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

Abstract

Vadose Zone Model-Data Fusion:State of the Art and Future Challenges Models are quan ta ve formula ons of assump ons regarding key physical processes, their mathema cal representa ons, and site-specifi c relevant proper es at a par cular scale of analysis. Models are fused with data in a two-way process that uses informa on contained in observa onal data to refi ne models and the context provided by models to improve informa on extrac on from observa onal data. This process of model-data fusion leads to improved understanding of hydrological processes by providing improved es mates of parameters, fl uxes, and states of the vadose zone system of interest, as well as of the associated uncertain es of these values. Notwithstanding recent progress, there are s ll 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) quan fying uncertainty. This special sec on presents nine contribu ons that address the state of the art of model-data fusion.Abbrevia ons: AFHO, ac vely heated fi ber op cs; GPR, ground penetra ng radar; MCMC, Markov Chain Monte Carlo.The past two decades have witnessed signifi cant advances in vadose zone modeling and measurement technologies that have allowed the vadose zone community to tackle more complex problems with increasingly sophisticated measurement technologies. Th e required fusion of models with data is ideally achieved in a two-way process. Information contained in observational data is used to refi ne models, and the context provided by models is used to improve information extraction from available observational data or to identify information-rich data worth collecting. Despite the availability of more and better measurements and continued increases in computational power, it has become apparent that these advances have not solved the diffi culties associated with such model-data fusion. Rather, these new capabilities have highlighted some of the more fundamental challenges common to all scientifi c analysis and challenged our approaches to model conceptualization, parameterization, validation, and hypothesis (model) reformulation. Specifi cally, they have tested our assumptions regarding the interpretation and value of observations, especially for indirect observations in complex environments. In addition, more complex models have encouraged vadose zone hydrologists to tackle problems with high parameter dimensionality and underdetermined inverse problems, which have shed light on shortcomings of our standard approaches for model parameterization that were not evident for less diffi cult problems. To address these shortcomings, there is a need for formal statistical methods that recognize the role of forcing data and model structural error in the analysis of parameter and predictive uncertainty.Th ere are numerous challenges in model-data fusion in vadose zone hydrology (e.g., see the review of Vrugt et al., 2008a). Th is special section focuses on thr...

Combining Models of Root-Zone Hydrology and Geoelectrical Measurements: Recent Advances and Future Prospects

“…The degree of integration of data with models may range from simple trial-and-error calibration (e.g., Binley et al, 2002) to fully coupled hydrogeophysical inversion and DA (e.g., Rajabi et al, 2018). Huisman et al (2012) discussed whether the information content of the data is sufficient to obtain reliable estimates of model parameters. However, in general, the availability of spatially extensive (and time intensive) data greatly improves the model capability to identify the relevant governing hydraulic parameters, and/or unknown forcing conditions.…”