Predicting and managing residential potable irrigation using parcel‐level databases

Friedman, Kenneth; Heaney, James P.; Morales, Miguel; Palenchar, John

doi:10.5942/jawwa.2013.105.0087

Cited by 22 publications

(27 citation statements)

References 9 publications

(16 reference statements)

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“…The ratio of estimated to calculated irrigation needs for the 11 utilities varied within the range of 0.46–1.02, with a weighted average of 0.78. A similar value of 0.72 was determined for Gainesville Regional Utilities irrigators (Friedman et al, 2013). These results suggest that the mean application rate of residential irrigation water can be reasonably predicted on the basis of irrigation requirements, which can be predicted by process‐level modeling.…”

Section: Process‐level Modeling Of Residential Water Usesupporting

confidence: 62%

“…Morales et al (2013) conducted a detailed review of service lives and costs for popular indoor end‐use devices in all urban water use sectors. The service lives and costs (including capital and installation costs) of fixtures modeled in the simulation of indoor residential use are shown in Table 5.…”

Section: Process‐level Modeling Of Residential Water Usementioning

confidence: 99%

“…The model for indoor use assumed a value of water (b) of $2/1,000 gal and a retrofit‐decision threshold (NB min ) of $100. The model for outdoor use assumed a constant ratio of average net irrigation to irrigation requirement of 0.75 (Friedman et al, 2013). Net irrigation requirements for Orlando, Fla., were used because the data for this city most closely represented the city of Sanford.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

“…Other population sources—e.g., temporary water users such as tourists and people in group quarters—are taken into account separately within the commercial, industrial, and institutional (CII) sectors. In contrast, CII water use estimates rely on a variety of size measures such as number of employees and heated area per building unit (Morales et al, 2013).…”

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confidence: 99%

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Using process models to estimate residential water use and population served

2014

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A key measure of efficiency in the field of urban water demand is gallons per capita per day of water use. One popular metric, gross gallons per capita per day, can be misleading because it includes nonresidential water uses. As an alternative, this article addresses the calculation of residential water use defined as the product of population and per capita use associated solely with people who are physically present and using water in single‐family and multifamily residences at a given time. This approach allows for consistent benchmarking because per capita use multiplied by population yields the actual amount of water delivered to the residential sector. Process models for residential population and indoor and outdoor per capita water use can be used to estimate total residential water use if they incorporate the variability among fixture end uses and irrigable area for every household served by a utility. When this approach was applied to the Sanford, Fla., case study area, the process models predicted single‐ and multifamily water use with reasonable accuracy.

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Section: Process‐level Modeling Of Residential Water Usesupporting

confidence: 62%

Section: Process‐level Modeling Of Residential Water Usementioning

confidence: 99%

Section: Model Calibration and Validationmentioning

confidence: 99%

mentioning

confidence: 99%

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Using process models to estimate residential water use and population served

2014

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“…A small number of single family residential potable water customers tend to have high irrigation use (DeOreo and Mayer 2012, Friedman et al 2013, while the majority of customers use less than the utility-wide mean. Irrigation conservation measures have the potential to make substantial reductions in potable water use, but these conservation measures should be targeted.…”

Section: Introductionmentioning

confidence: 99%

“Wat-er” They Irrigating: Characterizing Irrigation of Individual Residential Water Customers

Boyer¹,

Dukes²

2015

World Environmental and Water Resources Congress 2015

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Irrigation can account for a substantial portion of a home's water use, and reductions in irrigation can have a large water conservation impact. This project provides an improved understanding of residential irrigation practices for the purpose of assisting utilities in planning and managing potable water demand. Customers are classified into groups of occasional, low, medium, and high irrigators. All customer groups were compared to a planning design standard. Monthly potable combined (indoor and irrigation) water billing records, property appraiser data, daily weather data, and U.S. Census data for Northwest Hillsborough County in Florida were used for this analysis. Irrigation applied per unit area was estimated by subtracting estimated indoor use from total water use, then dividing by the estimated irrigated area. Theoretical gross irrigation required was calculated using an irrigation efficiency and a daily soil-water balance that included weather and soil inputs at each customer's parcel. Ratios of irrigation applied to irrigation required were used to classify customers into irrigating groups. The majority of customers were classified as occasional irrigators (69%), followed by low irrigators (19%), and medium and high irrigators (6% each). Low, medium, and high irrigator groups all had potential for reducing irrigation without compromising landscape quality. The demand of the high irrigating group exceeded the requirement by 623 mm/year (92%). IntroductionIrrigation can account for a substantial portion of a home's water use (Mayer et al. 1999, Haley et al. 2007, Romero and Dukes 2013, but irrigation behavior varies among customers. A small number of single family residential potable water customers tend to have high irrigation use Mayer 2012, Friedman et al. 2013), while the majority of customers use less than the utility-wide mean. Irrigation conservation measures have the potential to make substantial reductions in potable water use, but these conservation measures should be targeted. DeOreo and Mayer (2012) stress that eliminating excess irrigation while allowing under-irrigators to continue their practices is essential for reducing water consumption without compromising quality. With the wealth of water billing data available to utilities,

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Analytical optimization of demand management strategies across all urban water use sectors

Friedman

Heaney

Morales

et al. 2014

Water Resources Research

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An effective urban water demand management program can greatly influence both peak and average demand and therefore long-term water supply and infrastructure planning. Although a theoretical framework for evaluating residential indoor demand management has been well established, little has been done to evaluate other water use sectors such as residential irrigation in a compatible manner for integrating these results into an overall solution. This paper presents a systematic procedure to evaluate the optimal blend of single family residential irrigation demand management strategies to achieve a specified goal based on performance functions derived from parcel level tax assessor's data linked to customer level monthly water billing data. This framework is then generalized to apply to any urban water sector, as exponential functions can be fit to all resulting cumulative water savings functions. Two alternative formulations are presented: maximize net benefits, or minimize total costs subject to satisfying a target water savings. Explicit analytical solutions are presented for both formulations based on appropriate exponential best fits of performance functions. A direct result of this solution is the dual variable which represents the marginal cost of water saved at a specified target water savings goal. A case study of 16,303 single family irrigators in Gainesville Regional Utilities utilizing high quality tax assessor and monthly billing data along with parcel level GIS data provide an illustrative example of these techniques. Spatial clustering of targeted homes can be easily performed in GIS to identify priority demand management areas.

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Predicting and managing residential potable irrigation using parcel‐level databases

Cited by 22 publications

References 9 publications

Using process models to estimate residential water use and population served

Using process models to estimate residential water use and population served

“Wat-er” They Irrigating: Characterizing Irrigation of Individual Residential Water Customers

Analytical optimization of demand management strategies across all urban water use sectors

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