Predicting urban forest growth and its impact on residential landscape water demand in a semiarid urban environment

Abstract

a b s t r a c tWe present an innovative approach to estimating residential irrigation water demand for a large metropolitan area using GIS data, weather station data, and a water budget modeling approach commonly used by plant scientists and landscape management professionals. An important question addressed by our study is how a growing urban forest affects the overall irrigation water demand of a semiarid metropolitan area. To estimate the amount of water required by residential landscaping, we consider water demand to be a function of the areal extent of residential landscaping (i.e. tree/shrub or turf grass), the water-loss rate for different landscaping types, the efficiency with which the landscape is irrigated, and local climatic factors (i.e. reference evapotranspiration and precipitation). We estimated irrigation water demand for 542 residential neighborhoods in Salt Lake County, UT, USA for 2005. To investigate the effects of a maturing urban forest on water demand, we used simultaneous autoregression (SAR) models to predict the spatial extent of future forest canopy and future exposed turf grass in residential neighborhoods. For both the forest canopy model and the turf grass model we used the median age of housing stock as the dependent variable. Psuedo R 2 were 0.70 and 0.82 for the tree/shrub canopy and turf grass models, respectively. Based on projected areal extents of tree/shrub canopy, exposed turf grass, and turf grass under canopy, we estimated future water demands for the 542 residential neighborhoods. Our predictive model suggests that as urban tree canopy increases in residential urban areas, exposed turf grass decreases, with a net effect of a slight decrease in residential landscape water demand. This can be explained by the relative differences in water lost through evapotranspiration by different landscape types, namely; trees/shrub (i.e. woody plants), exposed turf grass, and turf grass under tree canopy.

Water scarcity in the Arabian Peninsula and socio-economic implications

“…tree/shrub or turf grass), the water-loss rate for different landscaping types, the efficiency with which the landscape is irrigated, and local climatic factors (i.e. reference evapotranspiration and precipitation) (Lowry et al 2011). No study has been conducted to quantify this but going by the high amounts of domestic and municipal water consumption it can be assumed that most of this goes towards watering plants and lawn.…”

Green areas and water management in residential developments in the European Western Mediterranean. A case study of Alicante, Spain

“…There are similar studies that focus on urban forest growth and its impact on the water demand of residential landscapes, conducted by Lowry, Ramsey, and Kjelgren (2011), water demand in the outdoor areas in the neighbourhoods of Salt Lake County (USA) was analysed. Studies of this type are not often conducted in Europe.…”

The tree cover and temperature disparity in US urbanized areas: Quantifying the association with income across 5,723 communities

“…While we recognize that block scale population density is not the only attribute describing urban form that is related to tree canopy cover, several studies have shown evidence of a decay in tree cover as population density increases [ 35 , 37 , 63 , 64 ]. Population density is also an easily accessible metric at broad spatial scales that is both correlated with and related to other aspects of urban form such as building type, compactness, and average block size [ 65 ], and some of these factors have also been shown to be related to tree cover [ 35 , 36 , 42 ].…”

“…First, tree cover is also associated with other factors, including differences in climate, biome, neighborhood age, urban density, the intensity of urban settlement, and other aspects of urban form [34][35][36][37][38][39][40]. In some studies, covariates such as terrain, housing type and density explained part of the variation between tree cover and socio-economic variables [25,29,39,41,42]. Furthermore, socio-economic variables are themselves also correlated with aspects of urban form, such as the degree of sprawl and population density [43,44].…”