Direct propagation of probability density functions in hydrological equations

Kunstmann, Harald; Kastens, Marko

doi:10.1016/j.jhydrol.2005.10.009

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…This approach is known as transformation of two random variables (e.g., (Papoulis and Pillai 2002, p. 139)), its univariate version (Z ¼ gðXÞ) has been used in several applications (e.g. Kunstmann and Kastens 2006;Ashkar and Aucoin 2011;Serinaldi 2013), and in the present case it yields Eq. 16.…”

Section: Kendall and Structure-based Return Periodsmentioning

confidence: 99%

Dismissing return periods!

Serinaldi

2014

Stoch Environ Res Risk Assess

230

192

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The concept of return period in stationary univariate frequency analysis is prone to misconceptions and misuses that are well known but still widespread. In this study we highlight how nonstationary and multivariate extensions of such a concept are affected by additional misconceptions, thus easily resulting in further ill-posed procedures and misleading conclusions. We also show that the concepts of probability of exceedance and risk of failure over a given design life period provide more coherent, general and well devised tools for risk assessment and communication.

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Section: Kendall and Structure-based Return Periodsmentioning

confidence: 99%

Dismissing return periods!

Serinaldi

2014

Stoch Environ Res Risk Assess

230

192

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“…Direct propagation of second moments was, among others, applied by Kunstmann and Kastens (2006a) for the delineation of stochastic wellhead protection zones and by Kunstmann et al (2002) for the inverse stochastic determination of groundwater recharge. Direct propagation of entire probability density functions in hydrological equations was demonstrated by Kunstmann and Kastens (2006b). Contrary to these studies, in the following the Monte Carlo method is substituted by propagation of first moments.…”

Section: Monte Carlo Approachmentioning

confidence: 99%

Effective SVAT-model parameters through inverse stochastic modelling and second-order first moment propagation

Kunstmann

2008

Journal of Hydrology

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“…Previous work in flood analysis incorporated parameter uncertainty through statistical analysis methods where a confidence level is calculated for the delineated floodplain based upon the uncertainty of input parameters (McBean et al ., ; Kunstmann and Kastens, ). Specific examples of statistical approaches include application of standard least squares, weighted least squares, Bayesian total error analysis, and non‐probabilistic information gap analysis (Thyer et al ., ; Hine and Hall, ).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in floodplain delineation: expression of flood hazard and risk in a Gulf Coast watershed

Christian

Dueñas‐Osorio

Teague³

et al. 2012

Hydrological Processes

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This paper investigates the development of flood hazard and flood risk delineations that account for uncertainty as improvements to standard floodplain maps for coastal watersheds. Current regulatory floodplain maps for the Gulf Coastal United States present 1% flood hazards as polygon features developed using deterministic, steady‐state models that do not consider data uncertainty or natural variability of input parameters. Using the techniques presented here, a standard binary deterministic floodplain delineation is replaced with a flood inundation map showing the underlying flood hazard structure. Additionally, the hazard uncertainty is further transformed to show flood risk as a spatially distributed probable flood depth using concepts familiar to practicing engineers and software tools accepted and understood by regulators. A case study of the proposed hazard and risk assessment methodology is presented for a Gulf Coast watershed, which suggests that storm duration and stage boundary conditions are important variable parameters, whereas rainfall distribution, storm movement, and roughness coefficients contribute less variability. The floodplain with uncertainty for this coastal watershed showed the highest variability in the tidally influenced reaches and showed little variability in the inland riverine reaches. Additionally, comparison of flood hazard maps to flood risk maps shows that they are not directly correlated, as areas of high hazard do not always represent high risk. Copyright © 2012 John Wiley & Sons, Ltd.

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Direct propagation of probability density functions in hydrological equations

Cited by 15 publications

References 13 publications

Dismissing return periods!

Dismissing return periods!

Effective SVAT-model parameters through inverse stochastic modelling and second-order first moment propagation

Uncertainty in floodplain delineation: expression of flood hazard and risk in a Gulf Coast watershed

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