标题： 基于人工智能的水文建模重塑，用于准确预测和解释以改变地球系统建模 显示英文标题

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

摘要： 传统基于方程的水文模型在预测像青藏高原这样的复杂区域地球系统中的径流时往往难以准确，而混合模型和现有的算法驱动模型在解释水文行为方面面临困难。 这项工作介绍了HydroTrace，一种算法驱动、数据无关的模型，其性能显著优于这些方法，在未见数据上表现出强大的泛化能力，达到了98%的纳什-萨特克莱效率。 此外，HydroTrace利用先进的注意力机制来捕捉时空变化和特征特定影响，能够量化和空间分辨径流分配，并解释水文行为，如冰川-积雪-径流相互作用和季风动态。 另外，基于大型语言模型（LLM）的应用使用户能够轻松理解和应用HydroTrace的见解以用于实际目的。 这些进展使HydroTrace成为水文和更广泛的地球系统建模中的变革性工具，提供了更高的预测精度和可解释性。 显示英文摘要

摘要： 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.