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Bayesian system for probabilistic river stage forecasting

Authors
Journal
Journal of Hydrology
0022-1694
Publisher
Elsevier
Publication Date
Volume
268
Identifiers
DOI: 10.1016/s0022-1694(02)00106-3
Keywords
  • Bayesian Analysis
  • Stochastic Processes
  • Statistical Analysis
  • Probability
  • Rivers
  • Floods
Disciplines
  • Computer Science
  • Design
  • Earth Science
  • Geography

Abstract

Abstract The purpose of this analytic-numerical Bayesian forecasting system (BFS) is to produce a short-term probabilistic river stage forecast based on a probabilistic quantitative precipitation forecast as an input and a deterministic hydrologic model (of any complexity) as a means of simulating the response of a headwater basin to precipitation. The BFS has three structural components: the precipitation uncertainty processor, the hydrologic uncertainty processor, and the integrator. A series of articles described the Bayesian forecasting theory and detailed each component of this particular BFS. This article presents a synthesis: the total system, operational expressions, estimation procedures, numerical algorithms, a complete example, and all design requirements, modeling assumptions, and operational attributes.

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