Parameter Uncertainty Analysis for Streamflow Simulation Using SWAT Model in Nashe Watershed, Blue Nile River Basin, Ethiopia

Abstract

e hydrological model is an important tool in water resource management, allocation, and prediction. However, the hydrological models are subject to the uncertainty resulting from di erent sources of errors involved in the large number of parameters. e hydrological models in the management of water resources play a very signi cant role in quantifying uncertainty. erefore, uncertainty analysis implementation is essential to advance con dence in modeling before performing the hydrological sim ulation. e purpose of the study was to assess the uncertainty parameters for the stream ow using the Soil and Water Assessment Tool (SWAT) hydrological model integrated sequential uncertainty tting (SUFI-2) algorithm to Nashe watershed located in the Northwestern, Upper Blue Nile River Basin. e required input data for this study were digital elevation model, land use, soil map and data, meteorological data (precipitation, minimum and maximum temperature, wind speed, solar radiation, and relative humidity), and stream ow data. e calibration and validation model was computed to simulate the observed stream ow data from 1985 to 2008 including two years of warm-up periods. Model calibration, validation, and analysis of parameter uncertainty were conducted for both daily and monthly observed stream ows at the gauging stations through SUFI-2, which is one of the algorithms of the SWAT-Calibration and Uncertainty Program (SWAT_CUP). e results show that CN_2, GW_DELAY, ALPHA_BNK, CH_N2, and SOL_AWC were the most sensitive parameter for the monthly period and had a great impact on the stream ow simulation. Modeling results indicated that the method provides better results for the monthly time period than the daily time period for both calibration and validation. e result indicated that R2 and NSE were 0.89 and 0.85 and 0.82 and 0.79, respectively, monthly and daily during the calibration. e validation likewise demonstrated a good performance with R2 and NSE results of 0.88 and 0.78 and 0.85 and 0.76, respectively, for monthly and daily time periods. e results of this study provide a scienti c reference based on uncertainty analysis to decision-makers to improve the decision support process in river basin management.