Statistics in Hydrology

Edited by
June 2022
218 pages
  • ISBN978-3-0365-4321-5 (Hardback)
  • ISBN978-3-0365-4322-2 (PDF)

This book is a reprint of the Special Issue Statistics in Hydrology that was published in

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

Statistical methods have a long history in the analysis of hydrological data for de-signing, planning, infilling, forecasting, and specifying better models to assess scenarios of land use and climate change in catchments. The effectiveness of statistical descriptions of hydrological processes reflects the enormous complexity of hydrological systems, which makes a purely deterministic description ineffective. This book hosts 11 papers devoted to statistics in hydrology, summarizing the recent advancement in statistical methods for hydrological studies such as statistical analysis of changes in hydrometeorological variables, forecasting and prediction of hydrological elements, hydrological forecasting uncertainty analysis, the use of new statistical methodologies for engineering hydrological design under stationary/nonstationary conditions, and so on. In general, the book will contribute to the promotion of the application of statistical methods in hydrology.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
regional l-moments; revision of frequency estimation of extreme precipitation; chow’s equation; annual maximum series; annual exceedance series; four-parameter exponential gamma distribution; levenberg-marquardt algorithm; maximum likelihood estimation; variance and covariance matrix; Weihe watershed; western Pacific subtropical high; the Yangtze River Valley; model output statistics (MOS); reanalysis-based (RAN) approach; extreme flood risk; climatic factors; nonstationary frequency analysis; Bayesian modeling; nonstationary return period; Xiangjiang River basin; frequency analysis; annual runoff; nonstationary; mechanism-based reconstruction; flash flood; spatiotemporal change; driving factor; Altay; rainfall; prediction; machine learning; stacking model; Taihu basin; extreme flood risk; mixture distribution; G–H copula; bivariate nonstationary flood frequency analysis; nonstationary return period; precipitation infiltration; groundwater–river interaction; multiscale time analysis; wavelet analysis; extreme rainstorms; driver identification; dominant factor; ERA5; uncertainty analysis; water resources; cluster analysis; Gaussian mixture model; probabilistic prediction; n/a