Abstract
Abstract. The increased availability of end use measurement studies allows for mechanistic and detailed approaches to estimating household water demand and conservation potential. This study simulates water use in a single-family residential neighborhood using end-water-use parameter probability distributions generated from Monte Carlo sampling. This model represents existing water use conditions in 2010 and is calibrated to 2006–2011 metered data. A two-stage mixed integer optimization model is then developed to estimate the least-cost combination of long- and short-term conservation actions for each household. This least-cost conservation model provides an estimate of the upper bound of reasonable conservation potential for varying pricing and rebate conditions. The models were adapted from previous work in Jordan and are applied to a neighborhood in San Ramon, California in the eastern San Francisco Bay Area. The existing conditions model produces seasonal use results very close to the metered data. The least-cost conservation model suggests clothes washer rebates are among most cost-effective rebate programs for indoor uses. Retrofit of faucets and toilets is also cost-effective and holds the highest potential for water savings from indoor uses. This mechanistic modeling approach can improve understanding of water demand and estimate cost-effectiveness of water conservation programs.
Highlights
Models predicting residential water use and conservation potential based on empirically estimated parameters, waterconsuming device turnover rates, and regression analysis are fairly common
In contrast to more inductive or empirical techniques for household water demand analyses, this paper presents a more deductive (“causal”) household end-use model based on physical parameters affecting water use that vary by household
The modeled results were comparable to measurements from other end use studies and were calibrated with little difficulty to the metered data
Summary
Models predicting residential water use and conservation potential based on empirically estimated parameters, waterconsuming device turnover rates, and regression analysis are fairly common. Some models estimate conservation potential by assuming natural replacement rates of appliances with more efficient appliances and calculating the expected amount of water saved (CALFED Bay Delta Program, 2006; Blokker et al, 2010; Gleick et al, 2003). The strength of the regression analysis results for estimating water demand of individual homes is often low, with coefficients of determination (R2) typically around 0.4 (DeOreo, 2011) Such models perform reasonably for estimating current average water use for groups of households, and are useful in estimating the effectiveness of and potential for water conservation measures under different scenarios. We employ a Monte Carlo approach to include variability in household physical characteristics and behavior when estimating distributions of household water use and conservation potential This modeling approach is applied to a neighborhood in the East Bay Municipal Utility District (EBMUD) service area, California. The inherent limitations and desirable extensions of this modeling approach are discussed
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