The U.S. Geological Survey, in cooperation with the Oregon Department of Transportation (ODOT), evaluated the effects of cold-weather chloride deicers (road deicing chemicals) on groundwater quality, with a focus on chloride, near the Siskiyou Pass in southwestern Oregon. The study covered the period during July 2018 through February 2021. Between the years 2016 and 2020 ODOT applied up to 16,000 gallons per mile of chloride deicer and 143,000 pounds per mile of road salt along an 11-mile stretch of Interstate 5 (I-5) through the Siskiyou Pass. Despite the benefit of safer driving conditions, there are potentially negative environmental effects associated with the use of chloride-based deicers (such as magnesium chloride and sodium chloride). The results from this study are intended to help ODOT assess the water-quality effects from the application of chloride deicers at the Siskiyou Pass and inform decisions on how those chemicals are used.
Dissolved chloride concentrations tended to be greater in groundwater downgradient from I-5 compared to groundwater upgradient from the interstate. Specific conductance was a good predictor of dissolved chloride concentration (R2 = 0.905). Continuous monitoring showed that specific conductance measurements were greater at four downgradient spring-fed sites at the end of the study period compared with measurements at the beginning of the study. The study results indicate that chloride levels in shallow groundwater downgradient from I-5 are increasing, but dissolved chloride concentrations in domestic wells are not above the U.S. Environmental Protection Agency drinking water recommendations. The approach and methods used in this study, with modifications as site conditions warrant, can be applied in other areas of chloride deicer application to determine if groundwater is affected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235107","collaboration":"Prepared in cooperation with Oregon Department of Transportation","usgsCitation":"Gingerich, S.B., Wise, D.R., and Stonewall, A.J., 2023, Assessing the effects of chloride deicer applications on groundwater near the Siskiyou Pass, southwestern Oregon, July 2018–February 2021 (ver. 1.1, July 2025): U.S. Geological Survey Scientific Investigations Report 2023–5107, 39 p., https://doi.org/10.3133/sir20235107.","productDescription":"Report: viii, 39 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-140151","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":492943,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2023/5107/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version history"},{"id":421408,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D6XDIJ","text":"USGS data release","description":"USGS data release","linkHelpText":"Specific conductance and other groundwater quality data, Siskiyou Pass area, southwestern Oregon, 2018 to 2021"},{"id":421403,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5107/coverthb2.jpg"},{"id":421404,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5107/sir20235107.pdf","text":"Report","size":"7.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5107"},{"id":421405,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235107/full","text":"Report","description":"SIR 2023-5107"},{"id":421406,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5107/images"},{"id":421407,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5107/sir20235107.XML"},{"id":421409,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5107/sir20235107_appendix1.xlsx","text":"Appendix 1","size":"45 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2023-5107 Appendix 1"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.79414603940278,\n 42.22368397834566\n ],\n [\n -122.79414603940278,\n 41.9878942723297\n ],\n [\n -122.42473672058753,\n 41.9878942723297\n ],\n [\n -122.42473672058753,\n 42.22368397834566\n ],\n [\n -122.79414603940278,\n 42.22368397834566\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: September 29, 2023; Version 1.1: July 25, 2025","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
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Introduction: Large dam removals provide a restoration opportunity for shrinking coastal wetland habitats. Dam removal can increase sediment delivery to sediment-starved river deltas and estuaries by restoring natural sediment transport and mobilizing reservoir-impounded sediment. However, rapid mobilization of massive quantities of sediment stored behind large dams also constitutes a major ecological perturbation. Information is lacking on coastal habitat responses to sediment pulses of this magnitude.
Methods: Removal of two large dams along the Elwha River (Washington, USA) in 2011–2014 released ~20.5 Mt of impounded sediment, ~5.4 Mt of which were deposited in the delta and estuary (hereafter, delta). We used time series of aerial imagery, digital elevation models, and vegetation field sampling to examine plant community responses to this sediment pulse across seven years during and after dam removal.
Results: Between 2011 and 2018, the Elwha River delta increased by ~26.8 ha. Vegetation colonized ~16.4 ha of new surfaces, with mixed pioneer vegetation on supratidal beach, river bars, and river mouth bars and emergent marsh vegetation in intertidal aquatic habitats. Colonization occurred on surfaces that were higher and more stable in elevation and farther from the shoreline. Compared to established delta plant communities, vegetation on new surfaces had lower cover of dominant species and functional groups, with very low woody cover, and lower graminoid cover than dunegrass and emergent marsh communities. Over time following surface stabilization, however, vegetation on new surfaces increased in species richness, cover, and similarity to established communities. By 2018, ~1.0 ha of vegetation on new surfaces had developed into dunegrass or willow–alder communities and ~5.9 ha had developed into emergent marsh. At the same time, dam removal had few discernible effects on established delta plant communities.
Discussion: Together, these results suggest that rapid sediment mobilization during large dam removal has potential to expand coastal wetland habitat without negatively affecting established plant communities. However, as sediment loads declined in 2016–2018, new delta surfaces decreased by ~4.5 ha, and ~1.6 ha of new vegetation reverted to no vegetation. Long-term persistence of the expanded coastal habitat will depend on ongoing erosional and depositional processes under the restored natural sediment regime.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1233903","usgsCitation":"Perry, L.G., Shafroth, P., Alfieri, S.J., and Miller, I.M., 2023, Coastal vegetation responses to large dam removal on the Elwha River: Frontiers in Ecology and Evolution, v. 11, 1233903, 21 p., https://doi.org/10.3389/fevo.2023.1233903.","productDescription":"1233903, 21 p.","ipdsId":"IP-153758","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":442000,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1233903","text":"Publisher Index Page"},{"id":435165,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O6NML1","text":"USGS data release","linkHelpText":"Vegetation and geomorphic surfaces in the Elwha River delta, Washington, after dam removal, derived from 2016 and 2018 aerial imagery and 2007, 2014, and 2018 field surveys"},{"id":422399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.8042115532547,\n 48.18296274689851\n ],\n [\n -123.8042115532547,\n 47.722552996994835\n ],\n [\n -123.34685932477454,\n 47.722552996994835\n ],\n [\n -123.34685932477454,\n 48.18296274689851\n ],\n [\n -123.8042115532547,\n 48.18296274689851\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Perry, Laura G.","contributorId":220048,"corporation":false,"usgs":false,"family":"Perry","given":"Laura","email":"","middleInitial":"G.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":887673,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":887674,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alfieri, Samuel J.","contributorId":329742,"corporation":false,"usgs":false,"family":"Alfieri","given":"Samuel","email":"","middleInitial":"J.","affiliations":[{"id":78705,"text":"self","active":true,"usgs":false}],"preferred":false,"id":887675,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Ian M. 0000-0002-3289-6337","orcid":"https://orcid.org/0000-0002-3289-6337","contributorId":41951,"corporation":false,"usgs":false,"family":"Miller","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":887676,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249968,"text":"70249968 - 2023 - Biocrusts indicators of livestock grazing effects on soil stability in sagebrush steppe: A case study from a long-term experiment in the northern Great Basin","interactions":[],"lastModifiedDate":"2024-07-17T21:34:54.964345","indexId":"70249968","displayToPublicDate":"2023-09-29T07:00:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6002,"text":"Rangeland Ecology & Management","active":true,"publicationSubtype":{"id":10}},"title":"Biocrusts indicators of livestock grazing effects on soil stability in sagebrush steppe: A case study from a long-term experiment in the northern Great Basin","docAbstract":"Biocrusts are sensitive to changes in livestock grazing intensity in arid rangelands and may be useful indicators of ecosystem functions, particularly soil properties like soil stability, which may suggest the potential for soil erosion. We compared biocrust community composition and surface soil stability in a big sagebrush (Artemisia tridentata) steppe rangeland in the northwestern Great Basin in several paired sites, with or without long-term cattle grazing exclusion, and similar soils (mostly sandy loams), climate, and vegetation composition. We found that livestock grazing was associated with both lower surface soil stability and cover of several biocrust morphogroups, especially lichens, compared with sites with long-term livestock exclusion. Surface soil stability did not modify the effects of grazing on most biocrust components via interactive effects. Livestock grazing effects on total biocrust cover were partially mediated by changes in surface soil stability. Though lichens were more sensitive to grazing disturbance, our results suggest that moss (mostly Tortula ruralis in this site) might be a more readily observable indicator of grazing-related soil stability change in this area due to their relatively higher abundance compared with lichens (moss: mean, 8.5% cover, maximum, 96.1%, lichens: mean, 1.0% cover, maximum, 14.1%). These results highlight the potential for biocrust components as sensitive indicators of change in soil-related ecosystem functions in sagebrush steppe rangelands. However, further research is needed to identify relevant indicator groups across the wide range of biocrust community composition associated with site environmental characteristics, variable grazing systems, other rangeland health metrics, and other disturbance types such as wildfire.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2023.09.001","usgsCitation":"Copeland, S., Condon, L.A., Rosentreter, R., Miller, J., and Kahn-Abrams, M., 2023, Biocrusts indicators of livestock grazing effects on soil stability in sagebrush steppe: A case study from a long-term experiment in the northern Great Basin: Rangeland Ecology & Management, v. 91, p. 82-86, https://doi.org/10.1016/j.rama.2023.09.001.","productDescription":"5 p.","startPage":"82","endPage":"86","ipdsId":"IP-147342","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":442004,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2023.09.001","text":"Publisher Index Page"},{"id":422476,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Great Basin","volume":"91","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Copeland, Stella M.","contributorId":196218,"corporation":false,"usgs":false,"family":"Copeland","given":"Stella M.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":887850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Condon, Lea A. 0000-0002-9357-3881","orcid":"https://orcid.org/0000-0002-9357-3881","contributorId":202908,"corporation":false,"usgs":true,"family":"Condon","given":"Lea","email":"","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":887851,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosentreter, Roger","contributorId":257441,"corporation":false,"usgs":false,"family":"Rosentreter","given":"Roger","affiliations":[{"id":52018,"text":"Biology Department, Boise State University, Boise, Idaho","active":true,"usgs":false}],"preferred":false,"id":887852,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Jesse","contributorId":147734,"corporation":false,"usgs":false,"family":"Miller","given":"Jesse","email":"","affiliations":[{"id":16916,"text":"Dept. of Zoology, University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":887853,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kahn-Abrams, Maya","contributorId":331492,"corporation":false,"usgs":false,"family":"Kahn-Abrams","given":"Maya","email":"","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":887854,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70251891,"text":"70251891 - 2023 - Benchmarking satellite-derived shoreline mapping algorithms","interactions":[],"lastModifiedDate":"2024-03-05T13:03:29.556435","indexId":"70251891","displayToPublicDate":"2023-09-29T06:59:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Benchmarking satellite-derived shoreline mapping algorithms","docAbstract":"Satellite remote sensing is becoming a widely used monitoring technique in coastal sciences. Yet, no benchmarking studies exist that compare the performance of popular satellite-derived shoreline mapping algorithms against standardized sets of inputs and validation data. Here we present a new benchmarking framework to evaluate the accuracy of shoreline change observations extracted from publicly available satellite imagery (Landsat and Sentinel-2). Accuracy and precision of five established shoreline mapping algorithms are evaluated at four sandy beaches with varying geologic and oceanographic conditions. Comparisons against long-term in situ beach surveys reveal that all algorithms provide horizontal accuracy on the order of 10 m at microtidal sites. However, accuracy deteriorates as the tidal range increases, to more than 20 m for a high-energy macrotidal beach (Truc Vert, France) with complex foreshore morphology. The goal of this open-source, collaborative benchmarking framework is to identify areas of improvement for present algorithms, while providing a stepping stone for testing future developments, and ensuring reproducibility of methods across various research groups and applications.
This report describes a sensitivity analysis of a water-budget model that was completed to identify the most important types of hydrologic information needed to reduce the uncertainty of model recharge estimates. The sensitivity of model recharge estimates for the Hawaiian Islands of Oʻahu and Maui was analyzed for seven model parameters potentially affected by land-cover changes within a watershed. The seven model parameters tested were canopy capacity, canopy-cover fraction, crop coefficient, fog-catch efficiency, root depth, stemflow, and trunk-storage capacity.
Results of the sensitivity analysis were used to (1) quantify the relative importance of the seven model parameters to recharge assessments for three moisture zones (dry, mesic, and wet) on Oʻahu and Maui and (2) prepare a list of critical information needs for each moisture zone. The list of critical information needs was developed for three general types of land cover (forest, shrubland, and grassland) that are assumed to be affected by watershed management in the Hawaiian Islands. Identified information needs included estimates or measurements of (1) evapotranspiration processes needed to determine crop coefficients for land-cover types in all moisture zones, (2) rooting depths for land-cover types in the dry and mesic moisture zones, (3) canopy-cover fraction for forests in the wet and mesic moisture zones, (4) ratios of fog interception to rainfall for forests and shrublands in the wet moisture zone, and (5) canopy capacity for forests in the wet and mesic moisture zones. The list of information needs can guide data-collection strategies of future projects. Collection and analysis of the identified hydrologic information may help model users develop a better parameterization scheme, reduce uncertainty of values that model users assign to land-cover dependent parameters, and therefore allow future applications of the water-budget model to more accurately quantify how recharge in the Hawaiian Islands might be affected by future land-cover changes within a watershed.
","language":"English","publisher":"U.S. Geological Center","publisherLocation":"Reston, VA","doi":"10.3133/sir20235022","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management","usgsCitation":"Johnson, A.G., Mair, A., and Oki, D.S., 2023, Identifying the relative importance of water-budget information needed to quantify how land-cover change affects recharge, Hawaiian Islands: U.S. Geological Survey Scientific Investigations Report 2023–5022, 28 p., https://doi.org/10.3133/sir20235022.","productDescription":"Report: vi, 28 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-129378","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":500874,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115438.htm","text":"Maui","linkFileType":{"id":5,"text":"html"}},{"id":500873,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115437.htm","text":"Oahu","linkFileType":{"id":5,"text":"html"}},{"id":421316,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X9ZEE3","text":"USGS Data Release","description":"Johnson, A.G., and Kāne, H.L., 2023, Model subareas and moisture zones used in a sensitivity analysis of a water-budget model completed in 2022 for the islands of Oahu and Maui, Hawaii: U.S. Geological Survey data release, https://doi.org/10.5066/P9X9ZEE3.","linkHelpText":"Model subareas and moisture zones used in a sensitivity analysis of a water-budget model completed in 2022 for the islands of Oahu and Maui, Hawaii"},{"id":421315,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5022/sir20235022.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":421314,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5022/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui, O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.75858532451014,\n 21.145379373074235\n ],\n [\n -156.75858532451014,\n 20.508739201099033\n ],\n [\n -155.89066540263516,\n 20.508739201099033\n ],\n [\n -155.89066540263516,\n 21.145379373074235\n ],\n [\n -156.75858532451014,\n 21.145379373074235\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -158.3516029026351,\n 21.83029675022702\n ],\n [\n -158.3516029026351,\n 21.17099365241016\n ],\n [\n -157.59354626201016,\n 21.17099365241016\n ],\n [\n -157.59354626201016,\n 21.83029675022702\n ],\n [\n -158.3516029026351,\n 21.83029675022702\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Most of the population of the Treasure Valley and the surrounding area of southwestern Idaho and easternmost Oregon depends on groundwater for domestic supply, either from domestic or municipal-supply wells. Current and projected rapid population growth in the area has caused concern about the long-term sustainability of the groundwater resource. In 2016, the U.S. Geological Survey, in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources, began a project to construct a numerical groundwater-flow model of the westernmost portion of the western Snake River Plain aquifer system, called the Treasure Valley.
The development of the model was guided by several objectives, including:
The model was constructed with a spatial scale and level of detail that aimed to meet these objectives while balancing the sometimes-competing goals of fast runtimes, numerical stability, usability, and parsimony.
The Treasure Valley Groundwater Flow Model (TVGWFM) is a three-dimensional finite-difference numerical model constructed using MODFLOW 6 (Langevin and others, 2017, Documentation for the MODFLOW 6 Groundwater Flow Model: U.S. Geological Survey Techniques and Methods, book 6, chap. A55, 197 p., https://doi.org/10.3133/tm6A55). The model covers the westernmost portion of the western Snake River Plain and is discretized into a regular grid of 64 by 65 cells with a side length of 1 mile and 6 layers of varying depth and active area. A historical model period was developed consisting of 360 month-long stress periods for 1986–2015. The model builds upon previous modeling efforts by adding a transient period, incorporating new head and discharge observations to constrain parameters, incorporating information from the hydrogeologic framework model (HFM) of Bartolino (2019, Hydrogeologic framework of the Treasure Valley and surrounding area, Idaho and Oregon: U.S. Geological Survey Scientific Investigations Report 2019–5138, https://doi.org/10.3133/sir20195138) and incorporating refined estimates of evapotranspiration and irrigation classification of lands in the study area.
The TVGWFM includes all significant components of recharge to and discharge from the aquifer. Inflows include canal seepage, irrigation and precipitation recharge, mountain-front recharge, rivers and stream seepage, and seepage from Lake Lowell. Outflows include discharge to agricultural drainage ditches, discharge to rivers and streams, pumping, and discharge to Lake Lowell. Each recharge or discharge component is represented separately using individual MODFLOW 6 packages.
Parameter values were derived with a combination of trial-and-error steps and automated parameter estimation using PEST software (Doherty, J.E., 2005, PEST, model-independent parameter estimation–User manual: Watermark Numerical Computing, https://pesthomepage.org/documentation). Parameter estimates were constrained with several types of observation data, including water levels, water level changes, vertical water level differences, drain discharges, change in drain discharges, river seepage, seepage from Lake Lowell, and change in seepage from Lake Lowell. Material properties from the hydrogeologic framework were also used to assign the minimum and maximum values of some parameters.
A final parameter realization was reached that minimized residuals between the observed and modelled values for the various observation groups. Mean residuals for the observation groups were 15.4 feet (ft) for water levels, 0.2 ft for water level changes, 19.4 ft for vertical water level differences, −3.9 cubic feet per second (ft3/s) for drain discharges, 0.0 ft3/s for changes in drain discharge, 45.0 ft3/s for river seepage, −40.1 ft3/s for Lake Lowell seepage, and 126.3 ft3/s for changes in Lake Lowell seepage. The quality of the model’s fit to observations varied spatially, with notable areas of under- or over-simulation of water levels present to the northwest and southwest of Lake Lowell, in the foothills along the eastern model boundary, and near the City of Eagle. Trends were observed in the residuals of many of the observation groups, indicating that the model is missing or not fully reproducing some phenomena that are observed in the system.
The TVGWFM can be used as a tool for water resource planning, for understanding the interactions of groundwater and surface water at a basin scale, and for facilitating conjunctive management, but may lack the precision needed for water rights administration at a local scale. Additional sources of uncertainty or limitations of the model are noted. The quantity and spatial distribution of canal seepage and infiltration of irrigation water recharge, the largest sources of recharge to the system, are unknown and approximated indirectly. There is poor understanding of how canal seepage and incidental recharge change as land is converted from agricultural (irrigated) to suburban (semi-irrigated). These uncertainties will affect any scenarios that investigate changes to land use or irrigation practices. Finally, the model has relatively high water-level residuals around and to the southwest of Lake Lowell and should not be used to estimate water level effects in that region.
The model was built with multiple, broadly expressed objectives and did not optimize performance for specific uses. However, the model and the tools included in an associated data release provide ample flexibility to improve the model for future uses. Adjustments and improvements could be made by refining the model in an area of interest, collecting additional calibration data, applying more rigorous boundary conditions, or re-estimating model parameters to optimize model performance for a specific model forecast.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235096","collaboration":"Prepared in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources","usgsCitation":"Hundt, S.A., and Bartolino, J.R., 2023, Groundwater-flow model of the Treasure Valley, southwestern Idaho, 1986–2015: U.S. Geological Survey Scientific Investigations Report 2023–5096, 107 p., https://doi.org/10.3133/sir20235096.","productDescription":"Report: xii, 107 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-127901","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":501062,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115439.htm","linkFileType":{"id":5,"text":"html"}},{"id":421318,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5096/sir20235096.pdf","text":"Report","size":"30.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5096"},{"id":421321,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5096/images"},{"id":421317,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5096/coverthb.jpg"},{"id":421320,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U6OOPH","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and archive for a groundwater flow model of the Treasure Valley aquifer system, southwestern Idaho"},{"id":421322,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5096/sir20235096.XML"}],"country":"United States","state":"Idaho","otherGeospatial":"Treasure Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.26392993194762,\n 44.27650517719664\n ],\n [\n -117.26392993194762,\n 42.71456173603502\n ],\n [\n -115.50611743194747,\n 42.71456173603502\n ],\n [\n -115.50611743194747,\n 44.27650517719664\n ],\n [\n -117.26392993194762,\n 44.27650517719664\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The Sparta-Memphis aquifer, present across much of eastern Arkansas, is the second most used groundwater resource in the State, with the Mississippi River Valley alluvial aquifer being the primary groundwater resource. The U.S. Geological Survey, in cooperation with Arkansas Department of Agriculture-Natural Resources Division, Arkansas Geological Survey, Natural Resources Conservation Service, Union County Water Conservation Board, and the Union County Conservation District, collects groundwater data across the Sparta-Memphis aquifer extent in Arkansas. This report presents water-level data for measurements conducted during two time periods, January–May 2013 and January–June 2015, and discusses water-level altitude changes for the 2011–13 and 2013–15 periods in the Sparta-Memphis aquifer. Accompanying water-level data in this report include groundwater-quality data for the period 2010–15 in the Sparta-Memphis aquifer. Groundwater data can guide ongoing and future groundwater-monitoring efforts and inform management of the aquifers in Arkansas.
Water levels measured at 306 wells from January to May 2013 and 273 wells from January to June 2015 are graphically presented as potentiometric-surface maps. Measurements from 2011, 2013, and 2015 were used in the construction of 2011–13 and 2013–15 water-level change maps. Select long-term hydrographs are included in the report to illustrate water-level changes at the local scale.
Water-level data show the influence of climate, pumping, and conservation and management efforts on groundwater levels. With respect to climate, the study area experienced extreme drought conditions between January 2011 and December 2012. The proximate effects of drought—increased evapotranspiration, decreased recharge, and increased irrigation needs—resulted in water-level declines that were particularly notable in the northern and central portions of the study area.
Groundwater sampled in 2010–15 from 148 wells completed in the Sparta-Memphis aquifer was analyzed for specific conductance, pH, chloride (Cl) concentration, and bromide (Br) concentration. In 2015, groundwater-quality data from 103 wells completed in the Sparta-Memphis aquifer had a median specific conductance of 356 microsiemens per centimeter at 25 degrees Celsius and a median Cl concentration of 9.5 milligrams per liter (mg/L). The data show two areas of higher Cl (greater than 10 mg/L) and higher Br (greater than 0.5 mg/L) concentrations in Union, Calhoun, and Bradley Counties in southern Arkansas and Monroe and Phillips Counties in eastern-central Arkansas. A Cl and Br mixing model indicates the two regions of wells may have different sources of higher salinity. In the greater Union County area, water in most wells may be a mixture of recharge or precipitation and higher salinity groundwater from the Nacatoch aquifer. Water in wells in eastern-central Arkansas may be sourced from aquifers having a higher Cl concentration (and thus, also a higher Cl-to-Br ratio).
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Removal of invasive Silver Carp Hypophthalmichthys molitrix is a primary control action in North America. Strong avoidance responses to underwater sound and electricity have been shown to facilitate herding and mass removal of these fish. We conducted a telemetry study on a closed population of Silver Carp (i.e., 10 telemetered fish) to assess fine-scale movement responses to herding stimuli. Two herding boats traveled along bank-to-bank transects through the study area (longitudinal progression rate = 0.37 m/s) emitting sound and electricity (“combination technique”) or no added stimuli (“control”). The combination technique was most effective in terms of increasing fish presence (2.2 x the control) in the refuge-zones when herding had concluded and effective range (i.e., fish reaction distance; 1.6 x the control) relative to the herding boats. Fish median (~1 m/s) and maximum (~2 m/s) swimming velocity was relatively stable across fixed effects, except for the negative influence of water depth on maximum velocity. Water depth also exhibited a negative effect on fish reaction distance. Our results suggest effective range of the combination technique was conservatively 200 m (~20 dB re 1 μPa > ambient level) when accounting for water depth in the study area. Herding deployments less than 1 m/s (longitudinal progression) could control fish passing and maintain fish movements towards an intended location. Information provided herein can serve to assist planning, design, and application of herding efforts used to manage, control, and remove these invasive fish.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10955","usgsCitation":"Ridgway, J.L., Acre, M.R., Hessler, T.M., Broaddus, D., Morris, J., and Calfee, R.D., 2023, Silver carp herding: A telemetry evaluation of efficacy and implications for design and application: North American Journal of Fisheries Management, v. 43, no. 6, p. 1750-1764, https://doi.org/10.1002/nafm.10955.","productDescription":"15 p.","startPage":"1750","endPage":"1764","ipdsId":"IP-144362","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":442012,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10955","text":"Publisher Index Page"},{"id":435166,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VQECFP","text":"USGS data release","linkHelpText":"Telemetry evaluation of invasive carp herding in Jonathan Creek Embayment, Kentucky Lake, Kentucky"},{"id":422098,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Jonathan Creek embayment, Kentucky Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.19285937344272,\n 36.82937300194537\n ],\n [\n -88.22829442891907,\n 36.81640599482829\n ],\n [\n -88.24424020388349,\n 36.764718425521735\n ],\n [\n -88.21310797657148,\n 36.76046024751608\n ],\n [\n -88.19285937344272,\n 36.82937300194537\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ridgway, Josey Lee 0000-0003-4157-7255","orcid":"https://orcid.org/0000-0003-4157-7255","contributorId":238277,"corporation":false,"usgs":true,"family":"Ridgway","given":"Josey","email":"","middleInitial":"Lee","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":886803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acre, Matthew Ross 0000-0002-5417-9523","orcid":"https://orcid.org/0000-0002-5417-9523","contributorId":268034,"corporation":false,"usgs":true,"family":"Acre","given":"Matthew","email":"","middleInitial":"Ross","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":886804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hessler, Tyler Michael 0000-0001-5062-2340","orcid":"https://orcid.org/0000-0001-5062-2340","contributorId":272075,"corporation":false,"usgs":true,"family":"Hessler","given":"Tyler","email":"","middleInitial":"Michael","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":886805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Broaddus, Dustin 0000-0002-3160-0477","orcid":"https://orcid.org/0000-0002-3160-0477","contributorId":331134,"corporation":false,"usgs":true,"family":"Broaddus","given":"Dustin","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":886806,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morris, Jessica","contributorId":331135,"corporation":false,"usgs":false,"family":"Morris","given":"Jessica","email":"","affiliations":[{"id":53972,"text":"Kentucky Department of Fish and Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":886807,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Calfee, Robin D. 0000-0001-6056-7023 rcalfee@usgs.gov","orcid":"https://orcid.org/0000-0001-6056-7023","contributorId":1841,"corporation":false,"usgs":true,"family":"Calfee","given":"Robin","email":"rcalfee@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":886808,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249582,"text":"70249582 - 2023 - Move it or lose it: Predicted effects of culverts and population density on Mojave desert tortoise (Gopherus agassizii) connectivity","interactions":[],"lastModifiedDate":"2023-10-19T13:20:32.889322","indexId":"70249582","displayToPublicDate":"2023-09-28T06:44:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Move it or lose it: Predicted effects of culverts and population density on Mojave desert tortoise (Gopherus agassizii) connectivity","title":"Move it or lose it: Predicted effects of culverts and population density on Mojave desert tortoise (Gopherus agassizii) connectivity","docAbstract":"Roadways and railways can reduce wildlife movements across landscapes, negatively impacting population connectivity. Connectivity may be improved by structures that allow safe passage across linear barriers, but connectivity could be adversely influenced by low population densities. The Mojave desert tortoise is threatened by habitat loss, fragmentation, and population declines. The tortoise continues to decline as disturbance increases across the Mojave Desert in the southwestern United States. While underground crossing structures, like hydrological culverts, have begun receiving attention, population density has not been considered in tortoise connectivity. Our work asks a novel question: How do culverts and population density affect connectivity and potentially drive genetic and demographic patterns? To explore the role of culverts and population density, we used agent-based spatially explicit forward-in-time simulations of gene flow. We constructed resistance surfaces with a range of barriers to movement and representative of tortoise habitat with anthropogenic disturbance. We predicted connectivity under variable population densities. Simulations were run for 200 non-overlapping generations (3400 years) with 30 replicates using 20 microsatellite loci. We evaluated population genetic structure and diversity and found that culverts would not entirely negate the effects of linear barriers, but gene flow improved. Our results also indicated that density is important for connectivity. Low densities resulted in declines regardless of the landscape barrier scenario (> 75% population census size, > 97% effective population size). Results from our simulation using current anthropogenic disturbance predicted decreased population connectivity over time. Genetic and demographic effects were detectable within five generations (85 years) following disturbance with estimated losses in effective population size of 69%. The pronounced declines in effective population size indicate this could be a useful monitoring metric. We suggest management strategies that improve connectivity, such as roadside fencing tied to culverts, conservation areas in a connected network, and development restricted to disturbed areas.
Causes of population collapse and failed recovery often remain enigmatic in marine forage fish like Pacific herring (Clupea pallasii) that experience dramatic population oscillations. Diseases such as ichthyophoniasis are hypothesized to contribute to these declines, but lack of long-term datasets frequently prevents inference. Analysis of pathogen surveillance and population assessment datasets spanning 2007–2019 indicate that the age-based prevalence estimate of Ichthyophonus infection was, on average, 54% greater among a collapsed population of Pacific herring (Prince William Sound, Alaska, USA) as compared to a nearby population (Sitka Sound, Alaska, USA) that is relatively robust. During the study years, the age-based infection prevalence ranged from 14 to 44% in Prince William Sound and 5 to 33% in Sitka Sound. At both sites, the age-based infection prevalence declined over time, with an average decrease of 7% per year. Statistical analyses indicated that infection prevalence between the two populations was reduced by regional factors affecting both sites, and that these factors were independent of herring density. Infection prevalence in both populations was positively correlated with herring age and negatively correlated with the Pacific Decadal Oscillation. This study demonstrates how synthesis of environmental, stock assessment, and disease assessment data can be leveraged to elucidate epidemiological trends in diseases of wild fish.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/icesjms/fsad147","usgsCitation":"Groner, M., Bravo-Mendosa, E.D., MacKenzie, A., Gregg, J.L., Conway, C.M., Trochta, J.T., and Hershberger, P., 2023, Temporal, environmental, and demographic correlates of Ichthyophonus sp. infections in mature Pacific herring populations: ICES Journal of Marine Science, fsad147, 14 p., https://doi.org/10.1093/icesjms/fsad147.","productDescription":"fsad147, 14 p.","ipdsId":"IP-131654","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":442017,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icesjms/fsad147","text":"Publisher Index Page"},{"id":421382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2023-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Groner, Maya L. 0000-0002-3381-6415","orcid":"https://orcid.org/0000-0002-3381-6415","contributorId":292708,"corporation":false,"usgs":false,"family":"Groner","given":"Maya","middleInitial":"L.","affiliations":[{"id":62985,"text":"Senior Research Scientist, Bigelow Laboratory for Ocean Sciences, 60 Bigelow Drive, East Boothbay, ME 04544","active":true,"usgs":false}],"preferred":false,"id":884529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bravo-Mendosa, Eliana D.","contributorId":330269,"corporation":false,"usgs":false,"family":"Bravo-Mendosa","given":"Eliana","email":"","middleInitial":"D.","affiliations":[{"id":78857,"text":"Previously a volunteer for the USGS Western Fisheries Research Center","active":true,"usgs":false}],"preferred":false,"id":884530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"MacKenzie, Ashley 0000-0002-7402-7877 amackenzie@usgs.gov","orcid":"https://orcid.org/0000-0002-7402-7877","contributorId":150817,"corporation":false,"usgs":true,"family":"MacKenzie","given":"Ashley","email":"amackenzie@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":884531,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gregg, Jacob L. 0000-0001-5328-5482 jgregg@usgs.gov","orcid":"https://orcid.org/0000-0001-5328-5482","contributorId":203912,"corporation":false,"usgs":true,"family":"Gregg","given":"Jacob","email":"jgregg@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":884532,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conway, Carla M. 0000-0002-3851-3616 cmconway@usgs.gov","orcid":"https://orcid.org/0000-0002-3851-3616","contributorId":2946,"corporation":false,"usgs":true,"family":"Conway","given":"Carla","email":"cmconway@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":884533,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Trochta, John T.","contributorId":279655,"corporation":false,"usgs":false,"family":"Trochta","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":57329,"text":"School of Aquatic and Fishery Sciences, Box 355020, University of Washington, Seattle WA, 98195, USA","active":true,"usgs":false}],"preferred":false,"id":884534,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hershberger, Paul 0000-0002-2261-7760","orcid":"https://orcid.org/0000-0002-2261-7760","contributorId":203322,"corporation":false,"usgs":true,"family":"Hershberger","given":"Paul","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":884535,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70248933,"text":"ofr20231069 - 2023 - Assessing the value and usage of data management planning and data management plans within the U.S. Geological Survey","interactions":[],"lastModifiedDate":"2023-10-26T20:09:51.762052","indexId":"ofr20231069","displayToPublicDate":"2023-09-27T14:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1069","displayTitle":"Assessing the Value and Usage of Data Management Planning and Data Management Plans Within the U.S. Geological Survey","title":"Assessing the value and usage of data management planning and data management plans within the U.S. Geological Survey","docAbstract":"As of 2016, the U.S. Geological Survey (USGS) Fundamental Science Practices require data management plans (DMPs) for all USGS and USGS-funded research. The USGS Science Data Management Branch of the Science Analytics and Synthesis Program has been working to help the USGS (Bureau) meet this requirement. However, USGS researchers still encounter common data management-related challenges that may be reduced or eliminated by better planning. In 2021, USGS staff were given a series of surveys aimed to better understand current data management planning practices, perceptions, and needs. The survey results indicated that adoption and integration of data management planning and DMPs into USGS research project workflows are broad, if inconsistent, across USGS Science Centers and programs. The USGS Science Data Management Branch can help improve clarity and guidance on the purpose, intended audience, content, workflows, and evaluation processes for DMPs. It would also be beneficial to provide additional supporting cyberinfrastructure to support DMP activities. Survey responses indicated it would be beneficial for the Science Data Management Branch to develop a strategy, other than through DMPs, for teaching and encouraging good data management practices. Although these surveys were an opportunity for USGS staff to provide feedback on their experiences, the surveys may also have revealed the desire for more frequent evaluations, cross-disciplinary communication, and training on research data management and DMP development and integration, in the context of USGS policy, Fundamental Science Practices requirements, and overall Bureau expectations. Data management-related roles such as data manager or steward, information technologist, and repository manager may need to be formally recognized as skilled professional career positions within the Bureau. At a minimum, the best practice for USGS would be to create and maintain DMPs as living documents, integrated with existing systems that are broadly accessible to all stakeholders, and include quantitatively measurable benefits tied directly to a clearly defined purpose.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231069","programNote":"Science Synthesis, Analysis, and Research Program","usgsCitation":"Langseth, M.L., Sellers, E.A., Donovan, G.C., and Liford, A.N., 2023, Assessing the value and usage of data management planning and data management plans within the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2023–1069, 44 p., https://doi.org/10.3133/ofr20231069.","productDescription":"Report: vi, 44 p.; 6 Appendixes; Data Release","onlineOnly":"Y","ipdsId":"IP-139788","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":421261,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91WKCA3","text":"USGS data release","description":"Data release associated with OFR 2023-1069","linkHelpText":"U.S. Geological Survey 2021 Data Management Planning Survey Results and Analyses"},{"id":422160,"rank":12,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231069/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1069"},{"id":421342,"rank":11,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1069/ofr20231069.xml"},{"id":421254,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2023/1069/ofr20231069_appendix3.pdf","text":"Appendix 3","size":"180 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2023-1069 Appendix 3","linkHelpText":"- Data Management Planning Questionnaire for Center Directors"},{"id":421255,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2023/1069/ofr20231069_appendix4.pdf","text":"Appendix 4","size":"168kB","description":"OFR 2023-1069 Appendix 4","linkHelpText":"- Data Management Planning Questionnaire for Program Coordinators and Bureau Approving Officials Appendix"},{"id":421257,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2023/1069/ofr20231069_appendix6.pdf","text":"Appendix 6","size":"88 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1069 Appendix 6","linkHelpText":"- Interview Questions for Data Managers"},{"id":421219,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2023/1069/ofr20231069_appendix1.pdf","text":"Appendix 1","size":"184 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1069 Appendix 1","linkHelpText":"- Data Management Planning Questionnaire for Researchers"},{"id":421253,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2023/1069/ofr20231069_appendix2.pdf","text":"Appendix 2","size":"184 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1069 Appendix 2","linkHelpText":"- Data Management Planning Questionnaire for Data Managers and Information Technologists"},{"id":421341,"rank":10,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1069/images"},{"id":421256,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2023/1069/ofr20231069_appendix5.pdf","text":"Appendix 5","size":"108 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1069 Appendix 5","linkHelpText":"- Interview Questions for Researchers"},{"id":421217,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1069/coverthb.jpg"},{"id":421218,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1069/ofr20231069.pdf","text":"Report","size":"1.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1069"}],"contact":"Director, Science Analytics and Synthesis Program
U.S. Geological Survey
P.O. Box 25046, Mail Stop 302
Denver, CO 80225
The U.S. Geological Survey and the Idaho Department of Water Resources measured groundwater levels during spring 2022 and autumn 2022 to create detailed potentiometric-surface maps for the alluvial aquifer in the Big Lost River Valley in south-central Idaho. Wells were assigned to shallow, intermediate, and deep water-bearing units based on well depth, groundwater potentiometric-surface altitude, and hydrogeologic unit. Potentiometric-surface contours were created for each of the three water-bearing units for spring 2022 and autumn 2022. Groundwater flow generally follows topography down valley to the south. The groundwater-level data also were used to calculate changes in groundwater levels from spring to autumn 2022 and from historical measurement events in 1968 and 1991 to 2022. Groundwater levels declined at most wells from spring 1968 to spring 2022 and from spring 1991 to spring 2022. Although groundwater-level changes are sensitive to interannual wet and dry periods, long-term groundwater-level declines suggest that recharge and down-valley groundwater flows are insufficient to fully recover groundwater-level declines from pumping in some parts of the alluvial aquifer in the Big Lost River Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3509","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Ducar, S.D., and Zinsser, L.M., 2023, Groundwater potentiometric-surface altitude in 2022 and groundwater-level changes between 1968, 1991, and 2022, in the alluvial aquifer in the Big Lost River Valley, south-central Idaho: U.S. Geological Survey Scientific Investigations Map 3509, 1 sheet, scale 1:150,000, 11-p. pamphlet, https://doi.org/10.3133/sim3509.","productDescription":"Pamphlet: viii, 11 p.; Map: 22.51 × 30.00 inches","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-140355","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":500438,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115436.htm","linkFileType":{"id":5,"text":"html"}},{"id":421346,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93NQAP9","text":"USGS data release","description":"USGS data release","linkHelpText":"Groundwater potentiometric-surface contours and well numbers used to map groundwater potentiometric-surface altitude in 2022 and groundwater-level changes between 1968, 1991, and 2022 in the alluvial aquifer in the Big Lost River Valley, south-central Idaho"},{"id":421275,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3509/sim3509_pamphlet.XML"},{"id":421274,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3509/images"},{"id":421270,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3509/coverthb.jpg"},{"id":421271,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3509/sim3509.pdf","text":"Sheet","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3509"},{"id":421272,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sim/3509/sim3509_pamphlet.pdf","text":"Pamphlet","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3509 Pamphlet"},{"id":421273,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3509/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"},"description":"SIM 3509 Pamphlet"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Lost River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.0,\n 44.15\n ],\n [\n -114,\n 43.30\n ],\n [\n -113.15,\n 43.3\n ],\n [\n -113.15,\n 44.15\n ],\n [\n -114,\n 44.15\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
Compound earthquakes involving simultaneous ruptures along multiple faults often define a region’s upper threshold of maximum magnitude. Yet, the potential for linked faulting remains poorly understood given the infrequency of these events in the historic era. Geological records provide longer perspectives, although temporal uncertainties are too broad to clearly pinpoint single multifault events. Here, we use dendrochronological dating and a cosmogenic radiation pulse to constrain the death dates of earthquake-killed trees along two adjacent fault zones near Seattle, Washington to within a 6-month period between the 923 and 924 CE growing seasons. Our narrow constraints conclusively show linked rupturing that occurred either as a single composite earthquake of estimated magnitude 7.8 or as a closely spaced double earthquake sequence with estimated magnitudes of 7.5 and 7.3. These scenarios, which are not recognized in current hazard models, increase the maximum earthquake size needed for seismic preparedness and engineering design within the Puget Sound region of >4 million residents.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.adh4973","usgsCitation":"Black, B., Pearl, J., Pearson, C., Pringle, P., Frank, D., Page, M.T., Buckley, B., Cook, E.R., Harley, G.L., King, K., Hughes, J.F., Reynolds, D.J., and Sherrod, B.L., 2023, A multifault earthquake threat for the Seattle metropolitan region revealed by mass tree mortality: Science Advances, v. 9, no. 39, eadh4973, 9 p., https://doi.org/10.1126/sciadv.adh4973.","productDescription":"eadh4973, 9 p.","ipdsId":"IP-143345","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adh4973","text":"Publisher Index Page"},{"id":482643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","city":"Seattle","geographicExtents":"{\n \"type\": 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F.","contributorId":184055,"corporation":false,"usgs":false,"family":"Hughes","given":"Jonathan","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":929124,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Reynolds, David J.","contributorId":279711,"corporation":false,"usgs":false,"family":"Reynolds","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":57351,"text":"Centre for Geography and Environmental Sciences, University of Exeter, Penryn, Cornwall, TR10 9EZ, UK","active":true,"usgs":false}],"preferred":false,"id":929125,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sherrod, Brian L. 0000-0002-4492-8631 bsherrod@usgs.gov","orcid":"https://orcid.org/0000-0002-4492-8631","contributorId":2834,"corporation":false,"usgs":true,"family":"Sherrod","given":"Brian","email":"bsherrod@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929126,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70250569,"text":"70250569 - 2023 - Thirteen years of turtle capture–mark–recapture in a small urban pond complex in Louisiana, USA","interactions":[],"lastModifiedDate":"2024-09-13T15:54:02.773662","indexId":"70250569","displayToPublicDate":"2023-09-27T06:36:12","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Thirteen years of turtle capture–mark–recapture in a small urban pond complex in Louisiana, USA","docAbstract":"Turtles are one of the most imperiled vertebrate groups in the world. With habitat destruction unabated in many places, urban and suburban greenspaces may serve as refugia for turtles, at least those species able to tolerate heavily altered landscapes. In south-central Louisiana, we have conducted a turtle capture–mark–recapture effort in two ponds in an urban greenspace for 13 yr to understand species composition, survival, and individual growth rates. We had 574 total captures of 251 individuals of five species from 2009–2021, with Trachemys scripta elegans (Red-Eared Sliders) and Sternotherus odoratus (Eastern Musk Turtles) being the most common. Apparent annual survival for T. scripta (0.79) was similar to estimates reported in other studies in altered habitats, whereas apparent annual survival for S. odoratus (0.89) was slightly or much higher than other published studies. Growth rates of T. scripta were comparable to other studies and showed both sexes have similar rates of growth until maturity, which is earlier and at a smaller size in males. The two ponds showed marked differences in captures by size, with significantly more juvenile T. scripta captured in the pond with more vegetation, depth, and a softer bottom. Most T. scripta (78.5%) that were recaptured came from the same pond from which they were originally captured. The basic demographic data gained in this study can serve as a starting point for broader questions on urbanization effects and as a comparison to more natural populations.
In 2021, the U.S. Geological Survey (USGS), in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, studied floods to produce a library of flood-inundation maps for the Sabinal River near Utopia, Texas. Digital flood-inundation maps were created for a 10-mile reach of the Sabinal River from USGS streamgage 08197936 Sabinal River below Mill Creek near Vanderpool, Tex., at the upstream boundary of the study reach, to USGS streamgage 08197970 Sabinal River at Utopia, Tex. (hereinafter referred to as the “Utopia gage”), at the downstream boundary of the study reach, and for a 7-mile reach of the West Sabinal River. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to selected gage heights (the water-surface elevation at a streamgage, commonly referred to as “stage”) at the Utopia gage. Water-surface elevations were computed for the stream reach by means of a two-dimensional unsteady-state diffusion wave model with the U.S. Army Corps of Engineers Hydrologic Engineering Center River Analysis System program. A synthetic stage-discharge rating curve at the Utopia gage was developed using a regional regression equation to construct the model boundary condition inputs, and the upper bound of the stage-discharge relation was matched to a major flood event in July 2002. The hydraulic model was used to compute water-surface elevations for 35 stages at 0.5-foot (ft) increments referenced to the Utopia gage datum and ranging from 11 ft (near bankfull) to 28 ft (estimated peak stage during the July 2002 flood event). These flood-inundation maps, in conjunction with the real-time stage data from the Utopia gage, are intended to help guide the public in taking individual safety precautions and provide emergency management personnel with a tool to efficiently manage emergency flood operations and postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235001","issn":"2328-0328 (online)","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., 2023, Flood-inundation maps created using a synthetic rating curve for a 10-mile reach of the Sabinal River and a 7-mile reach of the West Sabinal River near Utopia, Texas, 2021 (ver. 2.0, September 2023): U.S. Geological Survey Scientific Investigations Report 2023–5001, 18 p., https://doi.org/10.3133/sir20235001.","productDescription":"Report: viii, 18 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-136311","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":435168,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CIK9ZF","text":"USGS data release","linkHelpText":"Geospatial and model dataset for flood-Inundation maps in a 10-mile reach of the Sabinal River and a 7-mile reach of the West Sabinal River near Utopia, Texas, 2021"},{"id":421129,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5001/coverthb.jpg"},{"id":500486,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114428.htm","linkFileType":{"id":5,"text":"html"}},{"id":421198,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20233001","text":"Fact Sheet 2023–3001","description":"USGS Fact Sheet 2023–3001","linkHelpText":"- Flood Warning Toolset for the Sabinal River Near Utopia, Texas"},{"id":421197,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2023/5001/versionHist.txt","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2023-5001 version history"},{"id":421196,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5001/sir20235001.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5001 XML"},{"id":421193,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5001/images"},{"id":421599,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235001/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5001 HTML"},{"id":421194,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5001/sir20235001.pdf","text":"Report","size":"2.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5001"}],"country":"United States","state":"Texas","city":"Utopia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.38878556700459,\n 29.515266260991964\n ],\n [\n -99.38878556700459,\n 29.797981198043047\n ],\n [\n -99.67156342604174,\n 29.797981198043047\n ],\n [\n -99.67156342604174,\n 29.515266260991964\n ],\n [\n -99.38878556700459,\n 29.515266260991964\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: February 2023; Version 2.0: September 2023","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 2 (April–June) of 2023. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was a Landsat 8 Thermal Infrared Sensor (TIRS) Scene Select Mechanism (SSM) excursion anomaly. On April 21, 2023, a TIRS SSM excursion error flag was indicated in telemetry during a calibration activity when the SSM encoder was powered on and the mirror was between the nadir position and the deep space position. An initial recovery plan indicated the SSM was moving erratically, so the instrument was put into a safe state for additional troubleshooting. A second recovery plan was developed and successfully executed on April 23, 2023. Additional information about the Landsat 8 TIRS SSM excursion anomaly is available at https://www.usgs.gov/landsat-missions/news/landsat-8-level-1-product-processing-resumes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231075","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Kaita, E., Barsi, J., Levy, R., Miller, J., and Ding, L., 2023, ECCOE Landsat quarterly Calibration and Validation report—Quarter 2, 2023: U.S. Geological Survey Open-File Report 2023–1075, 39 p., https://doi.org/10.3133/ofr20231075.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154779","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":421208,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":421207,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1075/images/"},{"id":421206,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1075/ofr20231075.XML","linkFileType":{"id":8,"text":"xml"}},{"id":421209,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231075/full","linkFileType":{"id":5,"text":"html"}},{"id":421204,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1075/coverthb.jpg"},{"id":421205,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1075/ofr20231075.pdf","text":"Report","size":"126 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1075"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Cooperative Science and Monitoring Initiative (CSMI) Lake Huron 2017 final report","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Cooperative Science and Monitoring Initiative (CSMI)","usgsCitation":"Esselman, P., Hondorp, D.W., Roseman, E., Nevers, M., Wills, T., and Riley, S., 2023, Development of an integrated survey design to assess invasive round goby abundance and distribution across gradients in substrate and depth, in Cooperative Science and Monitoring Initiative (CSMI) Lake Huron 2017 final report, p. 245-263.","productDescription":"19 p.","startPage":"245","endPage":"263","ipdsId":"IP-119012","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":425802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425801,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://greatlakescsmi.org/publications/23718/","linkFileType":{"id":5,"text":"html"}}],"otherGeospatial":"Lake Huron, Thunder Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.26748319036578,\n 45.023341671651394\n ],\n [\n -83.37733171507917,\n 45.07966771596739\n ],\n [\n -83.44109098841496,\n 45.060880623088025\n ],\n [\n -83.4587983735888,\n 44.981984039965994\n ],\n [\n -83.42692382572686,\n 44.911772535907375\n ],\n [\n -83.26039618877164,\n 44.810053772517335\n ],\n [\n -82.78202358114997,\n 44.55558835438245\n ],\n [\n -82.57118528371684,\n 44.73081073254255\n ],\n [\n -83.26748319036578,\n 45.023341671651394\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wills, Todd","contributorId":237770,"corporation":false,"usgs":false,"family":"Wills","given":"Todd","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":807578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riley, Stephen C.","contributorId":246057,"corporation":false,"usgs":false,"family":"Riley","given":"Stephen C.","affiliations":[{"id":49411,"text":"USGS Great Lakes Science Center (Emeritus)","active":true,"usgs":false}],"preferred":false,"id":807579,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248950,"text":"70248950 - 2023 - A guide to creating an effective big data management framework","interactions":[],"lastModifiedDate":"2023-09-27T16:18:26.710593","indexId":"70248950","displayToPublicDate":"2023-09-26T11:15:46","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16876,"text":"Journal of Big Data","active":true,"publicationSubtype":{"id":10}},"title":"A guide to creating an effective big data management framework","docAbstract":"Many agencies and organizations, such as the U.S. Geological Survey, handle massive geospatial datasets and their auxiliary data and are thus faced with challenges in storing data and ingesting it, transferring it between internal programs, and egressing it to external entities. As a result, these agencies and organizations may inadvertently devote unnecessary time and money to convey data without existing or outdated standards. This research aims to evaluate the components of data conveyance systems, such as transfer methods, tracking, and automation, to guide their improved performance. Specifically, organizations face the challenges of slow dispatch time and manual intervention when conveying data into, within, and from their systems. Conveyance often requires skilled workers when the system depends on physical media such as hard drives, particularly when terabyte transfers are required. In addition, incomplete or inconsistent metadata may necessitate manual intervention, process changes, or both. A proposed solution is organization-wide guidance for efficient data conveyance. That guidance involves systems analysis to outline a data management framework, which may include understanding the minimum requirements of data manifests, specification of transport mechanisms, and improving automation capabilities.
","language":"English","publisher":"Springer","doi":"10.1186/s40537-023-00801-9","usgsCitation":"Arundel, S., McKeehan, K.G., Campbell, B.B., Bulen, A.N., and Thiem, P.T., 2023, A guide to creating an effective big data management framework: Journal of Big Data, v. 10, 146, 22 p., https://doi.org/10.1186/s40537-023-00801-9.","productDescription":"146, 22 p.","ipdsId":"IP-142351","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":442022,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40537-023-00801-9","text":"Publisher Index Page"},{"id":435170,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92KSPMB","text":"USGS data release","linkHelpText":"ADOM: A Data Orchestration Manager"},{"id":421266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2023-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":884325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKeehan, Kevin G 0000-0002-7242-6954","orcid":"https://orcid.org/0000-0002-7242-6954","contributorId":330206,"corporation":false,"usgs":true,"family":"McKeehan","given":"Kevin","email":"","middleInitial":"G","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":884326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, Bryan B 0000-0002-5425-6736","orcid":"https://orcid.org/0000-0002-5425-6736","contributorId":330207,"corporation":false,"usgs":true,"family":"Campbell","given":"Bryan","email":"","middleInitial":"B","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":884327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bulen, Andrew N. 0000-0002-2155-8412","orcid":"https://orcid.org/0000-0002-2155-8412","contributorId":330208,"corporation":false,"usgs":true,"family":"Bulen","given":"Andrew","email":"","middleInitial":"N.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":884328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thiem, Philip T. 0000-0002-3324-2589","orcid":"https://orcid.org/0000-0002-3324-2589","contributorId":287990,"corporation":false,"usgs":true,"family":"Thiem","given":"Philip","email":"","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":884329,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248930,"text":"sir20235102 - 2023 - Long-term water-quality constituent trends in the Little Arkansas River, south-central Kansas, 1995–2021","interactions":[],"lastModifiedDate":"2026-03-16T13:45:27.510092","indexId":"sir20235102","displayToPublicDate":"2023-09-26T10:49:03","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5102","displayTitle":"Long-Term Water-Quality Constituent Trends in the Little Arkansas River, South-Central Kansas, 1995–2021","title":"Long-term water-quality constituent trends in the Little Arkansas River, south-central Kansas, 1995–2021","docAbstract":"The Equus Beds aquifer and Cheney Reservoir are primary sources for the city of Wichita’s current (2023) water supply. The Equus Beds aquifer storage and recovery (ASR) project was developed by the city of Wichita in the early 1990s to meet future water demands using the Little Arkansas River as an artificial aquifer recharge water source during above-base-flow conditions. Little Arkansas River water is removed from the river at an ASR Facility intake structure, treated using National Primary Drinking Water Regulations as a guideline, and is infiltrated into the Equus Beds aquifer through recharge basins or injected into the aquifer through recharge wells for later use. The U.S. Geological Survey, in cooperation with the city of Wichita, completed this study to quantify and characterize Little Arkansas River water-quality data. Data in this report can be used to evaluate changing conditions, provide science-based information for decision making, and help meet regulatory requirements.
Continuous (hourly) physicochemical properties were measured, and discrete water-quality samples were collected from three Little Arkansas River sites located along the easternmost extent of the Equus Beds aquifer during 1995 through 2021 over a range of streamflow conditions. The Little Arkansas River at Highway 50 near Halstead, Kansas, streamgage (U.S. Geological Survey station 07143672; hereafter referred to as the “Highway 50 site”) is located upstream from the other two sites, and the Little Arkansas River near Sedgwick, Kans., streamgage (U.S. Geological Survey station 07144100; hereafter referred to as the “Sedgwick site”) is located downstream from the other two sites; these two sites bracket most of the easternmost part of the Equus Beds aquifer. The Little Arkansas River upstream of ASR Facility near Sedgwick, Kans., streamgage (U.S. Geological Survey station 375350097262800; hereafter referred to as the “Upstream ASR site”) is located between the Highway 50 and Sedgwick sites, about 14.7 river miles (mi) downstream from the Highway 50 site, about 1.7 river mi upstream from the Sedgwick site, and immediately upstream from the ASR Facility intake structure. Surrogate models for water-quality constituents of interest (including bromide, dissolved organic carbon, 2-chloro-4-isopropylamino-6-amino-s-triazine [deethylatrazine], atrazine, and metolachlor) were updated or developed using continuously measured and concomitant discrete data. These surrogate models, along with previously developed regression models, were used to compute concentrations (at the Highway 50, Sedgwick, and Upstream ASR sites) and loads (at the Highway 50 and Sedgwick sites) during the study period. Federal criteria were used to evaluate water quality. Where applicable, water-quality data were compared to Federal national drinking-water regulations. Flow-normalized water-quality constituent trends were evaluated using Weighted Regressions on Time, Discharge, and Season (WRTDS) statistical models and water-quality trends were described using WRTDS bootstrap tests.
Continuously computed primary ion concentrations were generally larger at the Highway 50 site compared to the Sedgwick site. During the study period, the Federal secondary maximum contaminant level (SMCL) for dissolved solids was exceeded 57 percent of the time at the Highway 50 site and 38 percent of the time at the Sedgwick site. Computed bromide concentrations were larger at the Highway 50 site and exceeded the city of Wichita treatment threshold about 70, 21, and 19 percent of the time at the Highway 50, Sedgwick, and Upstream ASR sites, respectively. Chloride concentrations exceeded the Federal SMCL about 16 percent of the time at the Highway 50 site and did not exceed the SMCL at the Sedgwick site. Continuous arsenic concentrations exceeded the Federal Maximum Contaminant Level (MCL) 9 to 15 percent of the time at the Sedgwick and Highway 50 sites, respectively, during the study. Atrazine concentrations exceeded the Federal MCL 10 percent of the time at the Highway 50 and Sedgwick sites and 14 percent of the time at the Upstream ASR site during the study; computed glyphosate concentrations at the Sedgwick site never exceeded the MCL during the study.
Little Arkansas River flow-normalized primary ion concentrations during 1995 through 2021 generally had downward trends and decreases were generally larger at the Highway 50 site compared to the Sedgwick site. Dissolved solids and chloride concentrations decreased at the Highway 50 and Sedgwick sites. Bromide had no trend at the Highway 50 site and a downward trend at the Sedgwick site. Nitrate plus nitrite and total phosphorus concentrations had upward trends at the Highway 50 site but downward trends at the Sedgwick site, whereas total organic carbon had upward trends at both sites. Nitrate plus nitrite, total nitrogen, total phosphorus, and total organic carbon fluxes had upward trends at the Highway 50 and Sedgwick sites. Suspended-sediment concentrations had an upward trend at the Highway 50 site and had no trend at the Sedgwick site. Arsenic concentrations had downward trends at the Highway 50 and Sedgwick sites.
About one-quarter to one-half of the Little Arkansas River loads, including nutrients and sediment, were transported during 1 percent of the time during the study. Because streamflows are highly sensitive to climatic variation and an increase of extreme precipitation events in the Great Plains is expected, similar disproportionately large pollutant loading events may increase into the future. Continuous measurement of physicochemical properties in near-real time allowed characterization of Little Arkansas River surface water during conditions and time scales that would not have been possible otherwise and served as a complement to discrete water-quality sampling. Continuation of this water-quality monitoring will provide data to characterize changing conditions in the Little Arkansas River and possibly identify new and changing trends. Information in this report allows the city of Wichita to make informed municipal water-supply decisions using past and present water-quality conditions and trends in the watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235102","collaboration":"Prepared in cooperation with the city of Wichita, Kansas","usgsCitation":"Stone, M.L., and Klager, B.J., 2023, Long-term water-quality constituent trends in the Little Arkansas River, south-central Kansas, 1995–2021: U.S. Geological Survey Scientific Investigations Report 2023–5102, 103 p., https://doi.org/10.3133/sir20235102.","productDescription":"Report: ix, 103 p.; 1 Figure; 9 Tables; 5 Appendixes; Dataset","numberOfPages":"118","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-146544","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":421187,"rank":26,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix10.zip","text":"Appendix 10","size":"46 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season Graphical Output at station 07144100"},{"id":421186,"rank":25,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix9.zip","text":"Appendix 9","size":"35 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season Graphical Output at station 07143672"},{"id":421177,"rank":24,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix6.zip","text":"Appendix 6","size":"2.6 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Surrogate Regression Model Archive Summaries for the Little Arkansas River upstream of ASR Facility near Sedgwick, Kansas"},{"id":421176,"rank":23,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix5.zip","text":"Appendix 5","size":"2.7 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Surrogate Regression Model Archive Summaries for the Little Arkansas River near Sedgwick, Kansas"},{"id":421175,"rank":22,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix4.zip","text":"Appendix 4","size":"1.1 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Surrogate Regression Model Archive Summaries for the Little Arkansas River at Highway 50 near Halstead, Kansas"},{"id":421185,"rank":19,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table8.3.csv","text":"Table 8.3","size":"9 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season estimated mean, flow-normalized, and generalized mean fluxes for sediment, indicator bacteria, and trace elements at the Little Arkansas River at Highway 50 near Halstead, Kansas, and Little Arkansas River near Sedgwick, Kans., 1995–2021"},{"id":421184,"rank":18,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table8.2.csv","text":"Table 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U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
PyLith, a community, open-source code for modelling quasi-static and dynamic crustal deformation with an emphasis on earthquake faulting, has recently been updated with a flexible multiphysics implementation. We demonstrate the versatility of the multiphysics implementation by extending the code to model fully coupled continuum poromechanics. We verify the newly incorporated physics using standard benchmarks for a porous medium saturated with a slightly compressible fluid. The benchmarks include the 1-D consolidation problem as outlined by Terzaghi, Mandel’s problem for the 2-D case, and Cryer’s problem for the 3-D case. All three benchmarks have been added to the PyLith continuous integration test suite. We compare the closed form analytical solution for each benchmark against solutions generated by our updated code, and lastly, demonstrate that the poroelastic material formulation may be used alongside the existing fault implementation in PyLith.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggad370","usgsCitation":"Walker, R.L., Knepley, M.G., Aagaard, B.T., and Williams, C.A., 2023, Multiphysics modelling in PyLith: Poroelasticity: Geophysical Journal International, v. 235, no. 3, p. 2442-2475, https://doi.org/10.1093/gji/ggad370.","productDescription":"34 p.","startPage":"2442","endPage":"2475","ipdsId":"IP-146478","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":424189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"235","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Walker, Robert L.","contributorId":333017,"corporation":false,"usgs":false,"family":"Walker","given":"Robert","email":"","middleInitial":"L.","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":891663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knepley, Matthew G.","contributorId":333018,"corporation":false,"usgs":false,"family":"Knepley","given":"Matthew","email":"","middleInitial":"G.","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":891664,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":891665,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Charles A.","contributorId":333019,"corporation":false,"usgs":false,"family":"Williams","given":"Charles","email":"","middleInitial":"A.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":891666,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248951,"text":"70248951 - 2023 - Informing ASR treatment practices in a Florida aquifer through a human health risk approach","interactions":[],"lastModifiedDate":"2023-09-27T12:02:41.775734","indexId":"70248951","displayToPublicDate":"2023-09-26T07:00:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16877,"text":"International Journal of Enviornmental Research and Public Health","active":true,"publicationSubtype":{"id":10}},"title":"Informing ASR treatment practices in a Florida aquifer through a human health risk approach","docAbstract":"Big sagebrush (Artemisia tridentata) ecosystems across the western United States have experienced many changes in ecosystem dynamics and vegetation composition over the last century due to livestock grazing, non-native species, and changing climate and fire regimes. We conducted the first systematic investigation of historical vegetation composition and vegetation change in a sagebrush landscape in the southwestern United States, asking whether sagebrush or grass dominated the landscape historically?
The Rio Grande del Norte National Monument (RGDN), northern New Mexico, USA.
We combined General Land Office (GLO) surveys from 1881 with modern vegetation maps, field vegetation surveys, and sagebrush ages from growth ring analysis to test for changes in vegetation in the RGDN over the last 140 years.
We found that big sagebrush presence across the study area increased significantly, from being present on 16% of section lines in 1881 to 79% in 2019, and only three section lines lost sagebrush presence during that period. Concurrently, the number of section lines with low grass index more than doubled since 1881, while moderate and high grass index declined. Grass declined equally in areas where sagebrush increased and areas with no change in sagebrush, suggesting that changes in both vegetation types were catalyzed by external factors, likely including overgrazing. The growth ring analysis of 93 sagebrush revealed a maximum age of 87 years and establishment in every decade since the 1930s, consistent with the GLO results.
The significant vegetation changes in the RGDN over the last century, including an increase of sagebrush, provide important context about the shifting mosaic of grasslands and shrublands relevant to current and future management and ecosystem dynamics.
","language":"English","publisher":"Wiley","doi":"10.1111/jvs.13202","usgsCitation":"Fox, K., Margolis, E.Q., Lopez, M.K., Kasten, E., and Stevens, J., 2023, Vegetation change over 140 years in a sagebrush landscape of the Rio Grande del Norte National Monument, New Mexico, USA: Journal of Vegetation Science, v. 34, no. 5, e13202, 17 p., https://doi.org/10.1111/jvs.13202.","productDescription":"e13202, 17 p.","ipdsId":"IP-151212","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":423620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio Grande del Norte National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.5540076425751,\n 37.026637990156544\n ],\n [\n -106.5540076425751,\n 36.224664603824266\n ],\n [\n -105.13109401921486,\n 36.224664603824266\n ],\n [\n -105.13109401921486,\n 37.026637990156544\n ],\n [\n -106.5540076425751,\n 37.026637990156544\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Fox, Kara","contributorId":261706,"corporation":false,"usgs":false,"family":"Fox","given":"Kara","email":"","affiliations":[],"preferred":false,"id":890344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":890345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lopez, Manuel K.","contributorId":298167,"corporation":false,"usgs":false,"family":"Lopez","given":"Manuel","email":"","middleInitial":"K.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":890346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kasten, Ella","contributorId":332521,"corporation":false,"usgs":false,"family":"Kasten","given":"Ella","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":890347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stevens, J.T.","contributorId":332522,"corporation":false,"usgs":false,"family":"Stevens","given":"J.T.","email":"","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":890348,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249458,"text":"70249458 - 2023 - Survey optimization for invasive Burmese pythons informed by camera traps","interactions":[],"lastModifiedDate":"2023-11-07T16:16:54.037489","indexId":"70249458","displayToPublicDate":"2023-09-25T14:56:48","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17053,"text":"Wildlife Letters","active":true,"publicationSubtype":{"id":10}},"title":"Survey optimization for invasive Burmese pythons informed by camera traps","docAbstract":"The Burmese python (Python bivittatus) is an invasive predator responsible for broad mammal declines in South Florida, United States. Despite their large size, pythons remain cryptic and require multifaceted approaches for detection. We evaluated a novel technique by deploying camera traps at known locations of radiotagged pythons in the Florida Keys. We estimated daily detection probabilities of snakes and plotted diel activity patterns. Our results suggest camera traps can effectively survey pythons but seasonality and camera trigger mechanisms affect utility. Pythons were most detectable with time-lapse camera traps and more detectable in winter. The diel activity pattern of pythons peaked midday through early afternoon, indicating an optimal survey time for other search methods. Artificial intelligence can alleviate photo volume, so we recommend a combination of motion detection and time-lapse with shorter time (1 min) intervals for python-specific surveys and where camera traps are deployed to monitor mammals to improve passive python detection.