The National Hydrography Dataset Plus High Resolution (NHDPlus HR) is a scalable hydrologic geospatial fabric or framework, built from (1) the High Resolution (1:24,000-scale or better) National Hydrography Dataset (NHD), (2) nationally complete Watershed Boundary Dataset (WBD), and (3) 1/3-arc-second 3D Elevation Program (3DEP) digital elevation model (DEM) data (at a 10-meter ground spacing; or 5-meter 3DEP DEM in Alaska only). The NHDPlus HR provides a modeling and assessment framework at a local 1:24,000 scale, while nesting seamlessly into the national context.
NHDPlus HR is modeled after the highly successful NHDPlus version 2 (NHDPlusV2). Like NHDPlusV2, the NHDPlus HR includes data for a nationally seamless network of stream reaches, elevation-based catchment areas, flow surfaces, and value-added attributes that enhance stream-network navigation, analysis, and data display. However, NHDPlus HR provides much greater spatial detail than NHDPlusV2, while NHDPlusV2 is, at present, more complete in its attribution of additions, removals, and diversions, as well as stream connectivity. This user’s guide is intended to provide necessary information and guidance in the use of NHDPlus HR data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255031","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","programNote":"National Geospatial Program","usgsCitation":"Moore, R.B., McKay, L.D., Rea, A.H., Bondelid, T.R., Price, C.V., Dewald, T.G., and Hayes, L., 2025, User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR): U.S. Geological Survey Scientific Investigations Report 2025–5031, 78 p., https://doi.org/10.3133/sir20255031. [Supersedes USGS Open-File Report 2019–1096.]","productDescription":"Report: xiii, 78 p.; 2 Data Releases; Project Site","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150034","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":496237,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5031/sir20255031.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5031 XML"},{"id":496238,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5031/images"},{"id":496239,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WFOBQI","text":"USGS data release","linkHelpText":"USGS National Hydrography Dataset Plus High Resolution National Release 1 FileGDB"},{"id":496240,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://apps.nationalmap.gov/downloader/#/","text":"USGS data release","linkHelpText":"The National Map downloader (ver. 2.0)"},{"id":496273,"rank":8,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/national-hydrography/nhdplus-high-resolution","text":"NHDPlus High Resolution"},{"id":496236,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255031/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5031 HTML"},{"id":496235,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5031/sir20255031.pdf","text":"Report","size":"9.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5031 PDF"},{"id":496234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5031/coverthb.jpg"}],"contact":"Director, National Geospatial Program
Core Science Systems
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Fostering a sense of classroom community in earth science classes supports students’ sense of belonging within the classroom and the broader scientific community, helping them build a sense of identity as a geoscientist. This study examines the effects of incorporating a 2-week, collaborative role-playing activity on sense of classroom community and science identity in an introductory hydrology class. Students assumed roles of residents, medical center representatives, government employees, and environmental activists to learn about flooding through a community-centered lens, focusing on a flood event in Harris County, Texas during Tropical Storm Allison. Pre-post-surveys were given immediately before and after the learning module to evaluate classroom community, science identity using Likert scales, and hydrologist identity using a pictorial scale. Qualitative analysis of a short-answer question in which students defined “hydrologist” provided context for quantitative identity data. Post-survey data on classroom community shows an increase in mean agreement as compared to the pre-survey. This increase was statistically significant for four classroom community statements. Paired science identity data show small effect size and no significant change, but pictorial identity as a hydrologist shows significant growth. Social aspects of the role-playing activity did not significantly alter student’s already high science identity but altered their conceptions of the social implications of hydrology and increased identity as hydrologists. The significant increase in classroom community has important implications for using role-playing as an active learning strategy to enhance student learning experience by creating a positive classroom climate and connecting hydrology concepts to community needs.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10899995.2025.2565990","usgsCitation":"Plenge, M., Dolan, W., Tomlinson, A., Hutson, B., and Pavelsky, T., 2025, Impact of a place-based role-playing exercise on student sense of classroom community and science identity in a hydrology class: Journal of Geoscience Education, https://doi.org/10.1080/10899995.2025.2565990.","ipdsId":"IP-171850","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":498550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Plenge, Megan","contributorId":365013,"corporation":false,"usgs":false,"family":"Plenge","given":"Megan","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":953582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dolan, Wayana 0000-0001-8405-4302","orcid":"https://orcid.org/0000-0001-8405-4302","contributorId":354442,"corporation":false,"usgs":true,"family":"Dolan","given":"Wayana","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":953583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tomlinson, Alexa","contributorId":365016,"corporation":false,"usgs":false,"family":"Tomlinson","given":"Alexa","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":953584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hutson, Bryant","contributorId":365018,"corporation":false,"usgs":false,"family":"Hutson","given":"Bryant","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":953585,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pavelsky, Tamlin","contributorId":149629,"corporation":false,"usgs":false,"family":"Pavelsky","given":"Tamlin","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":953586,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272027,"text":"70272027 - 2025 - Estimated ultimate recovery (EUR) Prediction for Eagle Ford Shale using integrated datasets and artificial neural networks","interactions":[],"lastModifiedDate":"2025-11-13T15:56:49.946165","indexId":"70272027","displayToPublicDate":"2025-09-30T08:51:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10757,"text":"Energies","active":true,"publicationSubtype":{"id":10}},"title":"Estimated ultimate recovery (EUR) Prediction for Eagle Ford Shale using integrated datasets and artificial neural networks","docAbstract":"The estimated ultimate recovery (EUR) is an important parameter for forecasting oil and gas production and informing decisions regarding field development strategies. In this study, we combined site-specific geologic, completion, and operational parameters with the predictive capabilities of machine learning (ML) models to predict EURs of the wells for the Eagle Ford Marl Continuous Oil Assessment Unit. We developed an extensive dataset of wells that have produced from the lower and upper Eagle Ford Shale intervals and reduced the model complexity using principal component analysis. We tested the ML models and estimated the sensitivities of ML-predicted EURs to changes in the values of different input variables. The results of applying the optimized ML model to the Eagle Ford suggest that the approach developed in this study could be promising. The ML estimates of the EURs fit the DCA-based values with an R2 ~ 0.9 and a mean absolute error of ~36 × 103 bbl. In the lower Eagle Ford Shale, the EUR estimates were found to be most sensitive to changes in porosity, net thickness of the interval, clay volume, and the API gravity of the oil; and that in the upper Eagle Ford Shale they were most sensitive to changes in the total organic carbon and water saturation, which suggests that it could be important to consider these parameters in assessing these intervals or close analogs.
","language":"English","publisher":"MDPI","doi":"10.3390/en18195216","usgsCitation":"Karacan, C.O., Anderson, S.T., and Cahan, S., 2025, Estimated ultimate recovery (EUR) Prediction for Eagle Ford Shale using integrated datasets and artificial neural networks: Energies, v. 18, no. 19, 5216, 21 p., https://doi.org/10.3390/en18195216.","productDescription":"5216, 21 p.","ipdsId":"IP-164247","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":496420,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/en18195216","text":"Publisher Index Page"},{"id":496401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.39816695432742,\n 30.86268720310727\n ],\n [\n -99.39816695432742,\n 27.387197803061596\n ],\n [\n -89.04028077792094,\n 27.387197803061596\n ],\n [\n -89.04028077792094,\n 30.86268720310727\n ],\n [\n -99.39816695432742,\n 30.86268720310727\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"19","noUsgsAuthors":false,"publicationDate":"2025-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":949769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Steven T. 0000-0003-3481-3424 sanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-3481-3424","contributorId":2532,"corporation":false,"usgs":true,"family":"Anderson","given":"Steven","email":"sanderson@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":949770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cahan, Steven M. 0000-0002-4776-3668","orcid":"https://orcid.org/0000-0002-4776-3668","contributorId":205929,"corporation":false,"usgs":true,"family":"Cahan","given":"Steven M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":949771,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70272111,"text":"70272111 - 2025 - Unique thermal mixing patterns in Lake Ontario revealed by novel year-round observations of thermal stratification","interactions":[],"lastModifiedDate":"2025-12-01T16:52:39.768257","indexId":"70272111","displayToPublicDate":"2025-09-30T08:32:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Unique thermal mixing patterns in Lake Ontario revealed by novel year-round observations of thermal stratification","docAbstract":"Year-round records of thermal stratification in the Great Lakes are rare, and there are few observations of thermal stratification during winter. In this paper, we analyze temperature data from 13 temperature logger chains and from over 130 benthic acoustic receivers that were deployed across Lake Ontario for 2 yr. The timing and duration of the fall overturn correlate with the local average water depth, and shallow sites (< 50 m depth) overturn up to a month before deep sites (> 100 m depths). Likewise, in spring, the shallow sites warm faster. Lake Ontario has partial ice cover, so wind-driven mixing stirs the water column throughout winter, and inverse thermal stratification is largely absent. The depth-averaged winter water temperatures vary between 0°C and 4°C, with the coldest temperatures (near 0.1°C) found in the shallow Kingston basin and warmest temperatures (near 4°C) at sites near the 244 m deep Rochester Basin. Lake Ontario appears to be a warm monomictic lake, rather than having a dimictic mixing pattern as previously described—there is no sustained ice cover or inverse stratification that inhibits vertical mixing in winter. Winter is a poorly understood season for many aquatic processes, including fish bioenergetics, fish distribution, biochemical processes, invertebrate distribution, and production. Moreover, the lack of knowledge of winter has hampered the use of correct initial conditions for running large lake hydrodynamic models.
","language":"English","publisher":"Wiley","doi":"10.1002/lno.70215","usgsCitation":"Wells, M., Johnson, T.B., Robinson, R., Midwood, J., Shi, Y., Larocque, S., Eddie, A., O’Malley, B., Morton, K., Gorsky, D., and Tufts, B., 2025, Unique thermal mixing patterns in Lake Ontario revealed by novel year-round observations of thermal stratification: Limnology and Oceanography, v. 70, no. 11, p. 3401-3416, https://doi.org/10.1002/lno.70215.","productDescription":"16 p.","startPage":"3401","endPage":"3416","ipdsId":"IP-172993","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":496724,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.70215","text":"Publisher Index Page"},{"id":496547,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": 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Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Different data for different goals: Exploring trade-offs and synergies in the use of spatial data inputs to optimize conservation action in sagebrush ecosystems","docAbstract":"Ecosystems worldwide continue to experience rapid rates of habitat and species loss. Management actions to conserve and restore functional habitats are needed to reduce these declines, but funding and resources for such actions are limited. Spatial conservation prioritization (SCP) can facilitate strategic decision-making for targeted conservation planning and delivery, but complexities arise when management objectives include multiple wildlife species and ecological or management constraints, all of which can be further complicated by data uncertainty and existing conservation plans. The Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET), an R package-based decision-support tool, supports strategic ecosystem management planning across the sagebrush biome by using SCP. We adapted PReSET to better address the needs of multiple wildlife species, evaluate the effects of different ecological or management constraints on conservation outcomes, assess the influence of data uncertainty, and integrate existing conservation plans. Specifically, we developed optimization problems to identify priority sagebrush protection and restoration across the state of Wyoming, USA, and evaluated the efficacy and trade-offs of various approaches to problem design. We evaluated trade-offs in targeting multiple species compared to a single species, including using greater sage-grouse as a potential umbrella species to benefit other sagebrush-dependent wildlife. We then evaluated multi-species protection and restoration problems aimed at minimizing the risks of inadequate connectivity, climate change, and restoration failure, and accounted for data uncertainty to assess relationships between risk aversion of managers and conservation outcomes. We also developed optimization problems within conservation areas identified by an existing sagebrush conservation plan to evaluate the efficacy of guiding local-scale conservation delivery within more broadly defined conservation areas. Our results demonstrate how SCP methods can leverage novel spatial data to develop targeted decision-support resources that can facilitate landscape conservation planning and improve management outcomes across a wide array of systems and species.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.72214","usgsCitation":"Shyvers, J.E., Tarbox, B.C., Monroe, A., Van Lanen, N.J., Robb, B.S., Buchholtz, E.K., Duchardt, C.J., Edmunds, D.R., O’Donnell, M.S., Van Schmidt, N.D., Heinrichs, J., and Aldridge, C.L., 2025, Different data for different goals: Exploring trade-offs and synergies in the use of spatial data inputs to optimize conservation action in sagebrush ecosystems: Ecology and Evolution, v. 15, no. 10, e72214, 25 p., https://doi.org/10.1002/ece3.72214.","productDescription":"e72214, 25 p.","ipdsId":"IP-150992","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":496709,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.72214","text":"Publisher Index Page"},{"id":496479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":949967,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":949968,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"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":949969,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70271935,"text":"fs20253022 - 2025 - Beaver dams and their effects on urban streams in the Tualatin River Basin, northwestern Oregon","interactions":[],"lastModifiedDate":"2026-02-03T16:22:51.589121","indexId":"fs20253022","displayToPublicDate":"2025-09-30T07:55:41","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3022","displayTitle":"Beaver Dams and Their Effects on Urban Streams in the Tualatin River Basin, Northwestern Oregon","title":"Beaver dams and their effects on urban streams in the Tualatin River Basin, northwestern Oregon","docAbstract":"In response to growing interest in beaver-assisted restoration in the Tualatin River Basin of northwestern Oregon, the U.S. Geological Survey (USGS), in partnership with Clean Water Services, collected data from 2016–17 and completed a series of studies to: (1) inventory known locations of beaver dams and activity in the Tualatin River Basin, (2) estimate the number of beaver dams in the Tualatin River Basin as of 2017 and the potential number of beaver dams that could be supported with riparian vegetation improvements, and (3) assess the effects of beaver dams and ponds on storm hydrology, hydraulics, and floodplain inundation, suspended-sediment transport and deposition, and water quality along two urban stream reaches (Fanno Creek at Greenway Park and Bronson Creek between Kaiser and Saltzman Roads). This fact sheet summarizes the results of these studies and implications for beaver-assisted restoration in the Tualatin River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253022","usgsCitation":"Jones, K.L., Smith, C.D., White, J.S., Rounds, S.A., Doyle, M.C., and Leahy, E.K., Beaver dams and their effects on urban streams in the Tualatin River Basin, northwestern Oregon: U.S. Geological Survey Fact Sheet 2025–3022, 6 p., https://doi.org/10.3133/fs20253022","productDescription":"6 p.","onlineOnly":"Y","ipdsId":"IP-138686","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":496063,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3022/fs20253022.XML"},{"id":496061,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3022/coverthb.jpg"},{"id":496062,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3022/fs20253022.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3022"},{"id":496206,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.3133/sir20255039","text":"SIR 2025-5039","description":"SIR 2025-5039","linkHelpText":"- Beavers in the Tualatin River Basin, Northwestern Oregon"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.375\n ],\n [\n -122.5,\n 45.375\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
601 SW 2nd Ave., Suite 1950
Portland, Oregon 97204
American beavers (Castor canadensis) are native to the Pacific Northwest, and their populations have increased in many locations after being nearly removed by historical trapping. Beaver dams have well-documented effects on water quality in forested streams, but their effects on water quality in urban streams have not been well characterized. The study documented the water-quality effects of beaver dams and beaver activity in selected urban streams of the Tualatin River Basin in northwestern Oregon. Variations in water quality upstream, downstream, and within ponded areas behind beaver dams were quantified with continuous measurements of water temperature, specific conductance, dissolved oxygen, and pH from May 2016 to November 2017 in two intensively monitored reaches of urban streams (Fanno and Bronson Creeks). Five other urban stream reaches were monitored upstream and downstream from beaver ponds using water-temperature sensors to document water-temperature changes in additional beaver-affected reaches. Spatial water-quality variations within a beaver pond along Fanno Creek were characterized in more detail on four hot summer afternoons with numerous measurements of temperature and dissolved oxygen. Results from the study were used to document and derive insights from measured patterns in the water-quality data, such as the following:
Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, Oregon 97204
This study investigated the effects of natural beaver dams and ponds on sediment transport and deposition in two urban beaver-affected reaches in the Tualatin River Basin, northwestern Oregon. Data were collected during 2016–17 from Fanno Creek at Greenway Park (between SW Hall Boulevard and SW Pearson Court) and Bronson Creek (between NW Laidlaw Road and NW Kaiser Road); each study reach contained multiple beaver dams. Continuous turbidity, discrete suspended-sediment samples, and streamflow measurements were collected during storms and baseflow periods to calculate suspended-sediment loads (SSLs) and to compare differences in SSLs upstream and downstream from the two beaver-affected reaches. Turbidity was measured continuously upstream, within, and downstream from these reaches to evaluate seasonal and longitudinal turbidity patterns and fluctuations. The volume and mass of sediment deposited in a large pond along the Fanno Creek study reach were also estimated. Study results include:
Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, Oregon 97204
Beaver dams can help streams connect to their floodplains. These floodplain connections can expand the range of available aquatic habitats and aid in the restoration of stream and floodplain function and processes. American beavers (Castor canadensis) occupy a wide variety of aquatic habitats; however, their ability to build dams, the agent of stream and floodplain change, is constrained in large part by three physical variables—local vegetation, topography, and hydrology.
These three physical variables are combined in the Beaver Restoration Assessment Tool (BRAT), a geographic information system-based utility that uses a Fuzzy Inference System (FIS) to estimate the capacity of each reach within a stream network to support beaver dams. In this study, version 1.0 of BRAT was adapted and applied to the entire perennial stream network of Tualatin River Basin in northwestern Oregon. Beaver-dam locations in the Tualatin River Basin were compiled to (1) define the distribution of dams in the basin during 2013–16 and (2) provide necessary data for calibrating and validating BRAT predictions. BRAT was calibrated to the current known distribution of dams, as compiled in the inventory. The input FIS equations of the original BRAT model were adjusted to account for local topographic conditions; specifically, the low gradient of many streams in the basin, although subsequent updates to BRAT may obviate the need for these changes.
Results from this modified BRAT model reasonably simulated the dam inventory. Results show that beavers can currently build the greatest density of dams, defined as number of dams per kilometer of stream, in the higher-gradient forested streams of the basin, whereas they can build the fewest number of dams per kilometer in urban streams along the lower-gradient valley floor. Estimated dam density was generally 5-15 dams per kilometer (km) for forested streams and 2-4 dams/km for urban streams. Improving riparian vegetation along urban streams may allow beavers to build on average four additional dams per kilometer compared to current conditions. Results from this study may help inform local stream and stormwater management by (1) identifying stream reaches with the most potential to support beaver dams, (2) determining the likely factors limiting potential for dam building, and (3) identifying potential areas where dam building may affect human infrastructure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255039A","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"White, J.S., Smith, C.D., Jones, K.L., and Rounds, S.A., 2025, Stream network capacity to support beaver dams in the Tualatin River Basin, northwestern Oregon, chap. A of Jones, K.L., and Smith, C.D., eds., Beavers in the Tualatin River Basin, northwestern Oregon: U.S. Geological Survey Scientific Investigations Report 2025–5039–A, 20 p., https://doi.org/10.3133/sir20255039A.","productDescription":"Report: viii, 20 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-102303","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":495854,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5039/a/sir20255039a.XML"},{"id":495853,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5039/a/images"},{"id":495852,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1SURYZ4","text":"USGS data release","description":"USGS data release","linkHelpText":"Stream network capacity to support beaver dams, Tualatin River Basin, northwest Oregon"},{"id":496245,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PZ57QP","text":"USGS data release","description":"USGS data release","linkHelpText":"Beaver dam locations and beaver activity in the Tualatin Basin, Oregon"},{"id":495851,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255039a/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5039-A"},{"id":495850,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5039/a/sir20255039a.pdf","size":"5.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5039-A"},{"id":495849,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5039/a/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.375\n ],\n [\n -122.5,\n 45.375\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, Oregon 97204
Growing interest in beaver-assisted restoration in the Tualatin River Basin of northwestern Oregon motivated a series of studies by the U.S. Geological Survey to assess the capacity of the stream network to support beaver dams and to evaluate the effects of beaver dams and ponds on urban streams. This multichapter volume describes the data collection from 2016–17 and the findings of these studies, which were done in partnership with Clean Water Services. Chapter A documents the locations of beaver dams in the Tualatin River Basin and how many beaver dams the stream network could support with existing and improved riparian vegetation. Beaver dam capacity was estimated by modifying existing tools to account for the low gradient of many streams in the Tualatin River Basin. Chapter B describes the effects of beaver dams and ponds on hydrologic and hydraulic responses of storm flows. Hydrologic and hydraulic responses for two urban stream reaches were compared with and without beaver dams and ponds and for a range of streamflow conditions using two-dimensional hydraulic models. Chapter C characterizes the effects of beaver dams and ponds on the transport and deposition of suspended sediment. Continuous turbidity, discrete suspended-sediment samples, and streamflow measurements collected during storms and base-flow periods were used to assess: (1) suspended-sediment loads upstream and downstream from two beaver-affected reaches, and (2) seasonal and longitudinal turbidity patterns. Chapter D describes the effects of beaver dams and ponds on longitudinal, spatial, and seasonal water-quality patterns. Continuous and synoptic water-quality data were collected along urban stream reaches, and net ecosystem production was calculated for two beaver-affected reaches. The findings of these studies illustrate that the effects of beaver dams and ponds on hydrology, hydraulics, suspended-sediment transport and deposition, and water quality are dependent on the characteristics of a stream reach (for example, channel gradient, groundwater exchange, and riparian vegetation) and the characteristics of beaver dams and ponds along that reach. This information can be used to consider the implications of beaver-assisted restoration in the Tualatin River Basin and the effects of beaver dams and ponds in urban streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255039","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"Jones, K.L, and Smith, C.D., eds., Beavers in the Tualatin River Basin, northwestern Oregon: U.S. Geological Survey Scientific Investigations Report 2025–5039, https://doi.org/10.3133/sir20255039.","productDescription":"Chapters A-D","onlineOnly":"Y","costCenters":[],"links":[{"id":496209,"rank":2,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.3133/fs20253022","text":"Fact Sheet 2025-3022","description":"FS 2025-3022","linkHelpText":"- Beaver dams and their effects on urban streams in the Tualatin River Basin, northwestern Oregon"},{"id":496091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5039/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.375\n ],\n [\n -122.5,\n 45.375\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, Oregon 97204
The U.S. Geological Survey is working with Federal land management agencies to develop a series of planned structured science syntheses to support environmental effects analyses that agencies conduct under the National Environmental Policy Act (NEPA). This report synthesizes science information relevant to environmental effects analyses concerned with potential increases in the distribution and abundance of invasive annual grasses (IAGs) from proposed vegetation treatments for habitat restoration. The focal environments for this synthesis are rangelands in the intermontane valleys of Montana and the northern Great Plains of Montana, North Dakota, and South Dakota. The synthesis is organized to align with the standard elements of NEPA analyses and provides information on relevant scientific studies, data availability, analysis methods, and mitigation measures. We found that the likelihood of increasing IAGs from vegetation treatments depends on treatment type and environmental context. In sagebrush ecosystems of the focal region, prescribed fire often reduces or does not increase IAGs. Treatments that cause soil disturbances, such as mechanical removals of sagebrush or firebreak constructions, are more likely to increase IAGs than other treatments. Herbicides applied to reduce sagebrush cover have not increased the proportion of IAGs in the plant community. Temperature and precipitation have been strong factors in determining IAG responses to vegetation treatments in sagebrush ecosystems of the focal region, where more precipitation in spring and summer likely provides a competitive edge to native, perennial grasses more than winter annual grasses like Bromus tectorum L. (cheatgrass). In grasslands, prescribed fire often reduces IAGs, but effects depend on the abundance of native species and are often short lived. Mowing can increase or decrease IAGs in grassland ecosystems. Grassland site conditions, such as southeast-facing slopes, sandier or rockier sites, or lower native species cover or richness affect the likelihood of invasion by annual grasses. Maintaining adequate cover of perennial vegetation creates rangelands that are resistant and resilient to annual grass invasions. Managers can minimize invasion potential by focusing on treatment type, placement, and seasonal timing. Herbicides also can provide effective mitigation, especially in combination with other controls such as prescribed fire or grazing. This report can be incorporated by reference in NEPA documentation, included in a project record, or provide a general reference for understanding and identifying literature about increases in IAGs associated with vegetation treatments in rangelands in this focal region.
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\"}}]}","contact":"Director, Northern Rocky Mountain Science Center
U.S. Geological Survey
2327 University Way, Suite 2
Bozeman, MT 59715
Between 1980 and 1986, the U.S. Geological Survey made a series of 1:2,000-scale topographic contour maps from aerial photographic surveys to monitor the eruption. These maps were made for operational purposes and were not intended for publication. Since then, advances in technology made it possible to digitize the original, highly detailed hardcopy maps and derive new digital data elevation models of the surface of the lava dome. These digital elevation models allow for the visualization of the progression of the eruption and reveal the rubbly, chaotic surface of the lava flows and dome. Additionally, these new data help fill gaps in the long-term record of topographic changes that have occurred at the volcano since the May 18, 1980, eruption.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip262","usgsCitation":"Bard, J.A., Friedle, C.M., Bartee, L., Dierker, B.C., Ganick, J.M., Gregory, N.M., Hill, K.R., Klug, J.G., Kruger, A., Mooney, D.T., Morrison, R.T., Rojas, I.I., Rollo, P., Stanton, S.A., Stewart, B., Stuhlmuller, B.E., Zyla, A.D., 2025, Rebuilding a volcano one lava flow at a time—Visualizing the lava dome-building eruption in the crater of Mount St. Helens, 1982–1986: U.S. Geological Survey General Information Product 262, https://doi.org/10.3133/gip262.","productDescription":"1 p.","onlineOnly":"N","ipdsId":"IP-180615","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":496232,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/262/coverthb.jpg"},{"id":496233,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/262/gip262.pdf","text":"Document","size":"33.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 262"},{"id":497787,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118911.htm"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.38015899040656,\n 46.32565684366381\n ],\n [\n -122.38015899040656,\n 46.080226964619754\n ],\n [\n -122.00665168620951,\n 46.080226964619754\n ],\n [\n -122.00665168620951,\n 46.32565684366381\n ],\n [\n -122.38015899040656,\n 46.32565684366381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Center Director, Science Analytics and Synthesis Program
U.S. Geological Survey
P.O. Box 25046, Mail Stop 302
Denver, CO 80225
This report describes the techniques and methods for computing the mean-channel velocity and discharge using the entropy-based probability concept (probability concept). The method is an alternative to or augments standard streamgaging methods adopted by the U.S. Geological Survey (USGS). Although sensor technology for measuring the mean velocity and discharge has advanced, standard streamgaging and computational methods have remained relatively unchanged since the USGS established its first streamgage at the Rio Grande at Embudo, New Mexico in 1889.
Standard streamgaging methods rely on integrating velocities and depths measured at multiple verticals at a channel cross section (standard cross section) to compute a discharge. The probability concept computes discharge at a single vertical (y-axis) using the ratio of the mean-channel velocity (mean velocity) and maximum velocity, the measured maximum velocity, and the area as a function of stage at the standard cross section. Proper siting and operation and maintenance are required. If siting is conducted appropriately, the probability concept parameters and the y-axis stationing will be similar for different streamflow conditions. The timing of operation and maintenance visits should be based on hydrologic and meteorologic occurrences and seasonality and should capture low, medium, high, and opportunistic streamflow conditions.
Advantages of the probability concept are the capacity to (1) compute discharge time series immediately after streamgage siting, (2) compute discharge for complex streamflow conditions that cannot be quantified by stage-discharge methods, (3) augment time-series data where gaps exist, and (4) integrate with surface velocity sensors such as Doppler velocity radars and cameras, which are not subject to damage caused by ice, debris, and flood flows. Potential sources of bias in discharge derived from the probability concept include (1) rain, (2) wind, and (3) geomorphologic and hydraulic instabilities. Recommendations to address these biases are provided.
This report guides users through the steps to parameterize the probability concept, process field data, and compute the mean velocity and discharge using the probability concept.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm3A26","usgsCitation":"Fulton, J.W., Engel, F.L., Eggleston, J.R., and Chiu, C.-L., 2025, Computing discharge using the entropy-based probability concept: U.S. Geological Survey Techniques and Methods book 3, chap. A26, 66 p., https://doi.org/10.3133/tm3A26.","productDescription":"Report: viii, 66 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-138301","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":496208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/03/a26/tm3a26.pdf","text":"Report","size":"6.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2-A26"},{"id":496210,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/03/a26/Appendix_3_Wind_Bias.csv","text":"Appendix 3","size":"8.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"T and M 2-A26 Appendix 3","linkHelpText":"Correction for Wind Bias"},{"id":496207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/03/a26/coverthb.jpg"}],"contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
This report describes the steps and the theory to compute the speed and flow of water in streams using the probability concept.
","publicationDate":"2025-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Fulton, John W, 0000-0002-5335-0720","orcid":"https://orcid.org/0000-0002-5335-0720","contributorId":213630,"corporation":false,"usgs":true,"family":"Fulton","given":"John","middleInitial":"W,","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engel, Frank L. 0000-0002-4253-2625","orcid":"https://orcid.org/0000-0002-4253-2625","contributorId":218208,"corporation":false,"usgs":true,"family":"Engel","given":"Frank","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eggleston, Jack R. 0000-0001-6633-3041","orcid":"https://orcid.org/0000-0001-6633-3041","contributorId":204628,"corporation":false,"usgs":true,"family":"Eggleston","given":"Jack R.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":949520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chiu, Chao-Lin","contributorId":361821,"corporation":false,"usgs":false,"family":"Chiu","given":"Chao-Lin","affiliations":[{"id":86362,"text":"Emeritus - University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":949521,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272101,"text":"70272101 - 2025 - Developing empirical fragility functions for lava flow building damage","interactions":[],"lastModifiedDate":"2026-02-10T13:33:34.995824","indexId":"70272101","displayToPublicDate":"2025-09-29T09:58:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2036,"text":"International Journal of Disaster Risk Reduction","active":true,"publicationSubtype":{"id":10}},"title":"Developing empirical fragility functions for lava flow building damage","docAbstract":"Fragility functions are vital tools in volcanic risk assessments to evaluate the probability of damage to structures at given hazard intensities. Traditionally, lava flow damage is assumed to be binary, whereby in contact with lava results in complete destruction and not in contact with lava remains undamaged. However, past studies present examples of structures exhibiting resistance to lava and not destruction. Developing empirical fragility functions requires damage data. We collected data from field campaigns and aerial imagery to assess damage across three case studies: 2021 Cumbre Vieja lava flows, La Palma, 2018 lower East Rift Zone lava flows, Kīlauea, Hawaiʻi, and 2014–2015 Fogo lava flows, Cabo Verde. This involved manually digitising 4545 structure footprints and assigning types and damage state categories to 10,439 structures. Of the impacted structures, 6 % were classified as damaged (not destroyed). Using this dataset, we developed the first empirical fragility functions from multiple eruptions for assessing lava flow damage, for masonry, metal, and timber building types. The functions reflect the probability of a structure sustaining any of six levels of damage severity given final lava flow thickness. Lava flows thicker than 6 m generally destroy structures, but some structures, particularly masonry buildings or those with a circular shape, can resist flows thinner than 6 m. The fragility functions reflect that lava flow impacts are not binary, and that structure types and shape are important. These empirical fragility functions can differentiate between structural attributes, thereby enhancing damage, risk, and impact assessments for lava flows, for places with similar building types.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2025.105844","usgsCitation":"Meredith, E.S., Jenkins, S.F., Hayes, J.L., Chee, D.J., Lallemant, D., Deligne, N.I., Meletlidis, S., and Felpeto, A., 2025, Developing empirical fragility functions for lava flow building damage: International Journal of Disaster Risk Reduction, no. 130, 105844, 19 p., https://doi.org/10.1016/j.ijdrr.2025.105844.","productDescription":"105844, 19 p.","ipdsId":"IP-178627","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":496718,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijdrr.2025.105844","text":"Publisher Index Page"},{"id":496503,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"130","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meredith, Elinor S. 0000-0002-3869-1180","orcid":"https://orcid.org/0000-0002-3869-1180","contributorId":270269,"corporation":false,"usgs":false,"family":"Meredith","given":"Elinor","email":"","middleInitial":"S.","affiliations":[{"id":56128,"text":"Earth Observatory of Singapore, Singapore","active":true,"usgs":false}],"preferred":false,"id":950066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkins, Susanna F. 0000-0002-7523-1423","orcid":"https://orcid.org/0000-0002-7523-1423","contributorId":270268,"corporation":false,"usgs":false,"family":"Jenkins","given":"Susanna","email":"","middleInitial":"F.","affiliations":[{"id":56128,"text":"Earth Observatory of Singapore, Singapore","active":true,"usgs":false}],"preferred":false,"id":950067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Josh L. 0000-0001-7099-1063","orcid":"https://orcid.org/0000-0001-7099-1063","contributorId":270275,"corporation":false,"usgs":false,"family":"Hayes","given":"Josh","email":"","middleInitial":"L.","affiliations":[{"id":56128,"text":"Earth Observatory of Singapore, Singapore","active":true,"usgs":false}],"preferred":false,"id":950068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chee, Denny J.","contributorId":362125,"corporation":false,"usgs":false,"family":"Chee","given":"Denny","middleInitial":"J.","affiliations":[{"id":16631,"text":"Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":950069,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lallemant, David","contributorId":334346,"corporation":false,"usgs":false,"family":"Lallemant","given":"David","affiliations":[{"id":16631,"text":"Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":950070,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deligne, Natalia I. 0000-0001-9221-8581","orcid":"https://orcid.org/0000-0001-9221-8581","contributorId":257389,"corporation":false,"usgs":true,"family":"Deligne","given":"Natalia","email":"","middleInitial":"I.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":950071,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meletlidis, Stravos 0000-0002-4629-0344","orcid":"https://orcid.org/0000-0002-4629-0344","contributorId":362128,"corporation":false,"usgs":false,"family":"Meletlidis","given":"Stravos","affiliations":[{"id":86475,"text":"Centro Geofísico de Canarias, Instituto Geográfico Nacional, Spain","active":true,"usgs":false}],"preferred":false,"id":950072,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Felpeto, Alicia 0000-0002-8152-7394","orcid":"https://orcid.org/0000-0002-8152-7394","contributorId":362129,"corporation":false,"usgs":false,"family":"Felpeto","given":"Alicia","affiliations":[{"id":86477,"text":"Observatorio Geofísico Central, Instituto Geográfico Nacional, Spain","active":true,"usgs":false}],"preferred":false,"id":950073,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70271933,"text":"sir20255083 - 2025 - Regional hydraulic geometry characteristics of stream channels in the Boston Mountains in Arkansas","interactions":[],"lastModifiedDate":"2026-02-03T16:07:29.146923","indexId":"sir20255083","displayToPublicDate":"2025-09-29T08:02:22","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5083","displayTitle":"Regional Hydraulic Geometry Characteristics of Stream Channels in the Boston Mountains in Arkansas","title":"Regional hydraulic geometry characteristics of stream channels in the Boston Mountains in Arkansas","docAbstract":"Many stream-channel infrastructure, habitat enhancement, and restoration projects are undertaken on streams throughout Arkansas by Federal, State, and local agencies as well as by private organizations and businesses with limited data on local geomorphology and streamflow conditions. Equations that relate drainage area above stable stream reaches to the basin characteristics, bankfull streamflow, and the associated channel dimensions can be used to estimate stream conditions. These equations, along with streambed material particle information, provide information that can be used to improve stream-channel projects. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, Little Rock District, completed a study to develop these equations for streams in the Boston Mountains in Arkansas.
Fourteen U.S. Geological Survey streamgages and stream reaches located in the Boston Mountains were selected for analysis. Geomorphic parameters of streams, including the mean bankfull channel dimensions (cross-sectional area, top width, mean depth, and streamflow), and the contributing drainage areas were investigated. Streambed materials were collected at eight of these sites to develop descriptive statistics of the streambed particle-size distributions and percentages of substrate type. Stream reaches at each study site were classified to Rosgen level II stream type based on the averages of stream-channel metrics collected from site cross sections and profiles. Of the 14 selected Boston Mountain stream reaches, 7 were classified as B-type streams, and 7 were classified as C-type streams. For these streams, the significant differences in measured parameters between stream types were that the B-type streams had greater depth, hydraulic radii, and bar D50 and D85 particle sizes, while C-type streams had greater watershed slopes. Streambed material particle size decreased with mean drainage basin elevation and decreased with increasing entrenchment ratios. Bar sediment size exhibited decreasing size with increasing sinuosity. Regional hydraulic geometry curves were constructed for the streams in the Boston Mountains by plotting measured bankfull geometry dimensions from stable reaches and the associated bankfull streamflow against the contributing drainage area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255083","issn":"2328-0328","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Little Rock District","usgsCitation":"Kroes, D.E., Ruhl-Whittle, L., Pieri, A.C., and Pugh, A.L., 2025, Regional hydraulic geometry characteristics of stream channels in the Boston Mountains in Arkansas: U.S. Geological Survey Scientific Investigations Report 2025–5083, 28 p., https://doi.org/10.3133/sir20255083.","productDescription":"Report: vii, 28 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-166842","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":497784,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118903.htm"},{"id":496007,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1XARR7X","text":"USGS Data Release","linkHelpText":"- Hydraulic geometry of stream channels in the Boston Mountains of Arkansas"},{"id":496006,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255083/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5083 HTML"},{"id":496005,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5083/sir20255083.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5083 XML"},{"id":496004,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5083/sir20255083.pdf","size":"2.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5083"},{"id":496003,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5083/images"},{"id":496002,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5083/coverthb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Boston Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95,\n 36.25\n ],\n [\n -95,\n 35.5\n ],\n [\n -91.5,\n 35.5\n ],\n [\n -91.5,\n 36.25\n ],\n [\n -95,\n 36.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Increased urban heat intensifies thermal discomfort, particularly in critical public spaces such as transit stops. This study investigated the predictors of transit users' thermal perceptions in Denver, Colorado—a semi-arid city. Sixty bus stops spanning a gradient of land cover compositions were selected for study. Micrometeorological data, including thermal comfort indices, were collected alongside survey responses from 77 users at 31 unique stops. Survey responses captured thermal sensation votes (TSV) and thermal comfort votes (TCV) as well as aesthetic preference votes (APV) of bus stop structure. Ordinal forest analysis revealed that for both TSV and TCV, aesthetic preferences and thermal comfort indices were the most influential predictors of transit user thermal perception. Multiple ordered logistic regression further demonstrated that, for TSV, higher APV was associated with lower odds of rating a thermal environment as hot (OR = 0.664, p < 0.002) while increased Physiological Equivalent Temperature (PET) raised these odds (OR = 1.101, p < 0.006). An interaction analysis demonstrated that APV significantly moderated the effect of PET on TCV (interaction OR = 1.040, p < 0.041), suggesting that aesthetic preferences are significantly correlated with an alleviation of thermal discomfort under high heat stress. Bivariate analyses further indicated that bus stops with greater tree canopy cover (OR = 1.032, p < 0.025) and higher visible vegetation view factors (OR = 10.350, p < 0.022) were more likely to be rated as aesthetically pleasing. These findings underscore the importance of aesthetic preferences in transit stop planning for urban heat resiliency.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.uclim.2025.102606","usgsCitation":"Steinharter, L., Ibsen, P.C., Lam, T.Y., Nesbit, L., Park, K., and McHale, M., 2025, Hot stops, cool looks: Aesthetic solutions for thermal comfort at transit stops: Urban Climate, v. 64, 102606, 25 p., https://doi.org/10.1016/j.uclim.2025.102606.","productDescription":"102606, 25 p.","ipdsId":"IP-174250","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":496501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Denver","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.1863006041697,\n 39.98477049655375\n ],\n [\n -105.1863006041697,\n 39.448378011936086\n ],\n [\n -104.68565284554944,\n 39.448378011936086\n ],\n [\n -104.68565284554944,\n 39.98477049655375\n ],\n [\n -105.1863006041697,\n 39.98477049655375\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"64","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Steinharter, Logan","contributorId":362081,"corporation":false,"usgs":false,"family":"Steinharter","given":"Logan","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":949970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ibsen, Peter Christian 0000-0002-3436-9100","orcid":"https://orcid.org/0000-0002-3436-9100","contributorId":260735,"corporation":false,"usgs":true,"family":"Ibsen","given":"Peter","email":"","middleInitial":"Christian","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":949971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lam, Tzeng Yih","contributorId":362084,"corporation":false,"usgs":false,"family":"Lam","given":"Tzeng","middleInitial":"Yih","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":949972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nesbit, Lorien","contributorId":362087,"corporation":false,"usgs":false,"family":"Nesbit","given":"Lorien","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":949973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Park, Keunhyun","contributorId":224296,"corporation":false,"usgs":false,"family":"Park","given":"Keunhyun","email":"","affiliations":[{"id":40852,"text":"Utah State University, Department of Landscape Architecture and Environmental Planning","active":true,"usgs":false}],"preferred":false,"id":949974,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McHale, Melissa R.","contributorId":362090,"corporation":false,"usgs":false,"family":"McHale","given":"Melissa","middleInitial":"R.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":949975,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271967,"text":"sir20255081 - 2025 - Multidecadal change in pesticide concentrations relative to human health benchmarks in the Nation’s groundwater","interactions":[],"lastModifiedDate":"2026-02-03T16:06:20.038024","indexId":"sir20255081","displayToPublicDate":"2025-09-26T15:05:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5081","displayTitle":"Multidecadal Change in Pesticide Concentrations Relative to Human Health Benchmarks in the Nation’s Groundwater","title":"Multidecadal change in pesticide concentrations relative to human health benchmarks in the Nation’s groundwater","docAbstract":"Groundwater-quality trend assessments identify aquifers that are responding to changes in pesticide use and the compounds that may pose a threat to water availability. The U.S. Geological Survey has been monitoring pesticide concentrations in groundwater for 25 principal aquifers across the conterminous United States since 1993. The groundwater well locations represent a range of soils, climate, and landforms. The wells are used to monitor groundwater underlying selected agricultural and urban settings and groundwater used for domestic supply. This study examined changes in relative concentrations, defined here as the percentage of wells with pesticide concentrations exceeding a human health benchmark (HHB). HHBs used in this report are legally enforceable drinking-water standards and nonenforceable drinking water levels. Relative pesticide concentration increases may lead to decreased water availability, as restrictions may be put in place for groundwater used as a drinking-water source.
This study focused on concentration changes in 22 pesticides that were included in laboratory analysis from 1993 to 2023. The analysis and interpretation of these pesticide concentrations in groundwater have been separated into approximate decadal intervals (decade 1 (1993–2001), decade 2 (2002–12), and decade 3 (2013–22). For one pesticide, 1,2-dibromo-3-chloropropane (DBCP), concentration data were also collected in decade 4 (2023–onward).
Atrazine, deethylatrazine, alachlor, prometon, and simazine were 5 pesticides detected at moderate concentrations (greater than 10 percent of the HHB but less than or equal to the HHB). The percentage of wells that had groundwater pesticide concentrations in the moderate concentration category decreased from 7 percent in decade 1 to 2 percent in decade 3. The agricultural networks had the highest percentages of wells with moderate concentrations, and these percentages decreased from 13 percent in decade 1 to 4 percent in decade 3. Moderate concentrations in the urban networks decreased between decades 1 and 2 from 4 percent to 0 percent. No moderate concentrations occurred in the urban networks in decade 3. The percentage of wells with moderate concentrations in the domestic supply networks (1 percent) was the lowest of all the network types and did not change across the three decades. Moderate atrazine or deethylatrazine concentrations occurred across all three decades in aggregated ecoregions representing similar soils, climate, and landforms in the Semiarid West, Midcontinent, and Northeastern United States. Moderate concentrations of prometon, alachlor, and simazine also occurred in the Midcontinent, Arid West, Northeast, South Atlantic Gulf, and Semiarid West regions, but the moderate concentrations did not persist across all three decades.
DBCP was the only pesticide that exceeded its respective HHB, and the exceedances occurred across all four decades. In this report, the DBCP analysis was limited to one well network in the Central Valley, California. Agricultural use of DBCP was suspended in 1977. Forty-five years after being banned, DBCP concentrations were greater than the maximum contaminant level of 2 micrograms per liter (μg/L), but the number of exceedances decreased from 50 percent to 15 percent of the samples between 1993 and 2023.
This assessment of decadal groundwater pesticide concentrations provides a characterization of changes in water availability because of pesticide contamination in areas where groundwater is used as a drinking-water source. The results highlight the importance of continued long-term monitoring and assessment of groundwater pesticides to identify locations and specific compounds that may pose a potential risk to human health.
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Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, Virginia 20192
Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Florida’s Freshwater Fisheries Long-Term Monitoring Program was implemented in 2006 to track changes in freshwater fish populations and communities. As part of an evaluation of the program, this study used a simulation framework to assess trend detection for fish abundance and biomass indices and how sampling intensity (number of samples per year) and frequency (number of years) can influence detection of these trends.
Using count and weight data from fall electrofishing samples collected between 2006 and 2021 from 21 lakes, trends were simulated for annual mean count and weight over a 10-year period that ranged from −70% to +200%. In all, simulations were performed for seven game fish species and three management-relevant groups (large nongame, nonnative, and prey species). For sampling intensity, data were simulated with a range of sample sizes, from 10 to 40 electrofishing transects or the maximum number available for a given lake. For sampling frequency, data were simulated for different sampling schedules that included sampling 1 year followed by 1- or 2-year breaks (4–5 years of sampling in a 10-year period), sampling two consecutive years followed by 1- or 2-year breaks (6–7 years of sampling in a 10-year period), and sampling the first 5 years only.
Simulations based on weight and count data yielded similar results, but the effect of sampling frequency and sampling schedule varied by species, management group, and lake. Trend detection was lower and more variable when mean counts and weights of fish in electrofishing samples were low. Overall, at least a 60% increase or 40% decrease over a 10-year period was typically needed for trends in mean weight and count to be detected at least 80% of the time in at least half of the lakes. Increasing sampling intensity did not substantially improve trend detection for lower-magnitude changes, but reducing sample intensity to a minimum of 10 electrofishing transects per year would have a large negative effect on trend detection in almost all lakes. Detection of trends improved as the number of years sampled increased, but ideally, sampling should be spaced throughout the entire 10-year period to capture the full magnitude of change. Sampling every year generally resulted in better trend detection and for many species and groups was the only sampling schedule that resulted in all study lakes achieving the 80% target detection level. Of the alternative schedules considered, those involving 2 years of consecutive sampling outperformed those with only 1 year of sampling followed by a 1- or 2-year break.
Relatively large changes in mean count and weight were required to detect trends over a 10-year period, but there was no clear advantage of using count or weight data for monitoring purposes. Further, study results support the current sampling intensity, but trend detection is optimized at higher mean catch and weight values. Although sampling every year is ideal, an alternative schedule involving sampling two consecutive years with 1- or 2-year breaks could be considered in certain situations. These results will be important for informing future decisions regarding Florida’s Freshwater Fisheries Long-Term Monitoring Program and other monitoring initiatives.
Wind erosion and sediment transport continue to increase in many parts of the world, leading to decreased soil quality, accelerated snow-melt, respiratory diseases, and traffic accidents. The processes that control sediment transport are well understood at small scales of mm to m but are less well understood at larger scales of km to hundreds of km. Here we test four approaches aimed at improving the variance explained in sediment transport measured in a network of 52 horizontal sediment flux collecting devices located on the Colorado Plateau, USA. First, switching from a regression tree to random forest statistical analysis increased the variance in sediment transport explained from 58% to 91%. Soil moisture as a single variable explained 52% of variation in sediment flux, but had a negligible effect on a random forest model with season (Winter, Spring, Summer), wind speed, and seasonal total precipitation. Similarly, adding four years of new data to an existing five-year dataset or adding measurements of soil roughness and grazing failed to improve variance explained. By explaining 91% of the variance in sediment transport, our model provides baseline model for understanding sediment transport on the landscape scale. Dust flux networks in new regions would likely need to collect at least 300-500 samples to describe variation in sediment transport values using random forest analyses of the effects of season, wind speed, seasonal rain and vegetation type.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0333166","usgsCitation":"Kulmatiski, A., Ozturk, M., Bladen, K.K., Brahney, J., and Duniway, M.C., 2025, Season, wind speed, and seasonal rain are major drivers of a regional aeolian sediment transport model: PLoS ONE, v. 20, no. 9, e0333166, 15 p., https://doi.org/10.1371/journal.pone.0333166.","productDescription":"e0333166, 15 p.","ipdsId":"IP-175885","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498292,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0333166","text":"Publisher Index Page"},{"id":498100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah","volume":"20","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Kulmatiski, Andrew","contributorId":210408,"corporation":false,"usgs":false,"family":"Kulmatiski","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":952968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ozturk, Mehmet mozturk@usgs.gov","contributorId":196300,"corporation":false,"usgs":false,"family":"Ozturk","given":"Mehmet","email":"mozturk@usgs.gov","affiliations":[],"preferred":false,"id":952969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bladen, Kelvyn K.","contributorId":364634,"corporation":false,"usgs":false,"family":"Bladen","given":"Kelvyn","middleInitial":"K.","affiliations":[{"id":86880,"text":"Department of Mathematics and Statistics, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":952970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brahney, Janice","contributorId":269810,"corporation":false,"usgs":false,"family":"Brahney","given":"Janice","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":952971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":952972,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271978,"text":"70271978 - 2025 - Validation of gridded precipitation datasets for flood-typing in select conterminous U.S. basins","interactions":[],"lastModifiedDate":"2025-09-29T15:03:21.41738","indexId":"70271978","displayToPublicDate":"2025-09-26T07:58:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Validation of gridded precipitation datasets for flood-typing in select conterminous U.S. basins","docAbstract":"Gridded precipitation datasets are required for flood-typing historical annual peak streamflow events in basins across the Conterminous United States. Selected gridded precipitation datasets were validated over the period 1981–2013 through comparisons with gage data from the NOAA Global Historical Climatology Network daily (GHCNd). The ability of each gridded dataset to capture the spatiotemporal characteristics of daily precipitation, including multi-day extremes over six selected regions, was assessed using the Kling-Gupta Efficiency metric and its component statistics. Overall, the Parameter-elevation Regression on Independent Slopes Model and Livneh-unsplit were found to best match the spatiotemporal variability of the GHCNd precipitation data, including extremes. The Analysis of Record for Calibration was found to be the third best-performing dataset in most regions except in the western U.S. The performance of reanalysis datasets evaluated appears to be poor compared to gage-based datasets. The reanalysis datasets might not be able to skillfully capture precipitation amounts at the correct location and time. Gage- and radar-based datasets were found to have relatively small biases (within +/-10% on an annual basis), while reanalysis datasets were found to have larger positive apparent biases, especially in winter and spring in most regions. It is possible that the apparent overestimation of winter and spring precipitation in the reanalysis datasets might reflect snow undercatch at gages especially in the central U.S. An overall deterioration of performance for correlation and/or variability was also observed for the summer season compared to other seasons in the reanalysis datasets. Various precipitation datasets might need to be used for flood-typing during different periods from the late 19th century to present. Datasets from different sources have different biases and errors and might have to be homogenized using downscaling and bias-adjustment methods. Alternatively, precipitation thresholds used in some flood-typing schemes might have to be adjusted as a function of time.","language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/JHYEFF.HEENG-6500","usgsCitation":"Irizarry-Ortiz, M.M., and Murphy, S.Y., 2025, Validation of gridded precipitation datasets for flood-typing in select conterminous U.S. basins: Journal of Hydrologic Engineering, v. 30, no. 6, 04025042, 13 p., https://doi.org/10.1061/JHYEFF.HEENG-6500.","productDescription":"04025042, 13 p.","ipdsId":"IP-167576","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":496323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/jhyeff.heeng-6500","text":"Publisher Index Page"},{"id":496227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous 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Center","active":true,"usgs":true}],"preferred":true,"id":949565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murphy, Sarah Yvette 0000-0001-5646-0936","orcid":"https://orcid.org/0000-0001-5646-0936","contributorId":361844,"corporation":false,"usgs":true,"family":"Murphy","given":"Sarah","middleInitial":"Yvette","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949566,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70272023,"text":"70272023 - 2025 - Unveiling coseismic deformation from differenced legacy aerial photography and modern lidar topography: The 1983 M6.9 Borah Peak earthquake, Idaho, USA","interactions":[],"lastModifiedDate":"2025-11-13T16:55:22.485744","indexId":"70272023","displayToPublicDate":"2025-09-25T10:48:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Unveiling coseismic deformation from differenced legacy aerial photography and modern lidar topography: The 1983 M6.9 Borah Peak earthquake, Idaho, USA","docAbstract":"The 1983 M6.9 Borah Peak, Idaho, earthquake is one of the largest historical normal fault earthquakes in the western United States. We quantified meter-scale vertical change along the 35 km-long rupture using topographic differencing of 1966 aerial imagery and 2019 lidar-derived data. The initial differencing results are largely obscured by horizontal and vertical georeferencing errors and flight-line stripes. Our error corrections are designed to be insensitive to the coseismic deformation and reduced error by 50%. We calculated vertical separation and resolved a maximum of 2.02 ± 0.46 m at Doublespring Pass. Our vertical separation measurements are generally consistent with those from prior studies using field data and post-earthquake topographic data. However, the differencing measurements are a few decimeters lower than these prior measurements, indicating that differencing can isolate historical from prehistoric earthquake deformation. Our study demonstrates that revisiting historical earthquakes can provide new insights into the magnitude and patterns of coseismic deformation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GL115882","usgsCitation":"Scott, C.P., Reitman, N.G., and Bello, S., 2025, Unveiling coseismic deformation from differenced legacy aerial photography and modern lidar topography: The 1983 M6.9 Borah Peak earthquake, Idaho, USA: Geophysical Research Letters, v. 52, no. 18, e2025GL115882, 12 p., https://doi.org/10.1029/2025GL115882.","productDescription":"e2025GL115882, 12 p.","ipdsId":"IP-177417","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":496426,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gl115882","text":"Publisher Index Page"},{"id":496410,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Borah Peak","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.1667,\n 44.25\n ],\n [\n -114.1667,\n 44\n ],\n [\n -113.667,\n 44\n ],\n [\n -113.667,\n 44.25\n ],\n [\n -114.1667,\n 44.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","issue":"18","noUsgsAuthors":false,"publicationDate":"2025-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Chelsea P 0000-0002-3884-4693","orcid":"https://orcid.org/0000-0002-3884-4693","contributorId":248847,"corporation":false,"usgs":false,"family":"Scott","given":"Chelsea","email":"","middleInitial":"P","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":949752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":949753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bello, Simone","contributorId":360174,"corporation":false,"usgs":false,"family":"Bello","given":"Simone","affiliations":[{"id":85980,"text":"3Department of Sciences, University G. d’Annunzio Chieti-Pescara, 66100, Chieti, Italy","active":true,"usgs":false}],"preferred":false,"id":949754,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}