Prediction Of Solute Transport Research Articles

Hydrogeomorphic controls on hyporheic and riparian transport in two headwater mountain streams during base flow recession

AbstractSolute transport along riparian and hyporheic flow paths is broadly expected to respond to dynamic hydrologic forcing by streams, aquifers, and hillslopes. However, direct observation of these dynamic responses is lacking, as is the relative control of geologic setting as a control on responses to dynamic hydrologic forcing. We conducted a series of four stream solute tracer injections through base flow recession in each of two watersheds with contrasting valley morphology in the H.J. Andrews Experimental Forest, monitoring tracer concentrations in the stream and in a network of shallow riparian wells in each watershed. We found hyporheic mean arrival time, temporal variance, and fraction of stream water in the bedrock‐constrained valley bottom and near large roughness elements in the wider valley bottom were not variable with discharge, suggesting minimal control by hydrologic forcing. Conversely, we observed increases in mean arrival time and temporal variance and decreasing fraction stream water with decreasing discharge near the hillslopes in the wider valley bottom. This may indicate changes in stream discharge and valley bottom hydrology control transport in less constrained locations. We detail five hydrogeomorphic responses to base flow recession to explain observed spatial and temporal patterns in the interactions between streams and their valley bottoms. Models able to account for the transition from geologically dominated processes in the near‐stream subsurface to hydrologically dominated processes near the hillslope will be required to predict solute transport and fate in valley bottoms of headwater mountain streams.

Combining 3D Hydraulic Tomography with Tracer Tests for Improved Transport Characterization.

Hydraulic tomography (HT) is a method for resolving the spatial distribution of hydraulic parameters to some extent, but many details important for solute transport usually remain unresolved. We present a methodology to improve solute transport predictions by combining data from HT with the breakthrough curve (BTC) of a single forced-gradient tracer test. We estimated the three dimensional (3D) hydraulic-conductivity field in an alluvial aquifer by inverting tomographic pumping tests performed at the Hydrogeological Research Site Lauswiesen close to Tübingen, Germany, using a regularized pilot-point method. We compared the estimated parameter field to available profiles of hydraulic-conductivity variations from direct-push injection logging (DPIL), and validated the hydraulic-conductivity field with hydraulic-head measurements of tests not used in the inversion. After validation, spatially uniform parameters for dual-domain transport were estimated by fitting tracer data collected during a forced-gradient tracer test. The dual-domain assumption was used to parameterize effects of the unresolved heterogeneity of the aquifer and deemed necessary to fit the shape of the BTC using reasonable parameter values. The estimated hydraulic-conductivity field and transport parameters were subsequently used to successfully predict a second independent tracer test. Our work provides an efficient and practical approach to predict solute transport in heterogeneous aquifers without performing elaborate field tracer tests with a tomographic layout.

Diffusive mass exchange of non-reactive substances in dual-porosity porous systems - column experiments under saturated conditions

Diffusive mass exchange into immobile water regions within heterogeneous porous aquifers influences the fate of solutes. The percentage of immobile water is often unidentified in natural aquifers though. Hence, the mathematical prediction of solute transport in such heterogeneous aquifers remains challenging. The objective of this study was to find a simple analytical model approach that allows quantifying properties of mobile and immobile water regions and the portion of immobile water in a porous system. Therefore, the Single Fissure Dispersion Model (SFDM), which takes into account diffusive mass exchange between mobile and immobile water zones, was applied to model transport in well-defined saturated dual-porosity column experiments. Direct and indirect model validation was performed by running experiments at different flow velocities and using conservative tracer with different molecular diffusion coefficients. In another column setup, immobile water regions were randomly distributed to test the model applicability and to determine the portion of immobile water. In all setups, the tracer concentration curves showed differences in normalized maximum peak concentration, tailing and mass recovery according to their diffusion coefficients. These findings were more pronounced at lower flow rates (larger flow times) indicating the dependency of diffusive mass exchange into immobile water regions on tracers' molecular diffusion coefficients. The SFDM simulated all data with high model efficiency. Successful model validation supported the physical meaning of fitted model parameters. This study showed that the SFDM, developed for fissured aquifers, is applicable in porous media and can be used to determine porosity and volume of regions with immobile water. Copyright © 2015 John Wiley & Sons, Ltd.

Prediction of solute transport in a heterogeneous aquifer utilizing hydraulic conductivity and specific storage tomograms

AbstractA sequential procedure of hydraulic tomographical inversion is applied to characterize at high resolution the spatial heterogeneity of hydraulic conductivity and specific storage at the field test site Stegemühle, Germany. The shallow aquifer at this site is examined by five short‐term multilevel pumping tests with 30 pumping‐observation pairs between two wells. Utilizing travel time diagnostics of the recorded pressure response curves, fast eikonal‐based inversion is shown to deliver insight into the sedimentary structures. Thus, the structural information from the generated travel time tomogram is exploited to constrain full calibration of the pressure response curves. Based on lateral extrapolation from the measured inter‐well profile, a three‐dimensional reconstruction of the aquifer is obtained. It is demonstrated that calibration of spatially variable specific storage in addition to hydraulic conductivity can improve the fitting of the model while the structural features are only slightly changed. At the field site, two tracer tests with uranine and sodium‐naphthionate were also performed and their concentrations were monitored for 2 months. The measured tracer breakthrough curves are employed for independent validation of the hydraulic tomographical reconstruction. It is demonstrated that major features of the observed solute transport can be reproduced, and structures relevant for macrodispersive tracer spreading could be resolved. However, for the mildly heterogeneous aquifer, the tracer breakthrough curves can also be approximated by a simplified homogeneous model with higher dispersivity. Therefore, improved validation results that capture specific characteristics of the breakthrough curves would require additional hydraulic measurements.

Two-point or multiple-point statistics? A comparison between the ensemble Kalman filtering and the ensemble pattern matching inverse methods

The Ensemble Kalman Filter (EnKF) has been commonly used to assimilate real time dynamic data into geologic models over the past decade. Despite its various advantages such as computational efficiency and its capability to handle multiple sources of uncertainty, the EnKF may not be used to reliably update models that are characterized by curvilinear geometries such as fluvial deposits where the permeable channels play a crucial role in the prediction of solute transport. It is well-known that the EnKF performs optimally for updating multi-Gaussian distributed fields, basically because it uses two-point statistics (i.e., covariances) to represent the relationship between the model parameters and between the model parameters and the observed response, and this is the only statistic necessary to fully characterize a multiGaussian distribution. The Ensemble PATtern matching (EnPAT) is an alternative ensemble based method that shows significant potential to condition complex geology such as channelized aquifers to dynamic data. The EnPAT is an evolution of the EnKF, replacing, in the analysis step, two-point statistics with multiple-point statistics. The advantages of EnPAT reside in its capability to honor the complex spatial connectivity of geologic structures as well as the measured static and dynamic data. In this work, the performance of the classical EnKF and the EnPAT are compared for modeling a synthetic channelized aquifer. The results reveal that the EnPAT yields a better prediction of transport characteristics than the EnKF because it characterizes the conductivity heterogeneity better. Issues such as uncertainty of multiple variables and the effect of measurement errors on EnPAT results will be discussed.

Relating Non-equilibrium Solute Transport and Porous Media Physical Characteristics

Breakthrough data for solute tracer transport at different velocities, covering a wide range of particle sizes and particle shapes corresponding to 324 breakthrough curves, were used in this study. Analysis was carried out for three granular porous media: crushed granite, gravel, and Leca® (a commercial insulation material). Mobile–immobile phase (MIM) solute transport parameters (dispersivity, mass transfer, and mobile (active) porosity) for non-equilibrium mass transport were determined for each breakthrough curve by fitting a MIM solute transport model to the breakthrough data. The resulting set of solute transport parameters was correlated with porous medium physical properties (particle size distribution and particle shape) to establish a set of simple expressions for estimating the MIM solute transport parameters. Linear expressions for predicting the solute dispersivity, mass transfer, and mobile phase porosity from porous medium particle size distribution (mean particle diameter and width of particle size distribution) and particle shape were developed based on regression analysis. A partial validation of these expressions indicated that the developed expressions are able to accurately predict solute transport parameters from porous medium physical properties.

Predicting flow and transport in highly heterogeneous alluvial aquifers

AbstractSuccessful prediction of groundwater flow and solute transport through highly heterogeneous aquifers has remained elusive due to the limitations of methods to characterize hydraulic conductivity (K) and generate realistic stochastic fields from such data. As a result, many studies have suggested that the classical advective‐dispersive equation (ADE) cannot reproduce such transport behavior. Here we demonstrate that when high‐resolution K data are used with a fractal stochastic method that produces K fields with adequate connectivity, the classical ADE can accurately predict solute transport at the macrodispersion experiment site in Mississippi. This development provides great promise to accurately predict contaminant plume migration, design more effective remediation schemes, and reduce environmental risks.

Solute transport routing in a small stream

The impact of pollution incidents on rivers and streams may be predicted using mathematical models of solute transport. Practical applications require an analytical or numerical solution to a governing solute mass balance equation together with appropriate values of relevant transport coefficients under the flow conditions of interest. This paper considers two such models, namely those proposed by Fischer and by Singh and Beck, and compares their performances using tracer data from a small stream in Edinburgh, UK. In calibrating the models, information on the magnitudes and the flow rate dependencies of the velocity and the dispersion coefficients was generated. The dispersion coefficient in the stream ranged between 0.1 and 0.9 m2/s for a flow rate range of 13–437 L/s. During calibration it was found that the Singh and Beck model fitted the tracer data a little better than the Fischer model in the majority of cases. In a validation exercise, however, both models gave similarly good predictions of solute transport at three different flow rates.

On the reliability of analytical models to predict solute transport in a fracture network

Abstract. In hydrogeology, the application of reliable tracer transport model approaches is a key issue to derive the hydrodynamic properties of aquifers. Laboratory- and field-scale tracer dispersion breakthrough curves (BTC) in fractured media are notorious for exhibiting early time arrivals and late time tailing that are not captured by the classical advection–dispersion equation (ADE). These "non-Fickian" features are proven to be better explained by a mobile–immobile (MIM) approach. In this conceptualization the fractured rock system is schematized as a continuous medium in which the liquid phase is separated into flowing and stagnant regions. The present study compares the performances and reliabilities of the classical MIM and the explicit network model (ENM), taking expressly into account the network geometry for describing tracer transport behavior in a fractured sample at bench scale. Though ENM shows better fitting results than MIM, the latter remains still valid as it proves to describe the observed curves quite well. The results show that the presence of nonlinear flow plays an important role in the behavior of solute transport. First, the distribution of solute according to different pathways is not constant, but it is related to the flow rate. Second, nonlinear flow influences advection in that it leads to a delay in solute transport respect to the linear flow assumption. However, nonlinear flow is not shown to be related with dispersion. The experimental results show that in the study case the geometrical dispersion dominates the Taylor dispersion. However, the interpretation with the ENM shows a weak transitional regime from geometrical dispersion to Taylor dispersion for high flow rates. Incorporating the description of the flow paths in the analytical modeling has proven to better fit the curves and to give a more robust interpretation of the solute transport.

Analysis of Uncertainties Affecting Numerical Transport Models for a Potential Radioactive Waste Disposal Site

Numerical models have been commonly used to study solute transport behaviours both in laboratory-and field-scale cases. However, numerical interpretation and predictions of solute transport are complicated by various uncertainties and therefore can be misleading and lead to incorrect understanding of solute transport behaviours in hydrogeological systems. Such uncertainties have been discussed with a case study of radionuclides transport at a potential site for disposal of Low-and Intermediate-Level radioactive Wastes (LILW) in southern China. Firstly, pre-modelling assumptions cause uncertainties for model performance and have a significant influence on the model output. Therefore, such assumptions and related factors should be thoroughly considered and minimized in laboratory experiments or investigated in the field. Secondly, difference in the calculated plumes caused by uncertainties in the development of conceptual models suggests that the hydrogeological conceptual model should be developed based on intrinsic understanding of the transport problems and relevant data collection. Thirdly, relative sensitivity (RS) has been calculated to analyse the parameter uncertainties. Results indicate that the uncertainties are tracer-, parameter-and time-dependent. Therefore, model uncertainties arising from parameters should be considered for each specific parameter with the specific period. Finally, prediction scenarios are associated with uncertainties in the model prediction stage. Results suggest that the uncertainties in scenarios of future transport conditions should be recognized prior to model prediction. This study illustrates some of the uncertainties that might affect numerical transport models and therefore will be a useful reference for application of solute transport models to assessment of contamination risks from a LILW disposal site.

Groundwater transport modeling with nonlinear sorption and intraparticle diffusion

Despite recent advances in the mechanistic understanding of sorption in groundwater systems, most contaminant transport models provide limited support for nonideal sorption processes such as nonlinear isotherms and/or diffusion-limited sorption. However, recent developments in the conceptualization of “dual mode” sorption for hydrophobic organic contaminants have provided more realistic and mechanistically sound alternatives to the commonly used Langmuir and Freundlich models. To support the inclusion of both nonlinear and diffusion-limited sorption processes in groundwater transport models, this paper presents two numerical algorithms based on the split operator approach. For the nonlinear equilibrium scenario, the commonly used two-step split operator algorithm has been modified to provide a more robust treatment of complex multi-parameter isotherms such as the Polanyi-partitioning model. For diffusion-limited sorption, a flexible three step split-operator procedure is presented to simulate intraparticle diffusion in multiple spherical particles with different sizes and nonlinear isotherms. Numerical experiments confirmed the accuracy of both algorithms for several candidate isotherms. However, the primary advantages of the algorithms are: (1) flexibility to accommodate any isotherm equation including “dual mode” and similar expressions, and (2) ease of adapting existing grid-based transport models of any dimensionality to include nonlinear sorption and/or intraparticle diffusion. Comparisons are developed for one-dimensional transport scenarios with different isotherms and particle configurations. Illustrative results highlight (1) the potential influence of isotherm model selection on solute transport predictions, and (2) the combined effects of intraparticle diffusion and nonlinear sorption on the plume transport and flushing for both single-particle and multi-particle scenarios.

해안지역에서의 용존성 물질의 이송확산 거동 수치모의

본 연구에서는 파랑과 흐름이 공존하고 있는 해안지역에 이용이 가능한 물에 용해된 물질의 정확한 이동을 예측하기 위한 수심적분형 수치모의 기법을 제시한다. 대상 영역에 일반적으로 발생하는 파랑의 전파와 변형 과정 및 쇄파와 흐름의 발달 과정에 대한 모의가 가능한 boussinesq equation 흐름모형과 동일한 과정을 거쳐 유도된 수심적분형 물질수송모형을 지배방정식으로 이용한다. 지배방정식은 approximate riemann solver를 이용하는 유한체적법을 이용하여 해석한다. 제시된 수치모형을 이용하여 해일발생에 의한 흐름양상을 계측한 실험을 재현하였으며, 해당 수역에 가상의 물질의 이송과 확산에 대한 수치모의를 수행하고 그 결과를 분석하였다. In this study, a numerical simulation technique for coastal area where wave and current interactions are observed is proposed. Considering the spatial scale of coastal area and the coastal processes such as wave, current, shoaling, wave breaking, and inundation processes, boussinesq equation model is used. A depth-integrated transport model based on the consistent assumption with the boussinesq equation model is used for the prediction of solute transport. To solve the equations, finite volume method with an approximate riemann solver is used. The proposed model is applied to a coastal area and reasonable computational results are obtained.

Prediction-Focused Subsurface Modeling: Investigating the Need for Accuracy in Flow-Based Inverse Modeling

The objective of most formulations of inverse modeling in the Earth Sci- ences is to estimate the model parameters given the observation data. In this paper, an additional element to these formulations is considered, namely, the prediction for which the models are built. An example of such modeling is the prediction of solute transport using geological models of the subsurface constrained to existing geophys- ical, flow dynamic and solute observations. The paper then illustrates and addresses a fundamental question relating data, model and prediction: does model inversion reduce uncertainty in prediction variables given the observed data? To investigate this question, a diagnostic tool is proposed to assess whether matching the observed data significantly reduces uncertainty in the prediction variables. In addition, for some cases, a quick estimate of uncertainty can be obtained without applying inverse mod- eling. It relies on a dimensionality reduction method using non-linear principal com- ponent analysis (NLPCA) calibrated from evaluating the prediction and data response variables on a few prior Earth models. The proposed diagnostic tool is applied on a simple example of tracer flow and, for the cases investigated, the NLPCA provided an accurate diagnostic and a satisfactory pre-estimation of uncertainty in the prediction variables.

Simulation and prediction of ion transport in the reclamation of sodic soils with gypsum based on the support vector machine.

The effect of gypsum on the physical and chemical characteristics of sodic soils is nonlinear and controlled by multiple factors. The support vector machine (SVM) is able to solve practical problems such as small samples, nonlinearity, high dimensions, and local minima points. This paper reports the use of the SVM regression method to predict changes in the chemical properties of sodic soils under different gypsum application rates in a soil column experiment and to evaluate the effect of gypsum reclamation on sodic soils. The research results show that (1) the SVM soil solute transport model using the Matlab toolbox represents the change in Ca2+ and Na+ in the soil solution and leachate well, with a high prediction accuracy. (2) Using the SVM model to predict the spatial and temporal variations in the soil solute content is feasible and does not require a specific mathematical model. The SVM model can take full advantage of the distribution characteristics of the training sample. (3) The workload of the soil solute transport prediction model based on the SVM is greatly reduced by not having to determine the hydrodynamic dispersion coefficient and retardation coefficient, and the model is thus highly practical.

What determines the strength of preferential transport in undisturbed soil under steady-state flow?

Preferential flow and transport has to be taken into account to successfully predict solute transport through the vadose zone. The relative 5%-arrival time of inert tracer can serve as a measure for the strength of preferential transport. As direct measurements of solute transport are not practical at large scales, soil susceptibility to preferential flow and transport has to be estimated using proxy variables. In this study we investigated how well the relative 5%-arrival time of inert tracer could be inferred from soil properties, site factors, scale and hydrologic conditions for 442 breakthrough curve experiments on undisturbed soil columns under steady state irrigation. Using a random forest as a global regression tool, we found a coefficient of determination of 0.561 in a ten-fold cross-validation. When predicting relative 5%-arrival times on a completely independent benchmark dataset of 149 experiments we obtained a still reasonable coefficient of determination of 0.336. When the soil columns had not been sampled from the same site and soil horizon, the random forest was able to rank the experiments correctly according to their relative 5%-arrival time, apart from one exception. Our study demonstrates that soil susceptibility to preferential flow and transport occurring under steady state initial and boundary conditions is to a large part predictable from proxy variables. We furthermore found evidence that the prediction performance should be considerably increased if information on the water saturation state during the experiment could be included into the random forest. An investigation of the importance of the predictors for estimating the relative 5%-arrival time yielded that the clay content was fundamental. Next important were the ratio between clay content and organic carbon, the lateral observation scale and whether the column had been slowly saturated from the bottom prior to the experiment or not. Flow rate, soil management and bulk density were found useful to further refine the predictions. A caveat has to be given that the investigated dataset includes few experiments on large columns and no experiments under natural transient hydrologic boundary conditions, since such experiments are scarce. Availability of such experiments is crucial to account for additional important preferential flow transport mechanisms caused by hydrophobicity, instabilities at infiltration fronts or funneling at soil horizon boundaries.

Modeling solute transport affected by heterogeneous sorption kinetics using single-rate nonequilibrium approaches

Single-rate transport models are commonly used for interpreting sorption-related mass transfer in porous media, often with the intention of approximating the kinetics of the sorption process. Among the most commonly used single-rate models are the two-site first-order (TSFO) and the two-site radial diffusion (TSRD) models. We fitted the parameters of the TSFO and TSRD models to simulated breakthrough data of hypothetical column experiments in which sorption rates were described by a γ-distributed sorption sites (GS) model. Our objective was to determine the conditions under which the assumption of a single-rate sorption parameter will be applicable to systems with heterogeneous sorption rates. We were further interested in knowing in what manner the fitted single-rate nonequilibrium model parameters depend upon the conditions under which the data were obtained. The considered hypothetical cases covered a range of experimental conditions and involved compounds with different sorption characteristics. The study revealed that the goodness of fit of the single rate models in simulating the transport of solutes exhibiting heterogeneous sorption rates is affected by solute residence time and pulse injection duration. Compared to the TSFO model, the TSRD model generally results in better prediction of solute transport affected by heterogeneous sorption kinetics. In addition, for such systems, the nonequilibrium parameters fitted using the TSFO model and their counterparts in the TSRD model are highly correlated. Moreover, an increase in the fitted mass transfer timescale of each of the single-rate models is coupled with an increase in the associated fraction of instantaneous sorption sites. A strong correlation was found between the time of the experiment and the product of the fitted characteristic time for mass transfer, pulse duration, and solute residence time. The correlation explains many of the variations in the mass transfer timescale encountered when single-rate sorption approaches were utilized to model solute transport in previous miscible displacement studies.

Hydrogeophysical data integration at larger scales: Application of Bayesian sequential simulation for the characterization of heterogeneous alluvial aquifers

Knowledge of the spatial distribution of hydraulic conductivity (K) within an aquifer is critical for reliable predictions of solute transport and the development of effective groundwater management and/or remediation strategies. While core analyses and hydraulic logging can provide highly detailed information, such information is inherently localized around boreholes that tend to be sparsely distributed throughout the aquifer volume. Conversely, larger-scale hydraulic experiments like pumping and tracer tests provide relatively low-resolution estimates of K in the investigated subsurface region. As a result, traditional hydrogeological measurement techniques contain a gap in terms of spatial resolution and coverage, and they are often alone inadequate for characterizing heterogeneous aquifers. Geophysical methods have the potential to bridge this gap. The recent increased interest in the application of geophysical methods to hydrogeological problems is clearly evidenced by the formation and rapid growth of the domain of hydrogeophysics over the past decade (e.g., Rubin and Hubbard, 2005).

Scaling and predicting solute transport processes in streams

We investigated scaling of conservative solute transport using temporal moment analysis of 98 tracer experiments (384 breakthrough curves) conducted in 44 streams located on five continents. The experiments span 7 orders of magnitude in discharge (10−3 to 103 m3/s), span 5 orders of magnitude in longitudinal scale (101 to 105 m), and sample different lotic environments—forested headwater streams, hyporheic zones, desert streams, major rivers, and an urban manmade channel. Our meta‐analysis of these data reveals that the coefficient of skewness is constant over time ( , ). In contrast, the of all commonly used solute transport models decreases over time. This shows that current theory is inconsistent with experimental data and suggests that a revised theory of solute transport is needed. Our meta‐analysis also shows that the variance (second normalized central moment) is correlated with the mean travel time ( ), and the third normalized central moment and the product of the first two are very strongly correlated ( ). These correlations were applied in four different streams to predict transport based on the transient storage and the aggregated dead zone models, and two probability distributions (Gumbel and log normal).

Regional-scale integration of multiresolution hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying true hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distances.

Effects of Soil Hydraulic and Transport Parameter Uncertainty on Predictions of Solute Transport in Large Lysimeters

Advanced numerical simulation models can potentially help improve guidelines for irrigation and salinity management. Many simulation model parameters have considerable uncertainty, and ideally that uncertainty should be reflected in model predictions and recommendations. In this work, we investigate solute transport predication intervals that can be generated by propagating model parameter uncertainty using Monte Carlo techniques. Flow and transport is simulated with a standard numerical model, while soil parameters and their uncertainty are estimated with pedotransfer functions. Generalized global sensitivity coefficients are computed to determine the parameters having the greatest impact on transport prediction and uncertainty. Simulations are compared with Br transport measured under unsaturated conditions in large lysimeters packed with clayey soil materials. In a 48 cm tall, homogeneous soil profile, model prediction intervals provided a reasonably good description of a single, relatively “noisy” breakthrough curve. In replicated 180 cm tall, layered soil profiles, model structural errors limited the accuracy of the prediction intervals under one irrigation water treatment, whereas under another treatment the predictions tracked the time course of the data reasonably well but tended to overestimate solute concentrations. The width of the prediction intervals tended to be small relative to the range of transport variability that existed across replicated lysimeters, particularly at shallow depths. Additional work aimed at operational field testing of model prediction uncertainty is needed if advanced water management models are to reach their full potential.