标题： 水文学中的科学机器学习：统一视角 显示英文标题

标题： Scientific machine learning in Hydrology: a unified perspective

摘要： 科学机器学习（SciML）提供了一种结构化的方法，将物理知识整合到数据驱动的建模中，为推动水文学研究带来了巨大潜力。 近年来，出现了多个方法学家族，包括基于物理学的机器学习、以物理学为导向的机器学习、混合物理-机器学习以及数据驱动的物理发现。 在这些家族中的每一个里，都独立发展出了多种异质性方法，往往缺乏概念上的协调。 这种碎片化使得评估方法创新变得复杂，并且在没有统一概念框架的情况下，很难确定仍可实现有意义进展的领域。 本综述是首个专注于水文学中SciML的概览，通过为每个SciML家族提出一个统一的方法学框架，将代表性贡献整合到一个连贯的结构中，从而促进概念清晰度并支持水文建模的累积性进步。 最后，我们强调了每个统一家族的局限性和未来机会，以指导水文学中的系统性研究，在该领域这些方法仍未得到充分利用。 显示英文摘要

摘要： Scientific machine learning (SciML) provides a structured approach to integrating physical knowledge into data-driven modeling, offering significant potential for advancing hydrological research. In recent years, multiple methodological families have emerged, including physics-informed machine learning, physics-guided machine learning, hybrid physics-machine learning, and data-driven physics discovery. Within each of these families, a proliferation of heterogeneous approaches has developed independently, often without conceptual coordination. This fragmentation complicates the assessment of methodological novelty and makes it difficult to identify where meaningful advances can still be made in the absence of a unified conceptual framework. This review, the first focused overview of SciML in hydrology, addresses these limitations by proposing a unified methodological framework for each SciML family, bringing together representative contributions into a coherent structure that fosters conceptual clarity and supports cumulative progress in hydrological modeling. Finally, we highlight the limitations and future opportunities of each unified family to guide systematic research in hydrology, where these methods remain underutilized.