1,720,989 research outputs found
Learning from landscapes: game theory for catchment science
No full textLinking how terrestrial drivers influence the quantity and quality of water in aquatic systems is a grand challenge of catchment science. This challenge grows more daunting as the effects of climate and land use change fundamentally shift catchment functions from their historic baselines to new norms. The current state-of-the-art models fall short in explaining the influence of this terrestrial-aquatic linkage in one of two ways: (1) black-box models provide unprecedented global predictive strength but lack clarity with respect to their local interpretability and (2) process-based models are locally interpretable but lack global transferability and predictive strength. Here, we propose an approach to bridge the gap in existing methods that conceptualizes catchment geochemistry as a game, where catchment drivers are players and riverine geochemistry is the payout, which is to be divided amongst the drivers based on their contribution to model prediction. This approach is built upon Shapley values, which explain why a model makes the predictions it does. Using this approach, we analyze several large-sample water quality and geochemistry datasets and extract from them local interpretability, global structure, and transferable insights. We interrogate six hypotheses related to catchment-scale nitrogen, carbon, and chloride fate and transport to glean physical insights and improve our understanding of catchment vulnerability to anthropogenic change. Given the recent surge in large-sample catchment datasets, the advent of new machine learning techniques, and the rapidly changing geophysical environment, this work has broad implications for model interpretability and science advancement across many geoscience domains.
Drivers of cyanotoxin and taste-and-odor compound presence within the benthic algae of human-disturbed rivers
No full textFreshwater benthic algae form complex mat matrices that can confer ecosystem benefits but also produce harmful cyanotoxins and nuisance taste-and-odor (T&O) compounds. Despite intensive study of the response of pelagic systems to anthropogenic change, the environmental factors controlling toxin presence in benthic mats remain uncertain. Here, we present a unique dataset from a rapidly urbanizing community (Kansas City, USA) that spans environmental, toxicological, taxonomic, and genomic indicators to identify the prevalence of three cyanotoxins (microcystin, anatoxin-a, and saxitoxin) and two T&O compounds (geosmin and 2-methylisoborneol). Thereafter, we construct a random forest model informed by game theory to assess underlying drivers. Microcystin (11.9 ± 11.6 µg/m2), a liver toxin linked to animal fatalities, and geosmin (0.67 ± 0.67 µg/m2), a costly-to-treat malodorous compound, were the most abundant compounds and were present in 100% of samples, irrespective of land use or environmental conditions. Anatoxin-a (8.1 ± 11.6 µg/m2) and saxitoxin (0.18 ± 0.39 µg/m2), while not always detected, showed a systematic tradeoff in their relative importance with season, an observation not previously reported in the literature. Our model indicates that microcystin concentrations were greatest where microcystin-producing genes were present, whereas geosmin concentrations were high in the absence of geosmin-producing genes. Together, these results suggest that benthic mats produce cyanotoxins in situ but that geosmin production may occur ex situ with its presence in mats attributable to adsorption by organic matter. Our study broadens the awareness of benthic cyanobacteria as a source of harmful and nuisance metabolites and highlights the importance of benthic monitoring for sustaining water quality standards in rivers
An index for inferring dominant transport pathways of solutes and sediment: assessing land use impacts with high-frequency conductivity and turbidity sensor data
No full textLand use change threatens aquatic ecosystems through freshwater salinization and sediment pollution. Effective river management requires an understanding of the dominant hydrologic pathways of sediment and solute delivery. To address this, we applied hysteresis analysis, hydrograph separation, and linear regression to hundreds of events across a decade of specific conductance and turbidity data from three streams along a rural-to-urban gradient. Thereafter, we developed an index ("β") to quantify the relative influence of surface runoff to event-scale suspended sediment generation, where a value of ‘1’ indicates complete alignment of suspended sediment generation with the temporal structure of runoff whereas ‘0’ indicates total alignment with baseflow. Solute hysteresis results showed a predominance of dilution for the rural and mixed-use streams irrespective of road salt presence. On the other hand, urban stream behavior shifted from dilution to flushing following salt application, which was largely driven by greater runoff coefficients and the connectivity of distal solutes to the stream corridor. The newly developed index ("β") indicated that suspended sediment dynamics were more aligned with runoff in all three streams: rural stream ("β" = 0.70), mixed stream ("β" = 0.57), and urban stream ("β" = 0.64). The relative importance of baseflow to sediment generation grows slightly in urbanizing streams, as impervious surfaces disconnect upland sediment, which would otherwise transport with runoff, while piston-flow baseflow erodes exposed streambanks. Our findings emphasize the need to consider the impact of human modification of the landscape on solute and sediment transport in freshwater systems for effective water quality management. Further, our "β" index provides a useful tool for assessing the relative influence of surface runoff on event-scale solute or sediment generation in streams, supporting river management and conservation efforts
Nitrate hysteresis as a tool for revealing storm-event dynamics and improving water quality model performance
No full textUnderstanding the physics of nitrate contamination in surface and subsurface water is vital for mitigating downstream water quality impairment. Though high frequency sensor data have become readily available and computational models more accessible, the integration of these two methods for improved prediction is underdeveloped. The objective of this study was to utilize high-frequency data to advance our understanding and model representation of nitrate transport for an agricultural karst spring in Kentucky, USA. We collected two-years of 15-minute nitrate and specific conductance data and analyzed source-timing dynamics across dozens of events to develop a conceptual model for nitrate hysteresis in karst. Thereafter, we used the sensing data, specifically discharge-concentration indices, to constrain modeled nitrate prediction bounds as well as the uncertainty of hydrologic and nitrogen processes, such as soil percolation and biogeochemical transformation. Observed nitrate hysteresis behavior at the spring was complex and included clockwise (n = 11), counterclockwise (n = 13), and figure-eight (n = 10) shapes, which contrasts with surface systems that are often dominated by a single hysteresis shape. Sensing results highlight the importance of antecedent connectivity to nitrate-rich storages in determining the timing of nitrate delivery to the spring. After integrating hysteresis analysis into our numerical model evaluation, simulated nitrate prediction bounds were reduced by 43 ± 12% and parameter uncertainty by 36 ± 20%. Taken together, this study suggests that discharge-concentration indices derived from high-frequency sensor data can be successfully integrated into numerical models to improve process representation and reduce modeled uncertainty
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