Simulation Of Rainfall-Runoff Process Using Hec-Hms Model For Meki River Watershed, Rift Valley River Basin, Ethiopia

Abstract

Understanding the complex relationship between rainfall and runoff processes is judicious for proper estimation of runoff generated at outlet point. The stream flow of Meki river watershed is always fluctuating from season to season and this causes flooding problem on surrounding agricultural land. Estimating the amount of flood is very important to take appropriate action to mitigate its impacts. Hence, the aim of this study is to model stream flow and forecast flood of Meki river watershed using Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS). The data used for this thesis were soil, Land Use Land Cover (LULC), Digital Elevation Model (DEM) 30 m x 30 m resolution, 31 years (1987-2017) daily rainfall and 24 years (1987-2010) daily stream flow data. Daily rainfall data was collected from Ethiopian Meteorological Agency whereas DEM, soil and daily stream flow data were collected from Ethiopian Ministry of Water Resources, Irrigation and Electricity and LULC data was collected from Ethiopian Mapping Agency. The missed rainfall data was filled using station average and normal ratio method while the missed stream flow data was filled using linear regression method. The HEC-HMS input parameters were generated using Hydrologic Engineering Center Geospatial Hydrologic Modeling System (HEC-GeoHMS). The Gage weight meteorological method was selected to assign the areal rainfall computed by Isohyetal method to each sub-basin. The Soil Conservation Service Curve Number, Soil Conservation Service Unit Hydrograph, monthly constant base flow and Muskingum method were employed for loss computation, excess rainfall computation, base flow modeling and flood routing respectively. The model was calibrated and validated with stream flow data of 18 years (1987-2004) and 6 years (2005-2010) respectively. Nash-Sutcliffe Error (NSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R 2 ) were used to evaluate efficiency of the model, giving values of 0.832, 0.50, and 0.91 and 0.804, 0.40, and 0.89 during calibration and validation respectively. This indicates very good performance rating of the model. Flood prediction was conducted using 24 hour rainfall depth of 2, 10, 25, 50 and 100 years return period and found to be 133.21, 178.1 239.7, 313.2 and 346.19 m 3 /s respectively. Hence, these predicted values will help further researchers to prepare flood inundation map and take appropriate actions to control flood for this study area.