Modelling soil moisture, water partitioning, and plant water stress under irrigated conditions in desert urban areas

Abstract

This study conducts a comparative analysis of the effects of irrigation scheduling at two urban residential sites in the Phoenix, Arizona, metropolitan area: a xeriscaped site (gravel base with low water use plants) and a mesiscaped site (turf grass and shade trees). A model of soil moisture dynamics was calibrated to observed soil moisture data from irrigated and non‐irrigated sensors at the xeric site and an irrigated sensor at the mesic site. Model simulations were conducted at both irrigated sites to investigate effects of irrigation scheduling; plant stress parameters; and precipitation variability on soil moisture dynamics, water balance partitioning, and plant water stress. Results indicated a substantial difference in soil water storage capacity at the two sites. At the xeric site, seasonal variation of irrigation input was shown to be critical in avoiding losses from the rooting zone, while reducing plant water stress. At the mesic site, sensitivity to irrigation scenarios was lower, though small amounts of water savings were achieved with seasonal applications. The model was then used to determine the minimum annual irrigation required to achieve specified levels of plant stress using long‐term meteorological records. A bimodal schedule consisting of low winter and summer irrigation was identified as a means to conserve water while keeping moderate levels of dynamic water stress. Results from the quantitative model can potentially assist water and landscape managers in desert urban areas by identifying opportunities for water savings, while providing a deeper understanding of the ecohydrological differences between the two types of landscape designs. Copyright © 2014 John Wiley & Sons, Ltd.

Urban plant physiology: adaptation-mitigation strategies under permanent stress

“…Because of the soil compaction, water shortage, especially in interaction with other environmental drivers, can be also an important factor in urban environments. A number of studies have recently focused on plant water use and thresholds of water stress [20,21]. It has been further demonstrated that higher temperature and increased drought in urban environments significantly affected the growth of urban trees [2,22].…”

Ecohydrological role of biological soil crusts across a gradient in levels of development

“…As shown in Figure 4, precipitation is partitioned into rainfall, snowfall, or a mixture based on T a . See text for details during the field experiment (e.g., Volo et al, 2014). We added snowmelt S m to total water input X (mm day −1 ) to the biocrust layer, if present.…”

“…One-dimensional soil water balance models have been shown to capture essential interactions between climate, soil, and vegetation under natural and irrigated conditions in drylands (e.g., Volo, Vivoni, Martin, Earl, & Ruddell, 2014). One-dimensional soil water balance models have been shown to capture essential interactions between climate, soil, and vegetation under natural and irrigated conditions in drylands (e.g., Volo, Vivoni, Martin, Earl, & Ruddell, 2014).…”

Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

“…Miller et al, (2007) calibrated soil moisture pdfs to project vegetation stress in a changing climate. Chen et al, (2008) related evapotranspiration observations at the stand scale to soil moisture values using a Bayesian inversion approach, and Volo et al, (2014) calibrated the soil moisture loss curve to investigate effects of irrigation scheduling and precipitation on soil moisture dynamics and plant stress. The functional form of the soil moisture losses was 25 approximated using conditionally averaged precipitation (Salvucci, 2001;Saleem and Salvucci, 2002) and remotely sensed data (Tuttle and Salvucci, 2014).…”

“…Soil saturation pdfs are less sensitive to the many sources of uncertainty and common gaps in soil moisture observations and do not require high quality co-located and concurrent hydrologic measurements that are often lacking. A number of studies have combined inverse modeling 10 approaches with ground and remotely sensed soil moisture data to successfully extract meaningful hydrologic information (Xu et al, 2006;Miller et al, 2007;Chen et al, 2008;Volo et al, 2014;Wang et al, 2016;Baldwin et al, 2017). In particular, Bayesian inference methods are effective in relating prior pdfs of observations to posterior estimates of model parameters (Xu et al, 2006;Chen et al, 2008;Baldwin et al, 2017).…”