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

Fok, Yu-Si
Fong, Ronald H.L.
Murabayashi, Edwin T.
Lo, Andrew
Hung, Jack
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Water Resources Research Center, University of Hawaii at Manoa
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.
cisterns, rainfall, water demand, storage capacity, water management (applied), dynamic programming, water conservation, droughts, rainfall data, Hawaii, Bayes-Markov analysis, rainfall probabilities, risk analysis, resign methodology, operational methodology, Pauoa Flats rain gage, Oahu
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.
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