Statistical Analysis and Stochastic Modelling of Hydrological Extremes

• Hoofdauteur: Tabari, Hossein (auth)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Onderwerpen: Meteorology & climatology; 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
• Online toegang: DOAB: download the publication; DOAB: description of the publication

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...