Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution

Freni, Gabriele; Mannina, Giorgio

doi:10.1016/j.jhydrol.2010.07.043

Cited by 99 publications

(55 citation statements)

References 45 publications

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“…The cutoff threshold selected to separate "behavioral" from "non-behavioral" parameter sets is another subjective choice within the GLUE method. Frequently, when the likelihood value is greater than zero, the corresponding simulations are considered "behavioral" (Freni and Mannina, 2010;Gong et al, 2011). In this study, model simulations with negative NS values are considered unacceptable and, therefore, the corresponding parameter sets are discarded from further analysis.…”

Section: Uncertainty Analysis (Ua)mentioning

confidence: 99%

Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

Sellami

Jeunesse

Benabdallah

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. In this study a method for propagating the hydrological model uncertainty in discharge predictions of ungauged Mediterranean catchments using a model parameter regionalization approach is presented. The method is developed and tested for the Thau catchment located in Southern France using the SWAT hydrological model. Regionalization of model parameters, based on physical similarity measured between gauged and ungauged catchment attributes, is a popular methodology for discharge prediction in ungauged basins, but it is often confronted with an arbitrary criterion for selecting the "behavioral" model parameter sets (Mps) at the gauged catchment. A more objective method is provided in this paper where the transferrable Mps are selected based on the similarity between the donor and the receptor catchments. In addition, the method allows propagating the modeling uncertainty while transferring the Mps to the ungauged catchments. Results indicate that physically similar catchments located within the same geographic and climatic region may exhibit similar hydrological behavior and can also be affected by similar model prediction uncertainty. Furthermore, the results suggest that model prediction uncertainty at the ungauged catchment increases as the dissimilarity between the donor and the receptor catchments increases. The methodology presented in this paper can be replicated and used in regionalization of any hydrological model parameters for estimating streamflow at ungauged catchment.

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Section: Uncertainty Analysis (Ua)mentioning

confidence: 99%

Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

Sellami

Jeunesse

Benabdallah

et al. 2014

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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“…Such a choice was driven by the fact that the prior information on the factors' behaviour was insufficient. As pointed out by Freni and Mannina (2010a;, a uniform distribution of model factors is preferred whenever relevant prior factor information is unavailable, as assuming a non-uniform shape may lead to wrong estimations of uncertainty in modelling results. For the SRC the sampling is carried out according to the LHS method.…”

Section: Gsa Methods Applicationmentioning

confidence: 99%

Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods

Vanrolleghem

Mannina

Cosenza

et al. 2015

Journal of Hydrology

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a b s t r a c tSensitivity analysis represents an important step in improving the understanding and use of environmental models. Indeed, by means of global sensitivity analysis (GSA), modellers may identify both important (factor prioritisation) and non-influential (factor fixing) model factors. No general rule has yet been defined for verifying the convergence of the GSA methods. In order to fill this gap this paper presents a convergence analysis of three widely used GSA methods (SRC, Extended FAST and Morris screening) for an urban drainage stormwater quality-quantity model. After the convergence was achieved the results of each method were compared. In particular, a discussion on peculiarities, applicability, and reliability of the three methods is presented. Moreover, a graphical Venn diagram based classification scheme and a precise terminology for better identifying important, interacting and non-influential factors for each method is proposed. In terms of convergence, it was shown that sensitivity indices related to factors of the quantity model achieve convergence faster. Results for the Morris screening method deviated considerably from the other methods. Factors related to the quality model require a much higher number of simulations than the number suggested in literature for achieving convergence with this method. In fact, the results have shown that the term ''screening'' is improperly used as the method may exclude important factors from further analysis. Moreover, for the presented application the convergence analysis shows more stable sensitivity coefficients for the Extended-FAST method compared to SRC and Morris screening. Substantial agreement in terms of factor fixing was found between the Morris screening and Extended FAST methods. In general, the water quality related factors exhibited more important interactions than factors related to water quantity. Furthermore, in contrast to water quantity model outputs, water quality model outputs were found to be characterised by high non-linearity.

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“…1) that are used to build likelihood functions (de la Varga and Wellmann, 2016). However, these schemes are known to not yield good results when incorrect informative priors are used (Freni and Mannina, 2010;Morita et al, 2010). Incorrect informative priors have low dispersion (high precision, "self-confident") and high bias (low accuracy, "off target").…”

Section: Disturbance Distribution Parameterizationmentioning

confidence: 99%

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

et al. 2018

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Abstract. Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the properties of the subsurface matters; they express our understanding of geometries in depth. For that reason, 3-D geological models have a wide range of practical applications including but not restricted to civil engineering, the oil and gas industry, the mining industry, and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) and the inherent lack of knowledge in areas where there are no observations combined with input uncertainty (observational, conceptual and technical errors). Because 3-D geological models are often used for impactful decision-making it is critical that all 3-D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data measurement uncertainty propagation in implicit 3-D geological modeling. This aim is achieved using Monte Carlo simulation for uncertainty estimation (MCUE), a stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3-D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed.The methods pertain to distribution selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of incorrect disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e., scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminish the occurrence of artifacts.

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Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution

Cited by 99 publications

References 45 publications

Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

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