Introduction to the Special Section in <i>Vadose Zone Journal</i>: Parameter Identification and Uncertainty Assessment in the Unsaturated Zone

Vrugt, Jasper A.; Neuman, Shlomo P.

doi:10.2136/vzj2006.0098

Cited by 4 publications

(4 citation statements)

References 12 publications

(10 reference statements)

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“…To better estimate the reliability of the model and provide guidance on future field studies, an analysis was conducted to determine the sensitivity of the model results to the input parameter values. While these types of analyses are well documented in the ground water and vadose zone modeling literature (Vrugt and Neuman, 2006; Hill and Tiedeman, 2007), this application is only rarely conducted on multicomponent reactive transport simulations (e.g., Spiessl et al, 2008).…”

Section: Resultsmentioning

confidence: 99%

Modeling Vadose Zone Processes during Land Application of Food‐Processing Waste Water in California's Central Valley

et al. 2008

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Land application of food-processing waste water occurs throughout California's Central Valley and may be degrading local ground water quality, primarily by increasing salinity and nitrogen levels. Natural attenuation is considered a treatment strategy for the waste, which often contains elevated levels of easily degradable organic carbon. Several key biogeochemical processes in the vadose zone alter the characteristics of the waste water before it reaches the ground water table, including microbial degradation, crop nutrient uptake, mineral precipitation, and ion exchange. This study used a process-based, multi-component reactive flow and transport model (MIN3P) to numerically simulate waste water migration in the vadose zone and to estimate its attenuation capacity. To address the high variability in site conditions and waste-stream characteristics, four food-processing industries were coupled with three site scenarios to simulate a range of land application outcomes. The simulations estimated that typically between 30 and 150% of the salt loading to the land surface reaches the ground water, resulting in dissolved solids concentrations up to sixteen times larger than the 500 mg L(-1) water quality objective. Site conditions, namely the ratio of hydraulic conductivity to the application rate, strongly influenced the amount of nitrate reaching the ground water, which ranged from zero to nine times the total loading applied. Rock-water interaction and nitrification explain salt and nitrate concentrations that exceed the levels present in the waste water. While source control remains the only method to prevent ground water degradation from saline wastes, proper site selection and waste application methods can reduce the risk of ground water degradation from nitrogen compounds.

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Section: Resultsmentioning

confidence: 99%

Modeling Vadose Zone Processes during Land Application of Food‐Processing Waste Water in California's Central Valley

et al. 2008

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“…Progress in model–data fusion can be assessed by comparing the scope and content of this special section with that of its predecessor in Vadose Zone Journal (Vrugt and Neuman, 2006). Over the intervening 6 years, the evaluation of model parameter uncertainty has become more established (Schelle et al, 2012; Shi et al, 2012; Scholer et al, 2012), and more advanced measurement technologies are being used for model conceptualization and parameterization (Ciocca et al, 2012, Keim et al, 2012; Jadoon et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

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

Huisman

Vrugt

Ferré

2012

Vadose Zone Journal

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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...

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“…Götzinger and Bárdossy (2008) and Beven (2018) present a very clear summary of the issues but there is a multitude of other contributions that treat the topic. Binley et al (1991) and Todini (2007), among others, dissected the problem related to the forecasting of discharges, but any model of any type involves errors and has its own error producing mechanisms, as can be seen, for instance, in Yeh (1986) or Hill and Tiedeman (2007) for groundwaters, Vrugt and Neuman (2006) for vadose zone variables, Post et al (2017) for ecohydrological modelling, Vrugt et al (2001) for root uptake, and Yilmaz et al (2010) for general watershed modelling.…”

Section: Assessing the Reliability Of Darthsmentioning

confidence: 99%

HESS Opinions: Participatory Digital Earth Twin Hydrology systems (DARTHs) for everyone: a blueprint for hydrologists

Rigon

Formetta²,

Bancheri

et al. 2022

Preprint

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Abstract. The Digital Earth (DE) metaphor is very useful for both end users and for hydrological modellers (i.e., the coders). However, in literature it can promote the erroneous view of models as a commodity, that is a basic good used in commerce that is interchangeable with other goods of the same type, without any warning about the fact that some models work better than others, some models just work while others can be simply wrong. These distinctions are at the core of doing good science. This opinion contribution, on the one hand, tries to accept the challenge of adopting models as commodities but, on the other, it wants to show that this acceptance comes with some consequences as to how models must be structured. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate infrastructure that provides some basic services for connection to input data and allows for a modelling-by-components strategy, which, we argue, is the right one for accomplishing the requirements of the DE. The urgency for DARTHs to be Open Source and well written pieces of codes is discussed here in light of the Open Science movement and its ideas. The need to tie predictions to an estimated confidence interval is also supported. Finally, it is argued that DARTHs must promote a new participatory way of doing hydrological science, where researchers can contribute cooperatively to characterize and control model outcomes in various territories. Furthermore, this has consequences for the engineering of the systems.

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Introduction to the Special Section in Vadose Zone Journal: Parameter Identification and Uncertainty Assessment in the Unsaturated Zone

Cited by 4 publications

References 12 publications

Modeling Vadose Zone Processes during Land Application of Food‐Processing Waste Water in California's Central Valley

Modeling Vadose Zone Processes during Land Application of Food‐Processing Waste Water in California's Central Valley

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

HESS Opinions: Participatory Digital Earth Twin Hydrology systems (DARTHs) for everyone: a blueprint for hydrologists

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