Estimating irrigation demand of urban green spaces and the influencing factors

【Objective】 Plants in most urban areas need irrigation, and understanding their demand for irrigation is important for urban water management. This paper analyzes the spatiotemporal variation in irrigation demand of green spaces in small and medium-sized metropolitan cities in arid areas of Northwest China, as well as the influencing factors. 【Method】 A net irrigation water accounting model was developed for plot scale by integrating meteorological data, root zone soil moisture, surface quantitative remote sensing data, and the principle of root zone soil water balance. Quantile random forest and Bayesian linear regression models were used to identify key factors that influence irrigation water demand. 【Result】 ① In 2022, the daily average net irrigation water demand of green space plots estimated from the model ranged from 0.45 to 0.85 mm, slightly lower than the measured values. Temporarily, irrigation demand peaked during the vegetation growth period. Spatially, plots with denser, healthier vegetation had high demand for irrigation, showing significant spatial autocorrelation. ② Key factors that affect irrigation demand included root zone soil moisture, surface temperature, vegetation growth, sunshine duration and air temperature. Meteorological data and surface remote sensing images explained 61% and 78% of the variation in net irrigation water demand, respectively, highlighting the multifaceted nature of irrigation demand. 【Conclusion】 Understanding the demand of urban green spaces for irrigation at plot scale has an important implication for urban water resource allocation and improving urban water management.