Position paper: Sensitivity analysis of spatially distributed environmental models- a pragmatic framework for the exploration of uncertainty sources

Koo, Hyeongmo; Iwanaga, Takuya; Croke, Barry; Jakeman, Anthony J.; Jian, Yang; Wang, Hsiao‐Hsuan; Sun, Xifu; Lv, Guonian; Li, Xin; Yue, Tianxiang; Yuan, Wenping; Liu, Xintao; Chen, Min

doi:10.1016/j.envsoft.2020.104857

Cited by 42 publications

(17 citation statements)

References 150 publications

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“…In all cases, the most important output information is the sensitivity order obtained using total indices. Total indices identify approximately the same sensitivity order for all types of SA; see Figures 11,14 and 15b. Sobol total indices can be a good proxy of quantileoriented total indices in cases where changes in the quantile are primarily influenced by changes in the variance and less by the shape of the probability distribution of the output variable R. It can be noted that although the case of strong discrepancy between quantile-oriented indices and Sobol indices has not yet been observed, some atypical (in practice, less real) tasks have not yet been solved, e.g., Sobol SA with strong interaction effects or SA of quantiles close to the mean.…”

Section: Discussionmentioning

confidence: 76%

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Global Sensitivity Analysis of Quantiles: New Importance Measure Based on Superquantiles and Subquantiles

Kala

2021

Symmetry

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The article introduces quantile deviation l as a new sensitivity measure based on the difference between superquantile and subquantile. New global sensitivity indices based on the square of l are presented. The proposed sensitivity indices are compared with quantile-oriented sensitivity indices subordinated to contrasts and classical Sobol sensitivity indices. The comparison is performed in a case study using a non-linear mathematical function, the output of which represents the elastic resistance of a slender steel member under compression. The steel member has random imperfections that reduce its load-carrying capacity. The member length is a deterministic parameter that significantly changes the sensitivity of the output resistance to the random effects of input imperfections. The comparison of the results of three types of global sensitivity analyses shows the rationality of the new quantile-oriented sensitivity indices, which have good properties similar to classical Sobol indices. Sensitivity indices subordinated to contrasts are the least comprehensible because they exhibit the strongest interaction effects between inputs. However, using total indices, all three types of sensitivity analyses lead to approximately the same conclusions. The similarity of the results of two quantile-oriented and Sobol sensitivity analysis confirms that Sobol sensitivity analysis is empathetic to the structural reliability and that the variance is one of the important characteristics significantly influencing the low quantile of resistance.

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Section: Discussionmentioning

confidence: 76%

“…SA is a multidisciplinary science and, therefore, review articles focused purely on SA have a multidisciplinary character [14][15][16][17][18][19]. Only approximately 2.5% of all articles on SA are focused on civil engineering.…”

Section: A Brief Review Of Sensitivity Analysis In Civil Engineeringmentioning

confidence: 99%

Global Sensitivity Analysis of Quantiles: New Importance Measure Based on Superquantiles and Subquantiles

Kala

2021

Symmetry

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“…The use of SA in environmental and urban modelling has become important due to the high complexity of natural and urban systems. Additionally, this complexity evokes models' complex nature, particularly if considered spatially distributed models that include parameters' spatial nature (Koo et al, 2020). This research is focussed on the local sensitivity analysis (LSA), paying attention to the local impact of input parameters on the model's output (Douglas-Smith et al, 2020).…”

Section: Sensitivity Analysis Of the Modelmentioning

confidence: 99%

Experimental Flash Floods Assessment Through Urban Flood Risk Mitigation (UFRM) Model: The Case Study of Ligurian Coastal Cities

2021

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Cities are vulnerable to extreme weather events, particularly by considering flash flood risk as a result of even more short-duration intensive rainfall. In the context of climate change, compound flooding due to simultaneous storm surges and increased runoff may further exacerbate the risk in coastal cities, and it is expected to be frequent and severe across several European urban areas. Despite this increasing evidence, the spatial knowledge of the hazardous events/vulnerabilities through modelling scenarios at the urban level is quite unexplored. Moreover, flood-prone areas often do not correspond to the traditional flood risk classification based on predicted return-period. The result that huge impacts (human losses and damages) occur everywhere throughout the city. Consequently, this new challenge requires stormwater flooding mitigation strategies to adapt to cities while mainstreaming urban flood resilience. In this paper, we considered the Urban Flood Risk Mitigation model through the employment of the open-source tool—Integrated Evaluation of Ecosystem Services and Trade-off (InVEST)—developed by the Natural Capital Project, integrated into a GIS environment. The model application in the three urban coastal territory of the Liguria Region (Italy) estimated the amount of runoff due to two extreme rainfall events for each watershed considered. These index calculation results help define examples of Natural Water Retention Measures (NWRM) per land-use type as resilient solutions by addressing site-specific runoff reduction. Local sensitivity analysis was finally conducted to comprehend the input parameter's influence of rain variation on the model.

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“…3B). Given this coarse spatial resolution of the soils data, we did not employ spatial sensitivity analysis methods that consider auto-and cross-correlations of soil parameter values (Koo et al, 2020b;Lilburne and Tarantola, 2009). RHESSys is typically calibrated using built-in parameter multipliers, which for this study would mean using 11 multipliers to adjust 40 of the 271 possible parameters.…”

Section: Hydrologic Model Description: Rhessysmentioning

confidence: 99%

Guidance on evaluating parametric model uncertainty at decision-relevant scales

Smith

Lin

Quinn

et al. 2021

Preprint

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Abstract. Spatially distributed hydrologic models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing decision objectives. Identifying parameters that control physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using parameter multipliers to further reduce dimensionality. We evaluate six sensitivity metrics that align with four decision objectives; two metrics consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results to those obtained by four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that 1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, 2) evaluating sensitivity metrics at only the basin outlet does not capture many parameters that control streamflows in hillslopes, and 3) for some parameter multipliers, calibration of just one of the parameters being adjusted may be the preferred approach for reducing dimensionality. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it.

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Position paper: Sensitivity analysis of spatially distributed environmental models- a pragmatic framework for the exploration of uncertainty sources

Cited by 42 publications

References 150 publications

Global Sensitivity Analysis of Quantiles: New Importance Measure Based on Superquantiles and Subquantiles

Global Sensitivity Analysis of Quantiles: New Importance Measure Based on Superquantiles and Subquantiles

Experimental Flash Floods Assessment Through Urban Flood Risk Mitigation (UFRM) Model: The Case Study of Ligurian Coastal Cities

Guidance on evaluating parametric model uncertainty at decision-relevant scales

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