2025 CSDMS meeting-024
From CSDMS
(if you haven't already)
Log in (or create account for non-CSDMS members)
Forgot username? Search or email:CSDMSweb@colorado.edu
Browse abstracts
High-Resolution Differentiable Models for Operational National and Global Water Modeling and Assessment
Yalan Song,
The Pennsylvania State University University Park Pennsylvania, United States. songyalan1@gmail.com
Chaopeng Shen, The Pennsylvania State University , United States. cshen@engr.psu.edu
Haoyu Ji, The Pennsylvania State University , United States. hjj5218@psu.edu
Jiangtao Liu, The Pennsylvania State University , United States. jql6620@psu.edu
Farshid Rahmani, The Pennsylvania State University , United States. fzr5082@psu.edu
Xiaofeng Liu, The Pennsylvania State University , United States. xzl123@psu.edu
Continental and global water models have long been trapped in slow growth and inadequate predictive power, as they are not able to effectively assimilate information from big data. While Artificial Intelligence (AI) models greatly improve performance, purely data-driven approaches do not provide strong enough interpretability and generalization. One promising avenue is “differentiable” modeling that seamlessly connects neural networks with physical modules and trains them together to deliver real-world benefits in operational systems. Differentiable modeling (DM) can efficiently learn from big data to reach state-of-the-art accuracy while preserving interpretability and physical constraints, promising superior generalization ability, predictions of untrained intermediate variables, and the potential for knowledge discovery. Here we demonstrate the practical relevance of a high-resolution, multiscale water model for operational continental-scale and global-scale water resources assessment. (https://bit.ly/3NnqDNB). Not only does it achieve significant improvements in streamflow simulation compared to the established national- and global water models, but it also produces much more reliable depictions of interannual changes in large river streamflow, freshwater inputs to estuaries, and groundwater recharge. As a related topic, we also showcase the value of foundation AI for global environmental change and its benefits for resource management.
