LEADER 04762nam 22011893a 450 001 9910367749703321 005 20250203235431.0 010 $a9783039216659 010 $a3039216651 024 8 $a10.3390/books978-3-03921-665-9 035 $a(CKB)4100000010106220 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/59997 035 $a(ScCtBLL)72baa98f-c679-4da0-b9f9-21b2e6b88eb0 035 $a(OCoLC)1163843987 035 $a(EXLCZ)994100000010106220 100 $a20250203i20192019 uu 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aStatistical Analysis and Stochastic Modelling of Hydrological Extremes$fHossein Tabari 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 electronic resource (294 p.) 311 08$a9783039216642 311 08$a3039216643 330 $aHydrological 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. 610 $aartificial neural network 610 $adownscaling 610 $ainnovative methods 610 $areservoir inflow forecasting 610 $asimulation 610 $aextreme events 610 $aclimate variability 610 $asparse monitoring network 610 $aweighted mean analogue 610 $asampling errors 610 $aprecipitation 610 $adrought indices 610 $adiscrete wavelet 610 $aSWSI 610 $ahyetograph 610 $atrends 610 $aclimate change 610 $aSIAP 610 $aKabul river basin 610 $aHurst exponent 610 $aextreme rainfall 610 $aevolutionary strategy 610 $athe Cauca River 610 $ahydrological drought 610 $aglobal warming 610 $aleast square support vector regression 610 $apolynomial normal transform 610 $aTRMM 610 $asatellite data 610 $aFiji 610 $aheavy storm 610 $aflood regime 610 $acompound events 610 $arandom forest 610 $auncertainty 610 $aseasonal climate forecast 610 $aINDC pledge 610 $aPakistan 610 $awavelet artificial neural network 610 $aHBV model 610 $atemperature 610 $aAPCC Multi-Model Ensemble 610 $ameteorological drought 610 $aflow regime 610 $ahigh resolution 610 $arainfall 610 $aclausius-clapeyron scaling 610 $astatistical downscaling 610 $aENSO 610 $aforecasting 610 $avariation analogue 610 $amachine learning 610 $aextreme rainfall analysis 610 $ahydrological extremes 610 $amultivariate modeling 610 $amonsoon 610 $anon-stationary 610 $asupport vector machine 610 $aANN model 610 $astretched Gaussian distribution 610 $adrought prediction 610 $anon-normality 610 $astatistical analysis 610 $aextreme precipitation exposure 610 $adrought analysis 610 $aextreme value theory 610 $astreamflow 610 $aflood management 700 $aTabari$b Hossein$01295065 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910367749703321 996 $aStatistical Analysis and Stochastic Modelling of Hydrological Extremes$93023355 997 $aUNINA