Data-Driven and AI-Enhanced Hydro-Environmental Systems: From Atmospheric Forcing to Intelligent Water Management

Climate change and intensifying human activities have profoundly altered the dynamics of hydro-environmental systems, posing unprecedented challenges to water resource management, agricultural sustainability, and pollution control. Traditional process-based models, while mechanistically sound, often struggle with computational efficiency and parameter uncertainty when addressing complex, nonlinear interactions across atmospheric, hydrological, and agricultural domains. Meanwhile, the proliferation of multi-source observation data and advances in artificial intelligence have opened new paradigms for understanding and predicting water-related processes at multiple scales. This Research Topic aims to bridge the gap between atmospheric sciences, hydrological modeling, environmental management, and intelligent decision-making systems. We invite cutting-edge research that integrates atmospheric forcing with hydrological and non-point source pollution models, leverages deep learning architectures (e.g., CNN-LSTM, Transformer, Graph Neural Networks) for enhanced predictions, and develops intelligent solutions for precision irrigation, water infrastructure optimization, and sustainable water-environment management. Key topics of interest include, but are not limited to: • Atmosphere-driven hydrological and water quality modeling under changing climate • Deep learning and hybrid models (physics-informed neural networks, data-model fusion) for streamflow, flood, and drought prediction • AI-based frameworks for non-point source pollution assessment and control • Intelligent irrigation systems integrating meteorological forecasts, soil moisture sensing, and crop water requirements • Digital twins and real-time decision support systems for water resource management • Uncertainties quantification and interpretability of AI models in hydro-environmental applications• Multi-scale modeling: from watershed to regional agricultural water management • Smart water infrastructure: reservoir operation, drainage systems, and hydraulic structures optimization • Remote sensing and IoT integration for water-agriculture-environment monitoring • Climate adaptation strategies for water security and agricultural resilience We particularly encourage submissions that demonstrate innovative integration of mechanistic understanding with data-driven approaches, cross-disciplinary methodologies, and practical applications that can inform policy and management decisions. Both original research articles and comprehensive reviews are welcome. This Research Topic Issue seeks to foster collaboration among hydrologists, environmental scientists, agricultural engineers, data scientists, and civil engineers to advance the frontier of intelligent water management in the era of big data and artificial intelligence. Topic editors: Xianyong Meng, Hao Wang, Yiping Wu, Liujun Zhu Source: https://www.frontiersin.org/research-topics/76488/data-driven-and-ai-enhanced-hydro-environmental-systems-from-atmospheric-forcing-to-intelligent-water-management