Solute Transport In Streams Research Articles

A comprehensive assessment and comparison of the impacts of storage parameters on solute transport in streams using a novel framework

Researchers must determine the storage parameter values to understand the storage processes and compare them across streams. These parameters are highly variable in time and space, making them challenging to measure. Individual studies of a single stream cannot characterize the interrelations among the processes involved in solute transport, hydraulic variables, and physical stream characteristics. Despite decades of research, a comprehensive framework is lacking. This study uses a simple and effective Monte Carlo approach to propose a general framework within which most solute transport models can undergo sensitivity analysis. This framework provides a comprehensive depiction of model performance under various scenarios, such as flow properties, stream geomorphology, and solute status. The relative and absolute sensitivities of three solute transport models, namely, the modified advection–dispersion equation (MADE), transient storage model (TSM), and variable residence time (VART), were evaluated and compared within the proposed framework. The main findings can be summarized as follows. In most conditions, storage models are more sensitive to the ratio of the storage zone cross-section to the stream cross-section than other parameters. Increasing the exchange rate between the stream and storage zones reduces the sensitivity to its indicative parameter and, consequently, its impact on the concentration time series. MADE's capability to identify modification parameters is higher for streams where dispersion dominates over advection, and their estimates have lower uncertainty. On the other hand, as advection becomes more dominant over dispersion in TSM and VART, estimated storage parameters have lower uncertainty.“

Discharge, Groundwater Gradients, and Streambed Micro-Topography Control the Temporal Dynamics of Transient Storage in a Headwater Reach.

Contradictory interpretations of transient storage modeling (TSM) results of past studies hamper the understanding of how hydrologic conditions control solute transport in streams. To address this issue, we conduct 30 instantaneous tracer experiments in the Weierbach stream, Luxembourg. Using an iterative modeling approach, we calibrate TSM parameters and assess their identifiability across various hydrologic conditions. Near-stream groundwater monitoring wells and LIDAR scans of the streambed are used to evaluate the area of the hyporheic zone and of the submerged sediments for each experiment. Our findings show that increasing discharge enhances parameters interaction requiring more samples of TSM parameters to obtain identifiable results. Our results also indicate that transient storage at the study site is influenced by in-stream and hyporheic exchange processes during low discharge, likely due to the hyporheic zone's large extent and the relatively low water level compared to the size of slate fragments on the streambed. However, as discharge increases, in-stream storage zones become part of the advective channel and the lower localized stream water losses to the adjacent groundwater suggests a decrease of the hyporheic exchange on transient storage. The results obtained were utilized to generate a hydrograph for the study site illustrating the dynamic evolution of in-stream and hyporheic storage with varying discharge, providing insights into the expected influence of different transient storage processes prior to tracer experiments. Overall, our study enhances the understanding of the role of the hyporheic area and in-stream storage zones in transient storage and helps estimate TSM parameters more accurately.

Master equation model for solute transport in river basins: part I channel fluvial scale

Normal and anomalous diffusion are ubiquitous in many physical complex systems. Here we define a system of diffusion equations generalized in time and space, using the conservation principles of mass and momentum at channel scale by a master equation. A numerical model for describing the steady one-dimensional advection-dispersion equation for solute transport in streams and channels imposed with point-loading is presented. We find the numerical model parameter as the solution of this system by estimating the transition probability that characterizes the physical phenomenon in the diffusion regime. The results presented (Part I) refer to the channel scale and represent the first part of a research project that has been extended to the basin scale.

An Explicit Solution for Characterizing Non-Fickian Solute Transport in Natural Streams

One-dimensional solute transport modeling is fundamental to enhance understanding of river mixing mechanisms, and is useful in predicting solute concentration variation and fate in rivers. Motivated by the need of more adaptive and efficient model, an exact and efficient solution for simulating breakthrough curves that vary with non-Fickian transport in natural streams was presented, which was based on an existing implicit advection-dispersion equation that incorporates the storage effect. The solution for the Gaussian approximation with a shape-free boundary condition was derived using a routing procedure, and the storage effect was incorporated using a stochastic concept with a memory function. The proposed solution was validated by comparison with analytical and numerical solutions, and the results were efficient and exact. Its performance in simulating non-Fickian transport in streams was validated using field tracer data, and good agreement was achieved with 0.990 of R2. Despite the accurate reproduction of the overall breakthrough curves, considerable errors in their late-time behaviors were found depending upon the memory function formulae. One of the key results was that the proper formula for the memory function is inconsistent according to the data and optimal parameters. Therefore, to gain a deeper understanding of non-Fickian transport in natural streams, identifying the true memory function from the tracer data is required.

Exploring tracer information in a small stream to improve parameter identifiability and enhance the process interpretation in transient storage models

Abstract. The transport of solutes in river networks is controlled by the interplay of processes such as in-stream solute transport and the exchange of water between the stream channel and dead zones, in-stream sediments, and adjacent groundwater bodies. Transient storage models (TSMs) are a powerful tool for testing hypotheses related to solute transport in streams. However, model parameters often do not show a univocal increase in model performances in a certain parameter range (i.e. they are non-identifiable), leading to an unclear understanding of the processes controlling solute transport in streams. In this study, we increased parameter identifiability in a set of tracer breakthrough experiments by combining global identifiability analysis and dynamic identifiability analysis in an iterative approach. We compared our results to inverse modelling approaches (OTIS-P) and the commonly used random sampling approach for TSMs (OTIS-MCAT). Compared to OTIS-P, our results informed about the identifiability of model parameters in the entire feasible parameter range. Our approach clearly improved parameter identifiability compared to the standard OTIS-MCAT application, due to the progressive reduction of the investigated parameter range with model iteration. Non-identifiable results led to solute retention times in the storage zone and the exchange flow with the storage zone with differences of up to 4 and 2 orders of magnitude compared to results with identifiable model parameters respectively. The clear differences in the transport metrics between results obtained from our proposed approach and results from the classic random sampling approach also resulted in contrasting interpretations of the hydrologic processes controlling solute transport in a headwater stream in western Luxembourg. Thus, our outcomes point to the risks of interpreting TSM results when even one of the model parameters is non-identifiable. Our results showed that coupling global identifiability analysis with dynamic identifiability analysis in an iterative approach clearly increased parameter identifiability in random sampling approaches for TSMs. Compared to the commonly used random sampling approach and inverse modelling results, our analysis was effective at obtaining higher accuracy of the evaluated solute transport metrics, which is advancing our understanding of hydrological processes that control in-stream solute transport.

One-dimensional numerical model for unsteady solute transport in open channels under multi-point loading

A numerical method for solving the unsteady-one-dimensional advection-dispersion equation for solute transport in streams and channels imposed with multiple point-loading is presented. The numerical technique proposes to solve the equation, which also includes a first-order decay term and a loading term, using techniques similar to those employed by the finite volume method (FVM). The channel length is segmented into cells and the solution aims at evaluating the concentrations at the cell interfaces or the internal nodes, at prescribed time steps for the steady condition of the flow. The results are computed commencing from a specified time level with known spatial concentration within the domain. It is assumed that the boundary nodes at the two ends of the channel, one each at the upstream and the downstream, are specified with solute concentration values or its spatial gradient at all times. Unlike the standard within-the-cell spatial approximations of a variable assumed in the finite volume or finite element schemes, the proposed method models the spatial variation of the solute concentration within a cell by an analytical expression in terms of the concentrations at the two boundary nodes of the cell and the influx rate of point-loading, if any, located within the cell. Using such a closed-form function based upon the true nature of the problem not only helps in capturing the spatial variation of concentration within a cell more precisely but also allows a better approximation of its spatial gradients at the cell interfaces, thus helping to achieve a more accurate computation of the inter-cell dispersive fluxes. The performance of the proposed algorithm is evaluated against analytical benchmarks, a standard finite difference scheme, and one public-domain computational package. Data sets reported from field tests are used for demonstrating the potential of the technique in simulating real situations.

An efficient numerical method for solving a class of variable-order fractional mobile-immobile advection-dispersion equations and its convergence analysis

This study provides a numerical scheme for solving a class of fractional partial differential equations with the variable order, referred to as fractional mobile-immobile advection-dispersion equations, using the sixth-kind Chebyshev polynomials. The fractional mobile-immobile advection-dispersion equations model complex systems involving heat diffusion, ocean acoustic propagation, and solute transport in rivers and streams. Integral operational matrices of integer and variable orders are derived and then the Collocation method is used to reduce the main equation to a system of algebraic equations. Appeared derivative and integral operators are in the Caputo and Riemann-Liouville sense, respectively. Two-variable Chebyshev polynomials of the sixth-kind are constructed and their operational matrices are derived using matrices obtained from the one-variable case. The existence and uniqueness of the solution of these equations are proved. A convergence analysis is performed in a two-dimensional Chebyshev-weighted Sobolev space and error bounds of approximate solutions are presented. It has been observed that approximation error tends to zero if the number of terms of the solution series is chosen sufficiently large. The proposed scheme is employed to solve three numerical examples in order to test its efficiency and accuracy. The convergence rate of the suggested method is numerically computed that confirms the exponential property of the convergence order of the method. Finally, the results are compared with the outcome of some existing methods to show the accuracy of the proposed method.

Assessment of turbulence effects on effective solute diffusivity close to a sediment-free fluid interface

Our work is focused on the analysis of solute mixing under the influence of turbulent flow propagating in a porous system across the interface with a free fluid. Such a scenario is representative of solute transport and chemical mixing in the hyporheic zone. The study is motivated by recent experimental results (Chandler et al. Water Res Res 52(5):3493–3509, 2016) which suggested that the effective diffusion parameter is characterized by an exponentially decreasing trend with depth below the sediment-water interface. This result has been recently employed to model numerically downstream solute transport and mixing in streams. Our study provides a quantification of the uncertainty associated with the interpretation of the available experimental data. Our probabilistic analysis relies on a Bayesian inverse modeling approach implemented through an acceptance/rejection algorithm. The stochastic inversion workflow yields depth-resolved posterior (i.e., conditional on solute breakthrough data) probability distributions of the effective diffusion coefficient and enables one to assess the impact on these of (a) the characteristic grain size of the solid matrix associated with the porous medium and (b) the turbulence level at the water-sediment interface. Our results provide quantitative estimates of the uncertainty associated with spatially variable diffusion coefficients. Finally, we discuss possible limitations about the generality of the conclusions one can draw from the considered dataset.

Developing an improved user interface for a physically-based stream solute transport model

There is a growing need to address water pollution that demands advanced tools to predict the fate and transport of water quality constituents. Existing stream solute transport models use simple first-order kinetics to evaluate nutrient loss, however these ignore biochemical reactions and lack a user-friendly interface. To address this shortcoming, we integrated the One-dimensional Transport with Inflow and Storage (OTIS) model and the Enhanced Stream Water Quality Model (QUAL2E) and developed an improved interface for a physically-based solute transport model. With background algal concentration as the only calibration parameter, a generalized model was developed and evaluated. The new model performed reasonably well in predicting nutrient uptake of newly collected experimental data and published data from 32 other datasets (R2 = 0.76, NSE = 0.47 and Percent Bias = −4.3%). Inclusion of biochemical reactions from QUAL2E improves model confidence and provides options for incorporating actual process-based data which is unfeasible in existing first-order decay-based models.

An improved process-based representation of stream solute transport in the soil and water assessment tools

Hydrological models have long been used to study the interactions between land, surface and groundwater systems, and to predict and manage water quantity and quality. The soil and water assessment tool (SWAT), a widely used hydrological model, can simulate various ecohydrological processes on land and subsequently route the water quality constituents through surface and subsurface waters. So far, in-stream solute transport algorithms of the SWAT model have only been minimally revised, even though it has been acknowledged that an improvement of in-stream process representation can contribute to better model performance with respect to water quality. In this study, we aim to incorporate a new and improved solute transport model into the SWAT model framework. The new process-based model was developed using in-stream process equations from two well established models—the One-dimensional Transport with Inflow and Storage model and the Enhanced Stream Water Quality Model. The modified SWAT model (Mir-SWAT) was tested for water quality predictions in a study watershed in Germany. Compared to the standard SWAT model, Mir-SWAT improved dissolved oxygen (DO) predictions by removing extreme low values of DO (<6 mg/L) simulated by SWAT. Phosphate concentration peaks were reduced during high flows and a better match of daily predicted and measured values was attained using the Mir-SWAT model (R2 = 0.17, NSE = −0.65, RSR = 1.29 with SWAT; R2 = 0.28, NSE = −0.04, RSR = 1.02 with Mir-SWAT). In addition, Mir-SWAT performed better than the SWAT model in terms of Chlorophyll-a content particularly during winter months, improving the NSE and RSR for monthly average Chl-a by 74 and 42%, respectively. With the new model improvements, we aim to increase confidence in the stream solute transport component of the model, improve the understanding of nutrient dynamics in the stream, and to extend the applicability of SWAT for reach-scale analysis and management.

Comparison of negative skewed space fractional models with time nonlocal approaches for stream solute transport modeling

Continuous time random walks (CTRW), multi-rate mass transfer (MRMT), and fractional advection-dispersion equations (FADEs) are three promising models of anomalous transport as commonly found in natural streams. Although these paradigms are mathematically related, understanding their advantages and limitations poses a challenge for model selection. In this paper, we quantitatively evaluate the advection-dispersion equation (ADE), fractional-mobile-immobile (FMIM), fractional-in-space ADE (sFADE), fractional in space transient storage (FSTS), truncated time-fractional model (TTFM), and CTRW models with truncated power-law waiting time distribution (CTRW-TPL) by fitting them first to synthetic data. We then applied these models to observations from tracer experiments conducted in several rivers. Based on the extensive analysis, we conclude that the performance of the FSTS model (in particular, the model with negative skewness β=-1) is comparable or superior to the other nonlocal models evaluated in the paper; therefore, the model represents an alternative to existing models for simulating stream solute transport for spatially-homogeneous flows.

Anomalous transport through free-flow-porous media interface: Pore-scale simulation and predictive modeling

Pore-scale velocity and turbulence structures near streambeds may control solute transport and dispersion in streams. In this study, pore-scale flow and transport simulations are performed to investigate the effects of pore-scale processes on anomalous transport, which is often manifested by remarkably long residence times of solute particles, in coupled free-flow and porous media systems. By solving the 2D Reynolds-averaged Navier–Stokes equations integrated with a renormalization group k − ε turbulence model, we resolve complex pore-scale flows featured by vortices and preferential flows. Then, we incorporate the simulated velocity and turbulence fields into a Lagrangian particle tracking model that considers advection, turbulent diffusion, and molecular diffusion. Simulation results reveal that the interplay between pore-scale vortices and turbulence structures near the free-flow-permeable bed interface controls anomalous transport. High porosity induces strong turbulence penetration and preferential flow paths within permeable beds. The enhanced subsurface turbulence facilitates the escape of solute particles from recirculation zones via turbulent diffusion, causing steep power-law slopes in breakthrough curves (BTCs). In contrast, low porosity introduces heavy tailing in BTCs from particles that are trapped in the near-interface recirculation zones characterized by low velocities and limited turbulence. We upscale and predict particle transport via a Spatial Markov model (SMM) honoring the interplay between Lagrangian velocity distribution and velocity correlation. The SMM reproduces anomalous transport behaviors obtained from the numerical simulations. These results demonstrate that Lagrangian velocity statistics effectively encode anomalous transport mechanisms in the coupled systems.

Modeling the Effect of Hyporheic Mixing on Stream Solute Transport

AbstractMixing in the hyporheic zone plays a key role in controlling the fate and transport of contaminants in streams and rivers. Consistently with recent experimental and numerical results, a physically based one‐dimensional solute transport model is presented that represents hyporheic mixing as a diffusion process exponentially attenuated with depth. When vertical diffusion in the sediment bed is not limited by an impermeable boundary, the moments of the breakthrough curves (BTCs) generated by the model exhibit persistent non‐Fickian behavior and are shown to scale consistently with existing experimental evidence. The ability of the exponentially attenuated mixing model to represent experimental BTCs was tested using data from field and flume tracer experiments, and its performance was compared with that of a classic two‐storage zone model. While both models provided an equally good approximation for the BTCs observed in field tracer tests, the exponentially attenuated mixing model provided better fits for the flume experiments. Analysis of the model equations and their solutions shows that, if the flow cross‐sectional area and wetted perimeter are not predetermined, there are infinite sets of parameter values that produce the same space‐time concentration distributions. The result implies that the physical parameters of hyporheic exchange cannot be determined by sole measurements of the solute BTCs in the surface water unless flow cross‐sectional area and average flow depth can be independently estimated.

Application of Asymmetrical Statistical Distributions for 1D Simulation of Solute Transport in Streams

Analytical solutions of the one-dimensional (1D) advection–dispersion equations, describing the substance transport in streams, are often used because of their simplicity and computational speed. Practical computations, however, clearly show the limits and the inaccuracies of this approach. These are especially visible in cases where the streams deform concentration distribution of the transported substance due to hydraulic and morphological conditions, e.g., by transient storage zones (dead zones), vegetation, and irregularities in the stream hydromorphology. In this paper, a new approach to the simulation of 1D substance transport is presented, adapted, and tested on tracer experiments available in the published research, and carried out in three small streams in Slovakia with dead zones. Evaluation of the proposed methods, based on different probability distributions, confirmed that they approximate the measured concentrations significantly better than those based upon the commonly used Gaussian distribution. Finally, an example of the application of the proposed methods to an iterative (inverse) task is presented.

Parameter uncertainty with flow variation of the one-dimensional solute transport model for small streams using Markov chain Monte Carlo

The transient storage model (TSM) is a popular solute transport model among hydrologists and ecologists for characterizing solute retention potential of small headwater streams. TSM parameters are most commonly estimated in an inverse modeling framework using tracer breakthrough data. A key challenge in interpreting TSM parameters is the inherent difficulty in the estimation process and relating these parameters with the physical characteristics of streams. While often recognized that multiple sources of uncertainty impact transient storage modeling, rigorous evaluation of TSM parameter uncertainty is rare. Stream flow magnitude has been identified as a key variable in characterizing stream solute transport dynamics, yet no study has shown the impact of flow magnitude on TSM parameter uncertainty. In this paper we study the impact of flow magnitude on TSM parameter uncertainty by using tracer data from repeated tracer studies with changing stream flow magnitudes, and introduce a software tool to perform Markov chain Monte Carlo based parameter estimation and uncertainty evaluation. The software tool developed is capable of performing thousands of model runs within a few hours, which is necessary for Monte Carlo based parameter estimation, and provides a potentially attractive framework for model testing and improvement. TSM parameters were found to be relatively less important during higher flows with large uncertainty bounds and clear evidence of parameter correlations. The importance of TSM parameters on solute transport seemed to increase as flow decreased with seasonal baseflow recession. Our findings highlight the need for improved field methodologies to reduce parameter uncertainty and the need for further improvement in the model structure for successful stream characterization.

Assessment of some numerical methods for estimating the parameters of the one-dimensional advection\u2013dispersion model

This study appraised optimisations of numerical solutions of the one-dimensional advection–dispersion model (AD-Model) to synthetic data generated using an analytical solution. The motivation for the work was to identify reliable methods for estimating stream solute transport parameters from observed events in small rivers. Numerical solutions of the AD-Model must contend with several effects that might disturb the solution, with the introduction of numerical diffusion and numerical dispersion being particularly important issues. This poses a problem if physical dispersion is being identified by optimising model coefficients using observations of solute transport from field experiments. The discretisation schemes used were the Backward-Time/Centred-Space, Crank–Nicolson, Implicit QUICK, MacCormack and QUICKEST methods. Optimisations were obtained for several grid resolutions by keeping the time step constant whilst varying the space step: the range of Peclet number, Pe, was 1.5–12.0. Generally, increasing the space step led to poorer estimated coefficients and poorer fits to the synthetic concentration profiles. For Pe < 5 only Crank–Nicolson, MacCormack and QUICKEST gave reliable optimised dispersion coefficients: those from Backward-Time/Centred-Space and Implicit QUICK being significantly underestimated. For Pe > 5 Crank–Nicolson and MacCormack gave slightly overestimated dispersion coefficients whilst the other methods gave significantly underestimated dispersion coefficients. These findings were generally consistent with the known presence of numerical diffusion and numerical dispersion in the methods.

New Potentiometric Wireless Chloride Sensors Provide High Resolution Information on Chemical Transport Processes in Streams

Quantifying the travel times, pathways, and dispersion of solutes moving through stream environments is critical for understanding the biogeochemical cycling processes that control ecosystem functioning. Validation of stream solute transport and exchange process models requires data obtained from in-stream measurement of chemical concentration changes through time. This can be expensive and time consuming, leading to a need for cheap distributed sensor arrays that respond instantly and record chemical transport at points of interest on timescales of seconds. To meet this need we apply new, low-cost (in the order of a euro per sensor) potentiometric chloride sensors used in a distributed array to obtain data with high spatial and temporal resolution. The application here is to monitoring in-stream hydrodynamic transport and dispersive mixing of an injected chemical, in this case NaCl. We present data obtained from the distributed sensor array under baseflow conditions for stream reaches in Luxembourg and Western Australia. The reaches were selected to provide a range of increasingly complex in-channel flow patterns. Mid-channel sensor results are comparable to data obtained from more expensive electrical conductivity meters, but simultaneous acquisition of tracer data at several positions across the channel allows far greater spatial resolution of hydrodynamic mixing processes and identification of chemical ‘dead zones’ in the study reaches.

Determination of hyporheic travel time distributions and other parameters from concurrent conservative and reactive tracer tests by local‐in‐global optimization

AbstractThe complexity of hyporheic flow paths requires reach‐scale models of solute transport in streams that are flexible in their representation of the hyporheic passage. We use a model that couples advective‐dispersive in‐stream transport to hyporheic exchange with a shape‐free distribution of hyporheic travel times. The model also accounts for two‐site sorption and transformation of reactive solutes. The coefficients of the model are determined by fitting concurrent stream‐tracer tests of conservative (fluorescein) and reactive (resazurin/resorufin) compounds. The flexibility of the shape‐free models give rise to multiple local minima of the objective function in parameter estimation, thus requiring global‐search algorithms, which is hindered by the large number of parameter values to be estimated. We present a local‐in‐global optimization approach, in which we use a Markov‐Chain Monte Carlo method as global‐search method to estimate a set of in‐stream and hyporheic parameters. Nested therein, we infer the shape‐free distribution of hyporheic travel times by a local Gauss‐Newton method. The overall approach is independent of the initial guess and provides the joint posterior distribution of all parameters. We apply the described local‐in‐global optimization method to recorded tracer breakthrough curves of three consecutive stream sections, and infer section‐wise hydraulic parameter distributions to analyze how hyporheic exchange processes differ between the stream sections.

Stream tracer breakthrough curve decomposition into mass fractions: A simple framework to analyze and compare conservative solute transport processes

AbstractEcological functions are coupled to the physical transport of water and solutes in streams. Transport of conservative tracers in lotic systems is subject to the processes of advection, dispersion, transient storage, and mass loss to groundwater. Stream tracer experiments and the simulation of observed tracer breakthrough curve (BTC) data with 1‐D numerical transient storage models (TSMs) are commonly used to quantify these processes. Results from TSMs can be useful, but issues related to model appropriateness and parameter identifiability suggest a need for conceptually simpler approaches to BTC analyses. We present a new approach to analyze BTCs to quantify the amount of stream water transported by each dominant process. BTCs are analyzed to parse the total quantity of injected tracer mass into three dominant process domains: advection and dispersion, transient storage, and gross loss of tracer. This method can be used to quantify the relative influence of transport processes within, and among streams, and estimate the proportion of stream water associated with each mode of transport. As proof of concept, we apply this approach to conservative tracer injections on two streams of contrasting morphology. Application of this method indicates that transport of injected solute mass in an alluvial stream is dominated by advection and dispersion, relative to a beaded, peat‐bottomed stream, where more tracer mass transport was found to be associated with longer timescale transient storage processes. This approach provides a simple, inexpensive, and useful quantification of dominant transport processes and provides an additional tool for analyzing experimental BTC data.

Biofilm growth in gravel bed streams controls solute residence time distributions

AbstractStreambed substrates harbor a rich biome responsible for biogeochemical processing in riverine waters. Beyond their biological role, the presence of benthic and hyporheic biofilms can play an important role in influencing large‐scale transport of solutes, even for conservative tracers. As biofilms grow and accumulate biomass, they actively interact with and influence surface and subsurface flow patterns. To explore this effect, we conducted experiments at the Notre Dame Linked Ecosystems Experimental Facility in four outdoor streams, each with different gravel beds. Over the course of 20 weeks we conducted transport experiments in each of these streams and observed different patterns in breakthrough curves as biofilms grew on the substrate. Biofilms played a major role in shaping the observed conservative transport patterns. Overall, while the presence of biofilms led to a decreased exchange rate between the fast (mobile) and slow (immobile) parts of the flow domain, water that was exchanged tended to be stored in the slow regions for longer times once biofilms had established. More specifically, we observed enhanced longitudinal dispersion in breakthrough curves as well as broader residence time distributions when biofilms were present. Biofilm colonization over time homogenized transport patterns across the four streams that were originally very distinct. These results indicate that stream biofilms exert a strong control on conservative solute transport in streams, a role that to date has not received enough attention.