Journal ArticleDOI
A Stochastic Model for Flood Analysis
P. Todorovic,E. Zelenhasic +1 more
Reads0
Chats0
TLDR
In this article, a stochastic model based on the theory of extreme values is presented to describe and analyze excessive streamflows, and the passage time T(x) of the process x(t) relevant to the risk evaluation in the design of hydraulic structures is also considered.Abstract:
A stochastic model, based on the recent developments in the theory of extreme values, is presented to describe and analyze excessive streamflows. The model is a particular stochastic process x(t) defined as the maximum term among a random number of random observations in an interval of time [0, t]. Since the number of hydrograph peaks in [0, t] that exceed a certain level x0 and the magnitudes of these peaks are random variables, the foregoing model seems to conform well to the flood phenomenon. The passage time T(x) of the process x(t) relevant to the risk evaluation in the design of hydraulic structures is also considered. The results obtained are applied on the 72-year record of the Susquehanna River at Wilkes-Barre, Pennsylvania. Theoretical and observed results agree reasonably well.read more
Citations
More filters
Journal ArticleDOI
Models for exceedances over high thresholds
Anthony C. Davison,Richard Smith +1 more
TL;DR: In this article, the authors discuss the analysis of the extremes of data by modelling the sizes and occurrence of exceedances over high thresholds, and the natural distribution for such exceedances, the generalized Pareto distribution, is described and its properties elucidated.
Journal ArticleDOI
Statistics of extremes in hydrology
TL;DR: In this article, statistical downscaling of hydrologic extremes is considered, and future challenges such as the development of more rigorous statistical methodology for regional analysis of extremes, as well as the extension of Bayesian methods to more fully quantify uncertainty in extremal estimation are reviewed.
Journal ArticleDOI
Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone
TL;DR: In this paper, a detailed analysis of ozone data collected in Houston, Texas is presented, showing no trend in overall levels of the series, but a marked downward trend in the extreme values.
Journal ArticleDOI
Towards operational guidelines for over-threshold modeling
TL;DR: This paper reviews tests and methods useful for modeling the process of over-threshold values, the choice of the threshold level, the verification of the independence of the values and the stationarity of the process, and also presents an application.
Journal ArticleDOI
Comparison of annual maximum series and partial duration series methods for modeling extreme hydrologic events: 1. At-site modeling
TL;DR: In this paper, two different models for analyzing extreme hydrologic events, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared in terms of the uncertainty of the T-year event estimator.
References
More filters
Journal ArticleDOI
Uniform Flood-Frequency Estimating Methods for Federal Agencies
TL;DR: It is recommended that all government agencies adopt a uniform procedure for flood-frequency analysis at sites where records are available and the log-Pearson Type III distribution has been selected as the base method.
Journal ArticleDOI
Mathematical Model for Flood Risk Evaluation
Richard M. Shane,Walter R. Lynn +1 more
TL;DR: In this paper, a mathematical model is developed for use with partial-duration data that relates design flow to several different measures of risk based on the Poisson probability law and the probability theory of sums of a random number of random variables.
Journal ArticleDOI
On the Random Occurrence of Major Floods
TL;DR: In this paper, it was shown that at sufficiently small exceedance probabilities the probability distributions and moments of the interexceedance time, the waiting time to the next exceedance, and the number of exceedances approach those implied by the occurrence of trials in a Poisson process.