A robust forward-chain approach for ex-post and ex-ante seasonal river water-quality forecasting using remote sensing and hydroclimate forecast datasets

作者：Kunwar Abhishek Singh, Dongryeol Ryu, Meenakshi Arora, Bhabagrahi Sahoo, Manoj Kumar Tiwari · 发表于：Journal of Water Process Engineering · 年份：2026 · DOI：10.1016/j.jwpe.2026.110920 · 研究领域：Flood Risk Assessment and Management、Hydrology and Watershed Management Studies、Water Quality and Pollution Assessment

Understanding pollution dynamics in urban riverine ecosystems is essential for effective water-quality management and ecological sustainability. However, reliable seasonal forecasting remains challenging due to sparse observations and uncertainty in hydrometeorological drivers. To fill this gap, this study presents a novel forward-chain (FC) forecasting framework for ex-post and ex-ante seasonal river water-quality prediction, explicitly designed to bridge the gap between retrospective model evaluation and operational forecast deployment. The framework uses a data-driven, long short-term memory (LSTM) architecture that integrates Sentinel-2 remote sensing, European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5), and Copernicus Climate Change Service (C3S) seasonal forecast datasets. Model training was conducted exclusively using ERA5 data, while forecast skill was evaluated under both ex-post ERA5-driven and ex-ante C3S-driven conditions. The framework was applied to the lower Ganges River (Hooghly River, India), a large, complex, and highly urbanized river system, and evaluated for dissolved oxygen (DO), electrical conductivity (EC), turbidity, and total suspended solids (TSS) across 1–6 month forecast horizons using Kling–Gupta Efficiency (KGE) and normalized mean absolute error (MAE %). Unlike conventional retrospective studies, the framework explicitly compares ex-post ERA5-driven forecasts with ex-ante C3S-driven forecasts to evaluate operational forecast...