AI-Driven Reinvention of Hydrological Modeling for Accurate Predictions and Interpretation to Transform Earth System Modeling

• URL: http://arxiv.org/abs/2501.04733v1
• Date: Tue, 07 Jan 2025 18:59:53 GMT
• Title: AI-Driven Reinvention of Hydrological Modeling for Accurate Predictions and Interpretation to Transform Earth System Modeling
• Authors: Cuihui Xia, Lei Yue, Deliang Chen, Yuyang Li, Hongqiang Yang, Ancheng Xue, Zhiqiang Li, Qing He, Guoqing Zhang, Dambaru Ballab Kattel, Lei Lei, Ming Zhou,
• Abstract summary: HydroTrace is an algorithm-driven, data-agnostic model for predicting streamflow. It achieves a Nash-Sutcliffe Efficiency of 98% and demonstrates strong generalization on unseen data.
• Score: 19.028024402759467
• License:
• 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.

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