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WRRCTR No.133 Bayes-Markov Analysis for Rain-Catchment Cisterns

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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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