Three complementary methods for sensitivity analysis of a water quality model

Sun, Xifu; Newham, Lachlan; Croke, Barry; Norton, J.P.

doi:10.1016/j.envsoft.2012.04.010

Cited by 83 publications

(31 citation statements)

References 60 publications

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“…Hence, the Morris method is more suitable to computationally expensive models, which often have a large number of uncertain parameters. However, the drawback of this method is that it cannot quantify the effects of different factors on outputs (Brockmann and Morgenroth, 2007;Sun et al, 2012), and type II errors (failing to identify some unimportant inputs as important parameters) might occur with the Morris screening method . Saltelli et al (2004) also highlighted that it cannot estimate individual interactions between parameters, thereby giving only the overall interaction of a parameter with the rest of the model.…”

Section: Morris Screening Methodsmentioning

confidence: 99%

“…For example, Francos et al (2003) integrate the Morris method with FAST for qualitative and quantitative analyses (the two-step analysis method) to estimate parameter sensitivity for the SWAT model. Sun et al (2012) also showed that when the number of input factors in the model is too high to allow a computationally expensive quantitative analysis, a more efficient two-step procedure based on a screening process (first stage) and a quantitative analysis method (second stage) can be adopted. In addition, Song et al (2014) integrated the Morris method, RSM, and the Sobol' method to allow the clear and efficient identification of the effects of parameters on the model outputs from the DTVGM and Xinanjiang models.…”

Section: Selection Of Sa Methods For Hydrological Modelsmentioning

confidence: 99%

“…Given the wide range of SA methods, practitioners require adequate resource to better understand the methods that are appropriate for a specific application (Ratto et al, 2007;Tang et al, 2007b;Pappenberger et al, 2008;Confalonieri et al, 2010;Yang, 2011;Sun et al, 2012;Gan et al, 2014). Different types of SA methods can be selected based on: (a) the objective of the analysis, (b) the number of uncertain input factors, (c) the degree of complexity of the model, (d) the computing time for a single model simulation, and (e) the analyst's time available to perform a SA (Cacuci et al, 2013;Saltelli et al, 2005Saltelli et al, , 2012Wallach et al, 2006;Zajac, 2010).…”

Section: Selection Of Sa Methods For Hydrological Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Song

Zhang

Zhan

et al. 2015

Journal of Hydrology

464

316

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s u m m a r y Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance.

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Section: Morris Screening Methodsmentioning

confidence: 99%

Section: Selection Of Sa Methods For Hydrological Modelsmentioning

confidence: 99%

Section: Selection Of Sa Methods For Hydrological Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Song

Zhang

Zhan

et al. 2015

Journal of Hydrology

464

316

View full text Add to dashboard Cite

show abstract

“…The different results obtained with the RSA were attributed to the choice of the filtering criteria. Sun et al (2012) compared three sensitivity analysis methods of a hydrological water quality model with 6 model factors: the local method, the Morris screening method and RSA. They concluded that the compared methods should be considered as complementary and not as mutually exclusive alternatives.…”

Section: Comparison Of Gsa Methodsmentioning

confidence: 99%

“…The peculiar features of each method can assist the modeller in characterising the behaviour of the model studied. In case of a model with a large number of factors, Sun et al (2012) suggested to use a two-step procedure including first a factors screening step (by using a local method) followed by a global sensitivity analysis step of the important factors identified during the first step.…”

Section: Comparison Of Gsa Methodsmentioning

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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Sources of uncertainty in modeling the glaciohydrological response of a Karakoram watershed to climate change

et al. 2013

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[1] In the headwater catchments of the main Asian rivers, glaciohydrological models are a useful tool to anticipate impacts of climatic changes. However, the reliability of their projections strongly depends on the quality and quantity of data that are available for parameter estimation, model calibration and validation, as well as on the accuracy of climate change projections. In this study the physically oriented, glaciohydrological model TOPKAPI-ETH is used to simulate future changes in snow, glacier, and runoff from the Hunza River Basin in northern Pakistan. Three key sources of model uncertainty in future runoff projections are compared : model parameters, climate projections, and natural climate variability. A novel approach, applicable also to ungauged catchments, is used to determine which model parameters and model components significantly affect the overall model uncertainty. We show that the model is capable of reproducing streamflow and glacier mass balances, but that all analyzed sources of uncertainty significantly affect the reliability of future projections, and that their effect is variable in time and in space. The effect of parametric uncertainty often exceeds the impact of climate uncertainty and natural climate variability, especially in heavily glacierized subcatchments. The results of the uncertainty analysis allow detailed recommendations on network design and the timing and location of field measurements, which could efficiently help to reduce model uncertainty in the future.

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Three complementary methods for sensitivity analysis of a water quality model

Cited by 83 publications

References 60 publications

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

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

Sources of uncertainty in modeling the glaciohydrological response of a Karakoram watershed to climate change

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