Climate change is increasing sulfate export and changing wetland extent in mountain regions. These changes may increase microbially mediated production of the neurotoxic substance methylmercury due to enhanced sulfate metabolism in mountain environments. Here, we assess methylmercury concentrations and formation rates across high-elevation wetlands in the Colorado Rocky Mountains. We also investigate sulfate controls on methylmercury production within subalpine peatlands by amending soils with sulfate to mimic increased stream export of sulfate from the alpine zone and measuring methylmercury formation rates for different sulfate treatments. We found that subalpine peatlands have statistically significant higher methylmercury concentrations and formation rates compared to alpine, mineral-soil wetlands. Methylmercury production in subalpine peatlands also increased significantly (p < 0.05) following sulfate additions; the highest rates occurred in sediments with intermediate extractable sulfate concentrations (∼0.60–1.4 mg sulfate g−1 dry soil). Our study is the first to identify soil sulfate-related thresholds for methylmercury production and sulfate-limitation of methylmercury production in subalpine peatlands. These findings highlight important linkages between climate-driven mineral weathering and mercury cycling in mountain regions globally.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/add8a5","usgsCitation":"Miller, H., Driscoll, C.T., Janssen, S., and Hinckley, E.S., 2025, Climate-driven sulfate export in alpine watersheds may stimulate methylmercury production: Environmental Research Letters, v. 20, no. 6, 064049, 10 p., https://doi.org/10.1088/1748-9326/add8a5.","productDescription":"064049, 10 p.","ipdsId":"IP-174502","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":492502,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/add8a5","text":"Publisher Index Page"},{"id":492344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.31242556501007,\n 40.97943186444155\n ],\n [\n -109.31242556501007,\n 37.13248278782403\n ],\n [\n -104.57001345468905,\n 37.13248278782403\n ],\n [\n -104.57001345468905,\n 40.97943186444155\n ],\n [\n -109.31242556501007,\n 40.97943186444155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Hannah R.","contributorId":358053,"corporation":false,"usgs":false,"family":"Miller","given":"Hannah R.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":943199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Charles T.","contributorId":167460,"corporation":false,"usgs":false,"family":"Driscoll","given":"Charles","email":"","middleInitial":"T.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":943200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hinckley, Eve-Lyn S.","contributorId":181894,"corporation":false,"usgs":false,"family":"Hinckley","given":"Eve-Lyn","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":943202,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70268378,"text":"70268378 - 2025 - Formation of the Mount Weld rare earth element deposit, Western Australia: A carbonatite-derived laterite","interactions":[],"lastModifiedDate":"2025-06-24T14:02:01.555543","indexId":"70268378","displayToPublicDate":"2025-05-29T08:52:34","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Formation of the Mount Weld rare earth element deposit, Western Australia: A carbonatite-derived laterite","docAbstract":"Carbonatite-hosted rare earth element (REE) deposits are the primary source of the world’s light REEs. The Mount Weld REE deposit in Western Australia is hosted in a lateritic sequence that reflects supergene enrichment of the underlying carbonatite. Water-rock interaction is a key to the formation of this world-class deposit. REE enrichment in the laterite is controlled by the breakdown of primary minerals, the release and transport of REEs, and the formation of secondary minerals. Secondary REE-bearing phosphate minerals are the primary REE-host phases in the laterite ore with monazite as the dominant phase; other REE-bearing phases include rhabdophane, cerianite, churchite, florencite, and crandallite subgroup minerals. Profiles through the laterite show that in the REE-rich zone, apatite and primary calcite and dolomite have broken down such that the loss of Ca and Mg, as well as Si and K, leads to a relative increase in the REEs. Sequestering of REEs in secondary mineral phases formed by groundwater further enhances the REE concentration.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 3rd IAGC international conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"UNICApress","usgsCitation":"Verplanck, P., Thompson, J.M., Mercer, C.M., Bhat, G., Lowers, H.A., and Boehlke, A., 2025, Formation of the Mount Weld rare earth element deposit, Western Australia: A carbonatite-derived laterite, in Proceedings of the 3rd IAGC international conference, p. 643-646.","productDescription":"4 p.","startPage":"643","endPage":"646","ipdsId":"IP-175346","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":491177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":491167,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://unicapress.unica.it/index.php/unicapress/catalog/book/978-88-3312-187-1"}],"country":"Australia","otherGeospatial":"Mount Weld Mine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 122.51875238583227,\n -28.852849393633186\n ],\n [\n 122.51875238583227,\n -28.883149678371097\n ],\n [\n 122.5640779834194,\n -28.883149678371097\n ],\n [\n 122.5640779834194,\n -28.852849393633186\n ],\n [\n 122.51875238583227,\n -28.852849393633186\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Verplanck, Philip L. 0000-0002-3653-6419","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":212813,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","middleInitial":"L.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":941148,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Jay M. 0000-0003-3322-0870","orcid":"https://orcid.org/0000-0003-3322-0870","contributorId":329664,"corporation":false,"usgs":true,"family":"Thompson","given":"Jay","middleInitial":"M.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":941149,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mercer, Cameron Mark 0000-0003-0534-848X","orcid":"https://orcid.org/0000-0003-0534-848X","contributorId":301880,"corporation":false,"usgs":true,"family":"Mercer","given":"Cameron","email":"","middleInitial":"Mark","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":941150,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bhat, Ganesh","contributorId":329666,"corporation":false,"usgs":false,"family":"Bhat","given":"Ganesh","email":"","affiliations":[{"id":78683,"text":"Lynas Rare Earths Ltd","active":true,"usgs":false}],"preferred":false,"id":941151,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":941152,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boehlke, Adam 0000-0003-4980-431X aboehlke@usgs.gov","orcid":"https://orcid.org/0000-0003-4980-431X","contributorId":3470,"corporation":false,"usgs":true,"family":"Boehlke","given":"Adam","email":"aboehlke@usgs.gov","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":941153,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70269899,"text":"70269899 - 2025 - Simulation of the impacts of projected climate change on groundwater resources in the urban, semiarid Yucaipa Valley watershed, southern California using an integrated hydrologic model","interactions":[],"lastModifiedDate":"2025-08-06T15:12:41.202838","indexId":"70269899","displayToPublicDate":"2025-05-29T08:03:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22158,"text":"Journal of Hydrology, Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Simulation of the impacts of projected climate change on groundwater resources in the urban, semiarid Yucaipa Valley watershed, southern California using an integrated hydrologic model","docAbstract":"Managing water resources in semiarid watersheds is challenging due to limited supply and uncertain future climate conditions. This paper examines the impact of future climate changes on an urban watershed in southern California using an integrated hydrologic model. GSFLOW modeling software is used to simulate the nonlinear relationships between climate trends and precipitation partitioning into ET, runoff, and subsurface storage. Four global circulation models (GCMs), each with two greenhouse-gas scenarios, RCP45 and RCP85 are used to project future climate conditions. GCMs include the CanESM2, CNRM-CM5, HadGEM2-ES, and MIROC5 models. The model's simulated hydrologic conditions are compared with historical data to assess changes in water budgets and groundwater supply. Results indicate decreased groundwater storage in most scenarios due to increased natural evapotranspiration, vegetation consumptive use, and streamflow out of the watershed. Only scenarios with substantially increased future precipitation show increased groundwater storage. The study also highlights increased future aridity despite the rise in precipitation and large precipitation events forecast by GCMs, which increase the risk of urban floods and decrease stream leakage and water available to vegetation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2025.102461","usgsCitation":"Ryter, D.W., Alzraiee, A.H., and Niswonger, R., 2025, Simulation of the impacts of projected climate change on groundwater resources in the urban, semiarid Yucaipa Valley watershed, southern California using an integrated hydrologic model: Journal of Hydrology, Regional Studies, v. 60, 102461, 16 p., https://doi.org/10.1016/j.ejrh.2025.102461.","productDescription":"102461, 16 p.","ipdsId":"IP-153865","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":493789,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2025.102461","text":"Publisher Index Page"},{"id":493644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yucaipa Valley watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.09271702186436,\n 34.0874203948469\n ],\n [\n -117.09271702186436,\n 33.9725594754417\n ],\n [\n -116.9002906646693,\n 33.9725594754417\n ],\n [\n -116.9002906646693,\n 34.0874203948469\n ],\n [\n -117.09271702186436,\n 34.0874203948469\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ryter, Derek W. 0000-0002-2488-626X dryter@usgs.gov","orcid":"https://orcid.org/0000-0002-2488-626X","contributorId":3395,"corporation":false,"usgs":true,"family":"Ryter","given":"Derek","email":"dryter@usgs.gov","middleInitial":"W.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944910,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niswonger, Richard G. rniswon@usgs.gov","contributorId":146547,"corporation":false,"usgs":false,"family":"Niswonger","given":"Richard G.","email":"rniswon@usgs.gov","affiliations":[],"preferred":false,"id":944911,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70267395,"text":"sir20245130 - 2025 - Estimating the magnitude and frequency of floods at ungaged locations on streams in Tennessee through the 2013 water year","interactions":[],"lastModifiedDate":"2025-05-29T15:06:41.309366","indexId":"sir20245130","displayToPublicDate":"2025-05-29T08:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5130","displayTitle":"Estimating the Magnitude and Frequency of Floods at Ungaged Locations on Streams in Tennessee Through the 2013 Water Year","title":"Estimating the magnitude and frequency of floods at ungaged locations on streams in Tennessee through the 2013 water year","docAbstract":"To improve estimates of the frequency of annual peak flows for ungaged locations on non-urban, unregulated streams in Tennessee, generalized least-squares multiple linear-regression techniques were used to relate annual peak flows from streamgages operated by the U.S. Geological Survey to physical, climatic, and land-use characteristics of their drainage basins. Geospatial data acquired since the previous study in 2003, annual peak-streamflow data through the 2013 water year, and Bulletin 17C methods for frequency analysis of annual peak-streamflow data were used in the study. Generalized least-squares regression equations were developed for four hydrologic areas with distinct hydrologic, geologic, and topographic characteristics. Drainage area was used as an explanatory variable in equations developed for each hydrologic area. In addition to drainage area, a 2-year recurrence-interval climate factor was used for hydrologic area 1, a 10–85 channel slope was used for hydrologic area 2, and percent imperviousness was used for hydrologic area 4. The regression equations can be used to estimate annual exceedance probability streamflows for ungaged locations on non-urban, unregulated streams in Tennessee. The term “unregulated” indicates that streamflow is not appreciably influenced by regulation from reservoirs or other impoundments. Average standard errors of prediction for the regression equations ranged from 44.4 to 51.4 percent for hydrologic area 1; 30.4 to 42.9 percent for hydrologic area 2; 35.7 to 42.6 percent for hydrologic area 3; and 32.5 to 47.4 percent for hydrologic area 4.
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":"No abstract available.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmicb.2025.1620877","usgsCitation":"Barnhart, E.P., Douterelo, I., Morgan, M., and Puzon, G., 2025, Editorial: Microbial ecology supporting growth of free-living amoebae in natural and engineered water systems: Frontiers in Microbiology, v. 16, 1620877, 2 p., https://doi.org/10.3389/fmicb.2025.1620877.","productDescription":"1620877, 2 p.","ipdsId":"IP-178465","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":490620,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2025.1620877","text":"Publisher Index Page"},{"id":489691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","noUsgsAuthors":false,"publicationDate":"2025-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douterelo, Isabel","contributorId":356353,"corporation":false,"usgs":false,"family":"Douterelo","given":"Isabel","affiliations":[{"id":84966,"text":"Civil and Structural Engineering Department, University of Sheffield, Sheffield S1 3JD, UK","active":true,"usgs":false}],"preferred":false,"id":939155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morgan, Matthew J.","contributorId":356354,"corporation":false,"usgs":false,"family":"Morgan","given":"Matthew J.","affiliations":[{"id":84967,"text":"CSIRO Environment, Canberra, ACT, Australia","active":true,"usgs":false}],"preferred":false,"id":939156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Puzon, Gregory J.","contributorId":356355,"corporation":false,"usgs":false,"family":"Puzon","given":"Gregory J.","affiliations":[{"id":84968,"text":"CSIRO Environment, Waterford, WA, Australia","active":true,"usgs":false}],"preferred":false,"id":939157,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267682,"text":"70267682 - 2025 - Visioning and conceptual framework for coordinating Great Lakes connecting waters research and monitoring","interactions":[],"lastModifiedDate":"2025-05-29T14:42:54.708079","indexId":"70267682","displayToPublicDate":"2025-05-26T09:34:42","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Visioning and conceptual framework for coordinating Great Lakes connecting waters research and monitoring","docAbstract":"The Laurentian Great Lakes are connected via naturally occurring straits and rivers: St. Marys River, Straits of Mackinac, St. Clair-Detroit River System, Niagara River, and the St. Lawrence River. Despite the historical ecological and economic importance of these waters, international agreements (e.g., Great Lakes Water Quality Agreement) only recently explicitly named the Great Lakes Connecting Waters (GLCWs), requiring governments to address the challenges of adequate restoration and protection from historical use and degradation. Standardized research and monitoring activities are needed; however, there is no established mechanism for coordination across the GLCWs. A three-day summit in 2023 convened experts to form the initial framework for a GLCWs Collaborative to increase standardizations and knowledge transfer. Participants drafted a governance structure and priorities following the principles of collective impact but allowed for place-based specificity for local connecting water organizations. Priorities and suggestions for success included: 1) co-development of the collaborative with all rights holders, stakeholders, and surrounding communities; 2) investment in research and technology specific to GLCWs; 3) investment in information transfer and training; 4) increased communication; and 5) better integration into existing Great Lakes research, monitoring, and funding programs. Expanding participation in all local GLCWs organizations with principles of inclusivity was identified as a larger collaborative goal. Next steps in the development of a GLCWs Collaborative include increased communication and formation of working groups and obtaining funding for a dedicated organization to begin supporting activities (communication, facilitation, logistics). By using a deliberate process for establishment, the potentially slower time frame for establishment may result in increased participation and success.
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Center","active":true,"usgs":true}],"preferred":true,"id":938530,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":938531,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moerke, Ashley H.","contributorId":350779,"corporation":false,"usgs":false,"family":"Moerke","given":"Ashley H.","affiliations":[{"id":35243,"text":"Lake Superior State University","active":true,"usgs":false}],"preferred":false,"id":938532,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fry, Lauren M","contributorId":355983,"corporation":false,"usgs":false,"family":"Fry","given":"Lauren M","affiliations":[{"id":12789,"text":"NOAA Great Lakes Environmental Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":938533,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Twiss, Michael R.","contributorId":194611,"corporation":false,"usgs":false,"family":"Twiss","given":"Michael R.","affiliations":[],"preferred":false,"id":938534,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tank, Samantha N.","contributorId":355984,"corporation":false,"usgs":false,"family":"Tank","given":"Samantha N.","affiliations":[{"id":13509,"text":"Great Lakes Commission","active":true,"usgs":false}],"preferred":false,"id":938535,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267782,"text":"70267782 - 2025 - Mobile radar provides insights into hydrologic responses in burn areas","interactions":[],"lastModifiedDate":"2025-06-02T14:28:32.775128","indexId":"70267782","displayToPublicDate":"2025-05-26T09:21:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"Mobile radar provides insights into hydrologic responses in burn areas","docAbstract":"Wildfires often occur in mountainous terrain, regions that pose substantial challenges to operational meteorological and hydrologic observing networks.
A mobile, post-fire hydrometeorological observatory comprising remote-sensing and in situ instrumentation was developed and deployed in a burnt area to provide unique insights into rainfall-induced post-fire hazards.
Mobile radar-based rainfall estimates were produced throughout the burn area at 75-m resolution and compared with rain gauge accumulations and basin response variables.
The mobile radar was capable of resolving details in intra-basin rain fields as well as detecting storms approaching the burn area with accuracy equivalent to rain gauges. Runoff responses were complex and dependent on spatiotemporal patterns and magnitude of rainfall intensity over the burn area.
The complement of the mobile radar with the near-field, non-contact instruments measuring the hydrologic response provided valuable information in regions that are difficult to access and are not routinely monitored by conventional observing networks.
Post-fire observatories equipped with mobile radars deployed on burn areas provide real-time data, early alerting capabilities and visualizations to potentially guide impact-based decision support for local authorities.
The calcified structures of fishes provide insight into their periodic growth rates and can be combined with other biological variables to identify metrics such as size or age at maturity and mortality rates. Collecting this information on growth and life history can help evaluate the success of conservation efforts and inform future management decisions for a species in need. However, before these life history data can be applied to larger stock assessments that direct management decisions, confidence in the validity of the data needs to be reported through metrics of accuracy and precision. For this report, we assessed the bias and precision of paired reader estimations of spawning marks on scales of Blueback herring (Alosa aestivalis) collected in the U.S. Fish & Wildlife (USFWS) Annual Adult River Herring Stock Assessment for the lower Connecticut River basin. The paired reads on scales from a total of 8,698 fish over the ten years of the long term monitoring program were evaluated for the annual presence of systematic bias and precision using a combination of qualitative (i.e., frequency tables) and quantitative (i.e., Evan’s and Hoenig’s Test of Symmetry, and Coefficient of Variation (CV) calculations) analyses. While seven out of the ten survey years had systematic bias detected by the tests of symmetry, only three years (2013, 2016, 2018) had imprecision values >10% CV threshold. Data were further categorized into specific age classes within survey years to increase our resolution on where bias and imprecision was most prevalent. While the ability for accurate bias detection was limited by sufficient sample sizes (>25 fish), average imprecision values increased with age, and median age classes (4 through 6) commonly had bias detected. However, the removal of insufficient age classes prior to calculating average annual CV did not significantly change the initial average. Lastly, 2023, which was the first year to implement a standardized training procedure prior to production estimating, had the highest precision for both the annual average and specific age classes compared to all prior survey years. This standardized training procedure will continue to be used by USFWS for the lower Connecticut River tributaries, and can be modified for other river systems. Overall, this report’s results highlight the importance of assessing precision and encourage the standardization of spawning mark identification quality control and assurance for future studies. With more quality assessments and baseline information on precision and bias, there can be more beneficial discussion on defining thresholds and how to implement spawning history variability into catch curve analyses.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css36742600","usgsCitation":"Stephens, J.B., Jordaan, A., Perkins, D., Sprankle, K., and Roy, A.H., 2025, Quality assessment of past spawning mark estimations from a long-term survey in the Connecticut River watershed: Cooperator Science Series CSS-168-2025, ii, 72 p., https://doi.org/10.3996/css36742600.","productDescription":"ii, 72 p.","ipdsId":"IP-176656","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Massachusetts","otherGeospatial":"Connecticut River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.96748212492314,\n 42.29494671693686\n ],\n [\n -72.95372317204433,\n 41.28995606733855\n ],\n [\n -72.16868601132036,\n 41.26947837383972\n ],\n [\n -72.16868601132036,\n 42.30090777357515\n ],\n [\n -72.96748212492314,\n 42.29494671693686\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2025-05-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Stephens, Jacqueline B.","contributorId":360502,"corporation":false,"usgs":false,"family":"Stephens","given":"Jacqueline","middleInitial":"B.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":947139,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jordaan, Adrian","contributorId":257709,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":37201,"text":"UMass Amherst","active":true,"usgs":false}],"preferred":false,"id":947140,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perkins, David","contributorId":342184,"corporation":false,"usgs":false,"family":"Perkins","given":"David","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":947141,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sprankle, Kenneth","contributorId":349559,"corporation":false,"usgs":false,"family":"Sprankle","given":"Kenneth","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":947142,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":947143,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266892,"text":"sir20255032 - 2025 - Flood-inundation maps for 14.8 miles of Little and Big Papillion Creeks in Omaha, Nebraska, 2023","interactions":[],"lastModifiedDate":"2025-08-07T21:16:38.983416","indexId":"sir20255032","displayToPublicDate":"2025-05-21T08:56:33","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":"2025-5032","displayTitle":"Flood-Inundation Maps for 14.8 Miles of Little and Big Papillion Creeks in Omaha, Nebraska, 2023","title":"Flood-inundation maps for 14.8 miles of Little and Big Papillion Creeks in Omaha, Nebraska, 2023","docAbstract":"Digital flood-inundation map libraries for two reaches that constitute 14.8 miles of Little and Big Papillion Creeks in Omaha, Nebraska, were created by the U.S. Geological Survey (USGS) in cooperation with the Papio-Missouri River Natural Resource District. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at Little Papillion Creek at Irvington, Nebr. (USGS station 06610750), Little Papillion Creek at Ak-Sar-Ben at Omaha, Nebr. (USGS station 06610765), and Big Papillion Creek at Q Street at Omaha, Nebr. (USGS station 06610770) streamgages. Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System database at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/.
Flood profiles were computed for two different reaches that constitute 14.8 miles of stream length in the study area by using hydraulic models. The models were calibrated by adjusting roughness coefficients to best represent the current (2022) stage-streamflow relation at the streamgages within the study reach.
The hydraulic models were then used to compute water-surface profiles at 1-foot stage intervals for selected stage ranges to represent various flooding scenarios at the streamgages in each reach. The simulated water-surface profiles then were combined with a digital elevation model using a geographic information system, which had a 10-foot grid spacing to delineate the flooding extents and water depths for each stage. The availability of these flood-inundation maps, along with information regarding current stage from the USGS streamgages, can provide emergency management personnel and residents with information that is critical for flood response activities and post flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255032","collaboration":"Prepared in cooperation with the Papio-Missouri River Natural Resource District","usgsCitation":"Strauch, K.R., and Hoefer, B.R., 2025, Flood-inundation maps for 14.8 miles of Little and Big Papillion Creeks in Omaha, Nebraska, 2023: U.S. Geological Survey Scientific Investigations Report 2025–5032, 14 p., https://doi.org/10.3133/sir20255032.","productDescription":"Report: vi, 14 p.; Data Release; Dataset","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152753","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":493771,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118579.htm","linkFileType":{"id":5,"text":"html"}},{"id":485915,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5032/coverthb.jpg"},{"id":485916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5032/sir20255032.pdf","text":"Report","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5032"},{"id":485920,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5032/sir20255032.XML"},{"id":485921,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5032/images/"},{"id":485922,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":485923,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OU7E42","text":"USGS data release","linkHelpText":"Flood-inundation geospatial datasets for 14.8 miles of the Little and Big Papillion Creeks in Omaha, Nebraska, 2023"},{"id":485924,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255032/full"}],"country":"United States","state":"Nebraska","city":"Omaha","otherGeospatial":"Little and Big Papillion Creeks","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.15305796306968,\n 41.36725587936067\n ],\n [\n -96.15305796306968,\n 41.117996650104345\n ],\n [\n -95.92667071234595,\n 41.117996650104345\n ],\n [\n -95.92667071234595,\n 41.36725587936067\n ],\n [\n -96.15305796306968,\n 41.36725587936067\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
A major storm caused catastrophic flooding in many parts of Vermont on July 9–12, 2023, resulting in millions of dollars in damages. The high amount of rainfall caused several rivers to peak at record levels, in some cases exceeding records set during Tropical Storm Irene in 2011. The U.S. Geological Survey, in cooperation with the Federal Emergency Management Agency, collected and analyzed data that characterized the flood in Vermont. The data collected included peak water-surface elevations, taken from high-water marks at bridges, dams, and roads, and peak streamflow and annual exceedance probabilities (AEPs) at streamgages, lake gages, and selected ungaged locations. At 11 of the 80 streamgages with 12 to 94 years of record, the July 2023 peak streamflow was the peak of record. Ten streamgages recorded a peak streamflow with an AEP of less than or equal to 1 percent (greater than or equal to a 100-year recurrence interval).
The July 2023 flood affected many of the same communities as the historical flood caused by Tropical Storm Irene in 2011. Twenty of the 45 continuous-recording streamgages running during both events recorded greater peak streamflows during the July 2023 flood than during Tropical Storm Irene in 2011. Four of the 11 U.S. Geological Survey streamgages with period-of-record maximum peak streamflows observed during the July 2023 flood had previously recorded their maximum period-of-record peak streamflows during Tropical Storm Irene. There were 17 rivers in Vermont that were surveyed for high-water marks during both Tropical Storm Irene and the July 2023 flood. On those 17 rivers, a total of 103 sites contained surveyed high-water marks for both events. Thirty-two of these sites had higher surveyed elevations for the July 2023 flood than Tropical Storm Irene, including Black River in Newport, Black River in Springfield, Jewell Brook, Middlebury River, Missisquoi River, Ottauquechee River, Otter Creek, Wells River, Whetstone Brook, and Winooski River. Peak water-surface elevations were not collected on the Lamoille River in 2011.
Federal Emergency Management Agency flood insurance studies were evaluated in the context of the July 2023 flood. Peak streamflows at streamgages and nearby locations were assessed to determine the influence of the July 2023 flood on the AEPs used in past studies. Overall, 21 of 26 streamflow-computation locations in the flood insurance studies had more than a 10 percent difference in the 1-percent AEP streamflow. A hydraulic evaluation of surveyed water-surface elevations following the July 2023 flood was compared with the AEP profiles from past studies. Four of the 10 streamgages analyzed had poor alignment between the AEPs of the observed streamflows and the AEPs of the observed peak water-surface elevations as computed from flood insurance studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255016","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Smith, T.L., Olson, S.A., LeNoir, J.M., Kalmon, R.D., and Ahearn, E.A., 2025, Flood of July 2023 in Vermont (ver. 1.1, May 22, 2025): U.S. Geological Survey Scientific Investigations Report 2025–5016, 21 p., https://doi.org/10.3133/sir20255016.","productDescription":"Report; vii, 21 p.; 2 Data Releases: Application Site","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-167054","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":493770,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118574.htm","linkFileType":{"id":5,"text":"html"}},{"id":486164,"rank":4,"type":{"id":31,"text":"Publication 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data viewer"},{"id":486167,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14XBBAT","text":"USGS data release","linkHelpText":"Vermont Flood of July 2023 Data"},{"id":486291,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2025/5016/versionHist.txt","size":"879 B","linkFileType":{"id":2,"text":"txt"}},{"id":486405,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/664e23efd34e702fe8744b3c","text":"USGS data release","linkHelpText":"Peak Streamflow, Peak Stage, and Annual Exceedance Probability Data of the July 2023 Flood in Vermont, with attached data tables 1.1 to 1.4 in Vermont_Flood_2023_tables.xlsx"}],"country":"United 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\"}}]}","edition":"Version 1.0: May 19, 2025; Version 1.1: May 22, 2025","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
A major storm caused catastrophic flooding in many parts of Vermont on July 9–12, 2023, resulting in millions of dollars in damages. The high amount of rainfall caused several rivers to peak at record levels, in some cases surpassing records set during Tropical Storm Irene in 2011. The U.S. Geological Survey, in cooperation with the Federal Emergency Management Agency, collected and analyzed data on peak water-surface elevations, peak streamflow, and annual exceedance probabilities at streamgages, lake gages, and selected ungaged locations. Of the 80 streamgages with records ranging from 12 to 94 years, 11 streamgages recorded peak streamflows during the July 2023 flood that were the highest on record, whereas 10 streamgages had peak streamflows with a recurrence interval of 100 years or more. The July 2023 flood affected many of the same communities as Tropical Storm Irene in 2011, but 20 of the 45 monitored streamgages recorded higher peak flows in the July 2023 flood. Seventeen rivers were surveyed for high-water marks during both Tropical Storm Irene and the July 2023 flood, and 32 of the 103 sites had higher water levels during the July 2023 flood compared with Tropical Storm Irene. Flood insurance studies by the Federal Emergency Management Agency were evaluated against the July 2023 flood data. Twenty-one of the 26 locations analyzed had more than a 10 percent difference in the 1-percent annual exceedance probability streamflow.
","publicationDate":"2025-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Travis L. 0000-0002-3448-2787 tlsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-2787","contributorId":297400,"corporation":false,"usgs":true,"family":"Smith","given":"Travis","email":"tlsmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":937647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olson, Scott A. 0000-0002-1064-2125","orcid":"https://orcid.org/0000-0002-1064-2125","contributorId":210173,"corporation":false,"usgs":true,"family":"Olson","given":"Scott A.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":937648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LeNoir, James M. 0000-0003-3295-4901","orcid":"https://orcid.org/0000-0003-3295-4901","contributorId":302690,"corporation":false,"usgs":true,"family":"LeNoir","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":937649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kalmon, Rena D. 0000-0002-3210-3210","orcid":"https://orcid.org/0000-0002-3210-3210","contributorId":206320,"corporation":false,"usgs":true,"family":"Kalmon","given":"Rena","email":"","middleInitial":"D.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":937650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ahearn, Elizabeth A. 0000-0002-5633-2640 eaahearn@usgs.gov","orcid":"https://orcid.org/0000-0002-5633-2640","contributorId":194658,"corporation":false,"usgs":true,"family":"Ahearn","given":"Elizabeth","email":"eaahearn@usgs.gov","middleInitial":"A.","affiliations":[{"id":377,"text":"Massachusetts-Rhode Island Water Science Center","active":false,"usgs":true},{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":false,"id":937651,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267230,"text":"sir20235064G - 2025 - Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70267230,"text":"sir20235064G - 2025 - Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064G","publicationYear":"2025","noYear":false,"chapter":"G","displayTitle":"Peak Streamflow Trends in Montana and Northern Wyoming and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2026-01-26T19:13:21.257304","indexId":"sir20235064G","displayToPublicDate":"2025-05-19T13:20:42","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":"2023-5064","chapter":"G","displayTitle":"Peak Streamflow Trends in Montana and Northern Wyoming and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020","docAbstract":"Frequency analysis on annual peak streamflow (hereinafter, peak flow) is essential to water-resources management applications, including critical structure design (for example, bridges and culverts) and floodplain mapping. Nonstationarity is a statistical property of a peak-flow series such that the distributional properties (the mean, variance, or skew) change either gradually (monotonic trend) or abruptly (shift, step change or change point) through time. Not incorporating or accounting for observed nonstationarity into peak-flow frequency analysis might result in a poor representation of the true probability of large floods and thus misrepresent the actual flood risks to life and property. This report summarizes how hydroclimatic variability might affect the temporal and spatial distributions of peak-flow data in the State of Montana (and northern Wyoming) and is part of a larger study to document peak-flow nonstationarity and hydroclimatic changes across a nine-State region consisting of Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin. A wide range of analyses and statistical approaches are applied to document the primary mechanisms controlling floods and characterize temporal changes in hydroclimatic variables and peak flows. This study was completed in cooperation with the Montana Department of Natural Resources and Conservation.
The purpose of this report is to characterize temporal and spatial patterns of nonstationarity in peak flows and hydroclimatology in Montana and northern Wyoming. In this evaluation, peak-flow, daily streamflow, and model-simulated gridded climatic data were examined for monotonic trends, change points, and other statistical properties that might indicate changing climatic and environmental conditions. This report includes background information on the study area, the history of U.S. Geological Survey peak-flow data collection and frequency analysis in Montana, and the review of research relating to hydroclimatic variability and change in Montana. This study might help provide a framework for addressing potential nonstationarity issues in peak-flow frequency updates that commonly are completed by the U.S. Geological Survey in cooperation with other agencies throughout the Nation.
The analytical structure of this study includes analyses of monotonic trends and change points in numerous hydroclimatic variables in assigned 30-, 50-, 75-, and 100-year analysis periods. For Montana and part of Wyoming, the 30-, 50-, 75, and 100-year analyses included 157, 70, 48, and 12 streamgages, respectively. For those streamgages, nonstationarities were analyzed in the following variables: (1) climatic variables, including annual and seasonal (winter, spring, summer, and fall) temperature and precipitation; (2) daily streamflow variables, including the annual center of volume duration, annual center of volume median, and peaks over threshold with a mean of four events per year; and (3) annual peak-flow variables, including peak-flow timing and magnitude. A likelihood approach was used to express statistical confidence and assign the nonstationarity results as likely upward or downward (highest statistical confidence), somewhat likely upward or downward (less statistical confidence), or about as likely as not (little statistical confidence; hereinafter, neutral). For the nonstationarity analyses of the climatic, daily streamflow, and peak-flow variables, the results are presented in detail and discussed with respect to statewide patterns and geographic variability. For each of the 30-, 50-, and 75-year analyses, peak-flow change-point and monotonic trend analyses were compiled for streamgages classified with likely downward or likely upward trends. For those streamgages, the associated basin characteristics and nonstationarity results for peak-flow timing, daily streamflow, and climatic variables were investigated and statistically compared to discern associations among other variables that might contribute to the peak-flow nonstationarity results.
The 50- and 75-year peak-flow nonstationarities identified in this study are mostly downward, in association with mostly upward temperature and potential evapotranspiration:precipitation monotonic trends. For the 50-, 75-, and 100-year analyses, the peak-flow change points are predominantly downward and are concentrated in the 1970s and 1980s, which indicates general consistency among the longer trend periods. These findings are in association with substantial research documenting globally rising temperature and atmospheric greenhouse gas concentrations that might be largely attributed to anthropogenic activities. Anthropogenic effects might represent long-term (on the order of several decades to more than a century) climate changes that might happen within highly variable natural climate fluctuations. Several paleo studies in the north-central United States have indicated that hydroclimatic extremes (that is, low- and high-streamflow conditions) before European settlement have been outside of extremes since the 1900s. Depending on the interactions of anthropogenic effects and natural climate variability, extreme high-streamflow conditions might occur in the future, even in the presence of long-term downward peak-flow trends.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064G","collaboration":"Prepared in cooperation with the Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, S.K., Barth, N.A., Sando, R., and Chase, K.J., 2025, Peak streamflow trends in Montana and northern Wyoming and their relation to changes in climate, water years 1921–2020, chap. G of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 129 p., https://doi.org/10.3133/sir20235064G.","productDescription":"Report: x, 129 p.; Data Release; Dataset","numberOfPages":"144","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-159092","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":486055,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/sir20235064g.pdf","text":"Report","size":"19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5064-G"},{"id":486054,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/coverthb.jpg"},{"id":486056,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/sir20235064g.XML"},{"id":486057,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5064/g/images/"},{"id":486059,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235064G/full"},{"id":499038,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118575.htm","linkFileType":{"id":5,"text":"html"}},{"id":486073,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":486058,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R71WWZ","text":"USGS data release","linkHelpText":"Peak streamflow data, climate data, and results from investigating hydroclimatic trends and climate change effects on peak streamflow in the Central United States, 1921–2020"}],"country":"United States","state":"Montana, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.16658108866669,\n 49\n ],\n [\n -116.16658108866669,\n 43\n ],\n [\n -104.0598539498924,\n 43\n ],\n [\n -104.0598539498924,\n 49\n ],\n [\n -116.16658108866669,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Objective
Nonnative fishes can modify ecosystems and harm economies when they are introduced to new environments. Climate change is likely to assist the spread and establishment of some nonnative fishes (e.g., warmwater species), but spatiotemporal gaps in water temperature monitoring and modeling may prevent ecologists and managers from forecasting thermal habitat suitability for these taxa. The purpose of this study was to develop a predictive model of winter water temperatures and thermal habitat suitability for two priority nonnative armored catfish, Vermiculated Sailfin Catfish Pterygoplichthys disjunctivus and Orinoco Sailfin Catfish P. multiradiatus, in the Suwannee River, Florida and Georgia.
Methods
Precipitation- and groundwater-corrected air–water temperature models were developed and evaluated using a model selection procedure to predict water temperatures at four sites in the Suwannee River. These models were chosen because they blend the simplicity of air–water temperature models with the accuracy of hydrometeorological models to create an efficient, economical, management-relevant approach for analyzing and forecasting water temperature.
Results
Most of the top-performing water temperature models (92%) had precipitation or groundwater corrections to air–water temperature formulations. Projected mean and maximum water temperatures increased as simulated climate change intensified. All four Suwannee River sites studied were projected to be thermally hospitable to the survival of Vermiculated Sailfin Catfish. Lower river sites, noticeably warmer than upper river sites, were conducive to the survival of Orinoco Sailfin Catfish throughout the winter months. The upper river sites were too cold for Orinoco Sailfin Catfish survival in some climate-change scenarios, but the Suwannee River has an abundance of constant-temperature springs that are likely hospitable to Vermiculated Sailfin Catfish and Orinoco Sailfin Catfish throughout the year.
Conclusions
The findings suggest that winter water temperatures will likely not be a barrier to the survival of Pterygoplichthys catfish in the Suwannee River, amplifying the importance of conservation and management approaches to inhibit their spread and establishment. If the Pterygoplichthys population remains small and isolated and decision makers are able to devote required staff time and resources to managing these species, removal and eradication at local if not broader scales may be reasonable goals. This study provides a water temperature modeling approach that can aid ecologists and managers in prioritizing sites to prevent the introduction, slow the dispersal, eradicate, and control Pterygoplichthys catfish and other nonnative fishes in the Suwannee River and beyond.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/tafafs/vnaf018","usgsCitation":"Carlson, A.K., 2025, Protected from Pterygoplichthys? Predicting thermal habitat suitability for nonnative armored catfish in the Suwannee River: Transactions of the American Fisheries Society, v. 154, no. 4, p. 398-413, https://doi.org/10.1093/tafafs/vnaf018.","productDescription":"16 p.","startPage":"398","endPage":"413","ipdsId":"IP-171826","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494463,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/tafafs/vnaf018","text":"Publisher Index Page"},{"id":494386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia","otherGeospatial":"Suwannee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.76698997402998,\n 31.112972868570907\n ],\n [\n -83.18475784303796,\n 30.177311490235\n ],\n [\n -83.16967351009271,\n 29.211264445611363\n ],\n [\n -82.78703329930818,\n 29.017838843725343\n ],\n [\n -82.37936790717382,\n 30.074113688375725\n ],\n [\n -82.26360657954834,\n 31.112972868570907\n ],\n [\n -82.76698997402998,\n 31.112972868570907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"154","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Andrew Kenneth 0000-0002-6681-0853","orcid":"https://orcid.org/0000-0002-6681-0853","contributorId":340581,"corporation":false,"usgs":true,"family":"Carlson","given":"Andrew","email":"","middleInitial":"Kenneth","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946650,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70267371,"text":"70267371 - 2025 - A review of standardization in Mississippi’s multidecadal inland fisheries monitoring program","interactions":[],"lastModifiedDate":"2025-05-21T14:29:44.778022","indexId":"70267371","displayToPublicDate":"2025-05-18T09:27:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6476,"text":"Fishes","active":true,"publicationSubtype":{"id":10}},"title":"A review of standardization in Mississippi’s multidecadal inland fisheries monitoring program","docAbstract":"Standardizing data collection, management, and analysis processes can improve the reliability and efficiency of fisheries monitoring programs, yet few studies have examined the operationalization of these tasks within agency settings. We reviewed the Mississippi Department of Wildlife, Fisheries, and Parks, Fisheries Bureau’s inland recreational fisheries monitoring program—a 30+-year effort to standardize field protocols, data handling procedures, and automated analyses through a custom-built computer application, the Fisheries Resources Analysis System (FRAS). Drawing on quantitative summaries of sampling trends and qualitative interviews with fisheries managers, we identified key benefits, challenges, and opportunities associated with the Bureau’s standardization efforts. Standardized procedures improved sampling consistency, data reliability, and operational efficiency, enabling the long-term tracking of fish population and angler metrics across more than 270 managed waterbodies. However, challenges related to analytical transparency and spatiotemporal comparisons persist. Simulations indicated that under current conditions, 5.8, 22.9, and 37.1 years would be required to sample (boat electrofishing) 50%, 75%, and 95% of the Bureau’s waterbodies at least once, respectively; these figures should translate to other agencies, assuming similar resource availability per waterbody. The monitoring program has reduced manual processing effort and enhanced staff capacity for waterbody-specific management, yet several opportunities remain to improve efficiency and utility. These include expanding FRAS functionalities for trend visualization, integrating mobile field data entry to reduce transcription errors, linking monitoring results with management objectives, and enhancing automated report generation for management support. Strengthening these elements could not only streamline workflows but better position agencies to apply standardized data in adaptive management embedded into the monitoring program.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes10050235","usgsCitation":"Aldridge, C.A., and Colvin, M.E., 2025, A review of standardization in Mississippi’s multidecadal inland fisheries monitoring program: Fishes, v. 10, no. 5, 235, 22 p., https://doi.org/10.3390/fishes10050235.","productDescription":"235, 22 p.","ipdsId":"IP-156974","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":487012,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes10050235","text":"Publisher Index Page"},{"id":486285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
The Long Island Sound watershed is home to nearly 9 million people in parts of Connecticut, Massachusetts, New Hampshire, New York, Rhode Island, Vermont, and Canada. Government agencies, nonprofits, and Tribal Nations have overseen numerous projects to monitor and protect the water resources of this watershed and the sound. Although there is an abundance of data, there is no easy way to search them or a central place to manage this information. To help, the U.S. Geological Survey, the U.S. Environmental Protection Agency, and the Long Island Sound Study have created an interactive map to help users find and understand data about the water resources of the Long Island Sound and its watershed.
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U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255024","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, High Desert Partnership, and Oregon Watershed Enhancement Board","usgsCitation":"Smith, C.D., 2025, Managing water for birds—A tool for the Malheur National Wildlife Refuge, southeastern Oregon: U.S. Geological Survey Scientific Investigations Report 2025–5024, 21 p., https://doi.org/10.3133/sir20255024.\n[Supersedes preprint https://doi.org/10.32942/X2N03N.]","productDescription":"Report: vii, 21 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-169682","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":485975,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5024/images"},{"id":485974,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1AJAYVS","text":"USGS data release","description":"USGS data release","linkHelpText":"Water for Birds—A spreadsheet-based tool for the Malheur National Wildlife Refuge for irrigation months in 2021–2024"},{"id":485973,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255024/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5024"},{"id":485972,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5024/sir20255024.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5024"},{"id":485971,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5024/coverthb2.jpg"},{"id":485976,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5024/sir20255024.XML"}],"country":"United States","state":"Oregon","otherGeospatial":"Malheur National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.5,\n 43.75\n ],\n [\n -119.5,\n 42.75\n ],\n [\n -118.5,\n 42.75\n ],\n [\n -118.5,\n 43.75\n ],\n [\n -119.5,\n 43.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW Second Avenue, Suite 1950
Portland, Oregon 97204
Objective
Best practices for conservation hatcheries to conserve genetic diversity and minimize adaptation to captivity have been established for decades, but how to apply them is not clear in every circumstance. As a growing number of aquatic species are propagated in captive settings, addressing the fit of these practices to each system will help managers operate optimally while conserving hatchery resources. Small-bodied freshwater fish present a unique set of traits compared with species that are typically considered for propagation (i.e., salmonids), including a patchy distribution within a watercourse. We examine the propagation and reintroduction program that supports the Topeka Shiner Miniellus topeka, an endangered minnow in the Midwestern USA.
Methods
We genotyped shiners from groups with different histories (two reintroduced, three captive, and two remnant populations) at 11 microsatellite loci and compared genetic diversity, genetic structure, effective population size, and evidence of population bottlenecks. We also looked at the breeding structure by genetically assigning hatchery-reared young (n = 148) to candidate parents.
Results
We documented high levels of genetic structure among the two natural populations in our study. We also noted lower diversity and evidence of bottlenecks in hatchery-reared groups. However, hatcheries may support sufficient (>50) effective population sizes with minimal space.
Conclusions
Hatcheries may avoid bottlenecks in other small-bodied freshwater fish by collecting wild fish from a broad area and frequently incorporating them into the captive population. Within the hatchery, we emphasize the need to reduce generational overlap by stocking all production fish and/or subdividing the captive populations.
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These failures result in the mass transport of sediment and organic materials into surface waters, with the potential to dramatically alter aquatic ecosystem function and biotic interactions. We coupled direct comparisons and long-term data of a suite of abiotic and biotic variables in a thermokarst-impacted lake and nearby reference lake to assess the impacts of thermokarst failure. After the thermokarst failure and relative to long-term averages, water transparency was substantially reduced. We hypothesized there would be subsequent changes to lower trophic levels and profound declines in fish foraging efficiency. However, these characteristics were within the range of natural variability and/or rapidly recovered to values within the range of long-term variability. In addition, although there was limited evidence of taxa-specific changes, we did not observe any strong changes in the total relative densities, growth rates, or composition of the bacterioplankton and zooplankton communities, benthic macroinvertebrates, or changes in fish diet, that could be attributed statistically to the thermokarst event. In sum, the thermokarst disturbance had substantial effects on water transparency and some lower trophic levels, which surprisingly were not manifested in higher trophic levels. Overall, the lake ecosystem appeared resistant to thermokarst disturbance with rapid recovery within two years after the disturbance.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s00442-025-05681-9","collaboration":"Alaska Dept. of Fish and Game","usgsCitation":"Budy, P., Pennock, C., Messenger, S., Pehrson, H., Adler, E., Thiede, G., Christman, N.R., Crump, B.C., Giblin, A., and Kling, G., 2025, Rapid recovery of an arctic lake ecosystem from a pulse disturbance caused by thermokarst failure: Oecologia, v. 207, 82, 16 p., https://doi.org/10.1007/s00442-025-05681-9.","productDescription":"82, 16 p.","ipdsId":"IP-162860","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487996,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00442-025-05681-9","text":"Publisher Index Page"},{"id":486523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Toolik Lake","geographicExtents":"{\n \"type\": 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R.","contributorId":329637,"corporation":false,"usgs":false,"family":"Christman","given":"Natasha","email":"","middleInitial":"R.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":938294,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crump, Byron C.","contributorId":329638,"corporation":false,"usgs":false,"family":"Crump","given":"Byron","email":"","middleInitial":"C.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":938295,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Giblin, Anne E.","contributorId":355866,"corporation":false,"usgs":false,"family":"Giblin","given":"Anne E.","affiliations":[{"id":84847,"text":"The Ecosystems Center, Marine Biological Laboratory","active":true,"usgs":false}],"preferred":false,"id":938296,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kling, George W.","contributorId":355867,"corporation":false,"usgs":false,"family":"Kling","given":"George W.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":938297,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70267261,"text":"70267261 - 2025 - Biocrust mosses and cyanobacteria exhibit distinct carbon uptake responses to variations in precipitation amount and frequency","interactions":[],"lastModifiedDate":"2025-05-19T14:58:24.230313","indexId":"70267261","displayToPublicDate":"2025-05-15T07:53:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Biocrust mosses and cyanobacteria exhibit distinct carbon uptake responses to variations in precipitation amount and frequency","docAbstract":"Dryland organisms exhibit varied responses to changes in precipitation, including event size, frequency, and soil moisture duration, influencing carbon uptake and reserve management strategies. This principle, central to the pulse-reserve paradigm, has not been thoroughly evaluated in biological soil crusts (biocrusts), essential primary producers on dryland surfaces. We conducted two experiments to investigate carbon uptake in biocrusts under different precipitation regimes. In the first, we applied a gradient of watering amounts to biocrusts dominated by moss or cyanobacteria, hypothesising distinct pulse-response strategies. The second experiment extended watering treatments over three months, varying pulse size and frequency. Our results revealed distinct carbon uptake patterns: moss crusts exhibited increased CO2 uptake with larger, less frequent watering events, whereas cyanobacteria crusts maintained similar carbon uptake across all event sizes. These findings suggest divergent pulse-response strategies across biocrust types, with implications for modelling dryland carbon dynamics and informing land management under changing precipitation regimes.","language":"English","publisher":"Wiley","doi":"10.1111/ele.70125","usgsCitation":"Young, K., Sala, O.E., Darrouzet-Nardi, A., Tucker, C.L., Finger-Higgens, R.A., Starbuck, M., and Reed, S., 2025, Biocrust mosses and cyanobacteria exhibit distinct carbon uptake responses to variations in precipitation amount and frequency: Ecology Letters, v. 28, no. 5, e70125, 10 p., https://doi.org/10.1111/ele.70125.","productDescription":"e70125, 10 p.","ipdsId":"IP-171245","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":489080,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2566615","text":"External Repository"},{"id":486154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Colorado Plateau, southeastern Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.03847502985644,\n 38.34758364168215\n ],\n [\n -111.03847502985644,\n 37.01866208836557\n ],\n [\n -109.01970607796514,\n 37.01866208836557\n ],\n [\n -109.01970607796514,\n 38.34758364168215\n ],\n [\n -111.03847502985644,\n 38.34758364168215\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"28","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, Kristina E.","contributorId":195945,"corporation":false,"usgs":false,"family":"Young","given":"Kristina E.","affiliations":[],"preferred":false,"id":937537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sala, Osvaldo E.","contributorId":139047,"corporation":false,"usgs":false,"family":"Sala","given":"Osvaldo","email":"","middleInitial":"E.","affiliations":[{"id":12629,"text":"Arizona State University, Tempe, AZ (DETAIL TO BE ADDED)","active":true,"usgs":false}],"preferred":false,"id":937538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Darrouzet-Nardi, Anthony adarrouzet-nardi@usgs.gov","contributorId":207292,"corporation":false,"usgs":false,"family":"Darrouzet-Nardi","given":"Anthony","email":"adarrouzet-nardi@usgs.gov","affiliations":[],"preferred":false,"id":937539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tucker, Colin L","contributorId":270737,"corporation":false,"usgs":false,"family":"Tucker","given":"Colin","email":"","middleInitial":"L","affiliations":[{"id":56205,"text":"U.S. National Forest Service, Northern Research Station, Houghton, MI 49931","active":true,"usgs":false}],"preferred":false,"id":937540,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Finger-Higgens, Rebecca A 0000-0002-7645-504X","orcid":"https://orcid.org/0000-0002-7645-504X","contributorId":290211,"corporation":false,"usgs":true,"family":"Finger-Higgens","given":"Rebecca","email":"","middleInitial":"A","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937541,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Starbuck, Megan Elyse 0000-0002-1363-6994","orcid":"https://orcid.org/0000-0002-1363-6994","contributorId":355528,"corporation":false,"usgs":true,"family":"Starbuck","given":"Megan Elyse","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937542,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937543,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266895,"text":"ofr20241070 - 2025 - Calibration of the Stream Salmonid Simulator (S3) model to estimate annual survival, movement, and food consumption by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the restoration reach of the Trinity River, California, 2006–18","interactions":[],"lastModifiedDate":"2025-05-16T14:44:27.229198","indexId":"ofr20241070","displayToPublicDate":"2025-05-15T07:39:36","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1070","displayTitle":"Calibration of the Stream Salmonid Simulator (S3) Model to Estimate Annual Survival, Movement, and Food Consumption by Juvenile Chinook Salmon (Oncorhynchus tshawytscha) in the Restoration Reach of the Trinity River, California, 2006–18","title":"Calibration of the Stream Salmonid Simulator (S3) model to estimate annual survival, movement, and food consumption by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the restoration reach of the Trinity River, California, 2006–18","docAbstract":"The Trinity River is managed in two sections: (1) from the upper 64-kilometer “restoration reach” downstream from Lewiston Dam to the confluence with the North Fork Trinity River, and (2) the 120-kilometer lower Trinity River downstream from the restoration reach. The Stream Salmonid Simulator (S3) has been previously applied to these reaches and the Klamath River. To estimate fish growth, past S3 calibration efforts in the Trinity and Klamath Rivers used maximum likelihood methods that considered only the abundance of juvenile Chinook salmon (Oncorhynchus tshawytscha) passing a fish trap to estimate survival and movement parameters, but not fish consumption. Previous calibrations did not estimate the average proportion of maximum consumption (Cy) when estimating survival (Sy) and movement (M0y) parameters across years (y) of data, but because no other information was available in the literature a fixed value of Cy=0.66 was assumed. Therefore, the goal of this report is to present an alternative approach that calibrates the S3 model to multivariate data (that is, abundance and size), enabling the estimation of the average proportion of maximum consumption, in conjunction with survival and movement parameters for a particular migration year. We fit the S3 model to individual years of weekly trap abundance estimates and mean fish sizes (fork length) at the Pear Tree Gulch (hereafter referred to as Pear Tree) fish trap representing the restoration reach. We used the Earth Mover’s Distance (EMD) as the objective value to be minimized in parameter optimization. This approach estimated survival, movement, and consumption parameters for each migration year. Because we had information on the abundance of natural and hatchery produced juvenile salmon at the fish traps, we estimated survival and movement for natural and hatchery fish.
S3 is a deterministic life-stage-structured population model that tracks daily growth, movement, and survival of juvenile Chinook Salmon. A key theme of the model is that river discharge affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily timeseries of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit drive survival and growth within each habitat unit and movement of fish among habitat units.
The physical template of the restoration reach of the Trinity River was classified into 356 meso-habitat units comprised of runs, riffles, and pools. For each habitat unit, we developed a timeseries of daily discharge, water temperature, amount of available spawning habitat, and fry and parr carrying capacity. Capacity time series were constructed using state-of-the-art models of spatially explicit hydrodynamics and quantitative fish habitat relationships developed for the Trinity River. These variables were then used to drive population dynamics such as egg maturation and survival, and in turn, juvenile movement, growth, and survival.
We estimated movement, survival, and consumption parameters by calibrating the model to 12 years of weekly juvenile abundance estimates and fish sizes at the Pear Tree fish trap near the downstream end of the restoration reach. We estimated parameters for 12 years that included a wide range of female spawner abundances (1,414–11,494) and water year types (critically dry–extremely wet). We contrast the estimated parameters to the corresponding number of female spawners and the total annual volume of water discharged for the Trinity River (Trinity River Restoration Program; https://www.trrp.net/restoration/flows/summary/).
The calibration consisted of replicating historical conditions as closely as possible (for example, discharge; temperature; spawner abundance, spawning location and timing, and hatchery releases), and then running the model to predict weekly abundance passing the trap location from each brood year of adults and subsequent migration year of their juvenile progeny. Because density-dependent movement was favored in past evaluations, we estimated S3 parameters based on density-independent survival and density-dependent movement. Likewise, each year’s estimated survival parameter for natural (SNy) and hatchery (SHy) fish may be interpreted as the mean daily survival probability from emergence or hatchery release to the Pear Tree fish trap. Under density dependence, the estimated movement parameter for natural (M0Ny) and hatchery (M0Hy) fish represents the intercept of the Beverton-Holt model; the probability of remaining in a habitat at near-zero abundance.
We estimated Cy by using EMD and incorporating abundance and fish size into model calibration. Average daily proportions of maximum consumption, , across the years were generally high (=0.640; standard deviation (SD) SD=0.176), suggesting that fish were feeding at about two-thirds of expected maximum consumption rates. This average proportion of maximum consumption,is very similar to what has been assumed (=0.66) in previous Trinity and Klamath River S3 calibration and simulation efforts. In 2017, we estimated the lowest Cy, suggesting lower average consumption for juvenile salmon in high-discharge water years. When this high discharge year was excluded, there was no apparent trend in Cy with annual water volume. Estimates of survival showed little trend over the range in spawner abundances, but a trend towards higher natural and hatchery fish survival with higher annual volumes of water was apparent. Over the 12 years, the average survival of hatchery fish was =0.888 (SD=0.079) and the average survival natural fish was=0.969 (SD=0.01).
With respect to fish movement, we estimated higher M0Ny and M0Hy with higher annual volumes of water in the Trinity River. Higher MN0y or MH0y suggest greater probability of remaining in a habitat at low fish densities, with potential for density-dependent processes in movement to occur. The highest M0Ny = 0.676 was estimated during brood year 2012, and the overall average for natural fish was =0.276 (SD=0.188) and for hatchery fish was=0.467 (SD=0.235). Under the Beverton-Holt model, as M0Ny or M0Hy approach zero, there is less capacity for change in fish movement as fish density increases.
The S3 model was initialized with only the spatiotemporal distribution of spawners, so it performed well at capturing the essential outmigration features that are ultimately governed by rates of growth, movement, and mortality. We used a new optimization method that could accommodate multivariate data on abundance and fish size collected at the Pear Tree fish trap, enabling the calibration of S3 to estimate five parameters for 12 separate years of data. Incorporating weekly fish size data for each year in our parameter optimization process made the estimation of Cy possible and represents a step forward in the fitting of the S3 model to fish trap data for the purposes of parameter calibration and the estimation of growth parameters with respect to annual conditions. We identified lack of fit and adding important effects into the S3 model may improve the S3 estimation and simulation of water scenarios.
The Trinity River Restoration Program (TRRP) Science Advisory Board recommended that the TRRP focus on developing core elements of a decision support system (DSS; Buffington and others, 2014). Toward that end, the habitat and S3 models described in this report are both core elements of the DSS. The structure of S3 makes it a particularly useful fish production model for the DSS because population dynamics are sensitive to (1) water temperature, (2) daily discharge management, and (3) habitat quality and quantity. Each of these variables are key management parameters under consideration in the TRRP. As such, the S3 model may provide valuable insights into the potentially variable effects of different management decisions on the Trinity River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241070","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation","usgsCitation":"Plumb, J.M., Perry, R.W., and De Juilio, K., 2025, Calibration of the Stream Salmonid Simulator (S3) model to estimate annual survival, movement, and food consumption by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the restoration reach of the Trinity River, California, 2006–18: U.S. Geological Survey Open-File Report 2024–1070, 21 p., https://doi.org/10.3133/ofr20241070.","productDescription":"vii, 22 p.","onlineOnly":"Y","ipdsId":"IP-156648","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":485969,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1070/images"},{"id":485968,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241070/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1070"},{"id":485967,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1070/ofr20241070.pdf","text":"Report","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1070"},{"id":485966,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1070/coverthb.jpg"},{"id":485970,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1070/ofr20241070.XML"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.1406578585185,\n 40.785753827016435\n ],\n [\n -123.1406578585185,\n 40.69151845163566\n ],\n [\n -122.79146166523228,\n 40.69151845163566\n ],\n [\n -122.79146166523228,\n 40.785753827016435\n ],\n [\n -123.1406578585185,\n 40.785753827016435\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Lampreys (Petromyzontiformes) are an ancient group of fishes with complex life histories. We created a life cycle model that includes an R Shiny interactive web application interface to simulate abundance by life stage. This will allow scientists and managers to connect available demographic information in a framework that can be applied to questions regarding lamprey biology and conservation. We used Pacific lamprey (Entosphenus tridentatus) as a case study to highlight the utility of this model. We applied a global sensitivity analysis to explore the importance of individual life stage parameters to overall population size, and to better understand the implications of existing gaps in knowledge. We also provided example analyses of selected management scenarios (dam passage, fish translocations, and hatchery additions) influencing Pacific lamprey in fresh water. These applications illustrate how the model can be applied to inform conservation efforts. This tool will provide new capabilities for users to explore their own questions about lamprey biology and conservation. Simulations can hone hypotheses and predictions, which can then be empirically tested in the real world.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0323408","usgsCitation":"Gomes, D.G., Benjamin, J.R., Clemens, B.J., Lampman, R., and Dunham, J., 2025, New technology for an ancient fish: A lamprey life cycle modeling tool with an R Shiny application: PLoS ONE, v. 20, no. 5, e0323408, 25 p., https://doi.org/10.1371/journal.pone.0323408.","productDescription":"e0323408, 25 p.","ipdsId":"IP-172919","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":489170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0323408","text":"Publisher Index Page"},{"id":486169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomes, Dylan Gerald-Everett 0000-0002-2642-3728","orcid":"https://orcid.org/0000-0002-2642-3728","contributorId":346160,"corporation":false,"usgs":true,"family":"Gomes","given":"Dylan","email":"","middleInitial":"Gerald-Everett","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":937518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":937519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clemens, Benjamin J.","contributorId":195098,"corporation":false,"usgs":false,"family":"Clemens","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":937520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lampman, Ralph","contributorId":215233,"corporation":false,"usgs":false,"family":"Lampman","given":"Ralph","email":"","affiliations":[],"preferred":true,"id":937521,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":937522,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267437,"text":"70267437 - 2025 - Regional analysis of the dependence of peak-flow quantiles on climate with application to adjustment to climate trends","interactions":[],"lastModifiedDate":"2025-05-23T15:07:59.713754","indexId":"70267437","displayToPublicDate":"2025-05-14T10:05:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10778,"text":"Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Regional analysis of the dependence of peak-flow quantiles on climate with application to adjustment to climate trends","docAbstract":"Standard flood-frequency analysis methods rely on an assumption of stationarity, but because of growing understanding of climatic persistence and concern regarding the effects of climate change, the need for methods to detect and model nonstationary flood frequency has become widely recognized. In this study, a regional statistical method for estimating the effects of climate variations on annual maximum (peak) flows that allows for the effect to vary by quantile is presented and applied. The method uses a panel–quantile regression framework based on a location-scale model with two fixed effects per basin. The model was fitted to 330 selected gauged basins in the midwestern United States, filtered to remove basins affected by reservoir regulation and urbanization. Precipitation and discharge simulated using a water-balance model at daily and annual time scales were tested as climate variables. Annual maximum daily discharge was found to be the best predictor of peak flows, and the quantile regression coefficients were found to depend monotonically on annual exceedance probability. Application of the models to gauged basins is demonstrated by estimating the peak-flow distributions at the end of the study period (2018) and, using the panel model, to the study basins as-if-ungauged by using leave-one-out cross validation, estimating the fixed effects using static basin characteristics, and parameterizing the water-balance model discharge using median parameters. The errors of the quantiles predicted as-if-ungauged approximately doubled compared to the errors of the fitted panel model.
","language":"English","publisher":"MDPI","doi":"10.3390/hydrology12050119","usgsCitation":"Over, T.M., Marti, M.K., and Podzorski, H.L., 2025, Regional analysis of the dependence of peak-flow quantiles on climate with application to adjustment to climate trends: Hydrology, v. 12, no. 5, 119, 43 p., https://doi.org/10.3390/hydrology12050119.","productDescription":"119, 43 p.","ipdsId":"IP-167316","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":487957,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/hydrology12050119","text":"Publisher Index Page"},{"id":486509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marti, Mackenzie K. 0000-0001-8817-4969 mmarti@usgs.gov","orcid":"https://orcid.org/0000-0001-8817-4969","contributorId":289738,"corporation":false,"usgs":true,"family":"Marti","given":"Mackenzie","email":"mmarti@usgs.gov","middleInitial":"K.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Podzorski, Hannah Lee 0000-0001-5204-2606 hpodzorski@usgs.gov","orcid":"https://orcid.org/0000-0001-5204-2606","contributorId":333626,"corporation":false,"usgs":true,"family":"Podzorski","given":"Hannah","email":"hpodzorski@usgs.gov","middleInitial":"Lee","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938197,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}