Rainfall Runoff Modeling Using HEC-HMS for Shaya Catchment, Genale Dawa Basin, Ethiopi

Abstract

Rainfall is a time-consuming and complex natural meteorological event to predict. Accurate rainfall data is essential for planning and managing water supplies, as well as river operation and flood control. Rainfall- runof models have been used to characterize nonlinear hydrological processes, predict extreme events, and assess future climate change and land use implications The HEC HMS model can also be calibrated and validated to simulate dif erent land uses and how they af ect hydrological reactions. Therefore, the main objective of this study was to look at the rainfall-runof relationship for water resource uses and management using SMA and SCS-CN loss method algorism in HEC-HMS to simulate in Shaya catchment, Genale-Dawa River Basin in Ethiopia. Long term daily rainfall data from 4 rain gauging stations from1983 to 2016 years, daily river flow of 1 stream gauging station from 1983 to 2016 years, land use and soil data of the watershed, and high resolution DEM were obtained from relevant sources. These data were then analyzed and interpreted, and used to set up the HEC-HMS model. In this study, SCS-CN and SMA loss method, SCS-UH and Clark UH transformation method, Recession and Linear Reservoir base flow method and Muskingum routing method were adopted. In order to fix the Hydrologic parameters of each watershed, first the sensitivity analysis was carried out with the base data, and then the model calibration was performed using data from 1983 to 2005 and validation for the period from 2006 to 2016 at a daily time step. The sensitivity analysis of dif erent model parameters was ranked according to their sensitivity in terms of percent change in simulated runof volume and peaks. Sensitivity analysis helped to understand the behavior of the model and relationships between the key model parameters and the variables. The model performance was evaluated based on computed statistical parameters and visual checking of plotted hydro graphs. For the calibration period, the performance of SCS- CN Algorism event model ranges from good to very good with a coef icient of determination R 2= 0.753, Nash- Sutclif e Ef iciency NSE = 0.79, percentage error in volume PEV=9.79%, percentage error in peak PEP =20.3%, Root Mean Squared Error (RMSE)= 0.46. Similarly, the event model performance for the validation period ranges from good to very good with R 2 = 0.8511, NSE =0.91, PEV =-10.21%, PEP = 22.9%, RMSE = 0.35. and for SMA Algorism continuous model ranges from good to very good with a coef icient of determination R 2= 0.754, Nash-Sutclif e Ef iciency NSE = 0.94, percentage error in volume PEV=-10.69%, percentage error in peak PEP = 0.61%, and Root Mean Squared Error (RMSE)=0.3. Similarly, the continuous model performance for the validation period ranges from good to very good with R 2 = 0.8564, NSE = 0.958, PEV = 11.15%, PEP = 14.2%, RMSE = 0.2. The performance results obtained showed that, both SCS-CN and SMA model in the HEC-HMS was found to give a good simulation of stream flow in the Shaya Catchment. the LULC time dependent factor, flood prediction, and regionalization of stream flow was not considered and assessed in this study, therefore extra study should be analyzed to increase runof .