Water Supply Peaking Factor Stochastics

WATER SUPPLY PEAKING FACTOR STOCHASTICS  
A study into the probability of occurrence of water supply demand peaking factors
L Donaldson
Publication Date (Web): 15 August 2018
DOI: https://doi.org/10.21139/wej.2018.025


Water supply codes generally do not recognise the stochastic nature of water supply demands and there is little published information about the exceedance probabilities, or return intervals of peaking factors. This is a limitation to water supply practitioners who generally can only use single value water supply peaking factors whereas the availability of stochastic based peaking factors would allow water supply infrastructure to be sized to match their levels of importance and quantitative risk assessments to be undertaken. Designers involved in drainage infrastructure can choose between a 1 in 5-year, 10-year or some other return interval flood event, depending on its importance. Shouldn’t that same flexibility also be available for practitioners working with water supply infrastructure?  

Many Australian water authorities now hold large demand data bases in the form of flow meter records kept for billing purposes and data loggers associated with the operation of demand management areas. This paper outlines the methodology adopted for the stochastic analysis of 685 twelve month sets of daily flow meter data collected from five south-east Queensland water supply authorities. That number of accepted data sets was reduced to 369 after the original data sets had been reviewed for completeness and uniformity. The data sets were also adjusted for in-catchment storage mitigation impacts. 

Stochastic peaking factors suitable for the investigation and design of water supply distribution infrastructure were prepared by arbitrarily separating the twelve month sets of daily flow meter records into thirteen average day demand bins ranging from 0-0.2 ML/d to 400-800 ML/d and undertaking exceedance probability analyses of the members of each bin. The representative probability lines from each bin were combined using the 95% and 5% confidence limits as a guide to produce a series of smooth shaped curves showing the 100, 50, 20, 10, 5, 2 and 1-year recurrence interval Maximum Day, 7 Day and 30 Day peaking factors for supply areas with average day demands between 0.1 and 600 ML/d. The location of storages within the data supply areas was found to have only a minor impact on the resultant peaking factors.

As part of the data investigations it was found that the Maximum Day, 7 Day and 30 Day peaking factors could be linked using a variation of the Goodrich Formula persistence curve which closely matched the raw data. Those persistence curves allowed the outcomes from the stochastic analyses to be confirmed by back-calculating the Maximum Day peaking factors using independently prepared 7 Day and 30 Day peaking factors.  

The prepared stochastic peaking factors were for the south-east Queensland supply which services a population of over 3 million people and extends over a distance of nearly 200 km. Testing found that those peaking factors also met acceptable confidence limits for individual supply areas within the overall supply area. The prepared stochastic peaking factors for the SEQ supply area therefore need only be defined by their AD demand and recurrence interval.  
 

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