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：精确的水文理解与水循环预测对于应对水资源管理相关的科学与社会挑战至关重要，尤其在人为气候变化的动态影响下。现有综述主要集中于机器学习（ML）在该领域的发展，但水文学与机器学习作为独立范式之间存在明显区别。本文引入物理感知机器学习作为一种变革性方法，以克服这一认知壁垒并革新这两个领域。具体而言，我们对物理感知机器学习方法进行了全面综述，构建了一个整合先验物理知识或基于物理建模的现有方法的结构化体系（PaML）。我们系统分析了这些PaML方法在四个方面的应用：物理数据引导的机器学习、物理信息机器学习、物理嵌入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设生成，加速了从大数据中获取洞见并推动科学发现。我们首先对PaML在水文学中的应用进行了系统回顾，包括降雨-径流水文过程与水动力过程，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。最后，我们发布了一个基于PaML的新型水文学平台——HydroPML，作为水文应用的基础。HydroPML提升了机器学习的可解释性与因果性，并为数字水循环的实现奠定了基础。HydroPML平台已在 https://hydropml.github.io/ 公开提供。