Accurate hydrological understanding and water cycle prediction are crucial for addressing scientific and societal challenges associated with the management of water resources, particularly under the dynamic influence of anthropogenic climate change. Existing reviews predominantly concentrate on the development of machine learning (ML) in this field, yet there is a clear distinction between hydrology and ML as separate paradigms. Here, we introduce physics-aware ML as a transformative approach to overcome the perceived barrier and revolutionize both fields. Specifically, we present a comprehensive review of the physics-aware ML methods, building a structured community (PaML) of existing methodologies that integrate prior physical knowledge or physics-based modeling into ML. We systematically analyze these PaML methodologies with respect to four aspects: physical data-guided ML, physics-informed ML, physics-embedded ML, and physics-aware hybrid learning. PaML facilitates ML-aided hypotheses, accelerating insights from big data and fostering scientific discoveries. We first conduct a systematic review of hydrology in PaML, including rainfall-runoff hydrological processes and hydrodynamic processes, and highlight the most promising and challenging directions for different objectives and PaML methods. Finally, a new PaML-based hydrology platform, termed HydroPML, is released as a foundation for hydrological applications. HydroPML enhances the explainability and causality of ML and lays the groundwork for the digital water cycle's realization. The HydroPML platform is publicly available at https://hydropml.github.io/.

翻译：精准理解水文过程与预测水循环，对于应对水资源管理中的科学与 societal 挑战至关重要，尤其是在人为气候变化动态影响下。现有综述主要聚焦机器学习在该领域的发展，但水文学与机器学习作为独立范式仍存在明显界限。本文提出物理感知机器学习这一变革性方法，以跨越这一认知壁垒并革新两个领域。具体而言，我们全面综述了物理感知机器学习方法，构建了现有方法论的结构化共同体（PaML），这些方法将先验物理知识或基于物理的建模融入机器学习。我们系统分析了这些PaML方法学的四个维度：物理数据引导的机器学习、物理知识约束的机器学习、物理嵌入的机器学习，以及物理感知混合学习。PaML促进了机器学习辅助的假说生成，加速从大数据中获取洞见并推动科学发现。我们首先系统评述了PaML在水文学中的应用（涵盖降雨-径流与水文动力学过程），并针对不同目标与PaML方法突出了最具前景与挑战性的方向。最终，发布了基于PaML的新型水文学平台——HydroPML，作为水文应用的基础。HydroPML增强了机器学习的可解释性与因果性，为数字水循环的实现奠定基础。HydroPML平台开源地址：https://hydropml.github.io/