Simulating Soil Water Status of Irrigated Fields: The Effects of Soil Data and Root Water Uptake Distribution

Abstract

<b>Highlights</b> <list list-type=bullet><list-item> The effects of variability in two key inputs to irrigation scheduling models were investigated. </list-item><list-item> Commonly used soil textural data underestimated sand particles, resulting in larger errors in simulated water content. </list-item><list-item> Sensor-based root water uptake distribution led to better model performance than constant and linear patterns. </list-item><list-item> Irrigation timing and amount variables were more impacted by variability in soil data than water uptake distributions. </list-item></list> <b>Abstract</b>. With recent advances in web-based irrigation scheduling tools and mobile applications and the possibility of using more complex modeling approaches, it is important to evaluate the effects of variable input data on the output of these tools and models. Two types of input data that are highly variable across irrigated fields and soil profiles are soil textural data and root water uptake distribution (RWUD). In this study, root zone soil textural data from two sources of commonly used, freely available web soil survey (WSS) and time-consuming, labor-intensive in-situ sampling (ISS) were used in combination with three RWUDs (constant, linear, and sensor-based) to simulate volumetric water content (θv) at four soil layers in six irrigated fields, using the HYDRUS model. The percentage of sand particles based on WSS was about half of the measured amount on average, resulting in a considerable difference in estimated hydraulic properties and soil water thresholds. Sensor data revealed that RWUDs were highly nonuniform, with more than 60% of water extraction occurring from the top 30 cm of the root zone. Among the six combinations of two sources of soil data and three RWUDs, ISS-sensor resulted in the smallest errors in simulated θv, and WSS-constant yielded the largest errors. Simulated θv data were translated to actionable end-user variables of irrigation trigger (IT) and soil water depletion (SWD), which determine the timing and the amount of irrigation applications, respectively. Relying on WSS resulted in irrigation trigger being called about four times more than when measured soil data were used. The average SWD based on WSS was 157 mm, about two times larger than the average SWD based on ISS (68 mm).