Translocation, often a management solution reserved for at-risk species, is a highly time-sensitive intervention in the face of a rapidly changing climate. The definition of abiotic and biotic habitat requirements is essential to the selection of appropriate release sites in novel environments. However, field-based approaches to gathering this information are often too time intensive, especially in areas of complex topography where common, coarse-scale climate models lack essential details. We apply a fine-scale remote sensing-based approach to study the 'akikiki (Oreomystis bairdi) and 'akeke'e (Loxops caeruleirostris), Hawaiian honeycreepers endemic to Kaua'i that are experiencing large-scale population declines due to warming-induced spread of invasive disease. We use habitat suitability modeling based on fine-scale light detection and ranging (lidar)-derived habitat structure metrics to refine coarse climate ranges for these species in candidate translocation areas on Maui. We found that canopy density was consistently the most important variable in defining habitat suitability for the two Kaua'i species. Our models also corroborated known habitat preferences and behavioral information for these species that are essential for informing translocation. We estimated a nesting habitat that will persist under future climate conditions on east Maui of 23.43 km2 for 'akikiki, compared to the current Kaua'i range of 13.09 km2. In contrast, the novel nesting range for 'akeke'e in east Maui was smaller than its current range on Kaua'i (26.29 vs. 38.48 km2, respectively). We were also able to assess detailed novel competitive interactions at a fine scale using models of three endemic Maui species of conservation concern: 'ākohekohe (Palmeria dolei), Maui 'alauahio (Paroreomyza montana), and kiwikiu (Pseudonestor xanthophrys). Weighted overlap areas between the species from both islands were moderate (<12 km2), and correlations between Maui and Kaua'i bird habitat were generally low, indicating limited potential for competition. Results indicate that translocation to east Maui could be a viable option for 'akikiki but would be more uncertain for 'akeke'e. Our novel multifaceted approach allows for the timely analysis of both climate and vegetation structure at informative scales for the effective selection of appropriate translocation sites for at-risk species.
Rapid and targeted management actions are a prerequisite to efficiently mitigate disease outbreaks. Targeted actions, however, require accurate spatial information on disease occurrence and spread. Frequently, targeted management actions are guided by non-statistical approaches that define the affected area by a pre-determined distance surrounding a small number of disease detections. As an alternative, we present a long-recognized but underutilized Bayesian technique that uses limited local data and informative priors to make statistically valid predictions and forecasts about disease occurrence and spread. As a case study, we use limited local data that were available after the detection of chronic wasting disease in Michigan, U.S. along with information rich priors obtained from a previous study in a neighboring state. Using these limited local data and informative priors, we generate statistically valid predictions of disease occurrence and spread for the Michigan study area. This Bayesian technique is conceptually and computationally simple, relies on little to no local data, and is competitive with non-statistical distance-based metrics in all performance evaluations. Bayesian modeling has added benefits because it allows practitioners to generate immediate forecasts of future disease conditions and provides a principled framework to incorporate new data as they accumulate. We contend that the Bayesian technique offers broad-scale benefits and opportunities to make statistical inference across a diversity of data-deficient systems, not limited to disease.
The 25 October 2022 Mw 5.1 Alum Rock earthquake shows strong evidence for southeast rupture directivity along the central Calaveras fault (CCF), as indicated by observed ground motions and simulated kinematic ruptures. Peak ground accelerations (PGAs) and peak ground velocities (PGVs) are notably higher to the southeast, with an order of magnitude difference for stations at the same distance but different azimuths. In addition, PGAs are lower than that predicted by ground‐motion models by a factor of 3 on average in all the directions, indicating a low stress drop (∼1.57 MPa). Directivity function modeling and ground‐motion simulations both indicate rupture propagation to the southeast with rupture velocity between 2.3 and 2.5 km/s. We suggest that the southward rupture propagation and relatively low stress drop may be typical of M ∼5 earthquakes on this portion of the CCF.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230013","usgsCitation":"Hirakawa, E.T., Parker, G.A., Baltay Sundstrom, A.S., and Hanks, T.C., 2023, Rupture directivity of the 25 October 2022 Mw 5.1 Alum Rock earthquake: The Seismic Record, v. 3, no. 2, p. 144-155, https://doi.org/10.1785/0320230013.","productDescription":"12 p.","startPage":"144","endPage":"155","ipdsId":"IP-151768","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230013","text":"Publisher Index Page"},{"id":482034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Califronia","otherGeospatial":"Alum Rock earthquake area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122,\n 37.5\n ],\n [\n -122,\n 37\n ],\n [\n -121.4,\n 37\n ],\n [\n -121.4,\n 37.5\n ],\n [\n -122,\n 37.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hirakawa, Evan Tyler 0000-0002-5720-0850","orcid":"https://orcid.org/0000-0002-5720-0850","contributorId":295776,"corporation":false,"usgs":true,"family":"Hirakawa","given":"Evan","email":"","middleInitial":"Tyler","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baltay, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanks, Thomas C. 0000-0003-0928-0056 thanks@usgs.gov","orcid":"https://orcid.org/0000-0003-0928-0056","contributorId":3065,"corporation":false,"usgs":true,"family":"Hanks","given":"Thomas","email":"thanks@usgs.gov","middleInitial":"C.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927342,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243845,"text":"70243845 - 2023 - Nest attendance, incubation constancy, and onset of incubation in dabbling ducks","interactions":[],"lastModifiedDate":"2023-05-23T13:56:58.32871","indexId":"70243845","displayToPublicDate":"2023-05-19T08:52:01","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Nest attendance, incubation constancy, and onset of incubation in dabbling ducks","docAbstract":"In birds, parents must provide their eggs with a safe thermal environment suitable for embryonic development. Species with uniparental incubation must balance time spent incubating eggs with time spent away from the nest to satisfy self-maintenance needs. Patterns of nest attendance, therefore, influence embryonic development and the time it takes for eggs to hatch. We studied nest attendance (time on the nest), incubation constancy (time nests were at incubation temperatures), and variation in nest temperature of 1,414 dabbling duck nests of three species in northern California. Daily nest attendance increased from only 1–3% on the day the first egg was laid to 51–57% on the day of clutch completion, and 80–83% after clutch completion through hatch. Variation in nest temperature also decreased gradually during egg-laying, and then dropped sharply (33–38%) between the day of and the day after clutch completion because increased nest attendance, particularly at night, resulted in more consistent nest temperatures. During the egg-laying stage, nocturnal nest attendance was low (13–25%), whereas after clutch completion, nest attendance was greater at night (≥87%) than during the day (70–77%) because most incubation recesses occurred during the day. Moreover, during egg-laying, nest attendance and incubation constancy increased more slowly among nests with larger final clutch sizes, suggesting that the number of eggs remaining to be laid is a major driver of incubation effort during egg-laying. Although overall nest attendance after clutch completion was similar among species, the average length of individual incubation bouts was greatest among gadwall (Mareca strepera; 779 minutes), followed by mallard (Anas platyrhynchos; 636 minutes) and then cinnamon teal (Spatula cyanoptera; 347 minutes). These results demonstrate that dabbling ducks moderate their incubation behavior according to nest stage, nest age, time of day, and clutch size and this moderation likely has important implications for egg development and overall nest success.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0286151","usgsCitation":"Hartman, C.A., Ackerman, J.T., Peterson, S.H., Fettig, B.L., Casazza, M.L., and Herzog, M.P., 2023, Nest attendance, incubation constancy, and onset of incubation in dabbling ducks: PLoS ONE, v. 18, no. 5, e0286151, 28 p., https://doi.org/10.1371/journal.pone.0286151.","productDescription":"e0286151, 28 p.","ipdsId":"IP-147141","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":443460,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0286151","text":"Publisher Index Page"},{"id":435323,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NSAKP8","text":"USGS data release","linkHelpText":"Nest Attendance, Incubation Constancy, and Onset of Incubation in Dabbling Ducks"},{"id":417335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Grizzly Island Wildlife Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.93120904361,\n 38.087026017844266\n ],\n [\n -121.92222832467957,\n 38.09123333537573\n ],\n [\n -121.90341158025386,\n 38.0839965992823\n ],\n [\n -121.88502248911058,\n 38.09022360125243\n ],\n [\n -121.89250642155254,\n 38.09846935507156\n ],\n [\n -121.88801606208753,\n 38.105031637082874\n ],\n [\n -121.88801606208753,\n 38.12353746197775\n ],\n [\n -121.89207876827012,\n 38.13463870601953\n ],\n [\n -121.90170096712419,\n 38.1351432679065\n ],\n [\n -121.9083295930017,\n 38.14170225498961\n ],\n [\n -121.93120904361,\n 38.1319476503748\n ],\n [\n -121.93698236292227,\n 38.13043388797155\n ],\n [\n -121.94788752162361,\n 38.14035686980549\n ],\n [\n -121.95344701429467,\n 38.13985234396577\n ],\n [\n -121.97376054520885,\n 38.156836128843764\n ],\n [\n -121.99236346299335,\n 38.1591900047855\n ],\n [\n -122.00391010161832,\n 38.15330517246224\n ],\n [\n -121.99835060894684,\n 38.142374938278834\n ],\n [\n -121.98209978421568,\n 38.13026569021102\n ],\n [\n -121.98209978421568,\n 38.1122662905006\n ],\n [\n -121.96371069307239,\n 38.105031637082874\n ],\n [\n -121.95644058727163,\n 38.09443073474577\n ],\n [\n -121.9457492552115,\n 38.09594524352207\n ],\n [\n -121.94296950887617,\n 38.08652112346789\n ],\n [\n -121.93120904361,\n 38.087026017844266\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131157,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Sarah H. 0000-0003-2773-3901 sepeterson@usgs.gov","orcid":"https://orcid.org/0000-0003-2773-3901","contributorId":167181,"corporation":false,"usgs":true,"family":"Peterson","given":"Sarah","email":"sepeterson@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fettig, Brady Lynn 0000-0002-3124-2606","orcid":"https://orcid.org/0000-0002-3124-2606","contributorId":302106,"corporation":false,"usgs":true,"family":"Fettig","given":"Brady","email":"","middleInitial":"Lynn","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873483,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873484,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243759,"text":"70243759 - 2023 - Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system","interactions":[],"lastModifiedDate":"2023-05-19T12:54:11.017578","indexId":"70243759","displayToPublicDate":"2023-05-19T07:29:23","publicationYear":"2023","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":"Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system","docAbstract":"Objective: The American Eel Anguilla rostrata historically was one of the most common fish species in Atlantic coast watersheds, but extensive dam construction and other factors caused a widespread population decline. One of the watersheds where American Eels have declined considerably is the Mohawk River in eastern and central New York. Recent attempts to characterize the distribution and abundance of American Eels in this watershed have been ineffective, and the extent to which a series of locks and dams on the Hudson River and lower Mohawk River limits use of the watershed is unclear.
Methods: We developed a model between environmental DNA (eDNA) quantity and American Eel abundance in the Hudson River watershed in which the DNA concentration in water samples explained up to 65% of the variability in eel density and 56% of the variability in eel biomass. We then used this relationship to interpret eDNA data collected twice from 36 sites across the Mohawk River watershed in 2021 and make inferences about the distribution and abundance of American Eels.
Result: American Eel DNA was detected almost exclusively in the downstream-most 4 km of the Mohawk River within a series of barriers. The concentration of DNA was reduced by approximately 80% across each successive upstream barrier before becoming too low to detect consistently. Our data suggest that eel population density was high in the Hudson River estuary and declined rapidly in the lower Mohawk River, and the species was nearly absent or undetectable in the Mohawk River and its tributaries upstream of the Crescent Dam and the Waterford Flight of Locks.
Conclusion: Barriers appear to be largely restricting American Eels from using over 99% of the Mohawk River watershed. Therefore, improvements in fish passage at dams and hydroelectric facilities in the region could help the American Eel to regain access to this part of its native range.
","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10404","usgsCitation":"George, S.D., Baldigo, B., Rees, C., Bartron, M.L., Wiley, J.J., Stich, D.S., Wells, S.M., and Winterhalter, D., 2023, Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system: Transactions of the American Fisheries Society, v. 152, no. 3, p. 310-326, https://doi.org/10.1002/tafs.10404.","productDescription":"17 p.","startPage":"310","endPage":"326","ipdsId":"IP-143996","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":443462,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10404","text":"Publisher Index Page"},{"id":417238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Mohawk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.46545959281143,\n 43.193210843337965\n ],\n [\n -75.36068780837059,\n 43.1807103892566\n ],\n [\n -75.25020119932465,\n 43.095917619682325\n ],\n [\n -75.11304540878423,\n 43.06391605114166\n ],\n [\n -75.0406576304439,\n 43.00264908923049\n ],\n [\n -74.84063876923905,\n 43.01240019708166\n ],\n [\n -74.66347920645833,\n 42.963629178932905\n ],\n [\n -74.58156672044112,\n 42.881327587792015\n ],\n [\n -74.45774552064795,\n 42.879931698082856\n ],\n [\n -74.25772665944312,\n 42.84083396491155\n ],\n [\n -74.11866592736799,\n 42.86317855831385\n ],\n [\n -74.03865838288596,\n 42.83524655344701\n ],\n [\n -74.02532379213918,\n 42.75417231574653\n ],\n [\n -74.00627437678641,\n 42.707996466290325\n ],\n [\n -73.81976366766865,\n 42.62025113498041\n ],\n [\n -73.79690436924501,\n 42.550123385716574\n ],\n [\n -73.8254784922747,\n 42.384309188935845\n ],\n [\n -73.81404884306264,\n 42.31351753996279\n ],\n [\n -73.93977498439143,\n 42.2035468271101\n ],\n [\n -73.97025404895584,\n 42.10469190424271\n ],\n [\n -74.00454299659106,\n 41.89801206244189\n ],\n [\n -73.91310580289745,\n 41.86681033353875\n ],\n [\n -73.88262673833307,\n 42.11317123319691\n ],\n [\n -73.72642153243986,\n 42.28251957771869\n ],\n [\n -73.74547094779265,\n 42.37828214920688\n ],\n [\n -73.71880176629864,\n 42.619854148073244\n ],\n [\n -73.61212504032306,\n 42.82697116015191\n ],\n [\n -73.66165352024026,\n 42.88841312774437\n ],\n [\n -73.94442157442768,\n 42.88448522793766\n ],\n [\n -74.22444798011406,\n 42.97932677531503\n ],\n [\n -74.37874824447162,\n 42.982114021563405\n ],\n [\n -74.51971391808242,\n 42.93192425763374\n ],\n [\n -74.65305982555196,\n 43.01415827143066\n ],\n [\n -74.86679009610465,\n 43.05871377504309\n ],\n [\n -75.00966071125109,\n 43.05314610631808\n ],\n [\n -75.22491910473742,\n 43.151897073297846\n ],\n [\n -75.36207489527783,\n 43.21717980329879\n ],\n [\n -75.47065656278859,\n 43.221344415722996\n ],\n [\n -75.46545959281143,\n 43.193210843337965\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"152","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rees, Christopher B.","contributorId":196308,"corporation":false,"usgs":false,"family":"Rees","given":"Christopher B.","affiliations":[],"preferred":false,"id":873175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartron, Meredith L.","contributorId":149109,"corporation":false,"usgs":false,"family":"Bartron","given":"Meredith","email":"","middleInitial":"L.","affiliations":[{"id":26874,"text":"USFWS, Lamar, PA","active":true,"usgs":false},{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":873176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiley, John J. Jr.","contributorId":305554,"corporation":false,"usgs":false,"family":"Wiley","given":"John","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":873177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stich, Daniel S.","contributorId":280276,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":33660,"text":"SUNY Oneonta","active":true,"usgs":false}],"preferred":false,"id":873178,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wells, Scott M.","contributorId":305556,"corporation":false,"usgs":false,"family":"Wells","given":"Scott","email":"","middleInitial":"M.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":873179,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Winterhalter, Dylan R. 0000-0003-1774-8034","orcid":"https://orcid.org/0000-0003-1774-8034","contributorId":251765,"corporation":false,"usgs":true,"family":"Winterhalter","given":"Dylan R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873180,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243768,"text":"70243768 - 2023 - Watershed carbon yield derived from gauge observations and river network connectivity in the United States","interactions":[],"lastModifiedDate":"2023-05-19T12:28:27.653939","indexId":"70243768","displayToPublicDate":"2023-05-19T07:04:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Watershed carbon yield derived from gauge observations and river network connectivity in the United States","docAbstract":"River networks play a critical role in the global carbon cycle. Although global/continental scale riverine carbon cycle studies demonstrate the significance of rivers and streams for linking land and coastal regions, the lack of spatially distributed riverine carbon load data represents a gap for quantifying riverine carbon net gain or net loss in different regions, understanding mechanisms and factors that influence the riverine carbon cycle, and testing simulations of aquatic carbon cycle models at fine scales. Here, we (1) derive the riverine load of particulate organic carbon (POC) and dissolved organic carbon (DOC) for over 1,000 hydrologic stations across the Conterminous United States (CONUS) and (2) use the river network connectivity information for over 80,000 catchment units within the National Hydrography Dataset Plus (NHDPlus) to estimate riverine POC and DOC net gain or net loss for watersheds controlled between upstream-downstream hydrologic stations. The new riverine carbon load and watershed net gain/loss represent a unique contribution to support future studies for better\nunderstanding and quantification of riverine carbon cycles.","language":"English","publisher":"Springer","doi":"10.1038/s41597-023-02162-7","usgsCitation":"Qiu, H., Zhang, X., Yang, A., Wickland, K., Stets, E.G., and Chen, M., 2023, Watershed carbon yield derived from gauge observations and river network connectivity in the United States: Scientific Data, v. 10, 278, 13 p., https://doi.org/10.1038/s41597-023-02162-7.","productDescription":"278, 13 p.","ipdsId":"IP-150043","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":443465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-023-02162-7","text":"Publisher Index 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Xuesong","contributorId":187519,"corporation":false,"usgs":false,"family":"Zhang","given":"Xuesong","email":"","affiliations":[],"preferred":false,"id":873198,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yang, Anni","contributorId":305567,"corporation":false,"usgs":false,"family":"Yang","given":"Anni","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":873199,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wickland, Kimberly 0000-0002-6400-0590","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":208471,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":873200,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stets, Edward G. 0000-0001-5375-0196 estets@usgs.gov","orcid":"https://orcid.org/0000-0001-5375-0196","contributorId":194490,"corporation":false,"usgs":true,"family":"Stets","given":"Edward","email":"estets@usgs.gov","middleInitial":"G.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":873201,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chen, Min","contributorId":56140,"corporation":false,"usgs":true,"family":"Chen","given":"Min","email":"","affiliations":[],"preferred":false,"id":873202,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243612,"text":"sir20225047 - 2023 - Simulation of flow and eutrophication in the central Salem River, New Jersey","interactions":[],"lastModifiedDate":"2026-02-23T19:12:37.435815","indexId":"sir20225047","displayToPublicDate":"2023-05-18T10:55:00","publicationYear":"2023","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":"2022-5047","displayTitle":"Simulation of Flow and Eutrophication in the Central Salem River, New Jersey","title":"Simulation of flow and eutrophication in the central Salem River, New Jersey","docAbstract":"The central Salem River in New Jersey is subject to periods of water-quality impairment, marked by elevated concentrations of phosphorus and chlorophyll-a, and low concentrations of and large diurnal swings in concentrations of dissolved oxygen. These seasonal eutrophic conditions are controlling factors for water quality in lower reaches, where the river is more lacustrine than in upper reaches, as a result of downstream damming. This biological productivity is supported by nutrient wash-off from agricultural areas in the surrounding watershed. To investigate this impairment, flow measurement and water-quality sampling were conducted during 2007–08 in support of development of a one-dimensional surface-water-quality model that simulates nutrient cycling and transformation processes.
The U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, used the U.S. Environmental Protection Agency Water Quality Analysis Simulation Program (WASP) to develop a receiving-water-quality model of the central Salem River between Woodstown and Deepwater, New Jersey, from April 2007 to October 2008. The main-stem river and largest tributary were simulated. In the flow model, kinematic wave flow is used to simulate flow in upper reaches and ponded weir flow is used to simulate flow in lower reaches. The water-quality model makes use of a mass-balance equation to simulate the fate and transport of nutrients, phytoplankton chlorophyll-a, dissolved oxygen, and oxygen demands (an indicator rather than a substance) in the river. Model input included channel characteristics, boundary conditions for flow and water quality, environmental parameters, vertical dispersion coefficients, settling rates, and kinetic constants. Inputs were estimated where field data were lacking, notably for tributary flows and nutrient loads.
The model was calibrated to observed flow variables and concentrations of dissolved oxygen, chlorophyll-a, and nutrients at sampling locations, with emphasis on growing-season conditions. Calibration was achieved through graphical and statistical comparison of simulated results to observed data. Sensitivity analyses were performed, and model limitations and applicability were evaluated. Simulated results closely matched observed data in most cases, although some were overpredicted slightly. The most important causes of overprediction were estimated tributary flows for the flow model and estimated tributary watershed loads for the water-quality model. Calibration of dissolved-oxygen concentrations was closer, and predicted diurnal variations were consistent with high algal photosynthesis/respiration, although lack of continuous dissolved-oxygen data precluded verifying these predictions. A similar caveat applies to predicted diurnal variations in chlorophyll-a. Simulated limitations on algal growth were consistent with those based on observed data and indicated phosphorus was the main limiting nutrient, except during certain periods when nitrogen was limiting.
Two water-quality management scenarios were simulated with the model to assess the effect of point- and nonpoint-source nutrient reductions on water-quality conditions in the river. Scenarios involved (1) a return of watershed land use to predevelopment natural conditions and (2) an extreme reduction in nutrient input. Although the extreme-nutrient-reduction scenario yielded improvements in water quality, the natural-conditions scenario yielded the largest improvements as indicated by minimal violations of surface-water-quality standards or thresholds. However, years may be needed to attain the full benefit of these management scenarios as a result of accumulation of phosphorus and organic carbon in riverbed sediments in lacustrine reaches. The results of this study indicate that the quality of water in the central Salem River will improve if management policies that mitigate the effects of nutrient-loading practices in the watershed, particularly those related to agriculture, are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225047","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Spitz, F.J., and DePaul, V.T., 2023, Simulation of flow and eutrophication in the central Salem River, New Jersey: U.S. Geological Survey Scientific Investigations Report 2022–5047, 72 p., https://doi.org/10.3133/sir20225047.","productDescription":"Report: x, 72 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109225","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":500449,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114735.htm","linkFileType":{"id":5,"text":"html"}},{"id":417027,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8JPJ","text":"USGS data release","linkHelpText":"WASP model used to simulate flow and eutrophication in the central Salem River, New Jersey"},{"id":417026,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5047/images/"},{"id":417025,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.XML"},{"id":417024,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225047/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5047"},{"id":417023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5047"},{"id":417022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5047/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Central Salem River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.51261142665348,\n 39.66506027345514\n ],\n [\n -75.13011677160785,\n 39.493754929673486\n ],\n [\n -75.01123329774249,\n 39.637202213256444\n ],\n [\n -75.41569555121957,\n 39.76545497451639\n ],\n [\n -75.51261142665348,\n 39.66506027345514\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
Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006810","usgsCitation":"Woodard, J.B., Mirus, B., Crawford, M., Or, D., Leshchinsky, B., Allstadt, K.E., and Wood, N.J., 2023, Mapping landslide susceptibility over large regions with limited data: Journal of Geophysical Research: Earth Surface, v. 128, no. 5, e2022JF006810, 21 p., https://doi.org/10.1029/2022JF006810.","productDescription":"e2022JF006810, 21 p.","ipdsId":"IP-142367","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":443478,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006810","text":"Publisher Index Page"},{"id":435324,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P959G9JN","text":"USGS data 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]\n}","volume":"128","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Woodard, Jacob Bryson 0000-0002-3095-0774","orcid":"https://orcid.org/0000-0002-3095-0774","contributorId":305507,"corporation":false,"usgs":true,"family":"Woodard","given":"Jacob","email":"","middleInitial":"Bryson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873052,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873053,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crawford, Matthew","contributorId":224687,"corporation":false,"usgs":false,"family":"Crawford","given":"Matthew","email":"","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":873054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Or, Dani","contributorId":267915,"corporation":false,"usgs":false,"family":"Or","given":"Dani","affiliations":[{"id":55530,"text":"ETH / DRI","active":true,"usgs":false}],"preferred":false,"id":873055,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leshchinsky, Ben","contributorId":267910,"corporation":false,"usgs":false,"family":"Leshchinsky","given":"Ben","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":873056,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873057,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":873058,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243709,"text":"70243709 - 2023 - Heavy: Software for forward-modeling gravity change from MODFLOW output","interactions":[],"lastModifiedDate":"2023-05-18T12:45:59.131321","indexId":"70243709","displayToPublicDate":"2023-05-18T07:43:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Heavy: Software for forward-modeling gravity change from MODFLOW output","docAbstract":"Fortran software, named Heavy, was developed to simulate gravity change due to water-storage change in MODFLOW groundwater models. Heavy is compatible with MODFLOW-2005 and MODFLOW-NWT models using the layer-property flow or upstream weighting packages. All of the necessary information for the gravity calculation—the geometry of the model cells, the storage coefficient, and head change—is present within the existing MODFLOW model files and no additional information is necessary. Gravity change is calculated at each time step, for each layer, at user specified locations or at a grid of hypothetical positions across the model. The software has been validated using analytical gravity solutions and three example MODFLOW models are included for demonstration. Heavy leverages the input/output routines from MODFLOW and is orders of magnitude faster than previous efforts using interpreted languages such as Python or MATLAB. The objective of the software is to facilitate repeat microgravity field measurements for groundwater-flow model calibration.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2023.105714","usgsCitation":"Kennedy, J.R., and Larsen, J., 2023, Heavy: Software for forward-modeling gravity change from MODFLOW output: Environmental Modelling and Software, v. 165, 105714, 7 p., https://doi.org/10.1016/j.envsoft.2023.105714.","productDescription":"105714, 7 p.","ipdsId":"IP-137200","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":435325,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IIHXN3","text":"USGS data release","linkHelpText":"Heavy"},{"id":417201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"165","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kennedy, Jeffrey R. 0000-0002-3365-6589 jkennedy@usgs.gov","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":176478,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","email":"jkennedy@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":873014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873015,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70244257,"text":"70244257 - 2023 - Future climate-induced changes in mixing and deep oxygen content of a caldera lake with hydrothermal heat and salt inputs","interactions":[],"lastModifiedDate":"2023-06-09T12:01:12.277301","indexId":"70244257","displayToPublicDate":"2023-05-18T06:55:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Future climate-induced changes in mixing and deep oxygen content of a caldera lake with hydrothermal heat and salt inputs","docAbstract":"Vertical profiles of temperature, salinity and dissolved oxygen in Crater Lake, a caldera lake in the Oregon Cascade Range that receives hydrothermal inputs of heat and salt, were simulated with a 1-dimensional model. Twelve Global Circulation Models and two Representative Concentration Pathways (RCPs) were used to develop boundary conditions from 1950 to 2099. The model simulated the ventilation of deep water initiated by reverse stratification and subsequent thermobaric instability. All models predicted a reduction in the frequency of deep ventilation events, from an ensemble median frequency of 5.4 winters decade−1 during 1950–2005 to 4.3 (RCP4.5) or 2.5 (RCP8.5) winters decade−1 during 2045–2099. Favorable conditions for thermobaric instability-induced mixing currently occur infrequently and will become rare in the future. The salinity gradient resulting from hydrothermal inputs presents an additional barrier to thermobaric instability that will continue through 2099. A redistribution of salt to the deep lake may prevent ventilation all the way to the bottom in the future. Hypolimnetic dissolved oxygen percent saturation remained above 75% within the 21st century, consistent with oligotrophy and very small oxygen demands. The rate of change in all variables accelerated approaching 2099, coincident with elimination of winter reverse stratification. Historically, about half of the hydrothermal heat added to Crater Lake has been vented to the atmosphere. In the RCP8.5 scenario, the hydrothermal heat will cease to be vented to the atmosphere by the end of the 21st century, and then the temperature of the deep waters will increase rapidly.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.03.014","usgsCitation":"Wood, T.M., Wherry, S., Piccolroaz, S., and Girdner, S.F., 2023, Future climate-induced changes in mixing and deep oxygen content of a caldera lake with hydrothermal heat and salt inputs: Journal of Great Lakes Research, v. 49, no. 3, p. 563-580, https://doi.org/10.1016/j.jglr.2023.03.014.","productDescription":"18 p.","startPage":"563","endPage":"580","ipdsId":"IP-150869","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":443494,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2023.03.014","text":"Publisher Index Page"},{"id":435329,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96NLDLX","text":"USGS data release","linkHelpText":"1-D Deep Ventilation (1DDV) model for Crater Lake, Oregon, 1950-2100"},{"id":417960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Crater Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.2356963721314,\n 43.03291163525574\n ],\n [\n -122.2356963721314,\n 42.848854312940176\n ],\n [\n -121.96965348050013,\n 42.848854312940176\n ],\n [\n -121.96965348050013,\n 43.03291163525574\n ],\n [\n -122.2356963721314,\n 43.03291163525574\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Tamara M. 0000-0001-6057-8080 tmwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6057-8080","contributorId":1164,"corporation":false,"usgs":true,"family":"Wood","given":"Tamara","email":"tmwood@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875049,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wherry, Susan 0000-0002-6749-8697 swherry@usgs.gov","orcid":"https://orcid.org/0000-0002-6749-8697","contributorId":140159,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan","email":"swherry@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875050,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Piccolroaz, Sebastiano","contributorId":297277,"corporation":false,"usgs":false,"family":"Piccolroaz","given":"Sebastiano","affiliations":[{"id":64342,"text":"University of Trento, Department of Civil, Environmental and Mechanical Engineering, Trento, Italy","active":true,"usgs":false}],"preferred":false,"id":875051,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Girdner, Scott F","contributorId":168526,"corporation":false,"usgs":false,"family":"Girdner","given":"Scott","email":"","middleInitial":"F","affiliations":[{"id":5106,"text":"National Park Service, Yellowstone National Park, Mammoth, Wyoming 82190","active":true,"usgs":false}],"preferred":false,"id":875052,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250988,"text":"70250988 - 2023 - Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes","interactions":[],"lastModifiedDate":"2024-01-18T11:54:48.157094","indexId":"70250988","displayToPublicDate":"2023-05-18T05:53:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes","docAbstract":"Small waterbodies have potentially high greenhouse gas emissions relative to their small footprint on the landscape, although there is high uncertainty in model estimates. Scaling their carbon dioxide (CO2) and methane (CH4) exchange with the atmosphere remains challenging due to an incomplete understanding and characterization of spatial and temporal variability in CO2 and CH4. Here, we measured partial pressures of CO2 (pCO2) and CH4 (pCH4) across 30 ponds and shallow lakes during summer in temperate regions of Europe and North America. We sampled each waterbody in three locations at three times during the growing season, and tested which physical, chemical, and biological characteristics related to the means and variability of pCO2 and pCH4 in space and time. Summer means of pCO2 and pCH4 were inversely related to waterbody size and positively related to floating vegetative cover; pCO2 was also positively related to dissolved phosphorus. Temporal variability in partial pressure in both gases weas greater than spatial variability. Although sampling on a single date was likely to misestimate mean seasonal pCO2 by up to 26%, mean seasonal pCH4 could be misestimated by up to 64.5%. Shallower systems displayed the most temporal variability in pCH4 and waterbodies with more vegetation cover had lower temporal variability. Inland waters remain one of the most uncertain components of the global carbon budget; understanding spatial and temporal variability will ultimately help us to constrain our estimates and inform research priorities.
Greater sage-grouse (Centrocercus urophasianus) are at the center of state and national land-use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with best-available science to help guide current management and conservation plans aimed at benefitting sage-grouse populations. This analysis relied on previously published population trend modeling methodology from Coates and others (2021, 2022a) and incorporated population lek count data through 2022. Bayesian state-space models estimated 2.9 percent average annual decline in sage-grouse populations across their geographical range, which varied among subpopulations at the largest scale of analysis, termed climate clusters (2.2–4.7). Cumulative declines were 40.9, 65.0, and 79.6 percent range-wide across short (19 years), medium (35 years), and long (55 years) temporal periods, respectively. These results indicate that the most recent nadir for range-wide populations occurred during 2021. However, growth during 2022 was modest, making 2021 a tentative final nadir at this point.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1175","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the Bureau of Land Management","programNote":"Species Management Research Program","usgsCitation":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2023, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2022: U.S. Geological Survey Data Report 1175, 17 p., https://doi.org/10.3133/dr1175.","productDescription":"Report: viii, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","ipdsId":"IP-151795","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":417065,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1175/full"},{"id":417060,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OQWGIV","text":"Trends and a targeted annual warning system for greater sage-grouse in the western United States (ver. 2.0, May 2023)","description":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2023, Trends and a targeted annual warning system for greater sage-grouse in the western United States (ver. 2.0, May 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9OQWGIV."},{"id":417061,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1175/covrthb.jpg"},{"id":417062,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1175/dr1175.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417063,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1175/dr1175.xml"},{"id":417064,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1175/images"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103,\n 49\n ],\n [\n -122,\n 49\n ],\n [\n -122,\n 36\n ],\n [\n -103,\n 36\n ],\n [\n -103,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Bioaccumulation of ionizable pharmaceuticals has been increasingly studied, with most reported aquatic tissue concentrations in field or laboratory experiments being from fish. However, higher levels of antidepressants have been observed in bivalves compared with fish from effluent-dominated and dependent surface waters. Such observations may be important for biodiversity because approximately 70% of freshwater bivalves in North America are considered to be vulnerable to extinction. Because experimental bioaccumulation information for freshwater bivalves is lacking, we examined accumulation dynamics in the freshwater pondmussel, Sagittunio subrostratus, following exposure to a model weak acid, acetaminophen (mean (±SD) = 4.9 ± 1 µg L–1), and a model weak base, sertraline (mean (±SD) = 1.1 ± 1.1 µg L–1) during 14-day uptake and 7-day depuration experiments. Pharmaceutical concentrations were analyzed in water and tissue using isotope dilution liquid chromatography–tandem mass spectrometry. Mussels accumulated two orders of magnitude higher concentrations of sertraline (31.7 ± 9.4 µg g–1) compared to acetaminophen (0.3 ± 0.1 µg g–1). Ratio and kinetic-based bioaccumulation factors of 28,836.4 (L kg–1) and 34.9 (L kg–1) were calculated for sertraline and for acetaminophen at 65.3 (L kg–1) and 0.13 (L kg–1), respectively. However, after 14 days sertraline did not reach steady-state concentrations, although it was readily eliminated by S. subrostratus. Acetaminophen rapidly reached steady-state conditions but was not depurated over a 7-day period. Future bioaccumulation studies of ionizable pharmaceuticals in freshwater bivalves appear warranted. Environ Toxicol Chem 2023;00:1–7. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.5590","usgsCitation":"Burket, S., Sims, J.L., Dorman, R.A., Kemble, N.E., Brunson, E., Steevens, J.A., and Brooks, B.W., 2023, Bioaccumulation kinetics of model pharmaceuticals in the freshwater unionid pondmussel, Sagittunio subrostratus: Environmental Toxicology and Chemistry, v. 42, no. 6, p. 1183-1189, https://doi.org/10.1002/etc.5590.","productDescription":"7 p.","startPage":"1183","endPage":"1189","ipdsId":"IP-136216","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":499335,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5590","text":"Publisher Index Page"},{"id":435331,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HVMQJL","text":"USGS data release","linkHelpText":"Morphometric measurements from unionid Pondmussel (Ligumia subrostrata) and concentrations of four per- and polyfluoroalkyl substances (PFAS) in water and mussels collected from a 14-day accumulation and 7-day elimination study"},{"id":435330,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OUHJ8N","text":"USGS data release","linkHelpText":"Concentration of sertraline and acetaminophen in freshwater mussel (Sagittunio subrostratus) and water from an exposure bioassay"},{"id":417132,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","county":"Boone County","otherGeospatial":"Lake Paragon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.214285899655,\n 38.856471915263995\n ],\n [\n -92.21393836929818,\n 38.85738644821262\n ],\n [\n -92.21326727619508,\n 38.85836629189188\n ],\n [\n -92.21301561628127,\n 38.85853426373845\n ],\n [\n -92.21300363247597,\n 38.858711566924285\n ],\n [\n -92.21312347053026,\n 38.85874889385451\n ],\n [\n -92.21278792397871,\n 38.85912216207768\n ],\n [\n -92.21269205353535,\n 38.859579412980565\n ],\n [\n -92.21283585920021,\n 38.85985002947703\n ],\n [\n -92.21317140575175,\n 38.85992468212277\n ],\n [\n -92.21335116283278,\n 38.8598686926461\n ],\n [\n -92.21345901708142,\n 38.85989668738969\n ],\n [\n -92.21386646646538,\n 38.85952342323205\n ],\n [\n -92.2140102721306,\n 38.85923414216251\n ],\n [\n -92.21423796443312,\n 38.85900085011963\n ],\n [\n -92.21499294417428,\n 38.85852493197967\n ],\n [\n -92.21517270125533,\n 38.85817965604173\n ],\n [\n -92.21585577816369,\n 38.85761041367235\n ],\n [\n -92.21593966480175,\n 38.85737711630321\n ],\n [\n -92.2160834704666,\n 38.85737711630321\n ],\n [\n -92.21628719515857,\n 38.85686385939647\n ],\n [\n -92.21625124374236,\n 38.85667721960269\n ],\n [\n -92.21640703321249,\n 38.85621061797582\n ],\n [\n -92.21664670932068,\n 38.85589332712095\n ],\n [\n -92.21614338949448,\n 38.85483205731646\n ],\n [\n -92.21580784294294,\n 38.854421437393256\n ],\n [\n -92.21558015064004,\n 38.85435611127798\n ],\n [\n -92.21512476603421,\n 38.85438410819165\n ],\n [\n -92.21462144620727,\n 38.854692073517924\n ],\n [\n -92.21435780248817,\n 38.85506536303453\n ],\n [\n -92.21439375390437,\n 38.85558796506638\n ],\n [\n -92.21435780248817,\n 38.85575594347523\n ],\n [\n -92.21441772151529,\n 38.855867928860704\n ],\n [\n -92.21442970532058,\n 38.85609189910329\n ],\n [\n -92.2142139968233,\n 38.85612922740793\n ],\n [\n -92.21413011018525,\n 38.85629720453841\n ],\n [\n -92.214285899655,\n 38.856471915263995\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-02-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Brunson, Eric 0000-0001-6624-0902","orcid":"https://orcid.org/0000-0001-6624-0902","contributorId":201761,"corporation":false,"usgs":true,"family":"Brunson","given":"Eric","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":872908,"contributorType":{"id":2,"text":"Editors"},"rank":5}],"authors":[{"text":"Burket, S. Rebekah","contributorId":303970,"corporation":false,"usgs":false,"family":"Burket","given":"S. Rebekah","affiliations":[{"id":13716,"text":"Baylor University","active":true,"usgs":false}],"preferred":false,"id":872902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sims, Jaylen L.","contributorId":305480,"corporation":false,"usgs":false,"family":"Sims","given":"Jaylen","email":"","middleInitial":"L.","affiliations":[{"id":13716,"text":"Baylor University","active":true,"usgs":false}],"preferred":false,"id":872903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dorman, Rebecca A. 0000-0002-5748-7046","orcid":"https://orcid.org/0000-0002-5748-7046","contributorId":28522,"corporation":false,"usgs":true,"family":"Dorman","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":872904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kemble, Nile E. 0000-0002-3608-0538 nkemble@usgs.gov","orcid":"https://orcid.org/0000-0002-3608-0538","contributorId":2626,"corporation":false,"usgs":true,"family":"Kemble","given":"Nile","email":"nkemble@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":872905,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brunson, Eric 0000-0001-6624-0902","orcid":"https://orcid.org/0000-0001-6624-0902","contributorId":201761,"corporation":false,"usgs":true,"family":"Brunson","given":"Eric","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":872930,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":872906,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brooks, Bryan W. 0000-0002-6277-9852","orcid":"https://orcid.org/0000-0002-6277-9852","contributorId":198868,"corporation":false,"usgs":false,"family":"Brooks","given":"Bryan","email":"","middleInitial":"W.","affiliations":[{"id":35352,"text":"Department of Environmental Science, Baylor University, Waco, TX, USA","active":true,"usgs":false}],"preferred":false,"id":872907,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70244211,"text":"70244211 - 2023 - Supervised versus unsupervised approaches to classification of accelerometry data","interactions":[],"lastModifiedDate":"2023-06-07T13:50:04.432165","indexId":"70244211","displayToPublicDate":"2023-05-17T08:46:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Supervised versus unsupervised approaches to classification of accelerometry data","docAbstract":"Sophisticated animal-borne sensor systems are increasingly providing novel insight into how animals behave and move. Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often used to meet this need. However, their relative effectiveness is not well known and, in the case of unsupervised tools, given that they do not use validation data, their accuracy can be difficult to assess. We evaluated the effectiveness of supervised (n = 6), semi-supervised (n = 1), and unsupervised (n = 2) approaches to analyzing accelerometry data collected from critically endangered California condors (Gymnogyps californianus). Unsupervised K-means and EM (expectation–maximization) clustering approaches performed poorly, with adequate classification accuracies of <0.8 but very low values for kappa statistics (range: −0.02 to 0.06). The semi-supervised nearest mean classifier was moderately effective at classification, with an overall classification accuracy of 0.61 but effective classification only of two of the four behavioral classes. Supervised random forest (RF) and k-nearest neighbor (kNN) machine learning models were most effective at classification across all behavior types, with overall accuracies >0.81. Kappa statistics were also highest for RF and kNN, in most cases substantially greater than for other modeling approaches. Unsupervised modeling, which is commonly used for the classification of a priori-defined behaviors in telemetry data, can provide useful information but likely is instead better suited to post hoc definition of generalized behavioral states. This work also shows the potential for substantial variation in classification accuracy among different machine learning approaches and among different metrics of accuracy. As such, when analyzing biotelemetry data, best practices appear to call for the evaluation of several machine learning techniques and several measures of accuracy for each dataset under consideration.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10035","usgsCitation":"Sur, M., Hall, J.C., Brandt, J., Astell, M., Poessel, S.A., and Katzner, T., 2023, Supervised versus unsupervised approaches to classification of accelerometry data: Ecology and Evolution, v. 13, no. 5, e10035, 11 p., https://doi.org/10.1002/ece3.10035.","productDescription":"e10035, 11 p.","ipdsId":"IP-144075","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":443508,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10035","text":"Publisher Index Page"},{"id":435332,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PAVUEZ","text":"USGS data release","linkHelpText":"Tri-axial acceleration data from California condors (Gymnogyps californianus), California, USA"},{"id":417910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Sur, Maitreyi","contributorId":191354,"corporation":false,"usgs":false,"family":"Sur","given":"Maitreyi","email":"","affiliations":[],"preferred":false,"id":874874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Jonathan C.","contributorId":202606,"corporation":false,"usgs":false,"family":"Hall","given":"Jonathan","email":"","middleInitial":"C.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":874875,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, Joseph","contributorId":127742,"corporation":false,"usgs":false,"family":"Brandt","given":"Joseph","affiliations":[{"id":7133,"text":"California Condor Recovery Program, US Fish and Wildlife Service, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":874876,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Astell, Molly","contributorId":199753,"corporation":false,"usgs":false,"family":"Astell","given":"Molly","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":874877,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":874878,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":874879,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243691,"text":"70243691 - 2023 - Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast","interactions":[],"lastModifiedDate":"2023-06-27T16:55:28.533795","indexId":"70243691","displayToPublicDate":"2023-05-17T08:23:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast","docAbstract":"A 38-year hindcast water level product is developed for the U.S. Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water level modelling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modelled water level components. Corrected water level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.","language":"English","publisher":"Springer","doi":"10.1007/s11069-023-05939-6","usgsCitation":"Parker, K.A., Erikson, L.H., Thomas, J.A., Nederhoff, C.M., Barnard, P.L., and Muis, S., 2023, Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast: Natural Hazards, v. 117, p. 2219-2248, https://doi.org/10.1007/s11069-023-05939-6.","productDescription":"30 p.","startPage":"2219","endPage":"2248","ipdsId":"IP-145520","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443513,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11069-023-05939-6","text":"Publisher Index 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However, the spatial variations and mechanisms in vertical accretion and soil organic carbon (SOC) sequestration across this highly urbanized estuary remains unclear. In this study, we collected soil cores to a depth of ~ 50 cm across Jamaica Bay to study the spatial variability in long-term (50–100 years) vertical accretion, the accumulation of mineral sediment and organic matter, and SOC sequestration. Results of gamma spectrometry analysis of 137Cs and 210Pb show that there was moderate spatial variability in long-term vertical accretion rates across Jamaica Bay study sites (mean: 0.48 ± 0.13 cm yr− 1, range: 0.36–0.78 cm yr− 1). This local scale spatial variability in vertical accretion is largely driven by spatial variations of sedimentation. The magnitude of the long-term vertical accretion in Jamaica Bay is significantly correlated with organic matter accumulation, but not with mineral sediment. However, the role of organic matter in contributing to vertical accretion has been declining. The declining role of organic matter to vertical accretion is reflected by the lower SOC sequestration rate (mean: 128 and range: 26–189 g C m− 2 yr− 1 using the 210Pb dating technique) compared to the global mean salt marsh SOC sequestration rate (244 g C m− 2 yr− 1). This is especially so on the marsh islands in the degrading western part of the bay where SOC sequestration was less than half the global average.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s13157-023-01699-y","usgsCitation":"Wang, H., Snedden, G., Hartig, E., and Chen, Q., 2023, Spatial variability in vertical accretion and carbon sequestration in salt marsh soils of an urban estuary: Wetlands, v. 43, no. 5, 49, 16 p.; Data Release, https://doi.org/10.1007/s13157-023-01699-y.","productDescription":"49, 16 p.; Data Release","ipdsId":"IP-147054","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":418859,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418858,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XQBYXU","text":"Soil property and geochronology (137Cs and 210Pb) data (2014) in salt marsh soils of Jamaica Bay Estuary, New York City"}],"country":"United States","state":"New York","city":"New York City","otherGeospatial":"Jamaica Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.9351951637045,\n 40.55401609118147\n ],\n [\n -73.76456876917054,\n 40.59793722111755\n ],\n [\n -73.73117649148077,\n 40.6337950544268\n ],\n [\n -73.7409498410488,\n 40.65326150244678\n ],\n [\n -73.75357375090694,\n 40.644919434207\n ],\n [\n -73.77637823323138,\n 40.626377771367885\n ],\n [\n -73.81832219179216,\n 40.65233465747326\n ],\n [\n -73.85334336107574,\n 40.6541883345451\n ],\n [\n -73.88632841586649,\n 40.64275650554836\n ],\n [\n -73.89813787992736,\n 40.61710500899537\n ],\n [\n -73.88592119296749,\n 40.58433093987168\n ],\n [\n -73.89895232572465,\n 40.590515956374276\n ],\n [\n -73.9315301576164,\n 40.59731881385059\n ],\n [\n -73.9547418628391,\n 40.58927899883426\n ],\n [\n -73.9351951637045,\n 40.55401609118147\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Hongqing 0000-0002-2977-7732","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":219641,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":877277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Snedden, Gregg 0000-0001-7821-3709","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":213411,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":877278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hartig, Ellen K.","contributorId":179351,"corporation":false,"usgs":false,"family":"Hartig","given":"Ellen K.","affiliations":[],"preferred":false,"id":877279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Q. 0000-0002-6540-8758","orcid":"https://orcid.org/0000-0002-6540-8758","contributorId":56532,"corporation":false,"usgs":false,"family":"Chen","given":"Q.","affiliations":[{"id":38331,"text":"Northeastern University","active":true,"usgs":false}],"preferred":true,"id":877280,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70260123,"text":"70260123 - 2023 - Breaking plates: Creation of the East Anatolian fault, the Anatolian plate, and a tectonic escape system","interactions":[],"lastModifiedDate":"2024-10-29T13:58:16.517135","indexId":"70260123","displayToPublicDate":"2023-05-16T08:45:28","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Breaking plates: Creation of the East Anatolian fault, the Anatolian plate, and a tectonic escape system","docAbstract":"Lateral movement of lithospheric fragments along strike-slip faults in response to collision (escape tectonics) has characterized convergent settings since the onset of plate tectonics and is a mechanism for the formation of new plates. The Anatolian plate was created by the sequential connection of strike-slip faults following ≥10 m.y. of distributed deformation that ultimately localized into plate-bounding faults. Thermochronology data and seismic images of lithosphere structure near the East Anatolian fault zone (EAFZ) provide insights into the development of the new plate and escape system. Low-temperature thermochronology ages of rocks in and near the EAFZ are significantly younger than in other fault zones in the region, e.g., apatite (U-Th)/He: 11–1 Ma versus 27–13 Ma. Young apatite (U-Th)/He ages and thermal history modeling record thermal resetting along the EAFZ over the past ~5 m.y. and are interpreted to indicate thermal activity triggered by strike-slip faulting in the EAFZ as it formed as a through-going, lithosphere-scale structure. The mechanism for EAFZ formation may be discerned from S-wave velocity images from the Continental Dynamics–Central Anatolian Tectonics (CD-CAT) seismic experiment. These images indicate that thin but strong Arabian lithospheric mantle extends ~50–150 km north beneath Anatolian crust and would have been located near the present surficial location of the Bitlis-Zagros suture zone (co-located with the EAFZ in our study area) at ca. 5 Ma. Underthrusting of strong Arabian lithosphere facilitated localization of the EAFZ and thus was a fundamental control on the formation of the Anatolian plate and escape system.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G51211.1","usgsCitation":"Whitney, D., Delph, J., Thomson, S.N., Beck, S.L., Brocard, G., Cosca, M., Darin, M.H., Kaymakci, N., Meijers, M.J., Okay, A., Rojay, B., Teyssier, C., and Umhoefer, P.J., 2023, Breaking plates: Creation of the East Anatolian fault, the Anatolian plate, and a tectonic escape system: Geology, v. 51, no. 7, p. 673-677, https://doi.org/10.1130/G51211.1.","productDescription":"5 p.","startPage":"673","endPage":"677","ipdsId":"IP-146320","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":467111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/g51211.1","text":"Publisher Index Page"},{"id":463320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","otherGeospatial":"East Anatolian 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(2018)","interactions":[],"lastModifiedDate":"2023-05-16T13:39:15.608596","indexId":"70243632","displayToPublicDate":"2023-05-16T08:37:56","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Rapid modeling of compound flooding across broad coastal regions and the necessity to include rainfall driven processes: A case study of Hurricane Florence (2018)","docAbstract":"In this work, we show that large-scale compound flood models developed for North and South Carolina, USA, can skillfully simulate multiple drivers of coastal flooding as confirmed by measurements collected during Hurricane Florence (2018). Besides the accuracy of representing observed water levels, the importance of individual processes was investigated. We demonstrate that across the area of interest, it is necessary to include marine, pluvial, and fluvial forcing and the processes of wind stress and infiltration to correctly model water levels along the coast and further inland. This work highlights the need to include these processes in modeling coastal compound flooding. By using high-resolution topo-bathymetry that is incorporated via subgrid derived tables in the Super-Fast INundation of CoastS (SFINCS) model, we improved the skill of the model at efficiently simulating flooding across large-scale domains with locally relevant results.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Coastal Sediments 2023: Proceedings of the Coastal Sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2023","conferenceDate":"April 11-15, 2023","conferenceLocation":"New Orleans, Louisiana, United States","language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0235","usgsCitation":"Leijnse, T., Nederhoff, C.M., Thomas, J.A., Parker, K.A., van Ormondt, M., Erikson, L.H., McCall, R.T., van Dongeren, A., O'Neill, A., and Barnard, P.L., 2023, Rapid modeling of compound flooding across broad coastal regions and the necessity to include rainfall driven processes: A case study of Hurricane Florence (2018), in Coastal Sediments 2023: Proceedings of the Coastal Sediments 2023, New Orleans, Louisiana, United States, April 11-15, 2023, p. 2576-2584, https://doi.org/10.1142/9789811275135_0235.","productDescription":"9 p.","startPage":"2576","endPage":"2584","ipdsId":"IP-147563","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":417087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, South 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,{"id":70246260,"text":"70246260 - 2023 - Rift basins and intraplate earthquakes: New high-resolution aeromagnetic data provide insights into buried structures of the Charleston, South Carolina seismic zone","interactions":[],"lastModifiedDate":"2023-06-28T13:26:15.350566","indexId":"70246260","displayToPublicDate":"2023-05-16T08:19:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Rift basins and intraplate earthquakes: New high-resolution aeromagnetic data provide insights into buried structures of the Charleston, South Carolina seismic zone","docAbstract":"The delineation of faults that pose seismic risk in intraplate seismic zones and the mapping of features associated with failed rift basins can help our understanding of links between the two. We use new high-resolution aeromagnetic data, previous borehole sample information, and reprocessed seismic reflection profiles to image subsurface structures and evaluate recent fault activity within the Charleston seismic zone, the associated Mesozoic South Georgia rift basin, and surrounds. The new aeromagnetic data provide an unprecedented view of buried basement structures. NE- and NW-trending lineaments of various lengths throughout the survey area are interpreted as Paleozoic orogenic structures and Mesozoic dikes, respectively. Within the rift basin, 15- to 20-km long ESE-trending lineaments are associated with faults in pre-Cretaceous strata of the reflection data and are interpreted as Mesozoic rift structures. Various intersections and terminations of interpreted faults suggest rift-related reactivation of Paleozoic faults and corresponding inheritance for Mesozoic structures. The reflection data show that several Paleozoic and Mesozoic faults are associated with deformation in Cretaceous and younger sediments, suggesting reactivation in the more recent passive margin setting. Two of these faults, one NE-striking and one ESE-striking, are coincident with surficial landforms, suggesting Quaternary slip; the ESE-striking fault is also well-aligned with a plan-view offset in modern seismicity. A favorable orientation for reverse motion on ESE-striking Mesozoic faults, a possible sub-basin, and potentially weakened lithosphere are failed rift basin features that may influence intraplate seismicity within the Charleston seismic zone.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GC010803","usgsCitation":"Shah, A.K., Pratt, T.L., and Horton,, J., 2023, Rift basins and intraplate earthquakes: New high-resolution aeromagnetic data provide insights into buried structures of the Charleston, South Carolina seismic zone: Geochemistry, Geophysics, Geosystems, v. 24, e2022GC010803, 25 p., https://doi.org/10.1029/2022GC010803.","productDescription":"e2022GC010803, 25 p.","ipdsId":"IP-144502","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443523,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gc010803","text":"Publisher Index Page"},{"id":418582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","otherGeospatial":"Charleston seismic zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.62681453773914,\n 34.845813308656375\n ],\n [\n -81.62681453773914,\n 32.343066119325954\n ],\n [\n -79.1425481149703,\n 32.343066119325954\n ],\n [\n -79.1425481149703,\n 34.845813308656375\n ],\n [\n -81.62681453773914,\n 34.845813308656375\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationDate":"2023-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":876471,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":876472,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horton,, J. Wright Jr. 0000-0001-6756-6365","orcid":"https://orcid.org/0000-0001-6756-6365","contributorId":219824,"corporation":false,"usgs":true,"family":"Horton,","given":"J. Wright","suffix":"Jr.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":876473,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243642,"text":"70243642 - 2023 - Incorporating wave climate complexity into modeling lower shoreface morphology and transport","interactions":[],"lastModifiedDate":"2023-05-16T12:58:28.507126","indexId":"70243642","displayToPublicDate":"2023-05-16T07:36:43","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Incorporating wave climate complexity into modeling lower shoreface morphology and transport","docAbstract":"The lower shoreface, a transitional subaqueous region extending from the seaward limit of the surf zone to beyond the closure depth, serves as a sediment reservoir and pathway in sandy beach environments over annual to millennial time scales. Despite the important role this region plays in shoreline dynamics, the morphodynamics of the lower shoreface remain poorly quantified and understood. To better understand controls on shoreface morphology, here we combine energetics-based suspended sediment transport formulae (Ortiz & Aston 2016) with empirical wave climate data to incorporate temporal complexity in modeled equilibrium profiles and sediment flux rates. The equilibrium shoreface shape computed using a full wave climate is steeper in shallower water and less steep in the deeper reaches compared to profiles computed using single wave characteristics. Using a full wave climate to simulate steady-state morphology will yield steeper profiles in shallow water. Suspended sediment transport rates also vary in direction and magnitude at different equilibrium profile depths and can potentially inform the location of morphodynamic boundaries in the shoreface. Our results reveal how infrequent storm waves affect shoreface slopes, with large events tending to drive sediment onshore in the deeper portions of the profile. This work explores a few ways to add complexity to simple energetics-based frameworks to reproduce empirical bathymetric data more accurately and provides insight toward refining coastal source-to-sink models.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Coastal Sediments 2023, proceedings of the 10th international conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2023","conferenceDate":"April 11-15, 2023","conferenceLocation":"New Orleans, Louisiana, United States","language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0260","usgsCitation":"Gillen, M., Ashton, A.D., Miselis, J.L., Ciarletta, D.J., Wei, E.A., and Sherwood, C.R., 2023, Incorporating wave climate complexity into modeling lower shoreface morphology and transport, in Coastal Sediments 2023, proceedings of the 10th international conference, New Orleans, Louisiana, United States, April 11-15, 2023, p. 2862-2874, https://doi.org/10.1142/9789811275135_0260.","productDescription":"13 p.","startPage":"2862","endPage":"2874","ipdsId":"IP-147824","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":417085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"editors":[{"text":"Wang, Ping","contributorId":78646,"corporation":false,"usgs":false,"family":"Wang","given":"Ping","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":872817,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Royer, Elizabeth","contributorId":305463,"corporation":false,"usgs":false,"family":"Royer","given":"Elizabeth","email":"","affiliations":[],"preferred":false,"id":872818,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Rosati, Julie D.","contributorId":112486,"corporation":false,"usgs":false,"family":"Rosati","given":"Julie D.","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":872819,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Gillen, Megan 0000-0002-2375-6519","orcid":"https://orcid.org/0000-0002-2375-6519","contributorId":267190,"corporation":false,"usgs":false,"family":"Gillen","given":"Megan","email":"","affiliations":[{"id":55436,"text":"MIT-WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering, Cambridge and Woods Hole, MA, USA","active":true,"usgs":false}],"preferred":false,"id":872692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ashton, Andrew D.","contributorId":300047,"corporation":false,"usgs":false,"family":"Ashton","given":"Andrew","email":"","middleInitial":"D.","affiliations":[{"id":16633,"text":"WHOI","active":true,"usgs":false}],"preferred":false,"id":872693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ciarletta, Daniel J. 0000-0002-8555-2239","orcid":"https://orcid.org/0000-0002-8555-2239","contributorId":256700,"corporation":false,"usgs":true,"family":"Ciarletta","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872695,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wei, Emily A. 0000-0003-4008-0933","orcid":"https://orcid.org/0000-0003-4008-0933","contributorId":223488,"corporation":false,"usgs":true,"family":"Wei","given":"Emily","email":"","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872696,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872697,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70244217,"text":"70244217 - 2023 - Guidance for parameterizing post-fire hydrologic models with in situ infiltration measurements","interactions":[],"lastModifiedDate":"2024-06-18T13:52:44.245324","indexId":"70244217","displayToPublicDate":"2023-05-16T07:20:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Guidance for parameterizing post-fire hydrologic models with in situ infiltration measurements","docAbstract":"Wildfire can alter soil-hydraulic properties, often resulting in an increased prevalence of infiltration-excess overland flow and greater potential for debris-flow hazards. Mini disk tension infiltrometers (MDIs) can be used to estimate soil hydraulic properties, such as field-saturated hydraulic conductivity (Kfs) and wetting front potential (Hf), and their spatial variability following wildfire. However, the small (point-scale) footprint of MDI measurements makes it challenging to use these data to parameterize hydrologic models at the hillslope and watershed scales where hydrologic hazards, such as debris flows, initiate. Here, we designed numerical experiments to estimate spatially constant or watershed-scale effective hydrologic parameters (EHPs) that approximate the response of spatially variable hydrologic parameters with distributions derived from MDI measurements at five sites in the southwestern United States. We found that it is possible to define EHPs for both Kfs and Hf based on the MDI measurements that lead to reasonable approximations of run-off hydrographs at the outlets of small watersheds (<1 km2). We found that watershed EHPs are functions of rainfall characteristics, although they are most sensitive to rainfall intensity and relatively less sensitive to the temporal distribution of rainfall. EHPs are lower than the arithmetic mean of the MDI measurements and are better approximated by the median or geometric mean of the MDI measurements, particularly for storms with recurrence intervals of approximately 1 year or less that commonly initiate post-fire debris flows. This work demonstrated that using the proposed upscaling method to estimate watershed-scale EHPs, as opposed to approximating EHPs based on the arithmetic mean of the MDI measurements, improved the ability of a hydrologic model to identify storms that are likely to produce debris flows. Results improved our ability to link point-scale MDI measurements and watershed-scale EHPs in post-fire settings and helped guide our ability to use MDI data to parameterize post-fire hydrologic models.
We trapped, anesthetized, and fit 16 female feral swine (Sus scrofa) with Global Positioning System (GPS) collars in Great Smoky Mountains National Park (GRSM) to develop predictive summer and winter models for more effective population control efforts. Given the highly diverse habitat and topography in GRSM and the spatial extent of our dataset, we employed Step Selection Function (SSF) to evaluate resource selection at the 3rd-order level and Resource Selection Function (RSF) models at the 2nd-order level for both summer and winter seasons. The summer SSF and RSF models suggested relatively similar levels of selection, whereas the winter models differed by method. We created a straightforward consensus model to better visualize the agreement and constraints of each set of models. In summer, feral swine used lower slopes regardless of elevation, especially those closer to human-dominated spaces such as along paved and gravel roadways. In winter, feral swine maintained preference for lower slopes but preferred oak-dominated forest areas and selection for human development was less than in summer. Wildlife managers can use these models to better focus feral swine surveillance and management in GRSM. Managers can identify areas of high use by season and plan control activities that are both accessible and highly efficient. The combination and consensus framework presented here can be applied to other systems where species’ habitat selection may result in incongruous results across different levels of selection or seasons of interest.
During the economic boom of the mid part of the first decade of the 2000s in northwestern Nevada, municipal and housing growth increased use of the water resources of this semi-arid region. In 2008, when the economy slowed, new housing development stopped, and immediate pressure on groundwater resources abated. The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, began a hydrogeologic study of the middle Carson River Basin. The first half of the study reviewed and synthesized previous geologic studies and contributed new datasets that served as a foundation for a three-dimensional, transient numerical model of groundwater and surface-water flow for the middle Carson River Basin extending from Eagle Valley to Churchill Valley. The model can be used to evaluate the effects of proposed alternative management strategies on groundwater sustainability, flows in the Carson River, and routine operation of Lahontan Reservoir and can also provide a basis for basin-wide investigations seeking to quantitatively evaluate the effects of climate change or yet-to-be-determined alternative management strategies.
The middle Carson model was constructed using the U.S. Geological Survey groundwater modeling software MODFLOW-NWT. MODFLOW is widely used groundwater modeling software and is well-suited for evaluating groundwater and surface-water interactions. The model uses 550-feet square grid cells that align with the previously published model for Carson Valley (adjacent upstream valley). Six grid layers with more finely resolved vertical resolution near the perimeter of the active model domain and near surface-water features, compared to other areas of the active model domain, hone the simulated groundwater and surface-water exchanges. In addition to simulating groundwater and surface-water interaction, crop and phreatophyte evapotranspiration, lake evaporation, mountain-front recharge, recharge from irrigation return flows, and groundwater pumping are also simulated. Surface-water flow entering the model domain, including the Carson River, tributary inflow from perennial streams in Eagle Valley, and trans-basin imports through the Truckee Canal (surface water diverted from the Truckee River) are specified according to U.S. Geological Survey streamgage records. Groundwater pumpage and surface-water diversions to 10 agricultural ditches and the managed release from Lahontan Reservoir, at the end of the middle Carson River Basin, are specified according to water-manager records.
The model simulation period extended from 2000 through 2010 (January 1, 2000, to December 31, 2010) using 574 weekly stress periods, with a single steady-state stress period at the beginning of the simulation that establishes initial conditions by approximating average conditions during the transient simulation period. All available observations for this period were used during the model calibration process, performed using automated parameter-estimation software. Calibration targets included observations of groundwater elevations in wells, streamflow, differences in observed streamflow between successive streamgages and actual evapotranspiration from irrigated lands. Among all 5,296 simulated and observed groundwater level pairs, the mean error was 1.42 feet; the mean absolute error, 7.71 feet; and the percent bias was −0.1 percent.
Three alternative management scenarios, run using the entire period of analysis (2000–10), were simulated to improve understanding of the potential effects of (1) loss of irrigated agricultural lands following conversion of water-rights to municipal groundwater rights; (2) reclaiming treated wastewater with induction wells; and (3) exercising permitted but under-utilized groundwater rights. Scenarios 2 and 3 were further explored using two and four subscenarios, respectively. Simulated scenario results ranged from having little effect on the groundwater system relative to a baseline simulation to having spatially extensive and large groundwater-level declines (10 to 20 feet) compared to the baseline simulation. None of the simulated scenarios increased delivery of river flows to Lahontan Reservoir. On the contrary, one of the subscenarios under alternative management scenario 3 led to surface-water delivery shortfalls of more than 10,000 acre-feet per year.
Future model improvements may include an extension of the model simulation period backward and forward in time and directly linking it to the upstream Carson Valley groundwater model. Furthermore, converting this MODFLOW model to a GSFLOW model, which fully integrates groundwater and surface-water flows including precipitation runoff and infiltration, may provide an improved tool for comprehensive management of water-resources in the middle Carson River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235008","collaboration":"Prepared in cooperation withv the Bureau of Reclamation","usgsCitation":"Morway, E.D., Buto, S.G., Niswonger, R.G., and Huntington, J.L., 2023, Assessing potential effects of changes in water use in the middle Carson River Basin with a numerical groundwater-flow model, Eagle, Dayton, and Churchill Valleys, west-central Nevada: U.S. Geological Survey Scientific Investigations Report 2023–5008, 112 p., https://doi.org/10.3133/sir20235008.","productDescription":"Report: xiii, 112 p.; 3 Data Releases","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-034336","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":416912,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D3XO1U","text":"Data for the report assessing potential effects of changes in water use in the middle Carson River Basin with a numerical groundwater-flow model, Eagle, Dayton, and Churchill Valleys, west-central Nevada","description":"Morway, E.D., Buto, S.G., and Medina, R.L., 2023, Data for the report assessing potential effects of changes in water use in the middle Carson River Basin with a numerical groundwater-flow model, Eagle, Dayton, and Churchill Valleys, west-central Nevada: U.S. Geological Survey data release, https://doi.org/10.5066/P9D3XO1U."},{"id":416913,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N9FNQZ","text":"MODFLOW-NWT model used to simulate potential effects of changes in water use in the middle Carson River Basin, Eagle, Dayton, and Churchill Valleys, west-central, Nevada","description":"Morway, E.D., Niswonger, R.G., and Buto, S.G., 2023, MODFLOW-NWT model used to simulate potential effects of changes in water use in the middle Carson River Basin, Eagle, Dayton, and Churchill Valleys, west-central, Nevada: U.S. Geological Survey data release, https://doi.org/10.5066/P9N9FNQZ."},{"id":416907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5008/covrthb.jpg"},{"id":416908,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5008/sir20235008.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416909,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5008/sir20235008.xml"},{"id":416910,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5008/images"},{"id":416911,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235008/full"},{"id":416921,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P5LJ3P","text":"Data for the report Geologic Framework and Hydrogeology of the middle Carson River basin, Eagle, Dayton, and Churchill Valleys, West-Central Nevada","description":"Maurer, D.K., and Medina, R.L., 2020, Data for the report Geologic Framework and Hydrogeology of the middle Carson River basin, Eagle, Dayton, and Churchill Valleys, West-Central Nevada: U.S. Geological Survey data release, https://doi.org/10.5066/P9P5LJ3P."}],"country":"United States","state":"Nevada","otherGeospatial":"Middle Carson River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120,\n 40.5\n ],\n [\n -120,\n 38\n ],\n [\n -118,\n 38\n ],\n [\n -118,\n 40.5\n ],\n [\n -120,\n 40.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701