedessa River) have been undertaken to derive some coefficients  for water balance. Hence, in the groundwater potential estimation of Dedessa River catchment both methods  have been employed in order to come up with concrete result as much as possible.
3.1.1  Base flow analysis
In order to employ  base flow analysis  approach for groundwater recharge estimation, the monthly  discharge of Dedessa River from 1961 - 2002 for a total of 15 years  have been used. The river flow data is  not consecutive due to some missing data for some years. Accordingly, the mean monthly  minimum flow of Arjo-Dedessa on annual base is  shown in Table 3.1.
The difference between the total flow and the Minimum monthly  flow is  supposed to be the surface runoff Accordingly, the surface runoff for Dedessa river catchment at the gauged station is  2,105,796,874 m3. Where as  the total minimum monthly  flow of the river is 1 962 092 938 m3 as shown in the Table 3.1. The basic assumption in deriving groundwater recharge from base flow analysis  is  that the monthly  minimum stream discharge is  equal to the base flow. This  approach has  been mentioned by  Wundt (1978) that the monthly minimum stream discharge best approximates the base flow, especially, for humid climate.
Therefore the base flow that is  supposed to correspond to the groundwater recharge is found to be 196.6mm. This value is obtained by dividing 1,962,092,938m3 by the area of the gauged catchments, which is 9,981 km . This amount of ground water recharge is very
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gauged catchments, which is 9.981 km . This amount of
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minimal  as  it  amounts  to  only  about  13.53%  of  the  total  annual  precipitation  of  the  area which is 1452.9mm.
Furthermore, based on the same method, the surface runoff of the River catchment has been estimated as  the difference between the total river flow of Dedessa and the minimum flow on annual base and works out to 2,105,796,874 m3 that is attributed to surface runoff. This  value is  used to derive the run off coefficient of the river catchment to asses  the surface run off for the entire Dedessa catchment that will be used in the water balance approach of the groundwater recharge estimation in the next section.
Table 3.1 Monthly total flow, and Minimum flow of Dedessa river near Arjo
Parameters
Qtot
Qmln
Months
Difference
M/se
c
M /month
M /sec
M3/month
M3/se
c
M3/month
J
1764
47238048
4 473
11980483
F
13.16
12.4
29994209
35257565
2.25
5443200
M
10.44
10.15
27973924
0.966
24551009
2587334
A
1564
9.48
40527302
4.557
25386589
11811744
M
29.14
78052326
11 08
10.364
I             28715558
27758938
J
98.47
255236832
------ ——— 18 78
45.433
117762336
J
263 37
705408958
53.04
1374744QA
137.365
A
423.39
367918416
1134009026
126
220.652
1
S
343.82
590994317
891191981
202 74
217.777
0
218.67
564477984
585697513
126 05
58.37T1
N
58.32
177074554
156356957
160 3
oZb!13997
47Q5ZH^cc
26.258
D
3565
95485139
68060736
. 42 06
13.792
36940493
.
21 86
uj
jhjuin((jAy 7rtn i uyu13o i o
Qtot
□oo44b4b
AC.AC
3
(m ) J
4,067,889,811
Q ,= total monthly discharge (m ). Mm Q = Minimum
l0
,,, nonthlv Hicrh^
1 962 092 938
,
2,105,796,874
the total monthly discharge
3.1.2 Water balance method
between
By  water balance it is  meant equating or balancing the amount of water inflowing and out flowing for a given system, such as hydrogeologic system, over a given duration for a given area Hence it is  a dynamic  system, (Detay, 1 997). The hydrogeologic  system can be a drainage basin/or catchment, groundwater basin, soil layer, or any  surface water reservoir (natural or artificial).
Water balance is
, k a valuable tool in the analysis of water availability in a region The method can be employed for different purposes: computation of
■■
...J groundwater recharge, evapo-
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transpiration, continuous record of soil moisture, and stream flow from a meteorological record and a few observations on the soil and vegetation, seasonal and geographic patterns of irrigation demand, the flux of water to lakes, prediction of human effect on hydrologic cycle. (Leopold and Dunne, 1978).
In this study the water balance method is aimed at estimation of groundwater recharge. The basic assumption that is considered in this case is that the surface water divide coincides with the subsurface drainage basin. Accordingly, other than the water that is  percolated within the limit of the surface water divide, there is no inter-aquifer flow (inflow or out flow) of groundwater.
The basic equation of water balance is:
Inflow = outflow +AS
Where AS is change in storage
For the study area, the main inflow component is precipitation, but the outflow components 
are evapotranspiration and surface runoff assuming all other components such as water abstraction by human for other purposes to be negligible. Besides this, the change in
storage, can be considered to be zero if the water balance is made by taking water year /or hydrologic year. Hence, the value of AS will be negligible/or zero since the calculation is to
be made on annual basis.
Since rainfall record can be obtained from meteorological stations in the area the main task left is  to estimate /or calculate the potential evapo-transpiration from which the Actual Evapotranspiration (AET) can be derived for the catchment. In addition to this  the surface runoff has  also to be estimated/or calculated since there is  no actual measurement of th7 component in the study area.
The  surface  runoff  for  Arjo  Dedessa  river  calchmenl  has  been  derived  (estimated!  -
follows.
as
onthly  minimum  stream  flow  has  been  considered  as  a  base  flow  as  mentioned ously    and     this     value      has      been      subtracted      from      the      month'y    total    runoff    to    the
onent that forms over land flow from which the runoff coefficient, Rc, is calculated.
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Rc = D/PA
Where:
Rc= Runoff coefficient
D = overland flow = 2,105,796,874 m3
P = annual precipitation =1452.9 mm = 1.452m
May 2007
3
2
2
2
A = drainage area = 9,981,000.000 m  (drainage area at the river gauging
station. But this does not mean that the total drainage area of the studied basin).
Hence, Rc = 2,105,796,874 m /1.4529 m x 9,981.000,000 m  = 0.1453
This Runoff coefficient is used to estimate the surface runoff for the river basin (Dedessa River) under study.
In  order  to  estimate  the  surface  runoff  of  Dedessa  area,  the  mean  annual  precipitation 1452.9mm has been used.
Hence, the surface runoff for the entire Dedessa River catchment becomes 0.1452 X
1.452m X 10.293,303.358.08 m  = 2.170.141.264.31 m’. Thus the surface runoff works 
out to 210.83mm This value is only about 14.51% of the total annual precipitation in the
area. Note that the total area of the studied river catchment at the out let of the command
2
area is 10,293,303,358.08 m .
By  assuming, the coincidence of groundwater basin with that of surface water divide and also by assuming that there is no artificial or natural intra-basin subsurface or surface flow, the groundwater recharge of Dedessa river catchment can be estimated as follows:
GWr = P - (Sr + AET)
Where. GWr = Groundwater recharge
p = Annual precipitation= 1452.9mm
AET = Actual Evapo-transpiration
Sr = Surface runoff - 210.83mm
The  Actual  Evapotranspiration,  (AET),  can  be  estimated  based  on  different  methods;  one  of th em is the empirical Turc method Turc method is represented by the following formula:
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2
AET = P/\[0.9 * (p/L) ]
Where, P = annual mean precipitation in mm - 1452.9mm
T = annual mean air temperature in °C = 20
L = 300 ♦ 25T * 0.05T3 [mm] = 1200mm
Hence, for Dedessa river catchment, AET = 944.57mm
Therefore, in order to estimate groundwater recharge for Dedessa River catchment, the value (AET = 944.57mm) obtained by Turc method is used in the water balance method of groundwater recharge estimation.
Hence, groundwater recharge in the Dedessa river catchment becomes as follows: GWr = P - (Sr + AET) = 1452.9mm - (944.57* 210.83) mm = 297.5mm
This value is about 20.48% of the total annual precipitation in the area. This high recharge rate may be attributed possibly to the relatively better vegetation cover and to the highly fractured rock prevailing in the area.
Finally, an average value from the two methods, the base flow analysis method (196 6mm) and the water balance method (297.5mm), which is 247.05 mm, has to be used as annual groundwater recharge of the area. This value is about 17% of the total annual precipitation in the area.
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4. WATER QUALITY
4.1             Water samples
May 2007
Natural water interacts with environment, and also it is affected by anthropogenic process changing  its  chemical,  physical  and  biological  constituents.  The  utilization  of  this  water requires  an  understanding  of  such  constituents.  Hence,  the  water  should  meet  certain quality standard that is set mainly based on its purpose in order to use it. Some important physical and chemical parameters have been determined for the water of the study area to compare  its  quality  with  certain  standards  set  by  different  Organization  such  as  WHO for specific water uses.
Water samples  from different sources  have been taken for physicochemical and microbiological analysis  based on the actual/existing geology, hydrogeology  and geomorphology of the area. Accordingly, a total of thirteen samples have been taken from springs and Wells. Fig. 4.1 shows the location of the water sample points. All the samples have been submitted to the laboratory  of WWDSE for analysis. In addition to these water samples  collected during the field work, previously  analyzed water quality  by  different institutions have been used for hydrogeological interpretation of the area.
Among the collected water samples, four samples  were taken from springs  and nine were from boreholes. The numbers  of samples  have been determined based on various conditions  such as  geological and hydrogeological setup of the area and availability  of previously analyzed physico-chemical and microbiological data in the area
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40
o
40 Kilometers
400000Arjo-Dedessa Irrigation Project
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4.2 Sampling techniques and analysis
May 2007
A  total  of  thirteen  samples  have  been  collected  from  different  sources.  One  duplicate sample is collected from spring in order to see the reproducibility of the laboratory results.
Natural water is sampled in view of carrying out various analyses on it. In order to undertake water quality  analysis  water samples  have been collected for physicochemical analysis. The collected water samples  were from ground water (springs  and Wells) sources. The samples have been taken with one liter capacity  plastic  bottles. In order to compare some physicochemical parameters, some physical parameters  such as  TDS (Total Dissolved Solids), Temperature and PH have been measured at field in order to compare the result with that of Laboratory.
4.3       Pictorial    representations    of    water    quality    analysis 
4.3.1 Piper diagram
The  Piper  diagram  plots  the  major  ions  as  percentages  of  milli-equivalents  in  two  base triangles. The Piper trilinear diagram for water samples in Arjo- Dedessa is available at Fig.
4.2. The total cations and the total anions are set equal to 100% and the data points in the two  triangles  are  projected  onto  an  adjacent  grid.  This  plot  reveals  useful  properties  and relationships  for  large  sample  groups.  The  main  purpose  of  the  Piper  diagram  is  to  show clustering of data points to indicate samples that have similar compositions.
The Piper diagram can be used to plot all samples in the open database or selected sample groups  In addition, the symbols  representing the sample values  can be customized according to shape and color. Other options  include individual multiplication factors  for each selected ion to prevent data point accumulation along a base line. The highlighted sample points  indicate samples  that are selected in the database and are also highlighted on all other open graphical displays. According to the water samples  plotted in Piper trilinear diagram for Arjo-Dedessa area, samples  can be categorized based on their clastering as Ad-01 AD-10, AD-12 and AD-13 as  one category; AD-02, AD-03, AD-04, AD-05, AD-09 an d AD-11 as  second category; AD-07 and AD-08 as  third category; and finally  AD-06 as fourth category. However, if we refine further the water type based on the chemical composition, they can be categorized in to five categories.
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•    Na- HCO3 type (Sample AD-01, AD-07, AD-08, AD-10, AD-12 and AD-13).
•    Ca- HCO3 type (Samples AD- 02 and AD- 11)
. Ca- Mg- HCO3 type (AD-03, AD- 06 and AD- 09)
. Ca- Mg- Na- HCO3 type (Sample AD- 04)
•    Ca- Na- HCO3 type (Sample AD-06)
The cation to anion ratio also varies for the different samples indicating various conditions of rock-water interaction.
4.3.2  Schoeller diagrams
These semi-logarithmic  diagrams  were developed to represent major ion analyses  in meq/l and to demonstrate different hydrochemical water types  on the same diagram. This  type of graphical representation has  the advantage that unlike the trilinear diagrams, actual sample concentrations  are displayed and compared. The Schoeller diagram in AquaChem can be
used  to  plot  all  samples  in  the  open  database  or  selected  sample  groups  only.  Up  to  10
different parameters  can be included along the x-axis  and the symbols  representing the sample points  can be customized according to shape and color. The highlighted lines indicate specific  samples  that are selected in the database and are also highlighted on all other open graphical displays. The Schoeller diagram for the water samples  of Arjo- Dedessa area is  available in Fig. 4-3. Water of similar source (type) shows  a similarly shaped curve; where as  water of different types  show differently  shaped curves. Accordingly, two major types  of water can be identified from the graph: Calcium bicarbonate types  (Samples  AD-02, AD- 03, AD- 04, AD- 05. AD- 06, AD- 09 and AD- 11) and the sodium bicarbonate types (Samples AD- 01, AD- 07, Ad- 08, AD- 10, AD- 11 and AD- 13)
4.3.3  Stiff diagram
The Stiff diagram method of water quality  representation uses  a scale for concentration of
ions in meq/L along the x-axis. The ions are arrannod
.
™ v., hu-ui, MU-UO, AU-10, AD-12 and AD 13)- r
(samples    AD-02.    AD-03,    AD-09    and    AD-11).    and    Ca-Na-HCO3    Ivn    a‘HC03      'Vpe
05 and AD-06).
a *03 type (samp.es AD-04. AD.
1990
90
90
90
40
40
X
20
SO4
O AD-O1
□ AD-02
♦    AC-43
* AC-04
x AC-45
» AC-06
O AC-47
A AC-08
V AC-09
- AC-JO
X AD-Tt
♦ AD-12
AC-t3
90
♦ AD-06
90
s
•«
40
40
o
X
•AD-12
-A — 
--
o$yp
• 3)
20
~•
- V
<
90
«
40
20 »
40
90
JO
Ca
Na*KHC03
Cl
Fig 4-2 A map showing Piper trilinear diagram for water samples in Arjen Dedessa area
MnanuConcentration (meq/1)
Legend:
♦
-
- H-----
— X—
—*-
- £b-.-
-e -
AD-01
AD-02
AD-03
AD-04
AD-05
AD-06
AD-07
AD-08
AD-O9
AD-10
AD-11
AD-12
Fig. 4-3 A map showing Schoeller diagram for water samples in Arjo-DedessaNa
Cl
/‘X
x
Ca
HC03
Mg
Ti
S04
C03
K
10        5 AD- 02
5
Na              —               Cl
Ca
Mg
K
-
\T1
'> HCC3 804
CC3
5-5 25 5
‘ AD- 04
5       z5Al>03Z5
Cl HCO3 S04 CO3
Na
Ca
Mg
K
Cl HCO3 804
15 AD-06 15
Cl HCO3 S04 CO3
cliff Hiaaram for water samples in Ago- Deaessa Project area (Concent. meq/L)
Fig<4-4AmapshwingStinaNa
Ca
Mg
K
Cl
Na
Cl
HC03
S04
Ca
x
HCO3
X
Mg
SO4
\T
C03
K
CO3
AD-08
10
5
AD- 09
5
10
Cl
Na
HC03
SC4
C03
Ca
Mg
K
Cl HCO3 SO4 CO3
50
25 AD-10
25
50
8
3
AD- 11
6
0
I
I
I
I
I
I
Na
Ca
-
Cl HCO3 S04 CO3
------ I------
AD- 12
t—
40
AD- 13       20                 40
rArjo Dedessa Irrigation Project
Hydrogeological investigations
4.4 Agricultural/irrigation water quality
May 2007
Agricultural suitability  of water depends  on crop type, climate, soil type, and amount of irrigation water use, (Davis  1966). The main parameters  to be analyzed to evaluate irrigation water quality  are Boron, Sodium hazard, and Salinity. These parameters  affect plants  either directly  (e.g. by  causing an adverse physiological effects) or indirectly  (e.g. by limiting plant root nutrient or moisture uptake). Salinity  is  best estimated based on Electrical Conductivity  (EC) value. Other direct indicator of salinity  is  Total Dissolved Solids  (TDS). The two parameters, TDS & EC are related by the following equation:
TDS  (mg/L)  =  640  x  EC  (dS/m),  where  mg/L  =  milligram  per  litre,  dS/m  =  deci  Siemens  per meter.
According   to   U.S.   Department   of   Agriculture,   water   with   an   EC   greater   than   4dS/m   is considered as saline.
Sodium hazard is  assessed by  the so-called Sodium Adsorption Ratio (SAR). It is  caused by  sodic  water. Sodic  water is  water that has  high sodium concentration relative to the concentration of calcium and magnesium. Water is  said to be sodic  if its  SAR is  greater than 12. SAR is calculated as follows:
SAR= [Na*]N0.5[Ca * + Mg *]
Where,   the   concentration   of   each   cation   is   expressed   in   meq/L.   If   the   concentration   is expressed in mmol/litre, SAR = [Na*]/ J [Ca * + Mg *]
According  to  Todd  (1980  and  references  there  in),  the  water  quality  evaluation  for  irrigation based on SAR and EC are as shown in Table 4.1.
Table 4.1 SAR and EC values in terms of irrigation water suitability
22
22
Water class
Excellent
Good
Fair
SAR
< 10
10- 18
Poor
18- 26
>26
Water class
Excellent
Good
Permissible
EC (ps/cm)
<250
250- 750
750- 2000
2000- 3000
1dS/m = 10ps/crn
----------------
Unsuitable >3000
--------------------- ----
In order to evaluate water quality for irrigation from Salinity and Sodium hazard for Arjo-Dedessa area, the parameters have been measured and calculm^ PT' shown in Table 4-2.
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“Arjo-Dedessa Irrigation Project
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Table  4.2  SAR  and  EC  values  for  surface  and  groundwater  in  Arjo-Dedessa  area
----- T------ -----
Sample code
---- - tatt » r> t
AD-1
ah.<
AD-2
AD-3
AD-4
AD-5
AD-6 ---
-- AADD^-7AD=a
r*DA-D9-8
AD-9
AADD--1100AD-
AdA1D2 -11 n-
Ad-12
AD-13
EC (us/cm)
604
436
439
496
418
2660
2370
665
4450
515
3160
3160
SAR
38 4
0.7
04
05
1.6
12 8
10 8
04
37 1
0.4
21.6
21 6
According to SAR values  obtained for the water samples, AD-01, AD-07, AD-10, AD-12 and AD-13 are all sodic  water and AD-08 is  slightly  sodic, where as, the remaining water samples  are all non sodic. From irrigation water quality  point of view, they  range from excellent quality to poor quality.
But from salinity  point of view, the quality  varies  from excellent to unsuitable; i.e., from 418 ps/cm to 4450 ps/cm. In other words, it can be said the salinity  ranges  from medium salinity to very  high salinity. Fig. 4.5 shows  values  of TDS and EC for different water samples  in the project area.
Figure 4-5 Electrical conductivity (EC) in ps/cm and Total Dissolved Solids (TDS) in mg/L for water samples in Arjo-Dedessa area
This graph clearly indicates the extent of salinity for different water sam
of for the EC and the TDS. Contour map of the TDS is avail.hi . c P
avd"aDie at Fiq 4 a fnr
trSnd
area. For description one the water samples mav be
* Udie9orized into th
based on their Electric conductivity and TDS values Accordi
The proJect
Cate9°ries
r Ir >gly, seven samples (AD-02
24Arjo-Dedessa Irrigation Project
Hydrogeological investigations
May 2007
AD-03,  AD-04,  AD-05  and  AD-06)  have
low  salinity,  four  samples  (AD-07,  AD-08,  AD-12
and AD-13) have high salinity ; and two samples (AD-01 and AD-10) are extremely saline. This  qualitative description doesn’t follow the standard procedure of water quality classification based on TDS and/or EC, but it is  simply  for the sake of description. Water samples  coded as  AD-01, AD-10, AD-12 and AD-13 are all from springs; all other water samples  are from boreholes  (drilled shallow Wells). Hence, it is  possible to conclude that even if they  are all from groundwater, they  are from different aquifers. The more saline water (having high TDS values) are mainly  from weathered basement rocks  (granite and granitic  pegmatite), and deep-seated volcanic  aquifers, especially  along fault lines  (e.g. thermal springs).
25□□e
Fig. 4-6 A map showing the contour for Total Dissolved Solids (TDS • mg/L) for
Arjo - Dedessa Project area
60
I
IArjoDedessa Irrigation Project
Hydrogeological investigations
May 2007
5.        PIEZOMETERS OBSERVATION WELLS AND WELL FILED
5.1 Distribution of Piezometers/Observation Wells
In order to monitor groundwater quality and quantity of the area both before and after the construction  of  the  dam  under  investigation,  a  total  of  fourteen  Piezometers/Observation wells    have    been    proposed    and    their    sites    have    been    located.    The    geographic configuration/distribution   of   the   piezometers/Observation   wells   is   selected   based   on geology,  hydrogeology,  topography,  groundwater  flow  direction,  etc  of  the  area.  Since  the depth  of  the  piezometers  to  be  constructed  rarely  exceeds  100  meters,  PVC  casing  is recommendable.  In  some  cases  the  existing  well  depth  at  the  immediate  down  stream  of the proposed dam axis are 50 - 70 meters depth
Table 5.1 shows the geographical coordinates of the proposed piezometers Table 5.1 Locations of the Proposed Piezometers
Geographical coordinates (UTM)
Ser.No.
Piezometer
code
Easting
Northing
Altitude (m)
1
M ------------------------
Pad-1
241283
945079
1332
2
Pad-2
239578
942443
1348
3
Pao-3
239229
948568
1354
4
Pad-4
232368
945622
1345
5
Pad-5
237407
951746
1391
6
Pad*6
228492
944575
1374
7
Pao-7
230198
952870
1386
8
Pad'8
225779
947133
1324
9
Pad-9
220119
954111
1341
Pad-10
217677
951707
1319
11
Pad-11
217135
961437
I 332
12
Pad-12
212716
952444
1354
13
Pad-13
214654
956087
1323
14
Pad-14
213336
961320
--------------
1320
The construction of these piezometers shall follow the standard procedu site-specific conditions such as hydrogeological set up will also mow proposed Piezometers is available on the map shown in Fig 5.1
a 'th°U9h some
01 the
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5.2         Future well field
May 2007
The potential aquifers for groundwater storage in the area are basaltic lava flows with minor salics including rhyolite and ignimbrite, and alluvial deposits in the river valley of Dedessa. This can be verified by existing boreholes data in the area. For example, Borehole data in Agaro area show this situation.
Well field sites  on which detail geophysical investigation shall be undertaken have been identified based on geological, hydrogeological and geomorphological setting/or approaches. Test wells  drilling and construction shall follow the detail geophysical investigation.
2811OO0OC
Sibu Sire
pcirfc
>uto Wayu
• Aat»r
pxnti
R\w’S7?arr-
- VI
••'■■’■* -v*i
Wama Qoneya
1060000
Thrnul
ixoxyj
*£000
Limu Seka
65CC0U
IKXX-X)
’’OJVArjo Dedessa Irrigation Project
Hydrogeological investigations
6.WATER LOGGING AND DRAINAGE
6.1 Topographic features and soil characteristics
May 2007
The  undulating  and  rolling  topography  in  upper  part  of  the  project  area  gradually  acquires gentle slope of flat land in the bottom valley of Dedessa, especially the lower part where the command  area  becomes  almost  flat.  This  flat  land  is  covered  with  sediments  mainly  of colluvial/delluvial  and  alluvial  origin.  This  sediment  is  underlain  mainly  by  basement  rocks. The  topography  and  land  characteristics  of  the  command  area  can  be  said  to  fall  under level  (0-  1%),  nearly  level  (1-  2%  slope),  very  gentle  (2-  4%  slope),  and  gentle  (4-  6% slope),  categories  as  about  35%  of  the  command  area  falls  under  first  two  categories  and about 23% of the command area falls under the last two categories. Though the command area  is plain, it has a rolling topography and there  are several small hills dispersed  in the command. There are rising hills also on the outer boundaries of the command area on both banks of river Dedessa.
The major part of the area is comprised of vertisol (47% of the command area) that has very low permeability. Next to vertisols  are cambisols  and luvisols  covering 15% and 8.68% of the area respectively. Hence, this  soil type hinders  the infiltration of the most incoming rainfall during rainy  season. On one hand this helps to restrain the rise of the groundwater table through reducing infiltration, on the other hand the plain nature of the land wouldn’t allow the surface run off to flow out easily.
6.2 Existing groundwater table
Groundwater level in the project area varies from 2 to 20m b.g.l. In the command area the groundwater table is 2 meters below surface during rainy season except in few exceptional cases of surface water logging. During dry season the groundwater table was found dee er than 5 meters below natural ground surface. The rise of the groundwater table can h
due to different natural conditions and man made activities The application f P^ practice is the major human interferences for rise of groundwater table The 'rr'9atlOri irrigation (drip, furrow, etc) that is practiced and the crops proposed to be
determine the extent to which it may affect the rise in groundwater level of the area^ W'"
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6.3
Future groundwater table and recharge from irrigation
Whe
never there is a major irrigation project with continued and
intensive
irrigation activity,
there is always an apprehension of rise of groundwater table. If the rise of the groundwater table is significant, and it continues unabated, it may cause serious adverse impact on the fertility of the land. Some of the factors that affect the rise of groundwater are:
•    Topographic features
•    Land characteristics
•    Natural drainage system
•    Intensity and amount of rainfall
•    Existing groundwater table
•    Soil characteristics
•    Crops and its water requirement
•    Seepage and other losses from canal system
•    Surface drainage system implemented and its effectiveness
The estimated annual crop water requirement at primary canal head, for various crops to be cultivated in the command area of the proposed Arjo- Dedessa project comprises four set of values  categorized under two phases  each phase having two rotations. This  has  been shown in Table 6.1.
Table 6.1 Annual gross crop water requirement at primary canal head
Ser. No
Phase
Rotation
GCWR (mm)
1
I
I
987 40
2
I
II
1086.14
3
II
I
1311.47
4
II
II
1423^234"
In order to assess groundwater recharge from irrigation water, the maximum Gross C rop Water R equirement (GCWR) in t he second p hase nf
Va 'US °f
or second rotation that •
1423 234 mm/yr has been used. Therefore, the groundwater recharge from of irrigation has been estimated under two main situations as follows-
'S
6 aPPl'CatlOn
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1.  According  to  groundwater  recharge  estimation  made  in  this  report,  out  of  the  total annual  precipitation  in  the  area,  only  17%  is  going  for  recharging  the  groundwater. If   the   same   scenario   is   considered,   the   annual   groundwater   recharge   from   the application  of  irrigation  in  the  command  area  becomes  241.95  mm.  This  value  is  a crude  estimation  since  various  factors  among  which  the  irrigation  method  can  affects the groundwater recharge.
2.  Along  with  other  crops,  cultivation  of  paddy  has  been  proposed  in  the  command area.  Paddy  is  the  only  crop  that  requires  constant  standing  water  in  the  field  for  its growth  and  development.  Thus  paddy  cultivation  results  into  percolation  loss,  and only  that  quantity  of  water  which  is  lost  through  percolation  has  the  chance  and tendency  of  reaching  groundwater.  It  may  be  noted  that  about  330  mm  of  water  is estimated  to  be  lost  through  percolation,  and  only  this  quantity  of  water  has  the scope  of  reaching  groundwater,  which  may  add  to  the  rise  of  the  groundwater  The intensity  of  paddy  is  only  30%  in  phase-  I,  which  will  increase  finally  to  45%  in phase-  II.  Considering  the  entire  command  on  average,  the  annual  contributions  of irrigation   water   to   the   groundwater   would   be   99mm   in   the   Phase-   I,   which   will increase to 148.5 mm under phase- II.
Regarding other crops, they are all dry irrigated i.e., no standing water is required The irrigation water applied to the field in case of all other crops except paddy will be contained as soil moisture within the root zone. Very small amount of water, which will be lost below the root zone, will reach the groundwater. Other losses will be in form of surface
which will be taken care by surface drainage system. As such the addition to groundw t
due to irrigation of all other crops except paddy may be considered verv nonr u.
therefore, may be neglected.
.
.          ...
y negiigible and
Seepage loss to the groundwater from the canals (main canak
secondary canals a h tertiary canals) has also been estimated. According to United States Burea
n°
(U.S.B.R.), seepage loss from unlined irrigation canal is 1 5 meter of wetted area for clay and clay loam soils. Based on thk =
° Reclamatlon
’ /J:>ec per one million
square
from Arjo- Dedessa irrigation canal is shown in Table 6 2 and 6 3
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Estimation of wetted area for Arjo.
Dedessa irrigation canal
Table 6.2
Canal
category
Total
Average
wetted
perimeter
(m /m)
Total length (m)
Area
Ser.
No
Right
1
2
3 Total
2.795 2.795
1.02       1.02
Right
42.80000 49,370.00
199,464.00
Left
60,180.00
47.190.00
239,100.00
Right
"279,639 00
Main canal
Secondary canal Tertiary canal
8.7115
2
Lett
10.211
740,512 00           ,
137.989 00
203.453.00
447335.00
1,457,732.05
621,081.00
836J5105
L
n  75W the .0.3) area has been cohered for - nrain cans.
o,
Table 6.3 Seepage loss from unlined Arjo- Dedessa irrigation canal
No
Command area (wing)
1      Right Bank
--------- -----------------------------------------------------------------------------------------------,
2
Wetted area of Canal (m )
Secondary canal
Tertiary canal
Total
wetted seepage
Main canal 279,639.00
—137\989?00 —203>5T66
—243.88Z00
2
3
4
LeftBank 
7
Total area Im )
Total Seepage (m )
740,512.00
269,885.05
'
621,081.00
836.651 05
1,457,732.05
40,330.960.80
loss (mm)
460.873.00     131,896.05
447.335.00
226.26
3
23,320,796.31 _ 6,400,809.75     10,609,354.73
J
the seepage loss calculation from the Main canal, only 75%, of the total area has been dered The duration of the seepage loss is considered to be eight months (October -
nd twenty four hours a day for the main canal; where as for six months of twenty four for secondary and tertiary canal. The command area on which the seepage loss has averaged out is the gross command area, i.e., 17,825 hectares (178, 250, 000 m  ),
P the total seepage loss of 40,330,960.80 n. is spread in the gross command area, „„ ovpraae seepaqe loss of 226.26mm from the irrigation canal,
2
which gave an average
H
a
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vestigations
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Therefore, the total
annual groundwater recharge from the application of
irrigation becomes 
the sum of the seepage loss from the canals (226.26 mm) and from the paddy irrigation (148.5 mm) that is 374.76 mm.
Although detail aquifer and soil sampling and analysis  is  required to come up with actual result of the porosity, its  value has  been estimated to be 35% in the command area by considering a range from clay  to sand soils. This  parameter will be used to estimate groundwater level rise due to the application of irrigation.
Finally, by knowing the groundwater recharge from the application of irrigation, and the porosity of the aquifer and the soil materials above it, the extent of the groundwater level rise due to the application of irrigation has been estimated under different scenarios as follows:
Scenario I: No groundwater flows out of the catchment (stagnant condition) Annual groundwater recharge from application of irrigation, GR = 241 95mm Effective porosity of aquifer (sand) in command area, n  = 0.35 (35%)
e
Groundwater level rise due to application of irrigation,
GWLr = GR/n  =241.95mm/0.35 = 0.691 m.
e
However, it should be noted that groundwater is  always  in a dynamic  condition; hence, the stagnant groundwater condition never prevails  in the area. Therefore, the stated value of groundwater level rise (0.691m) will never happen.
Scenario II: Groundwater flows out of the catchment (dynamic system)
Annual groundwater recharge from application of irrigation, GR = 241 95mm Effective porosity of aquifer (sand) in command area, n  = 0.35 (35%)
e
According to base flow separation method, the ratio of qroundwatAr n..w <
y uriuwaier outflow from the entire river catchment to the groundwater recharge of the catchment from precipitation
0
Hence, the remaining 20.42% of the groundwater recharge contributes to roc "
rise. Similarly, if we apply such dynamicity to the groundwater recharge from °
application, the groundwater outflow from the irrigation application becom '™9at'On Water
omes 192.54 mm.
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e
0.141m.
Scenario III: According to the paddy  cultivation and seepage loss  from canals, the annual groundwater recharge from the application of irrigation is  374.76 mm as  mentioned before. If this  is  the case, the groundwater level rise due to the application of irrigation under stagnant groundwater condition becomes:
Groundwater level rise due to application of irrigation, GWLr = GR/ne  =374.74 mm/0.35 = 1070.74mm= 1.07m.
Scenario IV: (Scenario III under dynamic condition): This  Scenario assumes  that groundwater is  continuously  flowing out of the catchment. Hence, only  20.42% (76.53 mm) of the groundwater recharge from irrigation water will contribute to the groundwater level rise. Therefore, the groundwater level rise under this  Scenario becomes, GWLr = GR/n = 76.53 mm/0.35 = 218.66mm = 0.219m.
In conclusion, the assumption of stagnant groundwater condition Scenarios  (scenario I and III), over estimates  the groundwater level rise due to the application of irrigation Because it
Hence, if we reduce this  amount from the recharge due to the application of irrigation mentioned above (241.95mm), the net recharge that contributes  to the groundwater level rise becomes only 49.4mm. Therefore, under this scenario the groundwater level rise due to the application of irrigation will be:
Groundwater Ievel rise due to application of irrigation, GWLr = GR/n  = 4 9.4 mm/0.35 =
assumes static/or stagnant groundwater condition that is not the
•
uic  case in reality  since
groundwater is usually in a dynamic condition The second Scenario (SrpnTi. m .
scenario   II)   relies   on groundwater recharge estimation from irrigation water application equals that
precipitation. However, groundwater recharge from irrigation water anniin
from paddy irrigation and canal loss should be higher than from Drecinit=>f
3
ppiication, particularlv 
scenario underestimates the groundwater recharge. Therefore
.....
investigation, the Scenario that works best is Scenario IV Hence level rise due to the application of irrigation water will be 0 219 m However,  it  has  to  be  noted  that  the  existing  soil  in  „
I
p ecipnarion. Hence thi^ ’
’ according to this
the annua' groundwater
a
e command area
loam soils as revealed from soil survey (sampling and anoi • 
y analysis) under the Sam -------------------------------------------------------- ne same project
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The  soil  has  an  a  verage  hydraulic  conductivity  of  0.0000056  m/s,  i.e.  0.482  m/day.  This value is very small inhibiting the infiltration of any water (precipitation or irrigation) to Groundwater.  Hence,  the  contribution  of  groundwater  recharge  from  the  application  of irrigation should be less than the amount stated here. Therefore, the groundwater level rise due to the application of irrigation cannot exceed 0.219m/year.
Another factor that can reduce the contribution of irrigation application to groundwater recharge is  the presence of number of creeks  that are transversal to the trend of the command area and Dedessa River. Some infiltrated water from the irrigation application will come out as regeneration through the creeks before reaching the groundwater table Profile across some creeks and Dedessa river in the command area is available in Fig. 6.1 (a, b, c, d and e). The lines along which the profiles have been taken are also shown in the map Fig.
6.2.
Although there is no well documented well completion data in the command area, the depth to groundwater in the area is  below 5 meters  during dry  seasons  except in some creeks such as Chuli area (eastern part of command area) where the water table is about 2 meters below ground surface. Only few boreholes and their data exist in the Dedessa River valley of project area since it is  inhabited mainly  through resettlements  only  2- 4 years  ago Hence, construction of contour map depicting depth to groundwater is hardly possible under such data limitation since they are entirely constructed for emergency purposes
36From Pos: 232335.077, 944107.179
fig 6f A map showaig profies aiong dfe 'ent ine$ in the command ares
To Pos: 213587.927, 956965.088
07 let bar/ ofDedessa river
—
-------T
15.0 km
----------- T---------
17.5 kmIFrom Pos: 219184.091. 943954.006
To Pos: 226412.470. 954446.814
c) ProHe Kross Dedessa river tom id bad: iorighlbanit
1400 m
1375 m
1350 m    - -
5.0 km
7.5 km
10.0 kmFrom Pos: 223707.657, 939756.803
To Pos: 230109.880, 953107.677
d) Proiie across Oedessa river bom Id bid tv rigid bantFrom Pos: 210789.844, 952208.348
e)Pr^xronDedes^^erhmldt^tori^b^
To Pos: 218531.206, 957198.261
1400 m
1375 m
1 350 m
7.0 km
8.0 km                        9.22 km10000
0
10000 Meter*Arjo Dedessa Irrigation Project
Hydrogeological investigations
May 2007
7.
SALINITY AND SODICITY
Salin
ity of soils/water can be defined
in
terms of
electr
ical
conductivity.
Particularly for
soils 
it is 
the electrical conductivity of the s
at
uration ex
tract.
How
ever, sodicity
is defined in
terms 
of exchangeable sodium percentage. Salinity  may  be categorized in to two based on its sources: primary  (inherent) salinity  and secondary  salinity. Primary  (inherent) salinity  is derived from the weathering/degradation of primary  rocks/minerals. It usually  occurs  when evaporation exceeds  precipitation. Where as  secondary  salinity  is  derived from the upward movement of salts  from saline groundwater through capillary  size under the situation of in adequate leaching. This  situation happens  mainly  under shallow groundwater. Mass  flow to the soil surface through capillary  rise is  known to cease when the water table is  below 2 meters  in a clay  and silty  clay; where as  the same value is 1.6 meters in coarse textured soil.
Water and soil salinity  occur due to different reasons. Among them are over irrigation that causes groundwater level rise due to in appropriate provision of drainage and/or crop water requirement calculation, poor method of irrigation, capillary  rise of groundwater due to naturally  high groundwater level below surface, etc. When such raised groundwater level is subjected to evaporation loss, the chemical constituents  of the water will be left over and becomes  concentrated on the surface leading to soil and/or water salinity. The leaching of some existing nutrients  in soil is  also the potential source of salinity  of water and soil in irrigation. In order to assess the possibility of salinity for the project area in the future, data have been collected and have been evaluated. Among them are the existing groundwater quality  (physicochemical characteristics  of groundwater), and soil chemistry. Other Parameters  that have been given emphasis  here is  the meteorological parameters  that cause high evapotranspiration. However, the high rate of precipitation (rainfall) in the area can counteract the problem by leaching the highly soluble salts such as chlorides, sulphates and carbonate salts.
The distribution of relatively saline water is sporadic in the area It is highl
basement (crystalline) rocks and thermal springs. Cold groundwater dSS°Clated With
volcanic  rocks  in the area has  very  low TDS; and hence verv  in.. .■ or'9lnated from
■ voy iow salinity, a man
Total Dissolved Solids of the project area is available at Fig 45
P snowing
Water Works Design & Supervision Enterprise ~---------------------------
In Association with Intercontinental Consultants and Technocrats Pvt Ltd
39Arjo-Dedessa Irrigation Project
Hydrogeological investigations
May 2007
Hence, the hazard soil/water salinity  during the design period of the project can be controlled naturally  (through high precipitation and topography  of the area) or artificially through appropriate irrigation water management.
Water Works Design & Supervision Enterprise
40
In Association with Intercontinental Consultants and Technocrats Pvt Ltd.ArjoDedessa Irrigation Project
Hydrogeological Investigations
May 2007
8. EXSITING AND FUTURE PROSPECT OF GROUNDWATER
8.1         Existing groundwater development
Presently,  the  majority  of  the  Woredas’  capital  town  and  the  rural  community  in  the  river catchment   are   supplied   potable   water   from   groundwater,   either   through   borehole construction  or  from  spring  development  with  the exception of Bedele town that  get water from Dabana River through the treatment of raw water. The yield of groundwater from some volcanic lava flow shows very promising result both for current and future water supply from groundwater for different purposes. Can be mentioned as examples are boreholes for Agaro town  where  the  yield  is  about  30lit/sec  from  a  single  borehole  Furthermore,  the  aquifer  in this particular area has multilayer system attributing to a confined aquifer.
Here, it is vital to discuss Agaro town as an example. The town is entirely supplied water
from groundwater through boreholes. There are three new boreholes and two old
boreholes. Among these boreholes, four boreholes are currently functional, one from the old
and three of the new boreholes.
In addition to this, the Quaternary sediments in the river valley are the potential aquifers for groundwater development, particularly shallow groundwater.
Other  Woreda  towns  that  are  supplied  potable  water  from  groundwater  are  Arjo  Atnago Gatira and Atnago.
8.2  Future groundwater prospect
The existing geological, meteorological, hydrogeological and geomorphological conditio
in the river catchment indicate there are areas that can be deveinnori
..
in order to develop qroundwater. I hese identified well field dtoc
likely limitation in the future groundwater development in the area is not the
.
quality (relatively high iron concentration). The map of potential „
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m ot/ailohlo at Pin M 1
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a 9rour>dwater
quantity but the
3^635 fOf
l"e
.
future development is available at Fig. 8.1
-------Water Works Design & Supervision Enterprise"               ~~ in Association with Intercontinental Consultants and Technocrats Pvt Ltd
41Fig. 8-1 A map showing Groundwater potential areas for future development
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Summery

Summary of Arjo-Dedessa Irrigation Project Feasibility Study

Arjo-Dedessa Irrigation Project Feasibility Study Summary

Project Location: Dedessa River, a major tributary of the Blue Nile (Abbay) in Ethiopia

Study Purpose: Feasibility assessment for irrigation development in the Dedessa sub-basin

Prepared by: Water Works Design & Supervision Enterprise in association with Intercontinental Consultants and Technocrats Pvt. Ltd.

Date: May 2007

1. Project Overview

The Arjo-Dedessa Irrigation Project is part of the larger Abbay River Basin development in Ethiopia. The Dedessa River is the largest tributary of the Abbay in terms of water volume contribution, draining an area of approximately 34,000 km².

Key Characteristics:

Catchment area at proposed dam site: 9,981 km²
Mean annual flow at dam site: 2,328 Mm³ (75% reliable year: 1,729 Mm³)
Climate: Temperate to tropical with distinct wet (May-September) and dry (November-February) seasons
Mean annual rainfall: 1,453 mm (ranging from 853 mm in dry years to 1,453 mm in wet years)

2. Meteorological Analysis

The study analyzed data from five meteorological stations in the region, with Jimma station providing the most reliable long-term data (since 1952).

Key Findings:

Parameter	Range/Value
Mean monthly temperature	20.0°C (December) to 25.4°C (March)
Relative humidity	56.63% (March) to 88.63% (September)
Wind speed	0.48 m/s (November) to 1.08 m/s (April)
Sunshine hours	3.7 hrs/day (July) to 8.3 hrs/day (December)
Reference evapotranspiration (ET₀)	1,397 mm annually (ranging 2.96-4.62 mm/day)

3. Hydrological Analysis

The study examined streamflow data from two main stations on the Dedessa River and three neighboring rivers.

Streamflow Characteristics:

Station	Catchment Area (km²)	Mean Annual Flow (Mm³)
Dedessa near Arjo	9,981	3,761
Upper Dedessa near Dembi	1,806	1,221

Flow Reliability Levels at Dam Site:

Reliability Level	Annual Flow (Mm³)
Mean year	2,328
75% year	1,729
80% year	1,625
90% year	1,384
95% year	1,224

4. Flood Analysis

The study conducted detailed flood frequency analysis using Log-Logistic and Generalized Extreme Value distributions.

Design Flood Estimates:

Return Period	Peak Discharge (m³/s)
10-year	575
20-year	654
50-year	771
100-year	871
500-year	1,155
1,000-year	1,303
10,000-year	1,948
Probable Maximum Flood (PMF)	3,690

5. Sediment Analysis

The study estimated sediment yield and distribution in the proposed reservoir:

Annual sediment load: 775,600 m³
50-year sediment accumulation: 38.78 Mm³
100-year sediment accumulation: 77.56 Mm³
Bed load assumed to be 15% of suspended load
Sediment density: 1.2 gm/cc (suspended), 1.4 gm/cc (bed load)

6. Water Resources Modeling

The study included reservoir simulation and sub-basin modeling to assess:

Irrigation water requirements for various scenarios
Reservoir characteristics at different storage levels
Potential for power generation at Dedessa Dam
Impact of upstream developments on downstream flows

7. Key Recommendations

Implementation of the proposed irrigation project with appropriate reservoir sizing
Consideration of sediment management strategies for long-term reservoir sustainability
Further studies on training needs for project implementation and management
Continued hydrological and meteorological monitoring

Conclusion: The feasibility study provides comprehensive hydrological and meteorological analysis to support the development of the Arjo-Dedessa Irrigation Project, demonstrating its technical viability while identifying key considerations for implementation and long-term operation.