Side channels in large floodplain rivers serve a variety of important ecological roles, particularly in reaches where habitat conditions have been degraded or diminished. We developed hypotheses regarding side channel ecological structure whereby we expected species richness of young-of-year fishes to generally be higher in shallower, more physically heterogeneous side channels with lower velocities, with differences based on reproductive guild. We also hypothesized species richness of adult fishes to be higher in side channels with greater heterogeneity that could support diverse foraging resources and provide refugia during extreme flow conditions. To test these hypotheses, we used a 28-year fish community dataset from the Upper Mississippi and Illinois Rivers. Across six study reaches, we assessed metrics of side channel physical size, heterogeneity, and connectivity that were hypothesized to explain variance of fish community response, while accounting for site-level factors across 52 side channels using multilevel models. We then used these side channel-level characteristics in a K-means cluster analysis to classify 1126 side channels across 32 reaches of the river system. Our results indicated that the relative explanatory contributions of physical metrics varied by response variable, providing varying evidence in support of our hypotheses, and indicating that different forms of heterogeneity matter in different ways. Side channel-level factors were more explanatory of fish community responses in side channels of upstream reaches compared to downstream reaches and percent wet forest was the most explanatory side channel-level factor of fish community responses across all models. Our classification of side channels indicated strong spatial contrasts in the abundance and diversity of side channels across reaches. Scaling up to understand how the diversity and abundance of different types of side channels contributes to landscape-scale ecological functions and processes would be useful for establishing targets for reach-scale physical heterogeneity.
Understanding how energy moves through food webs and limits productivity at various trophic levels is a central question in aquatic ecology and can provide insight into drivers of fish population dynamics since many fish populations are food limited. In this study, we seek to better understand what factors drove a decline of >85% in the number of Rainbow TroutOncorhynchus mykiss found in the tailwater portion of the Colorado River below Glen Canyon Dam during 2012–2016.
We estimate the production of dominant prey using data from previously published studies of Rainbow Trout abundance and growth alongside drift and diet samples. We test how prey production correlates to both proximate (e.g., nutrients) and distal (e.g., limnological conditions in the upriver reservoir) drivers.
Results suggest that gross consumption of invertebrate prey by the Rainbow Trout population declined from an annual mean of 423 to 69 kg/d. Daily production rates of dominant prey in aggregate declined from a high of 0.173 to 0.018 g·m−2·d−1. Chironomids accounted for 70% of the decline in prey production. Foraging efficiency by Rainbow Trout (range, 0.99–0.67) was high across the range of prey production rates. After the Rainbow Trout population had declined by ~90%, prey consumption saturated at higher rates of prey production and the gross quantity of daily drift exported from the reach increased from 8.9 to 12.7 kg/d.
Rainbow Trout population dynamics are largely influenced by changes in prey production, which is itself driven by soluble reactive phosphorus (SRP) concentrations in the reservoir. The SRP model predicted that prey production would increase by 32 kg/d (SE, 9) for each 1 μg/L increase in SRP. These concentrations were indirectly influenced by reservoir hydrology and biogeochemistry, linkages that may extend far beyond the confines of this tailwater fishery and into the downstream reaches of the Grand Canyon's Colorado River ecosystem.
We combined Rainbow Trout diet, growth, and abundance estimates with concentrations of drifting invertebrates to estimate the biomass of Rainbow Trout prey produced over time. Trends in prey biomass production track trends in phosphorous concentrations in the river.
A high-resolution multibeam survey from a portion of the San Simeon Channel (offshore Morro Bay, California) captured a zone of recurring troughs and ridges adjacent to prominent submarine meander bends. Through an integrated study using surveying data, sediment core analysis, radiocarbon dating, and stable isotope measurements, we hypothesize that turbidity current event frequency was higher during the late Pleistocene than at present conditions. We speculate that the rise in sea-level following the Last Glacial Maximum sequestered sedimentation largely to the shelf during the Holocene. This work suggests that the occurrence of sediment gravity flows in this region, particularly away from any submarine channels, is appreciably lower than at times of continental shelf subaerial exposure.
Censored and uncensored generalized additive models (GAMs) were developed using streamflow data from 941 U.S. Geological Survey streamflow-gaging stations (streamgages) to predict decadal statistics of daily streamflow for streams draining to the Gulf of Mexico. The modeled decadal statistics comprise no-flow fractions and L-moments of logarithms of nonzero streamflow for six decades (1950–2009). These statistics represent metrics of decadal flow-duration curves (dFDCs) derived from about 10 million daily mean streamflows. The L-moments comprise the mean, coefficient of L-variation, and the third through fifth L-moment ratios. The GAMs were fit to the statistics from 941 streamgages and 2,750 streamgage-decades by using watershed properties such as basin area and slope, decadal precipitation and temperature, and decadal values of flood storage and urban development percentages. The GAMs then estimated decadal statistics for 9,220 prediction locations (stream reaches) coincident with outlets of level-12 hydrologic unit codes. Both entire dataset (whole model) and leave-one-watershed-out model results are reported. No-flow fractions are censored data, and Tobit extensions to GAMs were used to model ephemeral streamflow conditions. Conversely, uncensored GAMs were used for estimation of the L-moments. The GAMs are shown, by coverage probabilities, to construct reliable 95-percent prediction limits. An example shows how no-flow fractions and L-moments may be used to approximate dFDCs by using selected probability distributions (mathematical formulas) including the asymmetric exponential power, generalized normal, and kappa distributions.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Frequent observations of surface water at fine spatial scales will provide critical data to support the management of aquatic habitat, flood risk and water quality. Sentinel-1 and Sentinel-2 satellites can provide such observations, but algorithms are still needed that perform well across diverse climate and vegetation conditions. We developed surface inundation algorithms for Sentinel-1 and Sentinel-2, respectively, at 12 sites across the conterminous United States (CONUS), covering a total of >536,000 km2 and representing diverse hydrologic and vegetation landscapes. Each scene in the 5-year (2017–2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables from Sentinel-1 and Sentinel-2, as well as variables derived from topographic and weather datasets. The Sentinel-1 algorithm was developed distinct from the Sentinel-2 model to explore if and where the two time series could potentially be integrated into a single high-frequency time series. Within each model, open water and vegetated water (vegetated palustrine, lacustrine, and riverine wetlands) classes were mapped. The models were validated using imagery from WorldView and PlanetScope. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for the Sentinel-1 algorithm and 3.1% and 0.5% for the Sentinel-2 algorithm, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. The Sentinel-2 algorithm showed higher accuracy (10.7% omission and 7.9% commission error) relative to the Sentinel-1 algorithm (28.4% omission and 16.0% commission error). Patterns over time in the proportion of area mapped as open or vegetated water by the Sentinel-1 and Sentinel-2 algorithms were charted and correlated for a subset of all 12 sites. Our results showed that the Sentinel-1 and Sentinel-2 algorithm open water time series can be integrated at all 12 sites to improve the temporal resolution, but sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for mixed-pixel, vegetated water. The methods developed here provide inundation at 5-day (Sentinel-2 algorithm) and 12-day (Sentinel-1 algorithm) time steps to improve our understanding of the short- and long-term response of surface water to climate and land use drivers in different ecoregions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2023.113498","usgsCitation":"Vanderhoof, M.K., Alexander, L., Christensen, J.R., Solvik, K., Nieuwlandt, P.J., and Prentiss, M.A., 2023, High-frequency time series comparison of Sentinel-1 and Sentinel-2 for open and vegetated water across the United States (2017-2021): Remote Sensing of Environment, v. 288, 113498, 28 p., https://doi.org/10.1016/j.rse.2023.113498.","productDescription":"113498, 28 p.","ipdsId":"IP-142670","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":444461,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2023.113498","text":"Publisher Index Page"},{"id":435452,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RSXQ2U","text":"USGS data release","linkHelpText":"Sentinel-1 and Sentinel-2 based frequency of open and vegetated water across 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R.","contributorId":238115,"corporation":false,"usgs":false,"family":"Christensen","given":"Jay","middleInitial":"R.","affiliations":[],"preferred":false,"id":867163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Solvik, Kylen 0000-0001-6537-1791","orcid":"https://orcid.org/0000-0001-6537-1791","contributorId":303316,"corporation":false,"usgs":false,"family":"Solvik","given":"Kylen","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":867164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nieuwlandt, Peter Joseph 0000-0002-8245-2873","orcid":"https://orcid.org/0000-0002-8245-2873","contributorId":303317,"corporation":false,"usgs":true,"family":"Nieuwlandt","given":"Peter","email":"","middleInitial":"Joseph","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":867165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Prentiss, Mallory Annelle 0000-0002-0010-0744","orcid":"https://orcid.org/0000-0002-0010-0744","contributorId":303318,"corporation":false,"usgs":true,"family":"Prentiss","given":"Mallory","email":"","middleInitial":"Annelle","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":867166,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240654,"text":"ofr20231011 - 2023 - The value of scientific information — An overview","interactions":[],"lastModifiedDate":"2023-02-14T17:33:13.350202","indexId":"ofr20231011","displayToPublicDate":"2023-02-14T12:20:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1011","displayTitle":"The Value of Scientific Information—An Overview","title":"The value of scientific information — An overview","docAbstract":"The U.S. Geological Survey (USGS) provides reliable science, data, information, and models (hereafter collectively referred to as “information”) to describe and understand the Earth. This information is used to minimize loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; and enhance and protect quality of life. USGS science informs public and private decisions, operations, and risk management in all major United States economic sectors, as defined by the Bureau of Economic Analysis, and provides critical information for natural resource and natural hazard management and stewardship decisions. Understanding the value of scientific information supports applications of USGS science in land- and water-management decisions, and better informs the public about the return on investment of USGS programs. USGS economists, social scientists, and physical scientists are engaged in collaborative efforts to advance methods to estimate the value of information (VOI) produced by the USGS. These efforts involve collaborating with an international community to develop and refine estimation methods, establish best practices to determine VOI, develop a study repository, and conduct projects to assess the VOI of specific information products and their application. This report focuses on economic valuation conducted by USGS specifically, although the methodology has much broader applicability within the U.S. government, academia, and beyond. Noneconomic valuation techniques for assessing the VOI also exist but are not addressed in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231011","usgsCitation":"Pindilli, E., Chiavacci, S., and Straub, C., 2023, The value of scientific information—An overview: U.S. Geological Survey Open-File Report 2023–1011, 5 p., https://doi.org/10.3133/ofr20231011.","productDescription":"iii, 5 p.","numberOfPages":"5","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146764","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":412985,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231011/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1011"},{"id":412987,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1011/ofr20231011.XML"},{"id":412950,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1011/ofr20231011.pdf","text":"Report","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1011"},{"id":412949,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1011/coverthb.jpg"},{"id":412986,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1011/images/"}],"contact":"Science and Decisions Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Conservation planning and decision-making can be enhanced by ecological models that reliably transfer to times and places beyond those where models were developed. Transferrable models can be especially helpful for species of conservation concern, such as grizzly bears (Ursus arctos). Currently, only four grizzly bear populations remain in the contiguous United States. We evaluated transferability of previously derived individual-based, integrated step selection functions (iSSFs) developed from GPS-collared grizzly bears in the Northern Continental Divide Ecosystem by applying them within the nearby Selkirk (SE), Cabinet-Yaak (CYE), and Greater Yellowstone Ecosystems (GYE). We simulated 100 replicates of 5000 steps for each iSSF in each ecosystem, summarized relative use into 10 equal-area classes for each sex, and overlaid GPS locations from bears in the SE, CYE, and GYE on resulting maps. Spearman rank correlations between numbers of locations and class rank were ≥ 0.96 within each study area, indicating models were highly predictive of grizzly bear space use in these nearby populations. Assessment of models using smaller subsets of data in space and time demonstrated generally high predictive accuracy for females. Although generally high across space and time, predictive accuracy for males was low within some watersheds and in summer within the SE and CYE, potentially due to seasonal effects, vegetation, and food assemblage differences. Altogether, these results demonstrated high transferability of our models to landscapes in the Northern Rocky Mountains, suggesting they may be used to evaluate habitat suitability and connectivity throughout the region to benefit conservation planning.
This report includes an updated geologic map and cross sections of the upper Santa Cruz River basin, southern Arizona. The map and cross sections describe the geometry, thickness, and structure of the Miocene to Holocene units which form the main aquifers in the basin. The report also includes results of new hydrogeologic studies including (1) mapping and defining depth to bedrock based on geophysical data in the map area to better define the geometry and structure of the basin aquifers, (2) describing newly recognized hydrologically significant faults in the Peck Canyon and Sopori Wash areas, and (3) evaluating groundwater sources and hydrogeology of the Potrero Creek wetlands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3490","programNote":"National Cooperative Geologic Mapping Program","usgsCitation":"Page, W.R., Bultman, M.W., Berry, M.E., Turner, K.J., Menges, C.M., Gray, F., Paces, J.B., VanSistine, D.P., Morgan, L.E., and Havens, J.C., 2023, Geologic map and hydrogeologic investigations of the upper Santa Cruz River basin, southern Arizona: U.S. Geological Survey Scientific Investigations Map 3490, 2 sheets, scale 1:50,000, 73-p. pamphlet, https://doi.org/10.3133/sim3490.","productDescription":"Report: ix, 73 p.; 4 Data Releases; 3 Sheets: 40.89 × 35.83 inches or smaller","onlineOnly":"Y","ipdsId":"IP-123665","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":412895,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PGUZV0","text":"USGS data release","linkHelpText":"Database for the geologic map of the upper Santa Cruz River basin, southern Arizona"},{"id":412893,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94NR0D9","text":"USGS data release","linkHelpText":"Argon data for Santa Cruz Basin, Arizona (ver. 1.1, November 2022)"},{"id":412892,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MBNX4O","text":"USGS data release","linkHelpText":"Sopori Wash sub-basin gravity data, Pima and Santa Cruz Counties, Arizona"},{"id":412891,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3490/sim3490_sheet2.pdf","text":"Cross Sections","size":"292 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3490 cross sections"},{"id":412890,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3490/sim3490_sheet1_georeferenced.pdf","text":"Georeferenced Geologic Map","size":"106 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3490 Georeferenced Geologic Map"},{"id":412889,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3490/sim3490_sheet1.pdf","text":"Geologic Map","size":"27.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3490 Geologic Map"},{"id":412888,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3490/ReadMe.txt","text":"Read Me","size":"12.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3490 Read Me file"},{"id":500197,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114341.htm","linkFileType":{"id":5,"text":"html"}},{"id":412894,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XXW25T","text":"USGS data release","linkHelpText":"Sr-, U-, H- and O-isotope data used to evaluate water sources in the Potrero Creek wetlands, upper Santa Cruz basin, southern Arizona, USA"},{"id":412886,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3490/coverthb_pamphlet.jpg"},{"id":412887,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3490/sim3490_pamphlet.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3490 pamphlet"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.37611177853266,\n 31.882514371272933\n ],\n [\n -111.37611177853266,\n 31.300216933285228\n ],\n [\n -110.49757862424259,\n 31.300216933285228\n ],\n [\n -110.49757862424259,\n 31.882514371272933\n ],\n [\n -111.37611177853266,\n 31.882514371272933\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
Anadromous fishes, such as Pacific salmon, spend portions of their life cycle in freshwater and marine systems, thus rendering them susceptible to a variety of natural and anthropogenic stressors. These stressors operate at different spatiotemporal scales, whereby freshwater conditions are more likely to impact single populations or subpopulations, while marine conditions are more likely to act on entire evolutionarily significant units (ESUs). Coherence in population parameters like survival and productivity can therefore serve as an indicator of relative influence. The goal of this study was to elucidate scale-dependent shifts in Oregon Coast coho salmon productivity. We used a multivariate state-space approach to analyze almost 60 years of stock-recruitment data for the Oregon Coast ESU. Analyses were conducted separately for time periods prior to and after 1990 to account for improvements in abundance estimation methods and significant changes in conservation and management strategies. Prior to 1990, productivity declined for most Oregon Coast populations, especially through the 1980s. From 1990–onward, coherence increased, and trends tracked closely with the North Pacific Gyre Oscillation (NPGO). The latter period is associated with reductions in harvest rates and hatchery production such that the relative influence of the marine environment may have grown more apparent following the removal of these stressors. Furthermore, the link between productivity and NPGO is consistent with trends observed for several other Pacific salmon ESUs. If Oregon Coast coho salmon populations become more synchronous, managers can expect to face new challenges driven by reductions in the population portfolio effect and increasingly variable marine conditions due to climate change.
Groundwater quality in the north Sacramento Valley (NSV) was studied in the Redding–Red Bluff shallow aquifer study unit (referred to as the NSV shallow aquifer or NSV-SA) as part of the Priority Basin Project (PBP) of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in Shasta and Tehama Counties and included two physiographic study areas: (1) the Redding area to the north and (2) the Red Bluff area to the south. The study was focused on groundwater resources used for domestic drinking-water supply, which are mostly drawn from shallower parts of aquifer systems than those of groundwater resources used for public drinking-water supply in the same area. This assessment characterized the quality of ambient groundwater in the aquifer before filtration or treatment, rather than the quality of drinking water delivered to the tap.
The water-quality evaluation in this study has three components: (1) a status assessment, which characterized the quality of the groundwater resources used for domestic supply for 2018–19, in reference to state and national benchmarks; (2) an understanding assessment, which evaluated the natural and human factors potentially affecting water quality in those resources; and (3) a comparison between the groundwater resources used for domestic supply and those used for public supply in the region.
The status assessment was based on data collected from 50 sites sampled by the U.S. Geological Survey for the GAMA-PBP in 2018–19. To provide context for the measured concentrations of groundwater constituents compared to U.S. Environmental Protection Agency and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality, relative concentrations (RCs) of groundwater constituents were calculated as the concentration in a sample divided by the respective benchmark. Health-based benchmarks include regulatory and non-regulatory human-health benchmarks such as a maximum contaminant level, notification level, or health-based screening level. Aesthetic-based benchmarks are regulatory or non-regulatory non-health-based benchmarks that can affect the color or taste of water. A grid-based method was used to estimate the proportions of the groundwater resources used for domestic drinking wells that have water-quality constituents below (low), approaching (moderate, greater than half the benchmark), or above (high) benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA-PBP study areas.
NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Streamflow data are critical for monitoring and managing water resources, yet there are significant spatial gaps in our federal monitoring networks with biases toward large perennial rivers. In some cases, streamflow monitoring exists in these spatial gaps, but information about these monitoring locations is challenging to obtain. Here, we present a streamflow catalog for the United States Pacific Northwest that includes current and historical streamflow monitoring location information obtained from 32 organizations (other than the U.S. Geological Survey), which includes 2661 continuous streamflow gaging locations (22% are currently active) and 30,557 discrete streamflow measurements. A stakeholder advisory board with representatives from organizations that operate streamflow monitoring networks identified metadata requirements and provided feedback on the Streamflow Data Catalog user interface. Engagement with the water resources community through this effort highlighted challenges that water professionals face in collecting and managing streamflow data so that data are findable, accessible, interoperable, and reusable (FAIR). Over 60% of the streamflow monitoring locations in the Streamflow Data Catalog are not available online and are thus not findable through web search engines. Providing organizations technical assistance with standard measurement procedures, metadata collection, and web accessibility could substantially increase the availability and utility of streamflow information to water resources communities.
","language":"English","publisher":"MDPI","doi":"10.3390/w15040679","usgsCitation":"Kaiser, K.E., Blasch, K.W., and Schmitz, S., 2023, Integration of distributed streamflow measurement metadata for improved water resource decision-making: Water, v. 15, no. 4, 679, 11 p., https://doi.org/10.3390/w15040679.","productDescription":"679, 11 p.","ipdsId":"IP-148240","costCenters":[{"id":65563,"text":"Northwest Pacific Islands Regional Director's Office","active":true,"usgs":true}],"links":[{"id":444520,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w15040679","text":"Publisher Index Page"},{"id":433499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Kaiser, Kendra E. 0000-0003-1773-6236","orcid":"https://orcid.org/0000-0003-1773-6236","contributorId":211475,"corporation":false,"usgs":false,"family":"Kaiser","given":"Kendra","email":"","middleInitial":"E.","affiliations":[{"id":38255,"text":"Boise State Unviersity","active":true,"usgs":false}],"preferred":false,"id":912398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blasch, Kyle W. 0000-0002-0590-0724","orcid":"https://orcid.org/0000-0002-0590-0724","contributorId":203415,"corporation":false,"usgs":true,"family":"Blasch","given":"Kyle","email":"","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmitz, Steven","contributorId":343922,"corporation":false,"usgs":false,"family":"Schmitz","given":"Steven","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":912400,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240476,"text":"ofr20231006 - 2023 - Improving temporal frequency of Landsat surface temperature products using the gap-filling algorithm","interactions":[],"lastModifiedDate":"2026-02-10T21:32:15.228526","indexId":"ofr20231006","displayToPublicDate":"2023-02-08T13:48:38","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1006","displayTitle":"Improving Temporal Frequency of Landsat Surface Temperature Products Using the Gap-Filling Algorithm","title":"Improving temporal frequency of Landsat surface temperature products using the gap-filling algorithm","docAbstract":"Remotely sensed surface temperature (ST) has been widely used to monitor and assess landscape thermal conditions, hydrologic modeling, and surface energy balance. Landsat thermal sensors have continuously measured the Earth surface thermal radiance since August 1982. The thermal radiance measurements are atmospherically compensated and converted to Landsat STs and delivered as part of the U.S. Geological Survey Landsat Collection 1 U.S. Analysis Ready Data; however, the low satellite revisit cycles combined with the presence of clouds and cloud shadows reduce the number of valid retrievals. This reduction can limit the ability to monitor annual or seasonal variations in the surface thermal budget. These factors reduce the ability to use the temperature data to fit time series for historical trend analysis to match background climate variations. In this study, we implemented an approach that uses linear harmonic least absolute shrinkage and selection operator regression models to fill gaps because of clouds, shadows, and coarse temporal resolution. The gap-filled data provide increased temporal density of Landsat ST records. The gap-filled Landsat ST, therefore, can allow for an improved monitoring of annual, seasonal, or even monthly landscape thermal conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231006","usgsCitation":"Xian, G., Shi, H., Arab, S., Mueller, C., Hussain, R., Sayler, K., and Howard, D., 2023, Improving temporal frequency of Landsat surface temperature products using the gap-filling algorithm: U.S. Geological Survey Open-File Report 2023–1006, 15 p., https://doi.org/10.3133/ofr20231006.","productDescription":"vi, 15 p.","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-144337","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412873,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1006/images"},{"id":412872,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1006/ofr20231006.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":412871,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1006/ofr20231006.pdf","text":"Report","size":"41.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1006"},{"id":412880,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20231006/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1006/coverthb.jpg"},{"id":499732,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114340.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.9318883744094,\n 34.338976979151155\n ],\n [\n -84.9318883744094,\n 33.376859208686255\n ],\n [\n -83.70224614831253,\n 33.376859208686255\n ],\n [\n -83.70224614831253,\n 34.338976979151155\n ],\n [\n -84.9318883744094,\n 34.338976979151155\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
Introduction: To sustain black bear (Ursus americanus) populations, wildlife managers should understand the coupled socio-ecological systems that influence acceptance capacity for bears.
Method: In a study area encompassing a portion of New York State, we spatially matched datasets from three sources: human-bear conflict reports between 2006 and 2018, estimates of local bear density in 2017–2018, and responses to a 2018 property owner survey (n=1,772). We used structural equation modeling to test hypothesized relationships between local human-bear conflict, local bear density, and psychological variables.
Results: The final model explained 57% of the variance in acceptance. The effect of bear population density on acceptance capacity for bears was relatively small and was mediated by a third variable: perception of proximity to the effects of human-bear interactions. The variables that exerted a direct effect on acceptance were perception of bear-related benefits, perception of bear-related risks, perceived proximity to effects of human-bear interactions, and being a hunter. Perception of bear-related benefits had a greater effect on acceptance than perception of bear-related risks. Perceived proximity to effects of human-bear interactions was affected by local bear density, but also was affected by social trust. Increased social trust had nearly the same effect on perceived proximity as decreased bear density. Social trust had the greatest indirect effect on acceptance of any variable in the model.
Discussion: Findings suggest wildlife agencies could maintain public acceptance for bears through an integrated approach that combines actions to address bear-related perceptions and social trust along with active management of bear populations.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2023.1041393","usgsCitation":"Siemer, W., Lauber, T., Stedman, R., Hurst, J., Sun, C., Fuller, A.K., Hollingshead, N., Belant, J., and Kellner, K., 2023, Perception and trust influence acceptance for black bears more than bear density or conflicts: Frontiers in Conservation Science, v. 4, 1041393, 13 p., https://doi.org/10.3389/fcosc.2023.1041393.","productDescription":"1041393, 13 p.","ipdsId":"IP-147449","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467120,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2023.1041393","text":"Publisher Index Page"},{"id":466002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.92499168267652,\n 40.760363749644455\n ],\n [\n -73.65063699090229,\n 40.98264101294393\n ],\n [\n -73.6996393039949,\n 41.10089280193728\n ],\n [\n -73.49391688239375,\n 41.2484207881225\n ],\n [\n -73.56250536378525,\n 41.3073292248944\n ],\n [\n -73.48423008439727,\n 42.05372989136586\n ],\n [\n -73.5233960109858,\n 42.126434934459525\n ],\n [\n -73.41562767810787,\n 42.34405165012723\n ],\n [\n -73.83681626701608,\n 42.54652252735795\n ],\n [\n -74.44841929328119,\n 42.6495653802757\n ],\n [\n -75.7909273246029,\n 43.03044245093176\n ],\n [\n -76.43773682001827,\n 43.50144007856014\n ],\n [\n -77.04532365530125,\n 43.25213523900416\n ],\n [\n -78.07429641881241,\n 43.3804649394846\n ],\n [\n -79.06407311309604,\n 43.2521232638133\n ],\n [\n -79.02485730522619,\n 42.98027715144707\n ],\n [\n -78.90726018672747,\n 42.90136300879922\n ],\n [\n -79.08365221595503,\n 42.69283803617958\n ],\n [\n -79.75001378472541,\n 42.331662242956355\n ],\n [\n -79.76953018691533,\n 42.01940417603805\n ],\n [\n -75.3404244202763,\n 41.98299557666223\n ],\n [\n -75.07586228888928,\n 41.75679559693819\n ],\n [\n -75.0464736918644,\n 41.515116258185316\n ],\n [\n -74.78190029599845,\n 41.44169802010788\n ],\n [\n -74.66431063355593,\n 41.3681952398467\n ],\n [\n -73.93913471760371,\n 41.02160483993919\n ],\n [\n -73.92499168267652,\n 40.760363749644455\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"4","noUsgsAuthors":false,"publicationDate":"2023-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Siemer, William F.","contributorId":348063,"corporation":false,"usgs":false,"family":"Siemer","given":"William F.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lauber, T. Bruce","contributorId":348064,"corporation":false,"usgs":false,"family":"Lauber","given":"T. Bruce","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922914,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stedman, Richard C.","contributorId":348065,"corporation":false,"usgs":false,"family":"Stedman","given":"Richard C.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hurst, Jeremy E.","contributorId":348066,"corporation":false,"usgs":false,"family":"Hurst","given":"Jeremy E.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":922916,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sun, Catherine C.","contributorId":348067,"corporation":false,"usgs":false,"family":"Sun","given":"Catherine C.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":922917,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922918,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hollingshead, Nicholas A.","contributorId":348068,"corporation":false,"usgs":false,"family":"Hollingshead","given":"Nicholas A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":922919,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Belant, Jerrold L.","contributorId":348069,"corporation":false,"usgs":false,"family":"Belant","given":"Jerrold L.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922920,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kellner, Kenneth III","contributorId":348070,"corporation":false,"usgs":false,"family":"Kellner","given":"Kenneth","suffix":"III","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922921,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70240637,"text":"70240637 - 2023 - A comparison of direct & indirect survey methods for estimating colonial nesting waterbird populations","interactions":[],"lastModifiedDate":"2023-02-10T13:08:59.72308","indexId":"70240637","displayToPublicDate":"2023-02-08T07:04:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of direct & indirect survey methods for estimating colonial nesting waterbird populations","docAbstract":"Population estimates derived from monitoring efforts can be sensitive to the survey method selected, potentially leading to biased estimates and low precision relative to true population size. While small unmanned aerial systems (UAS) present a unique opportunity to survey avian populations while limiting disturbance, relatively little is known about how this method compares with more traditional approaches. In this study we compared population estimates of Snowy (Egretta thula) and Cattle Egrets (Bubulcus ibis) in a mixed-species colony in the Chesapeake Bay (Maryland, USA) derived from UAS photo counts, flush counts, flight-line surveys, and in-colony nest counts along with the time required to derive an estimate via each approach. We found that UAS counts and flush counts produced lower pair estimates than nest counts and flight-line surveys (P < 0.05), and required dramatically less time (x̄ = 6, 8, 84 and 90 min, respectively). These results suggest that while UAS have the potential to collect valuable survey data from breeding colonies that are hard to reach or are especially sensitive to the disturbance inherent in other methods, inherent biases should be considered and caution should be used when comparing results between survey types.
The International Union for Conservation of Nature (IUCN) Red List is an important and widely used tool for conservation assessment. The IUCN uses information about a species’ range, population size, habitat quality and fragmentation levels, and trends in abundance to assess extinction risk. Genetic diversity is not considered, although it affects extinction risk. Declining populations are more strongly affected by genetic drift and higher rates of inbreeding, which can reduce the efficiency of selection, lead to fitness declines, and hinder species’ capacities to adapt to environmental change. Given the importance of conserving genetic diversity, attempts have been made to find relationships between red-list status and genetic diversity. Yet, there is still no consensus on whether genetic diversity is captured by the current IUCN Red List categories in a way that is informative for conservation. To assess the predictive power of correlations between genetic diversity and IUCN Red List status in vertebrates, we synthesized previous work and reanalyzed data sets based on 3 types of genetic data: mitochondrial DNA, microsatellites, and whole genomes. Consistent with previous work, species with higher extinction risk status tended to have lower genetic diversity for all marker types, but these relationships were weak and varied across taxa. Regardless of marker type, genetic diversity did not accurately identify threatened species for any taxonomic group. Our results indicate that red-list status is not a useful metric for informing species-specific decisions about the protection of genetic diversity and that genetic data cannot be used to identify threat status in the absence of demographic data. Thus, there is a need to develop and assess metrics specifically designed to assess genetic diversity and inform conservation policy, including policies recently adopted by the UN's Convention on Biological Diversity Kunming-Montreal Global Biodiversity Framework.
Elucidating the molecular structure of sedimentary organic matter (SOM) is key to understanding petroleum generation processes, as well as ancient sedimentary environments. SOM structure is primarily controlled by biogenic source material (e.g., marine vs. terrigenous), depositional conditions, and subsurface thermal history. Additional factors, e.g., strain, may also impact the molecular structure of SOM. Multiple spatially resolved approaches exist for in situ evaluation of SOM, including Raman and infrared spectroscopies, as well as mass spectrometric methods. While these methods have enabled increased understanding of the occurrence and distribution of SOM functional groups, they suffer from disadvantages including low spatial resolution (infrared spectroscopy), limited molecular information (Raman spectroscopy), and sample destruction (mass spectrometric methods). Recent technological advances have resulted in infrared spectrometers capable of breaking the Abbe diffraction limit, greatly increasing the spatial resolutions accessible for an infrared measurement.
Here we utilize optical photothermal infrared spectroscopy (O-PTIR) to record maps of functional group distributions at 500 nm spatial resolution in Tasmanites (algal microfossils) from the Upper Devonian Ohio Shale. These data allow for discrimination between Tasmanites, adjacent SOM, and fine-grained minerals. Additionally, functional group distributions within Tasmanites were found to be generally homogenous, although slight variations exist between the body and fold apices (zones of greatest deformation) which may indicate strain-induced chemical reactions. The data presented here represent the first application of O-PTIR to study SOM, highlighting the promise of this analytical approach for future studies evaluating the molecular composition of geologic materials at sub-micron scales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.orggeochem.2023.104569","usgsCitation":"Jubb, A., Stokes, M., McAleer, R.J., Hackley, P.C., Dillion, E., and Qu, J., 2023, Mapping ancient sedimentary organic matter molecular structure at nanoscales using optical photothermal infrared spectroscopy: Organic Geochemistry, v. 177, 104569, 9 p., https://doi.org/10.1016/j.orggeochem.2023.104569.","productDescription":"104569, 9 p.","ipdsId":"IP-140733","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":444534,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.orggeochem.2023.104569","text":"Publisher Index Page"},{"id":412936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"177","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":864038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stokes, Martha 0000-0002-2838-8380","orcid":"https://orcid.org/0000-0002-2838-8380","contributorId":269608,"corporation":false,"usgs":true,"family":"Stokes","given":"Martha","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":864040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":864041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":864039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dillion, Eoghan","contributorId":302334,"corporation":false,"usgs":false,"family":"Dillion","given":"Eoghan","email":"","affiliations":[{"id":65459,"text":"Photothermal Spectroscopy Corporation","active":true,"usgs":false}],"preferred":false,"id":864043,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Qu, Jing","contributorId":242671,"corporation":false,"usgs":false,"family":"Qu","given":"Jing","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":864042,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240663,"text":"70240663 - 2023 - The Volcanic Hazard Maps Database: An initiative of the IAVCEI Commission on Volcanic Hazards and Risk","interactions":[],"lastModifiedDate":"2023-02-13T12:31:58.70134","indexId":"70240663","displayToPublicDate":"2023-02-08T06:30:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3841,"text":"Journal of Applied Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"The Volcanic Hazard Maps Database: An initiative of the IAVCEI Commission on Volcanic Hazards and Risk","docAbstract":"In this work we present the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI) Commission on Volcanic Hazards and Risk (CVHR) Volcanic Hazard Maps Database and the accompanying volcanichazardmaps.org website. Using input from a series of IAVCEI CVHR Working Group on Hazard Mapping workshops, we developed a classification scheme and terminology framework for categorizing, discussing, naming, and searching for hazard maps. ≥ The database and website aim to serve as a resource for the volcanology community to explore how different aspects of hazard map development and design have been addressed in different countries, for different hazard processes, and for different intended purposes and audiences. Additionally, they act as a tool for presenting hazard map options to stakeholder groups and serve as a learning resource that can be incorporated into educational materials and training courses. In this work, we present the database and website, discuss the classification scheme, explore the enormous diversity of hazard maps, and suggest ways that the database and website can be used by the volcanic hazard mapping community.
This two-sheet publication displays a high-resolution colored shaded-relief bathymetric map (sheet 1) and orthomosaic (sheet 2) of part of the Lake Tahoe floor in California generated from a U.S. Geological Survey towed surface vehicle with multiple downward-looking underwater cameras. The system is named the Structure-from-Motion Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). The cameras were synchronized with each other and with a survey-grade Global Navigation Satellite System. A total of 42,939 photographs were collected with nearly complete overlapping coverage of an area approximately 250 meters by 250 meters. A digital terrain model and an orthomosaic were generated from the overlapping photographs using Structure-from-Motion and photogrammetry techniques. Gaps are present in the bathymetry data owing to data-collection or -processing artifacts. These two sheets display the very fine details of the lake floor mapped using SQUID-5.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3501","usgsCitation":"Hatcher, G.A., Warrick, J.A., and Dartnell, P., 2022, Colored shaded-relief bathymetric map and orthomosaic from structure-from-motion quantitative underwater imaging device with five cameras of the Lake Tahoe floor, California: U.S. Geological Survey Scientific Investigations Map 3501, 2 sheets, scale 1:700, https://doi.org/10.3133/sim3501.","productDescription":"2 Sheets: 35.00 × 34.00 inches; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-139653","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":412576,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9934I6U","text":"USGS data release","description":"USGS data release","linkHelpText":"Point clouds, bathymetric maps, and orthoimagery generated from overlapping lakebed images acquired with the SQUID-5 system near Dollar Point, Lake Tahoe, CA, March 2021"},{"id":412577,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V44ZYS","text":"USGS data release","description":"USGS data release","linkHelpText":"Overlapping lakebed images and associated GNSS locations acquired near Dollar Point, Lake Tahoe, CA, March 2021"},{"id":412573,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3501/coverthb.jpg"},{"id":412574,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3501/sim3501_sheet1.pdf","text":"Sheet 1","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3501 Sheet 1"},{"id":412575,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3501/sim3501_sheet2.pdf","text":"Sheet 2","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3501 Sheet 2"},{"id":500207,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114342.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"Lake Tahoe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.23690229714447,\n 39.300192154621016\n ],\n [\n -120.23690229714447,\n 38.87400239989947\n ],\n [\n -119.8635257065711,\n 38.87400239989947\n ],\n [\n -119.8635257065711,\n 39.300192154621016\n ],\n [\n -120.23690229714447,\n 39.300192154621016\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Detecting spatiotemporal changes in the abundances of organisms is key to effectively conserving species. While indices of abundance have long been used, there has been a shift toward model-based estimators that account for the detection process. Popular approaches including traditional occupancy models and N-mixture models entail tradeoffs. The traditional occupancy approach requires the researcher coarsen the characterization of abundance to the probability that a site is occupied or unoccupied. Conversely, N-mixture models make use of variation in counts, but perform poorly when individuals have low detectability or move into or out of sites between visits. Multistate occupancy models that differentiate relatively abundant from non-abundant states have the potential to fill this gap but have been underexplored. We conducted a simulation study to test whether multistate occupancy models could capture spatial abundance patterns and detect population declines in the face of low individual detection probability (p ≤ 0.3) and unmodeled heterogeneity (e.g., that arising from individual movement). We considered 10,773 scenarios to examine the effects of differing amounts of heterogeneity as well as alternative study designs, population parameters, and modeling choices. We tracked bias in the proportion of sites estimated to be in the abundant state for single-season models, and power to detect a declining trend across multiple years. We also evaluated data diagnostic metrics to provide guidance to users. Multistate occupancy models were able to differentiate sites with higher abundances from sites with lower abundances when there were at least medium levels of spatial heterogeneity in true abundances. If different sites were randomly selected each year, power to detect even large population declines (65%) was poor (power < 0.8). However, if the same sites were surveyed each year, and a dynamic multistate occupancy was used, multistate occupancy models could detect (power ≥ 0.8) relatively small declines (5-40%) in 20% of scenarios, and frequently detect large declines of 45-60% (mean power = 0.92). Conservation decisions rely on detecting change reliably, rarely needing absolute abundance information. Multistate occupancy models can improve our ability to detect changing abundance while accommodating low individual detection probability and heterogeneity in count monitoring data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2023.110303","usgsCitation":"Steen, V., Duarte, A., and Peterson, J., 2023, An evaluation of multistate occupancy models for estimating relative abundance and population trends: Ecological Modelling, v. 478, 110303, 9 p., https://doi.org/10.1016/j.ecolmodel.2023.110303.","productDescription":"110303, 9 p.","ipdsId":"IP-144901","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":444548,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2023.110303","text":"Publisher Index Page"},{"id":429891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"478","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Steen, Valerie A. 0000-0002-1417-8139","orcid":"https://orcid.org/0000-0002-1417-8139","contributorId":205994,"corporation":false,"usgs":false,"family":"Steen","given":"Valerie A.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":902764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":337608,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":902765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240642,"text":"70240642 - 2023 - Detection and monitoring of small-scale diamond and gold mining dredges using synthetic aperture radar on the Kadéï (Sangha) River, Central African Republic","interactions":[],"lastModifiedDate":"2023-02-10T13:01:32.364059","indexId":"70240642","displayToPublicDate":"2023-02-07T06:58:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Detection and monitoring of small-scale diamond and gold mining dredges using synthetic aperture radar on the Kadéï (Sangha) River, Central African Republic","docAbstract":"Although groundwater exchange processes are known to modulate atmospheric influences on stream temperature and flow, the implications for ecological stability are poorly understood. Here, we evaluated temporal change in stream fish communities across a gradient of groundwater influence defined by karst terrain (carbonate parent materials) within the Potomac River basin of eastern North America. We surveyed 12 sites in 2022 that had been sampled 29–30 years previously with similar methods. We also collected stream temperature data from each site and used the regression slope of the air-water temperature relationship to index stream thermal sensitivity and groundwater exchange processes. Sites in karst terrain exhibited strong groundwater controls on stream temperature, and fish communities were more stable over time in these locations than elsewhere. However, stream thermal sensitivity was a stronger predictor of species persistence than the spatial distribution of karst terrain in contributing areas, highlighting the ecological importance of local variation in groundwater discharge processes. The presence of calcium precipitates (marl) in stream substrates was associated with low thermal sensitivity and ecological stability over time, and we suggest such visible features may be a useful indicator of climate change refugia in stream ecosystems.
Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates.
Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey's 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network.
We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score < 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2023.102662","usgsCitation":"Mordensky, S.P., Lipor, J., DeAngelo, J., Burns, E., and Lindsey, C.R., 2023, When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates: Geothermics, v. 110, 102662, 22 p., https://doi.org/10.1016/j.geothermics.2023.102662.","productDescription":"102662, 22 p.","ipdsId":"IP-142531","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":444577,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geothermics.2023.102662","text":"Publisher Index Page"},{"id":435466,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V1Q9XM","text":"USGS data release","linkHelpText":"Geothermal resource favorability: select features and predictions for the western United States curated for DOI 10.1016/j.geothermics.2023.102662"},{"id":412868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -127.12518216384296,\n 49.86387644792583\n ],\n [\n -127.12518216384296,\n 31.096405781605768\n ],\n [\n -102.87766710543923,\n 31.096405781605768\n ],\n [\n -102.87766710543923,\n 49.86387644792583\n ],\n [\n -127.12518216384296,\n 49.86387644792583\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":863886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":863887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":863888,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":863889,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lindsey, Cary Ruth 0000-0001-5693-9664","orcid":"https://orcid.org/0000-0001-5693-9664","contributorId":292016,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":863890,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240880,"text":"70240880 - 2023 - Regolith of the crater floor units, Jezero crater, Mars: Textures, composition and implications for provenance","interactions":[],"lastModifiedDate":"2023-03-15T15:12:05.585557","indexId":"70240880","displayToPublicDate":"2023-02-07T06:33:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13276,"text":"JGR-Planets","active":true,"publicationSubtype":{"id":10}},"title":"Regolith of the crater floor units, Jezero crater, Mars: Textures, composition and implications for provenance","docAbstract":"A multi-instrument study of the regolith of Jezero crater floor units by the Perseverance rover has identified three types of regolith: fine-grained, coarse-grained, and mixed-type. Mastcam-Z, WATSON, and SuperCam RMI were used to characterize regolith texture, particle size, and roundedness where possible. Mastcam-Z multispectral and SuperCam LIBS data were used to constrain the composition of the regolith types. Fine-grained regolith is found surrounding bedrock and boulders, comprising bedforms, and accumulating on top of rocks in erosional depressions. Spectral and chemical data show it is compositionally consistent with pyroxene and a ferric-oxide phase. Coarse-grained regolith consists of 1-2 mm well-sorted gray grains that are found concentrated around the base of boulders and bedrock, and armoring bedforms. Its chemistry and spectra indicate it is olivine-bearing, and spatial distribution and roundedness indicate it has been transported, likely by saltation-induced creep. Coarse grains share similarities with the olivine grains observed in the Séítah formation bedrock, making that unit a possible source for these grains. Mixed-type regolith contains fine- and coarse-grained regolith components and larger rock fragments. The rock fragments are texturally and spectrally similar to bedrock within the Máaz and Séítah formations, indicating origins by erosion from those units, although they could also be a lag deposit from erosion of an overlying unit. The fine- and coarse-grained types are compared to their counterparts at other landing sites to inform global, regional, and local inputs to regolith formation within Jezero crater. The regolith characterization presented here informs regolith sampling efforts underway by Perseverance.
Groundwater quality and potential sources and migration pathways of chemical constituents associated with hydrocarbon-bearing formations were assessed by the U.S. Geological Survey for the California State Water Resources Control Board Oil and Gas Regional Monitoring Program (RMP). Groundwater samples were collected as part of the RMP from 21 preexisting wells used for public supply, monitoring, or irrigation in and near the Montebello Oil Field and analyzed for constituents associated with hydrocarbon-bearing formations and constituents used to identify recently recharged groundwater and older groundwater. The newly collected RMP data were supplemented with historical sample data from 849 groundwater wells and analyzed with respect to explanatory factors that have the potential to influence water quality. Potential sources and migration pathways of fluids (water, gas, or oil) from hydrocarbon-bearing formations that could affect groundwater quality in the Montebello Oil Field include large volumes of recycled produced water (water withdrawn from an oil well and brought to the surface that may include oil, water, and gas from the geologic formation and water or gas injected for enhanced recovery) that have been reinjected since the 1960s to enhance oil production, oil and gas wells with well-integrity issues, and oil and gas wells with an uncemented annulus that intersects groundwater resource zones.
Trace amounts of dissolved petroleum hydrocarbons, thermogenic gas (propane through pentane range), or both, were detected in seven groundwater samples collected in 2014 and 2018 as part of the RMP. Five of those samples also contained manufactured volatile organic compounds and at least some modern-age groundwater (recharged during or after 1953), indicating that the hydrocarbons could have originated from surficial, or shallow, sources unrelated to oil and gas development. Two samples contained low concentrations of petroleum hydrocarbons (less than 0.1 microgram per liter) and did not contain detections of manufactured volatile organic compounds in pre-modern groundwater. These samples were collected from relatively deep wells (greater than 140 meters below land surface) with perforations completed in marine sediments that may contain water with similar compositions to produced water.
The RMP sample results and available historical data in and near the Montebello Oil Field did not provide conclusive evidence that oil and gas development has adversely affected groundwater resources. All samples with detectable petroleum hydrocarbons, thermogenic gases, or both, were collected from sites that also are within 500 meters of anthropogenic hydrocarbon sources not associated with oil and gas development or sources. In addition, naturally occurring sources of hydrocarbons that exist at intervals shallower than, or are in areas outside of, economically productive oil- and gas-producing zones could affect groundwater quality.
A definitive analysis of relations of groundwater quality to potential anthropogenic and natural explanatory factors was not possible because of the low density of new and historical sampling data, particularly in parts of the Montebello Oil Field where the largest relative risks to groundwater from hydrocarbon-bearing formations exist. Areas to consider for more detailed monitoring and analysis in the future that may present the largest relative potential risks to groundwater quality include (1) areas downgradient from historical surface ponds and sumps and (2) areas with co-located high net injection (oil reservoir injection exceeds production), old oil and gas wells that may be more likely to develop well-integrity issues than newer wells, and oil and gas wells with uncemented boreholes intersecting groundwater zones. To help fill gaps resulting from sparse groundwater wells, temperature and resistivity borehole log data could be analyzed to locate anomalies that identify potential areas where relatively warm or saline water from deeper hydrocarbon-bearing formations is present in groundwater.
Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819