04963nam 22012373a 450 991036774970332120250203235431.09783039216659303921665110.3390/books978-3-03921-665-9(CKB)4100000010106220(oapen)https://directory.doabooks.org/handle/20.500.12854/59997(ScCtBLL)72baa98f-c679-4da0-b9f9-21b2e6b88eb0(OCoLC)1163843987(oapen)doab59997(EXLCZ)99410000001010622020250203i20192019 uu engurmn|---annantxtrdacontentcrdamediacrrdacarrierStatistical Analysis and Stochastic Modelling of Hydrological ExtremesHossein TabariMDPI - Multidisciplinary Digital Publishing Institute2019Basel, Switzerland :MDPI,2019.1 electronic resource (294 p.)9783039216642 3039216643 Hydrological extremes have become a major concern because of their devastating consequences and their increased risk as a result of climate change and the growing concentration of people and infrastructure in high-risk zones. The analysis of hydrological extremes is challenging due to their rarity and small sample size, and the interconnections between different types of extremes and becomes further complicated by the untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The complexity of analyzing hydrological extremes calls for robust statistical methods for the treatment of such events. This Special Issue is motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions. The papers of this Special Issue focus on six topics associated with hydrological extremes: Historical changes in hydrological extremes; Projected changes in hydrological extremes; Downscaling of hydrological extremes; Early warning and forecasting systems for drought and flood; Interconnections of hydrological extremes; Applicability of satellite data for hydrological studies.Meteorology & climatologybicsscartificial neural networkdownscalinginnovative methodsreservoir inflow forecastingsimulationextreme eventsclimate variabilitysparse monitoring networkweighted mean analoguesampling errorsprecipitationdrought indicesdiscrete waveletSWSIhyetographtrendsclimate changeSIAPKabul river basinHurst exponentextreme rainfallevolutionary strategythe Cauca Riverhydrological droughtglobal warmingleast square support vector regressionpolynomial normal transformTRMMsatellite dataFijiheavy stormflood regimecompound eventsrandom forestuncertaintyseasonal climate forecastINDC pledgePakistanwavelet artificial neural networkHBV modeltemperatureAPCC Multi-Model Ensemblemeteorological droughtflow regimehigh resolutionrainfallclausius-clapeyron scalingstatistical downscalingENSOforecastingvariation analoguemachine learningextreme rainfall analysishydrological extremesmultivariate modelingmonsoonnon-stationarysupport vector machineANN modelstretched Gaussian distributiondrought predictionnon-normalitystatistical analysisextreme precipitation exposuredrought analysisextreme value theorystreamflowflood managementMeteorology & climatologyTabari Hossein1295065ScCtBLLScCtBLLBOOK9910367749703321Statistical Analysis and Stochastic Modelling of Hydrological Extremes3023355UNINA