Data‐driven rainfall–runoff models have advanced rapidly, yet the majority of large‐scale applications still rely on lumped inputs that smooth out spatial variability in precipitation, temperature, and landscape properties. This simplification can introduce substantial biases in flood peaks, hydrograph timing, and wate...
Jin-Yang Li, Kuo-Lin Hsu, S. Sorooshian et al.· Water Resources Research· 0 citations
Streamflow prediction is essential for water resources management, flood forecasting, and climate resilience. Long short-term memory (LSTM) networks have advanced large-sample hydrology through cross-basin learning, but their recurrent architectures have limited ability to capture long-range temporal dependencies, part...
Electrical resistivity tomography (ERT) is a subsurface imaging geophysical technique. Traditional ERT inversion methods, such as smoothness‐constrained least‐squares approaches, often suffer from discretization artifacts when reconstructing electrical resistivity from resistance measurements. To address the limita...
Yusen Yuan, K. Carroll, Huichao Yin et al.· Journal of Geophysical Resea...· 0 citations
STORM is introduced, a one-stage generative AI framework that reformulates DA as diffusion-based Bayesian posterior sampling, replacing online PDE ensemble forecasts with scalable AI inference, enabling high-resolution, long-context Earth modeling and demonstrating improved accuracy.
Xiao Wang, Ze-Zhong Zhang, Isaac Lyngaas et al.· arXiv.org· 1 citation
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