Using monitoring and partnerships to provide management-relevant information about Chesapeake Bay rivers
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The effect of extraction methods on detecting hydrocarbon oxidation products (HOPs) in groundwater remains unclear. HOPs are polar, water-soluble byproducts of petroleum biodegradation. Our previous work showed that liquid–liquid extraction (LLE), a method commonly used in regulatory monitoring, has a significantly lower extraction efficiency for HOPs compared to solid-phase extraction (SPE). In this study, we evaluate the analytical limitations and compositional selectivity of LLE and SPE using groundwater samples from the Bemidji, MN, crude oil spill site. Optical properties were characterized using excitation–emission matrix spectroscopy (EEMs), and a three-component PARAFAC model was validated, showing consistent trends across both extracts and whole water samples. Ultrahigh-resolution mass spectrometry (UHR-MS) revealed that LLE selectively recovered aliphatic-like compounds but underrepresented more polar oxygenated HOPs. In contrast, SPE methods were more effective at isolating highly oxidized compound classes. These differences were consistent across a gradient of contamination. Overall, the LLE was less precise and less representative of polar HOPs, introducing bias in the characterization of HOPs. This study is the first to quantitatively demonstrate the compositional selectivity and analytical bias of LLE versus SPE for HOPs using combined EEM-PARAFAC and UHR-MS techniques, with implications for long-term monitoring and site assessment protocols.
Efficient operation of streamflow monitoring networks requires investments in technology and labor that provide the greatest benefits from available resources. Economic analyses comparing the costs and benefits from different types of alternatives for monitoring have not been practical to implement. Streamflow information provides a generic measure of benefits that can be incorporated into operational decisions as an objective for monitoring networks. A methodology for comparing how accuracy, monitoring period, and monitoring instead of modeling affects streamflow information is developed from information-theoretic approaches for network design but contributes three novel features: (1) a probability-difference model for conditional probability of monotonically paired variables, (2) explicit discounting of unverified information that may exceed the accuracy of streamflow records, and (3) run analysis to account for non-stationarity in streamflow probabilities. Application of the methodology to the U.S. Geological Survey streamflow monitoring network indicates the value of monitoring period to reduce the uncertainty of streamflow probabilities and, thus, increase streamflow information. The methodology has important limitations, particularly for sites with non-perennial streamflow, but demonstrates that probability difference could be used to evaluate operational alternatives to increase the efficiency of monitoring networks.
On July 9, 1926, monitoring well Oe-151 at Woodgate, New York (USGS ID 433112075091501) recorded its first groundwater data. Since then, the well has provided water data nearly continuously and has now reached a 100-year milestone for data collection. The well is part of the U.S. Geological Survey (USGS) Climate Response Network (CRN), which is a national network of wells selected to monitor natural groundwater conditions. Well Oe-151 is the first well in the network to reach a 100-year Centennial milestone.
Groundwater-quality trend assessments identify aquifers that are responding to changes in pesticide use and the compounds that may pose a threat to water availability. The U.S. Geological Survey has been monitoring pesticide concentrations in groundwater for 25 principal aquifers across the conterminous United States since 1993. The groundwater well locations represent a range of soils, climate, and landforms. The wells are used to monitor groundwater underlying selected agricultural and urban settings and groundwater used for domestic supply. This study examined changes in relative concentrations, defined here as the percentage of wells with pesticide concentrations exceeding a human health benchmark (HHB). HHBs used in this report are legally enforceable drinking-water standards and nonenforceable drinking water levels. Relative pesticide concentration increases may lead to decreased water availability, as restrictions may be put in place for groundwater used as a drinking-water source. This study focused on concentration changes in 22 pesticides that were included in laboratory analysis from 1993 to 2023. The analysis and interpretation of these pesticide concentrations in groundwater have been separated into approximate decadal intervals (decade 1 (1993–2001), decade 2 (2002–12), and decade 3 (2013–22). For one pesticide, 1,2-dibromo-3-chloropropane (DBCP), concentration data were also collected in decade 4 (2023–onward). Atrazine, deethylatrazine, alachlor, prometon, and simazine were 5 pesticides detected at moderate concentrations (greater than 10 percent of the HHB but less than or equal to the HHB). The percentage of wells that had groundwater pesticide concentrations in the moderate concentration category decreased from 7 percent in decade 1 to 2 percent in decade 3. The agricultural networks had the highest percentages of wells with moderate concentrations, and these percentages decreased from 13 percent in decade 1 to 4 percent in decade 3. Moderate concentrations in the urban networks decreased between decades 1 and 2 from 4 percent to 0 percent. No moderate concentrations occurred in the urban networks in decade 3. The percentage of wells with moderate concentrations in the domestic supply networks (1 percent) was the lowest of all the network types and did not change across the three decades. Moderate atrazine or deethylatrazine concentrations occurred across all three decades in aggregated ecoregions representing similar soils, climate, and landforms in the Semiarid West, Midcontinent, and Northeastern United States. Moderate concentrations of prometon, alachlor, and simazine also occurred in the Midcontinent, Arid West, Northeast, South Atlantic Gulf, and Semiarid West regions, but the moderate concentrations did not persist across all three decades. DBCP was the only pesticide that exceeded its respective HHB, and the exceedances occurred across all four decades. In this report, the DBCP analysis was limited to one well network in the Central Valley, California. Agricultural use of DBCP was suspended in 1977. Forty-five years after being banned, DBCP concentrations were greater than the maximum contaminant level of 2 micrograms per liter (μg/L), but the number of exceedances decreased from 50 percent to 15 percent of the samples between 1993 and 2023. This assessment of decadal groundwater pesticide concentrations provides a characterization of changes in water availability because of pesticide contamination in areas where groundwater is used as a drinking-water source. The results highlight the importance of continued long-term monitoring and assessment of groundwater pesticides to identify locations and specific compounds that may pose a potential risk to human health.
The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ET a ), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ET a , and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ET a yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ET a estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and −7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (r ah ) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agriculture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period.
Uranium (U) in rocks and soils of arid and semi-arid environments can be mobilized by irrigation and fertilization, posing environmental and health risks. Elevated U, along with selenium (Se) and nitrate (NO 3 ) co-constituents, necessitates careful monitoring and management. We developed a distributed-parameter numerical model to assess U pollution in an irrigated stream-aquifer system, applying it to a 552 km 2 region in Colorado's Lower Arkansas River Valley (LARV) over 14 years. A MODFLOW model, describing groundwater and stream flow, was coupled with an RT3D-OTIS model to portray reactive U transport. Calibration using the PESTPP-iES iterative ensemble smoother (iES) software indicated good agreement with observed U concentrations. The model revealed substantial and variable U levels across the LARV, highlighting potential hotspots and possible contributing factors, such as geological composition of the bedrock and near-surface shale and aquifer sediments derived from them, irrigation practices, and riparian landscape. U levels exceed the chronic standard (85th percentile = 30 μg/L, set by the US Environmental Protection Agency), which is the permissible regulatory threshold, in groundwater across 44 % of the region and along the river by an average factor of 2.9. Simulated average U concentrations in the non-riparian aquifer and river are 124 μg/L and 60 μg/L, respectively, compared with 112 μg/L and 62 μg/L for measured values. The average 85th percentile U concentration is 222 μg/L in the aquifer and 82 μg/L in the river. Average simulated U mass loading to the river is 0.17 kg/day per km, compared to an estimated 0.23 kg/day per km. Findings provide a baseline for comparing future simulated outcomes of alternative best management practices (BMPs) for U pollution mitigation and offer a methodology applicable to other irrigated regions.
El Paso County is the second-most populous county in Colorado and is projected to grow another 15 percent by 2030. Within El Paso County is the Upper Black Squirrel Creek Designated Groundwater Basin (Black Squirrel Basin), an area where surface water is scarce and water users rely primarily on groundwater from five different aquifers (the Upper Black Squirrel Creek alluvial aquifer and four bedrock aquifers within the Denver Basin aquifer system: the lower Dawson, Denver, Arapahoe, and Laramie-Fox Hills aquifers) to meet their needs. Currently (2024), land within the Upper Black Squirrel Creek Basin is primarily used for rural grazing and agriculture; however, municipal development is ongoing. In 2021, the U.S. Geological Survey, in cooperation with the Upper Black Squirrel Creek Ground Water Management District, began a study to establish a baseline dataset and assess the groundwater resources of the aquifers within the Black Squirrel Basin. A network of 39 wells was established in 2021; discrete groundwater-level measurements were made bimonthly. Nine of the 39 wells were equipped with pressure transducers to record hourly groundwater-level data. Seven wells had statistically significant seasonal trends, and trends at 3 wells were negative. For the discrete data, 16 wells had a significant trend for the study period, and 4 wells had negative trends. For the time-series data, 8 wells had significant trends, and 3 wells had negative trends. Potentiometric surface maps were created for this study using discrete, static groundwater levels measured in April 2023. These maps showed the estimated groundwater flow direction from the north-northwest to the south-southeast in the alluvial aquifer and from the northwest to the east-southeast for the lower Dawson and Denver aquifer wells. This study indicates the potential benefit of monitoring wells in the areas near municipal pumping. Additional monitoring could lead to a better understanding of connectivity between aquifers and be an important tool for assessing long-term sustainability of groundwater use.
This chapter reviews the state-of-the-art of geochemical volcano monitoring techniques. We cover in-situ monitoring technologies that rely on sampling fluids (direct sampling) and on instrumental analysis of the composition of such fluids in real-time (remote sensing of volcanic fluids is covered elsewhere in this book). We first review key concepts and principles in the field, and then review the results of some selected case studies and applications. We cover the large variety of fluid categories emitted by volcanoes, in both the near-(crater fumaroles and lakes, and plumes) and far-(degassing soils, groundwaters) fields. Our aim is to demonstrate the utility of measuring the chemistry of fluids released by volcanoes, and how these can help characterize volcano unrest, and eventually the increased likelihood of eruption. We conclude with a brief discussion of current challenges and knowledge gaps, and on future directions in geochemical monitoring.
Alaska's coastal communities face growing landslide hazards owing to glacier retreat and extreme weather intensified by the warming climate, yet hazard monitoring remains challenging. As part of ongoing experimental monitoring in Prince William Sound, we detected three large landslides (0.5–2.3 M m 3 ) at Surprise Inlet on 20 September 2024, within the span of an hour. These events were identified in near real-time through seismic data and later confirmed using satellite imagery, tidal records, and infrasound. The landslides generated a modest tsunami, and a 4 cm wave was recorded by a tide gauge 18 km away, marking the first recorded landslide to reach water since monitoring began in this region in 2021. Here, we examine the detection and interpretation of these landslides using multiple data sources and modeling. We demonstrate the effectiveness of this regional seismic monitoring system and show how complementary instrumentation, where available, can enhance detection capabilities.
Tectonic tremor is a weak, long-duration seismic signal often observed in subduction zones and on some other plate-bounding faults. Because of tremor's characteristically low amplitude (and low signal-to-noise) and lack of clear phase arrivals, detecting and locating tremor usually requires techniques distinct from those applied to typical earthquakes. Major advances in detection and understanding of tremor have derived in the past from a powerful combination of new data and new analysis techniques. In a recent study, Sagae et al. (2025, https://doi.org/10.1029/2025jb031348 ) exploit that combination again, developing a new machine-learning based workflow and applying it to the S-net cabled seismic network in the Japan trench offshore northern Honshu. Their approach, although complex, succeeds in detecting several times more tremor activity than earlier studies, resulting in new insights and providing a blueprint for similar approaches that could be applied elsewhere. As real-time earthquake monitoring adopts similar tools, it may present an opportunity to bring tremor monitoring into operational workflows. In turn, this could solidify tremor monitoring as a component of future operational earthquake forecasting.
Phosphorus load data were evaluated for Lake Okeechobee, Florida, for water years 1982 through 1991. Standard errors for load estimates were computed from available phosphorus concentration and daily discharge data. Components of error were associated with uncertainty in concentration and discharge data and were calculated for existing conditions and for 6 alternative load-monitoring scenarios for each of 48 distinct inflows. Benefit-cost ratios were computed for each alternative monitoring scenario at each site by dividing estimated reductions in load uncertainty by the 5-year average costs of each scenario in 1992 dollars. Absolute and marginal benefit-cost ratios were compared in an iterative optimization scheme to determine the most cost-effective combination of discharge and concentration monitoring scenarios for the lake. If the current (1992) discharge-monitoring network around the lake is maintained, the water-quality sampling at each inflow site twice each year is continued, and the nature of loading remains the same, the standard error of computed mean-annual load is estimated at about 98 metric tons per year compared to an absolute loading rate (inflows and outflows) of 530 metric tons per year. This produces a relative uncertainty of nearly 20 percent. The standard error in load can be reduced to about 20 metric tons per year (4 percent) by adopting an optimized set of monitoring alternatives at a cost of an additional $200,000 per year. The final optimized network prescribes changes to improve both concentration and discharge monitoring. These changes include the addition of intensive sampling with automatic samplers at 11 sites, the initiation of event-based sampling by observers at another 5 sites, the continuation of periodic sampling 12 times per year at 1 site, the installation of acoustic velocity meters to improve discharge gaging at 9 sites, and the improvement of a discharge rating at 1 site.
Headwaters—that is headwater streams and the watersheds they drain—strongly influence water quality, ecosystem services, and hydrologic connectivity, yet remain poorly mapped, monitored, and understood at large spatial scales. Using available spatial data to classify headwater “types” could therefore enhance the utility of sparse monitoring data to advance research and watershed planning—and help update literature-based perceptual models of headwaters as steep, forested, and minimally disturbed watersheds. Here, we analyze headwaters across the contiguous United States to quantify how their attributes (climate, topography, land cover, soils) vary regionally and differ from their downstream watersheds. Our analysis (a) identified seven dominant headwater types with distinct combinations of biophysical and climatic attributes and (b) revealed systematic contrasts with downstream watersheds, with strongest differences in attributes representing potential flowpath depth. Our findings offer a transferable framework for classifying, modeling, and managing these critical yet understudied components of the hydrologic network.
Wildlife agencies invest substantial resources in monitoring ungulate reproductive rates given ongoing concerns about population trends and their implications for management. For barren-ground caribou ( Rangifer tarandus ) in the North American Arctic, data on parturition and neonate survival have traditionally been collected using visual observations of antler retention and calf presence from small, fixed-wing aircraft. However, these surveys are weather-dependent, expensive, and dangerous, motivating the development of movement-based approaches that infer reproductive events from global positioning system (GPS) location data. We had the unique opportunity to use video camera collars to validate both antler- and movement-based methods for estimating reproduction in barren-ground caribou, using data from the Porcupine (2018–2023) and Western Arctic (2021, 2023) herds in Alaska, USA, and Yukon, Canada. For the movement-based approaches, we assessed individual-based and population-based methods, which both inferred parturition following a sharp decline in movement and inferred calf loss following a sharp increase in movement. We investigated these approaches using rarified 1-, 2-, 4-, and 8-hour fix intervals. Based on video collar observations, we found that antler retention at the onset of the calving period was highly accurate for predicting parturition (96% accuracy) but became unreliable as calving progressed, as approximately 80% of parturient females shed their antlers during the calving period. In contrast, both movement methods performed marginally for estimating caribou parturition (~70% accuracy), with estimates from the individual-based method exhibiting greater bias than those from the population-based method. The individual-based method also poorly predicted neonate survival (≤49% accuracy for detecting parturition and calf fate for the Porcupine Herd). Video data revealed that inaccuracies with the movement methods often occurred when caribou bedded during storms, rested after traversing mountainous terrain, lost a calf shortly after birth, or increased movement to evade insects. These results have important implications for management agencies using antler- and movement-based methods to estimate caribou reproduction, particularly when informing assessments of population decline or recovery. Our results demonstrate the utility of video collar data for validating reproductive monitoring approaches and provide insights into how these approaches can be improved.
Tapwater (TW) safety and sustainability are priorities in the United States. Per/polyfluoroalkyl substance(s) (PFAS) contamination is a growing public-health concern due to prolific use, widespread TW exposures, and mounting human-health concerns. Historically-rural, actively-urbanizing communities that rely on surficial-aquifer private wells incur elevated risks of unrecognized TW exposures, including PFAS, due to limited private-well monitoring and contaminant-source proliferation in urbanizing landscapes. Here, a broad-analytical-scope TW-assessment was conducted in a hydrologically-vulnerable, Mississippi River alluvial-island community, where PFAS contamination of the shallow-alluvial drinking-water aquifer has been documented, but more comprehensive contaminant characterization to inform decision-making is currently lacking. In 2021, we analyzed 510 organics, 34 inorganics, and 3 microbial groups in 11 residential and community locations to assess (1) TW risks beyond recognized PFAS issues, (2) day-to-day and year-to-year risk variability, and (3) suitability of the underlying sandstone aquifer as an alternative source to mitigate TW-PFAS exposures. Seventy-six organics and 25 inorganics were detected. Potential human-health risks of detected TW exposures were explored based on cumulative benchmark-based toxicity quotients ( ∑ TQ ). Elevated risks ( ∑ TQ ≥ 1) from organic and inorganic contaminants were observed in all alluvial-aquifer-sourced synoptic samples but not in sandstone-aquifer-sourced samples. Repeated sampling at 3 sites over 52–55 h indicated limited variability in risk over the short-term. Comparable PFAS-specific ∑ TQ for spatial-synoptic, short-term (3 days) temporal, and long-term (3 years quarterly) temporal samples indicated that synoptic results provided useful insight into the risks of TW-PFAS exposures at French Island over the long-term. No PFAS detections in sandstone-aquifer-sourced samples over a 3 year period indicated no PFAS-associated risk and supported the sandstone aquifer as an alternative drinking-water source to mitigate community TW-PFAS exposures. This study illustrated the importance of expanded contaminant monitoring of private-well TW, beyond known concerns (in this case, PFAS), to reduce the risks of a range of unrecognized contaminant exposures.
Compact streamgages requiring minimal infrastructure and equipped with submersible pressure transducers (PTs) are increasingly used to monitor small streams, yet disparate implementations obscure their accuracy under real-world conditions. This study isolated instrumentation-derived uncertainty in stage monitoring by co-locating various combinations of commercial vented and unvented PTs with seven U.S. Geological Survey (USGS) reference streamgages on small streams. Multi-year PT stage records from compact streamgages, collected and corrected following USGS protocols, were compared to concurrent reference observations. Vented PTs demonstrated an average measurement uncertainty of ±0.005 m and mean absolute percent error (MAPE) of ±0.2%. Unvented PTs exhibited higher uncertainty, averaging ±0.009 m and ±0.4% MAPE. Although both sensor types had stage errors up to ±0.3 m, 95% of vented PT and unvented PT observations were within 0.01 and 0.02 m of reference stage, respectively. Analysis of additional unvented PTs revealed stage errors of up to ±1.2% when using barometric sensors within 15 km of the in-water sensor. Propagation of stage error to discharge using reference rating models resulted in cumulative discharge MAPEs of ±4.5% for vented and ±5.5% for unvented PTs. These findings highlight PTs as practical alternatives to reference instrumentation when deployed with standardized procedures, potentially expanding access to reliable streamflow data.
The U.S. Geological Survey (USGS) operates a National Water Quality Network (NWQN) to monitor trends in groundwater quality and assess emerging contaminants of concern. It is a “network of networks” with 81 subnetworks being sampled on a decadal time scale. Each year, 8 of the subnetworks are sampled. Subnetworks have 20–30 wells each and include studies of domestic supply wells or shallow groundwater (20–50 feet deep) underlying urban land use or agricultural land use. Currently there are 2,089 wells in the network. All wells are sampled for physical properties, nutrients, major ions, trace elements, per- and polyfluoroalkyl substances (PFAS), and a subset of wells are sampled for pesticides, volatile organic compounds, radionuclides, and microbiological contaminants.
From July to October 2022, a non-eruptive volcanic earthquake swarm occurred within ~15 km of Taʻū Island, located in eastern American Samoa. Felt reports from local residents were the only available information about the swarm when it started, as American Samoa lacked a seismic monitoring network. We developed a consistent single-station catalog for the entire swarm, using seismic data from the nearest station IU.AFI, ~250 km away. We applied the EQTransformer deep-learning model (Mousavi et al., 2020), automatically picking Pn and Sn arrivals on IU.AFI continuous data. We retained only events with Sn-Pn times of 22.5–25 seconds, consistent with the expected locations based on felt reports, then detected smaller swarm events with subsequent template-matching. This single-station catalog characterized the swarm’s onset and escalation to peak activity before a multi-agency field response team installed a local seismic network in mid-August 2022. This permanent seismic network captured the swarm’s decline. EQTransformer identified short S-P times on the first two locally deployed seismometers, both Raspberry Shake sensors, to constrain the swarm’s distance from Taʻū Island. Modern seismological processing methods, combined with basic observations such as felt reports, can quickly contribute useful information during an earthquake response in a poorly monitored region.