Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.
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The data screening for both model types considered historical trends in analytical methods and detection quantitation limits, the extent of censored data, data density, and environmental relevance with respect to three U.S. Environmental Protection Agency water quality thresholds (drinking water guidelines, human health criteria, and aquatic life criteria). The result of this analysis was the production of a final list of potential models deemed suitable for further development based upon the data exclusion (or inclusion) scheme developed herein for each model type. In both cases, the final models included mostly the three crustal elements (iron, manganese, and aluminum) that are found at comparatively high concentrations in surface water, whereas most of the more pernicious elements were excluded from the final model lists owing to various data limitations. 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Thiamine (vitamin B1) deficiency in marine systems is a globally significant threat to marine life. In 2020, newly hatched Chinook salmon (Oncorhynchus tshawytscha) fry in California’s Central Valley (CCV) hatcheries swam in corkscrew patterns and died at unusually high rates due to a lack of this essential vitamin. We subsequently investigated the impacts and causes of thiamine deficiency in California’s anadromous salmonids. Our laboratory studies defined the relationship between thiamine concentrations in Chinook salmon eggs and early life-stage survival in offspring; we used these data to develop a model that estimated 26 to 48% thiamine-dependent fry mortality across consecutive years (2020–2021) for winter-run Chinook salmon. We established an egg surveillance effort that found widespread thiamine deficiency in CCV Chinook salmon in 2020 and 2021, and emerging thiamine deficiency in Klamath River and Trinity River coho salmon (Oncorhynchus kisutch) in 2021. We determined that thiamine injections into adults raised egg thiamine concentrations above levels found to impact early life-stage survival and swimming behavior. Ocean surveys, prey nutrition, salmon gut contents, and stable isotope data link thiamine deficiency to an ocean diet dominated by a booming population of northern anchovy (Engraulis mordax). This forage fish had low thiamine, high lipid, and high thiaminase activity levels consistent with both a thiaminase and oxidative stress hypothesis for causing thiamine deficiency in California salmon. Our research suggests California’s already stressed anadromous salmonids will continue to be impacted by thiamine deficiency as long as their ocean forage base and diet are dominated by northern anchovy.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2426011122","usgsCitation":"Mantua, N., Bell, H.M., Todgham, A.E., Daniels, M.E., Rinchard, J., Ludwig, J.R., Field, J., Lindley, S., Rowland, F.E., Richter, C.A., Walters, D., Finney, B., Haskell, A., Tillitt, D., Honeyfield, D.C., Lipscomb, T.N., Kwak, K., Kindopp, J., Cocherell, D.E., Ward, A., Williams, T.H., Harding, J., Fangue, N., Jeffres, C., Ruiz-Cooley, R., Litvin, S., Foott, S., Adkison, M., Kormos, B., Harte, P., Colwell, F.S., Suffridge, C., Shannon, K., Cranford, A., Ambrose, C., Reed, A.N., and Johnson, R.C., 2025, Widespread thiamine deficiency in California salmon linked to an anchovy-dominated marine prey base: Proceedings of the National Academy of Sciences, v. 122, no. 26, e2426011122, 12 p., https://doi.org/10.1073/pnas.2426011122.","productDescription":"e2426011122, 12 p.","ipdsId":"IP-168561","costCenters":[{"id":192,"text":"Columbia Environmental Research 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Population monitoring is an essential need for tracking biodiversity and judging efficacy of conservation management actions, both globally and in the Pacific. However, population monitoring efforts are often temporally inconsistent and limited to small scales. Motion-activated cameras (‘camera traps’) offer a way to cost-effectively monitor populations, but they also generate large amounts of data that are time intensive to process.
Aims
To develop an automated pipeline for processing videos of ungulates (Philippine deer, Rusa marianna; and pigs, Sus scrofa) on Andersen Air Force Base in Guam.
Methods
We processed camera videos with a machine learning model for object detection and classification. To estimate density using distance sampling methods, we used a separate machine learning model to estimate the distance of target animals from the camera. We compared density estimates generated using manual versus automated methods and assessed accuracy and processing time saved.
Key results
The object detection and classification model achieved an overall accuracy >80% and F1 score ≥0.9 and saved 36.9 h of processing time. The automated distance estimation was fairly accurate, with a 1.1 m (±1.4 m) difference from manual distance estimates, and saved 16.8 h of processing time. Density estimates did not differ substantially between manual and automated distance estimation.
Conclusions
Machine learning models accurately processed camera videos, allowing efficient estimates of density from camera data.
Implications
Further adoption of motion-activated cameras coupled with automated processing could lead to continuous, large-scale monitoring of populations, helping to understand and address changes in biodiversity.
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In historically frequent-fire forests, fuel buildup as a result of fire exclusion is contributing to increased fire severity. The probability of high-severity fire can be reduced by active forest management that reduces fuels, prompting federal and state agencies to commit significant resources to increase the pace and scale of fuel reduction treatments. However, lower severity areas of wildfires also have the potential to act as “treatments,” and even catastrophic fires with large areas of high severity can still have substantial areas of lower severity fire that may be improving forest conditions locally. We quantified active management and wildfire severity across yellow pine and mixed conifer (YPMC) forests in the Sierra Nevada of California over a 22-year period (2001–2022). We did not detect increases in the area treated through time, but the area of beneficial wildfire (low to moderate severity) increased substantially, exceeding active treatment area in 8 of 22 years. Overall, beneficial wildfire treated ~17% more area than all treatments combined, and roughly four times more area than fire-related treatments alone. We then used disturbance history to evaluate resistance to high-severity wildfire and forest loss across the YPMC range. Of the 2.3 million ha YPMC of forests in 2001, 20% lost mature forests due to high-severity fire by 2022, which is nearly half of all YPMC area burned. Most of the landscape (47%) remains at risk of high-severity fire because it had no restorative disturbances, but 33% of the study area has some level of resistance to high-severity wildfire. In these areas, resistance will need to be enhanced and maintained over time via active management or managed wildfire, but these treatment needs will likely outpace capacity even under optimistic implementation scenarios. Given limited resources for implementing active management and the likelihood of a more fiery future, incorporating beneficial wildfire into landscape-level treatment planning has the potential to amplify the impact of active management treatments.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70306","usgsCitation":"Shive, K., Knight, C.A., Steel, Z.L., Stanley, C., and Wilson, K., 2025, Leveraging wildfire to augment forest management and amplify forest resilience: Ecosphere, v. 16, no. 6, e70306, 23 p., https://doi.org/10.1002/ecs2.70306.","productDescription":"e70306, 23 p.","ipdsId":"IP-171515","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":492491,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70306","text":"Publisher Index Page"},{"id":492239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Sierra Nevada mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.49191600648966,\n 35.65099754384805\n ],\n [\n -118.41892399844924,\n 37.519967279694015\n ],\n [\n -119.44265195907964,\n 39.671467955184795\n ],\n [\n -120.0832792618448,\n 40.51478566722406\n ],\n [\n -122.12439984049409,\n 40.20490455276678\n ],\n [\n -121.63327673827555,\n 39.70452920126846\n ],\n [\n -121.05326397512911,\n 38.48323730930633\n ],\n [\n -118.5302083539183,\n 35.15085062254475\n ],\n [\n -117.49191600648966,\n 35.65099754384805\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Shive, Kristen I. 0000-0002-5633-2528","orcid":"https://orcid.org/0000-0002-5633-2528","contributorId":352132,"corporation":false,"usgs":false,"family":"Shive","given":"Kristen I.","affiliations":[{"id":84117,"text":"University of California Cooperative Extension and Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":943173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Clarke Alexandra 0000-0003-0002-6959","orcid":"https://orcid.org/0000-0003-0002-6959","contributorId":288487,"corporation":false,"usgs":true,"family":"Knight","given":"Clarke","email":"","middleInitial":"Alexandra","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":943174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steel, Zachary L 0000-0002-1659-3141","orcid":"https://orcid.org/0000-0002-1659-3141","contributorId":329821,"corporation":false,"usgs":false,"family":"Steel","given":"Zachary","email":"","middleInitial":"L","affiliations":[{"id":6643,"text":"University of California - Berkeley","active":true,"usgs":false}],"preferred":false,"id":943175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanley, Charlotte K. 0000-0002-5019-4427","orcid":"https://orcid.org/0000-0002-5019-4427","contributorId":358047,"corporation":false,"usgs":false,"family":"Stanley","given":"Charlotte K.","affiliations":[{"id":85576,"text":"The Nature Conservancy, San Francisco, California","active":true,"usgs":false}],"preferred":false,"id":943176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Kristen N. 0000-0003-4769-2086","orcid":"https://orcid.org/0000-0003-4769-2086","contributorId":358048,"corporation":false,"usgs":false,"family":"Wilson","given":"Kristen N.","affiliations":[{"id":85576,"text":"The Nature Conservancy, San Francisco, California","active":true,"usgs":false}],"preferred":false,"id":943177,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271508,"text":"70271508 - 2025 - Impact of gas/liquid phase change of CO2 during injection for sequestration","interactions":[],"lastModifiedDate":"2025-09-18T15:18:17.86038","indexId":"70271508","displayToPublicDate":"2025-06-20T09:58:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22362,"text":"Journal of the Mechanics and Physics of Solids","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Impact of gas/liquid phase change of CO2 during injection for sequestration","title":"Impact of gas/liquid phase change of CO2 during injection for sequestration","docAbstract":"Often found along the estuarine-marine interface, barrier islands and mainland coastal zones are shaped by tides, currents, extreme storms, and relative sea-level rise. These systems provide ecosystem services such as storm surge and wave attenuation, erosion protection to inland areas, habitat for fish and wildlife, recreation, and tourism. Given the importance of these ecosystems coupled with their dynamic nature, information on how these coastal systems are changing can help to inform natural resource management. Remote sensing advancements have led to an abundance of data for monitoring change in coastal settings. This study developed a multiscale framework that can provide trajectory information from screening-level analyses by using existing or custom moderate spatial resolution land cover maps. Using the north-central Gulf Coast as a case study, the trajectory of land cover area for barrier islands and mainland coastal zones was assessed using several geospatial data sets, including: (1) long-term moderate-resolution remote sensing products with an annual (or more frequent) temporal frequency; (2) a restoration database (e.g., beach/dune restoration, sediment placement, and dune enhancement); and (3) a tropical storm database. Due to the coarser spatial resolution of data sets used for screening-level analyses, detailed or application-specific analyses are often needed to reduce uncertainty in smaller changes that may not be captured. These may include land cover change analyses (i.e. this study), periodic land cover maps with higher spatial resolution and more detailed land cover classes, or elevation-related analyses (e.g., dune change or inundation change). Using this framework, abrupt changes in land cover on Dauphin Island, Alabama, resulting from extreme storms were detected using moderate spatial resolution screening-level data, while restoration impact analyses may require higher resolution data. Further, land cover change analyses that incorporate change allocation provide robust information for understanding land cover change in dynamic coastal settings.
","language":"English","publisher":"Coastal Education and Research Foundation, Inc.","doi":"10.2112/JCOASTRES-D-24-00084.1","usgsCitation":"Enwright, N., Dalyander, P.S., Stuht, C.M., Han, M., Palmsten, M.L., Davenport, T.M., Kingwill, C.J., Steyer, G., and La Peyre, M., 2025, Multiscale framework for assessing land cover change on barrier islands from extreme storms and restoration: Journal of Coastal Research, v. 41, no. 6, p. 1029-1042, https://doi.org/10.2112/JCOASTRES-D-24-00084.1.","productDescription":"14 p.","startPage":"1029","endPage":"1042","ipdsId":"IP-172879","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":497643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.40937976247565,\n 30.743725811842268\n ],\n [\n -88.40937976247565,\n 29.56478712694208\n ],\n [\n -83.91845836432758,\n 29.56478712694208\n ],\n [\n -83.91845836432758,\n 30.743725811842268\n ],\n [\n -88.40937976247565,\n 30.743725811842268\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":214839,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":952525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalyander, P. 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Thus, our understanding of long-term barrier island morphological change focuses on beach erosion, overwash, and inlet formation. In contrast, outwash events with inundation from the sound side, such as one that occurred in Cape Lookout National Seashore, North Carolina, USA during Hurricane Dorian (September 2019), are understudied. Studying such events can improve understanding of barrier island response and stability for a broader range of conditions. Here, we model the hydrodynamics and morphological evolution of a barrier island using a coupled wave-current-sediment transport modeling system. Wind-driven surge in Pamlico Sound led to overtopping from the sound side, which eroded outwash channels and transported sediment seaward into the nearshore. Simulations reproduce the channel features observed with aerial imagery and provide information not available from the remote-sensing observations, including channel depths (>2 m) and the fate of the eroded sand. We found that >99% of the eroded sand was deposited in the nearshore, within 1,000 m of the shoreline in depths <10 m, suggesting that the deposited sediment remains available for littoral transport and beach recovery. Simulations with combinations of coarse or fine sediment and vegetated or unvegetated landcover indicate that channel position did not vary with grain size or vegetation, while volume of erosion and channel morphology were more responsive to variations in grain size and less responsive to presence of vegetation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JF008309","usgsCitation":"Warner, J.C., Sherwood, C.R., Hegermiller, C., Defne, Z., Zambon, J., He, R., Xue, G., Bao, D., Yin, D., and Moulton, M., 2025, Numerical simulation of sound-side barrier-island inundation and breaching during Hurricane Dorian (2019): JGR Earth Surface, v. 130, no. 6, e2025JF008309, 23 p., https://doi.org/10.1029/2025JF008309.","productDescription":"e2025JF008309, 23 p.","ipdsId":"IP-170654","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491441,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jf008309","text":"Publisher Index Page"},{"id":491278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Outer Banks","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.5,\n 35.25\n ],\n [\n -76.5,\n 34.75\n ],\n [\n -75.5,\n 34.75\n ],\n [\n -75.5,\n 35.25\n ],\n [\n -76.5,\n 35.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hegermiller, Christie A.","contributorId":357332,"corporation":false,"usgs":false,"family":"Hegermiller","given":"Christie A.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":941219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Defne, Zafer 0000-0003-4544-4310 zdefne@usgs.gov","orcid":"https://orcid.org/0000-0003-4544-4310","contributorId":5520,"corporation":false,"usgs":true,"family":"Defne","given":"Zafer","email":"zdefne@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zambon, Joseph B.","contributorId":336620,"corporation":false,"usgs":false,"family":"Zambon","given":"Joseph B.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":941221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"He, Ruoying 0000-0001-6158-2292","orcid":"https://orcid.org/0000-0001-6158-2292","contributorId":202189,"corporation":false,"usgs":false,"family":"He","given":"Ruoying","email":"","affiliations":[],"preferred":false,"id":941222,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xue, George","contributorId":294533,"corporation":false,"usgs":false,"family":"Xue","given":"George","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":941223,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bao, Daoyang","contributorId":294534,"corporation":false,"usgs":false,"family":"Bao","given":"Daoyang","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":941224,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yin, Dongxiao","contributorId":294535,"corporation":false,"usgs":false,"family":"Yin","given":"Dongxiao","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":941225,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Moulton, Melissa","contributorId":305679,"corporation":false,"usgs":false,"family":"Moulton","given":"Melissa","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":941226,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70274598,"text":"70274598 - 2025 - Near-surface geophysics: Environmental applications","interactions":[],"lastModifiedDate":"2026-04-01T13:53:22.33473","indexId":"70274598","displayToPublicDate":"2025-06-20T08:47:58","publicationYear":"2025","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Near-surface geophysics: Environmental applications","docAbstract":"The field of geophysics encompasses a broad and diverse compilation of methodologies that employs principles of physics to characterize properties of earth materials within the subsurface. While geophysical methods have a long history in resource exploration and studies of Earth’s interior, the subdiscipline of “near-surface geophysics” has evolved in recent decades for examination of the shallow, near-surface environment for a range of purposes ranging from archaeological or forensic investigations to assessment of geologic, hydrologic, biologic, and geochemical properties and processes. “Environmental geophysics” are near-surface geophysical studies and methods that focus on understanding natural systems (e.g., watershed hydrology, groundwater–surface water connections, biophysical processes) as well as research pertaining to anthropogenic impacts and land management, (e.g., contamination and remediation, saltwater intrusion, agricultural practices). This field can be further subdivided into subdisciplines focused on specific topics and applications, such as water resources and hydrology (hydrogeophysics) or biologic and microbial processes (biogeophysics). Studies in environmental geophysics span a range of scales, from pore-scale laboratory tests to watershed-scale or regional field experiments. Methods vary by the nature of physics employed, the specific measurement acquired, and how that data is ultimately processed and analyzed to produce interpretable results. There exists further diversity in the acquisition logistics, geometry, and timing of data collection. Geophysical data can be collected in boreholes (one-dimensional, 1-D, vertical profiles), along survey lines (two-dimensional, 2-D, cross-sections), or in dense sensor arrays or gridded profiles (three-dimensional, 3-D, models). Regarding the temporal aspect, studies can conduct one-time geophysical surveys to obtain detailed imaging of subsurface structure or use timelapse and continuous monitoring to investigate variations in subsurface properties over time. The cumulation of all possible permutations of these factors (method, acquisition geometry, survey design, and target application) results in an immense diversity among environmental geophysical studies. Nevertheless, this field remains unified in the pursuit of understanding natural and human-impacted near-surface environments through geophysical investigations. Here we highlight some key references within environmental geophysics. Resources on geophysical theory, acquisition logistics, processing and inversion workflows, and example case studies are categorized into the most common geophysical classes within Geophysical Methods. Lastly, example references for the dominant types of applications in environmental geophysical studies are catalogued in Environmental Applications.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Oxford Bibliographies","largerWorkSubtype":{"id":11,"text":"Bibliography"},"language":"English","publisher":"Oxford University Press","doi":"10.1093/obo/9780199363445-0146","usgsCitation":"James, S.R., Glaser, D.R., and Garcia, A., 2025, Near-surface geophysics: Environmental applications, chap. of Oxford Bibliographies, HTML Document, https://doi.org/10.1093/obo/9780199363445-0146.","productDescription":"HTML Document","ipdsId":"IP-172909","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"James, Stephanie R. 0000-0001-5715-253X","orcid":"https://orcid.org/0000-0001-5715-253X","contributorId":260620,"corporation":false,"usgs":true,"family":"James","given":"Stephanie","email":"","middleInitial":"R.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":958466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glaser, Dan R.","contributorId":292710,"corporation":false,"usgs":false,"family":"Glaser","given":"Dan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":958467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, Alejandro","contributorId":369112,"corporation":false,"usgs":false,"family":"Garcia","given":"Alejandro","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":958468,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273971,"text":"70273971 - 2025 - Streamflow regime characterization in the changing boreal ecosystem: Wildfire impacts from stream-to-regional scales","interactions":[],"lastModifiedDate":"2026-02-20T17:06:56.875893","indexId":"70273971","displayToPublicDate":"2025-06-19T09:57:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Streamflow regime characterization in the changing boreal ecosystem: Wildfire impacts from stream-to-regional scales","docAbstract":"The boreal ecosystem has experienced significant changes over recent decades as wildfires become more frequent, intense, and severe. As streams are highly prevalent and ecologically relevant, understanding interactions among wildfire and hydrologic patterns is important for effective aquatic ecosystem management. This study used a Bayesian mixture model to classify streamflow regimes from modeled streamflow data for 32,730 stream reaches (totaling 295,880 km) across the Yukon and Kuskokwim basins and the Northwestern Boreal Ecosystem in Alaska, USA, and Yukon Territory, Canada. We assessed time since burn and calculated the total length of stream (km) within burn perimeters for each streamflow class from 1985 to 2015. Additionally, we used field observations (2018–2022) to compare streamflow regimes in four burned and four unburned headwater streams (drainage basins ≤150 km2) in interior Alaska. Modeled stream reaches were grouped into twenty-two classes and reduced to eleven metaclasses based on similarities in streamflow statistics. These metaclasses formed two broad groups: 1) large rivers with lower variability and strong seasonal signals, and 2) mid- to small-sized tributaries with high variability, frequent high flow events, and weaker seasonal signals. The stream length burned analysis indicated an average increase of 47 km per year with first- and second-order streams experiencing more frequent fire. Empirical streamflow metrics from headwater stream gages revealed additional differences in streamflow patterns between burned and unburned streams. This streamflow classification establishes a baseline for understanding boreal stream responses to wildfire, detecting climate-induced regime shifts, and facilitating management and conservation of important boreal aquatic species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.179770","usgsCitation":"Strohm, D.D., Sergeant, C.J., Paul, J.D., Falke, J.A., 2025, Streamflow regime characterization in the changing boreal ecosystem: Wildfire impacts from stream-to-regional scales: Science of the Total Environment, v. 991, 179770, 14 p., https://doi.org/10.1016/j.scitotenv.2025.179770.","productDescription":"179770, 14 p.","ipdsId":"IP-173153","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500353,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Boreal Yukon-Kuskokwim study area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -151.25073280649116,\n 65.22533418311923\n ],\n [\n -151.08883708497373,\n 64.1874521027234\n ],\n [\n -147.81756784832672,\n 64.90432777046252\n ],\n [\n -144.75370718998238,\n 64.258653756473\n ],\n [\n -144.56100126166737,\n 65.39083680482943\n ],\n [\n -148.2119885011982,\n 65.53590677988976\n ],\n [\n -151.25073280649116,\n 65.22533418311923\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"991","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Strohm, Deanna D.","contributorId":366469,"corporation":false,"usgs":false,"family":"Strohm","given":"Deanna","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":955951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sergeant, Christopher J.","contributorId":140496,"corporation":false,"usgs":false,"family":"Sergeant","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":955953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paul, Josh D.","contributorId":366470,"corporation":false,"usgs":false,"family":"Paul","given":"Josh","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":955954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":955952,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70268334,"text":"70268334 - 2025 - Disparate groundwater responses to wildfire","interactions":[],"lastModifiedDate":"2025-06-23T14:34:47.223906","indexId":"70268334","displayToPublicDate":"2025-06-19T09:32:25","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5067,"text":"WIREs Water","active":true,"publicationSubtype":{"id":10}},"title":"Disparate groundwater responses to wildfire","docAbstract":"Post-wildfire investigations of groundwater response reveal a range of outcomes, varying from substantial increases to notable decreases in recharge and baseflow, with some studies indicating negligible or short-lived effects. This review assesses these varied responses within five critical categories: climate, vegetation, hydrogeology, fire characteristics, and the cryosphere, examining both short-term (within 2 years) and intermediate (2–10 years post-fire) effects. Despite considerable variability, some consistent patterns emerge. For instance, in hydroclimatic settings where water input and evaporative demand cycles are out of sync, post-wildfire groundwater responses tend to be positive (i.e., increased flux or storage), whereas under low fire severity conditions or in vegetation types that quickly recover, groundwater responses tend to be negative (i.e., decreased flux or storage). We synthesize relevant findings into a compendium of testable hypotheses aimed at explaining the spatiotemporal variability in observed post-wildfire groundwater responses. A recurring theme is the critical influence of the pre-wildfire groundwater regime on expected response and recovery. We identify opportunities for specific improvements in post-wildfire monitoring and modeling that would further advance capabilities to predict groundwater response. A key area for further research is understanding how wildfire effects on snow dynamics and other cryospheric processes translate to changes in groundwater.
","language":"English","publisher":"Wiley Interdisciplinary Reviews","doi":"10.1002/wat2.70029","usgsCitation":"Walvoord, M.A., Ebel, B., Partridge, T.F., Rey, D., and Rosenberry, D., 2025, Disparate groundwater responses to wildfire: WIREs Water, v. 12, no. 3, e70029, 22 p., https://doi.org/10.1002/wat2.70029.","productDescription":"e70029, 22 p.","ipdsId":"IP-178452","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":491494,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.70029","text":"Publisher Index Page"},{"id":491099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":940839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":940840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":940841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":940842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosenberry, D.O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":38500,"corporation":false,"usgs":true,"family":"Rosenberry","given":"D.O.","affiliations":[],"preferred":true,"id":940843,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268363,"text":"70268363 - 2025 - Public supply water delivery analysis and estimation for the conterminous United States","interactions":[],"lastModifiedDate":"2025-06-25T13:12:44.133817","indexId":"70268363","displayToPublicDate":"2025-06-19T09:29:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Public supply water delivery analysis and estimation for the conterminous United States","docAbstract":"Public supply water withdrawals represent 14% of all withdrawals in the conterminous United States (CONUS), supplying approximately 87% of the population with fresh water. Deliveries for public water supply are crucial for associating water use amounts with populations because they often differ from total withdrawals due to wholesales, transfers, losses, and other factors. Understanding these differences helps identify the drivers for each type of delivery. The goal of this study was to compile all available public water supply delivery data for the CONUS and develop a data-driven model to estimate deliveries for all water service areas within the CONUS. Annual deliveries were estimated between 2010 and 2020, encompassing total water deliveries; combined commercial, industrial, and institutional deliveries (CII); and domestic deliveries. Data were compiled for 2,744 water service areas to produce the most comprehensive public water supply delivery data set for the CONUS to date. Three ensemble modeling approaches were developed to estimate total, CII, and domestic per capita (DPC) deliveries using a gradient boosted regression tree modeling approach. Estimates of daily domestic and CII per capita deliveries were generated from these models for approximately 18,800 water service areas, covering most public water systems in the CONUS. Domestic delivery was found to be lowest in the midwestern region and higher in the southern and southwest regions of the United States. Results indicate that climate and land use can be associated with regional differences in DPC delivery. Population metrics and land use were identified as significant contributors to CII delivery estimates.
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A.","affiliations":[],"preferred":true,"id":941112,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Luukkonen, Carol L. 0000-0001-7056-8599","orcid":"https://orcid.org/0000-0001-7056-8599","contributorId":208181,"corporation":false,"usgs":true,"family":"Luukkonen","given":"Carol","email":"","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941113,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stewart, Jana S. 0000-0002-8121-1373","orcid":"https://orcid.org/0000-0002-8121-1373","contributorId":211037,"corporation":false,"usgs":true,"family":"Stewart","given":"Jana","middleInitial":"S.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941114,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Paulinski, Scott 0000-0001-6548-8164","orcid":"https://orcid.org/0000-0001-6548-8164","contributorId":357291,"corporation":false,"usgs":false,"family":"Paulinski","given":"Scott","affiliations":[{"id":28165,"text":"No affiliation","active":true,"usgs":false}],"preferred":false,"id":941115,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miller, Lisa D. 0000-0002-3523-0768 ldmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-3523-0768","contributorId":1125,"corporation":false,"usgs":true,"family":"Miller","given":"Lisa","email":"ldmiller@usgs.gov","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941116,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Houston, Natalie 0000-0002-6071-4545","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":206533,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941117,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70268390,"text":"70268390 - 2025 - Rapid emplacement of the Keaiwa Lava Flow of 1823 from the Great Crack in the Southwest Rift Zone of Kilauea volcano","interactions":[],"lastModifiedDate":"2025-06-24T14:27:46.198157","indexId":"70268390","displayToPublicDate":"2025-06-19T09:20:50","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Rapid emplacement of the Keaīwa Lava Flow of 1823 from the Great Crack in the Southwest Rift Zone of Kīlauea volcano","title":"Rapid emplacement of the Keaiwa Lava Flow of 1823 from the Great Crack in the Southwest Rift Zone of Kilauea volcano","docAbstract":"The Keaīwa Lava Flow of 1823 in the Southwest Rift Zone of Kīlauea volcano is unusual for its expansive pāhoehoe sheet flow morphology and lack of constructive vent topography, despite having a similar tholeiitic basalt composition to other lavas erupted from Kīlauea. This lava flow issued from a ∼10-km-long continuous fissure now known as the Great Crack, and has an unusually thin sheet flow morphology with margin thicknesses of ∼15–110 cm (average of 42 cm). Based on field observations of the lava flow at its fissure vent (e.g., drain-back features), we propose that the Great Crack formed, or at least significantly widened, just prior to and syn-eruptively with this 1823 eruption. The absence of pyroclastic cones or spatter ramparts indicates that the eruption consisted of a rapid outpouring of relatively degassed lava as the fissure unzipped. The rapidly moving lava flow overtopped pre-existing tumuli and scoria cones (e.g., Lava Plastered Cones) up to ∼10 m tall. Glass and whole-rock chemistry yield homogeneous compositions for the lavas erupted from the Great Crack, with glass compositions of 6.40 ± 0.10 wt% MgO and whole-rock compositions of 7.39 ± 0.07 wt% MgO. Lava pads erupted from a short western fissure system are richer in mafic minerals (e.g., olivine and clinopyroxene), and show slightly more MgO-rich whole-rock compositions (7.79 ± 0.05 wt%). MgO-in-glass thermometry on juvenile spatter yield eruption temperatures of 1153 ± 13°C that are typical of Kīlauea lavas. Thus, the extensive sheet-like lava flow morphology is not a direct consequence of unusual magmatic or rheological conditions (i.e., low viscosity). Instead, the flow morphology is associated with high effusion rates caused by sudden drainage of uprift magma as it erupted from the Great Crack. Lava flow modeling on a 2-m-resolution digital elevation model indicates that a minimum bulk effusion rate of ∼5800 m3/s (∼3500 m3/s dense rock equivalent) and a minimum flow velocity of ∼11 m/s are required for the lava flow to overcome the topography of the Lava Plastered Cones. This effusion rate is among the highest inferred for eruptions in Hawaiʻi and around the world. This study highlights a less frequent eruption style at Hawaiian volcanoes characterized by a sudden outpouring of lava from an unusual fissure system. Local eyewitness accounts indicate that the 1823 eruption was preceded by seismicity. Given the complex magmatic-volcanic-tectonic relations across Kīlauea, we speculate that the south flank could have slipped over one or more events that ultimately triggered unzipping of the Great Crack and passive release of briefly stored uprift magma. An eruption similar to 1823 at Kīlauea or Mauna Loa, with an eruptive timeframe that could be as short as an hour, with high effusion rates and rapid flow front velocities, would not easily allow for a timely response.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2025.108391","usgsCitation":"Tonato, A., Shea, T., Downs, D.T., and Kelfoun, K., 2025, Rapid emplacement of the Keaiwa Lava Flow of 1823 from the Great Crack in the Southwest Rift Zone of Kilauea volcano: Journal of Volcanology and Geothermal Research, v. 466, 108391, 18 p., https://doi.org/10.1016/j.jvolgeores.2025.108391.","productDescription":"108391, 18 p.","ipdsId":"IP-169862","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":494405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2025.108391","text":"Publisher Index Page"},{"id":491181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Great Crack in the Southwest Rift Zone of Kīlauea volcano, Keaīwa Lava Flow","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.2184297752859,\n 19.437317498221987\n ],\n [\n -155.5,\n 19.437317498221987\n ],\n [\n -155.5,\n 19.1667\n ],\n [\n -155.2184297752859,\n 19.1667\n ],\n [\n -155.2184297752859,\n 19.437317498221987\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"466","noUsgsAuthors":false,"publicationDate":"2025-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Tonato, Andrea","contributorId":352882,"corporation":false,"usgs":false,"family":"Tonato","given":"Andrea","affiliations":[{"id":64253,"text":"University of Hawaiʻi at Mānoa","active":true,"usgs":false}],"preferred":false,"id":941184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shea, Thomas","contributorId":236886,"corporation":false,"usgs":false,"family":"Shea","given":"Thomas","affiliations":[{"id":47560,"text":"University of Hawaii Manoa","active":true,"usgs":false}],"preferred":false,"id":941185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Downs, Drew T. 0000-0002-9056-1404 ddowns@usgs.gov","orcid":"https://orcid.org/0000-0002-9056-1404","contributorId":173516,"corporation":false,"usgs":true,"family":"Downs","given":"Drew","email":"ddowns@usgs.gov","middleInitial":"T.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":941186,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelfoun, Karim","contributorId":333750,"corporation":false,"usgs":false,"family":"Kelfoun","given":"Karim","email":"","affiliations":[{"id":79967,"text":"Laboratoire Magmas et Volcans, Université Clermont Auvergne, Clermont-Ferrand, France","active":true,"usgs":false}],"preferred":false,"id":941187,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269030,"text":"70269030 - 2025 - Navigating the possibilities and pitfalls of biocrust recovery in a changing climate","interactions":[],"lastModifiedDate":"2025-07-14T13:33:03.584001","indexId":"70269030","displayToPublicDate":"2025-06-19T08:31:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":724,"text":"American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Navigating the possibilities and pitfalls of biocrust recovery in a changing climate","docAbstract":"Biological soil crusts are complex communities composed of lichens, mosses, bacteria, and cyanobacteria that create a living skin on the soil surface across drylands worldwide. Although small in size, the vast area that biocrusts cover and the critical functions they provide make them a cornerstone of dryland health and resiliency. In addition to being important, biocrusts are exceptionally vulnerable to certain types of disturbance. Although they can withstand a wide range of temperatures and long periods without precipitation, biocrusts are highly sensitive to land-use change and are vulnerable to physical and compressional disturbance (i.e., trampling, vehicles, cattle, heavy machinery). In the face of these disturbances, a critical, long-standing question of interest to dryland ecologists is: Can biocrusts recover following disturbance without active intervention. If so, how long does it take? Early estimates of biocrust recovery suggested recovery can be incredibly slow (on the order of thousands of years), with more modern studies finding potential for faster recovery, especially with intervention. Multiple lines of evidence agree that recovery is context dependent, differing across climates, soils, and with the types of disturbance and biocrust. Additionally, active restoration of biocrusts is becoming more common as tractable strategies are developed for facilitating the establishment of biocrusts after disturbance. Here, we add to the body of knowledge about biocrust recovery following disturbances by reviewing recovery patterns, their connection to climate change, considerations for recovery in changing climates, and the role of restoration.
","language":"English","publisher":"Botanical Society of AMerica","doi":"10.1002/ajb2.70055","usgsCitation":"Phillips, M.L., Young, K.E., Lauria, C.M., Jech, S., Giraldo-Silva, A., and Reed, S., 2025, Navigating the possibilities and pitfalls of biocrust recovery in a changing climate: American Journal of Botany, v. 112, e70055, 8 p., https://doi.org/10.1002/ajb2.70055.","productDescription":"e70055, 8 p.","ipdsId":"IP-176307","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":492481,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ajb2.70055","text":"Publisher Index Page"},{"id":492193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"112","noUsgsAuthors":false,"publicationDate":"2025-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Phillips, Michala Lee 0000-0001-7005-8740","orcid":"https://orcid.org/0000-0001-7005-8740","contributorId":245186,"corporation":false,"usgs":true,"family":"Phillips","given":"Michala","email":"","middleInitial":"Lee","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":942951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Kristina E.","contributorId":210572,"corporation":false,"usgs":false,"family":"Young","given":"Kristina","email":"","middleInitial":"E.","affiliations":[{"id":38116,"text":"Department of Biological Sciences, University of Texas at El Paso, El Paso, TX 79902, USA","active":true,"usgs":false}],"preferred":false,"id":942952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lauria, Cara Marie 0000-0001-8914-8041","orcid":"https://orcid.org/0000-0001-8914-8041","contributorId":271066,"corporation":false,"usgs":true,"family":"Lauria","given":"Cara","email":"","middleInitial":"Marie","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":942953,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jech, Sierra","contributorId":292726,"corporation":false,"usgs":false,"family":"Jech","given":"Sierra","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":942954,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Giraldo-Silva, Ana","contributorId":357982,"corporation":false,"usgs":false,"family":"Giraldo-Silva","given":"Ana","affiliations":[{"id":85571,"text":"Public University of Navarre","active":true,"usgs":false}],"preferred":false,"id":942955,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":207498,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":942956,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70268278,"text":"70268278 - 2025 - Discovery of an intact Quaternary paleosol, Georgia Bight, USA","interactions":[],"lastModifiedDate":"2025-06-20T14:16:38.069571","indexId":"70268278","displayToPublicDate":"2025-06-18T09:08:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5841,"text":"Applied Sciences","onlineIssn":"2076-3417","active":true,"publicationSubtype":{"id":10}},"title":"Discovery of an intact Quaternary paleosol, Georgia Bight, USA","docAbstract":"A previously buried paleosol was found on the continental shelf during a study of sea floor scour, nucleated by large artificial reef structures such as vessel hulks, barges, train cars, military vehicles, etc., called “scour nuclei”. It is a relic paleo-land surface of sapling-sized tree stumps, root systems, and fossil animal bone exhumed by scour processes active adjacent to the artificial reef structure. Over the span of five research cruises to the site in 2022–2024, soil samples were taken using hand excavation, PONAR grab samplers, split spoon, hollow tube auger, and a modified Shelby-style push box. High-definition (HD) video was taken using a Remotely Operated Vehicle (ROV) and diver-held cameras. Radiocarbon dating of wood samples returned ages of 42,015–43,417 calibrated years before present (cal yrBP). Pollen studies, together with the recovered macrobotanical remains, support our interpretation of the site as a freshwater forested wetland whose keystone tree species was Taxodium distichum—bald cypress. The paleosol was identified as an Aquult, a sub-order of Ultisols where water tables are at or near the surface year-round. A deep (0.25 m+) argillic horizon comprised the bulk of the preserved soil. Comparable Ultisols found in Georgia wetlands include Typic Paleaquult (Grady and Bayboro series) soils.
","language":"English","publisher":"MDPI","doi":"10.3390/app15126859","usgsCitation":"Garrison, E., Newton, M., Prueitt, B., Jones, E., and Willard, D., 2025, Discovery of an intact Quaternary paleosol, Georgia Bight, USA: Applied Sciences, v. 15, 6859, 13 p., https://doi.org/10.3390/app15126859.","productDescription":"6859, 13 p.","ipdsId":"IP-177145","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":491444,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/app15126859","text":"Publisher Index Page"},{"id":491020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Georgia Bight","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.6037065174064,\n 32.05700383452627\n ],\n [\n -80.87744613056587,\n 32.073340398276045\n ],\n [\n -81.16853543751718,\n 31.739498148416956\n ],\n [\n -81.50026399128812,\n 31.09261341173041\n ],\n [\n -81.51308221190993,\n 30.689275547735463\n ],\n [\n -81.13422317222164,\n 30.688050731677237\n ],\n [\n -80.6037065174064,\n 32.05700383452627\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Garrison, Ervan G.","contributorId":357067,"corporation":false,"usgs":false,"family":"Garrison","given":"Ervan G.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":940686,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Newton, Matthew","contributorId":357068,"corporation":false,"usgs":false,"family":"Newton","given":"Matthew","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":940687,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prueitt, Benjamin","contributorId":357071,"corporation":false,"usgs":false,"family":"Prueitt","given":"Benjamin","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":940689,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Emily C.","contributorId":357073,"corporation":false,"usgs":false,"family":"Jones","given":"Emily C.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":940690,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Willard, Debra A. 0000-0003-4878-0942","orcid":"https://orcid.org/0000-0003-4878-0942","contributorId":269840,"corporation":false,"usgs":true,"family":"Willard","given":"Debra A.","affiliations":[],"preferred":true,"id":940693,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268250,"text":"70268250 - 2025 - Mixed natal origins present management challenges for a non-native fish established throughout a modified river network","interactions":[],"lastModifiedDate":"2025-07-10T14:56:29.491021","indexId":"70268250","displayToPublicDate":"2025-06-18T08:25:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Mixed natal origins present management challenges for a non-native fish established throughout a modified river network","docAbstract":"Expansion of non-native brown trout (Salmo trutta) in the Colorado River below Glen Canyon Dam motivated reevaluation of suppression strategies to minimize potential impacts to native fishes in the Grand Canyon, Arizona, USA. Brown trout are one of several non-native fish species of management concern in this river reach, and understanding their natal sources and movement patterns may assist managers in planning suppression strategies. We identified trace elements in brown trout otoliths, which, when coupled with location-specific water chemistry data, identified brown trout natal origins over 19 years. Strontium and manganese concentrations revealed distinct emigration patterns from natal tributary streams and the mainstem Colorado River over two periods. Adult brown trout collected from throughout our study area showed mixed tributary and mainstem natal origins, which persisted during suppression efforts in a known spawning tributary. Unexpectedly, we found evidence of brown trout reproduction in the Colorado River for at least a decade before documentation through field monitoring. Our findings may inform but complicate the development of management strategies for system-wide brown trout suppression.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2024-0267","usgsCitation":"Akland, M., Limburg, K., Healy, B.D., and Pine, W.E., 2025, Mixed natal origins present management challenges for a non-native fish established throughout a modified river network: Canadian Journal of Fisheries and Aquatic Sciences, v. 82, p. 1-13, https://doi.org/10.1139/cjfas-2024-0267.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-168922","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":490920,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.63077077053144,\n 36.80786020404963\n ],\n [\n -111.65750425155792,\n 36.81141589124036\n ],\n [\n -111.76698028633918,\n 36.68345426369245\n ],\n [\n -111.88230163058687,\n 36.503023995019646\n ],\n [\n -111.84019781092644,\n 36.499299737249814\n ],\n [\n -111.68048884893217,\n 36.68934775789845\n ],\n [\n -111.63077077053144,\n 36.80786020404963\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Akland, Michael K.","contributorId":357023,"corporation":false,"usgs":false,"family":"Akland","given":"Michael K.","affiliations":[{"id":85311,"text":"Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, New York, USA","active":true,"usgs":false}],"preferred":false,"id":940600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Limburg, Karin E.","contributorId":356369,"corporation":false,"usgs":false,"family":"Limburg","given":"Karin E.","affiliations":[{"id":33387,"text":"SUNY-ESF","active":true,"usgs":false}],"preferred":false,"id":940601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Healy, Brian D. 0000-0002-4402-638X","orcid":"https://orcid.org/0000-0002-4402-638X","contributorId":304257,"corporation":false,"usgs":true,"family":"Healy","given":"Brian","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":940602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pine, William E. III","contributorId":139959,"corporation":false,"usgs":false,"family":"Pine","given":"William","suffix":"III","email":"","middleInitial":"E.","affiliations":[{"id":13332,"text":"Uni. of Florida Department of Wildlife Ecology and Conservation","active":true,"usgs":false}],"preferred":false,"id":940603,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269667,"text":"70269667 - 2025 - Fine-scale spatial risk models to predict avian collisions with power lines","interactions":[],"lastModifiedDate":"2025-08-19T15:31:40.484785","indexId":"70269667","displayToPublicDate":"2025-06-18T08:12:42","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fine-scale spatial risk models to predict avian collisions with power lines","docAbstract":"1. Avian fatalities caused by collisions with overhead power lines are an important conservation issue worldwide. Although mitigation strategies can help reduce mortalities, given their considerable cost and the vast scale of power line infrastructure, cost-effective action requires that these efforts be prioritised to areas with the highest potential risk to birds. To date, this risk assessment has usually been guided by potentially biased information on the location of recorded fatalities.
2. Here we use five years of GPS tracking data from endangered Tasmanian wedge-tailed eagles to develop an alternative approach to risk assessment: fine-scale spatial risk models based on behavioural analyses. We built and cross-validated a model that generates spatially explicit predictions of the probability that eagles would cross power lines at hazardous altitudes throughout the entire Tasmanian electricity distribution network.
3. In our model, probability of power line crossings was most strongly associated with the proportion of forest edges, wet forest, open habitat, freshwater sources, and rural residential developments in the area surrounding the power lines. Cross-validation indicated that the model effectively predicted where Tasmanian wedge-tailed eagles cross power lines at low altitude.
4. Model validation suggested our approach was a powerful predictor of the locations of power line collisions involving eagles. The locations of almost all (94%) confirmed eagle fatalities were in the half of the total Tasmanian power line area assigned the higher risk by the model, and 50% of incidents occurred in the 30% of the power line area estimated to be highest risk.
5. Synthesis and applications. Our study illustrates a framework for using bird movement data to provide insights into avian behaviour and the risk they encounter around power line infrastructure. Electricity delivery industries can use these models to identify the electrical infrastructure that poses the highest risk to avian survival and prioritise mitigation efforts, thereby optimizing the benefit of investments to reduce detrimental effects on biodiversity. Our model can direct pre-emptive mitigation across Tasmania’s 20,310 km of distribution infrastructure to meet management targets aiming to reduce the negative effects of power lines on the Tasmanian wedge-tailed eagle.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.70076","usgsCitation":"Pay, J.M., Cameron, E.Z., Hawkins, C.E., Johnson, C., Koch, A.J., Wiersma, J., and Katzner, T., 2025, Fine-scale spatial risk models to predict avian collisions with power lines: Journal of Applied Ecology, v. 62, no. 8, p. 1820-1830, https://doi.org/10.1111/1365-2664.70076.","productDescription":"11 p.","startPage":"1820","endPage":"1830","ipdsId":"IP-163294","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":493113,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":493326,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.70076","text":"Publisher Index Page"}],"country":"Australia","otherGeospatial":"Tasmania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 143.86088831282336,\n -40.405798345451885\n ],\n [\n 143.86088831282336,\n -44.063653865697944\n ],\n [\n 149.28385412411444,\n -44.063653865697944\n ],\n [\n 149.28385412411444,\n -40.405798345451885\n ],\n [\n 143.86088831282336,\n -40.405798345451885\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Pay, James M.","contributorId":245078,"corporation":false,"usgs":false,"family":"Pay","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":944335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cameron, Elissa Z.","contributorId":245084,"corporation":false,"usgs":false,"family":"Cameron","given":"Elissa","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":944336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawkins, Clare E.","contributorId":245079,"corporation":false,"usgs":false,"family":"Hawkins","given":"Clare","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":944337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Christopher","contributorId":334072,"corporation":false,"usgs":false,"family":"Johnson","given":"Christopher","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":944338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koch, Amelia J.","contributorId":245080,"corporation":false,"usgs":false,"family":"Koch","given":"Amelia","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":944339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wiersma, Jason M.","contributorId":358878,"corporation":false,"usgs":false,"family":"Wiersma","given":"Jason M.","affiliations":[{"id":85698,"text":"Forest Practices Authority, 30 Patrick St, Hobart, TAS, AustraliaForest Practices Authority, 30 Patrick St, Hobart, TAS, Australia","active":true,"usgs":false}],"preferred":false,"id":944340,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":944341,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70274278,"text":"70274278 - 2025 - Considerations for using tag-returns to monitor targeted removal of invasive fishes","interactions":[],"lastModifiedDate":"2026-03-24T16:46:08.980961","indexId":"70274278","displayToPublicDate":"2025-06-18T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Considerations for using tag-returns to monitor targeted removal of invasive fishes","docAbstract":"Objective
Targeted removals are used for management of some invasive fish populations. Tag–return studies are one approach that can be used to assess the efficacy of targeted removals. However, there are many decisions to make when designing a tag–return study. We used simulation modeling to outline general guidelines for consideration when designing efficient tag–return studies to measure annual removal rates of invasive fish, particularly invasive carps.
Methods
We simulated data sets using scenarios with varying numbers of fish tagged per year, removal rates, tag reporting rates, tag retention rates, and study durations. We generated the data sets under a set of “known” parameters with added stochasticity; we then fitted the simulated data sets to a Bayesian tag–return model and measured the precision and accuracy of the model-estimated removal rates.
Results
We found that the model was able to predict removal rates without bias for most of the scenarios. However, we did find patterns in the precision of the predictions that could help to inform tag–return studies. When the proportion of the population removed through harvest was constant, the proportion of the population removed per year and the probability that harvested tags were reported had the largest effect on precision. The number of tags released per year and the study duration also had moderate effects. For scenarios testing the ability of the model to predict removal rates in stochastic populations, the precision of the model was primarily influenced by the number of fish tagged, the underlying nature of the stochasticity, and whether fish were tagged during the year of the prediction.
Conclusions
Based on our simulations, we outline how study objectives, the underlying population variability, and the tolerance range for error can guide decisions regarding the number of fish to tag, how to monitor tag return rates, and how long to conduct a study.
","language":"English","publisher":"American Fisheries Society","doi":"10.1093/najfmt/vqaf049","usgsCitation":"Stanton, J.C., Marcek, B.J., and Brey, M.K., 2025, Considerations for using tag-returns to monitor targeted removal of invasive fishes: North American Journal of Fisheries Management, v. 45, no. 4, p. 669-683, https://doi.org/10.1093/najfmt/vqaf049.","productDescription":"15 p.","startPage":"669","endPage":"683","ipdsId":"IP-164064","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":501964,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13DJCBK","text":"USGS data release","linkHelpText":"Code release for simulated tag-return study for monitoring invasive fish removals"},{"id":501681,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/najfmt/vqaf049","text":"Publisher Index Page"},{"id":501474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":957555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marcek, Benjamin J.","contributorId":367732,"corporation":false,"usgs":false,"family":"Marcek","given":"Benjamin","middleInitial":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":957556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":957557,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70268060,"text":"sir20255051 - 2025 - Estimated hydrogeologic, spatial, and temporal distribution of self-supplied domestic groundwater withdrawals for aquifers of the Virginia Coastal Plain","interactions":[],"lastModifiedDate":"2025-08-14T19:36:34.867035","indexId":"sir20255051","displayToPublicDate":"2025-06-17T10:40:00","publicationYear":"2025","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":"2025-5051","displayTitle":"Estimated Hydrogeologic, Spatial, and Temporal Distribution of Self-Supplied Domestic Groundwater Withdrawals for Aquifers of the Virginia Coastal Plain","title":"Estimated hydrogeologic, spatial, and temporal distribution of self-supplied domestic groundwater withdrawals for aquifers of the Virginia Coastal Plain","docAbstract":"Water use from private-domestic wells accounts for nearly 40 percent of total groundwater withdrawals in the Virginia Coastal Plain Physiographic Province (henceforth called the Virginia Coastal Plain). However, because self-supplied domestic water use generally falls below the Virginia Department of Environmental Quality (VDEQ) reporting and management threshold of 300,000 gallons per month, quantifying these withdrawals is challenging. This report builds upon the foundation of previous U.S. Geological Survey investigations by providing revised techniques to improve estimates of the aquifer source, spatial distribution, and monthly magnitude of these groundwater withdrawals.
The aquifer sources of private-domestic wells in the Virginia Coastal Plain were estimated by cross-referencing 8,264 well records from the VDEQ and the Virginia Department of Health to a digital model of the Virginia Coastal Plain hydrogeologic framework. This analysis highlights the regional importance of the Yorktown-Eastover, Potomac, and surficial aquifers. Collectively, these three aquifers account for 80 percent of self-supplied domestic groundwater withdrawals.
The population using self-supplied domestic water was estimated using census blocks, well-use ratios, building footprints, and land-use and land-cover data to produce a high-resolution, disaggregated, raster-based dataset. This approach improves upon previous models at the census-block or road-network scale by reducing the low-density spread of the self-supplied domestic population across undeveloped areas and concentrating the population and its corresponding water use in the areas where it is most likely to occur. Results show that an estimated 475,332 people comprise the 2020 self-supplied domestic population of the Virginia Coastal Plain, an increase of 5.7 percent since 2010, and the greatest concentrations of self-supplied domestic population surround large cities. Estimates could be further refined with the addition of current and complete spatial data on public water-system service areas.
The quantity of water used by the self-supplied domestic population was estimated by modifying published state per-capita water-use coefficients with the corresponding monthly variability assessed from Virginia Coastal Plain public water-system withdrawal data. This analysis estimates an average increase of 12 percent from June through August and an average decrease of 8 percent from December through March from the baseline annual average of 80 gallons per day per capita, which generally matches similar studies in the eastern United States.
The application of these revised methodologies for the estimation of private-domestic wells and the self-supplied domestic population improves understanding of domestic groundwater use in the Virginia Coastal Plain across hydrogeologic, spatial, and temporal scales. These revisions help better inform water-resource managers and decision makers and support higher resolution groundwater modeling. Furthermore, these methods are transferrable to other areas where self-supplied domestic water withdrawals are important to the overall water budget.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255051","isbn":"978-1-4113-4609-3","collaboration":"Prepared in cooperation with Virginia Department of Environmental Quality","usgsCitation":"Kearns, M.R., and Pope, J.P., 2025, Estimated hydrogeologic, spatial, and temporal distribution of self-supplied domestic groundwater withdrawals for aquifers of the Virginia Coastal Plain: U.S. Geological Survey Scientific Investigations Report 2025–5051, 45 p., https://doi.org/10.3133/sir20255051.","productDescription":"Report: vii, 45 p.; Data release","numberOfPages":"45","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-168850","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":494146,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118651.htm","linkFileType":{"id":5,"text":"html"}},{"id":490433,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1QJQ2CB","text":"USGS data release","linkHelpText":"Estimated aquifer distribution for private domestic wells; estimated spatial distribution of the self-supplied domestic population for 2020 and 2010; and estimated monthly domestic self-supplied withdrawals of groundwater for the Virginia Coastal Plain"},{"id":490432,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5051/images/"},{"id":490431,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5051/sir20255051.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5051 XML"},{"id":490428,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5051/coverthb.jpg"},{"id":490429,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5051/sir20255051.pdf","text":"Report","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5051 PDF"},{"id":490430,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255051/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5051 HTML"}],"country":"United States","state":"Virginia","otherGeospatial":"Virginia Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.32710341724686,\n 36.556784944765795\n ],\n [\n -75.86947557927473,\n 36.55265452745853\n ],\n [\n -75.8900431225546,\n 37.161549677245205\n ],\n [\n -75.32957756818404,\n 38.02121666772172\n ],\n [\n -76.21912381502888,\n 37.907709637048185\n ],\n [\n -76.67160976718064,\n 38.146680392024706\n ],\n [\n -77.04182554621384,\n 38.31631983320398\n ],\n [\n -77.04182554621384,\n 38.40904986490523\n ],\n [\n -77.23721720737078,\n 38.34052172806261\n ],\n [\n -77.30406172302983,\n 38.39293143681613\n ],\n [\n -77.18579834917193,\n 38.64236292442064\n ],\n [\n -77.00583234547452,\n 38.722640223692224\n ],\n [\n -77.03717799709146,\n 38.83873960192227\n ],\n [\n -77.1282913174048,\n 38.95928536652022\n ],\n [\n -77.23580503537455,\n 38.99328476178536\n ],\n [\n -77.29958450463103,\n 39.054158449342225\n ],\n [\n -77.6385260561967,\n 39.00744529354114\n ],\n [\n -77.6421696520891,\n 38.07245238381515\n ],\n [\n -77.5875012530419,\n 36.77147021603372\n ],\n [\n -77.58567879299983,\n 36.54488432804379\n ],\n [\n -76.32710341724686,\n 36.556784944765795\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Quantifying lake ice loss is crucial for understanding the impact of climate change on lake ecosystems. In this study, we trained a deep learning model (Long-Short Term Memory with Landsat observations, 1984–2012) to simulate Northern Hemisphere lake ice changes at a fine spatial scale (> 0.1 km2) from 1980 to 2022. The model achieved good performance overall during the test period (2013–2022), and the derived ice-on and ice-off matched well with two independent ice phenology data sets. Results reveal a 76.8% increase in intermittently ice-covered lakes from the 1980s to the 2010s, alongside a 10.7-day shorter ice duration and a 3.9 percentage-points reduction in annual mean ice cover fractions. The model can track daily partial ice cover changes, providing a novel contribution to understanding shifts in lake ice cover with climate change. These findings can provide valuable insights for future limnology studies, such as improving estimates of greenhouse gas emissions from lakes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024gl113544","usgsCitation":"He, X., Andreadis, K.M., Roy, A.H., Langhorst, T., Kumar, A., and Butler, C.S., 2025, Modeling daily ice cover in northern hemisphere lakes with a long short‐term memory neural network: Geophysical Research Letters, v. 52, no. 12, e2024GL113544,10 p., https://doi.org/10.1029/2024gl113544.","productDescription":"e2024GL113544,10 p.","ipdsId":"IP-165442","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494970,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13ZGUGE","text":"USGS data release","linkHelpText":"A Long Short Term Memory model for predicting daily lake ice cover changes in the Northern Hemisphere"},{"id":494456,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl113544","text":"Publisher Index Page"},{"id":494313,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"12","noUsgsAuthors":false,"publicationDate":"2025-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"He, Xinchen","contributorId":316775,"corporation":false,"usgs":false,"family":"He","given":"Xinchen","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":946355,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andreadis, Konstantinos M.","contributorId":359867,"corporation":false,"usgs":false,"family":"Andreadis","given":"Konstantinos","middleInitial":"M.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":946356,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":946357,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langhorst, Theodore","contributorId":292528,"corporation":false,"usgs":false,"family":"Langhorst","given":"Theodore","email":"","affiliations":[{"id":27517,"text":"University of North Carolina - Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":946358,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kumar, Abhishek","contributorId":316778,"corporation":false,"usgs":false,"family":"Kumar","given":"Abhishek","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":946359,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butler, Caitlyn S.","contributorId":359869,"corporation":false,"usgs":false,"family":"Butler","given":"Caitlyn","middleInitial":"S.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":946360,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70268839,"text":"70268839 - 2025 - The impact of burial diagenesis on soil-formed minerals in paleosols using stable isotopes of phyllosilicates and carbonate clumped isotopes","interactions":[],"lastModifiedDate":"2025-07-08T17:13:17.514089","indexId":"70268839","displayToPublicDate":"2025-06-17T10:09:10","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"The impact of burial diagenesis on soil-formed minerals in paleosols using stable isotopes of phyllosilicates and carbonate clumped isotopes","docAbstract":"To understand the effects of burial diagenesis on the stable isotope geochemistry of soil-formed clay and carbonate minerals in paleosols, samples were collected from seven cores, spanning middle- to upper-Pennsylvanian strata of the Illinois Basin, with varied maximum burial depths of 1–3 km. Mixed-layer illite-smectite and kaolinite mixtures give δ2H and δ18O values of −83 ‰ to −36 ‰ and 11.9 ‰ to 21.1 ‰ (VSMOW), respectively. After carbonates were screened petrographically for diagenetic textures using transmitted light and cathodoluminescence, measured clumped isotope Δ47 values range from 0.504 to 0.563 ‰ (I-CDES). Resulting mineral formation temperatures for phyllosilicate mineral mixtures are 28 to 66 °C (mean = 47 °C), whereas T(Δ47) estimates for calcites are 36 to 61 °C (mean = 45 °C). Calculated δ18Owater values from which phyllosilicate minerals and calcites precipitated under isotopic equilibrium ranges from −7.1 to −1.2 ‰ and − 1.4 to +4.9 ‰, respectively. Closed and open-system phyllosilicate-fluid exchange modeling indicates that phyllosilicate alteration occurred in the presence of a low temperature brine or meteoric water and is interpreted to occur in a layer-by-layer illitization transformation. Due to the lack of diagenetic textures and positively correlated T(Δ47) and δ18Owater, calcites are interpreted to have undergone solid-state bond reordering. Despite low to moderate temperatures (<125 °C) and varying depths of shallow burial (1–3 km), solid-state transformation of phyllosilicates and calcites indicates paleosols had prolonged exposure to burial conditions which has implications for the use of paleosol minerals for paleoenvironmental reconstructions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2025.122941","usgsCitation":"McIntosh, J.A., Tabor, N., and Montañez, I., 2025, The impact of burial diagenesis on soil-formed minerals in paleosols using stable isotopes of phyllosilicates and carbonate clumped isotopes: Chemical Geology, v. 692, 122941, 17 p., https://doi.org/10.1016/j.chemgeo.2025.122941.","productDescription":"122941, 17 p.","ipdsId":"IP-171865","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":492072,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2025.122941","text":"Publisher Index Page"},{"id":491834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Kentucky","otherGeospatial":"Illinois Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.26016848796888,\n 41.70274182675729\n ],\n [\n -90.26016848796888,\n 36.61086981521096\n ],\n [\n -86.39382164848195,\n 36.61086981521096\n ],\n [\n -86.39382164848195,\n 41.70274182675729\n ],\n [\n -90.26016848796888,\n 41.70274182675729\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"692","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McIntosh, Julia A. 0000-0003-2819-8664","orcid":"https://orcid.org/0000-0003-2819-8664","contributorId":331662,"corporation":false,"usgs":true,"family":"McIntosh","given":"Julia","email":"","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":942314,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tabor, Neil J. 0000-0001-8582-9886","orcid":"https://orcid.org/0000-0001-8582-9886","contributorId":357718,"corporation":false,"usgs":false,"family":"Tabor","given":"Neil J.","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":942315,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Montañez, Isabel P. 0000-0003-0492-3796","orcid":"https://orcid.org/0000-0003-0492-3796","contributorId":357719,"corporation":false,"usgs":false,"family":"Montañez","given":"Isabel P.","affiliations":[{"id":17619,"text":"University of California at Davis","active":true,"usgs":false}],"preferred":false,"id":942316,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70268299,"text":"70268299 - 2025 - Are equilibrium shoreline models just convolutions?","interactions":[],"lastModifiedDate":"2025-06-20T14:52:37.1233","indexId":"70268299","displayToPublicDate":"2025-06-17T09:48:47","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7357,"text":"JGR Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Are equilibrium shoreline models just convolutions?","docAbstract":"Yes. Equilibrium shoreline models, which simulate wave-driven cross-shore erosion and accretion, are mathematically equivalent to a discrete convolution (i.e., a weighted, moving average) of a time series of wave-forcing conditions with a parameterized memory-decay kernel function. The direct equivalence between equilibrium shoreline models and convolutions reveals key theoretical aspects of equilibrium behavior. Convolutions (representing quasi-low-pass filter operations) provide an intuitive theoretical description of shoreline erosion and accretion behavior in response to waves: that is, shoreline position often mirrors the weighted moving average of wave time series. Model-convolution equivalence also provides a conceptual basis to interpret, evaluate, and construct data-driven Machine-Learning/Deep-Learning (ML/DL) models that use convolutions to extract features from data and then apply them for prediction (e.g., Convolutional Neural Networks (CNNs)). Finally, our findings provide a methodological pathway (based on Fourier transforms) for future understanding of wave-driven shoreline change, which can be used to interpret the coherence between the frequency spectrum of the processes of waves and shoreline change and construct more computationally efficient and effective shoreline-modeling approaches.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025JF008452","usgsCitation":"Vitousek, S., Buscombe, D.D., Gomez-de la Peña, E., Calcraft, K., Lundine, M.A., Splinter, K., Giovanni Coco, and Barnard, P.L., 2025, Are equilibrium shoreline models just convolutions?: JGR Earth Surface, v. 130, no. 6, e2025JF008452, 30 p., https://doi.org/10.1029/2025JF008452.","productDescription":"e2025JF008452, 30 p.","ipdsId":"IP-172699","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025jf008452","text":"Publisher Index Page"},{"id":491025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"130","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":198817,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","middleInitial":"D.","affiliations":[],"preferred":false,"id":940731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gomez-de la Peña, Eduardo","contributorId":357091,"corporation":false,"usgs":false,"family":"Gomez-de la Peña","given":"Eduardo","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":940732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calcraft, Kit","contributorId":357094,"corporation":false,"usgs":false,"family":"Calcraft","given":"Kit","affiliations":[{"id":65517,"text":"University of New South Wales - Sydney","active":true,"usgs":false}],"preferred":false,"id":940733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lundine, Mark A. 0000-0002-2878-1713","orcid":"https://orcid.org/0000-0002-2878-1713","contributorId":339934,"corporation":false,"usgs":true,"family":"Lundine","given":"Mark","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Splinter, Kristen D.","contributorId":357097,"corporation":false,"usgs":false,"family":"Splinter","given":"Kristen D.","affiliations":[{"id":65517,"text":"University of New South Wales - Sydney","active":true,"usgs":false}],"preferred":false,"id":940735,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Giovanni Coco","contributorId":357100,"corporation":false,"usgs":false,"family":"Giovanni Coco","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":940736,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940737,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266861,"text":"sir20245061 - 2025 - Estimating daily public supply water use by drinking water service area in New Jersey","interactions":[],"lastModifiedDate":"2025-06-17T13:40:29.748047","indexId":"sir20245061","displayToPublicDate":"2025-06-17T09:05:00","publicationYear":"2025","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":"2024-5061","displayTitle":"Estimating Daily Public Supply Water Use by Drinking Water Service Area in New Jersey","title":"Estimating daily public supply water use by drinking water service area in New Jersey","docAbstract":"This report, prepared in cooperation with the New Jersey Department of Environmental Protection, presents a method for estimating daily public supply water use by drinking water service area systems for New Jersey. The ability to accurately estimate daily public supply water use could help water supply planners in New Jersey better understand and manage the state’s limited water resources and balance the competing needs for freshwater resources. Data sources for this work include daily public supply water-use data from 2016 through 2020 acquired from New Jersey American Water for 15 drinking water service areas and monthly data exported from the New Jersey Department of Environmental Protection’s online water transfer data model database (known as NJWaTr). The two datasets were compared by aggregating the daily data to a monthly timescale. Statistical regression analysis was applied to the daily data, along with climate data, to evaluate what factors are influential in estimating daily fluctuations and trends in daily public supply water use. Fifteen regression equations were developed, one for each of the 15 drinking water service area systems for which daily data were acquired. Regression equations for systems that had seasonal patterns performed better than equations for non-seasonal systems. For the test year (2020), the average adjusted coefficient of determination for the linear regression with autoregressive errors model among systems with seasonality was 0.78; the average adjusted coefficient of determination for the linear regression with autoregressive errors model among systems with little or no seasonality was 0.25. The effects of anomalous data in the regression analysis were examined by comparing adjusted coefficient of determination values when the atypical data points were removed versus when they were retained in the analysis. Overall, including the anomalous data did not have a large effect on the results, and thus the data were retained for this study.
In addition to developing regression equations, all 589 unique drinking water service area systems in New Jersey were characterized based on socio-economic data and monthly water-use data from NJWaTr. Systems that are located near the New Jersey coast, serve populations larger than 1,970 people, or serve areas that have median property values over $256,250 tended to demonstrate seasonal water-use behaviors. Systems that have mostly urban residential land use tended to show little to no seasonal water-use behaviors. Finally, a method was developed to disaggregate monthly data to a daily timescale and was tested against systems for which daily data were not available. Two regression equation forms were developed to be applied to systems beyond the 15 systems from which the original equations were developed; one equation was developed for use when all drinking water service area systems showed little to no seasonality, and the other equation was developed for use when systems displayed seasonal behavior.
To the extent possible, uncertainty and possible sources of error were identified and examined in relation to the regression model equations developed. Additional daily data from these 15 systems (over different years) and daily data from different systems could be used to further evaluate the results of the disaggregation through a comprehensive assessment of error. Further adjustments to the regression equations could be made, ultimately enhancing their accuracy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245061","collaboration":"Prepared in cooperation with New Jersey Department of Environmental Protection","programNote":"Water Availability and Use Science Program","usgsCitation":"Shourds, J.L., and Scott, M.H., 2025, Estimating daily public supply water use by drinking water service area in New Jersey: U.S. Geological Survey Scientific Investigations Report 2024–5061, 90 p., https://doi.org/10.3133/sir20245061.","productDescription":"Report: xi, 90 p.; Appendix","numberOfPages":"90","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-151668","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":485928,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5061/sir20245061.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5061 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike
Suite 110
Lawrenceville, NJ 08648
Anthropogenic climate change is an existential threat to our planet, impacting everything from the delicate balance of ecosystems to the availability of vital resources. Coastal regions, particularly vulnerable to the impacts of climate change due to rising sea levels and changing weather patterns, are experiencing increased erosion, flooding, and habitat loss. Understanding how coastal regions responded to past warming is crucial for developing effective adaptation and mitigation strategies. One past interval commonly used to examine and compare with climate model projections of near future conditions is the mid-Piacenzian Warm Period (MPWP) which occurred between*3.3 and 3.0 Ma. Here we review the stratigraphy of Atlantic Coastal Plain (ACP) sediments to determine the stratigraphic position of the MPWP by evaluating ages based upon existing and new planktic foraminifer occurrence data calibrated to the current geologic time scale (GTS2020). We identify geologic formations representing pre-, syn-, and post-MPWP environments. The Sunken Meadow Member of the Yorktown Formation in Virginia and North Carolina and the Wabasso beds in the subsurface of Georgia and Florida both fall within Planktic Foraminiferal Zone PL1 and represent pre-MPWP Pliocene deposits. Parts of the Yorktown Formation in southeastern Virginia and northern North Carolina, the Duplin Formation in North Carolina and South Carolina, and the Raysor Formation in South Carolina and Georgia, fall within Planktic Foraminiferal Zone PL3 and were deposited following a major regression associated with a global drop in sea level during Marine Isotope Stage (MIS) M2 and represent syn-MPWP deposits. Representing the immediately post-MPWP climate conditions (Planktic Foraminiferal Zone PL5) are the Chowan River, Bear Bluff, and Cypresshead Formations. This work provides a record of the MPWP from Georgia to Virginia and provides a stratigraphic framework within which the impacts of a profound global warming on the east coast of the United States can be assessed.
","language":"English","publisher":"Micropaleontological Press","doi":"10.47894/stra.22.2.00","usgsCitation":"Dowsett, H., and Spivey, W., 2025, The stratigraphic record of the mid-Piacenzian warm period on the Atlantic Coastal Plain: Stratigraphy, v. 22, no. 2, p. 81-97, https://doi.org/10.47894/stra.22.2.00.","productDescription":"17 p.","startPage":"81","endPage":"97","ipdsId":"IP-167251","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":490297,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.micropress.org/microaccess/stratigraphy/issue-412/article-2424","linkFileType":{"id":5,"text":"html"}},{"id":495251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, North Carolina, South Carolina, Virginia","otherGeospatial":"Atlantic Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.2540064137628,\n 37.9661310516864\n ],\n [\n -77.45393664738447,\n 37.57788480662157\n ],\n [\n -82.81553889394615,\n 31.49173122752032\n ],\n [\n -82.4849451212944,\n 30.69984073209814\n ],\n [\n -81.58807068387608,\n 30.841401404258605\n ],\n [\n -78.7009680731669,\n 33.43137207763918\n ],\n [\n -75.88995103042635,\n 34.91166522689612\n ],\n [\n -75.2540064137628,\n 37.9661310516864\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":316789,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":939852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spivey, Whittney 0000-0003-1111-3361 wspivey@usgs.gov","orcid":"https://orcid.org/0000-0003-1111-3361","contributorId":214849,"corporation":false,"usgs":true,"family":"Spivey","given":"Whittney","email":"wspivey@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":939853,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70268362,"text":"70268362 - 2025 - Characterizing water-quality response after the 2020 Cameron Peak Fire using a novel application of the Weighted Regressions on Time, Discharge, and Season method","interactions":[],"lastModifiedDate":"2025-06-24T14:08:12.18671","indexId":"70268362","displayToPublicDate":"2025-06-16T09:02:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing water-quality response after the 2020 Cameron Peak Fire using a novel application of the Weighted Regressions on Time, Discharge, and Season method","docAbstract":"The frequency and severity of wildfire activity in the western United States emphasises the utility of hydrologic models to predict water-quality response. This study presents a novel application of the Weighted Regressions on Time, Discharge and Season (WRTDS) method to assess potential changes in water quality in two watersheds draining the North Fork Big Thompson River and Buckhorn Creek in Larimer County, Colorado that were affected by the 2020 Cameron Peak Fire. WRTDS models were developed using 12 years of pre-fire data and used to estimate the expected constituent concentrations for each sample collected in the post-fire record. The predicted constituent concentrations modelled in this manner are representative of conditions in the absence of fire and allow pre-fire and post-fire stream chemistry to be quantitatively compared. Nitrate and total phosphorus concentrations showed the greatest differences between the observed and predicted concentrations, which were up to 153% greater than expected. We linked changes in source inputs and elevation as likely controls on the difference in magnitude and timing of response between the two watersheds. Post-fire arsenic and manganese concentrations were greater than the predicted concentrations in both watersheds, with arsenic up to 42% greater and manganese up to 85% greater than the model predictions. Post-fire calcium, magnesium, chloride and sulphate concentrations were greater than model predictions at the North Fork and less than the predictions at Buckhorn. We argue that greater burn severity at Buckhorn likely reduced soil–water infiltration and led to bypassed subsurface flow paths through a major lithologic source of these constituents. Post-fire changes in total organic carbon and dissolved iron concentrations were weakly supported by the model results, as observed concentrations were largely within the bounds of expected values calculated from the pre-fire model. The novel approach to WRTDS presented in this study could be a useful tool for water-quality assessments after land disturbances in the western United States.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70178","usgsCitation":"Ruckhaus, M.H., Clow, D.W., Hirsch, R.M., and Chapin, T.W., 2025, Characterizing water-quality response after the 2020 Cameron Peak Fire using a novel application of the Weighted Regressions on Time, Discharge, and Season method: Hydrological Processes, v. 39, no. 6, e70178, 21 p., https://doi.org/10.1002/hyp.70178.","productDescription":"e70178, 21 p.","ipdsId":"IP-171701","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":491489,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70178","text":"Publisher Index Page"},{"id":491178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","county":"Larimer County","otherGeospatial":"Buckhorn Creek watershed, North Fork Big Thompson River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.1667,\n 40.75\n ],\n [\n -106,\n 40.75\n ],\n [\n -106,\n 40.333\n ],\n [\n -105.1667,\n 40.333\n ],\n [\n -105.1667,\n 40.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruckhaus, Manya Helene 0009-0006-3111-1127","orcid":"https://orcid.org/0009-0006-3111-1127","contributorId":344234,"corporation":false,"usgs":true,"family":"Ruckhaus","given":"Manya","email":"","middleInitial":"Helene","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":941105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chapin, Tanner William 0000-0003-3905-3241","orcid":"https://orcid.org/0000-0003-3905-3241","contributorId":297923,"corporation":false,"usgs":true,"family":"Chapin","given":"Tanner","email":"","middleInitial":"William","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941106,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}