Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling

Aim of study: To investigate the monitoring strategies that let us to build effective models able to best estimate water contents, θ and pressure heads, h with the least amount of data. Area of study: Field data was acquired in an experimental plot at Bahía Blanca (Argentina). Material and methods: Field data of θ(t), h(t) for six soil depth were used to optimize the SHP (θr, θs, α, n and Ks) by inverse modeling with HYDRUS 1D. Several scenarios of available data from θ(t) and h(t) were considered: (1) six monitoring depths (6-MD); (2) five monitoring depths (5-MD); (3) four monitoring depths (4-MD). Model accuracy was assessed by comparing the measured and predicted θ and h for each monitoring strategy. Additionally, field measured SHP with independent methods were compared to inversely optimized SHP. Main results: The best fit between predicted and observed θ and h was achieved with the 6-MD strategy. Nevertheless, deterioration of statistics EF and rRMSE in the 5-MD or 4-MD schemes were lower than 10%, depending on the location of the missing data. The observation points that had less importance in parameter prediction corresponded to the intermediate vadose zone and to the deeper layers. The proposed strategies presented a better performance than field measured SHP to reproduce soil water retention curves for each layer of the soil profile. Research highlights: By reducing the number of vertical observations in the profile without harming the final SHP estimation, the resources needed in data monitoring strategies can be greatly enhanced.