000 04673naaaa2201069uu 4500
001 https://directory.doabooks.org/handle/20.500.12854/59997
005 20220220103336.0
020 _abooks978-3-03921-665-9
020 _a9783039216659
020 _a9783039216642
024 7 _a10.3390/books978-3-03921-665-9
_cdoi
041 0 _aEnglish
042 _adc
100 1 _aTabari, Hossein
_4auth
245 1 0 _aStatistical Analysis and Stochastic Modelling of Hydrological Extremes
260 _bMDPI - Multidisciplinary Digital Publishing Institute
_c2019
300 _a1 electronic resource (294 p.)
506 0 _aOpen Access
_2star
_fUnrestricted online access
520 _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.
540 _aCreative Commons
_fhttps://creativecommons.org/licenses/by-nc-nd/4.0/
_2cc
_4https://creativecommons.org/licenses/by-nc-nd/4.0/
546 _aEnglish
653 _aartificial neural network
653 _adownscaling
653 _ainnovative methods
653 _areservoir inflow forecasting
653 _asimulation
653 _aextreme events
653 _aclimate variability
653 _asparse monitoring network
653 _aweighted mean analogue
653 _asampling errors
653 _aprecipitation
653 _adrought indices
653 _adiscrete wavelet
653 _aSWSI
653 _ahyetograph
653 _atrends
653 _aclimate change
653 _aSIAP
653 _aKabul river basin
653 _aHurst exponent
653 _aextreme rainfall
653 _aevolutionary strategy
653 _athe Cauca River
653 _ahydrological drought
653 _aglobal warming
653 _aleast square support vector regression
653 _apolynomial normal transform
653 _aTRMM
653 _asatellite data
653 _aFiji
653 _aheavy storm
653 _aflood regime
653 _acompound events
653 _arandom forest
653 _auncertainty
653 _aseasonal climate forecast
653 _aINDC pledge
653 _aPakistan
653 _awavelet artificial neural network
653 _aHBV model
653 _atemperature
653 _aAPCC Multi-Model Ensemble
653 _ameteorological drought
653 _aflow regime
653 _ahigh resolution
653 _arainfall
653 _aclausius-clapeyron scaling
653 _astatistical downscaling
653 _aENSO
653 _aforecasting
653 _avariation analogue
653 _amachine learning
653 _aextreme rainfall analysis
653 _ahydrological extremes
653 _amultivariate modeling
653 _amonsoon
653 _anon-stationary
653 _asupport vector machine
653 _aANN model
653 _astretched Gaussian distribution
653 _adrought prediction
653 _anon-normality
653 _astatistical analysis
653 _aextreme precipitation exposure
653 _adrought analysis
653 _aextreme value theory
653 _astreamflow
653 _aflood management
856 4 0 _awww.oapen.org
_uhttps://mdpi.com/books/pdfview/book/1751
_70
_zDOAB: download the publication
856 4 0 _awww.oapen.org
_uhttps://directory.doabooks.org/handle/20.500.12854/59997
_70
_zDOAB: description of the publication
999 _c81560
_d81560