Sensitivity analysis based on regional splits and regression trees (SARS-RT)

Pappenberger, Florian; Iorgulescu, I.; Beven, Keith

doi:10.1016/j.envsoft.2005.04.010

Cited by 66 publications

(49 citation statements)

References 45 publications

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“…These uncertainty sources can be analysed separately or in various combinations analogously to a sensitivity analysis as presented e.g., in (Hall et al, 2005b;Pappenberger et al, 2006a;Pappenberger et al, 2006b). In order to reduce computation time, a Latin Hypercube Sampling with n = 80 quantile divisions was applied for each selected return interval.…”

Section: Combination Of Uncertainty Sourcesmentioning

confidence: 99%

Quantification of uncertainties in flood risk assessments

Apel¹,

Merz²,

Thieken³

2008

International Journal of River Basin Management

167

165

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By a common definition, flood risk assessments are comprised of two parts: a hazard and vulnerability assessment. The hazard assessment investigates the extent and magnitude of usually large flood events, which are associated to a certain exceedance probability, whereas the vulnerability part assesses the impact of the flooding on specified targets, e.g., building, people or infrastructure. Being inherently speculative flood risk assessments should always be accompanied by an uncertainty assessment in order to assist consequent decision properly. In this paper a dynamic-probabilistic method is proposed, which enables a cumulated flood risk assessment of a complete river reach considering dike failures at all dike locations. The model uses simple but computational efficient modules to simulate the complete process chain of flooding. These modules are embedded into a Monte Carlo framework thus enabling a risk assessment which is physically based thus mapping the real flooding process, and which is also probabilistic and not based on scenarios. The model also provides uncertainty estimates by quantifying various epistemic uncertainty sources of the hazard as well as the vulnerability part in a second layer of Monte Carlo simulations. These uncertainty estimates are associated to defined return intervals of the model outputs, i.e., the derived flood frequencies at the end of the reach and the risk curves for the complete reach, thus providing valuable information for the interpretation of the results. By separating single uncertainty sources a comparison of the contribution of different uncertainty sources to the overall predictive uncertainty in terms of derived flood frequencies and monetary risks could be performed. This revealed that the major uncertainties are extreme value statistics, resp. the length of the data series used and the discharge-stage relation used for the transformation of discharge into water levels in the river.

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Section: Combination Of Uncertainty Sourcesmentioning

confidence: 99%

Quantification of uncertainties in flood risk assessments

Apel¹,

Merz²,

Thieken³

2008

International Journal of River Basin Management

167

165

View full text Add to dashboard Cite

show abstract

“…One approach to gain this understanding is through the use of sensitivity analysis, which evaluates the parameter's impacts on the model response (Hornberger and Spear, 1981;Freer et al, 1996;Wagener et al, 2001;Liang and Guo, 2003;Hall et al, 2005;Pappenberger et al, 2005;Sieber and Uhlenbrook, 2005). Sensitivity analysis results can be used to decide which parameters should be the focus of model calibration efforts, or even as an analysis tool to test if the model behaves according to underlying assumptions (e.g., Wagener et al, 2003).…”

mentioning

confidence: 99%

Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation

Tang

Reed

Wagener

et al. 2007

Hydrol. Earth Syst. Sci.

312

220

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Abstract. This study seeks to identify sensitivity tools that will advance our understanding of lumped hydrologic models for the purposes of model improvement, calibration efficiency and improved measurement schemes. Four sensitivity analysis methods were tested: (1) local analysis using parameter estimation software (PEST), (2) regional sensitivity analysis (RSA), (3) analysis of variance (ANOVA), and (4) Sobol's method. The methods' relative efficiencies and effectiveness have been analyzed and compared. These four sensitivity methods were applied to the lumped Sacramento soil moisture accounting model (SAC-SMA) coupled with SNOW-17. Results from this study characterize model sensitivities for two medium sized watersheds within the Juniata River Basin in Pennsylvania, USA. Comparative results for the 4 sensitivity methods are presented for a 3-year time series with 1 h, 6 h, and 24 h time intervals. The results of this study show that model parameter sensitivities are heavily impacted by the choice of analysis method as well as the model time interval. Differences between the two adjacent watersheds also suggest strong influences of local physical characteristics on the sensitivity methods' results. This study also contributes a comprehensive assessment of the repeatability, robustness, efficiency, and ease-of-implementation of the four sensitivity methods. Overall ANOVA and Sobol's method were shown to be superior to RSA and PEST. Relative to one another, ANOVA has reduced computational requirements and Sobol's method yielded more robust sensitivity rankings.

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“…The uncertainty of any model output (water surface elevations and inundation extent) is obtained by combining the standard deviation of the following variables: discharge rate, Manning's coefficient and topography [46,47]. Numerous approaches exist to quantify the resulting model uncertainties [48,49], including analytical methods [50], generalized likelihood uncertainty estimation (GLUE) [51,52], parameter estimation [53], approximation methods (e.g., first-order second-moment method), Melching [54], simulation and sampling-based methods [55], Bayesian methods [56], analytical methods, methods based on the analysis of model errors, methods based on fuzzy sets [57,58], etc.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of a Two-Dimensional Hydraulic Model

Oubennaceur

Chokmani

Nastev

et al. 2018

Water

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Abstract:A reliability approach referred to as the point estimate method (PEM) is presented to assess the uncertainty of a two-dimensional hydraulic model. PEM is a special case of numerical quadrature based on orthogonal polynomials, which evaluates the statistical moments of a performance function involving random variables. When applied to hydraulic problems, the variables are the inputs to the hydraulic model, and the first and second statistical moments refer to the mean and standard deviation of the model's output. In providing approximate estimates of the uncertainty, PEM appears considerably simpler and requires less information and fewer runs than standard Monte Carlo methods. An example of uncertainty assessment is shown for simulated water depths in a 46 km reach of the Richelieu River, Canada. The 2D hydraulic model, H2D2, was used to solve the shallow water equations. Standard deviations around the mean water depths were estimated by considering the uncertainties of three main input variables: flow rate, Manning's coefficient and topography. Results indicate that the mean standard deviation is <27 cm for the considered flow rates of 759, 824, 936, 1113 m 3 /s. Higher standard deviations were obtained upstream of the topographic shoal at the municipality of Saint-Jean-sur-Richelieu. The PEM method adds further value to the H2D2 model predictions as it indicates the magnitude and the spatial variation in uncertainties. The effort required to complete an uncertainty analysis using the PEM method is minimal and the resulting insight is meaningful. This knowledge should be incorporated into decision-making in the context of flood risk assessment.

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Sensitivity analysis based on regional splits and regression trees (SARS-RT)

Cited by 66 publications

References 45 publications

Quantification of uncertainties in flood risk assessments

Quantification of uncertainties in flood risk assessments

Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation

Uncertainty Analysis of a Two-Dimensional Hydraulic Model

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