Climate change impact assessment on water resources of Gumara watershed, upper Blue Nile River basin

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1 Internatonal Journal of Agrcultural Polcy and Research Vol.1 (5), pp , July 2013 Avalable onlne at Journal Issues Orgnal Research Paper Clmate change mpact assessment on water resources of Gumara watershed, upper Blue Nle Rver basn Accepted 24 June, 2013 Lemma Hanbal Department of Water Resources and Irrgaton Management, Bahr Dar Unversty P.O.Box 79 Bahr Dar, Ethopa. Correspondng author Emal: Tel: Recently, there s growng scentfc evdence that the global clmate has changed, s changng and wll contnue to change. Clmate change s also expected to aggravate current stresses on avalablty of water resources, due to rapd populaton growth and economc development. Consequently, the government has proposed to construct a dam and dverson wer on Gumara Rver to rrgate 14,100ha of land. However, the major uncertanty n water resources development s the varablty of water supply and demand pertanng to changes n clmate and n rver basn dynamcs. Therefore, water supply potental of a rver basn s senstve to clmate change and land use. The am of ths study s to assess the potental mpact of clmate change on water resources of Gumara watershed usng Relablty, Reslence and Vulnerablty (RRV) ndces. Generally, projected temperature shows ncreasng trend for the next century for all scenaros. However, precptaton shows decreasng trend for A2a and B2a scenaros and an ncreasng trend for the RegCM3-A1b scenaro. It s also observed that Relablty and Reslence are above 91% and 96%, respectvely; and Vulnerablty reveals less than 30% for all clmate scenaros. Hence, t s concluded that the proposed rrgaton project has hgher capablty to meet the requred target demand. Besdes, decson makers and water users n the area can be assured that the project has very good potental to rrgate the requred area under 2030s and 2090s clmatc condtons. Key words: Change, gumara watershed, water demand, downscalng, HEC-HMS INTRODUCTION Varous envronmental researches have contnued to reveal changes n global clmate (NRC, 1998). The latest Assessment Report (AR4) of the Intergovernmental Panel on Clmate Change (IPCC, 2007) projected that global average temperatures n 2100 wll be C hgher than the average. Sea levels are projected to rse m by In Ethopa, the trend analyss of annual ranfall over the last 50 years, shows that a declnng trend has been observed over the Northern half and South- Western Ethopa. It also reveals that there has been a warmng trend n temperature. The average annual mnmum temperature over the country has been ncreasng by about 0.25 o C every ten years whle average annual maxmum temperature ncreases at about 0.1 o C every decade. It s nterestng to note that the average annual mnmum temperature s ncreasng faster than the average annual maxmum temperature (MoWR and NMSA, 2001).A major uncertanty n water resources management s the varablty of water supply and demand pertanng to changes n clmatc varables, and n dynamcs of rver basns. The water supply potental of a rver basn s therefore, senstve to land use and clmate changes. Rapd populaton growth and economc development are also expected to put extra pressure on demand for water (Solomon, 2002). Hence, n many rver basns, steady clmatc (statonary) condtons are no longer consdered a vald assumpton for sustanable water resources management. Despte ts sgnfcant computatonal effort, water resources studes at the rver basn level are ncreasngly lnked to regonal clmate studes. In addton to natural varablty, whch s ncorporated n exstng water plannng methods, new water projects wll have to deal wth uncertanty assocated wth populaton growth and trends n clmate change (Mohamed et al. 2005).

2 Int.J.Agrc.Polcy Res. 143 countres. Clmate change can affect multple features of water resources, for nstance, quantty and qualty, hgh and low-flow extremes, tmng of events, among others (Km et al, 2008). Hence, assessng vulnerablty of water resources to clmate change at a watershed level s crucal. Ths offers an opportunty to plan approprate adaptaton measures taken ahead of tme, and consderaton gven to possble future rsks n all phases of water resource development projects. The man objectve of ths study s to evaluate the mpacts of future clmate change on both hydrologc regme and water resources of the Gumara watershed. Ths study used a statstcal downscalng technque to downscale HadCM3, Dynamcal downscaled RegCM3 outputs and a HEC-HMS hydrologcal model to smulate the possble mpacts of clmate change on the Gumara watershed. Ths technque provdes a valuable tool for future water resources management f clmate trends, both observed and modelled, can be translated nto hydrologcal mpact. MATERIALS AND METHODS Area Descrpton he study area Fgure 1: Locaton of the study area Despte ts economc mportance to the natonal economy, and for the survval of the people, the desgn study of the Gumara rrgaton project does not consder the future clmate mpact on the proposed rrgaton scheme. Although, clmate change s expected to have adverse mpacts on soco-economc development globally, the degree of the mpact wll vary across natons. It s expected that clmate change mpacts are gong to be most severe n a developng naton lke Ethopa, because of ts poor capacty to adapt to clmate varablty (Gosan et al. 2006). Besdes, a large part of Ethopa s ard and semard, and s hghly prone to desertfcaton and drought. And as MoWR and NMSA (2001) assert, clmate change and ts mpacts are cause for concern. In addton, both observed and Global Clmate Models (GCMs) future scenaros suggest a current amplfcaton of clmate contrasts across the globe (IPCC, 2007). Furthermore, studes by Conway (2005); Km et al (2008); Mohamed et al (2005); Solman et al, (2009) and Solman et al (2008), have manly focused at the basn level. It s advsable to study the mpact of clmate change n subbasn level (Yhun, 2009). Besdes, prevous clmate change mpact studes n the Nle Basn, mostly focused on the effect on runoff and the consequences for downstream The Gumara Rver s located to the east of Lake Tana and has a total dranage area of about 1893km 2 and 1394km 2 above the gaugng staton,.e. the gaugng staton at Gumara. After flowng for a length of km, the rver jons Lake Tana. It falls between lattude 11 o 45 and 11 o 55 N and longtude 37 o 30 and 37 o 50 E. The watershed conssts of rugged and undulatng topographes wth dfferent rdges, valleys and steep slopes whch vary from 1790 up to 3700 masl. The land-use of the study area s categorzed as agrcultural, agro-pastoral, pastoral and urban, and respectvely consttutes 59%, 36%, 3.4% and 0.1%. Wth regard to sol type, Vertsols and sols wth vertc characterstcs are the domnant sol groups. Chromc Luvsols, Orthc Luvsols, Chromc vertsols and Lthosols covers 56%, 26%, 14% and 2.2% respectvely. The annual Ranfall s relatvely hgh n the watershed, rangng between 1145 mm and 1523 mm. The maxmum and mnmum monthly temperature vares between 23 o C o C and 7 o C-14 o C respectvely. Total populaton of the sub-catchment s 1.1 mllon (MoWR, 1998 and 2008). As shown n Fgure 1, an earthen dam and a dverson wer wll be bult on the man trbutary of the Gumara and the Sendega rvers before t confluence wth the man stream for rrgaton. The dam has a total catchment area of 385 sq. km at the proposed dam ste and a capacty of 24Mm 3. The proposed dverson wll be located about 28km below the dam and has an area of 1166km 2. The dverson wer conssts of 16.5 m long scourng sluce bays and 75m long ogee shaped wer, wth a 2m wde dvde wall n between. The full supply dscharge s 19.7m 3 /s. The cultvable command area s 8940 ha on the left bank and

3 Hanbal ha on the rght bank, total beng 14,100 ha, whch s 84% of the gross command area that can be rrgated by the proposed rrgaton project. Methodology The general steps employed n the study of clmate change mpact on water resources of study area are descrbed below: Frst, an analyss was made of the observed data for a reference-perod ( ). And for the absence of trends and the stablty of the mean, TREND-program (Smple and relable tme seres analyss program to evaluate homogenety, consstency and ndependence of data) was used; mssed data was flled n wth Autoregressve (AR) model and data generated usng Long Ashton Research Staton Weather Generator (LARS-WG) to extend records for statons that have lmted length of data. LARS-WG s a stochastc weather generator, whch can be used for the smulaton of weather data at a sngle ste (Semenov and Brooks, 1999 and Semenov et al. 2002), under both current and future clmate condtons. These data are n the form of daly tme-seres of clmate varables, namely, precptaton (mm), maxmum and mnmum temperature ( C) and solar radaton (MJm-2day- 1). To evaluate the performance of LARS-WG model, the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Percent Bas (PBIAS) ndces were used. Sngh et al (2004) state that RMSE and MAE values less than half the observed standard devaton (SDev) of the measured data may be consdered low and ether s approprate for model evaluaton vald for ths study. Moras et al (2007), also states that PBIAS less than or equal to ±15 s very good for model evaluaton. Next, temporal clmate change scenaros of precptaton, temperature, and Potental Evapotranspraton (PET) were developed usng Downscalng Model from large-scale predctor varable nformaton of GCMs. Based on the avalablty of publc doman GCMs and tme, the study used GCM scenaros of HadCM3 from UK Hadley Centre and Regonal Clmate Model (RegCM). HadCM3, runnng under A2a and B2a emsson scenaros (where A2a s referred as the medum-hgh emssons scenaro and B2a as the medum-low emssons scenaro), represent ranfall patterns n East Afrca relatvely well (McHugh, 2005). The man reason for the selecton of ths model for mpact study was that, the GCM output was avalable together wth the downscalng tools called Statstcal Downscalng Model (SDSM). Among the wdely appled statstcal downscalng technques, the multple lnear regressons based model called Statstcal Downscalng Model (SDSM) was used n ths study. Statstcal downscalng was based on the vew that the regonal clmate s condtoned by two factors: the large scale clmate state, and regonal/local physographc features (e.g. topography, land-use). From ths perspectve, regonal or local clmate nformaton was derved by frst determnng a statstcal model, whch relates large-scale clmate varables (or predctors ) to regonal and local varables (or predctands ). Then large-scale output of a GCM smulaton was fed nto ths statstcal model to estmate the correspondng local and regonal clmate characterstcs. One of the prmary advantages of these technques s that they are computatonally nexpensve, and thus can be easly appled to output from dfferent GCM experments. Another advantage s that they can be used to provde ste-specfc nformaton, whch s crtcal for many clmate change mpact studes. But, the major theoretcal weakness of statstcal downscalng methods s that ther basc assumptons s not verfable,.e., that the statstcal relatonshps developed for the present day clmate also hold under the dfferent forcng condtons of possble future clmates (Wlby et al, 2004) The RegCM3 model nested wth the ECHAM5 GCM were appled n ths study. The RegCM3 predctor varables were avalable for the A1b experment. Solman et al (2009) and Solman et al (2008) calbrated and valdated RegCM3 over the Blue Nle basn doman. Whle comparng the model results wth dfferent observatonal data sets, they found that the model could accurately smulate the clmatology of the Blue Nle. The observed spatal and temporal pattern of temperature, seasonalty and spatal pattern of precptaton are all represented by the model outputs. The predctand for statstcal downscalng usng SDSM are mean areal precptaton and temperature derved from Bahr-Dar, Debre-Tabor and Woreta statons usng Inverse Dstance Weght (IDW) method for the base perod of , and PET calculated from the temperature usng FAO Penman-Monteth method (Doorenbos and Prutt, 1977). However, dynamcally downscaled RegCM3 outputs are avalable only for the perod , and at daly tme steps. These data have been collected from IWMI-Ethopa. After selectng the hydrologcal model usng the crtera of Cunderlk and Smonovc (2007) and Beven (2000), the HEC-HMS hydrologc model was set-up and calbrated wth clmate and stream flow data, whch represent the current clmate. Then, smulaton of stream flow correspondng to future clmate change scenaros was made. Moreover, the smulated stream flow correspondng to the dfferent tme perods were analysed to see f there s a flow n trend. The mpact of clmate change on water consumpton for some of these water uses s not yet clearly known. It s projected however, that clmate change wll ncrease rrgaton water requrements due to ncreased potental evapotranspraton for the doublng of CO 2 scenaro. The Gumara rrgaton project has been planned for cultvaton of cereals, pulses, ol seeds and other hortcultural crops. Therefore, the rrgaton requrement was calculated based on the proposed percentage of land allocated for the dfferent crops usng CROPWAT by assumng that the proposed croppng pattern s the same n ts desgn perod, and envronmental flow requrement of the area adopted

4 Int.J.Agrc.Polcy Res. 145 from MoWR (2008). Fnally, the clmate mpact was assessed for the perod 2030s ( ) and 2090s ( ) usng Relablty, Reslence and Vulnerablty ndces. Clmate Change Impact Assessment Indces The analyss of potental clmate change mpact on the water supply system requres smulaton of the water balance under dfferent clmate scenaros. There are dfferent measures for assessng system performance. Ths study used three performance ndces to evaluate the clmate change mpact on reservor comparatvely. These ndces are: Relablty, Reslence and Vulnerablty ndces. Relablty Relablty s defned as the probablty that a water supply system wll be able to meet- wthn the smulaton perodthe target demand n any gven nterval of tme (often a year or a month). There are several measures of relablty, whch are defned by Thomas et al (2004) as follows: Tme-based Relablty consders the proporton of ntervals durng the smulaton perod that the reservor can meet the target demand. A general expresson for estmatng ths metrc s: Ns Rt ; 0 Rt 1 (1) N Where Rt s the tme-based relablty, Ns s the number of ntervals that the target demand was fully met and N s the total number of ntervals coverng the hstorcal or smulaton analyss perod. When the tme nterval s monthly or annual, we speak about a monthly or an annual tme-based relablty, respectvely. Volumetrc Relablty s defned as the volume of water suppled to a demand dvded by the total target demand durng the entre smulaton perod,.e. n ( D D ) Rv 1 1 n D TotalShortfall TotalTarget Demand ; 0<Rv<1 (2) Where Rv s the volumetrc relablty, D s the target demand durng th perod, D t s the volume actually suppled durng the th perod, and n s the number of tme ntervals n the smulaton. So that Rv=1 f D s totally satsfed,.e. D = D for all. It should be noted that Rv wll always be equal to or greater than Rt because durng a tme nterval n whch a falure s recorded some releasealthough lower than the target demand- may stll be made. Reslence Reslence s a metrc, defnng how quckly a reservor wll recover from a falure. The measure adopted n ths study s: fs, 0 fd fd (3) Where φ s reslence, fs s the number of ndvdual contnuous sequences of falure perods and fd s the total duraton of all the falures. In other words, φ s the nverse of the average falure duraton. Reslence s the probablty of a year of success followng a year of falure. Vulnerablty The metrc known as vulnerablty measures the average volumetrc severty of falure durng a falure perod. fs j 1 max( S ) fs j (4) Where s the vulnerablty, S j s the volumetrc shortfall durng j th contnuous falure sequence, and fs s the number of contnuous falure sequences. Because Eq. (4) averages out the maxmum shortfall over all the contnuous falure perods, then a reducton n fs wll cause to ncrease when the numerator n Eq. (4) remans unchanged. A practcal stuaton where ths may occur s when the reservor capacty s ncreased, wth all other factors remanng constant. One way to avod ths anomaly s to remove the averagng n Eq. (4). Another pont to note about Eq. (5) s that s n volumetrc unts; a more useful expresson of vulnerablty s ts dmensonless form s gven by:, 0 1 Df (5) Where s the dmensonless vulnerablty metrc, known as the vulnerablty rato n ths paper, and Df s the (constant) target demand durng falure. (Note that DF=D,.e. target demand s the same for drought and non-drought perods.) RESULTS AND DISCUSSION Statstcal Test and Weather Generaton Statstcal tests were carred out for observed annual

5 Hanbal 146 Table 1. Summary Statstc and error ndces for LARS-WG generated data Error ndex Mnmum Temperature Maxmum Temperature Precptaton SDev RMSE MAE PBIAS (%) Table 2. HadCM3 Vs RegCM3 Future scenaro Scenaro HadCM3-A2a HadCM3-B2a RegCM-A1b Clmate Varables Changes n 2030s 2090s Precptaton -6.3% -10.6% Maxmum Temperature 0.50 o C 0.98 o C Mnmum Temperature 0.32 o C 1.17 o C PET 1.6% 2.8% Precptaton -5.1% 8.2% Maxmum Temperature 0.27 o C 0.8 o C Mnmum Temperature 0.31 o C 0.73 o C PET 1.6% 2% Precptaton 4.7% 3.0% Maxmum Temperature 2.61 o C 5.89 o C Mnmum Temperature 1.96 o C 5.11 o C PET 8.6% 17.8% Note: The negatve sgn (-) shows decrement ranfall and temperature data ( ) for absence of trend wth Spearman s rank-correlaton method, and by t- test for stablty of mean usng TREND-trend/change detecton program. Mnmum and maxmum temperature result ndcates an ncreasng trend, whereas precptaton does not show any sgnfcant trend. Because of the short record ( ), data were generated for Woreta staton usng LARS-WG for the perod , to ft the 30 years base perod crtera. The performance of LARS-WG model was evaluated usng RMSE, MAE and PBIAS ndces. Snce all ndces (as shown n Table 1) are wthn acceptable range, LARS-WG good performance s an ndcaton that representatve weather data can be generated from lmted data for the study area. Hence, precptaton; maxmum and mnmum temperatures for Woreta meteorologcal staton were generated usng LARS-WG for the perod Future HadCM3 and RegCM3 outputs The downscalng model was setup and valdated- ths model s used to downscale the future clmate change scenaro smulated by GCM. The statstcally downscaled outputs from A2a and B2a scenaro of HadCM3 and Dynamcally downscaled outputs of RegCm3 are shown n Table 2. The study wll not gve any concluson towards a partcular preference as the output comes from dfferent GCMs, wth dfferent downscalng technques. Here, the 1990s observed scenaro were used for comparng the GCMs and downscalng technques performance, whereas the 2030s and 2090s scenaro were used to predct the future clmate trend. Table 2 shows that future Precptaton may decrease at ncreasng rate n statstcally downscaled HadCM3-A2a and HadCM3-B2a. However, t may ncrease at decreasng rate n RegCM3-A1b. Future Temperature and PET ncrease at ncreasng rate for all scenaros, although, the rate s hgher for the RegCM3-A1b. It was also observed that Precptaton s shftng towards October n the case of RegCM3. HEC-HMS Hydrologcal Model Development HEC-HMS calbraton were performed from , and valdaton was carred out from , usng both daly and monthly tme step. The flow data at Gumara gaugng staton were collected from hydrologcal department of Ethopan Mnstry of Water Resources. Model valdaton was used to determne the effectveness of the parameterzaton and calbraton methodologes. Moreover, effcency crtera such as Nash-Sutclffe effcency (NSE), Coeffcent of determnaton (R 2 ), percent dfference (D) and RMSE-observatons standard, and devaton rato (RSR), were used for evaluaton of the performance of the model. A summary of HEC-HMS hydrologcal model development usng a combnaton of Defct constant loss, Clark s unt hydrograph

6 Int.J.Agrc.Polcy Res. 147 Table 3. The result of model performance crtera NSE R 2 D (%) RSR Calbraton( ) 0.7(0.88) 0.72(0.89) 0(0) 0.55(0.35) Valdaton ( ) 0.73(0.91) 0.73(0.92) -3.4(-3.42) 0.52(0.29) Note: The value outsde the bracket s for daly tme step and those n brackets s for monthly tme step Table 4. Changes n Flow and Clmate varables Perod Flow (%) Precptaton (%) Temperature( C) PET (%) HadCM3-A2a Scenaro 2030s s HadCM3-B2a Scenaro 2030s s RegCM3-A1b 2030s s transformaton, monthly constant baseflow and Muskngum routng method s shown n Table 3. In general the model performed reasonably n smulatng flows for perods outsde of the calbraton perod, based on adjusted parameters durng calbraton. Hydrologcal Model Smulaton correspondng to clmate scenaros The nflow to the Gumara gauge-staton was generated usng the downscaled clmate varable as an nput to the HEC-HMS hydrologcal model. For comparson purpose, the generated flow was compared wth the current ( ) mean monthly observed flow. Relatve to the (1990s) condton, the smulated future nflow to the gauge, based on the HadCm3- A2a scenaro, shows an average annual decrease n volume by 9.1% n 2030s and 16.4% n 2090s. Ths may be due to ncrement of future temperature, and reducton n future precptaton. In case of HadCM3-B2a scenaro, the nflow projected to decrease by 8.3 % n 2030s, whch exhbts an average annual absolute temperature ncrease by 0.29 o C. PET ncreased by 1.6% and the precptaton decreased by 5.1% n the same tme horzon, whle n 2090s, the nflow volume decreases by 14.2% -where the absolute annual average temperature ncreased by 0.63 C- PET ncreased by 2% and the precptaton decreased by 5.7%. Consderng RegCM3, the nflow has been projected to ncrease by 4.1% n 2030s, whch exhbts an average annual temperature ncrease by 2.3 o C, PET ncreased by 8.6% and the precptaton ncreased by 4.7% n the same tme horzon, whle n 2090s the nflow volume ncreases by 3.2%- where the absolute annual average temperature ncreased by 5.5 o C- PET ncreased by 17.8% and the precptaton ncreased by 3% as shown n Table 4. Clmate Impact Evaluaton: the Performance Indces The values of the performance measures were computed after generatng the nflow to the Gumara storage dam and Dverson wer. It was examned under the standard operaton polcy of the reservor,.e. the target demand s fully suppled whenever suffcent water exsts; otherwse all the avalable water s put nto supply and the reservor s left empty. Based on the avalablty of dynamcally downscaled RCM outputs, both current (1990s) and future (2030s and 2090s), generated nflow are consdered when quantfyng the performance ndces. The target demand conssts of rrgaton water demand and downstream release or envronmental demand for the same perod. The averaged tme-based relablty and volumetrc relablty of the Gumara Irrgaton Scheme revealed a value of about 90% and above 92% for all scenaros n respectve tme perods. The result value of above 80% ndcates that there exst very good potental at the ste to meet the demand n terms of tme as well as volume. Reslence ndcates how quckly a system wll recover from a falure. The reslence analyss result n Table 5, valued about 100% for all scenaros and tme perods, and showed that the rrgaton scheme recovers quckly for all scenaros n ts desgn perod. The volumetrc vulnerablty, whch ndcates the average

7 Hanbal 148 Table 5. Results of Performance ndces Scenaro Perod Rt(%) Rv(%) φ(%) η(mm3) ή Current HadCM3 A2a 2030s HadCM3 B2a 2030s RegCM3-A1b 2030s HadCM3-A2a 2090s HadCM3-B2a 2090s RegCM3-A1b 2090s of maxmum volumes of shortages, revealed that the shortage s found wthn the ranges of 3.33Mm3-3.44Mm 3 for A2a and B2a scenaros, and ,4Mm 3 for the RegCM3 scenaro. Comparng the scenaros, the maxmum shortage occurred n RCM scenaro where the temperature exhbts average ncrease. Conclusons The performance of the Gumara reservor and dverson wer under clmate change was quantfed usng the Relablty, Reslence and Vulnerablty ndces (RRVcrtera). Based on the study, the followng conclusons are drawn: LARS-WG shows a good performance, whch ndcates that representatve weather data can be generated from lmted data for the study area. In addton, projected temperature shows an ncreasng trend for the next century. However, the precptaton ndcates decreasng trend for A2a and B2a outputs, whereas t wll ncrease n case of the ReCM3 output. It s also concluded that the HEC-HMS model s able to capture daly and monthly patterns that can be proven by NSE, R 2, D and RSR values. Hence, HEC-HMS s able to accurately explan the hydrologcal characterstcs of the Gumara watershed. Wth regard to the nflow at the proposed dam, the average annual nflow volume wll decrease for the A2a and B2a scenaros, but ncrease for the RegCM3 scenaro for both the 2030s and 2090s. Snce a Relablty of about 90% and a Reslence of 100% s found, t s concluded that Gumara reservor and wer has hgh capablty to meet the requred target demand n the 2030s and 2090s, n terms of tme as well as volume. Moreover, the supply system wll recover quckly from falure to meet the demand, and satsfy the target draft. Hence, the proposed scheme has very good potental to rrgate the requred area n ts lfe perod s clmatc condton wth the consderaton of descrbed lmtatons. Based on the fndngs and lmtatons noted n ths study, the followng research gaps were drawn: the GCM outputs; the emsson scenaros and the downscalng methods used have certan level of uncertanty. Therefore, further study should reduce the uncertanty by usng addtonal GCMs; downscalng methods and emsson scenaros for longer perod (rather than only for 2030s and 2090s) to get better result. Besdes, such study can be extended by consderng change n land use, sol type and other clmate varables n addton to temperature and precptaton. Future research should also nclude adaptaton optons to clmate change. Fnally, to make the evaluaton of clmate change mpact more complete, t s apprecable to use addton of other performance ndces, such as rato of AET to PET, Drought Rsk Index (DRI) and Sustanablty Index (K). ACKNOWLEDGEMENTS Sustanable Water Harvestng and Insttutonal Strengthenng n Amhara (SWHISA) Project and Amhara Regonal Agrcultural Research Insttute (ARARI) fnancally supported ths research for whch they deserve great credt. I also acknowledge IWMI-Ethopa, the Mnstry of Water Resources and the Natonal Meteorologcal Servce Agency of Ethopa for provdng the data free of charge. REFERENCES Beven KJ (2000). Ranfall-Runoff Modellng. The Prmer, John Wlley and Sons, New York Conway D (2005). From headwater trbutares to nternatonal rver: Observng and adaptng to clmate varablty and change n the Nle Basn. Globa. Envron. Change 15: Cunderlk JM, Smonovc SP (2007). Hydrologc Models for Inverse Clmate Change Impact Modelng, the 18th Canadan Hydrotechncal Conference on Challenges for Water Resources Engneerng n a Changng World, Wnnpeg, Mantoba, August Doorenbos J and Prutt WO (1977). Crop water requrements. FAO, Irrgaton and Dranage paper No. 24 (revsed). FAO (Food and Agrculture Organzaton of the Unted Natons), Rome, Italy. Gosan AK, Sandhya Rao, Debajt Basuray (2006). Clmate change mpact assessment on hydrology of Indan Rver basns. Curr. Sc., 90: 3-10 IPCC (2007). Clmate Change 2007: Impacts, Adaptaton and Vulnerablty. Contrbuton of Workng Group II to

8 Int.J.Agrc.Polcy Res. 149 the Fourth Assessment Report of the Intergovernmental Panel on Clmate Change, M.L. Parry, O.F. Canzan, J.P. Palutkof, P.J. van der Lnden and C.E. Hanson(Eds.), Cambrdge Unversty Press, Cambrdge, Unted Kngdom, 976p. Km U, Kaluarachch JJ, Smakhtn VU (2008). Clmate change mpacts on hydrology and water resources of the Upper Blue Nle Rver Basn, Ethopa. Colombo, Sr Lanka: Internatonal Water Management Insttute. 27p (IWMI Research Report 126) McHugh MJ (2005). Mult-model trends n East Afrcan ranfall assocated wth ncreased CO2. Geophyscal Research Letters 32, L Mnstry of Water Resources, MoWR (2008). Gumara Irrgaton Project Feasblty Study Report, Man Report, Mnstry of Water Resources (MoWR), February, Adds Ababa. Mohamed YA, Vanden Hurk BJJM, Savenje1 HHG, Bastaanssen WGM (2005). Hydroclmatology of the Nle: results from a regonal clmate model. Hydrol Earth System Sc., 9: Moras DN, Arnold JG, Van Lew MW, Bngner RL, Harmel RD, Veth TL (2007). Model Evaluaton Gudelnes for Systematc Quantfcaton of Accuracy n Watershed Smulatons. Amercan Socety of Agrc. Bol. Eng., 50(3): MoWR, NMSA (2001). Clmate Change Natonal Adaptaton Programme of Acton (Napa) Of Ethopa. Abebe Tadege (Edt), The Federal Democratc Republc Of Ethopa Mnstry Of Water Resources And Natonal Meteorologcal Agency Semenov MA, Barrow EM (2002). LARS-WG: A Stochastc Weather Generator for Use of Clmate Impact Studes, User Manual, V3 Semenov MA, Brooks RJ, Barrow EM, Rchardson CW (1998). Comparson of the WGEN and LARS-WG stochastc weather generators for dverse clmates. Clmate Research 10: Sngh J, Knapp HV, Demsse M (2004): Hydrologc modelng of the Iroquos Rver watershed usng HSPF and SWAT. ISWS CR Champagn, Ill.: Illnos State Water Survey. Solman ESA, Sayed MAA, Nour El-Dn MM, Samy G (2008): Integraton of NFS wth Regonal Clmate Model to Smulate the Nle Basn Hydro-clmatology, Nle Water Sc. Eng. Mag. 1. Solman ESAM, Sayed MAA, Marc Jeuland (2009). Impact Assessment of Future Clmate Change for the Blue Nle Basn, Usng a RCM Nested n a GCM. Nle Basn Water Eng. Sc. Mag., Vol.2 Solomon Seyoum (2002). Assessng the Relablty of Water Yeld of a Rver Basn. Natonal Hydrology Semnar 2002: Water Resource Management: Sustanable Supply and Demand Programme and Papers, Natonal Unversty of Ireland, Galway Thomas AM, Adebayo JA, Sen-Ln Z (2004). Understandng performance measures of reservors. J. Hydrol. 324: Wlby RL, Charles SP, Zorta E, Tmbal B, Whetteon P, Mearns LO (2004). Gudelnes for Use of Clmate Scenaros Developed from Statstcal Downscalng Methods. Supportng materals of the IPCC Yhun Dle (2009). Hydrologcal Modellng to Assess Clmate Change Impact at Glgel Abay Rver, Lake Tana Basn, Ethopa. MSc thess done for the partal fulflment of the requrements of the Master of Scence degree at Lund Unversty, Lund, Sweden