The Kansas River is an essential water resource that provides drinking water to more than 950,000 people in northeastern Kansas. Water suppliers that rely on the Kansas River as a water-supply source use physical and chemical water-treatment strategies to remove contaminants before distribution. Water District No. 1 of Johnson County, Kansas (WaterOne), is the largest water supplier in the State and uses the Kansas and Missouri Rivers as water-supply sources to provide drinking water to the Kansas City metropolitan area. WaterOne has been using ozone disinfection as a primary water-treatment strategy since the summer of 2020. Water suppliers that rely on ozone disinfection have become increasingly concerned with the presence of elevated dissolved bromide (the negatively charged form of bromine; hereafter referred to as “bromide”) concentrations in their water-supply source. Ozone disinfection of source water containing elevated concentrations of bromide can lead to the formation of bromate, a regulated disinfection byproduct and probable carcinogen. Real-time computations of bromide concentrations upstream from the WaterOne source-water intake in the Kansas River can be used to assist WaterOne with proactive adjustment of water-treatment strategies. These computations can also be used to advance understanding of hydrologic processes affecting ozone disinfection and formation of bromate.
This report documents the development of the surrogate-regression model that computes bromide concentrations in real time at De Soto, Kansas, and characterizes daily and monthly bromide concentrations at this location during the study period. The U.S. Geological Survey (USGS), in cooperation with WaterOne, collected specific conductance and discrete bromide sample data at the USGS streamgage Kansas River at De Soto, Kans. (06892350; hereafter referred to as “De Soto”), during January 2021 through October 2023 to develop a surrogate-regression model using ordinary least-squares regression that computes bromide concentrations at De Soto, which is about 15 miles upstream from the WaterOne source-water intake in the Kansas River. Specific conductance explained about 85 percent of the variance in bromide concentrations at De Soto during the study period. The surrogate-regression model documented in this report estimated that bromide concentrations at De Soto were likely to exceed the WaterOne water-treatment level of concern (150 micrograms per liter [μg/L]) when specific conductance was greater than or equal to about 930 microsiemens per centimeter at 25 degrees Celsius. Surrogate-regression model computations of bromide concentrations documented in this report are available at the USGS National Real-Time Water-Quality website (https://nrtwq.usgs.gov/).
Bromide concentrations in discrete samples ranged from 31.9 to 251 μg/L and exceeded the water-treatment level of concern in about 34 percent of the 41 samples collected at De Soto during January 2021 through October 2023. Computed daily bromide concentrations ranged from 38.2 to 277 μg/L and exceeded the water-treatment level of concern about 46 percent of the time during January 2021 through October 2023. Generally, an inverse relation was observed between bromide and streamflow during the study period. Higher bromide concentrations were observed during September through February, and lower bromide concentrations were observed during March through August. Seasonal median bromide concentrations were significantly different in all pairwise seasonal combinations, except for summer versus spring. Computed median bromide concentrations were highest during winter, followed by fall, then spring and summer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245078","collaboration":"Prepared in cooperation with the Water District No. 1 of Johnson County, Kansas","usgsCitation":"Williams, T.J., and Totzke, G.S., 2024, Computation of bromide concentrations at the Kansas River at De Soto, Kansas, January 2021 through October 2023: U.S. Geological Survey Scientific Investigations Report 2024–5078, 18 p., https://doi.org/10.3133/sir20245078.","productDescription":"Report: vii, 18 p.; Appendix; Dataset","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-166673","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":433130,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245078/full"},{"id":433128,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5078/downloads/","text":"Appendix 1","linkHelpText":"—Model Archival Summary for Bromide Concentration at U.S. Geological Survey Streamgage 06892350, Kansas River at De Soto, Kansas, during January 2021 through October 2023"},{"id":499481,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117306.htm","linkFileType":{"id":5,"text":"html"}},{"id":433124,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5078/coverthb.jpg"},{"id":433127,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5078/images/"},{"id":433126,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5078/sir20245078.XML"},{"id":433125,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5078/sir20245078.pdf","text":"Report","size":"2.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5078"},{"id":433129,"rank":6,"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"}],"country":"United States","state":"Kansas","city":"De Soto","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.89522863083639,\n 39.3821856985449\n ],\n [\n -96.89522863083639,\n 38.76718861844998\n ],\n [\n -94.64788021564547,\n 38.76718861844998\n ],\n [\n -94.64788021564547,\n 39.3821856985449\n ],\n [\n -96.89522863083639,\n 39.3821856985449\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Cyclonic storms (i.e., hurricanes) are powerful disturbance events that often cause widespread forest damage. Storm-related canopy damage reduces rainfall interception and evapotranspiration, but impacts on streamflow regimes are poorly understood. We quantify streamflow changes in Puerto Rico following Hurricane Maria in September 2017, and evaluate whether forest cover and storm-related canopy damage account for the differences. Streams are particularly vulnerable to flooding in early post-disturbance stages during hurricane season, so we focus on 3 months (Oct–Dec) following the hurricane. To discern changes in rainfall responses, we partitioned streamflow into baseflow and quickflow using a digital filter. We collected 2010–2017 streamflow and rainfall data from 18 watersheds and compared the relative magnitude of post- to pre-hurricane double mass curve slopes of baseflow and quickflow volumes against rainfall. Several watersheds displayed higher post-hurricane quickflow and baseflow, however, the response was variable. The magnitude of quickflow increase was greater in watersheds with high forest damage. Under the same level of relative damage, watersheds with low initial forest cover had greater quickflow increases than highly forested ones. Conversely, baseflow generally increased, but increases were greater in highly forested watersheds and smaller in highly damaged watersheds. These results suggest that post-storm baseflow increases were due to recharge of hurricane-related rainfall, as well as forest transpiration interruption and soil disturbance enhancing recharge of post-hurricane rainfall, while increases to quickflow are related to loss of canopy rainfall interception and higher soil saturation decreasing infiltration. Our research demonstrates that forest damage from disturbance lowers quickflow and elevates baseflow in highly forested watersheds, and elevates quickflow and lowers baseflow in less-forested watersheds. Less-forested watersheds may be closer to the forest cover loss threshold needed to elicit a streamflow response following disturbance, suggesting higher flooding potential downstream, and a lower storm-related forest disturbance threshold than in heavily forested watersheds.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.15249","usgsCitation":"Hall, J.S., Scholl, M.A., Shanley, J.B., Matt, S., and Uriarte, M., 2024, Forest cover lessens hurricane impacts on peak streamflow: Hydrological Processes, v. 38, no. 8, e15249, 15 p., https://doi.org/10.1002/hyp.15249.","productDescription":"e15249, 15 p.","ipdsId":"IP-145696","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":466952,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.15249","text":"Publisher Index Page"},{"id":463115,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto 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Division","active":true,"usgs":true}],"preferred":true,"id":916574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matt, Serena 0000-0001-7489-1588","orcid":"https://orcid.org/0000-0001-7489-1588","contributorId":270681,"corporation":false,"usgs":true,"family":"Matt","given":"Serena","email":"","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Uriarte, Maria","contributorId":287019,"corporation":false,"usgs":false,"family":"Uriarte","given":"Maria","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":916577,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263810,"text":"70263810 - 2024 - Cold blood in warming waters: Effects of air temperature, precipitation, and groundwater on Gulf Sturgeon thermal habitats in a changing climate","interactions":[],"lastModifiedDate":"2025-02-25T15:28:39.78105","indexId":"70263810","displayToPublicDate":"2024-08-23T08:22:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Cold blood in warming waters: Effects of air temperature, precipitation, and groundwater on Gulf Sturgeon thermal habitats in a changing climate","docAbstract":"In a changing climate, the effects of air temperature, precipitation, and groundwater on water temperature and thermal habitat suitability for Gulf Sturgeon Acipenser desotoi, listed as threatened under the U.S. Endangered Species Act, are not well understood. Hence, we incorporated these factors into thermal habitat models to forecast how Gulf Sturgeon may be affected by wide‐ranging climate change scenarios in 2024–2074.
Using data from the Choctawhatchee River, Florida, we developed precipitation‐ and groundwater‐corrected air–water temperature models, compared their accuracy with that of conventional air–water temperature models used in fisheries management, and projected future Gulf Sturgeon thermal habitat suitability for normal physiological functioning and fieldwork (i.e., population sampling and telemetry surgeries) in summer (May–August) under 16 climate change scenarios.
Precipitation‐ and groundwater‐corrected models were more accurate than conventional air–water temperature models (mean improvement in adjusted R2 = +0.45; range = +0.09 to +0.75). Water temperature was projected to warm at widely variable rates across climate change scenarios encompassing different air temperature, precipitation, and groundwater regimes. Importantly, Gulf Sturgeon summer aggregation areas were cooler and influenced more by precipitation and groundwater and less by air temperature than were non‐aggregation areas. If precipitation and groundwater—as drivers of cooling—become warm in a changing climate, summer aggregation areas were projected to exhibit thermal habitat degradation equivalent to or greater than that of non‐aggregation areas.
Our results add hydrological context to the premise that aggregation areas provide cool water and energetic savings for Gulf Sturgeon during summer, underscoring the importance of protecting these habitats through groundwater conservation, water quality monitoring, and riparian/watershed habitat management. Our findings indicate that identifying thermally appropriate times for fieldwork activities will be increasingly important and time‐restricted as climate change intensifies. However, our research provides managers with a portfolio of water temperature models and an accurate, cost‐effective, management‐relevant approach to forecasting thermal habitat conditions for Gulf Sturgeon and other species in a changing climate.
Residents of Carson Valley, Douglas County, Nevada, rely on the basin-fill alluvial aquifer underlying the valley for drinking water. Since the 1980s, groundwater levels and water-quality data have been collected to monitor the status of the aquifer system and to assist in planning efforts to address current (2024) and future demand. The U.S. Geological Survey (USGS), in cooperation with Douglas County, Nevada, evaluated trends in water levels, nitrate, and arsenic concentrations from a network of monitoring and domestic wells in Carson Valley. This work also assessed the monitoring well network to determine the suitability of wells for characterizing the occurrence of arsenic in the groundwater. Monitoring of constituents, such as nitrate and arsenic concentrations, is needed to assess changes in contaminant distribution and to evaluate the effect that changing land use and groundwater pumping has on their occurrence and transport.
Results of the trend analysis indicate water levels are declining (p<0.05) in 17 of 26 selected monitoring wells (65 percent). Areas with the largest change in water levels, with more than 20 feet of declines, were within the community areas of Johnson Lane, Ruhenstroth, South Agricultural, East Valley, and Fish Springs. Variations in water levels measured in wells from the Central Agricultural, Minden, Foothill, Alpine County (one well), and Gardnerville Ranchos areas show periods of increase and decrease over time, but they also maintain long-term declining trends (p<0.05).
Increases in nitrate concentrations in groundwater samples collected from 9 out of 14 selected monitoring wells (64 percent) are statistically significant (p<0.05) within the Ruhenstroth, Gardnerville Ranchos, East Valley, Genoa, and Johnson Lane community areas. Samples collected from a well in Indian Hills/Jacks Valley indicated a decreasing trend in nitrate concentration over time. Nitrate concentrations in samples collected from wells in East Valley, Genoa, Johnson Lane, and Indian Hills/Jack Valley were consistently low (less than 3 milligrams per liter [mg/L]) and stable. Nitrate concentrations from selected wells in Johnson Lane and Garnerville Ranchos exceeded the U.S. Environmental Protection Agency (EPA) maximum contaminant level (MCL) of 10 mg/L (as nitrogen) and have trends that are increasing over time. In 2022, a sample collected from Johnson Lane had a concentration (7.3 mg/L) below the MCL with an increasing trend over time.
Temporal trend analyses for groundwater arsenic concentrations in Carson Valley could not be done because of a lack of temporal data. However, using available historical data, arsenic concentrations seem to be greater in groundwater from wells located on the eastern and northern areas of the valley than in wells located on the western or southern areas. Groundwater arsenic concentrations exceed 5 micrograms per liter (μg/L) in most samples collected from wells in Johnson Lane, Airport, Central Agricultural, and East Valley areas and in many cases exceed the U.S. Environmental Protection Agency (EPA) MCL of 10 μg/L. Data indicate that groundwater from domestic wells screened at deeper intervals are likely more vulnerable to elevated arsenic concentrations than shallower wells.
A groundwater network evaluation for Carson Valley identified potential modifications in the sampling locations and frequency to better understand the effect of groundwater pumping in communities where municipal and domestic demand are greatest, potentially enhancing understanding of contaminant transport in these areas. Potential modifications to the active well network include reducing the frequency of sample collection from existing network wells (6 out of 11) that have consistently shown low and stable nitrate concentrations, adding wells in areas where data are sparse, and increasing the number of wells in areas with elevated groundwater nitrate concentrations. Including the analysis of arsenic in samples from the active groundwater monitoring well network will provide more detail on the temporal and spatial variability of arsenic concentrations.
A visualization tool for the Carson River Basin was developed to provide access to discrete and near real-time hydrologic and water-quality data. The Carson River Basin Hydro Mapper (CBH; U.S. Geological Survey, 2023b) shows active and historical discrete water levels measured by the USGS and the State of Nevada Division of Water Resources, discrete groundwater nitrate and arsenic concentration data collected by the USGS, near real-time streamflow, and surface water levels for select waterbodies. The hydrologic data in the CBH provides resource managers, the public, and the scientific community with an easily accessible tool to present and communicate the most up-to-date information available about local and basin-wide water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241045","collaboration":"Prepared in cooperation with Douglas County, Nevada","programNote":"Water Resources Mission Area—Water's Cooperative Matching Funds","usgsCitation":"Naranjo, R.C., and Bubiy, A., 2024, Assessment of water levels, nitrate, and arsenic in the Carson Valley Alluvial Aquifer and the development of a data visualization tool for the Carson River Basin, Nevada (ver. 1.1, September 2024): U.S. Geological Survey Open-File Report 2024–1045, 29 p., https://doi.org/10.3133/ofr20241045.","productDescription":"vii, 29 p.","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-154652","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":434792,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2024/1045/versionHist.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}},{"id":432958,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1045/covrthb.jpg"},{"id":432959,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1045/ofr20241045.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":432960,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1045/ofr20241045.xml"},{"id":432961,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1045/images"},{"id":432962,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241045/full"},{"id":497966,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117224.htm","linkFileType":{"id":5,"text":"html"}},{"id":433077,"rank":6,"type":{"id":4,"text":"Application Site"},"url":"https://webapps.usgs.gov/carsonriverbasinhydromapper/","text":"Carson River Basin Hydro Mapper Webapp"}],"country":"United States","state":"Nevada","otherGeospatial":"Carson River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.6,\n 39.05\n ],\n [\n -119.6,\n 38.5\n ],\n [\n -119.3,\n 38.5\n ],\n [\n -119.3,\n 39.05\n ],\n [\n -119.6,\n 39.05\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: August 2024; Version 1.1: September 2024","contact":"Director,
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U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
Hydrological drought is a pervasive and reoccurring challenge in managing water resources. Reservoirs are critical for lessening the impacts of drought on water available for many uses. We use a novel and generalized approach to identify periods of unusually low reservoir storage—via comparisons to operational rule curves and historical patterns—to investigate how droughts affect storage in 250 reservoirs across the conterminous U.S. (CONUS). We find that the maximum amount of water stored in reservoirs is decreasing, and that periods of unusually low storage are becoming longer, more severe, and more variable in (a) western and central CONUS reservoirs, and (b) reservoirs with primarily over-year storage. Results suggest that reservoir storage has become less reliable and more vulnerable to larger deviations from desired storage patterns. These changes have coincided with ongoing shifts to the hydroclimate of CONUS, and with sedimentation further reducing available reservoir storage.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL109476","usgsCitation":"Simeone, C., Hammond, J., Archfield, S.A., Broman, D., Condon, L., Eldardiry, H., Olson, C.G., and Steyaert, J., 2024, Declining reservoir reliability and increasing reservoir vulnerability: Long-term observations reveal longer and more severe periods of low reservoir storage for major United States reservoirs: Geophysical Research Letters, v. 51, no. 16, e2024GL109476, 12 p., https://doi.org/10.1029/2024GL109476.","productDescription":"e2024GL109476, 12 p.","ipdsId":"IP-161001","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science 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C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":911462,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":911463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Broman, Dan","contributorId":343588,"corporation":false,"usgs":false,"family":"Broman","given":"Dan","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National 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System Processes Division","active":true,"usgs":true}],"preferred":true,"id":911467,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Steyaert, Jen","contributorId":343592,"corporation":false,"usgs":false,"family":"Steyaert","given":"Jen","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":911468,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70257924,"text":"70257924 - 2024 - The effect of drying boreal lakes on plants, soils, and microbial communities in lake margin habitats","interactions":[],"lastModifiedDate":"2024-09-03T14:04:52.601735","indexId":"70257924","displayToPublicDate":"2024-08-22T08:35:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9326,"text":"JGR Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"The effect of drying boreal lakes on plants, soils, and microbial communities in lake margin habitats","docAbstract":"Decadal scale lake drying in interior Alaska results in lake margin colonization by willow shrub and graminoid vegetation, but the effects of these changes on plant production, biodiversity, soil properties, and soil microbial communities are not well known. We studied changes in soil organic carbon (SOC) and nitrogen (N) storage, plant and microbial community composition, and soil microbial activities in drying and non-drying lakes in the Yukon Flats National Wildlife Refuge. Historic changes in lake area were determined using Landsat imagery. Results showed that SOC storage in drying lake margins declined by 0.13 kg C m−2 yr−1 over 30 years of exposure of lake sediments, with no significant change in soil N. Lake drying resulted in an increase in graminoid and shrub aboveground net primary production (ANPP, +3% yr−1) with little change in plant functional composition. Increases in ANPP were similar in magnitude (but opposite in sign) to losses in SOC over a 30-year drying trend. Potential decomposition rates and soil enzyme activities were lower in drying lake margins compared to stable lake margins, possibly due to high salinities in drying lake margin soils. Microbial communities shifted in response to changing plant communities, although they still retained a legacy of the previous plant community. Understanding how changing lake hydrology impacts the ecology and biogeochemistry of lake margin terrestrial ecosystems is an underexamined phenomenon with large impacts to landscape processes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007819","usgsCitation":"Patil, V.P., McFarland, J., Wickland, K., Manies, K.L., Winterstein, M., Hollingsworth, T., Euskirchen, E., and Waldrop, M., 2024, The effect of drying boreal lakes on plants, soils, and microbial communities in lake margin habitats: JGR Biogeosciences, v. 129, no. 8, e2023JG007819, 21 p., https://doi.org/10.1029/2023JG007819.","productDescription":"e2023JG007819, 21 p.","ipdsId":"IP-139844","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":439200,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jg007819","text":"Publisher Index Page"},{"id":433403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -150.14311428675356,\n 66.58339320669828\n ],\n [\n -150.0613509618515,\n 65.52747431340518\n ],\n [\n -143.489470862569,\n 65.54657614460567\n ],\n [\n -143.48736112738945,\n 66.54807699281983\n ],\n [\n -150.14311428675356,\n 66.58339320669828\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Patil, Vijay P. 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":203676,"corporation":false,"usgs":true,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":912009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McFarland, Jack 0000-0001-9672-8597","orcid":"https://orcid.org/0000-0001-9672-8597","contributorId":214819,"corporation":false,"usgs":true,"family":"McFarland","given":"Jack","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":912012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wickland, Kimberly 0000-0002-6400-0590","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":208471,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":912011,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Manies, Kristen L. 0000-0003-4941-9657 kmanies@usgs.gov","orcid":"https://orcid.org/0000-0003-4941-9657","contributorId":2136,"corporation":false,"usgs":true,"family":"Manies","given":"Kristen","email":"kmanies@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":912013,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Winterstein, Mark","contributorId":343792,"corporation":false,"usgs":false,"family":"Winterstein","given":"Mark","email":"","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":912014,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hollingsworth, Teresa N.","contributorId":343793,"corporation":false,"usgs":false,"family":"Hollingsworth","given":"Teresa N.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":912015,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Euskirchen, Eugénie S.","contributorId":83378,"corporation":false,"usgs":false,"family":"Euskirchen","given":"Eugénie S.","affiliations":[{"id":13117,"text":"Institute of Arctic Biology, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":912016,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":912010,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70256698,"text":"sir20245026v2 - 2024 - Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington","interactions":[{"subject":{"id":70256698,"text":"sir20245026v2 - 2024 - Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington","indexId":"sir20245026v2","publicationYear":"2024","noYear":false,"chapter":"D-E","displayTitle":"Numerical Model of the Groundwater-Flow System Near the Southeastern Part of Puget Sound, Washington","title":"Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington"},"predicate":"IS_PART_OF","object":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"id":1}],"isPartOf":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"lastModifiedDate":"2026-02-03T18:14:38.644212","indexId":"sir20245026v2","displayToPublicDate":"2024-08-21T14:09:17","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":"2024-5026","chapter":"D-E","displayTitle":"Numerical Model of the Groundwater-Flow System Near the Southeastern Part of Puget Sound, Washington","title":"Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington","docAbstract":"Groundwater flow in the active model area (AMA) was simulated using a groundwater-flow model. A steady-state model version of the model simulates equilibrium conditions, and a transient model version simulates monthly variability. The model corresponds to the physical and temporal dimensions of the conceptual model and groundwater budget. The steady-state model version represents average conditions for an 11-year period (January 1, 2005–December 31, 2015), and the transient model represents monthly hydrologic variability within that period. The 13-layer model was constructed using MODFLOW-NWT with a uniformly spaced grid consisting of 416 rows, 433 columns, and cells with a horizontal dimension of 500 feet (ft) on a side.
The model was calibrated to measured values of water levels in wells and lakes and estimated base flow for selected streamflow measurement stations, commonly referred to as streamgages. Model calibration was accomplished using a combination of manual and automatic methods, including the Model-Independent Parameter Estimation (PEST) program that adjusted model input parameters with the aim of minimizing the difference between estimated and model-simulated values of hydraulic head and base flow.
Model boundary conditions consist of all simulated groundwater inflow to and outflow from the AMA. For example, a stream reach that simulates a gain from or loss to groundwater is a boundary condition that allows water to exit or enter, respectively, the groundwater system. Other boundary conditions include springs, seeps, precipitation recharge, groundwater exchange with lakes and Puget Sound, and groundwater pumping. A comparison of the estimated groundwater budget to that simulated by the steady-state model version indicates that the relative percentages of total inflow or total outflow for six major categories of boundary conditions are similar for the two budgets.
The model was used to simulate three suites of scenarios of potential drought and water-use changes. Scenario 1 suite consisted of the steady-state model version that was run with 0, 15, 20, and 25 percent reduction of precipitation recharge to assess the corresponding reductions in base flow with decreasing recharge. The last simulation for the scenario 1 suite consisted of the transient model version simulating 3 years of consecutive seasonal drought, defined by the months of May through September, to assess the corresponding base-flow reductions. Scenario 2 suite consisted of the steady-state model version with all simulated groundwater use removed, compared with a simulation that includes current groundwater use to evaluate changes to potentiometric surfaces and base flows. Scenario 3 suite consisted of a transient model version of the model that simulated pumping increases for four different categories of water-supply wells (compared to no pumping increases) to evaluate resulting reductions in base flow. Although, these scenarios provide examples of model applications and useful insights, many other scenarios could be simulated. A description of how to download the model is described in the body of this report.
Uncertainty is associated with most model inputs. Groundwater levels, lake levels, and land-surface altitudes are relatively certain; other model inputs are far less certain, including precipitation recharge, base flow, hydraulic properties, water use, and the three-dimensional structure of subsurface hydrogeologic units. Models are useful not because of high levels of accuracy of all model inputs, but because they combine the best information and estimates available, thereby providing the best predictions available related to physical processes.
The model described in this report simulates groundwater flow on a regional scale, which has inherent limitations for simulating hydrologic scenarios at local scales. Model structures and inputs were generalized to be consistent with this regional scale. For example, the actual groundwater system has much greater heterogeneity of hydraulic conductivity than is possible within the model’s degrees of freedom. Variations in hydraulic gradients over distances less than 500 ft cannot be simulated. The distances between model features, such as a pumping well and a stream, must be placed at 500-ft intervals and are co-located if both features are within the same model cell.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245026v2","collaboration":"Prepared in cooperation with the Cities of Auburn, Milton, Puyallup, Sumner, and Tacoma; Pierce Conservation District; Pierce County Public Works; Washington State Department of Health; Washington State Department of Ecology; Thurston County Public Utility District; Cascade Water Alliance; Lakehaven Utility District; Lakewood Water District; Firgrove Mutual Water Company; Fruitland Mutual Water Company; Spanaway Water Company; Summit Water & Supply Company; and Mt. View-Edgewood Water Company","usgsCitation":"Long, A.J., Wright, E.E., Fuhrig, L.T., and Bright, V.A.L., 2024, Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington, v. 2 of Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington, 2 chap. 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Washington"},{"id":432079,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.11.csv","text":"Table 1.11","size":"8 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.11","linkHelpText":"- Supplemental hydraulic-head targets for the steady-state model version set equal to the land surface to prevent groundwater flooding and corresponding simulated values, near the southeastern part of Puget Sound, Washington"},{"id":432074,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.6.csv","text":"Table 1.6","size":"637 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.6","linkHelpText":"- Time-series records of measured and simulated hydraulic-head values (transient model version) for selected wells used, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432087,"rank":21,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/sir20245026v2.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5026 Vol 2 XML"},{"id":432066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/sir20245026v2.jpg"},{"id":432067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/sir20245026v2.pdf","size":"10.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5026 Vol 2 PDF"},{"id":432069,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.1.csv","text":"Table 1.1","size":"995 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.1","linkHelpText":"- Streamflow-Routing (SFR) Package specifications by reach"},{"id":432070,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.2.csv","text":"Table 1.2","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.2","linkHelpText":"- Estimated monthly average base flow estimated for Coal, Boise, and Scatter Creeks where they enter the active model area, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432071,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.3.csv","text":"Table 1.3","size":"10 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.3","linkHelpText":"- Estimated monthly average base flow estimated for selected streams where they enter the active model area, the Buckley diversion (inflow to Lake Tapps), and outflow from Lake Tapps, near the southeastern part of Puget Sound, Washington, 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2005–15"},{"id":432080,"rank":14,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.12.csv","text":"Table 1.12","size":"199 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.12","linkHelpText":"- Model calibration parameters showing input to the control file for the Model-Independent Parameter Estimation (PEST) program"},{"id":432081,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.13.csv","text":"Table 1.13","size":"12 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.13","linkHelpText":"- Simulated groundwater budget for the calibrated transient model version, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432083,"rank":17,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Tables1.1-1.14.xlsx","text":"Tables 1.1-1.14","size":"5.2 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More than 1 million people live within the active model area (AMA) in the southeastern part of the lowlands surrounding Puget Sound, or Puget Lowland, Washington, and groundwater is the source for approximately one-half of their public, domestic, and irrigation water demands. The 887-square-mile AMA, located in King and Pierce Counties, represents the area of analysis for the conceptual hydrogeologic framework and numerical groundwater-flow models within the study area and includes the Puyallup River and Chambers-Clover Creek watersheds. To assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources, conceptual and numerical groundwater-flow models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center in close cooperation with 18 water-resource agencies and stakeholders.
This report presents information used to characterize the groundwater-flow system and the development of a numerical model in the AMA. Included are descriptions of the geology and conceptual hydrogeologic framework, groundwater levels and flow directions, groundwater recharge and discharge, numerical groundwater-flow model construction and results, and model limitations. The study area encompasses the western part of Pierce County and the southwestern part of King County, Washington. The study area extends south to the Nisqually River, southwest to Tanwax Creek, northeast to the Green River, and north through the valley near Auburn and adjacent uplands. It is bounded on the east by foothills of the Cascade Range, and on the northwest by Puget Sound.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245026v1","collaboration":"Prepared in cooperation with the Cities of Auburn, Milton, Puyallup, Sumner, and Tacoma; Pierce Conservation District; Pierce County Public Works; Washington State Department of Health; Washington State Department of Ecology; Thurston County Public Utility District; Cascade Water Alliance; Lakehaven Utility District; Lakewood Water District; Firgrove Mutual Water Company; Fruitland Mutual Water Company; Spanaway Water Company; Summit Water & Supply Company; and Mt. View-Edgewood Water Company","usgsCitation":"Welch, W.B., Bright, V.A.L., Gendaszek, A.S., Dunn, S.B., Headman, A.O., and Fasser, E.T., 2024, Conceptual hydrogeologic framework and groundwater budget near the southeastern part of Puget Sound, Washington, v. 1 of Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington, 3 chap. (A–C): U.S. Geological Survey Scientific Investigations Report 2024–5026–A–C, [variously paged; 71 p.], 1 pl., https://doi.org/10.3133/sir20245026v1.","productDescription":"Report: 71 p.; Data Release: 1 Plate: 50.67 × 37.33 inches","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-135626","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":499451,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117216.htm","linkFileType":{"id":5,"text":"html"}},{"id":432059,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.jpg"},{"id":432065,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1_plate.pdf","text":"Plate 1","size":"5.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5026 Vol 1 Plate 1"},{"id":432064,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.XML"},{"id":432063,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/images"},{"id":433528,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1_versionHist.txt","description":"SIR 2024-5026 Vol 1 Version History"},{"id":432062,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JFKLMG","text":"USGS data release","description":"USGS data release","linkHelpText":"Spatial data in support of the characterization of water resources near the southeastern part of Puget Sound, Washington"},{"id":432060,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.pdf","text":"Report","size":"57.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5026 Vol 1"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.8989970759464,\n 47.52614250846048\n ],\n [\n -122.8989970759464,\n 46.60885290293453\n ],\n [\n -121.43135900005484,\n 46.60885290293453\n ],\n [\n -121.43135900005484,\n 47.52614250846048\n ],\n [\n -122.8989970759464,\n 47.52614250846048\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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More than 1 million people live within the active model area (AMA) in the southeastern part of the lowlands surrounding Puget Sound, or Puget Lowland, Washington, and groundwater is the source for approximately one-half of their public, domestic, and irrigation water demands. The 887-square-mile AMA, located in King and Pierce Counties, represents the area of analysis for the conceptual hydrogeologic framework and numerical groundwater-flow models within the study area and includes the Puyallup River and Chambers-Clover Creek watersheds. To assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources, conceptual and numerical groundwater-flow models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center in close cooperation with 18 water-resource agencies and stakeholders.
This multichapter volume documents the development of the conceptual and numerical groundwater-flow models of groundwater flow. Chapters A, B, and C provide an overall introduction to the multichapter volume (Chapter A), the conceptual hydrogeologic framework (Chapter B), and the groundwater budget (Chapter C). Chapters D and E describe numerical groundwater-flow model construction and calibration (Chapter D) and the numerical groundwater-flow model results (Chapter E). Collectively, these reports present a characterization and simulation tool for groundwater resources near the southeastern part of Puget Sound, Washington.
","doi":"10.3133/sir20245026","usgsCitation":"Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5026, https://doi.org/10.3133/sir20245026.","onlineOnly":"Y","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":433028,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.74462161441157,\n 49.512370286103504\n ],\n [\n -125.74462161441157,\n 46.69617446727628\n ],\n [\n -120.97655520816149,\n 46.69617446727628\n ],\n [\n -120.97655520816149,\n 49.512370286103504\n ],\n [\n -125.74462161441157,\n 49.512370286103504\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
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The U.S. Geological Survey has developed a transient, groundwater-flow model that simulates hydrologic conditions in the Long Island aquifer system as part of an ongoing (since 2016) multiyear, cooperative investigation with the New York State Department of Environmental Conservation. The goals of this investigation are to assist stakeholders and resource managers to evaluate the response of the hydrologic system to changes in future hydraulic stresses. Responses in the hydrologic system include changes in water levels in the hydrogeologic units; discharge to streams, coastal waters, and subsurface infrastructure; and the extent of saline groundwater in the aquifers. Hydraulic stresses include future water-supply management and changes in land use and infrastructure.
The numerical model synthesizes a diverse set of physiographic, geologic, climatic, land-use, and historical population, water use, and infrastructure data to physically represent the Long Island aquifer system from land surface to bedrock and to simulate annual hydrologic conditions between 1900 and 2019. A three-dimensional hydrogeologic framework was developed from existing and recently collected borehole geologic and geophysical data collected as part of a companion drilling program. Water-transmitting properties of the principal aquifer sediments were defined in three dimensions from new and existing lithologic logs. The distribution of recharge from precipitation was estimated from landscape characteristics and climate data. Anthropogenic recharge from wastewater, leaky infrastructure, and storm runoff were estimated from population, infrastructure, and pumping data.
Water-use data, including well locations, depths, and pumping rates, were obtained from historical sources and records and used to estimate pumping stresses continuously in time and space, at an annual average time scale. The data were incorporated into a three-dimensional numerical model using the U.S. Geological Survey finite difference modeling code MODFLOW 6; the model encompassed all of Long Island and surrounding surface waters and simulated historical hydrologic conditions from 1900 to 2019.
The calibration process involved trial and error adjustments using prior knowledge to improve general fit to observations followed by an inverse calibration to update and optimize input parameters, using an iterative ensemble smoother algorithm implemented in PEST++ version 5.0. This resulted in a model that generally was in good agreement with observed, dynamically varying hydrologic conditions from 1900 to 2019. The calibrated model was used to develop two base-case models for scenario testing of future, hypothetical conditions where one represented average-annual conditions, and one represented average-seasonal conditions from 2010 to 2019. The model representing average-annual conditions was modified further to represent an alternate sea-level position of 6 feet above the North American Vertical Datum of 1988, and the model representing average-seasonal conditions was modified to represent the average seasonal effects of a 5-year drought imposed upon current hydrologic conditions.
Recharge is the sole source of water to the aquifer system; groundwater discharges to coastal water and streams and is withdrawn by pumped wells. Model-estimated annual recharge ranged from about 11 inches in 1965 to 41 inches in 1983. On average, from 2010 to 2019, about 23 percent of water was pumped from wells, and about 47 and 27 percent discharged to coastal waters and streams, respectively; the remaining 4 percent was water that moved into storage in the aquifer matrix.
Water levels on Long Island vary naturally during time in response to changes in recharge; the amount of variation is largest in the interior of the island, in areas with highest water table altitudes near groundwater divides and lowest near streams and the coastal waters. The total range of water table altitudes on Long Island between 1900 and 2019 ranged from near 0 to more than 70 feet in western parts of Long Island. The largest range in altitudes is in New York City and is associated with areas of large historical withdrawals between the 1920s and the late 1980s. Water table altitudes generally varied by less than 10 feet in eastern Suffolk County, where the aquifer is under more natural conditions.
Saltwater intrusion is of great concern on Long Island, particularly in western Long Island where both the unconfined and confined parts of the aquifer system have been intruded in response to large-scale groundwater withdrawals; however, the volume of freshwater in the islandwide aquifer system only has changed by about 5 percent between 1900 and 2019. The decadal change in the freshwater volume was largest during the early and mid-20th century, corresponding to the largest historical pumping, but that volume change did not exceed 1 percent.
The negligible change in freshwater volume suggests that saltwater intrusion as of 2019 was limited at an islandwide scale but continues to occur in local areas of Queens and Nassau Counties, adversely affecting current water supplies and limiting future water supplies for affected communities. The regional groundwater model developed for this investigation is a tool that can be used to help determine the viability of current and future water supplies at a regional scale and can be used to support development of additional models at finer scale to support more focused assessments of groundwater sustainability.
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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Sedimentary landforms on Earth and other planetary bodies are built through scour, transport, and deposition of sediment. Sediment connectivity refers to the hypothesis that pathways of sediment transport do not occur in isolation, but rather are mechanistically linked. In dryland river systems, one such example of sediment connectivity is the transport of fluvially deposited sediment by wind. However, predictive tools that can forecast fluvial-aeolian sediment connectivity at meaningful scales are rare. Here we develop a suite of models for quantifying the availability of river-sourced sediment for aeolian transport as a function of river flow, wind regime, and land cover across 168 km of the Colorado River in Grand Canyon, USA. We compare and validate these models using topographic changes observed over 10 years in a coupled river sandbar-aeolian dunefield setting. The models provide a path forward for directly linking fluvial hydrology with the management and understanding of aeolian landscapes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL110106","usgsCitation":"Kasprak, A., Sankey, J., and Caster, J., 2024, Landscape-scale modeling to forecast fluvial-aeolian sediment connectivity in river valleys: Geophysical Research Letters, v. 51, no. 6, e2024GL110106, 10 p., https://doi.org/10.1029/2024GL110106.","productDescription":"e2024GL110106, 10 p.","ipdsId":"IP-165471","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":490135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl110106","text":"Publisher Index Page"},{"id":486217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.41686436104216,\n 36.95682017793375\n ],\n [\n -112.32831221274077,\n 36.95682017793375\n ],\n [\n -112.32831221274077,\n 36.00005971677052\n ],\n [\n -111.41686436104216,\n 36.00005971677052\n ],\n [\n -111.41686436104216,\n 36.95682017793375\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":937704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caster, Joshua 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":199033,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937706,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257288,"text":"sir20235094 - 2024 - Hydrogeologic conceptual model of groundwater occurrence and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado","interactions":[],"lastModifiedDate":"2026-02-02T20:23:29.622888","indexId":"sir20235094","displayToPublicDate":"2024-08-19T17:15:00","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":"2023-5094","displayTitle":"Hydrogeologic Conceptual Model of Groundwater Occurrence and Brine Discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado","title":"Hydrogeologic conceptual model of groundwater occurrence and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado","docAbstract":"Salinity, or total dissolved solids (TDS), of the Colorado River is a major concern in the southwestern United States where the river provides water to about 40 million people for municipal and industrial use and is used to irrigate about 5.5 million acres of land. Much of the salinity in the Colorado River Basin is derived from natural interactions of surface water and groundwater with various geologic materials (rocks, soils, and alluvial deposits). The Dolores River in southwest Colorado is a major tributary of the Colorado River that historically accounts for about 6 percent of the salinity load to the Upper Colorado River Basin with the Paradox Valley being the primary source of salinity to the Dolores River. The Paradox Valley, one of several salt-anticline valleys in the region, is a fault-bounded topographic basin aligned with and exposing an underlying salt-anticline core. Salt deposits in the Pennsylvanian Paradox Formation of the Hermosa Group form an elongated salt diapir oriented northwest to southeast that is up to 12,000 feet (ft) thick beneath the present valley floor. Surface erosion, groundwater circulation, and weathering during Tertiary and Quaternary valley formation contributed to development of a cap rock, collapse features, breccia, and brine at the top of the exposed salt diapir. Today (2023), brine occurring in the brecciated cap rock and underlying salt deposits is in hydraulic connection with an overlying freshwater alluvial aquifer, and depending on seasonal river stage and hydrologic conditions, the brine discharges to the Dolores River causing the observed increase in salinity as the river crosses the Paradox Valley.
To reduce salinity concentrations in the Dolores River, the Bureau of Reclamation (Reclamation) operates the Paradox Valley Unit (PVU). The PVU project consists of nine shallow brine pumping wells near the Dolores River and one deep disposal well where the brine is injected for disposal. When operational, the PVU pumping wells extract brine from the base of the alluvial aquifer that is piped and injected into a deep disposal well about 3 miles southwest of the PVU. The PVU became fully operational July 1, 1996, and by 2015, operation of the PVU had reduced salinity concentrations in the Dolores River by as much as 70 percent compared to pre-PVU conditions. In response to a 4.5 magnitude earthquake, injection operations, and thus PVU pumping, were ceased from March 2019 to June 2022. A trial period of PVU operation began in June 2022 with a reduced injection rate, and thus PVU pumping rate, of about two-thirds capacity to gather additional information and guide future operational decisions.
In cooperation with Reclamation, the U.S. Geological Survey (USGS) developed this report to present the current (2023) understanding of groundwater and brine occurrence and discharge to the Dolores River in the Paradox Valley. Results from the compilation of spatial datasets, groundwater sampling and age dating, and aquifer tests are presented to provide improved understanding of the Paradox Valley hydrogeology, to supply datasets for a numerical groundwater-flow and brine-transport model, and to support future operations of the PVU. The hydrogeologic data provided herein, along with the most recent loading analysis for the Dolores River in the Paradox Valley, and a previous conceptual model for brine discharge to the river are used to present a conceptual understanding of groundwater occurrence in the Paradox Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235094","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Paschke, S.S., Mast, M.A., Gardner, P.M., Newman, C.P., and Watts, K.R., 2024, Hydrogeologic conceptual model of groundwater occurrence and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado: U.S. Geological Survey Scientific Investigations Report 2023–5094, 58 p., https://doi.org/10.3133/sir20235094.","productDescription":"Report: x, 54 p.; 2 Data Releases; Database","onlineOnly":"Y","ipdsId":"IP-125569","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":432678,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245038","text":"Simulation of Groundwater Flow and Brine Discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado"},{"id":432677,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS database—","linkHelpText":"USGS water data for the nation: U.S. Geological Survey National Water Information System database"},{"id":432676,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CJQDDU","text":"USGS data release","linkHelpText":"Geospatial datasets developed for a hydrogeologic conceptual model of brine discharge to the Dolores River, Paradox Valley, Colorado"},{"id":432675,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NV5U6F","text":"USGS data release","linkHelpText":"Water-level and pumping data, water-level models, and estimated hydraulic properties for the Paradox Valley alluvial aquifer in Montrose County, Colorado, 2013"},{"id":432674,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FMWX2J","text":"USGS data release","linkHelpText":"Recharge temperatures and groundwater-age models for the Paradox Valley alluvial aquifer, 2011, Colorado"},{"id":432669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5094/sir20235094.pdf","text":"Report","size":"9.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5094"},{"id":499380,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117218.htm","linkFileType":{"id":5,"text":"html"}},{"id":432668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5094/coverthb.jpg"}],"country":"United States","state":"Colorado","county":"Montrose County","otherGeospatial":"Paradox Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.23469392755278,\n 38.62004006715256\n ],\n [\n -109.23469392755278,\n 38.0400613431201\n ],\n [\n -108.48695698861,\n 38.0400613431201\n ],\n [\n -108.48695698861,\n 38.62004006715256\n ],\n [\n -109.23469392755278,\n 38.62004006715256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, Colorado 80225
Salinity, or total dissolved solids (TDS), of the Colorado River affects agricultural, municipal, and industrial water users and is an important concern in the Western United States. In the Paradox Valley of southwestern Colorado, natural discharge of sodium-chloride brine to the Dolores River from the underlying core of a salt-valley anticline accounts for about 6 percent of the salinity load to the Colorado River. Formation of the Paradox Valley began during the Miocene, and subsequent erosion exposed the Pennsylvania Paradox Formation in the core of the anticline where a cap rock, collapse features, breccia, and sodium-chloride saturated brine developed at the top of the exposed salt diapir. The discharge of brine to the Dolores River is affected by these dissolution features, along with seasonal hydrologic conditions and density-dependent flow between older dense brine and the younger fresh groundwater in the overlying alluvial aquifer. To reduce TDS concentrations in the Dolores River through the Paradox Valley, the Bureau of Reclamation has pumped brine from a series of shallow wells adjacent to the river since July 1996. The pumped brine is collected and piped to a deep disposal well where it is injected into the Mississippian Leadville Limestone at a depth of about 4,570-meters below land surface. The pumping and injection operation is collectively known as the Paradox Valley Unit (PVU), and by 2015, the PVU had substantially reduced TDS concentrations in the Dolores River by about 70 percent. Since 2019, injection-pressure limits and related seismic activity have constrained deep-well injection and thus brine pumping at the PVU.
In cooperation with the Bureau of Reclamation, the U.S. Geological Survey developed a MODFLOW-6 three-dimensional, variable-density groundwater flow and TDS transport model of the Paradox Valley to evaluate the effects of PVU pumping operations on brine discharge to the Dolores River and to guide additional research. The finite-difference model grid consists of 76 rows and 48 columns oriented from northwest to southeast in alignment with valley topography and groundwater-flow directions in the near-surface freshwater alluvial aquifer. A 7-layer hydrogeologic framework was developed from existing datasets to represent the alluvial aquifer, cap rock, collapse breccia, and groundwater flow and TDS transport from the underlying Paradox Formation salt to the Dolores River. The model represents a 33-year transient calibration period from 1987 through 2020 that includes pre-PVU conditions from 1987 through June 1996 and post-PVU conditions from July 1996 through 2020. A 1,000-year simulation of groundwater flow and coupled TDS transport computed the initial conditions for the subsequent 33-year transient simulation. Observations of precipitation, streamflow, evaporation, agricultural land use, and PVU brine pumping rates were used to specify appropriate boundary conditions to the model representing time-varying recharge, tributary streamflow, groundwater underflow, evapotranspiration (ET), and PVU pumping. Values for average monthly streamflow and TDS concentration at the upstream streamgage, the Dolores River at Bedrock (USGS streamgage 09169500), were specified as model input where the Dolores River enters Paradox Valley. Observed pumping from the PVU, water levels and TDS concentrations in groundwater, and streamflow and estimated TDS concentrations at the downstream streamgage, the Dolores River near Bedrock (USGS streamgage 09171100), were calibration targets that constrained the manual calibration of model parameters representing aquifer hydraulic conductivity, storage, streambed conductance, recharge, and (ET).
Two primary model-calibration targets were the match between observed and simulated TDS mass flux from PVU pumping wells and the match between estimated and simulated TDS mass flux to the Dolores River. The simulated TDS mass withdrawn by pumping wells is calculated by the model as the product of the assigned pumping rate and simulated groundwater TDS concentrations. Because actual pumping rates were assigned as simulated values, the total simulated PVU pumping for the 33-year calibration is within 0.5 percent of the observed values. However, simulated concentrations and thus mass flux of TDS withdrawn by the PVU pumping wells were consistently about 26 percent less than observed values for all the simulated time periods (33-year simulation, pre-PVU, and post-PVU). The representation of brine inflow was explored through additional modeling to evaluate the effect of the simulated brine source on groundwater TDS concentrations. Results indicated that a saturated-salt constant-flux brine source best replicated the magnitude and transient pattern observed for TDS mass flux from PVU pumping wells.
The simulated TDS mass flux to the Dolores River is compared to estimates based on observed streamflow and specific conductance (SC) data for the downstream streamgage. The calibrated model provided a close fit of simulated to measured streamflow at the downstream streamgage, and the calibrated model fit to estimated TDS concentrations at the downstream streamgage was reasonable. The greatest differences between simulated and estimated values occurred during drought periods from June 2000 to March 2003, May 2012 to June 2013, and October 2013 to October 2014, when simulated TDS concentrations in the river were greater than estimated concentrations. In general, simulated TDS mass flux to the river for the pre-PVU period is in good agreement with estimated values (2-percent difference), but the model overestimated TDS mass flux to the river by about 41 percent during the post-PVU period. The model uncertainty with respect to TDS mass flux to the river indicates other processes or model parameters not well represented by the model are affecting the system, especially during drought. During model calibration, the most sensitive parameters were identified as vertical hydraulic conductivity of the alluvial aquifer, conductance of the Dolores River streambed, ET extinction depth and rate, and recharge rate.
Five 5-year scenarios of conditions for 2021–25 were simulated to assist evaluation of alternative strategies to manage the discharge of brine into the Dolores River. The first scenario simulates no PVU pumping and serves as a base case for comparison to the other scenarios. Two scenarios simulate the effects of varying withdrawal timing at an annual rate about one-third less than during 2010 through 2018. During high-flow spring snowmelt runoff periods when brine discharge is naturally minimized, PVU pumping does not substantially affect salinity in the Dolores River, and comparison of these two scenarios indicates that scheduling brine withdrawals during times of low river stage is nearly as effective at reducing TDS mass flux to the river as pumping brine year-round. Cessation of pumping during periods of high river stage may be advantageous for system maintenance, brine injection, and seismic-risk reduction. The fourth scenario tested the effect of reducing irrigation-return flow on brine discharge and predicted a slight reduction of TDS mass flux to the Dolores River, but not as great a reduction as that of using the PVU to remove brine. The fifth scenario simulated 5 years of drought conditions without PVU pumping and indicates brine discharge during drought about 15 percent greater than during average hydrologic conditions. Results from scenario 5 are consistent with the calibrated model results and indicate that aquifer properties and ET processes and parameters may be affecting simulation results during drought.
The Paradox Valley groundwater model provides a reasonable overall match to observed conditions in the Dolores River. The model is useful for evaluating relative differences between brine management scenarios to inform PVU operational decisions and to identify gaps in data and process understanding. Representation of the brine source, hydraulic-conductivity parameters, and recharge and ET processes were identified as potential areas for additional field and modeling research. Additional research in the Paradox Valley might include field-data collection that provides additional information on the hydrogeologic framework, groundwater levels, groundwater TDS concentrations, stream characteristics, and aquifer properties. Additional modeling efforts could benefit from applying advanced tools for model development, calibration, and visualization including parameter-estimation and sensitivity analysis. Statistical evaluation of known model uncertainties such as hydraulic conductivity, streambed conductance, representations of the brine source, recharge, and ET could improve the match between simulated and estimated TDS mass flux from PVU pumping wells and to the Dolores River further informing model predictions and system understanding for the Paradox Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245038","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Heywood, C.E., Paschke, S.S., Mast, M.A., and Watts, K.R., 2024, Simulation of groundwater flow and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado: U.S. Geological Survey Scientific Investigations Report 2024–5038, 47 p., https://doi.org/10.3133/sir20245038.","productDescription":"Report: viii, 47 p.; Data Release; 3 Databases","onlineOnly":"Y","ipdsId":"IP-130109","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":432768,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://waterdata.usgs.gov/monitoring-location/09171100/all-graphs/#period=P7D","text":"USGS database site information —","linkHelpText":"USGS 09171100 Dolores River near Bedrock, CO, in USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432764,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS database—","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5038/coverthb.jpg"},{"id":432763,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZW0FH5","text":"USGS data release","linkHelpText":"MODFLOW-6 model of variable-density groundwater flow and brine discharge to the Dolores River in the Paradox Valley, Colorado"},{"id":432766,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://waterdata.usgs.gov/monitoring-location/09169500/all-graphs/#period=P7D","text":"USGS database site information —","linkHelpText":"USGS 09169500 Dolores River at Bedrock, CO, in USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432760,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5038/sir20245038.pdf","text":"Report","size":"9.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5038"},{"id":499462,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117219.htm","linkFileType":{"id":5,"text":"html"}},{"id":432769,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235094","text":"Hydrogeologic Conceptual Model of Groundwater Occurrence and Brine Discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado"}],"country":"United States","state":"Colorado","county":"Montrose County","otherGeospatial":"Paradox Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.10372373203981,\n 38.51805273423872\n ],\n [\n -109.10372373203981,\n 38.1119253984173\n ],\n [\n -108.49529764389875,\n 38.1119253984173\n ],\n [\n -108.49529764389875,\n 38.51805273423872\n ],\n [\n -109.10372373203981,\n 38.51805273423872\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, Colorado 80225
Stable isotopes of water preserved in geologic archives, primarily as oxygen (δ18O), have proven critical for documenting Earth’s climatic and hydrologic
systems past and present. However, timescale differences of water isotope inputs to proxy systems and the signal embedded in long paleorecords often
confound translation to observed hydroclimatic metrics. Here, a unique 20-year dataset of meteorology, hydrology, and the isotopic composition of
weekly meteoric and surface water samples (δ18O, δ2 H) are combined with paleoclimate δ18O data from tree-ring cellulose and lake carbonate to better
understand proxy signals of Upper Colorado river basin drought. Annual tree-ring cellulose δ18O from Picea engelmannii growing within a glacier-fed creek
and a spring discharge area were used to derive annual source water δ18O using a cellulose source-water isotope model. Comparisons with the monitoring
record indicates that tree-ring cellulose δ18O tracks variations in wet and dry hydroclimatic extremes. Source water isotopes are shown to reflect the
hydroclimate of the current year and some number of previous years as an effective moisture-discharge proxy rather than a precipitation isotope proxy.
Results contextualize Holocene lake carbonate δ18O data. The contemporary-to-paleo comparison identifies changes in seasonal precipitation extremes
during recent millennia and several earlier arid and monsoon-dominated Holocene periods that exceed the arid maximum of the calibration period.
Water temperature is a primary control on the occurrence and distribution of cold-water species. Rivers draining Mount Rainier in western Washington, including the White River along its northern flank, support several cold-water fish populations, but the spatial distribution of water temperatures, particularly during late-summer base flow between August and September, and the climatic, hydrologic, and physical processes regulating this temperature distribution are not well understood. Spatial stream network (SSN) models, which are generalized linear models that incorporate streamwise spatial autocovariance structures, were fit to mean and seven-day average daily maximum water temperature for August and September for the White River basin located with Mount Rainier National Park. The SSN models were calibrated using water temperature measurements collected between 2010 and 2020. Significant covariates within the best-fit models included the proportion of ice cover and forest cover within the basin, mean August air temperature, the proportion of consolidated geologic units, and snow water equivalent. Statistical models that included spatial autocovariance structures had better predictive performance than those that did not. In addition, models of mean August and September water temperature had better predictive performance than those of seven-day average daily maximum temperature in August and September. Predictions of the spatial distribution of water temperature were similar between August and September with a general warming in the downstream part of main-stem White River compared to cooler water temperatures in the high-elevation headwater streams. Estimated water temperatures for the upper White River model are three to four degrees Celsius warmer for tributaries but one to two degrees cooler for the main stem compared to the regional-scale model. Differences between the upper White River SSN model and the regional-scale SSN model are attributed the upper White River SSN including water temperature observations specific to the upper White River, whereas water temperature observations from lower elevation streams and downstream of the Mount Rainer National Park boundary were used in the regional-scale model.
","language":"English","publisher":"EarthArXiv","doi":"10.31223/X5712P","usgsCitation":"Gendaszek, A., Leach, A.C., and Jaeger, K.L., 2024, Spatial variability of water temperature within the White River basin, Mount Rainier National Park Washington: EarthArXiv, https://doi.org/10.31223/X5712P.","productDescription":"33 p.","ipdsId":"IP-166723","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":433007,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439208,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.31223/x5712p","text":"External Repository"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gendaszek, Andrew S. 0000-0002-2373-8986","orcid":"https://orcid.org/0000-0002-2373-8986","contributorId":343378,"corporation":false,"usgs":false,"family":"Gendaszek","given":"Andrew","middleInitial":"S.","affiliations":[{"id":82076,"text":"King County","active":true,"usgs":false}],"preferred":false,"id":910876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leach, Anya Clare 0000-0001-7828-8858","orcid":"https://orcid.org/0000-0001-7828-8858","contributorId":339960,"corporation":false,"usgs":true,"family":"Leach","given":"Anya","email":"","middleInitial":"Clare","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":910878,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257042,"text":"sir20245022 - 2024 - Monitoring and simulation of hydrology, suspended sediment, and nutrients in selected tributary watersheds of Lake Erie, New York","interactions":[],"lastModifiedDate":"2026-02-03T18:05:12.063702","indexId":"sir20245022","displayToPublicDate":"2024-08-13T11:55:00","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":"2024-5022","displayTitle":"Monitoring and Simulation of Hydrology, Suspended Sediment, and Nutrients in Selected Tributary Watersheds of Lake Erie, New York","title":"Monitoring and simulation of hydrology, suspended sediment, and nutrients in selected tributary watersheds of Lake Erie, New York","docAbstract":"The U.S. Geological Survey, in cooperation with Erie County, New York, the New York State Department of Environmental Conservation, and the Great Lakes Restoration Initiative, collected water-quality samples in nine selected New York tributaries to Lake Erie, computed estimates of suspended sediment and nutrient loads using the R scripting package rloadest and used the Soil and Water Assessment Tool (SWAT) to simulate hydrology and suspended sediment and nutrient loads from these tributaries. This project was undertaken to better understand the water quality of New York’s inputs into eastern Lake Erie.
Water-quality samples for suspended sediment, nitrogen, and phosphorus were collected at 19 sampling sites in the Lake Erie Basin in New York. Daily and monthly suspended sediment and nutrient loads were computed with regressions of streamflow and suspended sediment and nutrient concentrations using rloadest.
SWAT models of nine watersheds were created using publicly available data; and the loads calculated by rloadest. Twenty-six SWAT model scenarios were created to explore the effects that best management practices (BMPs; 21 scenarios), point source discharges (4 scenarios), and green infrastructure (1 scenario) can have on the water quality of the nine tributaries to Lake Erie. BMP scenarios for the watershed models included combinations of agricultural BMPs applied at varying implementation levels across the study watersheds, including cover crops, reduced tillage, nutrient management plans, and filter strips. The BMP scenarios showed small reductions of total nitrogen and total phosphorus. The scenarios have variable suspended sediment load results, with both increases and decreases of sediment modeled. The point source scenarios result in lower total phosphorus loads. The green infrastructure scenario shows only minimal reduction of suspended sediment and nutrient loads from the Buffalo River watershed but shows substantial reductions locally.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245022","collaboration":"Prepared in cooperation with Erie County, New York, the New York State Department of Environmental Conservation, and the Great Lakes Restoration Initiative","usgsCitation":"Merriman, K.R., Fisher, B.N., Nystrom, E.A., Bunch, A.R., Welk, R.J., and Kappel, W.M., 2024, Monitoring and simulation of hydrology, suspended sediment, and nutrients in selected tributary watersheds of Lake Erie, New York: U.S. Geological Survey Scientific Investigations Report 2024–5022, 152 p., https://doi.org/10.3133/sir20245022.","productDescription":"Report: xii, 152 p.; 2 Data Releases","numberOfPages":"152","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-142479","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":432349,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5022/images/"},{"id":432347,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245022/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5022 HTML"},{"id":432346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5022/sir20245022.pdf","text":"Report","size":"49.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5022 PDF"},{"id":432345,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5022/coverthb.jpg"},{"id":432350,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N6IQZT","text":"USGS data release","linkHelpText":"Data and rloadest models used to estimate sediment and nutrient loads in selected New York tributaries to eastern Lake Erie"},{"id":499450,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117172.htm","linkFileType":{"id":5,"text":"html"}},{"id":432351,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U2IF5I","text":"USGS data release","linkHelpText":"SWAT Model Archive for Simulation of Hydrology, Suspended Sediment and Nutrients in Selected Tributary Watersheds of Lake Erie, New York"},{"id":432348,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5022/sir20245022.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5022 XML"}],"country":"United States","state":"New York","otherGeospatial":"Lake Erie watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.05117923155397,\n 43.180030390880034\n ],\n [\n -79.03978515463244,\n 43.13847304512555\n ],\n [\n -79.06582875902379,\n 43.119465987668804\n ],\n [\n -79.05606240737677,\n 43.10639520955428\n ],\n [\n -79.07233966012183,\n 43.07667851399174\n ],\n [\n -78.99420884694702,\n 43.056462925290475\n ],\n [\n -79.0235079018874,\n 43.00648994913769\n ],\n [\n -79.01048609969206,\n 42.9779156948116\n ],\n [\n -78.95514344035962,\n 42.9540936681897\n ],\n [\n -78.91933348432123,\n 42.94932815586105\n ],\n [\n -78.90305623157617,\n 42.9243031624776\n ],\n [\n -78.89817305575336,\n 42.889728119559265\n ],\n [\n -78.87212945136129,\n 42.800219981426636\n ],\n [\n -79.75923746535221,\n 42.27370078552943\n ],\n [\n -79.75435428952869,\n 42.04444584745826\n ],\n [\n -79.2155772236776,\n 42.08553142608457\n ],\n [\n -78.44891922533873,\n 42.52492114004454\n ],\n [\n -78.15592867593357,\n 42.82529655474477\n ],\n [\n -78.1005861716743,\n 43.11709104352599\n ],\n [\n -78.66540684191676,\n 43.21444355835797\n ],\n [\n -79.01536777592817,\n 43.19308678489239\n ],\n [\n -79.05117923155397,\n 43.180030390880034\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Species occupying dryland river ecosystems often experience “boom-and-bust” demographic cycles that coincide with shifts in habitat availability. Knowing whether declines are within natural thresholds versus those caused by acute human disturbance is critical for managing protected species. We investigated temporal shifts in abundance and habitat use of an endangered population of the threespine stickleback Gasterosteus aculeatus in southern California, where a Mediterranean climate leads to ephemeral habitat in one of the regions' least hydrologically modified rivers, the Santa Clara River. We conducted population surveys over a period of below-average rainfall in the upper watershed in Soledad Canyon, with predefined reaches surveyed multiple times per year to capture different hydrologic conditions. Abundances were stable across years but varied significantly depending on location, with some reaches remaining dry and others drying seasonally to varying degrees. Occupancy models showed that the presence of stable perennial reaches, drying regime, and other site-specific factors were important predictors of habitat use, and that certain reaches may be key to ensuring source-sink dynamics as flow dissipates over the dry season. Low occupancy in two sections was driven by different predominant mechanisms, one by diel cycles of evapotranspiration and the other by cattails (Typha spp.), with both having greater effects during the hotter, drier parts of the year. As dryland river ecosystems are vulnerable to the effects of anthropogenic-induced climate change, this study demonstrates how temporal monitoring can delimit dry-state benchmarks for improving management interventions (i.e., translocation and habitat restoration) for protected species under conditions that are predicted to worsen in the coming years.
","language":"English","publisher":"Wiley","doi":"10.1002/aqc.4211","usgsCitation":"Richmond, J.Q., Gould, P.R., Pareti, J., Aitken, A., Morrissette, E., Backlin, A.R., Dellith, C., and Fisher, R., 2024, Effects of temporal hydrologic shifts on the population biology of an endangered freshwater fish in a dryland river ecosystem: Aquatic Conservation: Marine and Freshwater Ecosystems, v. 34, no. 8, e4211, 13 p., https://doi.org/10.1002/aqc.4211.","productDescription":"e4211, 13 p.","ipdsId":"IP-166124","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":463600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Soledad Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.40734591539739,\n 34.491979597799684\n ],\n [\n -118.40734591539739,\n 34.36899750284367\n ],\n [\n -118.07881365289235,\n 34.36899750284367\n ],\n [\n -118.07881365289235,\n 34.491979597799684\n ],\n [\n -118.40734591539739,\n 34.491979597799684\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":917752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gould, Philip Robert 0000-0002-8871-0968","orcid":"https://orcid.org/0000-0002-8871-0968","contributorId":294694,"corporation":false,"usgs":true,"family":"Gould","given":"Philip","email":"","middleInitial":"Robert","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":917753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pareti, Jennifer","contributorId":345858,"corporation":false,"usgs":false,"family":"Pareti","given":"Jennifer","email":"","affiliations":[{"id":54562,"text":"cdfw","active":true,"usgs":false}],"preferred":false,"id":917754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aitken, Andrew","contributorId":345859,"corporation":false,"usgs":false,"family":"Aitken","given":"Andrew","email":"","affiliations":[{"id":54562,"text":"cdfw","active":true,"usgs":false}],"preferred":false,"id":917755,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morrissette, Eric","contributorId":237012,"corporation":false,"usgs":false,"family":"Morrissette","given":"Eric","email":"","affiliations":[],"preferred":false,"id":917756,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Backlin, Adam R. 0000-0001-5618-8426 abacklin@usgs.gov","orcid":"https://orcid.org/0000-0001-5618-8426","contributorId":3802,"corporation":false,"usgs":true,"family":"Backlin","given":"Adam","email":"abacklin@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":917757,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dellith, Chris","contributorId":139396,"corporation":false,"usgs":false,"family":"Dellith","given":"Chris","email":"","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":917758,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":917759,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70257129,"text":"70257129 - 2024 - Downstream decreases in water availability, tree height, canopy volume and growth rate in cottonwood forests along the Green River, southwestern USA","interactions":[],"lastModifiedDate":"2024-11-04T19:36:04.206703","indexId":"70257129","displayToPublicDate":"2024-08-07T06:23:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Downstream decreases in water availability, tree height, canopy volume and growth rate in cottonwood forests along the Green River, southwestern USA","docAbstract":"Hydrologic stress is increasing in Fremont cottonwood (Populus fremontii) forests across the southwestern United States because of increased temperature and streamflow diversion. The spatial variability of this stress is large yet poorly understood. Along the Yampa and Green Rivers in Colorado and Utah, vapour pressure deficit and flow diversions increase downstream. To investigate effects of this gradient on cottonwoods, we measured the percent live canopy and height of randomly selected trees at three sites: Deerlodge Park on the Yampa River (DLP), Island Park on the upper Green (ILP) and Canyonlands National Park on the lower Green (CAN). From these same trees, we took increment cores to understand differences in tree growth in each forest over time. We then related tree metrics to local water availability, streamflow and climatic data. Cottonwoods at CAN were shorter and had lower percent live canopy and growth rate than similarly aged trees upstream. CAN trees that grew higher above the water surface also tended to have lower tree growth, height and live canopy percentage. Furthermore, the correlation between tree growth and maximum vapour pressure deficit showed a much stronger negative shift since 1990 at CAN than at the other sites. All of these differences suggest higher hydrologic stress at CAN, which we attribute to the combined effects of peak flow declines from Flaming Gorge Reservoir, flow diversion and the higher and increasing vapour pressure deficit at CAN. Further research on the variability of hydrologic stress on cottonwoods could help managers anticipate and mitigate the effects of drought stress in these iconic forests.
The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, updated low-streamflow statistics for New York, excluding Long Island and including hydrologically connected watersheds in bordering States, for the first time since 1972. Historical daily streamflow data for active and inactive gages were considered for this study with periods of record as recent as March 31, 2022, adding 50 years of data to the last published low-streamflow statistics report for New York and including 119 new gages where low-streamflow statistics are calculated for the first time. Gages were evaluated across several criteria to identify gages that were not suitable for low-streamflow frequency analysis. In addition, gages were evaluated for the presence of alteration within the streamflow period of record based on previous studies and U.S. Geological Survey National Water Information System site metadata including peak flow codes.
A trend analysis was performed using the Wilcoxon rank-sum hypothesis test comparing data from the most recent 30 years of record to data from 30 years and earlier for each long-record gage (30 years or more of available data). Results from the trend analysis indicated that 45 unaltered and 32 altered long-record sites had a statistically significant trend for the annual minimum n-day time series; most gages showed increasing trends in the annual minimum n-day time series. Low-streamflow statistics were calculated using the most recent 30 years of record for gages with a statistically significant trend. Before and after 1972, the lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years (7Q10 and 30Q10 statistics respectively) increased significantly at 41 unaltered gages and decreased significantly at 3 unaltered gages where data were available.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245055","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stagnitta, T.J., Graziano, A.P., Woda, J.C., Glas, R.L., and Gazoorian, C.L., 2024, Low-flow statistics for selected streams in New York, excluding Long Island: U.S. Geological Survey Scientific Investigations Report 2024–5055, 39 p., https://doi.org/10.3133/sir20245055.","productDescription":"Report: vi, 39 p.; Data Release","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-157678","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":432052,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5055/images/"},{"id":499477,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117164.htm","linkFileType":{"id":5,"text":"html"}},{"id":432053,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NOM6FR","text":"USGS data release","linkHelpText":"Low-flow statistics for New York State, excluding Long Island, computed through March 2022"},{"id":432048,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5055/coverthb.jpg"},{"id":432049,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5055/sir20245055.pdf","text":"Report","size":"2.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5055 PDF"},{"id":432051,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5055/sir20245055.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5055 XML"},{"id":432050,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245055/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5055 HTML"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.91286009788664,\n 41.01178774841347\n ],\n [\n -74.0266841070774,\n 40.720375728237144\n ],\n [\n -73.9615635090029,\n 40.70803672971712\n ],\n [\n -73.67394830242374,\n 40.966707520822155\n ],\n [\n -73.67401345493704,\n 41.01587061494237\n ],\n [\n -73.7610093516628,\n 41.09363100726691\n ],\n [\n -73.47905430116539,\n 41.21618146660194\n ],\n [\n -73.54958995108733,\n 41.28551897494029\n ],\n [\n -73.48572425776553,\n 42.050669593361164\n ],\n [\n -73.52340772727291,\n 42.08675087137979\n ],\n [\n -73.27944022486994,\n 42.71925225128092\n ],\n [\n -73.2459886442373,\n 43.56229834373957\n ],\n [\n -73.31078219020274,\n 43.62532933066436\n ],\n [\n -73.37060286487959,\n 43.61346437461091\n ],\n [\n -73.43066543683648,\n 43.58573443689042\n ],\n [\n -73.35806708414735,\n 43.79014308086275\n ],\n [\n -73.40009937812863,\n 44.00975633545363\n ],\n [\n -73.41060974678811,\n 44.08529772972881\n ],\n [\n -73.35805219069586,\n 44.318863718851276\n ],\n [\n -73.33701549362132,\n 44.52903276098101\n ],\n [\n -73.36854225932441,\n 44.626354722690195\n ],\n [\n -73.3054828249653,\n 44.73844638732069\n ],\n [\n -73.37904970354246,\n 44.83541786386914\n ],\n [\n -73.34751720937287,\n 45.0140137308498\n ],\n [\n -74.34598531174339,\n 44.99914556300604\n ],\n [\n -74.8399460822856,\n 45.02144131236122\n ],\n [\n -75.34442725785361,\n 44.80559688881786\n ],\n [\n -75.71227781323351,\n 44.58894197821161\n ],\n [\n -75.93298792150392,\n 44.363963821867316\n ],\n [\n -76.22726798212784,\n 44.23609096764133\n ],\n [\n -76.4374686507537,\n 44.085295715094446\n ],\n [\n -76.80532026610759,\n 43.62298965505482\n ],\n [\n -78.73916539102125,\n 43.63059733126207\n ],\n [\n -79.1910965886907,\n 43.44011660394341\n ],\n [\n -79.02292338480896,\n 43.20308949268261\n ],\n [\n -79.06495062651008,\n 43.0880701724453\n ],\n [\n -78.88515521912908,\n 42.85650132783874\n ],\n [\n -79.77268100220316,\n 42.50253000668161\n ],\n [\n -79.7434614428243,\n 42.00043762996769\n ],\n [\n -75.398839703039,\n 41.97678364767139\n ],\n [\n -75.09566664827426,\n 41.829506046160375\n ],\n [\n -74.91860171317268,\n 41.43129306375607\n ],\n [\n -73.91286009788664,\n 41.01178774841347\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The thematic issue entitled, “Current Advances in Coastal Wetland Elevation Dynamics,” draws on topics from two special sessions at the CERF 2021 conference plus additional recent research describing scientific insights gained from the Surface Elevation Table–Marker Horizon (SET–MH) method and its application across the globe to quantify and understand subsurface process influences on wetland elevation change and wetland responses to sea-level rise. The findings group articles within each of five thematic topics. (1) A 30-year retrospective on the scientific insights gained on surface and shallow subsurface process dynamics. (2) Investigations of the subsurface soil process influences on wetland elevation. (3) How the scientific community applies the SET–MH method to quantify and understand wetland responses to RSLR and other environmental drivers such as altered hydrology and sediment supply. (4) How SET–MH data are used in long-term monitoring networks at different geographic scales. (5) Pairing the SET-MH method with (a) survey techniques to increase lateral coverage of wetland elevation trends and (b) geodetic measurements to increase vertical coverage of vertical land motion.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-024-01411-1","usgsCitation":"Cahoon, D., and Guntenspergen, G.R., 2024, Current advances in coastal wetland elevation dynamics: Introduction to the special issue: Estuaries and Coasts, v. 47, p. 1703-1707, https://doi.org/10.1007/s12237-024-01411-1.","productDescription":"5 p.","startPage":"1703","endPage":"1707","ipdsId":"IP-167659","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":496383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-024-01411-1","text":"Publisher Index Page"},{"id":483451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","noUsgsAuthors":false,"publicationDate":"2024-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Cahoon, Donald R. 0000-0002-2591-5667","orcid":"https://orcid.org/0000-0002-2591-5667","contributorId":219657,"corporation":false,"usgs":true,"family":"Cahoon","given":"Donald","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":930909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":930910,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256788,"text":"70256788 - 2024 - Low-flow period seasonality, trends, and climate linkages across the United States","interactions":[],"lastModifiedDate":"2024-08-13T14:41:37.568761","indexId":"70256788","displayToPublicDate":"2024-07-31T09:46:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1927,"text":"Hydrological Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"Low-flow period seasonality, trends, and climate linkages across the United States","docAbstract":"Low-flow period properties, including timing, magnitude, and duration, influence many key processes for water resource managers and ecosystems. We computed annual low-flow period duration and timing metrics from 1951 to 2020 for 1032 conterminous United States (CONUS) streamgages and analyzed spatial patterns, trends through time, and relationships to climate. Results show northwestern and eastern CONUS streamgages had longer and more inter-annually consistent low-flow period durations, while central CONUS periods were shorter and more variable. Low-flow periods most often occurred in summer months but start and end dates occurred later in north-central and mountainous western CONUS, which have the greatest number of low flows during cold seasons. Low-flow periods are becoming longer in southeastern and northwestern CONUS but shorter in much of the rest of CONUS. Temperature was correlated with low-flow period duration in southeastern and northwestern CONUS, and precipitation was correlated with duration everywhere, but most strongly in eastern CONUS.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02626667.2024.2369639","usgsCitation":"Simeone, C., McCabe, G.J., Hecht, J.S., Hammond, J., Hodgkins, G.A., Olson, C.G., Wieczorek, M., and Wolock, D.M., 2024, Low-flow period seasonality, trends, and climate linkages across the United States: Hydrological Sciences Journal, v. 69, no. 10, p. 1387-1398, https://doi.org/10.1080/02626667.2024.2369639.","productDescription":"12 p.","startPage":"1387","endPage":"1398","ipdsId":"IP-144967","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science 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1985–2021","interactions":[],"lastModifiedDate":"2026-02-03T18:28:42.919405","indexId":"sir20245033","displayToPublicDate":"2024-07-30T12:53:26","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":"2024-5033","displayTitle":"Assessment of Long-Term Changes in Surface-Water Extent Within Klamath Marsh, South-Central Oregon, 1985–2021","title":"Assessment of long-term changes in surface-water extent within Klamath Marsh, south-central Oregon, 1985–2021","docAbstract":"The annual maximum extent of surface water in Klamath Marsh has naturally fluctuated in response to periods of wet and dry conditions in the surrounding basin. Field observations during the 2010s indicate that the annual maximum extent of surface water has been declining and the marsh is not responding to hydrologic inputs as it had historically. This report describes the results of a hydrologic evaluation of Klamath Marsh to characterize and understand multi-year declines in the surface-water extent and increased intermittency of streamflow exiting the marsh.
Landsat imagery collected during 1985–2021 was processed to create a time series of annual maximum surface-water extent to assess changes in surface-water inundation within the marsh. A 50-percent decrease in the mean surface area of annual total open-water extent (TOWE) during the latter half of the study period (2003–21) compared to the first half (1985–2003) was observed in this 37-year time-series dataset. The change in open-water extent was offset by a corresponding increase in dry land in the marsh.
Time series of streamflow, groundwater level, total annual precipitation, annual mean temperature, and anthropogenic water use and water management were compiled and evaluated to improve understanding of the factors affecting TOWE. Statistically significant downward trends in the regional groundwater table and streamflow into and out of the marsh were identified as well as statistically significant upward trends in annual mean temperature. Statistically significant correlations among TOWE, streamflow, and groundwater level also were identified. The decreasing trends could not be attributed to changes in total annual precipitation or changing anthropogenic groundwater use within the study area.
Declines in the open-water extent of Klamath Marsh since 2000 principally are due to a decoupling of the groundwater and surface-water system beneath the marsh because of regional declines in groundwater level. Regional increases in air temperature and the reestablishment of more than 55,000 acres of forested land within the study area have likely contributed to increasing evapotranspiration, leaving less water available for groundwater recharge and stream base flow and resulting in basin-wide declines in streamflow and groundwater levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245033","collaboration":"Prepared in cooperation with the Klamath Tribes","usgsCitation":"Kennedy, J.J., Johnson, H.M., Gingerich, S.B., 2024, Assessment of long-term changes in surface-water extent within Klamath Marsh, south-central Oregon, 1985–2021: U.S. Geological Survey Scientific Investigations Report 2024–5033, 32 p., https://doi.org/10.3133/sir20245033.","productDescription":"Report: ix, 32 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-153514","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":499460,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117151.htm","linkFileType":{"id":5,"text":"html"}},{"id":431674,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5033/sir20245033.XML"},{"id":431673,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5033/images"},{"id":431672,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RC7RJM","text":"USGS data release","description":"USGS data release","linkHelpText":"Klamath Marsh January through June maximum surface water extent, 1985–2021"},{"id":431671,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245033/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5033"},{"id":431670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5033/sir20245033.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5033"},{"id":431669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5033/sir20245033.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.27115315992094,\n 43.15707158138778\n ],\n [\n -122.27115315992094,\n 42.30\n ],\n [\n -121.15,\n 42.30\n ],\n [\n -121.15,\n 43.15707158138778\n ],\n [\n -122.27115315992094,\n 43.15707158138778\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.
Intertribal networks for collecting and analyzing hydrologic and environmental data are growing. The U.S. Geological Survey can be a key partner with Tribal Nations in the further development of network capacity. A first step is the internship opportunity available through the partnership between the USGS and The Wildlife Society: The Native American Research Assistantship Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243026","usgsCitation":"Hare-Red Corn, E., Breault, R.F., and Sorenson, J.R., 2024, The Native American Research Assistantship Program—Building capacity for Indigenous water-resources monitoring: U.S. Geological Survey Fact Sheet 2024–3026, 2 p., https://doi.org/10.3133/fs20243026.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158895","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":431260,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3026/images/"},{"id":431259,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3026/fs20243026.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2024-3026 XML"},{"id":431258,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243026/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3026 HTML"},{"id":431257,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3026/fs20243026.pdf","text":"Report","size":"5.13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3026 PDF"},{"id":431256,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3026/coverthb.jpg"}],"contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349