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Root hydraulic properties are key physiological traits that determine the capacity of root systems to take up water, at a specific evaporative demand. They can strongly vary among species, cultivars or even within the same genotype, but a systematic analysis of their variation across plant functional types (PFTs) is still missing. Here, we reviewed published empirical studies on root hydraulic properties at the segment-, individual root-, or root system scale and determined its variability and the main factors contributing to it. This corresponded to a total of 241 published studies, comprising 213 species, including woody and herbaceous vegetation. We observed an extremely large range of variation (of orders of magnitude) in root hydraulic properties, but this was not caused by systematic differences among PFTs. Rather, the (combined) effect of factors such as root system age, driving force used for measurement, or stress treatments shaped the results. We found a significant decrease in root hydraulic properties under stress conditions (drought and aquaporin inhibition, p  < .001) and a significant effect of the driving force used for measurement (hydrostatic or osmotic gradients, p  < .001). Furthermore, whole root system conductance increased significantly with root system age across several crop species ( p  < .01), causing very large variation in the data (>2 orders of magnitude). Interestingly, this relationship showed an asymptotic shape, with a steep increase during the first days of growth and a flattening out at later stages of development. We confirmed this dynamic through simulations using a state-of-the-art computational model of water flow in the root system for a variety of crop species, suggesting common patterns across studies and species. These findings provide better understanding of the main causes of root hydraulic properties variations observed across empirical studies. They also open the door to better representation of hydraulic processes across multiple plant functional types and at large scales. All data collected in our analysis has been aggregated into an open access database (https://roothydraulic-properties.shinyapps.io/database/), fostering scientific exchange., Competing Interests: The Authors did not report any conflict of interest., (© 2024 The Authors. Plant Direct published by American Society of Plant Biologists and the Society for Experimental Biology and John Wiley & Sons Ltd.)

The effect of root hairs on water uptake remains controversial. In particular, the key root hair and soil parameters that determine their importance have been elusive. We grew maize plants (Zea mays) in microcosms and scanned them using synchrotron-based X-ray computed microtomography. By means of image-based modelling, we investigated the parameters determining the effectiveness of root hairs in root water uptake. We explicitly accounted for rhizosphere features (e.g. root-soil contact and pore structure) and took root hair shrinkage of dehydrated root hairs into consideration. Our model suggests that > 85% of the variance in root water uptake is explained by the hair-induced increase in root-soil contact. In dry soil conditions, root hair shrinkage reduces the impact of hairs substantially. We conclude that the effectiveness of root hairs on root water uptake is determined by the hair-induced increase in root-soil contact and root hair shrinkage. Although the latter clearly reduces the effect of hairs on water uptake, our model still indicated facilitation of water uptake by root hairs at soil matric potentials from -1 to -0.1 MPa. Our findings provide new avenues towards a mechanistic understanding of the role of root hairs on water uptake., (© 2023 The Authors New Phytologist © 2023 New Phytologist Foundation.)

The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil-plant continuum and the root images to develop and compare image analysis methods., (© 2023. Springer Nature Limited.)

Magnetic resonance imaging (MRI) is used to image root systems grown in opaque soil. However, reconstruction of root system architecture (RSA) from 3-dimensional (3D) MRI images is challenging. Low resolution and poor contrast-to-noise ratios (CNRs) hinder automated reconstruction. Hence, manual reconstruction is still widely used. Here, we evaluate a novel 2-step work flow for automated RSA reconstruction. In the first step, a 3D U-Net segments MRI images into root and soil in super-resolution. In the second step, an automated tracing algorithm reconstructs the root systems from the segmented images. We evaluated the merits of both steps for an MRI dataset of 8 lupine root systems, by comparing the automated reconstructions to manual reconstructions of unaltered and segmented MRI images derived with a novel virtual reality system. We found that the U-Net segmentation offers profound benefits in manual reconstruction: reconstruction speed was doubled (+97%) for images with low CNR and increased by 27% for images with high CNR. Reconstructed root lengths were increased by 20% and 3%, respectively. Therefore, we propose to use U-Net segmentation as a principal image preprocessing step in manual work flows. The root length derived by the tracing algorithm was lower than in both manual reconstruction methods, but segmentation allowed automated processing of otherwise not readily usable MRI images. Nonetheless, model-based functional root traits revealed similar hydraulic behavior of automated and manual reconstructions. Future studies will aim to establish a hybrid work flow that utilizes automated reconstructions as scaffolds that can be manually corrected., (Copyright © 2023 Tobias Selzner et al.)

Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using "RootPainter." Then, an automated feature extraction from the segments is carried out by "RhizoVision Explorer." To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation ( r = 0.9) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this article., (Copyright © 2022 Felix Maximilian Bauer et al.)

Soil hydraulic conductivity ( k soil ) drops significantly in dry soils, resulting in steep soil water potential gradients ( ψ s ) near plant roots during water uptake. Coarse soil grid resolutions in root system scale (RSS) models of root water uptake (RWU) generally do not spatially resolve this gradient in drying soils which can lead to a large overestimation of RWU. To quantify this, we consider a benchmark scenario of RWU from drying soil for which a numerical reference solution is available. We analyze this problem using a finite volume scheme and investigate the impact of grid size on the RSS model results. At dry conditions, the cumulative RWU was overestimated by up to 300% for the coarsest soil grid of 4.0 cm and by 30% for the finest soil grid of 0.2 cm, while the computational demand increased from 19 s to 21 h. As an accurate and computationally efficient alternative to the RSS model, we implemented a continuum multi-scale model where we keep a coarse grid resolution for the bulk soil, but in addition, we solve a 1-dimensional radially symmetric soil model at rhizosphere scale around individual root segments. The models at the two scales are coupled in a mass-conservative way. The multi-scale model compares best to the reference solution (-20%) at much lower computational costs of 4 min. Our results demonstrate the need to shift to improved RWU models when simulating dry soil conditions and highlight that results for dry conditions obtained with RSS models of RWU should be interpreted with caution., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Khare, Selzner, Leitner, Vanderborght, Vereecken and Schnepf.)

In this chapter, we present the Root and Soil Water Movement and Solute transport model R-SWMS, which can be used to simulate flow and transport in the soil-plant system. The equations describing water flow in soil-root systems are presented and numerical solutions are provided. An application of R-SWMS is then briefly discussed, in which we combine in vivo and in silico experiments in order to decrypt water flow in the soil-root domain. More precisely, light transmission imaging experiments were conducted to generate data that can serve as input for the R-SWMS model. These data include the root system architecture, the soil hydraulic properties and the environmental conditions (initial soil water content and boundary conditions, BC). Root hydraulic properties were not acquired experimentally, but set to theoretical values found in the literature. In order to validate the results obtained by the model, the simulated and experimental water content distributions were compared. The model was then used to estimate variables that were not experimentally accessible, such as the actual root water uptake distribution and xylem water potential., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

In this chapter, we discuss the issue of balance between spatial resolution and computational efficiency in the context of the R-SWMS model. Based on the equations governing the water fluxes within the model, we propose here an objective and quantitative criterion which can help fix root segment size to both minimize computational load and achieve simulation according to a given accuracy degree., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

Meaningful assessment of pesticide fate in soils and plants is based on fate models that represent all relevant processes. With mechanistic models, these processes can be simulated based on soil, substance, and plant properties. We present a mechanistic model that simulates pesticide uptake from soil and investigate how it is influenced, depending on the governing uptake process, by root and substance properties and by distributions of the substance and water in the soil profile. A new root solute uptake model based on a lumped version of the Trapp model (Trapp, 2000) was implemented in a coupled version of R-SWMS-ParTrace models for 3-D water flow and solute transport in soil and root systems. Solute uptake was modeled as two individual processes: advection with the transpiration stream and diffusion through the root membrane. We set up the model for a FOCUS scenario used in the European Union (EU) for pesticide registration. Considering a single vertical root and advective uptake only, the root hydraulic properties could be defined so that water and substance uptake and substance fate in soil showed a good agreement with the results of the 1D PEARL model, one of the reference models used in the EU for pesticide registration. Simulations with a complex root system and using root hydraulic parameters reported in the literature predicted larger water uptake from the upper root zone, leading to larger pesticide uptake when pesticides are concentrated in the upper root zone. Dilution of root water concentrations at the top root zone with water with low pesticide concentration taken up from the bottom of the root zone leads to larger uptake of solute when uptake was simulated as a diffusive process. This illustrates the importance of modeling uptake mechanistically and considering root and solute physical and chemical properties, especially when root-zone pesticide concentrations are non-uniform., (© 2021. The Author(s).)

Background and Aims: Upland rice is often grown where water and phosphorus (P) are limited. To better understand the interaction between water and P availability, functional-structural models that mechanistically represent small-scale nutrient gradients and water dynamics in the rhizosphere are needed., Methods: Rice was grown in large columns using a P-deficient soil at three P supplies in the topsoil (deficient, sub-optimal and non-limiting) in combination with two water regimes (field capacity vs. drying periods). Root system characteristics, such as nodal root number, lateral types, interbranch distance, root diameters and the distribution of biomass with depth, as well as water and P uptake, were measured. Based on the observed root data, 3-D root systems were reconstructed by calibrating the structural architecure model CRootBox for each scenario. Water flow and P transport in the soil to each of the individual root segments of the generated 3-D root architectures were simulated using a multiscale flow and transport model. Total water and P uptake were then computed by adding up the uptake by all the root segments., Key Results: Measurements showed that root architecture was significantly affected by the treatments. The moist, high P scenario had 2.8 times the root mass, double the number of nodal roots and more S-type laterals than the dry, low P scenario. Likewise, measured plant P uptake increased >3-fold by increasing P and water supply. However, drying periods reduced P uptake at high but not at low P supply. Simulation results adequately predicted P uptake in all scenarios when the Michaelis-Menten constant (Km) was corrected for diffusion limitation. They showed that the key drivers for P uptake are the different types of laterals (i.e. S- and L-type) and growing root tips. The L-type laterals become more important for overall water and P uptake than the S-type laterals in the dry scenarios. This is true across all the P treatments, but the effect is more pronounced as the P availability decreases., Conclusions: This functional-structural model can predict the function of specific rice roots in terms of P and water uptake under different P and water supplies, when the structure of the root system is known. A future challenge is to predict how the structure root systems responds to nutrient and water availability., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models, such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore, defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute toward improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development., (Copyright © 2020 Schnepf, Black, Couvreur, Delory, Doussan, Koch, Koch, Javaux, Landl, Leitner, Lobet, Mai, Meunier, Petrich, Postma, Priesack, Schmidt, Vanderborght, Vereecken and Weber.)

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.

Split root experiments have the potential to disentangle water transport in roots and soil, enabling the investigation of the water uptake pattern of a root system. Interpretation of the experimental data assumes that water flow between the split soil compartments does not occur. Another approach to investigate root water uptake is by numerical simulations combining soil and root water flow depending on the parameterization and description of the root system. Our aim is to demonstrate the synergisms that emerge from combining split root experiments with simulations. We show how growing root architectures derived from temporally repeated X-ray CT scanning can be implemented in numerical soil-plant models. Faba beans were grown with and without split layers and exposed to a single drought period during which plant and soil water status were measured. Root architectures were reconstructed from CT scans and used in the model R-SWMS (root-soil water movement and solute transport) to simulate water potentials in soil and roots in 3D as well as water uptake by growing roots in different depths. CT scans revealed that root development was considerably lower with split layers compared to without. This coincided with a reduction of transpiration, stomatal conductance and shoot growth. Simulated predawn water potentials were lower in the presence of split layers. Simulations showed that this was related to an increased resistance to vertical water flow in the soil by the split layers. Comparison between measured and simulated soil water potentials proved that the split layers were not perfectly isolating and that redistribution of water from the lower, wetter compartments to the drier upper compartments took place, thus water losses were not equal to the root water uptake from those compartments. Still, the layers increased the resistance to vertical flow which resulted in lower simulated collar water potentials that led to reduced stomatal conductance and growth.

Atrazine (2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine) groundwater monitoring in the Zwischenscholle aquifer in western Germany revealed concentrations exceeding the threshold value of 0.1 μg L and increasing concentration trends even 20 yr after its ban. Accordingly, the hypothesis was raised that a continued release of bound atrazine residues from the soil into the Zwischenscholle aquifer in combination with the low atrazine degradation in groundwater contributes to elevated atrazine in groundwater. Three soil cores reaching down to the groundwater table were taken from an agricultural field where atrazine had been applied before its ban in 1991. Atrazine residues were extracted from eight soil layers down to 300 cm using accelerated solvent extraction and analyzed using liquid chromatography-tandem mass spectrometry. Extracted atrazine concentrations ranged between 0.2 and 0.01 μg kg for topsoil and subsoil, respectively. The extracted mass from the soil profiles represented 0.07% of the applied mass, with 0.01% remaining in the top layer. A complete and instantaneous remobilization of atrazine residues and vertical mixing with the groundwater body below would lead to atrazine groundwater concentrations of 0.068 μg L. Considering the area where atrazine was applied in the region and assuming instantaneous lateral mixing in the Zwischenscholle aquifer would result in a mean groundwater concentration of 0.002 μg L. A conservative estimation suggests an atrazine half-life value of about 2 yr for the soil zone, which significantly exceeds highest atrazine half-lives found in the literature (433 d for subsurface soils). The long-term environmental behavior of atrazine and its metabolites thus needs to be reconsidered., (Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.)

Atrazine was banned in Germany in 1991 due to findings of atrazine concentrations in ground- and drinking waters exceeding threshold values. Monitoring of atrazine concentrations in the groundwater since then provides information about the resilience of the groundwater quality to changing agricultural practices. In this study, we present results of a monitoring campaign of atrazine concentrations in the Zwischenscholle aquifer. This phreatic aquifer is exposed to intensive agricultural land use and susceptible to contaminants due to a shallow water table. In total 60 observation wells (OWs) have been monitored since 1991, of which 15 are sampled monthly today. Descriptive statistics of monitoring data were derived using the "regression on order statistics" (ROS) data censoring approach, estimating values for nondetects. The monitoring data shows that even 20 years after the ban of atrazine, the groundwater concentrations of sampled OWs remain on a level close to the threshold value of 0.1 μg l(-1) without any considerable decrease. The spatial distribution of atrazine concentrations is highly heterogeneous with OWs exhibiting permanently concentrations above the regulatory threshold on the one hand and OWs were concentrations are mostly below the limit of quantification (LOQ) on the other hand. A deethylatrazine-to-atrazine ratio (DAR) was used to distinguish between diffuse - and point-source contamination, with a global mean value of 0.84 indicating mainly diffuse contamination. Principle Component Analysis (PCA) of the monitoring dataset demonstrated relationships between the metabolite desisopropylatrazine, which was found to be exclusively associated with the parent compound simazine but not with atrazine, and between deethylatrazine, atrazine, nitrate, and the specific electrical conductivity. These parameters indicate agricultural impacts on groundwater quality. The findings presented in this study point at the difficulty to estimate mean concentrations of contamination for entire aquifers and to evaluate groundwater quality based on average parameters. However, analytical data of monthly sampled single observation wells provide adequate information to characterize local contamination and evolutionary trends of pollutant concentration., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

The transport and biochemical transformations of the iodinated X-ray contrast medium (ICM) iomeprol were studied at the stream/groundwater interface. During a one-month field experiment piezometric pressure heads, temperatures, and concentrations of redox-sensitive species, iomeprol and 15 of its transformation products (TPs) were collected in stream- and groundwater. The data set was analyzed and transformation processes and rates identified by comparing conservative and reactive transport simulations. ICM and TP transformations were simulated as a cometabolic process during organic carbon degradation. Using iomeprol/TPs ratios as calibration constrain mitigated the uncertainties associated with the high variability of the ICM wastewater discharge into the investigated stream. The study provides evidence that biodegradation of ICM occurs at the field-scale also for predominantly denitrifying conditions. Under these anaerobically dominated field conditions shortest simulated half-life (21 days) was in the same range as previously reported laboratory-determined half-lives for aerobic conditions.

Standardised exposure scenarios play an important role in European pesticide authorisation procedures (a scenario is a combination of climate, weather and crop data to be used in exposure models). The European Food Safety Authority developed such scenarios for the assessment of exposure of soil organisms to pesticides. Scenarios were needed for both the concentration in total soil and for the concentration in the liquid phase. The goal of the exposure assessment is the 90th percentile of the exposure concentration in the area of agricultural use of a pesticide in each of three regulatory European zones (North, Centre and South). A statistical approach was adopted to find scenarios that are consistent with this exposure goal. Scenario development began with the simulation of the concentration distribution in the entire area of use by means of a simple analytical model. In the subsequent two steps, procedures were applied to account for parameter uncertainty and scenario uncertainty (i.e. the likelihood that a scenario that is derived for one pesticide is not conservative enough for another pesticide). In the final step, the six scenarios were selected by defining their average air temperature, soil organic-matter content and their soil textural class. Organic matter of the selected scenarios decreased in the order North-Centre-South. Because organic matter has a different effect on the concentration in total soil than it has on the concentration in the liquid phase, the concentration in total soil decreased in the order North-Centre-South whereas the concentration in the liquid phase decreased in the opposite order. The concentration differences between the three regulatory zones appeared to be no more than a factor of two. These differences were comparatively small in view of the considerable differences in climate and soil properties between the three zones.

Kinetic parameters of environmental fate processes are usually inferred by fitting appropriate kinetic models to the data using standard nonlinear least squares (NLS) approaches. Although NLS is appropriate to estimate the optimum parameter values, it implies restrictive assumptions on data variances when the confidence limits of the parameters must also be determined. Particularly in the case of degradation and metabolite formation, the assumption of equal error variance is often not realistic because the parent data usually show higher variances than those of the metabolites. Conventionally, such problems would be tackled by weighted NLS regression, which requires prior knowledge about the data errors. Instead of implicitly assuming equal error variances or giving arbitrary weights decided by the researcher, we use an iteratively reweighted least squares (IRLS) algorithm to obtain the maximum likelihood estimates of the model parameters and the error variances specific for the different species in a model. A study with simulated data shows that IRLS gives reliable results in the case of both unequal and equal error variances. We also compared results obtained by NLS and IRLS, with probability distributions of the parameters inferred with a Markov-Chain Monte-Carlo (MCMC) approach for data from aerobic transformation of different chemicals in soil. Confidence intervals obtained by IRLS and MCMC are consistent, whereas NLS leads to very different results when the error variances are distinctly different between different species. Because the MCMC results can be assumed to reflect the real parameter distribution imposed by the observed data, we conclude that IRLS generally yields more realistic estimates of confidence intervals for model parameters than NLS., (Copyright © 2011 SETAC.)

Background: For the registration of pesticides in the European Union, model simulations for worst-case scenarios are used to demonstrate that leaching concentrations to groundwater do not exceed a critical threshold. A worst-case scenario is a combination of soil and climate properties for which predicted leaching concentrations are higher than a certain percentile of the spatial concentration distribution within a region. The derivation of scenarios is complicated by uncertainty about soil and pesticide fate parameters. As the ranking of climate and soil property combinations according to predicted leaching concentrations is different for different pesticides, the worst-case scenario for one pesticide may misrepresent the worst case for another pesticide, which leads to 'scenario uncertainty'., Results: Pesticide fate parameter uncertainty led to higher concentrations in the higher percentiles of spatial concentration distributions, especially for distributions in smaller and more homogeneous regions. The effect of pesticide fate parameter uncertainty on the spatial concentration distribution was small when compared with the uncertainty of local concentration predictions and with the scenario uncertainty., Conclusion: Uncertainty in pesticide fate parameters and scenario uncertainty can be accounted for using higher percentiles of spatial concentration distributions and considering a range of pesticides for the scenario selection., (Copyright © 2010 Society of Chemical Industry.)

The worldwide use of veterinary antibiotics poses a continuous threat to the environment. There is, however, a lack of mechanistic studies on sorption and transformation processes for environmental assessment in soils. Two-week batch sorption experiments were performed with the antibiotic sulfadiazine (SDZ) in the plow layer and the subsoil of a loamy sand and a silty loam. The sorption and transformation parameters of SDZ and its main transformation products N1-2-(4-hydroxypyrimidinyl) benzenesulfanilamide (4-OH-SDZ) and 4-(2-iminopyrimidin-1(2H)-yl)aniline (An-SDZ) were estimated using a global optimization algorithm. A two-stage, one-rate sorption model combined with a first-order transformation model adequately described the batch data. Sorption of SDZ was nonlinear, time-dependent, and affected by pH, with a higher sorption capacity for the loamy sand. Transformation of SDZ into 4-OH-SDZ occurred only in the liquid phase, with half-life values of 1 month in the plow layers and 6 months in the subsoils. Under the exclusion of light, An-SDZ was formed in substantial amounts in the silty loam only, with liquid phase half-life values of 2 to 3 weeks. Despite the rather large parameter uncertainties, which may be reduced using additional information obtained from sequential solid phase extraction, the proposed method provides a framework to assess the fate of antibiotics in soils.

The flux of dissolved organic carbon (DOC) in soil facilitates transport of nutrients and contaminants in soil. There is little information on DOC fluxes and the relationship between DOC concentration and water flux in agricultural soils. The DOC fluxes and concentrations were measured during 2.5 yr using 30 automatic equilibrium tension plate lysimeters (AETPLs) at 0.4 m and 30 AETPLs at 1.20-m depth in a bare luvisol, previously used as an arable soil. Average annual DOC fluxes of the 30 AETPLS were 4.9 g C m(-2) y(-1) at 0.4 m and 2.4 g C m(-2) y(-1) at 1.2 m depth. The average leachate DOC concentrations were 17 mg C L(-1) (0.4 m) and 9 mg C L(-1) (1.2 m). The DOC concentrations were unrelated to soil moisture content or average temperature and rarely dropped below 9 mg C L(-1) (0.4 m) and 5 mg C L(-1) (1.2 m). The variability in cumulative DOC fluxes among the plates was positively related to leachate volume and not to average DOC concentrations at both depths. This suggests that water fluxes are the main determinants of spatial variability in DOC fluxes. However, the largest DOC concentrations were inversely proportional to the mean water velocity between succeeding sampling periods, suggesting that the maximal net DOC mobilization rate in the topsoil is limited. Elevated DOC concentrations, up to 90 mg C L(-1), were only observed at low water velocities, reducing the risks of DOC-facilitated transport of contaminants to groundwater. The study emphasizes that water flux and velocity are important parameters for DOC fluxes and concentrations.

The local scale dispersion tensor, Dd, is a controlling parameter for the dilution of concentrations in a solute plume that is displaced by groundwater flow in a heterogeneous aquifer. In this paper, we estimate the local scale dispersion from time series or breakthrough curves, BTCs, of Br concentrations that were measured at several points in a fluvial aquifer during a natural gradient tracer test at Krauthausen. Locally measured BTCs were characterized by equivalent convection dispersion parameters: equivalent velocity, v(eq)(x) and expected equivalent dispersivity, [lambda(eq)(x)]. A Lagrangian framework was used to approximately predict these equivalent parameters in terms of the spatial covariance of log(e) transformed conductivity and the local scale dispersion coefficient. The approximate Lagrangian theory illustrates that [lambda(eq)(x)] increases with increasing travel distance and is much larger than the local scale dispersivity, lambda(d). A sensitivity analysis indicates that [lambda(eq)(x)] is predominantly determined by the transverse component of the local scale dispersion and by the correlation scale of the hydraulic conductivity in the transverse to flow direction whereas it is relatively insensitive to the longitudinal component of the local scale dispersion. By comparing predicted [lambda(eq)(x)] for a range of Dd values with [lambda(eq)(x)] obtained from locally measured BTCs, the transverse component of Dd, DdT, was estimated. The estimated transverse local scale dispersivity, lambda(dT) = DdT/U1 (U1 = mean advection velocity) is in the order of 10(1)-10(2) mm, which is relatively large but realistic for the fluvial gravel sediments at Krauthausen.