Ensemble physically based semi-distributed models for the rainfall-runoff process modeling in the data-scarce Katar catchment, Ethiopia

Abstract

This study evaluates the performance of the soil and water assessment tool (SWAT), the hydrologiska byråns vattenbalansavdelning (HBV) and the hydrologic engineering center-hydrologic modeling system (HEC-HMS) for modeling rainfall-runoff in the data-scarce Katar catchment, Ethiopia. First, the rainfall-runoff process was simulated using the SWAT, HBV and HEC-HMS models individually. Second, simple average ensemble (SAE), weighted average ensemble (WAE) and neural network ensemble (NNE) techniques were developed by combining the results of individual models to improve overall accuracy. Statistical performance measures and flow duration curves (FDCs) were used to compare and evaluate the performance of the models. The results showed that the SWAT model outperformed the HBV and HEC-HMS models with the coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) of 0.857 and 0.83 for calibration and 0.85 and 0.799 for validation, respectively. The ensemble result showed that NNE outperformed the SAE and WAE techniques, with NSE and R2 values of 0.924 and 0.925 for calibration and 0.896 and 0.904 for validation, respectively. The NNE technique improved the performance of SWAT, HBV and HEC-HMS by 12.14, 22.7 and 26.8%, respectively, in the validation phase. Overall, the results showed that ensemble modeling is a promising option for accurate modeling of the rainfall-runoff process. HIGHLIGHTS Novel ensemble approach was proposed to simulate the complex and dynamic rainfall-runoff process.; Spatial, hydrological and meteorological data were used as input for the semi-distributed models.; Three ensemble techniques were developed by combining the runoff results of the semi-distributed models to boost the overall efficiency.; The proposed ensemble technique significantly improved the modeling performance.;

Keywords

• ensemble modeling
• katar catchment
• physically based modes
• rainfall-runoff modeling