Sensitivity analysis: A discipline coming of age

Saltelli, Andrea; Jakeman, Anthony J.; Razavi, Saman; Wu, Qingfeng

doi:10.1016/j.envsoft.2021.105226

Cited by 46 publications

(18 citation statements)

References 33 publications

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“…The decisions made in that phase will transfer to support the selection of the integrated model type (Kelly et al, 2013) and its computational model implementation, and enable the potential application of subsequent methods that may sharpen choices further. Quantitative methods that enable discrimination among technical scale choices principally include assessing the identifiability of the computational model such as via sensitivity and uncertainty analyses (Guillaume et al, 2017;Guillaume et al, 2019;Razavi et al, 2021;Saltelli et al, 2021).…”

Section: 3mentioning

confidence: 99%

Scale decisions and good practices in socio-environmental systems modelling: guidance and documentation during problem scoping and model formulation

Wang

Voorn

Grant

et al. 2023

SESMO

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Models of socio-environmental or social-ecological systems (SES) commonly address problems requiring interdisciplinary scientific expertise and input from a heterogeneous group of stakeholders. In SES modelling multiple interactions occur on different scales among various phenomena. These scale phenomena include the technical, such as system variables, process detail, inputs and outputs, which most often require spatial, temporal, thematic and organisational choices. From a good practice and project efficiency perspective, the problem scoping and conceptual model formulation phase of modelling is the one to address well from the outset. During this phase, intense and substantive discussions should arise regarding appropriate scales at which to represent the different phenomena. Although the details of these discussions influence the path of model development, they are seldom documented and as a result often forgotten. We draw upon personal experience with existing protocols and communications in recent literature to propose preliminary guidelines for documenting these early discussions about the scale(s) of the studied phenomena. Our guidelines aim to aid modelling group members in building and capturing the richness of their rationale for scoping and scale decisions. The resulting transcripts are intended to promote transparency of modelling decisions and provide essential support for the justification of the final model for its intended use. They also facilitate adaptive modifications of the pathway of model development via retracing decisions and iterative reflection upon alternative scale options.

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Section: 3mentioning

confidence: 99%

Scale decisions and good practices in socio-environmental systems modelling: guidance and documentation during problem scoping and model formulation

Wang

Voorn

Grant

et al. 2023

SESMO

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“…The variance of the output distribution will be then considered the uncertainty in the model output (i.e., ET estimate) associated with the input variables. The contribution of each input factor to the total uncertainty in the model output is determined by sensitivity analysis (Crosetto et al, 2001;Saltelli et al, 2021). Such analysis is primarily used to identify the factors that contribute most to the model uncertainty (Saltelli et al, 2019).…”

Section: Uncertainty Assessmentmentioning

confidence: 99%

“…The Extended Fourier Amplitude Sensitivity Test (EFAST) has also been applied for GSA (García et al, 2013). This limited number of studies shows the application of GSA onto RS-ET models has been under-researched during the last decade, despite the importance of GSA in environmental modeling (Saltelli et al, 2021).…”

Section: Sensitivity Analysis and Uncertainty Propagationmentioning

confidence: 99%

Uncertainty Assessment of Satellite Remote Sensing-based Evapotranspiration Estimates: A Systematic Review of Methods and Gaps

Tran

Kwast

Seyoum

et al. 2023

Preprint

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Abstract. Satellite remote sensing (RS) data are increasingly being used to estimate total evaporation or evapotranspiration (ET) over large regions. Since RS-based ET (RS-ET) estimation inherits uncertainties from several sources, many available studies have assessed these uncertainties using different methods and reference data. However, the suitability of methods and reference data subsequently affects the validity of these evaluations. This study summarizes the status of the various methods applied for uncertainty assessment of RS-ET estimates, discusses the advances and caveats of these methods, identifies assessment gaps, and provides recommendations for future studies. We systematically reviewed 601 research papers published from 2011 to 2021 that assessed the uncertainty or accuracy of RS-ET estimates. We categorized and classified them based on (i) the methods used to assess uncertainties, (ii) the context where uncertainties were evaluated, and (iii) the metrics used to report uncertainties. Our quantitative synthesis shows that the uncertainty assessments of RS-ET estimates are not consistent and comparable in terms of methodology, reference data, geographical distribution, and uncertainty presentation. Most studies used validation methods using Eddy Covariance (EC) based ET estimates as reference. However, in many regions such as Africa and the Middle East, other references are often used due to the lack of EC stations. The accuracy and uncertainty of RS-ET estimates are most often described by Root-Mean-Squared Error (RMSE). When validating against EC-based estimates, the RMSE of daily RS-ET varies greatly among different locations and levels of temporal support, ranging from 0.01 to 6.65 mm/day with a mean of 1.12 mm/day. We conclude that future studies need to report the context of validation, the uncertainty of the reference datasets, the mismatch in temporal and spatial scales of reference datasets to that of the RS-ET estimates, and multiple performance metrics with their variation in different conditions and statistical significance to provide a comprehensive interpretation to assist potential users. We provide specific recommendations in this regard. Furthermore, extending the application of RS-ET to regions that lack validation will require obtaining additional ground-based data and combining different methods for uncertainty assessment.

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“…Diagnostic sensitivity analysis (SA) has emerged as an important tool for characterizing hydrologic parameter uncertainty. SA clarifies which parameterized processes primarily influence the range of variability in specific model outputs of interest (Herman et al., 2013; Pianosi & Wagener, 2016; Reed, Hadjimichael, Malek, et al., 2022; Saltelli et al., 2021). However, the structural complexity and computationally intensive nature of CLM have hindered SA efforts.…”

Section: Introductionmentioning

confidence: 99%

Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)

Yan

Sun

Eldardiry

et al. 2023

J Adv Model Earth Syst

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Land surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance.

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Sensitivity analysis: A discipline coming of age

Cited by 46 publications

References 33 publications

Scale decisions and good practices in socio-environmental systems modelling: guidance and documentation during problem scoping and model formulation

Scale decisions and good practices in socio-environmental systems modelling: guidance and documentation during problem scoping and model formulation

Uncertainty Assessment of Satellite Remote Sensing-based Evapotranspiration Estimates: A Systematic Review of Methods and Gaps

Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)

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