Climate change in California is expected to alter future water availability, impacting water supplies needed to support future housing growth and agriculture demand. In groundwater-dependent regions like California's Central Coast, new land-use related water demand and decreasing recharge is already stressing depleted groundwater basins. We developed a spatially explicit state-and-transition simulation model that integrates climate, land-use change, water demand, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand in five counties along the Central Coast from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future from a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows. Results indicate that future changes in climate-driven groundwater recharge, coupled with cumulative increases in agricultural water demand, result in overall declines in future groundwater balance, with a Hot/Dry future resulting in cumulative groundwater decline in all but Santa Cruz County. Cumulative declines by 2060 are especially prominent in San Luis Obispo (−2.9 to −5.1 Bm3) and Monterey counties (−6.5 to −8.7 Bm3), despite limited changes in agricultural water demand over the model period. These two counties show declining groundwater reserves in a Warm/Wet future as well, while San Benito and Santa Barbara County barely reach equilibrium. These results suggest future groundwater supplies may not be able to keep pace with regional demand and declining climate-driven recharge, resulting in a potential reduction in water security in the region. However, our county-scale projections showed new housing and associated water demand does not conflict with California's groundwater sustainability goals. Rather, future climate coupled with increasing agricultural groundwater demand may reduce water security in some counties, potentially limiting available groundwater supplies for new housing.
Seismic velocity models of the crust are an integral part of earthquake monitoring systems at volcanoes. 1D models that vary only in depth are typically used for real‐time hypocenter determination and serve as critical reference models for detailed 3D imaging studies and geomechanical modeling. Such models are usually computed using seismic tomographic methods that rely on P‐ and S‐wave arrival‐time picks from numerous earthquakes recorded at receivers around the volcano. Traditional linearized tomographic methods that jointly invert for source locations, velocity structure, and station corrections depend critically on having reasonable starting values for the unknown parameters, are susceptible to local misfit minima and divergence, and often do not provide adequate uncertainty information. These issues are often exacerbated by sparse seismic networks, inadequate distributions of seismicity, and/or poor data quality common at volcanoes. In contrast, modern probabilistic global search methods avoid these issues only at the cost of increased computation time. In this article, we review both approaches and present example applications and comparisons at several volcanoes in the United States, including Mount Hood (Oregon), Mount St. Helens (Washington), the Island of Hawai’i, and Mount Cleveland (Alaska). We provide guidance on the proper usage of these methods as relevant to challenges specific to volcano monitoring and imaging. Finally, we survey‐published 1D P‐wave velocity models from around the world and use them to derive a generic stratovolcano velocity model, which serves as a useful reference model for comparison and when local velocity information is sparse.
Long-term, large-scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large-scale, long-term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species-specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large-scale, long-term datasets.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4961","usgsCitation":"Brusa, J., Farr, M., Evenson, J., Silverman, E., Murphie, B., Cyra, T., Tschaekofske, H., Spragens, K., and Converse, S.J., 2024, Correcting for measurement errors in a long-term aerial survey with auxiliary photographic data: Ecosphere, v. 15, no. 8, e4961, 15 p., https://doi.org/10.1002/ecs2.4961.","productDescription":"e4961, 15 p.","ipdsId":"IP-157545","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487744,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4961","text":"Publisher Index Page"},{"id":482967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Salish Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.31135582023572,\n 49.0056879632715\n ],\n [\n -123.31135582023572,\n 48.746723285327334\n ],\n [\n -122.69212389645517,\n 48.746723285327334\n ],\n [\n -122.69212389645517,\n 49.0056879632715\n ],\n [\n -123.31135582023572,\n 49.0056879632715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Brusa, Jamie L.","contributorId":351922,"corporation":false,"usgs":false,"family":"Brusa","given":"Jamie L.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":929751,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farr, Matthew T.","contributorId":351923,"corporation":false,"usgs":false,"family":"Farr","given":"Matthew T.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":929752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evenson, Joseph","contributorId":351924,"corporation":false,"usgs":false,"family":"Evenson","given":"Joseph","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929753,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silverman, Emily","contributorId":351925,"corporation":false,"usgs":false,"family":"Silverman","given":"Emily","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929754,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murphie, Bryan","contributorId":351927,"corporation":false,"usgs":false,"family":"Murphie","given":"Bryan","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929755,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cyra, Thomas A.","contributorId":351929,"corporation":false,"usgs":false,"family":"Cyra","given":"Thomas A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929756,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tschaekofske, Heather","contributorId":351931,"corporation":false,"usgs":false,"family":"Tschaekofske","given":"Heather","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929757,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Spragens, Kyle A.","contributorId":351933,"corporation":false,"usgs":false,"family":"Spragens","given":"Kyle A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929758,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":929759,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257668,"text":"ofr20241015 - 2024 - Occupancy dynamics of the California Gnatcatcher in southern California","interactions":[],"lastModifiedDate":"2024-08-26T22:23:15.258653","indexId":"ofr20241015","displayToPublicDate":"2024-08-26T13:34:24","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1015","displayTitle":"Occupancy Dynamics of the Coastal California Gnatcatcher in Southern California","title":"Occupancy dynamics of the California Gnatcatcher in southern California","docAbstract":"The Coastal California Gnatcatcher (Polioptila californica californica: “gnatcatcher”) is a resident species restricted to coastal sage scrub habitat in southern California. Listed as federally threatened, the gnatcatcher is subject to multiple threats, including habitat loss, fragmentation, and degradation, particularly in association with the increasing frequency of large wildfires. The California Gnatcatcher is a focal species under several habitat conservation plans and is monitored to determine population trends and evaluate the success of the plans in protecting the species.
Historically, gnatcatcher monitoring has been limited in geographic scope and has used differing methodologies, limiting the extent to which findings can be generalized across larger spatial scales and multiple populations. In 2015, we completed the first of an intended series of surveys following a standardized protocol designed to address two broad objectives. First, we sought to determine gnatcatcher occupancy at the regional scale, including habitat from throughout the species’ range in southern California, as well as in two subregions: Orange County and San Diego County, to address specific management objectives within those jurisdictions. In addition, we collected vegetation data to better understand gnatcatcher habitat associations that affect occupancy. In a parallel objective, we evaluated the effect of fire on gnatcatchers and their habitat by comparing occupancy and vegetation characteristics across sites varying in the length of time since the last fire. Data collected in 2020 allowed us to expand the study to include analyses of colonization (sites unoccupied in one year and occupied the next) and extinction (sites occupied in one year but not the next).
In 2020, we surveyed 327 regional points and 180 subregional points each in Orange and San Diego Counties. In addition, we surveyed 95–106 points within 4 postfire categories based on the year of the last fire at each point: before or during 2002 (“unburned”), 2003–06, 2007–10, and 2011–14. We surveyed for gnatcatchers during three area searches centered on each point at 2-week intervals commencing in mid-March. Vegetation data were collected during May–June using a modified point-intercept method along fixed transects.
Shrub and tree cover at our plots was dominated by California sagebrush (Artemisia californica), California buckwheat (Eriogonum fasciculatum), laurel sumac (Malosma laurina), sage (including Salvia mellifera and S. leucophylla), and sunflowers (including Encelia californica, E. farinosa, and Bahiopsis laciniata); however, most of the vegetation at plots consisted of non-native grass and herbaceous plants, indicating a high level of disturbance associated with fire. We documented vegetation differences at the subregional scale indicative of differences in fire history: in Orange County, overall shrub/tree cover was higher and herbaceous cover lower than in San Diego, where three large fires had burned within 17 years of this study. Both woody and herbaceous cover increased between 2016 and 2020 at the regional and two subregional scales, likely a response to above-average precipitation during 2 years preceding the 2020 surveys. Herbaceous vegetation also increased at postfire points; however, woody vegetation cover changed little between 2016 and 2020.
We modeled the effects of vegetation and physical (elevation, distance to Pacific coast, slope) covariates on gnatcatcher occupancy, colonization, and extinction probabilities in the regional, subregional, and postfire datasets. Cover of California sagebrush was the strongest predictor of gnatcatcher occupancy and appeared in the top models for every dataset. California buckwheat was another strong positive predictor of gnatcatcher occupancy in all datasets. Cover of sunflowers was a positive predictor of occupancy in the Orange County subregion, and both sunflowers and sage were positive predictors of occupancy at postfire points. In contrast, laurel sumac was negatively related to gnatcatcher occupancy in postfire habitats, with occupancy unlikely when sumac exceeded 50 percent cover. Herbaceous vegetation, including invasive grass, negatively affected gnatcatcher occupancy regionwide.
Covariates that were strong positive predictors of occupancy were also positive predictors of colonization and (or) negative predictors of extinction, and vice versa. Outside of the positive effects of California sagebrush and California buckwheat, and the negative effect of total herbaceous cover, we identified few covariates influencing colonization. In contrast, we identified many more predictors of extinction, including cover of laurel sumac and grass, which increased extinction risk, and cover of California sagebrush, sunflowers, and bare ground, along with time since last fire, which reduced extinction risk.
We used our modelled estimates of colonization and extinction probabilities to derive occupancy in 2020 based on starting occupancy in 2016. Gnatcatcher occupancy increased in 2020 at regional and subregional scales and in unburned habitat, growing by 19–35 percent since 2016. Among burned sites, occupancy tripled from 2016 to 2020 at points burned during 2011–14 but was unchanged at the 2007–10 postfire points and declined by 28 percent at points burned in 2003–06. The slow recovery of the gnatcatcher population in this latter category, which makes up 16 percent of the suitable habitat in San Diego County, is a matter of conservation concern warranting further attention.
Collectively, our rangewide results reveal a widespread and long-term effect of wildfire on California Gnatcatcher habitat, particularly in San Diego County. These data provide a baseline from which future monitoring can be used to evaluate changes in habitat condition over time and to improve our understanding of the factors and processes influencing gnatcatcher occupancy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241015","collaboration":"Prepared in cooperation with the San Diego Association of Governments, Natural Communities Coalition, California Department of Fish and Wildlife, and U.S. Fish and Wildlife Service","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Kus, B.E., Houston, A., and Preston, K.L., 2024, Occupancy dynamics of the Coastal California Gnatcatcher in southern California: U.S. Geological Survey Open-File Report 2024–1015, 34 p., https://doi.org/10.3133/ofr20241015.","productDescription":"Report: viii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-156536","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":433034,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241015/full"},{"id":433032,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1015/ofr20241015.xml"},{"id":433031,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1015/ofr20241015.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":433029,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PC30JX","text":"USGS Data Release","description":"Kus, B.E., and Houston, A., 2021, Rangewide occupancy and post-fire recovery of California gnatcatchers in southern California (ver 2.0, March 2023): U.S. Geological Survey data release, https://doi.org/10.5066/F7PC30JX.","linkHelpText":"Rangewide occupancy and post-fire recovery of California gnatcatchers in southern California (ver 2.0, March 2023)"},{"id":433033,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1015/images"},{"id":433030,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1015/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.95012019018054,\n 35.34010394860475\n ],\n [\n -119.95012019018054,\n 32.347823604041594\n ],\n [\n -116.28068659643058,\n 32.347823604041594\n ],\n [\n -116.28068659643058,\n 35.34010394860475\n ],\n [\n -119.95012019018054,\n 35.34010394860475\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The Kansas River is an essential water resource that provides drinking water to more than 950,000 people in northeastern Kansas. Water suppliers that rely on the Kansas River as a water-supply source use physical and chemical water-treatment strategies to remove contaminants before distribution. Water District No. 1 of Johnson County, Kansas (WaterOne), is the largest water supplier in the State and uses the Kansas and Missouri Rivers as water-supply sources to provide drinking water to the Kansas City metropolitan area. WaterOne has been using ozone disinfection as a primary water-treatment strategy since the summer of 2020. Water suppliers that rely on ozone disinfection have become increasingly concerned with the presence of elevated dissolved bromide (the negatively charged form of bromine; hereafter referred to as “bromide”) concentrations in their water-supply source. Ozone disinfection of source water containing elevated concentrations of bromide can lead to the formation of bromate, a regulated disinfection byproduct and probable carcinogen. Real-time computations of bromide concentrations upstream from the WaterOne source-water intake in the Kansas River can be used to assist WaterOne with proactive adjustment of water-treatment strategies. These computations can also be used to advance understanding of hydrologic processes affecting ozone disinfection and formation of bromate.
This report documents the development of the surrogate-regression model that computes bromide concentrations in real time at De Soto, Kansas, and characterizes daily and monthly bromide concentrations at this location during the study period. The U.S. Geological Survey (USGS), in cooperation with WaterOne, collected specific conductance and discrete bromide sample data at the USGS streamgage Kansas River at De Soto, Kans. (06892350; hereafter referred to as “De Soto”), during January 2021 through October 2023 to develop a surrogate-regression model using ordinary least-squares regression that computes bromide concentrations at De Soto, which is about 15 miles upstream from the WaterOne source-water intake in the Kansas River. Specific conductance explained about 85 percent of the variance in bromide concentrations at De Soto during the study period. The surrogate-regression model documented in this report estimated that bromide concentrations at De Soto were likely to exceed the WaterOne water-treatment level of concern (150 micrograms per liter [μg/L]) when specific conductance was greater than or equal to about 930 microsiemens per centimeter at 25 degrees Celsius. Surrogate-regression model computations of bromide concentrations documented in this report are available at the USGS National Real-Time Water-Quality website (https://nrtwq.usgs.gov/).
Bromide concentrations in discrete samples ranged from 31.9 to 251 μg/L and exceeded the water-treatment level of concern in about 34 percent of the 41 samples collected at De Soto during January 2021 through October 2023. Computed daily bromide concentrations ranged from 38.2 to 277 μg/L and exceeded the water-treatment level of concern about 46 percent of the time during January 2021 through October 2023. Generally, an inverse relation was observed between bromide and streamflow during the study period. Higher bromide concentrations were observed during September through February, and lower bromide concentrations were observed during March through August. Seasonal median bromide concentrations were significantly different in all pairwise seasonal combinations, except for summer versus spring. Computed median bromide concentrations were highest during winter, followed by fall, then spring and summer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245078","collaboration":"Prepared in cooperation with the Water District No. 1 of Johnson County, Kansas","usgsCitation":"Williams, T.J., and Totzke, G.S., 2024, Computation of bromide concentrations at the Kansas River at De Soto, Kansas, January 2021 through October 2023: U.S. Geological Survey Scientific Investigations Report 2024–5078, 18 p., https://doi.org/10.3133/sir20245078.","productDescription":"Report: vii, 18 p.; Appendix; Dataset","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-166673","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":433130,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245078/full"},{"id":433128,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5078/downloads/","text":"Appendix 1","linkHelpText":"—Model Archival Summary for Bromide Concentration at U.S. Geological Survey Streamgage 06892350, Kansas River at De Soto, Kansas, during January 2021 through October 2023"},{"id":499481,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117306.htm","linkFileType":{"id":5,"text":"html"}},{"id":433124,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5078/coverthb.jpg"},{"id":433127,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5078/images/"},{"id":433126,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5078/sir20245078.XML"},{"id":433125,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5078/sir20245078.pdf","text":"Report","size":"2.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5078"},{"id":433129,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Kansas","city":"De Soto","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.89522863083639,\n 39.3821856985449\n ],\n [\n -96.89522863083639,\n 38.76718861844998\n ],\n [\n -94.64788021564547,\n 38.76718861844998\n ],\n [\n -94.64788021564547,\n 39.3821856985449\n ],\n [\n -96.89522863083639,\n 39.3821856985449\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Constraining landslide occurrence rates can help to generate landslide hazard models that predict the spatial and temporal occurrence of landslides. However, most landslide inventories do not include any temporal data due to the difficulties of dating landslide deposits. Here we introduce a method for estimating the mean landslide occurrence rate of deep-seated rotational and translational slides derived solely from high-resolution (≤3 m) elevation data and globally available estimates of the diffusion coefficient for sediment flux. The method applies a linear diffusion model to the roughest landslide deposits until they reach a representative non-landslide roughness distribution. This estimates the time for a landslide deposit to be unrecognizable in high-resolution digital elevation data, which we term the mean lifetime of the landslide. Using the mean lifetime and number of landslides within an area of interest, we can estimate the mean occurrence rate of landslides over that domain. We validate this approach using a comprehensive temporal inventory of landslides in western Oregon created using age-roughness curves that are calibrated with high-resolution elevation data and radiocarbon data. We find good agreement between our diffusion method and the existing age-roughness-derived estimates, producing mean lifetimes of 4500 and 5200 years (4% difference), respectively. Hazard maps produced using the two methodologies generally agree, with the maximum differences in landslide probability reaching 0.1. Due to the relative abundance of high-resolution elevation data compared with age-dated landslides, our method could help constrain landslide occurrence rates in areas previously considered unfeasible.
Ozette Lake is an ~100-m-deep coastal lake located along the outer coast of the Olympic Peninsula (Washington, USA); it is situated above the locked portion of the northern Cascadia megathrust but also relatively isolated from active crustal faults and intraslab earthquakes. Here we present a suite of geophysical and geological evidence for earthquake-triggered mass transport deposits (MTDs) and related turbidite deposition in Ozette Lake since ca. 14 ka. Comprehensive high-resolution bathymetry data, seismic reflection profiles, and sediment cores are used to characterize the post-glacial stratigraphic framework and examine paleoseismic evidence in the lacustrine sediments. Stacked sequences of MTDs along the steep eastern flanks of the lake appear to grade basin-ward from thick, chaotic, blocky masses to thin, parallel-bedded turbidite beds. The discrete turbidite event layers are separated by fine-grained (silt and clay) lake sedimentation. The event layers are observed throughout the lake, but the physical characteristics of the deposits vary considerably depending on proximity to primary depocenters, steep slopes, and subaqueous deltas. A total of 30–34 event deposits are observed in the post-glacial record. Radiometric dating was used to reconstruct a detailed sedimentation history over the last ~5.5 k.y., develop an age model, and estimate the recurrence (365–405 yr) for the most recent 12 event layers. Based on sedimentological characteristics, temporal overlap with other regional paleoseismic chronologies, and recurrence estimates, at least 10 of the dated event layers appear to be sourced from slope failures triggered by intense shaking during megathrust ruptures; the recurrence interval for these 10 events is 440–560 yr. Thus, Ozette Lake contains one of the longest and most robust geological records of repeated shaking along the northern Cascadia subduction zone.
Cyclonic storms (i.e., hurricanes) are powerful disturbance events that often cause widespread forest damage. Storm-related canopy damage reduces rainfall interception and evapotranspiration, but impacts on streamflow regimes are poorly understood. We quantify streamflow changes in Puerto Rico following Hurricane Maria in September 2017, and evaluate whether forest cover and storm-related canopy damage account for the differences. Streams are particularly vulnerable to flooding in early post-disturbance stages during hurricane season, so we focus on 3 months (Oct–Dec) following the hurricane. To discern changes in rainfall responses, we partitioned streamflow into baseflow and quickflow using a digital filter. We collected 2010–2017 streamflow and rainfall data from 18 watersheds and compared the relative magnitude of post- to pre-hurricane double mass curve slopes of baseflow and quickflow volumes against rainfall. Several watersheds displayed higher post-hurricane quickflow and baseflow, however, the response was variable. The magnitude of quickflow increase was greater in watersheds with high forest damage. Under the same level of relative damage, watersheds with low initial forest cover had greater quickflow increases than highly forested ones. Conversely, baseflow generally increased, but increases were greater in highly forested watersheds and smaller in highly damaged watersheds. These results suggest that post-storm baseflow increases were due to recharge of hurricane-related rainfall, as well as forest transpiration interruption and soil disturbance enhancing recharge of post-hurricane rainfall, while increases to quickflow are related to loss of canopy rainfall interception and higher soil saturation decreasing infiltration. Our research demonstrates that forest damage from disturbance lowers quickflow and elevates baseflow in highly forested watersheds, and elevates quickflow and lowers baseflow in less-forested watersheds. Less-forested watersheds may be closer to the forest cover loss threshold needed to elicit a streamflow response following disturbance, suggesting higher flooding potential downstream, and a lower storm-related forest disturbance threshold than in heavily forested watersheds.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.15249","usgsCitation":"Hall, J.S., Scholl, M.A., Shanley, J.B., Matt, S., and Uriarte, M., 2024, Forest cover lessens hurricane impacts on peak streamflow: Hydrological Processes, v. 38, no. 8, e15249, 15 p., https://doi.org/10.1002/hyp.15249.","productDescription":"e15249, 15 p.","ipdsId":"IP-145696","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":466952,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.15249","text":"Publisher Index Page"},{"id":463115,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto 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Division","active":true,"usgs":true}],"preferred":true,"id":916574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matt, Serena 0000-0001-7489-1588","orcid":"https://orcid.org/0000-0001-7489-1588","contributorId":270681,"corporation":false,"usgs":true,"family":"Matt","given":"Serena","email":"","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Uriarte, Maria","contributorId":287019,"corporation":false,"usgs":false,"family":"Uriarte","given":"Maria","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":916577,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70260972,"text":"70260972 - 2024 - Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau","interactions":[],"lastModifiedDate":"2024-11-19T19:30:42.94833","indexId":"70260972","displayToPublicDate":"2024-08-25T13:08:57","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau","docAbstract":"Biological soil crusts (biocrusts) are widespread soil photosynthetic communities covering about 12% of Earth's land surface and play crucial roles in terrestrial carbon (C) and nitrogen (N) cycles, yet scalable quantifications of biocrusts and their biogeochemical contributions are notably lacking. While remote sensing has enormous potential to assess, scale, and contextualize biocrusts and their functions, the applicability of hyperspectral data in predicting C- and N-related biocrust traits remains largely unexplored. We address this issue by evaluating the potential of in situ hyperspectral data to predict C and N across a range of biocrust species and different environmental conditions. We found that in situ hyperspectral reflectance measurements can be used to predict biocrust tissue C/N ratios and N concentrations with relatively high accuracy but to a lesser extent for potential biocrust N2 fixation rates. Critical wavelength domains included the visible region of the spectrum from roughly 490–600 nm, which most effectively captured variations in biocrust tissue C, and the shortwave infrared region from 1,150 to 1,350 nm and 1,550–1,650 nm, which most effectively captured biocrust tissue N and N2 fixation potential. Finally, we provide evidence that multi- and hyperspectral missions with targeted band placement, such as the proposed 26-band Landsat Next, could be effective in predicting biocrust traits. This work provides a critical step in understanding how to apply data from new and upcoming satellite missions to the monitoring of biocrusts.
","language":"English","publisher":"AGU","doi":"10.1029/2024JG008089","usgsCitation":"Yan, D., Reed, S., Rutherford, W., Javadian, M., Reibold, R.H., Villarreal, M.L., Poulter, B., Song, S., and Smith, W., 2024, Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau: Journal of Geophysical Research: Biogeosciences, v. 129, no. 8, e2024JG008089, 14 p., https://doi.org/10.1029/2024JG008089.","productDescription":"e2024JG008089, 14 p.","ipdsId":"IP-153887","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":499268,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jg008089","text":"Publisher Index Page"},{"id":464299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","city":"Moab","otherGeospatial":"Sand Flats Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.52189096872196,\n 38.5875471924133\n ],\n [\n -109.52189096872196,\n 38.569668406221155\n ],\n [\n -109.46583788017041,\n 38.569668406221155\n ],\n [\n -109.46583788017041,\n 38.5875471924133\n ],\n [\n -109.52189096872196,\n 38.5875471924133\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Yan, Dong","contributorId":346341,"corporation":false,"usgs":false,"family":"Yan","given":"Dong","affiliations":[{"id":82831,"text":"Information and Data Center, China Renewable Energy Engineering Institute, Beijing 100120, China.","active":true,"usgs":false}],"preferred":false,"id":918762,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":918763,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rutherford, William A.","contributorId":346342,"corporation":false,"usgs":false,"family":"Rutherford","given":"William A.","affiliations":[{"id":36671,"text":"School of Natural Resources and the Environment, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":918764,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Javadian, 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mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":918767,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Poulter, Benjamin","contributorId":346344,"corporation":false,"usgs":false,"family":"Poulter","given":"Benjamin","affiliations":[{"id":82832,"text":"National Aeronautics and Space Administration, Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":918768,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Song, Shujun","contributorId":346345,"corporation":false,"usgs":false,"family":"Song","given":"Shujun","email":"","affiliations":[{"id":82833,"text":"nformation and Data Center, China Renewable Energy Engineering Institute","active":true,"usgs":false}],"preferred":false,"id":918769,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smith, William K.","contributorId":346346,"corporation":false,"usgs":false,"family":"Smith","given":"William K.","affiliations":[{"id":36671,"text":"School of Natural Resources and the Environment, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":918770,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262366,"text":"70262366 - 2024 - Trends in plant cover derived from vegetation plot data using ordinal zero-augmented beta regression","interactions":[],"lastModifiedDate":"2025-01-17T14:49:48.775588","indexId":"70262366","displayToPublicDate":"2024-08-23T11:48:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2490,"text":"Journal of Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"Trends in plant cover derived from vegetation plot data using ordinal zero-augmented beta regression","docAbstract":"Plant cover values in vegetation plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non-equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is whether we can detect species trends in vegetation plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners.
The Netherlands, Western Europe.
We used vegetation plot data collected from 10,000 fixed plots which were surveyed once every four years during 1999–2022. We used the ordinal zero-augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four-year period. Thereafter we assessed trends in cover and in occurrence across all periods.
We found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence.
Our model enables researchers to detect trends in cover using longitudinal vegetation plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring data sets.
","language":"English","publisher":"Wiley","doi":"10.1111/jvs.13295","usgsCitation":"van Strien, A., Irvine, K., and Retel, C., 2024, Trends in plant cover derived from vegetation plot data using ordinal zero-augmented beta regression: Journal of Vegetation Science, v. 35, no. 4, e13295, 11 p., https://doi.org/10.1111/jvs.13295.","productDescription":"e13295, 11 p.","ipdsId":"IP-156504","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":466954,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jvs.13295","text":"Publisher Index Page"},{"id":466650,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Netherlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 3.104622082077441,\n 51.56124200693006\n ],\n [\n 3.4349861187013175,\n 51.25787100973227\n ],\n [\n 3.8571179432763927,\n 51.21190422488573\n ],\n [\n 4.288426546646235,\n 51.36112842072012\n ],\n [\n 4.389371113392144,\n 51.36112842072012\n ],\n [\n 4.7747958227873255,\n 51.46415357027314\n ],\n [\n 5.059275965435631,\n 51.446998833246\n ],\n [\n 5.215281204952788,\n 51.2923159651817\n ],\n [\n 5.51811490519151,\n 51.28083718268144\n ],\n [\n 5.775064711454661,\n 51.16013668783256\n ],\n [\n 5.674120144707871,\n 50.84830939560217\n ],\n [\n 5.747534375068625,\n 50.778729633794114\n ],\n [\n 6.1146055268733335,\n 50.77292664103024\n ],\n [\n 6.05954485410291,\n 50.93513837713709\n ],\n [\n 5.894362835790986,\n 50.992934460491995\n ],\n [\n 5.90353961458581,\n 51.06219487139654\n ],\n [\n 6.169666199643643,\n 51.17164560996849\n ],\n [\n 6.1146055268733335,\n 51.22914715100595\n ],\n [\n 6.197196536029708,\n 51.43555876010598\n ],\n [\n 6.096251969282918,\n 51.669507847947216\n ],\n [\n 5.958600287356177,\n 51.78886977227111\n ],\n [\n 6.151312642054137,\n 51.879600723397715\n ],\n [\n 6.3990856695215825,\n 51.85126693759756\n ],\n [\n 6.821217494096743,\n 51.9644950265338\n ],\n [\n 6.738626484941193,\n 52.05487205707274\n ],\n [\n 7.05981374276945,\n 52.274401960865504\n ],\n [\n 6.986399512408639,\n 52.459312885357974\n ],\n [\n 6.701919369760333,\n 52.481673755016715\n ],\n [\n 6.756980042530671,\n 52.63231261791026\n ],\n [\n 7.050636963974682,\n 52.643450482948595\n ],\n [\n 7.206642203491782,\n 53.025972464426985\n ],\n [\n 7.179111867106656,\n 53.317509748322465\n ],\n [\n 6.867101388072314,\n 53.4488756479484\n ],\n [\n 6.3899088907268435,\n 53.65060475034829\n ],\n [\n 5.105159859411231,\n 53.465267895494804\n ],\n [\n 4.49031568013902,\n 52.82681609388678\n ],\n [\n 4.26089621026108,\n 52.257553484418736\n ],\n [\n 3.104622082077441,\n 51.56124200693006\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"35","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"van Strien, Arco","contributorId":349035,"corporation":false,"usgs":false,"family":"van Strien","given":"Arco","affiliations":[{"id":27734,"text":"Statistics Netherlands","active":true,"usgs":false}],"preferred":false,"id":923944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":220632,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":923945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Retel, Cas","contributorId":349036,"corporation":false,"usgs":false,"family":"Retel","given":"Cas","affiliations":[{"id":83417,"text":"Statistics Netherland","active":true,"usgs":false}],"preferred":false,"id":923947,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266317,"text":"70266317 - 2024 - Seasonal patterns in riverine carbon form and export from a temperate forested watershed in Southeast Alaska","interactions":[],"lastModifiedDate":"2025-05-06T13:11:07.378975","indexId":"70266317","displayToPublicDate":"2024-08-23T09:45:10","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal patterns in riverine carbon form and export from a temperate forested watershed in Southeast Alaska","docAbstract":"Riverine export of carbon (C) is an important part of the global C cycle; however, most riverine C budgets focus on individual forms of C and fail to comprehensively measure both organic and inorganic C species in concert. To address this knowledge gap, we conducted high frequency sampling of multiple C forms, including dissolved organic C (DOC), inorganic carbon (as alkalinity), particulate organic C (POC), coarse particulate organic C (CPOC), and invertebrate biomass C across the main run-off season in a predominantly rain-fed watershed in Southeast Alaska. Streamwater concentrations were used to model daily watershed C export from May through October. Concentration and modeled yield data indicated that DOC was the primary form of riverine C export (8708 kg C/km2), except during low flow periods when alkalinity (3125 kg C/km2) was the dominant form of C export. Relative to DOC and alkalinity, export of particulate organic C (POC: 992 kg C/km2; CPOC: 313 kg C/km2) and invertebrates (40 kg C/km2) was small, but these forms of organic matter could disproportionately impact downstream food webs because of their higher quality, assessed via C to nitrogen ratios. These seasonal and flow driven changes to C form and export likely provide subsidies to downstream and nearshore ecosystems such that predicted shifts in regional hydroclimate could substantially impact C transfer and incorporation into aquatic food webs.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10533-024-01175-7","usgsCitation":"Delbecq, C., Fellman, J.B., Bellmore, J.R., Whitney, E., Hood, E., Fitzgerald, K., and Falke, J.A., 2024, Seasonal patterns in riverine carbon form and export from a temperate forested watershed in Southeast Alaska: Biogeochemistry, v. 167, p. 1353-1369, https://doi.org/10.1007/s10533-024-01175-7.","productDescription":"17 p.","startPage":"1353","endPage":"1369","ipdsId":"IP-159555","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487559,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10533-024-01175-7","text":"Publisher Index Page"},{"id":485378,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kaxdigoowu Héen watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -134.75658445715095,\n 58.49000824721642\n ],\n [\n -134.75658445715095,\n 58.377070439919066\n ],\n [\n -134.52405757677857,\n 58.377070439919066\n ],\n [\n -134.52405757677857,\n 58.49000824721642\n ],\n [\n -134.75658445715095,\n 58.49000824721642\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"167","noUsgsAuthors":false,"publicationDate":"2024-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Delbecq, Claire","contributorId":337162,"corporation":false,"usgs":false,"family":"Delbecq","given":"Claire","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":935566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fellman, Jason B.","contributorId":198741,"corporation":false,"usgs":false,"family":"Fellman","given":"Jason","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":935567,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bellmore, J. Ryan","contributorId":271034,"corporation":false,"usgs":false,"family":"Bellmore","given":"J.","email":"","middleInitial":"Ryan","affiliations":[{"id":56260,"text":"U.S. Forest Service, Pacific Northwest Research Station, 11175 Auke Lake Way, Juneau, Alaska, 99801","active":true,"usgs":false}],"preferred":false,"id":935568,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitney, Emily J.","contributorId":354399,"corporation":false,"usgs":false,"family":"Whitney","given":"Emily J.","affiliations":[{"id":16298,"text":"University of Alaska Southeast","active":true,"usgs":false}],"preferred":false,"id":935569,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hood, Eran","contributorId":106802,"corporation":false,"usgs":false,"family":"Hood","given":"Eran","affiliations":[],"preferred":false,"id":935570,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fitzgerald, Kevin","contributorId":332288,"corporation":false,"usgs":false,"family":"Fitzgerald","given":"Kevin","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":935571,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":935572,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70263810,"text":"70263810 - 2024 - Cold blood in warming waters: Effects of air temperature, precipitation, and groundwater on Gulf Sturgeon thermal habitats in a changing climate","interactions":[],"lastModifiedDate":"2025-02-25T15:28:39.78105","indexId":"70263810","displayToPublicDate":"2024-08-23T08:22:47","publicationYear":"2024","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":"Cold blood in warming waters: Effects of air temperature, precipitation, and groundwater on Gulf Sturgeon thermal habitats in a changing climate","docAbstract":"In a changing climate, the effects of air temperature, precipitation, and groundwater on water temperature and thermal habitat suitability for Gulf Sturgeon Acipenser desotoi, listed as threatened under the U.S. Endangered Species Act, are not well understood. Hence, we incorporated these factors into thermal habitat models to forecast how Gulf Sturgeon may be affected by wide‐ranging climate change scenarios in 2024–2074.
Using data from the Choctawhatchee River, Florida, we developed precipitation‐ and groundwater‐corrected air–water temperature models, compared their accuracy with that of conventional air–water temperature models used in fisheries management, and projected future Gulf Sturgeon thermal habitat suitability for normal physiological functioning and fieldwork (i.e., population sampling and telemetry surgeries) in summer (May–August) under 16 climate change scenarios.
Precipitation‐ and groundwater‐corrected models were more accurate than conventional air–water temperature models (mean improvement in adjusted R2 = +0.45; range = +0.09 to +0.75). Water temperature was projected to warm at widely variable rates across climate change scenarios encompassing different air temperature, precipitation, and groundwater regimes. Importantly, Gulf Sturgeon summer aggregation areas were cooler and influenced more by precipitation and groundwater and less by air temperature than were non‐aggregation areas. If precipitation and groundwater—as drivers of cooling—become warm in a changing climate, summer aggregation areas were projected to exhibit thermal habitat degradation equivalent to or greater than that of non‐aggregation areas.
Our results add hydrological context to the premise that aggregation areas provide cool water and energetic savings for Gulf Sturgeon during summer, underscoring the importance of protecting these habitats through groundwater conservation, water quality monitoring, and riparian/watershed habitat management. Our findings indicate that identifying thermally appropriate times for fieldwork activities will be increasingly important and time‐restricted as climate change intensifies. However, our research provides managers with a portfolio of water temperature models and an accurate, cost‐effective, management‐relevant approach to forecasting thermal habitat conditions for Gulf Sturgeon and other species in a changing climate.
Although coastal islands are home to many endemic species, they are also at risk of inundation from storm surge and sea level rise. Three subspecies of mole skink (Plestiodon egregius egregius, P. e. insularis, and the Egmont Key Mole Skink known from a single occurrence) occur on a small number of islands off the Gulf Coast of Florida, USA. We used the most recent sea level rise projections and the latest storm surge simulation data to predict impacts to habitat for insular mole skinks in Florida from 2030 to 2150. Our models predicted that in <100 years (by 2100; intermediate sea level rise scenario; ~1.08–1.15 m sea level rise), >78% of preferred habitat for the Florida Keys Mole Skink, >65% of preferred habitat for the Cedar Key Mole Skink, and >36% of preferred habitat for the Egmont Key Mole Skink will be inundated from sea level rise. Storm surge from tropical cyclones presents a more immediate risk to insular mole skink habitat: our models predicted that between 58% and 75% of Florida Keys Mole Skink habitat is at risk of being submerged under an average maximum of between 0.60 (SD = 0.86) and 0.98 (SD = 0.36) m of storm surge water for a category 1 storm, and the amount of habitat predicted to be impacted increases for higher intensity storms. Our models predicted similar trends for Cedar Key and Egmont Key Mole Skink habitat. Given current sea level rise projections, our models predicted that all three subspecies could be extinct by 2140 due to habitat inundation. There remains uncertainty about how species and ecosystems will respond to sea level rise, thus research to fill these gaps could help mitigate the effects of sea level rise in areas most vulnerable to the effects of climate change.
Residents of Carson Valley, Douglas County, Nevada, rely on the basin-fill alluvial aquifer underlying the valley for drinking water. Since the 1980s, groundwater levels and water-quality data have been collected to monitor the status of the aquifer system and to assist in planning efforts to address current (2024) and future demand. The U.S. Geological Survey (USGS), in cooperation with Douglas County, Nevada, evaluated trends in water levels, nitrate, and arsenic concentrations from a network of monitoring and domestic wells in Carson Valley. This work also assessed the monitoring well network to determine the suitability of wells for characterizing the occurrence of arsenic in the groundwater. Monitoring of constituents, such as nitrate and arsenic concentrations, is needed to assess changes in contaminant distribution and to evaluate the effect that changing land use and groundwater pumping has on their occurrence and transport.
Results of the trend analysis indicate water levels are declining (p<0.05) in 17 of 26 selected monitoring wells (65 percent). Areas with the largest change in water levels, with more than 20 feet of declines, were within the community areas of Johnson Lane, Ruhenstroth, South Agricultural, East Valley, and Fish Springs. Variations in water levels measured in wells from the Central Agricultural, Minden, Foothill, Alpine County (one well), and Gardnerville Ranchos areas show periods of increase and decrease over time, but they also maintain long-term declining trends (p<0.05).
Increases in nitrate concentrations in groundwater samples collected from 9 out of 14 selected monitoring wells (64 percent) are statistically significant (p<0.05) within the Ruhenstroth, Gardnerville Ranchos, East Valley, Genoa, and Johnson Lane community areas. Samples collected from a well in Indian Hills/Jacks Valley indicated a decreasing trend in nitrate concentration over time. Nitrate concentrations in samples collected from wells in East Valley, Genoa, Johnson Lane, and Indian Hills/Jack Valley were consistently low (less than 3 milligrams per liter [mg/L]) and stable. Nitrate concentrations from selected wells in Johnson Lane and Garnerville Ranchos exceeded the U.S. Environmental Protection Agency (EPA) maximum contaminant level (MCL) of 10 mg/L (as nitrogen) and have trends that are increasing over time. In 2022, a sample collected from Johnson Lane had a concentration (7.3 mg/L) below the MCL with an increasing trend over time.
Temporal trend analyses for groundwater arsenic concentrations in Carson Valley could not be done because of a lack of temporal data. However, using available historical data, arsenic concentrations seem to be greater in groundwater from wells located on the eastern and northern areas of the valley than in wells located on the western or southern areas. Groundwater arsenic concentrations exceed 5 micrograms per liter (μg/L) in most samples collected from wells in Johnson Lane, Airport, Central Agricultural, and East Valley areas and in many cases exceed the U.S. Environmental Protection Agency (EPA) MCL of 10 μg/L. Data indicate that groundwater from domestic wells screened at deeper intervals are likely more vulnerable to elevated arsenic concentrations than shallower wells.
A groundwater network evaluation for Carson Valley identified potential modifications in the sampling locations and frequency to better understand the effect of groundwater pumping in communities where municipal and domestic demand are greatest, potentially enhancing understanding of contaminant transport in these areas. Potential modifications to the active well network include reducing the frequency of sample collection from existing network wells (6 out of 11) that have consistently shown low and stable nitrate concentrations, adding wells in areas where data are sparse, and increasing the number of wells in areas with elevated groundwater nitrate concentrations. Including the analysis of arsenic in samples from the active groundwater monitoring well network will provide more detail on the temporal and spatial variability of arsenic concentrations.
A visualization tool for the Carson River Basin was developed to provide access to discrete and near real-time hydrologic and water-quality data. The Carson River Basin Hydro Mapper (CBH; U.S. Geological Survey, 2023b) shows active and historical discrete water levels measured by the USGS and the State of Nevada Division of Water Resources, discrete groundwater nitrate and arsenic concentration data collected by the USGS, near real-time streamflow, and surface water levels for select waterbodies. The hydrologic data in the CBH provides resource managers, the public, and the scientific community with an easily accessible tool to present and communicate the most up-to-date information available about local and basin-wide water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241045","collaboration":"Prepared in cooperation with Douglas County, Nevada","programNote":"Water Resources Mission Area—Water's Cooperative Matching Funds","usgsCitation":"Naranjo, R.C., and Bubiy, A., 2024, Assessment of water levels, nitrate, and arsenic in the Carson Valley Alluvial Aquifer and the development of a data visualization tool for the Carson River Basin, Nevada (ver. 1.1, September 2024): U.S. Geological Survey Open-File Report 2024–1045, 29 p., https://doi.org/10.3133/ofr20241045.","productDescription":"vii, 29 p.","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-154652","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":434792,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2024/1045/versionHist.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}},{"id":432958,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1045/covrthb.jpg"},{"id":432959,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1045/ofr20241045.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":432960,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1045/ofr20241045.xml"},{"id":432961,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1045/images"},{"id":432962,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241045/full"},{"id":497966,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117224.htm","linkFileType":{"id":5,"text":"html"}},{"id":433077,"rank":6,"type":{"id":4,"text":"Application Site"},"url":"https://webapps.usgs.gov/carsonriverbasinhydromapper/","text":"Carson River Basin Hydro Mapper Webapp"}],"country":"United States","state":"Nevada","otherGeospatial":"Carson River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.6,\n 39.05\n ],\n [\n -119.6,\n 38.5\n ],\n [\n -119.3,\n 38.5\n ],\n [\n -119.3,\n 39.05\n ],\n [\n -119.6,\n 39.05\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: August 2024; Version 1.1: September 2024","contact":"Director,
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U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
The U.S. Geological Survey Community for Data Integration annually funds small projects focusing on data integration for interdisciplinary research, innovative data management, and demonstration of new technologies. This report provides a summary of the 12 projects funded in fiscal year 2020, outlining their goals, activities, and accomplishments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241027","programNote":"Science Synthesis, Analysis, and Research Program","usgsCitation":"Hsu, L., Chapin, E.G., Barnhart, T.B., Cravens, A.E., Erickson, R.A., Ferrante, J., Fox, A., Hitt, N.P., Hunter, M., Kolb, K., Peacock, J.R., Petkewich, M.D., Reed, S.C., Sohl, T.L., and Williamson, T.N., 2024, Community for Data Integration 2020 project report: U.S. Geological Survey Open-File Report 2024–1027, 21 p., https://doi.org/10.3133/ofr20241027.","productDescription":"iv, 21 p.","onlineOnly":"Y","ipdsId":"IP-157501","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":433035,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1027/coverthb.jpg"},{"id":433075,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1027/images"},{"id":433076,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1027/ofr20241027.xml"},{"id":433036,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1027/ofr20241027.pdf","text":"Report","size":"3.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1027"},{"id":433331,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241027/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1027"}],"contact":"Director, Science Analytics and Synthesis Program
U.S. Geological Survey
P.O. Box 25046, Mail Stop 302
Denver, CO 80225
The Aurora software package robustly estimates single station and remote reference electromagnetic transfer functions (TFs) from magnetotelluric (MT) time series. Aurora is part of an open-source processing workflow that leverages the self-describing data container MTH5, which in turn leverages the general mt-metadata framework to manage metadata. These pre-existing packages simplify the processing by providing managed data structures, allowing for transfer functions to be generated with only a few lines of code. The processing depends on two inputs -- a table defining the data to use for TF estimation, and a JSON file specifying the processing parameters, both of which are generated automatically, and can be modified if desired. Output TFs are returned as mt_metadata objects, and can be exported to a variety of common formats for plotting, modeling and inversion.
","language":"English","publisher":"Open Source Initiative","doi":"10.21105/joss.06832","usgsCitation":"Kappler, K., Peacock, J., Egbert, G.D., Frassetto, A., Heagy, L., Kelbert, A., Keyson, L., Oldenburg, D.W., Ronan, T., and Sweet, J., 2024, Aurora: An open-source Python implementation of the EMTF package for magnetotelluric data processing using MTH5 and mt-metadata: Journal of Open Source Software, v. 9, no. 100, 6832, 7 p., https://doi.org/10.21105/joss.06832.","productDescription":"6832, 7 p.","ipdsId":"IP-164541","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":466956,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.21105/joss.06832","text":"Publisher Index Page"},{"id":465194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"100","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kappler, Karl 0000-0002-1877-1255","orcid":"https://orcid.org/0000-0002-1877-1255","contributorId":345189,"corporation":false,"usgs":false,"family":"Kappler","given":"Karl","email":"","affiliations":[{"id":82517,"text":"IMDEX Technology USA, LLC","active":true,"usgs":false}],"preferred":false,"id":921185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Egbert, Gary D.","contributorId":187462,"corporation":false,"usgs":false,"family":"Egbert","given":"Gary","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":921187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frassetto, Andrew 0000-0002-8818-3731","orcid":"https://orcid.org/0000-0002-8818-3731","contributorId":345192,"corporation":false,"usgs":false,"family":"Frassetto","given":"Andrew","email":"","affiliations":[{"id":82518,"text":"Incorporated Research Institutes for Seismology","active":true,"usgs":false}],"preferred":false,"id":921327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heagy, Lindsey 0000-0002-1551-5926","orcid":"https://orcid.org/0000-0002-1551-5926","contributorId":345190,"corporation":false,"usgs":false,"family":"Heagy","given":"Lindsey","email":"","affiliations":[{"id":78772,"text":"University of British Columbia, Canada","active":true,"usgs":false}],"preferred":false,"id":921328,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":921329,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Keyson, Laura","contributorId":347262,"corporation":false,"usgs":false,"family":"Keyson","given":"Laura","email":"","affiliations":[{"id":83114,"text":"Earthscope USA","active":true,"usgs":false}],"preferred":false,"id":921188,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oldenburg, Douglas W. 0000-0002-4327-2124","orcid":"https://orcid.org/0000-0002-4327-2124","contributorId":304117,"corporation":false,"usgs":false,"family":"Oldenburg","given":"Douglas","email":"","middleInitial":"W.","affiliations":[{"id":65972,"text":"Geophysical Inversion Facility (GIF), Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":921330,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ronan, Timothy 0000-0001-8450-9573","orcid":"https://orcid.org/0000-0001-8450-9573","contributorId":345191,"corporation":false,"usgs":false,"family":"Ronan","given":"Timothy","email":"","affiliations":[{"id":82518,"text":"Incorporated Research Institutes for Seismology","active":true,"usgs":false}],"preferred":false,"id":921189,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sweet, Justin 0000-0001-7323-9758","orcid":"https://orcid.org/0000-0001-7323-9758","contributorId":347263,"corporation":false,"usgs":false,"family":"Sweet","given":"Justin","email":"","affiliations":[{"id":83114,"text":"Earthscope USA","active":true,"usgs":false}],"preferred":false,"id":921331,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70259304,"text":"70259304 - 2024 - Remote sensing large-wood storage downstream of reservoirs during and after dam removal: Elwha River, Washington, USA","interactions":[],"lastModifiedDate":"2024-10-03T12:15:08.069344","indexId":"70259304","displayToPublicDate":"2024-08-22T07:10:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing large-wood storage downstream of reservoirs during and after dam removal: Elwha River, Washington, USA","docAbstract":"Large wood is an integral part of many rivers, often defining river-corridor morphology and habitat, but its occurrence, magnitude, and evolution in a river system are much less well understood than the sedimentary and hydraulic components, and due to methodological limitations, have seldom previously been mapped in substantial detail. We present a new method for this, representing a substantial advance in automated deep-learning-based image segmentation. From these maps, we measured large wood and sediment deposits from high-resolution orthoimages to explore the dynamics of large wood in two reaches of the Elwha River, Washington, USA, between 2012 and 2017 as it adjusted to upstream dam removals. The data set consists of a time series of orthoimages (12.5-cm resolution) constructed using Structure-from-Motion photogrammetry on imagery from 14 aerial surveys. Model training was optimized to yield maximum accuracy for estimated wood areas, compared to manually digitized wood, therefore model development and intended application were coupled. These fully reproducible methods and model resulted in a maximum of 15% error between observed and estimated total wood areas and wood deposit size-distributions over the full spatio-temporal extent of the data. Areal extent of wood in the channel margin approximately doubled in the years following dam removal, with greatest increases in large wood in wider, lower-gradient sections. Large-wood deposition increased between the start of dam removal (2011) and winter 2013, then plateaued. Sediment bars continued to grow up until 2016/17, assisted by a partially static wood framework deposited predominantly during the period up to winter 2013.
The Redeye Bass Micropterus coosae is a piscivore introduced into California, which has become a threat to the state's endemic freshwater fishes. It has eliminated native fishes from the middle reaches of the Cosumnes River, our study stream, which is the largest stream without a major dam on its main stem in the Sacramento–San Joaquin River drainage, central California, USA. We thoroughly documented its novel life history and ecology in California to shed light on why it has been such a successful invader despite its relatively small native range.
Over 4000 stable carbon and nitrogen isotope samples were utilized to refine our understanding of fish trophic position within the river food web, along with a stable isotope mixing model that accounts for uncertainty in trophic enrichment data.
Growth was slow, with an adult size range of 9–25 cm standard length (SL), although few were larger than 15-cm SL (5–6 years old). Stable isotope analyses showed that Redeye Bass dominate the river ecosystem to the exclusion of most native fishes, occupying multiple trophic levels and microhabitats. Adults largely consumed non-native crayfish and large aquatic insects, while juveniles consumed aquatic insects, the size of prey increasing with Redeye Bass length. There was no evidence of cannibalism. Redeye Bass have effectively occupied the diverse trophic positions of at least four native fish species and have altered the trophic position of Rainbow Trout Oncorhynchus mykiss in sites where they co-occur with bass.
The introduction of Redeye Bass poses a continuing threat to native stream fishes in California and elsewhere.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10477","usgsCitation":"Long, B.C., Moyle, P.B., Young, M.J., and Crain, P.K., 2024, Age, growth, and trophic ecology of the Redeye Bass, an introduced invader of California rivers: Transactions of the American Fisheries Society, v. 153, no. 5, p. 559-575, https://doi.org/10.1002/tafs.10477.","productDescription":"17 p.","startPage":"559","endPage":"575","ipdsId":"IP-165585","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":439201,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10477","text":"Publisher Index Page"},{"id":433151,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"153","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Long, Beth C.","contributorId":343631,"corporation":false,"usgs":false,"family":"Long","given":"Beth","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":911566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moyle, Peter B.","contributorId":117099,"corporation":false,"usgs":false,"family":"Moyle","given":"Peter","email":"","middleInitial":"B.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":911567,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":911568,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crain, Patrick K.","contributorId":343634,"corporation":false,"usgs":false,"family":"Crain","given":"Patrick","email":"","middleInitial":"K.","affiliations":[{"id":13109,"text":"ICF International","active":true,"usgs":false}],"preferred":false,"id":911569,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70258136,"text":"70258136 - 2024 - DNA-based studies and genetic diversity indicator assessments are complementary approaches to conserving evolutionary potential","interactions":[],"lastModifiedDate":"2024-12-10T15:15:45.775254","indexId":"70258136","displayToPublicDate":"2024-08-21T09:21:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"DNA-based studies and genetic diversity indicator assessments are complementary approaches to conserving evolutionary potential","docAbstract":"Genetic diversity is essential for maintaining healthy populations and ecosystems. Several approaches have recently been developed to evaluate population genetic trends without necessarily collecting new genetic data. Such “genetic diversity indicators” enable rapid, large-scale evaluation across dozens to thousands of species. Empirical genetic studies, when available, provide detailed information that is important for management, such as estimates of gene flow, inbreeding, genetic erosion and adaptation. In this article, we argue that the development and advancement of genetic diversity indicators is a complementary approach to genetic studies in conservation biology, but not a substitute. Genetic diversity indicators and empirical genetic data can provide different information for conserving genetic diversity. Genetic diversity indicators enable affordable tracking, reporting, prioritization and communication, although, being proxies, do not provide comprehensive evaluation of the genetic status of a species. Conversely, genetic methods offer detailed analysis of the genetic status of a given species or population, although they remain challenging to implement for most species globally, given current capacity and resourcing. We conclude that indicators and genetic studies are both important for genetic conservation actions and recommend they be used in combination for conserving and monitoring genetic diversity.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-024-01632-8","usgsCitation":"Hoban, S.M., Paz-Vinas, I., Shaw, R.E., Castillo-Reina, L., da Silva, J.M., DeWoody, J., Ekblom, R., Fedorca, A., Forester, B.R., Funk, W., Geue, J.C., Heuertz, M., Hollingsworth, P.M., Hughes, A.C., Hunter, M., Hvilsom, C., Ishihama, F., Jordan, R., Kalamujic Stroil, B., Kershaw, F., Khoury, C.K., Koppa, V., Laikre, L., MacDonald, A.J., Mastretta-Yanes, A., Meek, M.H., Mergeay, J., Millette, K.L., O'Brien, D., Rincon-Parra, V.J., Rodriguez-Morales, M., Schuman, M.C., Segelbacher, G., Sunnucks, P., Taylor, R., Thurfjell, H., Vernesi, C., and Grueber, C.E., 2024, DNA-based studies and genetic diversity indicator assessments are complementary approaches to conserving evolutionary potential: Conservation Genetics, v. 25, p. 1147-1153, https://doi.org/10.1007/s10592-024-01632-8.","productDescription":"7 p.","startPage":"1147","endPage":"1153","ipdsId":"IP-160562","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439203,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-024-01632-8","text":"Publisher Index Page"},{"id":433496,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","noUsgsAuthors":false,"publicationDate":"2024-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoban, Sean M. 0000-0002-0348-8449","orcid":"https://orcid.org/0000-0002-0348-8449","contributorId":206582,"corporation":false,"usgs":false,"family":"Hoban","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":912310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paz-Vinas, Ivan","contributorId":239614,"corporation":false,"usgs":false,"family":"Paz-Vinas","given":"Ivan","email":"","affiliations":[{"id":47934,"text":"Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":912311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shaw, Robyn E.","contributorId":260838,"corporation":false,"usgs":false,"family":"Shaw","given":"Robyn","email":"","middleInitial":"E.","affiliations":[{"id":52690,"text":"Environmental and Conservation Sciences, Murdoch University, Perth, Australia","active":true,"usgs":false}],"preferred":false,"id":912312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Castillo-Reina, Luis","contributorId":340272,"corporation":false,"usgs":false,"family":"Castillo-Reina","given":"Luis","email":"","affiliations":[{"id":81533,"text":"Department of Biology, Faculty of Science, KU Leuven","active":true,"usgs":false}],"preferred":false,"id":912313,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"da Silva, Jessica M.","contributorId":290139,"corporation":false,"usgs":false,"family":"da Silva","given":"Jessica","email":"","middleInitial":"M.","affiliations":[{"id":62352,"text":"South African National Biodiversity Institute, Kirstenbosch Research Centre, Rhodes Drive, Private Bag X7, 7735 Cape Town, South Africa","active":true,"usgs":false}],"preferred":false,"id":912314,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeWoody, J. 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Alejandra","affiliations":[{"id":82257,"text":"Department of Biology, Faculty of Science, Pontificia Universidad Javeriana, Bogotá D.C","active":true,"usgs":false}],"preferred":false,"id":912340,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Schuman, Meredith C. 0000-0003-3159-3534","orcid":"https://orcid.org/0000-0003-3159-3534","contributorId":343905,"corporation":false,"usgs":false,"family":"Schuman","given":"Meredith","email":"","middleInitial":"C.","affiliations":[{"id":82258,"text":"Spatial Genetics, Remote Sensing Laboratories, Department of Geography, University of Zurich, Zurich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":912341,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Segelbacher, Gernot","contributorId":206584,"corporation":false,"usgs":false,"family":"Segelbacher","given":"Gernot","email":"","affiliations":[{"id":37345,"text":"University of Freiburg, Germany","active":true,"usgs":false}],"preferred":false,"id":912342,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Sunnucks, Paul 0000-0002-8139-7059","orcid":"https://orcid.org/0000-0002-8139-7059","contributorId":333555,"corporation":false,"usgs":false,"family":"Sunnucks","given":"Paul","email":"","affiliations":[{"id":27950,"text":"School of Biological Sciences, Monash University","active":true,"usgs":false}],"preferred":false,"id":912343,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Taylor, Rebecca S. 0000-0002-8916-4858","orcid":"https://orcid.org/0000-0002-8916-4858","contributorId":343906,"corporation":false,"usgs":false,"family":"Taylor","given":"Rebecca S.","affiliations":[{"id":82259,"text":"Landscape Science and Technology Division, Environment and Climate Change Canada, Ottawa, Canada","active":true,"usgs":false}],"preferred":false,"id":912344,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Thurfjell, Henrik","contributorId":305348,"corporation":false,"usgs":false,"family":"Thurfjell","given":"Henrik","email":"","affiliations":[{"id":66219,"text":"Swedish Species Information Centre, Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":912345,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Vernesi, Cristiano","contributorId":239922,"corporation":false,"usgs":false,"family":"Vernesi","given":"Cristiano","email":"","affiliations":[{"id":48051,"text":"Dept. of Sustainable Agroecosystems and Bioresources, Research and Innovation Centre - Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":912346,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Grueber, Catherine E.","contributorId":239927,"corporation":false,"usgs":false,"family":"Grueber","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":48055,"text":"School of Life and Environmental Sciences, Faculty of Science, The University of Sydney","active":true,"usgs":false}],"preferred":false,"id":912347,"contributorType":{"id":1,"text":"Authors"},"rank":38}]}} ,{"id":70257760,"text":"70257760 - 2024 - Despite regional variation, Gymnorhinus cyanocephalus (Pinyon Jay) densities generally increase with local pinyon–juniper cover and heterogeneous ground cover","interactions":[],"lastModifiedDate":"2024-12-26T16:34:01.74251","indexId":"70257760","displayToPublicDate":"2024-08-21T07:21:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Despite regional variation, Gymnorhinus cyanocephalus (Pinyon Jay) densities generally increase with local pinyon–juniper cover and heterogeneous ground cover","docAbstract":"Traditionally, local-scale habitat-relationship models are developed over small spatial extents, limiting model transferability and inference outside the study area. Thus, habitat managers frequently lack fine-scale information regarding the influence of vegetation composition and structure on site suitability or species abundance. Gymnorhinus cyanocephalus (Pinyon Jay) represents one declining species for which managers have limited information regarding the influence that vegetation composition and structure have on abundance at broad scales. To address this need, we developed a hierarchical Bayesian abundance model using summertime bird and vegetation data collected under the Integrated Monitoring in Bird Conservation Regions program to explain jay abundance as a function of local conditions. Our G. cyanocephalus abundance model allowed abundance relationships with pinyon pine (Pinus edulis and P. monophylla) and juniper (Juniperus spp.) to vary by ecoregion, thereby accounting for potential regional differences in habitat associations. We found G. cyanocephalus abundance was generally positively associated with pinyon pine and juniper cover; however, habitat relationships varied by ecoregion. Additionally, we found positive associations between jay abundance and grass cover, sagebrush cover, and percent bare ground. Our results agree with prior research suggesting mechanical removal of pinyon pine and juniper trees for sagebrush restoration or fuel treatments may negatively affect G. cyanocephalus. Managers wishing to reduce pinyon and juniper tree cover without negatively affecting G. cyanocephalus may benefit from targeting sites where both large-scale distribution models and our local habitat relationships suggest G. cyanocephalus are likely to occur in low numbers. Additionally, our modeled relationships indicate restoration that increases grass cover, sagebrush cover, and bare ground, while maintaining pinyon and (or) juniper cover, may lead to increased local densities of G. cyanocephalus.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/ornithapp/duae036","usgsCitation":"Van Lanen, N.J., Monroe, A., and Aldridge, C.L., 2024, Despite regional variation, Gymnorhinus cyanocephalus (Pinyon Jay) densities generally increase with local pinyon–juniper cover and heterogeneous ground cover: Ornithological Applications, v. 126, no. 4, duae036, https://doi.org/10.1093/ornithapp/duae036.","productDescription":"duae036","ipdsId":"IP-158852","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439204,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duae036","text":"Publisher Index Page"},{"id":433154,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Lanen, Nicholas J. 0000-0003-0871-0261","orcid":"https://orcid.org/0000-0003-0871-0261","contributorId":302927,"corporation":false,"usgs":true,"family":"Van Lanen","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":911621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":911622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":911623,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257808,"text":"70257808 - 2024 - On the uncertain intensity estimate of the 1859 Carrington storm","interactions":[],"lastModifiedDate":"2024-08-28T11:58:46.249223","indexId":"70257808","displayToPublicDate":"2024-08-21T06:57:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18352,"text":"Journal of Space Weather and Space Climate","active":true,"publicationSubtype":{"id":10}},"title":"On the uncertain intensity estimate of the 1859 Carrington storm","docAbstract":"A study is made of the intensity of the Carrington magnetic storm of September 1859 as inferred from visual measurements of horizontal-component geomagnetic disturbance made at the Colaba observatory in India. Using data from modern observatories, a lognormal statistical model of storm intensity is developed, to characterize the maximum-negative value of the storm-time disturbance index (maximum –Dst) versus geomagnetic disturbance recorded at low-latitude observatories during magnetic storms. With this model and a recently published presentation of the Colaba data, the most likely maximum –Dst of the Carrington storm and its credibility interval are estimated. A related model is used to examine individual Colaba disturbance values reported for the Carrington storm. Results indicate that only about one in a million storms with maximum –Dst like the Carrington storm would result in local disturbance greater than that reported from Colaba. This indicates that either the Colaba data were affected by magnetospheric-ionospheric current systems in addition to the ring current, or there might be something wrong with the Colaba data. If the most extreme Colaba disturbance value is included in the analysis, then, of all hypothetical storms generating the hourly average disturbance recorded at Colaba during the Carrington storm, the median maximum –Dst = 964 nT, with a 68% credibility interval of [855,1087] nT. If the most extreme Colaba disturbance value is excluded from the analysis, then the median maximum –Dst = 866 nT, with a 68% credibility interval of [768,977] nT. The widths of these intervals indicate that estimates of the occurrence frequency of Carrington-class storms are very uncertain, as are related estimates of risk for modern technological systems.
","language":"English","publisher":"EcoSciences","doi":"10.1051/swsc/2024015","usgsCitation":"Love, J.J., Rigler, E.J., Hayakawa, H., and Mursula, K., 2024, On the uncertain intensity estimate of the 1859 Carrington storm: Journal of Space Weather and Space Climate, v. 14, https://doi.org/10.1051/swsc/2024015.","productDescription":"21, 16 p.","startPage":"21","ipdsId":"IP-153188","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":439205,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1051/swsc/2024015","text":"External Repository"},{"id":433242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2024-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":911733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigler, E. Joshua 0000-0003-4850-3953 erigler@usgs.gov","orcid":"https://orcid.org/0000-0003-4850-3953","contributorId":4367,"corporation":false,"usgs":true,"family":"Rigler","given":"E.","email":"erigler@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":911734,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayakawa, H. 0000-0001-5370-3365","orcid":"https://orcid.org/0000-0001-5370-3365","contributorId":261775,"corporation":false,"usgs":false,"family":"Hayakawa","given":"H.","email":"","affiliations":[{"id":53009,"text":"Nagoya University, Rutherford Appleton Laboratory, Nishina Center","active":true,"usgs":false}],"preferred":false,"id":911735,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mursula, Kalevi 0000-0003-4892-5056","orcid":"https://orcid.org/0000-0003-4892-5056","contributorId":343695,"corporation":false,"usgs":false,"family":"Mursula","given":"Kalevi","email":"","affiliations":[{"id":82163,"text":"Oulu University","active":true,"usgs":false}],"preferred":false,"id":911736,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257626,"text":"ofr20241036 - 2024 - Airborne lidar accuracy analysis for dual photogrammetric and lidar sensor pilot project in Colorado, 2019","interactions":[],"lastModifiedDate":"2026-01-29T19:50:12.601231","indexId":"ofr20241036","displayToPublicDate":"2024-08-20T16:19:54","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1036","displayTitle":"Airborne Lidar Accuracy Analysis for Dual Photogrammetric and Lidar Sensor Pilot Project in Colorado, 2019","title":"Airborne lidar accuracy analysis for dual photogrammetric and lidar sensor pilot project in Colorado, 2019","docAbstract":"This report presents accuracy assessment results of the light detection and ranging (lidar) data collected in Colorado during a pilot project in fall 2019. The purpose of the pilot project was to assess the accuracy of lidar and imagery data collected simultaneously for the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program and the U.S. Geological Survey National Geospatial Program 3D Elevation Program (3DEP). A multiagency group consisting of U.S. Department of the Interior agencies and USDA agencies participated in the effort. Department of the Interior agencies included Bureau of Land Management, National Park Service, and U.S. Geological Survey; USDA agencies included the Farm Services Agency, the Natural Resource Conservation Service, and U.S. Forest Service. This pilot project was designed to help determine if a lidar sensor system has the potential to meet future 3DEP topographic lidar collection requirements, ideally at the same altitudes and leaf-on times that National Agriculture Imagery Program is flown.
The airborne sensor system from Leica Geosystems (part of Hexagon) (hereafter referred to as dual sensor system) was used in the pilot project and can collect imagery and three-dimensional point cloud data concurrently. This report examines the characteristics of lidar data from a geometric accuracy perspective. Field surveys were performed to evaluate the three-dimensional absolute and relative accuracy of the airborne lidar data and to determine if the data met 3DEP specifications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241036","usgsCitation":"Sampath, A., Irwin, J., and Kim, M., 2024, Airborne lidar accuracy analysis for dual photogrammetric and lidar sensor pilot project in Colorado, 2019: U.S. Geological Survey Open-File Report 2024–1036, 22 p., https://doi.org/10.3133/ofr20241036.","productDescription":"Report: v, 22 p.; Data Release","ipdsId":"IP-145014","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499258,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117220.htm","linkFileType":{"id":5,"text":"html"}},{"id":432948,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241036/full"},{"id":432946,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1036/ofr20241036.XML"},{"id":432945,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1036/images/"},{"id":432944,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1036/ofr20241036.pdf","text":"Report","size":"4.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1036"},{"id":432943,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1036/coverthb.jpg"},{"id":432947,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPDWUU","text":"USGS data release","linkHelpText":"Hybrid lidar/imagery sensor validation survey data, 2019"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.41673062084341,\n 40.689427754445916\n ],\n [\n -105.41673062084341,\n 40.45990268267724\n ],\n [\n -104.91983118255482,\n 40.45990268267724\n ],\n [\n -104.91983118255482,\n 40.689427754445916\n ],\n [\n -105.41673062084341,\n 40.689427754445916\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.24129047491809,\n 40.22901702069382\n ],\n [\n -106.24129047491809,\n 39.9645466499631\n ],\n [\n -105.60981370231355,\n 39.9645466499631\n ],\n [\n -105.60981370231355,\n 40.22901702069382\n ],\n [\n -106.24129047491809,\n 40.22901702069382\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The U.S. Geological Survey has developed a transient, groundwater-flow model that simulates hydrologic conditions in the Long Island aquifer system as part of an ongoing (since 2016) multiyear, cooperative investigation with the New York State Department of Environmental Conservation. The goals of this investigation are to assist stakeholders and resource managers to evaluate the response of the hydrologic system to changes in future hydraulic stresses. Responses in the hydrologic system include changes in water levels in the hydrogeologic units; discharge to streams, coastal waters, and subsurface infrastructure; and the extent of saline groundwater in the aquifers. Hydraulic stresses include future water-supply management and changes in land use and infrastructure.
The numerical model synthesizes a diverse set of physiographic, geologic, climatic, land-use, and historical population, water use, and infrastructure data to physically represent the Long Island aquifer system from land surface to bedrock and to simulate annual hydrologic conditions between 1900 and 2019. A three-dimensional hydrogeologic framework was developed from existing and recently collected borehole geologic and geophysical data collected as part of a companion drilling program. Water-transmitting properties of the principal aquifer sediments were defined in three dimensions from new and existing lithologic logs. The distribution of recharge from precipitation was estimated from landscape characteristics and climate data. Anthropogenic recharge from wastewater, leaky infrastructure, and storm runoff were estimated from population, infrastructure, and pumping data.
Water-use data, including well locations, depths, and pumping rates, were obtained from historical sources and records and used to estimate pumping stresses continuously in time and space, at an annual average time scale. The data were incorporated into a three-dimensional numerical model using the U.S. Geological Survey finite difference modeling code MODFLOW 6; the model encompassed all of Long Island and surrounding surface waters and simulated historical hydrologic conditions from 1900 to 2019.
The calibration process involved trial and error adjustments using prior knowledge to improve general fit to observations followed by an inverse calibration to update and optimize input parameters, using an iterative ensemble smoother algorithm implemented in PEST++ version 5.0. This resulted in a model that generally was in good agreement with observed, dynamically varying hydrologic conditions from 1900 to 2019. The calibrated model was used to develop two base-case models for scenario testing of future, hypothetical conditions where one represented average-annual conditions, and one represented average-seasonal conditions from 2010 to 2019. The model representing average-annual conditions was modified further to represent an alternate sea-level position of 6 feet above the North American Vertical Datum of 1988, and the model representing average-seasonal conditions was modified to represent the average seasonal effects of a 5-year drought imposed upon current hydrologic conditions.
Recharge is the sole source of water to the aquifer system; groundwater discharges to coastal water and streams and is withdrawn by pumped wells. Model-estimated annual recharge ranged from about 11 inches in 1965 to 41 inches in 1983. On average, from 2010 to 2019, about 23 percent of water was pumped from wells, and about 47 and 27 percent discharged to coastal waters and streams, respectively; the remaining 4 percent was water that moved into storage in the aquifer matrix.
Water levels on Long Island vary naturally during time in response to changes in recharge; the amount of variation is largest in the interior of the island, in areas with highest water table altitudes near groundwater divides and lowest near streams and the coastal waters. The total range of water table altitudes on Long Island between 1900 and 2019 ranged from near 0 to more than 70 feet in western parts of Long Island. The largest range in altitudes is in New York City and is associated with areas of large historical withdrawals between the 1920s and the late 1980s. Water table altitudes generally varied by less than 10 feet in eastern Suffolk County, where the aquifer is under more natural conditions.
Saltwater intrusion is of great concern on Long Island, particularly in western Long Island where both the unconfined and confined parts of the aquifer system have been intruded in response to large-scale groundwater withdrawals; however, the volume of freshwater in the islandwide aquifer system only has changed by about 5 percent between 1900 and 2019. The decadal change in the freshwater volume was largest during the early and mid-20th century, corresponding to the largest historical pumping, but that volume change did not exceed 1 percent.
The negligible change in freshwater volume suggests that saltwater intrusion as of 2019 was limited at an islandwide scale but continues to occur in local areas of Queens and Nassau Counties, adversely affecting current water supplies and limiting future water supplies for affected communities. The regional groundwater model developed for this investigation is a tool that can be used to help determine the viability of current and future water supplies at a regional scale and can be used to support development of additional models at finer scale to support more focused assessments of groundwater sustainability.
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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349