AI驱动的水文建模重塑：面向准确预测与解释以变革地球系统建模
AI-Driven Reinvention of Hydrological Modeling for Accurate Predictions and Interpretation to Transform Earth System Modeling
AI总结：
针对传统及混合水文模型预测不准、可解释性不足的问题，本文提出算法驱动且数据无关的HydroTrace，以注意力机制实现高精度径流预测、泛化与水文机制解析，并结合LLM支持实际应用。
摘要：
传统的方程驱动水文模型往往难以在青藏高原等具有挑战性的区域地球系统中准确预测径流，而混合模型和现有算法驱动模型在解释水文行为方面存在困难。本工作提出 HydroTrace，这是一种算法驱动、与数据无关的模型，显著优于上述方法，达到98%的Nash-Sutcliffe效率，并在未见过的数据上展现出很强的泛化能力。此外，HydroTrace利用先进的注意力机制捕捉时空变化和特定特征影响，从而能够对径流分割进行量化和空间分辨，并解释冰川—积雪—径流相互作用以及季风动力学等水文行为。另外，一个基于大型语言模型（LLM）的应用程序使用户能够轻松理解并将HydroTrace的洞见用于实际目的。这些进展使HydroTrace成为水文及更广泛地球系统建模中的变革性工具，提供更高的预测准确性和可解释性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
