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At least 163 records · Page 9Linked to original sources
USGS2022

Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data

Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll- a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon , and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella , a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.

New York, Oregon, Texas↗
USGS2016

Investigating dynamic sources of pharmaceuticals: Demographic and seasonal use are more important than down-the-drain disposal in wastewater effluent in a University City setting

Pharmaceutical pollution in surface waters poses risks to human and ecosystem health. Wastewater treatment facilities are primary sources of pharmaceutical pollutants, but little is known about the factors that affect drugs entering the wastewater stream. This paper investigates the effects of student pharmaceutical use and disposal behaviors and an annual demographic shift on pharmaceutical pollution in a university town. We sampled wastewater effluent during a ten-day annual spring student move-out period at the University of Vermont. We then interpreted these data in light of survey results that investigated pharmaceutical purchasing, use, and disposal practices among the university student population. Surveys indicated that the majority of student respondents purchased pharmaceuticals in the previous year. Many students reported having leftover drugs, though only a small portion disposed of them, mainly in the trash. We detected 51 pharmaceuticals in 80% or more of the wastewater effluent samples collected over the ten-day sampling period. Several increased in concentration after students left the area. Concentrations of caffeine and nicotine decreased weakly. Drug disposal among this university student population does not appear to be a major source of pharmaceuticals in wastewater. Increases in pharmaceutical concentration after the students left campus can be tied to an increase in the seasonal use of allergy medications directly related to pollen, as well as a demographic shift to a year-round older population, which supports national data that older people use larger volumes and different types of pharmaceuticals than the younger student population.

Vermont↗
USGS2017

A regional assessment of chemicals of concern in surface waters of four Midwestern United States national parks

Anthropogenic chemicals and their potential for adverse biological effects raise concern for aquatic ecosystem health in protected areas. During 2013–15, surface waters of four Midwestern United States national parks were sampled and analyzed for wastewater indicators, pharmaceuticals, personal care products, and pesticides. More chemicals and higher concentrations were detected at the two parks with greater urban influences (Mississippi National River and Recreation Area and Indiana Dunes National Lakeshore) than at the two more remote parks (Apostle Islands National Lakeshore and Isle Royale National Park). Atrazine (10–15 ng/L) and N,N-diethyl-meta-toluamide (16–120 ng/L) were the only chemicals detected in inland lakes of a remote island national park (Isle Royale National Park). Bisphenol A and organophosphate flame retardants were commonly detected at the other sampled parks. Gabapentin and simazine had the highest observed concentrations (> 1000 ng/L) in three and two samples, respectively. At the two parks with urban influences, metolachlor and simazine concentrations were similar to those reported for other major urban rivers in the United States. Environmental concentrations of detected chemicals were often orders of magnitude less than standards or reference values with three exceptions: (1) hydrochlorothiazide exceeded a human health-based screening value in seven samples, (2) estrone exceeded a predicted critical environmental concentration for fish pharmacological effects in one sample, and (3) simazine was approaching the 4000 ng/L Maximum Contaminant Level in one sample even though this concentration is not expected to reflect peak pesticide use. Although few environmental concentrations were approaching or exceeded standards or reference values, concentrations were often in ranges reported to elicit effects in aquatic biota. Data from this study will assist in establishing a baseline for chemicals of concern in Midwestern national parks and highlight the need to better understand the sources, pathways, and potential adverse effects to aquatic systems in national parks.

Indiana, Michigan, Minnesota, Wisconsin↗
USGS2020

The response of stream ecosystems in the Adirondack region of New York to historical and future changes in atmospheric deposition of sulfur and nitrogen

The present-day acid-base chemistry of surface waters can be directly linked to contemporary observations of acid deposition; however, pre-industrial conditions are key to predicting the potential future recovery of stream ecosystems under decreasing loads of atmospheric sulfur (S) and nitrogen (N) deposition. The integrated biogeochemical model PnET-BGC was applied to 25 forest watersheds that represent a range of acid sensitivity in the Adirondack region of New York, USA to simulate the response of streams to past and future changes in atmospheric S and N deposition, and calculate the target loads of acidity for protecting and restoring stream water quality and ecosystem health. Using measured data, the model was calibrated and applied to simulate soil and stream chemistry at all study sites. Model hindcasts indicate that historically stream water chemistry in the Adirondacks was variable, but inherently sensitive to acid deposition. The median model-simulated acid neutralizing capacity (ANC) of the streams was projected to be 55 μeq L −1 before the advent of anthropogenic acid deposition (~1850), decreasing to minimum values of 10 μeq L −1 around the year 2000. The median simulated ANC increased to 13 μeq L −1 by 2015 in response to decreases in acid deposition that have occurred over recent decades. Model projections suggest that simultaneous decreases in sulfate, nitrate and ammonium deposition are more effective in restoring stream ANC than individual decreases in sulfur or nitrogen deposition. However, the increases in stream ANC per unit equivalent decrease in S deposition is greater compared to decreases in N deposition. Using empirical algorithms, fish community density and biomass are projected to increase under several deposition-control scenarios that coincide with increases in stream ANC. Model projections suggest that even under the most aggressive deposition-reduction scenarios, stream chemistry and fisheries will not fully recover from historical acidification by 2200.

New York↗
USGS2020

Freshwater neurotoxins and concerns for human, animal, and ecosystemhealth: A review of anatoxin-a and saxitoxin

Toxic cyanobacteria are a concern worldwide because they can adversely affect humans, animals, and ecosystems. However, neurotoxins produced by freshwater cyanobacteria are understudied relative to microcystin. Thus, the objective of this critical review was to provide a comprehensive examination of the modes of action, production, fate, and occurrence of the freshwater neurotoxins anatoxin-a and saxitoxin as they relate to human, animal, and ecosystem health. Literature on freshwater anatoxin-a and saxitoxin was obtained and reviewed for both laboratory and field studies. Current (2020) research identifies as many as 41 anatoxin-a producing species and 15 saxitoxin-producing species of freshwater cyanobacteria. Field studies indicate that anatoxin-a and saxitoxin have widespread distribution, and examples are given from every continent except Antarctica. Human and animal health concerns can range from acute to chronic. However, few researchers studied chronic or sublethal effects of freshwater exposures to anatoxin-a or saxitoxin. Ecosystemhealth also is a concern, as the effects of toxicity may be far reaching and include consequences throughout the food web. Several gaps in knowledgewere identified for anatoxin-a and saxitoxin, including triggers of production and release, environmental fate and degradation, primary and secondary exposure routes, diel variation, food web effects, effects of cyanotoxin mixtures, and sublethal health effects on individual organisms and populations. Despite the gaps, this critical review facilitates our current understanding of freshwater neurotoxins and thus can serve to guide future research on anatoxin-a, saxitoxin, and other cyanotoxins.

Science of the Total Environment↗
USGS2022

Nitrogen reductions have decreased hypoxia in the Chesapeake Bay: Evidence from empirical and numerical modeling

Seasonal hypoxia is a characteristic feature of the Chesapeake Bay due to anthropogenic nutrient input from agriculture and urbanization throughout the watershed. Although coordinated management efforts since 1985 have reduced nutrient inputs to the Bay, oxygen concentrations at depth in the summer still frequently fail to meet water quality standards that have been set to protect critical estuarine living resources. To quantify the impact of watershed nitrogen reductions on Bay hypoxia during a recent period including both average discharge and extremely wet years (2016–2019), this study employed both statistical and three-dimensional (3-D) numerical modeling analyses. Numerical model results suggest that if the nitrogen reductions since 1985 had not occurred, annual hypoxic volumes (O 2 < 3 mg L −1 ) would have been ~50–120% greater during the average discharge years of 2016–2017 and ~20–50% greater during the wet years of 2018–2019. The effect was even greater for O 2 < 1 mg L −1 , where annual volumes would have been ~80–280% greater in 2016–2017 and ~30–100% greater in 2018–2019. These results were supported by statistical analysis of empirical data, though the magnitude of improvement due to nitrogen reductions was greater in the numerical modeling results than in the statistical analysis. This discrepancy is largely accounted for by warming in the Bay that has exacerbated hypoxia and offset roughly 6–34% of the improvement from nitrogen reductions. Although these results may reassure policymakers and stakeholders that their efforts to reduce hypoxia have improved ecosystem health in the Bay, they also indicate that greater reductions are needed to counteract the ever-increasing impacts of climate change.

Chesapeake Bay↗
USGS2026

Comparative assessment of STIC sensors, streamflow and rain gauges for quantifying river connectivity in intermittent systems

In intermittent stream systems, including those occurring in Texas, USA, the severity of low-flow conditions, duration of seasonal disconnection, and frequency of no-flow events have been amplified by drought. Documentation of these no-flow events is necessary to evaluate ecosystem health. However, many intermittent reaches remain un-gauged given that perennial river sec-tions are often prioritized for gauge placement. Our objectives were to 1) document stream flow using Stream Temperature, Intermittency, and Conductivity (STIC) loggers to determine the frequency and duration of no-flow events in intermittent tributaries of the Colorado River, Texas and 2) compare logger data to publicly available data from streamflow discharge and precipitation gauge networks to understand differences among these data types for drying event characterization. We use these comparisons to summarize benefits and limitations of the application of in-stream data loggers. STIC loggers were deployed at 19 sites, one in each pool and riffle habitat of a stream reach. STIC loggers recorded a measurement of relative conductance every six hours from June 2022 to March 2024, which was used to determine the presence or absence of flow connectivity in a reach. No-flow duration among intermittent reaches varied between 37 and 270 days across tributaries during an ongoing drought in the study area. Overall, logger data was more precise than discharge data for characterizing no-flow events or precipitation data when documenting presence of water in the stream channel due to runoff. Lack of discharge gauges in intermittent tributaries left large sections of stream reaches undocumented and resulted in mischaracterization of flow patterns. Drought severity across the tributaries did not follow longitudinal patterns that would be expected by the climatic precipitation gradient of the study area. More research is needed to determine if factors such as population size affect severity. Likewise, precipitation data did not correlate well with logger water presence data, lacking consideration for groundwater recharge, soil hydrophobicity, and surface compaction. This study shows that to monitor no-flow events, detailed spatial datasets are necessary and that STIC loggers are useful tools that provide data to fill spatial information gaps and facilitate more accurate flow characterization and water presence data in intermittent systems.

Texas↗
USGS2019

Improving ecological restoration to curb biotic invasion - A practical guide

Common practices for invasive species control and management include physical, chemical, and biological approaches. The first two approaches have clear limitations and may lead to unintended (negative) consequences, unless carefully planned and implemented. For example, physical removal rarely completely eradicates the targeted invasive species and can cause disturbances that facilitate new invasions by nonnative species from nearby habitats. Chemical treatments can harm native, and especially rare, species through unanticipated side effects. Biological methods may be classified as biocontrol and the ecological approach. Similar to physical and chemical methods, biocontrol also has limitations and sometimes leads to unintended consequences. Therefore, a relatively safer and more practical choice may be the ecological approach, which has two major components: (1) restoration of native species and (2) biomass manipulation of the restored community, such as selective grazing or prescribed burning (to achieve and maintain viable population sizes). Restoration requires well-planned and implemented planting designs that consider alpha-, beta-, and gamma-diversity and the abundance of native and invasive component species at local, landscape, and regional levels. Given the extensive destruction or degradation of natural habitats around the world, restoration could be most effective for enhancing ecosystem resilience and resistance to biotic invasions. At the same time, ecosystems in human-dominated landscapes, especially those newly restored, require close monitoring and careful intervention (e.g., through biomass manipulation), especially when successional trajectories are not moving as intended. Biomass management frequently uses prescribed burning, grazing, harvesting, and thinning to maintain overall ecosystem health and sustainability. Thus, the resulting optimal, balanced, and relatively stable ecological conditions could more effectively limit the spread and establishment of invasive species. Here we review the literature (especially within the last decade) on ecological approaches that involve biodiversity, biomass, and productivity, three key community/ecosystem variables that reciprocally influence one another. We focus on the common and most feasible ecological practices that can aid in resisting new invasions and/or suppressing the dominance of existing invasive species. We contend that, because of the strong influences from neighboring areas (i.e., as exotic species pools), local restoration and management efforts in the future need to consider the regional context and projected climate changes.

Invasive Plant Science and Management↗
USGS2020

Factors affecting nitrate concentrations in stream base flow

Elevated nitrogen concentrations in streams and rivers in the Chesapeake Bay watershed have adversely affected the ecosystem health of the bay. Much of this nitrogen is derived as nitrate from groundwater that discharges to streams as base flow. In this study, boosted regression trees (BRTs) were used to relate nitrate concentrations in base flow ( n = 156) to explanatory variables describing nitrogen sources, geology, and soil and catchment characteristics. From these relations, a BRT model was developed to predict base flow nitrate concentrations in streams throughout the Chesapeake Bay watershed. The highest base flow nitrate concentrations were associated with intensive agricultural land use, carbonate geology, and sparse riparian canopy, which suggested that reduced nitrogen inputs, particularly over carbonate terrane, are critical for limiting nitrate concentrations. The lowest nitrate concentrations in the BRT model were associated with extensive riparian canopy, high levels of organic carbon in soils, and suboxic conditions at shallow depths, which suggested that denitrification in the subsurface, particularly in the riparian zone, is limiting base flow nitrate concentrations. Nitrate transport from aquifers to streams can take decades to occur, resulting in decades-long lag times between the time when a land-use activity is implemented and when its effects are fully observed in streams. Predictive models of base flow nitrate concentrations in streams will help identify which portions of a watershed are likely to have large fractions of total stream nitrogen load derived from pathways with significant lag times.

Delaware, Maryland, New York, North Carolina, Penn↗
USGS2016

Hydrologic controls on nitrogen cycling processes and functional gene abundance in sediments of a groundwater flow-through lake

The fate and transport of inorganic nitrogen (N) is a critically important issue for human and aquatic ecosystem health because discharging N-contaminated groundwater can foul drinking water and cause algal blooms. Factors controlling N-processing were examined in sediments at three sites with contrasting hydrologic regimes at a lake on Cape Cod, MA. These factors included water chemistry, seepage rates and direction of groundwater flow, and the abundance and potential rates of activity of N-cycling microbial communities. Genes coding for denitrification, anaerobic ammonium oxidation (anammox), and nitrification were identified at all sites regardless of flow direction or groundwater dissolved oxygen concentrations. Flow direction was, however, a controlling factor in the potential for N-attenuation via denitrification in the sediments. Potential rates of denitrification varied from 6 to 4500 pmol N/g/h from the inflow to the outflow side of the lake, owing to fundamental differences in the supply of labile organic matter. The results of laboratory incubations suggested that when anoxia and limiting labile organic matter prevailed, the potential existed for concomitant anammox and denitrification. Where oxic lake water was downwelling, potential rates of nitrification at shallow depths were substantial (1640 pmol N/g/h). Rates of anammox, denitrification, and nitrification may be linked to rates of organic N-mineralization, serving to increase N-mobility and transport downgradient.

Massachusetts↗
USGS2025

Imidacloprid in United States rivers, 2013–2022: Persistent presence and emerging chronic hazard

Imidacloprid, a neonicotinoid insecticide, is used for agricultural and nonagricultural purposes and is toxic to nontarget organisms at low concentrations in aquatic ecosystems. A total of 12,547 water samples were collected from 2013 to 2022 from 77 rivers across the United States (U.S.) and were analyzed to evaluate detections and temporal trends in imidacloprid concentrations. Imidacloprid was detected in 44% of all samples, and the mean concentration, adjusted for nondetect samples, of 24.9 ng/L (median = 11.9 ng/L) was more than twice the chronic benchmark for freshwater invertebrates (10 ng/L). This potential hazard to aquatic life was persistent, with 44% of the sites having a median concentration exceeding the chronic benchmark. Half of the sites ( n = 38) had increasing trends, including large river sites along the Mississippi River. The mean increase was 10.6 ng/L over the past decade, while only six sites indicated decreasing trends. The estimated total loading of imidacloprid delivered to the Gulf of America from 2013 to 2022 was 129,489 kg (142.7 U.S. ton). The extensive presence of imidacloprid in U.S. waterways, the high percentage of sites with trends of increasing concentrations, and the prevalence of concentrations exceeding chronic benchmarks suggest widespread persistent risks to ecosystem health.

Environmental Science & Technology↗
USGS2012

Applications of fluorescence spectroscopy for predicting percent wastewater in an urban stream

Dissolved organic carbon (DOC) is a significant organic carbon reservoir in many ecosystems, and its characteristics and sources determine many aspects of ecosystem health and water quality. Fluorescence spectroscopy methods can quantify and characterize the subset of the DOC pool that can absorb and re-emit electromagnetic energy as fluorescence and thus provide a rapid technique for environmental monitoring of DOC in lakes and rivers. Using high resolution fluorescence techniques, we characterized DOC in the Tualatin River watershed near Portland, Oregon, and identified fluorescence parameters associated with effluent from two wastewater treatment plants and samples from sites within and outside the urban region. Using a variety of statistical approaches, we developed and validated a multivariate linear regression model to predict the amount of wastewater in the river as a function of the relative abundance of specific fluorescence excitation/emission pairs. The model was tested with independent data and predicts the percentage of wastewater in a sample within 80% confidence. Model results can be used to develop in situ instrumentation, inform monitoring programs, and develop additional water quality indicators for aquatic systems.

Oregon↗
USGS2000

Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude -0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude -0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude -0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude -0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at -0.3769.

Environmental and Ecological Statistics↗
USGS2023

Stream temperature prediction in a shifting environment: The influence of deep learning architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process-guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

Delaware River Basin↗
USGS2024

Climate-driven increases in stream metal concentrations in mineralized watersheds throughout the Colorado Rocky Mountains, USA

Increasing stream metal concentrations apparently caused by climate warming have been reported for a small number of mountain watersheds containing hydrothermally altered bedrock with abundant sulfide minerals (mineralized watersheds). Such increases are concerning and could negatively impact downstream ecosystem health, water resources, and mine-site remediation efforts. However, the pervasiveness and typical magnitude of these trends remain uncertain. We aggregated available streamwater chemistry data collected from late summer and fall over the past 40 years for 22 mineralized watersheds throughout the Colorado Rocky Mountains. Temporal trend analysis performed using the Regional Kendall Test indicates significant regional upward trends of ∼2% of the site median per year for sulfate, zinc, and copper concentrations in the 17 streams affected by acid rock drainage (ARD; median pH ≤ 5.5), equivalent to concentrations roughly doubling over the past 30 years. An examination of potential load trends utilizing streamflow data from eight “index gages” located near the sample sites provides strong support for regionally increasing sulfate and metal loads in ARD-affected streams, particularly at higher elevations. Declining streamflows are likely contributing to regionally increasing concentrations, but increasing loads appear to be on average an equal or greater contributor. Comparison of selected site characteristics with site concentration trend magnitudes shows the highest correlation for mean annual air temperature and mean elevation (R 2 of 0.42 and 0.35, respectively, with all others being ≤0.14). Future research on climate-driven controlling mechanisms should therefore focus on processes such as melting of frozen ground directly linked to site mean temperature and elevation.

Colorado↗
USGS2025

The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought

Quantitative understanding is lacking on how the depth of active groundwater circulation in bedrock affects mountain streamflow response to a multi‐year drought. We use an integrated hydrological model to explore the sensitivity of a variety of streamflow metrics to bedrock circulation depth and porosity under a plausible extreme drought scenario lasting up to 5 years. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. With drought, a deeper circulation system with higher drainable porosity more effectively buffers minimum flow and significantly limits perennial stream loss in comparison to a shallow circulation system. Streamflow buffering is accomplished through extensive groundwater storage loss. However, deeper circulation systems experience prolonged recovery from drought in comparison to storage‐limited shallow systems. Research highlights the importance of characterizing the deeper bedrock hydrogeology in mountainous watersheds to better understand and predict drought impacts on stream ecosystem health and water resource sustainability.

Colorado↗
USGS2024

Longitudinal and seasonal changes of organic matter sources through a semi-arid river-reservoir system

The quality and quantity of organic matter (OM) in a river system directly affects ecosystem health; thus, managers benefit from an in-depth understanding of the drivers and sources of OM. In the Snake River, a highly altered river-reservoir system in the semi-arid western United States, OM production and loading are key drivers of reservoir anoxia, which leads to several deleterious processes such as mercury methylation. However, sources and quantities of OM to the Snake River, and the effects of impoundment on OM moving through the river-reservoir system, are not well understood. Particulate organic carbon (POC), dissolved organic carbon (DOC), particulate nitrogen (PN), chlorophyll a (chl- a ), and δ 15 N–PN and δ 13 C–POC isotopic ratios were measured bi-weekly for over 2 years at four locations through the Snake River Hells Canyon Reservoir Complex to determine spatial and temporal patterns of OM quantities and sources. POC concentrations increased through the riverine zone upstream of the reservoirs, likely due to in situ primary production and/or inputs from tributaries and agricultural drains; then decreased through the most upstream reservoir likely due to particle settling. Isotopic ratios and other OM source indicators (δ 15 N–PN, δ 13 C–POC, POC:PN, chl- a :POC) show that the dominant source of particulate OM was phytoplankton with seasonal terrestrial/macrophytic inputs. Results highlight the effects of major tributary and agricultural drain inputs, primary production, and impoundment on OM composition and concentration through a large river-reservoir system and may inform water quality management efforts in this and similar systems.

Idaho, Oregon↗
USGS2026

Subsurface vertical connectivity shapes solute transport to montane streams: Insights from watershed-scale geophysics-informed modeling

Headwater catchments provide essential water and nutrients to downstream ecosystems. The timing and evolution of their transport is shaped in part by the “invisible” subsurface structure but have been largely unexplored at the watershed scale. This raises a fundamental question: how and to what extent does subsurface structure influence streamflow and solute transport in montane watersheds? Here, we test controls of three-dimensionally resolved subsurface structure on solute transport, which reflects source waters and water residence time. We integrate the geophysically mapped architecture of Coal Creek, a headwater catchment in the Colorado River Basin, into numerical flow-transport models. Our results reveal greater vertical connectivity elongates flow paths and enhances deep groundwater contributions to streams, therefore substantially influencing the timing and magnitude of solute transport. In contrast, watershed-integrated outlet discharge can be predicted without high-resolution subsurface information. These findings demonstrate incorporating subsurface architecture is critical for predicting ecosystem health and contaminant transport.

Colorado↗