The Blackfeet Nation seeks an increased scientific understanding of the water resources within the Blackfeet Indian Reservation of northwestern Montana. Hydrologic information is needed to better inform water-management decisions as the Blackfeet Nation implements the Blackfeet Water Rights Compact, initiates new water-use projects, and improves the Blackfeet Irrigation Project.
The U.S. Geological Survey and the Blackfeet Water Department began cooperating in 2019 to design and implement a hydrologic data-collection program. The program is being implemented in phases that include discrete and continuous discharge measurements of streams and canals, installation, operation of streamgages, groundwater-level monitoring, and database management. Data collected will be used to characterize current hydrologic conditions on the reservation and will act as a baseline for comparison as Blackfeet Nation water projects are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233012","collaboration":"Prepared in cooperation with the Blackfeet Water Department","usgsCitation":"Lawlor, S.M., Caldwell, R.R., Bartos, T.T., and Price, B., 2023, U.S. Geological Survey and Blackfeet Water Department Hydrologic Assessment of the Blackfeet Indian Reservation, Montana: U.S. Geological Survey Fact Sheet 2023–3012, 4 p., https://doi.org/10.3133/fs20233012.","productDescription":"Report: 4 p.; Dataset","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-141394","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":414914,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3012/coverthb2.jpg"},{"id":414918,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3012/images"},{"id":414919,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":415102,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233012/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":499662,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114650.htm","linkFileType":{"id":5,"text":"html"}},{"id":414917,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3012/fs20233012.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":414916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3012/fs20233012.pdf","text":"Report","size":"2.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3012"}],"country":"United States","state":"Montana","otherGeospatial":"Blackfeet Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.92382337824559,\n 49.00115786005651\n ],\n [\n -113.92382337824559,\n 47.9492225969058\n ],\n [\n -111.72750252269083,\n 47.9492225969058\n ],\n [\n -111.72750252269083,\n 49.00115786005651\n ],\n [\n -113.92382337824559,\n 49.00115786005651\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The development of sample processing techniques that recover a broad suite of pesticides from solid matrices, while mitigating coextracted matrix interferences, and reducing processing time is beneficial for high throughput analyses. The objective of this study was to evaluate the effectiveness of an automated extraction system for pesticide analyses in solid environmental samples. An Energized Dispersive Guided Extraction (EDGE) system was used to evaluate two different extraction solvents in optimizing the extraction of 210 pesticides and pesticide transformation products. A graphitized carbon cleanup step was implemented, and three elution solvents were evaluated separately for analyte recoveries. Recoveries between 70 and 130% were achieved for 167 compounds in a test soil using acetonitrile as an extraction solvent and carbon cleanup with acetonitrile and dichloromethane elutions. Nine field samples (soil, sediment, and biosolids) were extracted using the newly developed method and were compared with a previously validated pressurized liquid extraction (PLE) method using an Accelerated Solvent Extraction (ASE) system. Concentrations obtained from the two methods were comparable (linear R2 > 0.999), suggesting similar performance between the EDGE and PLE extractions in complex matrices. The new method provided slightly better sensitivities in comparison to the PLE method, ranging from 0.09 to 2.56 ng g−1. The method presented here significantly reduces extraction setup and runtimes while also minimizing the volume of carcinogenic solvents (e.g., dichloromethane) used in the laboratory and presents a sensitive multiresidue method for a wide range of pesticides in solid matrices.
The hillslope and channel dynamics that govern streamflow permanence in headwater systems have important implications for ecosystem functioning and downstream water quality. Recent advancements in process-based, semi-distributed hydrologic models that build upon empirical studies of streamflow permanence in well-monitored headwater catchments show promise for characterizing the dynamics of streamflow permanence in headwater systems. However, few process-based models consider the continuum of hillslope-stream network connectivity as a control on streamflow permanence in headwater systems. The objective of this study was to expand a process-based, catchment-scale hydrologic model to better understand the spatiotemporal dynamics of headwater streamflow permanence and to identify controls of streamflow expansion and contraction in a headwater network. Further, we aimed to develop an approach that enhanced the fidelity of model simulations, yet required little additional data, with the intent that the model might be later transferred to catchments with limited long-term and spatially explicit measurements. This approach facilitated network-scale estimates of the controls of streamflow expansion and contraction, albeit with higher degrees of uncertainty in individual reaches due to data constraints. Our model simulated that streamflow permanence was highly dynamic in first-order reaches with steep slopes and variable contributing areas. The simulated stream network length ranged from nearly 98±2% of the geomorphic channel extent during wet periods to nearly 50±10% during dry periods. The model identified a discharge threshold of approximately 1 mm d−1, above which the rate of streamflow expansion decreases by nearly an order of magnitude, indicating a lack of sensitivity of streamflow expansion to hydrologic forcing during high-flow periods. Overall, we demonstrate that process-based, catchment-scale models offer important insights on the controls of streamflow permanence, despite uncertainties and limitations of the model. We encourage researchers to increase data collection efforts and develop benchmarks to better evaluate such models.
Nuisance levels of benthic algae are becoming increasingly common in surface waters of the western United States and can compromise aesthetic quality, limit recreational activities, block water infrastructure, and negatively affect aquatic life. In cooperation with the White River and Douglas Creek Conservation Districts, the Colorado River Basin Salinity Control Forum, and the Colorado River Water Conservation District, the U.S. Geological Survey studied physical, chemical, and biological factors potentially controlling the occurrence of benthic algae in the upper White River Basin, Colorado, from 2018 through 2021. Multiple approaches were used to assess nutrients and physical conditions in the upper White River Basin. A linear mixed-effects model was used to evaluate the relative effect of different factors on algal biomass across water-quality sites.
The frequency and severity of algal blooms in the upper White River Basin may be affected by long-term changes in nutrient availability and streamflow, specifically changes in the timing and magnitude of high and low streamflow. The effects of large peak streamflow, including movement of the streambed, may be the dominant control on the occurrence of algal blooms through years. Large, late, and long-lasting peak streamflow may limit algal blooms during the same year and into subsequent years. Without streambed disturbance, other factors such as nutrients and water temperature may have a larger effect on algal biomass.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
In 2016, Colorado Parks and Wildlife identified filamentous algae collected from the main stem White River as Cladophora glomerata, a pervasive nuisance aquatic alga. Excessive levels of filamentous algae can compromise aesthetic quality, limit recreational activities, and have negative effects on aquatic life including strong fluctuations in dissolved oxygen levels and a reduction in overall biodiversity. To increase understanding of the biology of the upper White River Basin in Colorado, identify potential factors promoting or limiting nuisance algal abundance, and outline information to aid in the understanding and protection of water resources, the U.S. Geological Survey (USGS), in cooperation with the White River and Douglas Creek Conservation Districts and the White River Algae Technical Advisory Group, initiated a study to collect and analyze physical, chemical, and biological information for the upper White River Basin. The report describes long-term changes and spatial variations in streamflow and nutrient concentrations and loads in the upper White River Basin and identifies possible nutrient sources in the basin.
Long-term streamflow and nutrient data indicate that conditions in the upper White River Basin have become more favorable to benthic algae over varying timescales. Upward trends in total phosphorus concentrations and loads were found at three sites across the basin from 2000 to 2020. Total phosphorus loads increased around 50 percent, ranging from 18 to 48 pounds per year. Annual estimated concentrations of total phosphorus from 2005 to 2020 were above algal-specific nutrient criteria at the North Fork White River at Buford, Colo., indicating that phosphorus concentrations at this site likely promote algal growth. Discrete concentrations of total phosphorus exceeded algal-specific nutrient criteria on the South Fork and main stem White River during the summer season, though less frequently than samples collected from the North Fork White River. Nitrogen to phosphorus molar ratios collected from July to September indicate movement from colimitation (10–22) to nitrogen limited (less than 13) conditions at the North Fork White River at Buford, Colo. and the South Fork White River at Buford, Colo. starting in 2012. The magnitude of trends in phosphorus loads were generally greater than trends in concentrations across all sites, indicating that the largest changes in concentrations occurred during greater streamflow periods.
At White River above Coal Creek, near Meeker, Colo., significant downward trends in streamflow were found in August and September for mean streamflow (15 and 14 percent per decade, respectively) and 7-day minimum streamflows (23 and 22 percent per decade, respectively). Significant downward trends in annual 7-day minimum streamflows of 24 percent per decade, or 66 percent over the 40-year period of analysis, were also observed. Though not significant based on 90-percent confidence intervals, downward trends in 1-day maximum and mean streamflows in May and June and corresponding increases in April may indicate a shift toward earlier snowmelt runoff, as observed across western North America and the Colorado River Basin. Alteration of the annual hydrograph can influence factors that influence algae including nutrient input and dilution potential, water temperature, dissolved oxygen, light availability, and physical disturbance.
Results from a synoptic-style sampling identified the lower North Fork White River subbasin as a large source of phosphorus to the downstream system. Large increases in phosphorus loads were observed below Marvine Creek. Synoptic samples and samples collected during spring and summer of 2019 and 2020 also show large increases in total nitrogen, orthophosphate, and total phosphorus occurring at the furthest three downstream sites on the White River. To further evaluate sources of nitrogen in the upper White River Basin, the dual isotopic composition of nitrate was compared across four sites. The isotopic compositions of nitrate were all within the expected range of typical soil-derived nitrate, though the same values can also be derived from a mixture of agricultural fertilizer and manure or septic sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225112","collaboration":"Prepared in cooperation with the White River and Douglas Creek Conservation Districts","usgsCitation":"Day, N.K., 2023, Characterization of streamflow and nutrient occurrence in the upper White River Basin, Colorado, 1980–2020: U.S. Geological Survey Scientific Investigations Report 2022–5112, 37 p., https://doi.org/10.3133/sir20225112.","productDescription":"Report: vi, 37 p.; 2 Data Release","onlineOnly":"Y","ipdsId":"IP-133327","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":414992,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":415083,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5112/sir20225112.xml"},{"id":415082,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5112/images"},{"id":415567,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5112"},{"id":414990,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235009","text":"USGS Scientific Investigations Report 2023-5009—","linkHelpText":"Investigation of Potential Factors Controlling Benthic Algae in the Upper White River Basin, Colorado, 2018–21"},{"id":414991,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E82RMQ","text":"USGS data release","linkHelpText":"Channel Characteristics, benthic algae, and water quality model data for selected sites in the upper White River Basin, Colorado, 2018-21"},{"id":414989,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233005","text":"USGS Fact Sheet 2023-3005—","linkHelpText":"Potential Factors Controlling Benthic Algae in the Upper White River Basin, Colorado, 2018–21"},{"id":414988,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5112/sir20225112.pdf","text":"Report","size":"7.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5112"},{"id":414987,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5112/coverthb.jpg"},{"id":500459,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114622.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","otherGeospatial":"Upper White River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.20175418327835,\n 40.550962714804655\n ],\n [\n -108.20175418327835,\n 39.298023775605145\n ],\n [\n -105.58075670984697,\n 39.298023775605145\n ],\n [\n -105.58075670984697,\n 40.550962714804655\n ],\n [\n -108.20175418327835,\n 40.550962714804655\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, CO 80225
Nuisance levels of benthic filamentous green algae are becoming increasingly common in surface waters of Colorado and the western United States. In 2018 the U.S. Geological Survey began a study in cooperation with the White River and Douglas Creek Conservation Districts, Colorado River Basin Salinity Control Forum, and the Colorado River Water Conservation District to collect and analyze physical, chemical, and biological information for the upper White River Basin in Colorado and investigate causes of benthic algal blooms in the basin. This report (1) presents site-specific data including water temperature, riparian canopy cover, streambed particle size, and algal biomass and community composition; (2) describes the potential for streambed movement during spring runoff using physical channel characteristics and peak streamflow velocities; and (3) explains the results of a linear mixed-effects model used to test hypotheses about the influence of physical and chemical factors in explaining the occurrence of algal blooms across the basin.
Benthic algal biomass ranged from 0.7 to 309 milligrams per square meter during the summer (July–August) from 2018 through 2021 and exceeded the Colorado Department of Public Health and Environment criteria of 150 milligrams per square meter on four occasions, in 2018. Four genera of filamentous green algae were identified in the upper White River Basin, including Cladophora, Stigeoclonium, Ulothrix, and Spirogyra. Many genera of cyanobacteria were present, including some capable of producing toxins and taste and odor compounds. The nuisance diatom Didymosphenia geminata, commonly referred to as didymo, was found at two sites on the South Fork White River and along the main stem White River.
Hypotheses pertaining to the influence of measured variables on algal biomass were tested with a linear mixed-effects model. Median rock size and mean August water temperature had significant positive effects, meaning that greater bed stability and higher mean August water temperatures result in greater algal biomass. Total nitrogen to total phosphorus ratios had a significant negative effect on algal biomass, meaning that more nitrogen-limiting conditions, or greater phosphorus availability, corresponded to greater algal biomass.
Streamflow and water temperature data at White River above Coal Creek near Meeker, Colo., were used to assess possible causes of bloom conditions across years, including when algal blooms were first studied in the basin during 2016 and 2017. Early or low-magnitude peak streamflow conditions were not prerequisites for algal bloom occurrence. Conversely, relatively large, late, and long-lasting peak streamflows, such as those measured in 2019, may limit algal blooms during the same year and into subsequent years, as evidenced by extremely low algal biomass in 2019 and 2020. The broad spatial extent of bloom conditions indicates that the factors contributing to the occurrence of algal blooms are likely basinwide. Findings from this multiyear study indicate that the effects caused by larger peak streamflow, including movement of the streambed, may be the dominant control on the occurrence of an algal bloom. The findings also indicate that in the absence of disturbance other resources, including substrate size, water temperature, and nutrient availability, moderate algal biomass.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
In the present study, we evaluate the temporal variability in runup and total water level for sandy beaches along Cape Cod (Massachusetts, USA), and their impact on dune and beach erosion. We use a 43-year hindcast of waves and water levels and calculate runup and total water level based on the Stockdon formulation using previously extracted beach slopes. The dominant components of the runup are identified and their temporal variability evaluated. The seasonal and interannual variability of total water level is evaluated. For most locations along the outer Cape Cod coast, the comparison between total water level and dune elevations suggested that the coastal response remained predominantly under swash regime. The results over these study locations could be extended to other similar areas at regional scales to provide better characterization of total water level and coastal change at long temporal scales.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2023","conferenceDate":"April 11-15, 2023","conferenceLocation":"New Orleans, LA","language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0024","usgsCitation":"Aretxabaleta, A., Sherwood, C.R., Blanton, B., Over, J.R., Traykovski, P.A., and Sogut, E., 2023, Temporal variability of runup and total water level on Cape Cod sandy beaches, in The proceedings of the coastal sediments 2023, New Orleans, LA, April 11-15, 2023, p. 267-281, https://doi.org/10.1142/9789811275135_0024.","productDescription":"15 p.","startPage":"267","endPage":"281","ipdsId":"IP-142382","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416230,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.6829885324346,\n 42.11339300926423\n ],\n [\n -70.6829885324346,\n 41.483021409405666\n ],\n [\n -69.82916879983807,\n 41.483021409405666\n ],\n [\n -69.82916879983807,\n 42.11339300926423\n ],\n [\n -70.6829885324346,\n 42.11339300926423\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870463,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blanton, B.O.","contributorId":304434,"corporation":false,"usgs":false,"family":"Blanton","given":"B.O.","email":"","affiliations":[{"id":66069,"text":"Renaissance Computing Institute","active":true,"usgs":false}],"preferred":false,"id":870464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Over, Jin-Si R. 0000-0001-6753-7185 jover@usgs.gov","orcid":"https://orcid.org/0000-0001-6753-7185","contributorId":260178,"corporation":false,"usgs":true,"family":"Over","given":"Jin-Si","email":"jover@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870465,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Traykovski, Peter A. 0000-0002-8163-6857","orcid":"https://orcid.org/0000-0002-8163-6857","contributorId":69487,"corporation":false,"usgs":false,"family":"Traykovski","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":6706,"text":"Woods Hole Oceanographic Institution,","active":true,"usgs":false}],"preferred":false,"id":870466,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sogut, Erdinc 0000-0002-8291-9429","orcid":"https://orcid.org/0000-0002-8291-9429","contributorId":304424,"corporation":false,"usgs":true,"family":"Sogut","given":"Erdinc","email":"","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870467,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70242804,"text":"70242804 - 2023 - Fusing geophysical and remotely sensed data for observing overwash occurrence, frequency, and impact","interactions":[],"lastModifiedDate":"2023-06-08T14:49:26.09138","indexId":"70242804","displayToPublicDate":"2023-03-31T06:59:58","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Fusing geophysical and remotely sensed data for observing overwash occurrence, frequency, and impact","docAbstract":"Overwash is an important process that enables a barrier island to migrate landward to adapt to rising sea levels but can also impact vegetated areas and create coastal hazards for populated barrier islands. Our overall objectives were to hindcast overwash events from September 2008 to November 2009 and assess whether overwash impacts could be detected using moderate-resolution imagery (30 m). Estimates of wave and still water levels can be benchmarked against morphological characteristics from elevation datasets to predict overwash events. These observations can be combined with optical remote sensing data used to monitor for changes in vegetation greenness over time to evaluate potential impacts from overwash. This study highlighted how physical-based overwash data can be paired with observations of greenness. The results from our study highlighted that a discernable drop in greenness can be detected for major hurricanes, such as Hurricane Gustav in 2008, with a weaker signal observed for smaller magnitude events in 2009 like Hurricane Ida. Tracking overwash impacts to vegetation can be helpful for observing impacts to vegetation associated with restoration efforts and advancing our understanding of general overwash impacts and recovery.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0203","collaboration":"The Water Institute of the Gulf, U.S. Army Corps of Engineers","usgsCitation":"Enwright, N., Dalyander, P., Jenkins, R.L., Godsey, E.S., and Stelly, S.J., 2023, Fusing geophysical and remotely sensed data for observing overwash occurrence, frequency, and impact, in The proceedings of the coastal sediments 2023, p. 2206-2219, https://doi.org/10.1142/9789811275135_0203.","productDescription":"14 p.; Data Release","startPage":"2206","endPage":"2219","ipdsId":"IP-147117","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":415994,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417818,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A19Q8J"}],"noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217781,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":869824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":221891,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy","affiliations":[{"id":40456,"text":"St. Petersburg Coastal and Marine Science Center (Former Employee)","active":true,"usgs":false}],"preferred":false,"id":869825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jenkins, Robert L 0000-0002-9163-7773 rljenkins@usgs.gov","orcid":"https://orcid.org/0000-0002-9163-7773","contributorId":304231,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert","email":"rljenkins@usgs.gov","middleInitial":"L","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":869826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Godsey, Elizabeth S.","contributorId":304232,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","email":"","middleInitial":"S.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":869827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stelly, Spencer J. 0000-0003-1050-1733","orcid":"https://orcid.org/0000-0003-1050-1733","contributorId":215852,"corporation":false,"usgs":false,"family":"Stelly","given":"Spencer","email":"","middleInitial":"J.","affiliations":[{"id":39319,"text":"Student Services Contractor at the U.S. Geological Survey Wetland and Aquatic Research Center","active":true,"usgs":false}],"preferred":false,"id":869828,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241955,"text":"70241955 - 2023 - Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling","interactions":[],"lastModifiedDate":"2023-04-03T11:43:32.05906","indexId":"70241955","displayToPublicDate":"2023-03-31T06:40:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling","docAbstract":"Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods.
We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment.
Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.15163","usgsCitation":"Aunins, A.W., Mueller, S.J., Fike, J., and Cornman, R.S., 2023, Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling: PeerJ, v. 11, e15163, 34 p., https://doi.org/10.7717/peerj.15163.","productDescription":"e15163, 34 p.","ipdsId":"IP-146615","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":444004,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.15163","text":"Publisher Index Page"},{"id":435391,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NNZNVH","text":"USGS data release","linkHelpText":"Metabarcode sequencing of aquatic environmental DNA from the Potomac River Watershed, 2015-2020"},{"id":415048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2023-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":868369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mueller, Sara J.","contributorId":303889,"corporation":false,"usgs":false,"family":"Mueller","given":"Sara","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":868370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":868371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":868372,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70242810,"text":"70242810 - 2023 - Soil medium and watering frequency alter growth and allocation for Blue Diamond cholla (Cylindropuntia multigeniculata), a rare cactus of the northeast Mojave Desert, USA","interactions":[],"lastModifiedDate":"2023-04-19T11:34:57.506219","indexId":"70242810","displayToPublicDate":"2023-03-31T06:32:08","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7499,"text":"Native Plants Journal","active":true,"publicationSubtype":{"id":10}},"title":"Soil medium and watering frequency alter growth and allocation for Blue Diamond cholla (Cylindropuntia multigeniculata), a rare cactus of the northeast Mojave Desert, USA","docAbstract":"Blue Diamond cholla (Cylindropuntia multigeniculata (Clokey) Blackb. [Cactaceae]) is a rare cactus of the Mojave Desert. We explored whether cultivation from joint cuttings is a viable method for supporting threatened populations. Terminal joints were collected from adult plants at the type locality and grown in a shade house: We tested whether 2 soil mixes that varied in the ratio of inorganic and organic components (50:50 compared to 85:15) and 2 watering frequencies (250 ml every 5 d compared to 500 ml every 10 d) promote root growth important to outplanting survival. Plants grown from joint cuttings in the 50:50 soil had greater shoot and root biomass, produced more joint segments, and had higher initial and final survivorship over the 5-mo study. Neither soil nor watering treatments shifted biomass allocation to roots as hypothesized, but frequent watering produced longer roots, which may benefit reintroduced plants by assisting root access to deep soil moisture. Despite their vigor during collection, freshly cut joints rapidly declined in condition, resulting in approximately 50% mortality during the first month of the study. Initial mortality was not explained by the identity, condition, or size of the maternal plant. Prior-year weather patterns and collection procedures may influence quality and durability of joint cuttings and require further study. While larger plants were produced from the 50:50 mix, and root length was increased by frequent watering, reintroduction of nursery-grown plants will indicate whether such treatments aid establishment in the dry habitat where this species occurs.
","language":"English","publisher":"University of Wisconsin Press","doi":"10.3368/npj.24.1.4","usgsCitation":"Scoles-Sciulla, S.J., Stosich, A., and DeFalco, L., 2023, Soil medium and watering frequency alter growth and allocation for Blue Diamond cholla (Cylindropuntia multigeniculata), a rare cactus of the northeast Mojave Desert, USA: Native Plants Journal, v. 24, no. 1, p. 4-17, https://doi.org/10.3368/npj.24.1.4.","productDescription":"14 p.","startPage":"4","endPage":"17","ipdsId":"IP-149550","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":498864,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3368/npj.24.1.4","text":"Publisher Index Page"},{"id":415988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northeast Mojave Desert","volume":"24","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Scoles-Sciulla, Sara J. 0000-0003-1693-5030 sscoles@usgs.gov","orcid":"https://orcid.org/0000-0003-1693-5030","contributorId":2614,"corporation":false,"usgs":true,"family":"Scoles-Sciulla","given":"Sara","email":"sscoles@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":869845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stosich, Alexander","contributorId":304238,"corporation":false,"usgs":false,"family":"Stosich","given":"Alexander","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":869846,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeFalco, Lesley A. 0000-0002-7542-9261","orcid":"https://orcid.org/0000-0002-7542-9261","contributorId":208658,"corporation":false,"usgs":true,"family":"DeFalco","given":"Lesley A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":869847,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241829,"text":"ofr20221102 - 2023 - Bivalve effects on the food web supporting delta smelt—A spatially intensive study of bivalve recruitment, biomass, and grazing rate patterns with varying freshwater outflow in 2019","interactions":[],"lastModifiedDate":"2026-02-10T20:55:56.049435","indexId":"ofr20221102","displayToPublicDate":"2023-03-28T13:19:24","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1102","displayTitle":"Bivalve Effects on the Food Web Supporting Delta Smelt—A Spatially Intensive Study of Bivalve Recruitment, Biomass, and Grazing Rate Patterns with Varying Freshwater Outflow in 2019","title":"Bivalve effects on the food web supporting delta smelt—A spatially intensive study of bivalve recruitment, biomass, and grazing rate patterns with varying freshwater outflow in 2019","docAbstract":"Phytoplankton are an important and limiting food source in the Sacramento-San Joaquin Delta and San Francisco Bay. The decline of phytoplankton biomass is one potential factor in the decline of the protected Hypomesus transpacificus (delta smelt) and other pelagic organisms. The bivalves Corbicula fluminea and Potamocorbula amurensis (hereafter C. fluminea and P. amurensis, respectively) have been shown to control phytoplankton biomass in several locations throughout the San Francisco Bay and the Sacramento-San Joaquin Delta; therefore, knowledge of their distribution and population dynamics are of great interest.
Here, we describe the distribution and dynamics of bivalve biomass using samples collected by the California Department of Water Resources (DWR) as part of the benthic monitoring program in 2019. One element of DWR’s and the Bureau of Reclamation’s Environmental Monitoring Program—the Generalized Random Tessellation Stratified (GRTS) program—examines the spatial and temporal extent of C. fluminea and P. amurensis control on phytoplankton. Historically, the GRTS program sampled 175 benthic stations (50 stations that are monitored every year and 125 randomly selected new stations that are changed yearly) throughout the Sacramento-San Joaquin Delta and northern San Francisco Bay (San Pablo and Suisun Bays) during one week in May and October. In 2019, only the 50 annually replicated stations were sampled.
Corbicula fluminea and P. amurensis biomass and grazing rates had similar trends; therefore, the conclusions regarding biomass are applied to grazing rate data as well. Corbicula fluminea biomass decreased from May to October, whereas P. amurensis average biomass (reported increased from May (1 g ash-free-dry-tissue mass/square meter (g AFDM/m2) to October (2 g AFDM/m2). Although C. fluminea’s average biomass was lower in October (10 gAFDM/m2) than in May (20 gAFDM/m2), the highest single biomass value was also observed in October (300 gAFDM/m2). In both May and October, most stations that recorded high C. fluminea biomass values were located in the deep water (≥3 m of depth between the surface of the water and the surface of the substrate on the bottom) and were sampled in either rivers or sloughs. A relation between depth and biomass was not observed for P. amurensis.
Both C. fluminea and P. amurensis recruitment (recruits are considered animals ≤2.5mm in length in this study and recruitment is the process of recruits successfully settled to the bottom) increased from May to October. The total number of C. fluminea recruits more than doubled from May to October, whereas P. amurensis total recruitment increased by 8-fold during the same period. Most P. amurensis recruits in May can be attributed to one station, whereas the recruits in October were found at 14 stations. A relation between number of recruits and station depth was not evident for either C. fluminea or P. amurensis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221102","collaboration":"Prepared in cooperation with California Department of Water Resources","usgsCitation":"Zierdt Smith, E.L., Shrader, K.H., Thompson, J.K., Parchaso, F., Gehrts, K., and Wells, E., 2023, Bivalve effects on the food web supporting delta smelt—A spatially intensive study of bivalve recruitment, biomass, and grazing rate patterns with varying freshwater outflow in 2019: U.S. Geological Survey Open-File Report 2022–1102, 15 p., https://doi.org/10.3133/ofr20221102.","productDescription":"Report: vi, 15 p.; Data Release","numberOfPages":"15","onlineOnly":"Y","ipdsId":"IP-120563","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":414835,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93BAY64","description":"Zierdt Smith, E.L., Shrader, K.H., Parchaso, F., and Thompson, J.K., 2021, A spatially and temporally intensive sampling study of benthic community and bivalve metrics in the Sacramento-San Joaquin Delta (ver. 2.0, May 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P93BAY64.","linkHelpText":"A spatially and temporally intensive sampling study of benthic community and bivalve metrics in the Sacramento-San Joaquin Delta (ver. 2.0, May 2021)"},{"id":414837,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1102/covrthb.jpg"},{"id":414838,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1102/ofr20221102.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022–1102"},{"id":415721,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221101","text":"Open-File Report 2022-1101","linkHelpText":"- Bivalve Effects on the Food Web Supporting Delta Smelt—A One-Year Study of Bivalve Recruitment, Biomass, and Grazing Rate Patterns with Varying Freshwater Outflow"},{"id":499721,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114617.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.32432978338892,\n 37.803391256717845\n ],\n [\n -121.87545670853471,\n 37.791400721304846\n ],\n [\n -121.29205898127825,\n 37.80121221377027\n ],\n [\n -121.31951299197254,\n 38.399657702215876\n ],\n [\n -122.30511197590273,\n 38.40180916920502\n ],\n [\n -122.32432978338892,\n 37.803391256717845\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Water Resources, Earth System Processes Division
U.S. Geological Survey
411 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
In 2021, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, drilled and constructed borehole TAN-2336 for stratigraphic framework analyses and long-term groundwater monitoring of the eastern Snake River Plain aquifer at the Idaho National Laboratory in southeastern Idaho. Borehole TAN-2336 initially was cored from the depths of 34.0–255.8 ft below land surface (BLS) to collect continuous geologic data and then redrilled to complete construction as a monitoring well completed to about 255 ft BLS. Three sediment layers are described in geophysical data, but only one was recovered in core and described as fine sand with evidence of ash (pumice) near 203 ft BLS. Basalt texture for borehole TAN-2336 generally was described as aphanitic, phaneritic, diktytaxitic, and porphyritic. Basalt flows varied from highly fractured to dense with high to low vesiculation.
Geophysical data were examined with photographed core material to make lithologic descriptions as well as suggest zones where groundwater flow was anticipated. Primary pathways for groundwater, fractured basalt, occur in two areas with the first occurrence near 232.0 ft BLS and the second occurrence near 248.6 ft BLS in borehole TAN-2336. The first occurrence was identified near the top of the water column (232.0 ft BLS) and is more pronounced than the bottom interval (248.6 ft BLS). The location of these fractures in borehole TAN-2336 appear to impact the aquifer tests that were conducted following final well construction. Single-well aquifer tests were completed July 14, 2021, to provide estimates of transmissivity and hydraulic conductivity. Estimates for transmissivity and hydraulic conductivity during aquifer test 1 were 1.24×103 feet squared per day (ft2/d) and 1.76 feet per day (ft/d), respectively. Estimates for transmissivity and hydraulic conductivity during aquifer test 2 were 1.22×103 ft2/d and 1.75 ft/d, respectively. The transmissivity and hydraulic conductivity estimates for well TAN-2336 were within range of those considered from previous aquifer tests in other wells near Test Area North.
Water-quality samples were analyzed for cations, anions, metals, nutrients, volatile organic compounds, stable isotopes, and radionuclides. Water samples for select inorganic constituents showed concentrations consistent with signatures from regional groundwater. Water-quality samples analyzed for stable isotopes of oxygen and hydrogen are consistent with signatures from irrigation and agricultural recharge inputs to the aquifer. Results for trichloroethene, vinyl chloride, and strontium-90 were all measured above their respective maximum contaminant levels (MCLs) for public drinking water supplies. The nutrient concentration results are likely being impacted by the remediation amendment introduced to the aquifer to address trichloroethylene concentrations from past waste-disposal activities. These waste-disposal activities have resulted in volatile organic compound and radiochemical detections in groundwater samples collected at well TAN-2336.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235020","collaboration":"Prepared in cooperation with the U.S. Department of Energy","programNote":"DOE/ID-22260","usgsCitation":"Twining, B.V., Treinen, K.C., and Trcka, A.R., 2023, Completion summary for Borehole TAN-2336 at Test Area North, Idaho National Laboratory, Idaho: U.S. Geological Survey Scientific Investigations Report 2023–5020, 33 p. plus appendixes, https://doi.org/10.3133/sir20235020.","productDescription":"Report: vii, 33 p.; Appendix: 2","additionalOnlineFiles":"Y","ipdsId":"IP-137450","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":414342,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020.XML"},{"id":414336,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5020/coverthb.jpg"},{"id":414337,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020.pdf","text":"Report","size":"3.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5020"},{"id":414340,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235020/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5020"},{"id":500714,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114615.htm","linkFileType":{"id":5,"text":"html"}},{"id":414341,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5020/images"},{"id":414339,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020_appendix2.pdf","text":"Appendix 2","size":"43.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5020 Appendix 2"},{"id":414338,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5020/sir20235020_appendix1.pdf","text":"Appendix 1","size":"218 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5020 Appendix 1"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.07738340728746,\n 43.34536223650912\n ],\n [\n -112.07738340728746,\n 44.091416267461994\n ],\n [\n -113.46655634842513,\n 44.091416267461994\n ],\n [\n -113.46655634842513,\n 43.34536223650912\n ],\n [\n -112.07738340728746,\n 43.34536223650912\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
This research presents an evaluation of the accuracy and uncertainty of estimates of river discharge made using satellite observed data sources as input to a modified form of Manning’s equation. Conventional U.S. Geological Survey (USGS) streamflow gaging station data and in-situ measurements of width, depth, height, slope, discharge, and velocity from 30 USGS gage sites were used as ground-truth to assess accuracy. This study explores accuracy in relation to the amount of ground truth information available, the number of calibration points available, and the accuracy of the input data. This research indicates that remotely sensed discharge estimates associated with the modified Manning equation may be expected to have an uncertainty in range of 10% overall given a sufficient number of calibration points. The uncertainty associated with the modified Manning algorithm increased markedly for depths <3 meters (m) and for discharges <1000 cubic meters per second (m3 / s) for many rivers after calibration. Rivers that exhibit (1) a wide range of flow conditions, (2) a significant number of dams in the watershed and along the channel, and (3) a high baseflow index are more likely to have relatively large errors overall and particularly at the low end of the streamflow range. Uncertainty in remotely sensed measurements of water-surface elevation (WSE) and width in the expected range (WSE, + / − 10 cm; Width, + / − 15 m) introduces uncertainty in the discharge estimates on the order of 10% and is greatest at the low end of discharge as rivers get shallower and narrower. As WSE and width measurement uncertainty increases, discharge uncertainty increases accordingly. In general, the observation errors are greater than the errors associated with the algorithm for a well-calibrated model (e.g., 20 calibration points). |
Rehabilitation of large Anthropocene rivers requires engagement of diverse stakeholders across a broad range of sociopolitical boundaries. Competing objectives often constrain options for ecological restoration of large rivers whereas fewer competing objectives may exist in a subset of tributaries. Further, tributaries contribute toward building a “portfolio” of river ecosystem assets through physical and biological processes that may present opportunities to enhance the resilience of large river fishes. Our goal is to review roles of tributaries in enhancing mainstem large river fish populations. We present case histories from two greatly altered and distinct large-river tributary systems that highlight how tributaries contribute four portfolio assets to support large-river fish populations: 1) habitat diversity, 2) connectivity, 3) ecological asynchrony, and 4) density-dependent processes. Finally, we identify future research directions to advance our understanding of tributary roles and inform conservation actions. In the Missouri River United States, we focus on conservation efforts for the state endangered lake sturgeon, which inhabits large rivers and tributaries in the Midwest and Eastern United States. In the Colorado River, Grand Canyon United States, we focus on conservation efforts for recovery of the federally threatened humpback chub. In the Missouri River, habitat diversity focused on physical habitats such as substrate for reproduction, and deep-water habitats for refuge, whereas augmenting habitat diversity for Colorado River fishes focused on managing populations in tributaries with minimally impaired thermal and flow regimes. Connectivity enhancements in the Missouri River focused on increasing habitat accessibility that may require removal of physical structures like low-head dams; whereas in the Colorado River, the lack of connectivity may benefit native fishes as the disconnection provides refuge from non-native fish predation. Hydrologic variability among tributaries was present in both systems, likely underscoring ecological asynchrony. These case studies also described density dependent processes that could influence success of restoration actions. Although actions to restore populations varied by river system, these examples show that these four portfolio assets can help guide restoration activities across a diverse range of mainstem rivers and their tributaries. Using these assets as a guide, we suggest these can be transferable to other large river-tributary systems.
The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two-thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage-constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.
As the world develops sources of renewable energy, there is an intensifying interest in offshore wind energy production. The Northeast U.S. Continental Shelf (NES) ecosystem has favorable wind dynamics, with active development of wind energy. In this study, we present species distribution models that consider both occupancy and biomass responses for a broad spectrum of fish and macroinvertebrate taxa (n = 177). Building upon prior analyses, habitat was differentiated into overall and core habitats based on statistical distributions of habitat scores. Overall habitat was used to show each species' regional distribution based on fishery-independent survey captures between 1976 and 2019, whereas core habitat represented where the focus of the species' abundance was located as a subset of overall habitat. Wind energy developments may modify the water column in ways that impact lower-trophic-level productivity; therefore, added attention was given to the response of forage species. Over 20% of species showed preferential use of putative and potential wind development areas, including a disproportionate number of forage taxa. Principal usage varied by season, with forage species like Atlantic Menhaden Brevoortia tyrannus and Atlantic Mackerel Scomber scombrus preferentially using the lease areas in spring and Round Herring Etrumeus teres and longfin inshore squid Doryteuthis pealeii using lease areas in autumn. For species with relatively low usage of the lease areas, there was a tendency for the usage related to overall habitat to be lower than usage for core habitat; in contrast, for species with high usage of the lease areas, that usage was higher for overall habitat than for core habitat. The area of habitat tended to have positive trends across species, with these positive trends being disproportionately higher among forage taxa. These results frame the importance of wind lease areas for species in the NES, particularly forage taxa that fulfill many important ecological functions.
Drinking water supplies across the United States have been contaminated by firefighting and fire-training activities that use aqueous film-forming foams (AFFF) containing per- and polyfluoroalkyl substances (PFAS). Much of the AFFF is manufactured using electrochemical fluorination by 3M. Precursors with six perfluorinated carbons (C6) and non-fluorinated amine substituents make up approximately one-third of the PFAS in 3M AFFF. C6 precursors can be transformed through nitrification (microbial oxidation) of amine moieties into perfluorohexane sulfonate (PFHxS), a compound of regulatory concern. Here, we report biotransformation of the most abundant C6 sulfonamido precursors in 3M AFFF with available commercial standards (FHxSA, PFHxSAm, and PFHxSAmS) in microcosms representative of the groundwater/surface water boundary. Results show rapid (<1 day) biosorption to living cells by precursors but slow biotransformation into PFHxS (1–100 pM day–1). The transformation pathway includes one or two nitrification steps and is supported by the detection of key intermediates using high-resolution mass spectrometry. Increasing nitrate concentrations and total abundance of nitrifying taxa occur in parallel with precursor biotransformation. Together, these data provide multiple lines of evidence supporting microbially limited biotransformation of C6 sulfonamido precursors involving ammonia-oxidizing archaea (Nitrososphaeria) and nitrite-oxidizing bacteria (Nitrospina). Further elucidation of interrelationships between precursor biotransformation and nitrogen cycling in ecosystems would help inform site remediation efforts.
Aqueous film-forming foams historically were used during fire training activities on Joint Base Cape Cod, Massachusetts, and created an extensive per- and polyfluoroalkyl substances (PFAS) groundwater contamination plume. The potential for PFAS bioconcentration from exposure to the contaminated groundwater, which discharges to surface water bodies, was assessed with mobile-laboratory experiments using groundwater from the contamination plume and a nearby reference location. The on-site continuous-flow 21-day exposures used male and female fathead minnows, freshwater mussels, polar organic chemical integrative samplers (POCIS), and polyethylene tube samplers (PETS) to evaluate biotic and abiotic uptake. The composition of the PFAS-contaminated groundwater was complex and 9 PFAS were detected in the reference groundwater and 17 PFAS were detected in the contaminated groundwater. The summed PFAS concentrations ranged from 120 to 140 ng L–1 in reference groundwater and 6100 to 15,000 ng L–1 in contaminated groundwater. Biotic concentration factors (CFb) for individual PFAS were species, sex, source, and compound-specific and ranged from 2.9 to 1000 L kg–1 in whole-body male fish exposed to contaminated groundwater for 21 days. The fish and mussel CFb generally increased with increasing fluorocarbon chain length and were greater for sulfonates than for carboxylates. The exception was perfluorohexane sulfonate, which deviated from the linear trend and had a 10-fold difference in CFb between sites, possibly because of biotransformation of precursors such as perfluorohexane sulfonamide. Uptake for most PFAS in male fish was linear over time, whereas female fish had bilinear uptake indicated by an initial increase in tissue concentrations followed by a decrease. Uptake of PFAS was less for mussels (maximum CFb = 200) than for fish, and mussel uptake of most PFAS also was bilinear. Although abiotic concentration factors were greater than CFb, and values for POCIS were greater than for PETS, passive samplers were useful for assessing PFAS that potentially bioconcentrate in fish but are present at concentrations below method quantitation limits in water. Passive samplers also accumulate short-chain PFAS that are not bioconcentrated.
Greater drought frequency and severity due to climate change will result in greater drawdown of water storage reservoirs. However, changes to oxythermal regimes due to drawdown are reservoir specific and interface with fish species-specific habitat requirements, producing varying effects on coldwater fish populations. We examined the effect of drawdown on the oxythermal habitat and relative abundance of kokanee Oncorhynchus nerka, a coldwater salmonid, in Island Park Reservoir on the Henrys Fork of the Snake River, Idaho. A measure of relative kokanee abundance was negatively, exponentially related to drawdown. Oxythermal patterns measured in the reservoir during 2021, a severe drought year, revealed that drawdown reduced kokanee habitat by increasing water temperatures and decreasing dissolved oxygen concentrations. Oxythermal refugia for kokanee appeared to relate to inflow from the spring-fed Henrys Fork and other groundwater inflows. However, we did not quantify groundwater flow or connections, and we did not study kokanee population demographics or mortality. Reducing these sources of uncertainty is a priority for future study. Still, our study highlights a potential mechanism connecting reservoir drawdown to fish populations and the unique yet predictable mechanisms by which reservoir drawdown interacts with reservoir morphometry to affect fish habitat availability.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10879","usgsCitation":"McLaren, J.S., Van Kirk, R.W., Mabaka, A.J., Brothers, S., and Budy, P., 2023, Drawdown, habitat, and kokanee populations in a western U.S. reservoir: North American Journal of Fisheries Management, v. 43, no. 2, p. 339-351, https://doi.org/10.1002/nafm.10879.","productDescription":"13 p.","startPage":"339","endPage":"351","ipdsId":"IP-136834","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":499238,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10879","text":"Publisher Index Page"},{"id":430142,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Island Park Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.36271923298024,\n 44.45908039535735\n ],\n [\n -111.60966508255247,\n 44.45908039535735\n ],\n [\n -111.60966508255247,\n 44.35910672122833\n ],\n [\n -111.36271923298024,\n 44.35910672122833\n ],\n [\n -111.36271923298024,\n 44.45908039535735\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"McLaren, John S.","contributorId":337322,"corporation":false,"usgs":false,"family":"McLaren","given":"John","email":"","middleInitial":"S.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":903742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Kirk, Robert W.","contributorId":337326,"corporation":false,"usgs":false,"family":"Van Kirk","given":"Robert","email":"","middleInitial":"W.","affiliations":[{"id":81016,"text":"Henrys Fork Foundation","active":true,"usgs":false}],"preferred":false,"id":903745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mabaka, Arthur J.","contributorId":339021,"corporation":false,"usgs":false,"family":"Mabaka","given":"Arthur","email":"","middleInitial":"J.","affiliations":[{"id":16159,"text":"Washington and Lee University","active":true,"usgs":false}],"preferred":false,"id":903746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brothers, Soren","contributorId":339019,"corporation":false,"usgs":false,"family":"Brothers","given":"Soren","email":"","affiliations":[{"id":81013,"text":"Department of Natural History","active":true,"usgs":false}],"preferred":false,"id":903743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903744,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241876,"text":"70241876 - 2023 - Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains","interactions":[],"lastModifiedDate":"2023-05-25T15:54:56.920665","indexId":"70241876","displayToPublicDate":"2023-03-25T08:44:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains","docAbstract":"Yellow sweetclover (Melilotus officinalis; YSC) is an invasive biennial legume that bloomed across the Northern Great Plains in 2018–2019 in response to above-average precipitation. YSC can increase nitrogen (N) levels and potentially cause substantial changes in the composition of native plant species communities. There is little knowledge of the spatiotemporal variability and conditions causing substantial widespread blooms of YSC across western South Dakota (SD).
We aimed to develop a generalized prediction model to predict the relative abundance of YSC in suitable habitats across rangelands of western South Dakota for 2019. Our research questions are: (1) What is the spatial extent of YSC across western South Dakota? (2) Which model can accurately predict the habitat and percent cover of YSC? and (3) What significant biophysical drivers affect its presence across western South Dakota?
We trained machine learning models with in situ data (2016–2021), Sentinel 2A-derived surface reflectance and indices (10 m, 20 m) and site-specific variables of climate, topography, and edaphic factors to optimize model performance.
We identified moisture proxies (Shortwave Infrared reflectance and variability in Tasseled Cap Wetness) as the important predictors to explain the YSC presence. Land Surface Water Index and variability in summer temperature were the top predictors in explaining the YSC abundance. We demonstrated how machine learning algorithms could help generate valuable information on the spatial distribution of this invasive plant. We delineated major YSC hotspots in Butte, Pennington, and Corson Counties of South Dakota. The floodplains of major rivers, including White and Bad Rivers, and areas around Badlands National Park also showed a higher occurrence probability and cover percentage.
These prediction maps could aid land managers in devising management strategies for the regions that are prone to YSC outbreaks. The management workflow can also serve as a prototype for mapping other invasive plant species in similar regions.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-023-01613-1","usgsCitation":"Saraf, S., John, R., Amirkhiz, R.G., Kolluru, V., Jain, K., Rigge, M.B., Giannico, V., Boyte, S., Chen, J., Henebry, G.M., Jarchow, M., and Lafortezza, R., 2023, Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains: Landscape Ecology, v. 38, p. 1463-1479, https://doi.org/10.1007/s10980-023-01613-1.","productDescription":"17 p.","startPage":"1463","endPage":"1479","ipdsId":"IP-147289","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":489774,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/11586/429913","text":"External Repository"},{"id":414967,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota, Wyoming","otherGeospatial":"Northern Great Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.12403782089808,\n 44.50409639966341\n ],\n [\n -109.51917809297896,\n 42.193942536431706\n ],\n [\n -108.20829190347715,\n 42.16267357718297\n ],\n [\n -105.7013688001525,\n 42.82087900174628\n ],\n [\n -104.34504145487756,\n 42.926504293846904\n ],\n [\n -98.59145737799025,\n 42.98528857117202\n ],\n [\n -99.68928260951722,\n 44.21667147884969\n ],\n [\n -100.49416850734309,\n 44.66694248150202\n ],\n [\n -100.10156005668756,\n 46.943864430494756\n ],\n [\n -101.88476410598878,\n 48.95101635024733\n ],\n [\n -114.58909916045843,\n 49.00176092015144\n ],\n [\n -113.33884787447045,\n 45.40203203770008\n ],\n [\n -112.89637808484645,\n 44.61584592164604\n ],\n [\n -111.12403782089808,\n 44.50409639966341\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationDate":"2023-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Saraf, Sakshi 0000-0002-6785-8381","orcid":"https://orcid.org/0000-0002-6785-8381","contributorId":302161,"corporation":false,"usgs":false,"family":"Saraf","given":"Sakshi","email":"","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":868036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"John, Ranjeet 0000-0002-0150-8450","orcid":"https://orcid.org/0000-0002-0150-8450","contributorId":302162,"corporation":false,"usgs":false,"family":"John","given":"Ranjeet","email":"","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":868037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Amirkhiz, Reza 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mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":868041,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Giannico, Vincenzo","contributorId":303762,"corporation":false,"usgs":false,"family":"Giannico","given":"Vincenzo","email":"","affiliations":[{"id":65903,"text":"University of Bari “Aldo Moro\"","active":true,"usgs":false}],"preferred":false,"id":868042,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":868043,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chen, Jiquan 0000-0003-0761-9458","orcid":"https://orcid.org/0000-0003-0761-9458","contributorId":146126,"corporation":false,"usgs":false,"family":"Chen","given":"Jiquan","email":"","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":868044,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Henebry, Geoffrey M.","contributorId":124528,"corporation":false,"usgs":false,"family":"Henebry","given":"Geoffrey","email":"","middleInitial":"M.","affiliations":[{"id":5087,"text":"Geographic Information Science Center of Excellence (GIScCE), South Dakota State University, Brookings, USA","active":true,"usgs":false}],"preferred":false,"id":868045,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jarchow, Meghann","contributorId":303764,"corporation":false,"usgs":false,"family":"Jarchow","given":"Meghann","email":"","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":868046,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lafortezza, Raffaele","contributorId":303767,"corporation":false,"usgs":false,"family":"Lafortezza","given":"Raffaele","email":"","affiliations":[{"id":65904,"text":"University of Bari “Aldo Moro”","active":true,"usgs":false}],"preferred":false,"id":868047,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70241574,"text":"sir20235019 - 2023 - Assessing Escherichia coli and microbial source tracking markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21","interactions":[],"lastModifiedDate":"2026-03-02T22:13:04.87586","indexId":"sir20235019","displayToPublicDate":"2023-03-24T08:55:27","publicationYear":"2023","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":"2023-5019","displayTitle":"Assessing Escherichia coli and Microbial Source Tracking Markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21","title":"Assessing Escherichia coli and microbial source tracking markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21","docAbstract":"The Rio Grande, in southern Albuquerque, New Mexico, is a Clean Water Act Section 303(d) Category 5 impaired reach for Escherichia coli (E. coli). The reach is 5 miles in length, extending from Tijeras Arroyo south to the Isleta Pueblo boundary. An evaluation of E. coli and microbial source tracking markers (human-, canine-, and waterfowl-specific sources) was conducted by the U.S. Geological Survey to determine the extent and source of fecal bacteria within the impaired reach of the Rio Grande, primarily during the dry season (November through June) in 2020 and 2021. Samples were collected in the river cross section at three locations within each site and collected during both the dry season and the wet season, thereby providing data over a range of flow conditions to better understand the extent and source of fecal bacteria. Because fecal microorganisms may readily attach to sediments, riverbed material samples were also collected. During the dry season, E. coli concentrations in water were primarily detected below the New Mexico Surface Water Quality Standard of 410 colony forming units per 100 milliliters and mostly human and canine sources were detected. However, approximately 40 percent of the water samples exceeded the Isleta Pueblo water quality standard of 88 colony forming units per 100 milliliters. E. coli concentrations in bed material were detected at low concentrations, and the bed material was a sandy substrate, with little fine-grained material, a suitable habitat that would allow for bacterial growth during the dry season. Significant spatial and temporal differences, where p-values were less than 0.05, were found in water-quality samples for E. coli (seasonal) and the human tracker concentrations (between sites and within a cross section of a site). Given the lack of correlation between discharge and E. coli concentration and the human marker being most prevalent in the study area, the sources of E. coli in the dry season are likely nonpoint sources. The results from this study will help decision makers determine the efficacy of their best management practices and guide new practices to improve water quality in the reach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235019","issn":"ISSN 2328-0328","collaboration":"Prepared in cooperation with Bernalillo County","usgsCitation":"Travis, R.E., Wilkins, K.L., and Kephart, C.M., 2023, Assessing Escherichia coli and microbial source tracking markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21: U.S. Geological Survey Scientific Investigations Report 2023–5019, 48 p., https://doi.org/10.3133/sir20235019.","productDescription":"Report: viii, 48 p.; Data Release","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-139896","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":414732,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5019/sir20235019.XML","size":"328 KB","linkFileType":{"id":8,"text":"xml"}},{"id":414632,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q2ECYV","text":"U.S. Geological Survey data release—Fecal bacteria and microbial source tracking marker data in the Rio Grande, Albuquerque, New Mexico 2017–2020"},{"id":414629,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5019/images"},{"id":414627,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5019/coverthb.jpg"},{"id":414626,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5019/sir20235019.pdf","text":"Report","size":"2.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5019"},{"id":500713,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114616.htm","linkFileType":{"id":5,"text":"html"}},{"id":414733,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235019/full","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande, South Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107,\n 35.5\n ],\n [\n -107,\n 34.75\n ],\n [\n -106.25,\n 34.75\n ],\n [\n -106.25,\n 35.5\n ],\n [\n -107,\n 35.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113