Ensemble rainfall–runoff modeling of physically based semi-distributed models using multi-source rainfall data fusion

Abstract

Abstract This study was aimed at ensemble rainfall–runoff modeling by Soil and Water Analysis Tool (SWAT), the Hydrologic Engineering Center's Hydraulic Modeling System, and Hydrologiska Byråns Vattenbalansavdelning of Gilgel-Abay watershed, Blue Nile basin, Ethiopia. For modeling, daily rainfall datasets of five gauges and three satellites, streamflow, and spatial data were used. In the modeling stage, first, the runoff was simulated separately using the rainfall data of gauges, satellites, and their fusion. Second, ensemble rainfall–runoff simulation of the rainfall dataset fusion-based runoff result was conducted via the proposed weighted average, simple average, and neural network (NNE) ensemble techniques. The results exhibited that all models are good in capturing the rainfall–runoff relationship; however, SWAT showed slight superiority by Nash–Sutcliffe efficiency of 0.807 and 0.821 for gauge and fusion data, respectively. The rainfall fusion ensemble model revealed significant improvement over modeling by satellite rainfall owing to the bias-correcting gauge rainfall over satellite rainfall. The NNE technique enhanced the efficiency of the low-performing satellite-rainfall-based model by 17.5% and the rainfall fusion-based model by 13.3% at the validation stage. In general, the result of this study points out that the rainfall datasets’ fusion from multi-sources would be worthy for the rainfall–runoff simulation of ungauged basins.