Significance of Statistical Tests and Persistence in Hydrologic Processes

Salas, José D.; Gavilán, Germán; Salas, F.; Juliën, Pierre Y.; Abdullah, Jazuri

doi:10.1201/b16683-29

Cited by 20 publications

(13 citation statements)

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“…Beauchamp et al (2013), Lagos-Zuniga and Vargas (2014), and Felder and Weingartner (2016) discuss the role of the spatio-temporal distribution of the PMP on the PMF, while Rousseau et al (2014) and Stratz and Hossain (2014) discuss climate change and stationarity issues. Hence, Faulkner and Benn (2016), Micovic et al (2015), Rouhani and Leconte (2016), and Salas et al (2014) have proposed incorporating uncertainty bands into the PMP estimation.…”

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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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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show abstract

“…15 there are also uncertainties in the sample mean, sample standard deviation, and frequency factor which can affect the PMP estimation. Assuming that the extreme precipitation series followed the Gumbel distribution, Salas et al, 16 considered the uncertainty of PMP estimates arising from the uncertainty of sample mean and sample standard deviation. They calculated the expected value and standard deviation of the PMP values obtained from the Hershfield method and then estimated the design risk values of PMP using Chebyshev's inequality.…”

Section: Uncertainty In Pmp Estimatesmentioning

confidence: 99%

“…To quantify the uncertainty of PMP due to sample mean and sample standard deviation, a procedure developed by Salas et al, 23 was followed. To quantify the uncertainty required the calculation of variance ( ) (P Var ) of PMP estimates which was calculated as 16 16 used Monte Carlo simulation experiments assuming Gumbel distribution (GEV-Type I) as the underlying distribution of the process under construction. Then, for easy application, the variance and covariance terms were determined based on the normal approximation, and a correction factor used for obtaining them for the Gumbel distribution.…”

Section: Uncertainty In Pmp Estimates Due To Sample Mean and Sample Smentioning

confidence: 99%

“…The results for different record lengths of 30, 50, 70, and 100 years were not very different from the one using the values of α = 4.8 and κ = 1, as shown in Table 3. Hence, it was decided to use the values of α As in Salas et al, 16 the expected value of PMP estimate ) (P E was calculated as:…”

Section: Uncertainty In Pmp Estimates Due To Sample Mean and Sample Smentioning

confidence: 99%

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Risk analysis of probable maximum precipitation estimates

Singh¹

2018

IJH

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Flooding, caused by extreme precipitation, including probable maximum precipitation (PMP), causes considerable loss almost yearly in many parts of the U.S., such as Texas and Louisiana. A common method for estimating PMP is the statistical method but it involves sample mean, standard deviation, and frequency factor each of which can be considered as a random variable. As the uncertainty in the PMP values is due to the uncertainty in sample mean, sample standard deviation, and frequency factor, the relative contribution of each random variable to the total uncertainty was determined. The best-fit probability distribution was found and the hazard rate was calculated for different probability distributions. Using the best-fit probability distribution, design risk estimates along with probability bounds on the PMP values were determined. Considering the damage (in U.S. dollars) a PMP event can cause, risk analysis of extreme precipitation was done. The damage due to a single PMP event of 12-hour duration can be as high as $2 billion in Harris County, Texas.

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“…Beauchamp et al (2013), Lagos-Zuniga and Vargas 20 M., (2014), and Felder and Weingartner (2016) discuss the role of the spatio-temporal distribution of the PMP on the PMF, while Rousseau et al (2014) and Stratz and Hossain (2014) discuss climate change and stationarity issues. Hence, Faulkner and Benn (2016), Micovic et al (2015), Rouhani and Leconte (2016), and Salas et al (2014) propose to incorporate uncertainty bands into the PMP estimation.…”

Section: Introductionmentioning

confidence: 99%

Effects of variability in probable maximum precipitation patterns on flood losses

Zischg¹,

Felder²,

Weingartner³

et al. 2018

Preprint

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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 hydrologic and hydraulic models has gained interest in recent years.Herein, we examine whether variability in precipitation patterns exceeds or is below other uncertainties in flood loss 15 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 20 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 organisation of the settlements within the river basin need to be considered in the analyses of probable maximum flood losses. 25

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Significance of Statistical Tests and Persistence in Hydrologic Processes

Cited by 20 publications

References 1 publication

Effects of variability in probable maximum precipitation patterns on flood losses

Effects of variability in probable maximum precipitation patterns on flood losses

Risk analysis of probable maximum precipitation estimates

Effects of variability in probable maximum precipitation patterns on flood losses

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