A nonparametric bootstrapping method for synthetically generating daily precipitation, water supply, and irrigation demand for rainwater harvesting system storage sizing

• Main Author: Kurt Wurthmann
• Format: Article
• Language: English
• Published: Elsevier 2019-01-01
• Series: MethodsX
• Online Access: http://www.sciencedirect.com/science/article/pii/S2215016119302900

This article describes a nonparametric bootstrapping method for synthetically generating daily precipitation, water supply, and irrigation demand for rainwater harvesting (RWH) system storage sizing and reliability determination. The method is illustrated using the case example of determining storage size and associated reliability outcomes for residential RWH systems that provide for the outdoor landscape irrigation demands of single-family homes in Broward and Palm Beach Counties, located in Southeast Florida, U.S.A. The method is useful not only for individual property owners, RWH system designers, and contractors, but also for policy makers who wish to analyze potential savings in water and energy amounts and costs that could result from widespread deployment of residential RWH systems, as discussed in Wurthmann (2019). The method can be easily implemented in Excel and is unique in its combination of: • precision – determines daily levels of precipitation, water supply, and irrigation demand, incorporating the effects of seasonality, • adaptability – user specified historical rainfall data and functional relationships between precipitation and water supply and demand are fully customizable, and • portability – the nonparametric bootstrapping approach overcomes the key challenge posed by parametric stochastic methods; that statistical relationships describing rainfall processes derived in one location are likely not applicable to other locations Method name: Nonparametric bootstrapping method for rainwater harvesting system storage sizing and reliability determination, Keywords: Bootstrapped rainfall generation, Cistern sizing and reliability, Numerical mass balance analysis, Seasonality effects