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WRRCTR No.133 Bayes-Markov Analysis for Rain-Catchment Cisterns
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Item Summary
Title: | WRRCTR No.133 Bayes-Markov Analysis for Rain-Catchment Cisterns |
Authors: | Fok, Yu-Si Fong, Ronald H.L. Murabayashi, Edwin T. Lo, Andrew Hung, Jack |
Keywords: | cisterns rainfall water demand storage capacity water management (applied) show 12 moredynamic programming water conservation droughts rainfall data Hawaii Bayes-Markov analysis rainfall probabilities risk analysis resign methodology operational methodology Pauoa Flats rain gage Oahu show less |
LC Subject Headings: | Cisterns -- Mathematical models. Rain and rainfall -- Hawaii -- Oahu. Rain-water (Water-supply) -- Mathematical models. |
Date Issued: | Mar 1980 |
Publisher: | Water Resources Research Center, University of Hawaii at Manoa |
Citation: | Fok YS, Fong RHL, Hung J, Murabayashi ET, Lo A. 1980. Bayes-Markov analysis for rain-catchment cisterns. Honolulu (HI): Water Resources Research Center, University of Hawaii at Manoa. WRRC technical report, 133. |
Series: | WRRC Technical Report 133 |
Abstract: | In many parts of the world, public water-supply systems have shown signs of an inability to adequately service increasing demand. As a result, water shortages have occurred and moratoriums on new development areas have been imposed. And because of recurrent droughts and the rapid acceleration of urban development in recent years, the rain-catchment system and its cost effectiveness are now regaining the attention of researchers and planners as a "new" and important alternative water supply. Rainfall, catchment area, storage capacity of the cistern, and water demand are the four main elements considered in the design, operation and management of a cistern system. Expected weekly rainfall is the main uncontrollable element of concern to the cistern owner; therefore, to meet this problem, expected weekly rainfall probabilities were first simulated by the Bayesian analysis. Then the sequential property contained in the 25-yr rainfall record was utilized to generate the likelihood probability function by using the lag 1 Markov sequential analysis. The Bayes-Markov analysis was subsequently applied to obtain the weekly rainfall probabilities. As a result of these analyses, the Bayes-Markov probabilistic approach for weekly rainfall simulation has shown its usefulness. Design and operational methodologies are also presented in the report. |
Pages/Duration: | viii + 100 pages |
URI: | http://hdl.handle.net/10125/2317 |
Appears in Collections: |
WRRC Technical Reports |
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