Tana Sub-Basin Land Use Planning and Environmental Study Project

BoEPLAU Hydrology and Water Resource Assessment Report, ADSWE Page I

Amhara National Regional State

Bureau of Environmental Protection, Land Administration and Use (BoEPLAU)

Tana Sub-basin Land Use Planning and Environmental Study Project

Technical Report III: Hydrology and Water Resource Assessment (ADSWE, LUPESP/TaSB: sII, vIII/2014) January, 2014 Bahir Dar

Client: Bureau of Environmental Protection, Land Administration and Use (BoEPLAU) Address: P.O.Box: 145 Telephone: +251-582-265458 Fax: (058) 2265479 E-mail: Amhara [email protected]

Consultant: Amhara Design & Supervision Works Enterprise (ADSWE) Address: P.O.Box: 1921 Telephone: +251-582-181023/ 180638/181201/181254 Fax: (058) 2180550/0560 E-mail: amhara [email protected]

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List of report

Section I: MAIN REPORT Section II: SECTOR STUDIES Volume I: Soil Survey Volume II: Forest and Wildlife Assessment Volume III: Hydrology and Water Resource Assessment Volume IV: Land Use and Land Cover Volume V: Agro Climatic Assessment Volume VI: Crop Resource Assessment Volume VII: Watershed Management Volume VIII: Livestock and Feed Resource Assessment Volume IX: Human Health Assessment Volume X Animal Health Assessment Volume XI: Fish and Wetland Assessment Volume XII: Sociologic assessment Volume XIII: Economic Study Volume XIV: Tourism Assessment Section III PLANNING Volume I Approaches, Procedures and Methods Volume II Land Utilization Types Description and their Environmental Requirements Setting Volume III Planning Units Description Volume IV Land Suitability Evaluation Volume V Land Use Plan Volume VI Management plan Volume VII Implementation Guideline SECTION IV ANNEXES Maps albums and data base

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Executive Summary

Lake Tana and its basin are valuable for many people, including the communities who live in the basin, around the lakeshore, those living on islands and close to the Blue Nile River. The area has been identified as a region for irrigation and hydropower development, which are vital for food security and economic growth in Ethiopia. This report presents findings from an integrated multidisciplinary study that was conducted to investigate the implications of water resource development. The study comprised three components: i) Water resource development in the sub-basin which include irrigation and water supply development and their problems; ii) Assessment of water demand and use on water supply, irrigation, industrial, hydropower and others; iii) Assessment of drinking water quality and sanitation of the sub-basin; iv) Hydrological water balance of the sub-basin; v) Surface and groundwater potential of Tana Sub-Basin; and vi) Land Utilization Types (LUTs) for irrigation The study found that existing water resources development of Tana Sub-Basin holds the great portion of the resource found in Blue Nile Basin. Existing information indicates that from 1,001,000 ha irrigable land of Blue Nile Basin (WAPCOS, 1995), Tana Sub-Basin holds 188,637.39 ha (Based on ADSWE assessment) which is 19% from the total. Irrigation practices like Modern River and spring diversion, traditional river and spring diversion, pump irrigation and irrigation with fetching are common in the basin. For these irrigation practices 85% of the water sources are rivers. This study also identifies the water supply schemes development. There are totally 105 (93 functional and 12 non- functional) urban water supply schemes and 4818 (3497 functional and 1321 non-functional) rural water supply schemes inventoried. From the functional urban water supply schemes boreholes are 68, hand dug wells 10, shallow well 1, springs 13 and surface water 1. The rural water supply sources include 3738 (77.6%) Hand Dug Wells (HDW), 746 (15.5%) springs, 197(4.1%) pipe supply from the nearest towns and others 137 (2.8%) surface water (river, pond and lake). Wells and springs make 93.1 % of the total number of schemes. This figure indicates that drinking water supply for human use in Tana Sub-Basin is ground water. According to ADSWE rural water supply inventory actual coverage of safe drinking water of the sub-basin is 48.5% by considering developed hand dug wells and springs only. When undeveloped hand dug wells and springs are included in the analysis the coverage become 82.4%. Whereas Safe drinking water coverage with access of the sub-basin by considering developed hand dug wells and springs only is 46.7%. When undeveloped hand dug wells and springs are included

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Tana Sub-Basin Land Use Planning and Environmental Study Project in the analysis the coverage become 80.6%. The existing water resource development problems were also identified during the study. These are lack of land and water management practices, disturbance of river morphology and change of river mouth and Lake Boundary due to siltation and sedimentation.

Related to water demand and use of the sub-basin, the study identifies the main water user sectors and their annual water demand. The first and the large user of water of the Sub-basin is hydropower. In 2012/2013 the water used for Tana Beles hydropower generation was 2.58 BCM. The second water consumer is irrigation. For the existing practices of modern and traditional irrigation 1,760.904 MCM (1.8 BCM) of water is required. Futures, under construction and under medium and large scale irrigation practices are highly demanding sectors. 1,442 MCM, 72569 MCM and 61.56 MCM of water required for planned, under construction and under irrigation practices respectively. Under urban water demand 9.601, 2.125 and 0.582 MCM of water required for public, both public & commercial and industrial consumption respectively. Under rural water demand 12.867 and 26.5 MCM of water required for human and livestock respectively. Navigation, tourism, fisheries and floriculture are other users of water.

Drinking water quality and sanitation is the other part which this study addresses. One microbiological, two chemical and seven aesthetic parameters were assessed on 131 samples of urban and rural water supplies of the sub-basin. These parameters are total coliform from micro-biological; nitrate and floured from chemical; iron, manganese, turbidity, Total Dissolved Solids (TDS), salinity and pH from aesthetic parameters. For all the parameters all the samples are complied 98-100% with the national and WHO Guide line except turbidity, nitrate and total coliform which are 69.5%, 84 % and 31.3% complied respectively. This may be due to sanitation problems and turbid nature of rural water supply schemes especially hand dug wells.

Careful management of water in all aspects of practices like irrigation, water supply and other sectors play a significant role for beneficiaries for a sustainable use. To keep the basin water potential, the future land and water management practices should be implemented throughout the sub-basin.

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Table of Contents

LIST OF REPORT ------III

EXECUTIVE SUMMARY ------IV

LIST OF FIGURES ------IX

LIST OF TABLES ------XII

ABBREVIATIONS AND ACRONYMS ------XV

1. INTRODUCTION ------1

1.1. Background ------1

1.2. Water Resource and Irrigation Development ------2

1.3. Water Demand and Use ------3

1.4. Water Quality and Sanitation ------4 1.4.1. Drinking-Water Quality and Health------4 1.4.2. Drinking Water Sources and their Degree of Health Risk ------5 1.4.3. WHO‘s framework for safe drinking-water ------6 1.4.4. Rapid Assessments of Drinking-Water Quality ------7

1.5. Basin Water Balance------8

1.6. General Objectives of the Study ------9

1.7. Scope of the Study ------9

2. LITERATURE REVIEW ------10

2.1. Water Resource and Irrigation Development ------10 2.1.1. Water Resource and Irrigation Development in Ethiopia ------10 2.1.2. Irrigation and Development in Abbay River Basin ------12 2.1.3. Medium and Large Scale Irrigation Potential of Tana Sub-Basin ------14 2.1.4. Modern Small Scale Irrigation (SSI) Practices and Potential in Tana Sub-Basin ------15 2.1.5. Hydropower ------17 2.1.6. Existing Water Resource Development Problems ------18

2.2. Drinking Water Access, Quality and Sanitation in Ethiopia and Amhara Region ------18 2.2.1. Water Access and Safety ------18 2.2.2. Household Sanitation Facilities ------20

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2.3. Water Balance of Lake Tana Sub-Basin ------21 2.3.1. Lake and Basin Water Balance ------21

3. MATERIALS AND METHODS ------24

3.1. Description of the Study Area ------24

3.2. Materials used ------27

3.3. Data Collection and Description ------27 3.3.1. Primary Data ------27 3.3.2. Secondary Data ------28 3.3.3. Base Map Preparation ------28 3.3.4. Preparation of Data Collection Formats ------32 3.3.5. Data quality ------32

3.4. Methods of Data Analysis ------33 3.4.1. Water Resource and Irrigation Development ------33 3.4.2. Water Demand and Use ------33 3.4.3. Water Quality and Sanitation ------40 3.4.4. Hydrological Modeling and Water Balance ------53 3.4.5. Siltation and Sedimentation ------73 3.4.6. Surface and Ground Water Potential for Irrigation ------73

4. RESULT AND DISCUSSION ------78

4.4. Water Resource and Irrigation Development in Tana Sub-Basin------78 4.4.4. Irrigation and Development ------78 4.4.5. Water Supply and Distribution ------91 4.4.6. Existing Water Resource Development Problems ------110

4.5. Water Demand and Use of Tana Sub-Basin ------119 4.5.4. Urban Water Supply and Demand ------119 4.5.5. Rural Water Supply and Demand ------122 4.5.6. Agricultural Water Demand and Use ------127 4.5.7. Industrial Water Demand and Use ------130 4.5.8. Hydropower Water Demand and Use ------131 4.5.9. Other Water Demand and Use ------132

4.6. Water Quality and Sanitation of Tana Sub-Basin ------140 4.6.4. Sample Characteristics ------140 4.6.5. Microbiological parameters ------143 4.6.6. Chemical parameters ------147 4.6.7. Aesthetic parameters ------152

4.7. Hydrological Modeling and Water Balance ------162 4.7.1. Sensitivity Analysis ------162 4.7.2. Model Calibration ------163 4.7.3. Model Validation ------168 4.7.4. Baseflow contribution ------170 4.7.5. Water balance analysis ------172

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4.8. Siltation and Sedimentation ------179

4.9. Surface and Ground Water Potential for Irrigation ------182 4.9.4. Surface Water Potential for Irrigation ------182 4.9.5. Ground Water Potential for Irrigation ------184

4.10. Land Utilization Types (LUTs) of Irrigation ------189 4.10.4. Surface irrigation: ------189 4.10.5. Sprinkler Irrigation: ------190 4.10.6. Drip Irrigation ------190 4.10.7. Land Use Requirements (LURs) ------191

5. CONCLUSION AND RECOMMENDATION ------192

5.1. Conclusion ------192 5.1.1. Water Resource and Irrigation Development ------192 5.1.2. Water Demand and Use ------192 5.1.3. Water Quality and Sanitation ------193 5.1.4. Hydrological Modeling and Water Balance ------194

5.2. Recommendation ------195 5.2.1. Water Resource and Irrigation Development ------195 5.2.2. Water Demand and Use ------195 5.2.3. Water Quality and Sanitation ------196 5.2.4. Hydrological Modeling and Water Balance ------196

REFERENCES ------198

APPENDICES ------202

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List of Figures

Figure 1: Location of Small Scale Irrigation Projects in Tana Sub-Basin, 2011 ...... 16 Figure 2: Gauged catchment of Lake Tana flows (Shown in blue) and rainfall isohyets (Kebede et al., 2011)...... 22 Figure 3: Wetlands around Lake Tana, (Aragie, 2005) ...... 24 Figure 4: Location of the study area ...... 26 Figure 5: Base map that used for physical observation ...... 30 Figure 6: Base map that used for drinking water quality assessment ...... 31 Figure 7: Paqualab® 50 used for microbiological testing ...... 47 Figure 8: Maj-meter used for physical tests ...... 49 Figure 9: Sample exchange with laboratory for chemical testing (ADSWE Laboratory) ...... 50 Figure 10: Schematic representation of the hydrologic cycle (Source: (Arnold et al., 2005)) 57 Figure 11: Hydro-meteorological station locations ...... 59 Figure 12: Elevation distribution of Lake Tana Sub-Basin ...... 61 Figure 13: Land use map of Lake Tana Basin used for SWAT ...... 63 Figure 14: Soil map of Lake Tana Basin used for SWAT ...... 66 Figure 15: Lake Tana Sub-Basin major watersheds and their SWAT sub-basins ...... 69 Figure 16: THORNTHWAITE-TYPE MONTHLY WATER-BALANCE MODEL ...... 76 Figure 17: Overview of the methodology for groundwater potential assessment using integrated remote sensing and GIS techniques (Source: (Gumma and Pavelic, 2012)) ...... 77 Figure 18: Future irrigation potential of Tana Sub-Basin (ENTRO, 2013) ...... 79 Figure 19: Percentage of existing irrigation potential of Tana Sub-Basin ...... 80 Figure 20: Percentage of existing irrigation beneficiaries of Tana Sub-Basin ...... 82 Figure 21: Command locations of existing modern irrigation schemes ...... 86 Figure 22: Command locations of existing traditional irrigation schemes using water sources of springs (A), rivers (B), dug well (C) and swamp (D) ...... 87 Figure 23: Existing and future dams of Lake Tana Sub-Bain ...... 89 Figure 24: Observed dams of Tana Sub-Basin (Courtesy: ADSWE 2014) ...... 91 Figure 25: Sample bore hole (deep well) locations of urban water supply ...... 93 Figure 26: Spatial distribution of hand dug wells in the sub-basin ...... 99 Figure 27: Spatial distribution of springs in the sub-basin ...... 100

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Figure 28: Spatial distribution of pipe supply sources in the sub-basin ...... 101 Figure 29: Spatial distribution of river sources in the sub-basin ...... 102 Figure 30: Spatial distribution of pond sources in the sub-basin ...... 103 Figure 31: Spatial distribution of Lake Sources in the sub-basin ...... 104 Figure 32: Observed points of soil and water use ...... 113 Figure 33: Land and water management problems: Steep slope areas used for agriculture in the upstream (A and B) and improper water utilization on irrigation practices (C and D) (Courtesy: ADSWE 2014) ...... 114 Figure 34: Observed points of river morphology ...... 116 Figure 35: Observed problems of river morphology: Downstream sediment deposition at Megech river (A), upstream fluvial deposition (B), embankment erosion due to riverine deforestation (C) and riverine deforestation (D) (Courtesy: ADSWE 2014) ...... 117 Figure 36: Observed points of river mouth and lake boundary ...... 118 Figure 37: River mouth and Lake Tana boundary: Siltation, sedimentation and new land formation (A, B, C and D) (Courtesy: ADSWE 2014) ...... 119 Figure 38: Water supply connection type and demand ...... 120 Figure 39: Rural and urban water demand of Tana Sub-Basin ...... 124 Figure 40: Areal coverage of agro ecological zones of Tana Sub-Basin ...... 129 Figure 41: Annual water use and power generated (1999 – 2005) EFY [Note: the 2005 generated power is only for 8 months (July to February)] ...... 132 Figure 42: Port locations of Lake Tana transport ...... 134 Figure 43: Tourist attraction in Lake Tana and its surrounding ...... 136 Figure 44: Successfully assessed water quality sample points in Lake Tana Basin ...... 141 Figure 45: Total coliforms results in percent related to scheme type ...... 145 Figure 46: Total coliforms results in percent related to protection type ...... 145 Figure 47: Total coliforms results in percent related to sanitation ...... 146 Figure 48: Spatial distribution of nitrate in Tana Sub-Basin ...... 149 Figure 49: Spatial distribution of fluoride in Tana Sub-Basin ...... 151 Figure 50: Spatial distribution of iron in Tana Sub-Basin ...... 153 Figure 51: Spatial distribution of manganese in Tana Sub-Basin ...... 155 Figure 52: Spatial distribution of Turbidity in Tana Sub-Basin ...... 157

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Figure 53: Spatial distribution of conductivity (A), TDS (B), salinity (C) and pH (D) in Tana Sub-Basin ...... 161 Figure 54: Scatter plot of simulated versus observed daily flow for calibration period (a) Gilgel Abay, (b) Gumara, (c) Ribb and (d) Megech ...... 166 Figure 55: Observed and Simulated daily streamflow in comparison with areal rainfall for Upper Gilgel Abay (a), Gumara (b), Ribb (c) and Megech (d) watersheds on the calibration period (1996 to 2004) ...... 167 Figure 56: Scatter plot of simulated versus observed daily flow for validation period (a) Gilgel Abay, (b) Gumara, (c) Ribb and (d) Megech ...... 168 Figure 57: Observed and Simulated daily streamflow in comparison with areal rainfall for Upper Gilgel Abay (a), Gumara (b), Ribb (c) and Megech (d) watersheds on the validation period (2005 to 2008) ...... 169 Figure 58: Observed and Simulated daily base flow for Upper Gilgel Abay (a), Gumara (b), Ribb (c) and Megech (d) watersheds in all simulation years (1996 to 2008) ...... 171 Figure 59: Spatial distribution of rainfall station used to estimate lake areal rainfall ...... 172 Figure 60: Long-term monthly average rainfall distribution in the Lake Tana Basin (1994-2008) 173 Figure 61: Long-term monthly average flow of the major gauged rivers in the Lake Tana (1990-2007) ...... 174 Figure 62: Outflow and lake levels of Lake Tana (1996-2008) ...... 175 Figure 63: Simulated verses observed lake level of Lake Tana (1996 to 2008) ...... 177 Figure 64: Trend of Lake Tana storage from 1996 to 2008 ...... 179 Figure 65: Rating curves and their equations for the four major inflow rivers of Lake Tina 180 Figure 66: Rating curve and its equation for Abbay (Blue Nile) as an outflow river of Lake Tina 180 Figure 67: Sediment transport load of the five major rivers ...... 182 Figure 68: Mean annual surface runoff of Tana Sub-Basin ...... 183 Figure 69: Areal coverage of geomorphology, geology, drainage density and slope with their score ...... 187 Figure 70: Areal coverage of rainfall, land use, soil and ground water potential of the basin188

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List of Tables

Table 1: Ethiopian surface water resources by major river basins (Birhane, 2002) ...... 11 Table 2: WRDA's Estimate of Irrigation Potential (1986) ...... 12 Table 3: Irrigation Potential in the River Basins of Ethiopia ...... 13 Table 4: Summary Planned Medium and Large Scale Irrigation potential...... 14 Table 5: Medium and large scale irrigation under construction ...... 14 Table 6 : Current irrigated and design command area of irrigation projects in ACZ ...... 17 Table 7: Primary data types and their source ...... 27 Table 8: Secondary data types and their source ...... 28 Table 9: The category of urban centers (BoWRD, 2004) ...... 34 Table 10: Type of sources of water supply and their estimated yields (BoWRD, 2004)...... 35 Table 11 : Urban centers category type and connection type ( Users percentage and l/c/d) ...... 35 Table 12 : Public, commercial and industrial water demand share (BoWRD, 2002) ...... 36 Table 13: The type of water sources and estimated production (BoWRD, 2004) ...... 37 Table 14: Summary Planned Medium and Large Scale Irrigation potential ...... 39 Table 15: Medium and large scale irrigation under construction ...... 39 Table 16: Indicators/parameters for basic/initial RADWQ ...... 41 Table 17: Samples size per Wereda per for rural hand dug wells, springs and shallow wells ...... 43 Table 18: Samples per administrative town for deep well ...... 43 Table 19: Samples per town for deep well ...... 44 Table 20: National and WHO guideline values for the selected physical, chemical and bacteriological parameters (WHO, 2012, Tadesse et al., 2010) ...... 52 Table 21: Land cover as per ADSWE and their corresponding area and SWAT definition...... 64 Table 22: Used soil units, area contribution and their SWAT code ...... 67 Table 23: Major watershed areas, number of sub-basins and HRUs ...... 68 Table 24: Ground water potential indicative factor weights ...... 77 Table 25: Existing irrigation potential of Tana Sub-Basin ...... 81 Table 26: Existing irrigation beneficiaries of Tana Sub-Basin ...... 82 Table 27: Existing irrigation potential at Woreda level with each scheme types ...... 84 Table 28: Contributions of water sources for existing modern irrigation ...... 85 Table 29: Locations of existing and future dams of Tana Sub-Basin ...... 88 Table 30: Population and water resource schemes in Lake Tana Basin area enclosure ...... 93 Table 31: Summary inventory of water supply schemes ...... 95 Table 32: Rural water scheme types with respect to their status and availability of committee ...... 96 Table 33: Use type of rural water supply schemes ...... 97

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Table 34: Rural water supply schemes of Tana Sub-Basin at Woreda level (Source: ADSWE inventory, 2014) .98 Table 35: Major source of potable water in Tana Sub-Basin ...... 106 Table 36: Access to potable water for consumption in Tana Sub-Basin ...... 106 Table 37: Major source of potable water in Tana Sub-Basin Woredas (Source: ADSWE inventory, 2014) ...... 107 Table 38: Classification of channels and their characteristics ...... 115 Table 39 : Type of sources and estimated annual production ...... 120 Table 40: Summarized water supply connection type and demand ...... 121 Table 41 : Public, commercial and industrial water demand share ...... 121 Table 42: Water production and consumption of sample urban centers ...... 122 Table 43: The type of water sources and estimated yield (ADSWE, 2014) ...... 123 Table 44: The monthly water production of the schemes of ADSWE 2014 water inventory ...... 123 Table 45 : Rural water demand at (ADSWE, 2014) ...... 123 Table 46: Rural water demand based on Woreda (ADSWE, 2014) ...... 124 Table 47: Livestock population and water demand in Tana Sub-Basin (ADSWE, 2014) ...... 126 Table 48: Summary Planned Medium and Large Scale Irrigation monthly water demand ...... 127 Table 49: Medium and large scale irrigation under construction ...... 128 Table 50 : Gross Monthly water demand of Koga irrigation project ...... 128 Table 51 : Annual Irrigation water demand for modern and traditional irrigation practices (ADSWE, 2014) ....130 Table 52: The annual water use of the main water user industries in Tana Sub-Basin ...... 130 Table 53: Water used in BCM for Generating hydropower (1999-2005) EFY ...... 131 Table 54: Hydropower Generated, GWH (1999-2005) EFY ...... 131 Table 55: Scheme types and their sampled numbers ...... 142 Table 56: Water quality sample with scheme and protection type ...... 142 Table 57: Result of total coliform test of Tana Sub-Basin ...... 144 Table 58: Total coliforms results related to scheme type ...... 145 Table 59: Total coliform results related to protection type ...... 145 Table 60: Total coliform results related to sanitation ...... 147 Table 61: Physicochemical parameters included in this RADWQ study ...... 148 Table 62: Compliance with national standard value for nitrate ...... 150 Table 63: Compliance with national standard and WHO guideline value for fluoride ...... 152 Table 64: Compliance with national standard and WHO guideline value for Iron ...... 154 Table 65: Compliance with national standard value for Manganese ...... 156 Table 66: Compliance with the national standard and WHO ―suggested‖ value for turbidity ...... 158 Table 67: Compliance with the national standard and WHO ―suggested‖ value for conductivity, TDS and Salinity 159 Table 68: Compliance with the national standard and WHO ―suggested‖ value for pH ...... 162 Table 69: Flow sensitive parameters for Gilgel Abbay, Gumara, Ribb and Megech River Catchment ...... 162 Table 70: Calibrated and fitted values of sensitive parameters ...... 164

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Table 71: Calibrated model simulation performance ...... 165 Table 72: Baseflow and surface runoff contribution for total streamflow ...... 170 Table 73: Weights of rainfall station on the lake areal rainfall...... 172 Table 74: Mean annual water balance of Lake Tana (1996-2008) ...... 178 Table 75: Gauged catchment and their downstream ungauged catchments contribution ...... 179 Table 76: Sediment rating curves of five major rivers ...... 181 Table 77: Mean annual sediment transport load of five major rivers at the lake confluence (1996-2008) ...... 181 Table 77: Validation result of mean annual surface runoff ...... 184 Table 78: Define scores for individual features of the seven themes for groundwater potential zones ...... 184 Table 79: A general comparison of surface irrigation methods ...... 190 Table 80: Summary of Water Demand and Consumption in Tana Sub-Basin, 2014 ...... 193 Table 81: Summary of overall compliance with WHO guideline values ...... 194 Table ‎10-13: Lake Tana water balance components simulated from 1996 to 2008 ...... 195

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Abbreviations and Acronyms ABA - Abay Basin Authority ACZ - Agro Climatic Zone ADF - African Development Fund ADSWE - Amhara Design and Supervision Works Enterprise BCM - Billion Cubic Meters BoFED - Bureau of Finance and Economic Development BoWRD - Bureau of Water Resource Development EDHS - Ethiopia Demographic and Health Survey EEPCO - Ethiopian Electric Power Corporation EFY - Ethiopian Fiscal Year ENTRO - Eastern Nile Technical Regional Office EPLAU - Environmental Protection, Land Administration and Use EVDSA - Ethiopian Valleys Development Studies Authority FAO - Food and Agricultural Organization GIS - Geo-Information Science GIWR - Gross Irrigation Water Requirement GWH - Giga Watt Hour IDDP - Irrigation and Drainage Design Process IFAD - International Fund for Agricultural Development MCM - Million Cubic Meters MoA - Ministry of Agriculture ONCCP - Office of the National Committee for Central Planning RADWQ - Rapid Assessment of Drinking Water Quality SMEC - Snowy Mountains Engineering Corporation SSI - Small Scale Irrigation TLU - Tropical Livestock Unit UNICEF - United Nations International Children's Emergency Fund USBR - United States Bureau of Reclamation USBR - United States Bureau of Reclamation WAPCOS - Water & Power Consultancy Services (I) Ltd WHO - World Health Organization WRDA - Water Resources Development Authority

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1. INTRODUCTION

1.1. Background

Water is a mobile resource: it falls from the clouds, seeps into the soil, flows through aquifers, runs along stream courses, and eventually returns to the clouds. This natural cycle is the basis of all life forms and of the economy of nature. Water may be "managed" in different ways: it may be harvested, extracted from the ground, diverted, transported, and stored. This makes it different from all other natural resources. However, each form of management that interferes with the natural cycle exacts a price, not just in economic terms but in terms of environmental damage and greater health hazards. Moreover, water does not occur alone, it is rather part of a complex ecosystem consisting of the land, plants, aquatic and other life forms. The improper and unregulated use of water by humans will not only damage the water source but the ecosystem as well. Thus investment projects designed to enable users to have secure access to water will have to be examined from the standpoint of cost and economic benefit as well as in terms of their long-term impact on the environment. To be sustainable, water management schemes should respect the natural "logic" of water systems, and the ecology of which water is an important element.

Water exists in different forms, each of which may have multiple uses. There is surface water which appears to be stationery as in lakes and ponds, running water in the form of rivers and streams, and ground water in aquifers or mixed with the soil. But each form of water does not exist alone or independently of the others; on the contrary, they are all inter-connected through a complex natural process. A water system or water regime denotes the inter- connection among the different forms in a given geographical location. Individuals may make use of one water source or another (or a combination), depending on the nature of their livelihoods and their proximity to the sources of water.

Water is a common property resource and is critical for sustainable livelihoods (Rahmato, 1999). To begin with, all households need water for domestic use, i.e. for drinking, food preparation, washing, cleaning, etc. Access to adequate, clean water will greatly contribute to improved health and better productivity. Secondly, there are distinct population groups whose livelihoods are water-based, entirely or to a considerable extent; such groups include

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Tana Sub-Basin Land Use Planning and Environmental Study Project fishermen, and artisans such as tanners and potters. Thirdly, water resources can play a significant role in improving food security and household income. Irrigation is the most common means of ensuring sustainable agriculture and coping with periods of inadequate rainfall and drought. Fourthly, water is employed to generate power for use in industry, services, and by urban households. In Ethiopia (as well as many countries in Africa), power generation is a monopoly of the state. Finally, in the developed countries, water is an important asset for the leisure industry. The extent to which water resources will contribute to sustainable livelihoods will depend on availability, the nature of rights of access, the system of management and the technology with which the resources are exploited. Moreover, the specific relationship between livelihoods and water resources will determine the nature of the stakeholders and their interest in the resources.

This study mainly focused on the assessment water resource development, water demand and use, water quality and sanitation, hydrological water balance of the sub-basin, surface and groundwater potential and selected Land Utilization Types (LUTs) of irrigation of Tana Sub- Basin. There also discussed current and future critical problems of water resource development of the Sub-basin.

1.2. Water Resource and Irrigation Development

The development of water resources is essential for a wide range of human activities. In particular it is needed so that demands for energy and food can be met. However, certain adverse effects of water resources development have received considerable attention. The rate of population growth in developing countries continues to outstrip the capacity to meet the demands for food and basic services amidst increasing poverty.

Decisions on water resources management have to be made based on accurate information. However, water resources development is often carried out without properly knowing the amount and spatial and temporal distribution of the available water in most of the developing countries including Ethiopia thus causing only not improper planning and inefficient and ineffective use but also causes conflict and negative environmental effects. Ethiopia has planned and is implementing water resources development projects to avail adequate and good quality water for domestic and irrigation uses. This ambitious plan should be supported

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Tana Sub-Basin Land Use Planning and Environmental Study Project by appropriate decisions which in turn require accurate information on available water and its use.

The construction of dams and formation of reservoirs and irrigation systems can cause rapid environmental degradation and health risks may arise even before there is any awareness of the danger and before. Preparations have been made to overcome it. Water resources development does not occur in isolation. The construction of a dam creates changes in both the upstream and downstream areas (Limbe, 1998).

Under this study of water resource development for Tana Sub-Basin irrigation and drainage, water supply and distribution and existing problems associated to water resource development will be discussed and presented as an assessment using past study documents, field work data and consultation.

1.3. Water Demand and Use

Water is affecting human life in many ways-from health and sanitation to food security, from livelihood to leisure and environment, and in so many other contexts. Water demand has been growing to satisfy the need of ever-increasing population worldwide more than twice the rate of population increase in the last century. With growing population and limited water resources, there is an increasing water need worldwide to manage water resources properly and to use it efficiently and effectively.

The knowledge on water demand and supply of catchments is very crucial for managing the water resource of catchments. In the past water resource planning has been supply driven. However, as the demand for water is increasing at an alarming rate and the resource is becoming scarce the water demand and use should be managed efficiently. For managing the water resources effectively and efficiently the current and future demands and uses of water for all sectors should be estimated as accurately as possible.

Water use can be subdivided in to two: off stream use and in stream use. Off stream use is a water use that depends on water being diverted or withdrawn from surface or groundwater source and conveyed to the place of use. Off stream uses include municipal water supply, domestic, commercial, irrigation, livestock, industrial use. In stream use is a water use which

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Assessment of water demand and use in this study aims at estimating the current and previous water demands and use condition of the main water use sectors: Agriculture, urban, rural, industry, hydropower, navigation, tourism and fisheries. The spatial and temporal variation of the water demands and use has been assessed as far as data is available. The assessment will be used as a base line for forecasting the future water demand of the basin under different development scenarios so that it will be used to visualize the balance with the water supply so as to analyze the future reliability of the water resources of the sub-basin and to undertake different water demand management measures.

1.4. Water Quality and Sanitation

There is a direct link between improving access to potable water and sanitation. Communities with safe drinking water are in a better position to maintain hygienic environment. It is a not difficult to understand that improved personal hygiene, development of appropriate sanitation practices, reduction in the prevalence of water related diseases, and overall enhancement of livelihood of the public are achievable when there is safe drinking water. In conjunction with development of sustainable water supply systems, appropriate technology and low cost sanitation projects could be implemented. One of the added benefits of low-cost and improved solid waste disposal systems is their suitability as alternative energy sources, thus minimizing the stress on the already scarce forest resources.

1.4.1. Drinking-Water Quality and Health

Water has a profound effect on human health both as a means to reduce disease and as a medium through which disease-causing agents may be transmitted. The impact of water on health derives principally from the consumption of water containing pathogenic organisms or

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Some water sources may be considered unsuitable by individuals or communities on the basis of personal or local preferences. The taste, odor and appearance of water must normally all be considered good for water to be acceptable for local consumption. Perceptions about water quality, based on visual examination, taste and odor, are often unreliable. Waters that look or smell unpleasant may be safe to drink, and clear odorless waters may contain chemicals or microbial contaminants that are harmful to human health. Objective techniques for the assessment of water quality are therefore necessary. These may be performed using widely available analytical techniques and supported by a range of risk assessment tools (WHO, 2012).

1.4.2. Drinking Water Sources and their Degree of Health Risk

Surface water These are surface derived water sources and include rivers, impounded reservoirs, lakes, streams and others. Surface water is most vulnerable to pollution caused by untreated sewage, industrial wastes, agricultural run-off, vegetation etc. These sources are distributed to the consumers through gravity flow technology without any treatment.

Degree of Health Risk: Owing to the limited resources available for testing, it is recommended that priority be accorded to the contaminants that pose significant threat to the health of the community. Other contaminants that confer aesthetic defects to drinking water could cause consumers to revert to unsafe traditional sources which should also be tested. It is only when there is no alternative that surface water sources should be considered, as surface water has high probability of pathogenic (coliform) micro-organisms and chemical by- products infection (WarerAid, 2011).

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Groundwater sources These are derived from aquifers occurring within pervious strata from water which normally originates from the precipitation that percolates through the soil and is confined by an impervious stratum. The water may pick up considerable amounts of dissolved mineral compounds, organic matter, soil particles and mirco-organisms. Fertilizers and pesticides may also be found in dissolved form. Filtration and absorption take place naturally and may result in the removal of bacteria, much of the suspended matter and possibly dissolved minerals as well. Ground water resources include hand dug wells, tube wells and bore holes. Hand pumps on the tube wells and hand dug wells (with or without hand pumps) are common in Ethiopia.

Degree of health risk: Ground water contamination is nearly always the result of human activity. In areas where population density is high and human use of the land is intensive, ground water is especially vulnerable. Virtually any activity whereby chemicals or wastes may be released to the environment, either intentionally or accidentally, has the potential to pollute ground water. When ground water becomes contaminated, it is difficult and expensive to clean up. Groundwater is at risk from surface contamination: pathogenic bacteria, pesticides, chemical fertilizers, nitrates, industrial and domestic pollutants are likely to be the greatest problems encountered groundwater. Shallow ground water is more vulnerable for contamination than that of spring water and deep ground water in Ethiopia.

1.4.3. WHO’s framework for safe drinking-water

The continuous delivery of safe drinking-water requires effective management and operation throughout the drinking-water supply chain from the catchment and source through to the point of consumption, within national, regional and local contexts. The WHO Guidelines for Drinking- Water Quality suggest that this is most effectively achieved by establishing a preventative management framework for safe drinking-water that encompasses the following elements (WHO, 2011):

 establishment of health-based targets, including water quality targets, for drinking- water as a ―benchmark‖ for evaluation of the adequacy of existing installations and policies;  In order to meet these targets, the development and implementation of water safety plans (WSP). WSP provide a quality management system for water suppliers at

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different scales, which is based on principles of risk assessment and risk management, and which aims at continuous management and control of water sources, treatment, distribution, handling and storage together with documented management, actions and communication plans;  Establishment of a system of independent surveillance that verifies that WSP are working effectively and that health-based targets are met.

The use of Rapid Assessment of Drinking Water Quality (RADWQ) tools, methods and procedures can contribute to the implementation of the framework principles. For example, it provides useful baseline information on drinking-water quality in a given region or country that can be utilized for national target setting or for prioritizing surveillance efforts.

1.4.4. Rapid Assessments of Drinking-Water Quality

Routine (iterative, regular and frequent) assessment of drinking-water quality is undertaken to provide data and information for many reasons, including (WHO, 2012):

 determination of the overall background and trends in quality;  monitoring compliance with regulations, guidelines, targets and/or standards;  identification of water quality or pollution problems and issues;  planning and development of remedial actions; and  Informing policy decisions on issues such as water source and technology development, revision of regulations for drinking-water quality or effluent discharge consents.

The scope of this rapid assessment of drinking-water quality:  obtain baseline information through a systematic ―snapshot‖ of drinking-water quality in Tana Sub-Basin (e.g. rural areas);  assess the prevalence of a specific parameter (e.g. fluoride or manganese);  check compliance for a particular type of water supply (e.g. point sources)

Some of the key reported benefits of rapid assessments include (WHO, 2012):  thorough compilation (from various institutions) and review of already existing data on drinking-water quality and water supply coverage in a country, as well as identification of data or information gaps;

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 creation of a regional data base on drinking-water quality and sanitary conditions which provides solid baseline information for the establishment (or stepwise improvement) of routine water quality monitoring and surveillance programmes, and/or for targeting resources and efforts in surveillance and/or remediation programmes;  capacity building in water quality monitoring and assessment, including policy formulation, strengthening institutions, facilitating networking between national, regional and local institutions and development of the human resource base; and  raised public awareness of the importance

1.5. Basin Water Balance

A catchment/watershed/basin water balance is the process by which precipitation is separated into its components. For the entire system, consisting of surface water and groundwater, precipitation P separates into four components: i) evaporation (E), ii) evapotranspiration (T), iii) surface water runoff (Q), and iv) ground water flow (G), appearing eventually as base flow. Such that

P = E + T + Q + G 1

In the field of hydrology the budget idea is widely used. Water balances are based on the principle of continuity. This can be expressed with the equation:

S I(t) Q(t)  2 t

3 3 Where, I is the inflow in [L /T], Qt is the outflow in [L /T], and ΔS/Δt is the rate of change storage over a finite time step in [L3/T] of the considered control volume in the system. The equation holds for a specific period of time and may be applied to any given system provided that the boundaries are well defined. Other names for the water balance equation are Storage Equation, Continuity Equation and Law of Conservation of Mass. Therefore Change in groundwater storage (∆Sgw) in a groundwater system can be stated as

Sgw  inf low outflow 3

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Under natural conditions ∆Sgw=0 or inflow=outflow. From the equation above Inflow- outflow=∆Sgw will be come

P  Gi  AET  Q  Go W  0 4

Where P =precipitation, Gi, groundwater inflow, AET, actual evapotransipiration, Q, discharge, and Go, groundwater out flow and W withdrawal, which is negligible in this case. Therefore the above equation reduced to

P  AET  Q  G 5 1.6. General Objectives of the Study

The following are general objectives of the study:

 To assess water resource development (Irrigation and Water Supply) of Tana Sub-Basin and its associated problems;  To assess water demand and use on water supply, irrigation, industrial, hydropower and others;  To assess drinking water quality and sanitation of the sub-basin;  To investigate hydrological water balance of the sub-basin;  To investigate surface and groundwater potential of Tana Sub-Basin for irrigation; and  To select, define and rate Land Utilization Types (LUTs) for irrigation

1.7. Scope of the Study

The scope of this study encompasses assessment of water resource development of the sub- basin, assessment of water demand and use and assessment of drinking water quality and sanitation. The question about what are the critical problems of water resource development will have an answer from this report. The assessment tries to include each and every data and information from primary and secondary data sources. The study also includes hydrological possible methods to assess the water resource development, demand & use and water quality & sanitation the on the study area. The report also includes scientific recommendation in each subject area for future sustainable use of water.

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2. LITERATURE REVIEW 2.1. Water Resource and Irrigation Development

2.1.1. Water Resource and Irrigation Development in Ethiopia

The economy of Ethiopia is highly dependent on agriculture, which is in turn dependent on the availability of seasonal rainfall. Although the country‘s renewable surface and ground freshwater amounts to 123 and 2.6 billion cubic meters per annum, respectively, its distribution in terms of area and season does not give adequate opportunity for sustainable growth to the economy (Birhane, 2002). The intensity of recurrent droughts affects the livelihoods of agricultural communities and the whole economy. Even in a year of good rain, the occurrence of floods affects the livelihoods of riparian residents with little capacity to neither protect themselves from the seasonal flood nor mitigate the impact.

Excess water is also responsible for the soil erosion in the highlands. Recent studies show that the sediment yields in different rivers range between 180 and 900 t/year per km2 (Rodeco, 2002). It is estimated that the trans-boundary Rivers alone carry about 1.3 billion tons of sediment each year to neighboring countries (MoWE, 1993). Poor watershed management and farming practices have contributed to these rates.

Sustainability of the management of water supply schemes is also a challenge for the sector. Poor co-ordination among stakeholders is aggravating the situation and constraining the economic returns on investment. Lack of research and development in the sector has hampered the contribution of the sector to the socio-economic development of the country.

Basin studies were first undertaken in the country in the 1950s, and the United States Bureau of Reclamation (USBR) conducted the Abay River basin study in 1964. Subsequently, basin level studies have been carried out in the northern basin (Tekeze, Mereb-Gash and Guang) and the Wabi Shebele River basin. Basin development studies have been carried out recently in a more comprehensive and integrated manner in five of the twelve major basins in the country, and studies in two more basins are underway. To date, the implementation of these studies has been limited (Birhane, 2002). Table 1 presents the surface water resources available against the landmass of the major basins of the country. Two of the basins, Ogaden and Ayesha, which make up 7% of the country‘s landmass and serve as home to a number of

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Table 1: Ethiopian surface water resources by major river basins (Birhane, 2002)

Specific Catchments Share out of Annual run off discharge area total No. River basin (× 109 m3) (liters/km2) (km2) 1 Abay 199,812 52.6 7.8 17.56 2 Awash 112,700 4.6 1.4 9.90 Baro- 6.51 3 Akobbo 74,100 23.6 9.7 Genale- 15.03 4 Dawa 171,050 5.88 1.2 5 Mereb 5900 0.26 3.2 0.52 6 Omo-Ghibe 78,200 17.96 6.7 6.87 7 Rift Valley 52,740 5.64 3.4 4.63 8 Tekeze 90,000 7.63 3.2 7.91 Wabi- 17.59 9 Shebele 200,214 3.16 0.5 10 Danakil 74,000 0.86 0 6.50 11 Ogaden 77,100 0 0 6.77 12 Aysha 2200 0 0 0.19 Total 1,138,016 122.19 Ground water resource potential of Ethiopia is approximately 2.6 billion cubic meters. (Source: Different master plan studies.) Until recently, the water potential of Ethiopia was not accurately known, and even today this is still a contentious area. There have been different estimates of the irrigation potential of the country, and the issue has not been satisfactorily resolved. One of the earliest estimations was made by the (World-Bank, 1973), which suggested a figure of between 1.0 and 1.5 million hectares. Recent estimates, however, place the figure somewhat higher. According to the (MOA, 1986), the total irrigable land in the country measures 2.3 million hectares. The International Fund for Agricultural Development (IFAD, 1987), on the other hand, gives a figure of 2.8 million hectares, while the Office of the National Committee for Central Planning‘s 1990 figure, which is based on WRDA's estimations, is 2.7 million hectares. The Indian engineering firm Water and Power Consulting Services‘ 3.5 mil ha is the highest estimate so far and Ethiopian Valleys Development Studies Authority (EVDSA) accepted the figure and was using it in the early 1990s. Most of these figures are derived by adding up the irrigation potential of the country's eight river basins as shown in Table 2 below. Except for

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In the 1960s and 1970s, comprehensive reconnaissance and feasibility studies were carried out on the Abbay (Blue Nile), Awash and Wabi Shebelle river basins. In 1962, a German engineering team, and in 1964, the U.S. Bureau of Reclamation undertook extensive studies of the water resource potential of the Abbay River basin, the largest basin in the country. Both reports maintained that there were high hopes for the development of irrigated agriculture in the basin. The German study (Lahmeyer-Consulting-Engineers, 1962) which was confined to the Gilgel Abbay basin, a much smaller area, suggested that the production of oil seeds, pulses and fodder crops, using the waters of the Gilgel Abbay, would be very profitable and earn high foreign exchange. The U.S. study recommended that small-scale irrigation should be greatly encouraged but that large-scale schemes would be too costly. It argued that without a coordinated water development program in the basin there would be no prospects for agricultural development in north-west Ethiopia. Table 2: WRDA's Estimate of Irrigation Potential (1986) Irrigable Land River Basin (Ha.) Abbay 760,000 Tekeze & Northern 200,000 Baro-Akobbo 600,000 Gibe-Omo 250,000 Rift Valley (Lakes) 50,000 Genale-Dawa 300,000 Wabi Shebelle 355,000 Awash 185,000 Total 2,700,000 Source: (ONCCP, 1990) (based on WRDA figures)

2.1.2. Irrigation and Development in Abbay River Basin

Abbay river basin has a catchment area of 199,812 km2, covering parts of Amhara, Oromia and Benishangul-Gumuz regional states. It has the major sub-basins of Anger, Beles, Dabus, Debre Markos, Didesa, Ayma, Shinfa, Fincha, Guder, Jemma, Lake Tana, Mota and Muger. The major river in the basin is the Blue Nile (Abbay) river, which rises in Lake Tana flowing about 1,450 km long, and merges with the White Nile to form the Nile proper. The river basin

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has a lowest elevation of 500 m. and a highest elevation of 4261 m. The total mean annual flow from the river basin is estimated to be 54.8 BCM (Awulachew et al., 2007).

The Abbay river basin is well known as the source of Nile, a land of dramatic gorges and mountains. Abbay is the most important river basin in Ethiopia. It accounts for 20 percent of Ethiopia‘s land area, for about 50 percent of its total average annual runoff which emanates from the Ethiopian highlands, for 25 percent of its population and for over 40 percent of its agricultural production. The rivers of the Abbay basin contribute on average about 62 percent of Nile at Aswan; together with the contribution of Baro-Akobbo and Tekeze rivers, Ethiopia accounts for at least 86 percent of the runoff at Aswan.

According to MoWE data, it is identified that the Abbay river basin has a potential of 211 irrigation projects, of which 90 are small-scale, 69 are medium-scale and 52 are large-scale. A total of 815,581 hectares of potential irrigable land is estimated, out of which 45,856 ha are for small-scale, 130,395 hectares for medium-scale and 639,330 hectares for large-scale development. This figure clearly showed that there is a significant increase of irrigation potential from 1986 (Table 2) to 1995 by 115,581ha. Table 3: Irrigation Potential in the River Basins of Ethiopia

Irrigation potentials (Ha) (Respective recent Irrigation Potential (WAPCOS 1995) master plan studies)

Present Catchment Small- Medium- Large- Total Irrigable Area Total Area (km2) Scale Scale Scale Drainage Irrigable of the Basin Area (km2) Area (Ha) Country (%) Abbay 198,890.70 45,856 130,395 639,330 815,581 201,346 1,001,000 27 Tekeze 83,475.94 N/A N/A 83,368 83,368 90,001 3,17,000 8.5 Baro-Akobbo 76,203.12 N/A N/A 1,019,523 1,019,523 74,102 9,85,000 26.5 Omo-Ghibe 79,000 N/A 10,028 57,900 67,928 78,213 4,45,000 12 Rift Valley 52,739 N/A 4000 45,700 139,300 52,739 1,39,000 3.7 Awash 110,439.30 30,556 24,500 79,065 134,121 112,697 2,05,000 5.5 Genale Dawa 172,133 1,805 28,415 1,044,500 1,074,720 117,042 4,23,000 11.4 Wabi Shebele 202,219.50 10,755 55,950 171,200 237,905 102,697 200,000 5.4 Denakil 63,852.97 2,309 45,656 110,811 158,776 74,102 Ogaden 77,121 77,121 Ayisha (Gulf of Aden) 2,000 2,000 Total 1,118,074.53 3,731,222 982,060 3,715,000 100 Source: (Awulachew et al., 2007)

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2.1.3. Medium and Large Scale Irrigation Potential of Tana Sub-Basin

According to the Abay River Basin Integrated Master Plan (ARBIMP) (MoWE, 1998) in Lake Tana basin around 123,223 ha gross irrigable land is planned to be developed with large and medium scale irrigation on the major tributaries by constructing dams and using pumps directly from Lake Tana. Among the planned projects; the Koga dam is the first implemented to irrigate about 7,000 ha of land. The construction of Rib dam, Megech dam and pump irrigation projects are underway and other projects are under feasibility, prefeasibility and identification phase.

2.1.3.1. Planned Medium and Large Scale Irrigation of Tana Sub-Basin

The initial identification of medium and large scale irrigation in the sub-basin was carried out during Abbay river study in1954 by USBR. The Abby master plan study is the second study which is the recent study and used as baseline for most water resources assessment. Based on master plan study outcome on some the sites go through further feasibility study development study at project.

Table 4: Summary Planned Medium and Large Scale Irrigation potential Type of Water Supply Lake Tana and Dams Gross Area under Irrigation (Ha) 180,423 Net Area under Irrigation (Ha) 153,364

2.1.3.2. Medium and Large Scale Irrigation Under Construction

As mentioned above some the medium and large scale which have been undergo feasibility are under construction. Rib dam, Megech dam and Megech pump irrigation projects are under construction (MoWE, 2009a, MoWE, 2009b, MoWE, 2010b, MoWE, 2010a) . The amount of irrigable land under construction is shown on Table 5. Table 5: Medium and large scale irrigation under construction Schemes ID MEG 1 MEG 2 MEG 6 RIB 1 RIB 2 RIB 3 RIB 4 sum Gross Area under 5254 6532 4000 7650 3060 9360 3370 39226 Irrigation (Ha) Net Area under Irrigation 4466 5552 3400 6503 2601 7956 2865 33343 (Ha) Total sum 72569

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2.1.3.3. Medium and Large Scale Irrigation Under Irrigation Practicing

Currently Koga irrigation is the only medium and large scale irrigation project which started irrigation practicing. Koga irrigation project has a potential to irrigate 7000 ha.

2.1.4. Modern Small Scale Irrigation (SSI) Practices and Potential in Tana Sub- Basin The Amhara National Regional State Water Resource Development Bureau is responsible for the study, design and construction of small scale irrigation (SSI) projects. The bureau has made SSI schemes inventory and geo mapping by IDDP (BoWRD, 2011) at the regional level. Totally 42 modern small scale irrigation projects are identified in the sub-basin in 2011. Out of these schemes 32 (76%) are diversion weirs, 6 (14%) are intakes and the rest 4 (10%) are micro earth dams. In addition the use of irrigation pumps is increasing drastically though for this assessment we have not incorporated it. The schemes irrigate from 12 to 250 ha of land. Most of the schemes are located on the upper head waters of the sub-basin especially on the east and south direction as shown on Figure 1.

In some Woredas the use of motor pumps by small holder farmers has reached to 800 in number in one Woreda (from personal discussion with Woreda irrigation experts). The distribution of these motor pumps for irrigation is not supported with the water allocation and management sector. Therefore, the issue of water allocation and management is becoming very important in the sub-basin.

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Figure 1: Location of Small Scale Irrigation Projects in Tana Sub-Basin, 2011

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From the assessment it is determined that during the inventory (2010) of IDDP 2559 ha of land in Tana Sub-Basin is irrigated with the SSI modern irrigation projects. The design command area of the projects was 2712 ha in the basin. The irrigated lands of the modern small scale irrigation projects lie in the three traditional agro climatic zones with area proportion of Dega (46 %), Woinadega (40 %) and Kolla (13 %).

Table 6 : Current irrigated and design command area of irrigation projects in ACZ

Agro Climatic Current irrigated, % area of Designed Zone ha irrigated command area, ha Dega 1189 46 1046 Woinadega 1025 40 1204 Kolla 345 13 462 Total 2559 2712 Source: Amahara Water Resource Development Bureau, 2011

2.1.5. Hydropower

The sub-basin is one of the potential areas identified for generating hydropower electricity. The first hydroelectric Power project which uses the water resource of the sub-basin is Tis Abbay I which was constructed and installed in 1964. After the construction of the Chara- Chara weir in 1995 to regulate the flow of Abbay at the outlet of Lake Tana the second hydropower project: Tis Abbay II was installed. The power generating capacity of the two projects is 84.4 MW.

Currently, the Tana Belese Hydropower project is generating hydropower at its full capacity following the completion of its construction. This scheme involves the transfer of water from Lake Tana to the Beles River via a 12 km long, 7.1 m diameter tunnel (Salini and Mid-day 2006) to generate 460 MW. The aim of the inter-basin transfer is to generate hydropower by exploiting the 311 m elevation difference between the lake and the Beles River. According to the report of SMEC 2,985 MCM of water will be diverted through the tunnel each year to generate 2,310 GWh of electricity (SMEC, 2008). Both the Tis Abbay power stations will be moth-balled and only used in emergencies.

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2.1.6. Existing Water Resource Development Problems

In most parts of Tana Sub-Basin, there are clearly observed water resource development problems. The traditional development of the water sector is highly characterized by most, if not all, of the following (Birhane, 2002):  Lack of sustainable and reliable water resources management strategy  Lack of efficient utilization of water resources  Non-objective-oriented programmes and projects  Uncertainties and ambiguities in planning  Prevalence of intensive centralism of management that does not focus on rural development  Lack of institutional sustainability  Lack of operation and maintenance activities of water schemes  Ad hoc development practices lacking coherent objectives and continuity  Highly subsidized and considered as any other social good  Lack of stakeholder participation and belongingness to the development of the sector  Relatively low attraction of the private sector  Low investment on research and development and  Trans-boundary nature of the rivers and the conflict that comes along.

2.2. Drinking Water Access, Quality and Sanitation in Ethiopia and Amhara Region

2.2.1. Water Access and Safety

In 2011 Ethiopia Demographic and Health Survey (EDHS) more than half of the households in Ethiopia (54 percent) have access to an improved source of drinking water, with a much higher proportion among urban households (95 percent) than among rural households (42 percent). The most common source of improved drinking water in urban households is piped water, used by 87 percent of households. In contrast, only 19 percent of rural households have access to piped water. Eleven percent of rural households have access to drinking water from a protected spring, and 8 percent have access to drinking water from a protected well.

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Nationally, the proportion of Ethiopian households with access to piped water has increased from 18 percent in 2000 to 24 percent in 2005 and 34 percent in 2011. In the last six years there has been a rapid increase in the percentage of households in Ethiopia that use some type of improved source of drinking water, from 35 percent in the 2005 EDHS to 54 percent in the 2011 EDHS.

In the 2011 EDHS only 13 percent of households reported having water on their premises. Households not having water on their premises were asked how long it takes to fetch water. Thirty five percent of all households (30 percent in urban areas and 36 percent in rural areas) take less than 30 minutes to fetch drinking water. More than half of all households (53 percent) travel 30 minutes or more to fetch their drinking water (19 percent in urban areas and 62 percent in rural areas).

Women in Ethiopia, especially in rural areas, bear the burden of collecting drinking water. In six of every ten households (62 percent), adult women are responsible for water collection. In rural households adult women are ten times more likely than adult men to usually fetch the water for the household (71 percent versus 7 percent). Even in urban households women are almost four times more likely than men to collect water (34 percent versus 9 percent). Female children under age 15 are about three times more likely than male children of the same age group to fetch drinking water (12 percent versus 4 percent).

In the 2011 EDHS all households also were asked whether they treat their drinking water. An overwhelming majority, nine households in every ten, do not treat their drinking water. Urban households (12 percent) are somewhat more likely than rural households (8 percent) to use an appropriate treatment method to ensure that water is safe for drinking.

As of 2010, the national rural water supply coverage of Ethiopia, Amhara Region coverage was estimated at only 60% (WaterAidEthiopia, 2010 ). If the current trend of management and utilization of water supply facilities continues, a minimum of 35% of the currently functioning facilities will become non-functional (ADF, 2005). Poor operation and maintenance (O & M) of water facilities is one out of the many factors contributing to the failure of these schemes (Carter, 2009).

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2.2.2. Household Sanitation Facilities

Ensuring adequate sanitation facilities is another Millennium Development Goal that Ethiopia shares with other countries. At the household level, adequate sanitation facilities include an improved toilet and disposal that separates waste from human contact. A household is classified as having an improved toilet if it is used only by members of one household (that is, it is not shared) and if the facility used by the household separates the waste from human contact (WHO and UNICEF, 2010).

Ethiopia Demographic and Health Survey (EDHS) of 2011 showed that 8 percent of households in Ethiopia (weighted average of urban and rural areas) use improved toilet facilities that are not shared with other households, 14 percent in urban areas and 7 percent in rural areas. One in ten households (32 percent in urban areas and 3 percent in rural areas) use shared toilet facilities. The large majority of households, 82 percent, use non-improved toilet facilities (91 percent in rural areas and 54 percent in urban areas). The most common type of non-improved toilet facility is an open pit latrine or pit latrine without slabs, used by 45 percent of households in rural areas and 37 percent of households in urban areas. Overall, 38 percent of households have no toilet facility, 16 percent in urban areas and 45 percent in rural areas.

Despite the ambitious plan to achieve 100% improved sanitation and hygiene coverage by 2012, access to sanitation services in Ethiopia is currently reported as 43% (WaterAidEthiopia, 2010 ) approaching only the Millennium Development Goal (MDG) target. According to literature, the sanitation coverage in the Amhara Region increased from 4% in 2004 (O'Loughlin et al., 2006) to 63% in 2010 (WaterAidEthiopia, 2010 ). Despite such figures, latrines are virtually non-existent in rural communities with defecation taking place in fields, bushes or along drainage ditches. The non-functionality of the available latrines was estimated to be greater than 80% in the country (Gebreselassie, 2007) which is likely the same in the region. If this trend of non-functionality of sanitation facilities continues, the risk of fecal-oral transmission and the mortality rate of children due to poor sanitation increase.

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2.3. Water Balance of Lake Tana Sub-Basin

Various researchers and consultants have tried to evaluate the water balance of Lake Tana. Some of the studies have reviewed documents dated as back as 1964. Thus a short review of these studies might be used to justify the amount which will be used as a reference for water allocation and scenarios analysis. More than 50% of the sub-basin area is not gauged. This had contributed to the large uncertainty in the water balance estimation of the lake. More over the evaporative water loss in the vast plains adjoining the lake could not be fairly accurately estimated due to under instrumentation. Researchers used simplifying assumptions and as such very few efforts have been made to systematically estimate the contribution by the ungauged part of the sub-basin.

2.3.1. Lake and Basin Water Balance

The different water balance estimates from the reviews are summarized below in the order of their date of publication.

Aragie (2005) indicated that the inflow and outflow of the lake are 382 m3/sec and 118 m3/sec respectively. The departure from closure of the estimate was 264 m3/sec.

In a Nile Basin Initiative project undertaken by Waterwatch (2005); the water balance of the Tana sub-basin was described separately as the lake balance and sub-basin balance.

2.3.1.1. Lake Balance:

 Inflow (Precipitation + Inflow+ change in storage) = 10218 MCM/yr  Outflow (Evaporation + Outflow + Percolation) = 9776 MCM/yr  Departure from closure = 442 MCM/yr

2.3.1.2. Basin balance:

 Inflow (Precipitation + Inflow+ change in storage) = 15110 MCM/yr  Outflow (Evaporation + Outflow + Percolation) = 14652 MCM/yr  Departure from closure = 458 MCM/yr

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Using 1960 – 1992 data Kebede et al. (2006) estimated the lake water balance to have an annual inflow of 2613 mm and outflow of 2591 mm. The departure from closure of the estimates is 22 mm. This estimation assumed a zero net influx of ground water from ungauged part of the sub basin. However while the total un gauged area is more than the gauged the author concluded that the contribution of the un gauged area to the lake flow as only 7% of total inflow (Kebede et al., 2006).

Figure 2: Gauged catchment of Lake Tana flows (Shown in blue) and rainfall isohyets (Kebede et al., 2011).

The World Bank rapid assessment report indicated that the annual inflow to the lake is 8661MCM and the outflow as 9389 MCM (Engda et al., 2007). The departure from closure of this estimate is 728 MCM. In this estimate percolation loss is assumed to be negligible.

The hydrological study by SMEC (2008) had reported the inflow to be 8802MCM/yr and outflow 8850 MCM/yr. The departure from closure of this estimate was -48 MCM/yr. Percolation losses are estimated to be negligible.

In a SWAT model set by Setegn et al. (2008) an estimate was made both for the gauged and un-gauged part of the lake basin. Accordingly a total annual inflow of 8400MCM and outflow of 7900 MCM was reported. Taking the runoff coefficient of the sub basin as 0.22, runoff

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Wale and Rientjes (2008) had estimated the water balance of the lake by modeling the lake level variation. According to their conclusion the total annual inflow and outflow were 10452MCM and 9956 MCM respectively. The contribution of the un-gauged part of the sub basin was estimated to be 2729 MCM. The departure from closure was 496 MCM.

On a study conducted by Asmerom (2008) the inflow to the lake was estimated to be 2904 mm/yr and the out flow 2903 mm/yr. In this study the change of storage is taken to be zero. Surface runoff from un-gauged sub-watershed is estimated to be 303 mm/yr. The departure from closure was -1 mm/yr.

In a lake stage and water balance modeling study Chebud and Melesse (2009) found out the inflow as 4530 MCM/yr and outflow 9900 MCM/yr. The departure from closure was -5370 MCM/yr.

The study by Kebede et al. (2011) used isotope method to estimate the contribution of the ungauged part to the total flow. The results indicated that the total annual inflow is 9204 MCM and outflow is 8749 MCM. The departure from closure of this estimate is 455 MCM. This is the most complete water balance estimation so far presented. The inflow contribution from un-gauged part of the sub basin was estimated to be 1698 MCM. The un-accounted water loss (455 MCM/yr) is attributed to outflow through wetland evapo – transpiration.

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Figure 3: Wetlands around Lake Tana, (Aragie, 2005)

3. MATERIALS AND METHODS

3.1. Description of the Study Area

Tana Sub-Basin is located in northern part of Ethiopian and north east part of Blue Nile Basin. The drainage area of Tana Sub-Basin has a size of 15,320km2 (Excluding Guange watershed which have an area of 744.17 km2). The lake is fed by eight perennial rivers namely Gilgel Abay, Rib, Gumara and Megech which are the major ones (93% contribution) and Gelda, Arno Garno, Enfiranz and Dirma and other several seasonal streams (Figure 4). Abbay (Blue Nile) River is the only natural out flowing river from the lake. Lake Tana, which is the biggest fresh water natural reservoir in Ethiopia with a maximum depth of 14m and mean depth of 9m, is nature‘s special gift to Ethiopia. It is located at 11°36' N & 37°23' E with average natural altitude of 1786 masl (meters above sea level). At similar elevation the lake has a surface area of about 3050km2 with length of 74km & 68km width and storage volume of 29km3 (Belete, 2013).

The climate of the area is largely controlled by the movement of the inter-tropical convergence zone (ITCZ), which results in a single rainy season between June and September. Rainfall is highly correlated with elevation. The mean annual rainfall over the catchment is

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Figure 4: Location of the study area

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3.2. Materials used

For this assessment previous inventory reports, feasibility study documents and research papers have been reviewed and used accordingly. In addition to this the following digital and electronical materials were used during this study. o GPS o Water quality measuring instruments (PAQUALAB, pH meter, turbidity meter, EC meter and Maji meter) More descriptions are available for the above instruments in the methodology part. Data and information used for this study are well described in data collection and description part below.

3.3. Data Collection and Description

During the field work session both primary and secondary data were collected physically from the ground truth and respected offices. Primary data like drinking water point location, river outlets, irrigable command areas location, river gauging stations, meteorological stations locations using GPS 60 of Garmin product was collected. Water quality parameters for the sub-basin drinking water quality assessment were measured with PAQUALAB, pH meter, turbidity meter, EC meter and Maji meter.

3.3.1. Primary Data For the completion of surface and ground water potential assessment of the corridor, observation and measurement at the field is one of intelligence information and data collection mechanism. Specifically, information‘s to be collected during bio-physical field survey are tabulated on Table 7. Table 7: Primary data types and their source Data Type Source Existing irrigation schemes Bio-physical Field survey Location and identification of perennial gauged and un-gauged rivers Bio-physical Field survey Location of drinking water supply points Bio-physical Field survey Location and acreage of drainage problem /Swamp areas Bio-physical Field survey Location of hydo-meteorological stations location Bio-physical Field survey Drinking water quality Field measurement Water supply coverage with actual and access Socio-Economic Team

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3.3.2. Secondary Data Information and data that have been collected as secondary data is like the primary data including the water supply schemes and irrigation schemes and any untapped water bodies. Unlikely the information and data was intensively focusing on the attribute data of the schemes rather than the spatial data. The attribute data of the water supply schemes were type of scheme, number of beneficiaries, and status of the scheme and spatial data like administrative location and name of schemes. Specifically, information‘s collected during secondary data collection are tabulated on Table 8. Table 8: Secondary data types and their source Data Type Source 1. Daily Weather Data Rainfall, Temperature/ Min and max, Relative Humidity, Sunshine hour, Wind speed National Meteorological Agency (NMA) 2.Water resource (Surface and Sub surface) data Existing irrigation schemes Wereda, Zone and Regional sectors Daily hydrological/river flow data Ministry of Water and Energy (MoWE) Soil data (soil depth, soil properties, soil moisture and soil type) Soil Survey Team Land use land cover data Land use/Land cover Team Monthly water consumption of the hydropower sector Ethiopian Electric Power Corporation (EEPCO) Water consumption, production and loss of urban centers Urban water and sewerage services

3.3.3. Base Map Preparation

A base map provides a user with context for a map. In this case, such base maps have two functions (van Elzakker and van de Berg, 2010): 1. They allow the map user and the map producer to localize the thematic information (where is that?)

2. They help to explain the geographic distribution of the thematic information (why is that there?)

Ideally, the nature and contents of the base maps should be adjusted to (van Elzakker and van de Berg, 2010): • the purpose of the map as a whole

• the information needs of the users

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• the scale of representation of the map display

• the primary information layer

According to the above functions and facts field data collection and observation for the assessment of water resources potential should be done in reference to the base map. The base maps that used for physical observation and water quality sampling were shown below on Figure 5 and Figure 6.

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Figure 5: Base map that used for physical observation

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Figure 6: Base map that used for drinking water quality assessment

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3.3.4. Preparation of Data Collection Formats

Data collection techniques such as using a data collection form is helpful for different research purposes to gather information from service vendors while preparing to gather necessary information that may be studied further and acted upon, if needed. According to this importance primary and secondary data collection format on physical observation, measurement, irrigation and water supply developments were prepared for field data collection in Assessment of Water Resource Potential. In primary data collection five formats were used for physical observation, physical water quality measurement, rural water supply scheme, meteorological and hydrological stations locations, modern and traditional irrigation scheme (Appendix 1). In secondary data collection three types of formats were used for traditional and modern irrigation scheme, urban water supply and use, seasonal and perennial rivers (Appendix 2). Secondary data collection formats were distributed for Wereda experts to be filled at office level.

3.3.5. Data quality The data collection process has some elements of potential error. From the information collected the following may be listed as sources of gaps and errors: • Non-completeness or omissions. The questionnaires prepared were not completely filled in most cases. They were only partially filled. • Lack of editing of the completed questionnaires. The data collected were not scrutinized for errors, omissions or ambiguities • Inaccurate and in consistent response Other factors that have contributed to the error are: • Vast and remote area of survey • Questions that invite subjective answers However data quality check and clearance of collected primary and secondary data were the great task after main field work.

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3.4. Methods of Data Analysis

3.4.1. Water Resource and Irrigation Development In this specific part of the methodology only assessment irrigation and drinking water supply of the sub-basin is described, tabulated and mapped according to water resource assessments ways of description. Within the result and description part of this sub title one can easily get existing irrigation and water supply potential of Tana sub-basin in spatial and tabular formats prepared based on the collected data of primary and secondary.

3.4.2. Water Demand and Use

For this assessment previous inventory reports, feasibility study documents and research papers have been reviewed and used accordingly. In addition, data are collected from responsible organizations to know the current status of water user sectors. The monthly water consumption of the hydropower sector which uses the sub-basin water resource has been collected for (1999 – 2005) Ethiopian Fiscal Year (EFY) from EEPCO. The water consumption, production and unaccounted water for nine urban centers (Bahir Dar, Gondar, Dangila, Woreta, Koladiba, Addis Kidam, Enjibara, Aykel and Debre Tabor) from urban water service and sewerage services, traditional irrigation status of 21 Woredas in Lake Tana basin from Woreda Agriculture offices has been collected.

3.4.2.1. Urban Water Supply and Demand

Urban water demand includes the demand of water for domestic, public, commercial and industrial uses. Tana Sub-Basin is a region which is characterized with the occurrence of relatively better urban centers, infrastructure like asphalt road, air connectivity and tourist destination areas. This has an implication on the urban water demand of the sub-basin. In the basin it has been identified that there are 31 urban centers including the major urban centers of the area Bahir Dar and Gondar.

The main constraint in this assessment is none availability of recent data and luck cooperation of responsible stakeholders to give recently collected data of the location, source types and yields of water sources. Due to stated reason and none coherence of data collected from different sources the Amhara regional water supply and sanitation status assessment report

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The Amhara National Regional Water Resource Development Bureau has made water supply and sanitation inventory in (2004) by a consultant: Abay Engineering Consultant. This document has been reviewed to get relevant information for this assessment. Population census data (2007) for Amhara region urban and rural areas has been obtained from BoFED and projected for 2014 with growth rate of 5.52 on average.

The water demand of the urban centers highly depends on the types of connections they use, their economic development level, population growth and consumption level. The urban centers in Tana Sub-Basin are categorized in to seven based on their number of current (2014) projected population so as to estimate the percentages of water connection types and the relative per capita demands. The category of urban centers is shown below in the Table 9.

Table 9: The category of urban centers (BoWRD, 2004) No Category No of Population Urban centers categorized Number 1 Category 1 >250,000 Gondar 1 2 Category 2 80,000 – 250,000 Bahir Dar 1 3 Category 3 50,000 – 80,000 Debretabor 1 4 Category 4 30,000 – 50,000 Dangila, Wereta 2 5 Category 5 15,000 – 30,000 Adis Zemen, Merawi, Koladiba, Durbetie, 5 Maksegnit 6 Category 6 5,000 – 15,000 Aykel, Hamusit, Delgi, Chuahit, Yismala, 14 Ambesamie, Addis Kidam, Deq, Kunzila, Enfiranz, Yifag, Wetet Abay, Amed ber, Dengel Ber 7 Category 7 <5,000 Ayimba, Arb Gebya, Gassay, Ambo Meda, 7 Gorgora, Kimir Dengay, Enjibara Sum 31

3.4.2.1.1. Urban Water Supply and Water Source Yield

For this assessment the Amhara Regional water supply and sanitation status inventory of BoWRD report prepared by Abay Engineering consultant; (2004) is adopted and modified according to ADSWE assessment. The consultant has made inventory for the Amhara region urban centers to identify types and number of the water supply schemes and their yields. The study has determined the average yields of the water sources based on the types of water

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Table 10: Type of sources of water supply and their estimated yields (BoWRD, 2004) Source Minimum Average Maximum Spring l/min (m3/day) 10 (4.8) 20 (9.6) 30 (14.4) Hand Pump l/min (m3/day) 3.1 (1.5) 8.3 (4.0) 16.6(8) Shallow Well l/min(m3/day) 2(0.96) 5(2.4) 12(5.76) Borehole l/sec (m3/day) 1.0 (28.8) 3.0(86.4) 5.0(144.0)

3.4.2.1.2. Domestic Urban Water Demand

According to Amhara Regional water supply and sanitation status report prepared by (BoWRD, 2004) with Abay Engineering consultant, the relation among the population, connection type and per capita consumption are shown below on Table 11. There are four types of connections currently in use in the urban centers: house connection, yard connection, shared and public connections. The percentage of Yard and house connections varies according to the size of population as well as the level of economic development and level of water consumption. House connection implies higher consumption and the use of water within the house. The most common type of connection is the yard connection whereby the houses have yard taps within their compound. Usually yard taps are shared between neighbors, and it is very common to find one-yard tap serving more than 5 houses in the area. The other type of connection considered is the connection to stand pipes (public taps). Standpipes usually serve a larger portion of the community, which cannot to have Yard or House connection. They usually serve communities within 500-meter range from the public tap. Based on the above mentioned assumption, type of connection and per capita demands, the urban centers water demand for the sub-basin has been summarized on

Table 40.

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Table 11 : Urban centers category type and connection type ( Users percentage and l/c/d) House Yard Shared Public Total Urban centers category % users l/c/d % users l/c/d % users l/c/d % users l/c/d % users category1 (>250000) 5 80 55 40 35 30 5 20 100 category2 (80000-250000 5 80 55 40 35 30 5 20 100 category3 (50000-80000) 5 80 55 40 35 30 5 20 100 category4 (30000-50000) 5 80 55 40 35 30 5 20 100 category5 (15000-30000) 2.5 70 47.5 30 40 20 10 15 100 category6 (5000-15000) 0 70 30 30 55 20 15 15 100 category7 (<5000) 0 70 30 30 55 20 15 15 100 (Source: Amhara Regional water supply and sanitation status report prepared by Abay Engineering (BoWRD, 2004))

3.4.2.1.3. Public, Commercial and Industrial Water Demand

According to the Amhara Regional water supply and sanitation status inventory report, (BoWRD, 2002); the public, commercial and industrial water demands are considered separately. The industrial water demand includes those which do not have their own sources but have direct connections to the urban centers water supply. Therefore the public, commercial and industrial water demands are estimated as percentage of urban centers water demand with the following assumption Table 12. Table 12 : Public, commercial and industrial water demand share (BoWRD, 2002)

Population Percentage of total domestic water demand Category Public & commercial Industrial <30000 20% 5% >=30000 30% 10%

3.4.2.2. Rural Water Supply and Demand

According to this assessment, rural water demand comprises of the water use for human population for domestic purpose and the water demand for livestock populations in the sub- basin out of urban centers.

3.4.2.2.1. Rural Water Supply Schemes and Water Production

From this Amhara Design and Supervision Enterprise (ADSWE) inventory of rural water supply a total of 4818 water supply schemes were inventoried in rural areas of the region. Supply production estimations were taken from (BoWRD, 2004) Abby Engineering study

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(Table 13). The rural water supply production this study only consider hand dug well and springs which were functional at the time of inventory. This is because of the difficulty of estimation of yield of pipe supply, river, pond and lake and also they are not permanently used by the local community. The rate for undeveloped springs is used from developed spring‘s rate. Table 13: The type of water sources and estimated production (BoWRD, 2004) Type of water supply Unit estimated No scheme yield, (m3/day)

1 Hand dug well 4 2 Spring (Developed) 9.6 3 Spring (Undeveloped) 9.6

3.4.2.2.2. Rural Domestic Water Demand Rural domestic water demand comprises of the demand for water for the purposes of drinking, cooking, washing people and cloth and small gardening etc. The rural domestic water demand is affected by many factors: population, household occupancy rates, level of service of water supply, local knowledge and indigenous practices, cultural and traditional values, climate and water quality.

There are two methods to estimate the rural domestic water demand of catchments: the direct method which involves conducting socio economic survey and questionnaires for assessing the water demands of sample households. The second method for assessing the rural domestic water demands of catchments is the indirect method where the quantity of water consumed is calculated from population levels and estimated demand levels in terms of per capita consumption. Due to the difficulty of obtaining data concerning the water service level, metering of water services indirect method is preferred to estimate the rural domestic water demand. The rural population of Lake Tana Basin is estimated using the Census data of 2007 at Kebele level and projected for 2014 at rural population growth rate of 1.57 %. The population of rural kebeles which lie in the basin partially is estimated using the area proportion method. The total population of the sub-basin is finally estimated to be 2,350,077 for year 2014 (excluding Quarit (2554 pop), west Belesa (309 pop) and Lay Gaint (89 pop) because of the inventory not take place). Average per capita water demand of the area is taken to be 15 l/capita/day.

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3.4.2.2.3. Livestock Water Demand

To estimate livestock water use for the sub-basin we need to have the number of each type of livestock in the sub-basin. The water requirements of livestock are influenced by several factors, including: Type of livestock, pregnancy, and lactation, type of diet, feed intake and temperature.

Livestock population was collected from each Woreda during the baseline disaggregated by sub-basin. Disaggregation by sub-basin was made by the surveyors based on the location of kebeles (lowest administrative unit) in a sub-basin. The livestock population is reported by type of livestock which include cattle, sheep, goat, equines (horse and mule) and poultry. Livestock water requirement per head varies with type of animal, climate and animal- husbandry practices. However, to avoid the complexities, average consumption rate for each livestock type irrespective of agro-ecology was adopted. Considering the high livestock population in the lowlands where water is limited or fetching distance is long, livestock in such areas are not adequately watered. In some areas where water is in short supply, livestock are watered every other day. On the contrary, livestock water use rate in the highlands is lower than in the lowlands. Therefore, lower ranges of daily water use rates are adopted. Accordingly, 25, 5, 18, 12 and 15 and liters of water per head per day were used for cattle, sheep/goat, horse, donkey and mule respectively. In case of poultry 407 liters per day for 1000 poultry (FAO, 1991). Monthly livestock water demand for each sub-basin is then estimated by multiplying these daily rates by the corresponding number of livestock type and by the number of days in each month.

One Tropical Livestock Unit (TLU) is equivalent to an animal live weight of 250 kg on maintenance. The conversion factor used for converting each number of livestock to TLU is Cattle = 0.7, Sheep and goat = 0.1, Ass = 0.5, Mule = 0.7, Horse = 0.8, Camel = 1. One can get the TLU of the basin using this standard.

3.4.2.3. Agricultural Water Demand and Use

Irrigation water demand is the water demand to undertake irrigated agriculture. The amount of irrigated lands, types of crops grown and related data which affect the demand for irrigation

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The computation of Gross Irrigation Water Requirement (GIWR) is based on the equation (Yibeltal Tiruneh, 2013): 10* Ai * NIR GIWRi  6 E Where Ai = Monthly irrigated area (ha), E = Overall irrigation efficiency (decimal), GIWRi = Gross irrigation water requirement (mm) and NIR = Net irrigation water requirement (mm)

3.4.2.3.1. Planned Medium and Large Scale Irrigation of Tana Sub-Basin

Based on Abbay master plan study outcome on some the sites go through further feasibility study development study at project and their water demand estimated. The estimation of water demand considers the studies average gross irrigation water requirement and irrigation efficiency. Some of the sites which are at identification level are not able to found estimated water demand and their demand estimated using on others feasibility level average demand. Summary of planned medium and large scale irrigation potential used for the demand analysis is shown on Table 14.

Table 14: Summary Planned Medium and Large Scale Irrigation potential Type of Water Supply Lake Tana and Dams Gross Area under Irrigation (Ha) 180,423 Net Area under Irrigation (Ha) 153,364 Source: Pre-feasibility and feasibility studies of the projects

3.4.2.3.2. Medium and Large Scale Irrigation under Construction

Water demand of medium and large scale irrigation under construction was estimated from information gathered from feasibility study. The amount of irrigable land shown in Table 15 is used for this purpose. Table 15: Medium and large scale irrigation under construction Schemes ID MEG 1 MEG 2 MEG 6 RIB 1 RIB 2 RIB 3 RIB 4 sum Gross Area under 5254 6532 4000 7650 3060 9360 3370 39226 Irrigation (Ha) Net Area under Irrigation 4466 5552 3400 6503 2601 7956 2865 33343 (Ha) Total sum 72569 Source: Feasibility studies of the projects

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3.4.2.3.3. Existing Modern and Traditional Irrigation Water Demand

The irrigation water demand of total modern and traditional irrigation is thus estimated by multiplying unit irrigation water demands with the command areas of the three agro climatic zones. The unit irrigation water demands of 7433, 8382 and 14792 m3/ha for Dega (cool), Woinadega (tepid) and Kolla (Warm) agro ecological zones respectively will be considered for modern irrigation and 9291.25, 10477.5 and 1849 m3/ha for Dega (cool), Woinadega (Tepid) and Kolla (Warm) agro-ecological zones assuming the efficiency of irrigation schemes has no significant improvement (MacDonald, 2004). This figure was developed by reviewing agronomical studies of the projects and literatures. By considering total irrigated area currently with the area proportion of these agro ecological zones in the Sub-basin average of net irrigation demand of the zones are applied.

3.4.2.4. Industrial Water Demand and Use

Industrial water demand and use includes water demand and use for industrial processes such as fabrication, processing, washing, cooling and hydropower generation. This water user sector share water from urban centers (see section 3.4.2.1.3) and use its own source for manufacturing.

3.4.2.5. Hydropower Water Demand and Use

Hydro power water demand and use of Tana sub-basin is analyzed based on the observed data gathered from Ethiopian Electric Power Corporation (EEPCO). The data is annual abstraction of water in million meters cubic from Lake Tana to Beless River for hydropower generation.

3.4.2.6. Other Water Demand and Use

Besides the consumptive use and water transfer of the water resource of Lake Tana there are also other instream uses of water for different purposes. Some of the major instream uses of Lake Tana include navigation, tourism, fishery, aquaculture and environment. With the concept of demand and use of these instream uses is discussed in the result discussion part.

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3.4.3. Water Quality and Sanitation

3.4.3.1. Indicator/parameter selection for basic/initial assessments

In terms of priority the indicators/parameters to be included in water quality assessment and monitoring programmes can be summarized as follows: 1. microbial quality: micro-organisms which indicate fecal contamination (―indicator organisms‖ such as total coliforms, thermo-tolerant coliforms) and thus signal the potential presence in water of micro-organisms that are harmful to health (pathogens); 2. parameters that have been shown to influence microbial quality (such as disinfectant residuals, pH and turbidity); 3. chemical parameters of known health risk; and 4. Aesthetic parameters, i.e. those that cause rejection of water (notably turbidity, iron and manganese). Table 16: Indicators/parameters for basic/initial RADWQ Parameters Type Microbial and related Physical and chemical Chemical Inspections and risk assessments Total Coliform (TC) Electric Conductivity (EC) Nitrate Sanitary inspection Turbidity Manganese Land use type pH Fluoride Total Dissolves Solids (TDS) Iron Temperature Salinity

3.4.3.2. Sampling Method

The number of samples of water to be taken in the rapid assessment (sample size) can be calculated using the equation below which is derived from UNICEF (2006):

4P(1 P)D n  2 ...... e 7 n = required number of samples;

P = assumed proportion of water supplies with water quality exceeding the water- quality target(s);

D = design effect;

e = acceptable precision expressed as a proportion.

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Estimating the proportion (P)

Expert judgment will be probably being most effective to ‗pool‘ information on all water supplies about likelihood of contamination. Thus, it is believed that perhaps 95% of point sources and 50% of piped systems are contaminated, a compromise figure can be calculated based on the proportion of people served by the different technologies. 88% of the populations rely on point sources and 12% on piped sources, then a weighted estimate would be: P = (0.88*0.95)+(0.12*0.5) = 0.896

Design effect (D)

In determining the value of D, existing data and expert judgment need to be applied to estimate what is the likely similarity in water quality in adjacent water supplies. In the pilots implemented as part of the development of the RADWQ approach, a design effect of D = 4 was used for the survey design reflecting the reported variation in water quality from existing data sets and literature.

Bias and precision (e) A key aspect of survey design is to ensure that a representative sample is taken from the population under study. When estimating a proportion, therefore, it is important that the estimator is unbiased. Bias means that the estimator selected is skewed to one side or another of the distribution of the data (either higher or lower than the central tendency).

The precision of the estimator is a measure of its accuracy and is usually assessed by considering the variance of the estimator based on the normal distribution. The smaller the variance, the more precise or accurate is the estimator. Variance is a measure of the difference of the actual mean value from the range of possible values.

In devising survey designs there is a trade-off between bias and precision. In general terms, controlling bias (or preventing biased surveys) is considered more important than precision and therefore bias is rarely compromised for precision. There is little value in being precisely wrong, but much value in being imprecisely correct!

In the pilot assessments undertaken, an acceptable precision of e = ± 0.1 with a confidence level of 90% was used. Other values may be used to increase or decrease the acceptable precision depending on the degree to which managing precision and risk. Hence the sampling size was:

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4*0.896*(1 0.896)*4 n  149...... (0.1)2 8

3.4.3.3. Stratifying the sample size

The stratification of sampling size for Tana Sub-Basin development corridor assumes four different thinks that help to decide the sampling number for each towns and weredas. These are:

a. Road accessibility b. Area of Wereda included in the study area c. Assumed population size with in that weredas d. Climatic zones of the basin Table 17: Samples size per Wereda per for rural hand dug wells, springs and shallow wells Wereda Name No. of Samples Area (km2) Alefa 2 174.7 Bahir Dar Zuria 4 723.5 Banja 2 79.9 Chilga 3 309.8 Dangila 4 308.3 Dembeya 8 1285.3 Dera 5 649.9 Ebinat 3 260.8 Fagita Lekoma 4 384.7 Farta 8 947.4 Fogera 5 1028.1 Gondar zuria 5 959.8 Lay Armachiho 4 395.5 Libo Kemkem 5 714.6 Mecha 8 1272.9 Misrak Estie 3 246.8 North Achefer 4 661.1 Quarit 2 12.6 Sekela 4 416.4 South Achefer 5 648.5 Takusa 3 311.9

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Wereda Name No. of Samples Area (km2) Wogera 2 44.7 Yilmana Densa 1 5.6 Total 94 11842.6 Table 18: Samples per administrative town for deep well

Town Administration No. of Samples Dangila 2 Bahir Dar 4 Debre Tabor 4 Wereta 2 Addis Zemen 2 Gondar 5 Aykel 2 Merawi 1 Total 22 Table 19: Samples per town for deep well

Town Name Wereda No. of Samples Adis Kidame Fagita Lekoma 1 Adis Zemen Kemkem 1 Amed Ber Fogera 1 Anbesame Dera 1 Chandiba Chilga 1 Chwahit Mender Denbia 1 Delgi Alefa 1 Dengel Ber Alefa 1 Durbete Achefer 1 Fagita Fagita Lekoma 1 Gassay Farta 1 Gish Abay Sekela 1 Gorgora Denbia 1 Hamusit Mender Dera 1 Kola Diba Denbia 1 Kunzila Achefer 1 Maksegnit Gonder Zuria 1 Meshenti Bahir Dar 1 Wanzaye Fogera 1 Wetet Abay Merawi 1 Yifag Kemkem 1 Yismala Achefer 1

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Zegie Bahir Dar 1 Total 23

11 samples are left for water quality measurements of surface water that are used for drinking. Therefore totally 150 samples are selected for rural and urban drinking water quality measurements including hand dug well, springs, shallow well, deep well and other sources of water for drinking like rivers and lake. All the points selected and stratified are shown on Figure 6 as base map.

3.4.3.4. Field implementation and data recording

3.4.3.4.1. Preparation and implementation of fieldwork

Fieldwork started immediately after finalizing the field action plan and lasted approximately 47 days, from first week of May to second week of June. Field implementation was carried out by one teams assembled by three people: one sanitary engineer who acted as team leader and who had responsibility for conducting sanitary inspections and conducted physical and chemical water quality analysis; one experienced laboratory technician who conducted also microbial and chemical water quality analyses; and a driver. The composition of field teams did not change during the study. For some field activities see the following photos.

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To prepare for the fieldwork, an inventory of equipment and media supplied through this rapid assessment of water quality was prepared and reviewed by the team (see Appendix 6 for the inventory. Photocopies had to be made, to provide the field teams with the necessary forms and information (e.g. the daily record sheets, sanitary inspection and quality assurance record sheets, copies of all Wagtech manuals). Copies of fieldwork record sheets are shown on Appendix 1.

The biggest difficulties encountered by the team during fieldwork included:  The road access for the assessment is not enough to cover all the sample points in addition to the unexpected rainfall.  There was a shortage of sampling bottles and vaccine boxes for bacteriological testing.  There was a fluctuation of electric power during bacteriological testing at field.  There was miss-sampling due to non-functional wells or absence of the respected person assigned during sampling.

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 There was a shortage of Membrane lauryl sulphate broth media for bacteriological testing.

3.4.3.4.2. Field and laboratory-based approaches

A portable testing system for monitoring key drinking water parameters on-site; even in remote areas.

 Meets national and WHO guideline requirements

 Provides microbiological, chemical and physical testing facilities

Microbiological testing Data quality question centered on the location and method used to collect water samples for bacteria analysis. Extra care must be taken when sampling water supplies for coliform bacteria and E. coli bacteria because of the risk of contaminating the sample from hands and other surfaces during collection and subsequent handling. The results of the protocol used in this research suggest that the sampling strategies, including washing the bottle three times and not flame sterilizing the end of the faucets, did not cause any systematic bacterial contamination of the samples.

Paqualab® 50 provides the facility to indicate harmful bacteria and virus presence using membrane filtration and incubation of fecal and total coliforms. A manual vacuum filtration unit allows a measured sample to be drawn through a sterile gridded membrane filter and then applied to an absorbent pad which has been pre-soaked in membrane lauryl sulphate medium and held within a reusable aluminum petri dish. The sample pad is then incubated in a portable incubator compartment. Up to 50 dishes can be simultaneously incubated in two separate compartments at +37°C or +44°C depending on method (chambers are individually switchable). Components and consumables for up to 200 tests in total are included.

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Figure 7: Paqualab® 50 used for microbiological testing Physical testing Physical parameters like temperature, turbidity, PH, conductivity, TDS and salinity result might cause rejection of water. These parameters are tested and measured with separate meters and also with Maji meter which combines multi-parameters (See Figure 8).

Temperature: Cool water is generally more palatable than warm water, and temperature will impact on the acceptability of a number of other inorganic constituents and chemical contaminants that may affect taste. High water temperature enhances the growth of microorganisms and may increase taste, odor, color and corrosion problems.

Turbidity is usually included in the microbiological testing kit as shown on Figure 7 above. The assessment can be either through a turbidity meter or the use of turbidity tubes. Turbidity is the most important problem for the aesthetic value of water quality. Although it doesn‘t necessarily adversely affect human health, it can protect microorganisms from disinfection effects, can stimulate bacterial growth, and indicate problems with treatment processes

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(WHO, 2004). For effective disinfection, median turbidity should be below 0.1 NTU although turbidity of less than 5 NTU is usually acceptable to consumers (WHO, 2004).

An important operational water quality parameter is pH, although within typical ranges it has no direct impact on consumers. Low pH levels can enhance corrosive characteristics resulting in contamination of drinking-water and adverse effects on its taste and appearance (WHO, 2004). Higher pH levels can lead to calcium carbonate deposition. Careful consideration of pH is necessary to ensure satisfactory water disinfection with chlorine, which requires pH to be less than 8 (WHO, 2004).

Total dissolved solids (TDS) and electrical conductivity (EC) are measures of the total ions in solution and ionic activity of a solution respectively. As TDS and EC increase, the corrosive nature of the water increases. Conductivity indicates the presence of dissolved solids in water, but does not provide information about a specific chemical. Its change might indicate a water quality problem that requires further investigation.

Salinity is a measure of the total salt concentration, comprising mostly of Na+ and Cl- ions as + 2- well as small quantities of other ions (eg. Mg2 , K+, or SO4 ). It is the total of all non- carbonate salts dissolved in water, was expressed in parts per thousand (1ppt = 1000mg/L or 1g/L) but is now generally expressed in parts per million (ppm).

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Figure 8: Maj-meter used for physical tests Chemical testing The quality of drinking water can be influenced by several sources:  Depending on the original source of drinking water, the water may contain various natural inorganic substances, partly wholesome for human health and partly even with health concerns. It may contain particles or natural organic substances decomposing products) originating from forest or marsh areas.  Due to human activities, agriculture, industry or traffic, the water may contain impurities.  Drinking water can be contaminated by the contact of the materials within the network, e.g. metal from pipes.

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Harmful chemicals and metals tested in laboratory (Figure 9) based for this study are nitrate, fluoride, iron and manganese are discussed below.

Figure 9: Sample exchange with laboratory for chemical testing (ADSWE Laboratory)

1. Nitrate (NO3)

Nitrate (NO3) is a naturally occurring form of nitrogen found in soil. Nitrogen is essential to all life. Most crop plants require large quantities to sustain high yields. The formation of nitrates is an integral part of the nitrogen cycle in our environment. In moderate amounts, nitrate is a harmless constituent of food and water. Plants use nitrates from the soil to satisfy nutrient requirements and may accumulate nitrate in their leaves and stems. Usually plants take up these nitrates, but rain or irrigation water can leach them out due to its high mobility

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Tana Sub-Basin Land Use Planning and Environmental Study Project into groundwater. Although nitrate occurs naturally in some groundwater, in most cases, higher levels are thought to result from human activities.

Common sources of nitrate include: • Fertilizers and manure • Animal feedlots • Municipal wastewater and sludge • Septic systems and pit latrines Nitrate in drinking water can aggravate ―Blue Baby Disease‖ (Methaemoglobinaemia) as it is converted to nitrite in the body. Nitrite reacts with hemoglobin of the red blood cells to Methaemoglobin, affecting the blood‘s ability to carry oxygen to the cells of the body. Infants less than three months of age are particularly at risk. The intake of tea or other baby food prepared with nitrate-rich water can cause the baby to not get enough oxygen and to turn blue. This disease can be lethal, or it can damage the brain or nerves of the child. Older people may also be at risk because of decreased gastric acid secretion. In areas where natural iodine intake by the inhabitants is low, high nitrate concentrations in drinking water can increase the frequency of thyroid problems. 2. Fluoride (F) The appearance of fluoride in the groundwater is mostly of geogenic origin, but can also be caused by mining or industrial pollution. On one hand, fluoride is to some extent essential for the development of healthy bones and teeth, but on the other hand, long-term and increased intake of fluoride via water or other sources can cause severe problems with teeth and bones. 3. Iron (Fe) and Manganese (Mn)

The primary sources of iron in drinking water are natural geologic sources, as well as ageing and corroding distribution systems (household pipes). Iron-based materials, such as cast iron and galvanized steel, have been widely used in our water distribution systems and household plumbing.

Undesirable effects are tastes or odors. Iron in quantities greater than 0.3 mg/L in drinking water can cause an unpleasant metallic taste and rusty color. Iron and manganese are both known to stain the water supply. They can make water appear red or yellow, create brown or black stains in the sink, and give off an easily detectable metallic taste. Even laundry can get

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Tana Sub-Basin Land Use Planning and Environmental Study Project brown spots by washing with Fe- and Mn-rich water. Although these can all be aesthetically displeasing, iron and manganese are not considered to be unhealthy. Fortunately, they can be removed from the water easily. Furthermore, increased levels of iron can appear in the drinking water of galvanized pipes that are corroding and release iron. Because galvanized pipes consist of a mixture of metals, zinc or cadmium levels in the drinking water could also increase. Like iron, zinc is not considered to cause health risks.

3.4.3.5. Water Quality Data analysis

Data analysis is one of the most important aspects of the report, because it is the principal mechanism by which raw data are transferred into usable information for the assessment, communities and other decision-makers. Raw data itself is of little use – most people will not understand what it means and few will have sufficient time or interest to analyze the data. What is required is simple, direct and comprehensible information that can be used without further manipulation and is meaningful to the target audience.

All water quality and sanitary inspection results were entered and stored to Microsoft Excel for data analysis. After entry, the data were checked for plausibility or for unrecognized characters. Data analysis was performed with Excel.

Data were analyzed following the guidelines provided by the international consultant. This included an analysis by broad area and supply technology to see if microbiological, physical and chemical parameters were in compliance with WHO guideline values (Table 20).

Table 20: National and WHO guideline values for the selected physical, chemical and bacteriological parameters (WHO, 2012, Tadesse et al., 2010)

Parameters Guideline values Remarks Volume of water consumed and intake from other sources should be considered when setting national Fluoride 1.5 standards Concentrations of the substance at or below the health-based guideline value may affect the appearance, taste or odor of the water, resulting in Manganese 0.5 consumer complaints. Temperature (0C) 30 PH 6.5-8.5 Conductivity (µs/cm) 1400 Turbidity (NTU) 5

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Parameters Guideline values Remarks Total Dissolved Solid (mg/L) 1000 Nitrate (mg/L) 10  Not of health concern at concentrations normally observed in drinking-water, and taste and appearance of water are affected at concentrations below the health-based value  More than 0.3 ppm can cause staining of clothes and plumbing fixtures, encrustation of well screens, and plugging of pipes. Excessive Iron (mg/L) 0.3 quantities can stimulate growth of iron bacteria. Salinity (mg/L) 500 Aesthetic

Must not Total coliform (No of colonies) detectable

3.4.4. Hydrological Modeling and Water Balance

Hydrological models are simplified, conceptual representations of part of the hydrologic, or water cycle. They are primarily used for understanding hydrologic procuresses. Hydrological models are vital for a wide range of applications, including water resources planning, development and management, flood prediction and design, and coupled systems modeling including, for example, water quality, hydro-ecology and climate (Pechlivanidis et al., 2011).

3.4.4.1. Types of Hydrological models

Two major types of hydrologic models can be distinguished: 1. Stochastic Models. These models are black box systems, based on data and using mathematical and statistical concepts to link a certain input (for instance rainfall) to the model output (for instance runoff). Commonly used techniques are regression, transfer functions, neural networks and system identification. These models are known as stochastic hydrology models. 2. Process-Based Models. These models try to represent the physical processes observed in the real world. Typically, such models contain representations of surface runoff, subsurface flow, evapotranspiration, and channel flow, but they can be far more complicated. These models are known as deterministic hydrology models. Deterministic hydrology models can be subdivided into single-event models and continuous simulation models.

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3.4.4.2. Model Performance Criterion

Hydrologic model behavior and performance evaluation is commonly made and reported through comparisons of simulated and observed variables. There are a number of reasons why hydrologists need to evaluate model performance (Krause et al., 2005):

(1) to provide a quantitative estimate of the model‘s ability to reproduce historic and future watershed behavior;

(2) to provide a means for evaluating improvements to the modeling approach through adjustment of model parameter values, model structural modifications, the inclusion of additional observational information, and representation of important spatial and temporal characteristics of the watershed;

(3) to compare current modeling efforts with previous study results.

Hydrologists may use subjective and/or objective estimates of the closeness of the simulated behavior of the model to observations made within the watershed. Visual inspection of the simulated and observed hydrographs is the most fundamental approach to assessing model performance in terms of behaviors. Objective assessment, however, generally requires the use of a mathematical estimate of the error between the simulated and observed hydrologic variable(s). Among these objective criteria‘s the most commonly used with hydrologists (Krause et al., 2005, Moriasi et al., 2007, Elshamy, 2008) are described.

1. Coefficient of determination (R2)

Coefficient of determination (R2) describes the degree of co-linearity between simulated and measured data. R2 describes the proportion of the variance in measured data explained by the model. R2 ranges from 0 to 1, with higher values indicating less error variance, and typically values greater than 0.5 are considered acceptable (Santhi et al., 2001). R2 defined as:

2  n   (Oi  O )(Pi  P)  2  i1  R    n n 9  2 2   (Oi  O ) (Pi  P)   i1 i1 

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Where O: Observed, P: simulated Values and the over bar denotes the mean for the entire time period of the evaluation.

2. Nash-Sutcliffe efficiency (NSE)

The Nash-Sutcliffe efficiency (NSE) is a normalized statistic that determines the relative magnitude of the residual variance (―noise‖) compared to the measured data variance (―information‖). NSE indicates how well the plot of observed versus simulated data fits the 1:1 line. NSE is computed as:

n (Oi  Pi)2 i1 10 NSE 1 n (Oi  O )2 i1

NSE ranges between -∞ and 1.0 (1 inclusive), with NSE = 1 being the optimal value. Values between 0.0 and 1.0 are generally viewed as acceptable levels of performance, whereas values <0.0 indicates that the mean observed value is a better predictor than the simulated value, which indicates unacceptable performance.

3.4.4.3. SWAT Model Description

The SWAT model was developed by the U.S. Department of Agriculture – Agriculture Research Service (USDA-ARS). It is a conceptual model that functions on a continuous time step. Model components include weather, hydrology, erosion/sedimentation, plant growth, nutrients, pesticides, agricultural management, channel routing, and pond/reservoir routing. Agricultural components in the model include fertilizer, crops, tillage options, grazing, and the capability to include point source loads (Neitsch et al., 2010). The SWAT model predicts the influence of land management practices on constituent yields from a watershed. SWAT is a public domain model that is actively supported by USDA-ARS at the Grassland, Soil, and Water Research Laboratory in Temple, Texas. At this time, there are more than 700 publications in peer-reviewed scientific journals that report development and applications of the SWAT model.

SWAT is a theoretical model that operates on a daily time step. In order to adequately simulate hydrologic processes, the watershed is divided into sub-watersheds through which

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Tana Sub-Basin Land Use Planning and Environmental Study Project streams are routed. The sub-units of the sub-watersheds are referred to as hydrologic response units (HRUs) which are the unique combination of soil, land use, and slope characteristics and are considered to be hydrologically homogeneous. Both sub-watersheds and HRUs are user defined, providing model users with some control over the resolution considered in the SWAT model (Arnold et al., 2005). The model calculations are performed on a HRU basis and flow and water quality variables are routed from HRU to sub-watersheds and subsequently to the watershed outlet. The SWAT model simulates hydrology as a two-component system, composed of land hydrology and channel hydrology. The land portion of the hydrologic cycle is based on a water mass balance. Soil water balance is the primary considerations by the model in each HRU, which is represented as (Arnold et al., 1998b):

t SWt SW(RiQiETi  Pi  QRi )...... i1 11 where SW is the soil water content, i is time in days for the simulation period t, and R, Q, ET, P, and QR respectively are the daily precipitation, runoff, evapotranspiration, percolation, and return flow. The hydrologic cycle simulation by SWAT is shown in Figure 10. Water enters the SWAT model‘s watershed system boundary predominantly in the form of precipitation. Precipitation inputs for hydrologic calculations can either be measured data or simulated with the weather generator available in the SWAT model. Precipitation is partitioned into different water pathways depending on system characteristics. The water balance of each HRU in the watershed contains four storage volumes: snow, the soil profile (0-2 m), the shallow aquifer (2-20 m), and the deep aquifer (>20 m). The soil profile can contain several layers. The soil- water processes include infiltration, percolation, evaporation, plant uptake, and lateral flow. Surface runoff is estimated using the SCS curve number or the Green-Ampt infiltration equation. Percolation is modeled with a layered storage routing technique combined with a crack flow model. Potential evaporation can be calculated using Hargreaves, Priestly-Taylor or Penman-Monteith method (Arnold et al., 1998a).

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Figure 10: Schematic representation of the hydrologic cycle (Source: (Arnold et al., 2005))

3.4.4.6.1. Model Inputs

Weather data, long year streamflow data for calibration, soil properties, topography, vegetation, and land management practices occurring in the watershed are required inputs of SWAT.

1. Meteorological Data

Meteorological variables were required for input to SWAT hydrological model and by identifying number of stations which lays on four major watersheds (Gilgel Abay, Gumara, Ribb and Megech) of Lake Tana Sub-basin in addition to stations around the watershed (Figure 11 and Appendix 7); daily meteorological variables were collected from National Meteorological Agency (NMA) Bahir Dar Branch Directorate.

Considering 2008 as the end of the data year, each meteorological station has 15 years daily meteorological data. All the above meteorological stations and their measured variables were used as an input for the hydrological model.

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2. Hydrological Data

Streamflow measurements for the four major watersheds were used for calibrating and validating the SWAT model simulation. Daily streamflow data was obtained from Ministry of Water and Energy (MoWE). Observe Figure 11 for locations of the four major discharge gauging stations.

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Figure 11: Hydro-meteorological station locations

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3. Spatial Data

Digital Elevation Model (DEM)1, land use/land cover, and soil are the three spatial data inputs required by SWAT model.

a) Digital Elevation Model (DEM)

Digital Elevation Model (DEM) describes the elevation of any point in a given area at a specific spatial resolution as a digital file. DEM is one of the essential inputs required by SWAT: (1) to delineate the watershed in to a number of sub watersheds or sub basins; (2) to analyze the drainage pattern of the watershed, slope, stream length, width of channel within the watershed. The DEM was obtained from the NASA Shuttle Radar Topographic Mission (SRTM) with a resolution of 90m*90m. Topographically, Lake Tana Sub-basin is represented with Figure 12 which was prepared from the used DEM for SWAT.

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Figure 12: Elevation distribution of Lake Tana Sub-Basin b) Land use/ Land cover

Using the following methodology the Land Cover and Land Use study in Tana sub-basin performed. For more detail of the report see section II volume IV of this project report.

 Collecting and reviewing previous nationwide, regional and lake Tana area studies,

 Recent Satellite imagery collection and preprocessing,

 Necessary field data and signature collection,

 Post-field Land Cover and Land Use interpretation, analysis and classification based on field and imagery signatures of objects using ERDAS imaging software,

 Verification and validation through review and field data

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 Final Land Cover and Land Use map preparation,

 Data analysis and report writing,

Each unit of the land use/land cover with some unit covers from MoWE for open spaces was used for hydrological analysis. For spatial observation see Figure 13 below and Table 21 for legend and SWAT code description. The land use/land cover properties used for SWAT database are shown on Appendix 10.

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Figure 13: Land use map of Lake Tana Basin used for SWAT

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Table 21: Land cover as per ADSWE and their corresponding area and SWAT definition Value Unit Cover SWAT Code Area 1 Degraded wooded shrub land RNGB 1.86 2 Dense natural forest FRST 143.33 3 Dense shrub land FRSD 205.92 4 Farm village AGRC 1021.66 5 Intensively cultivated land AGRL 5648.66 6 Lake WATR 3065.51 7 Moderately cultivated land AGRC 2324.38 8 Open grass land PAST 886.90 9 Open shrub land RNGB 491.30 10 Permanent wetland WETL 193.89 11 Plantation forest FRSE 427.83 12 Ponds and dams WATR 20.62 13 Rivers WATR 23.18 14 Seasonal wetland WETN 46.76 15 Shrub grass land RNGB 6.14 16 Sparsely cultivated land AGRC 363.74 17 Sub-afro-alpine vegetation PAST 133.34 18 Town URLD 43.26 19 Intensively cultivated AGRL 12.41 20 Moderately cultivated AGRC 24.68 21 Grassland PAST 0.12 22 Water body WATR 0.81 23 Urban URLD 2.76 24 Shrub land FRSD 0.69 25 Woodland open FRSE 0.08 26 Afro alpine PAST 0.31

c) Soil Data A soil survey of the Tana sub-basin was carried out give detailed soil resources information for land evaluation team, characterize, classify and to show distribution of soils of sub-basin on maps so as to minimizing soil degradation, improving agricultural production and productivity of the Tana sub basin. The methodology had adapted review of previous studies and conducting of soil survey /assessment at field condition. A soil map was prepared at 1:20,000 scale. Land sat ETM+ 2014 image was used for the delineation and classification of major soil boundaries. Slope map of the study area was overlaid to generate the soil mapping units. Hence, a total of 101 soil mapping units with two replications were prepared.

Observations of soils were mostly carried out by profile description to a depth of 2m, unless obstructed at shallower depth by rock, stones and/or hard soil layer. In addition, soil

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Tana Sub-Basin Land Use Planning and Environmental Study Project characteristics are carefully studied wherever chance exposures (example road cuts, gully walls etc) are observed. Augering to a depth of 1.2m is also made on every accessible sites and mapping units along representative traverses. For soil resource investigation a total of 336 representative observation points at different sites were selected. Soil profile pits were dug and carefully studied and soil samples 845 samples were collected from the pits for intended chemical and physical parameters analyses conducted in soil laboratory. There are observation points that were observed to be soilless (rocky and stony or parent material entirely exposed having no soil cover) and sample is not collected from these areas. For detail observation of soil landscape units 45426 auger hole without sampling was conducted.

All relevant land qualities and characteristics used for land evaluation and planning were collected. Data were recorded on standard soil profile and auger description sheets giving particular emphasis to those characteristics, which affect present or potential land use and serve as a basis for soil classification and land evaluation. General site information, registration and location, soil morphological description, genetic and systematic interpretations were done based on ―Guidelines for soil description, FAO 2006, fourth edition‖. Soil classification was done based on World Reference for Soil Resources, FAO 2006 fourth edition. For more description and detail report of soil see section II volume I. For the hydrological modelling of this study some border areas of the basin like places downstream of Charachara weir up to the hydrological station which is found Bahir Dar University Pedagogy campus, Ministry of Water and Energy (MoWE) soil data was used. For spatial observation see Figure 14 below and Table 22 for legend and SWAT code description. The soil properties used for SWAT database are shown on Appendix 9.

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Figure 14: Soil map of Lake Tana Basin used for SWAT

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Table 22: Used soil units, area contribution and their SWAT code Value Soil Unit SWAT Code Area 1 Abruptic Alisols AbrAli 4.24 2 Abruptic Luvisols AbrLuv 8.77 3 Albic Luvisols AlbLuv 12.62 4 Alic Nitosols AliNit 217.04 5 Alic Vertisols AliVer 161.91 6 Calcic Vertisols CalVer 96.66 7 Chromic Lixisols ChrLix 1.42 8 Chromic Luvisols ChrLuv 26.99 9 Chromic Vertiols ChrVer 361.23 10 Dystric Luvisols DysLuv 221.90 11 Dystric Nitosols DysNit 369.21 12 Dystric Regosols DysReg 206.69 13 Eutric Cambisols EutCam 28.27 14 Eutric Fluvisols EutFlu 264.26 15 Eutric Gleysols EutGle 2.97 16 Eutric Leptosols EutLep 2.59 17 Eutric Nitosols EutNit 1476.30 18 Eutric Regosols EutReg 0.11 19 Eutric Vertisols EutVer 1393.19 20 Gelic Gleysols GelAli 172.14 21 Gleyic Alisols GleAli 0.40 22 Haplic Alisols HapAli 3.70 23 Haplic Luvisols HapLuv 11.40 24 Haplic Nitisols HapNit 0.35 25 Humic Alisols HumAli 28.79 26 Humic Nitosols HumNit 8.22 27 Hyperdystric Alisols HyDyAli 0.75 28 Hyperskeletic Alisols HypSkAli 108.70 29 Hyperskeletic Leptosols HypSkLep 1875.33 30 Water Body WATER 3077.54 31 Lamelic Luvisols LamLuv 68.10 32 Leptic Cambisols LepCam 62.56 33 Leptic Luvisols LepLuv 890.51 34 Leptic Regosols LepReg 225.23 35 Leptic Vertisols LepVer 37.67 36 Lithic Leptosols LitLep 123.64 37 Lixic Ferralsols LixCam 216.39 38 Molic Cambisols MolCam 1.84 39 Mollic Luvisols MolLuv 64.39

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Value Soil Unit SWAT Code Area 40 Nitic Alisols NitAli 1098.46 41 Nitic Luvisols NitLuv 39.24 42 Pellic Vertisols PelVer 448.60 43 Plinthic Acrisols PliAcr 24.49 44 Profondic Luvisols ProLuv 133.69 45 Skeletic Regosols SkeReg 57.25 46 Stagnic Fluvisols StaFlu 28.47 47 Towns URBAN LAND 373.03 48 Vertic Alisols VerAli 0.18 49 Vertic Cambisols VerCam 398.07 50 Vertic Leptosols VerLep 46.97 51 Vertic Luvisols VerLuv 607.86

3.4.4.6.2. Model Setup

Five sequential steps have been followed to set up the SWAT model, which are: watershed Delineation, HRU Definition, Weather Data Definition, Edit SWAT inputs and Simulation. The descriptions of these steps are given in the section below: 1. Watershed delineation

Watershed delineator tool in ArcSWAT allows the user to delineate the watershed and sub basins using Digital Elevation Model (DEM). Flow direction and accumulation were the concept behind to define the stream network of the DEM in SWAT. The monitoring point was added manually and the numbers of sub basin were adjusted accordingly. Finally, the four watersheds (Gilgel Abay, Gumara, Ribb and Megech) were delineated by taking the outlet point at Tana confluences. Table 23 show the four watersheds areas, number of sub-basins and number of HRUs and also Figure 15 shows the sub-basin and their corresponding areas are shown on Appendix 8. Table 23: Major watershed areas, number of sub-basins and HRUs Watershed Area (km2) Number of sub-basins Number of HRUs Gilgel Abay 4043.33 49 572 Gumara 1363.91 40 498 Ribb 1567.52 47 486 Megech 658.29 32 309

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Figure 15: Lake Tana Sub-Basin major watersheds and their SWAT sub-basins

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2. HRU definition

HRU analysis helps to load land use map, soil map and also incorporate classification of HRU in to different slope classes. The land use map as well as soil map was overlapped 100 % with the delineated watershed and HRUs were formed for the sub watersheds. Number of HRUs for the major watersheds of Lake Taba Sub-Basin is shown in Figure 15. Reclassifying land use and soil is nothing without preparing SWAT database and their corresponding lookup table. For land uses it is simply relating the existing land uses to SWAT crop database. Databases used for user soil, crop and urban are tabulated on Appendix 9, Appendix 10 and Appendix 11 and the classified landuse/soil/slop are also shown on Appendix 12. Generally, key procedures of HRU analysis are listed below (Hassan, 2012):

 Define the land use dataset

 Reclassify the land use layer

 Define the soil dataset

 Reclassify the soil layer

 Reclassify the slope layer

 Overlay land use, soil, and slope layers 3. Weather data definition

The climate of a watershed provides the moisture and energy inputs that control the water balance and determine the relative importance of the different components of the hydrologic cycle. Available meteorological records (1994-2008) (i.e. precipitation, minimum and maximum temperature, relative humidity and wind speed) and location of Meteorological station are prepared based on ArcSWAT 2012 input format and integrated with the model using weather data input wizards. Bahir Dar meteorological station data were defined and used as weather generator for this study and its calculated parameters used for SWAT database are tabulated on Appendix 13.

4. Sensitivity Analysis

Sensitivity analysis is a simple technique for assessing the effect of uncertainty on the system performance. It is also a measure of the effect of change of one parameter on another. The

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5. Calibration and Validation

Model calibration involves modification of input parameters and comparison of predicted output with observed values until a defined objective function is achieved (James and Burges, 1982). Parameters identified in sensitivity analysis that influence significantly the simulation result were used to calibrate the model.

For this study the measured stream data of Gilgel Abay river near Merawi, Gumara river near Bahir Dar, Ribb near Addis Zemen and Megech near Azezo were used to calibrate the model by SWAT-CUP using Sequential Uncertainty Fitting Version-2 (SUFI-2) uncertainty analysis routine (Abbaspour, 2011) from a period of 1996-2004 by skipping two years data as a warm up period. The rest of the data was used for validation. The result is presented in result and discussion part.

The calibration process using SUFI-2 algorithm was selected for this research from other two algorisms (ParaSol and GLUE) due to its final fitted parameters for Gilgel Abay, Gumara, Ribb and Megech catchments (Setegn, 2008). In SUFI-2, parameter uncertainty accounts for all sources of uncertainties such as uncertainty in driving variables (e.g., rainfall), conceptual model, parameters, and measured data.

6. Simulation Method

The first simulation of SWAT is done from the SWAT simulation menu selecting the Run SWAT button. The printout frequency is specified as three forms in the window: daily, monthly and yearly. In this simulation the printout setting is defined as daily and the simulation period is selected as 1994 to 2008. To describe the distribution of rainfall amounts SWAT provides two options: a skewed normal distribution and a mixed exponential distribution. The skewed distribution has been used to generate representative stream flow

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Tana Sub-Basin Land Use Planning and Environmental Study Project whereas the exponential distribution is an alternative to the skewed distribution. In this simulation the skewed normal probability distribution function has been used to describe the distribution of rainfall amounts. SWAT uses Manning‗s equation to define the rate and velocity of flow. Water is routed through the channel network using the variable storage routing method or the Muskingum River routing method. In this simulation the Muskingum method has been used. For estimating runoff, the SCS curve number method has been used and the variable CN: Moisture condition II curve number is specified on the text file named ‗.mgt‘. For calculating Potential Evapotranspiration (PET) the Hargreaves method has been used since it requires less data (air temperature only).

3.4.4.6.3. Water balance analysis A water balance is based on the principle that any mass is conserved within a specified control volume or domain and specific time period. Dingman (2002) refers to a water balance as ―the amount of a conservative quantity entering a control volume during a defined period minus the amount of quantity leaving the control volume during the same time period equals the change in the amount of the quantity stored in the control volume during the same time period‖. A water balance often leads to understanding of hydrological systems. Lake Tana water balance is simulated from 1996 to 2008.

The water balance equation in its simplest form reads: S  Inflow Outflow 12 T Where ΔS/ΔT is the change in storage for a selected period of time [L3 T-1]. The general water balance equation of a lake can be written as: S  (P  SI  SI  GW )  (E  S  GW )  SS 13 T Ungauged Gauged I o o o

Where: P - Lake area rainfall [L3 T-1] 3 -1 SIUngauged - Surface water inflow from ungauged catchments into the lake [L T ] 3 -1 SIGauged - Surface water inflow from gauged catchments into the lake [L T ] 3 -1 So - Surface water outflow from the lake [L T ] 3 -1 GWI - Subsurface water inflow into the lake [L T ] 3 -1 Eo - Open water evaporation from the lake surface [L T ] 3 -1 GWo - Subsurface water outflow from the lake, and [L T ] SS - Sink source term. [L3 T-1].

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3.4.5. Siltation and Sedimentation

Here discharge sediment rating curves are developed for the five major rivers of Tana Sub-Basin. This is due to that there was not enough data of suspended sediment measured continuously in the sub- basin. It was found that some measurements of suspended sediment concentration in the water together with the discharge flowing in the river at the same time were available at the five stations. The number of observations at each of these stations is reasonable (about 16 to 56). But the method for sampling is not perfectly clears because: integrated depth method, samples taken near the surface or the bed, definition of the verticals where samples are extracted from, etc. The dates of sampling show that they have not been performed on a systematic basis, but rather at random times: campaigns of measurements alternate with long periods without any observation.

Attempts were made, for all stations, to link the sediment concentration to the discharge, in the form of sediment rating curves with equations:

QL = a x (H-H0)b linking flow to water level

and QS =  x QL linking solid discharge to liquid discharge.

To estimate the sediment load into and out of the lake is estimated by using the developed sediment-discharge rating curve equation. The equation is important to get the sediment load by using the estimated stream flow using SWAT at the lake coenfluence.

3.4.6. Surface and Ground Water Potential for Irrigation

3.4.6.1. Surface Water Potential for Irrigation

Thornthwaite - type Monthly Water Balance model was used for the computation of annual runoff. Thornthwaite-type monthly water-balance models are conceptual models that can be used to simulate steady-state seasonal (climatic average) or Continuous values of watershed or regional vater input, snowpack, soil moisture, and evapotranspiration. Input for such models consists of monthly values of precipitation, Pm and temperature, Tm. representative of the region of interest. For steady-state applications, these values are monthly climatic averages, In which case m=1,2, ..., 12; for continuous simulations they are actual monthly averages,ln which case m=1,2....., 12•N,where N is the number of years of record. Such models typically have a single parameter, the soil-water storage capacity of the soil in the region, SOILmax which is defined as

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SOILmax  fc  Zrz 14

Where, θfc is the field capacity and Zrz the vertical extent of the root zone. Typically SOILmax

=100 or 150 mm. For continuous applications an initial value of soil moisture. SOIL0, must also be specified.

All water quantities in the model represent depths (volumes per unit area) of liquid water; inputs and outputs are monthly totals and snowpack and soil storage are end – of - month values.

Snowpack, Snowmelt, and Water Input

Monthly precipitation is divided into rain, RAINm, and snow, SNOWm, where

RAINm  Fm  Pm 15

SNOWm  (1 Fm ) Pm 16 And Fm is the melt factor. A simple approach to calculating Fm is

0 Fm  0,Tm  0 C; F  0.167 T ,00 C  T  60 C : m m m 17 0 Fm 1,Tm  6 C

To determine the monthly snowmelt MELTm, as

MELTm  Fm (PACKm1  SNOWm ) 18

Where, PACKm-1 is the snowpack water equivalent at the end of month m - 1. The snowpack at the end of month m is then computed as

2 PACKm  (1 Fm )  Pm  (1 Fm ) PACKm1 19

By definition, the water input, Wm, is

Wm  RAINm  MELTm 20

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Evapotranspiration and Soil Moisture

Following Alley(1984),if Wm≥PETm,ET takes place at the potential rate,

ETm  PETm 21 and soil moisture increases or, if already at SOILmax, remains constant. Thus

SOILm  minWm  PETm  SOILm 1,SOILmax  22

Where […] min indicates the smaller of the quantities in the braces. Most current applications of the method use a simpler temperature-based method or, if data are available one of the other approaches for estimating PETm.

If Wm < PETm, ETm is the sum of water input and an increment removed from soil storage.

ETm Wm  SOILm1  SOILm 23 Where the decrease in soil storage is computed via the following conceptual model:

 PET m Wm  SOILm1  SOILm  SOILm1  1 exp  24  SOILmax  Computation If the model is used with climatic monthly averages, the computations in equations 18, 19, 20, and 22 are ―wrapped around‖ from m= l2 to m= 1 so that m — 1 = 12 when m = 1. Thus the computations are circular and must be iterated until all the monthly quantities converge to constant values. This iteration is automatically carried out In Excel spreadsheets when the ―Enable iterative calculations‖ option is activated. Otherwise you will get an error message: ―Cannot resolve circular references.‖

Overall Water Balance

The model output is a table of monthly values that can be graphed to give a concise picture of the annual cycle of inputs, soil and snowpack storage, evaporation, and water available for ground-water recharge and streamflow at any location. This model has been programmed in a spreadsheet called ThornEx.xls.

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Figure 16: THORNTHWAITE-TYPE MONTHLY WATER-BALANCE MODEL Therefore, the mean annual runoff (1996-2008) of each meteorological station was interpolated with Inverse Distance Weighting (IDW) interpolation method to show the result spacially.

3.4.6.2. Ground Water Potential for Irrigation

The methodology developed here to determine groundwater potential consists of four main steps (Figure 17). The first step starts with the identification of the thematic layers which are relevant to groundwater potential. The second involves preprocessing these thematic layers to ensure uniform projection (projection: UTM, datum: WGS84) and resolution, assigning scores, and weightages. The third step integrates all thematic layers along with scores using the spatial analysis tool in GIS software. The final step categorizes the outputs into five classes and compares the results with borehole yield information.

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Figure 17: Overview of the methodology for groundwater potential assessment using integrated remote sensing and GIS techniques (Source: (Gumma and Pavelic, 2012)) Using the above methodology the following weights are given for each themes and the raster overlay was undertaken. Table 24: Ground water potential indicative factor weights No Theme Theme weight 1 Geomorphology 18 2 Geology 15 3 Slope 13 4 Drainage density 15

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5 Rainfall 15 6 LULC 12 7 Soils 12 Total 100

4. RESULT AND DISCUSSION

4.4. Water Resource and Irrigation Development in Tana Sub-Basin

4.4.4. Irrigation and Development

Land and Water are some of the available natural resources in the region. Our country, Ethiopia, is called water tower of East Africa. Especially our region Amhara is origin of biggest rivers in the country. Those countries, which have limited amount of these resources, are creating paradise and oasis in the desert using these limited natural resources efficiently. On the contrary, due to inefficient (unwise) utilization of these resources our people are suffering from drought and famine. The majority of country‘s populations are agrarian, so to alleviate the food insecurity problem utilizing these available resources is mandatory. Because water sustains irrigation and irrigation sustains agriculture.

Amhara region has enormous potential both in land and water resources. Different development activities have been underway to utilize these resources. Currently, there are 310 irrigation schemes developed in Amhara region. The irrigation schemes developed have covered an irrigated area of 8,469.26 hectares with 17,443 people beneficiaries. Out of these total irrigated areas, 5,718.68 hectares is from small-scale and 2,750.58 is from medium-scale irrigation schemes (Awulachew et al., 2007).

According to Ministry of Water and Energy (MoWE, Accessed Dec 26, 2014) posted from its website in Tana Sub-Basin there are irrigation projects under feasibility study and construction. The project areas are located in Abbay Basin. It includes North East Lake Tana, North West Lake Tana, South West Lake Tana, Jemma, Megech, Rib and Gilgel Abbay Irrigation Project. They constitute a total area of 62,457 ha. The spatial map obtained from Eastern Nile Technical Regional Office (ENTRO) showed that when these irrigation projects will implemented our large scale irrigation potential will be increased up to 138,281.27 ha.

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This will have its own contribution on sustainable development of the basin people (Figure 18).

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Figure 18: Future irrigation potential of Tana Sub-Basin (ENTRO, 2013) To deal with the problems and to come to the solution the existing situation of the Tana Sub- Basin irrigation work has to be known and analyzed. This part of the report discusses about the irrigation schemes, irrigation water sources and area developed.

In Tana Sub-Basin irrigation scheme type is broadly divided in to two: Traditional and modern scheme. Traditional scheme is traditional irrigation practiced by use of indigenous knowledge of the beneficiaries. This practice is ancient in its history and it is working still now a days. The major irrigation structure utilized under this practice were diversion, hand dug wells (shallow wells) and ponds. Whereas Modern scheme is an irrigation scheme with or more of the following modern technology application: water abstraction, conveyance and distribution structures (equipment). It may also include irrigation with all its inputs and management. The structures included under this scheme were diversion (weir, barrage, and intake), dam, pump, pond and hand dug wells.

According to secondary data collected from Woreda sectors and our inventory in 2013/2014 out of the total 1,182,445.39 ha of rural land of Tana Sub-Basin 188637.39 ha is irrigable land. It constitutes 16% of the area. This result includes both traditional and model irrigation schemes. One can see each Woreda contribution for this existing irrigation potential from Table 25. From the 21 Woredas included for this study Fogera (43.4%), Estie (29.9%), Bahir Dar Zuria (27.6%) and Fagita Lekoma (19.1%) contribute largely for the basin. The percentage shows how much of their area is irrigable.

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Figure 19: Percentage of existing irrigation potential of Tana Sub-Basin Table 25: Existing irrigation potential of Tana Sub-Basin Total Area Total Area Irrigable Irrigable No Zone Woreda Name from the from the Land Land Basin(km2) Basin (ha) (Ha) (%) 1 North Gondar Alefa 174.69 17469.44 686.70 3.9 2 West Gojam Bahir Dar Zuria 723.54 72353.63 19938.86 27.6 3 Awi Banja 79.90 7990.18 1113.05 13.9 4 North Gondar Chilga 309.78 30977.96 345.20 1.1 5 Awi Dangila 308.28 30828.49 4320.08 14.0 6 North Gondar Dembia 1285.28 128527.91 19065.46 14.8 7 South Gondar Dera 649.90 64990.44 8223.00 12.7 8 South Gondar Ebinat 260.75 26075.25 371.70 1.4 16 South Gondar Estie 246.79 24678.59 7382.35 29.9 9 Awi Fagita Lekoma 384.72 38472.45 7345.25 19.1 10 South Gondar Farta 947.44 94743.75 9166.1 9.7 11 South Gondar Fogera 1028.09 102808.89 44586.97 43.4 12 North Gondar Gondar zuria 959.77 95977.04 7604.1 7.9 13 North Gondar Lay Armachiho 395.46 39546.00 2178.17 5.5 14 South Gondar Libo Kemkem 714.55 71455.32 14283.7 20.0 15 West Gojam Mecha 1272.86 127285.89 14781.11 11.6 17 West Gojam North Achefer 661.11 66110.86 7440.58 11.3 18 West Gojam Sekela 416.41 41640.58 8324.01 20.0 19 West Gojam South Achefer 648.54 64854.06 5289.93 8.2 20 North Gondar Takusa 311.87 31186.76 5412.58 17.4 21 North Gondar Wogera 44.72 4471.90 591.85 13.2 Total 11824.45 1182445.39 188637.39 16.0 Source: ADSWE Assessment ,2014 The assessment also tried to see the beneficiaries of the irrigation in Woreda Level from the total population projected for 2014. Out of 2,348,410 of the rural population 430,814 are benefited with the existing irrigation both in traditional and modern. This number constitutes 18.3% of the basin rural population. The numbers of beneficiaries are large in Estie (39.8%), Fogera (36.2%), Bahir Dar Zuria (35.1%) and Fagita Lekoma (25.2%) as compared to the other Woredas (Table 26 and Figure 20).

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Figure 20: Percentage of existing irrigation beneficiaries of Tana Sub-Basin

Table 26: Existing irrigation beneficiaries of Tana Sub-Basin Total Beneficiaries Beneficiaries No Zone Woreda Name Population (2014) (%) (2014) 1 North Gondar Alefa 26323 2762 10.5 2 West Gojam Bahir Dar Zuria 102961 36176 35.1 3 Awi Banja 18496 2788 15.1 4 North Gondar Chilga 69874 637 0.9 5 Awi Dangila 76779 6990 9.1 6 North Gondar Dembia 271931 40353 14.8 7 South Gondar Dera 136780 34220 25.0 8 South Gondar Ebinat 43788 1431 3.3 9 South Gondar Estie 46251 18425 39.8 10 Awi Fagita Lekoma 73331 18483 25.2 11 South Gondar Farta 224021 36756 16.4 12 South Gondar Fogera 226931 82240 36.2 13 North Gondar Gondar zuria 158170 22385 14.2 14 North Gondar Lay Armachiho 79387 7304 9.2 15 South Gondar Libo Kemkem 159268 37957 23.8 16 West Gojam Mecha 268755 17846 6.6 17 West Gojam North Achefer 116680 25285 21.7 18 West Gojam Sekela 70143 12088 17.2 19 West Gojam South Achefer 107207 23224 21.7 20 North Gondar Takusa 59103 14207 24.0 21 North Gondar Wogera 12231 679 5.6 Total 2348410 430814 18.34 Source: ADSWE Assessment ,2014

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The assessments increase its scale by collecting the irrigation potential data with scheme type. Some of the Woredas like Dangila, Fogera and Gondar Zuria lacks this information due to the absence of the data of each Kebele with each scheme types. In addition Chilga, Dera and Mecha have not beneficiaries with Kebele level. These missing data are shown on Table 27 with cells having gray color. From the table one can clearly observe that most of the irrigation potential of the basin is traditional river diversion and motor pump. This result showed that source of water for majority of the irrigation is river and spring to the next place.

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Table 27: Existing irrigation potential at Woreda level with each scheme types

Woreda

Alefa Bahir Dar Zuria Banja Chilga Dangila Dembia Dera Ebinat Estie Fagita Lekoma Farta Fogera Gondar Zuria Lay Armachih o Libo Kemkem Mecha North Achefer Sekela South Achefer Takusa Wogera

Modern Irrigated Area 1357.3 35.6 360.0 92.7 4060.0 501.0 167.0 203.8 408.9 River Diversion Beneficiaries 1143 1096 402 5440 440 1273 1161 1381. Traditional Irrigated Area 15.9 4884.3 432.9 78.0 7868.5 1064.0 175.7 873.9 6387.0 5 6578.8 5193.5 1077.8 5112.7 1002.1 1714.5 110.5 River Diversion Beneficiaries 67 6023 788 12966 670 3190 4027 15644 4148 5538 3559 4650.5 346

Modern Irrigated Area 68 3.5 Spring Diversion Beneficiaries 14

Traditional Irrigated Area 7.6 976.3 487.8 35.3 460.9 676.0 5.5 268.8 1296.0 590.1 639.4 3667.8 514.7 2589.0 831.1 18.5 Spring Diversion Beneficiaries 51 2887 1331 1024 43 1076 2143 2098 2129 4451 3775 139 Irrigated Area 66.0 30.0 64.6 Dam Beneficiaries 520. 139. Irrigated Area 2 8661.9 119.8 5 8595.7 2588.0 88.3 1774.0 918.5 106.8 6538.7 3505.9 2993.4 316.3 1052.0 3329.0 438.0

Motor Pump Beneficiaries 1256 14119 431 13876 233 7096 576 13898 3626 282 2960 7906 21

Irrigated Area 0.4 24.3 2.8 7.0 2.1 9.6 2.0 54.1 11.7 1.7 0.2 10.6 35.8 8.0

Pedal Pump Beneficiaries 15 32 13 38.5 308 12.8 8 11 33 168 29

Irrigated Area 4.5 4.7 7.9 0.5 48.0 1.0 47.6 22.8 2.1 32.8

Rope Pump Beneficiaries 45 32 150 91 17 70

Irrigated Area 2.1 32.0 14.3 0.1 42.5 1.1 0.7 10.9

Pond Beneficiaries 14 0.75 57 679 3 56 Hand Dug Irrigated Area 58.3 4052.2 63.0 170.9 2320.0 1.6 255.8 15.5 34.0 275.2 1460.2 2594.5 99.9 1384.1 5.9 Well Beneficiaries 504 11958 639 19 1023 191 4858 13523 516 7998 51 Irrigated Area 80.3 1.4 32.1 919.8 676.0 2.3 126.0 15.0 11.5 30.4 840.5 235.0 557.3 323.5 11.0 Fetching Beneficiaries 839 72 10435 37 504 57 327 1757 3611 1418 93

Drip Irrigated Area 5.0 0.3 0.1 9.8 Irrigation Beneficiaries 82 2 1 64 Recession Irrigated Area 1042.0 678.0 Farming Beneficiaries 1299 686. 19938. 1113. 345. 4320. 19065. 7345. 44587. 7604. 2178. 14283. 14781. Irrigated Area 7 9 1 2 1 5 8223.0 371.7 7382.4 2 9166.1 0 1 2 7 1 7440.6 8324.0 5289.9 5412.6 591.9 1848 2238 Total Beneficiaries 2762 36176 2788 637 6990 40353 34220 1431 18425 3 36756 82240 5 7304 37957 17846 25285 12088 23224 14207 679 Source: ADSWE Assessment ,2014

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According to the inventory water source used for both traditional and modern irrigation are river, spring, lake, pond, ground water, dam and others. ‗Others‘ includes swampy area, regression of water body (bahire sheshi) and tap water. For modern irrigation the top two water source were river (84.6%) and spring (15.4%) (Table 28). Locations of these modern irrigation schemes are shown on Figure 21. From the figure one can understand that most of the points of modern irrigation schemes lied on rivers and around the rivers. This is an indication where the source is. In addition to modern one, traditional irrigation practice is shown point spatially on Figure 22.

Table 28: Contributions of water sources for existing modern irrigation Source of Water Number of Contribution schemes (%) River 159 84.6 Spring 29 15.4 Total 188 100 Source: ADSWE Assessment ,2014

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Figure 21: Command locations of existing modern irrigation schemes

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Figure 22: Command locations of existing traditional irrigation schemes using water sources of springs (A), rivers (B), dug well (C) and swamp (D)

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In medium and large scale irrigation development, construction of dams plays a significant role. Currently in Tana Sub-Basin there is one dam used for large scale and three dams for medium scale irrigation. The dam used for large scale irrigation is Koga and the three dams used for medium scale irrigation are Shina in Dera Woreda, Bebeks in Fogera Woreda and Selamko in Farta Woreda. Bebeks dam is totally filled with sediment and used as diversion weir and the others are also in danger due to the surrounding land degradation. One dam which is used for water supply of Gondar town is Angereb. The others like Megech, Ribb, Jema, Gilgel Abay and Gumara are dams under construction and to be constructed. All these will be sued for large scale irrigation practice. For more visual observation see Figure 23, Figure 24 and Table 29.

Table 29: Locations of existing and future dams of Tana Sub-Basin NAME X Y Megech 333430.2 1384981.0 Ribb 390935.8 1331618.1 Gumara 370116.2 1299958.8 Gilgel Abbay 282689.3 1267482.8 Koga 296902.0 1254443.8 Jema 301496.1 1238263.0 Bebeks 354331.0 1304566.0 Angereb 335487.0 1394971.0 Shina 338177.0 1305294.0 Selamko 394658.7 1313096.0 Source: ADSWE inventory, 2014

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Figure 23: Existing and future dams of Lake Tana Sub-Bain

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Figure 24: Observed dams of Tana Sub-Basin (Courtesy: ADSWE 2014)

4.4.5. Water Supply and Distribution

4.4.5.6. Urban Water Supply Schemes

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Tana Sub-Basin Land Use Planning and Environmental Study Project urban centers to identify types and number of the water supply schemes and their yields. The study has determined the average yields of the water sources based on the types of water supply source types as shown below on Table 30. From the GIS data of the assessment, the urban water supply schemes in Tana Sub-Basin are identified by clipping with the sub-basin boundary map.

Totally in Lake Tana basin 93 functional urban water supply sources have been identified out of these water supply sources the number of Bore holes are 68, hand dug wells 10, shallow well 1, springs 13 and surface water 1. In the sub-basin it has been identified only one surface water supply source (Angereb dam) which is the source of water supply for Gondar town. The rest of water supply schemes in the sub-basin are ground water sources.

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Figure 25: Sample bore hole (deep well) locations of urban water supply

Table 30: Population and water resource schemes in Lake Tana Basin area enclosure

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No. of Schemes Population, S/N Town HDW SP SHW BH SW Total 2014 FN NFN FN NFN FN NFN FN NFN FN NFN FN NFN

1 Addis Kidame 9021 1 1 0

2 Addis Zemen 23758 3 3 0

3 Ambasame 9283 1 1 0

4 Ambo Meda 2757 1 1 0

5 Amed Ber 5851 1 1 0

6 Arb Gebeya -Dera 2923 1 1 0

7 Ayikel 11986 1 2 3 0

8 Ayimba 3683 2 2 1 1 3 3

9 Bahir Dar 101295 3 8 11 0

10 Chuahit 10047 2 0 2

11 Dangela 36607 3 3 0

12 Debre Tabor 77667 7 6 1 6 14 6

13 Deq 8252 1 1 0

14 Delgi 10450 1 1 0

15 Dengel Ber 5019 1 1 0

16 Durbete 17329 1 1 0

17 Enferanz 6613 2 1 2 1

18 Enjebara 1087 1 1 0

19 Gassay 2794 1 1 0

20 Gonder 305281 2 24 1 27 0

21 Gorgora 2385 1 1 0

22 Hamusit 10492 1 1 0

23 Kimir Dingay 1994 1 1 0

24 Koladiba 17872 1 2 3 0

25 Kunzela 6893 1 1 0

26 Maksegnit 16874 2 2 0

27 Merawi 27546 1 1 0

28 Wereta 31291 3 3 0

29 Wetet Abay 5891 1 1 0

30 Yesmala 9677 1 1 0

31 Yifag 6003 1 1 0

Total 788621 10 8 13 0 1 1 68 3 1 0 93 12

HDW-Hand Dug Well, SP-Spring, SHW-Shallow Well, BH-Bore Hole, SW-Surface Water, FN-Functional and NFN-Non-Functional Source: BoWRD (2004) and ADSWE inventory (2014)

4.4.5.7. Rural Water Supply Schemes

The rural water supply schemes in general characterized by the following properties:

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• Dispersed - The schemes are distributed over a large area. The Situation makes outreach to the sites difficult • Remote - Most of the schemes are remote and have no access to Infrastructures. • Small - The schemes are small and level of service is low • Lack physical and social infrastructures - Lack the basic infrastructures like road , electricity and telephone • Not functional – due to different man made problems A total of 4818 water supply schemes were inventoried in rural areas of the region. This figure includes total schemes which were functional and not-functional. The sources include 3738 (77.6%) Hand Dug Wells (HDW), 746 (15.5%) springs, 197(4.1%) pipe supply from the nearest towns and others 137 (2.8%) surface water (river, pond and lake). Wells and springs make 93.1 % of the total number of schemes. This figure indicates that drinking water supply for human use in Tana Sub-Basin is ground water. Table 31: Summary inventory of water supply schemes Number of Contribution Scheme Type Scheme (%) Hand Dug Wells (HDW) 3738 77.6 Spring (Developed) 435 9.0 Spring (Not developed) 311 6.5 Pipe supply 197 4.1 River 121 2.5 Pond 10 0.2 Lake 6 0.1 Basin total 4818 100 Source: ADSWE inventory, 2014 From the above seven types of schemes 3,497 (72.6%) were functional at the time of inventory. The other 1,321 (27.4%) schemes were not functional. Most of the reasons of non- functionalities of schemes are:

 Animal disturbance  Steal  Breaking during usage  Construction problem  Flood and siltation  Dry of the scheme  Land slide  Micro-pollutant (sanitation)

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 Quality problem  Sweet less On the other hand from 4818 schemes 4,187 (86.9%) have their own committee and the other 631 (13.1%) have not. All the information related to scheme status and availability of respected committee with scheme type is shown in Table 32.

Table 32: Rural water scheme types with respect to their status and availability of committee Not No Number Functional Committee Scheme Type of functional Committee Scheme No % No % No % No % Hand Dug Wells (HDW) 3738 2720 72.8 1018 27.2 3500 93.6 238 6.4 Spring (Developed) 435 290 66.7 145 33.3 338 77.7 97 22.3 Spring (Not developed) 311 246 79.1 65 20.9 114 36.7 197 63.3 Pipe supply 197 140 71.1 57 28.9 188 95.4 9 4.6 River 121 90 74.4 31 25.6 40 33.1 81 66.9 Pond 10 7 70.0 3 30.0 3 30.0 7 70.0 Lake 6 4 66.7 2 33.3 4.0 66.7 2 33.3 Basin total 4818 3497 72.6 1321 27.4 4187.0 86.9 631 13.1 Source: ADSWE inventory, 2014

The inventory of rural water supply also includes the type of scheme use. Use of schemes are characterized by human only, livestock only and both human and livestock. In this case from the total number of schemes 3912 (81.2%), 94 (2%) and 812 (16.9%) were used for human, livestock and both for human and livestock respectively. The status of each scheme type is tabulated on Table 33. For instance, from the total 3738 well sources of water 3368 (90.1%) schemes used for human use only, 340 (9.1%) used for both human and livestock, and 30 (0.8%) used for livestock only. The wells used for livestock only were all undeveloped hand dug wells. This statistics include all functional and un-functional schemes. From schemes used for both human and livestock 218 (64.1%) schemes have not trough for the livestock and the other 122 (35.1%) schemes have trough. This shows that livestock has a large probability to be contaminant for drinking water.

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Table 33: Use type of rural water supply schemes Number Human Livestock of only only Both Scheme Type Scheme No. % No. % No. % Hand Dug Wells (HDW) 3738 3368 90.1 30 0.8 340 9.1 Spring (Developed) 435 248 57.0 4 0.9 183 42.1 Spring (Not developed) 311 104 33.4 22 7.1 185 59.5 Pipe supply 197 173 87.8 2 1.0 22 11.2 River 121 14 11.6 30 24.8 77 63.6 Pond 10 2 20.0 5 50.0 3 30.0 Lake 6 3 50.0 1 16.7 2 33.3 Basin total 4818 3912 81.2 94 2.0 812 16.9 Source: ADSWE inventory, 2014 Within the sub-basin Woredas rural water supply schemes are tabulated on Table 34. From the table one can easily understand that Dembia, Fogera and Farta Woredas hold the great number of schemes in the sub-basin. One can also see the special distribution of these schemes on Figure 26, Figure 27, Figure 28, Figure 29 and Figure 30. From the figures of hand dug wells the distribution is dense to the downstream of rivers. This clearly assures that downstream are good sources of water for wells. Whereas springs are highly populated to upstream of the sub-basin micro watersheds. Related to water supply sources for rural kebeles; the rural kebeles adjacent to towns which have not water source for their consumption they use urban pipe supplies as an option.

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Tana Sub-Basin Land Use Planning and Environmental Study Project Table 34: Rural water supply schemes of Tana Sub-Basin at Woreda level (Source: ADSWE inventory, 2014) Source Type Total Hand Dug Well Spring (Developed) Spring (Not Developed) Pipe supply River Pond Lake Woreda Non- Non- Non- Non- Non- Non- Non- Func. Func Non-Func Func Func Func Func Func Func Func Func Func Func Func Func Func

Alefa 73 9 4 0 3 0 0 0 3 0 0 0 1 0 84 9

Bahir Dar Town 9 6 0 0 0 0 18 4 0 0 0 0 0 0 27 10

Bahir Dar Zuria 139 83 2 1 2 0 4 2 1 0 0 0 0 0 148 86

Banja 27 1 4 0 0 0 0 0 0 0 0 0 0 0 31 1

Chilga 102 36 22 7 27 1 0 0 3 0 0 0 0 0 154 44

Dangila Town 33 5 1 1 0 0 1 0 0 0 0 0 0 0 35 6

Dangila 217 28 9 0 2 0 1 0 1 0 1 0 0 0 231 28

Dembia 200 96 13 12 8 4 60 30 10 0 0 0 0 0 291 142

Dera 31 50 6 5 3 10 11 2 15 0 0 2 0 0 66 69

Ebinat 73 44 4 5 2 3 1 0 5 1 0 1 0 0 85 54

Estie 7 5 2 11 2 1 2 7 0 0 0 0 0 0 13 24

Fagita Lekoma 80 45 13 1 6 2 3 1 1 2 0 0 0 0 103 51

Farta 278 65 70 55 48 13 1 1 3 0 1 0 0 0 401 134

Fogera 337 131 13 3 34 1 2 1 21 3 1 0 1 0 409 139

Gondar Zuria 183 63 20 1 40 11 5 0 26 6 1 2 1 1 276 84

Lay Armachiho 29 9 18 3 13 0 3 1 6 0 0 0 0 0 69 13

Libo Kemkem 222 67 10 10 10 6 1 0 1 2 0 0 0 0 244 85

Mecha 242 78 21 5 17 1 3 3 2 1 0 0 0 0 285 88

North Achefer 138 29 26 11 2 0 10 0 0 0 0 0 0 0 176 40

Sekela 103 46 15 5 14 1 13 2 0 0 1 0 0 0 146 54

South Achefer 71 96 5 5 2 4 2 2 3 1 0 0 0 0 83 108

Takusa 120 18 9 3 1 0 0 0 2 0 0 0 1 1 133 22

Wogera 6 5 2 2 10 7 0 0 2 0 0 0 0 0 20 14

Yilmana Densa 3 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0

Sub-Total 2723 1015 289 146 246 65 141 56 105 16 5 5 4 2 3513 1305

Total 3738 435 311 197 121 10 6 4818

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Figure 26: Spatial distribution of hand dug wells in the sub-basin

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Figure 27: Spatial distribution of springs in the sub-basin

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Figure 28: Spatial distribution of pipe supply sources in the sub-basin

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Figure 29: Spatial distribution of river sources in the sub-basin

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Figure 30: Spatial distribution of pond sources in the sub-basin

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Figure 31: Spatial distribution of Lake Sources in the sub-basin

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4.4.5.8. Rural Water Supply Schemes Coverage

4.4.5.8.1. Actual Water Supply Scheme Coverage

To assess rural water supply schemes coverage sampling of households with specified kebeles were implemented with socio-economy group. Major quantitative data pertaining to livelihood and related concerns was secured on the basis of survey instruments which were designed to be administered on randomly selected 4002 household heads in 25 Woredas and 101 selected kebeles based on the prevailing agro climatic zone (ACZ) within the project area. Probability sampling was utilized to identify the sampled kebeles and households. From among the probability sampling, both proportionate random sampling and stratified sampling were employed to select the interviewed farmers. Accordingly, the project area had been clustered stratified in to six agro climatic zones. Samples had been selected from each agro climatic zone.

Since Woredas and Kebeles under the study are containing different ACZs with different proportion, the sample size determined is again proportionately distributed to the area of each ACZ. Some Kebeles that contain unique ACZ‗s of the Woreda are purposively selected. After determining the sample size of the kebeles and deciding which one of them are included, lists of all household heads were taken from each Kebele administration. From the list, 40 respondents had been chosen randomly by using lottery method in each Kebele.

According to the above methodology from 4002 selected household interviewees 3,962 respond about sources of portable water for their consumption. Majority of the respondents use developed hand dug wells (42.2%) for their consumption undeveloped springs hold the next place of source with 24%. For more detail of the proportions of schemes used by the households in the sub-basin as well as at Woreda level are tabulated on Table 35 and Table 37. Safe drinking water coverage of the sub-basin is therefore 48.5% by considering developed hand dug wells and springs only. When undeveloped hand dug wells and springs are included in the analysis the coverage become 82.4%.

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Table 35: Major source of potable water in Tana Sub-Basin

Scheme Types Frequency Valid Percent Cumulative Percent River 485 12.2 12.2 Pond 29 .7 13.0 Lake 63 1.6 14.6 Rain 11 .3 14.8 Undeveloped Hand dug well 393 9.9 24.8 Developed Hand dug well 1671 42.2 67.0 Undeveloped spring 952 24.0 91.0 Developed spring 248 6.3 97.2 Water Pipe 101 2.5 99.8 Other 8 .2 100.0 Total 3962 100.0 Source: ADSWE inventory, 2014

4.4.5.8.2. Water Supply Scheme Coverage with Access

The above sampling technique was also applied to study water supply schemes coverage with access. The standard to say one water supply scheme is accessible is that; if it is found with 1.5 km radius? With this standard the data was collected by considering double trip which was 3 km. Therefore from 3892 respondents; 92.9% of the people access portable water for their consumption from all types of schemes. Safe drinking water coverage with access of the sub-basin by considering developed hand dug wells and springs only is 46.7%. When undeveloped hand dug wells and springs are included in the analysis the coverage become 80.6%. Table 36: Access to potable water for consumption in Tana Sub-Basin

Distance (km) Frequency Valid Percent Cumulative Percent 766.0 19.7 19.7 0-0.5 1103.0 28.3 48.0 0.5-1 483.0 12.4 60.4 1-1.5 16.5 77.0 1.5-2 643.0 4.0 81.0 2-2.5 156 11.9 92.9 2.5-3 464 .9 93.8 3-3.5 34 3.3 97.0 3.5-4 128 1.0 98.0 4-5 37 1.8 99.8 5-10 71 .2 100.0 10-20 7

Total 3892.0 100.0 Source: ADSWE inventory, 2014

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Tana Sub-Basin Land Use Planning and Environmental Study Project Table 37: Major source of potable water in Tana Sub-Basin Woredas (Source: ADSWE inventory, 2014) Scheme Types

Undeveloped Developed Undeveloped Developed Woreda River Pond Lake Rain Hand dug well Hand dug well spring spring Water Pipe Other Total Bahir Dar Count 0 0 40 0 9 1 30 0 0 0 80 City (Rural) % within Woreda 0.0% 0.0% 50.0% 0.0% 11.3% 1.3% 37.5% 0.0% 0.0% 0.0% 100.0% % of Total 0.0% 0.0% 1.0% 0.0% .2% .0% .8% 0.0% 0.0% 0.0% 2.0% Bahir Dar Count 11 10 3 2 43 80 32 14 0 1 197 Zuriya % within Woreda 5.6% 5.1% 1.5% 1.0% 21.8% 40.6% 16.2% 7.1% 0.0% .5% 100.0%

% of Total .3% .3% .1% .1% 1.1% 2.0% .8% .4% 0.0% .0% 5.0% Banja Count 1 0 0 1 10 29 40 0 0 0 81 % within Woreda 1.2% 0.0% 0.0% 1.2% 12.3% 35.8% 49.4% 0.0% 0.0% 0.0% 100.0% % of Total .0% 0.0% 0.0% .0% .3% .7% 1.0% 0.0% 0.0% 0.0% 2.0% Dangila Count 1 0 0 0 16 94 47 0 1 0 159 % within Woreda .6% 0.0% 0.0% 0.0% 10.1% 59.1% 29.6% 0.0% .6% 0.0% 100.0% % of Total .0% 0.0% 0.0% 0.0% .4% 2.4% 1.2% 0.0% .0% 0.0% 4.0% Dera Count 69 1 1 0 12 91 17 9 0 0 200 % within Woreda 34.5% .5% .5% 0.0% 6.0% 45.5% 8.5% 4.5% 0.0% 0.0% 100.0% % of Total 1.7% .0% .0% 0.0% .3% 2.3% .4% .2% 0.0% 0.0% 5.0% Este Count 8 1 0 0 9 79 17 28 13 1 156 % within Woreda 5.1% .6% 0.0% 0.0% 5.8% 50.6% 10.9% 17.9% 8.3% .6% 100.0% % of Total .2% .0% 0.0% 0.0% .2% 2.0% .4% .7% .3% .0% 3.9% Quarit Count 11 0 0 0 0 0 28 0 0 0 39 % within Woreda 28.2% 0.0% 0.0% 0.0% 0.0% 0.0% 71.8% 0.0% 0.0% 0.0% 100.0%

% of Total .3% 0.0% 0.0% 0.0% 0.0% 0.0% .7% 0.0% 0.0% 0.0% 1.0% South Count 10 0 0 0 48 78 6 14 0 0 156 Achefer % within Woreda 6.4% 0.0% 0.0% 0.0% 30.8% 50.0% 3.8% 9.0% 0.0% 0.0% 100.0% % of Total .3% 0.0% 0.0% 0.0% 1.2% 2.0% .2% .4% 0.0% 0.0% 3.9% Fagita Count 18 0 0 0 2 48 90 1 0 2 161 Lekoma % within Woreda 11.2% 0.0% 0.0% 0.0% 1.2% 29.8% 55.9% .6% 0.0% 1.2% 100.0% % of Total .5% 0.0% 0.0% 0.0% .1% 1.2% 2.3% .0% 0.0% .1% 4.1% Mecha Count 74 1 0 1 48 109 71 6 10 0 320 % within Woreda 23.1% .3% 0.0% .3% 15.0% 34.1% 22.2% 1.9% 3.1% 0.0% 100.0% % of Total 1.9% .0% 0.0% .0% 1.2% 2.8% 1.8% .2% .3% 0.0% 8.1% North Count 5 1 1 1 19 98 19 42 8 1 195 Achefer % within Woreda 2.6% .5% .5% .5% 9.7% 50.3% 9.7% 21.5% 4.1% .5% 100.0% % of Total .1% .0% .0% .0% .5% 2.5% .5% 1.1% .2% .0% 4.9% Sekela Count 37 2 0 0 7 63 71 18 0 0 198

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Scheme Types

Undeveloped Developed Undeveloped Developed Woreda River Pond Lake Rain Hand dug well Hand dug well spring spring Water Pipe Other Total % within Woreda 18.7% 1.0% 0.0% 0.0% 3.5% 31.8% 35.9% 9.1% 0.0% 0.0% 100.0% % of Total .9% .1% 0.0% 0.0% .2% 1.6% 1.8% .5% 0.0% 0.0% 5.0% Farta Count 2 1 0 0 3 101 97 33 2 0 239 % within Woreda .8% .4% 0.0% 0.0% 1.3% 42.3% 40.6% 13.8% .8% 0.0% 100.0% % of Total .1% .0% 0.0% 0.0% .1% 2.5% 2.4% .8% .1% 0.0% 6.0% Libo Count 10 3 5 1 32 107 50 15 17 0 240 Kemkem % within Woreda 4.2% 1.3% 2.1% .4% 13.3% 44.6% 20.8% 6.3% 7.1% 0.0% 100.0% % of Total .3% .1% .1% .0% .8% 2.7% 1.3% .4% .4% 0.0% 6.1% Fogera Count 13 1 0 1 46 106 29 3 0 0 199 % within Woreda 6.5% .5% 0.0% .5% 23.1% 53.3% 14.6% 1.5% 0.0% 0.0% 100.0% % of Total .3% .0% 0.0% .0% 1.2% 2.7% .7% .1% 0.0% 0.0% 5.0% Ebinat Count 17 0 0 1 2 34 23 2 0 0 79 % within Woreda 21.5% 0.0% 0.0% 1.3% 2.5% 43.0% 29.1% 2.5% 0.0% 0.0% 100.0% % of Total .4% 0.0% 0.0% .0% .1% .9% .6% .1% 0.0% 0.0% 2.0% Gondar Zuria Count 90 5 4 0 15 147 37 12 8 1 319 % within Woreda 28.2% 1.6% 1.3% 0.0% 4.7% 46.1% 11.6% 3.8% 2.5% .3% 100.0% % of Total 2.3% .1% .1% 0.0% .4% 3.7% .9% .3% .2% .0% 8.1% Wegera Count 35 0 0 1 1 17 6 1 16 1 78 % within Woreda 44.9% 0.0% 0.0% 1.3% 1.3% 21.8% 7.7% 1.3% 20.5% 1.3% 100.0% % of Total .9% 0.0% 0.0% .0% .0% .4% .2% .0% .4% .0% 2.0% Lay Count 8 0 1 0 6 2 111 16 14 0 158 Armachiho % within Woreda 5.1% 0.0% .6% 0.0% 3.8% 1.3% 70.3% 10.1% 8.9% 0.0% 100.0% % of Total .2% 0.0% .0% 0.0% .2% .1% 2.8% .4% .4% 0.0% 4.0% Chilga Count 27 1 0 0 7 50 36 11 7 0 139 % within Woreda 19.4% .7% 0.0% 0.0% 5.0% 36.0% 25.9% 7.9% 5.0% 0.0% 100.0%

% of Total .7% .0% 0.0% 0.0% .2% 1.3% .9% .3% .2% 0.0% 3.5% Dembia Count 30 1 1 1 43 252 62 18 4 0 412 % within Woreda 7.3% .2% .2% .2% 10.4% 61.2% 15.0% 4.4% 1.0% 0.0% 100.0%

% of Total .8% .0% .0% .0% 1.1% 6.4% 1.6% .5% .1% 0.0% 10.4% Takusa Count 3 0 0 0 13 49 8 4 1 1 79 % within Woreda 3.8% 0.0% 0.0% 0.0% 16.5% 62.0% 10.1% 5.1% 1.3% 1.3% 100.0%

% of Total .1% 0.0% 0.0% 0.0% .3% 1.2% .2% .1% .0% .0% 2.0% Alefa Count 5 1 7 1 2 36 25 1 0 0 78 % within Woreda 6.4% 1.3% 9.0% 1.3% 2.6% 46.2% 32.1% 1.3% 0.0% 0.0% 100.0%

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Scheme Types

Undeveloped Developed Undeveloped Developed Woreda River Pond Lake Rain Hand dug well Hand dug well spring spring Water Pipe Other Total % of Total .1% .0% .2% .0% .1% .9% .6% .0% 0.0% 0.0% 2.0% Total Count 485 29 63 11 393 1671 952 248 101 8 3962 % within Woreda 12.2% .7% 1.6% .3% 9.9% 42.2% 24.0% 6.3% 2.5% .2% 100.0% % of Total 12.2% .7% 1.6% .3% 9.9% 42.2% 24.0% 6.3% 2.5% .2% 100.0%

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4.4.6. Existing Water Resource Development Problems

From the observed problems of water resource development, lack of land and water management practices, disturbance of river morphology and change of river mouth and lake boundary due to siltation and sedimentation are critical. These problems are selectively discussed on the following parts.

4.4.6.6. Lack of Land and Water Management Practices

Ethiopia for the last couple of decades has faced serious ecological imbalances because of large scale deforestation and soil erosion caused by improper farming practices, destructive forest exploitation, wild fire and uncontrolled grazing practices. This has resulted in a declining agricultural production, water depletion, disturbed hydrological conditions, poverty and food insecurity.

Over the past three decades, many governmental and non-governmental organizations have been involved in massive soil and water conservation activities. However, the results achieved in reducing soil erosion problem and improving agricultural productivity have been unsatisfactory (Danano, 2002).

The challenge of land and water management in Ethiopia is great – and great efforts have been, and are being, made to meet this challenge. This section looks at the range of interventions that have been undertaken to enhance sustainable land and water management in the Blue Nile. It will look at both successful and failed interventions to provide insight for focusing detailed research efforts. Interventions will be characterized and described with an eye to whether they could be replicable, scalable and sustainable (environmentally, socially and economically) (Haileslassie et al., 2008). In relation to water resources management, Ethiopia as a country has formulated and acted for implementation of water resources management policies by considering the long-term effect of water resource depletion. Box 4-1, Box 4-2 and Box 4-3 clearly show this issue.

Box 4-1: Goal of water resources management policy The overall goal of the water resources policy (WRP) is to enhance and promote all national efforts towards the efficient, equitable and optimum utilization of the available water resources for significant socio-economic development on a sustainable basis.

Box 4-2: Objectives of the water resources management policy

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1. Development of the water resources of the country for economic and social benefits of the people on an equitable and sustainable basis. 2. Allocation and apportionment of water based on comprehensive and integrated plans and optimum allocation principles that incorporate efficiency of use, equity of access, and sustainability of the resource. 3. Managing and combating drought through, inter alia, efficient allocation, redistribution, transfer, storage and efficient use of water resources. 4. Combating and regulating floods through sustainable mitigation, prevention, rehabilitation and other practical measures. 5. Conserving, protecting and enhancing water resources and the overall aquatic environment on a sustainable basis.

Box 4-3: Fundamental principles of water resource management policy 1. Water is a natural endowment commonly owned by all the peoples of Ethiopia. 2. As far as conditions permit, every Ethiopian shall have access to sufficient water of acceptable quality, to satisfy basic human needs. 3. In order to significantly contribute to development, water shall be recognized both as an economic and a social good. 4. Integrated framework of water resources development a rural-centered, decentralized management and the participatory approach shall underpin. 5. Management of water resources shall ensure social equity, economic efficiency, system reliability and sustainability norms. 6. Promotion of the participation of all stakeholders, particularly women and user communities in the relevant aspects of water resources management.

Watershed management is a solution in improving land and water resource potential of an area. Watershed management plays a significant role in improving the standard of living of the population living within the watersheds, decrease population pressure, and increase land productivity so that sustainable livelihoods and land use practices can be secured for the target populations. Without action, the challenges of food insecurity and famine, environmental degradation, and rapid population growth will intensify. Yet, through coordinated efforts of different stakeholders, production of food and energy, mitigation of droughts, arresting watershed degradation, reducing sedimentation, and improving the environment can be achieved. It is possible to capture these opportunities in a sustainable manner to benefit the people.

The first benefit of appropriate watershed management is to reduce soil erosion and the subsequent siltation rate of reservoirs thereby maximizing the benefits of irrigation and hydropower projects. The second important benefit will be an overall increase in land

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The above describe land and water resource management problems are also observed in Tana Sub-Basin. From the 52 observed irrigation schemes and practices of the basin more than the 90% have problems of land and water management practices. The observation includes modern and traditional river diversions, modern and traditional spring diversions pump irrigations and so on. Improper utilization of water was high in traditional irrigation practices more than the modern one. The following pictures on Figure 33 are indications of improper land utilizations in the upstream and loss of water due to traditional irrigation practices. Observed points of soil and water use practices are shown on Figure 32 and Appendix 3 b.

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Figure 32: Observed points of soil and water use

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Figure 33: Land and water management problems: Steep slope areas used for agriculture in the upstream (A and B) and improper water utilization on irrigation practices (C and D) (Courtesy: ADSWE 2014)

4.4.6.7. River Morphology

Channel processes reflected in river morphology are erosion, transportation and sedimentation. These processes develop drainage basins. Every drainage basin has its own shape. Some indices are proposed to explain the shape of a drainage basin quantitatively. As for channels in a drainage basin, concept of stream order is introduced and is related to the total length and the gradient of channels and the area of drainage basins. The movement of water and the kinds of sediment load affect the depth and width of a channel (Matsuda, 2004).

Every channel has its own characteristics. However, channels show some common characteristics in areas of similar landform. Table 1 lists some common characteristics of channels classified by where they run.

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Table 38: Classification of channels and their characteristics Classification of Channels in Channels in alluvial plains channels mountains River bed rock, gravel gravelly sandy muddy materials bifurcated, strait, meandering, Channel pattern braided strait, braided strait meandering Transportation by debris flow, traction, traction suspension running water traction suspension shallow to Depth of channel various shallow deep deep Gradient of river steeper than 1/50 to 1/100 to greater than bed 1/100 1/500 1/2000 1/1000

Regarding this truth under field observation river course, embankment erosion, changes in river morphology, siltation and fluvial deposition problems of rivers are observed. From 68 observation points of river course more than 82% have river morphology problems and the biodiversity around is disturbed (Figure 34and Appendix 3 a). Figure 35 indicates some of the observation place in different observation types. During the observation time most of the rivers of Tana Sub-Basin have similar problems of riverine forest disappearance. This really disturb the river morphology totally like changing the river course from the primary water way, series and dangerous embankment erosion and also gully formation, upstream fluvial deposition and downstream siltation were commonly observed. This is clearly observed on the following pictures taken from the observation.

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Figure 34: Observed points of river morphology

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Figure 35: Observed problems of river morphology: Downstream sediment deposition at Megech river (A), upstream fluvial deposition (B), embankment erosion due to riverine deforestation (C) and riverine deforestation (D) (Courtesy: ADSWE 2014)

4.4.6.8. River Mouth and Lake Boundary

Sediments entering and settling in the estuary is terrestrial. As rivers and creeks flow Lake Ward, they carry with them sediments from the land. Runoff from rain carries sediments into nearby streams. Removal of vegetation as from agricultural, logging, and construction operations promotes erosion and increases the sediment loads of rivers and creeks. This is the reason why observation is needed to interpret how much sedimentation is a great problem in total biodiversity, disappearance of wetlands, decreasing of lake depth and area and similar problems. This is a clear indication how much Lake Tana and its wetlands are endangered due to siltation and sedimentation. To clearly understand how much the problem is series watch Figure 37 and observation points on Figure 36and Appendix 3 c.

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Figure 36: Observed points of river mouth and lake boundary

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Figure 37: River mouth and Lake Tana boundary: Siltation, sedimentation and new land formation (A, B, C and D) (Courtesy: ADSWE 2014)

4.5. Water Demand and Use of Tana Sub-Basin

4.5.4. Urban Water Supply and Demand

Except the water supply sources of the major urban centers, Gondar and Bahir Dar, Table 10 in the methods part is used to estimate the urban water productions of other urban centers shown in Table 39. For Gondar the main source of surface water supply, Angereb dam which contributes 83 % of the total urban water supply has a capacity of supplying 116 liter /sec (10022.4 m3/day) and the main sources of Bahir Dar water supply are very high yielding springs of Areki and Lomi with respective yields of 108 liter/sec (7824 m3/day) and 54

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Table 39 : Type of sources and estimated annual production

% of Annual water No Source No. of scheme scheme production, MCM/year

1 Bore hole(BH) 68 73.1 2.144 2 Hand dug well(HDW) 10 10.8 0.015 3 Spring(SP) 13 14.0 4.144 4 Shallow well(SHW) 1 1.1 0.001 5 Surface water(SW) 1 1.1 3.658 Total 93 100 9.962 Based on the assumptions shown on the methodology part section 3.4.2.1.2 the annual urban domestic water demand is estimated for each type of connection for 2014 and is summarized as shown on Figure 38 and

Table 40.

Figure 38: Water supply connection type and demand

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Table 40: Summarized water supply connection type and demand House Yard Shared Public Urban centers Population % Demand % Demand Demand % Demand category l/c/d l/c/d % users l/c/d l/c/d users (MCM) users (MCM) (MCM) users (MCM)

Category 1,2,3 552141 5 80 0.806 55 40 4.434 35 30 2.116 5 20 0.202 and 4 (>30,000) Category 5 103379 2.5 70 0.066 47.5 30 0.538 40 20 0.302 10 15 0.057 (15,000-30,000) Category 6 and 133101 0 70 0.000 30 30 0.437 55 20 0.534 15 15 0.109 7 (<15,000) Total 788621 0.872 5.409 2.952 0.367

Basin Total 9.601

The public, commercial and industrial water demands are considered separately from the total urban water demand. Therefore the public, commercial and industrial water demands are estimated as percentage of urban centers water demand with the assumption on Table 12 of the methodology. According to the assumption public and commercial water demand is estimated at 2.125 MCM and the industrial water demand at 0.582 MCM for the sub-basin. The total water demand of the urban centers is therefore 12.31 MCM/year. The estimated existing water supply coverage from the demand is 81%. The urban sector still demands 19% to balance the supply. Spatially water demand of administrative towns is shown on Figure 39.

Table 41 : Public, commercial and industrial water demand share

Percentage of total domestic water demand Population Category Public & Total Demand Industrial (MCM) commercial <30000 7.557 20% 1.51 5% 0.378 >=30000 2.045 30% 0.61 10% 0.205 Total 9.602 2.1249 0.582

4.5.4.6. Water Consumption of Sample Urban Centers The urban water consumptions of sample urban centers in Tana Sub-Basin area have been collected for Bahir Dar, Gondar, Dangila, Woreta, Koladiba, Addis Kidam, Enjibara, Aykel and Debre Tabor. As these urban centers use meters to measure the water consumptions at the water use points relatively better water consumption data is obtained at the respective urban water and sewerage service offices. A comparison of production and consumption of water in

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the sample urban centers show that the unaccounted for water ranges from 0.4 % up to 54.1%. The minimum one is for Aykel which has critical shortage of water supply and the maximum one is for Debre Tabor. The loss has aggravated the water scarcity around urban centers. One of the first resources management in case should have to be the identification of the type of loss and possible measures to alleviate the shortage problems. Table 42: Water production and consumption of sample urban centers

Year Urban center Efficiency 1998 1999 2000 2001 2002 2003 2004 2005 Production (MCM) 5.120 5.870 6.000 6.460 Consumption (MCM) 3.480 4.300 4.340 4.650 Bahir Dar Loss (%) 32.0 26.7 27.7 28.0 Production (MCM) 2.250 2.353 2.853 2.948 2.980 Consumption (MCM) 1.585 1.824 2.157 2.074 2.368 Gondar Loss (%) 29.53 22.48 24.38 27.63 20.52 Production (MCM) 0.225 0.256 0.258 0.268 Consumption (MCM) 0.172 0.187 0.216 0.212 Dangila Loss (%) 23.69 27.07 15.96 19.03 Production (MCM) 0.170 0.240 0.300 0.280 0.350 Consumption (MCM) 0.140 0.200 0.200 0.230 0.290 Woreta Loss (%) 17.65 16.67 33.33 17.86 17.14 Production (MCM) 0.102 0.111 0.129 0.126 Consumption (MCM) 0.083 0.084 0.104 0.103 Koladiba Loss (%) 18.98 23.80 19.65 18.61 Production (MCM) 0.079 0.071 0.088 Consumption Addis Kidam (MCM) 0.048 0.042 0.060 Loss (%) 38.63 40.19 31.16 Production (MCM) 0.077 0.067 0.105 0.139 0.129 Consumption (MCM) 0.059 0.051 0.097 0.115 0.107 Enjibara Loss (%) 23.30 24.68 41.62 17.20 17.57 Production (MCM) 0.0347 0.0419 0.0410 0.0369 0.0459 0.0526 0.0455 Consumption (MCM) 0.0345 0.0400 0.0365 0.0307 0.0419 0.0425 0.0442 Aykel Loss (%) 0.43 4.45 11.05 16.83 8.67 19.84 2.87 Production (MCM) 0.178 0.175 0.201 0.245 0.247 0.252 0.218 0.199 Consumption (MCM) 0.118 0.106 0.096 0.112 0.186 0.128 0.134 0.138 Debre Tabor Loss (%) 33.68 39.28 52.39 54.10 24.75 49.11 38.53 30.85

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4.5.5. Rural Water Supply and Demand From this Amhara Design and Supervision Enterprise (ADSWE) inventory of rural water supply a total of 4818 water supply schemes were inventoried in rural areas of the region. This figure includes total schemes which were functional and not-functional. The sources include 3738 (77.6%) Hand Dug Wells (HDW), 746 (15.5%) springs, 197(4.1%) pipe supply from the nearest s and others 137 (2.8%) surface water (river, pond and lake). Wells and springs make 93.1 % of the total number of schemes. This figure indicates that drinking water supply for human use in Tana Sub-Basin is ground water. Based on the assumptions discussed in the methods part of the water yields of each water supply schemes the rural water supply production of the sub-basin area during the study time is estimated at 5.849 MCM.

Table 43: The type of water sources and estimated yield (ADSWE, 2014) No of scheme Type of water supply Unit estimated Total water yield, No in Tana Sub- scheme yield, (m3/day) (MCM) Basin 1 Hand dug well 4 2720 3.971 2 Spring (Developed) 9.6 290 1.016 3 Spring (Undeveloped) 9.6 246 0.862 Total 3256 5.849

Table 44: The monthly water production of the schemes of ADSWE 2014 water inventory Monthly water production, MCM Month Spring Total Hand dug well Spring (Developed) (Undeveloped) Jan 0.337 0.086 0.073 0.497 Feb 0.305 0.078 0.066 0.449 Mar 0.337 0.086 0.073 0.497 Apr 0.326 0.084 0.071 0.481 May 0.337 0.086 0.073 0.497 Jun 0.326 0.084 0.071 0.481 Jul 0.337 0.086 0.073 0.497 Aug 0.337 0.086 0.073 0.497 Sep 0.326 0.084 0.071 0.481 Oct 0.337 0.086 0.073 0.497 Nov 0.326 0.084 0.071 0.481 Dec 0.337 0.086 0.073 0.497 Total 3.971 1.016 0.862 5.849

The water demand of the sub-basin is estimated by multiplying the population with the per capita demand discussed on section 3.4.2.2.2 and summarized as shown below.

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Table 45 : Rural water demand at (ADSWE, 2014)

Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Domestic demand, 1.093 0.987 1.093 1.058 1.093 1.058 1.093 1.093 1.058 1.093 1.058 1.093 12.867 MCM The annual rural domestic water demand of the sub-basin is estimated at 12.867 MCM. From use and demand point of view rural population of Tana Sub-Basin require 7.013 MCM of water to succeed its demand which is 54.5% from the total demand. This increase in percentage of demand may be due to the increase number of un-functional schemes. When the calculation considers the un-functional schemes; the use increase to 8.071 MCM and the net demand decrease to 37.3%. This clearly showed that the Woreda water resource development offices should emphasis on maintenance of the un-functional schemes.

Similarly to understand the spatial condition of rural water demand of the sub-basin in Woreda level it is downscaled by considering the Woreda population. The population and respective water demands for each Woreda which contributes to Tana Sub-Basin are summarized on Table 46 shown below. This result also mapped on Figure 39 including urban centers water demand.

Figure 39: Rural and urban water demand of Tana Sub-Basin

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Table 46: Rural water demand based on Woreda (ADSWE, 2014) Population, Annual water demand, Woreda 2014 MCM Alefa 26323 0.144 Bahir Dar Zuria 102961 0.564 Banja 18496 0.101 Chilga 69874 0.383 Dangila 76779 0.420 Dembia 271931 1.489 Dera 136780 0.749 Ebinat 43788 0.240 Estie 46251 0.253 Fagita Lekoma 73331 0.401 Farta 224021 1.227 Fogera 226931 1.242 Gondar Zuria 158170 0.866 Lay Armachiho 79387 0.435 Libo Kemkem 159268 0.872 Mecha 268755 1.471 North Achefer 116680 0.639 Sekela 70143 0.384 South Achefer 107207 0.587 Takusa 59103 0.324 Wogera 12231 0.067 Yilmana Densa 1667 0.009 Total 2350077 12.867

4.5.5.6. Livestock Water Demand The demand of water by Woreda level with respect to each livestock type was estimated and tabulated on Table 47. Using the applied assumption the annual amount of water required for livestock in the sub-basin is estimated at 26.5 MCM.

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Table 47: Livestock population and water demand in Tana Sub-Basin (ADSWE, 2014)

Cattle Sheep Goats Horses Mules Donkeys Poultry Total

Water Water Water Water Water Water Water Water Water Water Demand Demand Demand Demand Demand Demand Demand Demand Livestock Demand Demand Woredas Number (Liter/day) Number (Liter/day) Number (Liter/day) Number (Liter/day) Number (Liter/day) Number (Liter/day) local (Liter/day) exotic (Liter/day) No. (Liter/day) (MCM/year)

Banja 9365 234125 14163 70815 1342 6710 4888 87984 214 3210 109 1308 4711 1917 275 112 35067 406181 0.1

Dangila 62138 1553450 31430 157150 9894 49470 93 1674 907 13605 6720 80640 156668 63764 15824 6440 283674 1926193 0.7

Fagita Lekoma 43564 1089100 36468 182340 12878 64390 8357 150426 1753 26295 2447 29364 41880 17045 6149 2503 153496 1561463 0.6

Sekela 32119 802975 64936 324680 8559 42795 2642 47556 1633 24495 5066 60792 23639 9621 232 94 138826 1313008 0.5

South Achefer 112184 2804600 49448 247240 23145 115725 1233 22194 2685 40275 15659 187908 99641 40554 3355 1365 307350 3459861 1.3

North Achefer 175655 4391375 50206 251030 24455 122275 387 6966 2182 32730 15423 185076 119734 48732 2834 1153 390876 5039337 1.8

Mecha 193029 4825725 117206 586030 51826 259130 542 9756 4302 64530 26179 314148 226725 92277 4007 1631 623816 6153227 2.2

Bahir Dar Zuria 87431 2185775 14322 71610 9144 45720 48 864 179 2685 7612 91344 58384 23762 3628 1477 180748 2423237 0.9

Bahir Dar town 13462 336550 1996 9980 527 2635 12 216 516 7740 1697 20364 915 372 15730 6402 34855 384260 0.1

Dera 113274 2831850 30819 154095 19082 95410 236 4248 1688 25320 12686 152232 78309 31872 1327 540 257421 3295567 1.2

Estie 28422 710550 27586 137930 18931 94655 767 13806 559 8385 1236 14832 20171 8210 410 167 98082 988534 0.4

Fogera 253709 6342725 35181 175905 27174 135870 11 198 509 7635 22874 274488 145004 59017 3691 1502 488153 6997340 2.6

Alefa 64416 1610400 15559 77795 13126 65630 19 342 456 6840 4713 56556 26451 10766 4228 1721 128968 1830049 0.7

Takusa 68854 1721350 9456 47280 9441 47205 16 288 1685 25275 3942 47304 36531 14868 7123 2899 137048 1906469 0.7

Wegera 18294 457350 30621 153105 4825 24125 822 14796 0 0 5404 64848 57115 23246 122 50 117203 737519 0.3

Chilga 119933 2998325 28401 142005 33355 166775 120 2160 128 1920 7356 88272 107220 43639 1204 490 297717 3443586 1.3

Gondar town 76939 1923475 24058 120290 20615 103075 971 17478 222 3330 8213 98556 62271 25344 3277 1334 196566 2292882 0.8

Gondar Zuria 200719 5017975 74117 370585 76042 380210 985 17730 592 8880 25142 301704 165276 67267 7861 3199 550734 6167551 2.3

Dembia 234534 5863350 65083 325415 14475 72375 54 972 253 3795 18027 216324 60022 24429 9782 3981 402230 6510641 2.4

L/Armachiho 116717 2917925 35627 178135 19124 95620 338 6084 266 3990 14837 178044 98609 40134 12773 5199 298291 3425130 1.3

Libokemkem 121109 3027725 71075 355375 76828 384140 814 14652 633 9495 13064 156768 82507 33580 4567 1859 370597 3983594 1.5

Ebenat 115209 2880225 25299 126495 35442 177210 83 1494 478 7170 19230 230760 49538 20162 2435 991 247714 3444507 1.3

Farta and D/Tabor 144037 3600925 98925 494625 39548 197740 7658 137844 7248 108720 20771 249252 88139 35873 4391 1787 410717 4826766 1.8

Total 2405113 60127825 951982 4759910 549778 2748890 31096 559728 29088 436320 258407 3100884 1809460 736450 115225 46897 6150149 72516904 26.5

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4.5.6. Agricultural Water Demand and Use

In the sub-basin Agriculture is undertaken under both rain fed and irrigation. As the sub-basin is characterized with mono modal type of rainfall there is one rain fed agriculture season and one to two irrigation seasons in irrigated areas of the sub-basin. In some areas of the sub- basin sometimes the rain fall comes late and early withdrawal which affects the productivity and production of agriculture. In these cases the application of supplementary irrigation is very essential to avoid the risk of crop failure due to moisture stress. However from the practical point of view this practice is not well implemented in the area due to several problems even in the modern irrigation schemes.

Irrigation is the highest water user sector which needs attention for the management of the water resources of the sub-basin. For ensuring food security, reducing poverty and contributing for the national economic development of the country, the importance of irrigated agriculture in the sub-basin is very crucial. The government has identified the Tana Sub-Basin as a region which needs priority attention for constructing irrigation and hydropower infrastructure due to its water resource potential.

Generally currently the design and construction of irrigated agriculture is undertaken at three scales: Large and medium scale irrigation by Ministry of Water and Energy (MoWE), Small scale modern irrigation by Amhara Region Water Resource Development Bureau (BoWRD) and traditional irrigation and water harvesting using small scale structures by Bureau of Agriculture. The projects which irrigate 200 to 3000 ha and more than 3000 ha are categorized under the medium and large scale projects respectively.

4.5.6.6. Planned Medium and Large Scale Irrigation of Tana Sub-Basin

Based on considerations and information applied on the methodology part, monthly and annual water demands of the projects in Tana Sub-Basin and outside the sub-basin are summarized on the following Table 48 and the detailed one is shown on Appendix 3: Observation points of water resource and irrigation development problems

a) River morphology

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4 B_Zuria Endemo 332985 1292737 1841 Embankment erosion Disturbed 5 B_Zuria Gelda 336719 1296254 1808 Embankment erosion Disturbed 6 B_Zuria Chimbil 330421 1282469 1875 Embankment erosion Disturbed 7 B_Zuria G/Abay 299961 1294352 1811 Embankment erosion Disturbed 8 Banja Gugi 292612 1214542 2552 Embankment erosion Disturbed 9 Chilga Aras Wusha 304024 1385761 1988 Embankment erosion Disturbed 10 Chilga Gabi Kura 287828 1370087 1958 Embankment erosion Disturbed 11 Dangila Zuma 278905 1237176 2106 No embankment erosion Not disturbed 12 Dangila Biranti 266301 1237435 2202 Embankment erosion Disturbed 13 Dangila Gazh 257917 1252609 2072 Embankment erosion Disturbed 14 Dangila Befta 258036 1251169 2109 Embankment erosion Disturbed 15 Dangila Kilti 260602 1250752 2059 Embankment erosion Disturbed 16 Dangila Ashar 274173 1244303 1989 Embankment erosion Disturbed 17 Dangila Twon Amen 264692 1245031 2110 Embankment erosion Disturbed 18 Dembia Megech 319383 1367776 1800 Embankment erosion Disturbed 19 Dembia Megech 330180 1389063 2039 Embankment erosion Disturbed 20 Dembia Dirma 315537 1356907 1787 Embankment erosion Disturbed 21 Dembia Ambagenen 307887 1371414 1856 Embankment erosion Disturbed 22 Dembia Ambagenen 303222 1366628 1820 Embankment erosion Disturbed 23 Dembia Sewgedel 301142 1367471 1828 Embankment erosion Disturbed 24 Dembia Sarwuha 293363 1362334 1805 Embankment erosion Disturbed 25 Dera Gelda 349971 1297555 1944 Embankment erosion Disturbed 26 Ebinat Ribb (Upstream) 391917 1330361 1892 Embankment erosion Disturbed 27 Ebinat Ziha 393927 1336693 1985 Embankment erosion Disturbed 28 Estie Estie 401742 1296331 2577 Embankment erosion Disturbed 29 Fagita Agzi 285888 1219456 2412 Embankment erosion Disturbed 30 Fagita Agzi 286457 1225633 2271 Embankment erosion Disturbed 31 Fagita Fagitit 281242 1226289 2398 Embankment erosion Disturbed 32 Fagita Guder 272996 1228874 2341 No embankment erosion Not disturbed 33 Fagita Quashini 266973 1230832 2360 No embankment erosion Not disturbed 34 Fagita Shihanti 284510 1224756 2285 Embankment erosion Disturbed 35 Fagita Libsi 283399 1225141 2292 Embankment erosion Disturbed 36 Fagita Zuma 276885 1227505 2346 Embankment erosion Disturbed 37 Farta Ribb (Upstream) 407296 1304790 2773 Embankment erosion Disturbed 38 Farta Zufil 396325 1309867 2618 No embankment erosion Not disturbed 39 Fogera Werk Wuha 381834 1317896 2064 Embankment erosion Disturbed 40 G_Zuria Gumara 342893 1370074 1906 Embankment erosion Disturbed 41 G_Zuria Megech 331430 1381224 1867 Embankment erosion Disturbed 42 Gondar Twon Dimaza 330180 1389063 2039 No embankment erosion Not disturbed 43 L_Armachiho 334213 1402304 2296 Embankment erosion Disturbed 44 Mecha Bered 299496 1262885 2001 Embankment erosion Disturbed 45 Mecha G/Abay 285484 1256761 1893 Embankment erosion Disturbed 46 Mecha Andid 289244 1259375 1945 No embankment erosion Not disturbed 47 Mecha Koga 286418 1257604 1898 No embankment erosion Not disturbed 48 N_Achefer Arboch 272362 1280758 2030 Embankment erosion Disturbed

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49 N_Achefer Merfi 282072 1308429 1837 Embankment erosion Disturbed 50 S_Achefer Kilti 276316 1269216 1880 Embankment erosion Disturbed 51 S_Achefer Zabza 276157 1273802 1934 Embankment erosion Disturbed 52 Sekela Munziriti 298800 1213289 2560 No embankment erosion Not disturbed 53 Sekela Fetam 296024 1211980 2583 Embankment erosion Disturbed 54 Sekela Weynitie 300194 1214067 2553 No embankment erosion Not disturbed 55 Wegera Megech 340339 1409230 2687 Embankment erosion Disturbed 56 Wegera Megech 340982 1409716 2767 Embankment erosion Disturbed 57 Chilga Guang 303557 1385663 1699 Embankment Erosion Disturbed 58 Alefa Kachela 283804 1328315 1798 Embankment Erosion Disturbed 59 Alefa War 283811 1321034 1793 Embankment Erosion Disturbed 60 Alefa Kachela 280599 1328194 1852 Embankment Erosion Disturbed 61 Alefa War 283127 1320450 1786 Embankment Erosion Disturbed 62 Alefa War 282458 1321068 1805 Embankment Erosion Disturbed 63 Takusa Bela Gedel 293349 1362175 1808 Embankment Erosion Disturbed 64 Takusa Tima 290861 1353682 1788 Embankment Erosion Disturbed 65 Takusa Segie Wenz 288254 1350253 1790 No embankment erosion Not disturbed 66 Takusa Gibera 287760 1349126 1788 Embankment Erosion Disturbed 67 Takusa Gibera 288840 1348779 1790 Embankment Erosion Disturbed 68 Takusa Tirikura 283129 1349338 1843 Embankment Erosion Disturbed b) Soil and water management

No Wereda River/Scheme Name X Y Z Management 1 Alefa Kachela 283804 1328315 1798 Poor management 2 B_Zuria Shimana and Gelda 337134 1296209 1803 Poor management 3 B_Zuria Chimbil 334747 1287215 1830 Good management 4 B_Zuria Gilgel Abay 300318 1294417 1808 Poor management 5 B_Zuria Enfiranz 313040 1284808 1818 Good management 6 B_Zuria Lake Tana (Zegie) 316105 1291175 1792 Poor management 7 Banja Gugri 292612 1214542 2552 Poor management 8 Chilga Aukibin 302179 1387885 1823 Good management 9 Chilga Gabi Kura 287771 1370040 1966 Poor management 10 Chilga Guang 303611 1385657 1706 Good management 11 Dangila Ashar (Downstream) 274173 1244303 1989 Poor management 12 Dangila Zuma 279366 1237350 2119 Poor management 13 Dangila Ashar (Upstream) 274765 1243412 1993 Poor management 14 Dembia Girargie 307698 1358393 1846 Poor management 15 Dembia Dirma 315537 1356907 1787 Poor management 16 Dembia Kibir Andiye 307876 1371320 1856 Poor management 17 Dembia Amba Genen 303252 1366647 1817 Poor management 18 Dembia Jenda 307876 1371320 1856 Good management 19 Dembia Amba Genen 303252 1366647 1817 Poor management 20 Dera Gumara 350272 1310304 1796 Poor management 21 Dera Shina 337566 1306229 1800 Poor management 22 Dera Lake Tana (korata) 331316 1300751 1798 Poor management

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23 Ebinat Zhiza 390854 1336277 1964 Poor management 24 Estie Gumara (Gelawdios) 375991 1287245 2280 Poor management 25 Estie Gumara (Estie) 401569 1296284 2577 Poor management 26 Fagita Agzi and Balesunga 286684 1225924 2279 Poor management 27 Fagita Agzi 286603 1233723 2097 Poor management 28 Fagita Shihanti 284510 1224756 2285 Poor management 29 Fagita Libsi 283399 1225141 2292 Poor management 30 Fagita Zuma 276885 1227505 2346 Poor management 31 Farta Selamko 395073 1313985 2497 Poor management 32 Farta Ribb Nr Gassay 407916 1304789 2785 Poor management 33 Farta Melo 391446 1313925 2576 Poor management 34 Fogera Kuhar 353570 1310614 1803 Poor management 35 Fogera Gumara Near Shina 351254 1311843 1797 Poor management 36 Fogera Ribb Near Kokit 360018 1326047 1799 Poor management 37 G_Zuria Arno 361343 1345720 1991 Poor management 38 G_Zuria Garno 350735 1352986 1862 Poor management 39 G_Zuria Mitraha 345569 1344516 1790 Poor management 40 G_Zuria Arbaitu Ensisat 335281 1360456 1794 Poor management 41 Gondar Town Angereb 335478 1394104 2095 Poor management 42 Gondar Town Azezo 330264 1388889 2036 Poor management 43 L_Kemkem Shini 366978 1339966 1929 Poor management 44 L_Kemkem Bikutie 384535 1336259 1933 Poor management 45 Mecha Koga 294930 1256188 2011 Poor management 46 N_Achefer Lake Tana (Kunzila) 285750 1314070 1798 Poor management 47 S_Achefer Kilti 276316 1269216 1880 Poor management 48 Sekela Weynitie 300194 1214067 2553 Poor management 49 Takusa Gibera 287709 1349153 1790 Poor management 50 Takusa Segie Wenz 288254 1350253 1790 Poor management 51 Takusa Near Gibera 288647 1348826 1787 Poor management 52 Takusa Gibera 288647 1348826 1787 Poor management c) River mouse and lake boundary

No Wereda Observation type X Y Z Remark 1 B_Zuria Lake bondary (Zegie) 316105 1291175 1792 Endanger wetland 2 Dembia Lake boundary (Abirja) 315604 1356384 1790 High siltation deposition 3 Dembia Megech river mouse 327142 1357522 1790 High siltation deposition 4 Dembia Dirma river mouse 315537 1356907 1787 Siltation 5 Dera Lake boundary (Korata) 331363 1300668 1798 Good 6 G_Zuria Arno river Tana coenfluence 345476 1344219 1788 Siltation 7 L_Kemkem Lake boundary (Agid Kiragna) 349408 1339376 1788 Settlement 8 N_Achefer Lake bondary (Kunzila) 285750 1314070 1798 Water quality problem 9 Alefa Kachela river Tana coenfuence 283804 1328315 1798 Siltation 10 Alefa War river Tana coenfuence 283811 1321034 1793 Siltation 11 Alefa Lake boundary (Dengelber) 283811 1321034 1793 Siltation (Wetland endanger) 12 Alefa Lake Boundary (Essey Debir) 283892 1329039 1798 Eucalyptus plantation

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13 Takusa Segie river Tana coenfuence 288820 1350043 1784 Wetland and Sand Mining 14 Takusa Lake Boundary 288820 1350043 1784 Wetland and Sand Mining 15 Takusa Gibera river Tana coenfluence 288840 1348779 1790 Siltation 16 Takusa Lake boundary (Delgi) 288647 1348826 1787 Siltation Appendix 4 and Appendix 5.

Table 48: Summary Planned Medium and Large Scale Irrigation monthly water demand Months Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total Water Demand 264 252 252 194 32 0 23 0 0 18 193 214 1442 (MCM)

4.5.6.7. Medium and Large Scale Irrigation Under Construction

The amount of irrigable land and the estimated water demand for medium and large scale irrigation under construction is shown on Table 49. Table 49: Medium and large scale irrigation under construction Schemes ID MEG 1 MEG 2 MEG 6 RIB 1 RIB 2 RIB 3 RIB 4 sum Gross Area under 5254 6532 4000 7650 3060 9360 3370 39226 Irrigation (Ha) Net Area under Irrigation 4466 5552 3400 6503 2601 7956 2865 33343 (Ha) Total sum 72569 Months Water Demand (in MCM) Jan 6.93 8.63 5.23 0 8.86 0 9.76 39.41 Feb 8.46 10.54 6.38 0 4.23 0 4.66 34.27 Mar 10.29 12.81 7.77 0 4.75 0 5.24 40.86 Apr 4.9 6.11 3.66 0 6.03 0 6.64 27.34 May 0 0 0 0 1.84 0 2.02 3.86 Jun 0 0 0 0 0 0 0 0 Jul 0 0 0 0 0 0 0 0 Aug 0 0 0 0 0 0 0 0 Sep 0 0 0 0 0 0 0 0 Oct 1.25 1.57 0.87 0 1.63 0 1.8 7.12 Nov 6.45 8.02 4.87 0 2.38 0 2.63 24.35 Dec 7.17 8.92 5.43 0 0.53 0 0.58 22.63 Total 45.45 56.6 34.21 0 30.25 0 33.33 199.84

4.5.6.8. Medium and Large Scale Irrigation Under Irrigation Practicing

According to MacDonald, (2004) the annual water demand for irrigation has been estimated at 61.5 MCM for the project. The monthly gross water demand for the irrigation project is shown below on Table 50.

Table 50 : Gross Monthly water demand of Koga irrigation project month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

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Gross water 4.94 10.74 14.91 10.74 1.09 0 0 0 3.94 9.38 4.81 1 61.56 demand (MCM)

4.5.6.9. Existing Modern and Traditional Irrigation Water Demand With the assumption discussed in section 3.4.2.3.3 the water demand of existing modern and traditional irrigation is estimated that 1,760 MCM of water is demanded for undertaking the irrigation of 188637.39 ha of land with modern and traditional irrigation practices. The data for traditional and modern irrigation practices were collected from Woreda districts and shown on Table 25.

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Figure 40: Areal coverage of agro ecological zones of Tana Sub-Basin

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Table 51 : Annual Irrigation water demand for modern and traditional irrigation practices (ADSWE, 2014) Area Average Modern Traditional Agro Total Area Contribution Irrigation Total Irrigation Irrigation Climatic Land Area Contribution from the Unit GIWD Unit GIWR Unit GIWR Zone (Ha) (%) Irrigable Land GIWR (MCM) (m3/ha) (m3/ha) (Ha) (m3/ha) Dega (Cool) 124890 9.83 18537 7433 9291.25 8362.125 155.008 Woinadega 1144270 90.03 169838 8382 10477.5 9429.75 1601.533 (Moist) Kolla (Warm) 1767 0.14 262 14792 18490 16641 4.364 Total 1270927 100 188637.39 1760.904

4.5.7. Industrial Water Demand and Use

The location and types of large and medium scale industries in the sub-basin has been collected from the Amhara Region Industry and Urban Planning Bureau. The industries are mainly concentrated on the major cities of Bahir Dar and Gondar except Saba Engineering at Debre Tabor and Guna Spring at Guna rural area. Totally 46 large and medium scale industries have been identified in the sub-basin. Most of these industries get their water supply from urban water services.

However those industries which use huge amount of water do have their own water sources by drilling bore holes or pumping from river and lake. Abay basin authority has made an assessment of the main sources of water pollution and waste management trends in major industries of Abay River basin in (ABA, 2012). From the report of the assessment the water uses of the major industries which use high amount of water in Tana Sub-Basin have been identified and compiled in Error! Reference source not found. as shown below. Table 52: The annual water use of the main water user industries in Tana Sub-Basin No Name of industry Location Source of water Annual volume of Final industrial Supply water use in m3 product 1 Dashen Brewery Gondar Borehole 468,000 Packed beer and Draft Factory 2 Moha Soft drink Gondar Municipal water 10,500 Pepsi , Mirinda, factory 7up,Mirinda Tonic 3 Bahir Dar Textile B/Dar River water/Abay Un known Different types and colored sheets, cotton cloths, threads 4 Hileta (Abubekr) B/Dar Pipe Water 8,720 Flour Flour Factory 5 Ashraf Agro Bahir Dar Municipal borehole 12,319.8 Meat, oil Industry

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No Name of industry Location Source of water Annual volume of Final industrial Supply water use in m3 product 6 Amhara Plastic Bahir Dar Ground Water 21,900 Plastic different Factory PLC diameter and size

7 Gudar Agro Industry Bahir Dar Pipe water 11,618 Flour 8 Bahir Dar Edible oil Bahir Dar Lake water 7,200m3 Oil Sc. 9 Bahir Dar Tannery B/Dar Bore hole Unlimited Tanned leather Factory 10 Habesha Tannery B/Dar River water Unlimited Tanned leather 11 Shva industry Gondar Pipe water ND 12 Biscuit factory Gondar Pipe water ND Biscuit

4.5.8. Hydropower Water Demand and Use

The data of hydropower generated and the amount of water diverted to generate the power was collected from EEPCO. According to the record the annual volume of water (BCM) diverted for hydropower and the amount of power generated from 1999 to 2005 is summarized as shown on Table 53 and Table 54. By considering current use of Tana – Beles hydropower project 2.58 BCM of water is required for hydropower generation.

Table 53: Water used in BCM for Generating hydropower (1999-2005) EFY

Year Tis-Abbay I & II Tana Belese Total Remark 1999 4.47 4.47 2000 4.16 4.16 2001 4.07 4.07 2002 2.33 2.33 2003 0.43 1.13 1.56 2004 0.47 2.58 3.05 2005 1.50 2.29 3.79 Up to February Source: EEPCO Table 54: Hydropower Generated, GWH (1999-2005) EFY Tis-Abbay I & Year II Tana Belese Total Remark 1999 1146.9 1146.9 2000 1089.4 1089.4 2001 1063.3 1063.3 2002 607.0 343.9 950.9 2003 115.5 1962.7 2078.2 2004 124.2 3884.0 4008.2 2005 131.7 2875.4 3007.1 Up to February(8 months) Source: EEPCO

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Figure 41: Annual water use and power generated (1999 – 2005) EFY [Note: the 2005 generated power is only for 8 months (July to February)]

4.5.9. Other Water Demand and Use

4.5.9.6. Navigation

Lake Tana is one of the country‘s inland freshwater resources that provide intensive transportation to the local community, tourists and different goods like construction materials (sand). Lake navigation is of particular importance for the people leaving on islands but also for others living out of islands as there are very few roads by the Lake, especially in the western area. Public transportation on the Lake is assured by the ―lake Tana Transport Enterprise‖, established in 1942. About 100,000 passengers (including 11,000 tourists and 75 to 85,000 local passengers) are recorded in average each year (SMEC, 2008).

In facilitating the water transport, there are eight (8) ports around Lake Tana which are functional and giving services by now. All the ports are found on the western part of Lake Tana. On the eastern part of the lake there is no functional port yet. Those ports which are functional at this time are: Zegie, Gurer, Kunzla, Esey Debre, Delgi, Gorgora, Bahir dar and Sekelet. The total number of beneficiaries with the existing water transport system is around 865,149 (ADSWE, 2012b). For spatial observation of ports see Figure 42.

The presence of different historical and cultural tourist attractions on the islands and islets, and adjacent terrestrial areas, together with the occurrence of villages/s at its peripheries make the transportation sector in Lake Tana very important. According to Lake Tana

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Transport Enterprise the minimum operation level for the boats in the lake is 1785 m.a.s.l. In 2003 when the lake level dropped to its historic low level (i.e.1784.39 m.a.s.l.) the enterprise lost an estimated sum of 4,207,202.13 birr both for maintenance of damaged boats and service restriction (Alemayehu, 2008). However, (SMEC, 2008) suggested the minimum operation level for the navigation is 1784.75 m.a.s.l.

Navigation water demand is therefore not consumptive instead it requires the existence of Lake Tana for transportation. This means that decrement of water level due to siltation and sedimentation and also decrement of lake water quality has its own impact on the navigation.

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Figure 42: Port locations of Lake Tana transport

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4.5.9.7. Tourism

Lake Tana region is one of the best tourist destination areas in the country owing to the occurrence of many historical, cultural and natural assets like the lake itself and islands, Fasil castle, ancient monasteries and wetland areas with abundant bird and fish (see Figure 43 for tourist attractions in Lake Tana as well as its surrounding). According to (EPLAUA, 2006) an average of 27,189 people visits the area per year (both domestic and foreign). Moreover, one can imagine how much benefit can be collected from Lake Tana cultural resources and tourist associated services (hotels, lodges, boating, fishing etc.) in the region.

The development of these immense resources in Lake Tan and its environs are poorly exploited owing to a number of problems including (ADSWE, 2012a)

 weak coordination among stakeholders,

 inadequate information provided to visitors,

 inadequate infrastructures and services,

 poor linkage between hotels and the local community,

 poor promotion and marketing activities, less diversify of developed tourism products,

 lack connectivity between water and road transport,

 less quality and diversity of handcraft products,

 lack of protection and preservation of resources,

 lack of land use plans in and around the Lake and

 financial constraints.

Similar to that of navigation water demand of tourism is not consumptive instead it requires the existence of Lake Tana other water bodies for tourism industry. This means that decrement of water level due to siltation and sedimentation and also decrement of lake water quality has its own impact on the tourism also.

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Figure 43: Tourist attraction in Lake Tana and its surrounding

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4.5.9.8. Fisheries and Aquaculture

Surprisingly, fish production uses no more water—and in many cases much less—than the production of other animal foods and, for the case of rainfed systems, the periodicity of water supply is also much less critical for fish than for crops such as maize. However, care must be taken in comparing water consumption by various types of aquaculture and livestock and crops.

Although aquaculture has tended to become more efficient in use of water, it is nonetheless highly variable, with water use for intensive aquaculture generally being higher where there are no incentives to reduce use. Water use includes both consumptive use (losses in pond systems associated with seepage and evaporation) and non-consumptive use (water that passes through the aquaculture system and is returned to the river or lake from which it was taken with little need for treatment). Unlike the case in agriculture, non-consumptive losses can be high in aquaculture.

Rivers and associated wetlands—maintaining environmental flows

Reduced flows in main river channels lead to significant changes, in particular reductions in area of associated wetlands (floodplains and floodplain swamps and lakes). This results in net production losses through direct habitat loss. The direct conversion of wetlands to agricultural use has similar consequences.

Many species of fish and other aquatic organisms are sensitive to variations in the timing, quantity, quality, and temperature of water, which are important, for example, as essential triggers to migration and breeding [well established]. Different fish species generally use different parts of the aquatic system, including the main channels of rivers, seasonally attached wetlands, lakes and reservoirs, and estuaries and near-shore marine areas. Some species use all or many of these areas and need to migrate between them. All these areas have to be treated as a continuum. Different species have different flow needs, and most species react negatively to changes in the hydrograph.

Changes to river morphology due to altered flows may interfere with the connectivity and channel diversity that are essential for the survival of many species. Other changes include silting or removal of critical substrates that act as spawning sites for many species and a source of invertebrate food for others. Where water quality is poor, reduced flows increase the risk of deoxygenation and other effects of contamination. Hydrological conditions that fall outside the range of natural variation in rivers may cause the fauna to become simplified,

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Lakes and reservoirs—a dependence on river flows

The productivity of reservoirs and dams can be influenced by filling, drawdown, and abstraction regimes. Abrupt changes in water level can be detrimental to certain species. For example, tilapia species, which are commonly present in reservoirs and lakes, nest on the shallow bottom, and rapid changes in level may submerge or expose the nests, resulting in breeding failure [established but incomplete].

Water management—an ecosystem approach is crucial

The ecosystem approach to managing watersheds, with the rivers, wetlands, lakes, estuarine areas, and land viewed as part of a continuum, is fundamental to managing water for inland fisheries. This approach should consider not only water quantity and quality but also the connectivity of the system because many species of fish must be able to move between spawning, nursery, and feeding areas within a basin. This management approach needs to consider land-use practices, such as agriculture and forestry, as well as the needs of industry, urban areas, and waterborne transport that affect basin processes and the quality, quantity, and timing of flows. The approach is further complicated by the fact that many river basins are trans-boundary and may be located within several countries, necessitating international mechanisms to regulate and manage river flows.

Lake Tana is an important area used for fish production. 26 different fish species are present in the Lake, of which 15 are endemic to it and the fish catch potential is of about 7-10,000 tons/year while the current production is estimated to be about 3,000 ton/year, but quickly growing. Lake Tana represents the only source of commercial fishing in the Abbay basin and about 5000 persons are directly or indirectly involved in the fisheries activities (SMEC, 2008). To keep growing this fish resource the above opportunities and demands should be considered in the basin

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4.5.9.9. Floriculture

The Amhara Regional Government has set an effort to intensify floriculture investment in the Lake Tana shores following the success story that takes place in the proximity of Addis Ababa and the Rift Valley. The regional government has allocated 700 ha of land on Lake Tana shores for investment in horticultural (flowers) production. So far investors have received some of the allocated lands and started production of horticulture and flower. The investments are going to use water by pumping Lake Tana water and drilling ground water. This floriculture investment is one of the sectors demanding water for production. Floriculture needs 20 times more water than cotton cultivation (Sharma, 2007).

4.5.9.10. Environmental Water Demand

Environmental water demand defined at its simplest is the amount of water needed in a watercourse to sustain a healthy ecosystem. Behind this simple definition are two considerations: 1) riparian and aquatic ecosystems depend on dynamic flows also known as the ―natural flow regime‖ and 2) the definition of a ―healthy‖ ecosystem is determined by the community allocating water to the environment. Dynamic flows for the environment are commonly described according to the natural flow regime, which contains five elements of water flow: magnitude (how much), duration (how long), frequency (how often), timing (how predictable) and rate of change (how variable). Each of these five elements can be determined for individual species‘ needs as well as the entire ecosystems. Determining environmental demand, however, goes beyond the ecology and hydrology of a system because it also involves determining how much water is required to achieve a certain level of river health, as agreed upon by the water-using community. In other words, defining environmental demand is a social process with a scientific eco-hydrological core.

Understanding how we determine environmental water demand takes three forms (Lacroix and Xiu):

1) the science of identifying environmental flow needs and flow responses;

2) the process for prioritizing water for the environment; and

3) policy and management tools for considering environmental demand based on a community‘s priorities.

Previously, a number of preliminary studies in this field were reported. For examples, the Southwest Florida Water Management District (SWFWMD) developed a variety of

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As a recommendation for Tana Sub-basin by adopting SWFWMD experience 10% of the natural flow of streams should be protected for environmental flow.

4.6. Water Quality and Sanitation of Tana Sub-Basin

4.6.4. Sample Characteristics The 150 samples planned and stratified were not successfully assessed during field water quality assessment. From the planned samples the team was succeeded with 131 samples but 19 samples were lost due to the following reasons:

 Some of the sample schemes were not functional during the assessment,

 Some of the deep well samples keepers were not found at the time of measurement and

 Some of the sample schemes were not easily accessible for assessment

Successfully assessed sample points in the project area are presented spatially in the following figure.

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Figure 44: Successfully assessed water quality sample points in Lake Tana Basin

The characteristics of the total samples (131) can be categorized as DW, DW (bono), DW (Private faucet), collection chamber, HDW, HDW (Traditional), spring and river. From the total samples HDW holds more than half which is 54.9 %. DW is on the second place having

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21.4%. Developed springs which are applicable for rural and urban water supply also have 11.5% from the total samples. For more detail see Table 55.

Table 55: Scheme types and their sampled numbers Scheme Type Number of Assessed Samples Percent of Samples DW* 28 21.4 DW (bono) 2 1.5 DW (Private faucet) 9 6.9 Collection Chamber 1 0.8 HDW** 70 53.4 HDW (Traditional) 2 1.5 Spring 15 11.5 River 4 3.1 Total 131 100.0 *Deep well, ** Hand Dug Well From the assessed scheme types during rapid water quality assessment 64.9% was well protected with fence or barrel, 28.2 % was totally not protected and the rest 6.9% was protected with fence but have no door (easily accessible by animals). When we see separately each scheme type all the samples of DW were well protected with fence except 2 samples from faucet. From the sampled HDWs whereas (advanced or traditional) 35 samples were safely protected, 25 samples were not protected, 9 samples protected with fence but have no door and the rest 3 samples were protected with barrel. Spring samples were 9 protected and 6 not protected. Additionally one sample of protected collection chamber 4 samples of unprotected rivers which are used for drinking was assessed. For more figurative understanding see Table 56.

Table 56: Water quality sample with scheme and protection type

Protection Type Well Not Protected Protected Protected DW Scheme Type Protected Protected (no door) with barrel (Private faucet) Total DW 28 0 0 0 0 28 DW (bono) 2 0 0 0 0 2 DW (Private faucet) 5 2 0 0 2 9 Collection Chamber 1 0 0 0 0 1 HDW 35 25 9 1 0 70 HDW (Traditional) 0 0 0 2 0 17 Spring 9 6 0 0 0 0 River 0 4 0 0 0 4 Total (No.) 80 37 9 3 2 131 Total (%) 61.1 28.2 6.9 2.3 1.5 100.0

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*Deep well, ** Hand Dug Well

4.6.5. Microbiological parameters

4.6.5.6. The coliform group The coliform group is made up of bacteria with defined biochemical and growth characteristics that are used to identify bacteria that are more or less related to fecal contaminants. The total coliforms represent the whole group, and are bacteria that multiply at 37°C. The thermo-tolerant coliforms are bacteria that can grow at a higher temperature (44.2°C) and Escherichia coli is a thermo-tolerant species that is specifically of fecal origin (WHO, 2003).

A finding of any coliform bacteria, whether thermo-tolerant or not, in water leaving the treatment works requires immediate investigation and corrective action. There is no difference in the significance of total coliforms, thermo-tolerant coliforms and E. coil in water leaving a treatment works, as they all indicate inadequate treatment, and action should not be delayed pending the determination of which type of coliform has been detected. Upon detection in a distribution system, investigations must be initiated immediately to discover the source of the contamination (WHO, 2003).

Total Coliforms

Total coliforms are a group of bacteria that are naturally found on plants and in soils, water, and in the intestines of humans and warm-blooded animals. Because total coliforms are widespread in the environment, they can be used as one of the many operational tools to determine the efficacy of a drinking water treatment system.

Coliform organisms, better referred to as total coliforms to avoid confusion with others in the group, are not an index of fecal pollution or of health risk, but can provide basic information on source water quality. Total coliforms have long been utilized as a microbial measure of drinking water quality, largely because they are easy to detect and enumerate in water (WHO, 2003).

The total coliform test is the starting point for determining the biological quality of drinking water. This test is performed frequently because of the acute risk that disease causing organisms pose to the users of that water supply.

Samples were tested for coliforms by the Most Probable Number technique (Cheesbrough, 1989). As per Ethiopian drinking water quality standard, samples with 0 coliform/100 mL of

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According to the above classification from the total samples taken during the assessment 31.3% have 0 coliform/100 mL, 20.6% have 0-10 coliform(s)/100 mL and the rest 48.1% of the sample have greater than 10 coliforms (Table 57). Therefore 31.3% of the sample has excellent and acceptable result for drinking. The other 68.7% of the sample was polluted during the sampling time. These results not directly show health risks of the water instead it indicates the status of water for the sample sites. Table 57: Result of total coliform test of Tana Sub-Basin

Total coliform Samples Samples (CFU/100mL) (No.) (%) 0 41 31.3 1-10 27 20.6 >10 63 48.1 Total 131 100.0 Samples which have zero, zero to ten and greater than ten total coliform result can distribute in relation to their scheme type (Table 58 and Figure 45). One can clearly observe from the table and figure that 46.4% of the deep well, 46.3% of the hand dug wells and 7.3% the springs have excellent result of total coliform. The samples in scheme type which have acceptable result were deep well (40.7%), hand dug well (55.6 %) and spring (3.7%). From the samples having the probability of pollution; 15.9% was deep well, 60.3% was hand dug well, 17.5 % was spring and 6.3% was river. The result indicates that majority of the samples taken from rural hand dug well and springs were advanced to the pollution related to bacteria. Most of the deep well samples were safe for drinking.

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Figure 45: Total coliforms results in percent related to scheme type

Table 58: Total coliforms results related to scheme type Total coliform (0 Total coliform (0-10 Total coliform (>10 CFU/100mL) CFU/100mL) CFU/100mL) Scheme Samples Samples Samples Samples Samples Samples (No.) (%) (No.) (%) (No.) (%) DW 13 31.7 8 29.6 7 11.1 DW (bono) 2 4.9 0 0.0 0 0.0 DW (Private faucet) 4 9.8 3 11.1 2 3.2 Collection Chamber 0 0.0 0 0.0 1 1.6 HDW 18 43.9 15 55.6 37 58.7 HDW (Traditional) 1 2.4 0 0.0 1 1.6 Spring 3 7.3 1 3.7 11 17.5 River 0 0.0 0 0.0 4 6.3 Total 41 100.0 27 100.0 63 100.0

Protection is a critical thing that can be related the presence of bacteria. This assessment tried to link the zero, zero to ten and greater than total coliform result in relation to scheme protection. 73.2% of the samples having zero total coliform were well protected with fence and barrel while 2.4% of the samples have protected but have not door showed zero result. Whereas from the 41 samples; 24.4% was not protected but have zero total coliform result. 77.8 % of the samples having zero to ten total coliform were protected with fence whereas 22.2% was not protected. Similarly half of the samples having greater than ten total coliform was not protected at the time of observation (Table 59 and Figure 46). This result clearly shows that bacterial pollution is directly related to animal and human contacts.

Figure 46: Total coliforms results in percent related to protection type

Table 59: Total coliform results related to protection type

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Total coliform (0 Total coliform (0-10 Total coliform (>10 CFU/100mL) CFU/100mL) CFU/100mL) Protection Type Samples Samples Samples Samples Samples Samples (No.) (%) (No.) (%) (No.) (%) Well Protected 27 65.9 17 63.0 36 57.1 Not Protected 10 24.4 6 22.2 21 33.3 Protected (no door) 1 2.4 4 14.8 4 6.3 Protected with barrel 2 4.9 0 0.0 1 1.6 Protected DW (Open faucet) 1 2.4 0 0.0 1 1.6 Total 41 100.0 27 100.0 63 100.0 Lack of sanitation is detrimental to water portability concentrating pathogenic organisms (Tambe et al., 2008). There is a substantial body of evidence that relates improvements in water supply and sanitation in general and in drinking water quality in particular, to specific health outcomes (most frequently reductions in diarrhoeal disease) (WHO, 2003).

From the samples having zero total coliforms in the project area; 83% have no latrine within 100 m radius. Plus to this 81.5 percent of samples having zero to ten total coliforms were free from 100m latrine problems; 85.7 % of samples greater than ten coliforms were free from latrines but most of the values were populated in the range of eleven to twelve (Table 60 and Figure 47). This finding shows that bacterial infection is directly related to the presence of sanitation problems.

Figure 47: Total coliforms results in percent related to sanitation

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Table 60: Total coliform results related to sanitation Total coliform (0 Total coliform (0-10 Total coliform (>10 CFU/100mL) CFU/100mL) CFU/100mL) Sanitation Samples Samples Samples Samples Samples Samples (No.) (%) (No.) (%) (No.) (%) No latrine 34 82.9 22 81.5 54 85.7 Latrine (10m) 1 2.4 1 3.7 1 1.6 Latrine (20m) 1 2.4 0 0.0 0 0.0 Latrine (30m) 1 2.4 3 11.1 1 1.6 Latrine (50m) 2 4.9 0 0.0 4 6.3 Latrine (75m) 2 4.9 1 3.7 3 4.8 Total 41 100 27 100.0 63 100.0

The causes of the sanitary risks were classified into three categories:

 Poor workmanship or lack of maintenance

 Poor site selection and failure to minimize sanitary risks

 Poor sanitary conditions

4.6.6. Chemical parameters Water gathers impurities from both natural and anthropogenic sources, and these cause the physical and chemical parameters of drinking-water to vary over time and by location. Natural and anthropogenic sources of water contamination include (WHO, 2004):

 naturally occurring chemicals and other substances;

 chemicals from industrial sources and human dwellings;

 chemicals from agricultural activities;

 chemicals used in water treatment or from materials in contact with drinking-water;

 pesticides used in water for public-health purposes;

 cyanobacterial toxins and other contaminants derived from biological sources.

Many chemicals found in drinking-water sources may be the cause of adverse human health effects (e.g. arsenic, fluoride), affect the acceptability of water (i.e. turbidity, iron, conductivity) and lower the effectiveness of water treatment. Although some chemicals can cause acute health effects, their concentrations rarely reach sufficient levels in drinking- water, except as a result of the accidental contamination of a water supply. The main problems associated with chemical constituents of drinking-water arise primarily from their ability to cause adverse health effects after prolonged periods of exposure. Contaminants that

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It would be expensive, difficult and largely unnecessary to test for all chemicals that might be of concern in drinking-water and so the chemical parameters to measure have to be prioritized. Priority should be given to parameters that have the greatest impact on the health of the general population and on infants and young children. People who are debilitated, sick or elderly, or who live under unsanitary conditions may be particularly vulnerable to chemicals in drinking-water. The parameters should be country specific (and region-specific, if possible). Judgment should be based on criteria and standards of WHO (2004). In the case of Lake Tana Basin, the physicochemical parameters recommended for RADWQ assessments were relevant and were therefore adopted for this study (Table 61).

Table 61: Physicochemical parameters included in this RADWQ study Parameter Reason for inclusion Fluoride* Health Nitrate* Health, indicator of sanitary quality Iron* Aesthetic Manganese* Aesthetic Conductivity ** Aesthetic, indirect health Turbidity** Aesthetic PH* Aesthetic Temperature** Aesthetic Salinity* Health TDS* Aesthetic *Chemical parameters, **Physical parameters

4.6.6.6. Nitrate Nitrate is one of the most ubiquitous chemical constituents/contaminants of water bodies worldwide as it is derived from human activities, particularly from the disposal of human and animal wastes and the use of nitrogenous fertilizers in agriculture. The intensification of farming practices, for example, has increased nitrate levels in many groundwater resources (WHO, 2004, Howard, 2002). In rare cases, nitrate in groundwater resources derives from geological formations like caliche (Taye, 1999).

In Lake Tana Sub-Basin rapid water quality assessment, from the total of 131 water samples collected from 39 deep wells, 72 hand dug wells, 15 springs, 4 rivers and 1 collection chamber were analyzed for nitrate (Table 62). Of these, 110 of samples complied with the national standard. From these total assessed samples the median concentration for the whole

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of the sub-basin was 4.6 mg NO3/L, with maximum concentrations of 33 mg NO3/l in Gondar

Zuria Wereda Burbuaks Kebele. Values greater than 10 mg NO3/l show indications of health problem (indicator of sanitary quality). This may be a source of health risk for infants. Spatial distribution of nitrate is shown on the following figure.

Figure 48: Spatial distribution of nitrate in Tana Sub-Basin

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Table 62: Compliance with national standard value for nitrate Scheme Type Number of Assessed Samples Compliance (%) DW* 39 89.3 DW (bono) 2 100 DW (Private faucet) 9 100 Collection Chamber 1 100 HDW** 70 82.9 HDW (Traditional) 2 50 Spring 15 66.7 River 4 100 Total 131 84.0 *Deep well, ** Hand Dug Well The high nitrate levels in groundwater resources (Taye, 1999) are likely caused by:

 a lack of proper sewers and other waste disposal facilities;

 the presence of more than 20 000 open pit latrines; and,

 geological conditions.

4.6.6.7. Fluoride Fluoride is one of the most important chemicals to affect the quality of drinking-water (Tadesse et al., 2010). The high fluoride concentrations are primarily associated with (Tadesse et al., 2010):

 volcanic and fumarolic activity, which adds fluoride to the groundwater;  water interacting with fluoride-bearing volcanic and sedimentary rocks, such as pumice, ignimbrite, obsidian and rhyolite; and,

 low calcium concentrations, which restrict the precipitation of fluoride as fluorite (CaF2).

In Lake Tana Sub-Basin rapid water quality assessment, from the total of 131 water samples collected from 39 deep wells, 72 hand dug wells, 15 springs, 4 rivers and 1 collection chamber were analyzed for fluoride (Table 63). Of these, all samples complied with national standard and WHO guideline. From these total assessed samples the median concentration for the whole of basin was 0.18 mg F/L, with maximum concentrations of 1.5 mg F/l in Dera Wereda Jigna Kidane Mihret Kebele. All the assessed samples were safe for drinking in case of fluoride. Spatial distribution of fluoride is shown on the following figure.

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Figure 49: Spatial distribution of fluoride in Tana Sub-Basin

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Table 63: Compliance with national standard and WHO guideline value for fluoride Scheme Type Number of Assessed Samples Compliance (%) DW* 28 100 DW (bono) 2 100 DW (Private faucet) 9 100 Collection Chamber 1 100 HDW** 70 100 HDW (Traditional) 2 100 Spring 15 100 River 4 100 Total 131 100 *Deep well, ** Hand Dug Well

4.6.7. Aesthetic parameters

4.6.7.6. Iron Iron is one of the most abundant metals in the earth‘s crust. Iron contamination is a particular problem for anaerobic groundwater supplies, but iron can get into drinking-water from the use of iron coagulants or from corrosion of galvanized iron, steel and cast-iron pipes in the distribution system. Iron also promotes the growth of iron bacteria, which oxidize ferrous iron to ferric iron, and in the process corrode the piping and deposit a slimy coating on its surface (Howard, 2002, WHO, 2004). Some surface waters also have iron problems, particularly related to colloidal iron.

In Lake Tana Sub-Basin rapid water quality assessment, from the total of 131 water samples collected from 39 deep wells, 72 hand dug wells, 15 springs, 4 rivers and 1 collection chamber were analyzed for Iron (Table 64). Of these, all samples complied with the national standard and WHO guideline except one spring which has a value of 0.32 mg Fe/L located in Farta Wereda Arga Kebele. From these total assessed samples the median concentration for the whole of basin was 0.01 mg Fe/L, with maximum concentrations of 0.32 mg Fe/L. Therefore, the water is safe for drinking in Tana Sub-Basin in case of iron concentration. Spatial distribution of iron is shown on the following figure.

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Figure 50: Spatial distribution of iron in Tana Sub-Basin

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Table 64: Compliance with national standard and WHO guideline value for Iron Scheme Type Number of Assessed Samples Compliance (%) DW* 28 100 DW (bono) 2 100 DW (Private faucet) 9 100 Collection Chamber 1 100 HDW** 70 100 HDW (Traditional) 2 100 Spring 15 93.3 River 4 100 Total 131 99.2 *Deep well, ** Hand Dug Well

4.6.7.7. Manganese Iron and manganese are both known to stain the water supply. They can make water appear red or yellow, create brown or black stains in the sink, and give off an easily detectable metallic taste. Even laundry can get brown spots by washing with Fe- and Mn-rich water. Although these can all be aesthetically displeasing, iron and manganese are not considered to be unhealthy. Fortunately, they can be removed from the water easily.

In Lake Tana Sub-Basin rapid water quality assessment, from the total of 131 water samples collected from 39 deep wells, 72 hand dug wells, 15 springs, 4 rivers and 1 collection chamber were analyzed for Manganese (Table 65). Of these, all samples complied with the national standard. From these total assessed samples the median concentration for the whole of basin was 0.01 mg Mn/L, with maximum concentrations of 0.45 mg Mn/L located in Dera Wereda Arga Kebele. All water tests of the manganese in Tana Sub-Basin show good compliance for drinking. Spatial distribution of manganese is shown on the following figure.

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Figure 51: Spatial distribution of manganese in Tana Sub-Basin

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Table 65: Compliance with national standard value for Manganese Scheme Type Number of Assessed Samples Compliance (%) DW* 28 100 DW (bono) 2 100 DW (Private faucet) 9 100 Collection Chamber 1 100 HDW** 70 100 HDW (Traditional) 2 100 Spring 15 100 River 4 100 Total 131 100 *Deep well, ** Hand Dug Well

4.6.7.8. Turbidity Turbidity in water is caused by suspended matter, such as clay, silts, finely divided organic and inorganic matter, soluble colored organic matter, and plankton and other microscopic organisms. It can arise in drinking-water if the water is inadequately treated or if sediment is re-suspended. Turbidity can also come from biofilm or corrosion products in the distribution system. High levels of turbidity can protect microorganisms from the effects of disinfection and can stimulate bacterial growth. Low turbidity minimizes both the amount of chlorine required for disinfection of water and the potential for transmitting infectious diseases.

In Lake Tana Sub-Basin rapid water quality assessment, from the total of 131 water samples collected from 39 deep wells, 72 hand dug wells, 15 springs, 4 rivers and 1 collection chamber were analyzed for turbidity (Table 66). Overall, 101 samples (69.5% of the total) complied with the national standard and WHO ―suggested‖ value. In hand dug wells and springs had the lowest compliance levels of all the water-supply technologies, at 62.9%, and 66.7% respectively. The sampled rivers and collection chamber also have lowest values. As expected, deep wells had the highest level of compliance (89%). From these total assessed samples the median concentration for the whole of basin was 1.86 NTU, with maximum value of 49.6 NTU located in Mecha Wereda Abro Menor Kebele. See the detail compliance on Table 66 and the spatial distribution on the following figure.

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Figure 52: Spatial distribution of Turbidity in Tana Sub-Basin

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Table 66: Compliance with the national standard and WHO ―suggested‖ value for turbidity Scheme Type Number of Assessed Samples Compliance (%) DW* 28 89.3 DW (bono) 2 50.0 DW (Private faucet) 9 88.9 Collection Chamber 1 0.0 HDW** 70 62.9 HDW (Traditional) 2 0.0 Spring 15 66.7 River 4 50.0 Total 131 69.5 *Deep well, ** Hand Dug Well From these 40 samples which fail turbidity standard of WHO; 7 are from Mecha Wereda, 5 from each Weredas of Dera and Fogera, 4s from South Achefer Wereda, 3 from each Weredas of Bahir Dar Zuria, Ebinat, Farta and Libo Kemkem, 2s from Fagita Lekoma Wereda, 1from each Weredas of Gondar Zuria, Misrak Estie, North Achefer, Sekela and Wegera.

4.6.7.9. Conductivity, TDS and Salinity Conductivity is a proxy indicator of total dissolved solids, and therefore an indicator of the taste or salinity of the water. Although conductivity, total dissolved solids (TDS) and salinity parameters do not provide information about specific chemicals in water, these acts as a good indicator of water-quality problems, particularly when these changes with time. There is little direct health risk associated with these parameters, but high values are associated with poor taste, customer dissatisfaction and complaints. High conductivity water, for example, can cause excessive scaling in water pipes, heaters, boilers and household appliances. The conductivity of water varies considerably by geological region, owing to differences in the mineral and chemical properties of the water body. However, changes in conductivity over time, and high conductivity values, indicate the water is contaminated, which can cause corrosion in rising mains and pipes (Tadesse et al., 2010). The relation between conductivity and TDS is:

TDS (mg/L) ≈ EC (µS/cm) X 640……………….25

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Table 67: Compliance with the national standard and WHO ―suggested‖ value for conductivity, TDS and Salinity Compliance (%) Scheme Type Number of Assessed Samples EC TDS Salinity DW* 28 100 100 96.4 DW (bono) 2 100 100 100 DW (Private faucet) 9 100 100 100 Collection Chamber 1 100 100 100 HDW** 70 100 100 100 HDW (Traditional) 2 100 100 100 Spring 15 100 100 100 River 4 100 100 100 Total 131 100 100 99.2 *Deep well, ** Hand Dug Well

4.6.7.10. pH The pH of natural water can provide important information about many chemical and biological processes and provides indirect correlations to a number of different impairments. The pH is the measurement of the acid/base activity in solution.

In unpolluted or pure waters, the pH is governed by the exchange of carbon dioxide with the atmosphere. Carbon dioxide is soluble in water and the amount of CO2 that will dissolve in the water will be a function of temperature and the concentration of CO2 in the air. As the gaseous CO2 becomes aqueous, the CO2 will be converted into H2CO3 which will acidify the water to a pH of about 6. If any alkaline earth metals such as sodium are present, the carbonates and bicarbonate formed from the solubilization of CO2 will interact with sodium increasing the alkalinity shifting the pH up over 7. Lower values in pH are indicative of high

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In Lake Tana Sub-Basin rapid water quality assessment, from the total of 131 water samples collected from 39 deep wells, 72 hand dug wells, 15 springs, 4 rivers and 1 collection chamber were analyzed for pH (Table 68). Overall, 129 samples (98.5% of the total) complied with the national standard and WHO ―suggested‖ value. Two samples decrease the compliance of deep well by 7.1 %. These samples have a value greater than 8.5 (which become base). Aykel town deep well is the first one which has a value of 9.1 located on Baskura Kebele and the second one is Dangila deep well which is known by DTW1 with a value of 8.6. Spatial distribution of manganese is shown on the Figure 53 D.

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Figure 53: Spatial distribution of conductivity (A), TDS (B), salinity (C) and pH (D) in Tana Sub-Basin

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Table 68: Compliance with the national standard and WHO ―suggested‖ value for pH

Scheme Type Number of Assessed Samples Compliance (%) DW* 28 92.9 DW (bono) 2 100 DW (Private faucet) 9 100 Collection Chamber 1 100 HDW** 70 100 HDW (Traditional) 2 50 Spring 15 100 River 4 100 Total 131 98.5 *Deep well, ** Hand Dug Well

4.7. Hydrological Modeling and Water Balance

4.7.1. Sensitivity Analysis

As mentioned on the methodology part flow sensitivity analysis has been carried out for 26 parameters. A t-test result of SWAT-CUP global sensitivity analysis provides a table output having columns of t-stat and p-values. A t-stat provides a measure of sensitivity (larger in absolute values are more sensitive) and p-values determined the significance of the sensitivity. A value close to zero has more significance. Nine for Gilgel Abbay, Gumara and Mehech and also eight for Ribb of the 26 parameters were sensitive for the observed flow from 1996- 2008 not including the warm up period. These parameters with their mean relative sensitivity value at the outlet for the runoff are tabulated in Table 69 below.

Table 69: Flow sensitive parameters for Gilgel Abbay, Gumara, Ribb and Megech River Catchment Rank Parameter Description t-stat p-value Gilgel Abbay 1 ALPHA_BF.gw Baseflow alpha factor (days) 10.007 0.000 Effective hydraulic conductivity in main channel 2 CH_K2.rte alluvium (mm/hr) 4.489 0.000 3 CANMX.hru Maximum canopy storage (mm H2O) -4.019 0.000 4 CH_N2.rte Manning's "n" value for main channel 3.723 0.000 5 GW_DELAY.gw Groundwater Delay time (days) -3.202 0.001 Threshold depth of water in the shallow aquifer required 6 GWQMN.gw for return flow to occur (mm H2O -2.272 0.023 7 BLAI.crop.dat Maximum potential leaf area index 2.077 0.038 8 GW_REVAP.gw Groundwater "revap" coefficient 1.961 0.050 9 CN2.mgt Initial SCS runoff curve number for moisture condition II -1.805 0.071 Gumara

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Rank Parameter Description t-stat p-value Effective hydraulic conductivity in main channel 1 CH_K2.rte alluvium (mm/hr) 15.807 0.000 2 ALPHA_BF.gw Baseflow alpha factor (days) 7.622 0.000 3 CANMX.hru Maximum canopy storage (mm H2O) -5.034 0.000 4 ESCO.hru Plant uptake compensation factor 3.713 0.000 5 CN2.mgt Initial SCS runoff curve number for moisture condition II -3.317 0.001 6 SURLAG.bsn Surface runoff lag coefficient -3.045 0.002 Threshold depth of water in the shallow aquifer required 7 GWQMN.gw for return flow to occur (mm H2O -3.018 0.003 8 BLAI.crop.dat Maximum potential leaf area index -2.524 0.012 9 SOL_Z.sol Depth from soil surface to bottom of layer (mm) -1.939 0.053 Ribb 1 CN2.mgt Initial SCS runoff curve number for moisture condition II -26.273 0.000 Effective hydraulic conductivity in main channel 2 CH_K2.rte alluvium (mm/hr) 10.670 0.000 3 CANMX.hru Maximum canopy storage (mm H2O) 5.805 0.000 4 ESCO.hru Soil evaporation compensation factor -3.536 0.000 Threshold depth of water in the shallow aquifer required 5 GWQMN.gw for return flow to occur (mm H2O 2.887 0.004 6 SURLAG.bsn Surface runoff lag coefficient -2.356 0.019 7 BLAI.crop.dat Maximum potential leaf area index 1.22 0.22 8 ALPHA_BF.gw Baseflow alpha factor (days) 1.16 0.25 Megech 1 CN2.mgt Initial SCS runoff curve number for moisture condition II -14.638 0.000 Effective hydraulic conductivity in main channel 2 CH_K2.rte alluvium (mm/hr) 8.583 0.000 3 CH_N2.rte Manning's "n" value for main channel 3.965 0.000 4 SOL_K.sol Saturated Hydraulic Conductivity (mm/hr) 2.982 0.003 5 GW_REVAP.gw Groundwater "revap" coefficient -2.483 0.013 6 ALPHA_BF.gw Baseflow alpha factor (days) 1.987 0.047 7 CANMX.hru Maximum canopy storage (mm H2O) -1.529 0.127 8 ESCO.hru Soil evaporation compensation factor -1.348 0.178 9 SOL_Z.sol Depth from soil surface to bottom of layer (mm) 1.230 0.219

4.7.2. Model Calibration

Model calibration is the process of adjustment of the model parameters and forcing within the margins of the uncertainties (in model parameters and /or model forcing) to obtain a model representation of the processes of interest that satisfies pre-agreed criteria. This approach aims to improve the model by developing correction factors that can be applied to generate predicted values and may result in an improved model description. The reliability of the model depends on the model simulated results and when the model results match with the observed values from stream flow measurement, the users get greater confidence. To facilitate the evaluation of model quality, visual comparison has been normally done between

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Result sensitivity analysis was helping to did calibration of the hydrological model. Upper Gilgel Abay, Gumara, Ribb and Megech streamflow data gauged near Merawi, Bahir Dar, Addis Zemen and Azezo respectively were used to calibrate the hydrological model for a period of Jan 1st. 1996 to Dec.31, 2004 using SWAT-CUP SUFI-2 algorithm. Therefore these four major streamflow were calibrated with the above sensitive flow parameters. After several iterations the parameters fitted value was earned (Table 70) Table 70: Calibrated and fitted values of sensitive parameters Lower Upper Fitted Values Parameter Name bound bound Gilgel Abay Gumara Ribb Megech r__CN2.mgt -0.25 0.25 -0.03 0.18 0.08 0.07 v__ESCO.hru 0 1 0.70 0.34 0.84 a__CANMX.hru 0 10 0.07 7.82 5.75 9.94 v__BLAI.crop.dat 0 1 0.63 0.28 0.72 v__GW_REVAP.gw -0.036 0.036 -0.02 0.01 v__GWQMN.gw -1000 1000 -457.85 -347.41 955.99 r__SOL_Z().sol -0.25 0.25 -0.02 0.15 a__CH_K2.rte 0 150 103.73 126.60 20.33 140.57 a__GW_DELAY.gw -10 10 -8.51 r__SOL_K().sol -0.25 0.25 0.07 v__CH_N2.rte 0 1 0.24 0.36 v__ALPHA_BF.gw 0 1 0.40 0.29 0.31 0.59 v__SURLAG.bsn 0 10 0.00 0.04 v__ means the existing parameter value found before calibration is to be replaced by the fitted value, a__ means the fitted value is added to the existing parameter value found before calibration, and r__ means the existing parameter value found before calibration is multiplied by (1+ a fitted value).

SWAT-CUP in parallel gives the best simulation of streamflow that was considering the above fitted parameters. Using calibration fitted parameters simulation was done for the validation period and checked its performance with the observed data. Performance of the best simulation of streamflow result which used these fitted parameter values for calibration and validation is shown on Table 71 and also observe Figure 54 for scatter plot of streamflow and sediment respectively.

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Santhi et al. (2001) stated that efficiency values greater than or equal to 0.50 are considered adequate for SWAT model application. Setegn et al. (2008) also stated on the model performance that can be judged as satisfactory if R2 is greater than 0.6 and ENS is greater than 0.5. Hence, it is observed that SWAT exhibited strong performance in representing the hydrological conditions of Gilgel Abay, Gumara and Ribb watersheds. It shows that the SUFI-2 did not capture the observations well during calibration period for Megech River.

This problem coupled with the lower values of NSE and R2 for Megech river indicate that there is uncertainty in simulated flow due to errors in input data such as rainfall and temperature and/or other sources of uncertainties such as upstream dam constructions for town water supply, diversion of streams for irrigation, and other unknown activities in the sub-basins (Setegn et al., 2008).

Table 71: Calibrated model simulation performance Coefficient of Nash-Sutcliffe determinatio efficiency Watershed Analysis n (R2) (NSE)

Calibration 0.81 0.80 Gilgel Abay Validation 0.73 0.73 Calibration 0.64 0.64 Gumara Validation 0.60 0.58 Calibration 0.75 0.73 Ribb Validation 0.62 0.60 Calibration 0.43 0.43 Megech Validation 0.39 0.37

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Figure 54: Scatter plot of simulated versus observed daily flow for calibration period (a) Gilgel Abay, (b) Gumara, (c) Ribb and (d) Megech

It can be seen from the flow hydrographs (Figure 54) that the simulated flows well replicate the observed flows except that the peak values couldn‘t be caught on the calibration period of Gilgel Abay, Gumara and Megech in daily basis. In case of Ribb simulation the peak flow is caught. This may be due to error reading of the flow. Over flow is the case that tops the staff gauge.

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Figure 55: Observed and Simulated daily streamflow in comparison with areal rainfall for Upper Gilgel Abay (a), Gumara (b), Ribb (c) and Megech (d) watersheds on the calibration period (1996 to 2004)

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4.7.3. Model Validation

The validations of the model for Gilgel Abay, Gumara, Ribb and Megech were done for an independent data set of four years from 2005 to 2008. Validation of the model showed the model‘s strong predictive capability through model performance criteria results shown on Table 71. On Figure 56 and Figure 57 the scatter plots and simulation plots for the validation period respectively are shown.

Figure 56: Scatter plot of simulated versus observed daily flow for validation period (a) Gilgel Abay, (b) Gumara, (c) Ribb and (d) Megech

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Figure 57: Observed and Simulated daily streamflow in comparison with areal rainfall for Upper Gilgel Abay (a), Gumara (b), Ribb (c) and Megech (d) watersheds on the validation period (2005 to 2008)

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4.7.4. Baseflow contribution

Baseflow (40% - 60%) is an important component of the total discharge within Lake Tana Sub-basin that contributes more than the surface runoff (Setegn et al, 2008). Separation of baseflow for the major watersheds either in observed and simulated streamflow shows this truth (Table 72). Model simulation base flow is best comparable to observed flow (Error! Reference source not found.). Table 72: Baseflow and surface runoff contribution for total streamflow Observed Total Streamflow Baseflow Surface Runoff Mean Daily Mean Daily Contribution Mean Daily Contribution (m3/s) (m3/s) (%) (m3/s) (%) Gilgel Abay 56.9 29.7 52.2 27.2 47.8 Gumara 38.6 18.5 48.1 20.0 51.9 Ribb 16.1 7.4 45.8 8.7 54.2 Megech 8.6 4.7 54.5 3.9 45.5 Simulated Total Surface Streamflow Baseflow Runoff Mean Daily Mean Daily Contribution Mean Daily Contribution (m3/s) (m3/s) (%) (m3/s) (%) Gilgel Abay 54.9 36.3 66.0 18.7 34.0 Gumara 33.9 20.7 61.0 13.2 39.0 Ribb 16.1 8.7 53.7 7.5 46.3 Megech 8.2 3.9 47.4 4.3 52.6

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Figure 58: Observed and Simulated daily base flow for Upper Gilgel Abay (a), Gumara (b), Ribb (c) and Megech (d) watersheds in all simulation years (1996 to 2008)

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4.7.5. Water balance analysis

4.7.5.1. Estimation of the water balance components

I. Lake areal rainfall

Precipitation data can be used in different ways for water balance calculations. For many hydrological applications that also include modelling, extrapolation or/and interpolation of point rainfall measurement is necessary. In Figure 59 it is shown that there are five meteorological stations in and around the lake. Daily observations from these stations are interpolated to obtain areal rainfall by using Thiessen polygon interpolation method.

Figure 59: Spatial distribution of rainfall station used to estimate lake areal rainfall

The Thiesson polygon of rainfall station (see Figure 59) shows, that Gondar station has no weight. Delgi station contributes the highest 35.9 % and Yifag and Chuahit the smallest 12.4 % of the Lake areal rainfall (see Table 73, weights of rainfall station on the lake areal rainfall.)

In the Lake Tana Basin, 85 per cent of the annual rainfall is from June to September. See Figure 60, the temporal distribution of rainfall in the nearby stations, January through April, November and December are the driest seasons.

Table 73: Weights of rainfall station on the lake areal rainfall

Bahir Dar Yifag Enfiranz Delgi Chuahit

24.8 12.4 14.4 35.9 12.4

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Figure 60: Long-term monthly average rainfall distribution in the Lake Tana Basin (1994- 2008)

II. Open water evaporation

The main factors influencing evaporation from open water surface are the supply of heat for vaporization and the process to transport vapour away from the evaporative surface. Influencing factors are solar radiation, wind velocity and the gradient of specific humidity in the air above the open water surface. Evaporation is a major component of the lake water balance, but it is still difficult to estimate and has rarely been measured directly.

Areal open water evaporation is estimated by Penman method, which is widely used as the standard method in hydrologic engineering applications to estimate potential evaporation from open water under varying locations and climatic conditions. The Penman combination equation for open water evaporation reads:

  6.43*(1 0.536U2 )* D Ep  *(Rn  Ah )  * ......        26

-1 Where: Ep: is potential evaporation that occurs from free water evaporation [mm day ], Rn: is net radiation exchange for the free water surface [mm day-1], Ah: is energy advected to the -1 -1 water body [mm day ], U2: is wind speed measured at 2m [m s ], D: is average vapor pressure deficit, [kPa], : is latent heat of vaporization [MJ kg-1], : is psychrometric

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The lake areal rainfall for the water balance simulation is computed using the nearby station Bahir Dar.

III. Surface water inflow from gauged catchments

Surface water inflow to the lake includes water by rivers, streams, and direct overland flow. According to Kebede et al.(2006) there are four major gauged rivers dominating surface water inflow contributing more that 93% of the inflow. In this study only rivers that have a reliable continuous daily flow data record are considered as gauged catchments and used for SWAT model calibration and validation to estimate each river contribution at the river mouse. This simulated stream flow data from each major watersheds used for the simulation of water balance and related lake levels, those are Gilgel Abay, Gumara, Ribb and Megech rivers. Figure 61, below shows the long-term monthly average flow from 1990 to 2007. Gilgel Abay contributes approximately 50% of the gauged lake inflow.

Figure 61: Long-term monthly average flow of the major gauged rivers in the Lake Tana (1990-2007)

IV. Groundwater inflow and outflow

There is no ground water monitoring station in the Lake Tana vicinity, so the groundwater flow to or from the lake is uncertain. Since, the lake is located in a wide depression of Plato, it seems there could be a likely groundwater flow towards the lake. But, according to SMEC (2008) study, an 80 m thick clay layer underlies the lake floor which makes relevant vertical inflow or outflow through the lake bottom highly unlikely. Due to the presence of the thick

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V. Lake level and outflow

Outflow from the Lake Tana and lake level data are available from 1959 with some missing flow data in 1982 and 1991; the lake level data at Bahir Dar station has partial missing data for only year 1991. Screening of outflow data indicates that the time series records for the years 1993 and 1994 are exactly the same.

In year 1996, a low height weir was constructed at Chara-Chara across the Abay River at the outlet of the lake. The weir is equipped with two radial gates which allow the release from Lake Tana to be totally controlled as long as the water level of the lake remains lower than the elevation of the spillway crest level (1787 m amsl), the minimum operating level of the weir is 1784 m amsl.

As shown in the Figure 62, the lake level varies approximately by 1.6 m annually with average lake level of 1786.2 m between 1996 and 2008. Since, the operation of the weir the lake level has dropped dramatically reaching the historical minimum water level of 1784.46 m amsl at 6/30/2003.

Starting from 2003 EFY, the data that show the water abstraction from lake Tana for Tana- Beles hydropower generation is considered in the water balance as outflow.

Figure 62: Outflow and lake levels of Lake Tana (1996-2008)

VI. Surface water inflow from ungauged catchments

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Ungauged catchments are catchments with inadequate records of data for hydrological computation. There are a number of Gauging stations in the Lake Tana Basin, but four of the watersheds have a reliable and a longer daily time series data. These ungauged catchments refer to catchments having topographic and climatic properties that are available without observed discharge data. The ungauged part of the Lake Tana Basin covers approximately 50 percent of the watershed.

A number of approaches are currently available for prediction of ungauged catchments flows. Methods appropriate include direct estimates of parameters for ungauged catchments using theatrical understanding of (small-scale) soil physics (Koren et al., 2000), transferring calibrated gauged model parameters to neighbouring ungauged catchment (Vandewiele and Elias, 1995) while the most common approach relates model parameters to catchment characteristics by means of statistics see (Seibert, 1999, Merz and Blöschl, 2004) among others.

In this study, runoff from ungauged catchment is estimated in two ways. The first one is transferring calibrated gauged model parameters to downstream neighbouring ungauged catchment and the second one is an indirect way, as a closing term in the water balance computation. The general water balance equation is rewritten as shown below:

S SIUngauged   P  SIGauged  Eo  So ...... T 27

Since, all the parameters in the right hand side are measured daily; the term in the left hand side will be computed as a closing term. The only advantage of this approach is it simplicity, every uncertainty associated with the measurement is considered as flow from ungauged catchments.

VII. Lake level simulation and runoff from ungauged catchment

The daily lake level is simulated by the observed data of lake areal rainfall, open water evaporation computed by Bahir Dar station, gauged inflow from the four dominant watersheds, river outflow by the Blue Nile and runoff from ungauged catchment as parameter tranisfer and a closing term by comparing the simulated lake level with observed lake level as a parameter.

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Daily areal rainfall: estimated from 5 meteorological station located nearby Lake Tina by Thiesson Polygon interpolation technique (1996-2008).

Open water evaporation: estimated by meteorological data of Bahir Dar stations, daily estimates of open water evaporation from 1996 to 2008.

Inflow from gauged catchments and their downstream ungauged: the SWAT daily river flow output of Gilgel Abay, Ribb, Megech and Gumara from 1996 to 2008.

River outflow: by the Blue Nile River measured at the outlet of the lake (1996 – 2008)

Inflow from ungauged catchment other than major watershed downstreams: is determined indirectly as a closing term by comparing the lake level simulation with the observed lake level.

Lake level is simulated using Elevation-Volume and Area-Volume polynomial fitted by Abeyou (2008). The result of lake level simulation (Figure 63) shows a comparable result with a maximum lake level difference of 70cm between the observed lake level and simulated lake level. The comparison of lake level simulation with the observed one shows a Nash- Sutcliffe Efficiency of 0.9 and a correlation coefficient of 0.91 R-square for a total river inflow of (runoff from gauged plus ungauged) 0.03 times inflow from the four gauged rivers and their downstream ungauged.

Figure 63: Simulated verses observed lake level of Lake Tana (1996 to 2008)

The lake level simulation indicated that, the ungauged part of the watershed which covers approximately 37% of the watershed area contributing 3% of the total river inflow, and the

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Tana Sub-Basin Land Use Planning and Environmental Study Project gauged part of the watershed which covers 63% of the watershed area contributing 97% of the total river inflow to the lake. The river flow gauging stations of gauged rivers are located at the midsection of the watershed, and the ungauged part of the watershed is located at the floodplain.

Therefore the summurized mean annual (1996-2008) water balance of Lake Tana is tabulated on the foll0wing table.

Table 74: Mean annual water balance of Lake Tana (1996-2008) Water Balance Contribution Contribution Item Total Components (BCM) (%) Lake areal rainfall 3.271 33.347 Gauged catchment and their downstream 6.302 64.247 Inflow ungauged 9.749 Ungauged catchments other than gauged 0.176 1.794 downstreams River 4.539 44.958 Open water 5.095 50.466 Outflow evaporation 10.096 Abstraction (Tana- 0.462 4.576 Beles hydropwer) Lake Agerage lake stogage 26.605 26.605 26.61 storage Change in Inflow-Outflow -0.347 3.437 -0.347 storage (ΔS)

The result in the above table showed that there is negative water storage in Lake Tana. The initial lake volume of the lake is 27460 MCM and it decreased to daily average of 26696.45 MCM. This is due to that the lake storage capacity (lake volume) is decreasing year to year because of siltation and sedimentation on one way and due to abstraction of the lake because of hydropower generation on the other hand since 2003 EFY. This is clearly observed on the following graph of Lake Tana storage.

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Figure 64: Trend of Lake Tana storage from 1996 to 2008

Gauged catchment and their downstream ungauged catchments contribution from the total inflow of 6.302 BCM is tabulated on the following table. As shown from the table 58% of the inflow for Lake Tana is from Gilgel Abbay River. Gumara River holds the second place.

Table 75: Gauged catchment and their downstream ungauged catchments contribution Watershed Inflow (BCM) Inflow (%) G_Abbay 3.657 58.0 Gumara 1.840 29.2 Ribb 0.530 8.4 Megech 0.275 4.4 Total 6.302 100

4.8. Siltation and Sedimentation

For the five stations, sediment-discharge rating curve equations were obtained; sediment rating equation coefficients and correlation coefficients r2 are tabulated on Table 76 and observed on Figure 65 and Figure 66 . The sediment data used for rating curve development are shown on Appendix 14. The rating curve correlation coefficients obtained for the five stations were better.

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Figure 65: Rating curves and their equations for the four major inflow rivers of Lake Tina

Figure 66: Rating curve and its equation for Abbay (Blue Nile) as an outflow river of Lake Tina

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Table 76: Sediment rating curves of five major rivers River Station a b r2 Gilgel Abbay Merawi 4.09 1.73 0.98 Gumara Bahir Dar 14.16 1.56 0.88 Ribb Addis Zemen 22.10 1.65 0.90 Megech Azezo 35.07 1.20 0.94 Abbay Peda 4.37 1.39 0.85

Therefore, by using the above rating curve equations the mean annual sediment transport load of the four major inflow rivers and the out flow sediment is tabulated on Table 77. The mean annual sediment inflow from the four major rivers which contribute 97% of the stream inflow is 16,705,498.97 tons. The mean annual sediment outflow by Abbay (Blue Nile) river is 1,848,104.19 tons. The outflow sediment is 11.1% of the inflow sediment. Table 77: Mean annual sediment transport load of five major rivers at the lake confluence (1996-2008)

Major rivers mean annual sediment transport load (tons) Month Gilgel Abbay Gumara Ribb Megech Abbay (Blue Nile)

January 9,734.15 30,286.31 2,015.57 1,590.07 97,321.05 February 1,269.88 10,457.05 514.95 519.30 76,048.23 March 286.63 5,099.18 160.75 475.70 84,059.85 April 443.45 11,143.81 90.44 549.99 76,000.99 May 4,193.26 55,092.94 214.76 1,913.38 61,870.01 June 181,190.51 154,849.33 1,069.36 8,252.38 62,689.25 July 1,680,093.81 703,233.65 130,782.04 33,696.67 100,549.74 August 3,687,370.62 1,227,461.99 731,316.03 63,866.49 180,123.58 September 2,933,113.04 1,041,013.90 654,442.96 47,728.64 439,963.48 October 1,558,884.69 561,915.37 220,695.04 24,180.01 324,522.04 November 435,486.66 240,146.91 54,846.22 11,206.16 211,981.85 December 72,466.50 96,036.20 9,600.71 4,501.52 132,974.11 Sub-total 10,564,533.20 4,136,736.63 1,805,748.83 198,480.31 1,848,104.19 Grand total 16,705,498.97 1,848,104.19

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Figure 67: Sediment transport load of the five major rivers

4.9. Surface and Ground Water Potential for Irrigation

4.9.4. Surface Water Potential for Irrigation

As discussed in the methodology part spatial distribution of mean annual surface runoff (1996-2008) of Lake Tana sub-basin was computed and processed by Thornthwaite - type Monthly Water Balance model and ArcGIS model builder respectively. The result is shown on Figure 68. Most of southern parts of the basin have high mean annual runoff as verified from the water balance part. Whereas northern parts of the basin have low mean annual runoff as compared to the southern part.

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Figure 68: Mean annual surface runoff of Tana Sub-Basin The above result was validated with the observed mean annual runoff (1996-2008) of Gilgel Abbay, Gumara, Ribb, Megech, Gemero and Garno. The comparison is between gauged watershed mean annual runoff estimated average and observed mean annual runoff. The

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Table 78: Validation result of mean annual surface runoff Observed Simulated No Watershed runoff runoff O-S (O-S)2 O-Ȏ (O-Ȏ)2 1 Gilgel_Abbay 1057.1 1236.5 -179.4 32183.7 467.1 218161.0 2 Gumara 890.0 760.0 130.0 16904.9 300.0 89996.4 3 Ribb 359.0 652.5 -293.5 86124.7 -231.0 53358.6 4 Megech 543.4 452.5 90.9 8261.5 -46.6 2174.5 5 Gemero 355.9 483.0 -127.1 16150.9 -234.1 54808.0 6 Garno 334.7 435.5 -100.8 10162.4 -255.3 65195.1 Sum 3540.1 4020.0 169788.1 483693.7 Av 590.0 NSE 0.65

4.9.5. Ground Water Potential for Irrigation

With the method discussed in methodology part, the ground water potential scores are assigned based on (Gumma and Pavelic, 2012). Their assigned score of the themes and their map and also the ground water potential map is shown on Table 79, Figure 69, and Figure 70

Table 79: Define scores for individual features of the seven themes for groundwater potential zones

Groundwater S potential Score no. Parameters/theme Identified units/score score assigned 0-10 Very Good 5 10-20 Good 4 1 Geomorphology 20-30 Moderate 3 30-50 Poor 2 >50 Very Poor 1 Upper lava flows Very Poor 1 Lake Tana Very Good 5 Water Body Very Good 5 Aiba basalt Moderate 3 Aiba Basalt Moderate 3 Guna tuff Moderate 3 2 Geology Middle basalt flows Moderate 3 Middle lava flows Poor 2 Pliocene Chilga sediments Very Good 5 Quaternary basalt Good 4 Quaternary lacustrine sediments Very Good 5 Quaternary trachyte flows Moderate 3 Quaternary volcanics Good 4

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Groundwater S potential Score no. Parameters/theme Identified units/score score assigned Rhyolite Good 4 Tarmaber (upper basalt and Trachyte) Moderate 3 Tub-Upper Basalt Moderate 3 0-3 Very Good 5 3-5 Good 4 3 Slope 5-7 Moderate 3 7-9 Poor 2 >9 Very Poor 1 0-0.1 Very Good 5 0.1-0.2 Good 4 4 Drainage Density 0.2-0.25 Moderate 3 0.25-0.3 Poor 2 0.3-0.5 Very Poor 1 <800 Very Poor 1 800-1000 Poor 2 5 Rainfall 1000-1500 Moderate 3 1500-2000 Good 4 >2000 Very Good 5 Degraded wooded shrub land Poor 2 Dense natural forest Very Poor 1 Dense shrub land Poor 2 Farm village Good 4 Intensively cultivated land Good 4 Lake Very Good 5 Moderately cultivated land Good 4 Open grass land Very Good 5 Open shrub land Good 4 6 Land Use Permanent wetland Very Good 5 Plantation forest Moderate 3 Ponds and dams Very Good 5 Rivers Very Good 5 Seasonal wetland Very Good 5 Shrub grass land Very Good 5 Sparsely cultivated land Good 4 Sub-afro-alpine vegetation Very Good 5 Town Poor 2 Acrisols Poor 2 Alisols Good 4 Cambisols Good 4 7 Soil Ferralsols Moderate 3 Fluvisols Very Good 5 Gleysols Moderate 3

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Groundwater S potential Score no. Parameters/theme Identified units/score score assigned Lake Tana Very Good 5 Leptosols Very Poor 1 Lixisols Good 4 Luvisols Moderate 3 Nitosols Good 4 Regosols Very Good 5 Towns Poor 2 Vertisols Moderate 3 Water Body Very Good 5

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Figure 69: Areal coverage of geomorphology, geology, drainage density and slope with their score

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Figure 70: Areal coverage of rainfall, land use, soil and ground water potential of the basin From the ground water potential result one can clearly observe that most of the basin parts have good and moderate ground water potential. Eastern and southern part parts of the basin have good ground water potential as compared to the northern part.

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The potential result is validated with functional shallow and deep wells of the basin that supply the urban centers. From the 45 wells used for validation 12 are located on good potentials and the rest 33‘s are located on moderate ground water potential zones.

4.10. Land Utilization Types (LUTs) of Irrigation

In planning an irrigation scheme there are four main questions: where is to be, how much water is available, how much irrigable land, and will it pay? The first is answered through soil and hydrological survey, linked by qualitative evaluation. The second question requires quantitative hydrological studies, the third special purpose soil survey, i.e. detail study at a scale of 1: 10, 000. The last question calls for economic land evaluation.

Land evaluation at 1:20,000 scales is a suitability evaluation directed at indicating the potential for irrigated agriculture development. The evaluation can be in qualitative terms, if it is relevant backed by sample economic feasibility studies at a generalized level. Once single sites have been selected there may be two further stages, a feasibility survey studies at a scale of 1:10,000(detail scale) is required. Thus, as the principal role of land evaluation at a scale of 1:20,000 (detail) is to indicate potential sites, the same holds true in this study.

To assess and delineate potentially suitable from non-suitable land units for the intended land use (irrigation) indicating constraints for use of the land. Therefore, in this study in addition to assessing, how much water is available; how much irrigable land is there was answer considering irrigable systems as major LUTs using Terrain and soil assessment method. However crops in irrigation at the scale of the study were seeing in irrigation crop production LUTs.  Surface irrigation(Irsur)  Drip irrigation(Irdr)  Sprinkler irrigation (Irspr)

4.10.4. Surface irrigation:

Surface irrigation is the oldest and most common method of applying water to croplands. Also referred to as ―flood irrigation‖, the essential feature of this irrigation system is that water is applied at a specific location and allowed to flow freely over the field surface and thereby apply and distribute the necessary water to refill the crop root zone. This can be contrasted to sprinkle or drip irrigation where water is distributed over the field in pressurized pipes and then applied through sprinklers or drippers to the surface.

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Surface irrigation has evolved into an extensive array of configurations which can broadly be classified as: (a) basin irrigation, (b) border irrigation, (c) furrow irrigation, and (d) wild flooding. The distinction between the various classifications is often subjective.

Table 80: A general comparison of surface irrigation methods

Selection Criteria Furrow Irrigation Border Irrigation Basin Irrigation Necessary Low Moderate to High High Development Costs Most Appropriate Field Rectangular Rectangular Variable Geometry Amount and Skill of High labor and high Moderate labor and Low labor and Labor Inputs Required skill required high skill required moderate skill required Land Leveling and Minimal required but Moderate initial Extensive land leveling Smoothing needed for high investment and regular required initially but efficiency. Smoothing smoothing is critical smoothing is less needed regularly critical if done periodically Soils Light to moderate Moderate to heavy Moderate to heavy texture soils textured soils textured soils Crops Row crops Solid-stand crops Solid-stand crops Water Supply Low discharge, long Moderately high High discharge, short duration, frequent discharge, short duration, infrequent supply. duration, infrequent supply supply Climate All, but better in low All, but better in low to All rainfall moderate rainfall Principal Risk Erosion Scalding Scalding Efficiency and Relatively low High with blocked- High Uniformity ends

4.10.5. Sprinkler Irrigation:

In the sprinkler method of irrigation, water is sprayed into the air and allowed to fall on the ground surface somewhat resembling rainfall. The spray is developed by the flow of water under pressure through small orifices or nozzles. The pressure is usually obtained by pumping. With careful selection of nozzle sizes, operating pressure and sprinkler spacing the amount of irrigation water required to refill the crop root zone can be applied nearly uniform at the rate to suit the infiltration rate of soil.

4.10.6. Drip Irrigation

Drip irrigation is defined as a method of micro-irrigation wherein water is applied at the soil surface as drops or small streams through emitters. Discharge rates are generally less than 2

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Tana Sub-Basin Land Use Planning and Environmental Study Project gallons per hour (7.6 l/h) for single-outlet emitters and 3 gallons per hour per 3.3 feet (11.4 l/h/m) for line source emitters (USDA, 2012).

Drip irrigation is one of the methods of irrigation that saves water and fertilizer. In drip irrigation method, water drips slowly to the roots of the plants either onto the soil surface or directly onto the root zone through a network of valves, pipes, tubing and emitters. The process is completed in narrow tubes so that water is given directly to the root of the plant. Drip irrigation is also termed as localized irrigation or micro irrigation.

4.10.7. Land Use Requirements (LURs)

Land use requirements are the conditions of land necessary or desirable for successful and sustained production of surface, sprinkler and drip irrigation systems. The land use requirements are described by the land characteristics or land qualities needed for sustained production for these LUTs. A land characteristic is an attribute of land that can be measured or estimated (soil drainage, soil depth, soil texture, salinity and soil slope).

Land qualities/characteristics that influence the soil suitability for irrigation assessment were Oxygen availability (w) defined by drainage class; water retaliation (r) of the soil, rooting condition and workability (consistence classes, top soil texture and soil depth), toxicity, mechanization requirement were consider for assessing the three irrigation system potential. For more detail of the three types of irrigation LUTs and their LURs see section III volume II of this project report.

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5. CONCLUSION AND RECOMMENDATION 5.1. Conclusion

5.1.1. Water Resource and Irrigation Development

The study found that existing water resources development of Tana Sub-Basin holds the great portion of the resource found in Blue Nile Basin. Existing information indicates that from 1,001,000 ha irrigable land of Blue Nile Basin (Awulachew et al., 2007), Tana Sub-Basin holds 188,637.39 ha which is 16% from the total (ADSWE, 2014). Irrigation practices like Modern River and spring diversion, traditional river and spring diversion, pump irrigation and irrigation with fetching are common in the basin. For these irrigation practices 85% of the water sources are rivers. This study also identifies the water supply schemes development. There are totally 105 (93 functional and 12 non-functional) urban water supply schemes and 4818 (3497 functional and 1321 non-functional) rural water supply schemes inventoried. From the functional urban water supply schemes bore holes are 68, hand dug wells 10, shallow well 1, springs 13 and surface water 1.The rural water supply sources include 3738 (77.6%) Hand Dug Wells (HDW), 746 (15.5%) springs, 197(4.1%) pipe supply from the nearest towns and others 137 (2.8%) surface water (river, pond and lake). Wells and springs make 93.1 % of the total number of schemes. This figure indicates that drinking water supply for human use in Tana Sub-Basin is ground water. The existing water resource development problems were also identified during the study. These are lack of land and water management practices, disturbance of river morphology and change of river mouse and Lake Boundary due to siltation and sedimentation are critical.

5.1.2. Water Demand and Use

The water demand and use of Tana sub-basin has been assessed to understand the characteristics of water uses in the region and estimate the water demand and uses of different sectors: urban, rural, irrigation, industrial, hydropower, Navigation, tourism and fishery. The demands are estimated based on both theoretical assumptions of key factors affecting the water requirements and actual water consumptions in cases of data availability.

Water demand and use of the sub-basin the study identifies the main water user sectors and their annual water demand. The first and the large user of water of the Sub-basin is hydropower. In 2012/2013 the water used for Tana Beles hydropower generation was 2.58 BCM. The second one water consumer is irrigation. For the existing practices of modern and

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Tana Sub-Basin Land Use Planning and Environmental Study Project traditional irrigation 1,760.904 MCM of water is required. Futures, under constructing and under irrigation medium and large scale irrigation practices are highly demanding sectors. 1,442 MCM, 72569 MCM and 61.56 MCM of water required for planned, under construction and under irrigation practices respectively. 9.601, 2.125 and 0.582 MCM of water required for public, both public & commercial and industrial consumption respectively under urban water demand. Under rural water demand 12.867 and 26.5 MCM of water required for human and livestock respectively. Navigation, tourism, fisheries and floriculture are other users of water.

Table 81: Summary of Water Demand and Consumption in Tana Sub-Basin, 2014 Demand, % No Demand Sector Remark MCM Demand 1 Urban 12.306 1.1 Domestic 9.601 1.2 Commercial & institutional 2.125 0.28 from municipal 1.3 Industrial 0.58 water 2 Rural 39.367 2.1 Human 12.867 0.88 2.2 Livestock 26.5 3 Irrigation 1,822.46 3.1 LM Scale (Koga) 61.56 40.91 Existing Modern Traditional 3.2 1760.904 SSI from their own 4 Industrial 0.5 0.01 source 5 Hydropower 2,580 57.92 Total 4,455 100

5.1.3. Water Quality and Sanitation

Water quality control is critical in reducing the potential for explosive epidemics, as a contaminated water supply provides one of the most effective pathways for mass transmission of pathogens to a large population. Due to these cases there is a need of drinking water quality and sanitation assessment of Tana Sub-Basin. For the assessment one microbiological, two chemical and seven aesthetic parameters were assessed on 131 samples of urban and rural water supplies of the sub-basin. These parameters are total coliform from micro-biological; nitrate and floured from chemical; iron, manganese, turbidity, Total Dissolved Solids (TDS), salinity and pH from aesthetic parameters. For the all the parameters

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Table 82: Summary of overall compliance with WHO guideline values Overall Parameter compliance (%) Total Coliform (TC) 31.3 Nitrate 84.0 Fluoride 100 Iron 99.2 Manganese 100 Turbidity 69.5 Electric Conductivity (EC) 100 Total Dissolves Solids (TDS) 100 Salinity 99.2 pH 98.5

5.1.4. Hydrological Modeling and Water Balance

Streamflow simulation results of SWAT model gave a satisfactory performance for the used five model efficiency criteria after calibrating the model with observed streamflow data of the four major rivers. For instance Nash-Sutcliffe Efficiency (NSE) of the Gilgel Abay, Gumara, Ribb and Megech was 0.80, 0.64, 0.73 and 0.43 for daily calibration respectively. During validation period NSE was 0.73, 0.58, 0.60 and 0.37 for Gilgel Abay, Gumara, Ribb and Megech respectively. Setegn et al. (2008) stated on the model performance that can be judged as satisfactory if R2 is greater than 0.6 and NSE is greater than 0.5. Hence, it is observed that SWAT exhibited strong performance in representing the hydrological conditions of the catchment.

In this study, Lake Tana lake level is simulated with the observed lake areal rainfall, simulated river inflow, observed river outflow by Blue Nile and open water evaporation to determine the runoff from the ungauged catchments indirectly, and the water balance components are tabulated in the

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Table 83 below:

Table 83: Lake Tana water balance components simulated from 1996 to 2008 Water Balance Contribution Contribution Item Total Components (BCM) (%) Lake areal rainfall 3.271 33.347 Gauged catchment and their downstream 6.302 64.247 Inflow ungauged 9.749 Ungauged catchments other than gauged 0.176 1.794 downtreams River 4.539 44.958 Open water 5.095 50.466 Outflow evaporation 10.096 Abstraction (Tana- 0.462 4.576 Beles hydropower) Lake Average lake storage 26.605 26.605 26.61 storage Change in Inflow-Outflow -0.347 3.437 -0.347 storage (ΔS)

5.2. Recommendation

5.2.1. Water Resource and Irrigation Development As a recommendation in Tana Sub-Basin there is significant potential for further socioeconomic development based on increased utilization of water in the catchment. However, great care is needed to ensure that such development is sustainable and does not adversely impact those communities that depend on the natural resources of the lake and the rivers that feed into it.

Key strategies suggested to the attainment of sustainable soil and water management activities should be considered and implemented for water resource development. These activities include community participation and empowerment, appropriate technology, issuing of policies, regulations and bylaws and considering of complementary strategies.

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Research and development in the field of water resources should also be given priority to sustainable development of the people.

5.2.2. Water Demand and Use

The in stream water requirements of navigation tourism and fisheries and the environment by large should be taken in to consideration while allocating water for other off stream water uses to maximize the benefit from the water resource and protect the environment. The water demands and uses of the floriculture investment need to be assessed well in the future.

Integrated water resource management approach should be strongly implemented in the sub- basin by participating all water user sectors and stakeholder which are responsible in managing the water resource of the sub-basin so that a compromise should be done among the water uses in allocating water to the sectors. As per the water policy of the country the environmental water use need to be given priority.

The future water demand has to be estimated under different scenarios based on the future plans of development and population forecasts. Since the future is highly subject to uncertainties plausible different scenarios need to be developed to estimate future water demands in the sub-basin. This is very important for planning and management of the water resource of the sub-basin.

5.2.3. Water Quality and Sanitation

As a recommendation promotion and implementation of sanitation and hygiene should be interlinked with the provision of water supply since poor sanitation practices affects the water source, which should be reasonably free from biological contamination for drinking water. Sanitation promoters or water supply providers of government or NGO‘s should no longer address sanitation or water supply independently.

Regular bacteriological assessment of water supply sources and storage in conjunction with sanitary and hygienic survey at the household level for drinking water should be planned and conducted to monitor the impact of using latrine and hygienic facilities on drinking water supply quality. Sources of contamination of water and then preventive strategies could be defined from regular assessment.

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5.2.4. Hydrological Modeling and Water Balance

To further improve and understand of the lake water balance components, the following recommendations are forwarded in addition with a general recommendation based on field observations:

. Runoff from Ungauged catchments other than gauged downtreams is estimated indirectly as closing terms after the water balance simulation. Even though, the result is close to the previous estimations, a further study is recommended to estimate the runoff from ungauged catchments through hydrological modelling and regionalization.

. The river gauging stations are located close to the main road for the purpose of easy access and are not located on the outlet and as such a large portion of the catchment is downstream of gauging stations. Traditionally manual daily gauging stations have to be positioned by considering the time of concentration to observe the peak flow; else the flow data recorded will not be representative for further study.

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KOREN, V. I., SMITH, M., WANG, D. & ZHANG, Z. 2000. Use of soil property data in the derivation of conceptual rainfall-runoff model parameters. 80th Annual Meeting of the AMS, Long Beach, Ca. January 15th conf. Hydrology (AMS, Long Beach, California, USA), 103-106. KRAUSE, P., BOYLE, D. P. & BÄSE, F. 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5, 89-97. LACROIX, K. M. & XIU, B. C. White Paper: Calculating and Considering Environmental Water Demand for Arizona. LAHMEYER-CONSULTING-ENGINEERS 1962. Gilgel Abbay Scheme. Imperial Ethiopian Government. Ministry of Public Works, Addis Ababa. LIMBE, W. 1998. WATER RESOURCES DEVELOPMENT AND VECTOR BORNE DISEASES IN MALAWI. WATER RESOURCES DEVELOPMENT AND VECTOR-BORNE DISEASES IN MALAWI, 38. MACDONALD, M. 2004. Koga irrigation project interim report. Ministry of Water Resource, Addis Ababa, Ethiopia. MAIDMENT, D. R. 1993. Handbook of Hydrology, McGraw-Hill. MERZ, R. & BLÖSCHL, G. 2004. Regionalisation of catchment model parameters. Journal of Hydrology, 287, 95-123. MOA 1986. Strategies for Small-Scale Irrigation Development [Amharic] Ministry of Agriculture Irrigation Development Department, Addis Ababa, September. MORIASI, D. N., ARNOLD, J. G., VAN LIEW, M. W., BINGNER, R. L., HARMEL, R. D. & VEITH, T. L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50, 885-900. MOWE 1993. Improvement of the resource-population sustainability balance. Water Resources Development, MoWR (Ministry of Water Resources), Addis Ababa, Ethiopia. MOWE 1998. Abay River Basin Integrated Master Plan Project, Phase II-Data Collection: Site Investigation Survey and Analysis. Volume 5. MOWE 2009a. Final feasibility study of Gilgel Abay Irrigation Project. Addis Ababa. MOWE 2009b. Final feasibility study of Jema Irrigation Project. Addis Ababa. MOWE 2010a. Environmental and Social Impact Assessment of Ribb Irrigation and Drainage Project. Volume 1/2: Main Report. MOWE 2010b. Environmental and Social Impact Assessment of the Megech Pump (Seraba) Irrigation and Drainage Project. Volume 1/2: Main Report. MOWE Accessed Dec 26, 2014. Lake Tana sub basin irrigation project. http://www.mowr.gov.et/index.php?pagenum=4.2&pagehgt=1000px&ContentID=62. NEITSCH, S., ARNOLD, J. G., KINIRY, J. R., SRINIVASAN, R. & WILLIAMS, J. R. 2010. Soil and Water Assessment tool input/output file documentation version 2009. Texas Water resources institute technical report, 365. O'LOUGHLIN, R., FENTIE, G., FLANNERY, B. & EMERSON, P. M. 2006. Follow‐up of a low cost latrine promotion programme in one district of Amhara, Ethiopia: characteristics of early adopters and non‐adopters. Tropical Medicine & International Health, 11, 1406-1415. ONCCP 1990. National Irrigation Policy and Strategy Workshop Discussion Paper. FAO,TCP/ETH/8963. Addis Ababa, April. PECHLIVANIDIS, I., JACKSON, B., MCINTYRE, N. & WHEATER, H. 2011. Catchment scale hydrological modelling: a review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications. Global NEST journal, 13, 193-214. RAHMATO, D. 1999. Water resource development in Ethiopia: Issues of sustainability and participation. FSS Discussion Paper (Ethiopia). RODECO 2002. Assessment and monitoring of erosion and sedimentation problems in Ethiopia-Final Report. . Rodeco Consulting GmbH, Hydrology Studies Department, Ministry of Water Resources, Addis Ababa, Ethiopia.

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SANTHI, C., ARNOLD, J. G., WILLIAMS, J. R., DUGAS, W. A., SRINIVASAN, R. & HAUCK, L. M. 2001. VALIDATION OF THE SWAT MODEL ON A LARGE RWER BASIN WITH POINT AND NONPOINT SOURCES1. Wiley Online Library. SEIBERT, J. 1999. Regionalisation of parameters for a conceptual rainfall-runoff model. Agricultural and forest meteorology, 98, 279-293. SETEGN, S. G. 2008. Hydrological and sediment yield modelling in Lake Tana basin, Blue Nile Ethiopia. SETEGN, S. G., SRINIVASAN, R. & DARGAHI, B. 2008. Hydrological modelling in the Lake Tana Basin, Ethiopia using SWAT model. The Open Hydrology Journal, 2, 49-62. SHARMA, D. 2007. Floriculture needs 20 times more water than cotton cultivation [Online]. http://infochangeindia.org/water-resources/analysis/floriculture-needs-20-times-more- water-than-cotton-cultivation.html. SMEC 2008. Hydrological Study of the Tana-Beles sub-basins, main report. Addis Ababa, Ethiopia: Ministry of Water Resources. SMEC (Snowy Mountains Engineering Corporation). TADESSE, D., DESTA, A., GEYID, A., GIRMA, W., FISSEHA, S. & SCHMOLL, O. 2010. Rapid assessment of drinking-water quality in the Federal Democratic Republic of Ethiopia: country report of the pilot project implementation in 2004-2005. Geneva: WHO/UNICEF. TAMBE, P. V., DASWANI, P. G., MISTRY, N. F., GHADGE, A. A. & ANTIA, N. H. 2008. A community- based bacteriological study of quality of drinking-water and its feedback to a rural community in Western Maharashtra, India. Journal of health, population, and nutrition, 26, 139. TAYE, A. 1999. Pollution of the hydrogeologic system of Dire Dawa. Preprint for the 25th WEDC Conference on Integrated Development for Water Supply and Sanitation. Addis Ababa, Berhanena Selam Printing Enterprise. UNICEF 2006. Monitoring the situation of children and women: Multiple indicator cluster survey manual 2005. Division of policy and planning, New York. USDA 2012. Chapter 7 Microirrigation, Part 623 Irrigation, National Engineering Handbook. United States Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS). VAN ELZAKKER, C. & VAN DE BERG, W. P. E. Topographic base maps for physical planning maps: user research for generalization. Special joint symposium of ISPRS technical commission IV and AutoCarto 2010, 2010. VANDEWIELE, G. L. & ELIAS, A. 1995. Monthly water balance of ungauged catchments obtained by geographical regionalization. Journal of Hydrology, Volume 170, Number 1, August 1995 , pp. 277-291(15). WALE, A. & RIENTJES, T. 2008. Hydrological Balance of Lake Tana Upper Blue Nile Basin, Ethiopia. Hydrology and Ecology of the Nile River Basin under Extreme Conditions, 159. WAPCOS 1995. The National Water Resources Master Plan. Water & Power Consultancy Services (I) Ltd (WAPCOS), Addis Ababa, Ethiopia. WARERAID 2011. Water quality standards and testing policy. A WaterAid in Nepal publication, WaterAid in Nepal Protocol. WATCH, W. 2005. Remote sensing studies of Tana-Beles Sub Basins. A Nile Basin Initiative project Ministry of Water Resources, Ethiopia. WATERAIDETHIOPIA 2010 Regional Water Supply and Sanitation Coverage in Ethiopia: according to 2001 EFY reports. WHO 2003. Assessing Microbial Safety of Drinking Water Improving Approaches and Methods: Improving Approaches and Methods, World Health Organization, OECD Publishing. WHO 2004. Guidelines for drinking-water quality. World Helth Organization, Disponibile all’indirizzo: http://www. who. int/water sanitation_health/dwq X, 924156251. WHO 2011. Guidelines for drinking-water quality, 4th edition. World Health Organization, Geneva. . Available at http://whqlibdoc.who.int/publications/2011/9789241548151_eng.pdf (accessed 13 October 2014).

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Appendices Appendix 1: Primary data collection formats A. Data collection Format for Physical Observation

Observation type: Observation Date:______ Pond GPS Code:______ River course Photo Code: ______ Dam  Weir  Deep well  Diversion  Irrigation command  River mouse  Lake boundary Relative location Region______Zone______Wereda______Kebele______Gote______Easting (X): ______Northing (Y): ______Elevation (Z):______Observed hydrological benefit of the structure: ______Observed existing water use strengths and weaknesses: - ______Observed land degradation around the structure and hydrological estimation of the cause: - ______Observed structural/ topographic problems of the observation type: - ______Observed siltation/fluvial problems and its hydrological explanation: ______Other observed sensitive things to be explained:______

Name of Expert: ______Signature: ______

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B. Water Quality Data Collection Sheet (Physical)

Well name: ______Region:______Zone:______Wereda:______Kebele :______Gote:______X: ______Y: ______Elevation: ______Sampling runs number: ______Sample No.: ______GPS Code: ______Photo Code: ______Time start: ______Finishes: ______Field measurement: Parameters Result Temperature (oC) pH Electrical conductivity (µS/cm) TDS (mg/L) Salinity (ppm) Turbidity (NTU)

Field observation on:  Sanitation and hygiene  Protection of the scheme  Land use Description______

Expert name: ______Date: ______

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C. Water Quality Samples List (Chemical)

Region: ______Zone: ______Wereda/Admin: ______Samp. GPS Photo Kebele Well/Gote X Y Z Remark No. code code name

Expert name: ______Date: ______

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D. Rural Water Supply Scheme Data collection Format

Region: ______Zone: ______Wereda: ______

Gote/Well Longitude Beneficiaries Operational Status No Kebele name (X) Latitude (Y) (HH) Fun Unfun

Expert name: ______

Date: ______

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E. Data collection Format for Hydrological and Meteorological Stations Locations

Hydrological Stations No Woreda Kebele Station Name X Y Z Remark

Meteorological Stations No Woreda Kebele Station Name X Y Z Remark

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F. ዘመናዊና ባህላዊ የመስኖ እንቅስቃሴ መረጃ መሰብሰቢያ ቅፅ

ክልል ______ዞን ______ወረዳ ______ቀበሌ ______ኮድ______የኤክስፐርቱ ስም ______በአገልግሎት ላይ ያለ የመስኖ ስራ ዘመናዊ የመስኖ እንቅስቃሴ ተ. ወንዝ ቁ በመስኖ የለማው መሬት ስፋት (ha) የጎለበተ ምንጭ 1ኛ ዙር 2ኛ ዙር በመስኖ ተጠቃ ሎንግ ከፍታ ስፋት ስፋት ተጠቃሚ የለማው ተጠቃሚ ስም ላት(Y) ሚ ስም ሎንግ (X) ላት(Y) ከፍታ(Z) ምርመራ (X) (Z) (ha) (ha) (HH) መሬት ስፋት (HH) (HH) (ha)

በቀበሌው ለመስኖ የሚውል (ሊውል የሚችል) ሌላ የውሃ ሀብት ካለ የማልማት አቅሙን ይግለፁ______

ባህላዊ የመስኖ እንቅስቃሴ (የወንዝ የውሃ ሀብት) በመስኖ የለማው መሬት ስፋት (ha)

ኛ ኛ 1 ዙር 2 ዙር ተ.ቁ የወንዙ ስም ሎንግ (X) ላት (Y) ከፍታ (Z) ተጠቃሚ ተጠቃሚ ምርመራ ስፋት (ha) (HH) ስፋት( ha) (HH)

በቀበሌው ለመስኖ የሚውል (ሊውል የሚችል) ሌላ የውሃ ሀብት ካለ የማልማት አቅሙን ይግለፁ______

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ክልል ______ዞን ______ወረዳ ______ቀበሌ ______ኮድ______የኤክስፐርቱ ስም ______በአገልግሎት ላይ ያለ የመስኖ ስራ

ባህላዊ የመስኖ እንቅስቃሴ (የምንጭ የውሃ ሀብት) የሚሰጠው የውሃ በመስኖ የለማው ተጠቃሚ ምንጭ ተ.ቁ መጠን (L/sec) መሬት ስፋት (ha) (HH) ምርመራ ስም ሎንግ (X) ላት(Y) ከፍታ(Z)

በቀበሌው ለመስኖ የሚውል (ሊውል የሚችል) ሌላ የውሃ ሀብት ካለ የማልማት አቅሙን ይግለፁ______

ባህላዊ የመስኖ እንቅስቃሴ (የጉድጓድ/የረግረጋማ መሬት የውሃ ሀብት) የጉድጓድ ዉሃ የረግረጋማ መሬት ምርመራ በመስኖ በመስኖ የለማው ከፍታ ተጠቃሚ ሎንግ የለማው ተጠቃሚ ስም ሎንግ (X) ላት(Y) መሬት ስፋት ስም ላት (Y) ከፍታ (Z) (Z) (HH) (X) መሬት ስፋት (HH) (ha) ተ.ቁ (ha)

በቀበሌው ለመስኖ የሚውል (ሊውል የሚችል) ሌላ የውሃ ሀብት ካለ የማልማት አቅሙን ይግለፁ______

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Appendix 2፡ Secondary data collection formats

A. Data collection Format for Irrigation Scheme ክልል፡______ዞን፡______ወረዳ:______

የማልሚያ ዘዴ

ጠለፋ

በአመቱ

(

)

ፓምፕ

ወንዝ ምንጭ

ፓምፕ

ወንዝ ምንጭ

ሸሽ

ፓምፕ

ጉድጓድ

የለማና

ቀበሌ ተጠቃሚ

ዘመናዊ ባህላዊ ዘመናዊ ባህላዊ በግድብ በሞትር በፔዳል በሮፕ በኩሬ በእጅ በመቅዳት በጠብታ በባህር አጠቃላይ መጨረሻ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ የለማ (ሄር) ተጠቃሚ

መረጃውን የሞላው ኤክስፐርት ስም፡______ፊርማ፡ ______

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B. የከተማ ውሃና ፍሳሽ አገልግሎት ሁለተኛ ደረጃ መረጃ መሰብሰቢያ ቅፅ

ክልል፡______ዞን፡______ወረዳ፡______ከተማ አስተዳደር:______

ተ.ቁ፣ ዓመት ምርት(ሜ.ኩ) ሽያጭ(ሜ.ኩ) ብክነት(ሜ.ኩ) የደምበኛ ብዛት የህዝብ ቁጥር

መረጃውን የሞላው ኤክስፐርት ስም፡______ፊርማ፡ ______

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C. Data collection Format for Seasonal Rivers

Region ______

Zone ______Name of Expert ______

Stream Data

Seasonal Rivers No Woreda Kebele River Name Period of flow

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D. Data collection Format for Perennial Rivers

Region ______

Zone ______Name of Expert ______

Stream Data

Perennial Rivers No Woreda Kebele River Name

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A. Data collection Format of Wash Inventory

Type of Source [1.Spring If it is used (developed), 2.Spring (not Type of Use [1. for livestock developed), 3. Deep Well, Human only, 2. Is there Status Respected 4. River, 5. Pond, 6. Pipe Livestock only, 3. trough? [1.Funcional, If it is not committee Supply, 7. Lake, 8. Hand Benefited Both human & [1.Yes, 2. 2.Not functional [1.Available, X- Y- Altitude s/n Wereda Kebele Dug Well] House Hold livestock] No] Functional) [Reason] 2.Not available) Coordinate Coordinate (meter)

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Appendix 3: Observation points of water resource and irrigation development problems d) River morphology

No Wereda River Name X Y Z Embankment Morphology 1 B/Dar Twon Abbay 325873 1279010 1789 No embankment erosion Not disturbed 2 B_Zuria Enfiranz 313498 1285315 1798 No embankment erosion Not disturbed 3 B_Zuria Yifilisht/Yigasho 331882 1291108 1830 Embankment erosion Disturbed 4 B_Zuria Endemo 332985 1292737 1841 Embankment erosion Disturbed 5 B_Zuria Gelda 336719 1296254 1808 Embankment erosion Disturbed 6 B_Zuria Chimbil 330421 1282469 1875 Embankment erosion Disturbed 7 B_Zuria G/Abay 299961 1294352 1811 Embankment erosion Disturbed 8 Banja Gugi 292612 1214542 2552 Embankment erosion Disturbed 9 Chilga Aras Wusha 304024 1385761 1988 Embankment erosion Disturbed 10 Chilga Gabi Kura 287828 1370087 1958 Embankment erosion Disturbed 11 Dangila Zuma 278905 1237176 2106 No embankment erosion Not disturbed 12 Dangila Biranti 266301 1237435 2202 Embankment erosion Disturbed 13 Dangila Gazh 257917 1252609 2072 Embankment erosion Disturbed 14 Dangila Befta 258036 1251169 2109 Embankment erosion Disturbed 15 Dangila Kilti 260602 1250752 2059 Embankment erosion Disturbed 16 Dangila Ashar 274173 1244303 1989 Embankment erosion Disturbed 17 Dangila Twon Amen 264692 1245031 2110 Embankment erosion Disturbed 18 Dembia Megech 319383 1367776 1800 Embankment erosion Disturbed 19 Dembia Megech 330180 1389063 2039 Embankment erosion Disturbed 20 Dembia Dirma 315537 1356907 1787 Embankment erosion Disturbed 21 Dembia Ambagenen 307887 1371414 1856 Embankment erosion Disturbed 22 Dembia Ambagenen 303222 1366628 1820 Embankment erosion Disturbed 23 Dembia Sewgedel 301142 1367471 1828 Embankment erosion Disturbed 24 Dembia Sarwuha 293363 1362334 1805 Embankment erosion Disturbed 25 Dera Gelda 349971 1297555 1944 Embankment erosion Disturbed 26 Ebinat Ribb (Upstream) 391917 1330361 1892 Embankment erosion Disturbed 27 Ebinat Ziha 393927 1336693 1985 Embankment erosion Disturbed 28 Estie Estie 401742 1296331 2577 Embankment erosion Disturbed 29 Fagita Agzi 285888 1219456 2412 Embankment erosion Disturbed 30 Fagita Agzi 286457 1225633 2271 Embankment erosion Disturbed 31 Fagita Fagitit 281242 1226289 2398 Embankment erosion Disturbed 32 Fagita Guder 272996 1228874 2341 No embankment erosion Not disturbed 33 Fagita Quashini 266973 1230832 2360 No embankment erosion Not disturbed 34 Fagita Shihanti 284510 1224756 2285 Embankment erosion Disturbed 35 Fagita Libsi 283399 1225141 2292 Embankment erosion Disturbed 36 Fagita Zuma 276885 1227505 2346 Embankment erosion Disturbed 37 Farta Ribb (Upstream) 407296 1304790 2773 Embankment erosion Disturbed

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38 Farta Zufil 396325 1309867 2618 No embankment erosion Not disturbed 39 Fogera Werk Wuha 381834 1317896 2064 Embankment erosion Disturbed 40 G_Zuria Gumara 342893 1370074 1906 Embankment erosion Disturbed 41 G_Zuria Megech 331430 1381224 1867 Embankment erosion Disturbed 42 Gondar Twon Dimaza 330180 1389063 2039 No embankment erosion Not disturbed 43 L_Armachiho 334213 1402304 2296 Embankment erosion Disturbed 44 Mecha Bered 299496 1262885 2001 Embankment erosion Disturbed 45 Mecha G/Abay 285484 1256761 1893 Embankment erosion Disturbed 46 Mecha Andid 289244 1259375 1945 No embankment erosion Not disturbed 47 Mecha Koga 286418 1257604 1898 No embankment erosion Not disturbed 48 N_Achefer Arboch 272362 1280758 2030 Embankment erosion Disturbed 49 N_Achefer Merfi 282072 1308429 1837 Embankment erosion Disturbed 50 S_Achefer Kilti 276316 1269216 1880 Embankment erosion Disturbed 51 S_Achefer Zabza 276157 1273802 1934 Embankment erosion Disturbed 52 Sekela Munziriti 298800 1213289 2560 No embankment erosion Not disturbed 53 Sekela Fetam 296024 1211980 2583 Embankment erosion Disturbed 54 Sekela Weynitie 300194 1214067 2553 No embankment erosion Not disturbed 55 Wegera Megech 340339 1409230 2687 Embankment erosion Disturbed 56 Wegera Megech 340982 1409716 2767 Embankment erosion Disturbed 57 Chilga Guang 303557 1385663 1699 Embankment Erosion Disturbed 58 Alefa Kachela 283804 1328315 1798 Embankment Erosion Disturbed 59 Alefa War 283811 1321034 1793 Embankment Erosion Disturbed 60 Alefa Kachela 280599 1328194 1852 Embankment Erosion Disturbed 61 Alefa War 283127 1320450 1786 Embankment Erosion Disturbed 62 Alefa War 282458 1321068 1805 Embankment Erosion Disturbed 63 Takusa Bela Gedel 293349 1362175 1808 Embankment Erosion Disturbed 64 Takusa Tima 290861 1353682 1788 Embankment Erosion Disturbed 65 Takusa Segie Wenz 288254 1350253 1790 No embankment erosion Not disturbed 66 Takusa Gibera 287760 1349126 1788 Embankment Erosion Disturbed 67 Takusa Gibera 288840 1348779 1790 Embankment Erosion Disturbed 68 Takusa Tirikura 283129 1349338 1843 Embankment Erosion Disturbed e) Soil and water management

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9 Chilga Gabi Kura 287771 1370040 1966 Poor management 10 Chilga Guang 303611 1385657 1706 Good management 11 Dangila Ashar (Downstream) 274173 1244303 1989 Poor management 12 Dangila Zuma 279366 1237350 2119 Poor management 13 Dangila Ashar (Upstream) 274765 1243412 1993 Poor management 14 Dembia Girargie 307698 1358393 1846 Poor management 15 Dembia Dirma 315537 1356907 1787 Poor management 16 Dembia Kibir Andiye 307876 1371320 1856 Poor management 17 Dembia Amba Genen 303252 1366647 1817 Poor management 18 Dembia Jenda 307876 1371320 1856 Good management 19 Dembia Amba Genen 303252 1366647 1817 Poor management 20 Dera Gumara 350272 1310304 1796 Poor management 21 Dera Shina 337566 1306229 1800 Poor management 22 Dera Lake Tana (korata) 331316 1300751 1798 Poor management 23 Ebinat Zhiza 390854 1336277 1964 Poor management 24 Estie Gumara (Gelawdios) 375991 1287245 2280 Poor management 25 Estie Gumara (Estie) 401569 1296284 2577 Poor management 26 Fagita Agzi and Balesunga 286684 1225924 2279 Poor management 27 Fagita Agzi 286603 1233723 2097 Poor management 28 Fagita Shihanti 284510 1224756 2285 Poor management 29 Fagita Libsi 283399 1225141 2292 Poor management 30 Fagita Zuma 276885 1227505 2346 Poor management 31 Farta Selamko 395073 1313985 2497 Poor management 32 Farta Ribb Nr Gassay 407916 1304789 2785 Poor management 33 Farta Melo 391446 1313925 2576 Poor management 34 Fogera Kuhar 353570 1310614 1803 Poor management 35 Fogera Gumara Near Shina 351254 1311843 1797 Poor management 36 Fogera Ribb Near Kokit 360018 1326047 1799 Poor management 37 G_Zuria Arno 361343 1345720 1991 Poor management 38 G_Zuria Garno 350735 1352986 1862 Poor management 39 G_Zuria Mitraha 345569 1344516 1790 Poor management 40 G_Zuria Arbaitu Ensisat 335281 1360456 1794 Poor management 41 Gondar Town Angereb 335478 1394104 2095 Poor management 42 Gondar Town Azezo 330264 1388889 2036 Poor management 43 L_Kemkem Shini 366978 1339966 1929 Poor management 44 L_Kemkem Bikutie 384535 1336259 1933 Poor management 45 Mecha Koga 294930 1256188 2011 Poor management 46 N_Achefer Lake Tana (Kunzila) 285750 1314070 1798 Poor management 47 S_Achefer Kilti 276316 1269216 1880 Poor management 48 Sekela Weynitie 300194 1214067 2553 Poor management 49 Takusa Gibera 287709 1349153 1790 Poor management

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50 Takusa Segie Wenz 288254 1350253 1790 Poor management 51 Takusa Near Gibera 288647 1348826 1787 Poor management 52 Takusa Gibera 288647 1348826 1787 Poor management f) River mouse and lake boundary

Scheme Status Gross Net Area Water Demand (in Mm3) Name Area under Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec under Irrigation Irrigation (Ha) (Ha) MEG 1 PF 5254 4466 6.93 8.46 10.29 4.90 0.00 0.00 0.00 0.00 0.00 1.25 6.45 7.17 MEG 2 PF 6532 5552 8.63 10.54 12.81 6.11 0.00 0.00 0.00 0.00 0.00 1.57 8.02 8.92 MEG 3 UK 6640 5644 8.75 10.70 13.00 6.19 0.00 0.00 0.00 0.00 0.00 1.59 8.15 9.06 MEG 4 UK 10020 8517 13.23 16.17 19.65 9.37 0.00 0.00 0.00 0.00 0.00 2.41 12.31 13.69 BEL 1# EMP 63,200 53,720 98.91 103.39 89.18 85.72 27.21 0.00 0.00 0.00 0.00 0.00 95.79 122.90 NET 1 PF 1,920 1,632 2.89 3.68 3.87 1.78 0.00 0.00 0.00 0.00 0.00 0.00 2.18 2.41 NET 2 PF 2,080 1,768 3.13 3.99 4.19 1.92 0.00 0.00 0.00 0.00 0.00 0.00 2.36 2.61 NET 3 PF 990 842 1.49 1.90 1.99 0.92 0.00 0.00 0.00 0.00 0.00 0.00 1.12 1.24 NET 4 PF 1,450 1,233 2.18 2.78 2.92 1.34 0.00 0.00 0.00 0.00 0.00 0.00 1.65 1.82 NWT 1 PF 2,809 2,388 3.70 4.53 5.50 2.62 0.00 0.00 0.00 0.00 0.00 0.67 3.45 3.83 NWT 2 PF 1,266 1,076 1.67 2.04 2.48 1.18 0.00 0.00 0.00 0.00 0.00 0.30 1.55 1.73 NWT 3 PF 830 706 1.09 1.34 1.62 0.77 0.00 0.00 0.00 0.00 0.00 0.20 1.02 1.13 NWT 4 PF 3,000 2,550 3.95 4.83 5.87 2.80 0.00 0.00 0.00 0.00 0.00 0.72 3.68 4.09 SWT PF 1,225 1,041 1.71 2.26 2.47 1.39 0.00 0.00 0.00 0.00 0.00 0.00 1.11 1.70 SWT 2 PF 2,207 1,876 3.08 4.07 4.45 2.50 0.00 0.00 0.00 0.00 0.00 0.00 2.00 3.07 SWT 3 PF 2,306 1,960 3.21 4.25 4.65 2.62 0.00 0.00 0.00 0.00 0.00 0.00 2.09 3.21 SWT 4 PF 300 255 0.42 0.55 0.60 0.34 0.00 0.00 0.00 0.00 0.00 0.00 0.27 0.42 Sum 112029 95226 164.98 185.48 185.54 132.47 27.21 0.00 0.00 0.00 0.00 8.71 153.21 189.02 * Tana Beles Transfer, PF: Pre-Feasibility; EMP: Early Master Plan, UK: Unknown, # irrigation development outside Sub-basin

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Appendix 5: Irrigation Water Requirement Using Dam

Scheme Status Type of Water Supply Gross Net Area ID Area under under Irrigation Water Demand (in Mm3) Irrigation (Ha) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (Ha) MEG 6 PF River Gravity 4000 3400 5.23 6.38 7.77 3.66 0 0 0 0 0 0.87 4.87 5.43 RIB 1 PF Dam (Ribb) 7650 6503 RIB 2 PF Dam (Ribb) 3060 2601 8.86 4.23 4.75 6.03 1.84 0 0 0 0 1.63 2.38 0.53 RIB 3 PF Dam (Ribb) 9360 7956 RIB 4 PF Dam (Ribb) 3370 2865 9.76 4.66 5.24 6.64 2.02 0 0 0 0 1.8 2.63 0.58 JEM 1 PF Dam (Jemma) 9160 7786 11.87 16.05 14.69 5.68 0 0 0 0 0 0 10.54 11.5 GIL 1 PF Dam(Gilgel Abbay ) & Pump 660 561 1.88 0.96 0.9 1.17 0 0 0.8 0 0 0.12 0.48 0.1 GIL 2 PF Dam(Gilgel Abbay ) & Pump 3470 2950 9.89 5.06 4.73 6.15 0 0 4.2 0 0 0.63 2.55 0.52 GIL 3 PF Dam(Gilgel Abbay ) & Pump 2430 2066 6.93 3.55 3.31 4.31 0 0 2.94 0 0 0.44 1.78 0.36 GIL 4 PF Dam(Gilgel Abbay ) & Pump 2750 2338 7.84 4.01 3.75 4.88 0 0 3.33 0 0 0.5 2.02 0.41 GIL 5 PF Dam(Gilgel Abbay ) & Pump 2260 1921 6.44 3.3 3.08 4.01 0 0 2.74 0 0 0.41 1.66 0.34 GIL 6 PF Dam(Gilgel Abbay ) & Pump 1371 1165 3.91 2 1.87 2.43 0 0 1.66 0 0 0.25 1.01 0.2 GIL 7 PF Dam(Gilgel Abbay ) & Pump 428 364 1.22 0.62 0.58 0.76 0 0 0.52 0 0 0.08 0.31 0.06 GIL 8 PF Dam(Gilgel Abbay ) & Pump 1984 1686 5.66 2.89 2.71 3.52 0 0 2.4 0 0 0.36 1.46 0.3 GUM 1 Re Dam (Gravity/Pump) 2049 1742 2.38 1.61 1.6 1.48 0.12 0 0.56 0 0 0.21 0.95 0.61 GUM 2 Re Dam (Gravity/Pump) 1623 1380 1.89 1.27 1.27 1.17 0.09 0 0.44 0 0 0.17 0.75 0.48 GUM 3 Re Dam (Gravity/Pump) 499 424 0.58 0.39 0.39 0.36 0.03 0 0.14 0 0 0.05 0.23 0.15 GUM 4 Re Dam (Gravity/Pump) 2795 2376 3.25 2.2 2.18 2.01 0.16 0 0.76 0 0 0.29 1.29 0.83 GUM 5 Re Dam (Gravity/Pump) 4940 4199 5.74 3.88 3.86 3.56 0.28 0 1.34 0 0 0.51 2.29 1.47 GUM 6 Re Dam (Gravity/Pump) 4535 3855 5.27 3.56 3.54 3.26 0.26 0 1.23 0 0 0.47 2.1 1.35 Total 68394 58138 98.6 66.62 66.22 61.06 4.78 0 23.07 0 0 8.79 39.29 25.3

Appendix 6: Inventory of equipment and consumables

A. Inventory of equipment

Inventory no. Item description Unit Total no. 1 Battery Cell Each 10 2 Aluminum Petri dish Each 98 3 Ball-point pen Each 1 4 Membrane filtration unit Set 1 Components: 4.1. Bronze membrane support disc Each 1 4.2. Filter assembly base Each 1 4.3. Filter funnel and locking collar Each 1 4.4. Hand bellows pump Each 1 4.5. Sample cup and cable Each 1 4.6. Upper and lower O rings Each 1 5 5.1. JMP kit rucksack Each 1 5.2. Wag-sac Each 1 6 Lockable carry case Each 1 7 Cigarette lighter Each 3 8 Mains adaptor/Battery charger Each 1

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Inventory no. Item description Unit Total no. 9 Media measuring device Each 2 10 Membrane filters, 0.45µm, gridded Each 200 11 Membrane forceps Each 1 12 Membrane pad dispenser Each 2 13 Membrane pads (media pads) Each 200 14 Operating instructions for: 14.1. pH/Temperature meter Each 1 14.2. Conductivity/TDS meter Each 1 14.3. Turbidity meter Each 1 14.4. Bacteriological tests (ELE Paqualab 50) Each 1 14.5. Maji Meter Each 1 Pasteur pipettes (dropping pipettes), plastic, 1ml capacity with 15 markings at 0.5 ml and 1 ml Each 2 16 Petri dishes rack Each 1 17 Polypropylene bottle (autoclavable) Each 4 18 Rechargeable battery Each 1 19 Double pot incubator, switchable between 37oC and 440C Each 1 20 Incubator calibration lid Each 1 21 Hand lens Each 1 22 Screwdriver Each 1 23 JMP kit for bacteriological testing (ELE Paqualab 50) Set 1 24 pH/ Temperature meter Each 1 25 Conductivity/TDS meter Each 1 26 Turbidity meter Each 1 27 Maji Meter Each 1 28 Sampling Bottle (One liter) Each 1 29 Cotton (100g) Each 2 B. Inventory of reagent supplies II. Physical tests Inventory Water-quality Reagent Tests per pack Quantity Total number of tests number parameter 1 Electrical Conductivity standard, Bottle 1 Depends on calibration conductivity 1 413 µS/cm frequency 2 pH Buffer solution, pH 4.0 60 ml bottle 1 Depends on calibration frequency Buffer solution, pH 7.0 60 ml bottle 1

3 Turbidity Turbidity standards: Depends on 0.02 NTU Vial 1 calibration frequency 20 NTU Vial 1 100 NTU Vial 1 800 NTU Vial 1

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II. Microbiological media Total Inventory Water-quality Media Tests per pack Quantity number number parameter of tests Total Membrane lauryl sulphate broth 1 200/pack 1 131 coliforms media, 38.1g pack

III. Maji-Meter and its Components

Model Description WAG-WE51010 Hard Carry Case with foam inserts for Maji-Meter and Maji-Probe WAG-WE51001 Heavy Duty Flow-Through Cell WAG-WE51002 5m Extension Cable WAG-WE51003 10m Extension Cable WAG-WE51004 30m Extension Cable WAG-WE51005 Galvanic Dissolved Oxygen Membrane Kit WAG-WE51055 Optical Dissolved Oxygen Sensor Cap WAG-WE51006 pH/ORP Electrode Storage Cap WAG-WE51007 Replacement pH Electrode WAG-WE51008 Optional ORP Electrode WAG-WE51009 Combined pH/ORP Electrode WAG-WE51050 300ml Calibration Solution WAG-WE51052 600ml Calibration Solution

Appendix 7: Meteorological stations used for SWAT simulation for the four major watersheds

Meteorological Stations Used for Gilgel Abay Simulation Station Name Easting Northing Elevation Adet 335519 1246754 2179 Askuna 255551 1208608 2115 Bahir Dar 321191 1282805 1800 Dangila 264940 1244970 2125 Dek Estifanos 311578 1316083 1808 Dengel Ber 238150 1279885 1820 Enjibara 272648 1216262 2568 Gundil 292681 1200824 2587 Kessa 278508 1205848 2492 Meshenti 312978 1268688 1958 Quarit 328578 1215979 2147 Sekela 304822 1215257 2715 Shahura 268432 1319551 2205 Wetet Abay 286364 1257614 1920

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Zegie 316060 1292689 1801 Meteorological Stations Used for Gumara Simulation Station Name Easting Northing Elevation Bahir Dar 321191 1282805 1800 Dera Hamusit 343297 1303510 1930 Korata 332121 1300249 1820 Lewaye 398864 1295759 2709 Mekane Eysus 396892 1283335 2374 Wanzaye 355641 1303257 1821 Wereta 357978 1318290 1819 Meteorological Stations Used for Ribb Simulation Station Name Easting Northing Elevation Addis Zemen 366493 1339738 1940 Debre Tabor 390583 1312000 2612 Ebinat 396714 1340287 2212 Kimir Dingay 414746 1306056 2980 Wereta 357978 1318290 1819 Yifag 361783 1336065 1853 Meteorological Stations Used for Megech Simulation Name Easting Northing Elevation Ambagiorgis 350132 1412075 2948 Ayimba 315591 1386126 2038 Chandiba 287080 1371344 2071 Chuahit 307357 1364253 1925 Gondar 329614 1384688 1973 Maksegnit 342927 1369931 1912 Shembekit 337498 1401403 2403

Appendix 8: Sub-basins of the four major watersheds and their corresponding areas

Gilgel Abay Gumara Ribb Megech Sub-basin Area Sub-basin Area Sub-basin Area Sub-basin Area 1 708.63 1 10.14 1 16.47 1 14.90 2 98.48 2 23.53 2 27.43 2 22.88 3 54.68 3 31.82 3 42.50 3 3.41 4 88.83 4 16.34 4 51.76 4 30.86 5 213.30 5 3.21 5 34.81 5 23.22 6 90.06 6 16.23 6 17.17 6 11.22 7 56.37 7 1.81 7 0.90 7 13.42 8 66.17 8 48.48 8 32.31 8 2.61 9 36.91 9 5.49 9 63.59 9 18.77

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Gilgel Abay Gumara Ribb Megech Sub-basin Area Sub-basin Area Sub-basin Area Sub-basin Area 10 58.34 10 55.37 10 47.41 10 23.13 11 161.53 11 32.77 11 17.75 11 33.32 12 34.03 12 41.27 12 14.20 12 28.17 13 67.29 13 46.22 13 65.03 13 31.59 14 97.15 14 1.53 14 23.09 14 16.12 15 30.27 15 28.56 15 75.18 15 32.46 16 13.45 16 21.41 16 14.17 16 37.56 17 48.55 17 54.39 17 25.60 17 2.49 18 12.00 18 8.50 18 36.94 18 3.67 19 15.31 19 19.55 19 2.46 19 11.14 20 65.02 20 71.83 20 58.11 20 13.77 21 0.76 21 37.73 21 10.41 21 80.45 22 40.49 22 39.54 22 19.36 22 10.87 23 66.26 23 38.79 23 4.76 23 21.34 24 45.54 24 44.95 24 56.93 24 3.47 25 142.39 25 20.15 25 7.53 25 22.58 26 59.10 26 0.17 26 15.25 26 11.77 27 92.33 27 44.54 27 4.91 27 31.32 28 5.12 28 57.31 28 30.07 28 26.92 29 66.76 29 16.32 29 2.70 29 10.86 30 65.31 30 36.56 30 20.65 30 24.82 31 89.81 31 53.02 31 1.03 31 24.72 32 81.10 32 72.64 32 9.98 32 14.44 33 100.11 33 113.87 33 40.31 34 32.67 34 48.09 34 11.27 35 0.75 35 66.22 35 19.20 36 4.58 36 22.36 36 61.71 37 10.71 37 16.79 37 16.19 38 0.18 38 42.37 38 49.17 39 113.88 39 16.26 39 27.31 40 118.89 40 37.76 40 36.45 41 7.69 41 73.99 42 120.02 42 55.83 43 84.56 43 36.13 44 240.42 44 18.60 45 56.10 45 33.22 46 133.56 46 137.35 47 95.88 47 100.33 48 80.21 49 71.79

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Appendix 9: Parameters in SWAT data base for each soil layer in the catchment

SNAM NLAYERS HYDGRP SOL_ZMX ANION_EXCL SOL_CRK TEXTURE SOL_Z1 SOL_BD1 SOL_AWC1 SOL_K1 SOL_CBN1 CLAY1 SILT1 SAND1 AbrAli 2 C 700 0.5 0.5 C 300 1.31 0.13 2.80 3.35 45.00 32.83 22.17 AbrLuv 2 C 700 0.5 0.5 C 300 1.29 0.12 0.90 1.48 55.08 26.33 18.58 AlbLuv 2 C 700 0.5 0.5 C 300 1.37 0.12 0.86 2.89 48.67 19.00 32.33 AliNit 2 C 700 0.5 0.5 C 300 1.30 0.12 0.98 1.28 53.25 28.04 18.71 AliVer 2 C 700 0.5 0.5 C 300 1.36 0.14 2.82 2.51 41.02 33.90 25.07 CalVer 2 C 700 0.5 0.5 C 300 1.46 0.12 1.19 0.85 41.53 21.20 37.27 ChrLix 2 B 700 0.5 0.5 L 300 1.50 0.13 8.79 1.73 25.00 31.00 44.00 ChrVer 2 D 700 0.5 0.5 C 300 1.20 0.10 0.35 1.09 67.52 15.33 17.15 DysLuv 2 C 700 0.5 0.5 C 300 1.39 0.13 2.08 2.23 41.40 27.62 30.98 DysNit 2 C 700 0.5 0.5 C 300 1.42 0.12 1.03 1.66 48.49 25.55 25.96 DysReg 2 B 700 0.5 0.5 L 300 1.67 0.12 15.13 1.14 16.67 40.67 42.67 EutCam 2 C 700 0.5 0.5 C 300 1.40 0.13 1.06 1.30 45.50 24.56 29.94 EutFlu 2 C 700 0.5 0.5 CL 300 1.44 0.12 2.01 1.83 39.26 23.40 37.33 EutGle 2 C 700 0.5 0.5 C 300 1.36 0.13 2.06 2.37 45.00 32.00 23.00 EutNit 2 D 700 0.5 0.5 C 300 1.35 0.12 0.43 1.64 53.19 17.60 29.21 EutVer 2 C 700 0.5 0.5 C 300 1.38 0.12 0.48 1.26 50.53 17.95 31.52 GelAli 2 C 700 0.5 0.5 C 300 1.39 0.13 1.67 1.61 43.07 29.67 27.27 GleAli 2 B 700 0.5 0.5 SL 300 1.47 0.10 24.57 2.85 18.00 21.00 61.00 HapLuv 2 B 700 0.5 0.5 SL 300 1.47 0.11 22.67 2.66 18.00 26.00 56.00 HumAli 2 B 700 0.5 0.5 CL 300 1.42 0.13 4.95 2.89 33.20 26.13 40.67 HumNit 2 C 700 0.5 0.5 C 300 1.28 0.11 0.87 2.25 57.00 24.00 19.00 HyDyAli 2 C 700 0.5 0.5 C 300 1.39 0.13 1.66 2.44 43.67 25.00 31.33 HypSkAli 2 C 700 0.5 0.5 C 300 1.36 0.12 0.47 1.62 52.02 18.00 29.98 HypSkLep 2 C 700 0.5 0.5 CL 300 1.61 0.11 3.63 1.79 34.23 31.14 34.63 LamLuv 2 C 700 0.5 0.5 CL 300 1.42 0.13 3.88 2.51 35.17 28.44 36.39 LepCam 2 C 700 0.5 0.5 C 300 1.26 0.10 1.00 3.04 60.17 23.53 16.30 LepLuv 2 C 700 0.5 0.5 C 300 1.39 0.13 1.53 1.93 43.96 26.69 29.35 LepReg 2 B 700 0.5 0.5 CL 300 1.60 0.11 3.44 1.58 33.87 26.12 40.01 LepVer 2 C 700 0.5 0.5 C 300 1.35 0.12 0.12 0.65 57.00 11.00 32.00 LitLep 2 B 700 0.5 0.5 SCL 300 1.70 0.11 22.66 1.37 13.56 23.41 47.40 LixCam 2 D 700 0.5 0.5 C 300 1.08 0.07 1.37 1.23 77.23 17.37 5.40 MolCam 2 B 700 0.5 0.5 SL 300 1.47 0.12 37.95 2.28 11.23 33.00 55.77 MolLuv 2 B 700 0.5 0.5 L 300 1.44 0.13 17.28 2.89 20.67 30.67 48.67 NitAli 2 C 700 0.5 0.5 C 300 1.41 0.13 2.11 1.87 40.20 28.06 31.75 NitLuv 2 C 700 0.5 0.5 C 300 1.26 0.12 0.84 0.96 58.00 26.33 15.67 PelVer 2 C 700 0.5 0.5 C 300 1.33 0.12 0.51 1.33 54.44 20.56 25.00 PliAcr 2 A 700 0.5 0.5 SCL 300 1.63 0.08 16.26 1.14 21.00 11.00 68.00 ProLuv 2 C 700 0.5 0.5 C 300 1.46 0.12 0.67 1.59 44.40 11.60 44.00 SkeReg 2 B 700 0.5 0.5 CL 300 1.60 0.11 7.16 2.13 28.00 29.67 42.33 StaFlu 2 C 700 0.5 0.5 C 300 1.39 0.13 1.67 1.61 43.07 29.67 27.27 VerAli 2 B 700 0.5 0.5 L 300 1.40 0.14 20.85 3.25 18.80 37.60 43.60 VerCam 2 C 700 0.5 0.5 CL 300 1.42 0.14 2.18 1.10 38.55 35.08 26.37 VerLep 2 C 700 0.5 0.5 C 300 1.52 0.12 1.35 1.08 47.00 33.00 20.00 VerLuv 2 C 700 0.5 0.5 C 300 1.37 0.13 1.10 1.55 46.84 25.75 27.41 ChrLuv 2 D 1000 0.5 0.5 SCL 300 1.57 0.11 6.22 0.63 27.00 22.00 51.00 EutLep 1 C 300 0.5 0.5 L 300 1.75 0.10 12.19 0.72 20.00 30.00 50.00 EutReg 2 B 1000 0.5 0.5 SL 300 1.74 0.07 36.17 0.45 12.00 19.00 69.00 HapAli 2 C 1000 0.5 0.5 SCL 300 1.56 0.11 10.82 0.93 22.00 27.00 51.00 HapNit 2 D 1000 0.5 0.5 CL 300 1.53 0.12 4.07 0.95 31.00 24.00 45.00

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Continued

ROCK1 SOL_ALB1 USLE_K1 SOL_EC1 SOL_Z2 SOL_BD2 SOL_AWC2 SOL_K2 SOL_CBN2 CLAY2 SILT2 SAND2 ROCK2 SOL_ALB2 USLE_K2 1 0.06 0.12 1 700 1.33 0.13 1.22 1.95 49.40 27.70 22.90 1.00 0.16 0.12 1 0.22 0.12 1 700 1.27 0.12 0.70 0.86 57.39 24.78 17.84 1.00 0.33 0.14 1 0.08 0.10 1 700 1.34 0.12 0.35 1.68 55.20 16.60 28.20 1.00 0.19 0.10 1 0.25 0.13 1 700 1.21 0.11 0.44 0.74 64.75 18.35 16.90 1.00 0.36 0.13 1 0.11 0.12 1 700 1.35 0.12 0.71 1.46 51.03 22.67 26.30 1.00 0.22 0.12 1 0.33 0.14 1 700 1.34 0.12 0.43 0.49 53.91 20.64 25.45 1.00 0.43 0.14 1 0.18 0.13 1 700 1.48 0.13 2.69 1.01 34.95 28.75 36.30 1.00 0.30 0.15 1 0.28 0.11 1 700 1.18 0.11 0.39 0.64 67.90 15.93 16.18 1.00 0.39 0.12 1 0.13 0.12 1 700 1.25 0.12 0.93 1.30 58.49 26.48 15.04 1.00 0.25 0.13 11 0.19 0.12 1 700 1.51 0.11 0.77 0.97 49.12 24.28 26.61 26.00 0.31 0.13 23 0.27 0.16 1 700 1.76 0.11 13.33 0.67 16.62 41.08 42.31 34.00 0.38 0.18 1 0.25 0.12 1 700 1.41 0.13 0.91 0.76 45.47 24.50 30.02 1.00 0.36 0.14 1 0.17 0.12 1 700 1.43 0.13 1.34 1.06 42.17 25.74 32.09 1.00 0.29 0.14 4 0.12 0.12 1 700 1.39 0.13 1.53 1.38 45.00 32.00 23.00 5.00 0.23 0.13 1 0.19 0.10 1 700 1.13 0.09 0.73 0.96 71.41 17.64 10.95 1.00 0.31 0.13 1 0.25 0.12 1 700 1.24 0.11 0.35 0.73 62.45 17.64 19.90 1.00 0.36 0.13 1 0.20 0.12 1 700 1.43 0.13 1.85 0.94 39.44 32.20 28.36 1.00 0.31 0.15 1 0.08 0.12 1 700 1.54 0.10 13.64 1.66 22.00 19.80 58.20 1.00 0.19 0.13 1 0.10 0.13 1 700 1.49 0.12 4.92 1.55 30.40 27.60 42.00 1.00 0.21 0.13 1 0.08 0.12 1 700 1.51 0.11 3.63 1.68 33.20 18.00 48.80 1.00 0.19 0.11 1 0.13 0.11 1 700 1.24 0.11 0.52 1.31 62.80 19.80 17.40 1.00 0.24 0.12 1 0.11 0.11 1 700 1.38 0.12 0.81 1.42 48.25 22.50 29.25 1.00 0.23 0.12 1 0.20 0.11 1 700 1.34 0.12 0.54 0.94 52.74 21.60 25.66 1.00 0.31 0.13 31 0.17 0.12 1 700 1.48 0.13 2.83 1.04 34.61 30.39 35.00 1.00 0.29 0.15 1 0.11 0.12 1 700 1.44 0.12 1.33 1.46 41.90 21.35 36.75 1.00 0.22 0.12 1 0.07 0.11 1 700 1.25 0.11 0.77 1.77 60.38 23.18 16.43 1.00 0.18 0.11 1 0.16 0.12 1 700 1.37 0.13 0.99 1.12 47.31 26.50 26.20 1.00 0.28 0.13 23 0.20 0.12 1 700 1.68 0.10 2.40 0.92 35.65 24.68 39.67 34.00 0.32 0.14 1 0.38 0.11 1 700 1.34 0.12 0.12 0.38 57.00 11.00 32.00 1.00 0.46 0.12 31 0.23 0.15 1 700 1.57 0.13 13.28 0.80 17.02 25.31 42.05 1.00 0.35 0.16 3 0.26 0.15 1 700 1.04 0.08 1.33 0.72 78.66 15.84 5.50 3.00 0.37 0.17 1 0.12 0.14 1 700 1.54 0.12 16.89 1.33 17.62 32.60 49.78 1.00 0.24 0.15 1 0.08 0.13 1 700 1.50 0.12 9.88 1.68 23.71 31.43 44.86 1.00 0.19 0.13 1 0.17 0.12 1 700 1.40 0.13 1.12 1.09 44.66 25.79 29.55 1.00 0.28 0.13 1 0.31 0.14 1 700 1.31 0.13 0.76 0.56 53.20 27.40 19.40 1.00 0.41 0.15 1 0.24 0.12 1 700 1.16 0.10 0.69 0.78 67.87 19.71 12.43 1.00 0.35 0.14 9 0.27 0.12 1 700 1.61 0.08 15.49 0.66 21.00 11.00 68.00 1.00 0.38 0.14 1 0.20 0.10 1 700 1.52 0.12 3.12 0.92 33.29 22.71 44.00 1.00 0.32 0.14 23 0.14 0.12 1 700 1.69 0.11 5.04 1.24 29.40 29.13 41.47 34.00 0.26 0.14 1 0.20 0.12 1 700 1.43 0.13 1.85 0.94 39.44 32.20 28.36 1.00 0.31 0.15 1 0.06 0.13 1 700 1.49 0.13 15.51 1.89 18.80 37.60 43.60 1.00 0.16 0.14 1 0.28 0.15 1 700 1.41 0.13 1.20 0.64 43.37 29.27 27.36 1.00 0.39 0.15 31 0.29 0.15 1 700 1.36 0.13 1.16 0.63 47.00 33.00 20.00 1.00 0.39 0.16 1 0.21 0.12 1 700 1.33 0.12 0.62 0.90 52.68 23.45 23.87 1.00 0.32 0.13 1 0.39 0.15 1 700 1.54 0.12 2.53 0.35 34.00 21.00 45.00 1.00 0.47 0.15 31 0.37 0.17 1 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 23 0.44 0.16 1 700 1.63 0.08 32.98 0.21 13.00 17.00 70.00 1.00 0.52 0.16 1 0.32 0.16 1 700 1.56 0.11 3.98 0.33 30.00 24.00 46.00 1.00 0.48 0.16 1 0.31 0.15 1 700 1.44 0.12 0.73 0.34 45.00 21.00 34.00 1.00 0.47 0.14

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Appendix 10: Parameters in SWAT data base for each land cover layer in the catchment

OBJECTID ICNUM CPNM IDC CROPNAME BIO_E HVSTI BLAI FRGRW1 LAIMX1 FRGRW2 1 1 AGRL 4 Agricultural Land-Generic 33.5 0.45 3 0.15 0.05 0.5 3 3 AGRC 5 Agricultural Land-Close-grown 30 0.4 4 0.05 0.05 0.45 6 6 FRST 7 Forest-Mixed 15 0.76 5 0.05 0.05 0.4 7 7 FRSD 7 Forest-Deciduous 15 0.76 5 0.05 0.05 0.4 8 8 FRSE 7 Forest-Evergreen 15 0.76 5 0.15 0.7 0.25 9 9 WETL 6 Wetlands-Mixed 47 0.9 6 0.1 0.2 0.2 11 11 WETN 6 Wetlands-Non-Forested 47 0.9 6 0.1 0.2 0.2 16 16 RNGB 6 Range-Brush 34 0.9 2 0.05 0.1 0.25 18 18 WATR 6 Water 0 0 0 0 0 0 Continued LAIMX2 DLAI CHTMX RDMX T_OPT T_BASE CNYLD CPYLD BN1 BN2 BN3 BP1 BP2 0.95 0.64 1 2 30 11 0.0199 0.0032 0.044 0.0164 0.0128 0.006 0.0022 0.95 0.5 0.9 1.3 18 0 0.025 0.0022 0.0663 0.0255 0.0148 0.0053 0.002 0.95 0.99 6 3.5 30 10 0.0015 0.0003 0.006 0.002 0.0015 0.0007 0.0004 0.95 0.99 6 3.5 30 10 0.0015 0.0003 0.006 0.002 0.0015 0.0007 0.0004 0.99 0.99 10 3.5 30 0 0.0015 0.0003 0.006 0.002 0.0015 0.0007 0.0004 0.95 0.7 2.5 2.2 25 12 0.016 0.0022 0.035 0.015 0.0038 0.0014 0.001 0.95 0.7 2.5 2.2 25 12 0.016 0.0022 0.035 0.015 0.0038 0.0014 0.001 0.7 0.35 1 2 25 12 0.016 0.0022 0.02 0.012 0.005 0.0014 0.001 0 0 0 0 0 0 0 0 0 0 0 0 0 Continued BP3 WSYF USLE_C GSI VPDFR FRGMAX WAVP CO2HI BIOEHI RSDCO_PL OV_N CN2A CN2B 0.0018 0.25 0.2 0.005 4 0.75 8.5 660 36 0.05 0.14 67 77 0.0012 0.2 0.03 0.006 4 0.75 6 660 39 0.05 0.14 62 73 0.0003 0.01 0.001 0.002 4 0.75 8 660 16 0.05 0.1 36 60 0.0003 0.01 0.001 0.002 4 0.75 8 660 16 0.05 0.1 45 66 0.0003 0.6 0.001 0.002 4 0.75 8 660 16 0.05 0.1 25 55 0.0007 0.9 0.003 0.005 4 0.75 8.5 660 54 0.05 0.05 49 69 0.0007 0.9 0.003 0.005 4 0.75 8.5 660 54 0.05 0.05 49 69 0.0007 0.9 0.003 0.005 4 0.75 10 660 39 0.05 0.15 39 61 0 0 0 0 0 0 0 0 0 0 0.01 92 92 Continued CN2C CN2D FERTFIELD ALAI_MIN BIO_LEAF MAT_YRS BMX_TREES EXT_COEF BM_DIEOFF OpSchedule 83 87 1 0 0 0 0 0.65 0.1 AGRR 81 84 1 0 0 0 0 0.65 0.1 AGRR 73 79 0 0.75 0.3 50 1000 0.65 0.1 FRST 77 83 0 0.75 0.3 10 1000 0.65 0.1 FRSD 70 77 0 0.75 0.3 30 1000 0.65 0.1 FRSE 79 84 0 0 0 0 0 0.65 0.1 WETL 79 84 0 0 0 0 0 0.65 0.1 WETN 74 80 0 0 0 0 0 0.33 0.1 RNGB 92 92 0 0 0 0 0 0 0.1 WATR

Appendix 11: Parameters in SWAT data base for urban layer in the catchment OBJECTID IUNUM URBNAME URBFLNM FIMP FCIMP CURBDEN URBCOEF DIRTMX 4 4 URLD Residential-Low Density 0.12 0.1 0.24 0.18 225 Continued THALF TNCONC TPCONC TNO3CONC OV_N CN2A CN2B CN2C CN2D URBCN2 OpSchedule 0.75 460 196 6 0.1 31 59 72 79 98 URLD

Appendix 12: Detailed LANDUSE/SOIL/SLOPE distribution g) Gilgel Abbay Area[ha] Area[acres] Watershed 404332.52 999125.87 Area[ha] Area[acres] %Wat.Area LANDUSE: FRST 2438.22 6024.96 0.60

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FRSD 2654.33 6558.98 0.66 AGRC 146849.95 362873.58 36.32 AGRL 175158.55 432825.54 43.32 WATR 2726.36 6736.98 0.67 PAST 26193.27 64724.88 6.48 RNGB 9968.14 24631.77 2.47 WETL 7064.65 17457.10 1.75 FRSE 29764.58 73549.77 7.36 WETN 853.42 2108.84 0.21 URLD 661.04 1633.46 0.16 AbrAli 114.41 282.71 0.03 AbrLuv 322.89 797.88 0.08 AlbLuv 213.57 527.73 0.05 AliNit 8729.96 21572.17 2.16 AliVer 10018.99 24757.42 2.48 CalVer 892.40 2205.17 0.22 ChrLuv 287.30 709.93 0.07 ChrVer 6609.55 16332.53 1.63 DysLuv 5793.42 14315.83 1.43 DysNit 8382.49 20713.55 2.07 DysReg 4631.51 11444.70 1.15 EutCam 1340.72 3313.00 0.33 EutFlu 3775.55 9329.58 0.93 EutGle 116.95 289.00 0.03 EutLep 7.63 18.85 0.00 SOILS: EutNit 84455.44 208693.62 20.89 EutVer 33429.11 82605.01 8.27 GelAli 7804.51 19285.32 1.93 GleAli 16.10 39.79 0.00 HapAli 327.13 808.35 0.08 HapLuv 724.60 1790.53 0.18 HapNit 11.02 27.22 0.00 HumAli 1634.80 4039.68 0.40 HumNit 169.50 418.84 0.04 HyDyAli 16.95 41.88 0.00 HypSkAli 3383.17 8359.97 0.84 HypSkLep 55192.57 136383.60 13.65 LamLuv 152.55 376.95 0.04 LepCam 4575.58 11306.49 1.13 LepLuv 20169.34 49839.44 4.99

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LepReg 3933.19 9719.10 0.97 LepVer 106.78 263.87 0.03 LitLep 1933.12 4776.83 0.48 LixCam 8640.97 21352.28 2.14 MolLuv 2156.85 5329.69 0.53 NitAli 64492.89 159365.16 15.95 NitLuv 25.42 62.83 0.01 PelVer 16902.27 41766.37 4.18 PliAcr 1651.75 4081.56 0.41 ProLuv 6953.63 17182.76 1.72 SkeReg 783.93 1937.12 0.19 StaFlu 1915.32 4732.85 0.47 URBAN LAND 12643.65 31243.10 3.13 VerAli 17.80 43.98 0.00 VerCam 4000.14 9884.54 0.99 VerLep 550.87 1361.22 0.14 VerLuv 12136.86 29990.78 3.00 WATER 2187.36 5405.08 0.54 0-5 234984.33 580658.04 58.12 SLOPE: 5-15 117600.65 290597.08 29.09 15-9999 51747.54 127870.75 12.80 h) Gumara Area[ha] Area[acres] Watershed 136391.12 337029.28 Area[ha] Area[acres] %Wat.Area AGRC 43138.33 106596.98 31.63 AGRL 74222.16 183406.67 54.42 PAST 8049.32 19890.27 5.90 LANDUSE: RNGB 3027.05 7479.98 2.22 FRSD 5933.49 14661.96 4.35 FRSE 2020.77 4993.42 1.48 EutFlu 670.68 1657.30 0.49 EutVer 6311.97 15597.20 4.63 EutNit 7832.36 19354.16 5.74 LepLuv 35528.78 87793.39 26.05 SOILS: NitAli 8883.21 21950.84 6.51 PelVer 3905.64 9651.02 2.86 HypSkLep 60122.21 148564.98 44.08 URBAN LAND 649.76 1605.60 0.48 LepReg 866.16 2140.33 0.64

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CalVer 31.75 78.45 0.02 DysReg 805.60 1990.68 0.59 VerLuv 2857.95 7062.15 2.10 DysNit 328.91 812.76 0.24 DysLuv 337.63 834.29 0.25 ChrVer 370.40 915.27 0.27 VerCam 3848.51 9509.86 2.82 LamLuv 2513.11 6210.03 1.84 NitLuv 220.49 544.85 0.16 HypSkAli 304.30 751.94 0.22 HumNit 1.70 4.19 0.00 0-5 18617.62 46005.08 13.65 SLOPE: 5-15 64450.44 159260.26 47.25 15-9999 53323.06 131763.95 39.10 i) Ribb Area[ha] Area[acres] Watershed 156751.99 387342.00 Area[ha] Area[acres] %Wat.Area AGRC 20467.66 50576.60 13.06 AGRL 112666.60 278404.80 71.88 LANDUSE: RNGB 6412.15 15844.75 4.09 PAST 17090.89 42232.44 10.90 WETN 114.69 283.41 0.07 HypSkLep 55388.87 136868.66 35.34 LepReg 6358.08 15711.15 4.06 LitLep 340.06 840.29 0.22 NitAli 9781.47 24170.49 6.24 LepLuv 17496.51 43234.75 11.16 VerLuv 8932.71 22073.18 5.70 VerCam 3693.94 9127.91 2.36 EutNit 6792.51 16784.62 4.33 EutVer 19302.25 47696.84 12.31 SOILS: HypSkAli 474.55 1172.63 0.30 DysNit 742.10 1833.78 0.47 PelVer 3180.98 7860.35 2.03 MolLuv 8.48 20.95 0.01 DysReg 6267.54 15487.40 4.00 ChrVer 3911.63 9665.83 2.50 AliVer 83.10 205.35 0.05 EutFlu 3774.75 9327.60 2.41 CalVer 676.58 1671.86 0.43

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SkeReg 2008.67 4963.52 1.28 LixCam 2939.78 7264.35 1.88 URBAN LAND 2253.55 5568.63 1.44 LamLuv 2343.89 5791.88 1.50 0-5 39027.22 96438.21 24.90 SLOPE: 5-15 52248.53 129108.73 33.33 15-9999 65476.24 161795.07 41.77 j) Megech Area[ha] Area[acres] Watershed 65829.49 162667.96 Area[ha] Area[acres] %Wat.Area AGRC 44388.17 109685.40 67.43 FRSE 2236.47 5526.43 3.40 AGRL 15023.70 37124.30 22.82 PAST 1705.87 4215.29 2.59 RNGB 1717.36 4243.69 2.61 LANDUSE: URLD 757.92 1872.86 1.15 DysNit 10456.97 25839.70 15.88 HypSkLep 22691.73 56072.39 34.47 LamLuv 1673.33 4134.87 2.54 NitAli 385.36 952.25 0.59 ProLuv 1144.38 2827.83 1.74 VerCam 3765.21 9304.03 5.72 VerLuv 3546.62 8763.88 5.39 LepReg 930.76 2299.96 1.41 HypSkAli 612.57 1513.69 0.93 PelVer 3628.16 8965.36 5.51 AbrLuv 15.78 38.99 0.02 LepCam 12.14 29.99 0.02 LitLep 127.10 314.07 0.19 URBAN LAND 6686.90 16523.67 10.16 LepLuv 3376.66 8343.88 5.13 EutVer 1958.41 4839.33 2.97 VerLep 41.97 103.71 0.06 DysReg 726.75 1795.83 1.10 ChrVer 1616.13 3993.55 2.46 EutNit 2377.80 5875.65 3.61 SOILS: DysLuv 54.76 135.31 0.08 0-5 13367.12 33030.83 20.31 5-15 17722.36 43792.83 26.92 SLOPE: 15-9999 34740.01 85844.30 52.77

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Appendix 13: Parameters for SWAT data base used for WXGEN model

MONTH TMP_MAX TMP_MIN HMD DEWPT PCP_MM PCPSTD PCPSKW PR_W1 PR_W2 PCPD SOLARAV WNDAV Jan 27.5 7.7 6.9 9.4 38.3 42.3 0.5 0.0 0.0 0.2 53.5 0.8 Feb 29.4 9.6 0.0 8.3 0.5 0.5 2.3 0.0 0.0 0.5 52.7 1.1 Mar 30.5 11.8 0.4 8.8 6.6 6.6 0.9 0.1 0.0 2.0 53.8 1.3 Apr 30.7 14.1 0.8 10.5 21.6 21.3 0.9 0.2 0.0 3.4 51.2 1.4 May 29.9 14.8 2.2 13.1 74.5 73.1 1.5 0.4 0.0 7.4 46.5 1.3 Jun 27.3 14.2 1.2 14.8 124.8 122.2 2.8 1.0 0.0 13.8 45.8 1.2 Jul 24.3 14.0 2.4 15.1 296.3 291.7 0.9 1.0 0.1 20.3 40.3 0.9 Aug 24.3 13.8 1.2 15.4 257.8 252.7 1.4 1.0 0.0 20.2 38.1 0.8 Sep 25.6 12.9 2.8 15.0 135.5 133.3 2.3 1.0 0.1 14.5 45.5 0.9 Oct 26.7 13.0 18.8 13.3 155.0 160.4 1.9 0.4 0.1 7.2 48.0 0.9 Nov 27.1 10.5 0.4 11.6 7.9 7.9 2.4 0.1 0.0 1.8 49.2 1.0 Dec 27.4 7.9 0.1 9.7 1.1 1.1 3.0 0.0 0.0 0.7 51.7 0.9

Appendix 14: Sediment data used to develop sediment rating curve

Flow (Q) Sediment Sediment Load Year Month Day (m3/s) concentration (mg/l) (tons/day) Gilgel Abbay 1968 6 28 70.84 1868.16 11434.22 1968 7 22 150.68 1462.10 19034.90 1968 8 12 171.70 1089.61 16164.23 1968 8 26 195.94 1367.22 23145.96 1983 8 18 117.90 820.69 8360.01 1985 8 30 75.96 2009.00 13184.95 1986 2 10 3.45 145.33 43.32 1986 4 11 1.94 77.33 12.96 1987 4 25 2.18 31.46 5.93 1987 10 23 48.80 531.69 2241.78

1988 5 20 10.94 220.00 208.02

1988 7 20 213.45 2163.64 39902.02 1988 9 8 123.64 883.34 9436.28

1990 8 2 195.94 3298.85 55847.00 1993 5 7 3.59 177.45 54.98 1995 8 23 173.50 1637.60 24548.28 1996 2 17 2.55 121.14 26.65 2004 8 19 149.53 3436.10 44392.62

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2004 8 22 119.43 2491.90 25712.87 2004 8 23 179.49 3480.00 53967.92 2005 2 17 2.77 122.90 29.43 2007 12 6 9.77 258.80 218.53 Gumara 1964 7 27 195.00 6482.31 109213.96 1964 8 1 189.00 7247.08 118341.92 1968 7 22 3.48 12801.32 3849.00 1968 7 28 43.54 22.07 83.02 1968 7 23 108.99 1640.30 15446.27 1968 8 6 138.05 1733.37 20674.81 1968 8 21 34.80 784.17 2357.78 1968 9 3 53.10 615.08 2821.89 1980 8 6 141.29 1394.22 17019.88 1980 9 24 26.58 109.11 250.57 1983 8 17 182.68 2585.97 40815.79 1985 9 2 55.90 2912.29 14065.66 1986 2 11 0.67 22.40 1.30 1986 9 5 92.39 2595.18 20716.01 1987 2 29 0.39 857.10 28.66 1987 10 29 12.08 727.05 758.83 1988 5 25 0.41 389.30 13.66 1988 7 21 132.98 4121.93 47358.80 1988 11 17 9.13 84.22 66.44 1988 12 22 3.11 65.49 17.62 1989 2 10 1.47 60.19 7.63 1990 2 10 1.28 147.35 16.33 1992 7 1 37.64 10068.56 32743.92 1992 6 1 0.31 368.65 9.75 1993 5 3 0.45 536.87 20.87 1994 9 3 35.88 286.98 889.65 1995 8 3 181.95 8946.66 140645.79 1995 8 16 258.68 6894.40 154089.51 1996 2 29 2.40 122.68 25.44 1996 8 24 197.50 2540.00 43342.56 1996 8 24 197.50 2416.89 41241.81 1996 7 30 218.82 5331.60 100799.49 1996 11 14 3.60 161.60 50.31 1996 11 14 3.60 124.20 38.66

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1996 11 14 3.60 94.20 29.32 2004 8 16 117.10 3108.30 31446.93 2004 8 17 207.80 5371.45 96437.72 2005 9 5 95.13 5039.06 41415.49 2005 9 6 146.50 3356.78 42487.29 2005 9 7 152.71 3870.34 51066.45 2006 7 17 50.12 2631.70 11395.56 2006 7 18 62.99 5945.23 32353.90 2006 7 28 73.70 3264.14 20786.15 2007 8 10 129.79 3227.77 36196.55 2007 8 14 122.71 2068.39 21929.37 2007 8 22 73.37 648.18 4108.70 2007 8 23 129.32 3044.25 34014.12 2007 8 24 152.27 2186.25 28762.40 2007 8 25 180.53 4736.04 73872.99 2007 8 11 118.12 1547.37 15791.92 2007 12 4 3.19 167.99 46.26 2008 8 1 225.41 7243.28 141064.70 2008 8 2 176.56 5128.49 78231.79 2008 8 3 221.30 5265.93 100684.45 2008 8 4 171.11 2470.01 36515.70 2008 8 5 276.35 3937.58 94015.92 Ribb 1968 7 3 32.50 7713.24 21658.78 1968 7 23 44.70 2877.77 11114.18 1968 8 7 96.70 3543.78 29607.86 1998 8 20 123.00 3712.61 39454.65 1968 9 3 320.00 3515.40 97193.78 1980 8 7 34.56 2229.61 6657.58 1980 9 24 6.84 178.18 105.30 1985 9 3 27.31 3178.53 7500.01 1986 2 11 0.09 2.03 0.02 1986 8 7 77.73 4729.71 31764.13 1986 9 5 24.58 577.21 1225.83 1987 4 29 0.71 231.61 14.21 1987 10 29 16.00 389.61 538.60 1988 5 25 0.10 2164.50 18.51 1988 7 22 60.25 3527.19 18361.14 1988 9 2 56.03 4076.42 19733.92 1988 11 18 2.61 173.14 39.04 1988 12 22 1.08 95.09 8.91

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1989 2 11 0.58 103.92 5.20 1990 2 12 0.39 281.57 9.41 1990 7 6 46.10 10530.65 41947.64 1992 5 23 0.49 987.42 42.06 1992 7 20 36.30 19995.75 62713.07 1992 8 25 62.17 3126.02 16791.38 1993 5 12 0.26 372.65 8.40 1993 7 20 90.64 14556.98 114000.02 1994 8 26 113.85 8777.03 86336.48 1995 8 3 138.17 3269.10 39026.15 1996 8 24 212.69 8102.37 148892.47 1996 8 12 57.60 769.75 3830.41 1996 10 2 2.07 25.70 4.60 2004 8 9 104.06 5279.09 47461.79 2004 8 16 90.57 5818.56 45531.65 2005 9 5 57.28 2652.12 13125.78 2005 9 6 40.77 1566.71 5518.52 2005 9 7 91.38 8527.45 67326.91 2006 7 17 50.97 13943.54 61399.84 2006 7 18 38.12 7038.77 23180.85 2006 7 28 56.46 6608.22 32235.29 2007 8 10 96.20 6727.82 55920.65 2007 8 14 98.58 8448.41 71954.80 2007 8 22 73.91 3751.34 23955.38 2007 8 23 75.42 3482.81 22696.22 2007 8 24 103.31 5371.06 47941.56 2007 8 25 81.09 5806.93 40684.90 2007 8 28 85.63 4084.12 30215.40 2008 8 1 95.81 4496.70 37225.14 2008 8 2 49.40 2215.71 9457.37 2008 8 3 37.98 2977.19 9769.82 2008 8 4 78.28 4102.56 27748.31 2008 8 5 124.16 11045.65 118494.23 Megech 1990 2 21 0.04 197.94 0.75 1990 8 10 8.96 3117.60 2413.48 1992 5 27 0.05 350.33 1.39 1993 5 14 0.19 537.37 8.91 1994 10 6 2.61 229.09 51.66 2005 3 11 0.11 294.72 2.75 2005 9 2 9.61 254.43 211.34

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2005 9 3 9.78 205.33 173.54 2005 9 4 8.67 205.61 153.93 2007 8 15 17.24 581.08 865.34 2007 8 16 46.00 2037.86 8098.58 2007 8 18 49.33 2474.41 10546.63 2007 8 19 21.41 505.22 934.34 2007 8 20 23.99 714.31 1480.26 2007 8 21 41.82 1155.00 4172.89 2007 11 22 0.70 273.10 16.54 Abbay (Blue Nile) 1961 5 9 1.77 170.00 26.00 1961 7 5 42.13 466.81 1699.20 1961 9 23 158.10 986.18 13471.06 1961 10 7 94.00 764.75 6210.99 1961 11 8 21.10 245.19 446.99 1964 7 24 174.00 584.88 8792.85 1964 8 11 216.90 469.21 8793.07 1968 8 11 170.00 453.45 6660.27 1968 8 30 384.30 560.00 18593.97 1983 8 11 47.75 166.38 686.42 1985 8 22 87.46 199.42 1506.93 1986 4 7 19.89 315.23 541.72 1986 6 19 27.95 64.00 154.55 1986 8 14 110.05 595.00 5657.45 1986 10 18 83.12 129.10 927.14 1987 4 25 14.88 51.48 66.18 1987 10 19 181.63 407.87 6400.64 1988 7 1 20.98 388.10 703.50 1990 11 1 31.09 242.84 652.31 1990 8 3 153.87 247.80 3294.34 1993 4 28 18.07 50.38 78.66 1995 8 1 61.41 119.67 634.93 1996 2 20 19.94 75.35 129.78 2004 9 15 116.41 219.83 2210.99 2004 9 22 146.38 219.83 2780.25

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