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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...

Täydet tiedot

Tallennettuna:
Päätekijä: Tabari, Hossein
Aineistotyyppi: Online
Kieli:englanti
Julkaistu: MDPI - Multidisciplinary Digital Publishing Institute 2021
Aiheet:
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
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