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High nutrient loads are an indicator of pollution sources in a watershed that need to be identified and quantified. These loads in surface and groundwater have been a major concern that impacts water quality in the Midwestern US, including the Great Lakes Basin. To investigate the influence of land use change, especially at urban/rural interfaces, we used the Soil and Water Assessment Tool (SWAT) to model water, sediment, and nutrient export for the St. Joseph River Basin (SJRB), which drains an area of 12,200 km2 in Southwest Michigan/Northwest Indiana and enters Lake Michigan. The SWAT models were built, calibrated and validated for monthly streamflow, groundwater, total suspended solids (TSS), total nitrogen (TN), total phosphorous (TP), nitrate (NO3-N) and dissolved reactive phosphate (DRP; as orthophosphate), using two stream gages (Niles, USGS ID 04101500; Paw Paw, USGS ID 04102500) in Berrien County, MI. We found that monthly hydrology, sediments, and inorganic nutrients were well captured by the model with very good to excellent performance at the Niles gage, and, good to satisfactory performance at Paw Paw. The simulated average annual groundwater was 137 mm and 129 mm for Niles and Paw Paw, respectively, suggesting that on average 57% and 60% of long-term streamflow in the basin comes from groundwater and shallow subsurface flow. For water quality variables, TSS loads were strongly correlated with streamflow with R2 reaching 0.90. Using this model, we investigated how land use change (e.g., agriculture), and the planting of winter cover crops in the fallow season would impact water and nutrient yields from the SJRB. We found that the impact of changing land use and applying cover crops on water quality components was significant and dependent on the selected spatial scale. The simulated outputs indicated that cover crops have no impact on hydrology but significantly reduced DRP and NO3-N export to Lake Michigan by up to 30% and 50%, respectively. Application of this model will assist regional land and water managers in planning for future impacts of land use and climate change and their impacts on water quality and quantity, enabling stakeholders to implement conservation practices to sustain the SJRB and other similar basins in the Great Lakes region.

Private water supply systems consisting of a domestic well and septic system are used throughout the world where households lack access to public water supply and sewers. In residential areas with a high housing density, septic contamination of private wells is common and associated with multiple health concerns. This situation can give rise to social dilemmas, where individual costs de-incentivize homeowners from investing in enhanced septic systems that would reduce well contamination and bring communal benefits. We combine a stylized game theoretical model with a probabilistic groundwater model to characterize how economic and hydrogeological conditions interact to produce misaligned incentives conducive to social dilemmas. The occurrence of social dilemmas depends on the relative costs of well contamination versus the cost of installing an enhanced septic treatment system and the relative probabilities of cross-contamination versus self-contamination. The game reveals three types of social dilemmas that occur in such systems, with each calling for distinct policy solutions. We demonstrate how the model can be applied to existing systems, using a case study of St. Joseph County, Indiana, where high nitrate contamination rates have raised public health concerns. This analysis represents a step towards identifying alternative policy solutions for a problem that has remained difficult to address for decades.

We develop a multi-dimensional, parallelized domain decomposition strategy (DDC) for mass-transfer particle tracking (MTPT) methods. These methods are a type of Lagrangian algorithm for simulating reactive transport and are able to be parallelized by employing large numbers of CPU cores to accelerate run times. In this work, we investigate different procedures for "tiling" the domain in two and three dimensions, (2-d and 3-d), as this type of formal DDC construction is currently limited to 1-d. An optimal tiling is prescribed based on physical problem parameters and the number of available CPU cores, as each tiling provides distinct results in both accuracy and run time. We further extend the most efficient technique to 3-d for comparison, leading to an analytical discussion of the effect of dimensionality on strategies for implementing DDC schemes. Increasing computational resources (cores) within the DDC method produces a trade-off between inter-node communication and on-node work. For an optimally subdivided diffusion problem, the 2-d parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run up to around 2700 cores, reducing a 5-hour simulation to 8 seconds, and the 3-d algorithm maintains appreciable speedup up to 1700 cores., Comment: 26 pages, 21 figures, Submitted to Journal of Computational Physics (JCP)

Inland lakes are socially and ecologically important components of many regional landscapes. Exploring lake responses to plausible future climate scenarios can provide important information needed ...

While several studies have linked network and in-fracture scale properties to conservative transport behavior in subsurface fractured media, studies on reactive transport cases remain relatively underdeveloped. In this study, we explore the behavior of an irreversible kinetic reaction during the interaction of two solute plumes, one consisting of species A and the other species B. When the plumes converge, these species react kinetically to form a new species C via $$A+B\xrightarrow {k} C$$ . This reactive system is studied using a three-dimensional discrete fracture network (DFN) model coupled with reactive Lagrangian particle tracking. We find that the interplay of network topology and chemical properties of the reactive solutes controls reactive transport processes. The network topology drives species A and B together, and the chemical properties dictate whether and how quickly a reaction occurs. Results demonstrate that reactions are most likely to occur in high-velocity fractures that make up the network backbone. The interplay between species’ chemical properties and transport is characterized by a non-dimensional Damkohler (Da) number. We show that the spatial distribution of reactions is sensitive to Da, which subsequently influences late-time tailing behavior in outlet breakthrough time distributions. The results of this study provide initial insights into how an irreversible reaction occurs during transport in a fracture network, using a methodology that can be applied to study reactive transport in a wide range of fractured media environments and contexts.

The transport and retention of fine particulate organic matter (FPOM) in headwater streams plays a significant role in organic matter processing and represents a critical link between upstr...

Water is a resource shared at many spatial and temporal scales, and humans simultaneously influence and are influenced by the availability of water resources to support life and economic activity. The spatial pattern of human settlements is thus affected by landscape heterogeneity and resource availability, with river networks playing a fundamental role in enabling access and mobility. On the one hand, trade and migration routes have been established since ancient times to increase the connectivity among human settlements and therefore allocating resources where they are most needed (or demanded). On the other hand, excessive connectivity can also introduce inter-dependence between population centres, which can ultimately decrease their resilience: their ability to bounce back from an exogenous (e.g., climatic) shock. In this perspective, interactions between spatiotemporal variability in climate and human connectivity (migration and trade) may have not only played a role in the rise and fall of several ancient civilizations, but can still affect the distribution of human settlements across watersheds. Here, we seek to understand the mechanisms through which trade and migration fluxes, and the policies that influence them, may affect the fundamental ability of interconnected communities to cope with climate shocks and sustain a population. To this aim, we developed a stochastic predator-prey model to represent the complex relation between population growth and water resources across multiple predetermined networks of neutral communities. Our model embraces the endogenous nature of human connectivity as an emergent process that arises from numerous independent and self- optimizing agent decisions at individual nodes, and overlays an exogenously determined physical network (e.g., river system). The main novelty of our approach consists in developing a tractable socio-environmental spatially explicit model to capture the critical effect of landscape heterogeneity and (water)resources distribution. The proposed framework can help in developing policies for ensuring availability and sustainable management of water resources under conditions of variability and has potentially applications in a wide variety of settings including natural resources extraction, disease dynamics and climate change policy.

Implementing effective policy to protect human health from the adverse effects of air pollution, such as premature mortality, requires reducing the uncertainty in health outcomes models. Here we present a novel method to reduce mortality uncertainty by increasing the amount of input data of air pollution and health outcomes, and then quantifying tradeoffs associated with the different data gained. We first present a study of long-term mortality from fine particulate matter (PM

The final publication is available at Springer via http://dx.doi.org/10.1007/s11242-021-01721-z. Mixing is pivotal to conservative and reactive transport behaviors in porous media. Methods for investigating mixing processes include mathematical models, laboratory experiments and numerical simulations. The latter have been historically limited by the extreme computational resources needed for solving flow and transport at the microscopic scale within the complex pore structure of a three-dimensional porous medium, while dealing with a sufficiently large domain in order to generate meaningful emergent continuum-scale observables. We present the results of such a set of virtual column experiments, which have been conducted by taking advantage of modern high-performance computing infrastructure and Computational Fluid Dynamics software capable of massively parallel simulations. The computational approach has important advantages such as full control over the experimental conditions as well as high spatial and temporal resolution of measurements. Hydrodynamic dispersion results agree with the empirical and theoretical literature and link dispersivity to median grain size, while elucidating the impact of grain size variability on the critical Péclet number. Reactive transport results also indicate that the relative degree of incomplete mixing is related to the granular material’s mean hydraulic radius and not directly to the median grain size. When compared to a well-known laboratory experiment with similar configuration, less incomplete mixing is observed in our simulations. We offer a partial explanation for this discrepancy, by showing how an apparent nonlinear absorbance–concentration relationship may induce laboratory measurement error in the presence of local concentration fluctuations. The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by the Barcelona Supercomputing Center (research activity AECT-2019-3-0014). D. B. acknowledges financial support by the U.S. Army Research Office under Contract/Grant W911NF-18-1-0338. D. F. acknowledges financial support by the European Commission through project MARSOLUT (H2020-MSCA-ITN-2018), and by the Spanish Ministry of Science, Innovation and Universities through project MONOPOLIOS (RTI2018-101990-B-I00). The authors thankfully acknowledge Charles F. Harvey for sharing the G02 light absorbance data. This work was partially funded by the U.S. Army Research Office (W911NF-18-1-0338), the European Commission (H2020-MSCA-ITN-2018), and the Spanish Ministry of Science, Innovation and Universities (RTI2018-101990-B-I00).

The fate and transport of nanoparticles (NPs) in streams is critical for understanding their overall environmental impact. Using a unique field-scale stream at the Notre Dame-Linked Experimental Ecosystem Facility, we investigated the impact of biofilms and the presence of dissolved organic matter (DOM) on the transport of titanium dioxide (TiO

Multi-phase flow and transport in porous media is prevalent in a wide range of challenging fluid mechanics problems related to sustainability, energy, and the environment. Accurate prediction of the displacement and interaction of such flows is vital in addressing these problems. In particular it is critical to understand the small- or pore-scale flow and its spatial and temporal evolution, which can impact behaviors at system scales in a nontrivial manner. Intermittency is a phenomenon currently observed in numerical and experimental studies of single-phase flow (Anna et al., n.d.; Morales et al., n.d.), but the case of multi-phase flow has yet to receive much study due to challenges faced in both simulations and experiments. The underlying physics of spreading, mixing, and interfacial processes must be understood for accurate predictions of transport in multi-phase flow systems. Therefore, a comprehensive understanding of multi-phase flow at these very small scales is necessary in the development of accurate system-scale prediction models. We present results from a coordinated numerical and experimental study of intermittency effects over a range of viscous and inertial flow regimes in single- and multi-phase flows in 2D heterogeneous micromodels to quantify Lagrangian flow statistics to better inform pore-scale models. The applicability of different modeling frameworks such as the correlated-continuous time random walk is tested by studying statistics of particle trajectories obtained by particle tracking velocimetry (PTV) measurements and Lattice Boltzmann simulations from single- and multi-phase flows. The results make particular note of the influence of the pore Reynolds number (Re) and inertial effects on intermittency, and compare these effects in the two flow regimes.

In low-gradient, macrophyte-rich rivers, we expect that the significant change in macrophyte biomass among seasons will strongly influence both biological activity and hydraulic conditions resulting in significant effects on nutrient dynamics. Understanding seasonal variation will improve modelling of nutrient transport in river networks, including annual estimations of export, which could optimise decision-making and management outcomes. We explored the relationships among seasonal differences in reach-scale nutrient uptake, macrophyte abundance, solute transport and transient storage in the River Gudenå (Denmark), a large macrophyte-rich river. We used the minimal pulse addition technique to measure uptake of ammonium, nitrate, soluble reactive phosphorus, as well as reach-scale metabolism, and surface transient storage in spring, summer, and autumn. We found that riverine uptake changed among seasons and was linked to macrophyte biomass via both biological activity, reflected in reach-scale metabolism, and through physical processes, as solute transport was influenced by longitudinal dispersion. In this macrophyte-rich river, seasonal changes in macrophyte biomass affected contact time between the water and biota, which influenced ammonium and soluble reactive phosphorus uptake. Using stoichiometric scaling of reach-scale metabolism, we found that seasonal variation also influenced the relative contributions of autotrophic and heterotrophic biota in assimilatory uptake. In summary, riverine nutrient uptake was not static, highlighting the importance of seasonality, with significant implications for modelling of nutrient export in river networks. Moreover, current management strategies that remove macrophyte biomass (i.e. weed cutting and dredging) will short-circuit the positive effects of enhanced nutrient uptake resulting from abundant macrophytes in rivers.

We present and derive a novel double-continuum transport model based on pore-scale characteristics. Our approach relies on building a simplified unit cell made up of immobile and mobile continua. We employ a numerically resolved pore-scale velocity distribution to characterize the volume of each continuum and to define the velocity profile in the mobile continuum. Using the simplified unit cell, we derive a closed form model, which includes two effective parameters that need to be estimated: a characteristic length scale and a ratio of waiting times RD that lumps the effect of stagnant regions and escape process. To calibrate and validate our model, we rely on a set of pore-scale numerical simulation performed on a 2D disordered segregated periodic porous medium considering different initial solute distributions. Using a Global Sensitivity Analysis, we explore the impact of the two effective parameters on solute concentration profiles and thereby define a sensitivity analysis driven criterion for model calibration. The latter is compared to a classical calibration approach. Our results show that, depending on the initial condition, the mass exchange process between mobile and immobile continua impact on solute profile shape significantly. By introducing parameter RD we obtain a flexible transport model capable of interpreting both symmetric and highly skewed solute concentration profiles. We show that the effectiveness of the calibration of the two parameters closely depends on the content of information of calibration dataset and the selected objective function whose definition can be supported by of the implementation of model sensitivity analysis. By relying on a sensitivity analysis driven calibration, we are able to provide a good interpretation of the concentration profile evolution independent of the given initial condition relying on a unique set of effective parameter values., Comment: 38 pages of manuscript plus 4 pages of Supplementary Material

Turbulence causes rapid mixing of solutes and fine particles between open channel flow and coarse-grained streambeds. Turbulent mixing is known to control hyporheic exchange fluxes and the distribution of vertical mixing rates in the streambed, but it is unclear how turbulent mixing ultimately influences mass transport at the reach scale. We used a particle-tracking model to simulate local- and reach-scale solute transport for a stream with coarse-grained sediments. Simulations were first used to determine profiles of vertical mixing rates that best described solute concentration profiles measured within a coarse granular bed in flume experiments. These vertical mixing profiles were then used to simulate a pulse solute injection to show the effects of turbulent hyporheic exchange on reach-scale solute transport. Experimentally measured concentrations were best described by simulations with a non-monotonic mixing profile, with highest mixing at the sediment-water interface and exponential decay into the bed. Reach-scale simulations show that this enhanced interfacial mixing couples in-stream and hyporheic solute transport. Coupling produces an interval of exponential decay in breakthrough curves and delays the onset of power-law tailing. High streamwise velocities in the hyporheic zone reduced mass recovery in the water column and caused breakthrough curves to exhibit steeper power-law slopes than predictions from mobile-immobile modeling theory. These results demonstrate that transport models must consider the spatial variability of streamwise velocity and vertical mixing for both the stream and the hyporheic zone, and new analytical theory is needed to describe reach-scale transport when high streamwise velocities are present in the hyporheic zone.

We propose a spatial Markov model to simulate transport in three-dimensional complex porous media flows. Our methodology is inspired by the concept of training images from geostatistics. Instead of using a training image we use highly resolved training trajectories obtained by high-resolution particle tracking, from which we sample increments in our random walk model. To reflect higher-order processes, subsequent increments are correlated. The approach can be split into three steps. First, we subdivide (cut) the training trajectories to form an archive of trajectory segments. Next, we recursively sample segments, where subsequent samples are chosen conditioned to the previous one to ensure continuity and smoothness of velocity (conditional copy). Finally, we merge (paste) consecutive segments together to generate simulated trajectories of arbitrary length. This training trajectory approach aims to overcome three common shortcomings of spatial Markov models: (1) We simulate finite-Péclet transport in three dimensions without commonly made simplifications (e.g., dimensionality reduction, and neglecting diffusion). (2) We do not parameterize dependence via a high-dimensional transition matrix. (3) We simulate transport at the resolution of the (highly resolved) training trajectories, which can be important for processes such as mixing and reaction. To validate our methodology, we apply it to simulate transport within a three-dimensional sandstone sample and compare predictions of a broad range of benchmark metrics against measurements from direct numerical simulations. We demonstrate that the training trajectories approach accurately represents three-dimensional particle motion, suggesting that this method can capture the governing dependence structure and simulate transport processes in full complexity.

The exchange of surface and subsurface waters plays an important role in understanding and predicting large scale transport processes in streams and rivers. Accurately capturing the influence of small-scale features associated with turbulent dispersion on exchange in an upscaled framework is necessary for developing reliable predictive models at the reach scale. In this work, we use high-fidelity direct numerical simulations (DNS) to fully resolve turbulent flow and hyporheic exchange in an open channel. We parameterize a 2D particle tracking model with the average DNS velocity and scalar diffusivity profiles. Breakthrough curves and rate of surface mass loss to the subsurface in both models agree after a sufficient distance downstream from particle injection. Finally we find that the travel time/distance joint pdf contains enough information to parameterize a 1D dual domain coupled Continuous Time Random Walk (ddc-CTRW) model that successfully reproduces the behavior of both the DNS and the 2D particle tracking model, allowing accurate prediction of breakthrough curves. Predicting breakthrough curves with a fully parameterized ddc-CTRW reduces cpu time by orders of magnitude when compared with DNS.

Private water supply systems consisting of a domestic well and septic system are used throughout the world where households lack access to public water supply and sewers. In residential areas with high housing density, septic contamination of private wells is common and associated with multiple health concerns. This situation can give rise to social dilemmas, where individual costs dis-incentivize homeowners from investing in enhanced septic systems that would reduce well contamination and bring communal benefits. We combine a stylized game-theoretical model with a probabilistic groundwater model to characterize how economic and hydrogeological conditions interact to produce misaligned incentives conducive to social dilemmas. The occurrence of social dilemmas depends on the relative costs of well contamination versus the cost of installing an enhanced septic treatment system, and the relative probabilities of cross-contamination versus self-contamination. The game reveals three three types of social dilemmas that occur in such systems, with each calling for distinct policy solutions. We demonstrate how the model can be applied to existing systems using a case study of St Joseph County, Indiana, where high nitrate contamination rates have raised public health concerns. This analysis represents a step towards identifying alternative policy solutions for a problem that has remained difficult to address for decades.

The LAgrangian Transport Eulerian Reaction Spatial (LATERS) Markov model was developed to predict upscaled bimolecular reactive transport in a flow around an array of solid cylinders. This method combines the stochastic Lagrangian Spatial Markov model (SMM) to predict transport and a volume averaged reaction rate equation to predict reactions of the form $$A+B\rightarrow \emptyset$$ . Here, we extend the LATERS Markov model to upscale bimolecular reactive transport in a Darcy flow through an idealized heterogeneous porous medium. In agreement with previous literature, the accuracy of the prediction is a function of the Damkohler (Da) numbers, i.e., high Da are more challenging because of incomplete mixing. It was found that a key component which must be incorporated into the upscaled model in these high Da systems is the idea that nearby A and B particles should be more likely to react than those that are farther apart. This is here achieved by appropriately reducing the resolution of the spatial grid employed to resolve the reactive process.

In this work we employ a reduced-order basis of conservative chemical components to model reactive transport using a Lagrangian (particle tracking) method. While this practice is well-understood in the Eulerian (grid-based) context, its adaptation to a Lagrangian context requires a novel reformulation of particle transport properties. Because the number of conservative-species particles need not change during simulation, spatial resolution stays constant in time, and there is no increase in computational expense due to increasing numbers of product particles. Additionally, this treatment simplifies the interaction between equilibrium and kinetic reactions and allows the use of species-dependent transport operators at the same time. We apply this method to model a suite of simple test problems that include equilibrium and kinetic reactions, and results exhibit excellent match with base-case Eulerian results. Finally, we apply the new method to model a 2D problem concerning the mobilization of cadmium by a CO2 leak, showing the potential applicability of the proposed methodology. The authors thank Nicolae Suciu and two anonymous reviewers for their very useful comments and suggestions. This work was supported by the US Army Research Office under Contract/Grant W911NF-18-1-0338, as well as by the Spanish Ministry of Economy and Competitiveness through projects WE-NEED (PCIN-2015-248) and INDEMNE (CGL2015-69768-R). Sandia National Laboratories is a multi-mission laboratory managed and operated by the National Technology and Engineering Solutions of Sandia, L.L.C., a wholly owned subsidiary of Honeywell International, Inc., for the DOE's National Nuclear Security Administration under contract DE-NA0003525.

There are many hydrated surface niches that are neither static nor continuously flowing that are colonized by microbes such as bacteria. Such periodic hydrodynamic regimes are distinct from aquatic systems where microbial dissemination is reasonably predicted by assuming continuous flow or static systems where motile microbes largely control their own fate. Here, we show how nonmotile bacteria exhibit rapid, dispersive bursts of movement over surfaces using transient confluent hydration from the environment, which we term “surface hydrodispersion,” in which cells traverse thousands of cell lengths within minutes. The fraction of the population disseminated by surface hydrodispersion is small—on the order of 1 cell per million. Thus, surface hydrodispersion can promote isolated distribution of single cells, which is unlike other characterized active and passive surface motilities. We describe this translocation using a continuous-time random-walk modeling approach and found in computational simulations that transient fluid accumulation, dilution, and gravitational pull are the contributing factors. Surface hydrodispersion, consistent with advection, is unlike simple colony expansion, as it dramatically alters spatial relationships, shown in this study with Staphylococcus aureus, which becomes increasingly virulent when isolated from Corynebacterium striatum. Surface hydrodispersion of nonmotile bacteria exploiting transient fluid availability and gravity is a mechanism that can result in sporadic and sudden shifts in microbial community behavior. To better understand how this movement can impact biogeography on the millimeter scale, this work describes a system for study of primary factors behind this movement as well as a stochastic model describing this dispersal. IMPORTANCE Understanding the dynamics within microbiome communities is a challenge. Knowledge of phylogeny and spatial arrangement has led to increased understanding of numerous polymicrobial communities, yet these snapshots do not convey the dynamics of populations over time. The actual biogeography of any microbiome controls the potential interactions, governing any possible antagonistic or synergistic behavior. Accordingly, a shift in biogeography can enable new behavior. Little is known about the movement mechanisms of “nonmotile” microbes. Here, we characterize a universal means of movement we term hydrodispersion in which nonmotile bacteria are transported thousands of cell lengths in minutes. We show that only a small fraction of the population is translocated by hydrodispersion and describe this movement further using a random-walk mathematical model approach in silico. We demonstrate the importance of hydrodispersion by showing that Staphylococcus aureus can separate from a coculture inoculation with Corynebacterium striatum, thus permitting transition to a more virulent state.

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green’s function of an underlying PDE. As such, each particle is a sample from the Green’s function; therefore, each particle should be distributed according to the Green’s function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green’s function. When the Green’s function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.

With the increased use of nanoparticles (NPs) in consumer, food, and pharmaceutical products, their eventual release into streams is inevitable. Critical factors affecting the transport of NPs in streams are the hyporheic exchange between the water column and porous streambed substrate and the interaction with biofilms. In this study, the transport behavior of two titanium dioxide NPs – catalytic- (P90) and food-grade (E171) – was evaluated in four field streams lined with different streambed substrate sizes for varying seasonal biofilm conditions. When biofilm growth was minimal, NP retention in the streams increased with increasing substrate size due to increased hyporheic exchange and subsequent physical and chemical interactions between the NPs and substrate. For all streams, the average mass recovery at the 40 m sampling point for E171 and P90 was 44 ± 8.7% and 16 ± 8.0%, respectively. The greater mobility of E171 was due to the inherent presence of negatively charged surface phosphates that reduced aggregation and decreased its interaction with the substrate. When biofilms were thriving in the streams the average mass recovery at 40 m for both NPs decreased significantly (E171 = 5.8 ± 7.3%, P = 0.0017; P90 = 2.4 ± 0.7%, P = 0.041), and the mass recovery difference between the two NPs became insignificant (P = 0.38).

Photomineralization, the transformation of dissolved organic carbon (DOC) to CO2 by sunlight, is an important source of CO2 in arctic surface waters. However, quantifying the role of photomineralization in inland waters is limited by the understanding of hydrologic controls on this process. To bridge this gap, this study evaluates mixing limitations, i.e., whether and by how much vertical mixing limits the depth-integrated photomineralization rate, in freshwater systems. We developed a conceptual model to qualitatively assess mixing limitations across the range of light attenuation and hydrologic conditions observed in freshwaters. For the common case of exponential light attenuation over depth, we developed a mathematical model to quantify mixing limitation, and used this model to assess a range of arctic freshwater systems. The results demonstrate that mixing limitations are important when there is significant light attenuation by suspended sediment (SS), which is the case in some arctic, boreal and temperate waters. Mixing limitation is pronounced when light attenuation over depth is strong and when the photomineralization rate at the water surface exceeds the vertical mixing rate. Arctic streams and rivers have strong vertical mixing relative to surface photomineralization, such that model results demonstrate no mixing limitation regardless of how much SS is present. Our analysis indicates that well-mixed assumptions used in prior work are valid in many, but not all, arctic surface waters. The effects of mixing limitations in reducing the photomineralization rate must be considered in arctic lakes with high SS concentrations.

Groundwater supports essential societal and ecological functions by acting as a reservoir that buffers against natural variability. Increasing water scarcity and climate variability have resulted in more intensive management of groundwater resources, but groundwater often remains difficult to understand and manage. With this in mind, we develop a simple platform that provides a straightforward, web-based user interface applicable to a wide variety of end-user scenarios. Groundwater behavior is modeled using the method of images in a new R package, anem, which serves as the engine for the web platform, anem-app, produced using R Shiny. Both tools allow users to define aquifer properties and pumping wells, view maps of hydraulic head, and simulate particle tracking under steady-state conditions. These tools have the advantage of being platform independent and open source, so that they are freely available to anyone with a web browser and internet connection (anem-app) or computing platform with R installed (anem). We designed both tools to lower the learning curve and up-front costs to building simple groundwater models. The simplicity of the web application allows exploration of groundwater behavior under various conditions, and should be especially valuable in low-budget applications where advanced analysis may not be practical or necessary. Integration with the R language allows for advanced analysis and deeper exploration of groundwater dynamics. In this manuscript, we describe how anem and anem-app are built in the R environment and demonstrate how they might be used by planners or stakeholders.

Hydrological models are unable to fully resolve subsurface flow and transport down to the microscale. Instead, modelers usually work with upscaled flow and transport properties that represent the behavior of the system at a given coarse scale. While this approach is justified from a practical standpoint, it disregards the local heterogeneity of porous media flows, which tend to produce mixing-limited reactive transport behaviors that cannot be captured by classical modeling approaches. While some innovative methods have been suggested in the past in order to address this problem, none of them has proposed a mathematical formulation which can potentially reproduce the generation, transport and decay of local concentration fluctuations and their impact on chemical reactions, for general initial and boundary conditions. Here, we propose a Lagrangian approach based on the random motion of fluid particles that locally mix following a Multi-Rate Interaction by Exchange with the Mean (MRIEM) formulation. Concentration fluctuations in the proposed model display the typical behavior associated to transport in porous media with mixing-limited conditions. Experimental results of reactive transport are successfully reproduced by the model.

Upon their release into the subsurface, non-aqueous phase liquids (NAPLs) dissolve slowly in groundwater and/or volatilize in the vadose zone threatening the environment and public health over extended periods of time. The failure of a treatment technology at any given site is often due to the unnoticed presence of dissolved NAPL trapped in low permeability areas and/or the remaining presence of substantial amounts of pure phase NAPL after remediation efforts. The design of remediation strategies and the determination of remediation endpoints are traditionally carried out without quantifying risks associated with the failure of such efforts. We conduct a probabilistic risk analysis (PRA) to estimate the likelihood of failure of an on-site NAPL treatment technology. The PRA integrates all aspects of the problem (causes, pathways, and receptors) without resorting to extensive modeling. It accounts for a combination of multiple mechanisms of failure of a monitoring system, such as bypassing, insufficient sampling frequency and malfunctioning of the observation wells. We use a Bayesian framework to update the likelihood of failure of the treatment technology with observed measurements of concentrations at nearby monitoring wells.