Eutrophication in peel inlet—II. Identification of critical uncertainties via generalized sensitivity analysis

Spear, Robert C.

doi:10.1016/0043-1354(80)90040-8

Cited by 552 publications

(347 citation statements)

References 4 publications

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“…high or low) of the output, and that can be used for mapping and for dominant controls analysis. The idea was first proposed and investigated in Young et al (1978) and Spear and Hornberger (1980). Here, the input samples (typically parameters) are divided into two binary sets, 'behavioural' and 'non-behavioural', depending on whether the associated model simulation exhibits the expected pattern of state variable response or not.…”

Section: Regional Sensitivity Analysis (Or Monte-carlo Filtering)mentioning

confidence: 99%

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

Pianosi

Wagener

Rougier

et al. 2014

Vulnerability, Uncertainty, and Risk

257

416

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a b s t r a c tSensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in environmental modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. In this paper we review the SA literature with the goal of providing: (i) a comprehensive view of SA approaches also in relation to other methodologies for model identification and application; (ii) a systematic classification of the most commonly used SA methods; (iii) practical guidelines for the application of SA. The paper aims at delivering an introduction to SA for non-specialist readers, as well as practical advice with best practice examples from the literature; and at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research.

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Section: Regional Sensitivity Analysis (Or Monte-carlo Filtering)mentioning

confidence: 99%

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

Pianosi

Wagener

Rougier

et al. 2014

Vulnerability, Uncertainty, and Risk

257

416

View full text Add to dashboard Cite

show abstract

“…As a consequence, the parameters in these equations may only be calibrated as effective values that will, to some extent, compensate for such sub-grid heterogeneities and any errors in the input data and model structure. However, since there are no adequate data available to constrain the values of these parameters spatially and temporally within the model framework, the calibration of effective parameter values often results in many parameter sets (called "behavioural" models by Spear & Hornberger, 1980), scattered throughout the parameter space, producing acceptable predictions on the basis of one or more performance measures (Franks & Beven, 1999;Beven & Freer, 2001). This is the so-called model equifinality problem.…”

Section: Introductionmentioning

confidence: 99%

“…It is the interaction between the individual parameter values that leads to the model performance being behavioural or non-behavioural in the sense of providing acceptably good fits to any available observations. Therefore, it is helpful to reveal some sensitivity of parameters to the model performance with the generalized sensitivity analysis (GSA) of Spear & Hornberger (1980). Generalized sensitivity analysis evaluates the sensitivity of individual parameters by dividing their values into several sets and comparing the distributions of these sets that give rise to either behavioural or non-behavioural model performance.…”

Section: Introductionmentioning

confidence: 99%

Parameter conditioning and prediction uncertainties of the LISFLOOD-WB distributed hydrological model

Pappenberger

Beven

et al. 2006

Hydrological Sciences Journal

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Distributed hydrological models are considered to be a promising tool for predicting the impacts of global change on the hydrological processes at the basin scale. However, distributed models typically require values of many parameters to be specified or calibrated, which exacerbates model prediction uncertainty. This study uses the generalized likelihood uncertainty estimation (GLUE) technique to analyse the parameter sensitivities of a distributed hydrological model, LISFLOOD-WB. Discharge time series and event volume data of the Luo River at upstream and downstream sites, Lingkou and Lushi, are used to analyse parameter uncertainty. Eight key parameters in the model are selected for conditioning and sampled using the Monte Carlo method under assumed prior distributions. The results show that maximum efficiency of model performance is lower and the number of behavioural parameter sets giving acceptable performance is fewer in the Lingkou sub-basin than in the Lushi sub-basin with the same criteria of acceptability. For both sub-basins the distribution shape parameter B in the fast runoff generation scheme is the most sensitive in predicting both discharge time series and event volume at the outlet. It is also shown that the value of parameter B at which the highest efficiency is derived is shifted from a high value for Lushi to a low value for Lingkou, consistent with past experience of model calibration that the larger the basin, the larger the B value is. The channel Manning coefficient N c shows some sensitivity in the prediction of discharge time series, but less in the prediction of event volumes. The other key parameters show little sensitivity and good simulations are found across the full range of parameter values sampled. The uncertainty bounds of predicted discharges at the Lushi sub-basin are broad in the peak and narrow in the recession. The normalized difference between the upper and lower uncertainty bounds for both discharge and evapotranspiration are broad in summer and narrow in winter and that of recharge is the opposite.Key words distributed hydrological model; GLUE; parameter calibration; uncertainty Conditionnement de paramétrage et incertitudes de prévision du modèle hydrologique distribué LISFLOOD-WBRésumé Les modèles hydrologiques distribués sont considérés comme un outil prometteur pour la prévision des impacts du changement global sur les processus hydrologiques à l'échelle du bassin versant. Cependant, les modèles distribués nécessitent généralement la spécification ou le calage des valeurs de nombreux paramètres, ce qui aggrave l'incertitude de prévision de la modélisation. Cette étude utilise la technique GLUE (generalized likelihood uncertainty estimation) pour analyser les sensibilités de paramétrage du modèle hydrologique distribué LISFLOOD-WB. Des séries temporelles de débit et des données de volume événementiel de la Rivière Luo, au niveau des sites amont et aval de Lingkou et Lushi, sont utilisées pour analyser l'incertitude de paramétrage. Huit paramètres clefs du m...

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“…A combination of both generalized likelihood uncertainty estimation (GLUE) and global sensitivity analysis (GSA) techniques (Beven and Binley 1992;Spear and Hornberger 1980) were employed to assess model prediction uncertainty and quantitative sensitivity to model parameters. In brief, 100,000 statistically independent parameter sets were generated for each compartment as sampled randomly from previously selected prior distributions which were extracted from literature values/tabulations.…”

Section: Model Assessmentmentioning

confidence: 99%

Capturing Spatial Variability of Biogeochemical Mass Exchanges and Reaction Rates in Wetland Water and Soil through Model Compartmentalization

et al. 2017

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A common phenomenon observed in natural and constructed wetlands is short-circuiting of flow and formation of stagnant zones that are only indirectly connected with the incoming water. Biogeochemistry of passive areas is potentially much different than that of active zones. In the research reported in this paper, the spatial resolution of a previously developed wetland nutrient cycling model was improved in order to capture the spatial variability of concentrations and reaction rates regarding nitrogen and carbon cycles throughout active and passive zones of wetlands. The upgraded model allows for several compartments in the horizontal domain, with all neighboring compartments connected through advective and dispersive/diffusive mass transport. The model was applied to data collected from a restored wetland in California that was characterized by the formation of a large stagnant zone at the southern end of the wetland due to close vicinity of the inlet and outlet structures in the northern end. Mass balance analysis revealed that over the course of the research period, about 23.4 AE 3.9% of the incoming total nitrogen load was removed or retained by the wetland. It was observed that mass of all exchanges (physical and biogeochemical) regarding nitrogen cycling decreased along the activity gradient from active to passive zones. Model results also revealed that anaerobic processes become more significant along the activity gradient towards passive areas.

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Eutrophication in peel inlet—II. Identification of critical uncertainties via generalized sensitivity analysis

Cited by 552 publications

References 4 publications

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

Sensitivity Analysis of Environmental Models: A Systematic Review with Practical Workflow

Parameter conditioning and prediction uncertainties of the LISFLOOD-WB distributed hydrological model

Capturing Spatial Variability of Biogeochemical Mass Exchanges and Reaction Rates in Wetland Water and Soil through Model Compartmentalization

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