Impact of Land use and Land cover change on stream flow of Beshilo watershed, Ethiopia

Impact of Land use and Land cover change on stream flow of Beshilo watershed, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Land use and Land cover change on stream flow of Beshilo watershed, Ethiopia Asalf Shumete Eshete, Girum Metaferia Affessa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1729506/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Land degradation is a series problem in Ethiopia highlands, particular in abbay basin reflected in the form of soil erosion and soil fertility decline from time to time. Beshilo catchment is one of the Abbay basin tributary, which covers 5,700 km 2 ; this shows that the problem of the catchment is significant effect in Abbay basin development. In this study the impact of Land use/ Land cover change on annual outflow contribution of Beshilo Catchment is evaluated, distributed physically based hydrological model known as soil and water assessment tool (SWAT) model was used this study. Changes were analyzed and characterized impacts on surface runoff were evaluated. The model was calibrated and validated over the gauged upper reaches of catchments of Gummara River. The model was calibrated for the period from 1999–2008 and validated for the period from 2009–2014. The performance of the model was evaluated on the basis of performance rating criteria, coefficient of determination, Nash & Sutcliff efficiency. The overall performance of the models gives good result. For this study the land use land cover change scenario, were developed using its change. From the land cover change analysis results it was found that there has been a substantial decline of forest lands, shrub lands, grass lands and expansion of agricultural land. The SWAT modeling shows the result indicated that the mean annual stream flow were increase by 7.4% with 2004 LULC from 1996 LULC and increased by 6.8% with 2013 LULC from 1996 LULC. Arc SWAT Stream flow analysis SWAT-CUP Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction Land and water resources degradation are the major problems on Ethiopian highlands. Poor land use practices and improper management systems have significant role in causing high soil erosion rates, sediment transport and most importantly, the loss of water resources both in quantity and quality (Shimelis, 2008 ). Erosion disturbs the channel stability and as a result downstream areas may receive excessive sediment loads, leading to poor water quality (Robert, 2002 ). A river’s flow rate and water quality is dependent on the land-use practices within their entire watershed. Therefore, watershed management is an essential part of maintaining healthy productive rivers. Particularly a better understanding of the hydrological characteristics of different watersheds in the head of the Blue Nile River is one of considerable importance for government special interest towards developing water resources of this catchment at a larger scale. For instance, the Beshilo catchment is considered as one of tributary of Blue Nile.To enhance national economic development the great Ethiopian renaissance dam and the Ribb dam are among the many development attempts on the Blue Nile River. Beshilo River is one of most affected sub basin in the region as soil erosion, sediment transport and land degradation are great concerns. The land and water resources of the sub basin and its ecosystem are in danger due to the nature of catchment and rapid growth of population, deforestation and overgrazing, soil erosion or sediment transportation is serious problem for BeshiloRiver. So land use /land cover change impact on the flow of Beshilo river is important, the study was done using soil and water assessment tool (SWAT) model and GIS are adopted. SWAT Model Among the many hydrologic models developed in the past decade, the Soil and Water Assessment Tool (SWAT), developed by (Arnold et al. 1993 ), has been used extensively by researchers. This is because SWAT uses readily available inputs for weather, soil, land, and topography, it allows considerable spatial detail for basin scale modeling, and it is capable of simulating change in catchment characteristics using different scenarios. SWAT is recognized by the U.S. Environmental Protection Agency (EPA) and has been incorporated into the EPA’s BASINS (Better Assessment Science Integrating Point and Nonpoint Sources) (Di Luzio et al. 2002 a). [BASINS is a multipurpose environmental analysis software system developed by the EPA for performing watershed and water quality studies on various regional and local scales.]. In order to optimally calibrate the model parameters, especially for large-scale modeling, an auto-calibration routine has been added to SWAT. SWAT is a river basin scale, continuous time, a spatially distributed model developed to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions over long period of time (Neitsch et al., 2005 ). SWAT model (Arnold et al., 1998 ; Arnold), has proven to be an effective tool for assessing water resource and nonpoint source pollution problems for a wide range of scales and environmental conditions across the globe. SWAT is a physically based watershed-scale continuous time-scale model, which operates on a daily time step. The SWAT model can simulate runoff, sediment, nutrients, pesticide, and bacteria transport from agricultural watersheds (Arnold et al., 1998 ). The SWAT model delineates a watershed, and sub-divides that watershed in to sub-basins. In each sub-basin, the model creates several hydrologic response units (HRUs) based on specific land cover, soil, and topographic conditions. Model simulations that are performed at the HRU levels are summarized for the sub-basins. Water is routed from HRUs to associated reaches in the SWAT model. SWAT first deposits estimated pollutants within the stream channel system then transport them to the outlet of the watershed. The HRUs provide opportunity to include processes for possible spatial and temporal variations in model input parameters. The hydrologic module of the model quantifies a soil water balance at each time step during the simulation period based on daily precipitation inputs. The SWAT model distinguishes the effects of weather, surface runoff, evapotranspiration, crop growth, nutrient loading, water routing, and the long-term effects of varying agricultural management practices (Neitsch et al., 2005 ). In the hydrologic module of the model, the surface runoff is estimated separately for each sub-basin and routed to quantify the total surface runoff for the watershed. Runoff volume is commonly estimated from daily rainfall using modified SCS-CN method. The model needs several data inputs to represent watershed conditions which include: digital elevation model (DEM), land use and land cover, soils and climate data. 2. Materials And Methodology 2.1 Study Area Beshilo-watershed is located in the upper part Blue Nile, the biggest tributary of Blue Nile river, it starts with the water divide between these basins with mountains and extends with plateau of wet land of the Gerado area and then with very rough mountainous regions till, all perennial and intermittent rivers within the study area flows to joins the main river Beshilo River watershed in the Blue Nile basin with a drainage area of nearly 5700 km2. The sub-watershed is geographically Located between 38.67– 39.70 degree longitude and 10.82–11.72 degree latitude. The altitude in-watershed ranges between 4250 masl at the outlet and 1100 masl at the upper part of the River .The mean annual rainfall varies between 1080 mm in northern part to 937 mm in the lower part of the catchments. The majority of the watershed characterized by a humid tropical climate with heavy rainfall, and most of the annual is 971mm. 2.2 Data collection and sources The required data for the Arc SWAT model include: spatial data (Digital Elevation Model (DEM), land-use/land-cover map and soil map), meteorological data (daily rainfall data, daily max and min temperature, daily wind speed, daily radiation, and daily relative humidity), and hydrological data (stream flow data). These data obtained from various sources as described in Table 1 .These meteorological stations located within the study area (Beshilo -watershed) and contained same missing data. The missed data-filled using Normal ratio method and the double-mass curve method used to check the data’s consistency. Meteorological data preparation and quality assurance Daily meteorological data of 20 years (1997–2016 G.C) collected from the National Meteorological Service Agency (NMSA) (Table 1 ). The missed data computed by the linear regression method and filled before preparing as input for the Arc SWAT model. The consistency of precipitation data checked using a double-mass curve method and consistent. Finally, the daily precipitation, temperature (max and min), wind speed, solar radiation, and relative humidity of all stations arranged vertically with the spread sheet’s corresponding date. Then, the arranged data saved in a comma-delimited format as required for the Arc SWAT model input. The prepared meteorological data used as input to the model to be processed and generate an output. There were six meteorological stations in the study area, selected based on the availability of the station’s data and representativeness in the study area. The selected meteorological stations were Ambamariam, Kombolcha, Serinka, Tenta, Kutaber and Wogel tena. Table 1 Data type and Sources Data Type Source Scale / Period Description DEM Ministry of Water, Irrigation and Energy(MoWIE) 30mX30m Digital Elevation Model Land Cover Ministry of Water, Irrigation and Energy(MoWIE) 1986,2004,2013 G.C Land use classification map Soil Ministry of Water, Irrigation and Energy(MoWIE) 2004 G.C map Soil map classification Hydrological data Ministry of Water, Irrigation and Energy(MoWIE) 1999–2014 G.C Daily Stream flow Metrological data National Metrological Service Agency (NMSA) 1997–2016 G.C Daily rain fall Daily Max and min temperature Daily Wind speed Daily Solar Radiation Daily Relative Humidity 2.3 Data Consistency for rain fall Double-mass curve analysis technique. Double-mass curve analysis is a graphical method for identifying or adjusting inconsistencies in a station record by comparing its time trend with those of other stations. 2.4 Homogeneity test Homogeneity analysis is used to identify a change in the statistical properties of the time series data. The cause’s non homogeneity can be either natural or man-made; these include alterations to land use and relocation of the observation station. 2.5 Hydrological Data The stream flow data collected from MWRE has a longer time series data for Beshilo from Gummara Rivers, for this study a daily time series data from 1990 to 2014 were used. Gummara and Beshilo rivers have a similar runoff trend see Fig. 5 with a high correlation on a long-term monthly basis around 0.96 of R square for the study period. The long-term average monthly discharge of the two rivers is maximum in August and minimum around March and April. 2.6 Methodology 2.6.1 Overall frame work of the study The study required different materials and methods to arrive at the stated objectives. Meteorological, hydrological, digital elevation model, land use and land cover and soil data were required. The SWAT model inters face with Arc GIS and SWAT Cup is used to evaluate land use /land cover watershed on stream flow. Arc GIS 10.3 and its extension Arc SWAT 2012 were used for hydrological model. The stream flow simulation by the SWAT model was calibrated and validated by comparing simulated stream flow with observed values. 2.7 SWAT Model Setup 2.7.1 Watershed Delination Watershed and sub watershed delineation was carried out using 30m Resolution DEM data by means of Arc SWAT is in to set up a project so as to create necessary folders to store all the data. The watershed delineation process consists of five important steps, DEM setup, stream delineation, outlet and inlet definition, watershed outlet selection and definition and calculation of sub basin parameter. After the DEM setup was completed and the location of outlet was specified on the DEM, the model automatically calculates the flow direction and flow accumulation, consequently stream networks, sub water sheds and topographic were calculated using the respective tool. The size of the sub basin in the watershed will affect the assumption of homogeneity, because definition of watershed, sub basin boundaries and stream is decided by selecting a threshold area or the minimum drainage area to define stream, configuration of a lot of sub -basin required a long time simulation period and even difficult to run. On the other hand too small number of sub watershed is affecting the simulation result by ignoring spatial variability. In general the number of sub basins chosen to model the watershed depends on the size of the watershed, the spatial detail of available input data and the amount of detail required to meet the goals of the project. When subdividing the watershed, keep in mind that topographic attributes (slope, slope length, channel length, channel width, etc.) are calculated or summarized at the sub basin level. The sub basin delineation should be detailed enough to capture significant topographic variability within the watershed, for this study using 25000 ha threshold area in percentage and the watershed was divided in to eleven sub basin. 2.7.2 HRU Definition The Arc AWAT model required the creation of hydrologic Response units (HRUs), which are the unique combination of land use, soil and slope classes within each sub basin. In this study performed HRU analysis menu on the Arc SWAT tool bar, by loading land use and soil maps, evaluate slope characteristics and determine the land use/soil/slope class combination in the delineated sub watershed. In the model there are two options in defining HRU distribution assign a single HRU to each sub watershed or assign multiple HRUs to each sub watershed depends on a certain threshold value. The Arc SWAT use manual suggests that 20%land use threshold, 10% soil threshold and 20% slope threshold are adequate for most modeling application. So for this study using the above recommendation and the watershed divided in to 11 sub basin and 38 HRUs. In addition HRU analysis in Arc SWAT includes division of HRUs by slope class; multi slope was selected for this study. Slope classification carried out based on the elevation range of DEM, it reclassify in to 5 class. Table 2 the slop class used in the model Class Clss1 Clss2 Clss3 Clss4 Clss5 Slope range (%) 0–2 2–10 10–15 15–30 < 30 2.7.3 Land Use Classification Agriculture land These are the areas identified as dominantly cultivated on the land cover map. Although animals play an important role in these areas, they are considered as secondary to cultivation. The key economic activity in these areas is cultivation, especially for grains, and this area is sources of Crops production like Wheat and Teff. Pastoral land These are the grass land areas, Pastoral areas are particularly use for animal pastured Most cultivated land is pastured after harvest but not include in pasture land but include bush lands and shrub lands are all grazed. Planted area– it is difficult to define but for this stud land use including moderately forest, scarcely forest and land use by plantation area are grouped under this category. Barr land the land which are not used for human purpose Wet land areas which are lands but cover with water and used for wild life purpose Table 3 LULC classification at different year Land use land cover 1996 2004 2013 Land use type SWAT Code Area (km 2 ) Area (%) Area (km2) Area (%) Area (km2) Area (%) Barr land BARR 0.5174 0.01 5.69 0.11 1.04 0.14 Agriculture AGRL 3707.17 71.65 4477.6 86.54 4264 83.69 Planted area FRSD 706.78 13.66 211.62 4.09 662.3 12.04 Pasture PAST 626.57 12.11 472.9 9.14 242.12 4.06 Wet land WATR 132.98 2.57 5.69 0.11 4.66 0.09 2.7.4 Watershed in put table The weather data which are rain fall, temperature, relative humidity and wind seeped are prepare based on SWAT database format using text format and the table prepared using id, name, lat, long, elevation, finally insert to the SWAT. Weather Generator SWAT includes the WXGEN weather generator model (Sharpley and Williams, 1990 ) to generate climatic data or to fill in gaps in measured records.. The weather generator first independently generates precipitation for the day. Once the total amount of rainfall for the day is generated, the distribution of rainfall within the day is computed if the Green & Ampt method is used for infiltration, maximum temperature, minimum temperature, solar radiation and relative humidity are then generated based on the presence or absence of rain for the day. Finally, wind speed is generated independently. To generate the data, weather parameters were developed by using the weather parameter calculator WXPARM (Williams, 1995 ) and dew point temperature calculator DEW02. For this study there are seven stations from those four stations are synoptic station, the remaining two stations have rain fall and temperature records from this used 1997–2016 G.C. Dew exe Dew.exe using average daily temperature and average daily relative humidity in percent but the dew02 software used daily minimum and maximum temperature, and relative humidity in %. For this study used dew02 software and the other data’s for weather generator the rain fall data is prepared using PCP STAT. 3. Model Calibration And Validation 3.1 Model Calibration The time series of discharge at the outlet of the catchment was used as data for calibration and validation for SWAT model, the model was calibrated using the measured stream flow data from 1999 to 2008 with worm up period. The parameters were optimized first using the calibration tool, then calibration was done by adjusting parameters until the simulated and observed value showed good agreement. In this process, model parameters varied until recorded flow patterns are accurately simulated. Model calibration of SWAT run can be divided in to several steps. Among these Water balance and stream flow generation are the most important part is also considered. (Refsgaard, 1996 ). For this research work the measured stream flow data of were calibrated using SWAT Cup model from SWAT model output using 10 years flow, automatic calibration period (from January 1st, 1999 to December 31st, 2008) and the warm up period (from January 1st, 1997 to December 31st, 1998). From three SWAT model using three year land use land cover map,1196,2004,2013, and also the calibration is operated as flows the scenario development based on the watershed characteristics used for evaluation for my study the calibration is done for the three land use land cover map, for evaluate climate characteristics, rain fall effect, and slope effect in the catchment area, finally table 11 shows the average calibration value. 3.2 Model Validation In order to utilize the calibrated model for estimating the effectiveness of future potential management practices, the model tested against an independent set of measured data. This testing of a model on an independent set of data set is commonly referred to as model validation. As the model predictive capability was demonstrated as being reasonable in both the calibration and validation phases, the model was used for future predictions under different management scenario. For this research work the measured stream flow data of Beshilo from 01 January 2009 to 31 December, 2014 were used. The average model calibration output from deferent scenario, land use land cover map, 1996, 2004, 2013, and other characteristics shows the following relationship and value. After calibrating and validation for flow simulation was executed and the hydrographs are well captured. The agreement between the measurement and simulation is generally very good, which are verified by NSE and R2 and an acceptable result were obtained according to the model evaluation guideline (Moriasi et al., 2007). The calibration and validation period of the model was fifteen years from 1999 to 2008 for calibration and 2009-2014for validation G.C. The observed and simulated average monthly stream flow was computed. During calibration it was 121.79 m3/s and 105.45m3/s for observed and simulated respectively. On the other hand, the observed and simulated was 147.1m3/s and 112.33m3/s respectively during validation period. These indicates reasonable agreement between observed and simulated values in both calibration and validation periods 3.3 Sensitivity Analysis After the SWAT model setup has been finished, the next step is to run the model and analyze the global sensitivity. Sensitivity analysis is used to estimate the rate of change of model outputs with respect to change of model inputs. It is also useful to recognize how the model depends on the information fed into it (Willems, 2000 ). Sensitivity analysis provides for better understanding of the behavior of the system being modeled, such as model parameters and applicability, thus it increases the confidence level of the model and its predictions. SWAT model have large number of parameters and a number of outputs, thus, an initial parameter selection makes the calibration process easier and reduces the uncertainties related to diverse parameters. In the sensitivity process, by using SWAT CAP is used for this study. SWAT-CUP is an interface that was developed for SWAT, Using this generic interface, any calibration/uncertainty or sensitivity is operated .program can easily be linked to SWAT. This is demonstrated by the program links SWAT Cup is having 4 algorism GLUE, Parasol, SUFI2, and MCMC procedures to SWAT. In this particular study it was preferred to use sequential uncertainty fittings (SUFI2). It is automated. Table 4 Sensitivity Rank Flow parameter(SWAT)code Description Lower bound Upper bound Fitted value Sensitivity rank CN2 SCS curve number (%) -25% 25% 23.9167 2 ALPHA_BF Base flow alpha factor(days) 0 1 0.08927 1 GWQMN Threshold depth of water in the shallow aquifer required for return flow(mm) 0 5000 412.6 6 REVAPMN Threshold depth of water in the shallow aquifer for "revap" (mm) 0 500 312.500 5 SOL_AWC Soil available water capacity (water/ mm soil) -25% 25% 4.466 9 CANMX Maximum canopy storage(mm) 0–10 0.2976 4 ESCO Soil evaporation compensation factor 0–1 0.4918 12 SOL_K Saturated Hydraulic conductivity [mm/hr.] -25% 25% -0.7107 8 GW_REVAP Ground water "revap "coefficient 0.02–0.2 0.10209 7 SOL_Z Total soil depth(mm) -25% 25% -17.75 11 CH_K2 Effective hydraulic conductivity of the main channel(mm/hr) 0-150 331.9 3 RCHRG_DP Deep aquifer percolation fraction 0 1 0.708 10 3.4 Model Evaluation The performance of SWAT was evaluated using statistical measures to determine the quality and reliability of predictions when compared to observed values. Coefficient of determination (R 2 ) and Nash-Sutcliffe simulation efficiency (ENS) were the goodness of fit measures used to evaluate model prediction. The R 2 value is an indicator of strength of relationship between the observed and simulated values. The Nash-Sutcliffe simulation efficiency (ENS) indicates how well the plot of observed versus simulated value fits the 1:1 line. If the measured value is the same as all predictions, ENS is 1. If the ENS is between 0 and 1, it indicates deviations between measured and predicted values. If ENS is negative, predictions are very poor, and the average value of output is a better estimate than the model prediction (Nash, Sutcliffe, 1970). Table 5 General performance rating for recommended statistics for a monthly time (Moriasi, et al.2007) Performance Rating For Stream Flow RSR NSE PBIAS (%) Very good 0.0 < = RSR < = 0.5 0.75 < NSE < = 1 PBIAS < 10 Good 0.5 < = RSR < = 0.6 0.65 < NSE < = 0.75 10 < PBIAS < 15 Satisfactory 0.6 < = RSR < = 0.7 0.5 < NSE < = 0.65 15 < PBIAS = 0.7 NSE 25 4. Results And Discussion Evaluation the impact of land use land cover changes on Stream flow was the most important parts of this study. The study was performed for the period from 1997to 2016. The three produced land use land cover maps, six climate data, soil map, and stream flow values were used to assess the impact of land use and land cover change on stream flow. To evaluate the variability of surface runoff by land use land cover dynamics from 1997to 2016, three independent SWAT Runs were carried out on monthly time step using 1996,2004, and 2013 land use land cover maps, for the three runs the other SWAT parameter are unchanged. Based on the simulation output the seasonal stream flow variability caused by land use land cover change was assessed and comparison was made on surface run of contribution to stream flow for period 1999–2014 G.C The land cover map of 2004 is covered the following land class coverage that about 78.32% of the Beshilo catchment was covered by agricultural land, 13.85% by Grass Landor pasture land, -7.75% by forest land or planted area, 0.03% by wet land or water body and 0.0.5% covered by Barr land. The distribution of land cover class as indicates agricultural land cover large amount of percentage from the total area. The land cover map of 2004 is covered the following land class coverage that about 86.54% of the Beshilo catchment was covered by agricultural land, 9.14% by Grass Landor pasture land, 4.09% by forest land or planted area, 2.57% by wet land or water body. The distribution of land cover class as indicates agricultural land cover large amount of percentage from the total area and but increased by 14.8% from 1996 LLCU, forest land is reduced by 7.8% and grass land reduced by 2.97% compare to land use/cover at 1996 The land cover map of 2013 is covered the following land class coverage that about 82.41% of the catchment was covered by agricultural land, 4.68% by Grass Land or pasture land, 12.8% by forest land or planted area, 0.09% by wet land or water body. The distribution of land cover class as indicates agricultural land cover 3.8% more than 1996LULCand reduced by 4.13% 2004 LULC but the forest area increased by 8.7% and reduced 1.05%, grass land increased by 06 from 2004LULC and reduced by 9% and 4.5% respectively. 4.1 Model responses to land cover change The hydrological impacts of land use have received a considerable amount of interest in hydrology. LULC is an important characteristic in the runoff process that affects infiltration, erosion, and evapotranspiration. Understanding of the effects historic land use changes have had on river flow is required to understand the future effects of land use and land cover on hydrological regimes at a watershed level. Along with these changes, considerable consequences are expected in the hydrological cycles and subsequent effects on water resources (Githu, 2007 ). The SWAT model simulated for the three time periods corresponding to the land use cover of 1996, 2004 and 2013. Simulation runs were conducted on monthly basis to compare the modeling outputs using the 1996, 2004 and 2013 land covers. A comparison of the land covers 1996, 2004 and 2013 and average annual stream flows generated using 1996, 20004 and 2013 land covers respectively is presented in (Table: 18 and Fig. 30). The SWAT model run by using the sensitive parameter fitting value by multiplied and replaced. The result indicated that the mean annual surface flow for 2004 LULC increase by 7.4%, and4.8 for 2013 LULC. Average annual catchment stream flows are directly related to land cover type characteristics. In the study area, agricultural land/ cultivated land areas have increased between 1996, 2004 and slightly decrease at 2013 from 2004 and with most of the increase occurring in previously areas of grass land/ pasture, wetland (marsh) and forest land. Forest areas have the highest potential for decrease runoff because the land is improve cover in a watershed and reduces infiltrations. To understand the flow processes during different seasons under different land cover conditions, the average monthly stream flows were plotted for each map. 5. Conclusion SWAT model integrated with GIS was used as a powerful tool to evaluate the impact of watershed characteristics on surface runoff in Beshilo Watershed. From different scenario development which are climate characteristics scenario, slope scenario and land use land cover change analysis it can be concluded that there were a significant land use and land cover change in the study watershed during period from 1996 to 2004 and 2004to 2013. Agricultural land increased by 14.2%. The From performance evaluation of the model the results of the monthly coefficient of determination (R 2 ) and Nash- Sutcliffe coefficient was 0.76 and 0.88 for calibration period, 0.84 and 0.84 for validation period. The percent bias of the mode for this study is 18.4% for calibration 19.2% for validation. The SWAT modeling shows the result indicated that the mean annual stream flow were increase by 7.4% with 2004 LULC and increased by 6.8% with 2013 LULC from 1996 LULC. So the result shows the land use land cover change is a great effect on surface run off in Beshilo catchment. To reduce surface run off in the study area land use land cover amendment is important. Declarations Author Contributions: A.S, conceived and developed the research framework. A.S and G.M, undertook the data processing and analysis. G.M., wrote and revised the manuscript. A.S supervised and revised the manuscript. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest: The author declares no conflict of interest. Availability of data and material All data generated and analyzed during this study are included in this published article. Funding statement: - There is no funding Agency References Arnold, J. G., Allen, P. M., & Bernhardt, G. (1993).A comprehensive surface groundwater flow model. Journal of Hydrology, 142, 47-69. Arnold, J.G., J.R. Williams, R. Srinivasan, K.W. King, and R.H.Griggs. (1995). SWAT - Soilnand Water Assessment Tool. Draft User’s Manual. USDA-ARS, and Temple, TX. Arnold J. G., Srinivasan R., Muttiah R. S., and Williams J. R. (1998). Large area hydrologic modeling and assessment, Part I: model development. Journal of American Water Resources Association, Vol. 34(1), 73-89. Dessie Water Supply and Design project by Water Works Design and Supervision Enterprise 2007 Di Luzio, M. Di, R. Srinivasan, J.G. Arnold, S.L. Neitsch, (2002). ArcView Interface for SWAT2000 (AVSWAT2000), User’s Guide, Grassland Soil and Water Research Laboratory, Blackland Research Center, Texas Agricultural Experiment Station, Texas Water Resources Institute, Texas Water Resources Institute, College Station, Texas TWRI communication. 2nd edition. World Soil Resources Reports No. 103. FAO, Rome. Githu, (2007) Assessing the impacts of environmental change on the hydrology of the Nzoia catchment, in the Lake Victoria Basin Department of Hydrology and Hydraulic Engineering Faculty of Engineering Vrije Universities Brussel. Moriasi, D., Arnold, J., & & Van Liew, M. W. 2007. Model evaluation guideline for systematic quantification of accuracy in watershed simulation. Multi variable catchment,. 885-900 Nash, J., & Sutcliffe, J. (1970). River flow forecasting through conceptual models part I. A discussion of principles. Journal of Hydrology, 10, 282–290. Nash, J., & Sutcliffe, J. (1970). River flow forecasting through conceptual models part I. A discussion of principles. Journal of Hydrology, 10, 282–290. Neitsch S. L., Arnold J. G., Kiniry J. R., and Williams J. R. (2005). Soil and water assessment tool (SWAT), theoretical documentation, Blackland research center, grassland, soil and water research laboratory, agricultural research service: Temple, Texas. Priestley, C.H.B. and R.J. Taylor. (1972). on the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather. Rev. 100:81-92 Refsgaard, J.C. and Storm, B., (1996). Construction, calibration and validation of hydrological models, Distributed Hydrological Modelling (eds. M.B.Abbott and J.C. Refsgaard), Kluwer Academic Publishers, 41-54. Robert L. F. (2002). Handbook of water sensitive planning and design. Lewis publishers. Sharpley, A.N. and J.R. Williams, eds. (1990). EPIC-Erosion Productivity Impact Calculator, Model documentation. U.S. Department of Agriculture, Agricultural Research Service, Tech. Bull. 1768. Shimelis G. (2008). Hydrological and sediment yield modeling in Lake Tana basin, Blue Nile Ethiopia. M.Sc paper, KTH-hydraulic engineering research group. SWAT‐CUP: SWAT Calibration and Uncertainty Programs ‐ A User Manual. Williams, J.R. (1995). Chapter 25: The EPIC model. p. 909-1000. In V.P. Singh (ed). Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch Co. Willems, P. (2000). Probabilistic modeling of the emission receiving surface waters. PhD thesis, Faculty of Engineering, Katholieke Universiteit, Leuven, Belgium. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1729506","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":111369229,"identity":"3c041f2e-7848-4a5f-a0fa-4fa7224fc5ba","order_by":0,"name":"Asalf Shumete Eshete","email":"","orcid":"","institution":"Wollo University, Kombolcha institute of technology, Kombolcha, Ethiopia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Asalf","middleName":"Shumete","lastName":"Eshete","suffix":""},{"id":111369230,"identity":"fc7c7d74-8ff1-4f85-9574-6ec680883c7d","order_by":1,"name":"Girum Metaferia Affessa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACZjApIcfG3gCkDSyI1mJhzMdzAKRFgmi7KhLnSSSArSOsVt6d95nUzRwJxjbJ51c3/CiQYOBv707Aq8XwMLuZdO42CWY26Zyymz1Ah0mcObsBv5ZmNjaQFiCZk3aDB6jFQCKXOC08bJJn0m7+IUaLPDNEiwSbBPux20TZYsDMxmwN1GLAxpPDdlvGQIKHoF/k+48x3s7dVlc/v/34s5tv/tjI8bf3ErDlAJzJYwAm8SoH29IAZ7I/IKh6FIyCUTAKRiYAAPiIO+84qeL4AAAAAElFTkSuQmCC","orcid":"","institution":"Wollo University, Kombolcha institute of technology, Kombolcha, Ethiopia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Girum","middleName":"Metaferia","lastName":"Affessa","suffix":""}],"badges":[],"createdAt":"2022-06-06 08:44:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1729506/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1729506/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22587375,"identity":"3a20581b-643c-41f2-bdcb-024077e3a875","added_by":"auto","created_at":"2022-06-13 15:50:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155447,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/400642d56c743bcf3fbfa107.png"},{"id":22585983,"identity":"30e7a9d2-a84e-405a-90ca-5508bed6e296","added_by":"auto","created_at":"2022-06-13 15:40:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82764,"visible":true,"origin":"","legend":"\u003cp\u003eTheiysson Polygon of the study area to show the areal rain fall in depth\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/6a9cf8f84083b1b499db6fdd.png"},{"id":22585982,"identity":"f03a0f67-80fc-48d7-9571-05ce15a99b5c","added_by":"auto","created_at":"2022-06-13 15:40:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40899,"visible":true,"origin":"","legend":"\u003cp\u003eDouble mass curves for selected stations\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/bee404c40eb42af4186799b4.png"},{"id":22587377,"identity":"8e95ae9d-0d43-48be-90b8-8e4a14ab3e90","added_by":"auto","created_at":"2022-06-13 15:50:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46839,"visible":true,"origin":"","legend":"\u003cp\u003eHomogeneity test for selected station in Beshilo catchment\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/820931293ed07a46b2db16e6.png"},{"id":22585986,"identity":"875e860b-d269-42ad-9272-33778f8dce8e","added_by":"auto","created_at":"2022-06-13 15:40:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28565,"visible":true,"origin":"","legend":"\u003cp\u003eDischarge relations between gauged and ungagged River\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/863993a7ed3eba396cb9135e.png"},{"id":22586793,"identity":"a537a261-0a85-409a-b0e0-018c83f11936","added_by":"auto","created_at":"2022-06-13 15:45:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":100061,"visible":true,"origin":"","legend":"\u003cp\u003eOverall flowchart used in the study\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/87b2ad41afec4807b7fdcb3c.png"},{"id":22587376,"identity":"c4766ea6-803a-4fc1-bcf2-629292169ad3","added_by":"auto","created_at":"2022-06-13 15:50:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":93571,"visible":true,"origin":"","legend":"\u003cp\u003eSub basins in the catchment by SWAT\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/218b3497a8dbb280974ba646.png"},{"id":22586799,"identity":"df97a08e-8f45-46fe-8be2-07f0c2d51a83","added_by":"auto","created_at":"2022-06-13 15:45:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":241575,"visible":true,"origin":"","legend":"\u003cp\u003eSlope classifications at SWAT\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/da6fd2a9789887cfadda00a8.png"},{"id":22585992,"identity":"dc5e8c77-c03c-4851-a343-1cfc3b90b2da","added_by":"auto","created_at":"2022-06-13 15:40:40","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":166894,"visible":true,"origin":"","legend":"\u003cp\u003eLULC classifications at 1996\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/eece21643b520de1b6326fea.png"},{"id":22586796,"identity":"f1a49447-7926-47fd-bc54-7561d4128758","added_by":"auto","created_at":"2022-06-13 15:45:39","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":265008,"visible":true,"origin":"","legend":"\u003cp\u003eLULC classifications at 2004\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/6679dc3f7e05b925835b0b38.png"},{"id":22585994,"identity":"c2a431c1-916b-4f5c-bd8d-321d0c5f1d49","added_by":"auto","created_at":"2022-06-13 15:40:40","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":242663,"visible":true,"origin":"","legend":"\u003cp\u003eLULC classification at 2013\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/79c3910369a4f84b40f1fc2f.png"},{"id":22585989,"identity":"401444fc-2140-4213-9236-61e83a5ce309","added_by":"auto","created_at":"2022-06-13 15:40:39","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":109867,"visible":true,"origin":"","legend":"\u003cp\u003eSoil classifications by Arc SWAT\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/46264764702a5ecc7d7e61f4.png"},{"id":22586797,"identity":"bab81eae-1096-4a57-89e5-8ddf36182e15","added_by":"auto","created_at":"2022-06-13 15:45:40","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":107514,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis of flow with graph view from SWAT cup\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/699f4a6c6f0e1fe59972d629.png"},{"id":22585985,"identity":"8ab6d8fe-a7aa-4915-8d42-39b77f0db992","added_by":"auto","created_at":"2022-06-13 15:40:39","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":63499,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly and Mean annual simulated and observed surface flow\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/2fb8553876ba107da2234025.png"},{"id":22855094,"identity":"e59103da-7ef4-4b01-a0c8-facdad8728a6","added_by":"auto","created_at":"2022-06-20 17:14:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1961828,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1729506/v1/c9771144-b4bc-46e5-a402-96b1edeef043.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Land use and Land cover change on stream flow of Beshilo watershed, Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLand and water resources degradation are the major problems on Ethiopian highlands. Poor land use practices and improper management systems have significant role in causing high soil erosion rates, sediment transport and most importantly, the loss of water resources both in quantity and quality (Shimelis, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Erosion disturbs the channel stability and as a result downstream areas may receive excessive sediment loads, leading to poor water quality (Robert, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). A river\u0026rsquo;s flow rate and water quality is dependent on the land-use practices within their entire watershed. Therefore, watershed management is an essential part of maintaining healthy productive rivers. Particularly a better understanding of the hydrological characteristics of different watersheds in the head of the Blue Nile River is one of considerable importance for government special interest towards developing water resources of this catchment at a larger scale. For instance, the Beshilo catchment is considered as one of tributary of Blue Nile.To enhance national economic development the great Ethiopian renaissance dam and the Ribb dam are among the many development attempts on the Blue Nile River. Beshilo River is one of most affected sub basin in the region as soil erosion, sediment transport and land degradation are great concerns. The land and water resources of the sub basin and its ecosystem are in danger due to the nature of catchment and rapid growth of population, deforestation and overgrazing, soil erosion or sediment transportation is serious problem for BeshiloRiver. So land use /land cover change impact on the flow of Beshilo river is important, the study was done using soil and water assessment tool (SWAT) model and GIS are adopted.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSWAT Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAmong the many hydrologic models developed in the past decade, the Soil and Water Assessment Tool (SWAT), developed by (Arnold et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), has been used extensively by researchers. This is because SWAT uses readily available inputs for weather, soil, land, and topography, it allows considerable spatial detail for basin scale modeling, and it is capable of simulating change in catchment characteristics using different scenarios. SWAT is recognized by the U.S. Environmental Protection Agency (EPA) and has been incorporated into the EPA\u0026rsquo;s BASINS (Better Assessment Science Integrating Point and Nonpoint Sources) (Di Luzio et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003ea). [BASINS is a multipurpose environmental analysis software system developed by the EPA for performing watershed and water quality studies on various regional and local scales.]. In order to optimally calibrate the model parameters, especially for large-scale modeling, an auto-calibration routine has been added to SWAT.\u003c/p\u003e \u003cp\u003eSWAT is a river basin scale, continuous time, a spatially distributed model developed to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions over long period of time (Neitsch et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). SWAT model (Arnold et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Arnold), has proven to be an effective tool for assessing water resource and nonpoint source pollution problems for a wide range of scales and environmental conditions across the globe. SWAT is a physically based watershed-scale continuous time-scale model, which operates on a daily time step.\u003c/p\u003e \u003cp\u003eThe SWAT model can simulate runoff, sediment, nutrients, pesticide, and bacteria transport from agricultural watersheds (Arnold et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The SWAT model delineates a watershed, and sub-divides that watershed in to sub-basins. In each sub-basin, the model creates several hydrologic response units (HRUs) based on specific land cover, soil, and topographic conditions. Model simulations that are performed at the HRU levels are summarized for the sub-basins. Water is routed from HRUs to associated reaches in the SWAT model. SWAT first deposits estimated pollutants within the stream channel system then transport them to the outlet of the watershed. The HRUs provide opportunity to include processes for possible spatial and temporal variations in model input parameters. The hydrologic module of the model quantifies a soil water balance at each time step during the simulation period based on daily precipitation inputs. The SWAT model distinguishes the effects of weather, surface runoff, evapotranspiration, crop growth, nutrient loading, water routing, and the long-term effects of varying agricultural management practices (Neitsch et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In the hydrologic module of the model, the surface runoff is estimated separately for each sub-basin and routed to quantify the total surface runoff for the watershed. Runoff volume is commonly estimated from daily rainfall using modified SCS-CN method. The model needs several data inputs to represent watershed conditions which include: digital elevation model (DEM), land use and land cover, soils and climate data.\u003c/p\u003e"},{"header":"2. Materials And Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eBeshilo-watershed is located in the upper part Blue Nile, the biggest tributary of Blue Nile river, it starts with the water divide between these basins with mountains and extends with plateau of wet land of the Gerado area and then with very rough mountainous regions till, all perennial and intermittent rivers within the study area flows to joins the main river Beshilo River watershed in the Blue Nile basin with a drainage area of nearly 5700 km2. The sub-watershed is geographically\u003c/p\u003e \u003cp\u003eLocated between 38.67\u0026ndash; 39.70 degree longitude and 10.82\u0026ndash;11.72 degree latitude. The altitude in-watershed ranges between 4250 masl at the outlet and 1100 masl at the upper part of the River .The mean annual rainfall varies between 1080 mm in northern part to 937 mm in the lower part of the catchments. The majority of the watershed characterized by a humid tropical climate with heavy rainfall, and most of the annual is 971mm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection and sources\u003c/h2\u003e \u003cp\u003eThe required data for the Arc SWAT model include: spatial data (Digital Elevation Model (DEM), land-use/land-cover map and soil map), meteorological data (daily rainfall data, daily max and min temperature, daily wind speed, daily radiation, and daily relative humidity), and hydrological data (stream flow data). These data obtained from various sources as described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.These meteorological stations located within the study area (Beshilo -watershed) and contained same missing data. The missed data-filled using Normal ratio method and the double-mass curve method used to check the data\u0026rsquo;s consistency. Meteorological data preparation and quality assurance Daily meteorological data of 20 years (1997\u0026ndash;2016 G.C) collected from the National Meteorological Service Agency (NMSA) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The missed data computed by the linear regression method and filled before preparing as input for the Arc SWAT model. The consistency of precipitation data checked using a double-mass curve method and consistent. Finally, the daily precipitation, temperature (max and min), wind speed, solar radiation, and relative humidity of all stations arranged vertically with the spread sheet\u0026rsquo;s corresponding date. Then, the arranged data saved in a comma-delimited format as required for the Arc SWAT model input. The prepared meteorological data used as input to the model to be processed and generate an output. There were six meteorological stations in the study area, selected based on the availability of the station\u0026rsquo;s data and representativeness in the study area. The selected meteorological stations were Ambamariam, Kombolcha, Serinka, Tenta, Kutaber and Wogel tena.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData type and Sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScale / Period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Water, Irrigation and Energy(MoWIE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30mX30m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital Elevation Model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Water, Irrigation and Energy(MoWIE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1986,2004,2013 G.C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLand use classification map\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Water, Irrigation and Energy(MoWIE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2004 G.C map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil map classification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrological data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinistry of Water, Irrigation and Energy(MoWIE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1999\u0026ndash;2014 G.C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily Stream flow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetrological data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational Metrological Service Agency (NMSA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1997\u0026ndash;2016 G.C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily rain fall\u003c/p\u003e \u003cp\u003eDaily Max and min temperature\u003c/p\u003e \u003cp\u003eDaily Wind speed\u003c/p\u003e \u003cp\u003eDaily Solar Radiation\u003c/p\u003e \u003cp\u003eDaily Relative Humidity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Consistency for rain fall\u003c/h2\u003e \u003cp\u003eDouble-mass curve analysis technique. Double-mass curve analysis is a graphical method for identifying or adjusting inconsistencies in a station record by comparing its time trend with those of other stations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Homogeneity test\u003c/h2\u003e \u003cp\u003eHomogeneity analysis is used to identify a change in the statistical properties of the time series data. The cause\u0026rsquo;s non homogeneity can be either natural or man-made; these include alterations to land use and relocation of the observation station.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Hydrological Data\u003c/h2\u003e \u003cp\u003eThe stream flow data collected from MWRE has a longer time series data for Beshilo from Gummara Rivers, for this study a daily time series data from 1990 to 2014 were used. Gummara and Beshilo rivers have a similar runoff trend see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e with a high correlation on a long-term monthly basis around 0.96 of R square for the study period. The long-term average monthly discharge of the two rivers is maximum in August and minimum around March and April.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Methodology\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 Overall frame work of the study\u003c/h2\u003e \u003cp\u003eThe study required different materials and methods to arrive at the stated objectives. Meteorological, hydrological, digital elevation model, land use and land cover and soil data were required. The SWAT model inters face with Arc GIS and SWAT Cup is used to evaluate land use /land cover watershed on stream flow. Arc GIS 10.3 and its extension Arc SWAT 2012 were used for hydrological model. The stream flow simulation by the SWAT model was calibrated and validated by comparing simulated stream flow with observed values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.7 SWAT Model Setup\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.7.1 Watershed Delination\u003c/h2\u003e \u003cp\u003eWatershed and sub watershed delineation was carried out using 30m Resolution DEM data by means of Arc SWAT is in to set up a project so as to create necessary folders to store all the data. The watershed delineation process consists of five important steps, DEM setup, stream delineation, outlet and inlet definition, watershed outlet selection and definition and calculation of sub basin parameter. After the DEM setup was completed and the location of outlet was specified on the DEM, the model automatically calculates the flow direction and flow accumulation, consequently stream networks, sub water sheds and topographic were calculated using the respective tool. The size of the sub basin in the watershed will affect the assumption of homogeneity, because definition of watershed, sub basin boundaries and stream is decided by selecting a threshold area or the minimum drainage area to define stream, configuration of a lot of sub -basin required a long time simulation period and even difficult to run. On the other hand too small number of sub watershed is affecting the simulation result by ignoring spatial variability. In general the number of sub basins chosen to model the watershed depends on the size of the watershed, the spatial detail of available input data and the amount of detail required to meet the goals of the project. When subdividing the watershed, keep in mind that topographic attributes (slope, slope length, channel length, channel width, etc.) are calculated or summarized at the sub basin level. The sub basin delineation should be detailed enough to capture significant topographic variability within the watershed, for this study using 25000 ha threshold area in percentage and the watershed was divided in to eleven sub basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.7.2 HRU Definition\u003c/h2\u003e \u003cp\u003eThe Arc AWAT model required the creation of hydrologic Response units (HRUs), which are the unique combination of land use, soil and slope classes within each sub basin. In this study performed HRU analysis menu on the Arc SWAT tool bar, by loading land use and soil maps, evaluate slope characteristics and determine the land use/soil/slope class combination in the delineated sub watershed. In the model there are two options in defining HRU distribution assign a single HRU to each sub watershed or assign multiple HRUs to each sub watershed depends on a certain threshold value. The Arc SWAT use manual suggests that 20%land use threshold, 10% soil threshold and 20% slope threshold are adequate for most modeling application. So for this study using the above recommendation and the watershed divided in to 11 sub basin and 38 HRUs. In addition HRU analysis in Arc SWAT includes division of HRUs by slope class; multi slope was selected for this study. Slope classification carried out based on the elevation range of DEM, it reclassify in to 5 class.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ethe slop class used in the model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClss1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClss2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClss3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClss4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClss5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope range (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.7.3 Land Use Classification\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eAgriculture land\u003c/strong\u003e \u003cp\u003eThese are the areas identified as dominantly cultivated on the land cover map. Although animals play an important role in these areas, they are considered as secondary to cultivation. The key economic activity in these areas is cultivation, especially for grains, and this area is sources of Crops production like Wheat and Teff.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePastoral land\u003c/strong\u003e \u003cp\u003eThese are the grass land areas, Pastoral areas are particularly use for animal pastured Most cultivated land is pastured after harvest but not include in pasture land but include bush lands and shrub lands are all grazed.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePlanted area\u0026ndash;\u003c/b\u003e it is difficult to define but for this stud land use including moderately forest, scarcely forest and land use by plantation area are grouped under this category.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBarr land\u003c/strong\u003e \u003cp\u003ethe land which are not used for human purpose\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eWet land\u003c/strong\u003e \u003cp\u003eareas which are lands but cover with water and used for wild life purpose\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC classification at different year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLand use land cover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWAT Code\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea (km2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eArea (km2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarr land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBARR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3707.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4477.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanted area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFRSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e706.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e662.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e626.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e472.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e242.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWet land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWATR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.7.4 Watershed in put table\u003c/h2\u003e \u003cp\u003e \u003cb\u003eThe\u003c/b\u003e weather data which are rain fall, temperature, relative humidity and wind seeped are prepare based on SWAT database format using text format and the table prepared using id, name, lat, long, elevation, finally insert to the SWAT.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eWeather Generator\u003c/strong\u003e \u003cp\u003eSWAT includes the WXGEN weather generator model (Sharpley and Williams, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) to generate climatic data or to fill in gaps in measured records.. The weather generator first independently generates precipitation for the day. Once the total amount of rainfall for the day is generated, the distribution of rainfall within the day is computed if the Green \u0026amp; Ampt method is used for infiltration, maximum temperature, minimum temperature, solar radiation and relative humidity are then generated based on the presence or absence of rain for the day. Finally, wind speed is generated independently. To generate the data, weather parameters were developed by using the weather parameter calculator WXPARM (Williams, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and dew point temperature calculator DEW02. For this study there are seven stations from those four stations are synoptic station, the remaining two stations have rain fall and temperature records from this used 1997\u0026ndash;2016 G.C. Dew exe Dew.exe using average daily temperature and average daily relative humidity in percent but the dew02 software used daily minimum and maximum temperature, and relative humidity in %. For this study used dew02 software and the other data\u0026rsquo;s for weather generator the rain fall data is prepared using PCP STAT.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Model Calibration And Validation","content":"\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003e3.1 Model Calibration\u003c/h2\u003e\n \u003cp\u003eThe time series of discharge at the outlet of the catchment was used as data for calibration and validation for SWAT model, the model was calibrated using the measured stream flow data from 1999 to 2008 with worm up period. The parameters were optimized first using the calibration tool, then calibration was done by adjusting parameters until the simulated and observed value showed good agreement. In this process, model parameters varied until recorded flow patterns are accurately simulated. Model calibration of SWAT run can be divided in to several steps. Among these Water balance and stream flow generation are the most important part is also considered. (Refsgaard, \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e). For this research work the measured stream flow data of were calibrated using SWAT Cup model from SWAT model output using 10 years flow, automatic calibration period (from January 1st, 1999 to December 31st, 2008) and the warm up period (from January 1st, 1997 to December 31st, 1998). From three SWAT model using three year land use land cover map,1196,2004,2013, and also the calibration is operated as flows the scenario development based on the watershed characteristics used for evaluation for my study the calibration is done for the three land use land cover map, for evaluate climate characteristics, rain fall effect, and slope effect in the catchment area, finally table 11 shows the average calibration value.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003e3.2 Model Validation\u003c/h2\u003e\n \u003cp\u003eIn order to utilize the calibrated model for estimating the effectiveness of future potential \u0026nbsp;management practices, the model tested against an independent set of measured data. This \u0026nbsp;testing of a model on an independent set of data set is commonly referred to as model validation. \u0026nbsp;As the model predictive capability was demonstrated as being reasonable in both the calibration \u0026nbsp;and validation phases, the model was used for future predictions under different management \u0026nbsp;scenario. For this research work the measured stream flow data of Beshilo from 01 January 2009 to 31 December, 2014 were used. The average model calibration output from deferent scenario, land use land cover map, 1996, 2004, 2013, and other characteristics shows the following relationship and value. After calibrating and validation for flow simulation was executed and the hydrographs are well captured. The agreement between the measurement and simulation is generally very good, which are verified by NSE and R2 and an acceptable result were obtained according to the model evaluation guideline (Moriasi et al., 2007). The calibration and validation period of the model was fifteen years from 1999 to 2008 for calibration and 2009-2014for validation G.C.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe observed and simulated average monthly stream flow was computed. During calibration it was 121.79 m3/s and 105.45m3/s for observed and simulated respectively. On the other hand, the observed and simulated was 147.1m3/s and 112.33m3/s respectively during validation period. These indicates reasonable agreement between observed and simulated values in both calibration and validation periods\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec18\"\u003e\n \u003ch2\u003e3.3 Sensitivity Analysis\u003c/h2\u003e\n \u003cp\u003eAfter the SWAT model setup has been finished, the next step is to run the model and analyze the global sensitivity. Sensitivity analysis is used to estimate the rate of change of model outputs with respect to change of model inputs. It is also useful to recognize how the model depends on the information fed into it (Willems, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). Sensitivity analysis provides for better understanding of the behavior of the system being modeled, such as model parameters and applicability, thus it increases the confidence level of the model and its predictions. SWAT model have large number of parameters and a number of outputs, thus, an initial parameter selection makes the calibration process easier and reduces the uncertainties related to diverse parameters. In the sensitivity process, by using SWAT CAP is used for this study. SWAT-CUP is an interface that was developed for SWAT, Using this generic interface, any calibration/uncertainty or sensitivity is operated .program can easily be linked to SWAT. This is demonstrated by the program links SWAT Cup is having 4 algorism GLUE, Parasol, SUFI2, and MCMC procedures to SWAT. In this particular study it was preferred to use sequential uncertainty fittings (SUFI2). It is automated.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSensitivity Rank\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFlow parameter(SWAT)code\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003cp\u003ebound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003cp\u003ebound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFitted\u003c/p\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity rank\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCS curve number (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.9167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALPHA_BF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBase flow alpha factor(days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGWQMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThreshold depth of water in the shallow aquifer required for return flow(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e412.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREVAPMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThreshold depth of water in the shallow aquifer for \u0026quot;revap\u0026quot; (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e312.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOL_AWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil available water capacity (water/ mm soil)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCANMX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum canopy storage(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil evaporation compensation factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOL_K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaturated Hydraulic conductivity\u003c/p\u003e\n \u003cp\u003e[mm/hr.]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.7107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGW_REVAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGround water \u0026quot;revap \u0026quot;coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u0026ndash;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOL_Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal soil depth(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-17.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCH_K2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEffective hydraulic conductivity of the main channel(mm/hr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e331.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRCHRG_DP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep aquifer percolation fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec19\"\u003e\n \u003ch2\u003e3.4 Model Evaluation\u003c/h2\u003e\n \u003cp\u003eThe performance of SWAT was evaluated using statistical measures to determine the quality and reliability of predictions when compared to observed values. Coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) \u0026nbsp;and Nash-Sutcliffe simulation efficiency (ENS) were the goodness of fit measures used to evaluate model prediction. The R\u003csup\u003e2\u003c/sup\u003e value is an indicator of strength of relationship between the \u0026nbsp;observed and simulated values. The Nash-Sutcliffe simulation efficiency (ENS) indicates how well the plot of observed versus simulated value fits the 1:1 line. If the measured value is the \u0026nbsp;same as all predictions, ENS is 1. If the ENS is between 0 and 1, it indicates deviations between \u0026nbsp;measured and predicted values. If ENS is negative, predictions are very poor, and the average \u0026nbsp;value of output is a better estimate than the model prediction (Nash, Sutcliffe, 1970). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneral performance rating for recommended statistics for a monthly time (Moriasi, et al.2007)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePerformance Rating\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFor Stream Flow\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRSR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePBIAS (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;RSR\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026thinsp;\u0026lt;\u0026thinsp;NSE\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePBIAS\u0026thinsp;\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;RSR\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u0026thinsp;\u0026lt;\u0026thinsp;NSE\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026thinsp;\u0026lt;\u0026thinsp;PBIAS\u0026thinsp;\u0026lt;\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatisfactory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;RSR\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u0026thinsp;\u0026lt;\u0026thinsp;NSE\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026thinsp;\u0026lt;\u0026thinsp;PBIAS\u0026thinsp;\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnsatisfactory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSR\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNSE\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePBIAS\u0026thinsp;\u0026gt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Results And Discussion","content":"\u003cp\u003eEvaluation the impact of land use land cover changes on Stream flow was the most important parts of this study. The study was performed for the period from 1997to 2016. The three produced land use land cover maps, six climate data, soil map, and stream flow values were used to assess the impact of land use and land cover change on stream flow. To evaluate the variability of surface runoff by land use land cover dynamics from 1997to 2016, three independent SWAT Runs were carried out on monthly time step using 1996,2004, and 2013 land use land cover maps, for the three runs the other SWAT parameter are unchanged. Based on the simulation output the seasonal stream flow variability caused by land use land cover change was assessed and comparison was made on surface run of contribution to stream flow for period 1999\u0026ndash;2014 G.C\u003c/p\u003e \u003cp\u003eThe land cover map of 2004 is covered the following land class coverage that about 78.32% of the Beshilo catchment was covered by agricultural land, 13.85% by Grass Landor pasture land, -7.75% by forest land or planted area, 0.03% by wet land or water body and 0.0.5% covered by Barr land. The distribution of land cover class as indicates agricultural land cover large amount of percentage from the total area. The land cover map of 2004 is covered the following land class coverage that about 86.54% of the Beshilo catchment was covered by agricultural land, 9.14% by Grass Landor pasture land, 4.09% by forest land or planted area, 2.57% by wet land or water body. The distribution of land cover class as indicates agricultural land cover large amount of percentage from the total area and but increased by 14.8% from 1996 LLCU, forest land is reduced by 7.8% and grass land reduced by 2.97% compare to land use/cover at 1996\u003c/p\u003e \u003cp\u003eThe land cover map of 2013 is covered the following land class coverage that about 82.41% of the catchment was covered by agricultural land, 4.68% by Grass Land or pasture land, 12.8% by forest land or planted area, 0.09% by wet land or water body. The distribution of land cover class as indicates agricultural land cover 3.8% more than 1996LULCand reduced by 4.13% 2004 LULC but the forest area increased by 8.7% and reduced 1.05%, grass land increased by 06 from 2004LULC and reduced by 9% and 4.5% respectively.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Model responses to land cover change\u003c/h2\u003e \u003cp\u003eThe hydrological impacts of land use have received a considerable amount of interest in hydrology. LULC is an important characteristic in the runoff process that affects infiltration, erosion, and evapotranspiration. Understanding of the effects historic land use changes have had on river flow is required to understand the future effects of land use and land cover on hydrological regimes at a watershed level. Along with these changes, considerable consequences are expected in the hydrological cycles and subsequent effects on water resources (Githu, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The SWAT model simulated for the three time periods corresponding to the land use cover of 1996, 2004 and 2013. Simulation runs were conducted on monthly basis to compare the modeling outputs using the 1996, 2004 and 2013 land covers. A comparison of the land covers 1996, 2004 and 2013 and average annual stream flows generated using 1996, 20004 and 2013 land covers respectively is presented in (Table: 18 and Fig.\u0026nbsp;30). The SWAT model run by using the sensitive parameter fitting value by multiplied and replaced. The result indicated that the mean annual surface flow for 2004 LULC increase by 7.4%, and4.8 for 2013 LULC.\u003c/p\u003e \u003cp\u003eAverage annual catchment stream flows are directly related to land cover type characteristics. In the study area, agricultural land/ cultivated land areas have increased between 1996, 2004 and slightly decrease at 2013 from 2004 and with most of the increase occurring in previously areas of grass land/ pasture, wetland (marsh) and forest land. Forest areas have the highest potential for decrease runoff because the land is improve cover in a watershed and reduces infiltrations. To understand the flow processes during different seasons under different land cover conditions, the average monthly stream flows were plotted for each map.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eSWAT model integrated with GIS was used as a powerful tool to evaluate the impact of watershed characteristics on surface runoff in Beshilo Watershed. From different scenario development which are climate characteristics scenario, slope scenario and land use land cover change analysis it can be concluded that there were a significant land use and land cover change in the study watershed during period from 1996 to 2004 and 2004to 2013. Agricultural land increased by 14.2%. The From performance evaluation of the model the results of the monthly coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) and Nash- Sutcliffe coefficient was 0.76 and 0.88 for calibration period, 0.84 and 0.84 for validation period. The percent bias of the mode for this study is 18.4% for calibration 19.2% for validation. The SWAT modeling shows the result indicated that the mean annual stream flow were increase by 7.4% with 2004 LULC and increased by 6.8% with 2013 LULC from 1996 LULC. So the result shows the land use land cover change is a great effect on surface run off in Beshilo catchment. To reduce surface run off in the study area land use land cover amendment is important.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eA.S, conceived and developed the research framework. A.S and G.M, undertook the data processing\u0026nbsp;and analysis. G.M., wrote and revised the manuscript.\u0026nbsp;A.S supervised and revised the manuscript. All authors have\u0026nbsp;read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The author declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement: -\u003c/strong\u003eThere is no funding Agency\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArnold, J. G., Allen, P. M., \u0026amp; Bernhardt, G. (1993).A comprehensive surface groundwater \u0026nbsp;flow model. Journal of Hydrology, 142, 47-69.\u003c/li\u003e\n \u003cli\u003eArnold, J.G., J.R. Williams, R. Srinivasan, K.W. King, and R.H.Griggs. (1995). SWAT - Soilnand Water Assessment Tool. Draft User\u0026rsquo;s Manual. USDA-ARS, and Temple, TX.\u003c/li\u003e\n \u003cli\u003eArnold J. G., Srinivasan R., Muttiah R. S., and Williams J. R. (1998). Large area hydrologic \u0026nbsp;modeling and assessment, Part I: model development. Journal of American Water Resources Association, Vol. 34(1), 73-89.\u003c/li\u003e\n \u003cli\u003eDessie Water Supply and Design project by Water Works Design and Supervision Enterprise 2007\u003c/li\u003e\n \u003cli\u003eDi Luzio, M. Di, R. Srinivasan, J.G. Arnold, S.L. Neitsch, (2002). ArcView Interface for \u0026nbsp;SWAT2000 (AVSWAT2000), User\u0026rsquo;s Guide, Grassland Soil and Water Research \u0026nbsp;Laboratory, Blackland Research Center, Texas Agricultural Experiment Station, Texas \u0026nbsp;Water Resources Institute, Texas Water Resources Institute, College Station, Texas TWRI communication. 2nd edition. World Soil Resources Reports No. 103. FAO, Rome.\u003c/li\u003e\n \u003cli\u003eGithu, (2007) Assessing the impacts of environmental change on the hydrology of the Nzoia catchment, in the Lake Victoria Basin Department of Hydrology and Hydraulic \u0026nbsp;Engineering Faculty of Engineering Vrije Universities Brussel.\u003c/li\u003e\n \u003cli\u003eMoriasi, D., Arnold, J., \u0026amp; \u0026amp; Van Liew, M. W. 2007. Model evaluation guideline for \u0026nbsp;systematic quantification of accuracy in watershed simulation. Multi variable catchment,. \u0026nbsp;885-900\u003c/li\u003e\n \u003cli\u003eNash, J., \u0026amp; Sutcliffe, J. (1970). River flow forecasting through conceptual models part I. A discussion of principles. Journal of Hydrology, 10, 282\u0026ndash;290.\u003c/li\u003e\n \u003cli\u003eNash, J., \u0026amp; Sutcliffe, J. (1970). River flow forecasting through conceptual models part I. A discussion of principles. Journal of Hydrology, 10, 282\u0026ndash;290.\u003c/li\u003e\n \u003cli\u003eNeitsch S. L., Arnold J. G., Kiniry J. R., and Williams J. R. (2005). Soil and water \u0026nbsp;assessment tool (SWAT), theoretical documentation, Blackland research center, grassland, \u0026nbsp;soil and water research laboratory, agricultural research service: Temple, Texas.\u0026nbsp;\u0026nbsp; \u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePriestley, C.H.B. and R.J. Taylor. (1972). on the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather. Rev. 100:81-92\u003c/li\u003e\n \u003cli\u003eRefsgaard, J.C. and Storm, B., (1996). Construction, calibration and validation of \u0026nbsp;hydrological models, Distributed Hydrological Modelling (eds. M.B.Abbott and J.C. \u0026nbsp;Refsgaard), Kluwer Academic Publishers, 41-54.\u003c/li\u003e\n \u003cli\u003eRobert L. F. (2002). Handbook of water sensitive planning and design. Lewis publishers.\u003c/li\u003e\n \u003cli\u003eSharpley, A.N. and J.R. Williams, eds. (1990). EPIC-Erosion Productivity Impact Calculator,\u0026nbsp; \u0026nbsp;Model documentation. U.S. Department of Agriculture, Agricultural Research Service, \u0026nbsp;Tech. Bull. 1768.\u003c/li\u003e\n \u003cli\u003eShimelis G. (2008). Hydrological and sediment yield modeling in Lake Tana basin, Blue Nile Ethiopia. M.Sc paper, KTH-hydraulic engineering research group.\u003c/li\u003e\n \u003cli\u003eSWAT‐CUP: SWAT Calibration and Uncertainty Programs ‐ A User Manual.\u003c/li\u003e\n \u003cli\u003eWilliams, J.R. (1995). Chapter 25: The EPIC model. p. 909-1000. In V.P. Singh (ed). Computer \u0026nbsp;models of watershed hydrology. Water Resources Publications, Highlands Ranch Co.\u003c/li\u003e\n \u003cli\u003eWillems, P. (2000). Probabilistic modeling of the emission receiving surface waters. PhD \u0026nbsp;thesis, Faculty of Engineering, Katholieke Universiteit, Leuven, Belgium.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Arc SWAT, Stream flow analysis, SWAT-CUP","lastPublishedDoi":"10.21203/rs.3.rs-1729506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1729506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand degradation is a series problem in Ethiopia highlands, particular in abbay basin reflected in the form of soil erosion and soil fertility decline from time to time. Beshilo catchment is one of the Abbay basin tributary, which covers 5,700 km\u003csup\u003e2\u003c/sup\u003e; this shows that the problem of the catchment is significant effect in Abbay basin development. In this study the impact of Land use/ Land cover change on annual outflow contribution of Beshilo Catchment is evaluated, distributed physically based hydrological model known as soil and water assessment tool (SWAT) model was used this study. Changes were analyzed and characterized impacts on surface runoff were evaluated. The model was calibrated and validated over the gauged upper reaches of catchments of Gummara River. The model was calibrated for the period from 1999\u0026ndash;2008 and validated for the period from 2009\u0026ndash;2014. The performance of the model was evaluated on the basis of performance rating criteria, coefficient of determination, Nash \u0026amp; Sutcliff efficiency. The overall performance of the models gives good result. For this study the land use land cover change scenario, were developed using its change. From the land cover change analysis results it was found that there has been a substantial decline of forest lands, shrub lands, grass lands and expansion of agricultural land. The SWAT modeling shows the result indicated that the mean annual stream flow were increase by 7.4% with 2004 LULC from 1996 LULC and increased by 6.8% with 2013 LULC from 1996 LULC.\u003c/p\u003e","manuscriptTitle":"Impact of Land use and Land cover change on stream flow of Beshilo watershed, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-13 15:40:37","doi":"10.21203/rs.3.rs-1729506/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33e3879f-3d1d-41ec-9e60-caf94a43f25b","owner":[],"postedDate":"June 13th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-20T17:14:24+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-13 15:40:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1729506","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1729506","identity":"rs-1729506","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}