Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Edited by
October 2019
294 pages
  • ISBN978-3-03921-664-2 (Paperback)
  • ISBN978-3-03921-665-9 (PDF)

This book is a reprint of the Special Issue Statistical Analysis and Stochastic Modelling of Hydrological Extremes that was published in

Biology & Life Sciences
Chemistry & Materials Science
Environmental & Earth Sciences
Public Health & Healthcare

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.
  • Paperback
© 2019 by the authors; CC BY-NC-ND license
rainfall; monsoon; high resolution; TRMM; drought prediction; APCC Multi-Model Ensemble; seasonal climate forecast; machine learning; sparse monitoring network; Fiji; drought analysis; ANN model; drought indices; meteorological drought; SIAP; SWSI; hydrological drought; discrete wavelet; global warming; statistical downscaling; HBV model; flow regime; uncertainty; reservoir inflow forecasting; artificial neural network; wavelet artificial neural network; weighted mean analogue; variation analogue; streamflow; artificial neural network; simulation; forecasting; support vector machine; evolutionary strategy; heavy storm; hyetograph; temperature; clausius-clapeyron scaling; climate change; the Cauca River; climate variability; ENSO; extreme rainfall; trends; statistical downscaling; random forest; least square support vector regression; extreme rainfall; polynomial normal transform; multivariate modeling; sampling errors; non-normality; extreme rainfall analysis; statistical analysis; hydrological extremes; stretched Gaussian distribution; Hurst exponent; INDC pledge; precipitation; extreme events; extreme precipitation exposure; non-stationary; extreme value theory; uncertainty; flood regime; flood management; Kabul river basin; Pakistan; extreme events; innovative methods; downscaling; forecasting; compound events; satellite data