IMPLICATION of REPPRESENTATIVE CONCENTRATION Pathwhays on ARJO-DIDESSA CHATCHMENT, UPPER BLUE NILE BASIN, USING MULTIPLE CLIMATE MODELS

IMPLICATION OF REPPRESENTATIVE CONCENTRATION PATHWHAYs ON ARJO-DIDESSA CHATCHMENT, UPPER BLUE NILE BASIN, USING MULTIPLE CLIMATE MODELS

WUDENEH TEMESGEN BEKELE

A THESIS SUBMITTED TO THE DEPARTEMENTOF IRRIGATION AND WATER RESOURCE INGINEERING, INSTITUTE OF TECHNOLOGY, SCHOOL OF GRADUATE STUDIES ARBA MINCH UNIVERSITY IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEPARTEMENT OF MASTER OF SCIENCE IN HYDROLOGY

ARBA MINCH FEBRUARY, 2017

Declaration

I, WUDENEH TEMESGEN declare that the content of this thesis is entirely my own work with the exceptions of or references which have been attributed to their authors or source, and that this thesis has not been previously submitted to this or other university for a degree award.

Signature------Date------

SCHOOL OF GRADUET STUDIES ARBA MINCH UNIVERSITY ADVISORS‟ THESIS SUBMISSION APPROVAL SHEET

This is to certify that the thesis in title “Implication of representative concentration pathways on Arjo-Didessa catchment, Blue Nile Basin, using multiple climate models” submitted in partial fulfillment of the requirement for the degree of Master‟s with specialization in Hydrology, the Graduate Program of the Department of irrigation and water resource engineering and has been carried out by Mr. Wudeneh Temesgen Bekele ID.No RMSc/285/06, under/our supervision. Therefore, I/we recommended that the student has fulfilled the requirements and hence hereby can submit the thesis to the department for defense.

Alemseged Tamiru Haile (Ph.D.) …………...... ………………………..

Name of Principal advisor Signature Date

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Acknowledgement

Above all I thank the almighty of GOD for his mercy and grace upon me during all my works and in all my life.

Foremost, I would like to express my deeply indebted to my supervisor, Alemseged Tamiru Haile (PhD), for his help and support without which the completion of the thesis might not been possible. He is also the one practically taught me what responsibility, commitment and punctuality mean on working process of my research.

I would like to acknowledge the Institute of Technology, Arba Minch University, for granting me with financial support to do the research. I would like also to thank my family and Yeshimebet Ayaue they all have provided continuous support and encouragement throughout my studies.

Abbreviation and Acronyms

AR5 Fifth Assessment Report

CORDEX Coordinated Regional Climate Downscaling Experiment

CM5A-MR Coupled Model Version 5, Medium Resolution

MPI-ESM-LR Max Planck Institute, Earth System Modeling, Low Resolution

DEM Digital Elevation Model

ECHAM5 European Center Hamburg version 5

MWIEE Ministry of Water, Irrigation and Electricity of Ethiopia

GCM Global climate Model

GHGs Greenhouse gasses

HadGEM2-ES Hadley Global Environment Model 2 - Earth System

HEC- HMS Hydrologic Engineering Center of Hydrologic Modeling System

ICHEC-EC Irish Center for High-End Computing Earth Consortium

IPCC Intergovernmental Panel on Climate Change

ITCZ Inter tropical convergence zone

IWMI International Water Management Institute

NMA National Meteorological Agency

NZCC New Zealand Climate Center

RCM Regional Climate Model

RCP Representative Concentration Pathways

SEI Stockholm Environment Institute

SRES Special Report on Emission Scenario

SRTM Shuttle Radar Topography Mission

SWAT Soil Water Assessment Tool

UACE US Army Corps of Engineering

UACE TRM Army corps of Engineering Technical References Manual

USGS US Geology Service

UNFCC United nation framework convention climate change

WBGS West Bank and Gaza Strip

WEAP Water evaluation and planning system

WMO World Meteorological Organization

WWF World Wide Fund

Table of Contents

Acknowledgement ...... iv Abbreviation and Acronyms ...... v List of Tables ...... ix List of Table in the appendix ...... x List of Figure...... xi List of Figure in the appendix ...... xii ABSTRACT ...... xiii 1. INTRODUCTION ...... 1 1.1 Background ...... 1 1.2 Problem statement ...... 2 1.3 Objective ...... 3 1.4 Scope of the study ...... 3 2. LITERATURE REVIEW ...... 4 2.1 Climate change ...... 4 2.2 Climate scenario ...... 4 2.3 Two Degree Threshold...... 5 2.4 Representative Concentration Pathways (RCPs) ...... 6 2.5 Climate change in the Upper Blue Nile River basin ...... 7 2.6 Bias Correction method ...... 12 2.7 Semi- distributed rainfall-runoff HEC-HMS4.1 model ...... 14 3. MATERIALS AND METHODOLOGY ...... 18 3.1 General framework of the research ...... 18 3.2 Description of the Study Area ...... 19 3.2.1 Topography ...... 19 3.2.2 Climate ...... 20 3.2.3 Hydrology ...... 21 3.3 Characteristics of the collected data ...... 21 3.3.1 Meteorological data...... 21 3.3.2 Hydrological data ...... 23 3.3.3 Land cover and Soil type ...... 24

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3.4 Data Management and Analysis ...... 26 3.4.1 Missing Value Estimation ...... 26 3.4.2 Data quality, homogeneity & consistency test ...... 27 3.4.3 Areal rainfall estimation ...... 27 3.4.4 Potential evapotranspiration ...... 28 3.4.5 Bias correction method ...... 30 3.5 Evaluation on climate rainfall data ...... 30 3.6 Model setup ...... 31 3.6.1 Terrain pre-processing ...... 31 3.6.2 HEC-HMS4.1 model calibration and validation ...... 32 3.7 Sensitivity Analysis ...... 35 3.8 Model performance evaluations ...... 36 3.9 Impact of climate change on stream flow ...... 37 4. RESULT AND DISCUSSION ...... 39 4.1 Sensitivity analysis ...... 39 4.2 HEC-HMS Model calibration and validation ...... 40 4.3 Data quality, homogeneity & consistency test ...... 42 4.4 Evaluation of rainfall estimates from climate models ...... 43 4.5 Historical meteorological and hydrological trend ...... 44 4.6 Future meteorological and hydrological data trend ...... 46 4.6.1 Historical trend as simulated by climate models ...... 46 4.7 Climate change impact ...... 50 4.7.1 Impact on future rainfall, temperature and evapo- transpiration ...... 50 4.7.2 Impact on future mean monthly flow ...... 53 4.7.3 Impact on seasonal and annual mean flow ...... 54 5. CONCLUSION AND RECOMMENDATION ...... 57 5.1 Conclusion ...... 57 5.2 Recommendation ...... 58 REFERENCE ...... 60 APPENDICES ...... 63

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List of Tables

Table 2.1 Overview of representative concentration pathways (RCPs), Source: (Vuuren et al, 2011) ...... 7 Table 2.2 Summary of previous climate change studies on upper Blue Nile River ...... 10 Table 2.3 Calibrated parameters values for HEC-HMS model from previous studies ...... 17 Table 3.1 General description of GCMs and RCMs and their resolution ...... 23 Table 3.2 General description of the climate and hydrological data which were collected from relevant offices ...... 24 Table 3.3 Dominant land covers of Arjo-Didessa catchment and their corresponding area coverage (period, 2001) ...... 25 Table 3.4 Description of the HEC-HMS model parameters and their allowable value range ...... 35 Table 4.1 Summary of sensitive parameters of the model depending on objective criteria ...... 40 Table 4.2 shows the best estimated model parameters as obtained from manual model calibration ...... 41 Table 4.3 Objective function values for calibration and validation period ...... 42 Table 4.4 Performance of dynamically downscaled models simulation in capturing and representing mean annual rainfall over Arjo- didessa catchment over the period (1981-2005). .. 44 Table 4.5 Statistic of the Mann Kendall trend test for catchment averaged rainfall, evapo- transpiration, and average minimum and maximum temperature over the period (1981-2005). . 45 Table 4.6 Mann- Kendall trend test statistics result of catchment averaged precipitation under RCP4.5 scenario of the selected models over the period (1981-2005)...... 47 Table 4.7 Mann- Kendall trend test statistics result of average maximum temperature under RCP4.5 scenario of the selected models over the period (1981-2005)...... 48 Table 4.8 Mann- Kendall trend test statistics result of average minimum temperature under RCP4.5 scenario of the selected models over the period (1981-2005)...... 48 Table 4.9 Mann- Kendall trend test statistics result of average evapo-transpiration under RCP4.5 scenario of the selected models over the period (1981-2005)...... 49 Table 4.10 Mann- Kendall trend test statistics result of average annual flow under RCP4.5 scenario of the selected models over the period (1981-2005)…………………………………...50

List of Table in the appendix

Table B.1 Monthly Average Maximum and Minimum Temperatures of Arjo-Didessa Catchment over a period of 1981-2008 ...... 64 Table C.1 Future potential evapo-transpiration for middle time horizon under different scenario in mm/month (2041s) ...... 65 Table F.1 Bias correction factor for HadGM2-ES climate model for RCP 4.5 scenario ...... 68 Table F.2 Bias correction factors for HadGM2-ES climate model for RCP 8.5 scenario ...... 69 Table F.3 Bias correction factors for MPI-ESM-LR climate model for RCP 4.5 scenario ...... 70 Table F.4 Bias correction factors for MPI-ESM-LR climate model for RCP 8.5 scenario ...... 71 Table F.5 Bias correction factors for ICHEC-EC climate model for RCP 4.5 scenario ...... 72 Table F.6 Bias correction factors for ICHEC-EC climate model for RCP 8.5 scenario ...... 73 Table F.7 Bias correction factors for CM5A-MR climate model for RCP 4.5 scenario ...... 74 Table F.8 Bias correction factors for CM5A-MR climate model for RCP 8.5 scenario ...... 75 Table G. 1 Total missed value in percent for each station and each meteorological element in the station (1981-2008) ...... 75

List of Figure

Figure2.1 Typical HEC-HMS representation of watershed runoff ...... 15 Figure3.1 Flow chart showing the main procedures of this study ...... 18 Figure3.2 Map of the study area and its climate zones ...... 19 Figure3.3 Spatial distribution of the meteorological station in the study area ...... 22 Figure3.4 Land cover map of Arjo- Didessa Catchment (source, MoWIE) ...... 25 Figure3.5 Major soil types of Arjo- Didessa Catchment (source, MoWIE) ...... 26 Figure3.6 Thiessen polygon of rain gauge stations in Arjo- Didessa catchment ...... 28 Figure3.7 HEC-HMS basin model of Arjo-Didessa Catchment ...... 32 Figure3.8 Procedure to evaluate the impact of climate change on stream flow ...... 38 Figure4.1 Model sensitivity analysis evaluated in terms of the RVE objective function ...... 39 Figure4.2 Hydrograph of the observed and simulated flow model calibration (1983-2001) ...... 40 Figure4.3 The observed and simulated hydrographs for the validation period (2002 to 2008) .... 42 Figure4.4 Sample Homogeneity test using double mass curve analysis for Nekemte station ...... 43 Figure4.5 Rainfall annual cycle over Arjo- didessa catchment from dynamically downscaled climate models simulations and gauged data at monthly base ...... 44 Figure4.6 Observed climate and hydrological trend for the time period (1981 to 2005), (a) rainfall, (b) maximum temperature trend, (c) minimum temperature trend, (d) stream flow trend and (e) evapo- transpiration ...... 46 Figure4.7 Annual catchment rainfall changes over the period (2041-2070) ...... 51 Figure4.8 Average maximum and minimum temperature change over the period (2041-2070) . 52 Figure4.9 Annual average evapo-transpiration changes over the period (2041-2070) ...... 52 Figure4.10 HEC-HMS simulated monthly change in stream flow of Arjo-Didessa catchment for the medium future (2041-2070) under RCP4.5 scenario...... 53 Figure4.11 Change in stream flow of Arjo-Didessa catchment for the medium future (2041- 2070) under RCP8.5 scenario...... 54 Figure4.12 Change in seasonal and annual stream flow the medium future (2041-2070) under RCP4.5 scenario...... 55 Figure4.13 Change in seasonal and annual stream flow for the medium future (2041-2070) under RCP8.5 scenario...... 56

List of Figure in the appendix

Figure C.1 Rainfall distribution over Arjo-Didessa catcment (1981-2008) ...... 65 Figure C.2 Total Areal Rainfall for Arjo-Didessa Catchment (1981-2008) ...... 65 Figure D.1 Daily discharge and areal rainfall of Arjo- Didessa catchment (1983 to 2008) ...... 66 Figure E.1 HEC-HMS sensitivity result, (a) Nash Sutcliffe (b) coefficient of determination (c) flow Vs constant rate factor changing by 5% (d) flow Vs base flow changing by 5%...... 67 Figure G.1 Historical rainfall trend for the selected climate models for the time period (1981 to 2005) under RCP4.5 scenario...... 76

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ABSTRACT

Climate change impact is becoming a global agenda and its impact can be significant on water resources system. Therefore, this study has evaluated the impact of climate change on stream flow of Arjo-Didessa catchment, Upper Blue Nile basin. The projection of future climate variables was done by using the selected four climate models namely HadGM2-ES, MPI-ESM-LR, ICHEC-EC and CM5A-MR under RCP4.5 and RCP8.5 scenarios. The projection analysis was done for the medium future time period (2041-2070) by taking 1971- 2000 as a base line period. HEC-HMS rainfall runoff model was found suitable for stream flow simulation for the study area. It reasonably captured the observed hydrograph pattern and volume. The pattern of the simulated rainfall from the three climate models resembles the gauge rainfall. However, the bias was found too significant to ignore. As such bias correction was applied. Annual rainfall in 2041 to 2070 will likely decrease by 0.36% to 16% for RCP4.5 and by 0.5% to 21% for RCP8.5 whereas annual evapotranspiration will increase by 3% to 5% for RCP4.5 and by 4% to 7% for RCP8.5. Mean maximum temperature will increase up to 1.1 for RCP4.5 and up to 3.16 for RCP8.5 scenario. However, the minimum temperature will increase up to 1.24 under RCP4.5 and up to 1.5 under RCP8.5. The climate variables change is likely to have a significant impact on the stream flow volume. Annual flows will likely decrease by 1% to 3% for most models under RCP4.5. Seasonal flow will increases by 3% to 5% and increase by 11% to 18% in Kiremt (rainy) season for RCP4.5 and RCP8.5 respectively while it may decrease up to 5% in Belg (small rain) season under RCP4.5 scenario. A significance difference in projecting annual flow was showed for CM5A-MR and ICHEC-EC for RCP4.5 and RCP8.5 respectively. Unlike other models, annual flow for CM5A-MR under RCP4.5and ICHEC-EC under RCP8.5 will likely increase by 2% and 9% respectively while the other model in both scenario showed decreases. Generally it is observed that, there will be a net annual reduction of stream flow up to 3% in Arjo- Didessa catchment in the medium future period. Most climate models fully agree in prediction of steam flow change but there is slightly difference in magnitude.

Key words: Climate change, RCP, RCM, Stream flow, Arjo-Didessa, HEC-HMS

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1. INTRODUCTION

1.1 Background

Climate change is caused by natural and anthropogenic factors. The natural factors are solar radiation change, volcanic eruption, greenhouse gas, and the earth‟s orbital change while anthropogenic causes include burning of fossil fuels, changes in land use (agriculture and deforestation) and industrial revolutions. These factors are the causes that increased the global average surface temperature by 0.6 ±0.2oC over the 20th century (Smithson, 2002).

Precipitation pattern in East Africa shows a high degree of temporal and spatial variability. Historical records indicate that there has been an increased in rainfall over the last century (FAO and UNEP). The fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) indicates that rainfall over Eastern Africa has decreased between March and May/Jun for the last three decades while there has been increase in temperature over east Africa since the beginning of 1980s.

In region of high or complex topography such as the Ethiopian highlands, downscaled projection indicate likely increase in rainfall and extreme rainfall by the end of 21st century (Change, 2014). Under A2 and B1 climate scenarios, there may be a high frequency of heat wave and higher rates of evaporation in Ethiopia due to warming over the country (Change, 2014).

Flows in Blue Nile are reduced in the late century due to combination of climate change and upstream water development for irrigation and hydropower (Elshamy et al., 2009), (McCartney and Menker Girma, 2012). McCartney and Menker Girma, (2012) reported that as a consequence of hydrological and climatic variables of the basin will change in the future (increase average temperature, decline in rainfall, and increase in potential evapotranspiration and decline in flows). For example, temperatures over the basin will likely increase by between 2oC and 5oC at the end of 21st century compared with 1961 – 1990 (Elshamy et al, 2009).

In order to apply effective water resource management and adaptation strategies, it is important to assess the potential impact of climate change on hydrological regimes. In this study, future change in stream flow of Arjo-Didessa is evaluated. Most previous studies in Didessa catchment are based on general circulation models (GCM) (Gebre et al., 2015). GCM have course resolution compared to regional climate models (RCM). Implications of the newly developed scenarios called Representative concentration Pathways (RCP) still needs further evaluation. The RCPs are an important development in climate research and provide a potential foundation for further research and assessment, including emission mitigation as well as impact analysis (Van Vuuren et al., 2011).

1.2 Problem statement

The Upper Blue Nile (UBN) basin is an important source to the economic growth and social development of the communities in the basin and the riparian countries. It is an important source of water for hydropower, irrigation, domestic water supply, fisheries, and for livestock of people. Roughly a quarter of the total flow of UBN as measured at the Sudan border originates from Didessa sub-basin (Sima, 2011; Awulachew et al., 2008). However, the flow of Didessa is reduced due to the combination effects of climate change and upstream water development for irrigation (Shaka, 2008).

Few studies are conducted to investigate the impact of climate change in UBN. Previous findings indicate that the water resource is under serious pressure (McCartney and Menker Girma, 2012; Elshamy et al., 2009). Increasing temperature and potential evaporation may lead to reduction in stream flow (Taye et al., 2015).

Most of the previous studies in UBN used GCMs which have coarse resolution compared to regional circulation models (RCM). GCMs are typically run at horizontal resolutions in the range 250-600km which is too course for local impact assessment (Taye et al., 2008). In addition, the previous studies used SRES climate scenario (Bernstein et al., 2008) while very few studies used the newly developed representative concentration pathway scenario (RCPs) (Admasu, 2015). RCPs allow the modeling of climate system response to human activities

2 and they include information on a range of long lived GHGs, including emissions of radiatively active gases and aerosols, land use and socioeconomic condition (Vuuren, 2011).

Climate change affects human kind in several ways. Drought and flood are among the main effect of climate change which significantly affects the livelihood of the people. In developing country like Ethiopia where most of its people livelihood is farming, effects of climate change are numerous.

1.3 Objective

The main objective in this study is to evaluate implication of Representative concentration pathways (RCPs) scenario on the future flow of Arjo- Didessa catchment.

Specific objectives

 Evaluate accuracy of historical rainfall estimates from climate models.  Assess historical trends of precipitation, temperature, evapo-transpiration and stream flow.  Evaluate implications of multiple climate model outputs and RCP scenarios on Arjo- Didessa monthly, seasonal and annual stream flow.

1.4 Scope of the study

Results of this study will contribute to water resource management and planning efforts in Arjo-Didessa catchment and its sub-catchment. Evaluation of climate change scenario will help to gain new insight about water resource problems and to develop necessary solution for the problem.

However, uncertainty of climate model outputs and impact of land use and land cover change were not addressed in this study. These were considered out of the scope of this study.

2. LITERATURE REVIEW

2.1 Climate change

Climate is the long-term prevailing pattern of temperature, precipitation and other weather variables at a given location, described by statistics, such as means and extremes. The climate includes conditions in the atmosphere and ocean, and is often described in terms of intensity, frequency, and duration of sever and non-severe weather events. Current pattern in climate data show that our planet‟s global surface temperature is rising. This change is linked to the dramatic increase in greenhouse gases in the atmosphere that has occurred over the past two centuries (IPCC, 2014).

Climate variability may occur either due to external force such as changes in the sun‟s energy output that raises the earth‟s surface temperature generating changes in atmospheric winds and ocean currents, which are the main distributors of energy and driving force of the global climate system or by interactions among the different components of the global climate system, the atmosphere, oceans, biosphere, ice cover, and land surface.

In Ethiopia mean annual rainfall distribution is characterized by large spatial variation, which ranges from about 2000mm over some pocket areas in the southwest to less than 250 mm over the Afar and Ogaden lowlands (Tesfaye and Walker, 2004). Unlike most of the tropics where two seasons are common (wet and dry season), three seasons are known in Ethiopia, namely bega (from October-January, belg (from February-may), and kiremit (from june- september). Mean annual temperature distribution over the country varies from about 10oC over the highlands of northwest, central, and south east about 35oC over northeastern lowlands (Tesfaye and Walker, 2004).

2.2 Climate scenario

In order to determine the impact of climate change in the future, we have need to have an idea of the concentration of the greenhouse gas and other pollutants in the atmosphere to which climate is sensitive, in the year to come. These concentrations depend on their emissions from various sources, natural as well as man-made.

Emission scenario describe future release in to the atmosphere of greenhouse gases, aerosols, and other pollutants and, along with information on land use land cover, provide inputs to climate models. They are based on assumption about driving force such as pattern of economic and population growth, technology development and, other factors. Level of future emissions is highly uncertain, and so scenarios provide alternative images of how the future might unfold (WMO).

Climate scenarios are plausible representations of future climate condition such as temperature, precipitation, and other climatological phenomena. They can be produced using a variety of approach and they have been developed to investigate the potential consequences of anthropogenic climate change (Moss et al., 2010).

The IPCC Special Report on Emission Scenario (SRES) in replacing the old IPCC scenario (IS92) identifies 40 different scenarios following four families of story line. Six illustrative scenarios were drawn from these four families. That are A1FI (fossil intensive), A1T (predominantly non fossil), A1B (balanced across energy source), A2, B1 and B2.All emission scenarios were designated as equally valid and probable.

2.3 Two Degree Threshold

The united nation framework convention climate change (UNFCC) was agreed in 1992 in Rio de Janeiro. The ultimate objective of this agreement is to stabilize atmospheric greenhouse gas (GHGs) concentrations at levels sufficient to “prevent dangerous anthropogenic interference with the climate system.”

In 2010, in Cancun, Mexico, those countries agreed that, to meet the goal, global warming should not rise beyond 2oc above pre- industrial levels. This New Zealand Climate Center (NZCC) Climate Brief explains the scientific basis behind that agreement, and backgrounds the various emission and concentration targets used in climate change policy designed to achieve that 2oc limit (Hunt and Watkiss, 2011).

Scientific assessment shows that, if we are to limit global warming to 2oc, we need a globally coordinated program to reduce our net GHG emissions to near- zero during the 21st century.

Two degrees may not sound like a big increase, but it represents a rate of global climatic change unparalleled in human history. Limiting warming to 2oc is possible, but requires rapid and sustained international changes to energy production and consumption patterns to cut emissions from transport, industry, forestry, and waste (Hunt and Watkiss, 2011).

Global mean temperature increases of up to 2oc (relative to pre- industrial levels) are likely to allow adaptation to climate change for many human systems at globally acceptable economic, social, and environmental costs. However, the ability of many natural ecosystems to adapt to rapid climate change is limited and many be exceeded before a 2oc temperature increase is reached. The global mean temperature increase greater than 2oc will result in increasingly costly adaptation and considerable impacts that exceed the adaptive capacity of many systems and an increasing and unacceptable high risk of large scale irreversible effects.

2.4 Representative Concentration Pathways (RCPs)

The early identification of Representative concentration pathways (RCPs) will facilitate coordination of new integrated socioeconomic, emissions, and climate scenarios. RCPs are referred to as pathways in order to emphasize that their primary purpose is to provide time- dependent projections of atmospheric greenhouse gas (GHG) concentration. In addition, the term pathways is to emphasize that it is not only a specific long-term concentration or radiative forcing outcome, such as a stabilization level, that is of interest, but also the trajectory that is taken over time to reach that outcome they are representative in that they are one of several different scenarios that have similar radiative forcing and emissions characteristics (IPCC, 2007).

The RCPs are named according to radiative forcing target level for 2100. The radiative forcing estimates are based on the forcing of greenhouse gas and other forcing agents. The four selected RCPs were considered to be representative of the literature, and include one mitigation scenario leading to a very low forcing level (RCP2.6), two medium stabilization scenarios (RCP4.5/RCP6) and one very high baseline emission scenario (RCP8.5).

Table2.1 Overview of representative concentration pathways (RCPs), Source: (Vuuren et al, 2011)

Name Description Model RCP8.5 Rising radiative forcing pathway leading to 8.5 MESSAGE 2 W/m (≈1370 ppm Co2 equiv.) by 2100. RCP6 Stabilization without overshoot pathway to AIM 2 6W/m (≈850 ppm Co2 equiv.)at stabilization after 2100

RCP4.5 Stabilization without over shoot pathway to GCAM 2 4.5W/m (≈650 ppm Co2 equiv.) at stabilization after 2100 RCP2.6 Peak in radiative forcing at 3 W/m2 (≈490 ppm IMAGE

Co2 equiv.) before 2100 and then decline (the selected pathway declines to 2.6 W/m2 by 2100)

Approximate radiative forcing level was defined as ±5% of the stated level in W/m2 relative to pre- industrial levels. Radiative forcing values include the net effect of all anthropogenic GHGs and other forcing agents. Note: MASSAGE, Model for Energy Supply Strategy and their General Environmental Impact, International Institute for Applied System Analysis, Austria; AIM, Asia-Pacific Integrated Model, National Institute for Environmental Studies, Japan; GCAM, Global Change Assessment Model, Pacific Northwest National Laboratory, USA (previously referred to as Mini Cam); IMAGE, Integrated Model to Assess the Global Environment Netherland Environmental Assessment Agency, The Netherlands ((Iacono et al., 2008).

2.5 Climate change in the Upper Blue Nile River basin

7 will have an impact on irrigation, hydropower, and mainly on the food security in overall Ethiopia as the upper blue Nile basin covers about 14% of the total land area of Ethiopia.

Taye (2015) used statically downscaling and bias correcting GCMs outputs with subsequent simulation in hydrologic model as needed. The author used GCMs outputs based on the special report on emission scenario (SRES) extensively and then he stated that from GCMs outputs based on special report on emission scenario (SRES) in two periods 2050s and 2080 of 21st century expected change in precipitation characteristics are unclear while Elshamy et al. (2009) report almost no excepted change in precipitation considering the ensemble mean of 17GCMs. Shaka (2008) showed that the rainfall will experience a decrease of 18% and 11.2% in June from the base line period by 2080s for A2 and B2 scenario respectively whereas the rainfall increase by 8.5% and 5.7% in September by 2080s for A2 and B2 scenario respectively.

Also McCartney and Girma (2012) concluded that there will be slightly reduced in rainfall and increased potential evapotranspiration which will lead to influence the basin runoff regime. From the result of future climate change simulation using ECHAM5-A1B outputs as input to statistical downscaling models developed for basin shows a decrease in rainfall by 6 -12% during the small rainy period while mixed results observed for main rainy season (Girma, 2012). According to Kim, Kaluarachchi and Smakhtin (2008) report from six GCMs models simulated by weighted scenario, when compared to the previous studies for the same area there is a change in precipitation by 11%, temperature by 2.3oC and 4% in runoff.

Although this needs future exploration given that potential evaporation is a function of various metrological variables, increasing temperature projections indicates that potential evaporation may simultaneously increase and leads to reduction in stream flow (Taye, 2015). The projected temperature in 2020s for both A2 and B2 scenario indicates that the maximum temperature will rise by 0.6oC. In 2050s the increment will be 1.4oC and 1.1oC for A2 and B2 scenario respectively while in 2080s annual maximum temperature will be increased by 2.5oC and 1.8oC (Shaka, 2008).

In many rainy season (June – September) the runoff volume of Blue Nile will reduce by 11.6% and 10.1% for A2 and B2 scenario respectively in 2080s (Shaka, 2008). The author

8 also reported that the mean annual runoff will reduce by 2.6% and 2.9% for the same scenario respectively within the same period. Girma (2012) conclude that change in climate will affect the basin hydrology. Under mid-range climate change scenario (A1B), average annual flows decrease significantly at location of Lake Tana by approximately 74%, at kessie by 28% and at the border 20% where these are part of upper Blue Nile river basin.

Intergovernmental penal on climate change working group II (AR5) projected that temperature increase over the upper blue Nile of between 2oc and 5oc at the end of the 21st century under the A1B set scenario compared to a 1961 – 1990 baseline. Also GCM projections over Ethiopia indicate a wide range of rainfall spatial pattern changes and in some regions GCMs do not agree on the direction of precipitation change, For example, in the upper Blue Nile basin in the late 21st century (IPCC, 2014).

Many studies were conducted on the upper Blue Nile river basin to assess the impact of climate change on the hydrology of the river basin. The general limitation of these studies is, they did not include the newly introduced RCPs and dynamically downscaled RCMs, and their evaluation was done excluding future water resource development.

Table2.2 Summary of previous climate change studies on upper Blue Nile River No. Review of literature Limitation 1 Taye (2015) use GCM model to investigate the The assessment was done using implication of climate change on the Blue Nile GCMs models of horizontal river basin. The author focuses on the current resolution 250-600 km at regional understanding of hydrological extremes under scale (Blue Nile) which couldn‟t historic and future climate condition. address local scale. Also the study Characteristics of precipitation and stream flow did not recognize factors such as extremes including historic and future projection population growth and land use are considered. The author concludes that the change. consistent increasing temperature projections indicate that potential evapotranspiration may simultaneously increase and lead to reduction in stream flow. 2 Kim and Kaluarachchi (2008) assess climate Outcomes of multiple GCMs change impact on the hydrology and water perturb the baseline climate resource of upper Blue Nile river basin by using scenario may not represent the six GCMs climate models and simple two-tank current precipitation and hydrologic model. They use the outcome of temperature pattern of the whole GCMs to representing the current precipitation catchment. They use two tank and temperature of the baseline climate scenario. conceptual models which have the Finally the authors conclude that low flow may following weakness; the relation become higher and sever mid- to long-term between the parameters is drought are likely to become less frequent impossible to design clearly that thought the entire basin, the climate in most of means we cannot know physical the upper blue Nile river basin is likely to meaning of the parameters. And become wetter and warmer in the 2050s and also also the model which prepare for the potential future dame operations are unlikely one catchment may not work to significantly affect the water availability to correctly for the other catchment.

Sudan and Egypt based on outflows from six Climate change on stream flow for GCMs and many dam operation scenario. future water infrastructure did not evaluate clearly. 3 Girma (2012) evaluate the potential impact of The main limitation of this study is climate and land use change on the water that it is based on a single GCM resource of upper blue Nile river basin. The (ECHAM5) and the SRES A1B author use statistical downscaling and ECHAM5 scenario which represents a mid climate model under A1B to extract future range climate change scenario, climate data and soil water assessment tool which assumes a balance between (SWAT2009) to study the hydrological impact fossil intensive and non- fossil of climate and land use change. According to the energy source. In this study future output of single climate model ECHAM5-A1B, water resource development also The author concludes that the rainfall decreases did not considered. by 6 to 12% during small rainy season and mixed result was observed for the main rainy season. The flow results show that increase in runoff in wide range of change. 4 Shaka (2008) evaluate the impact of climate It does not use the newly climate change on hydrology of Gilgel Abay catchment modeling techniques and bias in Lake Tana basin by using HBV model for correction method for coarse runoff simulation and bias corrected GCM resolution errors. It addresses only climate model under A2 and B2 scenario. The the water balance of Gilgel Abay. statically downscaling technique was used to downscale future climate data. The conclusion is that in the main rainy season the runoff will be reduced by 12% in 2080s and as much as 33% of the seasonal and annual runoff will be reduced if there is increments of 2oC in temperature and reduction of 20% rainfall occur simultaneously in the catchment.

2.6 Bias Correction method

RCMs are prone to biases and the simulated climate is not always fully consistent with the observation which is critical in climate change impact research (Portoghese et al., 2011). Therefore, Bias correction is a feasible alternative to improve the current simulation and also to reduces model systematic deviation and provides more reliable outputs (Christensen et al., 2008).Bias correction methods are often applied with in climate impact studies to correct the climate input data provided by the general circulation model or regional circulation models for systematic statistical deviations from observational data. Climate models are not simulate climate data perfectly that means the simulated climatology will be differ somewhat from observed climate data. The model state will drift towards the model climate as the forecast progress and this drift will be confounded with the climate evolution that is being predicted. Therefore, before using simulation climate data to hydrological model, bias correction is necessary to approach to the accurate observed climate data (Argüeso et al., 2013).

Different published studies discusses different type of bias correction techniques and point out their effectiveness and robustness method of correcting the precipitation and temperature dataset that simulated by the RCM for subsequent use in a hydrological model. Lafon et al., (2013) compares the performance of four published bias correction method (linear, nonlinear, γ- based quantile mapping and empirical quantile mapping) in reducing the bias of regional climate model daily precipitation output using cross validation technique. The authors conclude that if both precipitation data sets can be approximated by a γ- distribution, the γ- based quantile mapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation data sets cannot adequately be approximated using a γ- distribution, the nonlinear method is more effective at reduces the bias, but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period.

In other hand, Chan et al (2013) & Lafon et al (2013) stated that all bias correction methods are able to somewhat improve the RCM-simulated precipitation. The Performance is depending on the choice of correction method. Moreover, the distribution-based methods are

12 consistently better than the mean-based methods for both climate projection and hydrological simulations.

From the literature, the commonly used bias correction methods for precipitation are;

Linear bias correction method

The monthly scaling factor is applied to each uncorrected daily observation of that month, generating the corrected daily time series. The linear correction method belongs to the same family as the „factor of change‟ or „delta change‟ method. In addition to correct the coefficient of variation and mean of rainfall time series, this method has the advantage of simplicity climatological information and modest data requirements only monthly climatological information is required to calculate monthly correction factor which can avoid the distortion of relative variability of the entire- monthly precipitation distribution of daily precipitations. (Lafon et al, 2013).

Nonlinear correction method

This method results in the mean and standard deviation of the daily precipitation distribution becoming equal to those of the observed distribution (Lafon et al., 2013).

(2.2) where is the corrected value of the variable (precipitation), „b‟ is scaling exponent which is calculated iteratively utile the coefficient of variation of the RCM precipitation time series matches that of the observed precipitation time series. The constant „a‟ is then calculated so that the mean of transformed precipitation values is equal to the observed mean.

γ - Distribution method

This correction method assumes that the probability distribution of both observed and RCM daily precipitation data sets can be approximated using a γ – Distribution.

(2.3)

where K> 0 and θ> 0 are the form and scaling parameters of γ – Distribution respectively and P is the RCM daily precipitation. K and θ are calculated as;

where and are the sample mean and standard deviation of P respectively.

Empirical distribution correction method

This method follows the same approach as γ – Distribution method, with the RCM distribution transformed to match the observed distribution through a transfer function (Lafon et al, 2013, Chen et al, 2013 & Gudmundsson et al, 2012). To apply the empirical distribution correction method, the observed precipitation distribution ranked and divided into a number of quantiles. Then a linear correction factor is calculated for each quantile division by dividing the mean observation in that quantile by the RCM simulated mean precipitation in the same quantile. This is being a transfer function (Lafon et al, 2013).

Bias correction for temperature is not the same as precipitation. Precipitation bias correction method use power law but for temperature this method is not applicable because temperature is approximately normally distributed (Terink et al, 2010). Correcting normally distributed data set by power law function is not giving a result normally distributed data sets. Therefore, it is preferable to use a correction method which involves only shifting and scaling to adjust the mean and variance (Terink et al, 2010)

2.7 Semi- distributed rainfall-runoff HEC-HMS4.1 model

HEC-HMS is a physically based, semi-distributed hydrologic model developed by the US Army Corps of Engineers to simulate the hydrologic response of a watershed subject to a given hydro-meteorological input (Scharffenber et al., 2010).It is designed to simulate the precipitation –runoff processes of dendritic watershed systems and also it is designed to be

14 applicable in a wide range of geographic areas for solving a broad range of problems. This includes large river basin water supply flood hydrology to small urban or natural watershed runoff. Hydrographs produced by the program can be used directly or in conjunction with other software for studies of water availability, urban drainage, flow forecasting, future urbanization impact, reservoir spillway design, flood damage reduction, flood plain regulation, wetlands hydrology, and system operation (Laouacheria and Mansouri, 2015).

These methods are selected on the basis of applicability and limitations of each method, availability of data, suitability for same hydrologic condition, well established, stable, and widely acceptable, researcher recommendation etc. In the HEC-HMS view of watershed hydrology, the response of a watershed is driven by precipitation that falls on the watershed and evapotranspiration from the watershed. The precipitation may be observed rainfall from historical event; it may be a frequency- based hypothetical rainfall, or it may be event that represents the upper limit of precipitation possible at a given location. Historical precipitation data are useful for calibration and verification of model parameters, for real-time forecasting, and for evaluating the performance of proposed designs or regulation (UACE TRM, 2000).

Figure2.1 Typical HEC-HMS representation of watershed runoff The main limitations of such models are selecting a loss model and estimating the model parameters are critical steps in developing HEC-HMS input. The other model limitation are HEC-HMS model is deterministic model, uncoupled model, no aquifer interactions, constant

15 parameter values, dendrite stream systems-Flow splits possible but limited capability and no downstream flow influence or reversal-Backwater possible but only if contained within a reach.

In this model the basin model comprises three vital processes; the loss, the transform, and the base flow. There are nine loss methods designed for simulating the events and continuous simulations. From these the main and well known methods are; Initial and Constant, Deficit and Constant, Exponential, SCS Curve Number, Green-Ampt, Smith Parlange, Gridded Loss and Soil Moisture Accounting (Halwatura and Najim, 2013). The underlying concept of the initial and constant rate loss model is that the maximum potential rate of precipitation loss is constant throughout an event. Deficit and Constant loss model is different from the initial and constant loss model in that the initial loss can “recover” after a prolonged period of no rainfall (RAZMKHAH et al., 2014). The SCS Curve Number (CN) model estimates precipitation excess as a function of cumulative precipitation, soil cover, land use, and antecedent moisture (Huizinga, 2014, Razmkhah, 2014). Gridded Loss Methods and Soil Moisture Accounting Loss Methods are not preferred for the simulation studies because they require a high number of parameters (Halwatura and Najim, 2013).

A total of seven different transformation methods are provided in HEC-HMS. Some of these methods are complicated which requests more inputs which are not available for most of the ungauged catchments. Snyder unit hydrograph and Clark unit hydrograph methods and SCS unit hydrograph have been applied successfully to simulate long term stream flows elsewhere (Yilma and Moges, 2007; Hunukumbura et al., 2012; Fang, 2005).

Table2.3 Calibrated parameters values for HEC-HMS model from previous studies

Past studies Modeling Model Parameter parameter value Authors range

Runoff Volume SCS CN CN(-) 52-86 Tahir et al.(2015);Yuan et al. Impre.sur (%) 0 - 45 Direct Runoff Lag time (min) 4-4860 (2001); Choudhari et Transformation SCS UH al. (2014); Razmkhah (2014) ; Base flow Constant Initial base flow 0 - 40 monthely (m3/s) Yusop et al. (2007)

and Halwatura et al. (2013) Routing Muskingum K (hr) 0.25 - 12.10 routing X (-) 0.2

3. MATERIALS AND METHODOLOGY

3.1 General framework of the research

The materials and methods of this study are described in this section. The digital elevation model (DEM) of the study area was obtained from Japan space system (ASTER GDEM) which has a resolution of 30m x30m. The DEM was processed by using HEC-GeoHMS embedded in Arc GIS10.1. The main outputs of HEC-GeoHMS are drainage network and watershed characteristics. HEC-HMS4.1 rainfall runoff model was used for stream flow simulation after calibration and validation using historically observed streamflow and climate data. For future projection, the bias of the RCM climate data was corrected. Fig. 3.1 shows the flow chart and of the overall procedure which was followed in this study.

DEM Observed stream Observed RCM Climate data flow data Climate data

HEC-GeoHMS Bias correction

HEC-HMS4.1 Catchment (calibration & validation) characteristics

Historical and Future head flow Data Develo Proces Impact analysis on flow volume s of Arjo didessa catchment Result

Figure3.1 Flow chart showing the main procedures of this study

3.2 Description of the Study Area 3.2.1 Topography

Didessa catchment is found in southwestern part of Upper Blue Nile (UBN) basin. It is the largest tributary in terms of volume of water contribution to UBN River. It originates from mountain Gomma in Oromia region of west Ethiopia to cover an estimated area of 23,690 km2. The geographical location of Didessa catchment is between 07o53‟N and 9o45‟N latitude and 35o31‟E and 37o10‟E longitude and an elevation of between 800 and 3211m.a.s.l, based on DEM of 30×30m resolution. According to Ethiopian climate classification based on elevation (i.e. <500m: Bereha, 500-1500: Kola, 1500-2440: Weina dega and >2440: Dega), Didessa catchment mostly covers Weina dega and Kola climate zones.

Figure3.2 Map of the study area and its climate zones

Arjo-Didessa Catchment is the upper part of Didessa Catchment which includes two sub- catchments namely the Wama and Upper-Didessa sub-catchments. It is located in the West part of Ethiopia between Jimma, Illubabora and West Wollega Zones of Oromia region and its geographical location is between 07o53‟N and 9o0‟N latitude and 35o50‟E and 37o0‟E longitude as shown in the fig.3.2 below. It has an estimated drainage area of about 9997km2.

3.2.2 Climate

Arjo-Didessa catchment is characterized by three climate zones according to elevation. These climate zones referred to as Kola, Weina dega and Dega. Most part of the catchment area falls into the Weina dega climate zone with annual average rainfall of about 1971mm over a period of 1981-2013. The highland area of Arjo-Didessa is Dega (cool zone) which has an annual average rainfall of about 1672mm (see Fig.C.1 in the Annex). The central part of the catchment which has an elevation range between 848 – 1500 m is with in tropical zone (Kola) with an annual average rainfall of about 1900mm.

About 80 percent of the total rainfall for all climate zones is received during summer (kiremit) season (Jun, July, August and September) with an average annual rainfall of 1779 mm. catchment.

The average maximum and minimum temperature of the south-western part of Arjo-Didessa sub -catchment ranges between 20.61 to 26.18oC and 11.68 to 12.98oC respectively. The south-eastern part of the catchment has an average maximum and minimum temperature of ranges between 24.32 to 29.73oC and 9.71 to 14.07oC over a period of 1981-2008 respectively. For similar time period the North-Eastern part of the study area has an average maximum and minimum temperature of ranges between 19.32 to 25.56oC and 11.3 to 13.13oC respectively (see Table B.1 in the Annex). The catchment average temperature is about 18oc.

3.2.3 Hydrology

Didessa river is the largest tributary of the blue Nile river in terms of volume of water with mean annual flow of about 5,673Mm3,contributing roughly a quarter of the total flow of as measured at the Ethio-Sudan border (Tena, 2015, Awulachew et al., 2008).

The major catchments of Didessa sub-basin are Dabana, Wama, Anger and Upper Didessa. Among these Wama and the Upper Didessa are constitute in the Arjo-Didessa catchment. The Wama River enters to Didessa River from the north-east direction with an estimated drainage area of about 3349km2 while Upper Didessa River stretches from west to east and then towards north before joining with Wama River. Upper Didessa has an estimated drainage area of about 5402 km2.

3.3 Characteristics of the collected data

3.3.1 Meteorological data

Historical climate data of the study area was collected from National Meteorology Agency (NMA) of Ethiopia. These data include rainfall, wind speed at 2m height, sunshine hours, maximum and minimum temperature and relative humidity. All of these climate variables were collected at daily time scale from six principal meteorological stations, which are found inside and outside of the study area. The observational period of these data cover 1981-2013. These stations were selected based on the data availability and proximity to the study area.

All stations have good record of rainfall, maximum and minimum temperature data and poor record of wind speed, relative humidity and sunshine hours. Exceptions are Jimma and Nekemte which have good records of all meteorological elements with missed value less than 50%. The missed value in percent for each meteorological parameter and the station distribution over the study area are shown clearly in table 3.1 and fig. 3.2 below respectively.

Figure3.3 Spatial distribution of the meteorological station in the study area The other necessary meteorological data to study climate change impact are future climate data that were downscaled dynamically from by CORDEX-Africa program (http://wcrp- cordex.ipsl.jussieufr) for the Representative Concentration Pathway scenario RCP 4.5 and 8.5 projection scenario. Therefore, climate change data that were Downscaled Regionally was taken from International Water Management Institute (IWMI). The data is derived from three regional climate models (RCMs) with four global climate models (GCMs). The regional climate models are Regional Climate Limited-area modeling (CCLM), Rossby Center regional atmospheric model (RCA4) and RAMO22T while the GCMs are HadGM2- ES, MPI-ESM-LR, ICHEC-EC and CM5A-MR. Table 3.1 shows the general description of GCMs and RCMs models with their resolutions and time period.

Table3.1 General description of GCMs and RCMs and their resolution

Spatial resolution Time period Temporal (in degree) GCMs RCMs resolution Latitude Longitude Historical Middle term

HadGM2-ES CCLM 1.25 1.875

MPI-ESM-LR CCLM 1.8653 1.875 ICHEC-EC RAMO22T Daily 1.1215 1.125 1971-2000 2041-2070 CM5A-MR RCA4 1.2676 2.5

3.3.2 Hydrological data

One of the important input data to investigate the impact of climate change on the water resources is observed stream flow data. Therefore, for this study daily Stream flow data of the study area from 1980-2008 was collected from Ministry of water, irrigation and electricity of Ethiopia (MWIEE). There are about four stream gauging stations which are operated by MWIEE in Arjo-Didessa. However, only one of the stations is reliable for this study while the others are not functional.

The reliability and quality of stream flow data was assessed based on its data availability and standard quality assessment methods. One of the reliability measures of flow data is missing percent. Unreliable peak flow data was also considered as missing value. For stream flow gauging station there is only 15% missing value from a total of 27 years daily flow record. The location of this stream flow gauging station is shown in fig.3.3 above. The gauging station is located downstream of Arjo-Didessa reservoir.

Table3.2 General description of the climate and hydrological data which were collected from relevant offices

Data type Data source Temporal Spatial Time Description resolution resolution period Climate National Daily and point 1981-2013 precipitation, minimum Meteorology agency monthly and maximum of Ethiopia temperature, wind speed , relative humidity & radiation Hydrology Ministry of Water, daily point 1980-2008 Irrigation and Flow data electricity of Ethiopia Land Ministry of Water, 30x30 2001 Prepared by Map Authority use/cover Irrigation and of Ethiopia electricity of Ethiopia Soils Ministry of Water, 1998 Prepared by Map Authority Irrigation and 30x30 of Ethiopia electricity Ethiopia Elevation USGS(SRTM) 30x30m 2013 Digital elevation model

3.3.3 Land cover and Soil type

According to FAO‟s land cover classification system, the study area has nine land cover types (Table 3.3). Nearly half of the study area is cultivated while forest cover is 14%. It is a rural watershed with negligible urban area.

Table3.3 Dominant land covers of Arjo-Didessa catchment and their corresponding area coverage (period, 2001)

Land cover Type Area coverage (%) Dominantly cultivated 26 Moderately cultivated 20 Perennial crops 1 Forest 14 Grassland 4 State farm 0.28 Urban 0.12 Woodland dense 4 Woodland open 30

Figure3.4 Land cover map of Arjo- Didessa Catchment (source, MoWIE)

According to FAO‟s soil classification system based on texture there are about seven major soil types in the study area. These are Clay, Loam, Sandy, Clay Loam, Sandy Loam, Sandy Clay Loam, and sandy clay. Most part of Arjo- Didessa sub- catchment is characterized by clay soil which covers 64%, clay loam 14% and sandy clay loam 11% of the total area of the catchment.

Figure3.5 Major soil types of Arjo- Didessa Catchment (source, MoWIE)

3.4 Data Management and Analysis

3.4.1 Missing Value Estimation

Sometimes there is missed data in a station due to failure of the observer to make necessary visit to the gauge site, vandalism of recording gauge and instrument failure because of mechanical or electrical malfunctioning. In this study, the following methods were used to fill missed data.

For rainfall stations with missed value within 10% of the time, Arithmetic mean method was used to fill in missing data. For above ten percent missed value Inverse distance weighted method (IDW) was used to estimate the missed value of precipitation data. For Inverse distance weighted method the missing data at a station was estimated as a weighted average of the observed rainfall at the neighboring stations. The weights are equal to the reciprocal of the distance or some power of the reciprocal of the distance of the estimator stations from the estimated stations. The same procedure and methods were used to estimate the missed value of temperature, humidity, sunshine, and wind speed data.

3.4.2 Data quality, homogeneity & consistency test

After collecting all hydro-meteorological data, their quality, homogeneity and consistency should be checked to improve model simulation output. Consistency of the data means the usability, or the validity of the data. But a time series of hydrological or meteorological data may exhibit jumps and unstable trend which is the manifestation of inconsistency and non- homogeneity. Therefore, Double mass curve analysis was used to check the consistency and homogeneity of hydro-meteorological time series for each individual station with regard to possible temporal and spatial variations or errors have been investigated. Double mass curve analysis is the plotting of accumulated values of the station under investigation against accumulated values at neighboring stations, over the same period of time.

If a Double mass curve reveals a change in slope that is significant (it may be due to measurement conditions at particular stations), the value of the earlier period of record should be adjusted to be consistent with latter period records by applying a correction ratio on inconsistent series.

3.4.3 Areal rainfall estimation

In this study, the Thiessen polygon method was used to estimate areal rainfall. This method as selected arbitrary as selection of interpolation methods is not easy. Thiessen polygon method assumes the recorded rainfall in a gauge is representative for the area half-way to the adjacent gauges. Thiessen polygons are formed around each station by drawing perpendicular

27 bisectors of the lines joining adjacent stations. The average depth of precipitation over the total area is calculated as;

∑ ( ) 3.0

Where: P = Areal average rainfall, Ps = Rainfall measured at station s, As = Area of sub- region covered by station s and A = total area of sub-regions. The Thiessen polygons are shown in figure 3.7.

Figure3.6 Thiessen polygon of rain gauge stations in Arjo- Didessa catchment

3.4.4 Potential evapotranspiration

One of the main input data of HEC-HMS4.1 rainfall-runoff model is potential evapotranspiration (PET). The well-known Penman- Monteith method, which is recommended by FAO(FAO, 1998) , was used to estimate potential evapotranspiration of the study area for calibration and validation of HEC-HMS4.1 model. PET was estimated using ETo calculator software which uses FAO Penman-Montieth method (http//www.FAO.org/nr/water/ETo).

Penman- Monteith method estimates grass ETo and explicitly incorporates both physiological and aerodynamic parameters. The inputs are maximum temperature, minimum temperature, sunshine hour, wind speed and relative humidity. These input data were collected from six first and two 3rd class meteorological stations in the Arjo-Didessa catchment over a period of 1981-2008. And then sub-catchment potential evapotranspiration was estimate using the well-known Thiesson polygon method.

∑ ( ) 3.1

where, s, is the number of neighboring stations, PET = Areal average potential evapotranspiration, PETi = Potential evapotranspiration estimated at a station, Ai= Area of each sub-catchment with corresponding station i and A= total area of the sub-catchment.

Hargreaves method was used to estimate PET for analysis of climate change. The main inputs to this method are minimum and maximum temperature. Hargreaves equation reads:

( ) √( ) 3.2 where:

= potential evapo-transpiration as estimated by Hargreaves method (mm);

Ra = Extraterrestrial radiation (MJm-2day-1) (calculated from latitude and time of year);

= Mean temperature ( );

= Minimum temperature ( ); and

= Maximum temperature ( )

3.4.5 Bias correction method

In this study, the rainfall and temperature data from the climate models were bias corrected on sub-catchment level. The minimum and maximum temperatures were bias corrected by linear shifting and scaling method which reads(Terink et al., 2010);

( ) ( ) ( ) 3.3 ( ) where: is the corrected daily temperature; is the uncoracted daily temprature from RCM model; refers to the observed daily temprature while is mean observed temprature and is mean simulated temprature. The correction was applied to data of each of the twelve months separately.

The rainfall data was bias corrected by using non-linear correction method. In common with the linear method, this approach has the advantage that it requires monthly observed statistics but, in addition to the mean, it needs information on the coefficient of variation (CV) of rainfall. This method results in the mean and standard deviation of the daily rainfall distribution becoming equal to those of the observed distribution (Lafon et al., 2013). The equation reads (Lafon et al., 2013);

3.4 where is the corrected value of the variable (precipitation), „b‟ is scaling exponent which is calculated iteratively utile the coefficient of variation of the RCM precipitation time series matches that of the observed precipitation time series. The constant „a‟ is then calculated so that the mean of transformed precipitation values is equal to the observed mean. Estimation of „a‟ and „b‟ was made on monthly for the time period 1981 to 2000.

3.5 Evaluation on climate rainfall data

The downscaled climate rainfall data from selected climate models was evaluate its performance by root mean square error, correlation coefficient, coefficient of variation and by calculating the bias for climate change detection and monitoring. The linear relationship

30 and the pattern of the simulated and observed rainfall were evaluated. From 1981 to 2000 time period for both the observed rainfall and simulated rainfall data which is derived from HadGM2-ES, MPI-ESM-LR, CM5A-MR and ICHEC-EC was used for evaluation. The ensemble mean of the selected models output was also used for comparison with the observed rainfall and it helps to understand the pattern of the ensemble model output and individual climate projection model.

3.6 Model setup

3.6.1 Terrain pre-processing

For this study, some important input hydrological parameters to HEC-HMS such as background maps, basin characteristics, and meteorological inputs were pre-processed using HEC- GeoHMS software which is embedded in Arc GIS 10.1. HEC-GeoHMS program allows users to visualize spatial information, watershed characteristics, perform spatial analysis, delineate sub basins and streams, construct inputs to hydrologic models, and assist with report preparation. It creates various files which can be used by HEC-HMS to develop a hydrologic model. The model schematization as derived by HEC- GeoHMS and specified in HEC-HMS is shown in the Fig.3.8.

The estimated parameters of HEC-HMS are time of concentration (Tc), initial losses and constant loss rate which are important to start model calibration. The sub-basin curve number (CN) was derived based on land use and soil type.

Sub basin

Reach Junction

Figure3.7 HEC-HMS basin model of Arjo-Didessa Catchment

3.6.2 HEC-HMS4.1 model calibration and validation

Evaluation of impact of climate change on water resources requires a proper model simulation of water availability. This can be achieved by rainfall-runoff modeling which is a complex task. Models should be well calibrated and validated to increase user confidence in their use. Calibration is the process of estimating model parameter with the objective to match characteristics of observed and simulated stream flow hydrographs. Validation is needed to test the model performance outside its calibration period.

In this study, HEC-HMS4.1 was calibrated from 1983 to 2001 and validated on time period of from 2002 to 2008. The period 1981 to 1982 was specified as warm-up period which is used for initializing the model smoothly. Both the automated and manual calibration method was used in this study. To get initial values of parameters, model sensitive to its parameters was evaluated first. This is followed by manual calibration which involves manually adjusting the most sensitive parameters until more than satisfactory match is achieved between simulated and observed hydrograph. Model performance is evaluated by visually

32 inspection of hydrographs and using quantitative measures as described by objective functions which measures the degree of match between observed and simulated hydrograph.

In this study, deficit and constant loss method, SCS Unit hydrograph, constant monthly base flow and Muskingum routing methods were used for model simulation. Deficit and constant loss method has been used successfully for continuous soil moisture simulation. It is a quasi- continuous model, easy to set up and use, not too much demanding in terms of data and is used in conjunction with a meteorological models that computes evapotranspiration. This model uses a single soil layer to account for continuous changes in moisture content (Saleh et al., 2011). Deficit and constant loss method is similar with initial and constant loss method except the initial loss can recover after a prolonged period of no rainfall (Homa, 2014). The parameters for this model include initial moisture deficit, maximum moisture deficit, constant loss rate, and impervious percentage. The percentage impervious was taking as 0% since no large urban settlements inside the catchment while the other were estimated by model calibration trial – error processes by taking the initial value arbitrarily but the selected value should be in the appropriate range of the model parameters (Table 3.4).

The soil conservation service (SCS) unit hydrograph transform method was used to compute direct runoff from excess precipitation. The SCS unit hydrograph is a parametric unit hydrograph model, based on the averages of unit hydrograph derived from gauged rainfall and runoff for a large number of small agricultural watersheds. The input parameter for this model is basin lag which is estimated, as 0.6 times time of concentration of the flow. The time of concentration can be estimated based on sub-basin characteristics including topography and the length of the reach (Kirpich‟s formula) as follows:

3.3

( ) 3.4

where: Tc is time of concentration in hr, L is the reach length in feet, and S is the slope in %.

The constant monthly base flow method was used to account for base flows. The method allows the specification of a constant base flow for each month of the year. For this study for

27 years of daily flow data the base flow is separated by considering the minimum flow in each month. Then, the values were averaged over the monthly time span.

The Muskingum routing method was used to route the flow in reaches using a simple conservation of mass approach. This routing method approximates storage in a system according to a wedge method (Rui and Wang, 2000). The development of the wedge is due to a flood wave that causes inflow to exceed outflow and creates a wedge of storage (Chow et al., 1988). It considers a linear relationship between inflow and outflow. The Muskingum method adequately approximates the storage within a river reach because of its linear nature. This method requires three parameters, K, X and number of sub reach.The Muskingum K is the travel time through the reach. The Muskingum X is the weighting between inflow and outflow influence; it ranges from 0 to 0.5. The value of K can be estimated as:

3.5

where: L is length of reach (m) and V is mean velocity (m/sec).

The value of K was first fixed based on equation (3.5) whereas the value of mean velocity was computed using HEC-GeoHMS (V=1.84m/sec), and then K was finally determined through calibration. Muskingum X parameter was set at the default value of 0.2. The HEC- HMS model parameters are summarized in Table 4.3.

Table3.4 Description of the HEC-HMS model parameters and their allowable value range

Modeling Model Parameter Range Descriptions

Initial deficit (mm) 0.001-1000 The amount of water required to fill the Runoff Volume Initial & soil layer to the maximum storage. constant Maximum deficit 0.001-1000 The amount of water the soil layer can rate loss (mm) hold. Constant rate 0.1-5 It is the percolation rate when the soil is (mm/hr) saturated

The length of time between the centroid Direct Runoff of precipitation mass and peak flow of Transformation SCS UH Lag-time(hr) 0.1-3000 the resulting hydrograph.

Base flow Monthly Initial base flow 0-100000 The amount of flow during dry season constant K(hr) 0.1-150 Is the travel time of the flood wave Routing Musking through routing reach; when the um amount of travel time increases the routing amount of runoff decreases X(-) 0-0.5 Is the weighting between inflow and out flow influence 3.7 Sensitivity Analysis

Sensitivity and calibration methods are critical to estimate values of model parameters which represented important characteristics of the real world that cannot be measured accurately (Foglia et al., 2009). Sensitivity analysis procedures help to explore and quantify the importance of possible errors in input or parameter data on simulated model outputs and model performance indicators. It is a method to determine which parameter of the model has greatest impact on the model result. It ranks model parameters based on their contribution to overall error in model predictions. Commonly, the effect of each input parameter is determined separately by keeping other model parameters constant.

In this study a local sensitivity analysis was performed to investigate the sensitive input parameter of the model. Nine important model parameters were taken for sensitivity analysis. The initial value for those parameters that investigated based on literature review and

35 catchment characteristics were kept as baseline parameter set. Using these parameter values the model was run repeatedly and the value of each baseline parameter was multiplied by 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, and 2.0. The procedure is repeated for all model parameter while keeping the value of other parameters at their initial values. Sensitivity was measured by visual inspection of simulated and observed hydrograph as well as using estimated values of objective functions for each model run.

3.8 Model performance evaluations

Evaluation of hydrological model performance is important to provide a quantitative estimate of the model‟s ability to reproduce historic and future watershed behavior and to provide a means for evaluating improvements to the modeling approach. The model performance in this study was evaluated by visual and statistical comparison of the measured and simulated stream flow data. The most straightforward possibility to evaluate visually is to use graphical representation and compare the observed and simulated values and patterns. This graphical technique provided an initial general overview. Interpretation of the graph first focused on hydrograph pattern, base flow and then on peak flow.

The frequently used efficiency criteria in hydrologic modeling studies and which are widely reported in literature are Nash-Sutcliff efficiency (NSE), coefficient of determination (R2), the mean fourth-power error (M4E), and relative volumetric error (RVE). In this study, the model ability to reproduce the pattern of the observed and predicted stream flow hydrographs was evaluated by NSE and R2. The volumetric error of the predicted stream flow was evaluated by RVE.

Nash- Sutcliff efficiency (NSE); the range of NSE lies between 1.0 (perfect fit) and -1. An NSE value of less than zero indicates that the mean value of the observed time series would have been a better predictor than the model. The performance of the model is very good (NES>0.8), good (0.6-0.8), satisfactory (0.5-0.6) and unsatisfactory (NSE<0.5) (Pachepskey et al.). It is calculated as;

∑ ( )

( ) ∑ ( ̅ )

36 where, is the observed discharge at the time step i, ̅ is the mean of the observed discharge, is the simulation discharge at the time step i and N is the number of observations.

Coefficient of determination (R2); can be expressed as the squared ratio between the covariance and the multiplied standard deviations of the observed and predicted values (Krause et al., 2005). R2 ranges between 0 and 1. A value of zero means no correlation whereas a value of 1 means that the dispersion of the simulation is equal to that of the observation. It is calculated as;

∑ ( ̅ )( ̅ )

∑ ( ̅ ) ∑ ( ̅ ) where, all terms are the same as defined previously.

The percentage error in total runoff volume (RVE): It ranges between - to + and the model performance is very good if RVE value between -5% to 5%, and satisfactory performance if RVE value is lies between 5% to 10% and -10% to -5%. It is calculated by:

∑ ( )

∑ where, all terms are defined previously.

The percentage error of peak flow (PEPF): It is for peak sensitivity performance. It is estimated as;

( ) ( )

( )

Where, ( ) is the peak observed discharge and ( ) is the peak simulated discharge.

3.9 Impact of climate change on stream flow

For the projection of climate change, rainfall, evapo-transpiration and temperature data was downscaled from HadGM2-ES, EC-EARTH, MPI-ESM-LR and CM5A-MR climate models and bias corrected. As climate change is the change in the state of climate that can be

identified by changes in the mean and variability of its properties that spend for thirty years or more, the downscaled meteorological data was developed in to three scenarios based on the standard newly representative concentration pathway classification method to identify the change. These are from 1971 to 2000 historical, 2011 to 2040 short term, 2041 to 2070 middle term and 2071 to 2100 long term period for both intermediate emission RCP 4.5 and high emission RCP 8.5 projection scenarios. Since the short term is already in passing and the long term is too far, also IPCC AR4, 2007 reported that there will be an increase of temperature to 2oc threshold, in mid period; this study was deeply focused on the middle term period. However, since the change in climate variable and change in stream flow is calculate compared with the base or historical period, the trend analysis for historic period of simulated data and observed data was done by Mann Kendall trend test which is important to observe the pattern of the simulated climate data with the observed. Trend test for observed and simulated stream flow of historical period was also done.

Thereafter, the bias corrected simulated climate data (rainfall and ETo) of the historical period and the middle time period was given to the calibrated HEC-HMS4.1 to produce future stream flow. Climate change impact on stream flow was analyzed statistically at monthly, seasonally and annually scale while for climate variables it analyzed at annually scale for each climate model and corresponding projection scenarios. The general procedure is showed in figure 3.9 Impact analysis RCM data (monthly, Bias Calibrated Future Flow seasonal and correction HEC-HMS Observed annual flow climate data change)

Figure3.8 Procedure to evaluate the impact of climate change on stream flow

4. RESULT AND DISCUSSION

4.1 Sensitivity analysis

Model sensitivity to its parameters was evaluated manually by changing the value of one model parameter at a time while keeping the value of remaining parameter constant. In terms of relative volumetric error (RVE) objective function the model is most sensitive to change in parameters that have steep slope plot and not sensitive for parameters that have constant slope (figure 4.2). Simulated stream flow volume for the study area is most sensitive to the constant rate (CR) and moderately sensitive to base flow (BF) parameter.

250 200

150 100 50 RVE(%) 0 -50 -100 0.25 0.5 0.75 1 1.25 1.5 1.75 2 parameters change (%) BF CR MD K X Tlag ID

Figure4.1 Model sensitivity analysis evaluated in terms of the RVE objective function

For reason of brevity, the plots for Nash-Sutcliff efficiency and coefficient of determination are not shown here. However, the results are discussed. In terms of Nash- Sutcliff efficiency, the model is most sensitive to constant rate (CR) and slightly sensitive to base flow (BF) model parameters. Therefore, CR and BF affect both simulated hydrograph pattern and volume.

In terms of coefficient of determination (R2) the model is highly sensitive to constant rate (CR) and slightly sensitive to lag time (Tlag), base flow (BF) and initial deficit (ID). The sensitivity of those parameters was summarized in terms of objective function i.e. their effect on volume, pattern and peak (Table 4.1). The peak flow, volume and pattern are affected by

constant rate (CR), and base flow (BF). Lag time, K, X and initial deficit affect the hydrograph pattern.

Table4.1 Summary of sensitive parameters of the model depending on objective criteria Objective criteria parameters Volume Constant rate (CR) and Base flow (BF) Pattern Constant rate(CR), Base flow (BF), Lag time(Tlag), Initial deficit (ID) Peak flow Constant rate(CR), Base flow (BF), parameter X,

4.2 HEC-HMS Model calibration and validation

The simulated and observed hydrographs are shown in figure 4.3 for the calibration period. There is a good agreement between the simulated and observed flows. The observed hydrograph patter is well captured by the model. The rising and recession limbs of the simulated hydrograph are well reproduced though slightly early and late compared to the observed hydrograph. The main limitation of the model is in reproducing peak flows with noticeable overestimation for some years. In terms of NSE, model performance in capturing observed hydrograph patter is good (NSE = 0.65). The REV for the calibration period is 5.1% which suggest that the model has very good performance in estimating observed stream flow volume. The percentage error peak flow is 19%.

Figure4.2 Hydrograph of the observed and simulated flow model calibration (1983-2001)

Table4.2 shows the best estimated model parameters as obtained from manual model calibration

Sub Reach Parameters initial value optimized value basin W40 -- Constant Rate(CR) 1.2 1.88 W50 -- Constant Rate(CR) 1.3 1.94 W80 -- Constant Rate(CR) 1.8 2.3 W90 -- Constant Rate(CR) 1.7 1.94 W40 -- Initial Deficit (ID) 3.2 3.27 W50 Initial Deficit (ID) 2.2 2.26 W80 -- Initial Deficit (ID) 4.2 4.26 W90 -- Initial Deficit (ID) 4.5 4.56 All -- maximum deficit 152 152.1 (MD) -- R20 K 140 140.23 -- R30 K 145 150 -- R20 X 0.15 0.085 -- R30 X 0.21 0.26 W40 -- Lag time (Tlag) 2615 2615 W50 -- Lag time(Tlag) 1107 1107 W80 -- Lag time(Tlag) 1458 1458.5 W90 -- Lag time(Tlag) 1884.9 1885 Note: The time lag is in minute, the initial and maximum deficit is in millimeter, X (-), K in hour and the constant rate is in cm/hr.

The objective function values have slightly deteriorated during validation compared to calibration period (table 4.3). It is common for objective functions values to deteriorate during validation period due to differences in climatic conditions or due to the assumption that land use and land cover are unchanged (Remko et al., 2015). However, the model performance is still acceptable for the validation period and hence the model can be used to evaluate impact of climate change.

Table4.3 Objective function values for calibration and validation period

Objective function Calibration Validation Value Performance value Performance NSE(-) 0.66 good 0.51 satisfactory R2(-) 0.85 Very good 0.85 Very good RVE (%) 5.1 Very good -6.1 good PEPF (%) 19 good 16 good

The calibrated model satisfactory reproduced the pattern and underestimated the peak flow of the observed hydrograph in 2002 and 2003 while it significantly overestimated the peaks in 2004, 2005 and 2008 (Figure 4.4). However, the performance of the model in capturing the pattern of observed flow hydrograph during validation is satisfactory (NSE = 0.51). The model have a good performance in terms of representing the volume of observed flow, (RVE = -6.1%) and peak flow (PEPF= 16%).

Figure4.3 The observed and simulated hydrographs for the validation period (2002 to 2008)

4.3 Data quality, homogeneity & consistency test

The data qualities with regard to possible temporal and spatial variations or errors have been investigated by checking the homogeneity and consistency of each individual station record.

As an example Nekemte station shown in Fig.3.4 below, rainfall recorded over 7 other stations of the study area shows a consistent record as compared with other station.

60000

40000

Rainfall of of Rainfall R² = 0.999 20000

Nekemte (mm) Nekemte 0

Commulative 0 10000 20000 30000 40000 50000 60000 70000 Other Stations commulative rainfall (mm)

Figure4.4 Sample Homogeneity test using double mass curve analysis for Nekemte station

4.4 Evaluation of rainfall estimates from climate models

Annual rainfall over Arjo-didessa catchment from dynamically downscaled climate model simulations captured the annual gauged rainfall for HadGM2-ES, MPI-ESM-LR and ICHEC- EC models even if it is under estimate for rainy months (Figure 4.5). However, their bias is as large as -35 to -47%. Rainfall over the catchment demonstrates large bias for CM5A-MR (-72%) which is the worst model. Also Root Mean Square Errors (RMSE), Coefficient of variation and Correlation coefficient of the simulated rainfall for all models ranges 373- 599mmyear-1, 6-17% and -0.08-0.354 respectively. These values indicate that the simulated rainfall over the catchment have not good agreement with observed rainfall for all climate models (Table 4.4). This means that the simulated rainfall is too biased. Therefore, the bias should be corrected before use it for stream flow simulation.

350 300 HadGM2-ES 250 MPI-ESM-LR 200 ICHEC-EC 150 CM5A-MR 100 ensembel 50 Gauged Rainfall amount (mm) amount Rainfall 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (month) Figure4.5 Rainfall annual cycle over Arjo- didessa catchment from dynamically downscaled climate models simulations and gauged data at monthly base

Table4.4 Performance of dynamically downscaled models simulation in capturing and representing mean annual rainfall over Arjo- didessa catchment over the period (1981-2005).

Annual rainfall Bias (%) CV (%) RMSE Correlation (--) (mm) (mmyear-1) Gauged 1779 -- 7.6 -- -- HadGM2-ES 1320 -34.8 17.2 373 -0.312 MPI-ESM-LR 1209 -47.2 10.7 431 -0.080 ICHEC-EC 1296 -37.3 6.0 334 0.354 CM5A-MR 1037 -71.6 13.2 599 -0.316 Ensemble 1215 -46.4 6.5 412 -0.306

4.5 Historical meteorological and hydrological trend

The annual mean of the catchment averaged rainfall amount is 1779mm and the standard deviation is 165mm in the period 1981 to 2005 (Table 4.4.). According to Mann Kendall trend test, the annual precipitation slightly increased with a change magnitude of 0.101 mm/year.

o o The mean minimum and maximum temperature of the catchment is 20.9 C and 10.4 C with o o standard deviation of 0.30 C and 0.23 C, respectively. Both maximum and minimum

temperature showed statistically significant increasing trend with the annual rate of change of o o 0.595 C and 0.533 C respectively.

The potential evapo-transpiration for 1981 to 2005 period demonstrated statistically significant positive trend with the mean of 3.60mm and deviation of 0.04mm. It increases with the annual rate of 0.147mm.

The annual mean of the catchment averaged stream flow amount is 88.3m3s-1 and the standard deviation is 45.09m3s-1 in the period 1981 to 2005. The annual stream flow showed insignificant increasing trend with annual rate of 0.09m3s-1 over the period 1981 to 2005.

Table4. 5 Statistic of the Mann Kendall trend test for catchment averaged rainfall, evapo- transpiration, and average minimum and maximum temperature over the period (1981-2005).

Variables Kendall‟s tau S P-value Sen‟s slope Trend Trend significance nature

Rainfall 0.101 67 0.171 6.832 Positive Not significance

Tmin 0.533 187 0.0001 0.021 Positive Significance

Tmax 0.595 209 0.0001 0.031 Positive Significance

ETo 0.147 61 0.013 0.001 Positive Significance

Stream flow 0.090 34 0.518 0.374 Positive Not significance Significance level = 0.05 a

2100 b 21.4

1900 C)

o 21.1

1700 20.8

Tmax Tmax ( 20.5 Annual ranfall (mm) ranfall Annual 1500 20.2 1981 1986 1991 1996 2001 1981 1986 1991 1996 2001 Year Year

c d

10.8 165

142 )

C) 10.6

o s

3 119

10.4 (m

Tmin ( Tmin Q 10.2 73 10 50 1981 1986 1991 1996 2001 1981 1986 1991 1996 2001 Year Year

1360 e

) 1345 1330 1315

ETo (mm/year ETo 1300 1285 1980 1985 1990 1995 2000 2005 Year

Figure4.6 Observed climate and hydrological trend for the time period (1981 to 2005), (a) rainfall, (b) maximum temperature trend, (c) minimum temperature trend, (d) stream flow trend and (e) evapo- transpiration 4.6 Future meteorological and hydrological data trend

4.6.1 Historical trend as simulated by climate models

The catchment average areal rainfall has decreasing trend for RCP 4.5 scenario according to the climate models output except those from CM5A-MR model which showed an increasing trend. The trend is not statistically significant for all selected climate model output. According to MK test result, the precipitation was decreased by 0.43, 0.123, 0.159 mm/year for HadGEM2-ES, MPI-ESM-LR, and ICHEC-EC respectively while it increased by 0.283 mm/year for CM5A-MR. The change of observed precipitation for the calibration period (1981-2005) is decreasing by 0.101 mm/year which is nearly similar with the change of precipitation for MPI-ESM-LR (0.123 mm/year) projection model under RCP4.5 scenario.

Table4. 6 Mann- Kendall trend test statistics result of catchment averaged precipitation under RCP4.5 scenario of the selected models over the period (1981-2005).

Climate model Kendall‟s S P-value Sen‟s Trend Trend significance tau slope nature

HadGEM2-ES -0.43 -12 0.788 -1.201 Negative Not significance

CM5A-MR 0.283 78 0.056 11.2 Positive Not significance

MPI-ESM-LR -0.123 -34 0.418 -3.33 Negative Not significance

ICHEC-EC -0.159 -44 0.29 -2.976 Negative Not significance The significance level is 0.05.

The catchment average maximum and minimum temperature over the period 1981 to 2005 for all climate models output have positive Kendall‟s (S) values, which indicate that both variables have increasing trend. The value of test statistic (P-value) is less than the significance level( ) of 5% for all climate models (Table 4.7 & 4.8). This implies that a significance positive trend is demonstrated by maximum and minimum temperature under RCP4.5 scenario. The average maximum temperature of the catchment is increased by 0.42, 0.41, 0.36 and 0.39oc/year for HadGM2-ES, CM5A-MR, MPI-ESM-LR and ICHEC-EC respectively under RCP4.5 projection scenario. For the same projection scenario, average minimum temperature is increased by 0.43, 0.49, 0.44 and 0.38oc/year for HadGM2-ES, CM5A-MR, MPI-ESM-LR and ICHEC-EC respectively.

Table4.7 Mann- Kendall trend test statistics result of average maximum temperature under RCP4.5 scenario of the selected models over the period (1981-2005).

Climate model Kendall‟s S P-value Sen‟s Trend Trend significance tau slope nature

HadGEM2-ES 0.420 116 0.004 0.02 Positive Significance

CM5A-MR 0.410 115 0.014 0.025 Positive Significance

MPI-ESM-LR 0.362 100 0.013 0.028 Positive Significance

ICHEC-EC 0.386 94 0.023 0.013 Positive Significance The significance level is 0.05.

Table4.8 Mann- Kendall trend test statistics result of average minimum temperature under RCP4.5 scenario of the selected models over the period (1981-2005).

Climate model Kendall‟s S P-value Sen‟s Trend Trend significance tau slope nature

HadGEM2-ES 0.428 118 0.003 0.021 Positive Significance

CM5A-MR 0.486 134 0.001 0.02 Positive Significance

MPI-ESM-LR 0.437 117 0.002 0.011 Positive Significance

ICHEC-EC 0.384 106 0.008 0.015 Positive Significance The significance level is 0.05. The catchment average evapo-transpiration has increasing trend over the period 1981 to 2005 for RCP 4.5 scenario according to the climate models output. It has the MK statistic (P- value) of 0.192, 0.007, 0.007, and 0.044 for HadGM2-ES, CM5A-MR, MPI-ESM-LR and ICHEC-EC models respectively which are less than the significance level of 5% except for HadGM2-ES model. This implies that the average evapo-transpiration demonstrates a significant trend for CM5A-MR, MPI-ESM-LR and ICHEC-EC while it demonstrates an insignificant trend for HadGM2-ES model output. The average evapo-transpiration from the

analysis of climate models increased by 0.2, 0.39, 0.40 and 0.12 mm/year for HadGM2-ES, CM5A-MR, MPI-ESM-LR and ICHEC-EC models output respectively.

Table4.9 Mann- Kendall trend test statistics result of average evapo-transpiration under RCP4.5 scenario of the selected models over the period (1981-2005).

Climate model Kendall‟s S P-value Sen‟s Trend Trend significance tau slope nature

HadGEM2-ES 0.428 118 0.003 0.021 Positive Significance

CM5A-MR 0.486 134 0.001 0.02 Positive Significance

MPI-ESM-LR 0.437 117 0.002 0.011 Positive Significance

ICHEC-EC 0.384 106 0.008 0.015 Positive Significance The significance level is 0.05.

According to climate models output analysis, the Arjo-didessa catchment average annual flow showed a decreasing trend over the period 1981 to 2005 under RCP4.5 scenario for all climate models. But the trend is not significant for all climate models. The average annual flow is decreased by 0.01, 0.1, 0.18 and 0.2 for HadGM2-ES, MPI-ESM-LR, CM5A-MR and ICHEC-EC climate models respectively.

Table4. 10 Mann- Kendall trend test statistics result of average annual flow under RCP4.5 scenario of the selected models over the period (1981-2005).

Climate model Kendall‟s S P-value Sen‟s Trend Trend significance tau slope nature

HadGEM2-ES 0.428 118 0.003 0.021 Positive Significance

CM5A-MR 0.486 134 0.001 0.02 Positive Significance

MPI-ESM-LR 0.437 117 0.002 0.011 Positive Significance

ICHEC-EC 0.384 106 0.008 0.015 Positive Significance The significance level is 0.05.

4.7 Climate change impact

4.7.1 Impact on future rainfall, temperature and evapo- transpiration

The magnitude of averaged annual rainfall at 2041s (2041-2070) will decrease by 0.36% for ICHEC-EC and 16% for MPI-ESM-LR and HadGM2-ES while it will increase by 2% for CM5A-MR under RCP4.5 scenario compared with the base line period.

Future average annual rainfall in the middle time horizon (2041 to 2070) under RCP8.5 scenario will decrease in magnitude by 21%, 0.5% and 20% for MPI-ESM-LR, CM5A-MR and HadGM2-ES models respectively whereas it will increase by 6% for ICHEC-EC model compared with the baseline period (Figure 4.7).

10 RCP4.5 6 RCP8.5

2 -2 -6 -10

Rainfall change (%) change Rainfall -14 -18 -22 CM5A-MR ICHEC-EC MPI-ESM-LR HadGM2-ES Climate models

Figure4.7 Annual catchment rainfall changes over the period (2041-2070)

The annual mean maximum temperature over the Arjo-Didessa catchment for the time period of 2041 to 2070 increased in magnitude by 1.195 , 1.39 , 1.17 and 1.303 for HadGM2-ES, CM5A-MR, ICHEC-EC and MPI-ESM-LR models under intermediate emission RCP4.5 scenario respectively. For the same time period, the annual mean maximum temperature increased by 1.46 , 3.16 , 1.88 and 1.36 for HadGM2-ES, MPI-ESM- LR, CM5A-MR and ICHEC-EC under high emission RCP8.5 scenario respectively compared with base line period (Figure 4.8).

The annual mean minimum temperature in the period 2041- 2070 will increase by 1.08 , 0.98 , 1.2 and 1.24 for HadGM2-ES, CM5A-MR, ICHEC-EC and MPI-ESM-LR models respectively under RCP4.5 scenario whereas it increase by 1.458 , 1.379 1.23 , 1.33 for HadGM2-ES, MPI-ESM-LR, CM5A-MR and ICHEC-EC models under RCP8.5 scenario respectively compared with the baseline period (Figure 4.8).

3.5 CM5A-MR

3 C)

o ICHEC-EC 2.5 HadGM2-ES 2 MPI-ESM-LR 1.5

Temperature change ( change Temperature 0.5

0 Tmax Tmin Tmax Tmin RCP4.5 RCP8.5 Figure4.8 Average maximum and minimum temperature change over the period (2041-2070) The annual average catchment evapo-transpiration at 2041s will increased by 4%, for HadGM2-ES and MPI whereas it will increase by 5% and 3% for CM5A-ESM-LR and ICHEC-EC models respectively under RCP4.5 scenario. Similarly the potential evapo- transpiration will increases by 4% for HadGM2-ES, ICHEC-EC and MPI-ESM-LR while it will increase by 7% for CM5A-ESM-LR models output under RCP8.5 scenario compared with the base line period (Figure 4.9).

7.8 RCP4.5 6.5 RCP8.5

(%) 5.2

3.9 change

2.6 ETo 1.3

0 HadGM2-ES MPI-ESM-LR CM5A-MR ICHEC-EC Climate models

Figure4.9 Annual average evapo-transpiration changes over the period (2041-2070)

4.7.2 Impact on future mean monthly flow

As showed in (Figure 4.10), the direction of monthly stream flow change show decrease agreement for all climate models in Feb, Oct and Nov except for HadGM2-ES model in Oct and Nov. However, it shows an increasing agreement for all climate models in May, Jun and July except for MPI-ESM-LR model. Under RCP4.5 scenario, the monthly stream flows will likely decrease in magnitude up to 0.7% for all climate models in Feb whereas it will increase up to 27% in May, Jun and July except for MPI-ESM-LR model which will decrease in between 1% to 17% over the period (2041-2070). For the same scenario, the simulated monthly stream flow decrease up to 3% will expect for all climate models except for HadGM2-ES which will increase up to 8%. In the remaining months i.e. Mar, Apr, Aug, and Sep the monthly stream flow will likely increase up to 11% for all climate models except for MPI-ESM-LR in Mar and Apr and ICHEC-EC in Aug and Sep.

30 HadGM2-ES

25 MPI-ESM-LR 20 ICHEC-EC 15 CM5A-MR 10 5 0 -5

Change in stream flow (%) flow stream in Change -10

-15 -20 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (month) Figure4.10 HEC-HMS simulated monthly change in stream flow of Arjo-Didessa catchment for the medium future (2041-2070) under RCP4.5 scenario. Monthly stream flow change over the medium period (2041-2070) show a significant increase in May, Jun, Jul and Aug for all climate models except for MPI-ESM-LR model in May while it show a significant decrease in Oct, Nov and Dec for all climate models under RCP8.5 scenario (Figure 4.11). The percentage increase change of runoff in magnitude up to 4%, 21%, 20%, and 15% in May, Jun, Jul and Aug will be expected for all climate models

53 exclude MPI-ESM-LR in May under RCP8.5 compared with base period. However, it show a decrease change in stream flow in magnitude up to 6%, 4% and 2% in Oct, Nov and Dec for all climate models. In the remaining months the change is insignificant that is below 1%.

25 HadGM2-ES

20 MPI-ESM-LR 15 ICHEC-EC 10 CM5A-MR

0 Change in stream flow (%) flow stream in Change -5

-10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure4.11 Change in stream flow of Arjo-Didessa catchment for the medium future (2041- 2070) under RCP8.5 scenario.

4.7.3 Impact on seasonal and annual mean flow

Reported direction of annual stream flow changes show agreement for HadGM2-ES, MPI- ESM-LR and ICHEC-EC while reported direction of seasonal stream flow change in Bega, Kiremt and Belg seasons show agreement for all climate models except MPI-ESM-LR in Kiremt. Under RCP4.5 scenario, annual runoff will likely decrease in magnitude up to 1% to 3% for HadGM2-ES, MPI-ESM-LR and ICHEC-EC while it will likely increase in magnitude by 2% for CM5A-MR climate model. Seasonal flow increases by 5% in magnitude in rainy season called “Kiremt”. In both Bega (dry) and Belg (small rain) seasons, the stream flow will likely decrease by larger than 2% and 5%, respectively depending on the type of climate model.

6 HadGM2-ES

4 MPI-ESM-LR

ICHEC-EC 2 CM5A-MR

-2

Change in flow (%) flow in Change

-4

-6 Kiremt Belg Bega Annual

Figure4.12 Change in seasonal and annual stream flow the medium future (2041-2070) under RCP4.5 scenario.

As indicated in (Figure 4.13), the average seasonal and annual flow change under RCP8.5 show an agreement in change direction for all climate models except for ICHEC-EC in Belg (small rain) season and annual flow change. Strangely the other climate models, the annual flow change for ICHEC-EC climate model show an increase in magnitude by 9% while seasonal flow change increase by 11% in Belg (small rain) season. However the annual flow will likely decrease in magnitude up to 3% whereas seasonal runoff will likely decrease by 3% in Bega (dry) and Belg (small rain) season for the remaining three climate models. In the rainy season (Kiremt), runoff over the catchment will increase up to 18% for all climate models. The magnitude is varies depending on the type of climate model.

20 HadGM2-ES

15 MPI-ESM-LR ICHEC-EC CM5A-MR 10

change in flow (%) inflow change

-5 Kiremt Belg(meher) Bega Annual

Figure4.13 Change in seasonal and annual stream flow for the medium future (2041-2070) under RCP8.5 scenario.

5. CONCLUSION AND RECOMMENDATION

5.1 Conclusion

In this study, the impact of climate change on stream flow was evaluated for Arjo-Didessa catchment for 2041 to 2070 under RCP4.5 and RCP8.5 scenarios. HEC-HMS model was used to simulate historical and future runoff using climate data obtained from HadGM2-ES, MPI-ESM-LR, CM5A-MR, and ICHEC-EC GCM models. The data from these models was downscaled dynamically by CORDEX using CCLM, RCA4 and RACMO22T RCMs.

Results indicate that the sensitivity of the HEC-HMS model to its parameters was dependent on the type of objective functions used. The simulated hydrograph pattern is mostly controlled by constant rate, base flow, lag time and initial deficit while stream flow volume and peak flow are mainly controlled by constant rate and base flow.

For the calibration period the model well captured the stream flow hydrograph pattern (NSE=0.65) with some overestimation for some years. The observed hydrograph volume was also well reproduced (RVE=5.1%). Hence, the HEC-HMS model is an acceptable rainfall- runoff model to evaluate climate change impact on Arjo- didessa catchment.

Pattern of monthly rainfall from dynamically downscaled climate data resembles with the gauged rainfall for all selected climate models for Belg and Dry season except for CM5A- MR model. However, the models did not captured the observed rainfall (Bias= -34 to -72%). Performance of CM5A-MR was the worst (Bias= 72%). There is also weak trend relation between the observed and simulated rainfall (Correlation= -0.08 to -0.354).

Annual rainfall in 2041 to 2070 will decreased by up to 0.36% to 16% for ICHEC-EC, MPI- ESM-LR and HadGM2-ES while it increased by 2% for CM5A-MR under RCP4.5. However, it will decreased in magnitude by 20% and 21%, for MPI-ESM-LR, and HadGM2- ES whereas it increased by 6% for ICHEC-EC model output under RCP8.5 scenario.

The annual average catchment evapo-transpiration will increase by 3% to 5% under RCP4.5 while it will increases by 4% to 7% under RCP8.5 scenario. Mean maximum temperature over the Arjo-Didessa catchment in medium future time period will increase in magnitude by

1.17 to 1.39 under RCP4.5 while it will increase by 1.36 to 3.16 under RCP8.5 scenario. The mean minimum temperature will increase by ranges from 0.98oc to 1.24 under RCP4.5 scenario whereas it increases by ranges from 1.23 to 1.5 under RCP8.5 scenario compared with the baseline period.

Significant increase in stream flow up to27% is observed in May, Jun and Jul for all climate model simulation except for MPI-ESM-LR under RCP4.5 scenario. However, it will increases up to 20% in May, Jun, Jul and Aug except for MPI-ESM-LR in May under RCP8.5 scenario.

Seasonal flow in Belg (small rain) and Bega (Dry season) will decrease by 2% to 5% under RCP4.5 while it increase in Kiremt (rainy season) by 3% to 5% under RCP8.5 for all climate models except for MPI-ESM-LR. Annual stream flow with respect to different scenario there is no significant difference in magnitude and direction of change. However, with respect to model type, there is a significant change in stream flow in magnitude and direction for ICHEC-EC and CM5A-MR for RCP8.5 and RCP4.5 scenarios. For ICHEC-EC stream flow will increase by 9% while it increases by 2% for CM5A-MR. However, for the other climate models the Annual stream flow volume will likely decrease by 1% to 3% under both scenarios. Generally the annual and seasonal stream flow volume change varies with the type of climate model used for both climate scenarios.

5.2 Recommendation

Generally from this specific study the following two main pointes are strongly recommended;

 Hydrological and climate model uncertainty should be further evaluated using the state of the art of uncertainty estimation methods and for additional GCM-RCM combinations. Also the bias correction method and temperature based future ETo estimation method uncertainty should be further examined. Hence, the results of this study should be taken with care and be considered as indicative of the likely future rather than accurate predictions.

 The model simulation consider only the climate variable by assuming value all other factors keep constant. But change in land use and soil management activities can impact on rainfall runoff processes. Therefore, it is recommended for future studies to consider land use land cover change and effect of soil type.

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APPENDICES

Appendix A: Used equations for missing data estimation

A.1: Thiessen polygon method

Where n is number of neighboring stations, Nx is normal annual precipitation of the target stations; A1,A2,A3,….An are the areas of respective Thiessen- polygons P1,P2,P3,...Pn are daily precipitation of respective neighboring stations and N1,N2…and Nn are annual total precipitations of respective neighboring stations. A.2: Distance power method

∑

∑ ( ) ( )

Where: Di is the distance of the estimator station from the estimated station. PA is missing value at station. X and Y are the coordinates of the station whose data is estimated and Xi and Yi are the coordinates of stations whose data are used in estimation.

Appendix-B: Maximum and Minimum temperature

Table B.1 Monthly Average Maximum and Minimum Temperatures of Arjo-Didessa Catchment over a period of 1981-2008

Arjo-Didessa Catchment North -Eastern part South-Western part South-Eastern part

Average Average Average Average Average Average minimum Maximum minimum Maximum minimum Maximum Month temperature temperature temperature temperature temperature temperature (oC) (oC) (oC) (oC) (oC) (oC) Jan 11.39 23.89 11.87 24.69 10.51 28.57 Feb 12.32 25.18 12.48 25.97 11.41 29.73 Mar 13.07 25.56 12.86 26.18 12.92 29.72 Apr 13.13 24.84 12.98 25.78 13.92 28.85 May 12.61 23.30 12.69 24.15 14.07 27.94 Jun 11.57 20.77 12.14 22.05 13.81 26.00 Jul 11.33 19.34 11.98 20.61 13.84 24.32 Aug 11.49 19.32 12.08 21.06 13.97 24.60 Sep 11.60 20.60 11.96 22.28 13.63 25.51 Oct 11.82 22.10 11.89 22.90 12.27 26.65 Nov 11.84 22.67 11.89 23.49 10.47 27.44 Dec 11.30 23.14 11.68 24.13 9.71 27.93

Appendix C: Rainfall and Potential Evapo- transpiration distribution over Arjo-Didessa catchment

Figure C.1 Rainfall distribution over Arjo-Didessa catcment (1981-2008)

400 300 200 100

Total monthly monthly Total 0

areal rainfall(mm) areal Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

Figure C.2 Total Areal Rainfall for Arjo-Didessa Catchment (1981-2008) Table C.1 Future potential evapo-transpiration for middle time horizon under different scenario in mm/month (2041s)

RCP4.5 Model Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec CM5A-MR 107.2 101.0 116.6 118.9 125.7 118.4 110.4 104.9 99.4 109.1 105.0 106.8 ICHEC-EC 118.0 108.3 123.1 116.4 113.8 102.3 104.9 107.0 104.8 118.0 119.5 121.4 MPI-ESM-LR 117.7 110.4 125.4 122.8 120.3 109.0 108.6 109.8 110.3 121.6 119.7 118.7 HadGM2-ES 151.3 144.0 167.4 162.9 156.3 143.9 136.9 135.6 137.9 151.0 152.9 152.6

RCP8.5 Model Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec CM5A-MR 105.0 98.5 113.6 115.7 122.2 115.4 110.2 104.5 99.3 107.3 104.1 104.9 ICHEC-EC 117.6 108.1 122.9 117.3 114.6 104.0 107.0 108.1 106.0 117.7 118.6 120.7 HadGM2-ES 113.4 106.6 123.6 121.5 119.3 111.6 108.2 107.0 107.9 115.4 114.1 114.7 MPI-ESM-LR 127.3 119.1 135.4 133.6 132.4 121.7 121.1 121.7 121.9 132.8 128.9 128.8

Appendix D: HEC-HMS initial model parameter

Figure D.1 Daily discharge and areal rainfall of Arjo- Didessa catchment (1983 to 2008)

Appendix E: HEC-HMS sensitivity analysis result.

2 a

0 0.25 0.5 0.75 1 1.25 1.5 1.75 2

Nash -2 BF CR MD K X Tlag ID -4 Parameter change (%)

1 b 0.995

R2 0.99 BF CR MD K X Tlag ID 0.985 0.25 0.5 0.75 1 1.25 1.5 1.75 2 Parameter change (%) 600 c

CR(+5%) 400 CR(-5% )

200 referance flow Q (m3/s) Q

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (month) 500 BF(+5%)

) 1

- 300 BF(‐5%)

s 3 - referance flow

100 Q (m Q

-100 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (month)

Figure E.1 HEC-HMS sensitivity result, (a) Nash Sutcliffe (b) coefficient of determination (c) flow Vs constant rate factor changing by 5% (d) flow Vs base flow changing by 5%.

Appendix- F, Bias correction factors for rainfall at sub- catchment level

Table F.1 Bias correction factor for HadGM2-ES climate model for RCP 4.5 scenario

Outlet Wama Middle Upper

a b a b a b a b

Jan 8.31350 0.10728 14.70341 0.78721 7.29465 0.34652 10.10996 0.06412

Feb 6.97752 0.28931 5.83433 0.19741 11.39547 0.22397 8.27952 0.11561

Mar 13.62858 0.19635 8.76275 0.24773 5.91199 0.43099 7.04131 0.38097

Apr 2.27420 0.73968 2.25373 0.69570 1.18580 0.84887 1.72785 0.77178

May 3.43635 0.66632 2.75853 0.64402 1.88450 0.71423 2.40186 0.74041

Jun 3.82496 0.75616 3.52627 0.65895 2.37874 0.78828 4.54058 0.60402

Jul 2.11746 0.87649 2.34291 0.78848 1.38363 0.87649 2.39464 0.76621

Aug 1.82311 0.93373 1.85043 0.88152 1.07314 0.92407 1.45258 0.95285

Sep 1.82152 0.89207 1.32827 0.94328 0.92474 0.95851 1.32320 0.96189

Oct 1.61018 0.95466 1.17508 0.98292 0.85707 0.99780 1.37628 0.88204

Nov 3.19674 0.54921 3.53436 0.37067 1.50138 0.78946 2.37621 0.76023

Dec 13.25851 0.18473 9.62349 0.15906 11.05283 0.39531 3.99976 0.86310 Note: Outlet, Wama, Middle and Upper are sub-catchments of Arjo-Didessa catchment.

Table F.2 Bias correction factors for HadGM2-ES climate model for RCP 8.5 scenario

Outlet Wama Middle Upper

a b a b a b a b

Jan 9.30624 0.05559 4.512297 0.026854 8.957567 0.064785 9.843806 0.067412

Feb 7.707119 0.064576 6.155257 0.139929 13.13484 0.141281 7.879206 0.026162

Mar 11.22473 0.304437 8.762909 0.247731 7.448287 0.430989 7.04132 0.380972

Apr 2.274199 0.739679 2.253728 0.695704 1.868992 0.848871 1.727862 0.771778

May 3.436345 0.666319 2.758525 0.644027 2.763429 0.714227 2.40188 0.740409

Jun 3.824965 0.756159 3.526286 0.658954 3.629425 0.788276 4.540597 0.604023

Jul 2.109405 0.877692 2.335487 0.789464 2.188328 0.881435 2.386185 0.767358

Aug 1.823109 0.93373 1.850417 0.881523 1.760984 0.924071 1.452592 0.952843

Sep 1.821517 0.892072 1.328312 0.943261 1.545731 0.958506 1.323212 0.961891

Oct 1.610182 0.954663 1.1751 0.982913 1.46312 0.9978 1.37629 0.882042

Nov 3.196737 0.549211 3.534348 0.370669 2.292227 0.789464 2.376233 0.760228

Dec 13.25851 0.184726 9.623512 0.159056 13.66131 0.395315 9.914993 0.46363

Table F.3 Bias correction factors for MPI-ESM-LR climate model for RCP 4.5 scenario

Outlet Wama Middle Upper a b a b a b a b

January 5.95545868 0.46331 4.760264407 0.18616 4.61355 0.551594 7.322793 0.420782

February 8.04907615 0.249021 6.403205417 0.281105 7.512437 0.444299 8.010818 0.288092

March 7.23792509 0.451276 4.704559964 0.480851 3.889318 0.585998 4.022193 0.545027

April 3.02289453 0.677396 2.297786129 0.721791 1.965016 0.810479 2.054159 0.757441

May 2.56700213 0.742004 2.297372789 0.675894 1.775525 0.900138 1.871117 0.849898

June 1.84224615 0.947146 2.329763289 0.760628 1.410536 1.079222 1.906603 0.877445

July 1.8361916 0.902629 2.733049425 0.716605 1.924858 0.906588 1.948272 0.861569

August 2.88918359 0.760416 3.271571545 0.690433 2.629323 0.799407 2.667375 0.785735

September 2.06528499 0.837267 2.012641351 0.759883 1.788326 0.857105 1.630971 0.891533

October 2.05015191 0.836949 1.937222031 0.755977 1.745438 0.889965 1.586355 0.861289

November 3.11573657 0.542686 3.390067418 0.380584 1.278419 0.898522 2.602418 0.666315

December 4.89526689 0.639692 3.588092605 0.559376 2.170898 0.927586 3.310899 0.724706

Table F.4 Bias correction factors for MPI-ESM-LR climate model for RCP 8.5 scenario

Outlet Wama Middle Upper

a b a b a b a b

Jan 5.955962 0.4636302 5.20972 0.164521 6.357217 0.443264 7.322793 0.420782

Feb 8.049075 0.2490209 6.855083 0.217423 8.920167 0.364287 8.010819 0.288092

Mar 7.237925 0.451276 5.598123 0.447625 4.992428 0.544217 4.385479 0.545027

Apr 3.022896 0.6773956 2.316858 0.702023 2.409831 0.83061 2.054161 0.75744

May 2.567001 0.7420038 1.96273 0.723762 1.985898 0.875463 1.871117 0.849898

Jun 1.842245 0.9471467 1.730197 0.851148 1.734463 0.969628 1.906604 0.877444

Jul 1.83619 0.9026289 2.08025 0.789115 1.878645 0.914881 1.948273 0.861569

Aug 2.889182 0.7604162 2.642395 0.734168 2.78279 0.781275 2.667371 0.785736

Sep 2.065283 0.8372673 1.834665 0.787446 2.333833 0.760228 1.630972 0.891533

Oct 2.050149 0.8369497 1.866683 0.756886 2.395957 0.80107 1.586354 0.86129

Nov 3.115737 0.5426864 3.707239 0.330432 1.682007 0.977824 2.602418 0.666315

Dec 4.895266 0.6396924 4.21942 0.537674 3.929262 0.799941 3.310898 0.724706

Table F.5 Bias correction factors for ICHEC-EC climate model for RCP 4.5 scenario

Outlet Wama Middle Upper a b a b a b a b

Jan 9.969022 1.017758 6.604324 0.82510104 2.821277 1.469236 3.794942 1.186686

Feb 1.771029 0.905632 1.902729 0.82794592 1.310607 1.184262 1.860286 0.996398

Mar 2.583739 0.841864 2.402419 0.77708996 1.306848 1.122702 1.496767 1.036962

Apr 0.031248 2.408835 0.080885 2.02009218 0.038213 2.48262 0.04592 2.367927

May 0.034004 2.401626 0.105453 1.90840088 0.042396 2.506573 0.059207 2.407259

Jun 0.852144 1.356086 0.705714 1.3246884 0.566404 1.600275 0.852144 1.356086

Jul 2.143047 0.873037 3.054885 0.73678182 2.318861 0.932085 2.434769 0.864601

Aug 2.301869 0.858904 2.830614 0.78019268 2.399672 0.904697 2.435081 0.90745

Sep 0.299244 1.547335 0.479576 1.33846749 0.436115 1.557547 0.545008 1.449692

Oct 0.077978 2.0631 0.147649 1.81810716 0.307623 1.762963 0.348873 1.655896

Nov 4.35 0.85 3.279417 0.65265904 3.354611 0.997626 4.350783 0.845745

Dec 5.66947 0.781486 4.606192 0.81738228 3.3756 1.179286 3.899062 1.082993

Table F.6 Bias correction factors for ICHEC-EC climate model for RCP 8.5 scenario

Outlet Wama Middle Upper

a b a b a b a b

Jan 8.970512 1.0991 4.866603 0.673453 7.734564 1.075249 3.794942 1.186686

Feb 1.878027 0.861675 2.109505 0.749194 2.832456 0.891577 1.860287 0.996398

Mar 2.578589 0.838834 2.544713 0.721101 2.715367 0.838208 1.496766 1.036963

Apr 0.038288 2.327942 0.041943 2.228532 0.079591 2.192224 0.04592 2.367926

May 0.035506 2.435835 0.034277 2.27378 0.050862 2.398775 0.059207 2.407257

Jun 0.687479 1.406119 0.530883 1.344381 0.502586 1.597255 0.852144 1.356086

Jul 2.775744 0.804177 2.281804 0.804046 2.429319 0.89469 2.434768 0.864601

Aug 3.131 0.767822 2.024743 0.862824 2.478965 0.878673 2.435081 0.90745

Sep 0.442123 1.439672 0.228472 1.559256 0.417352 1.528719 0.545006 1.449694

Oct 0.067835 2.139013 0.06585 2.051858 0.306142 1.69461 0.348873 1.655896

Nov 1.798131 0.961913 2.250318 0.749745 4.881579 0.819877 4.350783 0.845745

Dec 5.435382 0.804955 4.65574 0.699748 6.174708 0.967532 3.899062 1.082993

Table F.7 Bias correction factors for CM5A-MR climate model for RCP 4.5 scenario

Outlet Wama Middle Upper

a b a b a b a b

Jan 7.786991 0.381527 7.809137 0.329996 7.809137 0.329996 7.831282 0.278464

Feb 6.551627 0.343869 6.564015 0.328356 6.564015 0.328356 6.576403 0.312844

Mar 9.561291 0.426945 9.904014 0.30236 9.904014 0.30236 10.24674 0.177776

Apr 8.523416 0.378875 10.70656 0.362606 13.66255 0.201647 12.88971 0.093646

May 1.38795 0.989726 6.240547 0.56536 16.54676 0.121065 7.29641 0.392012

Jun 6.694286 0.490311 7.784438 0.248418 2.353081 0.987054 0.715428 1.140341

Jul 0.309646 1.510887 0.928389 1.142578 0.502888 1.506463 0.112765 1.711788

Aug 0.481908 1.271646 0.665145 1.186215 0.792581 1.203172 0.206471 1.464952

Sep 4.301247 0.526196 5.035127 0.410074 3.013871 0.755733 0.723689 1.084091

Oct 2.998338 0.582702 3.724195 0.765763 16.41393 0.166551 8.757203 0.337268

Nov 0.884122 0.838555 1.526544 1.040895 4.068285 0.666912 6.220488 0.429255

Dec 6.123101 0.450132 0.355985 1.258644 12.1009 0.369895 7.818795 0.341633

Table F.8 Bias correction factors for CM5A-MR climate model for RCP 8.5 scenario

Outlet Wama middle upper a b a b a b a b Jan 6.721571 0.352581 3.964869 0.257849 8.050041 0.168907 7.050334 0.343603 Feb 6.930789 0.057161 5.842803 0.222449 9.040255 0.204574 7.467725 0.082709 Mar 11.4078 0.157057 7.223336 0.329426 11.49533 0.113014 9.291752 0.232992 Apr 13.76724 0.038142 12.21716 0.070618 16.2362 0.073988 12.83934 0.135627 May 13.65612 0.091945 10.86621 0.119685 16.62973 0.111985 14.80639 0.049453 Jun 14.89657 0.14401 13.68747 0.047274 6.87249 0.575042 2.332738 0.763539 Jul 0.685217 1.281505 2.069963 0.898516 0.600255 1.477033 0.117966 1.71559 Aug 0.32922 1.388499 0.489139 1.277916 0.724347 1.228995 0.20128 1.470862 Sep 0.714728 1.141395 0.838035 1.069188 0.802892 1.2284 0.156715 1.545421 Oct 14.01777 0.114738 11.02374 0.194759 10.73173 0.400369 3.142439 0.693281 Nov 7.515987 0.167735 5.894819 0.026788 8.536976 0.372378 4.080824 0.542548

Dec 9.878075 0.29968 8.13089 0.192171 12.43813 0.284304 10.50474 0.167704

Appendix G. Total Missed Value

Table G. 1 Total missed value in percent for each station and each meteorological element in the station (1981-2008)

Total missed in% Station Maximum Minimum Wind Relative Sunshine Rainfall Name Temperature Temperature speed(u2 ) humidity(RH) hour(hr) Bedele 17 25 27 52 87 96 Gatira 16 61 61 68 83 94 Limu genet 15 53 53 50 94 95 Arjo 12 33 36 33 48 73 Jimma 5 5 5 30 5 11 Nekemte 5 9 8 93 19 32

Appendix F. Trend graphs and Tables

HadGM2-ES MPI-ESM-LR

2200 2250 2050 2100 1900 1950 1750 1800 1600

1450 1650

annual annual rainfall (mm) annual rainfall (mm) rainfall annual 1300 1500 1980 1990 2000 1980 1990 2000 year Year

CM5A-MR

ICHEC-EC

2200

) 1800 2100 1650 2000 1500 1900 1350 1800 1200

1700 1050

annual rainnfall (mm) rainnfall annual annual rainfall (mm rainfall annual 1600 900 1980 1990 2000 1980 1990 2000 Year Year

Figure G.1 Historical rainfall trend for the selected climate models for the time period (1981 to 2005) under RCP4.5 scenario.