Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.
Translocated animals undergo a phase of behavioral adjustment after being released in a novel environment, initially prioritizing exploration and gradually shifting toward resource exploitation. This transition has been termed post-release behavioral modification. Post-release behavioral modification may also manifest as changes in habitat selection through time, and these temporal dynamics may differ between individuals. We aimed to evaluate how post-release behavioral modification is reflected in temporal dynamics of habitat selection and its variability across individuals using a population of translocated female greater sage-grouse as a case study. Sage-grouse were translocated from Wyoming to North Dakota (USA) during the summers of 2018–2020. We analyzed individual habitat selection as a function of sagebrush cover, herbaceous cover, slope, and distance to roads. Herbaceous cover is a key foraging resource for sage-grouse during summer; thus, we expected a shift from exploration to exploitation to manifest as temporally-varying selection for herbaceous cover. For each individual sage-grouse (N = 26), we tested two competing models: a null model with no time-dependence and a model with time-dependent selection for herbaceous cover. We performed model selection at the individual level using an information-theoretic approach. Time-dependence was supported for five individuals, unsupported for seven, and the two models were indistinguishable based on AICc for the remaining fourteen. We found no association between the top-ranked model and individual reproductive status (brood-rearing or not). We showed that temporal dynamics of post-release habitat selection may emerge in some individuals but not in others, and that failing to account for time-dependence may hinder the detection of steady-state habitat selection patterns. These findings demonstrate the need to consider both temporal dynamics and individual variability in habitat selection when conducting post-release monitoring to inform translocation protocols.
A robust method for characterizing the biophysical environment of terrestrial vegetation uses the relationship between Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD). These variables are usually estimated from a water balance model rather than measured directly and are often more representative of ecologically-significant changes than temperature or precipitation. We evaluate trends and spatial patterns in AET and CWD in the Continental United States (CONUS) during 1980–2019 using a gridded water balance model. The western US had linear regression slopes indicating increasing CWD and decreasing AET (drying), while the eastern US had generally opposite trends. When limits to plant performance characterized by AET and CWD are exceeded, vegetation assemblages change. Widespread increases in aridity throughout the west portends shifts in the distribution of plants limited by available moisture. A detailed look at Sequoia National Park illustrates the high degree of fine-scale spatial variability that exists across elevation and topographical gradients. Where such topographical and climatic diversity exists, appropriate use of our gridded data will require sub-setting to an appropriate area and analyzing according to categories of interest such as vegetation communities or across obvious physical gradients. Recent studies have successfully applied similar water balance models to fire risk and forest structure in both western and eastern U.S. forests, arid-land spring discharge, amphibian colonization and persistence in wetlands, whitebark pine mortality and establishment, and the distribution of arid-land grass species and landscape scale vegetation condition. Our gridded dataset is available free for public use. Our findings illustrate how a simple water balance model can identify important trends and patterns at site to regional scales. However, at finer scales, environmental heterogeneity is driving a range of responses that may not be simply characterized by a single trend.
Tilting of the ground due to loading by the variable atmosphere is known to corrupt very long period horizontal seismic records (below 10 mHz) even at the quietest stations. At BFO (Black Forest Observatory, SW-Germany), the opportunity arose to study these disturbances on a variety of simultaneously operated state-of-the-art broad-band sensors. A series of time windows with clear atmospherically caused effects was selected and attempts were made to model these ‘signals’ in a deterministic way. This was done by simultaneously least-squares fitting the locally recorded barometric pressure and its Hilbert transform to the ground accelerations in a bandpass between 100 and 3600 s periods. Variance reductions of up to 97 per cent were obtained. We show our results by combining the ‘specific pressure induced accelerations’ for the two horizontal components of the same sensor as vectors on a horizontal plane, one for direct pressure and one for its Hilbert transform. It turned out that at BFO the direct pressure effects are large, strongly position dependent and largely independent of atmospheric events for instruments installed on piers, while three post-hole sensors are only slightly affected. The infamous ‘cavity effects’ are invoked to be responsible for these large effects on the pier sensors. On the other hand, in the majority of cases all sensors showed very similar magnitudes and directions for the vectors obtained for the regression with the Hilbert transform, but highly variable from event to event especially in direction. Therefore, this direction most certainly has to do with the gradient of the pressure field moving over the station which causes a larger scale deformation of the crust. The observations are very consistent with these two fundamental mechanisms of how fluctuations of atmospheric surface pressure causes tilt noise. The results provide a sound basis for further improvements of the models for these mechanisms. The methods used here can already help to reduce atmospherically induced noise in long-period horizontal seismic records.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab336","usgsCitation":"Zurn, W., Forbriger, T., Widmer-Schnidrig, R., Duffner, P., and Ringler, A.T., 2021, Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise: Geophysical Journal International, v. 228, no. 2, p. 927-943, https://doi.org/10.1093/gji/ggab336.","productDescription":"17 p.","startPage":"927","endPage":"943","ipdsId":"IP-131609","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":450964,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.5445/ir/1000140172","text":"External Repository"},{"id":391423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"228","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Zurn, W.","contributorId":268322,"corporation":false,"usgs":false,"family":"Zurn","given":"W.","affiliations":[{"id":55624,"text":"Black Forest Observatory (Schiltach)","active":true,"usgs":false}],"preferred":false,"id":826410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forbriger, T.","contributorId":268323,"corporation":false,"usgs":false,"family":"Forbriger","given":"T.","email":"","affiliations":[{"id":55625,"text":"Black Forest Observatory (Schiltach); Karlsruhe Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":826411,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Widmer-Schnidrig, R.","contributorId":221153,"corporation":false,"usgs":false,"family":"Widmer-Schnidrig","given":"R.","email":"","affiliations":[{"id":40338,"text":"Black Forest Observatory, Institute of Geodesy, Stuttgart University, Wolfach, Germany","active":true,"usgs":false}],"preferred":false,"id":826412,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duffner, P.","contributorId":268324,"corporation":false,"usgs":false,"family":"Duffner","given":"P.","email":"","affiliations":[{"id":55625,"text":"Black Forest Observatory (Schiltach); Karlsruhe Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":826413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":826414,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223694,"text":"sir20205150 - 2021 - Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013","interactions":[],"lastModifiedDate":"2021-09-02T11:51:45.887677","indexId":"sir20205150","displayToPublicDate":"2021-09-01T16:37:37","publicationYear":"2021","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":"2020-5150","displayTitle":"Precipitation-Runoff Processes in the Merced River Basin, Central California, with Prospects for Streamflow Predictability, Water Years 1952–2013","title":"Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013","docAbstract":"The U.S. Geological Survey, in cooperation with the California Department of Water Resources (DWR), has constructed a new spatially detailed Precipitation-Runoff Modeling System (PRMS) model for the Merced River Basin, California, which is a tributary of the San Joaquin River in California. Operated through an Object User Interface (OUI) with Ensemble Streamflow Prediction (ESP) and daily climate distribution preprocessing functionality, the model is calibrated primarily to simulate (and eventually, forecast) year-to-year variations of inflows to Lake McClure during the critical April–July snowmelt season. The model is intended to become part of a suite of methods used by DWR for estimating daily streamflow from the Merced River Basin, especially during the snowmelt season. This study describes the results of the application of an analysis tool that simulates responses to climate and land-use variations at a higher spatial resolution than previously available to DWR.
A geographic information system was used to delineate the model domain, that is, areas draining to a single outlet at U.S. Geological Survey streamflow-gaging station 11270900, Merced River below Merced Falls Dam, near Snell, CA (also known as California Data Exchange Center station MRC), and subdrainage areas, including four draining to internal gages used as calibration targets. Using this delineation, three contiguous subbasins were recognized and, along with the model domain and nested calibration targets, are the simulation units evaluated in this report.
An auto-calibration tool, LUCA (Let Us CAlibrate), was used for each calibration node, from headwaters to basin outlet, and then parameters were manually adjusted to complete the calibration. The main objective was to match April–July snowmelt seasonal discharge values of simulated streamflow to observed (measured or reconstructed) discharge values. Calibration or validation periods used site-specific streamflows—mostly from October 1, 1988, through September 30, 2013—but differed according to the period-of-record available for the measurements collected at internal gages or reconstructed flows for the single outlet.
The accuracy of the Merced PRMS streamflow simulations varied seasonally, as compared to observed values. Based on statistical results, the Merced PRMS model satisfactorily simulated snowmelt seasonal streamflows. April–July calibrations for all areas had small negative bias (not greater than 7 percent) and low relative error (less than 8 percent). Less satisfactory performance for other seasons was attributed to several factors: (1) high uncertainty in low or zero flows in summer and fall, (2) lack of accounting for basin withdrawals and anthropogenic water use, (3) unavailability and (or) inaccuracy of observed (measured) meteorological input data, and (4) uncertainty in reconstructed streamflow data.
With some additional refinement, the Merced PRMS model may be used for forecasting seasonal and longer-term streamflow variations; evaluating forecasted and past climate and land cover changes; providing water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites within the basin; and aiding environmental studies, hydraulic design, water management, and water-quality projects in the Merced River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205150","collaboration":"Prepared in cooperation with California Department of Water Resources","usgsCitation":"Koczot, K.M., Risley, J.C., Gronberg, J.M., Donovan, J.M., and McPherson, K.R., 2021, Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013: U.S. Geological Survey Scientific Investigations Report 2020–5150, 61 p., https://doi.org/10.3133/sir20205150.","productDescription":"Report: ix, 61 p.; 1 Figure: 16.0 x 10.0 inches; Data Release","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-028665","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":388739,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3KFR","linkHelpText":"Archive of Merced River Basin Precipitation-Runoff Modeling System, with forecasting, climate-file preparation, and data-visualization tools"},{"id":388738,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5150/sir20205150_fig11_sheet.pdf","text":"Figure 11 (16\" x 10\" sheet)","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Physical architecture of the Merced River Basin Precipitation-Runoff Modeling System."},{"id":388737,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5150/sir20205150.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388736,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5150/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Merced River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.3055419921875,\n 36.88401445049676\n ],\n [\n -119.27307128906249,\n 36.88401445049676\n ],\n [\n -119.27307128906249,\n 37.69251435532741\n ],\n [\n -121.3055419921875,\n 37.69251435532741\n ],\n [\n -121.3055419921875,\n 36.88401445049676\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
Freshwater streams can exchange nutrients and carbon with the surrounding terrestrial environment through various mechanisms including physical erosion, flooding, leaf drop, and snowmelt. These aquatic-terrestrial interactions are crucial in carbon mobilization, transformation, ecosystem productivity, and have important implications for the role of freshwater ecosystems in the global carbon budget. We utilized high-frequency oxygen, temperature, and carbon dioxide (CO2) data to infer watershed connectivity in Boulder Creek, a mid-sized (1160 km2) watershed located in Colorado, USA. Daily modeled gross primary production (GPP), ecosystem respiration (ER), net ecosystem production (NEP), and reaeration coefficients (K600) were paired with high-frequency, in-situ dissolved CO2 data to characterize changes in metabolic regime and carbon flux on a stream influenced by seasonal snowmelt. GPP and ER were correlated (ρ = −0.72, p ≪ 0.001) during the non-snowmelt period and NEP was frequently negative. Mean FCO2 during the non-snowmelt period was approximately 302 (±171) mmol C m−2 d−1 and was primarily supported by watershed CO2 inputs. During snowmelt, GPP and ER were not significantly correlated (ρ = −0.22, p = 0.05), and mean NEP was significantly more negative than during non-snowmelt. Watershed connectivity was higher during snowmelt, as evidenced by significantly higher FCO2 (843 ± 338 mmol C m−2 d−1) and greater allochthonous CO2 inputs than during non-snowmelt periods, emphasizing the effects of seasonal differences in aquatic-terrestrial linkages in this stream. We suggest that our understanding of watershed carbon budgets is subject to temporal dynamics which control the degree of connectivity between terrestrial and aquatic ecosystems.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006296","usgsCitation":"Reed, A.P., Stets, E.G., Murphy, S.F., and Mullins, E., 2021, Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt: Journal of Geophysical Research Biogeosciences, v. 126, no. 9, e2021JG006296, 16 p., https://doi.org/10.1029/2021JG006296.","productDescription":"e2021JG006296, 16 p.","ipdsId":"IP-113327","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":450975,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021jg006296","text":"Publisher Index Page"},{"id":436214,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P991TMNQ","text":"USGS data release","linkHelpText":"Modeled Stream Metabolism in Boulder Creek near Boulder, CO (2016 - 2018)"},{"id":390389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Boulder","otherGeospatial":"Boulder Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.43922424316406,\n 39.95343802330847\n ],\n [\n -105.15975952148438,\n 39.95343802330847\n ],\n [\n -105.15975952148438,\n 40.054949943999496\n ],\n [\n -105.43922424316406,\n 40.054949943999496\n ],\n [\n -105.43922424316406,\n 39.95343802330847\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Ariel P. 0000-0002-0792-5204","orcid":"https://orcid.org/0000-0002-0792-5204","contributorId":219992,"corporation":false,"usgs":true,"family":"Reed","given":"Ariel","email":"","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824893,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stets, Edward G. 0000-0001-5375-0196 estets@usgs.gov","orcid":"https://orcid.org/0000-0001-5375-0196","contributorId":194490,"corporation":false,"usgs":true,"family":"Stets","given":"Edward","email":"estets@usgs.gov","middleInitial":"G.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":824894,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":824895,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullins, Emily 0000-0002-6710-0327","orcid":"https://orcid.org/0000-0002-6710-0327","contributorId":219993,"corporation":false,"usgs":true,"family":"Mullins","given":"Emily","email":"","affiliations":[],"preferred":true,"id":824896,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228264,"text":"70228264 - 2021 - Developing bare-earth digital elevation models from structure-from-motion data on barrier islands","interactions":[],"lastModifiedDate":"2023-06-09T14:08:15.215958","indexId":"70228264","displayToPublicDate":"2021-09-01T08:49:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Developing bare-earth digital elevation models from structure-from-motion data on barrier islands","docAbstract":"Unoccupied aerial systems can collect aerial imagery that can be used to develop structure-from-motion products with a temporal resolution well-suited to monitoring dynamic barrier island environments. However, topographic data created using photogrammetric techniques such as structure-from-motion represent the surface elevation including the vegetation canopy. Additional processing is required for estimating bare-earth elevation, which is critical for understanding the underlying geomorphology of these islands. In this study, we used a vegetation and elevation survey to produce bare-earth digital elevation models from structure-from-motion-derived elevation products for two sites on Dauphin Island, Alabama (USA). One site was exposed to high wave energy and included a mix of beach, dune, and barrier flat habitats that were dominated by supratidal/upland herbaceous vegetation. The second site was exposed to low wave energy and was dominated by intertidal marsh. Aerial imagery was collected in late fall of 2018 and spring of 2019. We tested several machine learning algorithms for predicting and removing elevation bias for vegetated areas using predictors that included spectral indices from unoccupied aerial systems-based multispectral imagery and landscape position information (e.g., relative topography and distance from shore). Models were developed for each site and season. We also explored how well the model from one season generalized to data from a different season for the same site. For developing initial digital surface models, we found that utilizing a minimum bin algorithm, as opposed to interpolation, led to lower elevation bias. For bias removal, Gaussian process regression performed the best and led to a root mean square error for the bare-earth digital elevation models of around 0.10 m for the high energy site and 0.15 m for the low energy site. Compared to the digital surface models, the root mean square error for the bare-earth digital elevation models was reduced by at least 29 percent for the high energy site and 69 percent for the low energy site. For all models, common predictors included surface elevation, vegetation greenness, and distance from the shoreline. The models produced comparable results when trained using data from a different season. The error estimates for all analyses were within published elevation standards for lidar data for vegetated areas. With calibration, this approach could be portable to other areas or data, such as aerial lidar (conventional or unoccupied), to provide an efficient and repeatable framework for monitoring geomorphology or provide baseline elevations for predicting changes to these environments under future conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2021.08.014","usgsCitation":"Enwright, N., Kranenburg, C.J., Patton, B., Dalyander, P., Brown, J., Piazza, S., and Cheney, W.C., 2021, Developing bare-earth digital elevation models from structure-from-motion data on barrier islands: ISPRS Journal of Photogrammetry and Remote Sensing, v. 180, p. 269-282, https://doi.org/10.1016/j.isprsjprs.2021.08.014.","productDescription":"14 p.; Data Release","startPage":"269","endPage":"282","ipdsId":"IP-127598","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450987,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2021.08.014","text":"Publisher Index Page"},{"id":436215,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RA15I0","text":"USGS data release","linkHelpText":"Barrier island vegetation and elevation survey, Dauphin Island, AL, 2018-19"},{"id":395611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417857,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PX0O3"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.33969116210938,\n 30.211608223816906\n ],\n [\n -88.06159973144531,\n 30.211608223816906\n ],\n [\n -88.06159973144531,\n 30.286938665455985\n ],\n [\n -88.33969116210938,\n 30.286938665455985\n ],\n [\n -88.33969116210938,\n 30.211608223816906\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"180","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217794,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranenburg, Christine J. 0000-0002-2955-0167 ckranenburg@usgs.gov","orcid":"https://orcid.org/0000-0002-2955-0167","contributorId":169234,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine","email":"ckranenburg@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patton, Brett 0000-0002-7396-3452 pattonb@usgs.gov","orcid":"https://orcid.org/0000-0002-7396-3452","contributorId":5458,"corporation":false,"usgs":true,"family":"Patton","given":"Brett","email":"pattonb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":833555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":221891,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy","affiliations":[{"id":40456,"text":"St. Petersburg Coastal and Marine Science Center (Former Employee)","active":true,"usgs":false}],"preferred":false,"id":833556,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Jenna A. 0000-0003-3137-7073","orcid":"https://orcid.org/0000-0003-3137-7073","contributorId":208564,"corporation":false,"usgs":true,"family":"Brown","given":"Jenna A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833557,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Piazza, Sarai 0000-0001-6962-9008","orcid":"https://orcid.org/0000-0001-6962-9008","contributorId":220329,"corporation":false,"usgs":true,"family":"Piazza","given":"Sarai","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cheney, Wyatt C 0000-0003-1009-8411","orcid":"https://orcid.org/0000-0003-1009-8411","contributorId":274998,"corporation":false,"usgs":false,"family":"Cheney","given":"Wyatt","email":"","middleInitial":"C","affiliations":[{"id":56693,"text":"Cheney Consulting at the U.S. Geological Survey Wetland and Aquatic Research Center","active":true,"usgs":false}],"preferred":false,"id":833559,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224258,"text":"70224258 - 2021 - Hydrate formation on marine seep bubbles and the implications for water column methane dissolution","interactions":[],"lastModifiedDate":"2021-09-16T12:27:12.757011","indexId":"70224258","displayToPublicDate":"2021-09-01T07:25:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9107,"text":"Journal of Geophysical Research - Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Hydrate formation on marine seep bubbles and the implications for water column methane dissolution","docAbstract":"Methane released from seafloor seeps contributes to a number of benthic, water column, and atmospheric processes. At seafloor seeps within the methane hydrate stability zone, crystalline gas hydrate shells can form on methane bubbles while the bubbles are still in contact with the seafloor or as the bubbles begin ascending through the water column. These shells reduce methane dissolution rates, allowing hydrate-coated bubbles to deliver methane to shallower depths in the water column than hydrate-free bubbles. Here, we analyze seafloor videos from six deepwater seep sites associated with a diverse range of bubble-release processes involving hydrate formation. Bubbles that grow rapidly are often hydrate-free when released from the seafloor. As bubble growth slows and seafloor residence time increases, a hydrate coating can form on the bubble's gas-water interface, fully coating most bubbles within ∼10 s of the onset of hydrate formation at the seafloor. This finding agrees with water-column observations that most bubbles become hydrate-coated after their initial ∼150 cm of rise, which takes about 10 s. Whether a bubble is coated or not at the seafloor affects how much methane a bubble contains and how quickly that methane dissolves during the bubble's rise through the water column. A simplified model shows that, after rising 150 cm above the seafloor, a bubble that grew a hydrate shell before releasing from the seafloor will have ∼5% more methane than a bubble of initial equal volume that did not grow a hydrate shell after it traveled to the same height.
Conservation efforts need reliable information concerning the status of a species and their trends to help identify which species are in most need of assistance. We completed a comparative evaluation of the occurrence of breeding for Cascades Frog (Rana cascadae), an amphibian that is being considered for federal protection under the U.S. Endangered Species Act. Specifically, in 2018–2019 we resurveyed 67 sites that were surveyed approximately 15 y prior and fit occupancy models to quantify the distribution of R. cascadae breeding in the Cascade Range, Oregon, USA. Furthermore, we conducted a simulation exercise to assess the power of sampling designs to detect declines in R. cascadae breeding at these sites. Our analysis of field data combined with our simulation results suggests that if there was a decline in the proportion of sites used for R. cascadae breeding in Oregon, it was likely a < 20% decline across our study period. Our results confirm that while R. cascadae detection probabilities are high, methods that allow the sampling process to be explicitly modeled are necessary to reliably track the status of the species. This study demonstrates the usefulness of investing in baseline information and data quality standards to increase capacity to make similar comparisons for other species in a timeframe that meet the needs of land managers and policy makers.
","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Duarte, A., Pearl, C., McCreary, B., Rowe, J., and Adams, M.J., 2021, An updated assessment of status and trend in the distribution of the Cascades frog (Rana cascadae) in Oregon, USA: Herpetological Conservation and Biology, v. 16, no. 2, p. 361-373.","productDescription":"13 p.","startPage":"361","endPage":"373","ipdsId":"IP-127196","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":398216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398175,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/contents_vol16_issue2.html"}],"country":"United States","state":"Oregon","otherGeospatial":"Cascade Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.398681640625,\n 45.537136680398596\n ],\n [\n -122.82714843749999,\n 44.88701247981298\n ],\n [\n -123.07983398437499,\n 44.07969327425713\n ],\n [\n -123.26660156249999,\n 42.58544425738491\n ],\n [\n -123.145751953125,\n 42.00848901572399\n ],\n [\n -121.61865234375,\n 42.00032514831621\n ],\n [\n -121.77246093750001,\n 42.98053954751642\n ],\n [\n -121.278076171875,\n 44.134913443750726\n ],\n [\n -121.025390625,\n 45.034714778688624\n ],\n [\n -121.124267578125,\n 45.68315803253308\n ],\n [\n -121.57470703125,\n 45.744526980468436\n ],\n [\n -121.871337890625,\n 45.729191061299915\n ],\n [\n -122.398681640625,\n 45.537136680398596\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":28492,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":839736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearl, Christopher 0000-0003-2943-7321 christopher_pearl@usgs.gov","orcid":"https://orcid.org/0000-0003-2943-7321","contributorId":172669,"corporation":false,"usgs":true,"family":"Pearl","given":"Christopher","email":"christopher_pearl@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":839737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCreary, Brome 0000-0002-0313-7796 brome_mccreary@usgs.gov","orcid":"https://orcid.org/0000-0002-0313-7796","contributorId":3130,"corporation":false,"usgs":true,"family":"McCreary","given":"Brome","email":"brome_mccreary@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rowe, Jennifer 0000-0002-5253-2223 jrowe@usgs.gov","orcid":"https://orcid.org/0000-0002-5253-2223","contributorId":172670,"corporation":false,"usgs":true,"family":"Rowe","given":"Jennifer","email":"jrowe@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":839739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Michael J. 0000-0001-8844-042X","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":211916,"corporation":false,"usgs":true,"family":"Adams","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839740,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224304,"text":"70224304 - 2021 - Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections","interactions":[],"lastModifiedDate":"2021-11-16T15:44:27.13098","indexId":"70224304","displayToPublicDate":"2021-08-31T07:54:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections","docAbstract":"Land use and climate change are anticipated to affect phytoplankton of lakes worldwide. The effects will depend on the magnitude of projected land use and climate changes and lake sensitivity to these factors. We used random forests fit with long-term (1971–2016) phytoplankton and cyanobacteria abundance time series, climate observations (1971–2016), and upstream catchment land use (global Clumondo models for the year 2000) data from 14 European and 15 North American lakes basins. We projected future phytoplankton and cyanobacteria abundance in the 29 focal lake basins and 1567 lakes across focal regions based on three land use (sustainability, middle of the road, and regional rivalry) and two climate (RCP 2.6 and 8.5) scenarios to mid-21st century. On average, lakes are expected to have higher phytoplankton and cyanobacteria due to increases in both urban land use and temperature, and decreases in forest habitat. However, the relative importance of land use and climate effects varied substantially among regions and lakes. Accounting for land use and climate changes in a combined way based on extensive data allowed us to identify urbanization as the major driver of phytoplankton development in lakes located in urban areas, and climate as major driver in lakes located in remote areas where past and future land use changes were minimal. For approximately one-third of the studied lakes, both drivers were relatively important. The results of this large scale study suggest the best approaches for mitigating the effects of human activity on lake phytoplankton and cyanobacteria will depend strongly on lake sensitivity to long-term change and the magnitude of projected land use and climate changes at a given location. Our quantitative analyses suggest local management measures should focus on retaining nutrients in urban landscapes to prevent nutrient pollution from exacerbating ongoing changes to lake ecosystems from climate change.
Non-native organisms have invaded novel ecosystems for centuries, yet we have only a limited understanding of why their impacts vary widely from minor to severe. Predicting the impact of non-established or newly detected species could help focus biosecurity measures on species with the highest potential to cause widespread damage. However, predictive models require an understanding of potential drivers of impact and the appropriate level at which these drivers should be evaluated. Here, we used non-native, specialist herbivorous insects of forest ecosystems to test which factors drive impact and if there were differences based on whether they used woody angiosperms or conifers as hosts. We identified convergent and divergent patterns between the two host types indicating fundamental similarities and differences in their interactions with non-native insects. Evolutionary divergence time between native and novel hosts was a significant driver of insect impact for both host types but was modulated by different factors in the two systems. Beetles in the subfamily Scolytinae posed the highest risk to woody angiosperms, and different host traits influenced impact of specialists on conifers and woody angiosperms. Tree wood density was a significant predictor of host impact for woody angiosperms with intermediate densities (0.5–0.6 mg/mm3) associated with highest risk, whereas risk of impact was highest for conifers that coupled shade tolerance with drought intolerance. These results underscore the importance of identifying the relevant levels of biological organization and ecological interactions needed to develop accurate risk models for species that may arrive in novel ecosystems.
Hydrologic processes are often important determinants of successful recruitment of native fishes. However, water management practices can result in abnormal changes in daily and seasonal hydrology patterns. Rarely has fish recruitment across river–reservoir landscapes been considered in relation to flow management, despite the direct relationship between reservoir water management and the resulting upstream and downstream hydrology. We evaluated the relationships between lotic and lentic hydrology and recruitment of two native broadcast-spawning fishes, Freshwater Drum Aplodinotus grunniens and Gizzard Shad Dorosoma cepedianum. Four seasonal periods for each species were identified that related to the species’ spawning biology, from which we derived our remaining hydrology variables. Annual hydrology variables were also considered in our analysis. We developed regression models in conjunction with a model-selection procedure for each species and habitat type based on the catch-curve residuals from fish populations in hydrologically connected river–reservoir systems in the Ozark Highland and Ouachita Mountain ecoregions, USA. Our results indicated that recruitment of reservoir Freshwater Drum was negatively correlated to annual reservoir retention time. In lotic habitats, Freshwater Drum recruitment was positively correlated with prespawn discharge conditions and negatively correlated with annual flow variability. Similarly, riverine Gizzard Shad recruitment was positively correlated to the frequency of high-flow pulses during the spawning period. Our results indicate that releasing reservoir water to best mimic relatively natural flow patterns may benefit some broadcast-spawning species that occupy both lentic and downstream lotic environments, especially during the spring. This information, combined with future efforts on additional spawning guilds, will provide a foundation for developing holistic river–reservoir water-allocation plans.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10692","usgsCitation":"Dattilo, J., Brewer, S.K., and Shoup, D., 2021, Flow dynamics influence fish recruitment in hydrologically connected river-reservoir landscapes: North American Journal of Fisheries Management, v. 41, no. 6, p. 1752-1763, https://doi.org/10.1002/nafm.10692.","productDescription":"12 p.","startPage":"1752","endPage":"1763","ipdsId":"IP-096322","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Oklahoma","otherGeospatial":"Elk River, Grand Lake O’ the Cherokee, Kiamichi River, Sardis Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.372314453125,\n 36.43012234551576\n ],\n [\n -93.80126953124999,\n 36.43012234551576\n ],\n [\n -93.80126953124999,\n 37.142803443716836\n ],\n [\n -95.372314453125,\n 37.142803443716836\n ],\n [\n -95.372314453125,\n 36.43012234551576\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.95458984375,\n 33.779147331286474\n ],\n [\n -94.493408203125,\n 33.779147331286474\n ],\n [\n -94.493408203125,\n 34.488447837809304\n ],\n [\n -95.95458984375,\n 34.488447837809304\n ],\n [\n -95.95458984375,\n 33.779147331286474\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Dattilo, J.","contributorId":274267,"corporation":false,"usgs":false,"family":"Dattilo","given":"J.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":832863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoup, D. E.","contributorId":242905,"corporation":false,"usgs":false,"family":"Shoup","given":"D. E.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":832864,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223282,"text":"sir20215064 - 2021 - Geohydrology and water quality of the stratified-drift aquifers in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York","interactions":[],"lastModifiedDate":"2021-08-30T15:07:53.555341","indexId":"sir20215064","displayToPublicDate":"2021-08-30T10:00:00","publicationYear":"2021","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":"2021-5064","displayTitle":"Geohydrology and Water Quality of the Stratified-Drift Aquifers in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York","title":"Geohydrology and water quality of the stratified-drift aquifers in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York","docAbstract":"From 2011 to 2016, the U.S. Geological Survey, in cooperation with the Town of Newfield and the Tompkins County Planning Department, performed a study of the stratified-drift aquifers in the West Branch Cayuga Inlet and Fish Kill Valleys in Newfield, Tompkins County, New York. Both confined and unconfined aquifers were identified, mostly in the valleys. The confined aquifer consists of a discontinuous sand and gravel layer that overlies bedrock and is commonly confined by overlying fine-grained sediments. The unconfined aquifer consists of surficial ice contact sand and gravel, alluvial silt, sand and gravel, and areas where several large tributary streams deposited alluvial fans in the valley, all of which were deposited during and after the last glacial recession.
The unconfined aquifers are primarily recharged by direct infiltration of precipitation at the land surface, by surface runoff and shallow subsurface flow from adjacent hillsides, and by seepage loss from streams crossing the aquifer, especially on alluvial fans. The confined aquifers are primarily recharged by groundwater stored in the overlying sand and gravel aquifer that slowly seeps downward through the underlying confining layer. Other sources of recharge are precipitation that falls directly on the surficial confining unit and adjacent valley walls, which then slowly seeps downward and enters the confined aquifer, and groundwater flow from bordering till and bedrock and from bedrock below the valley. There may also be some recharge where confining units are absent or where parts of the confining units contain sediments with moderate permeability.
The groundwater naturally discharges to the Fish Kill and West Branch Cayuga Inlet streams and to wetlands overlying the aquifer boundaries, with additional losses due to evapotranspiration. Groundwater is pumped from the aquifers by domestic, municipal, and agricultural wells. Approximately 57.9 million gallons per year was withdrawn from the stratified-drift (sand and gravel) aquifers.
Groundwater samples were collected from 11 wells, and surface water samples were collected at 2 sites, one each from Fish Kill and West Branch Cayuga Inlet. None of the common ions (for example, sodium, chloride, and magnesium) exceeded existing drinking water standards at either surface water site. The concentration of nitrate plus nitrite detected was 0.4 milligram per liter as nitrogen in the West Branch Cayuga Inlet site. Total phosphorus was detected at 0.01 milligram per liter as phosphate for both sites. Of the 11 wells sampled, 8 were finished in confined sand and gravel aquifers, 1 was finished in unconfined sand and gravel, and 2 were finished in shale bedrock. Groundwater quality in the study area generally met Federal and State drinking-water standards. However, of the 11 samples taken, 2 exceeded the U.S. Environmental Protection Agency drinking water advisory taste threshold of 20 milligrams per liter for sodium, 8 exceeded the secondary maximum contaminant level of 300 micrograms per liter for iron, and 9 exceeded the secondary maximum contaminant level of 50 micrograms per liter for manganese.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215064","collaboration":"Prepared in cooperation with the Town of Newfield and the Tompkins County Planning Department","usgsCitation":"Fisher, B.N., Heisig, P.M., and Kappel, W.M., 2021, Geohydrology and water quality of the stratified-drift aquifers in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York: U.S. Geological Survey Scientific Investigations Report 2021–5064, 42 p., https://doi.org/10.3133/sir20215064.","productDescription":"Report: vii, 42 p.; 2 Tables; 2 Data Releases","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103464","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":388165,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5064/coverthb.jpg"},{"id":388166,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5064/sir20215064.pdf","text":"Report","size":"5.46 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5064"},{"id":388167,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94Y3E81","text":"USGS data release","linkHelpText":"Geospatial datasets for the geohydrology and water quality of the stratified-drift aquifers in West Branch Cayuga Inlet/Fish Kill aquifers in Newfield, Tompkins County, New York"},{"id":388169,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2021/5064/sir20215064_table03.01.csv","text":"Table 3.1","size":"4.74 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Physical properties and concentrations of common ions, nutrients, radiochemical properties, and dissolved gases in groundwater samples from confined aquifers in the West Branch Cayuga Inlet and Fish Kill Creek Valleys, Newfield, Tompkins County, New York"},{"id":388217,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5064/images/"},{"id":388218,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5064/sir20215064.XML"},{"id":388170,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2021/5064/sir20215064_table03.02.csv","text":"Table 3.2","size":"2.98 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Concentrations of trace elements in groundwater samples from confined aquifers in the West Branch Cayuga Inlet and Fish Kill Creek Valleys, Newfield, Tompkins County, New York"},{"id":388168,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N6AZ4E","text":"USGS data release","linkHelpText":"Horizontal-to-vertical spectral ratio and depth-to-bedrock for geohydrology and water quality of the stratified-drift aquifer in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York, July 2011–November 2016"}],"country":"United States","state":"New York","otherGeospatial":"West Branch Cayuga Inlet and Fish Kill Valleys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.83333333,\n 42.1666\n ],\n [\n -76.83333333,\n 42.8333\n ],\n [\n -76.00,\n 42.83333333\n ],\n [\n -76.00,\n 42.1666\n ],\n [\n -76.83333333,\n 42.1666\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Adaptive traits can be dramatically altered by genome duplication. The study of interactions among traits, ploidy, and the environment are necessary to develop an understanding of how polyploidy affects niche differentiation and to develop restoration strategies for resilient native ecosystems.
Growth and fecundity were measured in common gardens for 39 populations of big sagebrush (Artemisia tridentata) containing two subspecies and two ploidy levels. General linear mixed-effect models assessed how much of the trait variation could be attributed to genetics (i.e., ploidy and climatic adaptation), environment, and gene–environment interactions.
Growth and fecundity variation were explained well by the mixed models (80% and 91%, respectively). Much of the trait variation was attributed to environment, and 15% of variation in growth and 34% of variation in seed yield were attributed to genetics. Genetic trait variation was mostly attributable to ploidy, with much higher growth and seed production in diploids, even in a warm-dry environment typically dominated by tetraploids. Population-level genetic variation was also evident and was related to the climate of each population's origin.
Ploidy is a strong predictor growth and seed yield, regardless of common-garden environment. The superior growth and fecundity of diploids across environments raises the question as to how tetraploids can be more prevalent than diploids, especially in warm-dry environments. Two hypotheses that may explain the abundance of tetraploids on the landscape include selection for drought resistance at the seedling stage, and greater competitive ability in water uptake in the upper soil horizon.
Surface energy balance (SEB) strongly influences the thermal state of permafrost, cryohydrological processes, and infrastructure stability. Road construction and snow accumulation affect the energy balance of underlying permafrost. Herein, we use an experimental road section of the Alaska Highway to develop a SEB model to quantify the surface energy components and ground surface temperature (GST) for different land cover types with varying snow regimes and properties. Simulated and measured ground temperatures are in good agreement, and our results show that the quantity of heat entering the embankment center and slope is mainly controlled by net radiation, and less by the sensible heat flux. In spring, lateral heat flux from the embankment center leads to earlier disappearance of snowpack on the embankment slope. In winter, the insulation created by the snow cover on the embankment slope reduces heat loss by a factor of three compared with the embankment center where the snow is plowed. The surface temperature offsets are 5.0°C and 7.8°C for the embankment center and slope, respectively. Furthermore, the heat flux released on the embankment slope exponentially decreases with increasing snow depth, and linearly decreases with earlier snow cover in fall and shorter snow-covered period in spring.
","language":"English","publisher":"Wiley","doi":"10.1002/ppp.2129","usgsCitation":"Chen, L., Voss, C., Fortier, D., and McKenzie, J.M., 2021, Surface energy balance of sub-Arctic roads with varying snow regimes and properties in permafrost regions: Permafrost and Periglacial Processes, v. 32, no. 4, p. 681-701, https://doi.org/10.1002/ppp.2129.","productDescription":"21 p.","startPage":"681","endPage":"701","ipdsId":"IP-121759","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":410466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Yukon","otherGeospatial":"Beaver Creek area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -140.9,\n 62.4\n ],\n [\n -140.9,\n 62.3\n ],\n [\n -140.85,\n 62.3\n ],\n [\n -140.85,\n 62.4\n ],\n [\n -140.9,\n 62.4\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Chen, Lin","contributorId":299914,"corporation":false,"usgs":false,"family":"Chen","given":"Lin","email":"","affiliations":[],"preferred":false,"id":858867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voss, Clifford I. 0000-0001-5923-2752","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":211844,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":858868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fortier, Daniel","contributorId":194641,"corporation":false,"usgs":false,"family":"Fortier","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":858869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKenzie, Jeffrey M.","contributorId":176299,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":858870,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70253092,"text":"70253092 - 2021 - GeoAI in the US Geological Survey for topographic mapping","interactions":[],"lastModifiedDate":"2024-04-18T12:16:33.943595","indexId":"70253092","displayToPublicDate":"2021-08-30T07:14:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3618,"text":"Transactions in GIS","active":true,"publicationSubtype":{"id":10}},"title":"GeoAI in the US Geological Survey for topographic mapping","docAbstract":"Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in terrain feature extraction, hydrographic network extraction, and semantic modeling. This article is a documentation of the recent work and current state of research and development. The article helps define the accomplishments and directions of research and applications in fields of GeoAI for topographic mapping within the USGS and more broadly.
Groundwater-dependent ecosystems and species (GDEs) are found throughout watersheds at locations of groundwater discharge, yet not all GDEs are the same, nor are the groundwater systems supporting them. Groundwater moves along a variety of flow paths of different lengths and with different contributing areas, ranging from shorter local flow paths with low discharge and large seasonal variability to streams, springs and wetlands to longer regional flow paths with potentially larger discharge and low seasonal variability, commonly at low basin elevations. How does this variation in physical hydrology affect the type and distribution of GDEs? Using data on hypsographic position, groundwater-dependent species distributions, groundwater pumping and streamflow from Oregon, USA, we provide a conceptual model and initial supporting evidence demonstrating that spatial variation in groundwater flow path scales, illustrated using basin hypsography, is a driver of non-random distribution of GDEs across watersheds. Further, we posit that the spatial variation in primary stressors to groundwater (e.g. pumping and climate change) will differentially affect GDEs depending on their hypsographic position. Furthermore, because of their use for irrigation and municipal water supply, regional groundwater systems and associated species are more likely to be studied and receive regulatory protection. Our initial data point to a disproportionate focus on larger discharge, lower elevation GDEs, which leads to a bias in our understanding of the full suite of biodiversity associated with groundwater discharge as well as their stressors and potential mechanisms for protection.
We developed a model for analyzing multi-year demographic data for long-lived animals and used data from a population of Agassiz’s desert tortoise (Gopherus agassizii) at the Desert Tortoise Research Natural Area in the western Mojave Desert of California as a case study. The study area was 7.77 square kilometers and included two locations: inside and outside the fenced boundary. The wildlife-permeable, protective fence was designed to prevent entry from vehicle users and sheep grazing. We collected mark-recapture data from 1,123 tortoises during seven annual surveys consisting of two censuses each over a 34-year period. Additional data were collected when marked tortoises were recovered dead and removed between survey years. We used a Bayesian modeling framework to develop a multistate Jolly-Seber model because of its ability to handle unobserved (latent) states and modified this model to incorporate the additional data from non-survey years. Three size-age states (juvenile, immature, adult), sex (female, male), two location states (inside and outside the fenced boundary), and three survival states (not-yet-entered, entered/alive, and dead/removed) were incorporated into the model. We calculated population densities and estimated probabilities of growth of the tortoises from one size-age state to a larger size-age state, survival after 1 year and 5 years, and detection. Our results show a declining population with low estimates for survival after 1 year and 5 years. The probability for tortoises to move from outside to inside the boundary fence was greater than for tortoises to move from inside the fence to outside. The probability for detecting tortoises differed by size-age state and was lowest for the smallest tortoises and highest for the adult tortoises. The framework for the model can be used to analyze other animal populations where vital rates are expected to vary depending on multiple individual states.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181094","usgsCitation":"Berry, K.H., and Yee, J.L., 2021, Development of demographic models to analyze populations with multi-year data—Using Agassiz’s Desert Tortoise (Gopherus agassizii) as a case study: U.S. Geological Survey Open-File Report 2018–1094, 55 p., https://doi.org/10.3133/ofr20181094.","productDescription":"vi, 55 p.","numberOfPages":"55","onlineOnly":"Y","ipdsId":"IP-086643","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":388564,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2018/1094/images"},{"id":388563,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2018/1094/ofr20181094.xml"},{"id":388562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1094/ofr20181094.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1094/covrthb.jpg"}],"contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
We introduce a Bayesian framework for incorporating time-varying noisy reported data on damage and loss information to update near real-time loss estimates/alerts for the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) system. Initial loss estimation by PAGER immediately following an earthquake includes several uncertainties. Historically, the PAGER’s alerting on fatality and economic losses has not incorporated location-specific reported data on physical damage or casualties for a given earthquake. The proposed framework provides the ability to include early reports on fatalities at any given time and improve the overall impact forecast for the earthquake. The reported data on fatalities or damage are generally incomplete and noisy, especially in the early hours of the disaster. To address these challenges, we develop a recursive Bayesian updating framework that takes into account the loss projection model and the measurement and model uncertainties. The framework is applied to loss data for three example earthquakes, and the results show that the proposed updating improves the loss estimates and alert level to the correct level within the first day of the earthquake.
","language":"English","publisher":"Sage Journals","doi":"10.1177/8755293020944177","usgsCitation":"Noh, H.Y., Jaiswal, K.S., Engler, D.T., and Wald, D.J., 2021, An efficient Bayesian framework for updating PAGER loss estimates: Earthquake Spectra Journal, v. 36, no. 4, p. 1719-1742, https://doi.org/10.1177/8755293020944177.","productDescription":"24 p.","startPage":"1719","endPage":"1742","ipdsId":"IP-118585","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":389706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Noh, Hae Young","contributorId":265961,"corporation":false,"usgs":false,"family":"Noh","given":"Hae","email":"","middleInitial":"Young","affiliations":[{"id":54844,"text":"Carnegie Mellon University (now at Stanford University)","active":true,"usgs":false}],"preferred":false,"id":823863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823864,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engler, Davis T. 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265962,"corporation":false,"usgs":true,"family":"Engler","given":"Davis","email":"","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823865,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823866,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224924,"text":"70224924 - 2021 - Flooding duration and volume more important than peak discharge in explaining 18 years of gravel–cobble river change","interactions":[],"lastModifiedDate":"2022-01-06T17:24:33.238441","indexId":"70224924","displayToPublicDate":"2021-08-27T07:22:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Flooding duration and volume more important than peak discharge in explaining 18 years of gravel–cobble river change","docAbstract":"Floods play a critical role in geomorphic change, but whether peak magnitude, duration, volume, or frequency determines the resulting magnitude of erosion and deposition is a question often proposed in geomorphic effectiveness studies. This study investigated that question using digital elevation model differencing to compare and contrast three hydrologically distinct epochs of topographic change spanning 18 years in the 37-km gravel–cobble lower Yuba River in northern California, USA. Scour and fill were analysed by volume at segment and geomorphic reach scales. Each epoch's hydrology was characterized using 15-min and daily averaged flow to obtain distinct peak and recurrence, duration, and volume metrics. Epochs 1 (1999–2008) and 3 (2014–2017) were wetter than average with large floods reaching 3206 and 2466 m3/s, respectively, though of different flood durations. Epoch 2 (2008–2014) was a drought period with only four brief moderate floods (peak of 1245 m3/s). Total volumetric changes showed that major geomorphic response occurred primarily during large flood events; however, total scour and net export of sediment varied greatly, with 20 times more export in epoch 3 compared to epoch 1. The key finding was that greater peak discharge was not correlated with greater net and total erosion; differences were better explained by duration and volume above floodway-filling stage. This finding highlights the importance of considering flood duration and volume, along with peak, to assess flood magnitude in the context of flood management, frequency analysis, and resulting geomorphic changes.
In 2015, the U.S. Geological Survey started a topical study to quantify water use in areas of continuous oil and gas (COG) development. The first phase of the study was completed in 2019 and analyzed the Williston Basin. The second phase of the study analyzed the Permian Basin using the same techniques and approaches used for the Williston Basin analysis. The Permian Basin was selected for the second phase of water-use analysis for the following reasons: (1) the basin has the largest undiscovered technically recoverable oil and gas resource in the United States, (2) the basin has a continuous resource in tight shale that primarily produces oil, and (3) the basin is within the contiguous United States. This study used data from 60 counties in Texas and New Mexico with spatial coverage based on the Permian Basin extent defined by the U.S. Energy Information Administration, a representation of the geologically defined Permian Basin.
Data from several sources were used in the analysis of direct, indirect, and ancillary water use associated with COG development in the Permian Basin and are available in an associated data release. Hydraulic fracturing water-use data were used to determine the start of the recent (before 2019) COG development boom in oil production in the Permian Basin in the same way that the data were used for the Williston Basin study. Water-use data were aggregated by county and year, which were the sampling units used in the analysis.
The water-use analysis of the Permian Basin contained three elements: (1) estimates of water use, in million gallons, by county and year; (2) coefficients of water use from regression models, in million gallons per developed oil and gas well; and (3) performance (based on goodness-of-fit metrics) of the regression models in estimating the observed water use.
Coefficients from the linear and quantile regression models of direct, indirect, and ancillary water use in the Permian Basin were produced as aggregate values for the counties and years. The mean estimate of direct water use had a 95-percent confidence interval of 4.13–5.45 million gallons (Mgal) per developed oil and gas well. The coefficient from the linear regression model of indirect water use was 0.111 Mgal per well, with a 95-percent confidence interval of 0.104–0.117 Mgal per well. The mean estimate of ancillary water use in the Permian Basin was 1.09 Mgal per well, with a 95-percent confidence interval of 1.05–1.13 Mgal per well. Model performance was evaluated with goodness-of-fit metrics including coefficient of determination (R2), root mean square error, and the ratio of root mean square error to standard deviation of observations computed from leave-one-out cross validation of the linear and quantile regression models of direct, indirect, and ancillary water use. Model performance for direct water use was acceptable, with an R2 value of 0.91. The model performance of indirect water use was acceptable, with an R2 value of 0.89. Values of R2 for the ancillary water-use categories were at least 0.89.
Annual mean estimates for hydraulic fracturing, cementing, drilling, indirect, and ancillary water use per well for the years 2010–17 were comparable between the Permian and Williston Basins. Hydraulic fracturing water use increased similarly from 2010 to 2015 in the Permian Basin and the Williston Basin, increasing from 0.6 Mgal per well in 2010 to 5.4 Mgal per well in 2015 in the Permian Basin and from 1.4 Mgal per well in 2010 to 4.7 Mgal per well in 2015 in the Williston Basin.
By design, the Permian water-use assessment is a simplification of a complex and continually developing system and therefore has uncertainty and limitations in the interpretation of results. Despite the modeling limitations, the results summarized in the report, when compared to other studies, compare well with water-use estimations. The favorable comparison highlights the transferability of the water-use methodology to other areas of COG development.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215090","programNote":"Water Availability and Use Science Program","usgsCitation":"Valder, J.F., McShane, R.R., Thamke, J.N., McDowell, J.S., Ball, G.P., Houston, N.A., and Galanter, A.E., 2021, Estimates of water use associated with continuous oil and gas development in the Permian Basin, Texas and New Mexico, 2010–19: U.S. Geological Survey Scientific Investigations Report 2021–5090, 27 p., https://doi.org/10.3133/sir20215090.","productDescription":"Report: vii, 27 p.; Data Releases: 3; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-126972","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":388522,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JIOU3V","text":"USGS data release","description":"USGS data release","linkHelpText":"R scripts and results of estimated water use associated with continuous oil and gas development, Permian Basin, United States, 2010–19"},{"id":388521,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPKRLW","text":"USGS data release","description":"USGS data release","linkHelpText":"Data to estimate water use associated with continuous oil and gas development, Williston Basin, United States, 1980–2017 (ver. 2.0, September 2019)"},{"id":388520,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LAWIPH","text":"USGS data release","description":"USGS data release","linkHelpText":"Data to estimate water use associated with continuous oil and gas development, Permian Basin, United States, 1980–2019"},{"id":391022,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/fs20213053","text":"FS 2021–3053","size":"4.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3053","linkHelpText":"— Estimates of Water Use Associated with Continuous Oil and Gas Development in the Permian Basin, Texas and New Mexico, 2010–19, with Comparisons to the Williston Basin, North Dakota and Montana"},{"id":388523,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5090/coverthb.jpg"},{"id":388519,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5090/sir20215090.pdf","text":"Report","size":"2.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5090"}],"country":"United States","state":"New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.18359375,\n 34.27083595165\n ],\n [\n -104.8974609375,\n 33.797408767572485\n ],\n [\n -105.0732421875,\n 32.43561304116276\n ],\n [\n -104.8974609375,\n 31.87755764334002\n ],\n [\n -103.447265625,\n 30.334953881988564\n ],\n [\n -102.0849609375,\n 30.259067203213018\n ],\n [\n -100.634765625,\n 29.916852233070173\n ],\n [\n -100.01953125,\n 29.305561325527698\n ],\n [\n -99.0966796875,\n 29.38217507514529\n ],\n [\n -98.701171875,\n 30.41078179084589\n ],\n [\n -99.0966796875,\n 31.353636941500987\n ],\n [\n -99.84374999999999,\n 32.39851580247402\n ],\n [\n -100.8544921875,\n 32.39851580247402\n ],\n [\n -100.986328125,\n 33.100745405144245\n ],\n [\n -101.0302734375,\n 33.65120829920497\n ],\n [\n -101.6455078125,\n 34.52466147177172\n ],\n [\n -103.18359375,\n 34.27083595165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702