The California Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP) investigated water quality of groundwater resources used for drinking-water supplies in the Madera-Chowchilla, Kings, Kaweah, Tule, and Tulare Lake groundwater subbasins of the southeastern San Joaquin Valley during 2013–15. The study focused primarily on groundwater resources used for domestic-supply wells in the southeastern San Joaquin Valley (SESJV-D), which correspond mostly to shallower parts of aquifer systems, compared to the groundwater resources used for public-supply wells in the southeastern San Joaquin Valley (SESJV-P). The investigation had three components: (1) characterization of the status of water quality in the SESJV-D, (2) comparison between water quality in the SESJV-D and SESJV-P, and (3) identification of natural and anthropogenic factors that potentially could affect water quality in these resources.
The characterization of water quality in the SESJV-D was based on data collected from 198 domestic wells sampled during 2013–15 by the U.S. Geological Survey (USGS); characterization of water quality in the SESJV-P was based on data collected from 124 wells sampled by the USGS during 2005–18 and an additional 1,577 wells with publicly available data reported to the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW). Measured concentrations were compared to regulatory and non-regulatory drinking-water quality benchmarks. A grid-based method was used to estimate the areal proportions of each study area and the whole southeastern San Joaquin Valley with high (greater than benchmark concentration), moderate (greater than half of the benchmark for inorganic and one-tenth of the benchmark for organic), and low concentrations relative to those benchmarks.
Natural and anthropogenic factors that could affect groundwater quality for the SESJV-D were identified in the context of the hydrogeologic setting of the southeastern San Joaquin Valley. The considered factors represented hydrologic conditions and position in the groundwater flow system (well depth, lateral position, presence of hydric soils, percentage of coarse-grained sediment, and aridity index), land-use characteristics (percentages of agricultural, urban, and natural land use, percentage of orchard or vineyard land use, and densities of septic tanks and underground storage tanks near the wells), and geochemical conditions (groundwater age class, oxidation-reduction class, pH, and dissolved oxygen and bicarbonate concentrations). Factors are compared between SESJV-D and SESJV-P at the scale of the five study areas.
One or more inorganic constituents with U.S. Environmental Protection Agency (EPA) or California maximum contaminant levels (MCLs) were detected at high concentrations in 47 percent of the SESJV-D and in 32 percent of the SESJV-P. The inorganic constituents most commonly present at high concentrations in the SESJV-D were nitrate, uranium, and arsenic. Within the SESJV-D, the proportion of the study area with high concentrations of inorganic constituents ranged from 19 percent in Madera-Chowchilla to 60 percent in Kings and Tulare Lake. One or more inorganic constituents with California State Water Resources Control Board Division of Drinking Water secondary maximum contaminant levels (SMCL-CAs) were detected at high concentrations in 14 percent of the SESJV-D and in 19 percent of the SESJV-P. The constituents most commonly present at high concentrations were manganese, iron, and total dissolved solids (TDS). Although the proportion of SESJV-D and SESJV-P with high concentrations of TDS greater than the upper SMCL were similar at 4 percent, the proportion of the SESJV-D with moderate concentrations (between the recommended and upper SMCL-CA), 30 percent, was greater than the proportion of the SESJV-P with moderate concentrations, 12 percent.
One or more organic constituents with MCLs were present at high concentrations in 19 percent of the SESJV-D and in 12 percent of the SESJV-P. All the constituents detected at high concentrations in the SESJV-D were fumigants, primarily 1,2,3-trichloropropane (1,2,3-TCP) and 1,2-dibromo-3-chloropropane (DBCP). Fumigants also were the constituents most commonly detected at high concentrations in the SESJV-P, although high concentrations of solvents also were detected. The SESJV-D dataset included analysis of many organic constituents without MCL benchmarks and with detection levels far below drinking water benchmark concentrations; detections at these low concentrations can be used as tracers of anthropogenic influence on groundwater. Pesticides and degradates of pesticides were detected in 60 percent of the SESJV-D; the most frequently detected pesticides were the herbicides simazine, didealkylatrazine (CAAT, a degradate of simazine and atrazine), diuron, and bromacil.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245009","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Burow, K.R., Shelton, J.L., and Fram, M.S., 2024, Status of water quality in groundwater resources used for drinking-water supply in the southeastern San Joaquin Valley, 2013–15—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2024–5009, 135 p., https://doi.org/10.3133/sir20245009.","productDescription":"Report: xiii, 135 p.; Data Release","numberOfPages":"136","onlineOnly":"Y","ipdsId":"IP-094434","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":428122,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245009/full"},{"id":493742,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116370.htm","linkFileType":{"id":5,"text":"html"}},{"id":428123,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DCTLXV","text":"USGS Data Release","description":"Balkan, M., Burow, K.R., and Shelton, J.L., and Fram, M.S., 2024, Data sets for: Status of water quality in groundwater resources used for drinking water supply in the southeast San Joaquin Valley, 2013–2015—California GAMA Priority Basin Project: U.S. Geological Survey data release, accessed January, 22, 2024, at https://doi.org/10.5066/P9DCTLXV","linkHelpText":"Data sets for: Status of water quality in groundwater resources used for drinking water supply in the southeast San Joaquin Valley, 2013–2015—California GAMA Priority Basin Project"},{"id":428120,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5009/sir20245009.xml"},{"id":428118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5009/covrthb.jpg"},{"id":428121,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5009/images"},{"id":428119,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5009/sir20245009.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.36728753741212,\n 37.719936264455484\n ],\n [\n -121.36728753741212,\n 35.78355104851377\n ],\n [\n -118.20322503741215,\n 35.78355104851377\n ],\n [\n -118.20322503741215,\n 37.719936264455484\n ],\n [\n -121.36728753741212,\n 37.719936264455484\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Wildlife species confront threats from climate and land use change, exacerbating the influence of extreme climatic events on populations and biodiversity. Migratory waterbirds are especially vulnerable to hydrological drought via reduced availability of surface water habitats. We assessed how whooping cranes (Grus americana) modified habitat use and migration strategies during drought to evaluate their resilience to changing conditions and adaptive capacity. We categorized >8000 night-roost sites used by 146 cranes from 2010 to 2022 and examined relative use during non-drought, moderate drought, and extreme drought conditions. We found cultivated and uncultivated palustrine and lacustrine wetlands were generally used less during droughts than non-drought conditions. Conversely, impounded palustrine and lacustrine systems and rivers served more frequently as drought refugia (i.e., used more during drought than non-drought conditions). Night roosts occurred primarily on private lands (86% overall); public land use decreased with latitude and increased with drought severity, with greatest use (56%) occurring during severe autumn drought in the southern Great Plains. Quantifying use of identified critical habitats in the United States indicated that Cheyenne Bottoms State Waterfowl Management Area and Quivira National Wildlife Refuge were used less during drought, and the Central Platte River and Salt Plains National Wildlife Refuge received similar use during drought compared to non-drought conditions. Our findings provide insights into compensatory use of habitats, where impounded surface water may function in a complementary fashion with natural wetlands. Collectively, these and other types of wetlands distributed across the migration corridor provided a reliable network of habitat available across the Great Plains. A diversity of wetlands available during variable environmental conditions would be useful in supporting continued recovery of whooping cranes and likely have benefits for a wide array of migratory birds.
Environmental DNA (eDNA) sampling is an increasingly important tool for answering ecological questions and informing aquatic species management; however, several factors currently limit the reliability of ecological inference from eDNA sampling. Two particular challenges are 1) determining species source location(s) and 2) accurately and precisely measuring low concentration eDNA samples in the presence of multiple sources of ecological and measurement variability. The recently introduced eDNA Integrating Transport and Hydrology (eDITH) model provides a framework for relating eDNA measurements to source locations in riverine networks, but little empirical work has been done to test and refine model assumptions or accommodate low concentration samples, that can be systematically undermeasured. To better understand eDNA fate and transport dynamics and our ability to reliably quantify low concentration samples, we developed a hierarchical model and used it to evaluate a fate and transport experiment. Our model addresses several low concentration challenges by modeling the number of copies in each PCR replicate as a latent variable with a count distribution and conditioning detection and quantification on replicate copy number. We provide evidence that the eDNA removal rate declined through time, estimating that over 80% of eDNA was removed over the first 10 meters, traversed in 41 seconds. After this initial period of rapid decay, eDNA decayed slowly with consistent detection through our farthest site 1km from the release location, traversed in 250 seconds. Our model further allowed us to detect extra-Poisson variation in the allocation of copies to replicates. We extended our hierarchical model to accommodate a continuous effect of inhibitors and used our model to provide evidence for the inhibitor hypothesis and explore the potential implications. While our model is not a panacea for all challenges faced when quantifying low-concentration eDNA samples, it provides a framework for a more complete accounting of uncertainty.
","language":"English","publisher":"BiorXiv","doi":"10.1101/2024.03.27.586987","usgsCitation":"Augustine, B., Hutchins, P., Jones-Slobodian, D.N., Williams, J., Leinonen, E., and Sepulveda, A., 2024, A hierarchical model for eDNA fate and transport dynamics accommodating low concentration samples: BioRxiv, https://doi.org/10.1101/2024.03.27.586987.","productDescription":"61 p.","ipdsId":"IP-161502","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":459969,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2024.03.27.586987","text":"External Repository"},{"id":465483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Augustine, Ben 0000-0001-6935-6361","orcid":"https://orcid.org/0000-0001-6935-6361","contributorId":245736,"corporation":false,"usgs":true,"family":"Augustine","given":"Ben","email":"","affiliations":[{"id":49304,"text":"Department of Natural Resources, Cornell University","active":true,"usgs":false}],"preferred":false,"id":912510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hutchins, Patrick Ross 0000-0001-5232-0821","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":256658,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick Ross","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones-Slobodian, Devin Nicole 0000-0001-9215-2930","orcid":"https://orcid.org/0000-0001-9215-2930","contributorId":305357,"corporation":false,"usgs":true,"family":"Jones-Slobodian","given":"Devin","middleInitial":"Nicole","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Jacob R.","contributorId":343977,"corporation":false,"usgs":false,"family":"Williams","given":"Jacob R.","affiliations":[{"id":82269,"text":"Turner Institute of Ecoagriculture","active":true,"usgs":false}],"preferred":false,"id":912513,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leinonen, Eric","contributorId":343978,"corporation":false,"usgs":false,"family":"Leinonen","given":"Eric","affiliations":[{"id":82269,"text":"Turner Institute of Ecoagriculture","active":true,"usgs":false}],"preferred":false,"id":912514,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912515,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70253177,"text":"70253177 - 2024 - Groundwater sustainability and land subsidence in California’s Central Valley","interactions":[],"lastModifiedDate":"2025-05-09T19:57:50.689646","indexId":"70253177","displayToPublicDate":"2024-04-22T06:38:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater sustainability and land subsidence in California’s Central Valley","docAbstract":"The application of Long Short-Term Memory (LSTM) models for streamflow predictions has been an area of rapid development, supported by advancements in computing technology, increasing availability of spatiotemporal data, and availability of historical data that allows for training data-driven LSTM models. Several studies have focused on improving the performance of LSTM models; however, few studies have assessed the applicability of these LSTM models across different hydroclimate regions. This study investigated the single-basin trained local (one model for each basin), multi-basin trained regional (one model for one region), and grand (one model for several regions) models for predicting daily streamflow in water-limited Great Basin (18 basins) and energy-limited New England (27 basins) regions in the United States using the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) data set. The results show a general pattern of higher accuracy in daily streamflow predictions from the regional model when compared to local or grand models for most basins in the New England region. For the Great Basin region, local models provided smaller errors for most basins and substantially lower for those basins with relatively larger errors from the regional and grand models. The evaluation of one-layer and three-layer LSTM network architectures trained with 1-day lag information indicates that the addition of model complexity by increasing the number of layers may not necessarily increase the model skill for improving streamflow predictions. Findings from our study highlight the strengths and limitations of LSTM models across contrasting hydroclimate regions in the United States, which could be useful for local and regional scale decisions using standalone or potential integration of data-driven LSTM models with physics-based hydrological models.
Climate change and climate variability impacts such as rising sea levels have the potential to exacerbate seawater intrusion and the strain on coastal freshwater resources in already stressed groundwater basins such as those in the Pajaro Valley groundwater basin, California. Regional hydrologic models are often coupled with climate projections to forecast future hydrologic conditions and inform adaptive resources management strategies. However, there is high uncertainty in the future projections of water resources due to uncertainties from downscaling global general circulation models (GCMs) to local scale climate change projections, future land use changes, and the inherent uncertainty of developed hydrologic models. Future climate projections and the magnitude of their influence on modeled hydrologic drivers are highly variable. Therefore, to develop a forecast model, an ensemble of different projections can be used to capture a wider range of basin responses and the associated uncertainties in the modeled forecasts. Understanding the reliability and uncertainty of forecasts is important for developing climate adaptation strategies such as developing protective thresholds, particularly at the basin scale where the impacts are felt, and adaptation is implemented. To demonstrate this, an uncertainty analysis of groundwater level and seawater intrusion forecasts for the Pajaro Valley groundwater basin was performed using an ensemble of three future climate projections with the Pajaro Valley Integrated Hydrologic Model (PVIHM) and the first-order second moment (FOSM) method. FOSM uncertainty analysis of hydrologic forecasts across a multi-GCM climate ensemble provides an upper and lower bound of potential impacts of climate change on sustainability targets related to mitigating seawater intrusion. The groundwater level forecasts’ narrow range of variability can help policymakers in adaptation planning by constraining possible outcomes to a focused range for risk-management decisions. However, less than one-third of groundwater level forecasts met the current protection thresholds to prevent chronic lowering of groundwater. Therefore, sustainability targets may need to be reassessed. Relative to groundwater level changes, the seawater intrusion forecasts had larger uncertainty due to the GCM climate projections and the simulated hydrologic response that were compounded by the propagation of scaling and bias from the GCMs and model simplifications in simulating the coastal boundary.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.131226","usgsCitation":"Earll, M.M., Henson, W.R., Lockwood, B., and Boyce, S.E., 2024, Evaluating seawater intrusion forecast uncertainty under climate change in the Pajaro Valley, California: Journal of Hydrology, v. 636, 131226, 17 p., https://doi.org/10.1016/j.jhydrol.2024.131226.","productDescription":"131226, 17 p.","ipdsId":"IP-118873","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":484763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Pajaro Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.92596725165258,\n 37.07637156314877\n ],\n [\n -121.92596725165258,\n 36.716849299804664\n ],\n [\n -121.42957626930755,\n 36.716849299804664\n ],\n [\n -121.42957626930755,\n 37.07637156314877\n ],\n [\n -121.92596725165258,\n 37.07637156314877\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"636","noUsgsAuthors":false,"publicationDate":"2024-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Earll, Marisa M. 0000-0002-4367-2013 mearll@usgs.gov","orcid":"https://orcid.org/0000-0002-4367-2013","contributorId":223723,"corporation":false,"usgs":true,"family":"Earll","given":"Marisa","email":"mearll@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henson, Wesley R. 0000-0003-4962-5565 whenson@usgs.gov","orcid":"https://orcid.org/0000-0003-4962-5565","contributorId":384,"corporation":false,"usgs":true,"family":"Henson","given":"Wesley","email":"whenson@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lockwood, Brian","contributorId":80202,"corporation":false,"usgs":true,"family":"Lockwood","given":"Brian","email":"","affiliations":[],"preferred":false,"id":933960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933820,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252963,"text":"sir20245018 - 2024 - Hydrologic study of green infrastructure in poorly drained urbanized soils at RecoveryPark, Detroit, Michigan, 2014–21","interactions":[],"lastModifiedDate":"2026-02-03T17:56:18.918058","indexId":"sir20245018","displayToPublicDate":"2024-04-15T13:10:07","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5018","displayTitle":"Hydrologic Study of Green Infrastructure in Poorly Drained Urbanized Soils at RecoveryPark, Detroit, Michigan, 2014–21","title":"Hydrologic study of green infrastructure in poorly drained urbanized soils at RecoveryPark, Detroit, Michigan, 2014–21","docAbstract":"Uncontrolled stormwater runoff volume is a legacy stressor on sewer-system capacity that is further compromised by the effects of aging infrastructure. Green stormwater infrastructure (GSI) has been used in a variety of designs and configurations (for example, bioretention) with the goal of increasing evapotranspiration and infiltration in the local water cycle. In practice, GSIs have variable effectiveness in reducing runoff volume.
An urban residential site near Detroit, Michigan, called RecoveryPark was monitored for 8 years before and after GSI construction to evaluate how effectively the GSI reduced volumes of stormwater flowing to Detroit’s Water Resource Recovery Facility through combined sewer systems. In addition to the GSI, the study site included an urban farm where salad crops were grown in hoop houses. The monitoring approach was to characterize the urban water cycle through high-frequency measurements of inflows and outflows. Datasets included meteorological data, soils and sediment characteristics, groundwater levels, flows within the combined sewer system, and soils and water chemistry with specific focus on the disposition of road salt.
Although land cover within the RecoveryPark sewershed was high-density residential in the 1950s, the sewershed included only one residence within the 8.74-acre sewershed during this study. Measurements of annual precipitation at the site exceeded long-term annual averages by more than 10 inches during 3 of the 8 years of study. Potential evapotranspiration was often greater than the measured precipitation that averaged 28–34 inches per year. As compared to underlying clay-rich sediments, soils data indicated relatively permeable sediments near land surface with estimated hydraulic conductivity of 0.75 inches per hour; however, these values decreased with increasing depth. Groundwater-level data revealed increases in groundwater storage as indicated by increases in seasonal groundwater levels and development of a groundwater mound adjacent to the GSI. These increases in groundwater levels were directly adjacent to swales designed to infiltrate stormwater and only became evident after installing the GSI.
Flows within the combined sewer system included rainwater, septic effluent, groundwater infiltration, leakage from water-supply lines, and release of water stored in abandoned foundations. Dry-weather flows (no rain fell within the prior 3 days) averaged 7–10 gallons per minute, which were much greater than flows estimated by septic outflow alone. A set of estimated water budgets were compiled, and results showed large discrepancies in unaccounted flows. To further examine these discrepancies, dye-tracing within the combined sewer system helped examine the sources of water by relating flow volumes to drainage area. For one of the monitoring sites within the combined sewer system along the southeast side of the study area, flows estimated by dye concentrations were more than 10 percent greater than those measured by standard methods. Through peak-flow-regression analysis, a minimum of 2.4 million gallons of water per year were infiltrated or lost to evapotranspiration because of GSI construction. After site modifications were made by excavating gravel drains to improve drainage characteristics, estimated stormwater volumes within the combined sewer system returned to near preconstruction levels. The GSI was effectively bypassed to address slow infiltration rates and standing water; the bypass all but eliminated the potential benefits of volume reduction.
Late in the project, a water-quality study was added to examine the transport of road salt and associated chloride within the GSI and the combined sewer system. Continuous specific conductance was used as a surrogate for chloride concentrations to estimate that 2,790 pounds of dissolved chloride passed through the sewershed during the winter months of late 2020 through early 2021. These data were collected after GSI modification, therefore most, if not all, of the chloride was transported directly to Detroit’s Water Resource Recovery Facility via the combined sewer system. Mixing diagrams using chloride and bromide concentrations of road salt, potable water, rainwater, groundwater, and water from the combined sewer system confirmed that water within the combined sewer system is a mix of these sources. The poor condition of the combined sewer system pipes and resulting unaccounted inflows added to the challenge of accurately monitoring and identifying sources and sinks of water within the RecoveryPark sewershed.
Our research results suggest that—along with clear and quantifiable objectives—the catchment and site conditions should be well-characterized before determining the GSI design. In addition, the work presented in this report provides implications and lessons learned for effectiveness and future studies of GSI in urban settings. These efforts can be improved through increased communication between stakeholders, use of high-quality soils in GSI that have suitable hydraulic characteristics, redundant data-collection networks for critical data streams, and focusing meteorological-data collection within the GSI to obtain relevant evapotranspiration data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245018","collaboration":"Prepared in cooperation with United States Environmental Protection Agency","usgsCitation":"Haefner, R.J., Hoard, C.J., and Shuster, W., 2024, Hydrologic study of green infrastructure in poorly drained urbanized soils at RecoveryPark, Detroit, Michigan, 2014–21: U.S. Geological Survey Scientific Investigations Report 2024–5018, 29 p., https://doi.org/10.3133/sir20245018.","productDescription":"Report: viii, 29 p.; Dataset; 2 Data Releases","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154558","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499447,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116361.htm","linkFileType":{"id":5,"text":"html"}},{"id":427762,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96GBEXW","text":"USGS data release","linkHelpText":"Select pipe-flow monitoring data from RecoveryPark in Detroit, MI (2015–2016)"},{"id":427761,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FP21N9","text":"USGS data release","linkHelpText":"Select pipe-flow monitoring data from RecoveryPark in Detroit, MI (2015–2021)"},{"id":427759,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5018/images/"},{"id":427758,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5018/sir20245018.XML"},{"id":427757,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5018/sir20245018.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5018"},{"id":427756,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5018/coverthb.jpg"},{"id":427763,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":427760,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245018/full"}],"country":"United States","state":"Michigan","city":"Detroit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.0495294507557,\n 42.37379642239543\n ],\n [\n -83.0495294507557,\n 42.36384762590089\n ],\n [\n -83.0332708011147,\n 42.36384762590089\n ],\n [\n -83.0332708011147,\n 42.37379642239543\n ],\n [\n -83.0495294507557,\n 42.37379642239543\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1992 Folwell Avenue
St. Paul, MN 55108
This report characterizes changes in peak streamflow in Missouri and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-streamflow frequency analysis in Missouri. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend periods (100 years, water years 1921–2020; 75 years, 1946–2020; 50 years, 1971–2020; and 30 years, 1991–2020) for trends, change points (abrupt changes in the streamflow time series), and other statistical properties indicative of changing conditions. Peak streamflow magnitudes generally exhibit upward trends across the State for the 100-, 75-, and 50-year trend periods and only in southern Missouri for the 30-year trend period. The medians of the trend magnitudes (normalized by median peak streamflow) range from a 10-percent increase during the 30-year trend period to a 40-percent increase during the 100-year trend period. Changes in the 90-percent quantile of peak streamflow, which correspond to the 10-percent exceedance probability often used for the design of drainage structures, are not as substantial or widespread, showing consistent increases mainly in the southern part of the State in the 50- and 30-year trend periods. Streamgages with trends in peak streamflow often also have change points, or abrupt changes, in streamflow magnitude. Change points in peak streamflows generally follow that of the peak streamflow trends, with upward change points throughout most of the State at the 100- and 75-year trend periods and in southern Missouri at the 30-year trend period. Temporally, clusters upward of change points are observed in the 1970s through 1980s for the 100-, 75-, and 50-year trend periods and around 2006 and 2007 for the 50- and 30-year trend periods.
A peaks-over-threshold analysis, which evaluates changes in the frequency of peak streamflows over a certain threshold, indicates that high flows have increased in frequency at 50 to 64 percent of streamgages in the 100- and 75-year trend periods. Most streamgages in the 50- and 30-year trend periods exhibit no change. Although the frequency of high flows has increased at some streamgages and trend periods in Missouri, these increases are not as widespread as the increases in the magnitude of peak streamflow.
Upward trends in observed temperature and observed annual precipitation dominate in all trend periods, with no downward trends in precipitation and only two somewhat likely downward trends in temperature for the 100-year trend period. Increases in annual precipitation mostly are limited to southern Missouri for the 30-year trend period. The proportion of precipitation falling as snow has largely decreased in the study basins across the State, which is expected in response to increasing temperature. Upward trends in modeled annual runoff, which in this study incorporates only the effects of climatic variation, are observed in the same geographic areas where there are increases in observed annual precipitation. When peak streamflow and climatic trends are considered together, widespread upward trends in peak streamflows for the 100-, 75-, and 50-year trend periods and for the 30-year trend period mainly in southern Missouri (encompassing both trends and abrupt change) appear to be driven largely by increases in precipitation based on spatial patterns and statistical relations.
The prevalence of nonstationarity in peak streamflow in Missouri has important implications for peak-flow frequency analysis. Winter and spring precipitation and the occurrence of extreme precipitation events are expected to increase across the State. If precipitation continues to increase as expected, peak-flow frequency estimates based on older records may no longer represent the hydrologic regime of today, and methods for nonstationary peak-flow frequency analysis may be needed.
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\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Groundwater-resource quality is assumed to be less responsive to drought compared to that of surface water due to relatively long transit times of recharge to drinking-supply wells. Here, we evidence dynamic perturbations in aquifer pressure dynamics during drought and subsequent recovery periods cause dramatic shifts in groundwater quality on a basin scale. We used a novel application of time-series clustering on annual nitrate anomalies at >450 public-supply wells (PSWs) across California's San Joaquin Valley during 2000–22 to group sub-populations of wells with similar water-quality responses to drought. Additionally, we statistically evaluated the direction and magnitude of multi-constituent water-quality changes across the San Joaquin Valley using a broader dataset of >3000 PSWs with data during two select hydrologic stress periods representing an extreme drought (2012–16) and subsequent recovery (2016–19). Results of time-series clustering and stress-period change analyses corroborate a predominant regional response to pumping stress characterized by increased concentrations of anthropogenic constituents (nitrate, total dissolved solids) and decreased concentrations of geogenic constituents (arsenic, fluoride), which largely reversed during recovery. Cluster analysis also identified a secondary, less commonly occurring group of PSWs where nitrate decreased during drought, but explanatory factor analysis was not able to discern hydrogeologic drivers for these two divergent response patterns. Long-term tracer data support the hypothesis that the predominant regional signal of nitrate increase during drought is caused by enhanced capture of modern-aged groundwater by PSWs during periods of pumping stress, which can drive rapid changes in water quality on seasonal and multiannual timescales. Pumping-induced migration of modern, oxic groundwater to depth during drought may affect geochemical conditions in deeper portions of regional aquifers controlling the mobility of geogenic contaminants over the long term.
Knowledge of the spatial and temporal distribution of surface water is important for water resource management, flood risk assessment, monitoring ecosystem health, constraining estimates of biogeochemical cycles and understanding our climate. While global-scale spatiotemporal change detection of surface water has significantly improved in recent years due to planetary-scale remote sensing and computing, it has remained challenging to distinguish the changing characteristics of rivers and lakes. Here we analyze the spatial extent of permanent and seasonal rivers and lakes globally over the past 38 years based on new data of river system extents and surface water trends. Results show that while the total permanent surface area of both rivers and lakes has remained relatively constant, the areas with intermittent seasonal coverage have increased by 12 % and 27 % for rivers and lakes, respectively. The increase is statistically significant in over 84 % of global water catchments based on Spearman's rank correlations (rho) above 0.05 and p values less than 0.05. The seasonal river extent is nearly 32 % larger than the previously observed annual mean river extent, suggesting large seasonal variations that impact not only ecosystem health but also estimations of terrestrial biogeochemical cycles of carbon. The outcomes of our analysis are shared as the Surface Area of Rivers and Lakes (SARL) database, serving as a valuable resource for monitoring and research of hydrological cycles, ecosystem accounting, and water management.
The frequency and extent of wildfires have increased in recent decades with immediate and cascading effects on water availability in many regions of the world. Precipitation is used as primary input to hydrologic models and is a critical driver of post-wildfire hydrologic hazards including debris flows, flash floods, water-quality effects, and reservoir sedimentation. These models are valuable tools for understanding the hydrologic response to wildfire but require accurate precipitation data at suitable spatial and temporal resolutions. Wildfires often occur in data-sparse, headwater catchments in complex terrain, and post-wildfire hydrologic effects are particularly sensitive to high-intensity, short-duration precipitation events, which are highly variable and difficult to measure or estimate. Therefore, the assessment and prediction of wildfire-induced changes to watershed hydrology, including the associated effects on ecosystems and communities, are complicated by uncertainty in precipitation data. When direct measurements of precipitation are not available, datasets of indirect measurements or estimates are often used. Choosing the most appropriate precipitation dataset can be difficult as different datasets have unique trade-offs in terms of spatial and temporal accuracy, resolution, and completeness. Here, we outline the challenges and opportunities associated with different precipitation datasets as they apply to post-wildfire hydrologic models and modeling objectives. We highlight the need for expanded precipitation gage deployment in wildfire-prone areas and discuss potential opportunities for future research and the integration of precipitation data from disparate sources into a common hydrologic modeling framework.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1728","usgsCitation":"Partridge, T.F., Johnson, Z., Sleeter, R., Qi, S.L., Walvoord, M.A., Murphy, S.F., Peterman-Phipps, C.L., and Ebel, B., 2024, Opportunities and challenges for precipitation forcing data in post-wildfire hydrologic modeling applications: WIREs Water, v. 11, no. 5, e1728, 27 p., https://doi.org/10.1002/wat2.1728.","productDescription":"e1728, 27 p.","ipdsId":"IP-155206","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":427613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439910,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1728","text":"Publisher Index Page"}],"volume":"11","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":898395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sleeter, Rachel 0000-0003-3477-0436 rsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-0436","contributorId":666,"corporation":false,"usgs":true,"family":"Sleeter","given":"Rachel","email":"rsleeter@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":898396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peterman-Phipps, Cara L. 0000-0003-1822-2552","orcid":"https://orcid.org/0000-0003-1822-2552","contributorId":259166,"corporation":false,"usgs":true,"family":"Peterman-Phipps","given":"Cara","email":"","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":898400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898401,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70253068,"text":"70253068 - 2024 - Eutrophication saturates surface elevation change potential in tidal mangrove forests","interactions":[],"lastModifiedDate":"2024-08-26T14:38:23.436483","indexId":"70253068","displayToPublicDate":"2024-04-06T06:52:04","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Eutrophication saturates surface elevation change potential in tidal mangrove forests","docAbstract":"Coastal mangrove forests are at risk of being submerged due to sea-level rise (SLR). However, mangroves have persisted with changing sea levels due to a variety of biotic and physical feedback mechanisms that allow them to gain and maintain relative soil surface elevation. Therefore, mangrove’s resilience to SLR is dependent upon their ability to build soil elevation at a rate that tracks with SLR, or well-enough to migrate inland. Anthropogenic disturbances, such as altered hydrology and eutrophication, can degrade mangrove forest health and compromise this land building process, placing mangroves at greater risk. Much of Florida’s mangroves are adjacent to highly urbanized areas that produce nutrient-loaded runoff. This study assesses how experimental nutrient inputs in the eutrophic Caloosahatchee Estuary influence the soil surface elevation change (SEC) in two distinct mangrove zones. Annual rates of SEC were reduced by phosphorus additions and differed by mangrove zone, ranging from 0.67 ± 0.59 to 2.13 ± 0.61 and 4.21 ± 0.58 to 6.39 ± 0.59 mm year−1 in the fringe and basin zone, respectively. This suggests that eutrophication can reduce the maximum potential SEC response to SLR and that a mangrove forest’s vulnerability to SLR is not uniform throughout forest but can differ by mangrove zone.
From 2018 through 2020, the U.S. Geological Survey, in cooperation with the Air Force Civil Engineering Center, conducted an integrated study of the Fountain Creek alluvial aquifer located near Colorado Springs, Colorado. The objective of the study was to characterize hydrologic conditions for the alluvial aquifer pertinent to the potential for transport of solutes. Specific goals of this report were to characterize the groundwater hydrology of the area, to quantify groundwater and surface-water interactions, to estimate hydraulic properties of the aquifer using aquifer testing, and to complete numerical simulations of groundwater flow.
Synoptic groundwater-level elevation measurements completed throughout this study, and as part of other U.S. Geological Survey programs between 1994 and 2020, indicate groundwater-level elevations fluctuate on annual and interannual timeframes. Groundwater-level fluctuations likely were caused by temporally variable groundwater recharge and discharge components in the area, with many wells showing maximum groundwater-level elevations during the winter months (November through March). From an interannual perspective, groundwater-level fluctuations appear to have reached maximum values during 2000 to 2003, decreased during 2003 to 2006, and remained relatively constant since that time, with the exception of several wells which have displayed rising groundwater-level elevations since 2018. Spatial evaluation of groundwater-level elevations indicates groundwater flow is generally from northeast to southwest within the vicinity of several alluvial paleochannels occurring along the northeastern margin of the aquifer. Within the center of the aquifer along Fountain Creek, groundwater flow is generally from north to south, approximately paralleling surface-water flow. To quantitatively understand the potential effect of groundwater recharge and groundwater pumping on fluctuations in groundwater-level elevation, a statistical transfer-function-noise model was applied. Results of the statistical model indicate throughout most of the aquifer, fluctuations were primarily the result of recharge seasonality. In the main stem of the aquifer where groundwater pumping wells were more concentrated, however, groundwater-level elevation fluctuations were also attributable to groundwater pumping through time.
Three-dimensional evaluation of the aquifer geometry near Fountain Creek was combined with synoptic streamflow measurement and accounting of stream gains and losses to evaluate groundwater and surface-water interactions in the study area. Streamflow gain or loss calculations indicate Fountain Creek both gains from and loses flow to the alluvial aquifer, and gaining or losing reaches of the stream may be partially controlled by the depth to bedrock near the stream. Reaches with streamflow gains tend to coincide with areas where the estimated depth to bedrock is decreasing, meaning the alluvial aquifer is likely thinning in these areas and groundwater-flow paths may be converging and discharging groundwater to the stream. Losing reaches tended to coincide with locally greater depth to bedrock where the alluvial aquifer is likely thicker and has greater storage potential for surface water lost from Fountain Creek.
Results of aquifer testing indicate hydraulic conductivity, estimated from slug tests and single-well pumping tests, ranged from 0.32 to 1,410 feet per day (ft/d) and 4.13 to 664 ft/d, respectively. These results are similar to the range of values from previous aquifer tests in the study area. Hydraulic conductivities from aquifer testing for this study were generally greater than the estimates of previous slug tests and had a mean value less than the estimates from previous pumping tests. Spatial evaluation of aquifer testing results indicates hydraulic conductivity tends to be greater in the main stem of the alluvial aquifer and lower in paleochannels upgradient from the main stem of the aquifer. The spatial variation in hydraulic conductivity may be attributed to the geomorphologic processes that formed the alluvial aquifer. Compacted sediment in the paleochannels has not been potentially transported sufficient distance to cause grain-size sorting, resulting in a poorly sorted deposit and lower hydraulic conductivities. In the central portion of the alluvial aquifer, near Fountain Creek, the sediments have been transported farther from their source areas and are likely better sorted, removing finer grained sediments that would cause lower hydraulic conductivity.
A numerical groundwater-flow model was calibrated for the Fountain Creek alluvial aquifer for 2000–19 to simulate water-budget components, groundwater-flow directions, and groundwater-flow paths. The model simulated precipitation recharge, groundwater and surface-water interactions, evapotranspiration, high-volume groundwater pumping by pumping wells, and external inflows and outflows occurring along the boundaries of the alluvial aquifer. Model calibration was completed using manual and automated approaches, the latter of which assisted in quantifying model results sensitivity to input parameters. The calibrated model corresponds well with groundwater-level elevation observations, with a mean residual (observed minus simulated groundwater-level elevation) equal to −0.60 feet. Simulated groundwater base flow to streams was typically within 10 percent of base flow estimated by independent methods. Groundwater and surface-water interactions represented the largest water-budget components of the aquifer, with the second largest groundwater discharge component coming from pumping wells. Groundwater and surface-water interactions represent both the largest gain and loss terms in the water budget, because these interactions differ spatially, meaning in some areas of the model domain groundwater is being recharged by streams, whereas in other areas, groundwater is discharged to streams. Estimates of advective groundwater-flow paths indicate pumping wells may capture groundwater recharged from losing streams and groundwater that flows into the main stem of the alluvial aquifer from paleochannels.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, Colorado 80225
The release of over 4500 Gt (gigatonnes) of carbon at the Paleocene–Eocene boundary provides the closest geological analog to modern anthropogenic CO2 emissions. The cause(s) of and responses to the resulting Paleocene–Eocene Thermal Maximum (PETM) and attendant carbon isotopic excursion (CIE) remain enigmatic and intriguing despite over 30 years of intense study. CIE records from the deep sea are generally thin due to its short duration and slow sedimentation rates, and they are truncated due to corrosive bottom waters dissolving carbonate sediments. In contrast, PETM coastal plain sections along the US mid-Atlantic margin are thick, generally having an expanded record of the CIE. Drilling here presents an opportunity to study the PETM onset to a level of detail that could transform our understanding of this important event. Previous drilling in this region provided important insights, but existing cores are either depleted or contain stratigraphic gaps. New core material is needed for well-resolved marine climate records. To plan new drilling, members of the international scientific community attended a multi-staged, hybrid scientific drilling workshop in 2022 designed to maximize not only scientifically and demographically diverse participation but also to protect participants' health and safety during the global pandemic and to reduce our carbon footprint. The resulting plan identified 10 sites for drill holes that would penetrate the Cretaceous–Paleogene (K–Pg) boundary, targeting the pre-onset excursion (POE), the CIE onset, the rapidly deposited Marlboro Clay that records a very thick CIE body, and other Eocene hyperthermals. The workshop participants developed several primary scientific objectives related to investigating the nature and the cause(s) of the CIE onset as well as the biotic effects of the PETM on the paleoshelf. Additional objectives focus on the evidence for widespread wildfires and changes in the hydrological cycle, shelf morphology, and sea level during the PETM as well as the desire to study both underlying K–Pg sediments and overlying post-Eocene records of extreme hyperthermal climate events. All objectives address our overarching research question: what was the Earth system response to a rapid carbon cycle perturbation?
This study examined the abundance and distribution of nutrients and phytoplankton in the tidal aquatic environments of the Sacramento–San Joaquin Delta (Delta) and Suisun Bay, comprising three spatial surveys conducted in May, July, and October of 2018 that used continuous underway high frequency sampling and measurements onboard a high-speed boat to characterize spatial variation across the extent of the Delta. The method used involves simultaneously collecting information about the concentration and spatial distribution of all major nutrient forms with analogous information about the major classes of phytoplankton and associated water-quality conditions. The results showed substantial variation across space and time, providing an unprecedented snapshot of the dynamic environmental processes that shape the ways nutrients interact with and affect aquatic habitats in the Delta.
The purposes of this study were to improve our understanding of how hydrodynamics, landscape features, and aquatic primary productivity interact to drive nutrient cycling and transport in the Delta and to provide insights into the underlying processes most directly responsible for the conditions at the time of this study, and thus into the range of conditions that may be expected following the wide array of prospective future changes to the Delta. One major anticipated change at the time of this study was the planned upgrade to the Sacramento Regional Wastewater Treatment Plant, but the study also informs our understanding of potential effects from other changes to the Delta, such as those caused by other nutrient-management actions, flow actions, large-scale wetland restoration, drought, flood, levee failure, and changes to water management.
Nutrient loading is the primary driver of nutrient concentrations in the Delta, but several other major drivers interact to shape their distribution and effects: geomorphology, hydrodynamics, landscape features, and aquatic productivity. Hydrodynamics affect timescales of transport and dilution of nutrient loads in the Delta. During transit through the system, channel geometry, tidal mixing, and water exports affect hydrodynamics in diverse ways that influence water-residence and transport times, thereby markedly affecting the range of times during which natural internal cycling can alter nutrient concentrations and forms. Channel geometry and location shape tidal energy and river currents into these observed dynamics. Interactions with Delta aquatic landscapes such as herbaceous tidal marsh, submerged aquatic vegetation, and large expanses of intertidal or subtidal sediments (all highly productive landscapes) exert demand on available nutrient supplies but can also simultaneously transform and generate nutrients. Finally, while phytoplankton require nutrients to sustain production and thus are a potential nutrient sink, the amount and form of nutrients also can influence the occurrence of harmful algal blooms (HABs) that adversely affect aquatic organisms as well as affect the occurrence of beneficial algal blooms that result in production of algae that are favorable for imperiled Delta pelagic aquatic food webs.
The surveys revealed a complex mosaic of spatial variation, with nutrient concentrations varying from near zero to well above concentrations considered eutrophic; nutrient concentrations were more often related to the extent of hydrologic transport and mixing than to specific geographic locations or to specific landscape features. Similarly, the surveys identified phytoplankton abundance ranging from near detection to the level of large phytoplankton blooms, with large variation in phytoplankton community composition. Although the study occurred during a period of low bloom activity, phytoplankton productivity appeared to be the strongest potential sink for inorganic nutrients in the Delta, indicating that it is a larger control on nutrient concentrations and distribution than previously understood. Cycling and transformation within the water column only appeared to substantially lower total nutrient concentrations at the longest estimated transport timescales. Contrary to expectations, we did not observe substantial nutrient depletion near landscape-scale features such as open-water habitats, submerged aquatic vegetation beds, extensive wetlands, or exposed sediments, indicating that these habitat types did not act as major sinks for nutrients in the Delta during these surveys. These results indicated that nutrient reduction efforts may have the greatest effect on pelagic phytoplankton productivity in the more productive reaches of the Delta and estuary, but these effects are unlikely to be magnified by changes to nutrient loss within the Delta over conceivable changes in flow conditions, Delta water management actions, or large-scale wetland restoration activities. Nevertheless, local processes were shown to cause substantial loss, and thus integrating of nutrient effects with other indicators of aquatic habitat conditions will help inform planning future actions at specific sites.
Finally, we note that the primary contribution of this study was intended to be the survey data themselves. Aside from the results highlighted in this report, the surveys are a benchmark against which future environmental change may be evaluated, including changes to nutrient management or water exports, drought, large-scale wetland restoration, and climate change. Further, although we highlight some of the main findings from the surveys in this report, the necessarily limited scope precludes examination of many topics for which these surveys may be highly informative. To facilitate the utility of these data to stakeholders, managers, and researchers, we have released the data online (Bergamaschi and others, 2020) and created an online data exploration portal (https://ca.water.usgs.gov/bay-delta/2018-delta-wide-mapping-surveys.html) where users may query the surveys in a variety of ways to test hypotheses, examine relationships, assess spatial trends, and download data. The data exploration portal is intended to be an immersive experience that allows users to gain greater understanding of the complex interactions that shape Delta aquatic environments. This report is intended as a companion to the portal, allowing the reader to challenge and further explore the highlighted findings.
This study was a collaboration between the U.S. Geological Survey and the Delta Regional Monitoring Program, with additional funding provided from U.S. Geological Survey Cooperative Matching Funds Program.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Surface-water supplies are important sources of drinking water for residents in the Triangle area of North Carolina, which is located within the upper Cape Fear and Neuse River Basins. Since 1988, the U.S. Geological Survey and a consortium of local governments have participated in a cooperative effort, known as the Triangle Area Water Supply Monitoring Project, to track water-quality and quantity conditions in several of the area’s water-supply reservoirs and streams. This report summarizes the hydrologic and water-quality monitoring activities through this cooperative effort, including an overview of previous and current data collection and quality-assurance and quality-control activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241003","issn":"2331-1258","collaboration":"Prepared in cooperation with the Triangle Area Water Supply Monitoring Project Steering Committee","usgsCitation":"Diaz, J.C., and Fanelli, R.M., 2024, Triangle Area Water Supply Monitoring Project, North Carolina—Overview of hydrologic and water-quality monitoring activities and data quality assurance: U.S. Geological Survey Open-File Report 2024–1003, 8 p., https://doi.org/10.3133/ofr20241003.","productDescription":"Report: vi, 8 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-140656","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":499203,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116211.htm","linkFileType":{"id":5,"text":"html"}},{"id":426743,"rank":1,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1003/images"},{"id":426744,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1003/coverthb.jpg"},{"id":426745,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1003/ofr20241003.pdf","size":"1.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1003"},{"id":426747,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1003/ofr20241003.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1003 XML"},{"id":426746,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241003/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1003 HTML"},{"id":426748,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MU5BAZ","text":"USGS Data Release","linkHelpText":"Associated data for the Triangle Area Water Supply Monitoring Project, North Carolina, October 2019–September 2022"}],"country":"United States","state":"North Carolina","otherGeospatial":"Triangle area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.65,\n 36.25\n ],\n [\n -79.375,\n 36.25\n ],\n [\n -79.375,\n 35.5\n ],\n [\n -78.65,\n 35.5\n ],\n [\n -78.65,\n 36.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Clumped isotope paleothermometry using pedogenic carbonates is a powerful tool for investigating past climate changes. However, location-specific seasonal patterns of precipitation and soil moisture cause systematic biases in the temperatures they record, hampering comparison of data across large areas or differing climate states. To account for biases, more systematic studies of carbonate forming processes are needed. We measured modern soil temperatures within the San Luis Valley of the Rocky Mountains and compared them to paleotemperatures determined using clumped isotopes. For Holocene-age samples, clumped isotope results indicate carbonate accumulated at a range of temperatures with site averages similar to the annual mean. Paleotemperatures for late Pleistocene-age samples (ranging 19–72 ka in age) yielded site averages only 2°C lower, despite evidence that annual temperatures during glacial periods were 5–9°C colder than modern. We use a 1D numerical model of soil physics to support the idea that differences in hydrologic conditions in interglacial versus glacial periods promote differences in the seasonal distribution of soil carbonate accumulation. Model simulations of modern (Holocene) conditions suggest that soil drying under low soil pCO2 favors year-round carbonate accumulation in this region but peaking during post-monsoon soil drying. During a “glacial” simulation with lowered temperatures and added snowpack, more carbonate accumulation shifted to the summer season. These experiments show that changing hydrologic regimes could change the seasonality of carbonate accumulation, which in this study blunts the use of clumped isotopes to quantify glacial-interglacial temperature changes. This highlights the importance of understanding seasonal biases of climate proxies for accurate paleoenvironmental reconstruction.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GC011221","usgsCitation":"Hudson, A.M., Kelson, J.R., Paces, J., Ruleman, C.A., Huntington, K.W., and Schauer, A.J., 2024, Clumped isotopes record a glacial-interglacial shift in seasonality of soil carbonate accumulation in the San Luis Valley, southern Rocky Mountains, USA: Geochemistry, Geophysics, Geosystems, v. 25, no. 4, e2023GC011221, 23 p., https://doi.org/10.1029/2023GC011221.","productDescription":"e2023GC011221, 23 p.","ipdsId":"IP-114357","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":440002,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gc011221","text":"Publisher Index Page"},{"id":435011,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TJF3PZ","text":"USGS data release","linkHelpText":"Isotopic, geochronologic and soil temperature data for Holocene and late Pleistocene soil carbonates of the San Luis Valley, Colorado and New Mexico, USA"},{"id":427311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, new Mexico","otherGeospatial":"San Luis Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107,\n 38.5\n ],\n [\n -107,\n 35.5\n ],\n [\n -105,\n 35.5\n ],\n [\n -105,\n 38.5\n ],\n [\n -107,\n 38.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Hudson, Adam M. 0000-0002-3387-9838 ahudson@usgs.gov","orcid":"https://orcid.org/0000-0002-3387-9838","contributorId":195419,"corporation":false,"usgs":true,"family":"Hudson","given":"Adam","email":"ahudson@usgs.gov","middleInitial":"M.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":897780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelson, Julia R.","contributorId":335224,"corporation":false,"usgs":false,"family":"Kelson","given":"Julia","email":"","middleInitial":"R.","affiliations":[{"id":80344,"text":"Department of Geosciences, University of Michigan, Ann Arbor, MI, USA, Department of Earth and Atmospheric Sciences, Indiana University, Indianapolis, Indiana, USA","active":true,"usgs":false}],"preferred":false,"id":897781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paces, James B. 0000-0002-9809-8493","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":118216,"corporation":false,"usgs":true,"family":"Paces","given":"James B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":897782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruleman, Chester A. 0000-0002-1503-4591 cruleman@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-4591","contributorId":1264,"corporation":false,"usgs":true,"family":"Ruleman","given":"Chester","email":"cruleman@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":897783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huntington, Katharine W.","contributorId":195423,"corporation":false,"usgs":false,"family":"Huntington","given":"Katharine","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":897784,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schauer, Andrew J.","contributorId":140713,"corporation":false,"usgs":false,"family":"Schauer","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":897785,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252251,"text":"sir20235142 - 2024 - Evaluation of the characteristics, discharge, and water quality of selected springs at Fort Irwin National Training Center, San Bernardino County, California","interactions":[],"lastModifiedDate":"2026-01-30T19:53:52.01101","indexId":"sir20235142","displayToPublicDate":"2024-03-29T12:07:33","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5142","displayTitle":"Evaluation of the Characteristics, Discharge, and Water Quality of Selected Springs at Fort Irwin National Training Center, San Bernardino County, California","title":"Evaluation of the characteristics, discharge, and water quality of selected springs at Fort Irwin National Training Center, San Bernardino County, California","docAbstract":"Eight springs and seeps at Fort Irwin National Training Center were described and categorized by their general characteristics, discharge, geophysical properties, and water quality between 2015 and 2017. The data collected establish a modern (2017) baseline of hydrologic conditions at the springs. Two types of springs were identified: (1) precipitation-fed upland springs (Cave, Desert King, Devouge, No Name, and Panther Springs) and (2) groundwater discharge-fed basin springs (Garlic, Bitter, and Jack Springs). Comparison of electrical resistivity tomography data collected at groundwater basin springs from 2015 to 2017 indicated that spring discharge and connection to the underlying groundwater system is highly focused, although the springs themselves appear diffuse and are spread out over a large area.
Spring discharge was consistently less than reported by Thompson (1929), except at Garlic Spring where discharges and vegetation have increased in recent years. Multiple discrete flume and seepage meter measurements taken between October 2015 and April 2016 indicated that discharge changed predictably on diurnal and seasonal timescales in response to evapotranspiration. These preliminary results and the lush vegetation noted at some of the springs, particularly at Bitter, Garlic, and Jack Springs, indicated plant evapotranspiration accounts for a substantial part of the discharge from these springs.
The quality of water ranges from fresh in precipitation-fed upland springs (Cave, Desert King, Devouge, and Panther Springs) to slightly saline (Garlic and Jack Springs) and moderately saline (Bitter Spring) in groundwater-fed discharge springs. Nitrate concentrations from water at most of the springs were less than 3 milligrams per liter, except for samples from Devouge and Desert King Springs and one sample from Jack Spring. An analysis of delta nitrogen-15 in nitrate (δ15N-NO3) and delta oxygen-18 in nitrate (δ18O-NO3) indicates high nitrate concentrations in excess of the U.S. Environmental Protection Agency maximum contaminant level at Jack Spring and Desert King Spring resulting from the dissolution of nitrate-bearing caliche deposits; nitrate concentrations at Devouge Spring are a result of algal growth within the spring, and the source of nitrate concentrations in Garlic Spring are consistent with a treated wastewater origin from Langford Valley-Irwin subbasin upgradient. The source of water in upland springs, indicated by values of delta oxygen-18 (δ18O) and delta deuterium (δD) are consistent with recharge from winter precipitation. In groundwater basin springs, values of δ18O and δD are consistent with groundwater sampled from nearby wells. Summer monsoonal precipitation appears to contribute little water to spring flow. Most springs contain low levels of tritium and appear to be primarily older (pre-1950s) groundwater. Groundwater basin springs with detectable tritium may result from occasional streamflow in nearby washes. These springs could be susceptible to decreases in flow during extended dry periods when the localized recharge may be reduced due to the loss of focused recharge through nearby washes.
Groundwater samples from Garlic and Bitter Springs contained arsenic concentrations above the U.S. Environmental Protection Agency maximum contaminant level. Groundwater samples from all springs, except Cave, Desert King, and Devouge Springs, exceeded the State of California maximum contaminant level for fluoride. Garlic Spring was the only sampled spring that contained vanadium concentrations that exceeded the State of California notification level. Only a single water sample from Jack Spring contained uranium at a concentration that exceeded the U.S. Environmental Protection Agency maximum contaminant level.
Many other constituents of concern were analyzed, including those from anthropogenic sources that may be a result of military activities. Most of these constituents were not detected above their respective reporting levels in spring water; only 15 were detected in spring waters. Diesel and gasoline degradants, many of which also occur naturally, were the most commonly detected compounds. Several other organic compounds, primarily solvents or their degradants, were detected in groundwater basin springs. These constituents, in order of decreasing detection frequency, were carbon disulfide; perchlorate; mercury; acetone; methylnaphthalene; toluene; methyl ethyl ketone; cyanide; and styrene; 4-iso-propyl-toluene; isopropylbenzene; methyl salicylate; and phenol. Except for Garlic Spring, which is affected by discharges of treated wastewater, the quality of water from most springs appears to be relatively unaffected by activities at the Fort Irwin National Training Center.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235142","collaboration":"Prepared in cooperation with the U.S. Army Fort Irwin National Training Center","programNote":"Water Availability and Use Science Program","usgsCitation":"Densmore, J.N., Thayer, D.C., Dick, M.C., Swarzenski, P.W., Ball, L.B., Rosecrans, C.Z., and Johnson, C., 2024, Evaluation of the characteristics, discharge, and water quality of selected springs at Fort Irwin National Training Center, San Bernardino County, California: U.S. Geological Survey Scientific Investigations Report 2023–5142, 87 p., https://doi.org/10.3133/sir20235142.","productDescription":"Report: xii, 87 p.; 2 Data Releases","numberOfPages":"87","onlineOnly":"Y","ipdsId":"IP-098665","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":426854,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P901E9C2","text":"USGS Data Release","description":"Mesmer, R.D., Dick, M.C., and Densmore, J.N., 2024, Temperature and discharge data of selected springs at Fort Irwin National Training Center, San Bernardino County, California: U.S. Geological Survey data release, available at https://doi.org/10.5066/P901E9C2.","linkHelpText":"Temperature and discharge data of selected springs at Fort Irwin National Training Center, San Bernardino County, California"},{"id":499404,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116216.htm","linkFileType":{"id":5,"text":"html"}},{"id":426868,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5142/images"},{"id":426867,"rank":6,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5142/covrthb.jpg"},{"id":426866,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235142/full"},{"id":426865,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5142/sir20235142.xml"},{"id":426864,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5142/sir20235142.pdf","text":"Report","size":"25.9 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426853,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77W6BF0","text":"USGS Data Release","description":"Thayer, D.C., Ball, L.B., Densmore, J.N., Swarzenski, P.W., and Johnson, C., 2018, Electrical resistivity tomography data at Fort Irwin National Training Center, San Bernardino County, California, 2015 and 2017: U.S. Geological Survey data release, available at https://doi.org/10.5066/F77W6BF0.","linkHelpText":"Electrical resistivity tomography data at Fort Irwin National Training Center, San Bernardino County, California, 2015 and 2017"}],"country":"United States","state":"California","otherGeospatial":"Fort Irwin National Training Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.65077467771744,\n 36.01045506303355\n ],\n [\n -117.65077467771744,\n 34.68622540325404\n ],\n [\n -115.49481045780325,\n 34.68622540325404\n ],\n [\n -115.49481045780325,\n 36.01045506303355\n ],\n [\n -117.65077467771744,\n 36.01045506303355\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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The Arctic National Wildlife Refuge is a unique landscape in northern Alaska with limited water resources, substantial biodiversity of rare and threatened species, as well as oil and gas resources. The region has unique hydrology related to perennial springs, and the formation of large aufeis fields—sheets of ice that grow in the river channels where water reaches the surface in the winter and freezes. This work aims to update our understanding of water resources and water quality in the springs, streams, rivers, and lakes of this region, returning to sites sampled by the U.S. Geological Survey in the 1970s. We resampled eight streams, four springs, and six lakes for hydrological metrics, water quality, and macroinvertebrates, and recalculated flood-frequency metrics for rivers using updated data and modern techniques. Aufeis field melt rates were also assessed for the past several decades. Although the available data preclude trend determinations in most cases, our analysis and comparison to the historical sampling indicates an increase in dissolved ions for streams and springs, faster and earlier aufeis melt, and similar macroinvertebrate populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245008","usgsCitation":"Koch, J.C., Best, H., Baughman, C., Couvillion, C., Carey, M.P., and Conaway, J., 2024, A comparison of contemporary and historical hydrology and water quality in the foothills and coastal plain of the Arctic National Wildlife Refuge, Arctic Slope, northern Alaska: U.S. Geological Survey Scientific Investigations Report 2024–5008, 24 p., https://doi.org/10.3133/sir20245008.","productDescription":"Report: viii, 24 p.; 2 Data Releases; Correction Note","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-151990","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":499422,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116206.htm","linkFileType":{"id":5,"text":"html"}},{"id":498378,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2024/5008/correctionNote.txt","text":"Correction note","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":430506,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KK98VP","text":"USGS data release","description":"USGS data release","linkHelpText":"Rasters of observed aufeis deposits within rivers of the 1002 Area based on historical Landsat imagery, 1985-2022"},{"id":430429,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7X34VHM","text":"USGS data release","description":"USGS data release","linkHelpText":"Macroinvertebrates from streams and springs in the 1002 region of the Arctic National Wildlife Refuge, Alaska, 2021"},{"id":427077,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5008/sir20245008.XML"},{"id":427076,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5008/images"},{"id":427075,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245008/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5008"},{"id":427074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5008/sir20245008.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5008"},{"id":427073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5008/sir20245008.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.35991062435545,\n 70.47943246978403\n ],\n [\n -147.35991062435545,\n 68.94103321326239\n ],\n [\n -141.60307468685548,\n 68.94103321326239\n ],\n [\n -141.60307468685548,\n 70.47943246978403\n ],\n [\n -147.35991062435545,\n 70.47943246978403\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
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We investigated wetland vegetation before, during, and after dike construction at the Metzger Marsh project in western Lake Erie, which was designed to restore a 300-ha wetland that had been degraded following the loss of a protective barrier beach. A dike was constructed in 1995 to replace the function of the eroded barrier beach, but it contained a water-control structure to allow managed hydrologic connection to the lake. The control structure contained a fish passageway to allow movement of fish across the dike, while restricting entry of large common carp. Color-infrared aerial photos from project start in 1994 through 2010 (and 2022) were analyzed to track vegetation changes, and major vegetation types were sampled quantitatively. Drawdown of water levels in 1996 after dike construction elicited a response of mudflat species from the seed bank, as well as tree seedlings. Over half of the marsh was vegetated then and in subsequent years. The water-control structure was opened in 1998, and by 2000, invasive Phragmites australis had gained dominance. Most trees were eventually eliminated by herbicide treatment and flooding, and extent of Phragmites was reduced by management actions. Typha spp. and emergents Sagittaria latifolia and Schoenoplectus tabernaemontani became dominant by 2022. This restoration project increased habitat values for fish and wildlife; it also provided lessons for future projects on lands managed by multiple agencies with differing missions. More importantly, it showed that long-term monitoring data are critical for assessing wetland restoration projects and guiding management decisions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102309","usgsCitation":"Wilcox, D., Kowalski, K., and Bozimowski, A.A., 2024, The Metzger marsh restoration: A vegetation-centric look after 27 years: Journal of Great Lakes Research, v. 50, no. 2, 102309, 12 p., https://doi.org/10.1016/j.jglr.2024.102309.","productDescription":"102309, 12 p.","ipdsId":"IP-157465","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":427396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Metzger Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.21526317873082,\n 41.63861198193294\n ],\n [\n -83.24120906559943,\n 41.650434477459726\n ],\n [\n -83.2513342897436,\n 41.64523284668019\n ],\n [\n -83.25101787648906,\n 41.63813903699173\n ],\n [\n -83.23867775956381,\n 41.63766608857978\n ],\n [\n -83.21526317873082,\n 41.63861198193294\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wilcox, Douglas A.","contributorId":244846,"corporation":false,"usgs":false,"family":"Wilcox","given":"Douglas A.","affiliations":[{"id":48999,"text":"Department of Environmental Science and Ecology, The College at Brockport – State University of New York, Brockport, NY","active":true,"usgs":false}],"preferred":false,"id":898064,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":898065,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bozimowski, Alexandra A 0000-0002-0835-1089","orcid":"https://orcid.org/0000-0002-0835-1089","contributorId":328563,"corporation":false,"usgs":true,"family":"Bozimowski","given":"Alexandra","email":"","middleInitial":"A","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":898066,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252249,"text":"sir20245017 - 2024 - Groundwater model of the Harney Basin, southeastern Oregon","interactions":[],"lastModifiedDate":"2026-02-02T22:27:22.663466","indexId":"sir20245017","displayToPublicDate":"2024-03-22T06:23:53","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5017","displayTitle":"Groundwater Model of the Harney Basin, Southeastern Oregon","title":"Groundwater model of the Harney Basin, southeastern Oregon","docAbstract":"Groundwater development, mainly for large-scale irrigation, has increased substantially in the Harney Basin of southeastern Oregon since 2010. Concurrently, some areas of the basin experienced groundwater-level declines of more than 100 feet, and some shallow wells have gone dry. The Oregon Water Resources Department has limited new groundwater development in the basin until an improved understanding of the groundwater-flow system is available. The groundwater resources report by Gingerich and others (2022, U.S. Geological Survey Scientific Investigations Report 2021–5103, https://doi.org/10.3133/sir20215103) provides that understanding. This report describes the development of a numerical groundwater-flow model that can be used as a tool to help improve that understanding. The Harney Basin Groundwater Model was developed using the finite-difference groundwater-modeling software U.S. Geological Survey modular finite-difference groundwater-flow model (MODFLOW 6) and associated Python pre- and post-processing routines. The groundwater model encompasses the entire 5,240-square-mile Harney Basin and adjacent areas and is calibrated to the hydrologic conditions from 1930 to 2018. The model has a uniform grid consisting of 78,064 nearly square cells, each covering 2,005 by 2,007 feet (about 92 acres) and has 10 layers (780,640 total cells) representing the vertical distribution of hydrogeologic units. The results from the calibrated model simulations indicate that groundwater pumpage exceeded recharge since about the mid-1980s, resulting in an estimated net cumulative depletion of groundwater storage (discharge minus recharge) of about 840,000 acre-feet and also indicated declines in groundwater evapotranspiration and spring and stream discharge. Model simulations show as much as 100 feet of groundwater-level decline in some areas and more than 40 feet of decline in widespread areas in recent decades. Model simulations are consistent with field observations of groundwater levels through time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245017","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Gingerich, S.B., Boschmann, D.E., Grondin, G.H., and Schibel, H.J., 2024, Groundwater model of the Harney Basin, southeastern Oregon: U.S. Geological Survey Scientific Investigations Report 2024–5017, 104 p., https://doi.org/10.3133/sir20245017.","productDescription":"Report: xii, 104 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-152081","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":499430,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116178.htm","linkFileType":{"id":5,"text":"html"}},{"id":426855,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5017/sir20245017.jpg"},{"id":426856,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5017/sir20245017.pdf","text":"Report","size":"47.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5017"},{"id":426869,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OEKEIO","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW 6 model used to simulate groundwater flow in the Harney Basin, southeastern Oregon"},{"id":426859,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5017/sir20245017.XML"},{"id":426858,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5017/images"}],"country":"United States","state":"Oregon","otherGeospatial":"Harney Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.44430319880412,\n 44.965776942074626\n ],\n [\n -121.44430319880412,\n 42.262073209475204\n ],\n [\n -117.31344382380401,\n 42.262073209475204\n ],\n [\n -117.31344382380401,\n 44.965776942074626\n ],\n [\n -121.44430319880412,\n 44.965776942074626\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
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601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
Communities throughout Massachusetts face the potential effects of climate change, ranging from more extreme rainfall to more pronounced and frequent droughts. Understanding the effects of climate change on hydrology is important to State and community officials to evaluate the potential effects on infrastructure and water systems. To better understand the effects of climate change on hydrology, the U.S. Geological Survey, in partnership with Cornell University and Tufts University, conducted a study in cooperation with the Massachusetts Executive Office of Energy and Environmental Affairs to develop tools for projecting 21st-century climate and hydrologic characteristics in Massachusetts.
A stochastic weather generator was developed to project future climatic characteristics for Massachusetts. The stochastic weather generator estimates daily precipitation, minimum temperature, and maximum temperature for 17 warming scenarios (from 0 to 8 degrees Celsius, in 0.5-degree increments). To project future hydrologic characteristics, the stochastic weather generator output data were input to the Precipitation-Watershed Modeling System deterministic watershed model for the Squannacook River watershed, which is the watershed selected as the pilot study location for investigating future hydrologic characteristics. Hydrologic data output from the deterministic watershed model were then input to a stochastic watershed model developed for this study to correct model errors (model errors are often observed in the output from deterministic models at the high- and low-flow extremes). The output from the stochastic watershed model was then used to characterize hydrology for the 17 warming scenarios. For the Squannacook River watershed, the results project more extreme flood and low streamflows under the warming scenarios.
Output from the tools allows the characterization of future streamflows for the years 2030, 2050, 2070, and 2090, which expands our understanding of 21st-century climatic and hydrologic risk in Massachusetts. These tools could improve Federal, State, and community officials’ ability to mitigate the effects of climate change over the next several decades.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Assessing the sensitivity of freshwater species to climate change is an essential component of prioritizing conservation efforts for threatened freshwater ecosystems and organisms. Sensitivity to climate change can be systematically evaluated for multiple species using geographic attributes such as range size and climate niche breadth, and using species traits associated with climate change sensitivity. These systematic evaluations produce relative rankings of species sensitivity to aid conservation prioritization and to identify relatively sensitive species that may otherwise be understudied or overlooked. Due in part to biogeographic constraints, species assemblages change across regions and spatial extents; yet, the degree to which spatial factors influence relative rankings of species sensitivity is unclear. The spatial extent of multispecies analyses may alter relative rankings of species climate sensitivity; alternatively, relative climate sensitivity may be conserved among spatial scales, resulting in consistent identification of sensitive species among regions and spatial extents. We investigated how spatial extent influences our understanding of relative climate sensitivity for 137 native freshwater fishes of the United States that were representative of taxonomic, trait, and geographic diversity. Using publicly available occurrence data from the Global Biodiversity Information Facility, we calculated a systematic, geographically derived index of climate change sensitivity for study species at national and regional extents, including within four major hydrologic subregions of the United States. We examined the effects of spatial extent on the relative ranking of climate sensitivity among species, and we explored relationships among climate sensitivity, species traits, and conservation status at regional and national extents. We found that climate sensitivity rankings of species were influenced by spatial extent in some specific instances, but that relative rankings were largely conserved across spatial scales. However, correlations among geographically derived climate sensitivity rankings and species traits associated with climate sensitivity were variable across scales and regions, suggesting that links between geographic rarity and species traits may be scale-dependent in some cases. Finally, we found few associations between climate sensitivity and current conservation status among species. Systematic approaches to quantifying climate sensitivity may offer an opportunity to identify sensitive but overlooked species for pre-listing actions such as monitoring or conservation agreements.
StreamStats is a U.S. Geological Survey (USGS) web application that provides streamflow statistics, such as the 1-percent annual exceedance probability peak flow, the mean flow, and the 7-day, 10-year low flow, to the public through a map-based user interface. These statistics are used in many ways, such as in the design of roads, bridges, and other structures; in delineation of floodplains for land-use zoning and setting of insurance rates; for regulatory purposes, such as the permitting of wastewater discharges; and for hydrologic and climate change studies. StreamStats was first developed for Massachusetts and released in 2001. The application provided users with the ability to obtain streamflow statistics computed from data collected at USGS streamgages and to obtain estimates of streamflow statistics for user-selected ungaged sites. Massachusetts StreamStats used geographic information system software and digital mapping to compute drainage-basin characteristics, which were then used in statistical models to estimate streamflow statistics for the user-selected sites. The statistical models were in the form of equations that were developed through a process known as regression analysis. StreamStats was the first known web application with the ability to do interactive geoprocessing.
The utility of Massachusetts StreamStats was instantly apparent, leading the USGS to develop a version of StreamStats that could be implemented nationally. USGS State offices normally were required to develop custom regression equations and prepare local digital mapping data needed for implementing StreamStats for their States. Funding needed to complete this work usually was provided through cooperative agreements between the USGS and State agencies. In 2004, Idaho became the first to be released in the national version of StreamStats. By 2023, 44 States were fully implemented and six were undergoing implementation.
StreamStats has undergone many modifications over the years to keep up with changes to the underlying software and to add functionality. Customized functionality and separate linked StreamStats applications were developed for several States. Meeting the high demand for additions and improvements to StreamStats while also adhering to budgetary constraints has, at times, been challenging. The StreamStats development team has identified numerous additional improvements that could be made to provide better performance and more functionality. The lessons learned from the experience of building and operating StreamStats for nearly 25 years could be relevant to others interested in pursuing efforts of a similar scale.
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U.S. Geological Survey
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Less than a year after the 2018 Kīlauea caldera collapse and eruption, water appeared in newly deepened Halemaʻumaʻu crater. The lake—unprecedented in the written record—grew to a depth of ∼50 m before lava from the December 2020 eruption boiled it away. Surface water heightened concerns of potential phreatic or phreatomagmatic explosions but also offered a new means of possibly identifying eruption precursors. The U.S. Geological Survey Hawaiian Volcano Observatory (HVO) monitored the lake via direct visual observation, webcams, thermal imaging, colorimetry, and laser rangefinders. HVO also employed uncrewed aircraft systems to sample the water and measure near-lake gas composition. The lake's δD and δ18O indicate a groundwater source with substantial evaporation. The initial sample had a salinity (total dissolved solids concentration) of 71,000 mg/L and was rich in sulfate (∼53,000 mg/L), iron (∼500 mg/L), and magnesium (∼10,000 mg/L). Subsequent samples were slightly more dilute. The water's pH (∼4), δ34S (+4.3‰), and surface temperatures (up to 85°C) suggest, rather than significant scrubbing of magmatic volatiles, leaching of basalt and reactions with sulfate minerals resulted in high concentrations of sulfate and other solutes. Thermodynamic modeling and precipitate mineralogy indicate that water composition was controlled by iron oxidation and sulfate dissolution. Although the lake exhibited no detectable precursors before the next eruption, and phreatic or phreatomagmatic explosions did not materialize, our multi-parameter approach to monitoring yielded an enhanced understanding of the hydrologic, geologic, and magmatic conditions that led to the formation of the unique and short-lived lake.