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Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Resumen/Descripción – provisto por la editorial

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Palabras clave – provistas por la editorial

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

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Información

Tipo de recurso:

libros

ISBN electrónico

978-3-03921-665-9

País de edición

Suiza