Vadose Zone Model–Data Fusion: State of the Art and Future Challenges

Abstract

Models are quantitative formulations of assumptions regarding key physical processes, their mathematical representations, and site‐specific relevant properties at a particular scale of analysis. Models are fused with data in a two‐way process that uses information contained in observational data to refine models and the context provided by models to improve information extraction from observational data. This process of model–data fusion leads to improved understanding of hydrological processes by providing improved estimates of parameters, fluxes, and states of the vadose zone system of interest, as well as of the associated uncertainties of these values. Notwithstanding recent progress, there are still numerous challenges associated with model–data fusion, including: (i) dealing with the increasing complexity of models, (ii) considering new and typically indirect measurements, and (iii) quantifying uncertainty. This special section presents nine contributions that address the state of the art of model–data fusion.