Soil Hydraulic Modeling Outcomes with Four Parameterization Methods: Comparing Soil Description and Inverse Estimation Approaches

Abstract

Different methods for parameterizing soil hydraulic models can lead to substantially varied predictions of soil-plant-atmosphere water fluxes. This study investigated, for a heterogeneous stony soil, four methods of soil hydraulic parameterization: (i) use of a pedotransfer function with a four-layer soil profile based on detailed soil physical and textural description; (ii) use of a pedotransfer function with a single-layer soil description; (iii) inverse estimation from soil moisture data; and (iv) inverse estimation from lysimeter drainage. Soil drainage, volumetric water content, and evapotranspiration were each modeled using HYDRUS-1D for an irrigated pasture in New Zealand during the time period 1 July 2011 to 15 Mar. 2014. The first 15 mo were used for model spin-up and inverse parameter estimation, while the remainder of the study period was used as a validation period, during which model results were compared against field data. Predictions from each model parameterization were compared with field-measured fluxes from lysimeter, soil moisture sensors, and eddy covariance to determine the approach most appropriate for our site and application. The parameters estimated inversely from field data improved the modeling of soil drainage, leading to total drainage within 5 to 7% of the measured volume and prediction of 35 to 80% more drainage peaks than parameterizations based on detailed soil physical description. While all methods underpredicted evapotranspiration by 18 to 30% compared with eddy covariance, improvement in drainage estimates with inverse estimation from field data led to decreased capability for modeling evapotranspiration. We suggest this approach for application in other settings to select the most appropriate parameterization approach for a given soil hydraulic model application.

Water sources for woody shrubs on hillslopes: An investigation using isotopic and sapflow methods

“…Winter precipitation appears to replenish water bound in both shallow and deeper soil. These results are in general agreement with lysimeter and eddy covariance studies conducted in the Canterbury region of New Zealand by Duncan, Srinivasan, and McMillan () and Graham, Srinivasan, Faulkner, and Carrick (, in press). Those studies showed that the majority of groundwater recharge takes place after a succession of winter rainfall events and that the majority of summer rainfall is evaporated or transpired during summer months.…”

Dynamic groundwater recharge simulations based on cosmic‐ray neutron sensing in a tropical wet experimental basin

“…The simulation of water fluxes depends on the proper calibration of soil hydraulic properties (retention and conductivity) and is crucial for consistently simulating the vertical percolation rates under shallow groundwater conditions (Wessolek et al, 2008), such as in GEB. A study comparing four parametrization methods showed that the inverse calibration based on field soil moisture data implemented in the HYDRUS-1D model provided the best water flux predictions (Graham et al, 2018). Therefore, HYDRUS-1D was used in this study for calculating the matric-potentialdependent soil water retention [i.e., θ(h)] and hydraulic conductivity [i.e., K(h)] through the Mualem-van Genuchten soil hydraulic model (van Genuchten, 1980), including the option of air-entry detailed by Vogel et al (2000) (Equations 7-12):…”

“…The vertical percolation rate from the numerical simulation of the vadose zone is often considered as a proxy of groundwater recharge (Mathias et al., 2017). The simulation of these groundwater fluxes, however, come with the necessity of the proper calibration of some soil hydraulic parameters by means of soil moisture observations (Graham et al., 2018).…”

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

“…Both soil heterogeneity at different scales, from millimeters to kilometers, and the large amount of required data restrict the use of pedotransfer functions for the determination of SHPs and especially the soil water retention function [θ( h )] (Graham et al, 2018). Methods to measure SHPs, including pressure plates and in situ techniques like internal drainage experiments, are commonly laborious and costly.…”

“…Direct measurement of K(h) is laborious, therefore K(h) is most commonly indirectly derived by using the measured saturated hydraulic conductivity, the q(h) function, and an empirical parameter related to tortuosity and connectivity, which, in its turn, exhibits a very large spatial variability (Durner et al, 1999). Consequently, any method to directly quantify K(h) represents an improvement of modeling quality, but this is predominantly neglected (Weninger et al, 2018;Weller et al, 2011). Both soil heterogeneity at different scales, from millimeters to kilometers, and the large amount of required data restrict the use of pedotransfer functions for the determination of SHPs and especially the soil water retention function [q(h)] (Graham et al, 2018). Methods to measure SHPs, including pressure plates and in situ techniques like internal drainage experiments, are commonly laborious and costly.…”