Hybrid Hydroinformatics: Integrating Machine Learning and Process-Based Modeling for Climate-Resilient Water Systems

The hydrologic system is subjected unprecedented stresses and increasing demands driven by climate variabilities, land use changes, groundwater overexploitation and the increasing pressures of freshwater utilization. These pressures have heightened the need for robust groundwater vulnerability assessment methods aimed at identify aquifers at risk of contamination, depletion, and salinization, and to support sustainable groundwater management under changing climatic and anthropogenic conditions. Meanwhile, significant breakthroughs of environmental sensing, high-resolution geospatial data, cloud supercomputing and AI technologies have revolutionized the field of hydroinformatics. Machine learning (ML), deep learning (DL) and hybrid models are increasingly used to couple with physically-based hydrologic models to enhance prediction accuracy, scaling capability, and operational decision support. New generation of hydroinformatics approaches now facilitate a spectrum of applications, including evapotranspiration, soil moisture and groundwater prediction/protection, flood forecasting, watershed management, and climate resilience evaluation. While numerous purely data-driven methods have shown promising results, many challenges related to physical consistency, interpretability, uncertainty quantification, and independent transferability are still encountered. This Research Topic endeavors to provide a forum for fostering integrated process-based and data-driven models to improve robustness, interpretability, and resilience of water systems.

This Research Topic focuses on integrating process-based hydrologic modeling with machine learning, deep learning, remote sensing, and broader hydroinformatics approaches. The goal is to encourage studies that bridge physically based understanding with data-driven intelligence while maintaining scientific rigor, interpretability, and physical consistency. We particularly welcome contributions related to agrohydrology, groundwater–surface water interactions, irrigation dynamics, groundwater protection, evapotranspiration processes, and climate-resilient agricultural water systems. Emphasis is also placed on hybrid approaches that improve parameterization of physically based hydrologic models where direct field measurements are difficult, sparse, uncertain, or expensive to acquire. This includes ML-assisted estimation of soil hydraulic properties, groundwater recharge, crop coefficients, aquifer parameters, stream–aquifer exchange, and land-surface process representation. The collection seeks scalable and transferable frameworks that improve prediction, uncertainty quantification, and operational water-resource decision support under changing climatic and environmental conditions.

This collection welcomes original research articles, reviews, methodological advances, datasets, and case studies related to hydroinformatics and hybrid hydrologic modeling. Topics include machine learning and deep learning applications in hydrology; integration of process-based models such as SWAT, APEX, MODFLOW, WRF-Hydro, HYDRUS, and agrohydrologic modeling systems with ML/DL frameworks; remote sensing and geospatial AI; data assimilation; surrogate modeling; uncertainty quantification; hybrids vulnerability indices and scalable computational workflows. Contributions focused on agrohydrology, irrigation systems, groundwater–surface water interactions, watershed processes, hydroclimate forecasting, and climate-resilient water management are especially encouraged. Particular interest is given to studies addressing parameter estimation and calibration challenges in physically based models, especially where observational data are limited or difficult to obtain. This includes AI-assisted parameterization, inverse modeling, transfer learning, explainable AI, and hybrid approaches for improving representation of soil, vegetation, aquifer, and land–atmosphere processes across diverse hydrologic and climatic regions.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Hydrology, Agricultural Water Management, Agrohydrology, Remote Sensing and Acquisition, Parameterization of any physical models, Open dataset, Groundwater vulnerability, data driven model, Machine/Deep Learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.