ALBERT

Description: Flow paths and residence times in the hyporheic zone are known to influence biogeochemical processes such as nitrification and denitrification. The exchange across the sediment-water interface may involve mixing of surface water and groundwater through complex hyporheic flow paths that contribute to highly variable biogeochemically active zones. Despite the recognition of these patterns in the literature, conceptualization and analysis of flow paths and nitrogen transformations beneath riffle-pool sequences often neglect to consider bed form driven exchange along the entire reach. In this study, the spatial and temporal distribution of dissolved oxygen (DO), nitrate (NO 3 - ) and ammonium (NH 4 + ) were monitored in the hyporheic zone beneath a riffle-pool sequence on a losing section of the Truckee River, NV. Spatially-varying hyporheic exchange and the occurrence of multi-scale hyporheic mixing cells are shown to influence concentrations of DO and NO 3 - and the mean residence time (MRT) of riffle and pool areas. Distinct patterns observed in piezometers are shown to be influenced by the first large flow event following a steady 8 month period of low flow conditions. Increases in surface water discharge resulted in reversed hydraulic gradients and production of nitrate through nitrification at small vertical spatial scales (0.10 to 0.25 m) beneath the sediment-water interface. In areas with high downward flow rates and low MRT, denitrification may be limited. The use of a longitudinal two-dimensional flow model helped identify important mechanisms such as multi-scale hyporheic mixing cells and spatially varying MRT, an important driver for nitrogen transformation in the riverbed. Our observations of DO and NO 3 - concentrations and model simulations highlight the role of multi-scale hyporheic mixing cells on MRT and nitrogen transformations in the hyporheic zone of riffle-pool sequences. This article is protected by copyright. All rights reserved.

Description: Abstract Optimizing the remediation of light nonaqueous phase liquids (LNAPLs) to achieve an acceptable endpoint status for a site is not trivial. Recently, Sookhak Lari, Johnston, et al. (2018, https://doi.org/10.1016/j.jhazmat.2017.11.006), Sookhak Lari, Rayner, and Davis (2018, https://doi.org/10.1016/j.jenvman.2018.07.041) conducted three‐dimensional multiphase, multicomponent simulations to address LNAPL remediation endpoints for a single recovery well. However, optimized LNAPL remediation for multiple wells is not addressed in the literature. In the first part of this paper, we establish a matrix of 10 simulation scenarios to show the sensitivity of the remedial endpoint (i.e., what is feasibly achieved) to several parameters including viscosity and partitioning attributes of the LNAPL, heterogeneity of the formation, and the location and number of the recovery wells. While this addresses the variability of LNAPL removal from the subsurface and is valuable in its own right, it does not address the optimal removal of LNAPL. We address this in the second part of the paper by linking a genetic algorithm to TMVOC‐MP to allow, for example, the assessment of the optimal number and location of LNAPL recovery wells in a field‐scale problem. Using supercomputing facilities and within 49 genetic algorithm generations, each including 150 members, highly optimized answers to different objective functions were obtained. For the first time, such a multiphase, multicomponent optimization tool promises the possibility of optimizing LNAPL remediation at field scale to achieve practicable endpoint conditions, within computational affordability.

Description: Coal seam gas production involves generation and management of large amounts of co-produced water. One of the most suitable methods of management is injection into deep aquifers. Field injection trials may be used to support the predictions of anticipated hydrological and geochemical impacts of injection. The present work employs reactive transport modeling (RTM) for a comprehensive analysis of data collected from a trial where arsenic mobilization was observed. Arsenic sorption behavior was studied through laboratory experiments, accompanied by the development of a surface complexation model (SCM). A field-scale RTM that incorporated the laboratory-derived SCM was used to simulate the data collected during the field injection trial and then to predict the long-term fate of arsenic. We propose a new practical procedure which integrates laboratory and field-scale models using a Monte Carlo type uncertainty analysis and alleviates a significant proportion of the computational effort required for predictive uncertainty quantification. The results illustrate that both arsenic desorption under alkaline conditions and pyrite oxidation have likely contributed to the arsenic mobilization that was observed during the field trial. The predictive simulations show that arsenic concentrations would likely remain very low if the potential for pyrite oxidation is minimized through complete deoxygenation of the injectant. The proposed modeling and predictive uncertainty quantification method can be implemented for a wide range of groundwater studies that investigate the risks of metal(loid) or radionuclide contamination.

Description: Accurately estimating basin-wide snow water equivalent (SWE) is the most important unsolved problem in mountain hydrology. Models that rely on remotely sensed inputs are especially needed in ranges with few surface measurements. The NASA Airborne Snow Observatory (ASO) provides estimates of SWE at 50-meter spatial resolution in several basins across the Western US during the melt season. Primarily, water managers use this information to forecast snowmelt runoff into reservoirs; another impactful use of ASO measurements lies in validating and improving satellite-based snow estimates or models that can scale to whole mountain ranges, even those without ground-based measurements. We compare ASO measurements from 2013 to 2015 to four methods that estimate spatially distributed SWE: two versions of a SWE reconstruction method, spatial interpolation from snow pillows and courses, and NOAA's Snow Data Assimilation System (SNODAS). SWE reconstruction downscales energy forcings to compute potential melt, then multiplies those values by satellite-derived estimates of fractional snow-covered area to calculate snowmelt. The snowpack is then built in reverse from the date the snow is observed to disappear. The two SWE reconstruction models tested include one that employs an energy balance calculation of snowmelt, and one that combines net radiation and degree-day approaches to estimate melt. Our full energy balance model, without ground observations, performed slightly better than spatial interpolation from snow pillows, having no systematic bias and 26% mean absolute error when compared to SWE from ASO. Both reconstruction models and interpolation were more accurate than SNODAS. This article is protected by copyright. All rights reserved.

Description: Abstract Understanding streamflow generation and its dependence on catchment characteristics requires large spatial datasets and is often limited by convoluted effects of multiple variables. Here we address this knowledge gap using data‐informed physics‐based hydrologic modelling in two catchments with similar vegetation and climate but different lithology (Shale Hills, SH, Shale, 0.08 km2 and Garner Run, GR, Sandstone, 1.34 km2), which influences catchment topography and soil properties. The sandstone catchment, Garner Run, is characterized by lower drainage density, extensive valley fill, and boulder soils. We tested the hypothesis that the influence of topographic characteristics is more significant than that of soil properties and catchment size. Transferring calibration coefficients from the previously‐calibrated SH model to GR cannot reproduce monthly discharge until after incorporating measured boulder distribution at GR. Model calibration underscored the importance of soil properties (porosity, van Genuchten parameters, and boulder characteristics) in reproducing daily discharge. Virtual experiments were used to swap topography, soil properties, and catchment size one at a time to disentangle their influence. They showed that clayey SH soils led to high nonlinearity and threshold behavior. With the same soil and topography, changing from SH to GR size consistently increased dynamic water storage (Sd) from ~ 0.12 m to ~ 0.17 m. All analyses accentuated the predominant control of soil properties, therefore rejecting the hypothesis. The results illustrate the use of physics‐based modelling for illuminating mechanisms and underscore the importance and challenges for subsurface characterization as we move toward hydrological Prediction in Ungauged Basins (PUB).