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Probability and Statistical Theory for Hydrometeorology

  • Zengchao HaoEmail author
  • Vijay P. Singh
  • Wei Gong
Reference work entry

Abstract

The hydrometeorological forecasting plays an important role in water resources planning and management. The fundamentals of probability and statistics in hydrometeorology are reviewed in this chapter to aid the forecasting practices. We first introduce the elements of probability theory in the classical statistics, including random variables, probability distribution, joint probability, and total probability theorem. The probability estimation is among the key topics in hydrometeorology for statistical inferences, such as uncertainty analysis and statistical forecasting. The probability inference in the univariate case is first introduced with different methods, including parametric distribution, nonparametric distribution, and mixed distribution. Many hydroclimatic variables are mutually correlated, and the dependence modeling of multivariate random variables through the construction of the joint distribution is then introduced. Most of the context is introduced from the view of classical statistics, while a preliminary introduction of the Bayesian approach is also provided. At last, some commonly used methods for hydrometeorological forecasting are introduced, along with a short summary of this chapter.

Keywords

Probability Statistics Distribution Forecasting 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Water SciencesBeijing Normal UniversityBeijingChina
  2. 2.Department of Biological and Agricultural Engineering and Zachry Department of Civil EngineeringTexas A&M UniversityCollege StationUSA
  3. 3.State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  4. 4.Institute of Land Surface System and Sustainable Development, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina

Section editors and affiliations

  • James Brown
    • 1
  • Wei Gong
    • 2
  1. 1.Hydrologic Solutions LimitedSouthamptonUK
  2. 2.College of Global Change and Earth System Science, Beijing Normal UniversityBeijingChina

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