Diked and drained coastal lowlands rely on hydraulic and protective infrastructure that may not function as designed in areas with relative sea-level rise. The slow and incremental loss of the hydraulic conditions required for a well-drained system make it difficult to identify if and when the flow structures no longer discharge enough water, especially in tidal settings where two-way flows occur through the dike. We developed and applied a hydraulic mass-balance model to quantify how water levels in the diked and tidally restricted coastal wetlands and water bodies dynamically respond to sea-level rise, specifically applied to the Herring River Estuary in MA, USA, from 2020 to 2100. Sensitivity testing of the model parameters indicated that primary outcomes were not sensitive to many of the chosen input values, though the terrestrial water input rate to the estuary and the flow coefficient for the hydraulic infrastructure were important. The relative importance of parameters, however, is expected to be site specific. We introduced a drainability metric that quantifies the net water volume drained over every tidal cycle to monitor and forecast how rising water levels on either side of the dike affected the net draining or impounding conditions of the system. Ensembles of model results across parameter and sea-level scenario uncertainties indicated that substantial impoundment of the Herring River Estuary was expected within ~ 20 years with the existing flow structures, a sluice and two flap gates. Simulations with up to three additional gates did not dampen this trend toward impoundment, suggesting that rising impounded water levels are likely even with major construction upgrades. Increasingly impounded diked coastal waterbodies present a hydrologic challenge with socioecological implications due to projected flooding and ecosystem impacts. Solutions to this challenge may be to allow coastal wetland restoration pathways or require substantial and recurring infrastructure improvement projects.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-023-01174-1","usgsCitation":"Befus, K.A., Kurnizki, A., Kroeger, K.D., Eagle, M.J., and Smith, T.P., 2023, Forecasting sea level rise-driven inundation in diked and tidally restricted coastal lowlands: Estuaries and Coasts, v. 46, no. 6, p. 1157-1169, https://doi.org/10.1007/s12237-023-01174-1.","productDescription":"13 p.","startPage":"1157","endPage":"1169","ipdsId":"IP-145736","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1007/s12237-023-01174-1","text":"Publisher Index Page"},{"id":421746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Herring River Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.06356706053543,\n 41.92990690084844\n ],\n [\n -70.0599719567331,\n 41.93094108028947\n ],\n [\n -70.05877358879943,\n 41.93322334800044\n ],\n [\n -70.05954054427677,\n 41.93450708770192\n ],\n [\n -70.05915706653809,\n 41.9355411925597\n ],\n [\n -70.05752728614821,\n 41.93611172599333\n ],\n [\n -70.057239677844,\n 41.93678922781737\n ],\n [\n -70.05786282916928,\n 41.937965924404836\n ],\n [\n -70.05656859180117,\n 41.937751981185585\n ],\n [\n -70.0528776185643,\n 41.939463506843225\n ],\n [\n -70.05426772536764,\n 41.94014097305953\n ],\n [\n -70.057239677844,\n 41.93889300339495\n ],\n [\n -70.05862978464737,\n 41.93903562973523\n ],\n [\n -70.05934880540781,\n 41.93871472002016\n ],\n [\n -70.05915706653809,\n 41.93760935197429\n ],\n [\n -70.06025956503724,\n 41.93746672244353\n ],\n [\n -70.06131412881953,\n 41.935826459914495\n ],\n [\n -70.06323151751364,\n 41.93464972385124\n ],\n [\n -70.06275217033973,\n 41.93340164672611\n ],\n [\n -70.06529271035981,\n 41.93233184689518\n ],\n [\n -70.06356706053543,\n 41.92990690084844\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"46","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Befus, Kevin A.","contributorId":299488,"corporation":false,"usgs":false,"family":"Befus","given":"Kevin","email":"","middleInitial":"A.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":885528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kurnizki, A","contributorId":330654,"corporation":false,"usgs":false,"family":"Kurnizki","given":"A","email":"","affiliations":[{"id":78949,"text":"epartment of Civil and Architectural Engineering, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":885529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":885530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":885531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Timothy P.","contributorId":220144,"corporation":false,"usgs":false,"family":"Smith","given":"Timothy","email":"","middleInitial":"P.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":885532,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243276,"text":"70243276 - 2023 - So goes the snow: Alaska snowpack changes and impacts on pacific salmon in a warming climate","interactions":[],"lastModifiedDate":"2023-05-05T11:38:45.159335","indexId":"70243276","displayToPublicDate":"2023-04-30T06:36:59","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":691,"text":"Alaska Park Science","printIssn":"1545- 496","active":true,"publicationSubtype":{"id":10}},"title":"So goes the snow: Alaska snowpack changes and impacts on pacific salmon in a warming climate","docAbstract":"In Alaska’s watersheds, climate change is altering the nature and role of the snowpack, defined as snow accumulation that melts in spring. Generally, the amount of precipitation that falls as snow and the length of the snow-cover season both decrease as temperatures exceed 0°C (32°F) more frequently. The impacts of climate change on snowpack vary among watersheds. In southern, coastal parts of Alaska, large decreases in spring snowpack are expected by the mid-21st century, even with more winter precipitation because temperatures warm to above freezing, causing a shift from snow to rain or more melt during the winter. In contrast, modest early spring increases in the snowpack are expected in watersheds where temperatures remain below freezing. In these locations temperatures warm but remain cold enough for the extra winter precipitation to fall as snow, even though the snowpack will begin accumulating later in the fall and melt earlier in the spring as temperatures rise during those warmer seasons. Because potential impacts on hydrological and ecological systems will vary among watersheds, it is difficult to generalize the resulting ecological impacts at broad spatial scales. Here, we explore likely impacts on hydrology in critical anadromous fish habitat in southwest Alaska.","language":"English","publisher":"US National Park Service","usgsCitation":"Littell, J., Reynolds, J.H., Bartz, K.K., McAfee, S., and Hayward, G.D., 2023, So goes the snow: Alaska snowpack changes and impacts on pacific salmon in a warming climate: Alaska Park Science, v. 19, no. 1, p. 62-75.","productDescription":"14 p.","startPage":"62","endPage":"75","ipdsId":"IP-112750","costCenters":[{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":416748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416743,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.nps.gov/articles/aps-19-1-10.htm"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -167.0502487962713,\n 69.32812262696825\n ],\n [\n -167.0502487962713,\n 63.68078746979131\n ],\n [\n -146.31697991983825,\n 63.68078746979131\n ],\n [\n -146.31697991983825,\n 69.32812262696825\n ],\n [\n -167.0502487962713,\n 69.32812262696825\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":871776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reynolds, Joel H.","contributorId":140498,"corporation":false,"usgs":false,"family":"Reynolds","given":"Joel","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":871777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartz, Krista K.","contributorId":200705,"corporation":false,"usgs":false,"family":"Bartz","given":"Krista","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":871778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McAfee, Stephanie A.","contributorId":167115,"corporation":false,"usgs":false,"family":"McAfee","given":"Stephanie A.","affiliations":[{"id":24618,"text":"Department of Geography, University of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":871779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayward, Gregory D.","contributorId":209846,"corporation":false,"usgs":false,"family":"Hayward","given":"Gregory","email":"","middleInitial":"D.","affiliations":[{"id":38010,"text":"US Forest Service, Alaska Region","active":true,"usgs":false}],"preferred":false,"id":871780,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241229,"text":"sir20235006 - 2023 - Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Results","interactions":[],"lastModifiedDate":"2026-03-02T18:01:49.725089","indexId":"sir20235006","displayToPublicDate":"2023-04-28T13:18:00","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":"2023-5006","displayTitle":"Magnitude and Frequency of Floods for Rural Streams in Georgia, South Carolina, and North Carolina, 2017—Results","title":"Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Results","docAbstract":"Reliable estimates of the magnitude and frequency of floods are an important part of the framework for hydraulic-structure design and flood-plain management in Georgia, South Carolina, and North Carolina. Annual peak flows measured at U.S. Geological Survey streamgages are used to compute flood‑frequency estimates at those streamgages. However, flood‑frequency estimates also are needed at ungaged stream locations. A process known as regionalization was used to develop regression equations to estimate the magnitude and frequency of floods at ungaged locations.
A multistate approach was used to update estimates of the magnitude and frequency of floods in rural, ungaged basins in Georgia, South Carolina, and North Carolina. Annual peak-flow data through September 2017 were analyzed for 965 streamgages with 10 or more years of data on rural streams in Georgia, South Carolina, North Carolina, and adjacent parts of Alabama, Florida, Tennessee, and Virginia. Flood‑frequency estimates of the 50‑, 20‑, 10‑, 4‑, 2‑, 1‑, 0.5‑, and 0.2‑percent annual exceedance probability streamflows, which correspond to flood-recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively, were computed for the 965 streamgages following national guidelines. As part of the computation of flood‑frequency estimates for the streamgages, an updated value for the regional skew coefficient (0.048) was developed using a Bayesian generalized least squares regression model. The new regional skew has a mean square error or average variance of prediction of 0.092. Additionally, basin characteristics for these stations were computed using a geographical information system.
Exploratory analyses on the 965 streamgages confirmed the five hydrologic regions for Georgia, South Carolina, and North Carolina defined in a previous rural flood‑frequency study. From the 965 streamgages, streamgages with 30 or more years of record were used to complete a peak-flow trend analysis. Of the 965 streamgages, 164 streamgages were found to be redundant and were excluded from the regional regression analyses. Data from the remaining 801 streamgages (292 in Georgia, 75 in South Carolina, 303 in North Carolina, 15 in Alabama, 12 in Florida, 39 in Tennessee, and 65 in Virginia) were used in a regional regression analysis relating basin characteristics to flood‑frequency estimates. This analysis, based on generalized least squares regression, was used to develop a set of predictive equations to estimate the 50‑, 20‑, 10‑, 4‑, 2‑, 1‑, 0.5‑, and 0.2‑percent annual exceedance probability streamflows for rural, ungaged basins in Georgia, South Carolina, and North Carolina. The final set of predictive equations are all functions of drainage area and percentage of the drainage basin within each of the five hydrologic regions. Average errors of prediction for these regression equations range from 35.8 to 44.4 percent.
Flood‑frequency estimates also were computed for 72 regulated (for example, a streamgage where flow is altered by a dam or weir) streamgages in Georgia, South Carolina, and North Carolina with 20 or more years of post-regulation record using data through water year 2019. The water year is the annual period from October 1 through September 30 and is designated by the year in which the period ends. Of the 72 regulated streamgages, 18 had pre-regulated periods of record that also were analyzed as part of this study. Flow adjustments were applied to historic peaks and large floods from the pre-regulated period, if available, for use in the post-regulation frequency analysis. Estimates of large floods provide valuable information in frequency analysis and, thus, were included in the post-regulation frequency analysis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235006","collaboration":"Prepared in cooperation with the Georgia Department of Transportation (Engineering Division, Office of Bridge Design and Maintenance), South Carolina Department of Transportation (Hydraulic Design Support Office), North Carolina Department of Transportation (Division of Highways, Hydraulics Unit), and the North Carolina Department of Crime Control and Public Safety (Division of Emergency Management, Floodplain Mapping Program)","usgsCitation":"Feaster, T.D., Gotvald, A.J., Musser, J.W., Weaver, J.C., Kolb, K.R., Veilleux, A.G., and Wagner, D.M., 2023, Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Results: U.S. Geological Survey Scientific Investigations Report 2023–5006, 75 p., https://doi.org/10.3133/sir20235006.","productDescription":"Report: ix, 75 p.; 2 Data 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U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Because use of high-resolution hydrologic models is becoming more widespread and estimates are made over large domains, there is a pressing need for systematic evaluation of their performance. Most evaluation efforts to date have focused on smaller basins that have been relatively undisturbed by human activity, but there is also a need to benchmark model performance more comprehensively, including basins impacted by human activities. This study benchmarks the long-term performance of two process-oriented, high-resolution, continental-scale hydrologic models that have been developed to assess water availability and risks in the United States (US): the National Water Model v2.1 application of WRF-Hydro (NWMv2.1) and the National Hydrologic Model v1.0 application of the Precipitation–Runoff Modeling System (NHMv1.0). The evaluation is performed on 5390 streamflow gages from 1983 to 2016 (∼ 33 years) at a daily time step, including both natural and human-impacted catchments, representing one of the most comprehensive evaluations over the contiguous US. Using the Kling–Gupta efficiency as the main evaluation metric, the models are compared against a climatological benchmark that accounts for seasonality. Overall, the model applications show similar performance, with better performance in minimally disturbed basins than in those impacted by human activities. Relative regional differences are also similar: the best performance is found in the Northeast, followed by the Southeast, and generally worse performance is found in the Central and West areas. For both models, about 80 % of the sites exceed the seasonal climatological benchmark. Basins that do not exceed the climatological benchmark are further scrutinized to provide model diagnostics for each application. Using the underperforming subset, both models tend to overestimate streamflow volumes in the West, which could be attributed to not accounting for human activities, such as active management. Both models underestimate flow variability, especially the highest flows; this was more pronounced for NHMv1.0. Low flows tended to be overestimated by NWMv2.1, whereas there were both over and underestimations for NHMv1.0, but they were less severe. Although this study focused on model diagnostics for underperforming sites based on the seasonal climatological benchmark, metrics for all sites for both model applications are openly available online.
Reservoirs in arid landscapes provide critical water storage and hydroelectric power but influence the transport and biogeochemical cycling of mercury (Hg). Improved management of reservoirs to mitigate the supply and uptake of bioavailable methylmercury (MeHg) in aquatic food webs will benefit from a mechanistic understanding of inorganic divalent Hg (Hg(II)) and MeHg fate within and downstream of reservoirs. Here, we quantified Hg(II), MeHg, and other pertinent biogeochemical constituents in water (filtered and associated with particles) at high temporal resolution from 2016–2020. This was done (1) at inflow and outflow locations of three successive hydroelectric reservoirs (Snake River, Idaho, Oregon) and (2) vertically and longitudinally within the first reservoir (Brownlee Reservoir). Under spring high flow, upstream inputs of particulate Hg (Hg(II) and MeHg) and filter-passing Hg(II) to Brownlee Reservoir were governed by total suspended solids and dissolved organic matter, respectively. Under redox stratified conditions in summer, net MeHg formation in the meta- and hypolimnion of Brownlee reservoir yielded elevated filter-passing and particulate MeHg concentrations, the latter exceeding 500 ng g−1 on particles. Simultaneously, the organic matter content of particulates increased longitudinally in the reservoir (from 9–29%) and temporally with stratified duration. In late summer and fall, destratification mobilized MeHg from the upgradient metalimnion and the downgradient hypolimnion of Brownlee Reservoir, respectively, resulting in downstream export of elevated filter-passing MeHg and organic-rich particles enriched in MeHg (up to 43% MeHg). We document coupled biogeochemical and hydrologic processes that yield in-reservoir MeHg accumulation and MeHg export in water and particles, which impacts MeHg uptake in aquatic food webs within and downstream of reservoirs.
Chromium concentrations in rock and aquifer material in Hinkley and Water Valleys in the Mojave Desert, 80 miles northeast of Los Angeles, California, are generally low compared to the average chromium concentration of 185 milligrams per kilogram (mg/kg) in the average bulk continental crust. Chromium concentrations in felsic, coarse-textured “Mojave-type” deposits, composed of Mojave River stream (alluvium) and lake-margin (beach) deposits sourced from the Mojave River, are as low as 5 mg/kg, with a median concentration of 23 mg/kg in aquifer materials adjacent to the screened intervals of sampled wells. The most abundant chromium-containing mineral within aquifer materials in Hinkley and Water Valleys is magnetite. Magnetite is resistant to weathering, and about 90 percent of chromium remains within unweathered mineral grains. However, chromium-containing hornblende diorite and basalt are present in surrounding uplands, and chromium-containing actinolite is present within some aquifer materials.
Although geologic abundance of chromium is clearly important, hexavalent chromium, Cr(VI), concentrations in alkaline oxic groundwater are related to additional factors. Hexavalent chromium concentrations in groundwater are influenced by a combination of processes including (1) mineralogy and the weathering rates of chromium-containing minerals; (2) texture of aquifer deposits; (3) accumulation of chromium weathered from minerals within surface coatings on mineral grains; (4) oxidation of accumulated Cr(III) to Cr(VI) in the presence of manganese oxides (Mn oxides), including the abundance and oxidation states of those Mn oxides; (5) pH-dependent desorption of chromium from coatings on the surfaces of mineral grains into groundwater during appropriate aqueous geochemical conditions; and (6) age (time since recharge) of groundwater. The pH of groundwater increases with groundwater age (time since recharge) as a result of silicate weathering, and desorption of Cr(VI) from aquifer deposits increases with increasing pH as long as groundwater remains oxic. In the absence of the detailed geologic, geochemical, and hydrologic data collected as part of this study, pH-dependent sorption, evaluated as the Cr(VI) occurrence probability at the measured pH, is an effective indicator of natural or anthropogenic Cr(VI).
The Pacific Gas and Electric Company (PG&E) Hinkley compressor station is used to compress natural gas as it is transported through a pipeline from Texas to California. Between 1952 and 1964, cooling water containing Cr(VI) was discharged to unlined ponds and released into groundwater in unconsolidated aquifers. The extent of groundwater containing evidence of at least some anthropogenic Cr(VI) was 5.5 square miles (mi2) and was estimated using a summative scale incorporating geologic, geochemical, and hydrologic data collected from more than 100 wells between March 2015 and November 2017. The summative-scale Cr(VI) plume extent is larger than the 2.2 mi2 extent of the October–December 2015 (Q4 2015) regulatory Cr(VI) plume but is smaller than the 8.3 mi2 maximum mapped extent of Cr(VI) greater than the interim regulatory Cr(VI) background concentration of 3.1 micrograms per liter (μg/L). The summative-scale Cr(VI) plume is within felsic, low-chromium aquifer material deposited by the Mojave River described as Mojave-type deposits and is within the area covered by the PG&E monitoring well network.
Background Cr(VI) concentrations were calculated using the computer program ProUCL 5.1 as the upper 95-percent tolerance limit, UTL95, using data from wells outside the summative-scale Cr(VI) plume extent collected between April 2017 and March 2018. The overall UTL95 for undifferentiated, unconsolidated deposits in the eastern and western subareas and the northern subarea upgradient of the Mount General fault in Hinkley Valley was 3.8 μg/L; this value is similar to the overall UTL95 value of 3.9 μg/L calculated for Mojave-type deposits in Hinkley and Water Valleys, and is similar to the maximum Cr(VI) concentration of older groundwater in contact with Mojave-type deposits of 3.6 μg/L.
In most cases the overall UTL95 value may be an acceptable Cr(VI) background value near the Cr(VI) plume margin; however, UTL95 values for the various subareas in Hinkley and Water Valleys provide greater resolution of Cr(VI) background that may be important for some purposes. The UTL95 values for undifferentiated, unconsolidated deposits in the eastern, western, and northern subareas upgradient of the Mount General fault were 2.8, 3.8, and 4.8 μg/L, respectively. The UTL95 value of 2.8 μg/L for wells screened in undifferentiated, unconsolidated deposits in the eastern subarea is important for plume management because the Hinkley compressor station and most of the summative-scale Cr(VI) plume are within the eastern subarea. A UTL95 value of 2.3 μg/L was calculated for Mojave-type deposits downgradient from the Hinkley compressor station. This value represents Cr(VI) concentrations that may have been present in that part of the aquifer had Cr(VI) not been released from the Hinkley compressor station, and it reflects coarser textured deposits in this area and the proximity of those deposits to recharge areas along the Mojave River that results in younger (post-1952), less alkaline groundwater than in wells farther downgradient. This value may be a suitable metric for Cr(VI) cleanup goals within the Cr(VI) plume after regulatory updates. A separate UTL95 value of 5.8 μg/L was calculated for mudflat/playa deposits and older groundwater near Mount General in the eastern subarea. The UTL95 values calculated for undifferentiated, unconsolidated deposits in the northern subarea downgradient from the Mount General fault and in Water Valley, including lacustrine (lake) deposits and material eroded from basalt and Miocene deposits, were 9.0 and 6.4 μg/L, respectively.
Hexavalent chromium concentrations in more than 70 domestic wells sampled between January 27 and 31, 2016, ranged from less than the study reporting level of 0.1–4.0 μg/L, with a median concentration of 1.2 μg/L. Hexavalent chromium concentrations in water from domestic wells did not exceed UTL95 values within subareas where the wells were located. Water from 47 percent of domestic wells sampled between January 27 and 31, 2016, had arsenic, uranium, or nitrate concentrations above a maximum contaminant level.
Anthropogenic Cr(VI) within groundwater downgradient from the Hinkley compressor station is treated by PG&E using bioremediation by adding ethanol as a reductant within a volume of aquifer known as the in situ reactive zone (IRZ). Laboratory microcosm studies showed that Cr(VI) is rapidly reduced to Cr(III) with additions of ethanol. Reduced Cr(III) is sorbed and is sequestered into crystalline iron and manganese oxides on the surfaces of mineral grains within the microcosms during a period of several months. Trivalent chromium was reoxidized back to Cr(VI) within 2 weeks of return to oxic (oxygen present) conditions within the microcosms. As much as 10 percent of added Cr was oxidized to Cr(VI) in microcosms prepared using recent Mojave River aquifer material, and as much as 20 percent of added Cr was oxidized to Cr(VI) in microcosms prepared using older Mojave River aquifer material. Less Cr(VI) (less than 3 percent of Cr added before reduction) was released to the aqueous phase, and this release occurred following longer time periods of oxygen exposure. Sequestration of chromium with manganese oxides during reduction facilitates reoxidation of Cr(III) to Cr(VI) under oxic conditions. Future maintenance of anoxic (oxygen absent) conditions would ensure continued sequestration of chromium as Cr(III) within IRZ treated portions of the Cr(VI) plume.
Although Cr(VI) within the summative-scale Cr(VI) plume may have an anthropogenic history associated with releases from the Hinkley compressor station, Cr(VI) concentrations less than the UTL95 values for the various subareas may not require regulatory attention. The regulatory Cr(VI) plume can be updated using the UTL95 values calculated as part of this study. The updated regulatory Cr(VI) plume extent would lie within the summative-scale Cr(VI) plume extent. The authority to establish regulatory Cr(VI) background values, clean-up goals, and future site management practices resides with the Lahontan Regional Water Quality Control Board.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1885J","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board","usgsCitation":"Izbicki, J.A., Groover, K.D., Seymour, W.A., Miller, D.M., Warden, J.G., and Miller, L.G., 2023, Summary and conclusions, Chapter J of Natural and anthropogenic (human-made) hexavalent chromium Cr(VI), in groundwater near a mapped plume, Hinkley, California: U.S. Geological Survey Professional Paper 1885-J, 55 p., https://doi.org/10.3133/pp1885J.","productDescription":"Report: x, 55 p.; 5 Data Releases","numberOfPages":"55","additionalOnlineFiles":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":416312,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CU0EH3","text":"Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California","description":"Groover, K.D., and Izbicki, J.A., 2018, Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9CU0EH3."},{"id":416309,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1885/j/images"},{"id":416308,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1885/j/pp1885j.xml"},{"id":416307,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1885/j/pp1885j.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416306,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1885/j/covrthb.jpg"},{"id":417468,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231043","text":"Open-File Report 2023-1043","linkHelpText":"- Natural and Anthropogenic Hexavalent Chromium, Cr(VI), in Groundwater near a Mapped Plume, Hinkley, California"},{"id":416315,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HUPMG0","text":"Grain size, mineralogic, and trace-element data from field samples near Hinkley, California","description":"Morrison, J.M., Benzel, W.M., Holm-Denoma, C.S., and Bala, S., 2018, Grain size, mineralogic, and trace-element data from field samples near Hinkley, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9HUPMG0."},{"id":416314,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U8C82V","text":"Aqueous and solid phase chemistry of sequestration and re-oxidation of chromium in experimental microcosms with sand and sediment from Hinkley, CA","description":"Miller, L.G., Bobb, C., Bennett, S., and Baesman, S.M., 2020, Aqueous and solid phase chemistry of sequestration and re-oxidation of chromium in experimental microcosms with sand and sediment from Hinkley, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9U8C82V."},{"id":416313,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BUXAX1","text":"Hydrologic data in Hinkley and Water Valleys, San Bernardino County, California, 2015–2018","description":"Groover, K.D., Izbicki, J.A., Larsen, J.D., Dick, M.C., Nawikas, J., and Kohel, C.A., 2021, Hydrologic data in Hinkley and Water Valleys, San Bernardino County, California, 2015–2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9BUXAX1."},{"id":416311,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ENBLGY","text":"Optical Petrography, Bulk Chemistry, Microscale Mineralogy/Chemistry, and Bulk/Micron-Scale Solid-Phase Speciation of Natural and Synthetic Solid Phases Used in Chromium Sequestration and Re-oxidation Experiments with Sand and Sediment from Hinkley, CA","description":"Foster, A.L., Wright, E.G., Bobb, C., Choy, D., and Miller, L.G., 2023, Optical petrography, bulk chemistry, micro-scale mineralogy/chemistry, and bulk/micro-scale speciation of solid phases used in chromium sequestration and re-oxidation experiments with sand and sediment from Hinkley, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9ENBLGY."}],"country":"United States","state":"California","city":"Hinkley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116,\n 35.25\n ],\n [\n -117.75,\n 35.25\n ],\n [\n -117.75,\n 34.25\n ],\n [\n -116,\n 34.25\n ],\n [\n -116,\n 35.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Groundwater containing hexavalent chromium, Cr(VI), downgradient from the Pacific Gas and Electric Company (PG&E) Hinkley compressor station in the Mojave Desert, 80 miles northeast of Los Angeles, California, is undergoing bioremediation using added ethanol as a reductant in a volume of the aquifer defined as the in situ reactive zone (IRZ). This treatment reduces Cr(VI) to trivalent chromium, Cr(III), which is rapidly sequestered by sorption to aquifer particle surfaces and by co-precipitation within iron (Fe) or manganese (Mn) bearing minerals forming in place as reduction proceeds. Successful mitigation of the Cr(VI) plume is projected to require 10–95 years, at which time bioremediation with ethanol will likely cease. This projection assumes that Cr(VI) removal is permanent and that no Cr(III) will oxidize back to Cr(VI) in the event of changing hydrologic conditions that may cause oxygen-rich water to re-enter the IRZ. Laboratory microcosm experiments were done to explore the process of reductive sequestration of Cr(VI) to Cr(III) and the potential for reoxidation of Cr(III) to Cr(VI).
In reductive sequestration experiments, batch microcosms were prepared with aquifer materials collected from sites upgradient of the Cr(VI) regulatory plume. Control microcosms were prepared using Fe- and Mn-coated quartz sand. Unfiltered Mojave River groundwater containing an added tracer of isotopically labeled chromium-50 were reacted with microcosm materials for up to 2 years; during this time, bio-reduction was stimulated by repeated additions of diluted ethanol to maintain reduced conditions within appropriate ranges, avoiding sulfate reducing or methanogenic conditions as much as possible while mimicking field conditions. Analysis of chromium-50, Fe, and Mn obtained by sequential extraction from microcosms harvested (incubation terminated and microcosm contents analyzed) at various times showed that some aqueous chromium (Cr) was sorbed to particle surfaces within hours; reduction to Cr(III) and incorporation into amorphous and crystalline solid phases occurred during the next few months. Amorphous Cr-containing fractions included Fe and Mn hydroxides and organic matter. Ultimately, most of the chromium-50 tracer was present in the less reactive crystalline phase. However, Fe and Mn were broadly distributed at later stages of reduction, and both were spatially co-located with Cr on a micrometer (μm) scale. Solid-phase data collected using scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) and X-ray absorption spectroscopy (XAS) indicated that some Cr(III) was associated with mixed valence Fe oxides like magnetite and Fe-Mn oxides like jacobsite. Additionally, Cr(III) was observed within several μm of Fe and Mn embedded in clays and in mineral coatings.
To evaluate the potential for reoxidation of Cr(III) to Cr(VI), additional batch microcosms of aquifer materials and mixtures of Fe- and Mn-coated sand were first reduced for more than 1 year and subsequently oxidized for almost 2 years. Hexavalent chromium was formed and was available for release to the aqueous phase during oxidation of all materials; however, the timing and amount of Cr(VI) formed and released varied among substrates. Artificial substrates containing more Mn produced more Cr(VI). Site material characteristic of recent Mojave River deposits contained within the IRZ produced the least Cr(VI) during oxidation, while site materials composed of older Mojave River aquifer material (containing more Mn) produced more Cr(VI). Site material collected from within the IRZ contained more Cr but produced an intermediate amount of Cr(VI) following oxidation. The combined results of microcosm chemistry and solid-phase analyses showed that the nature and locus of Cr(III) sequestration influenced its vulnerability to reoxidation to Cr(VI). It was concluded that co-location of Cr with Mn at later stages of reduction influenced the susceptibility of Cr(III) to reoxidation in microcosms.
Reoxidation of Cr(III) to Cr(VI) was observed in experiments with previously reduced material after just 14 days exposure to oxygen. As much as 10 percent of added Cr was oxidized to Cr(VI) in microcosms prepared using recent Mojave River aquifer material, and as much as 20 percent of added Cr was oxidized to Cr(VI) in microcosms prepared using older Mojave River aquifer material. Less Cr(VI) (less than 3 percent of Cr added before reduction) was released to the aqueous phase, and this release occurred following longer oxygen exposure. Site managers may need to plan for long-term monitoring and the possibility of active maintenance of anoxic conditions within the IRZ to ensure permanent sequestration of Cr after bioremediation with ethanol ceases.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1885I","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board","usgsCitation":"Miller, L.G., Bobb, C.E., Foster, A.L., Wright, E.G., Bennett, S.C., Groover, K.D., and Izbicki, J.A., 2023, Sequestration and reoxidation of chromium in experimental microcosms, Chapter I of Natural and anthropogenic (human-made) hexavalent chromium, Cr(VI), in groundwater near a mapped plume, Hinkley, California: U.S. Geological Survey Professional Paper 1885-I, 72 p., https://doi.org/10.3133/pp1885I.","productDescription":"Report: xii, 72 p.; 4 Data Releases","numberOfPages":"72","additionalOnlineFiles":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":417467,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231043","text":"Open-File Report 2023-1043","linkHelpText":"- Natural and Anthropogenic Hexavalent Chromium, Cr(VI), in Groundwater near a Mapped Plume, Hinkley, California"},{"id":416302,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1885/i/images"},{"id":416300,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1885/i/pp1885i.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416299,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1885/i/covrthb.jpg"},{"id":416298,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HUPMG0","text":"Grain size, mineralogic, and trace-element data from field samples near Hinkley, California","description":"Morrison, J.M., Benzel, W.M., Holm-Denoma, C.S., and Bala, S., 2018, Grain size, mineralogic, and trace-element data from field samples near Hinkley, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9HUPMG0."},{"id":416297,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U8C82V","text":"Aqueous and solid phase chemistry of sequestration and re-oxidation of chromium in experimental microcosms with sand and sediment from Hinkley, CA","description":"Miller, L.G., Bobb, C., Bennett, S., and Baesman, S.M., 2020, Aqueous and solid phase chemistry of sequestration and re-oxidation of chromium in experimental microcosms with sand and sediment from Hinkley, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9U8C82V."},{"id":416296,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CU0EH3","text":"Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California","description":"Groover, K.D., and Izbicki, J.A., 2018, Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9CU0EH3."},{"id":416295,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ENBLGY.","text":"Optical petrography, bulk chemistry, micro-scale mineralogy/chemistry, and bulk/micro-scale speciation of solid phases used in chromium sequestration and re-oxidation experiments with sand and sediment from Hinkley, California","description":"Foster, A.L., Wright, E.G., Bobb, C., Choy, D., and Miller, L.G., 2023, Optical petrography, bulk chemistry, micro-scale mineralogy/chemistry, and bulk/micro-scale speciation of solid phases used in chromium sequestration and re-oxidation experiments with sand and sediment from Hinkley, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9ENBLGY."},{"id":416301,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1885/i/pp1885i.xml"}],"country":"United States","state":"California","city":"Hinkley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116,\n 35.25\n ],\n [\n -117.75,\n 35.25\n ],\n [\n -117.75,\n 34.25\n ],\n [\n -116,\n 34.25\n ],\n [\n -116,\n 35.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Hydrologic and geophysical data were collected to support updates to an existing groundwater-flow model of Hinkley Valley, California, in the Mojave Desert about 80 miles northeast of Los Angeles, California. These data provide information on predevelopment (pre-1930) water levels, groundwater recharge, and selected hydrologic properties of aquifer materials.
A predevelopment groundwater-level map, drawn using water-level measurements from 48 wells collected as early as 1918, showed groundwater movement from recharge areas along the Mojave River to evaporative discharge areas near the margin of Harper (dry) Lake in Water Valley. During predevelopment conditions, depth to water ranged from near land surface along the Mojave River to above land surface near Harper (dry) Lake, consistent with flowing wells in Water Valley at that time. Depths to water in much of Hinkley Valley downgradient from the Lockhart fault were less than 20 feet below land surface. By 2017, water-level declines as a result of agricultural pumping, were as much as 60 feet near the Hinkley compressor station.
Areal recharge from infiltration of precipitation on the valley floor is negligible. Average annual recharge as infiltration of runoff from upland drainages to Hinkley and Water Valleys averages 64.7 acre-feet per year. In most years recharge does not occur; in years when it occurs, recharge to Hinkley Valley is typically about 296 acre-feet. In contrast, average recharge as infiltration of streamflow from the Mojave River from 1931 to 2015 was between 13,400 and 17,100 acre-feet per year; in some years recharge from the Mojave River exceeded 100,000 acre-ft. Estimates of predevelopment groundwater movement through Hinkley Gap and groundwater discharge to Harper (dry) Lake ranged from 570 to 1,900 and 820 to 2,460 acre-feet per year, respectively; at the time of this study in 2017, groundwater movement through Hinkley Gap was estimated to be about 83 acre-feet per year.
Hydraulic-conductivity values estimated from slug-test data for 95 monitoring wells ranged from less than 0.1 to 680 feet per day (ft/d); values generally decreased with depth. Median hydraulic-conductivity values calculated from nuclear magnetic resonance (NMR) data for Mojave River alluvium and near-shore lake deposits were 73 and 11 ft/d, respectively; median hydraulic-conductivity values for locally derived alluvium and weathered bedrock were 6 and 2 ft/d, respectively. Hydraulic-conductivity values, estimated from NMR data for formerly saturated deposits overlying the 2017 water table, were as high as 300 ft/d near the Hinkley compressor station. Downgradient from the Hinkley compressor station, formerly saturated deposits had hydraulic-conductivity values of about 150 ft/d, which were higher than values in saturated material. Coarse-textured, permeable material in formerly saturated deposits above the 2017 water table may have allowed groundwater, released from the Hinkley compressor station that may have contained Cr(VI), to move rapidly downgradient.
The Lockhart fault is an impediment to groundwater flow within Hinkley Valley. Groundwater-flow directions from horizontal point-velocity probe data were deflected to the west on the upgradient side of the fault compared to the nominal direction of groundwater flow estimated from water-level data. Younger groundwater was present on the upgradient and downgradient sides of the fault, and older groundwater with unadjusted carbon-14 ages as old as 5,650 years before present was in water from wells within splays of the Lockhart fault, consistent with limited groundwater movement across the fault. As a result, groundwater and Cr(VI) released from the Hinkley compressor station moved to the northwest along the downgradient side of the fault.
Coupled well-bore flow and depth-dependent water-quality data show water from wells C-01 and IW-03 within the Q4 2015 (October–December 2015) regulatory Cr(VI) plume was yielded from thin layers within the aquifer that are composed of well-sorted lake-margin (beach) deposits that likely have high lateral and longitudinal connectivity. Collectively, data show highly permeable deposits above the regional water table and thin permeable deposits within saturated portions of the upper aquifer that may have conducted groundwater and Cr(VI) downgradient when releases from the Hinkley compressor station first occurred.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1885H","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board","usgsCitation":"Groover, K.D., Izbicki, J.A., Seymour, W.A., Brown, A.N., Bayless, R.E., Johnson, C.D., Pappas, K.L., Smith, G.A., Clark, D.A., Larsen, J., Dick, M.C., Flint, L.E., Stamos, C.L., and Warden, J.G., 2023, Predevelopment water levels, groundwater recharge, and selected hydrologic properties of aquifer materials, Hinkley and Water Valleys, California, Chapter H of Natural and anthropogenic (human-made) hexavalent chromium, Cr(VI), in groundwater near a mapped plume, Hinkley, California: U.S. Geological Survey Professional Paper 1885-H, 64 p., https://doi.org/10.3133/pp1885H.","productDescription":"Report: x, 64 p.; Data Release; 5 Appendixes","numberOfPages":"64","additionalOnlineFiles":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":417466,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231043","text":"Open-File Report 2023-1043","linkHelpText":"- Natural and Anthropogenic Hexavalent Chromium, Cr(VI), in Groundwater near a Mapped Plume, Hinkley, California"},{"id":416347,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/h/tables/pp1885h_appendtable_h.1.5.xlsx","text":"Appendix table H 1.5","linkFileType":{"id":3,"text":"xlsx"}},{"id":416346,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/h/tables/pp1885h_appendtable_h.1.4.xlsx","text":"Appendix table H 1.4","linkFileType":{"id":3,"text":"xlsx"}},{"id":416345,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/h/tables/pp1885h_appendtable_h.1.3.xlsx","text":"Appendix table H 1.3","linkFileType":{"id":3,"text":"xlsx"}},{"id":416344,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/h/tables/pp1885h_appendtable_h.1.2.xlsx","text":"Appendix table H 1.2","linkFileType":{"id":3,"text":"xlsx"}},{"id":416343,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/h/tables/pp1885h_appendtable_h.1.1.xlsx","text":"Appendix table H 1.1","linkFileType":{"id":3,"text":"xlsx"}},{"id":416293,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1885/h/images"},{"id":416291,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1885/h/pp1885h.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416290,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1885/h/covrthb.jpg"},{"id":416292,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1885/h/pp1885h.xml"},{"id":416289,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BUXAX1","text":"Hydrologic data in Hinkley and Water Valleys, San Bernardino County, California, 2015–2018","description":"Groover, K.D., Izbicki, J.A., Larsen, J.D., Dick, M.C., Nawikas, J., and Kohel, C.A., 2021, Hydrologic data in Hinkley and Water Valleys, San Bernardino County, California, 2015–2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9BUXAX1."}],"country":"United States","state":"California","city":"Hinkley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116,\n 35.25\n ],\n [\n -117.75,\n 35.25\n ],\n [\n -117.75,\n 34.25\n ],\n [\n -116,\n 34.25\n ],\n [\n -116,\n 35.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Hexavalent chromium, Cr(VI), was released between 1952 and 1964 from the Pacific Gas and Electric Company (PG&E) Hinkley compressor station, in the Mojave Desert about 80 miles northeast of Los Angeles, California. Geologic, geochemical, and hydrologic data from more than 100 wells collected between March 2015 and November 2017 were interpreted using a summative-scale analysis to define the extent of anthropogenic (human-made) Cr(VI) in groundwater. The summative scale consisted of eight questions requiring binary (yes or no) answers for each sampled well. The questions were intended to (1) provide a transparent framework for data interpretation in which all stakeholders participated; (2) provide unbiased interpretation of data traceable to numerical measurements; (3) provide a framework that enabled geologic, geochemical, and hydrologic data to be considered collectively; and (4) consolidate different types of data into a simple, easy-to-understand interpretation. When data from each well are scored using questions and metrics within the summative scale, all stakeholders would score each well the same way and would draw the same summative-scale Cr(VI) plume extent.
The areal extent of the summative-scale Cr(VI) plume was 5.5 square miles (mi2); this is larger than the 2.2-mi2 extent of the October–December 2015 (Q4 2015) regulatory Cr(VI) plume but smaller than the 8.3-mi2 maximum mapped extent of Cr(VI) greater than the interim regulatory Cr(VI) background value of 3.1 micrograms per liter (μg/L). The summative-scale Cr(VI) plume is within the area covered by the PG&E monitoring well network and lies within “Mojave-type” deposits composed of low-chromium stream and near-shore lake deposits sourced from the Mojave River. The summative-scale Cr(VI) plume included all shallow wells within the footprint of the Q4 2015 regulatory Cr(VI) plume, but summative-scale scores indicate that anthropogenic Cr(VI) was not present in several wells within the footprint of the regulatory Cr(VI) plume that were screened within the deep zone of the upper aquifer. The summative-scale Cr(VI) plume extent was consistent with mineralogic and geochemical data collected as part of this study that were not used within the summative-scale analysis.
Data from wells outside the summative-scale Cr(VI) plume collected for regulatory purposes from April 2017 through March 2018 were used to estimate Cr(VI) background concentrations as the upper 95-percent tolerance limit (UTL95) in different parts of Hinkley and Water Valleys. The UTL95 values were calculated using the computer program ProUCL 5.1 and are suitable for use by regulatory agencies in support of (1) updating the regulatory Cr(VI) plume extent and management of Cr(VI) near the plume margins, (2) establishing cleanup goals for Cr(VI) within the updated regulatory Cr(VI) plume, and (3) identifying unusual Cr(VI) concentrations outside the regulatory Cr(VI) plume. The nonparametric UTL95 values for wells screened in Mojave-type deposits in the eastern, western, and northern subareas of Hinkley Valley were 3.7, 3.9, and 4.0 μg/L, respectively. The normal UTL95 values for wells screened in undifferentiated, unconsolidated deposits in the eastern and western subareas and the northern subarea upgradient from the Mount General fault were 2.8, 3.8, and 4.8 μg/L, respectively. An overall normal UTL95 value of 3.8 μg/L was calculated for undifferentiated, unconsolidated deposits in these areas. This value is similar to the overall nonparametric UTL95 value of 3.9 μg/L calculated for Mojave-type deposits and similar to the maximum Cr(VI) concentration of older groundwater in contact with Mojave-type deposits of 3.6 μg/L. The provenance of most PG&E monitoring wells is not precisely known, and the UTL95 values for wells screened in undifferentiated, unconsolidated deposits in the different subareas may be more widely applicable for regulatory purposes than the UTL95 values for Mojave-type deposits.
The UTL95 value of 2.8 μg/L for wells screened in undifferentiated, unconsolidated deposits in the eastern subarea is important for plume management because most of the summative-scale Cr(VI) plume is within the eastern subarea. A UTL95 value of 5.8 μg/L was calculated for older (pre-1952) groundwater associated with mudflat/playa deposits in the eastern subarea near Mount General. A UTL95 value of 2.3 μg/L was calculated for Mojave-type deposits within the Cr(VI) plume downgradient from the Hinkley compressor station after regulatory updates. This lower value is consistent with neutral to slightly alkaline, younger (post-1952) groundwater within coarse-textured, low-chromium Mojave-type deposits in this area and may be a suitable metric for Cr(VI) cleanup goals. The UTL95 value of 4.8 μg/L for wells screened in undifferentiated, unconsolidated deposits in the northern subarea upgradient from the Mount General fault provides for possible increases in Cr(VI) concentrations if water levels continue to decline. Downgradient from the Q4 2015 regulatory Cr(VI) plume and the summative-scale Cr(VI) plume, UTL95 values of 9.0 and 6.4 μg/L were calculated for wells screened in undifferentiated, unconsolidated deposits in the northern subarea downgradient from the Mount General fault and for Water Valley, respectively, consistent with different geologic and geochemical conditions in these areas.
The UTL95 values calculated as part of this study provide scientifically defensible estimates of background Cr(VI) concentrations that differ with local geologic, geochemical, and hydrologic conditions in Hinkley and Water Valleys. The regulatory Cr(VI) plume extent can be updated on the basis of these values. The summative-scale Cr(VI) plume extent may contain wells having anthropogenic Cr(VI) concentrations less than the UTL95 values for their respective subareas that may not require regulatory attention, and an updated regulatory Cr(VI) plume extent may be less than the summative-scale Cr(VI) plume extent. The UTL95 values are not background Cr(VI) concentrations for regulatory purposes, and the authority to establish regulatory values resides solely with the Lahontan Regional Water Quality Control Board.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1885G","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board","usgsCitation":"Izbicki, J.A., Warden, J.G., Groover, K.D., and Seymour, W.A., 2023, Evaluation of natural and anthropogenic (human-made) hexavalent chromium, Chapter G of Natural and anthropogenic (human-made) hexavalent chromium, Cr(VI), in groundwater near a mapped plume, Hinkley, California: U.S. Geological Survey Professional Paper 1885-G, 51 p., https://doi.org/10.3133/pp1885G.","productDescription":"Report: x, 51 p.; 2 Data Releases; 3 Appendixes","numberOfPages":"51","additionalOnlineFiles":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":417465,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231043","text":"Open-File Report 2023-1043","linkHelpText":"- Natural and Anthropogenic Hexavalent Chromium, Cr(VI), in Groundwater near a Mapped Plume, Hinkley, California"},{"id":416337,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/g/tables/pp1885g_appendtable_g.2.2.csv","text":"Appendix table 2.2","linkFileType":{"id":7,"text":"csv"}},{"id":416336,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/g/tables/pp1885g_appendtable_g.2.1.csv","text":"Appendix table 2.1","linkFileType":{"id":7,"text":"csv"}},{"id":416335,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/g/tables/pp1885g_appendtable_g.1.1.csv","text":"Appendix table 1.1","linkFileType":{"id":7,"text":"csv"}},{"id":416287,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1885/g/images"},{"id":416286,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1885/g/pp1885g.xml"},{"id":416285,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1885/g/pp1885g.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416284,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1885/g/covrthb.jpg"},{"id":416283,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CU0EH3","text":"Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California","description":"Groover, K.D., and Izbicki, J.A., 2018, Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9CU0EH3."},{"id":416282,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ENBLGY","text":"Optical petrography, bulk chemistry, micro-scale mineralogy/chemistry, and bulk/micro-scale speciation of solid phases used in chromium sequestration and re-oxidation experiments with sand and sediment from Hinkley, California","description":"Foster, A.L., Wright, E.G., , Bobb, C., Choy, D., and Miller, L.G., 2023, Optical petrography, bulk chemistry, micro-scale mineralogy/chemistry, and bulk/micro-scale speciation of solid phases used in chromium sequestration and re-oxidation experiments with sand and sediment from Hinkley, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9ENBLGY."}],"country":"United States","state":"California","city":"Hinkley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116,\n 35.25\n ],\n [\n -117.75,\n 35.25\n ],\n [\n -117.75,\n 34.25\n ],\n [\n -116,\n 34.25\n ],\n [\n -116,\n 35.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Hexavalent chromium, Cr(VI), was discharged in cooling wastewater to unlined surface ponds from 1952 to 1964 and reached the underlying unconsolidated aquifer at the Pacific Gas and Electric Company (PG&E) Hinkley compressor station in the Mojave Desert, 80 miles northeast of Los Angeles, California. A suite of environmental tracers was analyzed in water samples collected from more than 100 wells to characterize the source, age, and geochemical evolution of groundwater within and near the Cr(VI) plume in Hinkley and Water Valleys. This information was used to help determine the extent of Cr(VI) associated with releases from the Hinkley compressor station and to identify Cr(VI) associated with natural sources.
The source of water in most wells, indicated by stable oxygen and hydrogen isotope values for water, delta oxygen-18 and delta deuterium, was recharge by infiltration of intermittent surface flows in the Mojave River. With the exception of small flows in 1958, the Mojave River was largely dry between 1952 and 1969. This dry period spans the period of Cr(VI) releases from the Hinkley compressor station; 1952–69 also spans the period of high tritium levels in precipitation resulting from the atmospheric testing of nuclear weapons and, as a consequence, tritium concentrations in groundwater in Hinkley Valley were comparatively low. Groundwater ages (time since recharge) increased downgradient from the Mojave River and with depth. Tritium, measured by helium ingrowth with a study reporting level of 0.05 tritium unit, was detected in water from 51 percent of wells, with detectable tritium as far as 7 miles downgradient from the Mojave River. Tritium concentrations were higher, and tritium/helium-3 groundwater ages younger, in water from wells near the Mojave River and in water from shallower wells downgradient. Agricultural pumping has decreased groundwater levels as much as 60 feet since 1952. As a result of this pumping, some groundwater containing tritium, and presumably anthropogenic Cr(VI), has been removed from the aquifer. The distribution of wells having carbon-14 activities near or greater than 100-percent modern carbon, consistent with post-1952 recharge water, was similar to the distribution of wells containing detectable tritium. Carbon-14 activities as low as 8.9-percent modern carbon, with carbon-14 ages (unadjusted for reactions with aquifer materials) of almost 20,000 years before present (ybp), were sampled in water from some deep wells. Hexavalent chromium concentrations in older groundwater were as high as 11 micrograms per liter but did not exceed 3.6 micrograms per liter in older water from wells completed in “Mojave-type” deposits (composed of felsic Mojave River stream and near-shore lake deposits sourced from the Mojave River); this value may represent an upper limit on Cr(VI) concentrations in groundwater within Mojave-type deposits that likely approximates background Cr(VI) concentrations in the study area. Chlorofluorocarbons were released to the atmosphere and hydrologic cycle as a result of industrial activity beginning in the 1930s. Chlorofluorocarbon data were not generally suitable for groundwater-age dating in Hinkley and Water Valley because of nonatmospheric contributions from local sources.
Strontium-87/86 isotope ratios and stable chromium isotopes, delta chromium-53, provide information on the geochemical evolution of groundwater in the aquifer. Highly radiogenic strontium-87/86 ratios greater than 0.71000 were present in water from wells completed in coarse-textured Mojave-type deposits having low chromium concentrations but were not diagnostic of these materials. Nonradiogenic strontium-87/86 ratios less than 0.70950 were diagnostic of weathered materials in the northern subarea of Hinkley and in Water Valley that were eroded from Miocene (23–5 million ybp) deposits east of the study area. Values for delta chromium-53 ranged from near 0 to 2.8 parts per thousand (‰) difference. The extent of reductive fractionation, mixing with native groundwater, and longitudinal dispersion within the October–December 2015 (Q4 2015) regulatory Cr(VI) plume can be estimated on the basis of the delta chromium-53 isotope composition of groundwater within the plume. Reduction of Cr(VI) to trivalent chromium, Cr(III), can occur in the presence of natural reductants in oxic groundwater. Although not diagnostic of anthropogenic chromium at the concentrations of interest near the Q4 2015 regulatory Cr(VI) plume margin, delta chromium-53 data indicate anthropogenic Cr(VI) within the plume is not conservative and has reacted with aquifer materials; these reactions have removed some anthropogenic Cr(VI) from groundwater.
Environmental tracers, and the distribution of modern (post-1952) and premodern (pre-1952) groundwater, inform understanding of the extent of anthropogenic and naturally occurring Cr(VI) near the Q4 2015 regulatory Cr(VI) plume and the understanding of geochemical processes occurring in and near the margins of the Cr(VI) plume. The oxygen and hydrogen isotope compositions of water, tritium/helium-3 groundwater-age data, and carbon-14 data were used with mineralogy and chemistry data as part of a summative-scale analysis to determine the Cr(VI) plume extent later in this professional paper (chapter G).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1885F","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board","usgsCitation":"Warden, J.G., Izbicki, J.A., Sültenfuß, J., Scheiderich, K., and Fitzpatrick, J., 2023, Environmental tracers of groundwater source, age, and geochemical evolution, Chapter F of Natural and anthropogenic (human-made) hexavalent chromium, Cr(VI), in groundwater near a mapped plume, Hinkley, California: U.S. Geological Survey Professional Paper 1885-F, 74 p., https://doi.org/10.3133/pp1885F.","productDescription":"Report: xii, 74 p.; 2 Data Releases; 2 Appendixes","numberOfPages":"74","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":416331,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/f/tables/pp1885f_appendtable_f.2.1.xlsx","text":"Appendix table 2.1","linkFileType":{"id":3,"text":"xlsx"}},{"id":416277,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1885/f/covrthb.jpg"},{"id":417464,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231043","text":"Open-File Report 2023-1043","linkHelpText":"- Natural and Anthropogenic Hexavalent Chromium, Cr(VI), in Groundwater near a Mapped Plume, Hinkley, California"},{"id":416275,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CU0EH3","text":"Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California","description":"Groover, K.D., and Izbicki, J.A., 2018, Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9CU0EH3."},{"id":416276,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HUPMG0","text":"Grain size, mineralogic, and trace-element data from field samples near Hinkley, California","description":"Morrison, J.M., Benzel, W.M., Holm-Denoma, C.S., and Bala, S., 2018, Grain size, mineralogic, and trace-element data from field samples near Hinkley, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9HUPMG0."},{"id":416278,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1885/f/pp1885f.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416279,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1885/f/pp1885f.xml"},{"id":416280,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1885/f/images"},{"id":416330,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1885/f/tables/pp1885f_appendtable_f.1.1.csv","text":"Appendix table 1.1","linkFileType":{"id":7,"text":"csv"}}],"country":"United States","state":"California","city":"Hinkley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116,\n 35.25\n ],\n [\n -117.75,\n 35.25\n ],\n [\n -117.75,\n 34.25\n ],\n [\n -116,\n 34.25\n ],\n [\n -116,\n 35.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Louisiana has high coastal wetland loss rates due to natural processes such as subsidence and anthropogenic activities such as construction of river levees and dams, pervasive alteration of surface hydrology by local industries such as oil and gas, and navigation. With the exception of the Atchafalaya River discharge area, most of Louisiana's marsh coastline is retreating and coastal marshes are degrading. In the inactive degrading delta regions, there exists a previously uncharacterized landform referred to colloquially as coastal ‘land bridge’ marshes. Land bridge marshes are saline or brackish marshes fronting large estuarine bays or lakes with sufficient fetch and wave energy to supply high levels of resuspended sediments to the marsh surface. They are generally linear features that are oriented parallel to the coast and the shoreline front retreats landward due to erosion from wave energy. These marshes persist over time vertically due to input of resuspended sediments but are experiencing rapid edge erosion due to wave attack. Comparison of data from Louisiana's Coastal Reference Monitoring System (CRMS) sites show that land bridge marshes have a greater frequency of higher soil surface elevation and higher soil bulk density than non-land bridge marshes. Because land bridges are vertically stable relative to other coastal wetlands, identification of measures to sustain these landscape features is important. Simulations using MarshMorpho2D, a process-based reduced-complexity morphology model, suggest that protection barriers installed on the seaward side of land bridge marshes will attenuate wave energy and, thus, edge erosion. Shoreline protection that can reduce wave energy but still allow sediment input to marshes include living shorelines, rock barriers, and/or breakwaters. Periodic thin layer nourishment of the marsh surface may be necessary to help sustain vertical growth. Further, marsh creation projects directly landward of land bridge marshes may benefit from their protection from waves and as a source of sediment. Consideration of land bridge marshes as distinct marsh types in the State Master Plan and integrated modeling could help to identify measures to sustain these landscape features.
Salinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at time scales relevant to ecology (e.g., daily, monthly, and seasonally). Here, we propose a framework for modeling coastal salinity with these overall goals: (1) quantify uncertainty in salinity associated with important terrestrial and oceanographic drivers, (2) examine time scales of salinity response to river streamflow events, and (3) predict salinity continuously over space at key time scales. Salinity is modeled as quantile surfaces related to river discharge, tidal dynamics, wind, and spatial location, applied to Suwannee Sound estuary, FL, USA, where salinity has been monitored spatially since 1981. Each quantile level is regressed independently, and together they comprise a distribution of salinity uncertainty across space, with upper and lower quantiles describing salinity extremes. Effects of physical drivers on salinity are compared through four base models with various combinations of tide and wind variables, each including spatial coordinates and a single streamflow metric (in cubic meters per second). Multiple time scales of streamflow are considered by taking means across various periods, from 1 to 12 days, and at various lagged intervals prior to salinity sample, totaling 144 streamflow metrics. We found that the Suwannee coastal salinity regime is dynamic at multiple time scales and varies nonlinearly across space from the river effluence outward. Salinity increases nonlinearly with decreasing river flow rates below 200 m3/s, most prominently in the lower quantiles of salinity (τ = 0.05–0.25). Wind appears to have a stronger influence on salinity than astronomic tides for this estuary. The regression approach developed here can be applied to any coastal system that has sufficient spatial and temporal monitoring coverage to capture multiple flood and drought events. It is implemented with a simple R routine, and is less computationally-intensive than finite difference hydrodynamic modeling. The characterizations of salinity uncertainty developed in these analyses can be directly applied to future studies of fish and wildlife responses to changes in watershed management.
The U.S. Geological Survey, in cooperation with Gwinnett County Department of Water Resources, established the Long-Term Trend Monitoring program in 1996 to monitor and analyze the hydrologic and water-quality conditions in Gwinnett County, Georgia. Gwinnett County is a suburban to urban area northeast of the city of Atlanta in north-central Georgia. The monitoring program currently consists of 15 watersheds ranging in size from 1.3 to about 161 square miles. This report synthesizes watershed characteristics and hydrologic and water-quality monitoring data collected for water years (WYs) 2002–20.
The 15 study watersheds were characterized for land-surface elevations, average land-surface slopes, septic densities, sanitary sewer densities, and detention pond areas. Temporal patterns in watershed characteristics were determined for land cover (2001–19), percent imperviousness (2000–20), population density (2000–20), and building density (1950–2022). In 2001, most of the watersheds had at least 45 percent of their land cover composed of developed land cover groups, and by 2019, at least 59 percent of each watershed was developed. Land cover changes occurred most rapidly between 2004 and 2008 at most watersheds. Percent imperviousness in the study watersheds varied substantially and ranged from 14.75 to 55.13 percent in 2019.
Precipitation and runoff were quantified at all study watersheds for WYs 2002–20, and the hydrologic cycle was evaluated both annually and seasonally. Several 1-year or longer droughts occurred during this period. Study area precipitation averaged 51.5 inches per year and runoff averaged 22.5 inches per year. Variations in annual runoff were largely determined by annual precipitation but were also dependent upon watershed storage. Runoff varied seasonally because of high evapotranspiration rates in the summer and changes in base flow associated with seasonal changes in watershed storage. Fifty-one percent of runoff in the study area occurred as base flow. Watersheds with higher imperviousness had higher stormflows because of increased surface runoff and lower base flows because of reduced infiltration that recharges watershed storage.
Turbidity, water temperature, and specific conductance were continuously measured at each study site. These constituents varied seasonally, diurnally, and with streamflow. A minimum of two base-flow and six stormflow samples were collected per year at each watershed and were analyzed for 21 water-quality constituents (water temperature, laboratory specific conductance, pH, and turbidity, biochemical and chemical oxygen demand, suspended sediments, nutrients, base cations, trace metals, and total dissolved solids). Concentrations of most particulate constituents were approximately one-half or more orders of magnitude higher in stormflow samples than in base-flow samples. Total copper and zinc stormflow concentrations exceeded the national recommended aquatic life criteria for acute conditions to varying degrees.
Annual loads and yields were estimated for 12 constituents (which include suspended sediments, nutrients, base cations, trace metals, and total dissolved solids) using a surrogate regression model approach and the Beale load estimator. Loads were typically higher for years with higher runoff. The proportional range of annual loads for total suspended solids, suspended-sediment concentrations, total phosphorus, and total lead, however, were 3.2 to 4.8 times larger than for annual runoff. Higher-than-expected annual sediment loads occurred in the years that also had some of the highest peak flows during the period, indicating that large storms are responsible for much of the sediment transport. Large development projects in proximity to streams also were related to years with high sediment loads. Yields from the Crooked Creek and North Fork Peachtree Creek watersheds were typically among the highest for 8 of the 12 constituents. These watersheds had the two highest amounts of developed medium plus high intensity land cover and the two highest percentages of imperviousness. Moderate to strong correlations were identified between seven of the constituent yields and the percentage of developed medium and high intensity land cover groups. Temporal trends in concentrations and loads were identified for 140 of the 300 possible watershed-time period-constituent combinations. There were substantially more negative than positive temporal trends identified during WYs 2003–10, whereas the number of negative and positive temporal trends were similar during WYs 2010–20. Measures of sediment transport had the most negative temporal trends. A few watersheds had consistent trends across several constituents; however, these trends did not appear to be associated with temporal changes in development or imperviousness.
This study provides a thorough assessment of watershed characteristics, hydrology, and water-quality conditions and trends for the 15 study watersheds and can be used to identify possible factors that affect runoff and water quality and determine changes in water-quality conditions. Watershed managers can use these data and analyses to inform management decisions regarding the designated uses of streams, minimization of flooding, protection of aquatic habitats, and optimization of the effectiveness of best management practices.
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South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
https://www.usgs.gov/centers/lsawsc
The groundwater and surface-water supply of the Central Platte Natural Resources District supports a large agricultural economy from the High Plains aquifer and Platte River, respectively. This study provided the Central Platte Natural Resources District with an advanced numerical modeling tool to assist with the update of their Groundwater Management Plan.
An integrated hydrologic model, called the Central Platte Integrated Hydrologic Model, was constructed using the MODFLOW-One-Water Hydrologic Model code with the Newton solver. This code integrates climate, landscape, surface water, and groundwater-flow processes in a fully coupled approach. Model framework included 163 rows; 327 columns; 2,640 feet cell sides; and 3 vertical layers. A predevelopment model simulated steady-state hydrologic conditions prior to April 30, 1895, and a development period model discretized into 610 stress periods simulated transient hydrologic conditions from May 1, 1895, to December 31, 2016, using 170 biannual stress periods from 1895 to 1980, and monthly stress periods from May 1, 1980, to December 31, 2016.
Calibration of the Central Platte Integrated Hydrologic Model involved two phases: a manual adjustment of parameters, followed by the automated calibration completed using BeoPEST that was facilitated by the employment of the singular value decomposition-assist features of PEST that specified 50 super parameters assembled from the 435 adjustable parameters and Tikhonov regularization. The average absolute groundwater-level residuals for model layers one, two, and three were 6.1, 12.4, and 7.4 feet, respectively. Calibrated horizontal hydraulic conductivity was about 70, 32, and 35 feet per day for layers 1, 2, and 3, respectively. The largest development period inflow to groundwater was recharge from deep percolation past the root zone, averaging 1,122,257 acre-feet per year (2.7 inches per year), and the largest outflow was to irrigation wells, averaging 693,171 acre-feet per year (10.2 inches per year for the Central Platte Natural Resources District). Other substantial groundwater outflows included evapotranspiration and base flow. For the total development period, there was a net change in storage of −122,393 acre-feet per year (−0.3 inch per year).
The calibrated Central Platte Integrated Hydrologic Model was used to simulate eight different potential future climate and irrigation pumping conditions from January 1, 2017, to December 31, 2049. Simulated future groundwater levels within the Central Platte Natural Resources District varied significantly between scenarios and locally, from 13.8 feet below to 7.6 feet above baseline 1982 groundwater levels. Most areas exhibited groundwater-level declines for the drought scenarios and rises for the alternate irrigation scenarios. Changes in scenario groundwater levels correlated with the relations between farm net recharge and irrigation pumping. Linear “first order second moment” techniques indicated that the uncertainty in projected groundwater altitudes was reduced by 15.33 feet through model calibration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235024","collaboration":"Prepared in cooperation with the Central Platte Natural Resources District and the Nebraska Natural Resources Commission","usgsCitation":"Traylor, J.P., Guira, M., and Peterson, S.M., 2023, An integrated hydrologic model to support the Central Platte Natural Resources District Groundwater Management Plan, central Nebraska: U.S. Geological Survey Scientific Investigations Report 2023–5024, 143 p., https://doi.org/10.3133/sir20235024.","productDescription":"Report: xii, 143 p.; 2 Tables; Data Release; Dataset; 3 Figures: 11.00 x 8.50 inches","numberOfPages":"160","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123254","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":416070,"rank":12,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235024/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":500878,"rank":13,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114681.htm","linkFileType":{"id":5,"text":"html"}},{"id":416058,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024_tables1.1_to_4.24.zip","text":"Appendix tables","size":"36 kB","linkFileType":{"id":7,"text":"csv"}},{"id":416057,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G3Q5XK","text":"USGS data release","linkHelpText":"MODFLOW-One-Water model used to support the Central Platte Natural Resources District Groundwater Management Plan, central Nebraska"},{"id":416056,"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"},{"id":416068,"rank":11,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024_fig11.pdf","text":"Figure 11 (layered)","size":"1.60 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416055,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5024/downloads","text":"Appendix tables","linkFileType":{"id":3,"text":"xlsx"}},{"id":416054,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5024/images"},{"id":416067,"rank":10,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024_fig07b.pdf","text":"Figure 7B (layered)","size":"3.37 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416053,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":416052,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024.pdf","text":"Report","size":"14.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5024"},{"id":416066,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2023/5024/sir20235024_fig04b.pdf","text":"Figure 4B (layered)","size":"847 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":416051,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5024/coverthb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.333,\n 41.51085969164163\n ],\n [\n -100.35,\n 41.51085969164163\n ],\n [\n -100.35,\n 40.11583169634787\n ],\n [\n -97.333,\n 40.11583169634787\n ],\n [\n -97.333,\n 41.51085969164163\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
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Monthly temperature and precipitation data for 923 United States Geological Survey 8-digit hydrologic units are used as inputs to a monthly water balance model to compute monthly actual evapotranspiration, soil moisture storage, and runoff across the western United States (U.S.) for the period 1900 through 2020. Time series of these water balance variables are examined to characterize and explain the dry conditions across the western U.S. since the year 2000. Results indicate that although precipitation deficits account for most of the changes in actual evapotranspiration and runoff, increases in temperature primarily explain decreases in soil moisture storage. Specifically, temperature has been particularly impactful on the magnitude of negative departures of soil moisture storage during the spring (April through June) and summer (July through September) seasons. These effects on soil moisture may be particularly detrimental to agriculture in regions already stressed by drought such as the western U.S.
Potential sources of suspended sediment and sediment-bound phosphorus (sedP) were studied in the Kinnickinnic River (51 square kilometers), a heavily urbanized tributary to Lake Michigan (90% urban land use) in Milwaukee, Wisconsin. The river is 60% concrete lined channels, with few unlined reaches. From September 2019 through August 2020, an integrated study of sediment budget and sediment fingerprinting was conducted to quantify upland and stream corridor sources of suspended sediment and sedP using Sediment Source Assessment Tool (SedSAT) methods with a suite of trace elements. Passive suspended sediment samplers were installed at three sites. Soft, fine-grained streambed sediment was collected at 10 rapid geomorphic assessment (RGA) sites. An inventory of bank erosion and soft-sediment deposition was done at each of the 18 RGA sites, which were selected to represent a range of stream sizes and geomorphic conditions. Sources of suspended sediment varied with streamflow; the primary source was from roadways in residential areas followed by eroding streambanks. Industrial/commercial areas contributed 1% of the suspended sediment at the streamgage during the study period, whereas green space contributed 18% of the suspended sediment at a mid-basin monitoring location downstream of an unlined reach. The dominant sources of streambed sediment, like suspended sediment, throughout the basin were eroding banks and residential areas, with green space and industrial/commercial area signatures present locally. In contrast with previous studies in agricultural and mixed-use basins, the urban Kinnickinnic River had limited storage of soft sediment, due to the hydrologically flashy system and concrete lined channels. However, eroding streambank sources contribute 50% of the streambed sediment, but only 9% of suspended sediment within this tributary to the Great Lakes.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","publisher":"SEDHYD","usgsCitation":"Blount, J.D., Lenoch, L., and Fitzpatrick, F., 2023, Stream corridor sources of suspended sediment and sediment-bound phosphorus from an urban tributary to the Great Lakes, in Proceedings of SEDHYD 2023, St. Louis, MO, May 8-12, 2023, 15 p.","productDescription":"15 p.","ipdsId":"IP-148078","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":419006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":415763,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s264.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","otherGeospatial":"Kinnickinnic River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.01,\n 43.02\n ],\n [\n -88.01,\n 42.95\n ],\n [\n -87.85,\n 42.95\n ],\n [\n -87.85,\n 43.02\n ],\n [\n -88.01,\n 43.02\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Blount, James D. 0000-0002-0006-3947 jblount@usgs.gov","orcid":"https://orcid.org/0000-0002-0006-3947","contributorId":200231,"corporation":false,"usgs":true,"family":"Blount","given":"James","email":"jblount@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869476,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lenoch, Leah 0000-0003-4613-0858","orcid":"https://orcid.org/0000-0003-4613-0858","contributorId":270181,"corporation":false,"usgs":true,"family":"Lenoch","given":"Leah","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869477,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209444,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869478,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243605,"text":"70243605 - 2023 - A conceptual workflow for projecting future riverine and coastal flood hazards to support the federal flood risk management standard","interactions":[],"lastModifiedDate":"2023-05-16T15:01:56.170564","indexId":"70243605","displayToPublicDate":"2023-04-15T10:33:54","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"A conceptual workflow for projecting future riverine and coastal flood hazards to support the federal flood risk management standard","docAbstract":"In 2021, the reinstatement of the Federal Flood Risk Management Standard (FFRMS) required\nfederally funded projects to recognize potential increases in flood hazards over their service lives due to climate change or local anthropogenic perturbations. Recognizing that the state of the science had advanced since the implementation guidelines for this standard were published in 2015 (WRC, 2015, Appendix H), an interagency state-of-the-science review committee\nconceptualized a workflow to guide the mapping and risk communication of projected future\nflood hazards in both riverine and coastal settings. This five-element workflow connects climate,\nhydrologic, and hydraulic models, incorporates land and water management impacts and\nongoing geomorphic changes, and can be tailored to the unique nature of different agency needs and resources. These conceptual workflows also provide a basis for a Climate-Informed Science Approach (CISA) implementation roadmap that identifies incremental steps for addressing the research and data gaps elucidated in our review. Many of these incremental steps present opportunities for interagency collaboration that would facilitate the rollout of the FFRMS in diverse riverine and coastal settings of the United States. We conduct case-study thought experiments to evaluate the implementation of the riverine and coastal workflows at three different locations in the United States: central Indiana, Galveston, Texas, and a small coastal community in western Alaska (Shaktoolik). Our thought experiments consider different project horizons, data availability, failure consequences, technical training requirements, and\ncomputational resources.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD-2023, Sedimentation and Hydrologic Modeling Conference","conferenceDate":"May 8-12, 2023","conferenceLocation":"St Louis, MO","language":"English","publisher":"SEDHYD","usgsCitation":"Hecht, J.S., Marcy, D.C., Overbeck, J.R., Schmied, L., Fitzpatrick, F., Kinsman, N.E., Honeycutt, M.G., Mason, Krolak, J., Veatch, W.C., Prokopec, J.G., Pollard, H., Gellis, A.C., Sharar-Salgado, D., Clark, E., and Weaver, C.P., 2023, A conceptual workflow for projecting future riverine and coastal flood hazards to support the federal flood risk management standard, in SEDHYD 2023, St Louis, MO, May 8-12, 2023, 17 p.","productDescription":"17 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The upper and middle reaches of Caulks Creek flow intermittently (only immediately after precipitation), whereas the lower reach flows perennially. This study examines the effects of climate change and added storage on the hydrologic and hydraulic response of the Caulks Creek Basin to design storms. The study uses hydrologic (Hydrologic Engineering Center Hydrologic Modeling System – HEC-HMS) and hydraulic (Hydrologic Engineering Center River Analysis System – HEC-RAS) models furnished by the Federal Emergency Management Agency (FEMA). HEC-HMS simulations were used to quantify the peak, volume, and timing of the flow response to a suite of design storms under both normal and wet antecedent conditions and for both the existing storage structures and new storage in the basin. The suite of design storms included all combinations of the following: (a) storm durations: 6-hour and 24-hour, (b) annual exceedance probabilities: 0.5, 0.2, 0.1, 0.04, 0.02, and 0.01, (c) climate conditions: current, 30-year, and 80-year predictions of future climate from the Coupled Model Intercomparison Project (CMIP) Climate Data Processing Tool. Additionally, for a selection of scenarios, results from the HEC-HMS simulations were used as boundary conditions for two-dimensional (2D) HEC-RAS simulations aimed at understanding how the distribution of velocity, shear stress, and stream power throughout the stream may be affected by projected changes in climate.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD-2023, Sedimentation and Hydrologic Modeling Conference","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","usgsCitation":"LeRoy, J.Z., Heimann, D.C., Burk, T.J., Cigrand, C.V., and Hix, K.D., 2023, Effects of climate change on the hydrologic and hydraulic response of the Caulks Creek basin, Wildwood, Missouri, in SEDHYD 2023, St. Louis, MO, May 8-12, 2023, 5 p.","productDescription":"5 p.","ipdsId":"IP-148407","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416240,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s26.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Missouri","city":"Wildwood","otherGeospatial":"Caulks Creek basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.7,\n 38.6667\n ],\n [\n -90.7,\n 38.566667\n ],\n [\n -90.55,\n 38.566667\n ],\n [\n -90.55,\n 38.6667\n ],\n [\n -90.7,\n 38.6667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"LeRoy, Jessica Z. 0000-0003-4035-6872 jzinger@usgs.gov","orcid":"https://orcid.org/0000-0003-4035-6872","contributorId":174534,"corporation":false,"usgs":true,"family":"LeRoy","given":"Jessica","email":"jzinger@usgs.gov","middleInitial":"Z.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heimann, David C. 0000-0003-0450-2545 dheimann@usgs.gov","orcid":"https://orcid.org/0000-0003-0450-2545","contributorId":3822,"corporation":false,"usgs":true,"family":"Heimann","given":"David","email":"dheimann@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burk, Tyler Joseph 0000-0002-9142-1454","orcid":"https://orcid.org/0000-0002-9142-1454","contributorId":304060,"corporation":false,"usgs":true,"family":"Burk","given":"Tyler","email":"","middleInitial":"Joseph","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869111,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cigrand, Charles V. 0000-0002-4177-7583","orcid":"https://orcid.org/0000-0002-4177-7583","contributorId":201575,"corporation":false,"usgs":true,"family":"Cigrand","given":"Charles","email":"","middleInitial":"V.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869112,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hix, Kyle D. 0000-0002-6316-7436","orcid":"https://orcid.org/0000-0002-6316-7436","contributorId":260630,"corporation":false,"usgs":true,"family":"Hix","given":"Kyle","email":"","middleInitial":"D.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869113,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247427,"text":"70247427 - 2023 - Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models","interactions":[],"lastModifiedDate":"2023-08-07T14:02:02.242741","indexId":"70247427","displayToPublicDate":"2023-04-15T08:48:17","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models","docAbstract":"Process-based, large-scale (e.g., conterminous United States [CONUS]) hydrologic models have struggled to achieve reliable streamflow drought performance in arid regions and for low-flow periods. Deep learning has recently seen broad implementation in streamflow prediction and forecasting research projects throughout the world with performance often equaling or exceeding that of process-based models. Deep learning models are a possible approach to increase the accuracy of streamflow drought predictions and to expand the spatial coverage of river locations with available streamflow drought forecasts.
As part of a multi-component Data-Driven Drought Prediction project, the U.S. Geological Survey is developing and testing deep learning models for streamflow drought forecasting. In this work, we present preliminary results of a deep learning model capable of predicting streamflow drought occurrence at ungaged locations for the Colorado River Basin (CRB). A long short-term memory (LSTM) neural network model was trained using 40 years (1980-2020) of daily streamflow data from 425 streamgages within and surrounding the CRB using static watershed attributes as well as meteorological and remotely sensed dynamic forcing inputs. Model tests were performed to evaluate model accuracy for now-casting streamflow drought conditions at ungaged locations and for forecasting drought conditions at lead times ranging from 0 to 14 days. Nearly all model configurations showed behavioral performance for predicting daily streamflow percentiles. Comparisons of LSTM model performance for predicting drought using fixed drought thresholds (calculated over all days and years) and variable drought thresholds (unique threshold calculated for each day of the year) identify differences in model skill between locations with implications for model design.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","publisher":"SEDHYD","usgsCitation":"Hamshaw, S.D., Goodling, P.J., Hafen, K., Hammond, J., McShane, R., Sando, R., Shastry, A.R., Simeone, C.E., Watkins, D., White, E., and Wieczorek, M., 2023, Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models, in Proceedings of SEDHYD 2023, St. Louis, MO, May 8-12, 2023, 15 p.","productDescription":"15 p.","ipdsId":"IP-151973","costCenters":[{"id":227,"text":"Earth Surface Dynamics Program","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","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 Center","active":true,"usgs":true}],"links":[{"id":419560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419548,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s181.html"}],"country":"United States","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.60045372623478,\n 31.258330936607123\n ],\n [\n -110.59254302741863,\n 31.054063075754513\n ],\n [\n -108.59802952983767,\n 31.359920476818175\n ],\n [\n -107.77329728728904,\n 32.667013027627036\n ],\n [\n -105.33291815667258,\n 38.21996621737378\n ],\n [\n -105.64401667449579,\n 40.61584869706027\n ],\n [\n -108.25906974720414,\n 42.992213339755665\n ],\n [\n -110.41906640637575,\n 43.12924273929244\n ],\n [\n -111.27275669412631,\n 41.39663030950132\n ],\n [\n -112.47810183593663,\n 38.504465490675386\n ],\n [\n -113.09970923969854,\n 37.353465042204334\n ],\n [\n -114.3929667988645,\n 37.49906345159148\n ],\n [\n -114.6266795454161,\n 38.107130943367025\n ],\n [\n -115.48463166591264,\n 39.43479765478551\n ],\n [\n -115.68353845853977,\n 37.41554450267796\n ],\n [\n -115.1115276804997,\n 33.75507968847421\n ],\n [\n -115.55059550089697,\n 31.937316150454635\n ],\n [\n -114.60045372623478,\n 31.258330936607123\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hamshaw, Scott Douglas 0000-0002-0583-4237","orcid":"https://orcid.org/0000-0002-0583-4237","contributorId":305601,"corporation":false,"usgs":true,"family":"Hamshaw","given":"Scott","email":"","middleInitial":"Douglas","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":879573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hafen, Konrad 0000-0002-1451-362X","orcid":"https://orcid.org/0000-0002-1451-362X","contributorId":215959,"corporation":false,"usgs":true,"family":"Hafen","given":"Konrad","email":"","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McShane, Ryan R. 0000-0002-3128-0039","orcid":"https://orcid.org/0000-0002-3128-0039","contributorId":219009,"corporation":false,"usgs":true,"family":"McShane","given":"Ryan R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879577,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sando, Roy 0000-0003-0704-6258","orcid":"https://orcid.org/0000-0003-0704-6258","contributorId":3874,"corporation":false,"usgs":true,"family":"Sando","given":"Roy","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":879578,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shastry, Apoorva Ramesh 0000-0002-3996-4857","orcid":"https://orcid.org/0000-0002-3996-4857","contributorId":317867,"corporation":false,"usgs":true,"family":"Shastry","given":"Apoorva","email":"","middleInitial":"Ramesh","affiliations":[{"id":227,"text":"Earth Surface Dynamics Program","active":true,"usgs":true}],"preferred":true,"id":879579,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Simeone, Caelan E. 0000-0003-3263-6452 csimeone@usgs.gov","orcid":"https://orcid.org/0000-0003-3263-6452","contributorId":221126,"corporation":false,"usgs":true,"family":"Simeone","given":"Caelan","email":"csimeone@usgs.gov","middleInitial":"E.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879580,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Watkins, David 0000-0002-7544-0700","orcid":"https://orcid.org/0000-0002-7544-0700","contributorId":317375,"corporation":false,"usgs":true,"family":"Watkins","given":"David","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":879581,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"White, Elaheh 0000-0003-1248-5247","orcid":"https://orcid.org/0000-0003-1248-5247","contributorId":295260,"corporation":false,"usgs":true,"family":"White","given":"Elaheh","email":"","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":879582,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wieczorek, Michael 0000-0003-0999-5457","orcid":"https://orcid.org/0000-0003-0999-5457","contributorId":207911,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879583,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70244030,"text":"70244030 - 2023 - Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time","interactions":[],"lastModifiedDate":"2023-05-31T13:46:59.201583","indexId":"70244030","displayToPublicDate":"2023-04-15T08:43:15","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time","docAbstract":"Sediment connectivity is a framework for transfer and storage of sediment among different geomorphic compartments across upland and channel network of the catchment sediment cascade. Sediment connectivity and dysconnectivity (i.e., source delivery and storage processes) are linked to the water cycle and hydrologic systems with the associated multiscale interactions with climate, soil, topography, ecology, and landuse/landcover under natural variability and human intervention. We review the sediment connectivity concept and frameworks developed in the last few decades to examine and quantify water and sediment transfer in catchment systems. Past conceptual models of connectivity have attempted to integrate multiple processes into sediment domain, including geomorphic, hydrologic, and ecological processes (i.e., “holistic approach to connectivity”). In particular, multiple studies highlight the importance of sediment and water interaction in defining landscape connectivity. There are also efforts to quantify the topographic controls on sediment connectivity, in the advent of increasingly high-resolution digital terrain models. More recent modeling efforts have integrated structural and functional connectivity through coupling topographic information with hydrologic simulation models. Though this recent modeling development is encouraging, a comprehensive sediment connectivity framework that integrates geomorphic and hydrologic processes across spatiotemporal scales is yet to be conceived. Such an effort will require understanding the governing hydrologic and geomorphic processes that control sediment source, storage, and transport. A conceptual model is proposed to describe dominant hydrologic-sediment connectivity regimes through spatial-temporal feedbacks between hydrologic processes (rainfall, flow routing, and water residence time) and geomorphic drivers (upland soil erosion and deposition, and geomorphic channel erosion and deposition response). Recent advancements in landscape monitoring techniques using geochemical tracers, remote-sensing, increasing availability of hydrologic monitoring data, and the integration of various analytic methods (e.g., isotopic hydrograph separation, stormflow concentration-discharge, hysteretic behavior analysis) have the potential to broaden the spatial and temporal scales of geomorphic observations and understanding of landscape sediment connectivity. Using the conceptual model as a “thinking” space, we examine sediment and hydrologic interactions in real world examples of watershed studies using multiple lines of evidence and modeling techniques.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD-2023, Sedimentation and Hydrologic Modeling Conference","language":"English","publisher":"SEDHYD","usgsCitation":"Cho, J., Karwan, D., Skalak, K., Pizzuto, J., and Huffman, M., 2023, Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time, in SEDHYD 2023, 14 p.","productDescription":"14 p.","ipdsId":"IP-150563","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":417576,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417575,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s252.html","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cho, Jong 0000-0001-5514-6056","orcid":"https://orcid.org/0000-0001-5514-6056","contributorId":291384,"corporation":false,"usgs":true,"family":"Cho","given":"Jong","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":874198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karwan, Diana","contributorId":305967,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":874199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":874200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pizzuto, James","contributorId":305968,"corporation":false,"usgs":false,"family":"Pizzuto","given":"James","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":874201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huffman, Max","contributorId":305969,"corporation":false,"usgs":false,"family":"Huffman","given":"Max","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":874202,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70242712,"text":"pp1837D - 2023 - Evaluation of hydrologic processes in the eastern Snake River Plain aquifer using uranium and strontium isotopes, Idaho National Laboratory, eastern Idaho","interactions":[],"lastModifiedDate":"2026-02-18T22:12:09.217716","indexId":"pp1837D","displayToPublicDate":"2023-04-14T06:48:58","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1837","chapter":"D","displayTitle":"Evaluation of Hydrologic Processes in the Eastern Snake River Plain Aquifer Using Uranium and Strontium Isotopes, Idaho National Laboratory, Eastern Idaho","title":"Evaluation of hydrologic processes in the eastern Snake River Plain aquifer using uranium and strontium isotopes, Idaho National Laboratory, eastern Idaho","docAbstract":"Waste constituents discharged to the eastern Snake River Plain aquifer at the U.S. Department of Energy (DOE) Idaho National Laboratory (INL) pose risks to the water quality of the aquifer. To understand these risks, the U.S. Geological Survey, in cooperation with the DOE, is conducting geochemical studies to better understand the hydrologic processes at the INL that affect the movement of groundwater and waste constituents. In this study, we used natural uranium (234U/238U) and strontium (87Sr/86Sr) isotope ratios of surface water and groundwater to identify the sources of water, the mixing of different source waters, and the flow directions in the shallow part (upper 250 feet) of the aquifer at the INL.
Samples were collected from 17 sites at and near the INL that represent the source-water contributions to the aquifer. These source-water sites included surface water, regional groundwater, and springs. Groundwater samples from 63 sites were collected at and near the INL. For all sites, sample collection dates ranged from 1979 to 2019, but groundwater samples collected at the INL are representative of wet climate cycles when the Big Lost River (BLR) was flowing onto the INL.
The 234U/238U activity ratios and 87Sr/86Sr from groundwater at the INL were plotted on graphs within ternary mixing webs in which the three end members of the mixing web represented specific sources of recharge. The large number of sources of recharge required numerous mixing webs, representing various geographic locations at the INL, so that each mixing web represented an area with just three sources of recharge. Considerations for determining the sources of recharge to groundwater sites included chemical signatures in addition to 234U/238U and 87Sr/86Sr, hydrologic context, and geographic location. The mixing webs were used to estimate the percentage of recharge from specific sources to groundwater at wells.
The results of this study identified groundwater from the Lemhi Range as a source of recharge to the INL, which was a previously unsuspected source of recharge. The estimated spatial distribution of recharge from the BLR and groundwater from the Lost River Range also decreased and increased, respectively, relative to the spatial distribution estimated from an earlier study. Upwelling geothermal water was identified at only one well, which indicates that the upward movement of deep groundwater to the shallow part of the aquifer is largely nonexistent. Mixing between surface water and groundwater, different groundwater recharge sources, or both is ubiquitous at the INL. Mixing of water fully explains the distribution of 234U/238U and 87Sr/86Sr in groundwater at the INL and thus renders unnecessary the hypothesis that fast and slow flow zones at the INL are required to explain the distribution of 234U/238U and 87Sr/86Sr.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1837D","collaboration":"DOE/ID-22259Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
Waste constituents discharged to the eastern Snake River Plain aquifer at the U.S. Department of Energy (DOE) Idaho National Laboratory (INL) pose risks to the water quality of the aquifer. To understand these risks, the U.S. Geological Survey, in cooperation with the DOE, used geochemical mass-balance modeling to identify three-dimensional hydrologic processes in that portion of the aquifer underlying the southwestern part of the INL that affect the movement of groundwater and waste constituents. Modeling was performed using water chemistry of 74 water samples collected from 30 wells. Fifty-four of the water samples were collected from 11 wells equipped with multilevel monitoring systems with vertically discrete sampling zones that encompass the upper 750 feet of the aquifer. Water samples from these multilevel wells were collected during 2007‒13, a period when conditions in the aquifer were approximately steady-state because there was little or no recharge from the Big Lost River.
The primary source of water in groundwater at the multilevel wells during 2007‒13 was the Big Lost River. Other sources of water include groundwater from the Little Lost River valley, precipitation, and wastewater. Horizontal groundwater-flow directions appear to be similar in both the shallow and deep parts of the aquifer, and surface-water sources of water in most deep groundwater shows that groundwater moves downward. Surface-water sources of water in deep groundwater noticeably decrease within and below the Matuyama flow and associated sedimentary interbeds, which indicates that these units are semi-impermeable and retard the downward movement of groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1837C","collaboration":"DOE/ID-22258Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
Changes in riparian vegetation cover and composition occur in relation to flow regime, geomorphic template, and climate, and can have cascading effects on aquatic and terrestrial ecosystems. Tracking such changes over time is therefore an important part of monitoring the condition and trajectory of riparian ecosystems. Maintaining diverse, self-sustaining riparian vegetation comprised of mostly native species is identified in the Glen Canyon Dam Long-Term Experimental and Management Plan as a key resource objective for the section of the Colorado River between Glen Canyon Dam and Lake Mead. The U.S. Geological Survey Grand Canyon Monitoring and Research Center implemented an annual monitoring program in 2014 to assess the status and trends of riparian vegetation along this section of river, particularly as they relate to flow regime. In this report, we summarize plant species composition and cover data collected under the annual monitoring program from 2014 to 2019, with special consideration given to the hydrologic position, associated geomorphic feature class, local climate patterns, native and nonnative species, and floristic region for key vegetation metrics and species. We divided the study area into four river segments (referred to as Glen Canyon, Marble Canyon, eastern Grand Canyon, and western Grand Canyon) on the basis of geography and floristic composition and calculated each recorded plant species’ relative frequency and foliar cover by river segment. These data were then used to evaluate species composition relationships among river segments, hydrologic zones, geomorphic features, and sampling years through ordination analysis. Temporal trends in our focal resource objectives—species richness, total foliar cover, proportion of native to nonnative species richness, proportion of native to nonnative species cover, Tamarix cover, Pluchea sericea cover, and Baccharis species cover—were assessed using mixed-effects models. Four patterns related to species composition emerged: (1) species composition of fixed-site sandbars differed from that of randomly selected sites (including randomly selected sandbars), (2) species composition of Glen Canyon sites differed from that of other previously identified floristic regions, (3) species composition differed across hydrologic zones related to dam operations, and (4) species composition within river segments did not change across years. For temporal patterns, four main findings emerged: (1) trends differed between fixed-sites and randomly selected sites; (2) although few directional changes were observed from 2014 to 2019, Baccharis species cover increased at randomly selected sites in areas influenced by daily water fluctuations; (3) native species cover and richness were greater than nonnative species cover and richness across all hydrologic zones; and (4) the temporal trend metrics used here can be used across floristic groups, enabling assessment of the Colorado River ecosystem as a whole. In addition to these findings, lists of recorded plant species are included as appendixes. The variations and patterns in vegetation status and trends presented in this report can be used as a baseline against which future monitoring can be compared.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231026","collaboration":"Prepared in cooperation with the Bureau of Reclamation Glen Canyon Adaptive Management Program","usgsCitation":"Palmquist, E.C., Butterfield, B.J., and Ralston, B.E., 2023, Assessment of riparian vegetation patterns and change downstream from Glen Canyon Dam from 2014 to 2019: U.S. Geological Survey Open-File Report 2023–1026, 55 p., https://doi.org/10.3133/ofr20231026.","productDescription":"Report: vii, 55 p.; Data Release","numberOfPages":"55","onlineOnly":"Y","ipdsId":"IP-132835","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499774,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114661.htm","linkFileType":{"id":5,"text":"html"}},{"id":415675,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1026/images"},{"id":415674,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1026/ofr20231026.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":415673,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1026/covrthb.jpg"},{"id":415672,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KEHY2S","text":"Riparian vegetation data downstream of Glen Canyon Dam in Glen Canyon National Recreation Area and Grand Canyon National Park, AZ from 2014 to 2019","description":"Palmquist, E.C., Butterfield, B.J., and Ralston, B.E., 2022, Riparian vegetation data downstream of Glen Canyon Dam in Glen Canyon National Recreation Area and Grand Canyon National Park, AZ from 2014 to 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9KEHY2S."}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.06028247701303,\n 36.94784441270309\n ],\n [\n -114.06028247701303,\n 35.55756259875736\n ],\n [\n -111.24899178190306,\n 35.55756259875736\n ],\n [\n -111.24899178190306,\n 36.94784441270309\n ],\n [\n -114.06028247701303,\n 36.94784441270309\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000–2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.
Arctic freshwater ecosystems and fish populations are largely shaped by seasonal and long-term watershed hydrology. In this paper, we hypothesize how changing air temperature and precipitation will alter freeze and thaw processes, hydrology, and instream habitat to assess potential indirect effects, such as the change to the foraging and behavioral ecology, on Arctic fishes, using Broad Whitefish Coregonus nasus as an indicator species. Climate change is expected to continue to alter hydrologic pathways, flow regimes, and, therefore, habitat suitability, connectivity, and availability for fishes. Warming and lengthening of the growing season will likely increase fish growth rates; however, the exceedance of threshold stream temperatures will likely increase physiological stress and alter life histories. We expect these changes to have mixed effects on Arctic subsistence fishes and fisheries. Management and conservation approaches focused on preserving the processes that create heterogeneity in aquatic habitats, genes, and communities will help maintain the resilience of Broad Whitefish and other important subsistence fisheries. Long-term effects are uncertain, so filling scientific knowledge gaps, such as identifying important habitats or increasing knowledge of abiotic variables in priority watersheds, is key to understanding and potentially mitigating likely impacts to Arctic fishes in a rapidly changing landscape.