Publications

2024

1. WRR

   Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

   Alexander Y. Sun, Peishi Jiang, Pin Shuai , and 1 more author

   Water Resources Research, 2024

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   Ensemble-based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process-based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite-dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet-based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling-Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics-informed, seq-to-seq streamflow forecasting, which further reduces biases in the pre-trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.

2. JoH

   Optimizing Parameter Learning and Calibration in an Integrated Hydrological Model: Impact of Observation Length and Information

   Peishi Jiang, Pin Shuai, Alexander Y. Sun , and 1 more author

   Journal of Hydrology, Nov 2024

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   Integrated hydrological modeling is gaining popularity due to its mechanistic representation of the surface and subsurface processes. However, estimating the parameters of such process-based models can be computationally expensive if careful consideration is not given to the length of streamflow observations used during model calibration. Here we evaluate the influence of the calibration period, the role of streamflow information content, and the gage location in parameter learning and calibration of a fully integrated hydrological model, the Advanced Terrestrial Simulator (ATS). We conducted the study at the Upper Neversink River Watershed within the Delaware River Basin, where streamflow observations are available at 11 gages with varying record lengths. We leveraged a recently proposed knowledge-informed deep learning technique for parameter estimation. To assess the impact of observation period and gage location, model parameters were learned on scenarios using different chunks of streamflow observations, including (1) using only one year or consecutive multiple years of streamflow observations at the watershed outlet and (2) using one shared year of observations at each of the sub-catchment gages, with the period from 1991-10-01 through 1999-09-30 as the overall calibration period. Using the estimated parameters, ATS was rerun for each scenario and evaluated on a subsequent period from 1999-10-01 through 2002-09-30. Results show that the basin outlet discharge prediction is mostly improved when using at least four years of observations for parameter estimation. Further, the performance of the calibrated ATS run correlates with the information content of the observed streamflows, suggesting that the information-theoretic metrics could be indicators for selecting the observation period for parameter estimation. Finally, we find that observations from an informative gage can be used in learning parameters to predict the streamflow at a nearby gage, which would potentially lower the computational expense by reducing the watershed domain used in calibration. Our success underscores the potential of using information theory to achieve robust model parameter estimation on a reduced computational budget.

3. HP

   River–Aquifer Interactions Enhancing Evapotranspiration in a Semiarid Riparian Zone: A Modelling Study

   Bowen Zhu, Maoyi Huang, Xingyuan Chen , and 3 more authors

   Hydrological Processes, Nov 2024

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   The hydrologic flows across the river–aquifer interface play an important role in groundwater dynamics and biogeochemical reactions within the subsurface; however, little is known about the effects of river–aquifer interactions on land surface processes. In this study, we developed a fully coupled three-dimensional (3D) land surface and subsurface model at a high resolution ( 1 km) that accounts for high-frequency hydrologic exchange flow conditions to investigate how river–aquifer interactions modulate surface water budgets in the Upper Columbia-Priest Rapids watershed, a typical semiarid watershed located in the northwestern United States where river stage fluctuates in response to reservoir releases changing. Our results show that the spatiotemporal dynamics of river–aquifer interactions are highly heterogeneous, driven mainly by river-stage fluctuations. Adding 6.64 \texttimes 106 m3 year-1 of water over the watershed from the river to groundwater owing to the lateral flow, river–aquifer interactions led to an increase in soil evaporation and transpiration supplied by higher soil moisture content, particularly in deeper subsurface. In a hypothetic future scenarios where a 5-m rise in river stage was assumed, the hydrologic flow exchange rates were intensified, resulting in higher surface water over the entire watershed. Overall, lateral flow induced by river–aquifer exchanges leads to an increase in evapotranspiration of 75% in the historical period and of 83% in the hypothetical future scenario. Our study demonstrates the potential of coupled model as an effective tool for understanding river–aquifer–land surface interactions, and indicates that river–aquifer interactions fundamentally alter the water balance of the riparian zone for the semiarid watershed and will likely become more frequent and intense in the future under the effects of climate change.

2023

1. HP

   The Importance of Explicitly Representing the Streambed in Watershed Models

   Pin Shuai, Peishi Jiang, Ethan T. Coon , and 1 more author

   Hydrological Processes, Nov 2023

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   The streambed is the critical interface between the aquatic and terrestrial systems and hosts important biogeochemical hot spots within river corridors. Although the streambed characteristics are significantly different from those of its surrounding soil, the streambed itself has not been explicitly represented in watershed models. Here, we explicitly incorporated a streambed layer into an integrated hydrologic model through model parameterization and discretization. We examined the hydrological effects of streambed characteristics, including hydraulic conductivity (K), layer thickness, and resolution, on the exchange fluxes across the streambed as well as the streamflow at the watershed outlet. The numerical experiments were performed in the American River Watershed, a headwater, mountainous watershed within the Yakima River Basin in central Washington. Despite having a negligible effect on the watershed streamflow, an explicit representation of the streambed with distinctive properties dramatically changed the magnitude and variability of the exchange flux. In general, a larger streambed K along with a thicker streambed layer induced larger exchange fluxes. The exchange flux was most sensitive to the streambed resolution. A finer streambed resolution increased exchange fluxes per unit area while reducing the overall exchange volumes across the entire streambed. The amount of baseflow decreased by 6% as the streambed resolution increased from 250 to 50 m. This finding is important because these hydrological changes may, in turn, affect the exchange of nutrients and contaminants between surface water and groundwater and the associated biogeochemical processes. Our work demonstrated the importance of representing streambeds in fully distributed, process-based watershed models to better capture the exchange flow dynamics in river corridors.

2. HESS

   Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

   Peishi Jiang, Pin Shuai, Alexander Sun , and 2 more authors

   Hydrology and Earth System Sciences, Jul 2023

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   Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

2022

1. HESS

   The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses

   Pin Shuai, Xingyuan Chen, Utkarsh Mital , and 2 more authors

   Hydrology and Earth System Sciences, May 2022

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   Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (tens of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) – Daily Surface Weather and Climatological Summaries (Daymet), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the 4 km-resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using the temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), and sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications for model calibration and watershed management decisions.

2. EMS

   Watershed Workflow: A toolset for parameterizing data-intensive, integrated hydrologic models

   Ethan T Coon, and Pin Shuai

   Environmental Modelling & Software, Nov 2022

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   Integrated, distributed hydrologic models leverage advances in computational power and data accessibility to improve predictive understanding of the water cycle. While impressive advances in this area of environmental modeling have been accomplished, such models are still rarely used, partially because of difficulty integrating model and data. This research describes the release of Watershed Workflow version 1.2, a new library aiming to automate and enable complex workflows defining inputs to high resolution, integrated, distributed hydrologic models. Watershed Workflow provides tools enabling the discovery, acquisition, mapping, and coordination of watershed geometry, land cover, soil properties, and meteorological data. It enables the construction of unstructured meshes that incorporate this data, and provides tools for automating a “first” simulation on any watershed in the United States. We present the design of the workflow tool, and describe best practices for its usage, culminating in a final example from watershed specification to simulation at the Coweeta Hydrologic Laboratory.

3. WRR

   Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions

   Kewei Chen, Xingyuan Chen, Xuehang Song , and 6 more authors

   Water Resources Research, Nov 2022

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   Quantifying dynamic hydrologic exchange flows (HEFs) within river corridors that experience high-frequency flow variations caused by dam regulations is important for understanding the biogeochemical processes at the river water and groundwater interfaces. Heat has been widely used as a tracer to infer steady-state flow velocities through analytical solutions of heat transport defined by the diurnal temperature signals. Under sub-daily dynamic flow conditions, however, such analytical solutions are not applicable due to the violation of their fundamental assumptions. In this study, we developed a data assimilation-based approach to estimate the sub-daily flux under highly dynamic flow conditions using multi-depth temperature observations at a 5-min resolution. If the hydraulic gradient is measured, Darcy’s law was used to calculate the flux with permeability estimated from temperature responses below the riverbed. Otherwise, flux was estimated directly by assimilating multi-depth temperature data at 1- or 2-hr time intervals assuming one-dimensional flow and heat transport governing equation. By comparing estimated fluxes with model-generated synthetic truth, we demonstrated that both schemes have robust performance in estimating fluxes under highly dynamic flow conditions. This data assimilation-based flux estimation method was able to capture the vertical sub-daily fluxes using multi-depth high-resolution temperature data alone, even in the presence of multi-dimensional flow. This approach has been successfully applied to real field temperature data collected at the Hanford site, which experiences highly dynamic HEFs. Our study shows the promise of adopting distributed 1-D temperature monitoring to capture spatial and temporal exchange dynamics in river corridors at a watershed scale or beyond.

2021

1. Front. Earth Sci.

   Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks

   Erol Cromwell, Pin Shuai, Peishi Jiang , and 5 more authors

   Frontiers in Earth Science, Feb 2021

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   Subsurface permeability is a key parameter in watershed models that controls the contribution from the subsurface flow to stream flows. Since the permeability is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed watershed models, estimation through inverse modeling has had a long history in subsurface hydrology. The wide availability of stream surface flow data, compared to groundwater monitoring data, provides a new data source to infer soil and geologic properties using integrated surface and subsurface hydrologic models. As most of the existing methods have shown difficulty in dealing with highly nonlinear inverse problems, we explore the use of deep neural networks for inversion owing to their successes in mapping complex, highly nonlinear relationships. We train various deep neural network (DNN) models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models.

2020

1. WRR

   River Dynamics Control Transit Time Distributions and Biogeochemical Reactions in a Dam‐Regulated River Corridor

   Xuehang Song, Xingyuan Chen, John M. Zachara , and 4 more authors

   Water Resources Research, Sep 2020

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   Transit time distributions (TTDs) exert important controls on biogeochemical processes in watershed systems. TTDs are often assumed to follow time-invariant exponential, lognormal, or heavy-tailed power law distributions in headwater or low-order streams. However, under dynamic hydrological forcing, transit time could exhibit more complex distribution patterns with strong spatial and temporal variability. In this study, we used a numerical particle tracking approach to characterize TTDs along the Hanford Reach of the Columbia River under the influences of river stage fluctuations and evaluate the associated effects on biogeochemical reaction potentials within the river corridor. Particle tracking was conducted using velocity fields simulated by high-resolution three-dimensional groundwater flow models that capture both the river stage fluctuations and physical heterogeneity. Our results revealed that multifrequency flow variations led to multimodal TTDs that varied in time and space. Such characteristics can only be captured by multiyear numerical simulations supported by multiyear field monitoring. Dam-induced high-frequency (subweekly) flow variations increased additional hydrologic exchange flows with short (subweekly) transit times, which accounted for up to 44% of reactant consumption in the river corridor along the Hanford Reach. The dam-induced river stage fluctuations have more significant impacts on faster biogeochemical reactions because they cause a larger fraction of shorter transit times. Numerical particle tracking provides an efficient alternative for characterizing TTDs for large complex systems where in situ field experiments are not feasible. Such a numerical approach is thus essential for improving large-scale biogeochemical modeling from watersheds to basins.

2. Front. Water

   High-Performance Simulation of Dynamic Hydrologic Exchange and Implications for Surrogate Flow and Reactive Transport Modeling in a Large River Corridor

   Yilin Fang, Xuehang Song, Huiying Ren , and 7 more authors

   Frontiers in Water, Sep 2020

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   Hydrologic exchange flows (HEFs) have environmental significance in riverine ecosystems. Key river channel factors that influence the spatial and temporal variations of HEFs include river stage, riverbed morphology, and riverbed hydraulic conductivity. However, their impacts on HEFs were often evaluated independently or on small scales. In this study, we numerically evaluated the combined interactions of these factors on HEFs using a high-performance simulator, PFLOTRAN, for subsurface flow and transport. The model covers 51 square kilometers of a selected river corridor with large sinuosity along the Hanford Reach of the Columbia River in Washington, US. Three years of spatially distributed hourly river stages were applied to the riverbed. Compared to the simulation when riverbed heterogeneity is not ignored, the simulation using homogeneous riverbed conductivity underestimated HEFs, especially upwelling from lateral features, and overestimated the mean residence times derived from particle tracking. To derive a surrogate model for the river corridor, we amended the widely used transient storage model (TSM) for riverine solute study at reach scale with reactions. By treating the whole river corridor as a batch reactor, the temporal changes in the exchange rate coefficient for the TSM were derived from the dynamic residence time estimated from the hourly PFLOTRAN results. The TSM results were evaluated against the effective concentrations in the hyporheic zone calculated from the PFLOTRAN simulations. Our results show that there is potential to parameterize surrogate models such as TSM amended with biogeochemical reactions while incorporating small-scale process understandings and the signature of time-varying streamflow to advance the mechanistic understanding of river corridor processes at reach to watershed scales. However, the assumption of a well-mixed storage zone for TSM should be revisited when redox-sensitive reactions in the storage zones play important roles in river corridor functioning.

3. WRR

   Kilometer‐Scale Hydrologic Exchange Flows in a Gravel Bed River Corridor and Their Implications to Solute Migration

   John M Zachara, Xingyuan Chen, Xuehang Song , and 3 more authors

   Water Resources Research, Feb 2020

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   A well-characterized field site along a major, gravel bed river corridor was used to investigate the dynamic pathways and impacts of subsurface hydrogeologic structure on kilometer-scale hydrologic exchange flows between river water and groundwater. An aqueous uranium (Uaq) plume exists within a hyporheic alluvial aquifer at the site that discharges to the Columbia River. We performed temporally intensive monitoring of specific conductance (SpC) and Uaq concentrations within the plume for a 2-year period at varying distances from the river shoreline, both within and outside a presumed subsurface pathway of lateral hydrologic exchange. SpC and Uaq were utilized as in situ tracers of hydrologic exchange and associated groundwater-surface water mixing. Seasonal river stage variations by more than 2 m caused distinct events of river water intrusion and retreat from the nearshore, hyporheic alluvial aquifer, resulting in highly dynamic SpC and Uaq patterns in monitoring wells. Simulations of hydrologic exchange and mixing were performed with PFLOTRAN to understand the observed SpC and Uaq behaviors linked to predominant flow directions and velocities in the river corridor as influenced by river stage dynamics and variable aquitard topography. By coupling robust monitoring with numerical flow and transport modeling, we demonstrate complicated multidirectional flow behaviors at the kilometer scale that strongly influenced plume dynamics. Therefore, hyporheic aquifer must be frequently monitored under different flow conditions if water quality is of concern. The resulting hydrologic understanding enables improved interpretation of hydrogeochemical data from this site and other large gravel bed river corridors in the United States and elsewhere.

2019

1. WRR

   Dam Operations and Subsurface Hydrogeology Control Dynamics of Hydrologic Exchange Flows in a Regulated River Reach

   Pin Shuai, Xingyuan Chen, Xuehang Song , and 7 more authors

   Water Resources Research, Feb 2019

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   Hydrologic exchange flows (HEFs) across the river-aquifer interface have important implications for biogeochemical processes and contaminant plume migration in the river corridor, yet little is known about the hydrogeomorphic factors that control HEFs dynamics under dynamic flow conditions. Here, we developed a 3-D numerical model for a large regulated river corridor along the Columbia River to study how HEFs are controlled by the interplays between dam-regulated flow conditions and hydrogeomorphic features of such river corridor system. Our results revealed highly variable intra-annual spatiotemporal patterns in HEFs along the 75-km river reach, as well as strong interannual variability with larger exchange volumes in wet years than dry years. In general, the river was losing during late spring to early summer when the river stage was high, and river was gaining in fall and winter when river stage was low. The magnitude and timing of river stage fluctuations controlled the timing of high exchange rates. Both river channel geomorphology and the thickness of a highly permeable river bank geologic layer controlled the locations of exchange hot spots, while the latter played a dominant role. Dam-induced, subdaily to daily river stage fluctuations drove high-frequency variations in HEFs across the river-aquifer interfaces, resulting in greater overall exchange volumes as compared to the case without high-frequency flows. Our results demonstrated that upstream dam operations enhanced the exchange between river water and groundwater with strong potential influence on the associated biogeochemical processes and on the fate and transport of groundwater contaminant plumes in such river corridors.

2018

1. Env. Chem.

   The fate of arsenic in groundwater discharged to the Meghna River, Bangladesh

   Michelle Berube, Katrina Jewell, Kimberly D. Myers , and 9 more authors

   Environmental Chemistry, Feb 2018

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   Arsenic contamination of groundwater is a major environmental problem in many areas of the world. In south-east Asia, iron-rich reducing groundwater mixes with oxidising river water in hyporheic zones, precipitating iron oxides. These oxides can act as a natural reactive barrier capable of accumulating elevated solid-phase concentrations of arsenic. Abstract Shallow, anoxic aquifers within the Ganges-Brahmaputra-Meghna Delta (GBMD) commonly contain elevated concentrations of arsenic (As), iron (Fe) and manganese (Mn). Highly enriched solid-phase concentrations of these elements have been observed within sediments lining the banks of the Meghna River. This zone has been described as a Natural Reactive Barrier (NRB). The impact of hydrological processes on NRB formation, such as transient river levels, which drive mixing between rivers and aquifers, is poorly understood. We evaluated the impact of groundwater flow dynamics on hydrobiogeochemical processes that led to the formation of an Fe- and Mn-rich NRB containing enriched As, within a riverbank aquifer along the Meghna River. The NRB dimensions were mapped using four complementary elemental analysis methods on sediment cores: X-ray fluorescence (XRF), aqua regia bulk extraction, and HCl and sodium phosphate leaching. It extended from 1.2 to 2.4 m in depth up to 15 m from the river’s edge. The accumulated As was advected to the NRB from offsite and released locally in response to mixing with aged river water. Nearly all of the As was subsequently deposited within the NRB before discharging to the Meghna. Significant Fe II release to the aqueous phase was observed within the NRB. This indicates the NRB is a dynamic zone defined by the interplay between oxidative and reductive processes, causing the NRB to grow and recede in response to rapid and seasonal hydrologic processes. This implies that natural and artificially induced changes in river stages and groundwater-tables will impact where As accumulates and is released to aquifers.

2017

1. Groundwater

   The Impact of the Degree of Aquifer Confinement and Anisotropy on Tidal Pulse Propagation

   Pin Shuai, Peter S. K. Knappett, Saddam Hossain , and 4 more authors

   Groundwater, Jul 2017

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   Oceanic tidal fluctuations which propagate long distances up coastal rivers can be exploited to constrain hydraulic properties of riverbank aquifers. These estimates, however, may be sensitive to degree of aquifer confinement and aquifer anisotropy. We analyzed the hydraulic properties of a tidally influenced aquifer along the Meghna River in Bangladesh using: (1) slug tests combined with drilling logs and surface resistivity to estimate Transmissivity (T); (2) a pumping test to estimate T and Storativity (S) and thus Aquifer Diffusivity (DPT); and (3) the observed reduction in the amplitude and velocity of a tidal pulse to calculate D using the Jacob-Ferris analytical solution. Average Hydraulic Conductivity (K) and T estimated with slug tests and borehole lithology were 27.3 m/d and 564 m2/d, respectively. Values of T and S determined from the pumping test ranged from 400 to 500 m2/d and 1 to 5 × 10−4, respectively with DPT ranging from 9 to 40 × 105 m2/d. In contrast, D estimated from the Jacob-Ferris model ranged from 0.5 to 9 × 104 m2/d. We hypothesized this error resulted from deviations of the real aquifer conditions from those assumed by the Jacob-Ferris model. Using a 2D numerical model tidal pulses were simulated across a range of conditions and D was calculated with the Jacob-Ferris model. Moderately confined (Ktop/Kaquifer \textless 0.01) or anisotropic aquifers (Kx/Kz \textgreater 10) yield D within a factor of 2 of the actual value. The order of magnitude difference in D between pumping test and Jacob-Ferris model at our site argues for little confinement or anisotropy.

2. WRR

   Denitrification in the banks of fluctuating rivers: The effects of river stage amplitude, sediment hydraulic conductivity and dispersivity, and ambient groundwater flow

   Pin Shuai, M. Bayani Cardenas, Peter S. K. Knappett , and 2 more authors

   Water Resources Research, Jul 2017

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   Hyporheic exchange induced by periodic river fluctuations leads to important biogeochemical processes, particularly nitrogen cycling, in riparian zones (RZs) where chemically distinct surface water and groundwater mix. We developed a two-dimensional coupled flow, reactive transport model to study the role of bank storage induced by river fluctuations on removing river-borne nitrate. Sensitivity analyses were conducted to quantify the effects of river amplitude, sediment hydraulic conductivity and dispersivity, and ambient groundwater flow on nitrate removal rate. The simulations showed that nitrification occurred in the shallower zone adjacent to the bank where oxic river water and groundwater interacted while denitrification occurred deeper into the aquifer and in the riverbed sediments where oxygen was depleted. River fluctuations greatly increased the amount of nitrate being removed; the nitrate removal rate increased as river amplitude increased. Similarly, increasing hydraulic conductivity increased overall nitrate removal since it expanded the denitrifying zone but decreased efficiency. In contrast, increasing sediment dispersivity increased the removal efficiency of nitrate because it promoted mixing between electron acceptors and donors. The presence and direction of ambient groundwater flow had a significant impact on nitrate removal rate when compared to neutral conditions. A losing river showed a larger nitrate removal rate, whereas a gaining river showed a smaller nitrate removal rate. Our results demonstrated that daily river fluctuations created denitrification hot spots within the RZ that would not otherwise exist under naturally neutral or gaining conditions.