Scientific machine learning in Hydrology: a unified perspective
This review addresses the problem of methodological disorganization for researchers in hydrology, but it is incremental as it synthesizes existing approaches rather than introducing new techniques.
The paper tackles the fragmentation in scientific machine learning (SciML) methods applied to hydrology by proposing a unified framework for different methodological families, aiming to enhance conceptual clarity and support cumulative progress in hydrological modeling.
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.