Soil Water Flow Model Research Articles

Effects of Xerophytic Vegetation-Salix on Soil Water Redistribution in Semiarid Region

Xerophytic vegetation re-regulates and allocates water resources through canopy interception, root water uptake and transpiration, and changes the water budget among precipitation, runoff, interception and infiltration, thus having a significant impact on the processes of the hydrological cycle. In this study, we investigated the effect of xerophytic shrub-Salix on soil water redistribution and water budget through an in situ monitoring experiment combined with two-dimensional vegetation water consumption modeling. The results showed that, due to the interception effect of root water uptake, it was difficult for precipitation infiltration to recharge deep soil water and groundwater. The measured data of soil moisture content, hydraulic head and precipitation were used to verify and calibrate the performance of the soil water flow model in the vadose zone by HYDRUS-2D. The effect of roots system on soil water was simulated, and the appropriate spacing of Salix replanting was estimated. Combined with the relationship between the transverse roots system and the crown width obtained by the investigation, it was determined that the spacing between the Salix should be greater than five times the crown width, so that the balance between the water consumption of Salix and the water supply of deep soil by precipitation could be considered. The results of this study are important for estimating groundwater recharge in arid areas and provide practical vegetation replanting options for similar regions.

Root distributions predict shrub–steppe responses to precipitation intensity

Abstract. Precipitation events are becoming more intense around the world, changing the way water moves through soils and plants. Plant rooting strategies that sustain water uptake under these conditions are likely to become more abundant (e.g., shrub encroachment). Yet, it remains difficult to predict species responses to climate change because we typically do not know where active roots are located or how much water they absorb. Here, we applied a water tracer experiment to describe forb, grass, and shrub root distributions. These measurements were made in 8 m by 8 m field shelters with low or high precipitation intensity. We used tracer uptake data in a soil water flow model to estimate how much water respective plant root tissues absorb over time. In low-precipitation-intensity plots, deep shrub roots were estimated to absorb the most water (93 mm yr−1) and shrubs had the greatest aboveground cover (27 %). Grass root distributions were estimated to absorb an intermediate amount of water (80 mm yr−1) and grasses had intermediate aboveground cover (18 %). Forb root distributions were estimated to absorb the least water (79 mm yr−1) and had the least aboveground cover (12 %). In high-precipitation-intensity plots, shrub and forb root distributions changed in ways that increased their water uptake relative to grasses, predicting the increased aboveground growth of shrubs and forbs in these plots. In short, water uptake caused by different rooting distributions predicted plant aboveground cover. Our results suggest that detailed descriptions of active plant root distributions can predict plant growth responses to climate change in arid and semi-arid ecosystems.

Data worth analysis within a model-free data assimilation framework for soil moisture flow

Abstract. Conventional data worth (DW) analysis for soil water problems depends on physical dynamic models. The widespread occurrence of model structural errors and the strong nonlinearity of soil water flow may lead to biased or wrong worth assessment. By introducing the nonparametric data worth analysis (NP-DWA) framework coupled with the ensemble Kalman filter (EnKF), this real-world case study attempts to assess the worth of potential soil moisture observations regarding the reconstruction of fully data-driven soil water flow models prior to data gathering. The DW of real-time soil moisture observations after Gaussian process training and Kalman update was quantified with three representative information metrics, including the trace, Shannon entropy difference and relative entropy. The sequential NP-DWA framework was examined by a number of cases in terms of the variable of interest, spatial location, observation error, and prior data content. Our results indicated that, similarly to the traditional DW analysis based on physical models, the overall increasing trend of the DW from the sequential augmentation of additional observations within the NP-DWA framework was also susceptible to interruptions by localized surges due to never-experienced atmospheric conditions (i.e., rainfall events). The difference is that this biased DW in the former is caused by model structural errors triggered by contrasting scenarios, which is difficult to be compensated for by assimilating more prior data, while this performance degradation in the NP-DWA can be effectively alleviated by enriching training scenarios or the appropriate amplification of observational noise under extreme meteorological conditions. Nevertheless, a substantial expansion of the prior data content may cause an unexpected increase in the DW of future potential observations due to the possible introduction of ensuing observation noises. Hence, high-quality and representative small data may be a better choice than unfiltered big data. Compared with the observations in the surface layer with the strongest time variability, the soil water content in the middle layer robustly exhibited remarkable superiority in the construction of model-free soil moisture models. We also demonstrated that the DW assessment performance was jointly determined by 3C, i.e., the capacity of potential observation realizations to capture actual observations, the correlation of potential observations with the variables of interest and the choice of DW indicators. Direct mapping from regular meteorological data to soil water content within the NP-DWA mitigated the adverse effects of nonlinearity-related interference, which thus facilitated the identification of the soil moisture covariance matrix, especially the cross-covariance.

How to put plant root uptake into a soil water flow model: A documentation with complete computer code.

The need for improved crop water use efficiency calls for flexible modelingplatforms to implement new ideas in plant root uptake and its regulation mechanisms. Thispaper documents the details of modifying a soil infiltration and redistribution model to include(a) dynamic root growth, (b) non-uniform root distribution and water uptake, (c) the effect ofwater stress on plant water uptake, and (d) soil evaporation. The paper also demonstratesstrategies of using the modified model to simulate soil water dynamics and plant transpirationconsidering different sensitivity of plants to soil dryness and different mechanisms of rootwater uptake. In particular, the flexibility of simulating various degrees of compensateduptake (whereby plants tend to maintain potential transpiration under mild water stress)is emphasized. The paper also describes how to estimate unknown root distributionand rooting depth parameters by the use of a simulation-based searching method. Thefull documentation of the computer code will allow further applications and new development.

Interaction Simulation of Vadose Zone Water and Groundwater in Cele Oasis: Assessment of the Impact of Agricultural Intensification, Northwestern China

Agricultural intensification has boosted land productivity, but it has also created new sustainability issues. As one of the most important human habitations and agricultural farming areas in arid areas, the Cele Oasis has a very developed agricultural system. This paper studies the long-term effects of different types of agricultural intensification strategies on groundwater level fluctuations in the Cele Oasis. A soil water flow (HYDRUS-1D) and aquifer simulation (MODFLOW) coupling model were used to construct the geometric structures of the vadose zone and saturated zone in the Cele Oasis and to analyze the recharge and discharge mechanism of the oasis. The results showed that HYDRUS-1D accurately simulated soil moisture transport in the Cele Oasis, providing reliable data for calibration of the MODFLOW model. The groundwater level simulated by MODFLOW was in good agreement with the observed value. The results of the R2, RMSE, and NSE were ranges of 0.77–0.90, 0.45–0.74 m, and 0.76–0.87, respectively. The errors were acceptable limits. The coupling model predicted the responses of different agricultural types and cropping scenarios to groundwater. Predictions showed that the groundwater level in the Cele Oasis remained stable under the current cropping scenario (100% cropping intensity), and that the groundwater level decreased slightly under the cropping scenario (110% cropping intensity and 130% cropping intensity). When the cropping scenario was at 170% cropping intensity, the groundwater level decreased rapidly, and the maximum drawdown value was 7 m. Therefore, the maximum cropping intensity of the Cele Oasis in the future should be 130% of the current cropping intensity.

From hydraulic root architecture models to macroscopic representations of root hydraulics in soil water flow and land surface models

Abstract. Root water uptake is an important process in the terrestrial water cycle. How this process depends on soil water content, root distributions, and root properties is a soil–root hydraulic problem. We compare different approaches to implement root hydraulics in macroscopic soil water flow and land surface models. By upscaling a three-dimensional hydraulic root architecture model, we derived an exact macroscopic root hydraulic model. The macroscopic model uses the following three characteristics: the root system conductance, Krs, the standard uptake fraction, SUF, which represents the uptake from a soil profile with a uniform hydraulic head, and a compensatory matrix that describes the redistribution of water uptake in a non-uniform hydraulic head profile. The two characteristics, Krs and SUF, are sufficient to describe the total uptake as a function of the collar and soil water potential, and water uptake redistribution does not depend on the total uptake or collar water potential. We compared the exact model with two hydraulic root models that make a priori simplifications of the hydraulic root architecture, i.e., the parallel and big root model. The parallel root model uses only two characteristics, Krs and SUF, which can be calculated directly following a bottom-up approach from the 3D hydraulic root architecture. The big root model uses more parameters than the parallel root model, but these parameters cannot be obtained straightforwardly with a bottom-up approach. The big root model was parameterized using a top-down approach, i.e., directly from root segment hydraulic properties, assuming a priori a single big root architecture. This simplification of the hydraulic root architecture led to less accurate descriptions of root water uptake than by the parallel root model. To compute root water uptake in macroscopic soil water flow and land surface models, we recommend the use of the parallel root model with Krs and SUF computed in a bottom-up approach from a known 3D root hydraulic architecture.

Evapotranspiration estimation by inverse soil water flow modelling

Evapotranspiration estimation by inverse soil water flow modelling

Simulation of palm oil root water uptake by using 2D numerical soil-water flow model.

The lack of water resource in these past decades encourages the implementation of the precision agriculture system towards the sustainability in palm oil plantation. Therefore, it requires a specific information about the palm oil performance regarding the water balance system that affect the water consumption through the plant root water uptake. However, the prediction of root water uptake distribution is still a challenge. Another method to investigate the soil water dynamics under the plant root system is through the numerical simulations that are widely use to assess the soil water flow of the plant. In alignment with the idea of promoting the sustainable palm oil plantation, the investigation of root water uptake and water content under oil palm tree is highly demanding. As an introduction, through this study, it is find of interest to simulate the root water uptake and water content pattern of oil palm tree using the 2D simulation soil-water flow. The study was performed by applying the 2D simulation soil-water flow model to 17th year old oil palm tree located in Siak, Riau with the loam soil type. The climate data was used as primary data to predict the rate of evapotranspiration. The soil properties and root dimension and distribution of oil palm was taken by the literature study. The simulation over 30 days illustrated the root water uptake distribution, water content change, pressure head and flow velocity. The most intensive root water uptake occurred in the upper root zone of oil palm tree as an impact of the higher root density. The significant root water uptake in the upper root zone lead to the decreasing of water content and increasing of pressure head in the soil. Consequently, there was a change of water flow direction from the wet area in the downward and sideward do dry root zone as the water supply to the oil palm tree.

Modelling 2D water table and soil moisture distribution in a field installed with sub surface drainage

The present study is part of the development of an environment-friendly water management system based on sub surface drainage technology. It is aimed at creating a multipurpose farmland with expectation of increase in both benefits and productivity. Sub-surface drainage pipes which are installed inside mole-drain, functions as water channel that flows water into the drainage channel. Simulation was run using a 2D numerical soil water flow model based on Richard’s equation. It integrates varying soil textures with aim of figuring the optimum setting of sub-drains in response to soil textures. The depth of sub-drain was established at 40cm beneath the surface for all conditions as it is the current installation available in Indonesia. Our simulation shows the variation of soil moisture and pressure over distance, time and different textures under specified spacing of sub-surface installation.

Small differences in root distributions allow resource niche partitioning.

Deep roots have long been thought to allow trees to coexist with shallow‐rooted grasses. However, data demonstrating how root distributions affect water uptake and niche partitioning are uncommon.We describe tree and grass root distributions using a depth‐specific tracer experiment six times over two years in a subtropical savanna, Kruger National Park, South Africa. These point‐in‐time measurements were then used in a soil water flow model to simulate continuous water uptake by depth and plant growth form (trees and grasses) across two growing seasons. This allowed estimates of the total amount of water a root distribution could absorb as well as the amount of water a root distribution could absorb in excess of the other rooting distribution (i.e., unique hydrological niche).Most active tree and grass roots were in shallow soils: The mean depth of water uptake was 22 cm for trees and 17 cm for grasses. Slightly deeper rooting distributions provided trees with 5% more soil water than the grasses in a drier season, but 13% less water in a wetter season. Small differences also provided each rooting distribution (tree or grass) with unique hydrological niches of 4 to 13 mm water.The effect of rooting distributions has long been inferred. By quantifying the depth and timing of water uptake, we demonstrated how even small differences in rooting distributions can provide plants with resource niches that can contribute to species coexistence. Differences in total water uptake and unique hydrological niche sizes were small in this system, but they indicated that tradeoffs in rooting strategies can be expected to contribute to tree and grass coexistence because 1) competitive advantages change over time and 2) plant growth forms always have access to a soil resource pool that is not available to the other plant growth form.

Simple functions for describing soil water retention and the unsaturated hydraulic conductivity from saturation to complete dryness

Many applications require accurate soil hydraulic functions that can describe the dynamics of water in variably saturated systems from full saturation to complete dryness. Current functions considering zero water content at the dry end involve the summation of capillary and adsorption functions. This study proposes relatively simple yet complete soil hydraulic functions from full saturation to oven dryness that can be incorporated into existing models. The proposed water retention model is constructed by scaling the saturation function of Fredlund and Xing (1994) to describe capillary and adsorptive water retention as well as zero water content at oven dryness. To describe the hydraulic conductivity due to capillary and film flow, the model of Wang et al. (2018) is used. We further incorporated hysteresis in the water retention function of Wang et al. (2018). Testing on 241 hydraulic property data sets covering a wide range of soil textures from sands to clays, shows good agreement with laboratory observations, especially for coarse-textured soils. The proposed model accounts for the following processes: hysteretic capillary and adsorptive water retention, hysteretic hydraulic conductivity as a function of pressure head, non-hysteretic hydraulic conductivity as a function of water content, the effects of entrapped air, and closed scanning curves. This paper successfully derives the parameters of the main wetting curve from the main drying curve. Using the proposed functions for soil water flow modelling should lead to improved dynamic flow predictions, especially for drying soil conditions.

Effective saturation-based weighting for interblock hydraulic conductivity in unsaturated zone soil water flow modelling using one-dimensional vertical finite-difference model

Abstract Richards equation is solved for soil water flow modeling in the unsaturated zone continuum. Interblock hydraulic conductivities, while solving for Richards equation, are estimated by some sort of averaging process based on upstream and downstream nodes hydraulic conductivity values. The accuracy of the interblock hydraulic conductivity estimation methods mainly depends on the distance between two adjacent discretized nodes. In general, the accuracy of the numerical solution of the Richards equation decreases as nodal grid discretization increases. Conventional interblock hydraulic conductivity estimation methods are mostly mere approximation approaches while the Darcian-based interblock hydraulic conductivities involve complex calculations and require intensive computation under different flow regimes. Therefore, in this study, we proposed an effective saturation-based weighting approach in the soil hydraulic curve functions for estimating interblock hydraulic conductivity using a one-dimensional vertical finite-difference model which provides a parametric basis for interblock hydraulic conductivity estimation while reducing complexity in the calculation and computational processes. Furthermore, we compared four test case simulation results from different interblock hydraulic conductivity methods with the reference solutions. The comparison results show that the proposed method performance in terms of percentage reduction in root mean square and mean absolute error over other methods compared in this study were 59.5 and 60%, respectively.

Predicting soil water balance for irrigated and non-irrigated lucerne on stony, alluvial soils

•Efficient use of irrigation is essential for minimising environmental impacts of agriculture, particularly on stony soils which are vulnerable to nitrogen leaching. Models of soil water balance are essential tools for effective agricultural water management. Here we use the soil water flow model HYDRUS-1D to predict evaporation, total 1.5 m soil profile water content, and drainage at 1.5 m soil depth for irrigated and non-irrigated lucerne (Medicago sativa L.) on stony, alluvial soils in central South Island, New Zealand. Modelled estimates were compared against measurements of evaporation by eddy covariance, profile soil water content from soil moisture sensors, and drainage from lysimeters from 1 October 2016 to 1 July 2018 at each site. Cumulative evaporation and drainage were within 4% of measurements and short-term variations were well predicted for non-irrigated lucerne. Under irrigation, modelled cumulative evaporation and drainage overestimated measurements by 6 and 24% respectively. This overestimation must be viewed within the context of uncertainties related to the measurements, particularly given non-closure of the measured water balance related to spatial variability and inherent biases in the measurement methods. Model inputs and concept are likewise sources of uncertainty. Total water content predicted for the 1.5 m depth soil profile was 40–50% greater than the measured volume for both non-irrigated and irrigated lucerne, although short term variations were well predicted, suggesting that further research is needed on estimation of soil hydraulic parameters which represent water storage in these stony, alluvial soils. Our results highlight the value of well-measured sites, such as those of the current study, for evaluating the quality of soil hydrological predictions.

Impact of Maize Roots on Soil–Root Electrical Conductivity: A Simulation Study

Core Ideas The impact of roots on bulk electrical conductivity was studied using a modeling approach. The presence of roots affects petrophysical relations. The effect of roots is more pronounced in sandy soil than in loamy soil. Root surface and soil–root electrical contrast affect ERT measurements the most. Electrical resistivity tomography (ERT) has become an important tool for studying root‐zone soil water fluxes under field conditions. The results of ERT translate to water content via empirical pedophysical relations, usually ignoring the impact of roots; however, studies in the literature have shown that roots in soils may actually play a non‐negligible role in the bulk electrical conductivity (σ) of the soil–root continuum, but we do not completely understand the impact of root segments on ERT measurements. In this numerical study, we coupled an electrical model with a plant–soil water flow model to investigate the impact of roots on virtual ERT measurements. The coupled model can produce three‐dimensional simulations of root growth and development, water flow in soil and root systems, and electrical transfer in the soil–root continuum. Our electrical simulation illustrates that in rooted soils, for every 1% increase in the root/sand volume ratio, there can be a 4 to 18% increase in the uncertainty of σ computed via the model, caused by the presence of root segments; the uncertainty in a loam medium is 0.2 to 1.5%. The influence of root segments on ERT measurements depends on the root surface area (r = ∼0.6) and the σ contrast between roots and the soil (r = ∼0.9), as revealed by correlation analysis. This study is important in the context of accurate water content predictions for automated irrigation systems in sandy soil.

Data assimilation of soil water flow by considering multiple uncertainty sources and spatial–temporal features: a field-scale real case study

Accurate estimates of soil moisture and soil hydraulic parameters via data assimilation largely depend on the quality of the model structure, input data and observations. Often, however, all of this information is subject to uncertainty under real circumstances. This real-case study seeks to understand the effects of different uncertainty sources and observation scales on data assimilation performance. Ensemble Kalman filter method based on the soil water-flow model, a sub-module of soil–water–atmosphere–plant model, is established to simultaneously estimate the model states and parameters. The soil hydraulic parameters are extensively measured or calibrated to examine the parameter estimation accuracy. Furthermore, considering the high spatial and temporal variability of soil moisture observation in the field-scale problem, an analysis of spatiotemporal characteristics is combined with data assimilation. Results indicated that simultaneously considering parameter and initial conditions uncertainty leads to a better soil moisture and parameter estimation than that ignoring initial uncertainty in realistic practice. Unlike the other error sources, an inadequate description to the meteorological forcing has a negative influence on surface soil moisture estimation, which might be attributed to the persistent disturbances of evaporation uncertainty and the lack of observations at shallow soil depth. Moreover, a prior knowledge of spatiotemporal features of soil moisture observation is beneficial to efficiently improve data assimilation performance. It is possible to implement field-scale data assimilation with a few representative points, instead of using the spatial average of all observations at a high cost. The assimilation results highlight the possibly positive outcomes of accounting for the multi-source of uncertainties and emphasize the significant importance of characterizing the spatial–temporal feature of soil moisture for a field-scale application.

Obtaining soil hydraulic parameters from soil water content data assimilation under different climatic/soil conditions

Obtaining reliable soil hydraulic properties is essential for correct simulations of soil water content (SWC), which is a key variable in countless applications such as agricultural management, soil remediation, aquifer protection, etc. Soil hydraulic properties can be measured in the laboratory; however, the procedures are laborious and costly, and may provide estimates different from those observed in the field. An alternative approach is to obtain soil hydraulic properties using a soil water flow model in conjunction with SWC monitoring data. The goal of the present study was to analyze the efficiency of obtaining hydraulic properties utilizing data assimilation (DA) based on the Ensemble Kalman Filter method. Two soil textures in homogeneous soil profiles, and four climatic conditions were considered; observations of soil moisture data were synthetically generated using HYDRUS-1D and subsequently perturbed by the application of the conditional multivariate normal distribution. When observed SWC varied in relatively narrow range as a consequence of the forcing imposed by dry climate atmospheric boundary conditions, data assimilation provided sets of properties that led to good Richards model performance, with the RMSE below 0.02 and/or R2 above 0.8 after a period of just 100days and above 0.98 after a period of three years in all climate/soil conditions. However, the closeness of parameters from DA to the parameters used to generate the synthetic data depended on weather conditions and soil properties. One year was adequate to obtain reliable soil hydraulic properties with data assimilation.

A Simple Representation of Plant Water Storage Effects in Coupled Soil Water Flow and Transpiration Stream Modeling

Core Ideas A previous soil water flow model was extended to allow simulation of transient plant water storage. Comparison with measured Norway spruce sap flow data indicates improved model performance. The proposed algorithm can be easily incorporated into existing soil water flow models. When describing the movement of water in a variably saturated plant root zone, most existing hydrological models use the assumption of quasi‐steady‐state flow to relate root water uptake to canopy transpiration, thereby neglecting the effect of changing plant water storage. This approach is known to be problematic, especially when considering relatively large volumes of water stored in the tissues of tall trees. We propose a simple algorithm, based on the concept of whole‐plant hydraulic capacitance, to deal with the problem. The algorithm is implemented in a one‐dimensional soil water flow model involving vertically distributed macroscopic root water uptake. In this study, the proposed transient storage approach was compared with the quasi‐steady‐state approach. Both approaches were used to simulate soil water flow and diurnal variations of transpiration at a forest site covered with Norway spruce [Picea abies (L.) H. Karst.]. The key parameter of the transient storage approach, the plant hydraulic capacitance, is estimated by comparing the variations in the potential transpiration rate, derived from micrometeorological measurements, with observed sap flow intensities. The application of the proposed algorithm leads to more realistic predictions of root water uptake rates at the site of interest. The inclusion of the plant water storage effects improved the ability of the model to capture the anticipated diurnal variations in actual transpiration rates. The algorithm can be easily implemented into existing soil water flow models and used to simulate transpiration stream responses to varying atmospheric and soil moisture conditions including isohydric and partly also anisohydric plant responses to drought stress.

Ground penetrating radar for underground sensing in agriculture: a review

AbstractBelowground properties strongly affect agricultural productivity. Traditional methods for quantifying belowground properties are destructive, labor-intensive and pointbased. Ground penetrating radar can provide non-invasive, areal, and repeatable underground measurements. This article reviews the application of ground penetrating radar for soil and root measurements and discusses potential approaches to overcome challenges facing ground penetrating radar-based sensing in agriculture, especially for soil physical characteristics and crop root measurements. Though advanced data-analysis has been developed for ground penetrating radar-based sensing of soil moisture and soil clay content in civil engineering and geosciences, it has not been used widely in agricultural research. Also, past studies using ground penetrating radar in root research have been focused mainly on coarse root measurement. Currently, it is difficult to measure individual crop roots directly using ground penetrating radar, but it is possible to sense root cohorts within a soil volume grid as a functional constituent modifying bulk soil dielectric permittivity. Alternatively, ground penetrating radarbased sensing of soil water content, soil nutrition and texture can be utilized to inversely estimate root development by coupling soil water flow modeling with the seasonality of plant root growth patterns. Further benefits of ground penetrating radar applications in agriculture rely on the knowledge, discovery, and integration among differing disciplines adapted to research in agricultural management.

How to put plant root uptake into a soil water flow model: A documentation with complete computer code.

The need for improved crop water use efficiency calls for flexible modelingplatforms to implement new ideas in plant root uptake and its regulation mechanisms. Thispaper documents the details of modifying a soil infiltration and redistribution model to include(a) dynamic root growth, (b) non-uniform root distribution and water uptake, (c) the effect ofwater stress on plant water uptake, and (d) soil evaporation. The paper also demonstratesstrategies of using the modified model to simulate soil water dynamics and plant transpirationconsidering different sensitivity of plants to soil dryness and different mechanisms of rootwater uptake. In particular, the flexibility of simulating various degrees of compensateduptake (whereby plants tend to maintain potential transpiration under mild water stress)is emphasized. The paper also describes how to estimate unknown root distributionand rooting depth parameters by the use of a simulation-based searching method. Thefull documentation of the computer code will allow further applications and new development.

Mesoscopic aspects of root water uptake modeling – Hydraulic resistances and root geometry interpretations in plant transpiration analysis

In the context of soil water flow modeling, root water uptake is often evaluated based on water potential difference between the soil and the plant (the water potential gradient approach). Root water uptake rate is modulated by hydraulic resistance of both the root itself, and the soil in the root vicinity. The soil hydraulic resistance is a function of actual soil water content and can be assessed assuming radial axisymmetric water flow toward a single root (at the mesoscopic scale). In the present study, three approximate solutions of mesoscopic root water uptake − finite difference approximation, steady-state solution, and steady-rate solution − are examined regarding their ability to capture the pressure head variations in the root vicinity. Insignificance of their differences when implemented in the macroscopic soil water flow model is demonstrated using the critical root water uptake concept. Subsequently, macroscopic simulations of coupled soil water flow and root water uptake are presented for a forest site under temperate humid climate. Predicted soil water pressure heads and actual transpiration rates are compared with observed data. Scenario simulations illustrate uncertainties associated with estimates of root geometrical and hydraulic properties. Regarding the actual transpiration prediction, the correct characterization of active root system geometry and hydraulic properties seems far more important than the choice of a particular mesoscopic model.