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Ming Pan
Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, University of California San DiegoMeeting roles in:
Advancing Precipitation Predictions with Physical Models and Artificial Intelligence I Oral
Knowledge-Guided Graph Machine Learning Enables High-Resolution Nitrogen Transport Modeling in the Upper Mississippi River Basin
Advancing Precipitation Predictions with Physical Models and Artificial Intelligence II Poster
Using FIRO to Understand Reservoir Operational Resilience Under Variable Hydrometeorological Conditions at Howard A. Hanson Dam in Washington State
Spatiotemporal Changes and Driving Factors of Riverine Nutrient Export at HUC12 Scale in the Mississippi/Atchafalaya River Basin
A Hybrid Modeling and Digital Twin Framework for Field-Scale Water and Nutrient Dynamics of Tile Drainage Systems
Seasonal Streamflow Forecasting in Colorado’s Yampa River Basin Using WRF-Hydro with LSTM Enhancement
Probabilistic Diffusion Models Advance Extreme Flood Forecasting
Spatiotemporal Patterns and Driving Factors of Event-scale Nitrate Concentration and Discharge Relationship in the Upper Mississippi River Basin
From Predictions to Patterns with AI: A Differentiable SPARROW Framework for Improved Water Quality Prediction and Attribution
Water, Agriculture, and Conservation: Sustaining Agricultural Production and Healthy Watersheds I Oral
Adaptive Reservoir Operations in a Snow-Dominated Basin Using Direct Policy Search
Water, Agriculture, and Conservation: Sustaining Agricultural Production and Healthy Watersheds II Oral
Developing a Targeted Conservation Framework for Nitrogen Loss Reduction Based on Watershed Typology in the Upper Mississippi River Basin
Water, Agriculture, and Conservation: Sustaining Agricultural Production and Healthy Watersheds III Poster
Global Reach Scale Hydrology: Progress and Challenges
LSTM-Enhanced Streamflow Forecasting for California-Nevada Region: a Post-Processing Framework for CHPS and WRF-Hydro
Global High-Resolution Hydrologic Simulation to Enhance Predictions in Ungauged Regions with Hybrid Differentiable Models
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