TY - BOOK ID - 42709 TI - Statistical Analysis and Stochastic Modelling of Hydrological Extremes AU - Tabari, Hossein PY - 2019 SN - 9783039216642 9783039216659 DB - DOAB KW - rainfall KW - monsoon KW - high resolution KW - TRMM KW - drought prediction KW - APCC Multi-Model Ensemble KW - seasonal climate forecast KW - machine learning KW - sparse monitoring network KW - Fiji KW - drought analysis KW - ANN model KW - drought indices KW - meteorological drought KW - SIAP KW - SWSI KW - hydrological drought KW - discrete wavelet KW - global warming KW - statistical downscaling KW - HBV model KW - flow regime KW - uncertainty KW - reservoir inflow forecasting KW - artificial neural network KW - wavelet artificial neural network KW - weighted mean analogue KW - variation analogue KW - streamflow KW - artificial neural network KW - simulation KW - forecasting KW - support vector machine KW - evolutionary strategy KW - heavy storm KW - hyetograph KW - temperature KW - clausius-clapeyron scaling KW - climate change KW - the Cauca River KW - climate variability KW - ENSO KW - extreme rainfall KW - trends KW - statistical downscaling KW - random forest KW - least square support vector regression KW - extreme rainfall KW - polynomial normal transform KW - multivariate modeling KW - sampling errors KW - non-normality KW - extreme rainfall analysis KW - statistical analysis KW - hydrological extremes KW - stretched Gaussian distribution KW - Hurst exponent KW - INDC pledge KW - precipitation KW - extreme events KW - extreme precipitation exposure KW - non-stationary KW - extreme value theory KW - uncertainty KW - flood regime KW - flood management KW - Kabul river basin KW - Pakistan KW - extreme events KW - innovative methods KW - downscaling KW - forecasting KW - compound events KW - satellite data UR - https://www.doabooks.org/doab?func=search&query=rid:42709 AB - 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 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. ER -