On the value of conditioning data to reduce conceptual model uncertainty in groundwater modeling

Rojas, Rodrigo; Feyen, Luc; Batelaan, Okke; Dassargues, Alain

doi:10.1029/2009wr008822

Cited by 44 publications

(37 citation statements)

References 149 publications

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“…This result is in full agreement with the findings of Rojas et al (2009a). For recharge inflows (Fig.…”

Section: Likelihood Response Surfacessupporting

confidence: 83%

“…In this way, conceptual models using conditional realizations of the hydraulic conductivity field show higher posterior model probabilities. Although this might be attributed to the number of parameters used to described the K-field, this is in agreement with the results obtained by Rojas et al (2009a). In addition, when the alternative recharge mechanism is considered (models M1b, M2b, M3b and M4b), posterior model probabilities also increase for detailed descriptions of the PTA.…”

Section: Likelihood Response Surfacessupporting

confidence: 79%

“…4. Based on the results of Rojas et al (2009a), an ensemble of 100 hydraulic conductivity realizations was defined as minimum number to initiate the Markov chains. That is, when a random set of parameters combined with a hydraulic conductivity realization agreed with the rejection criterion, the hydraulic conductivity field was set as fixed and the Markov chain proceeded for the set of parameters solely.…”

Section: Simulation Proceduresmentioning

confidence: 99%

“…1b), and the other (complementary to the previous one) due to a system of faults and deep fissures connecting the PTA with Altiplano aquifers (see Table 1). Based on the results of Rojas et al (2009a), we consider spatial conditioning of the hydraulic conductivity field to a set of available hydraulic conductivity measurements. Finally, we analyse the full predictive uncertainty on groundwater heads, groundwater balance components and groundwater fluxes following the approach described in Rojas et al (2008).…”

Section: Introduction and Scopementioning

confidence: 99%

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Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile

Rojas

Batelaan

Feyen

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. In this work we assess the uncertainty in modelling the groundwater flow for the Pampa del Tamarugal Aquifer (PTA) -North Chile using a novel and fully integrated multi-model approach aimed at explicitly accounting for uncertainties arising from the definition of alternative conceptual models. The approach integrates the Generalized Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods. For each member of an ensemble M of potential conceptualizations, model weights used in BMA for multi-model aggregation are obtained from GLUE-based likelihood values. These model weights are based on model performance, thus, reflecting how well a conceptualization reproduces an observed dataset D. GLUEbased cumulative predictive distributions for each member of M are then aggregated obtaining predictive distributions accounting for conceptual model uncertainties. For the PTA we propose an ensemble of eight alternative conceptualizations covering all major features of groundwater flow models independently developed in past studies and including two recharge mechanisms which have been source of debate for several years. Results showed that accounting for heterogeneities in the hydraulic conductivity field (a) reduced the uncertainty in the estimations of parameters and state variables, and (b) increased the corresponding model weights used for multi-model aggregation. This was more noticeable when the hydraulic conductivity field was conditioned on available hydraulic conductivity measurements. ContriCorrespondence to: R. Rojas (rodrigo.rojas@jrc.ec.europa.eu) bution of conceptual model uncertainty to the predictive uncertainty varied between 6% and 64% for ground water head estimations and between 16% and 79% for ground water flow estimations. These results clearly illustrate the relevance of conceptual model uncertainty.

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“…This result is in full agreement with the findings of Rojas et al (2009a). For recharge inflows (Fig.…”

Section: Likelihood Response Surfacessupporting

confidence: 83%

Section: Likelihood Response Surfacessupporting

confidence: 79%

Section: Simulation Proceduresmentioning

confidence: 99%

Section: Introduction and Scopementioning

confidence: 99%

See 2 more Smart Citations

Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile

Rojas

Batelaan

Feyen

et al. 2010

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…MC-BMA method is not popular in current multi-model researches, and it is mainly seen in Rojas's studies, such as [6,23,[71][72][73]. Furthermore, Raftery et al [74] proposed an expectation maximization (EM) method to solve the weight and variance of conceptual models iteratively.…”

Section: Uncertainty Analysis Of Groundwater Conceptual Modelmentioning

confidence: 99%

Review of the uncertainty analysis of groundwater numerical simulation

Zeng

2013

Chin. Sci. Bull.

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Groundwater system is a complex and open system, which is affected by natural conditions and human activities. Natural hydrological processes is conceptualized through relatively simple flow governing equations in groundwater models. Moreover, observation data is always limited in field hydrogeological conditions. Therefore, the predictive results of groundwater simulation often deviate from true values, which is attribute to the uncertainty of groundwater numerical simulation. According to the process of system simulation, the uncertainty sources of groundwater numerical simulation can be divided into model parameters, conceptual model and observation data uncertainties. In addition, the uncertainty stemmed from boundary conditions is sometimes refered as scenario uncertainty. In this paper, the origination and category of groundwater modeling uncertainty are analyzed. The recent progresses on the methods of groundwater modeling uncertainty analysis are reivewed. Furthermore, the researches on the comprehensive analysis of uncertainty sources, and the predictive uncertainty of model outputs are discussed. Finally, several prospects on the deveolpment of groundwater modeling uncetainty analysis are proposed.groundwater modeling, uncertainty analysis, model parameter, conceptual model, observation data Citation:

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Bayesian model averaging to explore the worth of data for soil‐plant model selection and prediction

Wöhling

Schöniger

Gayler

et al. 2015

Water Resources Research

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A Bayesian model averaging (BMA) framework is presented to evaluate the worth of different observation types and experimental design options for (1) more confidence in model selection and (2) for increased predictive reliability. These two modeling tasks are handled separately because model selection aims at identifying the most appropriate model with respect to a given calibration data set, while predictive reliability aims at reducing uncertainty in model predictions through constraining the plausible range of both models and model parameters. For that purpose, we pursue an optimal design of measurement framework that is based on BMA and that considers uncertainty in parameters, measurements, and model structures. We apply this framework to select between four crop models (the vegetation components of CERES, SUCROS, GECROS, and SPASS), which are coupled to identical routines for simulating soil carbon and nitrogen turnover, soil heat and nitrogen transport, and soil water movement. An ensemble of parameter realizations was generated for each model using Monte-Carlo simulation. We assess each model's plausibility by determining its posterior weight, which signifies the probability to have generated a given experimental data set. Several BMA analyses were conducted for different data packages with measurements of soil moisture, evapotranspiration (ET a ), and leaf area index (LAI). The posterior weights resulting from the different BMA runs were compared to the weight distribution of a reference run with all data types to investigate the utility of different data packages and monitoring design options in identifying the most appropriate model in the ensemble. We found that different (combinations of) data types support different models and none of the four crop models outperforms all others under all data scenarios. The best model discrimination was observed for those data where the competing models disagree the most. The data worth for reducing prediction uncertainty depends on the prediction to be made. LAI data have the highest utility for predicting ET a , while soil moisture data are better for predicting soil water drainage. Our study illustrates, that BMA provides an objective framework for data worth analysis with respect to both model discrimination and model calibration for a wide range of applications.

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On the value of conditioning data to reduce conceptual model uncertainty in groundwater modeling

Cited by 44 publications

References 149 publications

Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile

Assessment of conceptual model uncertainty for the regional aquifer Pampa del Tamarugal – North Chile

Review of the uncertainty analysis of groundwater numerical simulation

Bayesian model averaging to explore the worth of data for soil‐plant model selection and prediction

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