The Impact of Hydrological Parameters and Climate Inputs on Extreme Streamflow Simulation in Upper Awash River Basin
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Date
2021-06-08
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Addis Ababa University
Abstract
Quantifying possible sources of uncertainty in simulations of hydrological extreme events is very important for better risk management in extreme situations and water resource planning. The main objective of this research work is to identify and address the role of input data quality and hydrological parameter sets, and uncertainty propagation in hydrological extremes estimation. This includes identifying and estimating their contribution to flood and low flow magnitude using two objective functions Nash-Sutcliffe efficiency for flood and Log Nash-Sutcliffe efficiency for low flow, 20, 000 Hydrological ByransVattenBalansa Vdelning hydrological parameter sets, and three frequency distribution models (Log-Normal, Pearson-III, and Generalized Extreme Value). The influence of uncertainty on the simulated flow is not uniform across all the selected three catchments due to different flow regimes and runoff generation mechanisms. The result shows that the uncertainty in high flow frequency modeling mainly comes from the input data quality. In the modeling of low flow frequency, the main contributor to the total uncertainty is model parameterization. The total uncertainty of QT90 (extreme peak flow quintile at 90-year return period) quintile shows that the interaction of input data and hydrological parameter sets has a significant role in the total uncertainty. In contrast, in the QT10 (extreme low flow quintile at 10-year return period) estimation, the input data quality and hydrological parameters significantly impact the total uncertainty. This implies that the main factors and their interactions may cause considerable risk in water resources management and flood and drought risk management. Therefore, neglecting these factors and their interaction in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to big risk.
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Uncertainty, Propagation, GLUE Generalize Likelihood Uncertainty Estimation, ANOVA (Analysis of Variance), Hydrological Model Extremes, HBV( Hydrological Byrans Vatten Balansavdelning )