Sensitivity analysis of environmental models: A systematic review with practical workflow

Pianosi, Francesca; Beven, Keith; Freer, J. E.; Hall, Jim W.; Rougier, Jonathan; Stephenson, David B.; Wagener, Thorsten

doi:10.1016/j.envsoft.2016.02.008

Cited by 1,061 publications

(554 citation statements)

References 95 publications

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“…The approach is generally referred to as regional sensitivity analysis (first proposed by Spear and Hornberger, 1980; for a general introduction see Pianosi et al, 2016). We split the 10 000 model simulations into two sub-sets: those that produce slope failure (F <1) and those that simulate a stable slope (F >1).…”

Section: Resultsmentioning

confidence: 99%

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Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

Almeida

Holcombe

Pianosi

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslides have large negative economic and societal impacts, including loss of life and damage to infrastructure. Slope stability assessment is a vital tool for landslide risk management, but high levels of uncertainty often challenge its usefulness. Uncertainties are associated with the numerical model used to assess slope stability and its parameters, with the data characterizing the geometric, geotechnic and hydrologic properties of the slope, and with hazard triggers (e.g. rainfall). Uncertainties associated with many of these factors are also likely to be exacerbated further by future climatic and socio-economic changes, such as increased urbanization and resultant land use change. In this study, we illustrate how numerical models can be used to explore the uncertain factors that influence potential future landslide hazard using a bottom-up strategy. Specifically, we link the Combined Hydrology And Stability Model (CHASM) with sensitivity analysis and Classification And Regression Trees (CART) to identify critical thresholds in slope properties and climatic (rainfall) drivers that lead to slope failure. We apply our approach to a slope in the Caribbean, an area that is naturally susceptible to landslides due to a combination of high rainfall rates, steep slopes, and highly weathered residual soils. For this particular slope, we find that uncertainties regarding some slope properties (namely thickness and effective cohesion of topsoil) are as important as the uncertainties related to future rainfall conditions. Furthermore, we show that 89 % of the expected behaviour of the studied slope can be characterized based on only two variables -the ratio of topsoil thickness to cohesion and the ratio of rainfall intensity to duration.

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Section: Resultsmentioning

confidence: 99%

“…The bottom-up strategy is very similar to the problem of mapping in Global Sensitivity Analysis, where one tries to understand which parts of the input factor space produce a particular model output, for example output values exceeding a certain threshold (e.g. Saltelli et al, 2008;Pianosi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

Almeida

Holcombe

Pianosi

et al. 2017

Nat. Hazards Earth Syst. Sci.

Self Cite

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“…uncertainties related to the inundation modelling approach and to the chosen vulnerability functions. Hence, we conducted a global sensitivity analysis of the model chain with the objective to rank the uncertainty in the rainfall pattern and the uncertainties in the model setup (choice of sub-models) according to their relative contribution to the output variability after Pianosi et al (2016). The uncertainties in the model setup are considered in the sensitivity analysis by varying the setup of the submodules for flood modelling and loss modelling.…”

Section: Methodsmentioning

confidence: 99%

“…In contrast, sensitivity analysis focuses on apportioning output uncertainty to the different sources of uncertainty (input factors). A global sensitivity analysis investigates how the variation in the output of a numerical model can be attributed to variations of its input factors (Pianosi et al, 2016). However, uncertainty analyses and sensitivity analyses of coupled models or model chains are rarely investigated topics.…”

Section: Introductionmentioning

confidence: 99%

Effects of variability in probable maximum precipitation patterns on flood losses

Zischg

Felder

Weingartner

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The assessment of the impacts of extreme floods is important for dealing with residual risk, particularly for critical infrastructure management and for insurance purposes. Thus, modelling of the probable maximum flood (PMF) from probable maximum precipitation (PMP) by coupling hydrological and hydraulic models has gained interest in recent years. Herein, we examine whether variability in precipitation patterns exceeds or is below selected uncertainty factors in flood loss estimation and if the flood losses within a river basin are related to the probable maximum discharge at the basin outlet. We developed a model experiment with an ensemble of probable maximum precipitation scenarios created by Monte Carlo simulations. For each rainfall pattern, we computed the flood losses with a model chain and benchmarked the effects of variability in rainfall distribution with other model uncertainties. The results show that flood losses vary considerably within the river basin and depend on the timing and superimposition of the flood peaks from the basin's sub-catchments. In addition to the flood hazard component, the other components of flood risk, exposure, and vulnerability contribute remarkably to the overall variability. This leads to the conclusion that the estimation of the probable maximum expectable flood losses in a river basin should not be based exclusively on the PMF. Consequently, the basin-specific sensitivities to different precipitation patterns and the spatial organization of the settlements within the river basin need to be considered in the analyses of probable maximum flood losses.

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“…For a local sensitivity analysis (LSA) the model inputs are varied around a point (often an "optimum" point) in the model input space. Global sensitivity analysis (GSA) assesses the sensitivity of a model output for the entire feasible range of model inputs (Gupta and Razavi, 2017;Pianosi et al, 2016). Compared to LSA, GSA usually requires a larger number of computations.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive sensitivity and uncertainty analysis for discharge and nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions

Schürz¹,

Hollosi²,

Matulla³

et al. 2018

Preprint

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Abstract. Environmental modeling studies aim to infer the impacts on environmental variables that are caused by natural and human-induced changes in environmental systems. Changes in environmental systems are typically implemented as discrete scenarios in environmental models to simulate environmental variables under changing conditions. The scenario development of a model input usually involves several data sources and perhaps other models, that are potential sources of uncertainty. The setup and the parametrization of the implemented environmental model are additional sources of uncertainty for the simulation 5 of environmental variables. Yet, to draw well-informed conclusions from the model simulations it is essential to identify the dominant sources of uncertainty.In two Austrian impact studies the eco-hydrological model Soil and Water Assessment Tool (SWAT) was applied to simulate discharge and nitrate-nitrogen (NO In both case studies we employed global sensitivity analysis (GSA) to identify the impact of the scenario inputs, the model 15 setup and the parametrization on the simulation of discharge and NO − 3 -N loads. We accompanied the GSA with a visual analysis of the simulation outputs and their associated uncertainties that resulted from the simulations of the 7000 SWAT model combinations. We present visualizations of the results of the GSA and the simulation uncertainty bands that proved to be powerful diagnostic tools in this study. Based on the GSA we identified climate change and the model parametrization to be the most sensitive model inputs for the simulation of discharge and NO − 3 -N loads in both case studies. In contrast, the impact of the model setup on the simulation of discharge and NO − 3 -N loads was low and the changes in land use and point source emissions were found to have the least impact on the simulated discharge and NO − 3 -N loads. Additionally, the visual analysis of the uncertainty bands illustrated that the precipitation of the climate scenarios dominated the changes in simulation outputs, rather than changes in air temperature 5 in both case studies.

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Sensitivity analysis of environmental models: A systematic review with practical workflow

Cited by 1,061 publications

References 95 publications

Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change

Effects of variability in probable maximum precipitation patterns on flood losses

A comprehensive sensitivity and uncertainty analysis for discharge and nitrate-nitrogen loads involving multiple discrete model inputs under future changing conditions

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