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 km) 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.

中文翻译：

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一种混合模型，利用基于数值天气预报模型的气象强迫，增强以稻田利用为主的流域的水流预测

由于复杂的非线性流域响应、不同的作物管理实践以及有限的现场数据可用性，径流预测工具的开发成为一项具有挑战性的任务。对于提前时间长达 10 天的实时水流预报，本研究调查了 SWAT-pothole (PSWAT) 模块在偏差校正全球预报系统 (GFS) 气象变量的影响下的潜力；其中，对PSWAT模拟的径流预报进行误差更新。误差由分层数据驱动子模型更新，例如自回归（AR）、具有外源输入的自回归移动平均（ARMAX）、基于小波的神经网络（WNN）、具有外源输入的基于小波的非线性自回归（ WNARX）、长短期记忆（LSTM）和一种新颖的基于小波的双向 LSTM（WBiLSTM）。独立 PSWAT 模块的功效在印度东部的 Brahmani-Baitarani（约 49,000 公里）复合河流域的混合模型上进行了测试。结果表明，新型 PSWAT-WBiLSTM 混合模型最适合对 Brahmani 河流域和 Baitarani 河流域分别进行长达 8 天和 9 天提前期的可靠水流预报。总之，该模型可以成为稻田为主的流域的潜在中期径流预测工具。