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Practical Extreme Value Modelling of Hydrological Floods and Droughts: A Case Study
Authors:Kolbj?rn?Engeland  author-information"  >  author-information__contact u-icon-before"  >  mailto:kolbjorn@engeland.no"   title="  kolbjorn@engeland.no"   itemprop="  email"   data-track="  click"   data-track-action="  Email author"   data-track-label="  "  >Email author,Hege?Hisdal,Arnoldo?Frigessi
Affiliation:(1) Cemagref, Lyon, France;(2) Department of Geosciences, University of Oslo, and Norwegian Water Resources and Energy Directorate, Oslo, Norway;(3) Section of Medical Statistics, University of Oslo and Norwegian Computing Center, Oslo, Norway;(4) P.O.Box 1047, Blindern, 0316 Oslo, Norway
Abstract:Estimation of flood and drought frequencies is important for reservoir design and management, river pollution, ecology and drinking water supply. Through an example based on daily streamflow observations, we introduce a stepwise procedure for estimating quantiles of the hydrological extremes floods and droughts. We fit the generalised extreme value (GEV) distribution by the method of block maxima and the generalised Pareto (GP) distribution by applying the peak over threshold method. Maximum likelihood, penalized maximum likelihood and probability weighted moments are used for parameter estimation. We incorporate trends and seasonal variation in the models instead of splitting the data, and investigate how the observed number of extreme events, the chosen statistical model, and the parameter estimation method effect parameter estimates and quantiles. We find that a seasonal variation should be included in the GEV distribution fitting for floods using block sizes less than one year. When modelling droughts, block sizes of one year or less are not recommended as significant model bias becomes visible. We conclude that the different characteristics of floods and droughts influence the choices made in the extreme value modelling within a common inferential strategy.This revised version was published online in March 2005 with corrections to the cover date.
Keywords:flood  drought  generalised extreme value distribution  generalised Pareto distribution  maximum likelihood  penalized maximum likelihood  probability weighted moments
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