Ensemble data-driven rainfall-runoff modeling using multi-source satellite and gauge rainfall data input fusion

Abstract

Feed Forward Neural Network (FFNN), Adaptive Neuro-fuzzy Inference System (ANFIS), and Support Vector Regression (SVR) were applied for rainfall-runoff modeling of the Gilgel Abay catchment, Blue Nile basin, Ethiopia. Daily precipitations from satellite sources and rain gauge stations and outlet discharge were used. The dominant inputs were selected by non-linear sensitivity analysis. The study was conducted in two stages. First, single models for each data source with input fusion were trained. Second, ensemble runoff modeling using rainfall data fusion from only satellite products (strategy 1) and satellite and gauge (strategy 2) was conducted by Simple Average (SA), Weighted Average (WA), and Neural Network Ensemble (NNE) methods. NNE method using input fusion of strategy 2 improved performance of the best single satellite model up to 14.5% and a single gauge model up to 8% in the validation. Strategy 2 input data fusion ensemble rainfall-runoff modeling indicated substantial improvement over satellite data-based runoff modeling. This could be due to the bias correction ability of gauge rainfall over satellite rainfall products. Overall, results showed that ensemble modeling of input fusion from multiple source satellite rainfall products is a promising option for accurate modeling of the rainfall-runoff process for ungagged or sparsely gauged catchments.