Statistical Analysis and Stochastic Modelling of Hydrological Extremes

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 a...

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Autore principale:	Tabari, Hossein
Natura:	Online
Lingua:	inglese
Pubblicazione:	MDPI - Multidisciplinary Digital Publishing Institute 2021
Soggetti:	QC851-999 Q1-390 artificial neural network downscaling innovative methods reservoir inflow forecasting simulation extreme events climate variability sparse monitoring network weighted mean analogue sampling errors precipitation drought indices discrete wavelet SWSI hyetograph trends climate change SIAP Kabul river basin Hurst exponent extreme rainfall evolutionary strategy the Cauca River hydrological drought global warming least square support vector regression polynomial normal transform TRMM satellite data Fiji heavy storm flood regime compound events random forest uncertainty seasonal climate forecast INDC pledge Pakistan wavelet artificial neural network HBV model temperature APCC Multi-Model Ensemble meteorological drought flow regime high resolution rainfall clausius-clapeyron scaling statistical downscaling ENSO forecasting variation analogue machine learning extreme rainfall analysis hydrological extremes multivariate modeling monsoon non-stationary support vector machine ANN model stretched Gaussian distribution drought prediction non-normality statistical analysis extreme precipitation exposure drought analysis extreme value theory streamflow flood management thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RB Earth sciences::RBP Meteorology and climatology
Accesso online:	42709
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Statistical Analysis and Stochastic Modelling of Hydrological Extremes

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