ALBERT

Description: We adapted Newton's Law of Cooling to model downstream water temperature change in response to stream-adjacent forest harvest on small and medium streams (average 327 ha in size) throughout the Oregon Coast Range, USA. The model requires measured stream gradient, width, depth and upstream control reach temperatures as inputs and contains two free parameters which were determined by fitting the model to measured stream temperature data. This model reproduces the measured downstream temperature responses to within 0.4 C ° for 15 of the 16 streams studied and provides insight into the physical sources of site-to-site variation among those responses. We also use the model to examine how the pre-to-post harvest change in daily maximum stream temperature depends on distance from the harvest reach. The model suggests that the pre-to-post harvest temperature change approximately 300  m downstream of the harvest will range from roughly 82% to less than 1% of that temperature change which occurred within the harvest reach, depending primarily on the downstream width, depth, and gradient. Using study-averaged values for these channel characteristics the model suggests that for a stream representative of those in the study, the temperature change approximately 300  m downstream of the harvest will be 56% of the temperature change which occurred within the harvest reach. This adapted Newton's Law of Cooling procedure represents a highly practical means for predicting stream temperature behavior downstream of timber harvests relative to conventional heat budget approaches, and is informative of the dominant processes affecting stream temperature. This article is protected by copyright. All rights reserved.

Description: Soil moisture is a critical variable in the water and energy cycles. The prediction of soil moisture patterns, especially at high spatial resolution, is challenging. This study tests the ability of a land surface hydrologic model (Flux-PIHM) to simulate high-resolution soil moisture patterns in the Shale Hills watershed (0.08 km 2 ) in central Pennsylvania. Locally measured variables including a soil map, soil parameters, a tree map, and lidar topographic data, all have been synthesized into Flux-PIHM to provide model inputs. The predicted 10-cm soil moisture patterns for fifteen individual days encompassing seven months in 2009 are compared with the observations from 61 soil moisture monitoring sites. Calibrated using only watershed-scale and a few point-based measurements, and driven by spatially uniform meteorological forcing, Flux-PIHM is able to simulate the observed macro spatial pattern of soil moisture at ~10 m resolution (spatial correlation coefficient ~ 0.6) and the day-to-day variation of this soil moisture pattern, although it underestimates the amplitude of the spatial variability and the mean soil moisture. Results show that the spatial distribution of soil hydraulic parameters has the dominant effect on the soil moisture spatial pattern. The surface topography and depth to bedrock also affect the soil moisture patterns in this watershed. Using the National Land Cover Database (NLCD) in place of a local tree survey map makes a negligible difference. Field measured soil type maps and soil type-specific hydraulic parameters significantly improve the predicted soil moisture pattern as compared to the most detailed national soils database (Soil Survey Geographic Database, or SSURGO, 30-m resolution). This article is protected by copyright. All rights reserved.

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).

Description: ABSTRACT Organic compounds are removed from the atmosphere and deposited to the earth's surface via precipitation. In this study, we quantified variations of dissolved organic carbon (DOC) in precipitation during storm events at the Shale Hills Critical Zone Observatory, a forested watershed in central Pennsylvania (USA). Precipitation samples were collected consecutively throughout the storm during 13 events, which spanned a range of seasons and synoptic meteorological conditions, including a hurricane. Further, we explored factors that affect the temporal variability by considering relationships of DOC in precipitation with atmospheric and storm characteristics. Concentrations and chemical composition of DOC changed considerably during storms, with the magnitude of change within individual events being comparable or higher than the range of variation in average event composition among events. While some previous studies observed that concentrations of other elements in precipitation typically decrease over the course of individual storm events, results of this study show that DOC concentrations in precipitation are highly variable. During most storm events concentrations decreased over time, possibly as a result of washing out of the below-cloud atmosphere. However, increasing concentrations that were observed in the later stages of some storm events highlight that DOC removal with precipitation is not merely a dilution response. Increases in DOC during events could result from advection of air masses, local emissions during breaks in precipitation, or chemical transformations in the atmosphere that enhance solubility of organic carbon compounds. This work advances understanding of processes occurring during storms that are relevant to studies of atmospheric chemistry, carbon cycling, and ecosystem responses.

Description: Abstract Groundwater dependent ecosystems are often defined by the presence of deeply‐rooted phreatophytic plants. When connected to groundwater, phreatophytes in arid regions decouple ecosystem net primary productivity from precipitation, underscoring a disproportionately high biodiversity and exchange of resources relative to surrounding areas. However, groundwater dependent ecosystems are widely threatened due to the effects of water diversions, groundwater abstraction, and higher frequencies of episodic drought and heat waves. The resilience of these ecosystems to shifting ecohydrological‐climatological conditions will depend largely on the capacity of dominant, phreatophytic plants to cope with dramatic reductions in water availability and increases in atmospheric water demand. This paper disentangles the broad range of hydraulic traits expressed by phreatophytic vegetation to better understand their capacity to survive, or even thrive under shifting ecohydrological conditions. We focus on three elements of plant water relations: 1) hydraulic architecture (including root area to leaf area ratios and rooting depth), 2) xylem structure and function, and 3) stomatal regulation. We place the expression of these traits across a continuum of phreatophytic habits from obligate to semi‐obligate to semi‐facultative to facultative. Although many species occupy multiple phreatophytic niches depending on access to groundwater, we anticipate that populations are largely locally adapted to a narrow range of ecohydrological conditions regardless of gene flow across ecohydrological gradients. Consequently, we hypothesize that reductions in available groundwater and increases in atmospheric water demand will result in either 1) stand replacement of obligate phreatophytic species with more facultative species as a function of wide‐spread mortality in highly groundwater dependent populations, or 2) directional selection in semi‐obligate and semi‐facultative phreatophytes towards the expression of traits associated with highly facultative phreatophytes in the absence of species replacement. Anticipated shifts in the expression of hydraulic traits may have profound impacts on water cycling processes, species assemblages and habitat structure of groundwater dependent woodlands and riparian forests.

Description: The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.