Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.abj2479","usgsCitation":"Uhlemann, S., Dafflon, B., Wainwright, H.M., Williams, K.H., Minsley, B.J., Zamudio, K.D., Carr, B., Falco, N., Ulrich, C., and Hubbard, S.S., 2022, Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics: Science Advances, v. 8, no. 12, 15 p., https://doi.org/10.1126/sciadv.abj2479.","productDescription":"15 p.","ipdsId":"IP-134172","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":447933,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1126/sciadv.abj2479","text":"External Repository"},{"id":400125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"East River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.02726364135741,\n 38.86671143315032\n ],\n [\n -106.9134521484375,\n 38.86671143315032\n ],\n [\n -106.9134521484375,\n 38.97595868249733\n ],\n [\n -107.02726364135741,\n 38.97595868249733\n ],\n [\n -107.02726364135741,\n 38.86671143315032\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Uhlemann, Sebastian","contributorId":291360,"corporation":false,"usgs":false,"family":"Uhlemann","given":"Sebastian","email":"","affiliations":[{"id":36254,"text":"LBNL","active":true,"usgs":false}],"preferred":false,"id":842137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dafflon, Baptiste","contributorId":245441,"corporation":false,"usgs":false,"family":"Dafflon","given":"Baptiste","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":842138,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wainwright, Haruko Murakami","contributorId":291362,"corporation":false,"usgs":false,"family":"Wainwright","given":"Haruko","email":"","middleInitial":"Murakami","affiliations":[{"id":36254,"text":"LBNL","active":true,"usgs":false}],"preferred":false,"id":842139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Kenneth Hurst","contributorId":291364,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth","email":"","middleInitial":"Hurst","affiliations":[{"id":62696,"text":"LBNL, Rocky Mountain Biological Lab","active":true,"usgs":false}],"preferred":false,"id":842140,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":842141,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zamudio, Katrina D. 0000-0003-0278-0154","orcid":"https://orcid.org/0000-0003-0278-0154","contributorId":203252,"corporation":false,"usgs":true,"family":"Zamudio","given":"Katrina","email":"","middleInitial":"D.","affiliations":[],"preferred":true,"id":842142,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carr, Bradley","contributorId":175482,"corporation":false,"usgs":false,"family":"Carr","given":"Bradley","email":"","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":842143,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Falco, Nicola","contributorId":245431,"corporation":false,"usgs":false,"family":"Falco","given":"Nicola","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":842144,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ulrich, Craig","contributorId":175248,"corporation":false,"usgs":false,"family":"Ulrich","given":"Craig","email":"","affiliations":[],"preferred":false,"id":842145,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hubbard, Susan S.","contributorId":175249,"corporation":false,"usgs":false,"family":"Hubbard","given":"Susan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":842146,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70231476,"text":"70231476 - 2022 - Determination of optimal set of spatio-temporal features for predicting burn probability in the state of California, USA","interactions":[],"lastModifiedDate":"2022-05-11T12:07:04.245455","indexId":"70231476","displayToPublicDate":"2022-05-04T07:03:53","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Determination of optimal set of spatio-temporal features for predicting burn probability in the state of California, USA","docAbstract":"Wildfires play a critical role in determining ecosystem structure and function and pose serious risks to human life, property and ecosystem services. Burn probability (BP) models the likelihood that a location could burn. Simulation models are typically used to predict BP but are computationally intensive. Machine learning (ML) pipelines can predict BP and reduce computational intensity. In this work, we tested approaches to reduce the set of input features used in an ML model to estimate BP for the state of California, USA, without loss of predictive performance. We used Principal Component Analysis (PCA) to determine the optimal set of features to use in our ML pipeline. Then, we mapped BP and compared model performance when using the reduced set and when using the whole set of features. Models using optimized input achieved similar prediction performance while using less than 50% of the input features.
Older urban landscapes present unique and complex stressors to urban streams and their habitats through the introduction of legacy and emerging toxic contaminants. Contaminant sources are often associated with various developed land uses such as older residential areas, active and former industrial sites, contaminated sites, and effluents from municipal wastewater treatment plant discharges. These landscapes have a history of legacy contaminant use such as polychlorinated biphenyls (PCBs) resulting in impacts to sediment and water in these complex environments. Despite the ban of PCBs in new commercial use in 1979, PCB contamination is still widespread in the environment, with many fish consumption advisories throughout the Chesapeake Bay region based on elevated PCBs. Several watersheds in the Baltimore region have mandated reductions in PCBs per total maximum daily loads in tidal waters of the watersheds in order to promote compliance with water quality standards. Some of these mandated reductions (for example, regulated watershed runoff) specified in the total maximum daily loads are the responsibility of the local jurisdictions as part of their phase 1 National Pollutant Discharge Elimination System municipal separate storm sewer system permit. In cooperation with the Baltimore City Department of Public Works and Maryland Department of the Environment, the U.S. Geological Survey and University of Maryland, Baltimore County conducted a study from 2018 to 2020 to refine the sources of PCBs from the City of Baltimore into Back River and to use the results to improve the conceptual site model of PCBs in the Back River watershed.
PCB concentrations in the water column of the nontidal streams in Back River watershed are relatively consistent throughout both tributaries, with greater concentrations detected in samples collected from Moores Run but greater loads estimated in samples collected from Herring Run. PCB concentrations measured in the bed sediments and analysis of the flux between sediment porewater (hereafter porewater) and surface water within the tributaries suggest that there are no stationary legacy sources within the stream channels.
The bulk of PCB mass entering the system from these nontidal tributaries appears to be introduced primarily during storm events. While only one storm event was sampled and concentrations were quantified only in Herring Run, solids captured during the storm were characterized by increases in PCB mass and overall suspended solids concentrations. Although the bioavailability of the PCB-associated sediment is unknown, this mechanism appears to warrant additional attention to better understand how concentrations vary under different storm conditions and temporally. The importance of contaminated stormwater in loading to Herring Run is further supported by the PCB concentrations in storm drain sediments collected near the tributary, which were present in higher concentrations and were characterized by different homolog signatures compared to that in bed sediments.
The observations in the tributaries differed from PCB concentrations and sediment characteristics downstream from the City of Baltimore boundary, in the upper tidal area of the main stem of Back River, particularly at the passive sampler locations BRT–1 and BRT–3. This depositional environment is characterized by higher organic content in sediments and higher concentrations of PCBs in porewater, which result in the possible flux of contaminants from sediment to the water column. This flux is generally opposite of that observed in the nontidal tributaries and the farthest upstream tidal site (BRT–2) and may be a result of the possible settling of sediment particles introduced via suspended solids in stormwater.
Despite an observed considerable reduction in overall PCB mass loading to and from the Back River Wastewater Treatment Plant (BRWWTP) (and similar reductions observed in biosolids) compared to the estimates previously reported from 2015, effluent from the BRWWTP continues to be a primary source of PCBs to Back River. The current study confirmed the likeliness of fat, oil, and grease deposits within the miles of sewer pipe as a source of PCBs to the BRWWTP influent. The differences between PCB concentrations in fat, oil, and grease deposits found in pipes (during replacement) compared to that of the BRWWTP suggest that legacy deposits may contain higher PCB concentrations and may act as a source of PCBs to passing sewage, eventually entering the BRWWTP. Variation in freely dissolved concentrations in the sewer system was apparent through the analysis of PCBs in the primary pump stations using passive sampling, with the largest contribution to the influent attributed to a single pump station and associated piping.
The contribution of PCBs to Herring Run and Moores Run via sanitary sewer overflows compared to the BRWWTP effluent is negligible, similar to reports from another large urban wastewater treatment plant. Therefore, decreased occurrence of sanitary sewer overflows is not expected to largely decrease PCB loads.
Results of this study suggest that targeted, sediment-capture best management practices in Back River watershed could be an effective way to reduce PCB mass loading assuming that deposited contaminated sediments are effectively isolated. Recent studies of some common urban best management practices such as bioretention have shown removal of PCBs within the stormwater control structures. In addition, appropriately timed street sweeping practices with appropriate collection equipment may be an effective way to reduce contaminants such as PCBs from road runoff sources. Reductions in concentrations and mass loading within the sewer system measured in this study compared to that estimated 5 years prior reflect the possible success of ongoing gray infrastructure management actions. Reductions may be attributable to enhanced nutrient reduction upgrades to the BRWWTP and extensive capital improvements and maintenance to the sewer system.
This study employed a combined sampling approach and a variety of sampling methods to include low-density polyethylene passive samplers, high-volume water samples, and grab samples of both water and sediment to characterize the PCB inputs to Herring Run, Moores Run, and Back River. Incorporating the passive samplers provided a time-weighted average of the freely dissolved concentration in the surface water, porewater, WWTP influent and effluent, and pump station influent over the deployment period with picogram per liter detection limits. A similar monitoring approach from this study could be implemented within other subwatersheds or municipal separate storm sewer system jurisdictions to assist in refining primary sources of PCBs in order to inform appropriate mitigation approaches.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225012","collaboration":"Prepared in cooperation with Baltimore City Department of Public Works and Maryland Department of the Environment","usgsCitation":"Majcher, E., Ghosh, U., Needham, T., Lombard, N., Foss, E., Bokare, M., Joshee, S., Cheung, L., Damond, J., and Lorah, M., 2022, Refining sources of polychlorinated biphenyls in the Back River watershed, Baltimore, Maryland, 2018–2020: U.S. Geological Survey Scientific Investigations Report 2022–5012, 58 p., https://doi.org/10.3133/sir20225012.","productDescription":"Report: x, 58 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-123587","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":399906,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WFDIBM","text":"USGS data release","linkHelpText":"Polychlorinated Biphenyl (PCB) Concentrations of Passive Samplers, Solids, Fat, Oil, and Greases (FOG), and Road Sediments; and Dissolved Organic Carbon (DOC), Total Suspended Solids (TSS), and Particulate Organic Carbon (POC) Concentrations in the Back River Watershed, Baltimore City, Maryland, 2018–2020"},{"id":399905,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5012/sir20225012.pdf","text":"Report","size":"79.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5012"},{"id":399904,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5012/coverthb.jpg"},{"id":502298,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112977.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maryland","city":"Baltimore","otherGeospatial":"Back River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.84112548828125,\n 39.18969082109678\n ],\n [\n -76.32202148437499,\n 39.18969082109678\n ],\n [\n -76.32202148437499,\n 39.51675478434244\n ],\n [\n -76.84112548828125,\n 39.51675478434244\n ],\n [\n -76.84112548828125,\n 39.18969082109678\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
We explore the response of ground motions to topography during large crustal fault earthquakes by simulating several magnitude 6.5–7.0 rupture scenarios on the Seattle fault, Washington State. Kinematic simulations are run using a 3D spectral element code and a detailed seismic velocity model for the Puget Sound region. This model includes realistic surface topography and a near‐surface low‐velocity layer; a mesh spacing of ∼30 m at the surface allows modeling of ground motions up to 3 Hz. We simulate 20 earthquake scenarios using different slip distributions and hypocenter locations on a planar fault surface. Results indicate that average ground motions in simulations with and without topography are similar. However, shaking amplification is common at topographic highs, and more than a quarter of all sites experience short‐period (≤2 s) ground‐motion amplification greater than 25%–35%, compared with models without topography. Comparisons of peak ground velocity at the top and bottom of topographic features demonstrate that amplification is sensitive to period, with the greatest amplifications typically manifesting near a topographic feature’s estimated resonance frequency and along azimuths perpendicular to its primary axis of elongation. However, interevent variability in topographic response can be significant, particularly at shorter periods (<1 s). We do not observe a clear relationship between source centroid‐to‐site azimuths and the strength of topographic amplification. Overall, our results suggest that although topographic resonance does influence the average ground motions, other processes (e.g., localized focusing and scattering) also play a significant role in determining topographic response. However, the amount of consistent, significant amplification due to topography suggests that topographic effects should likely be considered in some capacity during seismic hazard studies.
In southeastern New York, the villages of Ellenville, Wurtsboro, Woodridge, the hamlet of Mountain Dale, and surrounding communities in the Neversink River and Rondout Creek drainage basins rely on wells that pump groundwater from valley-fill glacial aquifers for public water supply. Glacial aquifers are vulnerable to contamination because they are highly permeable and have a shallow depth to water table. To protect the quality of these water resources, water managers need accurate information about the areas that contribute recharge to production wells that pump from these aquifers. The New York State Department of Environmental Conservation and the New York State Department of Health designated eight priority wells in this region for which water supply protection is of primary concern.
The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health, began an investigation in 2019 with the general objectives of (1) improving understanding of regional groundwater-flow system, (2) delineating areas contributing recharge to eight priority production wells, and (3) quantifying the uncertainty of these contributing areas in a probabilistic way that can be used to inform decision-making related to priority well source-water protection. To complete these objectives, a MODFLOW 6 groundwater model was created encompassing the eight priority wells and the surrounding flow system, which includes parts of the Neversink River and Rondout Creek Basins in Sullivan County and Ulster County, New York. The model was built using Python tools (such as flopy, modflow-setup, and sfrmaker) that facilitate transparent and repeatable model development using existing datasets. The model parameters were estimated with a stepwise approach using an iterative ensemble smoother implementation of the Parameter ESTimation software PEST++ (version 5.0.0). We evaluated initial “best guess” parameter bounds with a prior Monte Carlo analysis. Results of the first prior Monte Carlo analysis were used to make informed adjustments to model parameter bounds (typically resulting in expanded bounds), and a second prior Monte Carlo analysis was run to identify improved ranges for model parameters during history matching.
The history matching effort produced an ensemble of parameter values for the groundwater-flow model that spans the range of values within prior uncertainty bounds. The ensemble is informed by the historical observation data, within a reasonable range of uncertainty on those observations. This history-matched ensemble was used in a particle tracking Monte Carlo analysis to delineate the areas contributing recharge to priority wells. The groundwater-flow and particle tracking (MODPATH7) models were run once for each ensemble member. Deterministic contributing areas computed for each ensemble member were aggregated to produce maps showing the probability that a location contributes recharge to priority wells. Finally, the particle tracking Monte Carlo analysis was repeated for six pumping scenarios, representing a wide range of possible pumping levels, to incorporate uncertainty in future pumping rates related to population growth or other management decisions. Increasing pumping rates generally led to larger contributing recharge areas and larger areas of high probability that a location contributes recharge to priority wells. These maps show the overall uncertainty of the areas contributing recharge to priority wells in the study area and provide a tool for risk-based decision making for protection of well source water.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215112","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health","usgsCitation":"Corson-Dosch, N.T., Fienen, M.N., Finkelstein, J.S., Leaf, A.T., White, J.T., Woda, J., and Williams, J.H., 2022, Areas contributing recharge to priority wells in valley-fill aquifers in the Neversink River and Rondout Creek drainage basins, New York: U.S. Geological Survey Scientific Investigations Report 2021–5112, 50 p., https://doi.org/10.3133/sir20215112.","productDescription":"Report: ix, 50 p.; 2 Data Releases","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125165","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399109,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":399101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5112/coverthb.jpg"},{"id":399102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5112/sir20215112.pdf","text":"Report","size":"22.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5112"},{"id":399104,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5112/sir20215112.XML"},{"id":399105,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5112/images/"},{"id":399107,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://ny.water.usgs.gov/maps/neversink/","text":"Neversink-Rondout Source Water Mapper"},{"id":399982,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20215112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5112"},{"id":399106,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225024","text":"Scientific Investigations Report 2022–5024","linkHelpText":"- Data Sources and Methods for Digital Mapping of Eight Valley-Fill Aquifer Systems in Upstate New York"},{"id":399108,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HWSOHP","text":"USGS data release","linkHelpText":"Groundwater model archive and workflow for Neversink/Rondout Basin, New York, source water delineation"},{"id":502119,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112974.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Neversink River and Rondout Creek Drainage Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.8663330078125,\n 41.40153558289846\n ],\n [\n -74.1961669921875,\n 41.40153558289846\n ],\n [\n -74.1961669921875,\n 41.99216023337633\n ],\n [\n -74.8663330078125,\n 41.99216023337633\n ],\n [\n -74.8663330078125,\n 41.40153558289846\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Digital hydrogeologic maps were developed in eight study areas in upstate New York by the U.S. Geological Survey in cooperation with the New York State Department of Environmental Conservation. The digital maps define the hydrogeologic framework of the valley-fill aquifers and surrounding till-covered uplands in the vicinity of the villages of Ellenville and Wurtsboro and hamlets of Woodbourne and South Fallsburg in Sullivan and Ulster Counties, town of Greene in Chenango County, city of Cortland and town of Cincinnatus in Cortland County, city of Jamestown in Chautauqua County, city of Olean and village of Ellicottville in Cattaraugus County, and villages of Fishkill and Wappinger Falls in Dutchess County. The hydrogeologic framework provided the foundation for groundwater-flow models that were used in the delineation of areas contributing groundwater flow to production wells screened in four of the eight valley-fill aquifers considered in this study. The hydrogeologic framework for the other four study areas was developed for potential future use in groundwater contributing-area studies.
Data used in the creation of all digital surfaces and thicknesses included published surficial geology; aquifer maps and hydrogeologic sections; light detection and ranging (lidar) datasets; the Soil Survey Geographic Database; and lithologic well logs from the National Water Information System, New York State Department of Environmental Conservation, New York State Department of Transportation, and Empire State Organized Geologic Information System databases. Digital maps of the surficial geology; thickness of the surficial sand and gravel aquifers; and tops of the confining lacustrine silt and clay units, confined sand and gravel aquifers, and bedrock surfaces were created by using ArcGIS (a geographic information system). All surfaces and thicknesses were generated by using one of the following ArcGIS interpolation tools: Topo to Raster, Natural Neighbors, Kriging, or Empirical Bayesian Kriging. The datasets developed in this study provide a greater understanding of the underlying hydrogeologic framework in glacial valley-fill aquifers and can be applied in the evaluation of groundwater-supply development and protection.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225024","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Finkelstein, J.S., Woda, J.C., and Williams, J.H., 2022, Data sources and methods for digital mapping of eight valley-fill aquifer systems in upstate New York: U.S. Geological Survey Scientific Investigations Report 2022–5024, 21 p., https://doi.org/10.3133/sir20225024.","productDescription":"Report: v, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122133","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":398708,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":398704,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5024/coverthb2.jpg"},{"id":398705,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5024/sir20225024.pdf","text":"Report","size":"4.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5024"},{"id":502380,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112976.htm","linkFileType":{"id":5,"text":"html"}},{"id":399981,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225024/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5024"},{"id":398710,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/sir20215112","text":"Scientific Investigations Report 2021–5112","linkHelpText":"- Areas Contributing Recharge to Priority Wells in Valley-fill Aquifers in the Neversink River and Rondout Creek Drainage Basins, New York"},{"id":398709,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/sir20215083","text":"Scientific Investigations Report 2021–5083","linkHelpText":"- Areas Contributing Recharge to Selected Production Wells in Unconfined and Confined Glacial Valley-Fill Aquifers in Chenango River Basin, New York"},{"id":398707,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5024/images/"},{"id":398706,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5024/sir20225024.XML"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.200439453125,\n 40.59727063442024\n ],\n [\n -73.223876953125,\n 40.59727063442024\n ],\n [\n -73.223876953125,\n 43.48481212891603\n ],\n [\n -79.200439453125,\n 43.48481212891603\n ],\n [\n -79.200439453125,\n 40.59727063442024\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
In the Chenango River Basin of central New York, unconfined and confined glacial valley-fill aquifers are an important source of drinking-water supplies. The risk of contaminating water withdrawn by wells that tap these aquifers might be reduced if the areas contributing recharge to the wells are delineated and these areas protected from land uses that might affect the water quality. The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health, began an investigation in 2019 to improve understanding of groundwater flow and delineate areas contributing recharge to 16 production wells clustered in three study areas in the basin as part of an effort to protect the source of water to these wells. Areas contributing recharge were delineated on the basis of numerical steady-state groundwater-flow models representing long-term average hydrologic conditions.
In the Cortland study area, four water suppliers operate 10 production wells that withdraw a total average rate of 2,480 gallons per minute from an unconfined aquifer consisting of well-sorted sand and gravel deposits. Simulated areas contributing recharge to these wells at their average pumping rates covered a total area of 6.93 square miles. Simulated areas contributing recharge extend upgradient from the wells to upland till deposits and to groundwater divides. Some simulated areas contributing recharge include isolated areas remote from the wells. Short simulated groundwater traveltimes from recharging locations to discharging wells indicated that the wells are vulnerable to contamination from land-surface activities; 50 percent of the traveltimes were 10 years or less. Land cover in some of the areas contributing recharge included a substantial amount of urban and agriculture land use.
The groundwater-flow model of the Cortland study area was calibrated to available hydrologic data by inverse modeling using nonlinear regression. The parameter variance-covariance matrix from model calibration was used to create parameter sets that reflect the uncertainty of the parameter estimates and the correlation among parameters to evaluate the uncertainty associated with the single, predicted contributing areas to the wells. This analysis led to contributing areas expressed as a probability distribution. Because of the effects of parameter uncertainty, the size of the probabilistic contributing areas was larger than the size of the single, predicted contributing area for the wells. Thus, some areas not in the single, predicted contributing area might actually be in the contributing area, including additional areas of urban and agriculture land use that have the potential to contaminate groundwater. Additional areas that might be in the contributing area included recharge originating near the pumping wells that have relatively short groundwater-flow paths and traveltimes.
In each of the Greene and Cincinnatus study areas, one water supplier operates three wells that are screened near the top of the bedrock surface in a confined aquifer consisting of poorly to well-sorted sand and gravel deposits. This confined aquifer is overlain by a lacustrine confining unit of very fine sand, silt, and clay, which in turn is overlain by a thin unconfined aquifer of sand and gravel. The groundwater-flow models for these two areas were manually calibrated because of the limited hydrologic data. Simulated areas contributing recharge to the Greene study area wells covered a total area of 0.35 square mile for the average pumping rate of 170 gallons per minute. The contributing areas extended southeastward of the wells to the groundwater divide in the till uplands. The contributing areas also included remote, isolated areas on the opposite side of the Chenango River from the wells primarily in the till uplands. For the Cincinnatus study area wells, which have a low average pumping rate (34 gallons per minute), the simulated contributing areas totaled 0.06 square mile and were on the same side of the river as the wells, but they are isolated areas remote from the wells primarily in the till-covered bedrock uplands. Land cover in these contributing areas for both study areas is primarily agriculture and forested, with the contributing areas to the Greene study area wells also including some urban land uses. Because the Greene and Cincinnatus study area wells are screened relatively deep and some flow paths to the wells partly travel through the confining unit, which impedes the connection with surface sources of recharge, overall groundwater traveltimes are greater than for wells in the Cortland study area. Fifty percent of Cortland study area wells, but only 9 and 44 percent of Greene and Cincinnatus study area wells, respectively, have groundwater traveltimes of 10 years or less.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215083","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation and New York State Department of Health","usgsCitation":"Friesz, P.J., Williams, J.H., Finkelstein, J.S., and Woda, J.C., 2022, Areas contributing recharge to selected production wells in unconfined and confined glacial valley-fill aquifers in Chenango River Basin, New York (ver. 1.1, 2026): U.S. Geological Survey Scientific Investigations Report 2021–5083, 48 p., https://doi.org/10.3133/sir20215083.","productDescription":"Report: vi, 48 p.; 2 Data Releases; Database","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126791","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":502109,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112975.htm","linkFileType":{"id":5,"text":"html"}},{"id":500551,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5083/versionHist.txt","size":"892 B","linkFileType":{"id":2,"text":"txt"}},{"id":398545,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/sir20225024","text":"Scientific Investigations Report 2022–5024","linkHelpText":"- Data Sources and Methods for Digital Mapping of Eight Valley-Fill Aquifer Systems in Upstate New York"},{"id":398544,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":398543,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":398541,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5083/images/"},{"id":398540,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5083/sir20215083.XML"},{"id":398539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5083/sir20215083.pdf","text":"Report","size":"18.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5083"},{"id":398538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5083/coverthb3.jpg"},{"id":399980,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215083/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5083"},{"id":398542,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HU2G1K","text":"USGS data release","linkHelpText":"MODFLOW -NWT groundwater-flow models used to delineate areas contributing recharge to selected production wells in unconfined and confined glacial valley-fill aquifers in Chenango River Basin, New York"}],"country":"United States","state":"New York","otherGeospatial":"Chenango River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125000001,\n 42.13896840458089\n ],\n [\n -75.16845703125,\n 42.13896840458089\n ],\n [\n -75.16845703125,\n 42.90011265525331\n ],\n [\n -76.11328125000001,\n 42.90011265525331\n ],\n [\n -76.11328125000001,\n 42.13896840458089\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: May 2022; Version 1.1: April 2026","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The U.S. Geological Survey completed gravity transects in 2015, 2018, and 2019 over four features: the Bright Angel Fault, Bright Angel Monocline, Tusayan Graben, and Redlands Ranch Fault Zone in the Coconino Plateau, Coconino County, Arizona, to determine if the existence and width of high porosity (low density) zones could be inferred from the resulting gravity contrasts, which could be used to update groundwater models of the region. Faults and other geological structures in the Coconino Plateau are commonly thought to play a role in the movement of groundwater in the area, but limited data exist to constrain their influence. Some groundwater models of the region have used zones of enhanced permeability and porosity along or near features to model their effect on groundwater flow but have not shown sensitivity to the width of the zones used. Enhanced porosity zones in the subsurface, such as those included along or near features in some groundwater models of the region, could create small mass deficiencies detectable by microgravity methods. However, 3 of the 4 gravity transects, the Bright Angel Fault, Bright Angel Monocline, and Tusayan Graben, showed no negative gravity anomaly over the features that could indicate the presence of a low-density zone. Only the Redlands Ranch Fault Zone that had nearby collapse features showed a negative gravity anomaly that was modeled as a zone of 0.017 increased porosity about 800 meters wide, corresponding to the relative dimension and enhanced porosity used in groundwater models of the area. This study was unable to verify the existence of enhanced porosity zones at the selected locations along the other features. However, faults and other features may affect groundwater flow in different ways at different locations, and this work does not preclude the existence of enhanced porosity zones at other places along these faults.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225031","usgsCitation":"Wildermuth, L.M., 2022, Gravity surveys for estimating possible width of enhanced porosity zones across structures on the Coconino Plateau, Coconino County, north-central Arizona: U.S. Geological Survey Scientific Investigations Report 2022–5031, 22 p., https://doi.org/10.3133/sir20225031.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","ipdsId":"IP-121579","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":502389,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112973.htm","linkFileType":{"id":5,"text":"html"}},{"id":399839,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZYHEBB","text":"Data from “Gravity surveys for estimating possible width of enhanced porosity zones across structures 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Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The relationships that control seed production in trees are fundamental to understanding the evolution of forest species and their capacity to recover from increasing losses to drought, fire, and harvest. A synthesis of fecundity data from 714 species worldwide allowed us to examine hypotheses that are central to quantifying reproduction, a foundation for assessing fitness in forest trees. Four major findings emerged. First, seed production is not constrained by a strict trade-off between seed size and numbers. Instead, seed numbers vary over ten orders of magnitude, with species that invest in large seeds producing more seeds than expected from the 1:1 trade-off. Second, gymnosperms have lower seed production than angiosperms, potentially due to their extra investments in protective woody cones. Third, nutrient-demanding species, indicated by high foliar phosphorus concentrations, have low seed production. Finally, sensitivity of individual species to soil fertility varies widely, limiting the response of community seed production to fertility gradients. In combination, these findings can inform models of forest response that need to incorporate reproductive potential.
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Pearse, I.S., Perez-Ramos, I., Piechnik, L., Poulsen, J., Poulton-Kamakura, R., Redmond, M., Reid, C.D., Rodman, K., Rodrigues-Sanchez, F., Sanguinetti, J., Scher, C.L., Schlesinger, W.H., Schmidt Van Marle, H., Seget, B., Sharma, S., Silman, M., Steele, M.A., Stephenson, N.L., Straub, J.N., Sun, I., Sutton, S., Swenson, J., Swift, M., Thomas, P., Uriarte, M., Vacchiano, G., Veblen, T., Whipple, A.V., Whitham, T.G., Wion, A., Wright, B., Wright, S.J., Zhu, K., Zimmermann, J., Zlotin, R., Zywiec, M., and Clark, J.S., 2022, Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery: Nature Communications, v. 13, 2381, 12 p., https://doi.org/10.1038/s41467-022-30037-9.","productDescription":"2381, 12 p.","ipdsId":"IP-132526","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research 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Understanding how landscape composition impacts gene flow (i.e., connectivity) and interacts with scale is essential to conservation decision-making. We used a landscape genetics approach implementing a recently developed statistical model based on the generalized Wishart probability distribution to identify the primary landscape features affecting gene flow and estimate the degree to which each component influences connectivity for Gunnison sage-grouse (Centrocercus minimus). We were interested in two spatial scales: among distinct populations rangewide and among leks (i.e., breeding grounds) within the largest population, Gunnison Basin. Populations and leks are nested within a landscape fragmented by rough terrain and anthropogenic features, although requisite sagebrush habitat is more contiguous within populations. Our best fit models for each scale confirm the importance of sagebrush habitat in connectivity, although the important sagebrush characteristics differ. For Gunnison Basin, taller shrubs and higher quality nesting habitat were the primary drivers of connectivity, while more sagebrush cover and less conifer cover facilitated connectivity rangewide. Our findings support previous assumptions that Gunnison sage-grouse range contraction is largely the result of habitat loss and degradation. Importantly, we report direct estimates of resistance for landscape components that can be used to create resistance surfaces for prioritization of specific locations for conservation or management (i.e., habitat preservation, restoration, or development) or as we demonstrated, can be combined with simulation techniques to predict impacts to connectivity from potential management actions.
National Park Service (NPS) units in the northern Great Plains (NGP) were established to preserve and interpret the history of the United States, protect and showcase unusual geology and paleontology, and provide a home for vanishing large wildlife. A unifying feature among these national parks, monuments, and historic sites is northern mixed-grass prairie, which not only provides background scenery and habitat but is the foundation of many park missions. As recognition of the prairie’s importance to park fundamental resources and values has grown, so too has the realization that invasive plants threaten these values by reducing native species diversity, altering food webs, and marring the visitor experience. Cheatgrass (Bromus tectorum) and Japanese brome (Bromus japonicus)—collectively referred to as “annual bromes”—are of particular concern because of their documented increase through time, and their association with lower native plant diversity, in NGP parks. A variety of grazing, herbicide-application, and prescribed-fire experiments have shown promising short-term results for controlling annual bromes in research-scale plots in the NGP, but it is unclear whether these management actions will be as effective at the larger spatial and longer temporal scales relevant to park management. When uncertainties about the effectiveness of different management actions cannot be answered with traditional research approaches in time to prevent resource degradation, yet recurrent management decisions must be made, an adaptive management approach may be appropriate. Thus, in 2017, we began to develop the ABAM—Annual Brome Adaptive Management—framework. The aim of this framework is to reduce uncertainties about methods for controlling annual bromes in seven NGP parks through a formal process of learning from the application of on-going management. A uniform framework across seven parks provides greater opportunities for reducing these uncertainties compared to a single park acting alone or to multiple parks using different adaptive management frameworks.
This technical report details the development and expected implementation of the ABAM framework. After briefly introducing the issue (Section 1) and describing the context in which the framework was developed (Section 2), the report describes how a structured decision-making process was used to frame the problem, determine concrete objectives, and decide the alternative actions for achieving those objectives that the framework would be designed around (Section 3). Then the report describes the process used to develop the ABAM decision support system (Section 4). At the core of this system is the ABAM decision support tool, a Bayesian decision network built on nearly two decades of vegetation monitoring data from NGP parks, as well as current literature and ABAMspecific experiments. This tool, referred to as the ABAM model by its intended users, is built to work with the existing vegetation monitoring, prescribed fire, and invasive plant management programs that support the seven ABAM parks. In Section 5, the report describes how output from the ABAM model is produced and used in annual vegetation management decision making. It describes the ABAM R package (Baldwin et al. 2021) and an example R script that leads a user through an annual workflow using the model and the package. This workflow updates the model with information from new monitoring events following management actions of prescribed fire, herbicide application, or a combination thereof. With the updated model, data describing the current condition of vegetation in park management units, and current data for environmental factors included in the model (soil texture, slope, weather, and grazing), the user then runs the model to predict future vegetation conditions—and managers’ happiness with the outcome—in response to each of 10 management actions for each management unit in each park. These predictions inform managers’ decisions regarding locations and types of management actions to apply in the upcoming year.
The ABAM framework is in its infancy, and the report concludes (Section 6) with a discussion of its longer-term viability. Successful adaptive management requires commitment for the long term, likely decades. Currently, the predictions of the decision support tool are not expected to be highly accurate, but they ideally will improve over time as more management actions are applied and their outcomes are captured by monitoring. We designed the ABAM decision support tool to work with the existing management and monitoring resources in ABAM parks to maximize the sustainability of the model’s use, but the ABAM framework requires more than the model. Because this application of an adaptive management framework supported by a quantitative decision support tool to guide vegetation management is unique within the NPS (to our knowledge), institutional knowledge and mechanisms for long-term implementation of the ABAM framework do not exist within the agency. Additionally, the ABAM model and the data that inform it could be improved in a variety of ways. Thus, this report concludes with a discussion of ways to both sustain and improve upon the work completed so far.
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baldwinh@usgs.gov","orcid":"https://orcid.org/0000-0003-1939-5439","contributorId":5635,"corporation":false,"usgs":true,"family":"Baldwin","given":"Heather","email":"baldwinh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Post van der Burg, Max 0000-0002-3943-4194 maxpostvanderburg@usgs.gov","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":4947,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","email":"maxpostvanderburg@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844198,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239128,"text":"70239128 - 2022 - Status and trends of North American bats: Summer occupancy analysis 2010-2019","interactions":[],"lastModifiedDate":"2022-12-28T15:45:33.125558","indexId":"70239128","displayToPublicDate":"2022-05-01T09:32:59","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Status and trends of North American bats: Summer occupancy analysis 2010-2019","docAbstract":"• We developed an analytical pipeline supported by web-based infrastructure for integrating continental scale bat monitoring data (stationary acoustic, mobile acoustic, and capture records) to estimate summer (May 1–Aug 31) occupancy probabilities and changes in occupancy over time for 12 North American bat species. This serves as one of multiple lines of evidence that inform the status and trends of bat populations.
• We analyzed data from a total of 12 bat species (Table 1), 11 of which have tested positive for Pseudogymnoascus destructans (Pd), a fungal pathogen that causes white-nose syndrome (WNS)—a disease that has led to significant rates of mortality for subterranean hibernating bat species in North America. A twelfth species was also selected because of high rates of mortality at wind energy facilities. Additional species were considered but not selected due to data limitations.
• We estimated occupancy probabilities for 2010 through 2019 for three species (Myotis lucifugus, MYLU; Myotis septentrionalis, MYSE; and Perimyotis subflavus, PESU). For an additional nine species, we estimated occupancy probabilities for 2016 through 2019 (Myotis evotis, MYEV; Myotis grisescens, MYGR; Myotis leibii, MYLE; Myotis thysanodes, MYTH; Myotis volans, MYVO; Myotis yumanensis, MYYU; Eptesicus fuscus, EPFU; Lasionycteris noctivagans, LANO; and Lasiurus cinereus, LACI). • For each species, we provide range-wide occupancy probability predictions (e.g., predicted summer occupancy distribution maps) each year at a spatial resolution of 100 km2 and provide regional estimates of mean occupancy probability aggregated at larger spatial scales (state/province/territory, range-wide).
• For each species, we also provide trends over time (average annual change rate and total change rate) in mean occupancy probabilities at multiple spatial scales (state/province/territory, range-wide) and when possible, over multiple timescales (short, medium, long).
• Results suggest that over the short-term (2016-2019), two (Myotis lucifugus and Perimyotis subflavus) of 12 species have experienced declines in range-wide average occupancy probability with at least 95% certainty. Seven species showed either minor increases or decreases in range-wide average occupancy probability but with less than 95% certainty in both trend indicators. Results over the longer term (eight years and 10 years of sampling) suggest that three hibernating species known to be highly affected by white-nose syndrome (Myotis lucifugus, Myotis septentrionalis, and Perimyotis subflavus) have experienced marked declines in range-wide average occupancy probabilities, with severity varying by species and region. Finally, the results for three species (Eptesicus fuscus, Lasiurus cinereus, Lasionycteris noctivagans) were inconclusive due to 1) borderline convergence issues in the model fitting procedure which suggests potentially unreliable estimates, 2) failure to reliably distinguish between false positives and true positive detections for ambiguous detections, and 3) largely uninformative covariates for occupancy and detection.
• For Myotis lucifugus, Myotis septentrionalis, and Perimyotis subflavus we found meaningful associations in space and time between declining winter populations (likely a result of WNS) and summer occupancy distributions.
• The representativeness of sampling data for each species’ status and trend estimates (e.g., state/province/territory) were also evaluated based on the percent of grid cells sampled each year with a goal of understanding the reliability of regional estimates and improving future monitoring efforts.
• This work represents the most comprehensive effort to date to model North American bat distributions across their continental ranges. Despite current limitations highlighted in the discussion, the analytical methods and resulting status and trends estimates provide the best available science on summer bat populations across North America and will continue to improve over time as monitoring data sets and analytical methods improve.
• Moving forward, our occupancy analyses will continue to improve with submission of more 1) data from currently underrepresented areas (i.e., improved geographic representation), 2) manually-vetted acoustic recordings, 3) capture records, and 4) roost location and count data (summer and winter).
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.7944/P927I36K","usgsCitation":"Udell, B.J., Straw, B., Cheng, T.L., Enns, K., Frick, W., Gotthold, B., Irvine, K., Lausen, C., Loeb, S., Reichard, J., Rodhouse, T., Smith, D., Stratton, C., Thogmartin, W.E., and Reichert, B., 2022, Status and trends of North American bats: Summer occupancy analysis 2010-2019, xvi, 231 p., https://doi.org/10.7944/P927I36K.","productDescription":"xvi, 231 p.","ipdsId":"IP-136669","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":435864,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92JGACB","text":"USGS data release","linkHelpText":"Status and Trends of North American Bats Summer Occupancy Analysis 2010-2019 Data 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0000-0002-9640-0695","orcid":"https://orcid.org/0000-0002-9640-0695","contributorId":204260,"corporation":false,"usgs":true,"family":"Reichert","given":"Brian","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":860288,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70241893,"text":"70241893 - 2022 - On the role of climate in monthly baseflow changes across the continental United States","interactions":[],"lastModifiedDate":"2023-03-30T13:35:11.224943","indexId":"70241893","displayToPublicDate":"2022-05-01T08:27:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"On the role of climate in monthly baseflow changes across the continental United States","docAbstract":"Baseflow is the portion of streamflow that comes from groundwater and subsurface sources. Although baseflow is essential for sustaining streams during low flow and drought periods, we have little information about how and why it has changed over large regions of the continental United States. The objective of this study was to evaluate how changes in the climate system have affected observed monthly baseflow records at 3,283 USGS gauges over the last 30 years (1989–2019). We developed a statistical modeling framework to determine the relationship between monthly baseflow and monthly climate predictors (i.e., precipitation, temperature, and antecedent wetness). Overall, we found that baseflow trends and the factors influencing them vary by region and month. In the US Northeast, increases were detected earlier in the year (February and March) and in the summer (May and June), and were likely due to increasing precipitation, warmer temperature, and subsequent changes in snowmelt. Increasing baseflow in the US Pacific Northwest and Midwest were associated with increases in precipitation and antecedent wetness throughout the year. Decreasing trends were located in the US Southeast and Southwest. Baseflow trends in the US Southeast were only detected in March, possibly as a result of decreased precipitation during the spring. On the other hand, decreases in baseflow in the Central Southwestern United States occurred throughout the year. These trends were associated with a lack of precipitation and increases in temperature. Finally, we examined the relationship between monthly baseflow trends and changes in total water storage using monthly Gravity Recovery and Climate Experiment mascon products from the Jet Propulsion Laboratory. In this study, trends in total water storage were strongly associated with baseflow trends across the United States. The spatial and temporal variability in baseflow response to climate reported here can aid water managers in adapting to future climate change.
The Arctic Ocean has experienced orbital and millennial-scale climate oscillations over the last 500 kilo-annum (ka) involving massive changes in global sea level and components of the Arctic cryosphere, including sea-ice cover, land-based ice sheets and ice shelves. Although these climate events are only partially understood, micropaleontological studies utilizing ostracodes and benthic foraminifera have demonstrated that major changes in faunas have occurred at different timescales that signify ecosystem regime changes linked to sea-ice cover, surface productivity, bottom temperature and other factors. In addition to faunal changes characterizing glacial-interglacial cycles, Arctic sediments contain several unusual faunal events that cannot be explained by orbital-scale sea level and cryospheric changes. One indicator of such events involves the ostracode Rabilimis mirabilis (Brady 1868), a shallow-water species that inhabits continental shelves in the modern Arctic. We conducted studies of the stratigraphic distribution of R. mirabilis in cores from the Northwind, Mendeleev, Lomonosov, and Alpha Ridges; the Siberian and North American (Beaufort Sea) continental margins; and the Lincoln Sea off North Greenland and in the northern Greenland Sherard Osborn Fjord. Evidence from these records suggests that this species occurs as a fossil in deeper water sediment cores on the upper parts of submarine ridges (mainly 700-900 meters water depth, mwd), in significant numbers (from 1%to 50% of total ostracodes) during Marine Isotope Stages (MIS) 5a (125-109 ka), MIS 4 (71-57 ka), and MIS 3 (57-29 ka). Furthermore, it occurs in cores from various depths on the Siberian margin, the Beaufort and Lincoln Seas during MIS 1 (the Holocene, approx. 11-0 ka). These occurrences involve well-preserved, stratigraphically consistent adult and juvenile populations, which are autochthonous in nature and not caused by downslope transport or ice rafting. Based on their age and associated paleoceanographic conditions in the Arctic, we interpret these R. mirabilis events as signifying basin-ward migration during abrupt changes in growth and decay of massive ice shelves and may be useful as biostratigraphic markers.
This paper summarizes the conditions, applied techniques, results, and lessons of major field gas production attempts from gas hydrates in the past and the necessity of longer term production testing with the scale of years to fulfill the gap between the currently available information and the knowledge required for commercial development. The temporal and spatial scales of field production test projects employing depressurization have expanded since 2002. The results from the projects have proved the applicability of these techniques in both onshore and offshore conditions. However, many technical and reservoir condition-related issues have emerged in gas production, and the gap between current status and industrial requirements is still large. Sand control, artificial lift, and related flow assurance issues are common technical issues that impact onshore and offshore production testing operations. Different reservoir responses were observed well by well, and discrepancy between model predictions and actual field measurements were seen, although reasonable matches were made for short-term behaviors. Those observations suggest that temporal change of the wellbore and near-wellbore conditions and reservoir heterogeneity that cannot be fully modeled have caused complex short-term responses to the depressurization operations. To ensure the long-term operational stability and reliability of the prediction technologies for production behaviors that are essential for commercialization of gas hydrate resources, gas hydrate production testing with comparable duration with commercial operations are necessary. Due to the locality of geological conditions in gas hydrate reservoirs, numerous gas production tests will be required to understand the factors controlling gas production.
The clearest statistical signal in aftershock locations is that most aftershocks occur close to their mainshocks. More precisely, aftershocks are triggered at distances following a power‐law decay in distance (Felzer and Brodsky, 2006). This distance decay kernel is used in epidemic‐type aftershock sequence (ETAS) modeling and is typically assumed to be isotropic, even though individual sequences show more clustered aftershock occurrence. The assumption of spatially isotropic triggering kernels can impact the estimation of ETAS parameters themselves, such as biasing the magnitude‐productivity term, alpha, and assigning too much weight to secondary rather than primary (direct) triggering. Here we show that aftershock locations in southern California, at all mainshock–aftershock distances, preferentially occur in the areas of previous seismicity. For a given sequence, the scaling between aftershock rates and the previous seismicity rate is approximately linear. However, the total number of aftershocks observed for a given sequence is independent of background rate. We explain both of these observations within the framework of rate‐and‐state friction (Dieterich, 1994).
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320220005","usgsCitation":"Page, M.T., and van der Elst, N., 2022, Aftershocks preferentially occur in previously active areas: The Seismic Record, v. 2, no. 2, p. 100-106, https://doi.org/10.1785/0320220005.","productDescription":"7 p.","startPage":"100","endPage":"106","ipdsId":"IP-134184","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":447977,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320220005","text":"Publisher Index Page"},{"id":400694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"southern California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 32.62087018318113\n ],\n [\n -114.60937499999999,\n 32.676372772089834\n ],\n [\n -114.47753906249999,\n 32.93492866908233\n ],\n [\n -114.6533203125,\n 33.37641235124676\n ],\n [\n -114.45556640625,\n 33.99802726234877\n ],\n [\n -114.14794921875,\n 34.32529192442733\n ],\n [\n -114.98291015625,\n 35.35321610123823\n ],\n [\n -118.87207031250001,\n 38.22091976683121\n ],\n [\n -122.54150390625,\n 37.45741810262938\n ],\n [\n -120.673828125,\n 34.56085936708384\n ],\n [\n -117.158203125,\n 32.62087018318113\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":843106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van der Elst, Nicholas 0000-0002-3812-1153 nvanderelst@usgs.gov","orcid":"https://orcid.org/0000-0002-3812-1153","contributorId":147858,"corporation":false,"usgs":true,"family":"van der Elst","given":"Nicholas","email":"nvanderelst@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":843107,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231213,"text":"70231213 - 2022 - Identifying monitoring information needs that support the management of fish in large rivers","interactions":[],"lastModifiedDate":"2022-05-03T11:37:15.435279","indexId":"70231213","displayToPublicDate":"2022-04-29T06:33:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Identifying monitoring information needs that support the management of fish in large rivers","docAbstract":"Management actions intended to benefit fish in large rivers can directly or indirectly affect multiple ecosystem components. Without consideration of the effects of management on non-target ecosystem components, unintended consequences may limit management efficacy. Monitoring can help clarify the effects of management actions, including on non-target ecosystem components, but only if data are collected to characterize key ecosystem processes that could affect the outcome. Scientists from across the U.S. convened to develop a conceptual model that would help identify monitoring information needed to better understand how natural and anthropogenic factors affect large river fishes. We applied the conceptual model to case studies in four large U.S. rivers. The application of the conceptual model indicates the model is flexible and relevant to large rivers in different geographic settings and with different management challenges. By visualizing how natural and anthropogenic drivers directly or indirectly affect cascading ecosystem tiers, our model identified critical information gaps and uncertainties that, if resolved, could inform how to best meet management objectives. Despite large differences in the physical and ecological contexts of the river systems, the case studies also demonstrated substantial commonalities in the data needed to better understand how stressors affect fish in these systems. For example, in most systems information on river discharge and water temperature were needed and available. Conversely, information regarding trophic relationships and the habitat requirements of larval fishes were generally lacking. This result suggests that there is a need to better understand a set of common factors across large-river systems. We provide a stepwise procedure to facilitate the application of our conceptual model to other river systems and management goals.
Large lahars pose substantial threats to people and property downstream from Mount Rainier volcano in Washington State. Geologic evidence indicates that these threats exist even during the absence of volcanic activity and that the threats are highest in the densely populated Puyallup and Nisqually River valleys on the west side of the volcano. However, the precise character of these threats can be difficult to anticipate.
To help predict depths and rates of possible lahar inundation in the area, this report presents the results of simulations of hypothetical future lahars that originate high on the west side of Mount Rainier and travel downstream into the Puyallup and Nisqually River valleys. Many of the results portrayed as still images in the figures of this report are also available as animated files that can be accessed at the web address provided in the figure captions. We simulated eight scenarios, including worst-case scenarios in which the simulated lahars are similar in size and mobility to the approximately 260 million cubic meter (Mm3; 340 million cubic yard) Electron Mudflow lahar that descended from Mount Rainier and inundated the Puyallup River valley about 500 years ago. The other six scenarios place the worst-case scenarios in perspective by simulating lahars that originate from the same source areas but have smaller volumes or lesser mobilities.
We perform our simulations using an open-source software package that we developed called D-Claw. The numerical model composing the kernel of D-Claw solves a system of five hyperbolic partial differential equations that describe the depth-averaged dynamics of static or flowing grain-fluid mixtures interacting with three-dimensional topography. In D-Claw, the volume fraction occupied by solid grains is a dependent variable that can freely evolve, enabling simulation of landslide liquefaction and of lahar interaction with static bodies of water. The latter feature facilitates a seamless simulation of a lahar in the Nisqually River valley entering Alder Lake reservoir.
In the event of an approximately 260 Mm3 high-mobility lahar originating on the west side of Mount Rainier, our results point to two areas of pronounced hazard. One area, comprising the densely populated lowlands of Orting, Washington, and environs, could be inundated by lahars originating from either the Sunset Amphitheater or Tahoma Glacier headwall areas. In the worst-case scenario we consider for the Orting lowlands, which involves a 260 Mm3 high-mobility lahar originating from a landslide in the Sunset Amphitheater, a flow front approximately 4 meters deep and traveling about 4 meters per second reaches the Orting lowlands about 1 hour after the onset of slope failure. After passing through the Orting lowlands, the simulated lahar slows down and comes to rest in the valleys surrounding Sumner and Puyallup. A second area of pronounced hazard is the stretch of the Nisqually River valley beginning in Mount Rainier National Park and extending downstream to Alder Lake reservoir and Alder Dam. This area would be substantially affected in the worst-case scenario that involves a 260 Mm3 high-mobility lahar originating from the Tahoma Glacier headwall area—the locality identified by a previous study as the sector of Mount Rainier most prone to large-scale gravitational collapse. The simulated lahar passes through the area of Ashford, Washington, within about 20 minutes of the onset of slope failure and reaches the head of Alder Lake within about 50 minutes. The lahar ultimately displaces enough reservoir water to cause overtopping of the 100 meter (330 foot) tall Alder Dam, but consequences of such dam overtopping are not addressed in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211118","usgsCitation":"George, D.L., Iverson, R.M., and Cannon, C.M., 2022, Modeling the dynamics of lahars that originate as landslides on the west side of Mount Rainier, Washington: U.S. Geological Survey Open-File Report 2021–1118, 54 p., https://doi.org/10.3133/ofr20211118.","productDescription":"Report: vii, 54 p.;16 Companion Files","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-123581","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":399834,"rank":18,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig27.gif","text":"Supplemental animation for figure 27","size":"5 MB gif"},{"id":399833,"rank":17,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig26(T-52-HM).gif","text":"Supplemental animation for figure 26 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1300 SE Cardinal Court
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Our understanding of the up to 7 orders of magnitude partitioning of thallium (Tl) between seawater and ferromanganese (FeMn) deposits rests upon two foundations: (1) being able to quantify the Tl(I)/Tl(III) ratio that reflects the extent of the oxidative scavenging of Tl by vernadite (δ-MnO2), the principle manganate mineral in oxic and suboxic environments, and (2) being able to determine the sorption sites and bonding environments of the Tl(I) and Tl(III) complexes on vernadite. We investigated these foundations by determining the oxidation state and chemical form of Tl in FeMn crusts and nodules from the global oceans at a Tl concentration ranging from several hundred ppm (mg/kg) down to the low ppm level. Seventeen hydrogenetic crusts and eleven nodules from the Pacific, Atlantic, Arctic, and Indian Oceans and Baltic Sea were characterized by chemical analysis, X-ray diffraction, Raman spectroscopy, Mn K-edge X-ray absorption near-edge structure (XANES) spectroscopy, Tl L3-edge high energy-resolution XANES (HR-XANES) spectroscopy, and extended X-ray absorption fine structure (EXAFS) spectroscopy. The Tl concentration increases linearly from 1.5 to 319 ppm with the Mn/Fe ratio in Fe-vernadite from hydrogenetic crusts, whereas the percentage of Tl(III) to total Tl varies between 62 and 100% independent of both the Mn/Fe and Mn(III)/Mn(IV) ratios. The data, complemented by molecular modeling of the Tl(III) coordination and by XANES calculations, suggest that the enrichment of Tl in Fe-vernadite is driven by (1) the oxidative uptake of octahedrally coordinated Tl(III) above the vacant Mn(IV) sites and on the layer edges of the vernadite layers, and (2) the sorption of Tl(I) on the crystallographic site of Ba at the surface of the vernadite layers, which is an analogue to the surface site of K. Thus, Tl has a high affinity for vernadite regardless of its oxidation state, and the lack of correlation between Tl(III) and the Mn/Fe ratio in FeMn crusts is explained by the affinity of Tl(I) for the Ba site. The Tl concentration varies between 2 and 112 ppm in surface and buried nodules independent of the Mn/Fe ratio, and the percentage of Tl(III) varies between 0 and 100%. Nodules subjected to sediment diagenesis with replacement of layered vernadite by tunneled todorokite are depleted in Tl and have more reduced thallium. Knowledge of the complex interplay of mineralogy, surface chemical processes, and crystallographic siting is required to understand the variability of Tl concentrations, redox state, and acquisition processes by marine FeMn deposits.
The United States Department of Energy, the MH21-S Research Consortium of Japan, and the United States Geological Survey are collaborating to enable gas hydrate scientific drilling and extended-duration reservoir response testing on the Alaska North Slope. To feasibly execute such a test, a location is required that is accessible from existing roads and gravel pads and that can be occupied without disrupting ongoing industry operations. A review of potential locations meeting these criteria determined the likely occurrence of gas hydrate in two fine-grained marginal-marine sands of Tertiary age in the vicinity of the inactive “Kuparuk State 7-11-12” exploration pad in the western Prudhoe Bay Unit (PBU). Existing well and seismic data for that site were insufficient to preclude the potential for free gas occurrence within the deeper (and most prospective) target sand. Therefore, with support from the PBU Working Interest Owners, Alaska Department of Natural Resources, and Petrotechnical Resources Alaska, the Hydrate-01 Stratigraphic Test Well (STW) was drilled in December 2018 to confirm the suitability of the site for future gas hydrate scientific testing. The Hydrate-01 well was successfully drilled to −3290 ft (1003 m) subsea vertical depth at a bottom hole location of approximately 900 ft (∼275 m) east of the surface location. The drilling program featured acquisition of a full suite of logging while drilling data, the collection of side-wall pressure cores, and the installation of distributed temperature and distributed acoustic sensor fiber-optic cables. The log data acquired confirmed the occurrence of gas hydrate at high saturation in two target sands. Integrated evaluation of log and sidewall core data provide petrophysical and geomechanical property information that allow for potential reservoir response to depressurization to be simulated. The deeper “B1 sand” is deemed to be most favorable for reservoir response testing as a result of confirmed gas hydrate occurrence in sediments of high intrinsic permeability, location within 100 ft (30 m) of the base of gas hydrate stability, and minimal risk for direct communication with permeable water-bearing (hydrate-free) zones. The shallower “D1 sand” provides a secondary target that is differentiated by colder in situ temperatures and the interpreted direct hydraulic communication to a lower section of non-hydrate-bearing, water-saturated sand. The Hydrate-01 log data also confirm the occurrence of at least one sub-seismic fault in close proximity to the B1 sand reservoir. To better image the distribution of the gas-hydrate-bearing reservoir sections and associated faults, a three-dimensional (3D) vertical seismic profile was conducted in early 2019 using the distributed acoustic sensors installed as part of the Hydrate-01 STW completion. Detailed two-dimensional (2D) and 3D geologic models have been constructed to enable numerical simulations to inform the planning for potential future scientific tests of reservoir response to depressurization at the site.
Exotic species are often vilified as \"bad\" without consideration of the potential they have for contributing to ecological functions in degraded ecosystems. The red-eared slider turtle (RES) has been disparaged as one of the worst invasive species. Based on this review, we suggest that RES contribute some ecosystem functions in urban wetlands comparable to those provided by the native turtles they sometimes dominate or replace. While we do not advocate for releases outside their native range, or into natural environments, in this review, we examine the case for the RES to be considered potentially beneficial in heavily human-altered and degraded ecosystems where native turtles struggle or fail to persist. After reviewing the ecosystem functions RESs are known to provide, we conclude that in many modified environments the RES is a partial ecological analog to native turtles and removing them may obviate the ecological benefits they provide. We also suggest research avenues to better understand the role of RESs in heavily modified wetlands.
","language":"English","publisher":"Springer","doi":"10.1007/s43621-022-00083-w","usgsCitation":"Dupuis-Desormeaux, M., Lovich, J.E., and Gibbons, J.W., 2022, Re-evaluating invasive species in degraded ecosystems: A case study of red-eared slider turtles as partial ecological analogs: Discover Sustainability, v. 3, 15, 13 p., https://doi.org/10.1007/s43621-022-00083-w.","productDescription":"15, 13 p.","ipdsId":"IP-127491","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447999,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s43621-022-00083-w","text":"Publisher Index Page"},{"id":402540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationDate":"2022-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Dupuis-Desormeaux, Marc","contributorId":292578,"corporation":false,"usgs":false,"family":"Dupuis-Desormeaux","given":"Marc","email":"","affiliations":[{"id":62941,"text":"Department of Biology, Glendon College, York University, 2275 Bayview Avenue, Toronto, Ontario, M4N 3M6 CANADA","active":true,"usgs":false}],"preferred":false,"id":845237,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":845238,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibbons, J. Whitfield","contributorId":198690,"corporation":false,"usgs":false,"family":"Gibbons","given":"J.","email":"","middleInitial":"Whitfield","affiliations":[],"preferred":false,"id":845239,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230765,"text":"cir1492 - 2022 - The Volcano Hazards Program — Strategic science plan for 2022–2026","interactions":[],"lastModifiedDate":"2022-04-27T14:57:14.095507","indexId":"cir1492","displayToPublicDate":"2022-04-27T10:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1492","displayTitle":"The Volcano Hazards Program — Strategic Science Plan for 2022–2026","title":"The Volcano Hazards Program — Strategic science plan for 2022–2026","docAbstract":"The U.S. Geological Survey (USGS) Volcano Hazards Program (VHP) Strategic Science Plan, developed through discussion with scientists-in-charge of the USGS volcano observatories and the director of the USGS Volcano Science Center, specifies six major strategic goals to be pursued over the next 5 years. The purpose of these goals is to help fulfill the USGS VHP mission to enhance public safety and to minimize social and economic disruption caused by volcanic eruptions in the United States and its territories, through delivery of effective forecasts, warnings, and information on volcano hazards based on scientific understanding of volcanic processes. These six major strategic goals are to (1) continue—and when possible, accelerate—implementation of the National Volcano Early Warning System (NVEWS); (2) improve community preparedness for volcanic hazards by updating and standardizing essential components of volcano hazard assessments and providing training to land managers, emergency responders, and State and local communities; (3) develop the next generation of volcano hazard assessments using geographic information systems and other digital tools; (4) make observations with new instrumentation and take advantage of advances in real-time gas sensors; (5) rebuild the Hawaiian Volcano Observatory and its monitoring capabilities; and (6) form new partnerships and strengthen existing partnerships with other government agencies and with academia and industry, to advance volcano monitoring, increase understanding of volcanic processes, and disseminate USGS information.
In its effort to advance volcano science and monitoring techniques, the VHP has identified six scientific targets to pursue over the next 5 years, including: (1) increased understanding of volcano seismicity; (2) improved probabilistic forecasting; (3) deepened grasp of volcano eruption histories and geochronology; (4) newly developed and refined physical models of magmatic systems, leading to better situational awareness and accuracy of eruption forecasts; (5) improved warnings and forecasts of volcanic ash and gas clouds and characterization of volcanic smog sources; and (6) refined lava-flow modeling and forecasting of lava-flow paths.
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