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：准确理解水文过程和水循环预测对解决水资源管理相关的科学和社会挑战至关重要，尤其是在人为气候变化的动态影响下。现有综述主要关注机器学习（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分野。本文引入物理感知机器学习作为突破这一认知壁垒并革新两个领域的变革性方法。具体而言，我们系统综述了物理感知机器学习方法，构建了融合先验物理知识或基于物理建模的ML方法论结构化社区（PaML）。我们从四个维度系统分析PaML方法论：物理数据引导型ML、物理信息约束型ML、物理嵌入型ML以及物理感知混合学习。PaML通过ML辅助假说加速大数据洞察并促进科学发现。我们首先对水文学中的PaML开展系统性综述，涵盖降雨径流水文过程和水动力学过程，针对不同目标和PaML方法指出最具前景与挑战性的方向。最终，我们发布了基于PaML的新型水文学平台HydroPML，作为水文应用的基础设施。HydroPML增强了ML的可解释性与因果性，为数字水循环的实现奠定基础。HydroPML平台已于https://hydropml.github.io/ 公开上线。