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For the first time, a functional-structural root-system model is validated by combining a tracer experiment monitored with magnetic resonance imaging and three-dimensional modeling of water and solute transport., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

In recent years, many computational tools, such as image analysis, data management, process-based simulation, and upscaling tools, have been developed to help quantify and understand water flow in the soil-root system, at multiple scales (tissue, organ, plant, and population). Several of these tools work together or at least are compatible. However, for the uninformed researcher, they might seem disconnected, forming an unclear and disorganized succession of tools. In this article, we show how different studies can be further developed by connecting them to analyse soil-root water relations in a comprehensive and structured network. This 'explicit network of soil-root computational tools' informs readers about existing tools and helps them understand how their data (past and future) might fit within the network. We also demonstrate the novel possibilities of scale-consistent parameterizations made possible by the network with a set of case studies from the literature. Finally, we discuss existing gaps in the network and how we can move forward to fill them., (© The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background and Aims: Root architecture development determines the sites in soil where roots provide input of carbon and take up water and solutes. However, root architecture is difficult to determine experimentally when grown in opaque soil. Thus, root architecture models have been widely used and been further developed into functional-structural models that simulate the fate of water and solutes in the soil-root system. The root architecture model CRootBox presented here is a flexible framework to model root architecture and its interactions with static and dynamic soil environments., Methods: CRootBox is a C++-based root architecture model with Python binding, so that CRootBox can be included via a shared library into any Python code. Output formats include VTP, DGF, RSML and a plain text file containing coordinates of root nodes. Furthermore, a database of published root architecture parameters was created. The capabilities of CRootBox for the unconfined growth of single root systems, as well as the different parameter sets, are highlighted in a freely available web application., Key Results: The capabilities of CRootBox are demonstrated through five different cases: (1) free growth of individual root systems; (2) growth of root systems in containers as a way to mimic experimental setups; (3) field-scale simulation; (4) root growth as affected by heterogeneous, static soil conditions; and (5) coupling CRootBox with code from the book Soil physics with Python to dynamically compute water flow in soil, root water uptake and water flow inside roots., Conclusions: CRootBox is a fast and flexible functional-structural root model that is based on state-of-the-art computational science methods. Its aim is to facilitate modelling of root responses to environmental conditions as well as the impact of roots on soil. In the future, this approach will be extended to the above-ground part of the plant.

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. [ABSTRACT FROM AUTHOR]

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Lateral mass redistribution in soils is the key to understanding field-scale solute transport, but the underlying assumptions of common transport theories are violated under transient-flow conditions. We tested the applicability of two limiting cases for solute spreading under transient-flow conditions in the field after an appropriate time coordinate transformation: no lateral mass redistribution, described by the convective-lognormal transfer function (CLT), vs. perfect lateral mass redistribution, described by the convection-dispersion equation (CDE). A Br- transport experiment was performed in six zero-tension lysimeters and in the field for almost 3 yr under atmospheric conditions. Sampling in the field was performed by extracting soil cores during seven campaigns. According to time-domain reflectometry readings, only slight variations in water content were measured in space and time in the lysimeters. In contrast, water content was lower and more variable in the plow layer in the field. The variance of solute spreading was better predicted by the CDE assuming perfect lateral mass redistribution. This hints at the importance of molecular diffusion. Both models have the flexibility to fit the flux-averaged breakthrough curve in the lysimeters and the averaged concentration profiles in the field, but not with one set of parameters. The CLT parameters obtained from the lysimeter experiment better predicted the measured concentration profiles in the field for shorter times, but both models failed for longer times. Due to the occurrence of local saturation at the lower boundary of zero-potential lysimeters, differences in water regimes hamper the transferability of transport parameters from lysimeters to the field. [ABSTRACT FROM AUTHOR]

One-dimensional transport models that predict field-scale averaged solute fluxes are often used to estimate the risk of nonpoint source groundwater contamination by widespread surface-applied chemicals. However, within-field variability of soil hydraulic properties leads to lateral variation in local solute fluxes. When this smaller scale variability is characterized in a geostatistical sense, stochastic three-dimensional flow and transport equations can be used to predict field-scale averaged transport in terms of geostatistical parameters. We discuss the use of stochastic equations for the parameterization of equivalent one-dimensional models predicting averaged solute fluxes. First, we consider the equivalent one-dimensional convection dispersion model and the equivalent dispersivity, which characterizes the spreading of laterally averaged concentrations or solute fluxes. Second, we discuss the parameterization of a stream tube model to predict local transport variables (i.e., distributions of local concentrations and local arrival times) These local transport variables are shown to be important for predicting nonlinear local transport processes and useful for inversely inferring the spatial structure of soil properties. Stochastic flow and transport equations reveal a dependency of equivalent model parameters on transport distance and flow rate, which reflects the importance of smaller scale heterogeneities on field-scale transport. Approximate solutions of stochastic flow and transport equations are obtained for steady-state and uniform flow. The effect of transient flow conditions on transport is discussed. Throughout the paper we refer to experimental and numerical data that confirm or contradict results from stochastic flow and transport equations. [ABSTRACT FROM AUTHOR]

• A new expression of the groundwater − surface water conductance is derived. • It is function of the surface water network density and aquifer properties. • It is dependent on the vertical and horizontal model discretization sizes. • It is evaluated against 2D and 3D numerical experiments. • It accurately captures the scale dependency of the conductance. In regional hydrogeological models groundwater–surface water interaction is generally represented with a Cauchy boundary condition, in which a conductance parameter governs the exchange flux rate. In some models, the conductance is controlled by the streambed properties, since it has generally a lower hydraulic conductivity than the aquifer. However, depending on the specific system and the spatial discretization of the hydrogeological model, aquifer conductance can be a limiting factor for groundwater–surface water interactions. The present study introduces a new expression to represent the aquifer conductance as a function of aquifer properties, surface water network density and model discretization. This expression is based on the Dupuit-Forcheimer theory, the Ernst equation and vertical 2D numerical experiments at the field scale. The main assumptions used to derive our formulation are the presence of a no-flow boundary at the bottom of the hydrogeological model and the homogeneity of the aquifer. The expression is evaluated using simulations with 3D hydrogeological models at different spatial resolutions and compared against previously published parameterization approaches. The results show that the new expression outperforms the other approaches by capturing accurately both the grid-size and the surface water network density dependency of the conductance, which is caused by pressure head losses due to flow within the aquifer grid cell to the surface water, without any additional numerical calculation. Moreover, the proposed expression can be implemented directly in hydrogeological models thereby improving current approaches to represent groundwater–surface water interactions in regional hydrogeological models. [ABSTRACT FROM AUTHOR]

Understanding (and manipulating) water relationships in the soil-plant system is a crucial issue for current and future breeding efforts. In the recent years, many modelling and numerical tools were developed to help quantify and understand water flow in the soil-plant system, at multiple scales (organ, plant and field). Most of these tools were developed to work together, or at least be compatible. However, for the un-informed researcher, they might seem disconnected, forming an unclear and disorganised succession of tools. In this presentation, we present different tools developed to understand plant-water relations in a comprehensive and structured network. The aim of this "water tools network" is to inform researchers about existing tools and help them understand how their data (past and future) might fit within the network. We also demonstrate the power of the network with a set of case studies from the literature. Finally, we discuss existing gaps in the network and how we can move forward to complete it. [ABSTRACT FROM AUTHOR]

The Community Land Model (CLM4.0) was extended to account for an alternative root water uptake model, which is based on the root hydraulic architecture and allows for compensatory root water uptake in case of local water stress. In this study, the new approach was compared with the standard root water uptake model of CLM by checking the simulated evapotranspiration rates against measured data from a grassland lysimeter (Rollesbroich, Germany). On-site measured data on air temperature, air pressure, humidity, precipitation, wind speed, and incident radiation were used as atmospheric forcing input, and data on plant height, leaf area index and soil texture for the land surface data set. Some adjustments of the default CLM plant and soil parameters (as stomatal resistance and near-infrared reflectance) were necessary to avoid underestimation of transpiration rates and surface albedos as well as overestimated surface runoff. Hourly data of evapotranspiration and the radiation components obtained by measurements at the lysimeter station Rollesbroich were used to evaluate the model outputs. Under the usually sufficient water conditions of the test site, both models produced equal results. Significantly different rates of root water uptake and transpiration were obtained when water limitations were induced by eliminating the precipitation in warm periods. These findings will be validated with data from the European drought and heat wave in 2018. [ABSTRACT FROM AUTHOR]