High-resolution remote sensing-driven water management in semi-arid basins: A CNN-Attention-SWAT fusion framework for the Fen River

作者：Jiawen Liu, Xianqi Zhang, Yang Yang, Kaiqiang Fu, Kaimin Wang · 发表于：Science of Remote Sensing · 年份：2025 · DOI：10.1016/j.srs.2025.100333 · 被引用次数：4 · 研究领域：Hydrology and Watershed Management Studies、Flood Risk Assessment and Management、Groundwater and Watershed Analysis

The Fen River Basin (FRB), a critical ecological corridor in China's Yellow River Basin, faces escalating water-security challenges under climate change and intensive human activities. Pressures include concentrated precipitation patterns, severe agricultural non-point source pollution (contributing >60 % nitrogen loads), groundwater overdraft, and increasing river flow interruptions (67 days/year in 2020), demanding integrated solutions aligned with Sustainable Development Goal 6 (SDG6). We propose a physics-embedded deep learning (PIDL) paradigm with bidirectional coupling between mechanistic and data-driven engines: 1) SWAT-modeled soil water stress index (SWSI) and groundwater depth are embedded into CNN-Attention-BiLSTM inputs to enforce physical plausibility; 2) Deep learning prediction errors dynamically update SWAT parameters (e.g., SOL_K, CH_N2) via Bayesian inversion. NSGA-II multi-objective optimization generates management strategies, validated through Monte Carlo simulations and ecological feasibility checks. The coupled framework outperformed standalone models in spatio-temporal accuracy: Runoff prediction: R 2 = 0.94, RMSE = 0.12 mm/d (37 % improvement vs. unidirectional coupling); Pollution load error reduced by 14.3 % (hotspot identification accuracy: ±1.5 km); Ecological flow compliance reached 92 % (vs. 69 % baseline). NSGA-II-optimized strategies achieved synergistic benefits: drip irrigation (65 % coverage, 12 % groundwater reduction), vegetative buffers ...