Integrating data and models is essential for advancing our understanding and management of water resources in an era of rapid environmental change. Model–data fusion encompasses a spectrum of techniques—including sequential data assimilation, inverse modeling, machine learning, and hybrid physics–ML approaches, that link observations with numerical simulations to enhance predictive skill, uncover process interactions, and quantify uncertainty.

This Research Topic aims to bring together both methodological innovations and application-oriented studies that demonstrate how data integration enhances our capacity to simulate and predict hydrological, land surface, and subsurface processes. We invite contributions addressing theoretical developments, new frameworks, and case studies that provide insights into model performance, process representation, and decision-making across spatial and temporal scales.

Studies linking data and models in hydrometeorology, rainfall–runoff modeling, groundwater systems, and coupled surface–subsurface dynamics are particularly welcome. By uniting diverse approaches, this collection seeks to advance a data-informed, physically grounded modeling paradigm for water resources science.

We welcome contributions that address, but are not limited to, the following themes:

• Advances in Model–Data Fusion Methods

Development and evaluation of innovative data assimilation, inverse modeling, and hybrid physics–machine learning techniques for hydrological and environmental systems.

• Hybrid and Physics-Guided Machine Learning

Studies exploring how physics-informed neural networks, differentiable modeling, or hybrid architectures combine data-driven learning with process-based modeling.

• Emerging Data Sources and Computational Advances

Exploiting novel observation platforms (e.g., high-resolution remote sensing, sensor networks, citizen science) and high-performance or cloud computing frameworks to scale and accelerate model–data fusion.

• Benchmarking, Evaluation, Sensitivity and Uncertainty

Comparative and benchmarking studies assessing performance, transferability, and uncertainty quantification in model–data fusion across different models, scales, and datasets.

• Hydrometeorological and Land Surface Processes

Fusion of in situ, remote sensing, and reanalysis data to improve representation of precipitation, evapotranspiration, soil moisture, and surface energy dynamics.

• Surface and Subsurface Interactions

Integration of observational data and models for rainfall–runoff, groundwater, and coupled surface–subsurface systems to enhance process understanding and predictability.

• Operational and Decision-Support Applications

Case studies demonstrating the application of model–data fusion for real-time forecasting, early warning, water management, and climate adaptation.

All contributions must fall within the scope of Frontiers in Water and adhere to the journal’s rigorous standards of scientific quality and reproducibility. Submissions that integrate multidisciplinary perspectives and link fundamental science with applied water management are particularly encouraged.

We invite submissions in the form of: Original Research, Methods, Review, Mini Review, Perspective, and Brief Research Report.

Impact

Frontiers in Water

Water and Hydrocomplexity