The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.

We reviewed the use of the van Genuchten–Mualem (VGM) model to parameterize soil hydraulic properties and for developing pedotransfer functions (PTFs). Analysis of literature data showed that the moisture retention characteristic (MRC) parameterization by setting shape parameters m = 1 − 1/n produced the largest deviations between fitted and measured water contents for pressure head values between 330 (log10 pressure head [pF] 2.5) and 2500 cm (pF 3.4). The Schaap–van Genuchten model performed best in describing the unsaturated hydraulic conductivity, K The classical VGM model using fixed parameters produced increasingly higher root mean squared residual, RMSR, values when the soil became drier. The most accurate PTFs for estimating the MRC were obtained when using textural properties, bulk density, soil organic matter, and soil moisture content. The RMSR values for these PTFs approached those of the direct fit, thus suggesting a need to improve both PTFs and the MRC parameterization. Inclusion of the soil water content in the PTFs for K only marginally improved their prediction compared with the PTFs that used only textural properties and bulk density. Including soil organic matter to predict K had more effect on the prediction than including soil moisture. To advance the development of PTFs, we advocate the establishment of databases of soil hydraulic properties that (i) are derived from standardized and harmonized measurement procedures, (ii) contain new predictors such as soil structural properties, and (iii) allow the development of time‐dependent PTFs. Successful use of structural properties in PTFs will require parameterizations that account for the effect of structural properties on the soil hydraulic functions.

We studied water uptake variability at the plant scale using a three‐dimensional detailed model. Specifically, we investigated the sensitivity of the R‐SWMS model under different plant collar conditions by comparing computed water fluxes, flow variability, and soil water distributions for different case scenarios and different parameterizations. The relative radial root conductivity and soil hydraulic conductivity were shown to control the plant water extraction distribution. Highly conductive soils promote water uptake but at the same time decrease the variability of the soil water content. A large radial root conductivity increases the amount of water extracted by the root and generates very heterogeneous water extraction profiles. Increasing the xylem conductivity has less impact because the xylem is generally the most conductive part of the system. It was also determined that, due to the different magnitudes of soil and root conductivities, similar one‐dimensional sink‐term profiles can result in very different water content and flux distributions at the plant scale. Furthermore, an analysis based on soil texture showed that the ability of a soil to sustain high plant transpiration demand cannot be predicted a priori from the soil hydraulic properties only, as it depends on the evaporative demand and on the three‐dimensional distributions of the soil/root conductivity ratio and soil capacity, which continuously evolve with time. Combining soil and root hydraulic properties led to very complex one‐dimensional sink functions that are quite different from the simple reduction functions usually found in the literature. The R‐SWMS model could be used to develop more realistic one‐dimensional reduction functions.

Abstract. Plant root water uptake (RWU) has been documented for the past five decades from water stable isotopic analysis. By comparing the (hydrogen or oxygen) stable isotopic compositions of plant xylem water to those of potential contributive water sources (e.g., water from different soil layers, groundwater, water from recent precipitation or from a nearby stream), studies were able to determine the relative contributions of these water sources to RWU. In this paper, the different methods used for locating/quantifying relative contributions of water sources to RWU (i.e., graphical inference, statistical (e.g., Bayesian) multi-source linear mixing models) are reviewed with emphasis on their respective advantages and drawbacks. The graphical and statistical methods are tested against a physically based analytical RWU model during a series of virtual experiments differing in the depth of the groundwater table, the soil surface water status, and the plant transpiration rate value. The benchmarking of these methods illustrates the limitations of the graphical and statistical methods while it underlines the performance of one Bayesian mixing model. The simplest two-end-member mixing model is also successfully tested when all possible sources in the soil can be identified to define the two end-members and compute their isotopic compositions. Finally, the authors call for a development of approaches coupling physically based RWU models with controlled condition experimental setups.