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A Bayesian framework for the use of regional information in hydrology
Author(s) -
Vicens Guillermo J.,
RodriguezIturbe Ignacio,
Schaake John C.
Publication year - 1975
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr011i003p00405
Subject(s) - streamflow , bayesian probability , computer science , resource (disambiguation) , process (computing) , bayesian inference , bayesian network , hydrological modelling , natural (archaeology) , hydrology (agriculture) , operations research , environmental science , mathematics , machine learning , artificial intelligence , engineering , geography , geology , operating system , computer network , cartography , geotechnical engineering , climatology , archaeology , drainage basin
Water resource designs are perfect examples of decision making under uncertainty. In fact, three types of uncertainties may exist in any design problem: natural, parameter, and model uncertainties. The last two may be considered as informational uncertainties that are due to the lack of perfect information about the streamflow processes. The use of regional information has been suggested as a technique for reducing parameter uncertainties. The use of Bayesian methodology provides a framework for combining regional information with at‐site historical records. Moreover, Bayesian methods allow the hydrologist to consider the parameter uncertainties as well as the natural uncertainties within the decision‐making process. Because of these two advantages the Bayesian approach is a more complete and realistic approach to problems of uncertainty in hydrology and water resource planning than presently used methodologies.