|BUGHICI, THEODOR - University Of California|
|SCUDIERO, ELIA - University Of California|
Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/3/2022
Publication Date: 7/20/2022
Citation: Bughici, T., Skaggs, T.H., Corwin, D.L., Scudiero, E. 2022. Ensemble HYDRUS-2D modeling to improve apparent electrical conductivity sensing of soil salinity under drip irrigation. Agricultural Water Management. 272. Article 107813. https://doi.org/10.1016/j.agwat.2022.107813.
Interpretive Summary: Developing sustainable and profitable irrigated agriculture requires monitoring and maintaining the salt balance of irrigated soils. An estimated 30% of irrigated lands globally are salt-affected, significantly reducing agricultural productivity and profitability. Monitoring salinity in drip- or micro-irrigated lands has proven to be especially difficult due to the complex patterns of water and salinity variability that emerge under micro-irrigation. In this work, computer simulations of drip irrigation were employed to explore best practices for using surface measurements of soil electrical conductivity to monitor and map soil salinity. Results from two simulated case-studies indicated that positioning the electrical conductivity instrument one meter from a drip emitter was best when irrigation water salinity was low, and as close as possible to the emitter when irrigation water salinity was high. The probabilistic simulation approached developed in this work can be used by soil scientists and agricultural consultants seeking to improve monitoring and mapping of soil salinity in micro-irrigated lands.
Technical Abstract: Monitoring and mapping soil salinity are valuable for irrigation management and reclamation of salt-affected agricultural soils in arid and semi-arid regions. Proximal measurements of apparent soil electrical conductivity (ECa) can help characterize soil salinity spatial distributions. However, ECa is not solely a function of salinity. ECa is strongly influenced by soil salinity, water content, and edaphic properties such as texture and bulk density. Consequently, monitoring and mapping salinity based on geospatial ECa measurements is challenging in fields with dynamic and spatially complex patterns of salinity and water content, such as occurs under drip irrigation. We conducted a numerical modeling study to evaluate protocols for using proximal ECa sensing in drip irrigated systems, focusing specifically on the measurement distance from the drip-line that consistently identifies areas of high salinity in the rootzone. The measurement distance was evaluated as a function of six irrigation management parameters: soil hydraulic conductivity, irrigation discharge, irrigation interval, solute concentration, root-zone volume, and leaching fraction. HYDRUS-2D was used to run a 729 member ensemble of drip irrigation simulations of water and solute dynamics under different irrigation management scenarios. Two case studies were simulated for clay loam soil: (1) low salinity soil irrigated with high salinity irrigation water and (2) high salinity soil irrigated with low salinity water. Depth-averaged ECa measurements down to the 75 and 150 cm depths, such as can be obtained using an electromagnetic induction (EMI) sensor, were evaluated in the simulations. According to the ensemble results, a reliable EMI measurement distance from the drip-line was about 100 cm for the case of low salinity irrigation in saline soil and adjacent to the drip-line for the high salinity irrigation. The ensemble ECa and EC of saturated paste extract (ECe) distributions were twice as sensitive to the irrigation water salinity level as compared to the other irrigation management parameters. The probabilistic ensemble approach can be extended to a variety of case studies to aid soil scientists and agricultural consultants monitoring and mapping soil salinity with ECa-directed soil sampling for micro-irrigation systems.