The spatiotemporal distribution of root water uptake (RWU) depends on the dynamics of the root distribution and compensatory uptake from wetter regions in the root zone. This work aimed to parameterize three RWU models with different representations of compensation: the Feddes-Jarvis model that uses an empirical function, the Feddes model without compensation, and the Couvreur model that is based on a physical description of water flow in the soil-root system. These models were implemented in HYDRUS-1D, and soil hydraulic parameters were optimized by inverse modeling using soil water content and potential measurements and observations of root distributions of winter wheat (Triticum aestivum L.) in horizontally installed rhizotubes. Soil moisture was equally well predicted by the three models, and the soil hydraulic parameters optimized by the models with compensation were comparable. The obtained RWU parameters of the Feddes-Jarvis model were consistent with data reported in the literature, although the pressure heads h 3l and h 3h for lower and higher transpirations rates, respectively, could not be uniquely identified. Response surfaces of the objective function showed that the root-related parameters of the Couvreur model could be identified using inverse modeling. Furthermore, these parameters were consistent with combined root architectural and hydraulic observations from the literature. The Feddes-Jarvis and Couvreur models simulated similar root-system-scale stress functions that link total RWU to the effective root zone water potential, suggesting that parameters may be transferable between the two models. Simulated RWU profiles differed due to different water redistribution by the root system, but the measurements were not sufficiently precise to observe this redistribution.Abbreviations: C, Couvreur; ET, evapotranspiration; F, Feddes; FJ, Feddes-Jarvis; GA, genetic algorithm; NRLD, normalized root length density; OF, objective functions; RLD, root length density; RWU, root water uptake; SWC, soil water content; SWP, soil water pressure head.Numerous root water uptake (RWU) models have been developed with different assumptions, complexity, and parameters, but the description of this process and its parameterization remains challenging in soil hydrology (Kumar et al., 2014;Vereecken et al., 2015). Although it is commonly acknowledged that RWU is defined by water potential gradients and hydraulic resistances in the soil-plant system (Steudle and Peterson, 1998;van den Honert, 1948), this principle is seldom included in models.Root water uptake models can be divided into two main classes: functional-structural vs. macroscopic models. The former class defines a root system architectural domain facilitating the inclusion of explicit root hydraulic features and associated physical concepts to simulate water flow toward individual roots (Doussan et al., 1998;Javaux et al., 2008). Their complexity is particularly appropriate to address questions of interactions between root growth and soil properties (Pagès et ...

Streaming potential (SPs) is the electric potential generated by fluid flow in a charged porous medium. The SPs signals are related to pore water velocity, bulk electrical conductivity, pore water charge excess, and soil porosity. While several studies have estimated hydraulic properties of the saturated zone from SPs, there have been fewer attempts to infer unsaturated hydraulic properties from SPs. From numerical and laboratory experiments in which infiltration and subsequent drainage was monitored with nonpolarizable Ag/AgCl electrodes and tensiometers, we showed that it is feasible to estimate three key Mualem–van Genuchten hydraulic parameters (fitting parameters α and n and saturated hydraulic conductivity Ks) and Archie's saturation exponent (na) using a coupled hydrogeophysical inversion approach. In addition to a reasonably good estimate of na, coupled hydrogeophysical inversion of actual SPs measurements during drainage provided estimates of α, n, and Ks that were comparable to those obtained from an independent inversion of the tensiometric data (matric heads). We concluded that coupled hydrogeophysical inversion of time‐lapse SPs signals is a promising method for hydraulic characterization of the vadose zone. Accurate modeling of SPs signals is essential for reliable inversion results, but there is still debate about the appropriate model for the voltage coupling coefficient at partial saturation. Our experimental data showed a nonlinear and monotonic decrease in the absolute voltage coupling coefficient with decreasing saturation. A comparison of several available models with our experimental data showed that models that consider the relative permeability and the relative electrical conductivity in addition to the saturated coupling coefficient and water saturation were most appropriate.

Abstract. Stomatal regulation and whole plant hydraulic signaling affect water fluxes and stress in plants. Land surface models and crop models use a coupled photosynthesis–stomatal conductance modeling approach. Those models estimate the effect of soil water stress on stomatal conductance directly from soil water content or soil hydraulic potential without explicit representation of hydraulic signals between the soil and stomata. In order to explicitly represent stomatal regulation by soil water status as a function of the hydraulic signal and its relation to the whole plant hydraulic conductance, we coupled the crop model LINTULCC2 and the root growth model SLIMROOT with Couvreur's root water uptake model (RWU) and the HILLFLOW soil water balance model. Since plant hydraulic conductance depends on the plant development, this model coupling represents a two-way coupling between growth and plant hydraulics. To evaluate the advantage of considering plant hydraulic conductance and hydraulic signaling, we compared the performance of this newly coupled model with another commonly used approach that relates root water uptake and plant stress directly to the root zone water hydraulic potential (HILLFLOW with Feddes' RWU model). Simulations were compared with gas flux measurements and crop growth data from a wheat crop grown under three water supply regimes (sheltered, rainfed, and irrigated) and two soil types (stony and silty) in western Germany in 2016. The two models showed a relatively similar performance in the simulation of dry matter, leaf area index (LAI), root growth, RWU, gross assimilation rate, and soil water content. The Feddes model predicts more stress and less growth in the silty soil than in the stony soil, which is opposite to the observed growth. The Couvreur model better represents the difference in growth between the two soils and the different treatments. The newly coupled model (HILLFLOW–Couvreur's RWU–SLIMROOT–LINTULCC2) was also able to simulate the dynamics and magnitude of whole plant hydraulic conductance over the growing season. This demonstrates the importance of two-way feedbacks between growth and root water uptake for predicting the crop response to different soil water conditions in different soils. Our results suggest that a better representation of the effects of soil characteristics on root growth is needed for reliable estimations of root hydraulic conductance and gas fluxes, particularly in heterogeneous fields. The newly coupled soil–plant model marks a promising approach but requires further testing for other scenarios regarding crops, soil, and climate.

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Accurate estimation of topsoil hydraulic properties is important for understanding water flow and solute transport in the vadose zone. Coupled hydrogeophysical inversion schemes that enable the use of multiple geophysical and hydrological data for the estimation of soil hydraulic properties have recently been proposed. In these coupled inversion schemes, a hydrological model describing the process under investigation is coupled to a forward geophysical model and hydraulic parameters are directly estimated from geophysical measurements. While these schemes provide a suitable platform for the integration of multiple geophysical and hydrological data, efficient methods to combine these data types for improved parameter estimation still warrant investigation. In this study, we investigated the feasibility of estimating three topsoil Mualem-van Genuchten parameters from the fusion of inflow and electrical resistance measurements obtained under constant head infiltration. In addition to using only inflow or electrical resistances, we investigated three methods of combining these data for improved estimation of topsoil hydraulic parameters. Our results show that using inflow alone does not provide a unique solution to the inverse problem. Better results are obtained with the additional use of electrical resistance data. We show that successful data fusion within the coupled hydrogeophysical inversion framework depends on the choice of an appropriate objective function. We obtained the best data fusion results with an objective function defined as the sum of the root mean square error of both data types normalized by the standard deviation of the respective measurements. In this case, the inverted hydraulic parameters were very comparable to reference values obtained from a multi-step outflow experiment carried out with undisturbed soil cores from the experimental site. It is concluded that the coupled hydrogeophysical inversion framework is a promising tool for non-invasive near-surface hydrological investigations. Parkin et al. 1995;Huisman et al. 2002;Schwartz and Evett 2002) during infiltration events. Although the water content and matric potential of the vadose zone can exhibit large spatial variation in the horizontal and vertical direction (e.g., Flury et al. 1994), tensiometry and TDR only provide limited spatial coverage (0.01-1 dm 3 ), requiring time-consuming measurements at many locations for field scale sampling. Geophysical methods like ground-penetrating radar (GPR; Binley and Beven 2003; Huisman et al. 1994; Faybishenko 2000) and time-domain reflectometry (TDR;