Salinity levels in streams and tributaries of the Colorado River Basin have been a major concern for the United States and Mexico for over 50 years as the water is used by millions of people for domestic and industrial purposes. Recently, the United States Geological Survey expanded stream monitoring networks including the number of sites where continuous (15-min) specific conductance is measured in the Colorado River Headwaters and Gunnison River Basin located east of the Colorado-Utah state line (hereafter, UCOL). The purpose of this study is to apply a proxy method to determine salinity and total dissolved solids concentrations from specific conductance and major-ion water type that is applicable to monitoring sites in the UCOL. Within the UCOL, carbonate rich waters originate from high-elevation mountain regions in the eastern UCOL, calcium sulfate rich waters are mainly found in the western half of the UCOL including the Gunnison River Basin, and waters of variable composition are found along the lower reaches of the Colorado River and Eagle River. It was found that the chemistry of sites with variable composition changes seasonally and is impacted by both geogenic and anthropogenic processes, potentially including seasonal application of deicing road salt. The specific conductance – water type proxy can be used to reliably (±10 %) predict salinity and total dissolved solids at 66 monitoring sites in the UCOL. The method is rapid, can generate high-resolution measurements, is cost-effective, and greatly expands the utility of specific conductance measurements. Furthermore, the high-resolution estimates provide an accurate approach to determining long-term salinity loads as short-term events are accurately accounted for.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2025.106358","usgsCitation":"McCleskey, R., Cravotta, C., Miller, M., Chapin, T.W., Tillman, F.D., and Keith, G.L., 2025, Specific conductance and water type as a proxy model for salinity and total dissolved solids measurements in the Upper Colorado River Basin: Applied Geochemistry, v. 184, 106358, 11 p., https://doi.org/10.1016/j.apgeochem.2025.106358.","productDescription":"106358, 11 p.","ipdsId":"IP-170952","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":483579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.8482146743099,\n 40.404889992338354\n ],\n [\n -109.03080773170848,\n 40.404889992338354\n ],\n [\n -109.03080773170848,\n 38.16700844876755\n ],\n [\n -104.8482146743099,\n 38.16700844876755\n ],\n [\n -104.8482146743099,\n 40.404889992338354\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"184","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCleskey, R. 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Monitoring snow-water quality can inform on the effects to the albedo and energy balance of the snowpack, and the sources of natural and anthropogenic aerosol and gases. This study analyzed metals in the seasonal snowpack from water year (WY) 2018 for 49 sites. Calcium, lanthanum, and cerium concentrations support the importance of mineral dust to the southern Rocky Mountains. Mercury (Hg), zinc (Zn), and cadmium (Cd) concentrations showed a similar spatial pattern to mineral dust, whereas antimony (Sb) concentrations were highest in the northern Rocky Mountains. To assess the relative contributions from dust versus anthropogenic contaminant sources, enrichment factors (EF) were calculated, with values above 10 indicating anthropogenic contamination. For Cd, Hg, Sb, and Zn, EF values exceeded 10 at northern sites. These observations were compared to spatial trends of EF values of Hg from WY2009 to WY2018, regional monitoring networks, and back trajectory analyses. The agreement between these datasets revealed temporally consistent contaminant sources and/or transport processes to the northern Rocky Mountains snowpack. Sources include current and historical mining and smelting in the region. Strategies to limit the emissions of these metals to the Northern Rockies could benefit from focusing on remediation of contaminated sites, and continued monitoring and mitigation of active mining and smelting.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2025.126094","usgsCitation":"Arienzo, M., Gleason, K., Sexstone, G., Sexauer Gustin, M., Schwan, M., Choma, N., Dunham-Cheatham, S., McConnell, J.R., Weisberg, P., and Csank, A., 2025, Latitudinal gradients of snow contamination in the Rocky Mountains associated with anthropogenic sources: Environmental Pollution, v. 373, 126094, 12 p., https://doi.org/10.1016/j.envpol.2025.126094.","productDescription":"126094, 12 p.","ipdsId":"IP-173899","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":488496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2025.126094","text":"Publisher Index Page"},{"id":484909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado , Idaho, Montana, New Mexico, Utah, Wyoming","otherGeospatial":"Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116,\n 49\n ],\n [\n -116,\n 36\n ],\n [\n -105,\n 36\n ],\n [\n -105,\n 49\n ],\n [\n -116,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"373","noUsgsAuthors":false,"publicationDate":"2025-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Arienzo, Monica","contributorId":191065,"corporation":false,"usgs":false,"family":"Arienzo","given":"Monica","affiliations":[],"preferred":false,"id":934289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gleason, Kelly","contributorId":244890,"corporation":false,"usgs":false,"family":"Gleason","given":"Kelly","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":934290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934291,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sexauer Gustin, Mae","contributorId":353670,"corporation":false,"usgs":false,"family":"Sexauer Gustin","given":"Mae","affiliations":[{"id":84456,"text":"University of Nevada Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":934292,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schwan, Melissa","contributorId":353671,"corporation":false,"usgs":false,"family":"Schwan","given":"Melissa","affiliations":[{"id":84456,"text":"University of Nevada Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":934293,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Choma, Nicole","contributorId":353672,"corporation":false,"usgs":false,"family":"Choma","given":"Nicole","affiliations":[{"id":84456,"text":"University of Nevada Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":934294,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dunham-Cheatham, Sarrah","contributorId":353673,"corporation":false,"usgs":false,"family":"Dunham-Cheatham","given":"Sarrah","affiliations":[{"id":84456,"text":"University of Nevada Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":934295,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McConnell, Joseph R. 0000-0001-9051-5240","orcid":"https://orcid.org/0000-0001-9051-5240","contributorId":288526,"corporation":false,"usgs":false,"family":"McConnell","given":"Joseph","email":"","middleInitial":"R.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":934296,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Weisberg, Peter","contributorId":225093,"corporation":false,"usgs":false,"family":"Weisberg","given":"Peter","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":934297,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Csank, Adam","contributorId":295955,"corporation":false,"usgs":false,"family":"Csank","given":"Adam","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":934298,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70272248,"text":"70272248 - 2025 - Decadal stability in stream fish communities and contemporary ecological drivers of species occupancy in two Appalachian U.S. National Parks","interactions":[],"lastModifiedDate":"2025-11-20T16:04:16.55833","indexId":"70272248","displayToPublicDate":"2025-03-18T08:53:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Decadal stability in stream fish communities and contemporary ecological drivers of species occupancy in two Appalachian U.S. National Parks","docAbstract":"Objective
Although conserving fish biodiversity in lotic systems is challenging, protected areas can provide refuge from certain environmental stressors. In the Appalachian region, USA, the National Park Service manages Delaware Water Gap National Recreation Area (DEWA) and New River Gorge National Park & Preserve (NERI), which contain abundant and diverse freshwater resources. To assess the effectiveness of these protected areas in conserving stream fishes, we evaluated decadal changes and ecological drivers of species occupancy and detection.
Methods
Using fish assemblage data from backpack electrofishing surveys conducted in both parks during 2013–2014 and 2022–2023, we quantified temporal differences in species occupancy and detection probabilities using a Bayesian hierarchical multispecies occupancy modeling approach. For the 2022–2023 survey, we included habitat variables as predictors of occupancy and detection.
Results
Community composition and occupancy probabilities for species in both parks remained similar through time, with the most recent occupancy estimates ranging from 0.07 (90% CI = 0.02, 0.14) for Variegate Darter Etheostoma variatum and Rainbow Darter E. caeruleum to 0.73 (90% credible interval = 0.59, 0.85) for Blacknose Dace Rhinichthys atratulus. Changes in occupancy were more prominent at Delaware Water Gap National Recreation Area than New River Gorge National Park & Preserve, with Yellow Perch Perca flavescens having a posterior mean difference of −0.17 [90% credible interval = −0.35, −0.01] and American Eel Anguilla rostrata having a high posterior probability (>80%) of occupancy increasing by at least 1%. Habitat variables were related to community structure, but effects varied in significance, magnitude, and direction among species and parks. Conversely, species-specific detection probabilities were comparatively less affected by environmental and sampling effort predictors.
Conclusions
Between 2013 and 2023, occupancy estimates for 44 fish species across two protected, ecologically diverse landscapes remained relatively stable. Furthermore, we highlight the efficacy of national parks in maintaining freshwater fish biodiversity amidst rapid global change.
Historical and ongoing anthropogenic activities coupled with advancements in analytical techniques have led to the detection of large numbers of contaminants in the Laurentian Great Lakes. Consequently, identifying and prioritizing chemicals likely to cause ecological harm represents a challenge for natural resource managers. Previous prioritization efforts have focused on contaminants in sediment, water, and passive samplers, which may not be representative of compounds that bioaccumulate in aquatic organisms. Consequently, this study adopted a stepwise method to prioritize chemicals of potential concern detected in dreissenid mussels from samples collected across the Great Lakes from 2009–2018. The stepwise method considered environmental fate, detection frequency, and exceedance of toxicity quotients based on ecotoxicological effect concentrations. Overall, 153 compounds out of 267 analyzed were detected in dreissenid mussels, 47 of which had water quality effect concentrations, 56 had apical effect concentrations (Tier 1 ECOTOX or apical screening), 17 had nonapical effect concentrations (Tier 2 ECOTOX, Cytotoxic Burst, and ToxCast) and 33 had estimated effect concentrations (quantitative structure-activity relationship, estimated screening, and pharmacological potency). Of the compounds with water quality effect concentrations, nine were designated as high priority, including the herbicide atrazine and five polycyclic aromatic hydrocarbons that were previously identified as potentially hazardous within other matrices. Similar contaminants were identified as high priority in a related study of native unionid mussels in the Great Lakes. A total of 27 compounds were low priority, suggesting that these contaminants do not warrant further action based on this dataset. Overall, these findings will facilitate the development of management strategies to mitigate the effects of contaminants on aquatic organisms within the Great Lakes.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/etojnl/vgaf072","usgsCitation":"Fuller, N., Kimbrough, K., Edwards, M., Maloney, E., Corsi, S., Pronschinske, M.A., DeCicco, L., Frisch, J.R., Baldwin, A.K., Hummel, S.L., Vinas, N., and Villeneuve, D.L., 2025, Leveraging invasive mussel contaminant survey data for stepwise prioritization of chemicals of potential concern in the Great Lakes basin: Environmental Toxicology and Chemistry, v. 44, no. 7, p. 2070-2087, https://doi.org/10.1093/etojnl/vgaf072.","productDescription":"18 p.","startPage":"2070","endPage":"2087","ipdsId":"IP-153495","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501610,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/etojnl/vgaf072","text":"Publisher Index 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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255007","usgsCitation":"Fienen, M.N., Schachter, L.A., and Hunt, R.J., 2025, A model uncertainty quantification protocol for evaluating the value of observation data: U.S. Geological Survey Scientific Investigations Report 2025–5007, 12 p., https://doi.org/10.3133/sir20255007.","productDescription":"vi; 12 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-171702","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":483399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5007/sir20255007.pdf","text":"Report","size":"7.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025–5007"},{"id":483398,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5007/coverthb.jpg"},{"id":483404,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255007/full","text":"Report"},{"id":483400,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5007/sir20255007.XML","text":"Report","description":"SIR 2025–5007"},{"id":483403,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5007/images"},{"id":492790,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118495.htm","linkFileType":{"id":5,"text":"html"}}],"contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, Wisconsin 53726
Population monitoring is essential to document recovery efforts for threatened and endangered species. Mexican wolves (Canis lupus baileyi) are an endangered subspecies of gray wolves that historically occupied large portions of the American Southwest and Mexico. Recently, the Mexican wolf population in the United States has been growing rapidly and traditional approaches for population monitoring (e.g., capture and radio collaring) are becoming difficult and expensive as wolves expand into new areas. We developed predictive models of pup-rearing habitat (i.e., den and rendezvous sites) that could help guide future population monitoring efforts. We located 255 den sites and 129 rendezvous sites in Arizona and New Mexico, USA (1998–2023) using tracking collars and site visits. We sampled habitat conditions in wolf-occupied regions of Arizona and New Mexico and fit logistic regressions to these data following a use–available study design to estimate resource selection functions (RSF) for den and rendezvous sites. We hypothesized wolves would select areas that offered greater physical protection, lower human-disturbance, and access to reliable water sources for pup-rearing but that the relative importance of these features would differ between the denning and rendezvous site seasons. Mexican wolves selected den sites at higher elevations in steeper and rougher terrain that were closer to permanent waterbodies but farther from rural roads. Selection of rendezvous sites was also associated with higher elevations and proximity to waterbodies but varied with availability of green leaf biomass on the landscape. While still highly predictive, our rendezvous site model was less predictive than our den model (Spearman's correlation averaged 0.81 [SE = 0.05] vs. 0.90 [SE = 0.03], respectively), possibly because water and green leaf biomass are more spatially diffuse and variable because of monsoonal rains during the rendezvous site season. Our results suggest that terrain features associated with physical protection and access to reliable water were most important in characterizing suitable pup-rearing habitat for Mexican wolves. By predicting suitable den and rendezvous site habitat across portions of the Mexican Wolf Experimental Population Area, our models can help guide future population monitoring by reducing the total search area when surveying for wolves and increase the probability of detecting all members of a pack.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70017","usgsCitation":"Bassing, S., Oakleaf, J., Cain, J.W., Greenleaf, A., Gardner, C., and Ausband, D.E., 2025, Predicting pup-rearing habitat for Mexican wolves: Journal of Wildlife Management, v. 89, no. 5, e70017, 19 p., https://doi.org/10.1002/jwmg.70017.","productDescription":"e70017, 19 p.","ipdsId":"IP-169774","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486238,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":488960,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.70017","text":"Publisher Index Page"}],"country":"United States","state":"Arizona, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.15656068155297,\n 37.15236143729469\n ],\n [\n -114.79334387076136,\n 36.19839033323856\n ],\n [\n -114.82092420356022,\n 32.28431539821898\n ],\n [\n -111.22371663071222,\n 31.529018437535207\n ],\n [\n -108.37781874007106,\n 31.259432273304338\n ],\n [\n -108.00163404937481,\n 31.805280742585737\n ],\n [\n -103.03648352957026,\n 31.93273826397835\n ],\n [\n -103.13422840316065,\n 37.15236143729469\n ],\n [\n -114.15656068155297,\n 37.15236143729469\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Bassing, Sarah B.","contributorId":355638,"corporation":false,"usgs":false,"family":"Bassing","given":"Sarah B.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oakleaf, John K.","contributorId":355639,"corporation":false,"usgs":false,"family":"Oakleaf","given":"John K.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937836,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greenleaf, Allison R.","contributorId":355640,"corporation":false,"usgs":false,"family":"Greenleaf","given":"Allison R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937837,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gardner, Colby M.","contributorId":355641,"corporation":false,"usgs":false,"family":"Gardner","given":"Colby M.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937838,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937839,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274725,"text":"70274725 - 2025 - Ordovician stratigraphy, structure, and karst of the Falling Spring Valley, Alleghany County, Virginia, USA","interactions":[],"lastModifiedDate":"2026-04-08T14:35:10.261331","indexId":"70274725","displayToPublicDate":"2025-03-17T09:14:01","publicationYear":"2025","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Ordovician stratigraphy, structure, and karst of the Falling Spring Valley, Alleghany County, Virginia, USA","docAbstract":"This one-day trip highlights new findings on a preliminary bedrock geologic map that shows results from ongoing geologic mapping in the Falling Spring Valley of Alleghany County, Virginia, USA, which is the southern end of the larger Warm Springs Valley, an elongated anticlinal valley rimmed by Ordovician and Silurian siliciclastic rocks, and which is famous for its thermal springs. This mapping includes stratigraphic, structural, and karst field and lab research focused on the Ordovician strata exposed in the area, the oldest of which is the dolomitic upper part of the Beekmantown Formation (Lower Ordovician, Darriwilian), and the youngest of which is the Juniata Formation (Upper Ordovician, Katian), a sequence of siliciclastic redbeds. Warm Springs Valley is the location of the only known caves in the eastern United States—three at present—with thermal waters flowing in some of their passages. Stops on the trip will highlight key details from mapping efforts, primarily within the structurally deformed Ordovician carbonate sequence that is exposed in the core and limbs of the anticline, as well as the associated karst features that are developed in those carbonate rocks, including results of recent dye traces and water temperature monitoring that have improved our understanding of the karst hydrogeologic systems developed in these strata.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From the Ozark Plateaus and Arkansas River Valley to the Shenandoah Valley: Field guides for the 2025 Southeastern and South-Central Section Meetings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2025.0072(05)","usgsCitation":"Haynes, J.T., Lambert, R.A., Martin, D.C., Orndorff, R.C., and Parker, M., 2025, Ordovician stratigraphy, structure, and karst of the Falling Spring Valley, Alleghany County, Virginia, USA, chap. of From the Ozark Plateaus and Arkansas River Valley to the Shenandoah Valley: Field guides for the 2025 Southeastern and South-Central Section Meetings, v. 72, p. 69-91, https://doi.org/10.1130/2025.0072(05).","productDescription":"23 p.","startPage":"69","endPage":"91","ipdsId":"IP-175668","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":502268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","county":"Alleghany County","otherGeospatial":"Falling Spring Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.875,\n 38\n ],\n [\n -80,\n 38\n ],\n [\n -80,\n 37.75\n ],\n [\n -79.875,\n 37.75\n ],\n [\n -79.875,\n 38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"72","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"editors":[{"text":"Admassu, Yonathan","contributorId":369433,"corporation":false,"usgs":false,"family":"Admassu","given":"Yonathan","affiliations":[],"preferred":false,"id":958958,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Garcia, Ángel","contributorId":369434,"corporation":false,"usgs":false,"family":"Garcia","given":"Ángel","affiliations":[],"preferred":false,"id":958959,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Hutto, Richard","contributorId":369435,"corporation":false,"usgs":false,"family":"Hutto","given":"Richard","affiliations":[],"preferred":false,"id":958960,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Haynes, John T.","contributorId":369314,"corporation":false,"usgs":false,"family":"Haynes","given":"John","middleInitial":"T.","affiliations":[{"id":16809,"text":"James Madison University","active":true,"usgs":false}],"preferred":false,"id":958859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lambert, Richard A.","contributorId":369315,"corporation":false,"usgs":false,"family":"Lambert","given":"Richard","middleInitial":"A.","affiliations":[{"id":87760,"text":"Warm Springs Anticline Cave Survey","active":true,"usgs":false}],"preferred":false,"id":958860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Delbert C.","contributorId":369316,"corporation":false,"usgs":false,"family":"Martin","given":"Delbert","middleInitial":"C.","affiliations":[{"id":87760,"text":"Warm Springs Anticline Cave Survey","active":true,"usgs":false}],"preferred":false,"id":958861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Orndorff, Randall C. 0000-0002-8956-5803 rorndorf@usgs.gov","orcid":"https://orcid.org/0000-0002-8956-5803","contributorId":2739,"corporation":false,"usgs":true,"family":"Orndorff","given":"Randall","email":"rorndorf@usgs.gov","middleInitial":"C.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":958862,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Parker, Mercer 0000-0001-6683-6458 mercerparker@usgs.gov","orcid":"https://orcid.org/0000-0001-6683-6458","contributorId":203174,"corporation":false,"usgs":true,"family":"Parker","given":"Mercer","email":"mercerparker@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":958863,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264626,"text":"70264626 - 2025 - Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry","interactions":[],"lastModifiedDate":"2025-03-18T16:47:13.564948","indexId":"70264626","displayToPublicDate":"2025-03-16T11:34:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry","docAbstract":"Continuous, high-resolution data for characterizing freshwater habitat conditions can support successful management of endangered salmonids. Uncrewed aircraft systems (UAS) make acquiring such fine-scale data along river channels more feasible, but workflows for quantifying reach-scale salmon habitats are lacking. We evaluated the potential for UAS-based mapping of hydraulic habitats using spectrally based depth retrieval and particle image velocimetry (PIV) by comparing these methods to a more well-established flow modeling approach. Our results indicated that estimates of water depth, depth-averaged velocity, and flow direction derived via remote sensing and modeling techniques were comparable and in good agreement with field measurements. Predictions of spring-run Chinook salmon (Oncorhynchus tshawytscha) juvenile rearing habitat produced from PIV and model output were similar, with small errors relative to direct field observations. Estimates of hydraulic heterogeneity based on kinetic energy gradients in the flow field were generally consistent between PIV and flow modeling, but errors relative to field measurements were larger. PIV results were sensitive to the velocity index (α) used to convert surface velocities to depth-averaged velocities. Sun glint precluded PIV analysis along the margins of some images and a large degree of overlap between frames was thus required to obtain continuous coverage of the reach. Similarly, shadows cast by riparian vegetation caused gaps in spectrally based bathymetric maps. Despite these limitations, our results suggest that for sites with sufficient water surface texture, UAS-based PIV can provide detailed hydraulic habitat information at the reach scale, with accuracies comparable to traditional field methods and multidimensional flow modeling.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR038045","usgsCitation":"Harrison, L.R., Legleiter, C.J., Overstreet, B., and White, J., 2025, Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry: Water Resources Research, v. 3, no. 61, e2024WR038045, 21 p., https://doi.org/10.1029/2024WR038045.","productDescription":"e2024WR038045, 21 p.","ipdsId":"IP-163184","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":488333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr038045","text":"Publisher Index Page"},{"id":483481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"North Santiam River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.01923518970605,\n 44.68226153883313\n ],\n [\n -122.36818028867839,\n 44.68226153883313\n ],\n [\n -122.36818028867839,\n 44.857748774184074\n ],\n [\n -123.01923518970605,\n 44.857748774184074\n ],\n [\n -123.01923518970605,\n 44.68226153883313\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"61","noUsgsAuthors":false,"publicationDate":"2025-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":930994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":930995,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overstreet, Brandon 0000-0001-7845-6671 boverstreet@usgs.gov","orcid":"https://orcid.org/0000-0001-7845-6671","contributorId":169201,"corporation":false,"usgs":true,"family":"Overstreet","given":"Brandon","email":"boverstreet@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930996,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, James 0000-0002-7255-3785 jameswhite@usgs.gov","orcid":"https://orcid.org/0000-0002-7255-3785","contributorId":193492,"corporation":false,"usgs":true,"family":"White","given":"James","email":"jameswhite@usgs.gov","affiliations":[],"preferred":true,"id":930997,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264651,"text":"70264651 - 2025 - Dynamic baseflow storage estimates and the role of topography, geology and evapotranspiration on streamflow recession characteristics in the Neversink Reservoir Watershed, New York","interactions":[],"lastModifiedDate":"2025-03-18T16:31:27.341468","indexId":"70264651","displayToPublicDate":"2025-03-15T11:10:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic baseflow storage estimates and the role of topography, geology and evapotranspiration on streamflow recession characteristics in the Neversink Reservoir Watershed, New York","docAbstract":"Estimates of dynamic groundwater volumes supplying baseflow to streams are important for water availability projections during extended periods of drought. The primary goals of this study were to provide dynamic storage volume estimates, inferred from streamflow recession analysis, for baseflow regimes within seven gaged catchments within the Neversink Reservoir Watershed (NRW), a critical municipal water source for New York City. Additionally, geomorphological properties, surficial geology and hydro-meteorological processes were quantified and described in relation to time and spatially variable recession behaviour and storage estimates across the NRW. To explore these relationships, we (1) evaluated seasonal trends in streamflow recession behaviour in relation to modelled potential evapotranspiration (PET) and catchment runoff rates, (2) derived empirical streamflow models for cool-season runoff using both linear and nonlinear reservoir assumptions for baseflow and (3) calculated metrics related to the geology and geomorphology of each catchment and compared these metrics to area normalised baseflow dynamic storage estimates. Results show that baseflow recession behaves as a nonlinear reservoir, and applying linear groundwater reservoir assumptions may underestimate the total dynamic storage volumes compared to what would be predicted for a nonlinear reservoir. Increases in PET caused decreases in storage conditions that resulted in increased recession rates and nonlinearity in streamflow recession during the growing season. Additionally, we found that while no single physical catchment characteristic solely predicted catchment storage dynamics, sediment volume and stream gradients were stronger predictors of normalised storage volumes than catchment surface area or surface topography alone. Within the NRW, catchments with the highest sediment volume exhibited the lowest recession rates and higher dynamic storage volumes, while the smallest catchment, mostly devoid of sediment, had the fastest recession rate and lowest dynamic storage volume.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70106","usgsCitation":"Benton, J., and Doctor, D.H., 2025, Dynamic baseflow storage estimates and the role of topography, geology and evapotranspiration on streamflow recession characteristics in the Neversink Reservoir Watershed, New York: Hydrological Processes, v. 39, no. 3, e70106, 17 p., https://doi.org/10.1002/hyp.70106.","productDescription":"e70106, 17 p.","ipdsId":"IP-163356","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":483480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Neversink Reservoir Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.95860919620127,\n 42.2147005922842\n ],\n [\n -74.56712312527952,\n 42.2147005922842\n ],\n [\n -74.56712312527952,\n 41.956641367777735\n ],\n [\n -73.95860919620127,\n 41.956641367777735\n ],\n [\n -73.95860919620127,\n 42.2147005922842\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Benton, Joshua R. 0000-0002-1698-6455","orcid":"https://orcid.org/0000-0002-1698-6455","contributorId":352387,"corporation":false,"usgs":false,"family":"Benton","given":"Joshua R.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":84197,"text":"**please fill in","active":true,"usgs":false}],"preferred":false,"id":931072,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931073,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264474,"text":"sir20255011 - 2025 - Comparison of hydrologic data and water budgets between 2003–08 and 2018–23 for the eastern part of the Arbuckle-Simpson aquifer, south-central Oklahoma","interactions":[],"lastModifiedDate":"2025-07-23T17:07:13.510916","indexId":"sir20255011","displayToPublicDate":"2025-03-14T15:26:55","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":"2025-5011","displayTitle":"Comparison of Hydrologic Data and Water Budgets Between 2003–08 and 2018–23 for the Eastern Part of the Arbuckle-Simpson Aquifer, South-Central Oklahoma","title":"Comparison of hydrologic data and water budgets between 2003–08 and 2018–23 for the eastern part of the Arbuckle-Simpson aquifer, south-central Oklahoma","docAbstract":"The Arbuckle-Simpson aquifer is divided spatially into three parts (eastern, central, and western). The largest groundwater withdrawals are from the eastern part of the Arbuckle-Simpson aquifer, which provides water to approximately 39,000 people in Ada and Sulphur, Oklahoma, and surrounding areas. The Arbuckle-Simpson aquifer, including the eastern part, is designated a sole source aquifer for its service area. Based primarily on data collected between 2003 and 2008, a series of comprehensive hydrologic studies of the Arbuckle-Simpson aquifer was published to provide the information necessary to perform groundwater-flow model simulations so that the Oklahoma Water Resources Board could determine how much water could be withdrawn from the aquifer while maintaining flow to springs and streams. As part of the Phase 1 studies, an aquifer water budget was developed from a numerical model for the period 2003–08. For this report, Phase 1 refers to the 2003–08 data collection period, although for some of the analyses, data collected prior to 2003 were used to inform model development work. Allocation of water from this aquifer was then established by the Oklahoma Water Resources Board in 2013. Additional well-spacing rules were also established by the Oklahoma Water Resources Board for sensitive sole source groundwater basins. To determine how the water budget for the eastern part of the Arbuckle-Simpson aquifer has changed over time, recently collected hydrologic data (2018–23) were compared to data collected during 2003–08. The analysis of changes in the aquifer water budget from 2003–08 to 2018–23 could help resource managers better understand changes in the overall balance of water in storage and the potential effects on streamflow, changes in groundwater levels, and the effects of different water uses in the aquifer area on available water in the eastern part of the Arbuckle-Simpson aquifer and streams overlying the eastern part of the Arbuckle-Simpson aquifer.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"The U.S. Geological Survey, in cooperation with the Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and the Wyoming Water Development Office, has developed standard methods of peak-flow frequency analysis for studies in Montana, North Dakota, South Dakota, and Wyoming. These methods describe the implementation of national flood frequency guidelines described in Bulletin 17C (https://doi.org/10.3133/tm4B5) for the four States and deviations from Bulletin 17C standard procedures to accommodate unusual hydrologic conditions. A U.S. Geological Survey data release accompanying this report (https://doi.org/10.5066/P1WHRK8H) provides example peak-flow frequency analyses for selected streamgages in the study area. The methods described in this report can be used to publish similar data releases for other streamgages in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255019","collaboration":"Prepared in cooperation with the Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wyoming Water Development Office","usgsCitation":"Siefken, S.A., Williams-Sether, T., Barth, N.A., Chase, K.J., and Cedar Face, M.A., 2025, Methods for peak-flow frequency analysis for streamgages in or near Montana, North Dakota, South Dakota, and Wyoming: U.S. Geological Survey Scientific Investigations Report 2025–5019, 19 p., https://doi.org/10.3133/sir20255019.","productDescription":"Report: vii, 19 p.; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148119","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":483328,"rank":4,"type":{"id":34,"text":"Image 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The U.S. Geological Survey, in cooperation with the Suffolk County Water Authority, evaluated the aquifer transmissivity and storage properties at the Alvahs Lane well field north of the village of Cutchogue, New York. This analysis of aquifer properties provides the Suffolk County Water Authority with hydrogeologic information needed to develop water supplies to meet the increasing water demands of the residents of Suffolk County, New York.
An aquifer test was conducted at the Alvahs Lane well field from October 18 through October 21, 2022, when a production well was pumped at 550 gallons per minute for about 24 hours, and groundwater-level drawdown and recovery were measured in two monitoring wells. The three wells are screened in a glaciofluvial aquifer under unconfined (water table) conditions. Drawdown and recovery data were analyzed with an analytical solution for partial penetration and delayed yield in an unconfined aquifer to provide estimates of the glaciofluvial aquifer properties. Inclusion of lateral aquifer boundaries was not necessary for the analysis to result in satisfactory matches with the observed water-level responses. Aquifer transmissivity was estimated at 32,000 feet squared per day. Assuming a saturated aquifer thickness of 120 feet, this result is equivalent to a horizontal hydraulic conductivity value of 270 feet per day. Specific yield was estimated at 0.15 (dimensionless). The estimated properties are consistent with those of a highly transmissive unconfined aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245128","collaboration":"Prepared in cooperation with the Suffolk County Water Authority","usgsCitation":"Misut, P.E., 2025, Analysis of aquifer framework and properties, Alvahs Lane well field, Cutchogue, New York: U.S. Geological Survey Scientific Investigations Report 2024–5128, 9 p., https://doi.org/10.3133/sir20245128.","productDescription":"iv, 9 p.","numberOfPages":"9","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154731","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":492784,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118489.htm","linkFileType":{"id":5,"text":"html"}},{"id":483275,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5128/images/"},{"id":483274,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5128/sir20245128.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5128 XML"},{"id":483273,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/preview/sir20245128/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5128 HTML"},{"id":483272,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5128/sir20245128.pdf","text":"Report","size":"2.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5128 PDF"},{"id":483271,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5128/coverthb.jpg"}],"country":"United States","state":"New York","city":"Cutchogue","otherGeospatial":"Alvahs Lane well field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.5463729228096,\n 41.03435851242847\n ],\n [\n -72.5463729228096,\n 40.987047126294755\n ],\n [\n -72.46023151369637,\n 40.987047126294755\n ],\n [\n -72.46023151369637,\n 41.03435851242847\n ],\n [\n -72.5463729228096,\n 41.03435851242847\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Excessive nitrogen discharge is a major concern for the Long Island Sound. Programs have been implemented to reduce point sources of nitrogen to the sound, but little is known about the nonpoint sources. This study aims to better understand the current groundwater contributions of nitrogen from nonpoint sources in the Long Island Sound watershed.
During the spring and summer of 2022, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, collected water-quality samples to analyze nutrients (nitrogen and phosphorus), chloride, and bromide at 45 stations in the Long Island Sound watershed in Connecticut, New York, and Rhode Island. The stations were in small drainage watersheds (5 to 30 square kilometers) in the southern part of the Long Island Sound watershed. During two separate synoptic sampling events, water-quality samples and instantaneous streamflow measurements were collected under base-flow conditions (where the streamflow is dominated by groundwater inputs rather than overland flow or runoff flow). One sampling event was in the nongrowing season (April 24–25, 2022), and the other was in the growing season (June 30–July 1, 2022). To calculate instantaneous nitrogen loads and yields, streamflow was measured at the time of sample collection.
Nitrogen concentrations, loads, and yields varied among sampling stations and by season. Total filtered nitrogen concentrations were generally lower in the nongrowing season (from less than 0.14 to 1.9 milligrams per liter) than in the growing season (from less than 0.23 to 3.0 milligrams per liter). Nitrate plus nitrite concentrations showed little variation between the nongrowing and growing seasons. Unfiltered ammonia plus organic nitrogen concentrations were generally lower in the nongrowing season (from less than 0.07 to 0.83 milligram per liter) than in the growing season (from 0.11 to 0.98 milligram per liter). In contrast, total filtered and unfiltered nitrogen loads and yields were higher in the nongrowing season than during the growing season, likely because streamflows were higher during the nongrowing season. Total unfiltered nitrogen yields during the nongrowing season ranged from less than 0.15 to 5.0 kilograms per square kilometer per day. Total unfiltered nitrogen yields during the growing season ranged from less than 0.12 to 2.5 kilograms per square kilometer per day. Total filtered nitrogen yields during the nongrowing season ranged from less than 0.13 to 5.2 kilograms per square kilometer per day. Total filtered nitrogen yields during the growing season ranged from less than 0.06 to 2.5 kilograms per square kilometer per day.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1206","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Laabs, K.L., Barclay, J.R., and Mullaney, J.R., 2025, Base-flow sampling to enhance understanding of the groundwater flow component of nitrogen loading in small watersheds draining into Long Island Sound: U.S. Geological Survey Data Report 1206, 23 p., https://doi.org/10.3133/dr1206.","productDescription":"Report: v, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161925","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":492783,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118484.htm","linkFileType":{"id":5,"text":"html"}},{"id":483188,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1206/full","linkFileType":{"id":5,"text":"html"},"description":"DR 1206 HTML"},{"id":483186,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1206/coverthb.jpg"},{"id":483187,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1206/dr1206.pdf","text":"Report","size":"7.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1206 PDF"},{"id":483189,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1206/dr1206.XML","linkFileType":{"id":8,"text":"xml"},"description":"DR 1206 XML"},{"id":483190,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1206/images/"},{"id":483191,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99IAXUI","text":"USGS data release","linkHelpText":"Nitrogen loads, yields, and associated field data collected during baseflow conditions and site attributes for small basins draining to Long Island Sound"}],"country":"United States","state":"Connecticut, New York, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.91277546117404,\n 40.875888202775144\n ],\n [\n -73.5833363685162,\n 40.98693216297761\n ],\n [\n -72.40741106161018,\n 41.2504046305547\n ],\n [\n -71.4646381344502,\n 41.35702020040523\n ],\n [\n -71.40893145316154,\n 41.57350712794573\n ],\n [\n -71.49014822027934,\n 41.724945464467424\n ],\n [\n -72.43277552970126,\n 41.78157938825299\n ],\n [\n -72.757106016586,\n 41.53934120640511\n ],\n [\n -73.08115837726446,\n 41.93608782758082\n ],\n [\n -73.30909534441503,\n 41.8870827192506\n ],\n [\n -73.51694453574096,\n 41.73992349657644\n ],\n [\n -73.50192428145502,\n 41.280885844515296\n ],\n [\n -73.88228764759546,\n 40.963994488494365\n ],\n [\n -73.91277546117404,\n 40.875888202775144\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
In June 2022, a historic flood event occurred in the headwaters of the Yellowstone River Basin. The flood resulted in millions of dollars in damages and substantial interruptions to Yellowstone National Park. The 2022 flood event was substantially higher in magnitude than other high-peak flow events over the last 30 years. The high discharge was primarily due to the combination of hydrologic mechanisms initiated by rain-on-snow, including a high-elevation snowpack that peaked later than average. However, the contributions of each hydrologic driver, rain and snow, have not been quantified and could be important for understanding future flood events in the region. The contribution of snowmelt to the total terrestrial water input (TWI) varied throughout the area, yet was concentrated in the headwaters of the Yellowstone, Stillwater, and Boulder rivers, along with the headwaters of Rock Creek in Wyoming and Montana. The primary atmospheric contributions to the TWI during the 2022 event were precipitation from moisture transported from the Pacific Ocean that converged over the Greater Yellowstone Area (GYA) and snowmelt from residual snowpack in the northeast part of Yellowstone National Park.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70099","usgsCitation":"Giovando, J., Reis, W., Zhang, W., and Barth, N.A., 2025, Hydrologic mechanisms for 2022 Yellowstone River flood and comparisons to recent historic floods: Hydrological Processes, v. 39, no. 3, e70099, 10 p., https://doi.org/10.1002/hyp.70099.","productDescription":"e70099, 10 p.","ipdsId":"IP-164578","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":483479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.8645863474894,\n 45.53668572000356\n ],\n [\n -111.8645863474894,\n 43.909029192960446\n ],\n [\n -108.86233230449172,\n 43.909029192960446\n ],\n [\n -108.86233230449172,\n 45.53668572000356\n ],\n [\n -111.8645863474894,\n 45.53668572000356\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Giovando, Jeremy","contributorId":352388,"corporation":false,"usgs":false,"family":"Giovando","given":"Jeremy","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":931074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reis, Wyatt","contributorId":352389,"corporation":false,"usgs":false,"family":"Reis","given":"Wyatt","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":931075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Wei","contributorId":352390,"corporation":false,"usgs":false,"family":"Zhang","given":"Wei","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":931076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barth, Nancy A. 0000-0002-7060-8244 nabarth@usgs.gov","orcid":"https://orcid.org/0000-0002-7060-8244","contributorId":298020,"corporation":false,"usgs":true,"family":"Barth","given":"Nancy","email":"nabarth@usgs.gov","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":931077,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264582,"text":"70264582 - 2025 - Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks","interactions":[],"lastModifiedDate":"2025-08-19T15:28:24.757921","indexId":"70264582","displayToPublicDate":"2025-03-13T10:03:11","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20503,"text":"Journal of Fish of Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks","title":"Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks","docAbstract":"The mummichog Fundulus heteroclitus is a trophically important fish inhabiting Atlantic coastal salt marshes, with few in situ estimates of overwinter survival throughout the species range. We estimated overwinter apparent survival rates of F. heteroclitus at the approximate mid-latitudinal species range [coastal North Carolina (USA)] in four tidal creeks that experience variable winter water temperatures. To estimate apparent survival, we fitted a Cormack-Jolly-Seber model to daily mark-resight data autonomously obtained from fish marked with passive integrated transponder tags. Creek, year, mean daily water temperature, change in mean daily temperature, fish length and fish condition were considered for effects on the modelled parameters: apparent survival (Φ) (product of true survival and site fidelity) and detection probability (p). Modelling showed that water temperature and fish metrics were not related to Φ. Water temperature was directly related to p, indicating reduced fish activity and thus reduced detection probability or poor antenna detection performance at low temperatures. Creek was related to Φ and p, and the creek most open to its downstream estuary (lacking a culvert) had lower rates than the others. Greater loss (fish mortality plus emigration) in this one creek may more effectively transfer production of F. heteroclitus to larger waterbodies via emigration or predation. Conversely, lower Φ may reflect reduced detection efficiency. The results suggest that F. heteroclitus survival is insensitive to variable winter water temperatures typical of thermal dynamics in shallow estuaries in this region of its range. Median creek-specific overwinter Φ rates (range of median values, 2 × 10−8, 0.04) were roughly equal to previously published rates for these creeks during the growing season (April–October). At these latitudes and with increasingly moderate winters, the results indicate that natural mortality could arise equally or more so from predation during the growing season than mechanisms such as starvation, direct mortality, thermal morbidity and stress-related susceptibility to predation resulting from intermittently low water temperatures during the overwinter season.
","language":"English","publisher":"Wiley","doi":"10.1111/jfb.70020","usgsCitation":"Rudershausen, P.J., and O'Donnell, M.J., 2025, Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks: Journal of Fish of Biology, v. 107, no. 1, p. 188-200, https://doi.org/10.1111/jfb.70020.","productDescription":"13 p.","startPage":"188","endPage":"200","ipdsId":"IP-168105","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":488323,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jfb.70020","text":"Publisher Index Page"},{"id":483454,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.61589538108844,\n 34.739737828981234\n ],\n [\n -76.80342045072118,\n 34.74057409854757\n ],\n [\n -76.80219234343787,\n 34.68681714274115\n ],\n [\n -76.61593931817359,\n 34.68548980814643\n ],\n [\n -76.61589538108844,\n 34.739737828981234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"107","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Rudershausen, P. J.","contributorId":352331,"corporation":false,"usgs":false,"family":"Rudershausen","given":"P.","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":930816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Donnell, Matthew J. 0000-0002-9089-2377","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":295467,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Matthew","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930817,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70269914,"text":"70269914 - 2025 - Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change","interactions":[],"lastModifiedDate":"2025-08-07T17:05:49.242218","indexId":"70269914","displayToPublicDate":"2025-03-13T09:31:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change","docAbstract":"1. Population ecology and biogeography applications often necessitate the transfer of models across spatial and/or temporal dimensions to make predictions outside the bounds of the data used for model fitting. However, ecological data are often spatiotemporally unbalanced such that the spatial or the temporal dimension tends to contain more data than the other. This unbalance frequently leads model transfers to become substitutions, which are predictions to a different dimension than the predictive model was built on. Despite the prevalence of substitutions in ecology, studies validating their performance and their underlying assumptions are scarce.
2. Here, we present a successful case study demonstrating both space-for-time and time-for-space substitutions using emperor penguins (Aptenodytes forsteri) as the focal species. Using abundance-based species distribution models (aSDM) of adult emperor penguins in attendance during spring across 50 colonies, we predict long-term annual fluctuations in fledgling abundance and breeding success at a single colony, Pointe Géologie. Subsequently, we construct statistical models from time series of extended counts on Pointe Géologie to predict average fledgling abundance across 50 colonies.
3. Our analysis reveals that distance to nearest open water (NOW) exhibits the strongest association with both temporal and spatial data. aSDM’s space-for-time substitution performance, as measured by Pearson correlation coefficient was 0.63 and 0.56 when predicting breeding success and fledgling abundance time series, respectively. Linear regression of fledgling abundance on NOW yields similar time-for-space substitution performance when predicting abundance distribution of emperor penguin colonies with a correlation coefficient of 0.58.
4. We posit that such space-time equivalence arises because: 1) emperor penguins colonies conform to their existing fundamental niche; 2) there is not yet any environmental novelty when comparing the spatial vs temporal variation of distance to nearest open water; and 3) models of more specific components of life histories, such as fledgling abundance, rather than occurrence or total population abundance, are more transferable. Identifying these conditions empirically can enhance the qualitative validation of substitutions in cases where direct validation data are lacking.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.70025","usgsCitation":"Şen, B., Che-Castaldo, C., LaRue, M., Krumhardt, K., Landrum, L., Holland, M., Lynch, H., Delord, K., Barbraud, C., and Jenouvrier, S., 2025, Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change: Journal of Animal Ecology, v. 94, no. 5, p. 932-942, https://doi.org/10.1111/1365-2656.70025.","productDescription":"11 p.","startPage":"932","endPage":"942","ipdsId":"IP-170330","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":496436,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.70025","text":"Publisher Index Page"},{"id":493730,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Pointe Géologie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 154.44140675588028,\n -67.88310899484881\n ],\n [\n 154.44140675588028,\n -70.08242540623479\n ],\n [\n 161.81002372233837,\n -70.08242540623479\n ],\n [\n 161.81002372233837,\n -67.88310899484881\n ],\n [\n 154.44140675588028,\n -67.88310899484881\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"94","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Şen, Bilgecan","contributorId":359058,"corporation":false,"usgs":false,"family":"Şen","given":"Bilgecan","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":944928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Che-Castaldo, Christian Joseph 0000-0002-7670-2178","orcid":"https://orcid.org/0000-0002-7670-2178","contributorId":347906,"corporation":false,"usgs":true,"family":"Che-Castaldo","given":"Christian Joseph","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":944929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaRue, Michelle A.","contributorId":348627,"corporation":false,"usgs":false,"family":"LaRue","given":"Michelle A.","affiliations":[{"id":37172,"text":"University of Canterbury","active":true,"usgs":false}],"preferred":false,"id":944930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krumhardt, Kristen M.","contributorId":359059,"corporation":false,"usgs":false,"family":"Krumhardt","given":"Kristen M.","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landrum, Laura","contributorId":359060,"corporation":false,"usgs":false,"family":"Landrum","given":"Laura","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944932,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holland, Marika M.","contributorId":359062,"corporation":false,"usgs":false,"family":"Holland","given":"Marika M.","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944933,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lynch, Heather J.","contributorId":347911,"corporation":false,"usgs":false,"family":"Lynch","given":"Heather J.","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":944934,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Delord, Karine 0000-0001-6720-951X","orcid":"https://orcid.org/0000-0001-6720-951X","contributorId":197702,"corporation":false,"usgs":false,"family":"Delord","given":"Karine","email":"","affiliations":[],"preferred":false,"id":944935,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Barbraud, Christophe","contributorId":197701,"corporation":false,"usgs":false,"family":"Barbraud","given":"Christophe","email":"","affiliations":[],"preferred":false,"id":944936,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jenouvrier, Stéphanie","contributorId":359063,"corporation":false,"usgs":false,"family":"Jenouvrier","given":"Stéphanie","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":944937,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70264321,"text":"sir20255002 - 2025 - Evaluating drought risk of the Red River of the North Basin using historical and stochastic streamflow upstream from Emerson, Manitoba","interactions":[],"lastModifiedDate":"2025-07-23T16:49:39.233509","indexId":"sir20255002","displayToPublicDate":"2025-03-12T13:16:46","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":"2025-5002","displayTitle":"Evaluating Drought Risk of the Red River of the North Basin Using Historical and Stochastic Streamflow Upstream from Emerson, Manitoba","title":"Evaluating drought risk of the Red River of the North Basin using historical and stochastic streamflow upstream from Emerson, Manitoba","docAbstract":"Drought and its effect on streamflow are important to understand because of the potential to adversely affect water supply, agricultural production, and ecological conditions. The Red River of the North Basin in north-central United States and central Canada is susceptible to dry conditions. During an extended drought, streamflow conditions in the Red River of the North may become inadequate to support existing water supply needs in the basin for agriculture, industry, human use, and aquatic life. To understand potential future low-streamflow conditions in the Red River of the North Basin, the U.S. Geological Survey, in cooperation with the International Joint Commission, North Dakota Department of Water Resources, Red River Joint Water Resource District, and Red River Watershed Management Board, developed a water-balance model of the Red River of the North Basin upstream from Emerson, Manitoba, Canada, and coupled the model with stochastic weather inputs to simulate possible future low-streamflow conditions.
Historical changes in low-streamflow conditions were characterized across the Red River of the North Basin using multiple change-point analysis for 12 streamgages. Across these stations, significant change-point years in 1943 and 1994 marked increases in the magnitude of low-streamflow conditions. During 1920–2015, conversion of primary land (not affected by human use) to agricultural and secondary land was followed by a conversion from smalls grains to corn and soybeans as the dominant crop type. From land-use analysis, 1940–2000 was determined to have relatively stable land use and therefore was used as the calibration period for the water-balance model.
A deterministic water-balance model was developed for the Red River of the North Basin upstream from Emerson, Manitoba. The water-balance model was calibrated with data from 37 U.S. Geological Survey streamgages for 1940–2000 and verified using data for 2001–15. The calibrated water-balance model simulated streamflow distributions that mirrored the seasonal patterns of the observed mean monthly streamflow and the standard deviation of the monthly streamflow data, especially during the fall and winter months when streamflow was lowest. For the verification period, during the low-streamflow months of December through January, the difference between simulated and observed data was similar to the calibration comparison and successfully reproduced seasonal trends in the distribution of streamflow, even when using weather data that were outside the calibration period.
To determine the future risk of low-streamflow conditions in the Red River of the North Basin, a block-bootstrap method was used to generate multiple possible future climates. These stochastically generated weather time series were then input to a water-balance model to simulate a distribution of possible streamflows. Three sets of experiments were performed, with each experiment containing a set of scenarios. The first set of experiments from the stochastic streamflow model were designed to investigate how changes in reservoir management would affect the distribution of low streamflow. Relative to scenario 1 (present-day [2023] reservoir operation), scenario 2 (no reservoir operation) shifted the low-streamflow frequency curves downward, reducing the annual minimum monthly streamflow for the Emerson subbasin. Subbasins were defined by the contributing area upstream from a selected streamgage station. Relative to scenario 1, scenario 3 (regulated streamflow with an increased reservoir capacity of 10 percent) shifted the low-streamflow frequency curves upward for the Emerson subbasin. The magnitude of this upward shift, caused by increased reservoir capacity, was lower than the magnitude of the shift caused by the absence of the reservoirs, which indicates that the streamflow was most affected when the reservoirs were first constructed.
The second set of experiments from the stochastic streamflow model included two scenarios that were performed to better understand how the Red River of the North Basin responds to long periods of low or high precipitation. The results indicate that the model consistently overestimated streamflow, but the relative change between a wet and dry climate state of simulated streamflow distribution reasonably matched the relative change of historical streamflow. Across the subbasins, the model was most accurate for low-streamflow conditions associated with nonexceedance probabilities between 20 and 40 percent.
The third set of experiments from the stochastic streamflow model were done to investigate low-streamflow response across the basin to several drought events. Low-end streamflow was reduced when the basin was exposed to a drought, and the magnitude of the reduction increased with longer or more intense droughts. Compared to the low-intensity drought scenarios, the range of percent reductions (as indicated by the interquartile range) was larger for the high-intensity drought scenarios for all subbasins, and the subbasins of Grand Forks and Emerson had a smaller range of reductions compared to the other three subbasins. The larger drainage area—combined with the large contribution of the Red Lake River and several other Minnesota tributaries that generally experience wetter climate conditions—upstream from the Emerson and Grand Forks subbasins may contribute to the smaller range in reductions under the high intensity scenarios. Comparison of the percent reduction in low-end streamflow among subbasins also indicated that the effects of drought duration and intensity could be cumulative. Combining factors of time and intensity produced a larger reduction in streamflow than when each effect was isolated. The array of drought scenarios can be used to determine how a subbasin would respond to multiple possible future conditions. Based on climate predictions, the drought scenario that best matches a future anticipated drought scenario can be used to estimate a low streamflow response for a given subbasin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255002","collaboration":"Prepared in cooperation with the International Joint Commission, North Dakota Department of Water Resources, Red River Joint Water Resource District, and Red River Watershed Management Board","usgsCitation":"Redoloza, F.S., Glas, R.L., Nustad, R.A., and Ryberg, K.R., 2025, Evaluating drought risk of the Red River of the North Basin using historical and stochastic streamflow upstream from Emerson, Manitoba: U.S. Geological Survey Scientific Investigations Report 2025–5002, 58 p., https://doi.org/10.3133/sir20255002.","productDescription":"Report: viii, 58 p.; Data Release; Dataset","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160450","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":492779,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118481.htm","linkFileType":{"id":5,"text":"html"}},{"id":483206,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13XEN8U","text":"USGS data release","linkHelpText":"Red River of the North low flow water-balance model and supporting data"},{"id":483203,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5002/sir20255002.XML"},{"id":483202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5002/sir20255002.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5002"},{"id":483201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5002/coverthb.jpg"},{"id":483204,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5002/images/"},{"id":483207,"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":483205,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255002/full"}],"country":"Canada, United States","state":"Manitoba, Minnesota, North Dakota","otherGeospatial":"Red River of the North Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.08881710576394,\n 49.037592614274644\n ],\n [\n -97.61189514667993,\n 49.237246321777064\n ],\n [\n -97.98430773681044,\n 49.25792746144549\n ],\n [\n -98.47156466708684,\n 49.30311814027007\n ],\n [\n -98.18869515177104,\n 48.806510092341696\n ],\n [\n -98.92510212635796,\n 48.563256003263746\n ],\n [\n -98.41426650704827,\n 48.10818045779425\n ],\n [\n -97.49283622534884,\n 48.05240253638604\n ],\n [\n -97.3523048621935,\n 47.017146908752025\n ],\n [\n -97.10429716720503,\n 45.72316825195222\n ],\n [\n -95.52291599413297,\n 45.59412827523792\n ],\n [\n -94.77240339220764,\n 46.995377381657505\n ],\n [\n -94.76454577568065,\n 47.45566932791883\n ],\n [\n -93.68493211560182,\n 47.83688445747143\n ],\n [\n -94.11770692662546,\n 48.33322840096503\n ],\n [\n -95.73784376997673,\n 48.92757937919353\n ],\n [\n -97.08881710576394,\n 49.037592614274644\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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Ratings are used for several reasons in water-resources investigations. The simplest rating relates discharge to the stage of a river (the stage-discharge relation). From a pure hydrodynamics perspective, all rivers and streams have some form of hysteresis in the relation between stage and discharge because flow becomes unsteady as a flood wave passes. The stage-discharge relation is unable to represent hysteresis. However, a dynamic rating method can capture hysteresis, which is driven by the variable energy slope of a flood wave.
A dynamic rating method called DYNPOUND, which accommodates compact and compound channel geometry, was developed by simplifying the one-dimensional Saint-Venant equations. The DYNPOUND method was developed in the Python programming language and computes discharge from stage and stage from discharge. Stage and discharge time series computed with this dynamic rating method were compared to the U.S. Geological Survey (USGS) published stage and discharge time series. The results from the DYNPOUND method were also compared to in-person field measurements of stage and discharge made at 10 USGS streamgages.
DYNPOUND was calibrated for 10 USGS streamgages using published discharge time-series data computed with a simple rating method. The calibration objective was to minimize the mean squared logarithmic error (MSLE) of the DYNPOUND-computed discharge with respect to the discharge time series computed by a simple rating method. For each site, the calibration process also included comparing all field measurements within a selected water year to the corresponding DYNPOUND-computed discharge data points. The MSLE of the DYNPOUND-computed discharge time series for the 10 sites ranged from 8.51×10−4 to 1.36×10−1. For each site, an event-based period was selected to compare the discharge time series computed with the dynamic rating method to discharge field measurements made at the streamgages; the range of MSLE for the 10 DYNPOUND-computed discharge sites was from 4.79×10−4 to 2.30×10−2.
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U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Maintaining streamflow to support human water needs and ecosystem services requires a fundamental understanding of the relations between changes in streamflow processes and ecosystem responses. Changes in the natural patterns in flow, geology, and topography alter the habitats that aquatic organisms rely on for food, shelter, and reproduction. The U.S. Geological Survey (USGS) implemented an ecological-flow framework that encapsulates the basic principles of the Ecological Limits of Hydrologic Alteration (ELOHA) to compare the relations between hydrologic metrics and stream conditions and estimate ecological flow needs in the Barnegat Bay-Little Egg Harbor watershed. As a first step in the ELOHA process, streamflow from two historical time periods (occurring between 1933 and 1988) was compared to streamflow for a recent time period (from 2004-2020) for four major streams in the Barnegat Bay-Little Egg Harbor watershed (North Branch Metedeconk River, Toms River, Cedar Creek, and Westecunk Creek), to evaluate if there were statistically significant differences in streamflow metrics. Analysis of monthly, seasonal, and annual low-flow metrics; patterns in the streamflow record; and general land-use changes were used to develop a better understanding of flow conditions in the watershed.
The comparative streamflow analysis indicated that notable changes in flow processes for the study streams occurred between the three periods of record (PORs) evaluated in this study: period of record 1 (POR1, from water years 1933–1958), period of record 2 (POR2, from water years 1974–1988), and period of record 3 (POR3, from water years 2004–2020). For example, the mean of the daily streamflow decreased between the historical POR to the current POR in Cedar Creek but increased in North Branch Metedeconk and Toms Rivers. Larger and more significant changes (p-value <0.10) occurred during specific months or were related to the variability or seasonality of flow. North Branch Metedeconk River and Toms River, the two northern and most developed sites, exhibited changes in low-flow metrics and decreases in minimum n-day moving averages. Decreases in the normalized 75th-percentile exceedance flows were evident at three of the four study sub-basins during POR2 and POR3. In comparison, there was little to no evidence of negative changes to low-flow metrics at Westecunk Creek, the southernmost and least developed site, where all low-flow duration metrics increased as well as seasonal minimum consecutive 7-day average flows. Significant increases in monthly minimums (p-value <0.05) at Cedar Creek for spring months (April, May, and June) also were observed.
Natural and anthropogenic processes can alter the landscape resulting in concomitant changes in the streamflow regime. There is a need to assess these changes and synthesize the results into a scientifically defensible set of goals and standards that help support the management of environmental flows. This study represents the initial steps in building the hydrologic foundation to inform management and develop future ecological flow targets that balance water availability for human and ecosystem needs in the Barnegat Bay-Little Egg Harbor watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245096","collaboration":"Prepared in cooperation with the Barnegat Bay Partnership","usgsCitation":"Wieben, C.M., Kennen, J.G., and Suro, T.P., 2025, Determining low-flow conditions at select streams to Barnegat Bay-Little Egg Harbor as the first step towards the development of ecological-flow targets: U.S. Geological Survey Scientific Investigations Report 2024–5096, 39 p., https://doi.org/10.3133/sir20245096.","productDescription":"vii, 39 p.","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-149405","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":492774,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118479.htm","linkFileType":{"id":5,"text":"html"}},{"id":482893,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5096/coverthb.jpg"},{"id":482897,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5096/images/"},{"id":482896,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5096/sir20245096.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5096 XML"},{"id":482895,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245096/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5096 HTML"},{"id":482894,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5096/sir20245096.pdf","text":"Report","size":"7.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5096 PDF"}],"country":"United States","state":"New Jersey","otherGeospatial":"Barnegat Bay-Little Egg Harbor watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.5,\n 40.1667\n ],\n [\n -74.5,\n 39.5\n ],\n [\n -73.8333,\n 39.5\n ],\n [\n -73.8333,\n 40.1667\n ],\n [\n -74.5,\n 40.1667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Freshwater megafishes are among some of the most commercially and ecologically important aquatic organisms yet are disproportionately threatened with range and population reduction. Anthropogenic alterations of rivers influencing migrations are among the most significant causes for these declines. However, migratory fishes do not always respond similarly to movement barriers and thus it is necessary to develop models to predict movements of freshwater migratory fishes in the face of anthropogenic alteration. Predicting movement of freshwater fishes is often investigated using statistical packages. However, empirical studies assessing these packages have led to mixed results, questioning its applicability to all taxa. We argue that spatial, temporal, and environmental attributes are more influential for movement of a migratory megafish, the Alligator Gar (Atractosteus spatula), than the current parameters explored in a globally relevant fish movement model.
This study explored two independent mobile telemetry datasets investigating Alligator Gar movement on the Brazos and Trinity rivers. Environmental associations were investigated to predict Alligator Gar displacement and dispersal using generalized additive models, generalized linear models, and model selection. Leptokurtosis of Alligator Gar populations was also assessed. Predictability of the movement model was tested by comparing observed to model derived stationary and mobile components making up a leptokurtic movement distribution.
Our study suggests that current and antecedent measures of discharge and water temperature are positively correlated with Alligator Gar displacement and dispersal. However, these patterns are only detectable when monthly relocation intervals are explored rather than seasonal scales. Leptokurtosis was observed in both Alligator Gar populations. However, movement was normally distributed (i.e., mesokurtic) under tracking events following high flood pulses. Additionally, predicted Alligator Gar movement was significantly farther under modeled values compared to observed values, in part because the species undergoes cyclical migrations for reproduction that are sensitive to water temperature and discharge.
In conclusion, this study provides an alternative framework to assess the movement patterns of migratory fishes, which could be tested on additional freshwater fishes, and suggests that assessing spatial, environmental, and temporal processes simultaneously are necessary to capture the complexities of fish movement which currently are unavailable for the movement model we investigated.
","language":"English","publisher":"BMC","doi":"10.1186/s40462-025-00544-7","usgsCitation":"Roberts, H.C., Kappen, F., Acre, M.R., Daugherty, D.J., Smith, N.G., and Perkin, J., 2025, Meta-analysis of a megafish: Assessing patterns and predictors of Alligator Gar movement across multiple populations: Movement Ecology, v. 13, no. 1, 15, 18 p., https://doi.org/10.1186/s40462-025-00544-7.","productDescription":"15, 18 p.","ipdsId":"IP-172438","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":488371,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-025-00544-7","text":"Publisher Index Page"},{"id":483713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.70591756751243,\n 30.894440659055704\n ],\n [\n -96.70591756751243,\n 29.622760974861365\n ],\n [\n -94.82999949548378,\n 29.622760974861365\n ],\n [\n -94.82999949548378,\n 30.894440659055704\n ],\n [\n -96.70591756751243,\n 30.894440659055704\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts, Hayden C.","contributorId":335083,"corporation":false,"usgs":false,"family":"Roberts","given":"Hayden","email":"","middleInitial":"C.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":931580,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kappen, Florian","contributorId":352518,"corporation":false,"usgs":false,"family":"Kappen","given":"Florian","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":931581,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Acre, Matthew Ross 0000-0002-5417-9523","orcid":"https://orcid.org/0000-0002-5417-9523","contributorId":268034,"corporation":false,"usgs":true,"family":"Acre","given":"Matthew","email":"","middleInitial":"Ross","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":931582,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daugherty, Daniel J.","contributorId":335084,"corporation":false,"usgs":false,"family":"Daugherty","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":931583,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Nathan G.","contributorId":268036,"corporation":false,"usgs":false,"family":"Smith","given":"Nathan","email":"","middleInitial":"G.","affiliations":[{"id":55541,"text":"Heart of the Hills Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":931584,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perkin, Joshuah S.","contributorId":238286,"corporation":false,"usgs":false,"family":"Perkin","given":"Joshuah S.","affiliations":[{"id":47708,"text":"Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX","active":true,"usgs":false}],"preferred":false,"id":931585,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264306,"text":"70264306 - 2025 - 6PPD-quinone in water from the San Francisco-San Joaquin Delta, California, 2018-2024","interactions":[],"lastModifiedDate":"2025-03-11T14:21:30.245946","indexId":"70264306","displayToPublicDate":"2025-03-10T09:15:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"6PPD-quinone in water from the San Francisco-San Joaquin Delta, California, 2018-2024","docAbstract":"The Sacramento-San Joaquin Delta (Delta) is an expansive river delta supplying a large portion of California’s fresh water for agriculture and residential use, and it is also an area of critical habitat for numerous state and federally listed species of concern. In many locations, urban stormwater flows directly into the Delta. 6PPD-quinone (6PPD-Q), an ozonation byproduct of a tire antiozonant 6PPD, has been shown to enter surface water via these pathways and can cause various toxicological effects, including acute urban mortality syndrome to coho salmon (Oncorhynchus kisutch) at low levels (LC50 = 41 and 95 ng/L for juveniles and adults, respectively). Here, we quantified 6PPD-Q in 61 archived Delta water extracts collected between 2018 and 2024 and found concentrations up to 21 ng/L. Currently, no 6PPD-Q presence and/or quantitative data is available for this complex and diverse ecosystem. Little is known regarding long-term storage of 6PPD-quinone in solvent extracts, so 6PPD-Q observations document its presence in the study area and provide evidence that further sampling may be warranted to better quantify environmental concentrations. Consistent with the general understanding of 6PPD-Q transport, all detections observed were in samples collected during or immediately after a precipitation event. This work provides environmentally relevant concentration data to complement ongoing toxicological investigations of 6PPD-Q in Delta organisms and suggests there are research opportunities for a more robust survey of 6PPD-Q inputs into the Delta.
","language":"English","publisher":"Springer","doi":"10.1007/s10661-025-13757-5","usgsCitation":"Black, G.P., De Parsia, M., Uychutin, M., Lane, R.F., Orlando, J., and Hladik, M.L., 2025, 6PPD-quinone in water from the San Francisco-San Joaquin Delta, California, 2018-2024: Environmental Monitoring and Assessment, v. 197, no. 4, 369, 9 p., https://doi.org/10.1007/s10661-025-13757-5.","productDescription":"369, 9 p.","ipdsId":"IP-169662","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":483195,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco‑San Joaquin delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.4615589600206,\n 38.89855420973035\n ],\n [\n -122.4156538354604,\n 37.57763045605546\n ],\n [\n -120.7611068337572,\n 37.57763045605546\n ],\n [\n -120.7611068337572,\n 38.92128311603909\n ],\n [\n -122.4615589600206,\n 38.89855420973035\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"197","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Black, Gabrielle Pecora 0000-0002-1578-742X","orcid":"https://orcid.org/0000-0002-1578-742X","contributorId":303108,"corporation":false,"usgs":true,"family":"Black","given":"Gabrielle","email":"","middleInitial":"Pecora","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930372,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Parsia, Matthew D. 0000-0001-5806-5403","orcid":"https://orcid.org/0000-0001-5806-5403","contributorId":204707,"corporation":false,"usgs":true,"family":"De Parsia","given":"Matthew D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930373,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Uychutin, Matthew 0000-0003-2677-7902","orcid":"https://orcid.org/0000-0003-2677-7902","contributorId":339824,"corporation":false,"usgs":true,"family":"Uychutin","given":"Matthew","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930374,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lane, Rachael F. 0000-0001-9202-0612","orcid":"https://orcid.org/0000-0001-9202-0612","contributorId":222471,"corporation":false,"usgs":true,"family":"Lane","given":"Rachael","email":"","middleInitial":"F.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":930375,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Orlando, James 0000-0002-0099-7221","orcid":"https://orcid.org/0000-0002-0099-7221","contributorId":208413,"corporation":false,"usgs":true,"family":"Orlando","given":"James","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930376,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221087,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930377,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264193,"text":"sir20255004 - 2025 - Assessment of effects of channelization mitigation alternatives of Stoney Brook, Carlton and St. Louis Counties, Minnesota","interactions":[],"lastModifiedDate":"2025-07-23T16:37:17.642571","indexId":"sir20255004","displayToPublicDate":"2025-03-10T08:26:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5004","displayTitle":"Assessment of Effects of Channelization Mitigation Alternatives of Stoney Brook, Carlton and St. Louis Counties, Minnesota","title":"Assessment of effects of channelization mitigation alternatives of Stoney Brook, Carlton and St. Louis Counties, Minnesota","docAbstract":"The U.S. Geological Survey, in cooperation with the Fond du Lac Band of Lake Superior Chippewa (FDLB), studied the effects of channel modification alternatives on lake levels and floodplain inundation in the Stoney Brook watershed in northeast Minnesota. Northern wild rice (Zizania palustris), also referred to as manoomin by the Ojibwe/Chippewa people, is a natural and cultural resource to the FDLB and is sensitive to water levels and rates of water-level changes, particularly during the early stages of growth. Drainage ditches constructed in the early 1900s in the Stoney Brook watershed lowered lake-water levels, caused greater fluctuations in the lakes, and created a loss in wetland coverage. The FDLB is committed to minimizing large fluctuations of the lakes with natural wild rice production in the Stoney Brook watershed and restoring a more natural hydrology to Stoney Brook. The hydrologic response of these lakes and floodplain storage to simulated channel modification alternatives were examined.
Hydrologic and hydraulic models were developed for the watershed and calibrated to historical rainfall events. The models used probabilistic frequency rainfall events of 24-hour duration for 1-, 2-, 5-, and 10-year annual recurrence intervals (100-, 50-, 20-, and 10-percent annual exceedance probability) to simulate watershed management scenarios with existing and alternative conditions. The hydraulic model outputs for peak flows, volume accumulation, water levels, and inundation duration and depths were assessed to quantify the effects of the channel modification alternatives. The channel modification alternatives were simulated with four different terrain conditions: existing conditions, bank spoil breach, original channel reconnection, and original channel reconnection with bank spoil breach. Hydrologic characteristics from six distinct areas were used in the model to evaluate the effects from the channel modification alternatives.
The simulated results of two lakes in which wild rice was planted demonstrated that the lakes would take longer to draw down following an event with the channel modification alternatives compared to existing conditions with little change to peak water-surface elevations. The alternatives provided minor to no increases in flows or conveyances at the downstream reference location at Pine Drive bridge. The restored floodplain locations had increased flows and conveyances for the channel modification alternatives that could be considered substantial when compared to flows with existing conditions. The inundation extent, duration, and water-depth distribution were assessed within selected floodplain areas. Generally, the channel modification alternatives produced increases in the higher depth (3–4 and greater than 4 feet) and duration (10–14 and greater than 14 days) categories for these areas, which may be beneficial to increases in wetland coverage and floodplain storage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255004","collaboration":"Prepared in cooperation with the Fond du Lac Band of Lake Superior Chippewa","usgsCitation":"Cigrand, C.V., 2025, Assessment of effects of channelization mitigation alternatives of Stoney Brook, Carlton and St. Louis Counties, Minnesota: U.S. Geological Survey Scientific Investigations Report 2025–5004, 44 p., https://doi.org/10.3133/sir20255004.","productDescription":"Report: ix, 44 p.; Data Release; Dataset","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-132433","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":483061,"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":483062,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13KFQSL","text":"USGS data release","linkHelpText":"Archive of hydraulic and hydrologic models used in the Stoney Brook watershed in Carlton and St. Louis Counties, Minnesota, 2008–2024"},{"id":483057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5004/coverthb.jpg"},{"id":483058,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5004/sir20255004.pdf","text":"Report","size":"9.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025–5004"},{"id":483059,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5004/sir20255004.XML"},{"id":483060,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5004/images/"},{"id":483063,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255004/full"},{"id":492770,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118476.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Minnesota","county":"Carlton County, St. Louis County","otherGeospatial":"Stoney Brook watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.4667,\n 46.8667\n ],\n [\n -92.8,\n 46.8667\n ],\n [\n -92.8,\n 46.633\n ],\n [\n -92.4667,\n 46.633\n ],\n [\n -92.4667,\n 46.8667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Rainfall strongly affects landslide triggering; however, understanding how storm characteristics relate to the severity of landslides at the regional scale has thus far remained unclear, despite the societal benefits that would result from defining this relationship. As mapped landslide inventories typically cover a small region relative to a storm system, here we develop a dimensionless index for landslide-inducing rainfall, A*, based on extremes of modeled soil water relative to its local climatology. We calibrate A* using four landslide inventories, comprising over 11 000 individual landslides over four unique storm events, and find that a common threshold can be applied to estimate regional shallow-landslide-triggering potential across diverse climatic regimes in California (USA). We then use the spatial distribution of A*, along with topography, to calculate the landslide potential area (LPA) for nine landslide-inducing storm events over the past 20 years, and we test whether atmospheric metrics describing the strength of landfalling storms, such as integrated water vapor transport, correlate with the magnitude of hazardous landslide-inducing rainfall. We find that although the events with the largest LPA do occur during exceptional atmospheric river (AR) storms, the strength of landfalling atmospheric rivers does not scale neatly with landslide potential area, and even exceptionally strong ARs may yield minimal landslide impacts. Other factors, such as antecedent soil moisture driven by storm frequency and mesoscale precipitation features within storms, are instead more likely to dictate the patterns of landslide-generating rainfall throughout the state.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/nhess-25-1037-2025","usgsCitation":"Perkins, J.P., Oakley, N.S., Collins, B.D., Corbett, S.C., and Burgess, W.P., 2025, Characterizing the scale of regional landslide triggering from storm hydrometeorology: Natural Hazards and Earth Systems Sciences (NHESS), v. 25, no. 3, p. 1037-1056, https://doi.org/10.5194/nhess-25-1037-2025.","productDescription":"20 p.","startPage":"1037","endPage":"1056","ipdsId":"IP-144518","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":493301,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-25-1037-2025","text":"Publisher Index Page"},{"id":493184,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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