Simulating soil water status of irrigated fields: The effects of soil data and root water uptake distribution

Authors: MEHATA, MUKESH - Oklahoma State University; DATTA, SUMON - Oklahoma State University; TAGHVAEIAN, SALEH - Oklahoma State University; MIRCHI, ALI - Oklahoma State University; Moriasi, Daniel; Starks, Patrick

Submitted to: Journal of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/24/2021
Publication Date: 3/13/2022
Citation: Mehata, M., Datta, S., Taghvaeian, S., Mirchi, A., Moriasi, D.N., Starks, P.J. 2022. Simulating soil water status of irrigated fields: The effects of soil data and root water uptake distribution. Journal of the ASABE. 65(3):587-597. https://doi.org/10.13031/ja.14856.
DOI: https://doi.org/10.13031/ja.14856

Interpretive Summary: 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, we used a subsurface model to determine the impact of using freely available web soil survey (WSS) and labor intensive measured soil texture in combination with three RWUDs (constant, linear, sensor-based) on predicted soil water content ('v). On average, % sand particles based on WSS was about half of the measured amount, resulting in a large difference in estimated soil water flow properties and thresholds. Sensor-based data revealed that RWUDs varied greatly, with more than 60% of water extraction occurring in the top 30 cm of the root zone. Model results indicated that measured data led to the smallest errors in predicted 'v, which were 33% lower than those predicted using freely available data. The predicted 'v data were translated to actionable end-user variables of irrigation trigger and soil water depletion, which determine the timing and the amount of irrigation application, respectively. Results showed that relying on freely available data led to more frequent and more amount of irrigation application than when measured soil data were used, which can lead to wasting water and increasing pumping costs. Therefore, it is critical to use accurate input data for irrigation tools and models used to ensure accurate outputs needed by producers to manage irrigation water.

Technical Abstract: 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 of constant (CT), linear (LN), and sensor-based (SB) to simulate volumetric water content ('v) at four soil layers of six irrigated fields, using the HYDRUS model. 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 non-uniform, 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-SB resulted in the smallest errors in simulated 'v and WSS-CT yielded the largest errors. Simulated 'v 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).