AI-Driven Reinvention of Hydrological Modeling for Accurate Predictions and Interpretation to Transform Earth System Modeling
Abstract
Traditional equation-driven hydrological models often struggle to accurately predict streamflow in challenging regional Earth systems like the Tibetan Plateau, while hybrid and existing algorithm-driven models face difficulties in interpreting hydrological behaviors. This work introduces HydroTrace, an algorithm-driven, data-agnostic model that substantially outperforms these approaches, achieving a Nash-Sutcliffe Efficiency of 98% and demonstrating strong generalization on unseen data. Moreover, HydroTrace leverages advanced attention mechanisms to capture spatial-temporal variations and feature-specific impacts, enabling the quantification and spatial resolution of streamflow partitioning as well as the interpretation of hydrological behaviors such as glacier-snow-streamflow interactions and monsoon dynamics. Additionally, a large language model (LLM)-based application allows users to easily understand and apply HydroTrace's insights for practical purposes. These advancements position HydroTrace as a transformative tool in hydrological and broader Earth system modeling, offering enhanced prediction accuracy and interpretability.
Keywords
Cite
@article{arxiv.2501.04733,
title = {AI-Driven Reinvention of Hydrological Modeling for Accurate Predictions and Interpretation to Transform Earth System Modeling},
author = {Cuihui Xia and Lei Yue and Deliang Chen and Yuyang Li and Hongqiang Yang and Ancheng Xue and Zhiqiang Li and Qing He and Guoqing Zhang and Dambaru Ballab Kattel and Lei Lei and Ming Zhou},
journal= {arXiv preprint arXiv:2501.04733},
year = {2025}
}