A hybrid model enhancing streamflow forecasts in paddy land use-dominated catchments with numerical weather prediction model-based meteorological forcings

Abstract

The development of a streamflow forecasting tool becomes a challenging task due to the sophisticated nonlinear catchment response, varying crop management practices, and limited in situ data availability. For real-time streamflow forecasting with up to 10-days lead-time, this study investigates the potential of SWAT-pothole (PSWAT) module forced with bias-corrected Global Forecasting System (GFS) meteorological variables; wherein the error-updating is carried out for the streamflow forecasts simulated by PSWAT. The error is updated by hierarchical data-driven sub-models, such as AutoRegressive (AR), AutoRegressive Moving Average with eXogenous inputs (ARMAX), Wavelet-based Neural Network (WNN), Wavelet-based Non-linear AutoRegressive with eXogenous inputs (WNARX), Long-Short Term Memory (LSTM), and a novel Wavelet-based Bidirectional LSTM (WBiLSTM). The efficacy of the standalone PSWAT module is tested against the hybrid models in the Brahmani-Baitarani (≈ 49,000 km2) compound River Basin in eastern India. The results revealed that the novel PSWAT-WBiLSTM hybrid model is the best for reliable streamflow forecasts up to 8-days’ and 9-days’ lead-times in the Brahmani and Baitarani River basins, respectively. Conclusively, this model can be a potential medium-range streamflow forecasting tool for paddy-dominated catchments.