The U.S. Army Fort Belvoir (FTBL) installation is on the banks of the Potomac River in Fairfax County, northeastern Virginia. The installation was founded by the U.S. Army during World War I. It has been home to a variety of military organizations over the course of its more than 100-year history and currently houses more than 145 mission partners. The installation consists of two noncontiguous units, the Main Post, and a smaller area to the northwest, Fort Belvoir North Area (FTNA). FTBL encompasses 8.91 square miles.
There is concern that activities on FTBL, including a long history of training, operations, and maintenance, may have resulted in contamination of stream water, streambed sediment, and (or) soils. Of particular concern is the U.S. Environmental Protection Agency (EPA) Target Analyte List (TAL). TAL refers to “the list of inorganic compounds/elements designated for analysis as contained in the version of the EPA Contract Laboratory Program Statement of Work for Inorganics Analysis, Multi-Media, Multi-Concentration in effect as of the date on which the laboratory is performing the analysis” (https://www.nj.gov/dep/srp/guidance/tcl_tal/). Because of the potential for TAL contamination at FTBL, the U.S. Geological Survey (USGS), in cooperation with U.S. Army Fort Belvoir, conducted a survey of FTBL’s stream water, streambed sediment, and soils during calendar year 2019.
The terminology “ambient concentrations” is used in this report to represent the concentrations of the TAL and other constituents at the time of sampling. This is in contrast to “background concentrations,” a term that “refers to areas in which the concentrations of chemicals have not been elevated by site activities”. Although some of the samples collected for this project may represent “background concentrations,” there is no assurance that they do, so all data collected are described as having “ambient concentrations.”
The purpose of the study was to obtain environmental data to characterize ambient concentrations of EPA TAL constituents in stream water, streambed sediment, and soils in FTBL, Virginia. This report describes methods and results of sampling stream water, streambed sediment, and soils during 2019. The purpose of this report is four-fold: (1) to describe the field sampling methods used to collect stream water, streambed sediment, and soils; (2) to describe the laboratory methods used to analyze the samples; (3) to report summaries of the field and laboratory results; and (4) to report the quality assurance and quality control results.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201059","collaboration":"Prepared in cooperation with the U.S. Army, Fort Belvoir","usgsCitation":"Rice, K.C., and Chambers, D.B., 2020, Chemical constituent concentrations in stream water, streambed sediment, and soils of Fort Belvoir, Virginia—A characterization of ambient conditions in 2019: U.S. Geological Survey Open-File Report 2020–1059, 20 p., https://doi.org/10.3133/ofr20201059.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-116519","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":376670,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91P7OZJ","text":"USGS data release","linkHelpText":"Fort Belvoir, Virginia, stream-water, streambed-sediment, and soil data collected in 2019"},{"id":376668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1059/coverthb.jpg"},{"id":376669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1059/ofr20201059.pdf","text":"Report","size":"6.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1059"}],"country":"United States","state":"Virginia","city":"Fort Belvoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.19388961791992,\n 38.71096464102174\n ],\n [\n -77.20212936401367,\n 38.69033348573663\n ],\n [\n -77.12900161743163,\n 38.67117065551123\n ],\n [\n -77.11492538452148,\n 38.695290864945804\n ],\n [\n -77.12608337402344,\n 38.741766321754575\n ],\n [\n -77.14050292968749,\n 38.753012320665185\n ],\n [\n -77.16659545898438,\n 38.74390855335671\n ],\n [\n -77.18238830566406,\n 38.73051855149164\n ],\n [\n -77.19388961791992,\n 38.71096464102174\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.2184371948242,\n 38.73935623438332\n ],\n [\n -77.17826843261717,\n 38.73935623438332\n ],\n [\n -77.17826843261717,\n 38.7591700932071\n ],\n [\n -77.2184371948242,\n 38.7591700932071\n ],\n [\n -77.2184371948242,\n 38.73935623438332\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
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Despite the reliance on groundwater by approximately 2.4 million rural Pennsylvania residents, publicly available data to characterize the quality of private well water are limited. As part of a regional effort to characterize groundwater in rural areas of Pennsylvania, samples from 54 domestic wells in Clinton County were collected and analyzed in 2017. The samples were evaluated for a wide range of constituents and compared to drinking-water health standards and geochemical characteristics. The sampled wells were completed to depths ranging from 46 to 500 feet in bedrock that was of predominantly sandstone, shale, or carbonate lithology. Results of this study show that the sampled groundwater quality in Clinton County generally met most drinking-water standards that apply to public water supplies. However, a percentage of samples exceeded drinking-water maximum contaminant levels (MCLs) for total coliform bacteria (57.4 percent), Escherichia coli (E. coli) (25.9 percent), nitrate (1.9 percent), and arsenic (1.9 percent); and secondary maximum contaminant levels (SMCLs) for pH (31.5 percent), manganese (29.6 percent), iron (13 percent), total dissolved solids (7.4 percent), aluminum (1.9 percent), and chloride (1.9 percent). Sodium concentrations exceeded the U.S. Environmental Protection Agency drinking-water advisory recommendation in 16.7 percent of the samples. Radon-222 activities exceeded the proposed drinking-water standard of 300 picocuries per liter (pCi/L) in 59.3 percent of the samples. The only volatile organic compounds (VOCs) detected were acetone and methyl ethyl ketone in two separate samples; neither constituent exceeded drinking-water standards.
Higher median nitrate concentrations were found in the carbonate (3.26 milligrams per liter [mg/L]) versus shale (less than 0.04 mg/L) and sandstone (0.27 mg/L) aquifer subsets. Most of the elevated nitrate concentrations were associated with E. coli detections in the carbonate aquifers, where transmissive bedrock can facilitate groundwater contamination by human activities at the land surface.
The median pH of groundwater from the sandstone aquifers (6.53) was less than those for the shale aquifers (7.31) and carbonate aquifers (7.43). Generally, the lower pH samples had greater potential for elevated concentrations of dissolved metals, including beryllium, copper, lead, nickel, and zinc, whereas the higher pH samples had greater potential for elevated concentrations of total dissolved solids, sodium, fluoride, boron, and uranium. Near-neutral samples (pH 6.5 to 7.5) had greater hardness and alkalinity concentrations than other samples with pH outside this range. Many samples from the shale or sandstone aquifers, particularly those with pH less than 6.5, were identified as having serious potential corrosivity based on the combination of the calcite saturation index and the chloride to sulfate mass ratio; however, none of the samples from the carbonate aquifers was identified as seriously corrosive.
Groundwater from 3.7 percent of the wells had concentrations of methane greater than the Pennsylvania action level of 7 mg/L, and 48 of the 54 wells (88.9 percent) had detectable concentrations of methane greater than the 0.0002 mg/L detection limit. Greater methane concentrations were found more frequently in groundwater sampled from the shale aquifers than the carbonate or sandstone aquifers in the study area. Most of the samples containing elevated methane (greater than 0.2 mg/L) were located outside the area of the Appalachian Plateaus. The elevated concentrations of methane generally were associated with suboxic groundwater (dissolved oxygen less than 0.5 mg/L) that had near-neutral to alkaline pH and were correlated with concentrations of iron, manganese, ammonia, sodium, lithium, barium, fluoride, and boron. The stable carbon and hydrogen isotopic compositions of methane in two of four samples analyzed for isotopes were consistent with compositions reported for mud-gas logging samples from gas-bearing geologic units (thermogenic gas) in the Appalachian Plateaus region, whereas two others were consistent with methane of microbial origin or a mixture of microbial and thermogenic gas.
Forty-two percent of samples had chloride concentrations greater than 20 mg/L with variable bromide concentrations. Corresponding chloride/bromide ratios are consistent with low-bromide sources such as road-deicing salt and septic effluent or animal waste, or, in a few cases, high-bromide brine. Brines characterized by relatively high bromide are naturally present in deeper parts of the regional groundwater system and, in some cases, may be mobilized by gas drilling. The chloride, bromide, and other constituents in road-deicing salt or brine solutions tend to be diluted by mixing with fresh groundwater in shallow aquifers used for water supply. One of the four groundwater samples with methane concentrations greater than 4 mg/L had chloride and bromide concentrations and a chloride/bromide ratio that indicates mixing with a salinity source such as road-deicing salt, whereas the chloride and bromide concentrations and ratios for the other three high-methane samples indicate mixing with a small amount of brine (0.03 percent or less). In two other eastern Pennsylvania county studies where gas drilling is absent, groundwater with comparable chloride/bromide ratios, bromide, and chloride concentrations plus other element associations have been reported. Additional sampling and analysis, such as isotopic analysis of the dissolved gas, fracture analysis, and more detailed evaluation of surrounding land uses, may be warranted to better understand the origin of the methane and brine constituents in groundwater at specific locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205022","collaboration":"Prepared in cooperation with the Clinton County Commissioners","usgsCitation":"Clune, J.W., and Cravotta, C.A., III, 2020, Groundwater quality in relation to drinking water health standards and geochemical characteristics for 54 domestic wells in Clinton County, Pennsylvania, 2017 (ver 1.1, July 2020): U.S. Geological Survey Scientific Investigations Report 2020–5022, 72 p., https://doi.org/10.3133/sir20205022.","productDescription":"Report: vii, 72 p.; Data Release; Appendix","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109062","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":376698,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5022/sir20205022_appendix3.pdf","text":"Appendix 3","size":"130 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The effect of nutrients on phytoplankton biomass in lakes continues to be a subject of debate by aquatic scientists. However, determining whether or not chlorophyll a (CHL) is limited by phosphorus (P) and/or nitrogen (N) is rarely considered using a probabilistic method in studies of hundreds of lakes across broad spatial extents. Several studies have applied a unified CHL-nutrient relationship to determine nutrient limitation, but pose a risk of ecological fallacy because they neglect spatial heterogeneity in ecological contexts. To examine whether or not CHL is limited by P, N, or both nutrients in hundreds of lakes and across diverse ecological settings, a probabilistic machine learning method, Bayesian Network, was applied. Spatial heterogeneity in ecological context was accommodated by the probabilistic nature of the results. We analyzed data from 1382 lakes in 17 US states to evaluate the cause-effect relationships between CHL and nutrients. Observations of CHL, total phosphorus (TP), and total nitrogen (TN) were discretized into three trophic states (oligo-mesotrophic, eutrophic, and hypereutrophic) to train the model. We found that although both nutrients were related to CHL trophic state, TP was more related to CHL than TN, especially under oligo-mesotrophic and eutrophic CHL conditions. However, when the CHL trophic state was hypereutrophic, both TP and TN were important. These results provide additional evidence that P-limitation is more likely under oligo-mesotrophic or eutrophic CHL conditions and that co-limitation of P and N occurs under hypereutrophic CHL conditions. We also found a decreasing pattern of the TN/TP ratio with increasing CHL concentrations, which might be a key driver for the role change of nutrients. Previous work performed at smaller scales support our findings, indicating potential for extension of our findings to other regions. Our findings enhance the understanding of nutrient limitation at macroscales and revealed that the current debate on the limiting nutrient might be caused by failure to consider CHL trophic state. Our findings also provide prior information for the site-specific eutrophication management of unsampled or data-limited lakes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2020.116236","usgsCitation":"Liang, Z., Soranno, P., and Wagner, T., 2020, The role of phosphorus and nitrogen on chlorophyll a: Evidence from hundreds of lakes: Water Research, v. 185, 116236, 9 p., https://doi.org/10.1016/j.watres.2020.116236.","productDescription":"116236, 9 p.","ipdsId":"IP-113421","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":455860,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2020.116236","text":"Publisher Index Page"},{"id":396014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liang, Zhongyao","contributorId":279427,"corporation":false,"usgs":false,"family":"Liang","given":"Zhongyao","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":834941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soranno, Patricia A.","contributorId":279428,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":834942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834940,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211311,"text":"70211311 - 2020 - A guidebook to spatial datasets for conservation planning under climate change in the Pacific Northwest","interactions":[],"lastModifiedDate":"2021-01-12T16:10:06.706657","indexId":"70211311","displayToPublicDate":"2020-07-27T08:58:19","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"A guidebook to spatial datasets for conservation planning under climate change in the Pacific Northwest","docAbstract":"This guidebook provides user-friendly overviews of a variety of spatial datasets relevant to conservation and management of natural resources in the face of climate change in the Pacific Northwest, United States. Each guidebook chapter was created using a standardized template to summarize a spatial dataset or a group of closely related datasets. Datasets were selected according to standardized criteria based on input through a collaborative process involving researchers and natural-resource managers throughout the Pacific Northwest region. In each chapter, basic spatial and temporal information is provided for the dataset, along with a conceptual overview, glossary of key terms, links to download data and supporting documentation, a brief methods summary describing how the dataset was created, guidelines for dataset interpretation, assessment of uncertainties along with evaluation of caveats and simplifying assumptions, and information about potential and actual conservation applications of the dataset. Collectively, this information provides natural-resource managers with “snapshots” of a variety of datasets representing diverse processes and conditions, including climate projections, changes in hydrologic conditions, vegetation and fire-regime shifts, animal habitat changes, species movements, and topographic and soil conditions relevant to climate change. Along with other types of data and site-specific information, the datasets described in this guidebook have the potential to inform management of valued natural resources throughout the Pacific Northwest region in the context of adaptation to changing climate conditions.","language":"English","publisher":"Department of Interior Northwest Climate Adaptation Science Center","usgsCitation":"Cartwright, J.M., Belote, T., Blasch, K.W., Campbell, S., Chambers, J.C., Davis, R.J., Dobrowski, S., Dunham, J., Gergel, D., Isaak, D., Jaeger, K., Krosby, M., Langdon, J., Lawler, J.J., Littlefield, C.E., Luce, C.H., Maestas, J.D., Martinez, A., Meddens, A.J., Michalak, J., Parks, S.A., Peterman, W., Popper, K., Ringo, C., Sando, R., Schindel, M., Stralberg, D., Theobald, D.M., Walker, N., Wilsey, C., Yang, Z., and Yost, A., 2020, A guidebook to spatial datasets for conservation planning under climate change in the Pacific Northwest, 212 p.","productDescription":"212 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Because of high external phosphorus (P) inputs to the lakes, the lakes became highly eutrophic, with much P contained in their sediments. In developing a total maximum daily load (TMDL) for these lakes, it is important to determine how their phosphorus concentrations should respond to changes in external P loading. In many TMDLs, internal P loading is assumed to be negligible or it is estimated based on sediment release rates and dissolved oxygen conditions in the lake, and each lake is considered independently. To evaluate these assumptions, internal P loading and external P loading were quantified by developing detailed P budgets for the Winnebago Pool chain of lakes. This information was then inputted into 2 eutrophication models (BATHTUB and Jensen models), which were used to simulate the steady-state and transient effects of various P reduction strategies. The importance of internal P loading varied among lakes, from being a minor source to representing almost 60% of the summer P input. Model results indicate that each lake responds to external P reductions, but internal loading can delay the lake responses, especially in the most downstream lake, Lake Winnebago, where internal P loading was most important to its summer P budget. Accurately quantifying net internal P loading and using this information in lake models are important in evaluating how large shallow lakes should respond to P reduction strategies, setting realistic expectations from watershed P reductions, and guiding TMDL efforts.
In order to comply with a current U.S. Environmental Protection Agency watershed-based National Pollutant Discharge Elimination System permit, the City of Albuquerque required a better understanding of the rainfall-runoff processes in its small urban watersheds. That requirement prompted the initiation of the assessment of three existing watershed models that were developed to simulate those processes. Three existing rainfall-runoff modeling software packages—Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) (using two sets of methods), Program for Predicting Polluting Particle Passage Through Pits, Puddles, and Ponds (P8), and Arid-Lands Hydrologic Model (AHYMO)—were compared to determine which provided the best balance of accuracy and usability for simulating storm runoff in small watersheds in the urbanized area of Albuquerque, New Mexico. Additionally, results of this study could help inform model users who have interest in simulating storm runoff in similar urban areas throughout the United States. Each model was used to simulate storm runoff in the Hahn Arroyo watershed, an urbanized watershed with concrete-lined arroyo channels in the northeastern quadrant of Albuquerque that exhibits flashy, monsoonal-driven storm runoff. Model results were compared to observed discharge data, according to literature-recommended performance measures and performance evaluation criteria. The HEC-HMS model using the Soil Conservation Service (SCS) curve number (CN) and SCS unit hydrograph methods ranked the highest when averaging the individual performance measures (Nash-Sutcliffe Efficiency, percent bias, and coefficient of determination) rankings together across the hourly calibration and validation periods, followed by P8, which was tied with the HEC-HMS initial and constant approach. For daily rankings using the same rank-averaging approach, the HEC-HMS CN-based model and P8 were tied for the highest ranking, followed by the HEC-HMS initial and constant approach. Alternatively, rating performance using validation period results as an indication of the expected confidence in forecasted results for future conditions, the P8 model performed best for both hourly and daily time-steps, followed by the HEC-HMS CN-based model and the HEC-HMS initial and constant-based model. However, based on the literature performance evaluation criteria, the HEC-HMS and P8 models overall had marginally satisfactory performance only for operation at the daily time-step. Direct comparison of the HEC-HMS and P8 models to the AHYMO is difficult, given the different performance assessment criteria used to assess these models separately in this study, as recommended by the literature. The AHYMO results generally lacked precision, given the wide range in the performance assessment values across events in percent error in peak discharge, difference in timing of peak discharge, percent error in total runoff volume, and difference in duration of event relative to observed data. For some events, however, the AHYMO results were fairly accurate, and AHYMO was likely a good predictor of the timing of storm runoff and the shape of the hydrograph. This study did not assess the results for all potential applications of the models in the Albuquerque urbanized area. Further study may be required to assess the model performance capabilities in other modeling applications.
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Watershed nitrogen (N) budgets provide insights into drivers and solutions for groundwater and surface water N contamination. We constructed a comprehensive N budget for the transboundary Nooksack River Watershed (British Columbia, Canada, and Washington, USA) using locally derived data, national statistics, and standard parameters. Feed imports for dairy (mainly in the United States) and poultry (mainly in Canada) accounted for 30% and 29% of the total N input to the watershed, respectively. Synthetic fertilizer was the next largest source contributing 21% of inputs. Food imports for humans and pets together accounted for 9% of total inputs, lower than atmospheric deposition (10%). N imported by returning salmon representing marine‐derived nutrients accounted for <0.06% of total N input. Quantified N export was 80% of total N input, driven by ammonia emission (32% of exports). Animal product export was the second largest output of N (31%) as milk and cattle in the United States and poultry products in Canada. Riverine export of N was estimated at 28% of total N export. The commonly used crop nitrogen use efficiency (NUE) metric alone did not provide sufficient information on farming activities but in combination with other criteria such as farm‐gate NUE may better represent management efficiency. Agriculture was the primary driver of N inputs to the environment as a result of its regional importance; the N budget information can inform management to minimize N losses. The N budget provides key information for stakeholders across sectors and borders to create environmentally and economically viable and effective solutions.
The Chattahoochee River National Recreation Area (CRNRA) is a National Park Service unit/park with 48 miles of urban waterway in the Atlanta metropolitan area. The Chattahoochee River within the CRNRA is a popular place for water-based recreation but is known to periodically experience elevated levels of fecal-coliform bacteria associated with warm-blooded animals that can result in a variety of pathogen-related human illnesses. In 2000, the National Park Service entered into a public-private partnership with the U.S. Geological Survey (USGS) and the Chattahoochee Riverkeeper, called the Chattahoochee River BacteriALERT program, to monitor Escherichia coli (E. coli), which is a fecal indicator bacteria and a proxy for human health risk from waterborne pathogens. The BacteriALERT network monitors E. coli densities at three stations on the Chattahoochee River within the CRNRA, at Norcross (USGS station 02335000), Powers Ferry (USGS station 02335880), and Atlanta (USGS station 02336000). E. coli densities determined from water samples were compared to the U.S. Environmental Protection Agency’s Beach Action Value (BAV) of 235 colony forming units per 100 milliliters to assess whether conditions were considered safe for freshwater, primary contact recreational use. Sample E. coli densities exceeded the BAV for 15.5 percent of the samples collected at Norcross (n = 1,969) and 30.3 percent of the samples at Atlanta (n = 1,938) for the study period October 23, 2000, to May 23, 2019, and 33.6 percent of the samples from Powers Ferry (n = 134) for the study period May 5, 2016, to May 23, 2019.
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Stephenson Center, Suite 129
Columbia, SC 29210
Surface-water and suspended-sediment samples were collected and analyzed by the U.S. Geological Survey for multiple current-use pesticides and pesticide degradates approximately every 2 weeks at up to five sites in the Yolo Bypass and Cache Slough Complex before, during, and after augmented flow pulses in summer and fall 2016 and 2018 as well as during ambient flow conditions in summer and fall 2017 (no flow pulse). In 2016, augmented flows occurred during the summer (July) and required the pumping of Sacramento River water by local Reclamation Districts into the Colusa Basin Drain and Yolo Bypass Toe Drain. In contrast, augmented flows in 2018 occurred in the fall (August–September) and used agricultural tailwater (primarily rice field discharge water) to create the flow pulse. Water samples were analyzed by the U.S. Geological Survey for a suite of 175 current-use pesticides and pesticide degradates using gas chromatography with mass spectrometry and liquid chromatography with tandem mass spectrometry laboratory methods. Suspended sediments filtered from the water samples were analyzed for 143 pesticides and degradates by gas chromatography with mass spectrometry.
During the study, 53 pesticides were detected, and all the samples contained mixtures of multiple pesticides at concentrations ranging from below method detection limits to 8,780 nanograms per liter. Pesticides used in growing rice were the dominant pesticides present at four of the five sites sampled and urban-use pesticides dominated at the remaining site. Overall, total pesticide concentrations tended to be higher at sites in the northern part of the Yolo Bypass and lower at southern sites, except for the farthest downstream site which received additional pesticide inputs from the Sacramento River. Flow-pulse water source influenced total pesticide concentrations in the Yolo Bypass and Cache Slough Complex, and the highest total pesticide concentrations at each site were detected either immediately before or during the flow pulse generated with agricultural tailwater in 2018. Data gathered during this study will aid the California Department of Water Resources and other agencies working in the region in adaptively managing pulse flows in the Yolo Bypass and Cache Slough Complex, as one of several California Natural Resources Agency’s Delta Smelt Resiliency strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201076","collaboration":"Prepared in cooperation with the California Department of Water Resources and the State and Federal Contractors Water Agency","usgsCitation":"Orlando, J.L., De Parsia, M., Sanders, C., Hladik, M., and Frantzich, J., 2020, Pesticide concentrations associated with augmented flow pulses in the Yolo Bypass and Cache Slough Complex, California: U.S. Geological Survey Open-File Report 2020–1076, 101 p., https://doi.org/10.3133/ofr20201076.","productDescription":"Report: vi, 101 p.; Data release","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-109449","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376629,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"U.S. Geological Survey, 2019, National Water Information System: U.S. Geological Survey Web interface"},{"id":376628,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1076/ofr20201076.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376627,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1076/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yolo Bypass and Cache Slough Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.85760498046875,\n 38.45789034424927\n ],\n [\n -121.35772705078125,\n 38.45789034424927\n ],\n [\n -121.35772705078125,\n 39.06184913429154\n ],\n [\n -121.85760498046875,\n 39.06184913429154\n ],\n [\n -121.85760498046875,\n 38.45789034424927\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Noncontact measurements of spatially varied ground surface deformation during landslide motion can provide important constraints on landslide mechanics. Here, we present and test a new method for extracting measurements of rapid landslide surface displacement and velocity (accelerations of approximately 1 m/s2) using sequences of stereo images obtained from a pair of inexpensive, stationary 4K video cameras with nominal frame rates of 29.97 Hz. The method combines elements of Structure from Motion with those of optical flow to extract data on 3‐D evolution of the ground surface during slope failure. We apply the method to an experiment at the U.S. Geological Survey debris‐flow flume in which a high‐speed, liquefying landslide was triggered by gradually adding water to a 6‐m3 prism of loosely packed sediment on a 31° slope. Strip‐scanning lidar measurements made during the experiment corroborate our video‐based measurements, but the latter encompassed the entire landslide surface and were much lower in cost. Our video‐based measurements enabled computation of depth‐integrated landslide dilation/contraction rates. The range of computed rates was within the ranges inferred from independent measurements of evolving pore water pressures and reasonable estimates of the hydraulic permeability of the sediment. Dilation and contraction rates play a crucial role in landslide mechanics. The dilation and contraction we observe contradict the incompressible flow assumption used in many studies that have employed noncontact methods to infer landslide properties.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JF005348","collaboration":"Colorado School of Mines; Oregon State University","usgsCitation":"Rapstine, T.D., Rengers, F.K., Allstadt, K.E., Iverson, R.M., Smith, J.B., Obryk, M., Logan, M., and Olsen, M.J., 2020, Reconstructing the velocity and deformation of a rapid landslide using multiview video: Journal of Geophysical Research - Earth Surface, v. 125, e2019JF005348, 18 p., https://doi.org/10.1029/2019JF005348.","productDescription":"e2019JF005348, 18 p.","ipdsId":"IP-116947","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377599,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Blue River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.42340087890624,\n 44.08758502824516\n ],\n [\n -122.20642089843749,\n 44.08758502824516\n ],\n [\n -122.20642089843749,\n 44.209772586984485\n ],\n [\n -122.42340087890624,\n 44.209772586984485\n ],\n [\n -122.42340087890624,\n 44.08758502824516\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationDate":"2020-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Rapstine, Thomas D 0000-0001-5939-9587","orcid":"https://orcid.org/0000-0001-5939-9587","contributorId":224777,"corporation":false,"usgs":true,"family":"Rapstine","given":"Thomas","email":"","middleInitial":"D","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796609,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796610,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iverson, Richard M. 0000-0002-7369-3819 riverson@usgs.gov","orcid":"https://orcid.org/0000-0002-7369-3819","contributorId":536,"corporation":false,"usgs":true,"family":"Iverson","given":"Richard","email":"riverson@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":796611,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796612,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Obryk, Maciej K. 0000-0002-8182-8656","orcid":"https://orcid.org/0000-0002-8182-8656","contributorId":203477,"corporation":false,"usgs":true,"family":"Obryk","given":"Maciej","middleInitial":"K.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":796613,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Logan, M. 0000-0002-3558-2405","orcid":"https://orcid.org/0000-0002-3558-2405","contributorId":238816,"corporation":false,"usgs":true,"family":"Logan","given":"M.","affiliations":[{"id":47793,"text":"USGS - Cascades Volcano Observatory","active":true,"usgs":false}],"preferred":false,"id":796614,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Olsen, M. J.","contributorId":238817,"corporation":false,"usgs":false,"family":"Olsen","given":"M.","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":796615,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211304,"text":"70211304 - 2020 - The importance of explicitly modelling sea-swell waves for runup on reef-lined coasts","interactions":[],"lastModifiedDate":"2020-12-07T17:49:03.523874","indexId":"70211304","displayToPublicDate":"2020-07-23T08:44:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"The importance of explicitly modelling sea-swell waves for runup on reef-lined coasts","docAbstract":"The importance of explicitly modelling sea-swell waves for runup was examined using a 2D XBeach short wave-averaged (surfbeat, “XB-SB”) and a wave-resolving (non-hydrostatic, “XB-NH”) model of Roi-Namur Island on Kwajalein Atoll in the Republic of Marshall Islands. Field observations on water levels, wave heights, and wave runup were used to drive and evaluate both models, which were subsequently used to determine the effect of sea-level rise and extreme wave conditions on wave runup and its components. Results show that specifically modelling the sea-swell component (using XB-NH) provides a better approximation of the observed runup than XB-SB (which only models the time-variation of the sea-swell wave height), despite good model performance of both models on reef flat water levels and wave heights. XB-SB has a bias of −0.108 – 0.057 m and scatter index of 0.083–0.639, whereas XB-NH has bias of −0.132 – 0.055 m and 0.122–0.490, respectively. However, both models under-predict runup peaks. The difference between XB-SB and XB-NH increases for more extreme wave events and higher sea levels, as XB-NH resolves individual waves and therefore captures SS-wave motions in runup. However, for even larger forcing conditions with offshore wave heights of 6 m, the island is flooded in both XB-SB and XB-NH computations, regardless the sea-swell wave energy contribution. In such cases XB-SB would be adequate to model flooding depths and extents on the island while requiring 4–5 times less computational effort.","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2020.103704","usgsCitation":"Quataert, E., Storlazzi, C., van Dongeren, A., and McCall, R.T., 2020, The importance of explicitly modelling sea-swell waves for runup on reef-lined coasts: Coastal Engineering, v. 160, 103704, 11 p., https://doi.org/10.1016/j.coastaleng.2020.103704.","productDescription":"103704, 11 p.","ipdsId":"IP-108391","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455896,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2020.103704","text":"Publisher Index Page"},{"id":436863,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XUI9Y1","text":"USGS data release","linkHelpText":"Model parameter input files to compare wave-averaged versus wave-resolving XBeach coastal flooding models for coral reef-lined coasts"},{"id":376660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Republic of the Marshall Islands","otherGeospatial":"Roi-Namur Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 167.4605655670166,\n 9.386572562434718\n ],\n [\n 167.49506950378418,\n 9.386572562434718\n ],\n [\n 167.49506950378418,\n 9.40727655830451\n ],\n [\n 167.4605655670166,\n 9.40727655830451\n ],\n [\n 167.4605655670166,\n 9.386572562434718\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"160","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Quataert, Ellen","contributorId":193834,"corporation":false,"usgs":false,"family":"Quataert","given":"Ellen","email":"","affiliations":[],"preferred":false,"id":793666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":229614,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":793667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Dongeren, Ap","contributorId":149002,"corporation":false,"usgs":false,"family":"van Dongeren","given":"Ap","email":"","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":793668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCall, Robert T.","contributorId":148986,"corporation":false,"usgs":false,"family":"McCall","given":"Robert","email":"","middleInitial":"T.","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":793669,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211278,"text":"gip205 - 2020 - Brianna postcard","interactions":[],"lastModifiedDate":"2020-07-27T13:59:47.768622","indexId":"gip205","displayToPublicDate":"2020-07-23T07:08:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"205","displayTitle":"Brianna Postcard","title":"Brianna postcard","docAbstract":"Brianna is a hydrologist in the Hydrologic Investigations (Studies) Unit. She received a bachelor of science degree in chemical engineering and a master’s degree in civil engineering from the University of Kansas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip205","usgsCitation":"U.S. Geological Survey, 2020, Brianna postcard: U.S. Geological Survey General Information Product 205, 2 p., https://doi.org/10.3133/gip205.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117274","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376599,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0205/gip205.pdf","text":"Postcard","size":"393 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 205"},{"id":376598,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0205/coverthb.jpg"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Brad is a hydrologist in the Surface Water Investigation Unit. He received his bachelor of science degree in natural sciences from Concordia University in Wisconsin and his master’s degree in freshwater sciences from the University of Wisconsin-Milwaukee.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip204","usgsCitation":"U.S. Geological Survey, 2020, Brad postcard: U.S. Geological Survey General Information Product 204, 2 p., https://doi.org/10.3133/gip204.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117269","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376607,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0204/gip204.pdf","text":"Postcard","size":"367 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 204"},{"id":376606,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0204/coverthb.jpg"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Hydrologic technicians collect water data related to water quantity, quality, availability, and movement in surface-water and groundwater environments.
For more information, visit https://www.usajobs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip203","usgsCitation":"U.S. Geological Survey, 2020, Hydrologic technican postcard: U.S. Geological Survey General Information Product 203, 2 p., https://doi.org/10.3133/gip203.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117273","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0203/gip203.pdf","text":"Postcard","size":"326 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 203"},{"id":376604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0203/coverthb.jpg"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Chantelle is a hydrologist in the Surface Water Investigation Unit. She received her bachelor of science degree in environmental geology from the University of Kansas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip202","usgsCitation":"U.S. Geological Survey, 2020, Chantelle postcard: U.S. Geological Survey General Information Product 202, 2 p., https://doi.org/10.3133/gip202.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117268","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376591,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0202/coverthb.jpg"},{"id":376592,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0202/gip202.pdf","text":"Postcard","size":"490 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 202"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Hydrologists study the properties, distribution, and effects of water on the Earth’s surface, in the soil and underlying rocks, and in the atmosphere.
For more information, visit https://www.usajobs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip201","usgsCitation":"U.S. Geological Survey, 2020, Hydrologist postcard: U.S. Geological Survey General Information Product 201, 2 p., https://doi.org/10.3133/gip201.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117272","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376588,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0201/gip201.pdf","text":"Postcard","size":"269 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 201"},{"id":376587,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0201/coverthb.jpg"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Chemists design analytical methods, analyze samples, and review instrument results to ensure high-quality, defensible data are provided to our Nation’s decision makers.
For more information, visit https://www.usajobs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip200","usgsCitation":"U.S. Geological Survey, 2020, Chemist postcard: U.S. Geological Survey General Information Product 200, 2 p., https://doi.org/10.3133/gip200.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117271","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376583,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0200/gip200.pdf","text":"Postcard","size":"284 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 200"},{"id":376603,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0200/coverthb.jpg"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Michaelah is an environmental chemist in the Organic Geochemistry Research Unit. She received her bachelor of science degree in environmental chemistry from the University of Kansas and her master’s degree in biomimicry from Arizona State University.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip199","usgsCitation":"U.S. Geological Survey, 2020, Michaelah postcard: U.S. Geological Survey General Information Product 199, 2 p., https://doi.org/10.3133/gip199.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117270","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376579,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0199/coverthb.jpg"},{"id":376580,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0199/gip199.pdf","text":"Report","size":"303 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 199"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Forster’s terns (Sterna forsteri), historically one of the most numerous colonial-breeding waterbirds in South San Francisco Bay, California, have experienced recent decreases in the number of nesting colonies and overall breeding population size. The South Bay Salt Pond Restoration Project aims to restore 50–90 percent of former salt evaporation ponds to tidal marsh habitat in South San Francisco Bay. During phase 1 of the South Bay Salt Pond Restoration Project, the breaching of several pond levees to begin the process of tidal marsh restoration inundated island nesting habitat that had been used by Forster’s terns, American avocets (Recurvirostra americana), and other waterbirds. Additional nesting habitat could be lost as more managed ponds are converted to tidal marsh in the future. To address this issue, the South Bay Salt Pond Restoration Project organized the construction of new nesting islands in managed ponds that will not be restored to tidal marsh, thereby providing enduring island nesting habitat for waterbirds. In 2012, 16 new islands were constructed in Pond A16 in the Alviso complex of the Don Edwards San Francisco Bay National Wildlife Refuge, which increased the number of islands in this pond from 4 to 20. However, despite a long history of nesting on the four islands in Pond A16 before the 2012 construction activities, no Forster’s terns have nested in Pond A16 during the 7-year period (2012–18) after island construction.
During the 2017 and 2019 breeding seasons, we used social attraction measures (decoys and colony call playback systems) to attract Forster’s terns to islands within Pond A16 to re-establish nesting colonies. We maintained these systems from March through August in each year. To evaluate the effect of these social attraction measures, we completed surveys (between April and August) where we recorded the number and location of all Forster’s terns and other waterbirds using Pond A16, and we monitored waterbird nests. We compared bird survey and nest monitoring data collected in 2017 and 2019 to data collected in 2015 and 2016, prior to the implementation of social attraction measures, allowing for direct evaluation of the effect of social attraction efforts on Forster’s terns.
To increase the visibility and stakeholder involvement of this project, we engaged in multiple outreach activities in 2017, 2019, and 2020, including the development of a project website and educational video; publication of popular articles in 2017 and 2020; the development of outreach materials describing the project to the general public; and public presentations to relay findings to managers, stakeholders, and the general public.
The relative abundance of Forster’s terns in Pond A16, after adjusting for the overall South San Francisco Bay breeding population each year, was higher during the nesting period in 2017 and 2019 (when social attraction was used) than in 2015 and 2016 (before social attraction was used). Furthermore, more Forster’s terns were observed during the pre-nesting and nesting periods in the areas of Pond A16 where decoys and call systems were deployed. Although no Forster’s tern nests were observed in Pond A16 before social attraction was implemented (2015, 2016), or during the first-year social attraction was implemented (2017), 35 Forster’s tern nests were recorded during the second year of social attraction implementation in 2019. These 35 nests represent a re-establishment of a Forster’s tern nesting colony to Pond A16 for the first time in 8 years. As social attraction efforts often benefit from multiple years of decoy and call system deployment, results from 2017 and 2019 suggest that continued implementation of social attraction measures could help to ensure Forster’s tern breeding colonies persist in Pond A16 and other areas of South San Francisco Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201081","collaboration":"Prepared in cooperation with the San Francisco Bay Bird Observatory","usgsCitation":"Hartman, C.A., Ackerman, J.T., Herzog, M.P., Wang, Y., and Strong, C., 2020, Establishing Forster’s Tern (Sterna forsteri) nesting sites at pond A16 using social attraction for the South Bay Salt Pond restoration project: U.S. Geological Survey Open-File Report 2020–1081, 28 p., https://doi.org/10.3133/ofr20201081.","productDescription":"vii, 28 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-118152","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":376595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1081/covrthb.jpg"},{"id":376596,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1081/ofr20201081.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"South San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.28057861328124,\n 37.40452830389465\n ],\n [\n -121.90155029296875,\n 37.40452830389465\n ],\n [\n -121.90155029296875,\n 37.55709809310769\n ],\n [\n -122.28057861328124,\n 37.55709809310769\n ],\n [\n -122.28057861328124,\n 37.40452830389465\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
We studied the nesting ecology of White-faced Ibis (Plegadis chihi) at 3 sites within the Bear River Migratory Bird Refuge, Great Salt Lake, Utah, USA. Ibises built nests in small mounds (mean height = 14.4 ± 4.3 cm) above shallow water (mean depth = 12.0 ± 6.6 cm) located within patchy vegetation (mean percent vegetative cover = 17.2 ± 17.8% vegetative cover) with mean vegetation height of 31.7 ± 9.8 cm. White-faced Ibis typically laid a clutch of 3 or 4 eggs (mean clutch size = 3.08 ± 0.76) and initiated nests over a 50 d period between 24 April 2012 and 12 June 2012. Mean nest success was 38% (95% CI: 31–45%) and hatching success of eggs from successful nests was 76 ± 26%. Although most of the breeding parameters estimated for White-faced Ibis nesting in Utah were comparable to other populations in Oregon and Idaho (USA), nest success may now be lower than has been historically documented.
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"History of wetland science: A perspective from wetland leaders","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Amazon Print-on-Demand","usgsCitation":"Rosenberry, D.O., 2020, Groundwater and surface water: What, they’re actually connected?, chap. of History of wetland science: A perspective from wetland leaders, p. 274-278.","productDescription":"5 p.","startPage":"274","endPage":"278","ipdsId":"IP-109883","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":380121,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":803903,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211089,"text":"sir20205062 - 2020 - Discharge and dissolved-solids characteristics and trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018","interactions":[],"lastModifiedDate":"2020-07-22T13:53:50.908568","indexId":"sir20205062","displayToPublicDate":"2020-07-21T12:57:13","publicationYear":"2020","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":"2020-5062","displayTitle":"Discharge and Dissolved-Solids Characteristics and Trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018","title":"Discharge and dissolved-solids characteristics and trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018","docAbstract":"The headwaters of the Snake River are in the mountains of northwestern Wyoming. Maintaining the recognized high quality of water in Grand Teton National Park is a National Park Service (NPS) priority. To characterize and understand the water resources of Grand Teton National Park, the NPS established a monitoring program to monitor the quality of area surface waters. Beginning in 2006, water was sampled by the NPS and analyzed for a range of chemical species at the Snake River above Jackson Lake at Flagg Ranch streamgage 13010065 (hereafter referred to as “Snake River at Flagg Ranch”), a site where the U.S. Geological Survey (USGS) previously sampled and analyzed water from 1986 through 2004. The USGS, in cooperation with the NPS, evaluated water-quality data collected by both entities to determine if discharge and total dissolved solids (referred to as dissolved solids) have changed in the Snake River at the Flagg Ranch.
To understand potential changes with time in dissolved solids, discharge was analyzed between January 1986 and December 2018, which corresponds with the time period when water-quality data were collected. Mean annual discharge varied during this time, with high, low, mean, and median flows generally increasing from 1986 through 1998, decreasing through 2005, and then generally increasing through 2018.
Combining water-quality data collected by the USGS and NPS provides a longer, more complete dataset for analyses. During the period of time when NPS was the sampling agency, specific conductance data were collected, but dissolved-solids data were not. The specific conductance data from both agencies were evaluated to determine if combining the data was justified. The interquartile ranges of data collected by both agencies are similar, and rapid, large changes in values during the period of transition between USGS and NPS sampling do not occur. The USGS and NPS datasets are not statistically different in the spring, summer, or fall, but are statistically different in the winter. The winter differences could be a function of the lack of wintertime NPS sampling, which excludes higher-concentration, lower-discharge data or a function of changes in the actual concentration in the stream. Although there is some difference in the winter datasets, the similarity in sampling methods and general overall data characteristics justifies combining the data for trend analyses.
Because the dissolved-solids parameter is useful for managers, it is often calculated from specific conductance using a linear regression model when dissolved-solids data are absent. For this study, creating a modeled dataset of dissolved solids for the NPS data collection period of time provided a longer, more complete dataset of dissolved-solids concentrations.
The concentrations of dissolved solids over time are identified by season and indicate that samples collected in the fall and winter have higher concentrations than samples collected in spring and summer. Specifically, the mean dissolved-solids concentrations in fall and winter are around 188 milligrams per liter (mg/L), whereas the mean concentrations are around 130 mg/L in spring and summer. This difference is generally attributed to the dilution of spring and summer samples by snowmelt generated runoff during the high-flow period of the year.
Trend analyses of dissolved-solids concentrations and loads indicate that an upward trend in concentration from 1986 to 2018 is likely, and a downward trend in load is highly likely. Comparing 1986 to 2018, dissolved-solids concentration is estimated to have increased by 2.25 mg/L (1.4 percent). During that same period, the dissolved-solids load is estimated to have decreased 11.8 million kilograms per year (12-percent decrease). This decrease is consistent with the estimated decrease in annual mean of daily mean discharge. Because 10 percent of the total change in dissolved-solids load is related to a change in the concentration-discharge relationship and 2 percent is related to changes in discharge, the decreased load is related less to changes in discharge and more to landscape scale processes that are affecting the concentration-discharge relationship.
As noted above, the data collected by the USGS and NPS are generally comparable with regards to sampling and analytical methods, and data collected by both agencies were used as one dataset for trend analyses. The current NPS sampling schedule, however, is creating a dataset biased towards lower concentration dissolved-solids data, which occurs during higher summer flows, by only sampling during April through November. From 1986 to 2018, the percentage of NPS samples is small enough that the effect on trends is expected to be minimal. Because of the importance of low flow (winter season) data, it is likely that an April through November sampling regime may affect the ability to detect trends or determine seasonality in the future. Collection of winter data in particular is important based on the findings that the changes in the modeled concentration-discharge relationship over time have been most pronounced during the winter (represented by February) months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205062","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Miller, O.L., and Eddy-Miller, C.A., 2020, Discharge and dissolved-solids characteristics and trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018: U.S. Geological Survey Scientific Investigations Report 2020–5062, 19 p., https://doi.org/10.3133/sir20205062.","productDescription":"vi, 19 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-116863","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":376357,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5062/coverthb.jpg"},{"id":376358,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5062/sir20205062.pdf","text":"Report","size":"2.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5062"}],"country":"United States","state":"Wyoming","otherGeospatial":"Flagg Ranch watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.05804443359375,\n 44.081666311450526\n ],\n [\n -110.23681640625,\n 44.081666311450526\n ],\n [\n -110.23681640625,\n 44.457309801319305\n ],\n [\n -111.05804443359375,\n 44.457309801319305\n ],\n [\n -111.05804443359375,\n 44.081666311450526\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Boseman Avenue
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Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
West Valley City, UT 84119–2047