Soil Hydraulic Properties Determined by Inverse Modeling of Drip Infiltrometer Experiments Extended with Pedotransfer Functions

Abstract

Core Ideas A robust PTF was developed to predict water contents at −1, −10, and −158 m tension. Drip infiltrometer experiments were inversely modeled to predict soil hydraulic properties. Both θ(h) and K(h) can be accurately estimated from experimental data together with PTFs. A transient flow experiment using automated drip infiltrometers (ADIs) was performed on soil columns (about 6 dm3) large enough to incorporate macropore flow effects. We investigated to what extent the estimated soil hydraulic parameters obtained from inverse modeling of these experiments are reliable. A machine learning based pedotransfer function (PTF) for prediction of water content at −1, −10, and −158 m pressure head was developed. Sensitivity analysis of the van Genuchten parameters (residual and saturated water content θr and θs, fitting parameters α, n, and λ, and saturated hydraulic conductivity Ks) in soils of sandy, silty, and clayey textures showed that the temporal variation of pressure heads in ADI scenarios was not sensitive to θr and θs. The other parameters were accurately estimated from numerically synthesized data. The uniqueness of the estimated parameters did not change when a bias, representing experimental error, was added to the data set. In actual columns, using the temporal and spatial pressure head data from the ADIs and the water contents in the drier range predicted by the developed PTF resulted in a precise estimation of the van Genuchten parameters. Not including the PTF water contents resulted in non‐uniquely estimated van Genuchten parameters.

Parameterization of two‐dimensional approaches in HYDRUS‐2D: Part 2. Solute transport at the field and column scale

“…Determination of soil properties is essential for an adequate understanding of the mechanisms that dominate water flow and solute transport processes within a field, which is crucial for improving agriculture management practices and protecting water bodies from contamination. Many researchers have used data from field and laboratory experiments in order to estimate soil hydraulic properties via numerical inversion (Kodešová et al, 2008(Kodešová et al, , 2009(Kodešová et al, , 2010Kotlar et al, 2019). Spatial heterogeneity in soil physical and hydraulic properties within an agricultural field adds significant uncertainty in relation to the modeling results (Filipović et al, 2019;Kodešová et al, 2005).…”

Modeling water fluxes through containerized soilless substrates using HYDRUS

“…We analyzed the sensitivity of model outputs to three hydraulic parameters (θ s , α, and K s ), noting that these three parameters were shown in previous work to influence hydrologic simulation outputs (Chen, Jiao, & Li, 2016; Kotlar et al., 2019; Stewart, Lee, Shuster, & Darner, 2017). We used initial parameters determined via the evaporation method and then individually varied α and K s by factors of 0.1, 0.2, 0.5, 2, 5, and 10 and θ s by increments of 0.1 in a one‐at‐a‐time analysis (Šimůnek & Senja, 2011).…”

Development and Uncertainty Assessment of Pedotransfer Functions for Predicting Water Contents at Specific Pressure Heads

“…Considering a Gaussian likelihood function, the predictive mean y ts for a given test point ( x ts ) is where is the vector with the distances from x ts to each training point. The optimization of kernel parameters and other details are given in Kotlar et al (2019b, 2019c), who successfully applied Gaussian regression. The length scale of each predictor extracted from its squared exponential kernel function shows the weight or importance of the respective predictor in the prediction by a GP PTF.…”