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Citation: Menna BY (2017) Simulation of Hydro Climatological Impacts Caused by Climate Change: The Case of Hare Watershed, Southern RiftValley of Ethiopia. Hydrol Current Res 8: 276. doi: 10.4172/2157-7587.1000276Page 2 of 12correct RCPs data. Next feed the downscaled or corrected RCPs datainto calibrated and validated hydrological models. After that, simulatestreamflow for three future periods considering the climate changescenario. Finally, carry out hydro climatological impacts caused byclimate change.Study Area and DatasetStudy areaThe study area, Hare River watershed, is located in the AbayaChamo sub-basin of the southern Ethiopian Rift Valley and drainsto Lake Abaya, which is the second largest lake of the country. Thewatershed is situated between 37 27ꞌ and 37 37ꞌ Eastern longitudeand 6 03ꞌ and 6 18ꞌ Northern latitude and has a land area of 153km2. The topography of the study area is generally increasing inelevation from the downstream to the upstream. The middle reach ofthe watershed is mainly covered by steep slopes characterized throughabrupt faults [7].The climate of the Hare watershed ranges from tropical to alpinedue to its great difference in altitude and topographical elevation. Theaverage annual temperature is 23 C and 14 C, and mean annual rainfallare 750 mm and 1300 mm at the lowland and highland respectively.Generally, about 55.56% is Dega (Humid), 22.84% is Woyna Dega (Subhumid), 11.73% is Wurch (Alpine) and 9.25% is Kola (Sub-arid). Thelower watershed area is characterized by dry Kola while the middle partof the watershed is characterized by moist woyna-dega and much of thearea in the northern part is dominated by Dega and the tip is Wurch.The main and small rainy seasons at Hare watershed occur from AprilMay and September and October respectively. The spatial rainfalldistribution at Hare watershed indicates that major increase of rainfalltakes place with an increase in elevation from 1180 m up to 3,480 mabove sea level (a.s.l.) [6] (Figure 1).Dataset meteorological dataRequired long year daily precipitation data were collected fromthree meteorological stations such as, Arba Minch, Chencha, and MirabAbaya. Daily maximum and minimum temperature data were collectedfrom Arba Minch station. The historical weather data for above threestation were obtained from National Meteorological Service Agency(NMSA) from 1980 to 2006 (Figure 2).Downscaled RCPs dataThe Intergovernmental Panel for Climate Change (IPCC) haspublished projections of future climate change scenarios in a seriesof reports. There was a fundamental change between the fourth andfifth assessment reports (AR4 and AR5) [8-10] and in order to reflectsuch differences as well as model variability, the study looked at twofuture scenarios. The first scenario considers what the future climatewill be under conditions with a representative concentration path(RCP) that assumes that radiative forcing will stabilize at 8.5 W/m2 in2100 (RCP8.5); the second less extreme scenario assumes that radiativeforcing will stabilize at 4.5 W/m2 in 2100 (RCP4.5).Downscaled rainfall, and average, minimum and maximumtemperatures for the period 1951-2100 have been obtained fromCORDEX-Ethiopia database. The data correspond to three RCPscenarios- RCP2.6, RCP4.5, RCP6 and RCP8.5. In order to best conducta future climate change study, RCP 4.5 and RCP 8.5 forced scenarioswere selected from 2010 to 2100 for 3 climate stations, and downscaledto the same climate stations which were used for SWAT model for thehydro Climatological simulations (Table 1).Hydrol Current Res, an open access journalISSN: 2157-7587Hydrological dataStream flow was used for calibrating and validating the SWATmodel simulation. Daily stream flow data were obtained from Ministryof Water Irrigation and Energy (MWIE) for Hare River from 1980 to2006.Spatial dataDigital elevation model (DEM), land use/land cover, and soil arethe three spatial data inputs required by SWAT model. DEM describesthe elevation of any point in a given area at a specific spatial resolutionas a digital file. DEM is one of the essential inputs required by SWAT:(1) to delineate the watershed into a number of sub-watersheds or subbasins and (2) to analyze the drainage pattern of the watershed, slope,stream length, width of channel within the watershed. The DEM wasobtained from USGS website with a resolution of 30 m by 30 m.The land use map of the study area ware downloaded from USGSwebsite with path-169 and row-056, 20 January 2000 satellite image. Ithas 8-bands, and with special resolution of 30 m 30 m processed inArcGIS was used for the hydrologic study input data. The land use ofthe area has reclassified based on the available topographic map, Arialphotography and satellite images. The reclassification of land use mapwas done to represent the land use according to the specific land covertypes such as type of Forest land, Water body, Cultivated land, Bushland and Bare land.SWAT model requires different soil textural and physico-chemicalproperties such as soil texture, available water content, hydraulicconductivity, bulk density and organic car-bon content for differentlayers (up to 3 layers) of each soil type. These data were obtained mainlyfrom the following sources: Ministry of Agriculture (MoA) and AfricaCD-ROM (Food and Agriculture Organization of the United Nations[11], Major Soils of the world CD-ROM [12], Digital Soil Map of theWorld and Derived Soil Properties CD-ROM [13], Proper-ties andManagement of Soils of the Tropics CD-ROM [14] (Figure 3).MethodologyThis study, applying bias correction method for downscaled climatevariables (precipitation and temperature) is the first and basic step forthis impact assessment to be described. Stream flow modeling withSWAT is the second step in the methodology. Finally, climate changeimpact study using hydrological model is the subject to be discussed.Bias correction method of downscaled climate dataOften, the downscaled RCPs data cannot be directly used for impactassessment as the computed variables may differ systematically fromthe observed ones. Bias correction is therefore applied to compensatefor any tendency to overestimate or underestimate the mean ofdownscaled variables. Bias correction factors are computed from thestatistics of observed and simulated variables [15]. Two bias correctionmethods were tried in this study. First, the nonlinear bias correctionmethod proposed by Nader et al. [16] and the second method called“delta approach”. The formulas used for rainfall and temperature biascorrection are indicated in Equations 1 and 2. Corrections factors werecomputed for each month.Precipitation correctionIn the bias correction technique, nonlinear correction each dailyVolume 8 Issue 2 1000276

Citation: Menna BY (2017) Simulation of Hydro Climatological Impacts Caused by Climate Change: The Case of Hare Watershed, Southern RiftValley of Ethiopia. Hydrol Current Res 8: 276. doi: 10.4172/2157-7587.1000276Page 3 of 12Figure 1: Location of Hare Watershed.Figure 2: Location of climate station of Hare watershed.Corresponding output for the study areaStationsGrid CodeLatitudeLongitudeArba MinchGP1112075.7237.4Mirab ture correctionTable 1: Location of CORDEX-Ethiopia output grids for the study area [35].precipitation amount P is transformed to a corrected P* a powertransformation equation have been used.P* aPb(1)Where P* is the simulated data in the projection period, and a and bare the parameters obtained from calibration in the baseline period andHydrol Current Res, an open access journalISSN: 2157-7587subsequently applied to the projection period. They are determined bymatching the mean and coefficient of variation (CV) of simulated datawith that of observed data [16].For temperature, monthly systematic biases were calculated for thebaseline period by comparing RCPs outputs with the observations themonthly mean biases correction have been calculated according to thefollowing Equation [17].Tc Tom δ0( Tr Trm )δr(2)Where; Tc is bias corrected future temperature, Tom is meanof observed temperature in base period, Trm is mean of RCPstemperature in base period and Tr is RCPs temperature of base periodδr, and δ0, represent the standard deviation of the daily RCPs output andobservations in the reference period respectively.Volume 8 Issue 2 1000276

Citation: Menna BY (2017) Simulation of Hydro Climatological Impacts Caused by Climate Change: The Case of Hare Watershed, Southern RiftValley of Ethiopia. Hydrol Current Res 8: 276. doi: 10.4172/2157-7587.1000276Page 4 of 12Figure 3: Spatial inputs of SWAT defined Dem, land use and soil type.Bias correction performance evaluationThe performance of the bias correction method was evaluated usingthe root means square error (RMSE), the mean absolute error (MAE)and the Relative Error (RE) [18]. They are calculated by the followingequations (3), (4) and (5).NRMSE MAE (Y Y )i'i2iN1 N ' Yi YN i 11 N ( Yi' Y )N i 1RE Ymean(3)(4)(5)Where, Yi is the observed value at time step i, Yi is simulated valueat time step i, Ymean is the mean of observed values, and N is the numberof observations.Arc SWAT model approachWatersheds can be subdivided into sub watersheds and further intohydrologic response units (HRUs) to account for differences in soils,land use, crops, topography, weather, etc. The model has a weathergenerator that generates daily values of precipitation, air temperature,solar radiation, wind speed, and relative humidity from statisticalparameters derived from average monthly values. The model computessurface runoff volume either by using modified SCS curve numbermethod or the Green and Ampt infiltration method. Flow is routedthrough the channel using a variable storage coefficient method or theMuskingum routing method. SWAT has three options for estimatingpotential evapotranspiration: Hargreaves, Priestley-Taylor, and PenmanMonteith. The model also includes controlled reservoir operation andgroundwater flow model. The important equations used by the modelare discussed below. The detailed and complete descriptions are givenin the SWAT theoretical documentation. SWAT splits hydrologicalsimulations of a watershed into two major phases: the land phase andthe routing phase. The difference between the two lies on the fact thatHydrol Current Res, an open access journalISSN: 2157-7587water storage and its influence on flow rates is considered in channelizedflow [19].Weather generatorLack of full and realistic long period climatic data is the problemof developing countries. Weather generators solve this problem bygenerating data having the same statistical properties as the observedones [20]. SWAT requires daily values of precipitation, maximum andminimum temperature, solar radiation, relative humidity and windspeed. The climatic data collected from the three meteorological stationsin the study area however, have too many missing data. As SWAT hasa built-in weather generator called WGEN that is used to fill the gaps,all the missing values were filled with a missing data identifier, -99. Theweather generator first independently generates precipitation for theday. Maximum temperature, minimum temperature, solar radiation andrelative humidity are then generated based on the presence or absenceof rain for the day. Finally, wind speed is generated independently [21].For the sake of data generation, weather parameters were developedby using the weather parameter calculator WXPARM [22] and dewpoint temperature calculator DEW02 [23], which were downloadedfrom the SWAT website. The WXPARM program reads daily values ofsolar radiation (calculated from daily sunshine hours), maximum andminimum temperatures, precipitation, relative humidity, and windspeed data. It then calculates monthly daily averages and standarddeviations of all variables as well as probability of wet and dry days, skewcoefficient, and average number of precipitation days in the month. TheDEW02 programs reads daily values of relative humidity, and maximumand minimum temperature values and calculates monthly average dewpoint temperatures [21].Hydrological component of SWATThe simulation of the hydrology of a watershed is done in twoseparate divisions. One is the land phase of the hydrological cycle thatcontrols the amount of water, sediment, nutrient and pesticide loadingsto the main channel in each sub-basin. Hydrological componentssimulated in land phase of the hydrological cycle are canopy storage,infiltration, redistribution, evapotranspiration, lateral subsurface flow,surface runoff, ponds, tributary channels and return flow. The seconddivision is routing phase of the hydrologic cycle that can be defined asthe movement of water, sediments, nutrients and organic chemicalsthrough the channel network of the watershed to the outlet. In the landphase of hydrological cycle, SWAT simulates the hydrological cycleVolume 8 Issue 2 1000276

Citation: Menna BY (2017) Simulation of Hydro Climatological Impacts Caused by Climate Change: The Case of Hare Watershed, Southern RiftValley of Ethiopia. Hydrol Current Res 8: 276. doi: 10.4172/2157-7587.1000276Page 5 of 12based on the water balance equation.tSW SW0 ( R day - Qsurf - E a - Wseep - Qgw ) it(6)i 1In which SWt is the final soil water content (mm), SW0 is the initialsoil water content on day i (mm), t is the time (days), Rday is the amountof precipitation on day i (mm), Qsurf is th

The study area, Hare River watershed, is located in the Abaya-Chamo sub-basin of the southern Ethiopian Rift Valley and drains to Lake Abaya, which is the second largest lake of the country. The watershed is situated between 37 27ꞌ and 37 37ꞌ Eastern longitude and 6 03ꞌ and 6 18ꞌ Northern latitude and has a land area of