Reconstruction of Daily Runoff Series in Data-Scarce Areas Based on Physically Enhanced Seq-to-Seq-Attention-LSTM Model

With the advancement of remote sensing-based river discharge monitoring in data-scarce regions, reconstructing daily streamflow series from remote sensing data has become a critical hydrological challenge. To address the sparsity of remote sensing inversions and the discontinuity of discharge observations, we propose a physics-enhanced deep learning model—Physics-enhanced Seq-to-Seq Attention LSTM (PSAL)—to achieve high-accuracy daily streamflow reconstruction. The model incorporates input structures aligned with hydrological mechanisms, providing a physically meaningful basis for interpretability and enabling physics-guided learning. Results show that (1) PSAL achieves high reconstruction accuracy across five representative gauging sites on the Jinsha River (mean NSE = 0.81). Among lagged output configurations from T-1 to T-7 days, the T-7 setting yields the best performance (mean NSE = 0.85). (2) Compared with a baseline Seq-to-Seq Attention LSTM model without physics-enhanced features, PSAL significantly improves reconstruction skill (mean ΔNSE = 0.76). Feature ablation analysis further reveals that precipitation, as a key driver of runoff, has a strong influence on model performance (mean ΔNSE = 0.32). This study presents a novel approach that integrates physical knowledge with data-driven methods for streamflow reconstruction in remote sensing-dominated, data-scarce regions, offering theoretical support and methodological guidance for digital twin watershed development and historical hydrological data infilling.