Physiological biomarkers are commonly used to assess the health of taxa exposed to natural and anthropogenic stressors. Glucocorticoid (GC) hormones are often used as indicators of physiological stress in wildlife because they affect growth, reproduction and survival. Increased salinity from human activities negatively influences amphibians and their corticosterone (CORT; the main amphibian GC) physiology; therefore, CORT could be a useful biomarker. We evaluated whether waterborne CORT could serve as a biomarker of salt stress for three free-living amphibian species that vary in their sensitivity to salinity: boreal chorus frogs (Pseudacris maculata), northern leopard frogs (Rana pipiens) and barred tiger salamanders (Ambystoma mavortium). Across a gradient of contamination from energy-related saline wastewaters, we tested the effects of salinity on baseline and stress-induced waterborne CORT of larvae. Stress-induced, but not baseline, CORT of leopard frogs increased with increasing salinity. Salinity was not associated with baseline or stress-induced CORT of chorus frogs or tiger salamanders. Associations between CORT and salinity were also not related to species-specific sensitivities to salinity. However, we detected background environmental CORT (ambient CORT) in all wetlands and spatial variation was high within and among wetlands. Higher ambient CORT was associated with lower waterborne CORT of larvae in wetlands. Therefore, ambient CORT likely confounded associations between waterborne CORT and salinity in our analysis and possibly influenced physiology of larvae. We hypothesize that larvae may passively take up CORT from their environment and downregulate endogenous CORT. Although effects of some hormones (e.g. oestrogen) and endocrine disruptors on aquatic organisms are well described, studies investigating the occurrence and effects of ambient CORT are limited. We provide suggestions to improve collection methods, reduce variability and avoid confounding effects of ambient CORT. By making changes to methodology, waterborne CORT could still be a promising, non-invasive conservation tool to evaluate effects of salinity on amphibians.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/conphys/coab049","usgsCitation":"Tornabene, B., Hossack, B., Crespi, E.J., and Breuner, C., 2021, Evaluating corticosterone as a biomarker for amphibians exposed to increased salinity and ambient corticosterone: Conservation Physiology, v. 9, no. 1, coab049, 15 p., https://doi.org/10.1093/conphys/coab049.","productDescription":"coab049, 15 p.","ipdsId":"IP-127959","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":451642,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coab049","text":"Publisher Index Page"},{"id":387129,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.205078125,\n 47.45780853075031\n ],\n [\n -101.162109375,\n 47.45780853075031\n ],\n [\n -101.162109375,\n 48.951366470947725\n ],\n [\n -105.205078125,\n 48.951366470947725\n ],\n [\n -105.205078125,\n 47.45780853075031\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Tornabene, Brian J.","contributorId":200041,"corporation":false,"usgs":false,"family":"Tornabene","given":"Brian J.","affiliations":[],"preferred":false,"id":819028,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":819029,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crespi, Erica J","contributorId":260876,"corporation":false,"usgs":false,"family":"Crespi","given":"Erica","email":"","middleInitial":"J","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":819030,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breuner, Creagh W","contributorId":241893,"corporation":false,"usgs":false,"family":"Breuner","given":"Creagh W","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":819031,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223681,"text":"70223681 - 2021 - Earthquake source mechanisms and stress field variations associated with wastewater-induced seismicity in southern Kansas, USA","interactions":[],"lastModifiedDate":"2021-09-01T13:02:26.421325","indexId":"70223681","displayToPublicDate":"2021-07-02T07:57:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Earthquake source mechanisms and stress field variations associated with wastewater-induced seismicity in southern Kansas, USA","docAbstract":"The strong increase of seismicity rates in the contiguous USA over the last 10 years is linked to the injection of huge amounts of wastewater from oil and gas production in unconventional hydrocarbon reservoirs. We calculated 549 moment tensors of induced earthquakes (MW ≤ 4.9) in southern Kansas to study their source mechanisms and their relation to injection activity. Seventeen percent of the events analyzed contained significant volumetric (ISO%) components, and these events mostly occurred near the two largest local earthquakes during the 4 months of largest active wastewater disposal. Mapping the local stress field, we determined that most of the region lies within a transtensional stress regime, with a maximum horizontal stress σHmax trending N75°E. In the epicentral area of the MW 4.9 Milan earthquake, the σHmax trend is rotated to about S80°E. Locally, two areas display a change in the stress field orientation with depth, from transtensional above 5.5 km depth to strike slip deeper in the basement. Relating the resolved fault geometries to the obtained local stress field orientation, we find that most of the activated fault planes were optimally oriented to the current stress field and thus small stress perturbations caused by the water injection could lead to failure.
Tracing produced water origins from wells hydraulically fractured with freshwater-based fluids is sometimes predicated on assumptions that (1) each geological formation contains compositionally unique brine and (2) produced water from recently hydraulically fractured wells resembles fresher meteoric water more so than produced water from older wells. These assumptions are not valid in Williston Basin oil wells sampled in this study. Although distinct average 228Ra/226Ra ratios were found in water produced from the Bakken and Three Forks Formations, average δ2H, δ18O, specific gravity, and conductivity were similar but exhibited significant variability across five oil fields within each formation. Furthermore, initial produced water (“flowback”) was operationally defined based on the presence of glycol ether compounds and water from wells that had produced <56% of the amount of fluids injected and sampled within 160 days of fracturing. Flowback unexpectedly exhibited higher temperature, specific gravity, conductivity, δ2H, and δ18O, but lower oxidation–reduction potential and δ11B, relative to the wells thought to be producing formation brines (from wells with a produced-to-injected water ratio [PIWR] > 0.84 and sampled more than 316 days after fracturing). As such, establishing an overall geochemical and isotopic signature of produced water compositions based solely on chemical similarity to meteoric water and formation without the consideration of well treatments, well completion depth, or lateral location across the basin could be misleading if these signatures are assumed to be applicable across the entire basin. These findings have implications for using produced water compositions to understand the interbasin fluid flow and trace sources of hydraulic fracturing fluids.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c06789","usgsCitation":"Gallegos, T., Doolan, C.A., Caldwell, R.R., Engle, M.A., Varonka, M., Birdwell, J.E., Jolly, G.D., Coplen, T.B., and Oliver, T.A., 2021, Insights on geochemical, isotopic, and volumetric compositions of produced water from hydraulically fractured Williston Basin oil wells: Environmental Science and Technology, v. 55, no. 14, p. 10025-10034, https://doi.org/10.1021/acs.est.0c06789.","productDescription":"10 p.","startPage":"10025","endPage":"10034","ipdsId":"IP-118139","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":488266,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.0c06789","text":"Publisher Index Page"},{"id":386984,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.18310546875,\n 47.30903424774781\n ],\n [\n -102.667236328125,\n 47.30903424774781\n ],\n [\n -102.667236328125,\n 48.31242790407178\n ],\n [\n -105.18310546875,\n 48.31242790407178\n ],\n [\n -105.18310546875,\n 47.30903424774781\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"14","noUsgsAuthors":false,"publicationDate":"2021-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":818717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doolan, Colin A. 0000-0002-7595-7566 cdoolan@usgs.gov","orcid":"https://orcid.org/0000-0002-7595-7566","contributorId":3046,"corporation":false,"usgs":true,"family":"Doolan","given":"Colin","email":"cdoolan@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":818718,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caldwell, Rodney R. 0000-0002-2588-715X caldwell@usgs.gov","orcid":"https://orcid.org/0000-0002-2588-715X","contributorId":2577,"corporation":false,"usgs":true,"family":"Caldwell","given":"Rodney","email":"caldwell@usgs.gov","middleInitial":"R.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":818719,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engle, Mark A 0000-0001-5258-7374","orcid":"https://orcid.org/0000-0001-5258-7374","contributorId":228981,"corporation":false,"usgs":false,"family":"Engle","given":"Mark","email":"","middleInitial":"A","affiliations":[{"id":41535,"text":"The University of Texas at El Paso, Department of Geological Sciences, El Paso, TX 79968","active":true,"usgs":false}],"preferred":false,"id":818720,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Varonka, Matthew S. 0000-0003-3620-5262","orcid":"https://orcid.org/0000-0003-3620-5262","contributorId":203231,"corporation":false,"usgs":true,"family":"Varonka","given":"Matthew S.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":818721,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":818748,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jolly, Glenn D. 0000-0001-5876-5258 gdjolly@usgs.gov","orcid":"https://orcid.org/0000-0001-5876-5258","contributorId":260780,"corporation":false,"usgs":true,"family":"Jolly","given":"Glenn","email":"gdjolly@usgs.gov","middleInitial":"D.","affiliations":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"preferred":true,"id":818722,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":818723,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Oliver, Thomas A. 0000-0001-5994-2391 taoliver@usgs.gov","orcid":"https://orcid.org/0000-0001-5994-2391","contributorId":260781,"corporation":false,"usgs":true,"family":"Oliver","given":"Thomas","email":"taoliver@usgs.gov","middleInitial":"A.","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":818724,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70237360,"text":"70237360 - 2021 - Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning","interactions":[],"lastModifiedDate":"2022-10-11T16:24:49.683141","indexId":"70237360","displayToPublicDate":"2021-07-01T11:21:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning","docAbstract":"Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, meta-transfer learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well-monitored lakes using calibrated process-based (PB) modeling and a recently developed approach called process-guided deep learning (PGDL). We applied MTL to either PB or PGDL source models (PB-MTL or PGDL-MTL, respectively) to predict temperatures in 305 target lakes treated as unmonitored in the Upper Midwestern United States. We show significantly improved performance relative to the uncalibrated PB General Lake Model, where the median root mean squared error (RMSE) for the target lakes is 2.52°C. PB-MTL yielded a median RMSE of 2.43°C; PGDL-MTL yielded 2.16°C; and a PGDL-MTL ensemble of nine sources per target yielded 1.88°C. For sparsely monitored target lakes, PGDL-MTL often outperformed PGDL models trained on the target lakes themselves. Differences in maximum depth between the source and target were consistently the most important predictors. Our approach readily scales to thousands of lakes in the Midwestern United States, demonstrating that MTL with meaningful predictor variables and high-quality source models is a promising approach for many kinds of unmonitored systems and environmental variables.","language":"English","publisher":"Wiley","doi":"10.1029/2021WR029579","usgsCitation":"Willard, J., Read, J., Appling, A.P., Oliver, S.K., Jia, X., and Kumar, V., 2021, Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning: Water Resources Research, v. 57, no. 7, e2021WR029579, 20 p., https://doi.org/10.1029/2021WR029579.","productDescription":"e2021WR029579, 20 p.","ipdsId":"IP-119147","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":451661,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr029579","text":"Publisher Index Page"},{"id":436285,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9I00WFR","text":"USGS data release","linkHelpText":"Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release)"},{"id":408165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":854263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854265,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854266,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223240,"text":"70223240 - 2021 - National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project","interactions":[],"lastModifiedDate":"2021-08-19T15:13:17.0885","indexId":"70223240","displayToPublicDate":"2021-07-01T10:01:38","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"2021/2285","title":"National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project","docAbstract":"The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program is an effort to classify, describe, and map existing vegetation communities in national park units throughout the United States. The NPS VMI Program is managed by the NPS Natural Resource Stewardship and Science Inventory and Monitoring Program and provides baseline vegetation information to natural resource managers, researchers, and ecologists. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, and NPS Great Smoky Mountains National Park (GRSM, also referred to as the “Park”) have completed vegetation classification and mapping of GRSM, including the Foothills Parkway, for the NPS VMI Program.
Mappers, ecologists, and botanists collaborated to affirm vegetation types of GRSM and to determine how best to map the vegetation types by using aerial imagery. A vegetation classification developed in 2003 by NatureServe and the NPS served as a foundation to further classify and map the vegetation types of the Park. Data from an additional 10 vegetation plots supported vegetation types either rare or not documented in the 2003 classification. Data from 203 verification sites were collected to test the field key to vegetation types and the application of vegetation types to a sample set of map polygons. Furthermore, data from 972 accuracy assessment (AA) sites were collected (of which 966 were used to test accuracy of the vegetation map layer). This GRSM vegetation mapping project identified 112 vegetation types consisting of 105 association types in the U.S. National Vegetation Classification (USNVC), 2 “park-special” types, 1 “map-special” type, and 4 cultural types in the USNVC.
To map the vegetation and land cover of GRSM, 52 map classes were developed. Of these 52 map classes, 46 represent natural (including ruderal) vegetation types, most of which types are recognized in the USNVC. For the remaining 6 of the 52 map classes, 4 represent USNVC cultural types for agricultural and developed areas, and 2 represent non-USNVC types for nonvegetated open water and nonvegetated rock. Features were interpreted from viewing four-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems; digital aerial imagery was collected during September 23–October 30, 2015. The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in a geographic information system. Polygon units were mapped to either a 0.5- or 0.25- hectare (ha) minimum mapping unit, depending on vegetation type.
A geodatabase containing several feature-class layers and tables provides the locations and data of USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, AA sites, project boundary extent, and aerial image centers and flight lines.
Covering 210,875 ha, the feature-class layer and related tables for the vegetation map layer provide 34,084 polygons of detailed attribute data when special modifiers are not considered (average polygon size of 6.2 ha) and 36,589 polygons of detailed attribute data when special modifiers are considered (average polygon size of 5.8 ha). Each map polygon is assigned a map-class code and name and, when applicable, are linked to USNVC classification tables within the geodatabase. The vegetation map extent includes the administrative boundary for GRSM and the Foothills Parkway.
A summary report, generated from the vegetation map layer, concludes that the 46 map classes representing natural (including ruderal) vegetation types apply to 99.2% of polygons (33,797 polygons; average size of 6.2 ha) and cover 98.6% of the Park (207,971.4 ha). Further broken down, map classes representing natural vegetation types indicate that the Park is 97.7% forest and woodland (205,882.5 ha), 0.6% shrubland (1,174.6 ha), and 0.4% herbaceous (914.3 ha). Map classes representing cultural vegetation types apply to 0.8% of polygons (259 polygons; average size of 4.9 ha) and cover 0.6% of the Park (1,277.4 ha). Map classes representing nonvegetation open and flowing water and unvegetated rock apply to 0.08% of polygons (28 polygons; average size of 58.1 ha) and cover 0.8% of the Park (1,625.9 ha).
A thematic AA study was completed of map classes representing the natural (including ruderal) vegetation types of the Park. Initial AA results were discussed with NPS staff from the Park. Following input from NPS staff on how to handle map classes that fell below accuracy standards, adjustments were made to the vegetation map layer. Final results indicate an overall accuracy of 80.64% (kappa index of 79.96% for chance agreements) based on data from 966 of the 972 AA sites. Most individual map-class themes exceed the NPS VMI Program standard of 80% with a 90% confidence interval.
The GRSM vegetation mapping project delivers many geospatial and vegetation data products, including an in-depth project report discussing methods and results, which includes map classification and map-class descriptions. This suite of products also includes descriptions and a field key to vegetation types; a database of vegetation plots, verification sites, and AA sites; digital images of field sites; field data sheets; digital aerial imagery; hardcopy and digital maps; a geodatabase of vegetation and land cover (map layer), field sites (vegetation plots, verification sites, and AA sites), aerial imagery index, project boundary, and metadata; and a contingency table listing AA results. Geospatial products are projected in the Universal Transverse Mercator, Zone 17 North, by using the North American Datum of 1983. Information on the NPS VMI Program and completed mapping projects are on the internet at https://www.nps.gov/im/vegetation-inventory.htm.
","language":"English","publisher":"National Park Service","doi":"10.36967/nrr-2286888","usgsCitation":"Hop, K.D., Strassman, A.C., Sattler, S., White, R., Pyne, M., Govus, T., and Dieck, J., 2021, National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project: Natural Resource Report 2021/2285, 220 p., https://doi.org/10.36967/nrr-2286888.","productDescription":"220 p.","ipdsId":"IP-120204","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.023681640625,\n 35.594785665487244\n ],\n [\n -83.09783935546875,\n 35.806676609227054\n ],\n [\n -83.40545654296875,\n 35.762114795721\n ],\n [\n -83.88336181640625,\n 35.68853320738875\n ],\n [\n -84.034423828125,\n 35.545635932499415\n ],\n [\n -83.90808105468749,\n 35.43605776486772\n ],\n [\n -83.5565185546875,\n 35.39800594715108\n ],\n [\n -83.30657958984375,\n 35.47409160773029\n ],\n [\n -83.023681640625,\n 35.594785665487244\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hop, Kevin D. 0000-0002-9928-4773 khop@usgs.gov","orcid":"https://orcid.org/0000-0002-9928-4773","contributorId":1438,"corporation":false,"usgs":true,"family":"Hop","given":"Kevin","email":"khop@usgs.gov","middleInitial":"D.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strassman, Andrew C. 0000-0002-9792-7181 astrassman@usgs.gov","orcid":"https://orcid.org/0000-0002-9792-7181","contributorId":4575,"corporation":false,"usgs":true,"family":"Strassman","given":"Andrew","email":"astrassman@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sattler, Stephanie 0000-0003-4417-2480 ssattler@usgs.gov","orcid":"https://orcid.org/0000-0003-4417-2480","contributorId":191016,"corporation":false,"usgs":true,"family":"Sattler","given":"Stephanie","email":"ssattler@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Rickie","contributorId":201063,"corporation":false,"usgs":false,"family":"White","given":"Rickie","email":"","affiliations":[],"preferred":false,"id":821498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pyne, Milo","contributorId":201061,"corporation":false,"usgs":false,"family":"Pyne","given":"Milo","email":"","affiliations":[],"preferred":false,"id":821499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Govus, Tom","contributorId":264417,"corporation":false,"usgs":false,"family":"Govus","given":"Tom","email":"","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":821500,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dieck, Jennifer 0000-0002-4388-4534 jdieck@usgs.gov","orcid":"https://orcid.org/0000-0002-4388-4534","contributorId":149647,"corporation":false,"usgs":true,"family":"Dieck","given":"Jennifer","email":"jdieck@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821501,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225734,"text":"70225734 - 2021 - 2020 Status of the Lake Ontario lower trophic levels","interactions":[],"lastModifiedDate":"2021-11-09T15:08:17.039839","indexId":"70225734","displayToPublicDate":"2021-07-01T09:04:48","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"2","title":"2020 Status of the Lake Ontario lower trophic levels","docAbstract":"Significant Findings for Year 2020: Note that due to covid-19 restrictions, offshore sampling was limited in 2020.
1) May – Oct total phosphorus (TP) in 2020 was 10.6 µg/L (offshore) and 7.7 µg/L (nearshore), higher than the long-term (1995-2019) average in the offshore (6.2 µg/L) and close to average in the nearshore (7.8 µg/L); mean TP values for the past decade (2010-2019) were 6.0 µg/L and 7.9 µg/L in the offshore and nearshore, respectively. In 2020, TP concentrations were significantly higher (p=0.03) in the offshore compared to the nearshore. Note that offshore duplicate samples had high relative percent difference (average 54%. 6-117%) making inferences for the offshore in 2020 uncertain.
2) May – Oct epilimnetic chlorophyll-a was similar at nearshore (1.9 µg/L) and offshore (2.1 µg/L) sites. These values were slightly higher than the average for 1995 – 2019 (1.7 µg/L, offshore; 1.5 µg/L, nearshore) and higher than for the last decade (1.4 µg/L, both offshore and nearshore).
3) May – Oct Secchi depth ranged from 3.5 m to 10.4 m (11 ft to 34 ft) at individual sites and was not significantly different between nearshore (6.3 m; 20.7 ft) and offshore (6.5 m; 21.3 ft) locations. Long-term (1995-2019) average was 7.2 m in the offshore and 6.4 m in the nearshore; means for the last decade were 7.8 m in the offshore and 6.2 m in the nearshore.
4) Despite higher TP values in 2020 than in recent years, TP, chlorophyll-a and Secchi depth are indicative of oligotrophic conditions in the offshore of Lake Ontario.
5) Nearshore summer zooplankton biomass was 10.5 µg/L, near the all-time low (9.4 µg/L, 2017) since monitoring began in 1995. Offshore epilimnetic summer zooplankton biomass was 11.7 µg/L. These values are similar to biomass in the last decade (2010-2019).
6) Peak (July) epilimnetic biomass of Cercopagis was 3.0 µg/L in the nearshore and represented 25% of the zooplankton community at that time; Cercopagis was absent from the July offshore epilimnetic samples in 2020 but was present in whole water column samples taken in August by other agencies. Epilimnetic biomass of Bythotrephes peaked in late-September in both the nearshore (1.0 µg/L) and offshore (1.8 µg/L) and represented 10% and 18% of the zooplankton community at those times, respectively.
7) Summer nearshore and offshore epilimnetic zooplankton density and biomass declined significantly 1995 – 2020. The declines were due mainly to reductions in cyclopoid copepods in both habitats.
Nonnative crayfish are an immediate and pervasive threat to aquatic environments and their biodiversity. Crayfish control can be achieved by physical methods, water chemistry modification, biological methods, biocidal application, and application of crayfish physiology modifiers. The purpose of this report is to identify suitable candidates for potential control of nonnative crayfish through a comprehensive literature review. This review focuses on control methods, specifically on the available data to support registration of a crayfish pesticide. The literature search resulted in 28,058 documents, which were searched to determine if they contained information on physical, chemical, biological, and (or) biocidal approaches to control crayfish. Pesticides directly toxic to crayfish in this literature review include: pyrethroids (natural pyrethrins and synthetic), fipronil, mirex, antimycin-A, and rotenone. Some chemicals, such as diflubenzuron and emamectin benzoate, alter crayfish physiology resulting in a lower pesticide dose needed to control crayfish. Environmental damage, application rate, exposure duration, nontarget effects, environmental persistence, and registration data gaps were used as criteria to define which pesticides are potentially selective to crayfish, along with which have the greatest amount of data to support registration by the U.S. Environmental Protection Agency.
Synthetic pyrethroids were identified as the most likely candidate to be developed into a crayfish pesticide. A type-2 synthetic pyrethroid, cyfluthrin, has the greatest potential for eradicating nonnative crayfish. Although other invertebrate species will be negatively affected at the concentrations required for crayfish control, compared with other pyrethroids and other potential control chemicals, cyfluthrin offers rapid ecosystem recovery due to being more selective, having fewer effects on native fish, and having a short aquatic persistence. Cyfluthrin also has few data gaps for U.S. Environmental Protection Agency registration purposes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211048","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Schueller, J.R., Smerud, J.R., Fredricks, K.T., and Putnam, J.G., 2021, Literature review for candidate chemical control agents for nonnative crayfish: U.S. Geological Survey Open-File Report 2021–1048, 32 p., https://doi.org/10.3133/ofr20211048.","productDescription":"vii, 32 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-115061","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":386879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1048/ofr20211048.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1048"},{"id":386878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1048/coverthb.jpg"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
Wetlands of the Prairie Pothole Region (PPR) in the Great Plains of central North America are numerous, densely distributed, and have highly productive plant and animal communities (Photo 49). When in a natural, unaltered condition, these wetlands store relatively large amounts of organic carbon in their soils (Photo 50). Human alterations, such as extensive drainage and land-use conversion for agriculture (Figure 7), have been linked with the loss of soil organic carbon (SOC) and associated emission of carbon dioxide (CO2), as well as impacts to other ecosystem services provided by these wetlands, such as wildlife and waterfowl habitat, plant biodiversity, flood mitigation, groundwater recharge, nutrient removal and retention, and recreation (Gleason et al., 2011). It has been estimated that more than half of the wetlands of the PPR have been lost due to drainage and other disturbances, with losses approaching 90 percent in some areas (Dahl, 2014; Serran et al., 2018). The goal of this case study was to identify land-management strategies that are consistent with maintaining and increasing SOC stocks of PPR wetlands.
Two overarching strategies generally are promoted to preserve and enhance SOC stocks of PPR wetlands: avoided drainage and rewetting or restoration. Avoided drainage involves protecting natural, unaltered wetlands from impacts of human actives with the purpose of retaining wetland functions and services such as carbon storage. Rewetting or restoration involves reestablishing natural hydrology and land use with the purpose of enhancing wetland functions and services that were previously lost due to human activities. Avoided drainage provides immediate and long-lasting benefits, while replenishing SOC through rewetting and restoration requires many decades. Both strategies are associated with higher methane (CH4) emissions but lower CO2 and nitrous oxide (N2O) emissions.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Recarbonizing global soils– A technical manual of recommended management practices","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"United Nations","collaboration":"United Nations","usgsCitation":"Bansal, S., and Tangen, B., 2021, Preserving soil organic carbon in prairie wetlands of central North America, chap. 19 of Recarbonizing global soils– A technical manual of recommended management practices, v. 6, p. 203-212.","productDescription":"10 p.","startPage":"203","endPage":"212","ipdsId":"IP-120160","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":389552,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389551,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fao.org/documents/card/en/c/cb6605en"}],"country":"Canada, United States","state":"Alberta, Iowa, Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota","otherGeospatial":"central North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.8564453125,\n 41.27780646738183\n ],\n [\n -92.3291015625,\n 43.26120612479979\n ],\n [\n -93.8232421875,\n 45.1510532655634\n ],\n [\n -95.44921875,\n 46.40756396630067\n ],\n [\n -96.6357421875,\n 47.69497434186282\n ],\n [\n -97.42675781249999,\n 49.866316729538674\n ],\n [\n -98.9208984375,\n 50.708634400828224\n ],\n [\n -100.1953125,\n 50.28933925329178\n ],\n [\n -102.3046875,\n 51.45400691005982\n ],\n [\n -107.490234375,\n 52.64306343665892\n ],\n [\n -112.763671875,\n 53.9560855309879\n ],\n [\n -114.5654296875,\n 53.61857936489517\n ],\n [\n -113.7744140625,\n 49.468124067331644\n ],\n [\n -109.1162109375,\n 49.06666839558117\n ],\n [\n -105.2490234375,\n 48.951366470947725\n ],\n [\n -104.23828125,\n 48.40003249610685\n ],\n [\n -101.4697265625,\n 47.931066347509784\n ],\n [\n -99.6240234375,\n 46.437856895024204\n ],\n [\n -97.734375,\n 43.35713822211053\n ],\n [\n -95.3173828125,\n 42.90816007196054\n ],\n [\n -94.0869140625,\n 41.21172151054787\n ],\n [\n -92.8564453125,\n 41.27780646738183\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":823460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":823461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223832,"text":"70223832 - 2021 - Toward improved decision-support tools for Delta Smelt management actions","interactions":[],"lastModifiedDate":"2021-09-09T16:00:10.030009","indexId":"70223832","displayToPublicDate":"2021-06-30T10:48:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":419,"text":"White Paper","active":false,"publicationSubtype":{"id":9}},"title":"Toward improved decision-support tools for Delta Smelt management actions","docAbstract":"The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level.
Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions.
Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance.
Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement.
• DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions.
• DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth.
• DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt.
There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address.
For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models.
Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time.
Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires:
- Engagement of a committed group of technical experts with appropriate expertise.
- The development of a coordinated workplan including appropriate project management and tracking.
- Dialog between potential users (i.e., managers and policy makers) and technical experts.
- Resources to pursue DST development including personnel and computational resources.
This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.
","language":"English","publisher":"Collaborative Adaptive Management Team","usgsCitation":"Reed, D., Acuna, S., Ateljevich, E., Brown, L.R., Geske, B., Gross, E., Hobbs, J., Kimmerer, W.J., Lucas, L., Nobriga, M., and Rose, K.A., 2021, Toward improved decision-support tools for Delta Smelt management actions: White Paper, v, 34 p.","productDescription":"v, 34 p.","ipdsId":"IP-127826","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":389005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389004,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.baydeltalive.com/CSAMP/docs/24756"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Denise","contributorId":215697,"corporation":false,"usgs":false,"family":"Reed","given":"Denise","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":822849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acuna, Shawn","contributorId":257756,"corporation":false,"usgs":false,"family":"Acuna","given":"Shawn","email":"","affiliations":[{"id":52106,"text":"Metropolitan Water District of Southern California","active":true,"usgs":false}],"preferred":false,"id":822850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ateljevich, Eli","contributorId":187437,"corporation":false,"usgs":false,"family":"Ateljevich","given":"Eli","email":"","affiliations":[],"preferred":false,"id":822851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Geske, Ben","contributorId":265520,"corporation":false,"usgs":false,"family":"Geske","given":"Ben","email":"","affiliations":[{"id":54715,"text":"Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":822853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gross, Edward","contributorId":264402,"corporation":false,"usgs":false,"family":"Gross","given":"Edward","affiliations":[{"id":28024,"text":"UCDavis","active":true,"usgs":false}],"preferred":false,"id":822854,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hobbs, Jim","contributorId":200389,"corporation":false,"usgs":false,"family":"Hobbs","given":"Jim","email":"","affiliations":[],"preferred":false,"id":822855,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kimmerer, Wim J.","contributorId":59169,"corporation":false,"usgs":false,"family":"Kimmerer","given":"Wim","email":"","middleInitial":"J.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":822856,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":822857,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nobriga, Matthew","contributorId":139247,"corporation":false,"usgs":false,"family":"Nobriga","given":"Matthew","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":822858,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rose, Kenneth A","contributorId":147274,"corporation":false,"usgs":false,"family":"Rose","given":"Kenneth","email":"","middleInitial":"A","affiliations":[{"id":16815,"text":"Dept. of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge","active":true,"usgs":false}],"preferred":false,"id":822859,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70223729,"text":"70223729 - 2021 - Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading","interactions":[],"lastModifiedDate":"2021-09-16T15:12:14.688708","indexId":"70223729","displayToPublicDate":"2021-06-30T09:26:46","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading","docAbstract":"Green Lake is the deepest natural inland lake in Wisconsin, USA, with a maximum depth of about 72 meters (m). In the early 1900’s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity), with low dissolved oxygen (DO) concentrations only in the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake increased, which led to increased algal production and low DO concentrations not only occurring in its deepest areas but also in the middle of the water column (metalimnion). Routine monitoring of the lake and its tributaries has been conducted by the U.S. Geological Survey since 2004 and 1988, respectively. Results from this monitoring led to the Wisconsin Department of Natural Resources (WDNR) listing the lake as impaired because of low DO concentrations in the metalimnion, with elevated TP concentrations identified as the cause of impairment.
As part of this study, comprehensive sampling of the lake and its tributaries was conducted in 2017–2018 to augment ongoing monitoring and further describe the low DO concentrations in the lake (especially in the metalimnion). Empirical and process-driven water quality models were then used to determine the causes of the low DO concentrations and the magnitude of P load reductions needed to improve the water quality of the lake to meet multiple water-quality goals, including the WDNR criteria for TP and DO.
Data from previous studies showed that DO concentrations in the metalimnion decreased slightly as summer progressed in the early 1900’s, but since the late 1970s have typically dropped below 5 milligrams per liter (mg/L), which is the WDNR criterion for impairment. During 2014–2018 (baseline period for this study), the near-surface geometric-mean TP concentration during June–September in the east side of the lake was 0.020 mg/L and in the west side was 0.016 mg/L (both were below the 0.015 mg/L WDNR criterion for the lake), and the minimum metalimnetic DO concentrations measured in August ranged from 1.0 to 4.7 mg/L. It was believed that the degradation in water quality was caused by excessive P inputs to the lake; therefore, the total P inputs to the lake were estimated. The mean annual external P load during 2014–2018 was estimated to be 8,980 kilograms per year (kg/yr), of which monitored and unmonitored tributary inputs contributed 84 percent, atmospheric inputs contributed 8 percent, waterfowl contributed 7 percent, and septic systems contributed 1 percent. At fall turnover, internal sediment recycling contributed an additional 7,040 kg that increased TP concentrations in shallow areas of the lake by about 0.020 mg/L. The elevated TP concentrations then persisted until the following spring. On an annual basis, however, there is a net deposition of P to the bottom sediments.
Empirical models were used to describe how the near-surface water quality of Green Lake would be expected to respond to changes in external P loading. Predictions from the models showed a relatively linear response between P loading and TP and chlorophyll-a (Chl-a) concentrations in the lake, with the changes in TP and Chl-a concentrations being less on a percentage basis (50–60 percent for TP and 30–70 percent for Chl-a) than the changes in P loading. Mean summer water clarity, indicated by Secchi disk depths, had a larger response to decreases in P loading than to increases in loading. Based on these relations, external P loading to the lake would need to be decreased from 8,980 kg/yr to about 5,460 kg/yr for the geometric mean June–September TP concentration on the east side of the lake, with higher TP concentrations than the west side, to reach the WDNR criterion of 0.015 mg/L. This reduction of 3,520 kg/yr equates to a 46-percent reduction in the potentially controllable external P sources (all external sources except precipitation, atmospheric deposition, and waterfowl) from that measured during water years (WYs) 2014–2018. The total external P loading would need to be decreased to 7,680 kg/yr (17-percent reduction in potentially controllable external P sources) for near-surface June–September TP concentrations in the west side of the lake to reach 0.015 mg/L. Total external P loading would need to be decreased to 3,870–5,320 kg/yr for the lake to be classified as oligotrophic, with a near-surface June-September TP concentration of 0.012 mg/L.
Results from the hydrodynamic water-quality model GLM-AED (General Lake Model coupled to the Aquatic Ecodynamics modeling library) indicated that metalimnetic DO minima are driven by external P loading and internal sediment recycling that lead to high TP concentrations during spring and early summer, which in turn lead to high phytoplankton production, high metabolism and respiration, and ultimately DO consumption in the upper, warmer areas of the metalimnion. GLM-AED results indicated that settling of organic material during summer may be slowed by the colder, denser, and more viscous water in the metalimnion and increase DO consumption. Based on empirical evidence comparing minimum metalimnetic DO concentrations with various meteorological, hydrologic, water quality, and in-lake physical factors, lower metalimnetic DO concentrations occurred when there was warmer metalimnetic water temperatures, higher near-surface Chl-a and TP concentrations, and lower Secchi depths during summer. GLM-AED results indicated that the external P load would need to be reduced to about 4,010 kg/yr, a 57-percent reduction from that measured in 2014–2018, to eliminate the occurrence of metalimnetic DO minima of less than 5 mg/L in over 75 percent of the years (the target provided by the WDNR).
Large reductions in external P loading are expected to have an immediate effect on the near-surface TP concentrations and metalimnetic DO concentrations in Green Lake. However, it may take several years for the full effects of the external load reduction to be observed because internal sediment recycling is an important source of P for the following spring.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Diagnostic and feasibility study findings: Water quality improvements for Green Lake, Wisconsin","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Green Lake Association","usgsCitation":"Robertson, D., Siebers, B.J., Ladwig, R., Hamilton, D., Reneau, P., McDonald, C.P., Prellwitz, S., and Lathrop, R.C., 2021, Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading, vii, 115 p.","productDescription":"vii, 115 p.","ipdsId":"IP-129488","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":389346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388824,"type":{"id":15,"text":"Index Page"},"url":"https://www.greenlakeassociation.org/research/"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07920837402344,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.75894467245554\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siebers, Benjamin J. 0000-0002-2900-5169","orcid":"https://orcid.org/0000-0002-2900-5169","contributorId":206518,"corporation":false,"usgs":true,"family":"Siebers","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladwig, Robert","contributorId":265278,"corporation":false,"usgs":false,"family":"Ladwig","given":"Robert","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":822505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":822506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reneau, Paul 0000-0002-1335-7573","orcid":"https://orcid.org/0000-0002-1335-7573","contributorId":217293,"corporation":false,"usgs":true,"family":"Reneau","given":"Paul","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822507,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDonald, Cory P. 0000-0002-1208-8471","orcid":"https://orcid.org/0000-0002-1208-8471","contributorId":261754,"corporation":false,"usgs":false,"family":"McDonald","given":"Cory","email":"","middleInitial":"P.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":822508,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prellwitz, Stephanie","contributorId":265281,"corporation":false,"usgs":false,"family":"Prellwitz","given":"Stephanie","email":"","affiliations":[{"id":54642,"text":"Green Lake Association","active":true,"usgs":false}],"preferred":false,"id":822509,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lathrop, Richard C","contributorId":172075,"corporation":false,"usgs":false,"family":"Lathrop","given":"Richard","email":"","middleInitial":"C","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":822510,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70236252,"text":"70236252 - 2021 - The drying regimes of non-perennial rivers and streams","interactions":[],"lastModifiedDate":"2022-08-31T13:36:51.788646","indexId":"70236252","displayToPublicDate":"2021-06-30T08:34:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The drying regimes of non-perennial rivers and streams","docAbstract":"The flow regime paradigm is central to the aquatic sciences, where flow drives critical functions in lotic systems. Non-perennial streams comprise the majority of global river length, thus we extended this paradigm to stream drying. Using 894 USGS gages, we isolated 25,207 drying events from 1979 to 2018, represented by a streamflow peak followed by no flow. We calculated hydrologic signatures for each drying event and using multivariate statistics, grouped events into drying regimes characterized by: (a) fast drying, (b) long no-flow duration, (c) prolonged drying following low antecedent flows, (d) drying without a distinctive hydrologic signature. 77% of gages had more than one drying regime at different times within the study period. Random forests revealed land cover/use are more important to how a river dries than climate or physiographic characteristics. Clustering stream drying behavior may allow practitioners to more systematically adapt water resource management practices to specific drying regimes or rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GL093298","usgsCitation":"Price, A.N., Jones, C.N., Hammond, J., Zimmer, M., and Zipper, S., 2021, The drying regimes of non-perennial rivers and streams: Geophysical Research Letters, v. 48, no. 14, e2021GL093298, 12 p., https://doi.org/10.1029/2021GL093298.","productDescription":"e2021GL093298, 12 p.","ipdsId":"IP-127641","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":405993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"14","noUsgsAuthors":false,"publicationDate":"2021-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Price, Adam N. 0000-0002-7211-4758","orcid":"https://orcid.org/0000-0002-7211-4758","contributorId":295971,"corporation":false,"usgs":false,"family":"Price","given":"Adam","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":850332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, C. Nathan 0000-0002-5804-0510","orcid":"https://orcid.org/0000-0002-5804-0510","contributorId":295972,"corporation":false,"usgs":false,"family":"Jones","given":"C.","email":"","middleInitial":"Nathan","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":850333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":850334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zimmer, Margaret 0000-0001-8287-1923","orcid":"https://orcid.org/0000-0001-8287-1923","contributorId":225158,"corporation":false,"usgs":false,"family":"Zimmer","given":"Margaret","affiliations":[{"id":41054,"text":"Earth and Planetary Sciences, University of California, Santa Cruz, CA, 95064, USA","active":true,"usgs":false}],"preferred":false,"id":850335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zipper, Samuel 0000-0002-8735-5757","orcid":"https://orcid.org/0000-0002-8735-5757","contributorId":225160,"corporation":false,"usgs":false,"family":"Zipper","given":"Samuel","email":"","affiliations":[{"id":41056,"text":"Kansas Geological Survey, University of Kansas, Lawrence KS 66047, USA","active":true,"usgs":false}],"preferred":false,"id":850336,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221670,"text":"ofr20211060 - 2021 - Estimated water withdrawals and use in Puerto Rico, 2015","interactions":[],"lastModifiedDate":"2021-07-01T11:41:28.037511","indexId":"ofr20211060","displayToPublicDate":"2021-06-30T08:33:16","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1060","displayTitle":"Estimated Water Withdrawals and Use in Puerto Rico, 2015","title":"Estimated water withdrawals and use in Puerto Rico, 2015","docAbstract":"Water withdrawals and use in Puerto Rico for 2015 were estimated at 2,372 million gallons per day (Mgal/d), which was 21 percent less than withdrawals and use for 2010. The 2015 total water withdrawal and use estimates were the lowest since 1990 and coincided with a substantial decline of 25 percent in saline-water withdrawals for thermoelectric-power cooling processes from 2010 to 2015. Freshwater withdrawals were 671 Mgal/d, or 28 percent of total water withdrawals, and saline-water withdrawals were 1,701 Mgal/d, or 72 percent of total withdrawals. Fresh surface-water withdrawals were estimated at 548 Mgal/d, 10 percent less than in 2010, whereas fresh groundwater withdrawals were estimated at 122 Mgal/d, 2 percent less than in 2010. Saline surface-water withdrawals were 25 percent less than in 2010.
Freshwater withdrawals were greatest for public-supply water and irrigation in 2015 and, combined, accounted for 98 percent of Puerto Rico’s total freshwater withdrawals. Withdrawals in 2015 for public-supply water (576 Mgal/d) were 14 percent lower and withdrawals for irrigation (78 Mgal/d) were 104 percent greater than in 2010, possibly because of drought conditions in agricultural counties along the south and southeast coasts in 2015. The sources for public-supply water withdrawals in 2015 included surface water (88 percent) and groundwater (12 percent). Withdrawals for other uses, which account for the remaining 2 percent of Puerto Rico’s total freshwater withdrawals, were lower in 2015 than in 2010; specifically, withdrawals for domestic self-supplied use decreased by 78 percent, industrial withdrawals decreased by 15 percent, and withdrawals for livestock decreased by 25 percent. Freshwater withdrawals for thermoelectric power and mining were greater in 2015 than in 2010, increasing by 23 percent and 5 percent, respectively.
The total population of Puerto Rico decreased by 7 percent from 2010 to 2015, from 3.73 million people in 2010 to 3.47 million people in 2015. The number of people who obtained potable water from public-supply water facilities in 2015 was about 3.47 million, or about 100 percent of the population of Puerto Rico.
Public-supply water deliveries for domestic use accounted for 338 Mgal/d in 2015, which is 47 percent greater than in 2010, indicating an increase in domestic per capita use from 62 to 98 gallons per person per day from 2010 to 2015. Domestic self-supplied withdrawals were estimated at 0.52 Mgal/d in 2015, for an estimated 4,708 people (less than 1 percent of Puerto Rico’s population). All domestic self-supplied withdrawals were assumed to be from groundwater sources.
Irrigation freshwater withdrawals were 78 Mgal/d in 2015 and accounted for 12 percent of the total freshwater withdrawals for all uses. Surface-water deliveries from irrigation districts accounted for 44 percent of total irrigation withdrawals, whereas groundwater withdrawals accounted for 56 percent. About 37,000 acres were irrigated in 2015, a decrease of 11 percent or about 4,000 acres compared to 2010. About 99 percent of the acreage was irrigated by micro-irrigation and sprinkler systems in 2015. About 65 percent of the irrigation withdrawals were accounted for by four municipalities: Santa Isabel, Salinas, Lajas, and Juana Díaz.
Altogether, freshwater withdrawals for livestock, industrial, mining, and thermoelectric power accounted for 2 percent (16.2 Mgal/d) of freshwater withdrawals for all uses, 9 percent less than in 2010. About 71 percent of the freshwater withdrawn for these categories was from groundwater sources.
In 2015, 50 percent of the total freshwater withdrawn in Puerto Rico was apportioned to six municipalities: Arecibo, Trujillo Alto, Toa Alta, Villalba, Aguada, and Mayagüez. Arecibo accounted for about 18 percent of the total freshwater withdrawals, predominantly for public-supply water use. Trujillo Alto, Toa Alta, Villalba, Aguada, and Mayagüez accounted for about 32 percent (213 Mgal/d) of the total freshwater withdrawals, which were predominantly for public-supply water uses. Withdrawals in some of these municipalities are subsequently distributed to other municipalities such as those in the San Juan metro area. The Puerto Rico Aqueduct and Sewer Authority water service area for the San Juan metro area (referred to as W–102) accounted for about 28 percent of the total water delivered from public-supply water facilities to domestic users, which includes about 34 percent of the total population of Puerto Rico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211060","collaboration":"Prepared in cooperation with the Puerto Rico Aqueduct and Sewer Authority and the Puerto Rico Environmental Quality Board","usgsCitation":"Molina-Rivera, W.L., and Irizarry-Ortiz, M.M., 2021, Estimated water withdrawals and use in Puerto Rico, 2015: U.S. Geological Survey Open-File Report 2021–1060, 38 p., https://doi.org/10.3133/ofr20211060.","productDescription":"Report: vii, 38 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-096352","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":386796,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9POVNC6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Spatial and tabular datasets of water withdrawals and use in Puerto Rico, 2015"},{"id":386795,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1060/ofr20211060.pdf","text":"Report","size":"6.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1060"},{"id":386794,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1060/coverthb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.445068359375,\n 17.764381077782076\n ],\n [\n -65.1873779296875,\n 17.764381077782076\n ],\n [\n -65.1873779296875,\n 18.651449894396634\n ],\n [\n -67.445068359375,\n 18.651449894396634\n ],\n [\n -67.445068359375,\n 17.764381077782076\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Plant community response to climate change will be influenced by individual plant responses that emerge from competition for limiting resources that fluctuate through time and vary across space. Projecting these responses requires an approach that integrates environmental conditions and species interactions that result from future climatic variability. Dryland plant communities are being substantially affected by climate change because their structure and function are closely tied to precipitation and temperature, yet impacts vary substantially due to environmental heterogeneity, especially in topographically complex regions. Here, we quantified the effects of climate change on big sagebrush (Artemisia tridentata Nutt.) plant communities that span 76 million ha in the western United States. We used an individual-based plant simulation model that represents intra- and inter-specific competition for water availability, which is represented by a process-based soil water balance model. For dominant plant functional types, we quantified changes in biomass and characterized agreement among 52 future climate scenarios. We then used a multivariate matching algorithm to generate fine-scale interpolated surfaces of functional type biomass for our study area. Results suggest geographically divergent responses of big sagebrush to climate change (changes in biomass of −20% to +27%), declines in perennial C3 grass and perennial forb biomass in most sites, and widespread, consistent, and sometimes large increases in perennial C4 grasses. The largest declines in big sagebrush, perennial C3 grass and perennial forb biomass were simulated in warm, dry sites. In contrast, we simulated no change or increases in functional type biomass in cold, moist sites. There was high agreement among climate scenarios on climate change impacts to functional type biomass, except for big sagebrush. Collectively, these results suggest divergent responses to warming in moisture-limited versus temperature-limited sites and potential shifts in the relative importance of some of the dominant functional types that result from competition for limiting resources.
In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid-flow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fluid-flow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.
The hydrology of seasonally inundated depression wetlands can be highly sensitive to climatic fluctuations. Hydroperiod—the number of days per year that a wetland is inundated—is often of primary ecological importance in these systems and can vary interannually depending on climate conditions. In this study we re-examined an existing hydrologic model to simulate daily water levels in Sinking Pond, a 35-hectare seasonally inundated karst-depression wetland in Tennessee, USA. We recalibrated the model using 22 years of climate and water-level observations and used the recalibrated model to reconstruct (hindcast) daily water levels over a 165-year period from 1855 to 2019. A trend analysis of the climatic data and reconstructed water levels over the hindcasting period indicated substantial increases in pond hydroperiod over time, apparently related to increasing regional precipitation. Wetland hydroperiod increased on average by 5.9 days per decade between 1920 and 2019, with a breakpoint around the year 1970. Hydroperiod changes of this magnitude may have profound consequences for wetland ecology, such as a transition from a forested wetland to a mostly open-water pond at the Sinking Pond site. More broadly, this study illustrates the needs for robust hydrologic models of depression wetlands and for consideration of model transferability in time (i.e., hindcasting and forecasting) under non-stationary hydroclimatic conditions. As climate change is expected to influence water cycles, hydrologic processes, and wetland ecohydrology in the coming decades, hydrologic model projections may become increasingly important to detect, anticipate, and potentially mitigate ecological impacts in depression wetland ecosystems.
Daily mean streamflow data from 28 U.S. Geological Survey streamflow-gaging stations in Puerto Rico with 10 or more years of unregulated or minimally affected flow record through water year 2018 were used to develop regression equations for flow duration and annual n-day low-flow statistics. Ordinary least-squares and generalized least-squares regression techniques were used to develop regional regression equations for flow-duration statistics at the 99th, 98th, 95th, 90th, 80th, 70th, 60th, and 50th percent exceedance probabilities and annual n-day low-flow frequency statistics for the 1-, 7-, 14-, and 30-day mean low flows with the 2-year (0.5 nonexceedance probability), 5-year (0.2 nonexceedance probability), and 10-year (0.1 nonexceedance probability) recurrence intervals. A StreamStats web application was developed to estimate basin and climatic characteristics for the regional regression equation analysis. Basin and climatic characteristics determined to be significant explanatory variables in one or more regression equations included drainage area, mean total annual reference evapotranspiration, and minimum basin elevation. The adjusted coefficient of determination for the flow-duration regression equations ranged from 57.7 to 81.4 percent. The pseudo coefficient of determination for the annual n-day low-flow regression equations ranged from 64.6 to 70.7 percent. The StreamStats web application incorporates the flow duration, and annual n-day low-flow regression equations and can provide streamflow estimates for most ungaged sites in the island.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215054","collaboration":"Prepared in cooperation with the Puerto Rico Environmental Quality Board","usgsCitation":"Williams-Sether, T., 2021, Estimating flow-duration statistics and low-flow frequencies for selected streams and the implementation of a StreamStats web-based tool in Puerto Rico: U.S. Geological Survey Scientific Investigations Report 2021–5054, 18 p., https://doi.org/10.3133/sir20215054.","productDescription":"Report: v, 17 p.; Data Release; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-118184","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386816,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5054/coverthb.jpg"},{"id":386817,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5054/sir20215054.pdf","text":"Report","size":"5.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5054"},{"id":386819,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":386818,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y2QVJ6","text":"USGS data release","linkHelpText":"Data files for the development of regression equations for flow-duration statistics and n-day low-flow frequencies for ungaged streams in Puerto Rico through water year 2018"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3187255859375,\n 17.85590441431915\n ],\n [\n -65.5828857421875,\n 17.85590441431915\n ],\n [\n -65.5828857421875,\n 18.557739984085266\n ],\n [\n -67.3187255859375,\n 18.557739984085266\n ],\n [\n -67.3187255859375,\n 17.85590441431915\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
The California Department of Transportation, commonly known as CalTrans, and other municipal separate storm sewer system permittees in California as well as other State departments of transportation nationwide need information about potential loads and yields (loads per unit area) of constituents of concern in stormwater runoff and discharges from stormwater best management practices (BMPs). Although its National Pollution Discharge Elimination System stormwater permit is focused on areas subject to total maximum daily load (TMDL) regulations, CalTrans builds and maintains BMPs to minimize the adverse effects of roadway runoff on receiving waters throughout the State. This report describes approaches used by the U.S. Geological Survey in cooperation with CalTrans for using the Stochastic Empirical Loading and Dilution Model (SELDM) to assess long-term annual yields of highway and urban runoff in selected areas of California. In this study, a series of regional and local yields were simulated to provide statewide planning-level estimates and more refined TMDL-specific yield values. SELDM was used to analyze 368 State roadway and urban runoff yields for 53 runoff quality constituents. The analyses included 222 random-seed analyses, 60 regional State roadway-runoff analyses, 24 regional urban roadway-runoff analyses, and 62 focused TMDL-area analyses.
This report describes approaches and statistics used to analyze available hydrologic and runoff quality data in all analyses. Results for all analyses are provided in the model archive, but only a selected subset of results are presented as examples in this report. State roadway runoff, urban runoff, and BMP discharge yields for total suspended solids, total nitrogen, total phosphorus, and total zinc were selected as examples because they are widespread constituents of concern with substantial amounts of State roadway and urban runoff monitoring data. In this report, a hypothetical basin was specified by using available geographic information to demonstrate use of the State roadway and urban runoff yields to estimate long-term annual stormwater loads from developed areas. Application of these yields to the hypothetical basin indicates that although State-roadway yields may be higher than urban-runoff yields for some constituents, State-roadway loads may be a small proportion of total stormwater loads because State roadways themselves are a small fraction of the total impervious area in such basins. Although application of results from this study may have considerable uncertainty for any particular stormwater outfall, the study does provide robust estimates to support basin-scale runoff-load analyses in California. These analyses also provide estimates for the 12 U.S. Environmental Protection Agency level III ecoregions that are completely or partially within the boundaries of the State of California.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215043","collaboration":"Prepared in cooperation with the California Department of Transportation","usgsCitation":"Granato, G.E., and Friesz, P.J., 2021, Approaches for assessing long-term annual yields of highway and urban runoff in selected areas of California with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2021–5043, 37 p., https://doi.org/10.3133/sir20215043.","productDescription":"Report: vii, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124902","costCenters":[{"id":466,"text":"New England Water Science 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
A study was conducted by the U.S. Geological Survey in cooperation with the U.S. Navy (the Navy) to determine the status of volatile organic compounds (VOCs) and per- and polyfluoroalkyl substances (PFASs) in groundwater at the former Naval Air Warfare Center (NAWC) in West Trenton, New Jersey. Wells contaminated with VOCs were sampled in 2014, 2015, 2016, and 2017 as part of the Navy’s long-term monitoring program. The results for trichloroethene (TCE), cis-1,2-dichloroethene (cisDCE), and vinyl chloride (VC) were plotted in map view to determine whether the areal extent of the contamination had changed over the 4-year period. TCE, cisDCE, and VC concentrations were plotted along nine lines of section across the former NAWC site to determine whether the vertical distribution of VOCs had changed during 2014–17. TCE, cisDCE, and VC concentrations over time were plotted on graphs for each well to determine long-term trends and changes in VOC concentrations. Data from 1990 to 2017 were used, if available, to make these graphs.
Results show that the areas of VOC concentrations greater than or equal to 1 microgram per liter decreased slightly on the northwestern side and the northeastern side of the NAWC site from 2014 to 2017 under the influence of a pump-and-treat system, natural attenuation processes, and engineered bioaugmentation experiments ongoing at the site. The pump-and-treat system continued to hydraulically contain the VOC contamination and kept it from moving offsite to the south and west of NAWC. One well northeast of the NAWC site, 50BR, was found to have detectable TCE and cisDCE concentrations. These detections indicated that VOC contamination had migrated offsite and that the pump-and-treat system was not containing the VOC contamination on the eastern side of the facility. Detectable VOC concentrations were present in wells as deep as 200 and 221 feet on the eastern and western sides of the NAWC site. TCE concentrations in most wells were found to be stable or to have slowly decreased since the facility closed in 1999. Only 7 wells, including 3 pump-and-treat extraction wells, showed substantial increases in TCE concentration from 2014 to 2017. Continuing sources of TCE to the system are desorption of TCE from organic materials in the aquifer, back diffusion of TCE from the contaminated bedrock matrix, and dissolution of remaining dense nonaqueous phase TCE in the aquifer.
Wells at the former NAWC site were sampled for PFASs in 2015, 2016, and 2017. Perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) results were plotted in map and cross-section views to determine the areal and vertical extent of the PFAS contamination at the site. PFOS, PFOA, and PFNA concentrations greater than their established maximum contaminant levels were detected in 25, 24, and 21 of the 26 wells sampled, respectively, on the eastern side of NAWC in 2017. Vertically, the highest PFAS concentrations were present in shallow wells along the fence near the firehouse and along the railroad tracks where the aqueous film-forming foam discharge reportedly occurred back in 1990. PFAS concentrations were detected in one well (54BR) as deep as 200 feet on the eastern side of the NAWC site. PFASs were present in wells east of the railroad tracks, indicating that PFAS-contaminated groundwater had moved offsite. In a limited test of five wells, samples collected with regenerated cellulose dialysis membrane (RCDM) passive samplers contained PFAS concentrations equal to those in samples from low-flow purging.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201105","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Imbrigiotta, T.E., and Fiore, A.R., 2021, Distribution of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in monitoring wells at the former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17: U.S. Geological Survey Open-File Report 2020–1105, 107 p., https://doi.org/10.3133/ofr20201105.","productDescription":"Report: xii, 107 p.; Data Release; 4 Appendixes","numberOfPages":"107","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-110205","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":386575,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix2.xlsx","text":"Appendix 2","size":"288 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Appendix 2. Volatile organic compounds, per- and polyfluoroalkyl substances, and 1,4-dioxane concentrations measured in samples from wells at the former Naval Air Warfare Center site, West Trenton, New Jersey, 1990–2017"},{"id":386577,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix2.csv","text":"Appendix 2 as CSV file","size":"187 KB","linkFileType":{"id":7,"text":"csv"}},{"id":386576,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix1.csv","text":"Appendix 1 as CSV file","size":"22.9 KB","linkFileType":{"id":7,"text":"csv"}},{"id":386573,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCAQ5N","text":"USGS data release","linkHelpText":"Concentrations of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in groundwater and surface water, former Naval Air Warfare Center, West Trenton, New Jersey"},{"id":386572,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105.pdf","text":"Report","size":"9.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1105"},{"id":386571,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1105/coverthb.jpg"},{"id":386574,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix1.xlsx","text":"Appendix 1","size":"43.7 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Appendix 1. Descriptions of boreholes, well locations, and well construction at the former Naval Air Warfare Center, West Trenton, New Jersey"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.80979204177856,\n 40.26746805544402\n ],\n [\n -74.80759263038635,\n 40.27155298671227\n ],\n [\n -74.8130750656128,\n 40.27224060619094\n ],\n [\n -74.81433033943176,\n 40.26832763061523\n ],\n [\n -74.81412649154663,\n 40.268139343654944\n ],\n [\n -74.80979204177856,\n 40.26746805544402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
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The U.S. Geological Survey, in cooperation with the U.S. Air Force Civil Engineering Center, investigated the use of surface geophysical methods to delineate the top of the Cretaceous Pierre Shale along survey transects in selected areas within and near Ellsworth Air Force Base, South Dakota. Two complementary geophysical methods—electrical resistivity and passive seismic—were used along 26 co-located transect surveys within and near Ellsworth Air Force Base for a total of 12.7 line-kilometers. Electrical resistivity results were analyzed using EarthImager2D electrical resistivity tomography processing and inversion software. Two-dimensional earth models showing the electrical properties of the subsurface were evaluated by directly comparing the high and low subsurface resistivity values to a surficial geologic map and nearby wells with driller logs. Passive seismic data were analyzed using the horizontal-to-vertical spectral ratio method to determine the depth to the Pierre Shale at each survey point. The electrical resistivity and passive seismic results were compared to driller logs from nearby wells to delineate the top of the Pierre Shale. The depth to the Pierre Shale along the transects ranged from about 2.4 to 20.3 meters, and mean and median depths were about 9.2 and 9.0 meters, respectively. The elevation of the Pierre Shale and thickness of unconsolidated deposits generally increased with land-surface elevation from south to north; however, some transects displayed topographically high and low areas that sometimes did not correlate with land-surface topography and may affect local groundwater flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3474","collaboration":"Prepared in cooperation with U.S. Air Force Civil Engineering Center","usgsCitation":"Medler, C.J., and Anderson, T.M., 2021, Delineating the Pierre Shale from geophysical surveys within and near Ellsworth Air Force Base, South Dakota, 2019: U.S. Geological Survey Scientific Investigations Map 3474, 3 sheets, 16-p. pamphlet, https://doi.org/10.3133/sim3474.","productDescription":"Pamphlet: ix,16 p.; 3 Sheets: 48.00 x 40.00 inches or smaller; Data Release; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-126004","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_pamphlet.pdf","text":"Pamphlet","size":"2.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Pamphlet"},{"id":386682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3474/coverthb2.jpg"},{"id":398136,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3474/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":398000,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3474/images"},{"id":386688,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":386687,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJH17","text":"USGS data release","linkHelpText":"Electrical Resistivity Tomography (ERT) and Horizontal-to-Vertical Spectral Ratio (HVSR) data collected within and near Ellsworth Air Force Base, South Dakota, from 2014 to 2019"},{"id":386686,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_sheet03.pdf","text":"Sheet 3","size":"9.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Sheet 3","linkHelpText":"— Electrical resistivity tomography inversion results with depth to Pierre Shale from horizontal-to-vertical spectral ratio results for transects 9A, 9B, 9C, 11, 8A, 8B, 8C, 10, and 12, Ellsworth Air Force Base, South Dakota"},{"id":386685,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_sheet02.pdf","text":"Sheet 2","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Sheet 2","linkHelpText":"— Electrical resistivity tomography inversion results with depth to Pierre Shale from horizontal-to-vertical spectral ratio results for transects 4A1, 4A2, 2, 3A, 3B, 3C, and 5, Ellsworth Air Force Base, South Dakota"},{"id":386684,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_sheet01.pdf","text":"Sheet 1","size":"8.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Sheet 1","linkHelpText":"— Electrical resistivity tomography inversion results with depth to Pierre Shale from horizontal-to-vertical spectral ratio results for transects 1C1, 1C2, 14, 15, 13A, 13B, 1A, 1B, 4B, and 4C, Ellsworth Air Force Base, South Dakota"},{"id":397999,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3474/sim3474.XML"}],"country":"United States","state":"South Dakota","otherGeospatial":"Ellsworth Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.14857482910155,\n 44.10977494207831\n ],\n [\n -103.04145812988281,\n 44.10977494207831\n ],\n [\n -103.04145812988281,\n 44.17136989600329\n ],\n [\n -103.14857482910155,\n 44.17136989600329\n ],\n [\n -103.14857482910155,\n 44.10977494207831\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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The U.S. Department of Agriculture's Conservation Reserve Program (CRP) is one of the largest private lands conservation programs in the United States, establishing perennial vegetation on environmentally sensitive lands formerly in agricultural production. Over its 35 year existence, the CRP has evolved to include diverse conservation practices (CPs) while concomitantly meeting its core goals of reducing soil erosion, improving water quality, and providing wildlife habitat. Ongoing threats to grasslands and decreased CRP acreage highlighted the need for a national evaluation of the effectiveness in providing the program's intended benefits. To address this need, we conducted edge-of-field surveys of erosional features, vegetation, and soil cover on 1 786 fields across 10 CPs and 14 central and western states from 2016 to 2018. We grouped practices into three types (grassland, wetland, and wildlife) and states into six regions for analysis. Across practice types, ≥99% of fields had no evidence of rills, gullies, or pedestaling from erosion, and 91% of fields had <20% bare soil cover, with region being the strongest predictor of bare soil cover. Seventy-nine percent of fields had ≥50% grass cover, with cover differing by practice type and region. Native grass species were present on more fields in wildlife and wetland practices compared to grassland practices. Forb cover >50% and native forb presence occurred most frequently in wildlife practices, with region being the strongest driver of differences. Federally listed noxious grass and forb species occurred on 23% and 61% of fields, respectively, but tended to constitute a small portion of cover in the field. Estimates from edge-of-field surveys and in-field validation sampling were strongly correlated, demonstrating the utility of the edge-of-field surveys. Our results provide the first national-level assessment of CRP establishment in three decades, confirming that enrolled wildlife and wetland practices often have diverse perennial vegetation cover and very few erosional features.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ac06f8","usgsCitation":"Vandever, M.W., Carter, S.K., Assal, T.J., Elgersma, K., Wen, A., Welty, J.L., Arkle, R.S., and Iovanna, R., 2021, Evaluating establishment of conservation practices in the Conservation Reserve Program across the central and western United States: Environmental Research Letters, v. 16, 074011, 16 p., https://doi.org/10.1088/1748-9326/ac06f8.","productDescription":"074011, 16 p.","ipdsId":"IP-122262","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":451746,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac06f8","text":"Publisher Index Page"},{"id":436287,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCC65W","text":"USGS data 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\"}}]}","volume":"16","noUsgsAuthors":false,"publicationDate":"2021-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Vandever, Mark W. 0000-0003-0247-2629 vandeverm@usgs.gov","orcid":"https://orcid.org/0000-0003-0247-2629","contributorId":197674,"corporation":false,"usgs":true,"family":"Vandever","given":"Mark","email":"vandeverm@usgs.gov","middleInitial":"W.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":819089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":819090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Assal, Timothy J. 0000-0001-6342-2954","orcid":"https://orcid.org/0000-0001-6342-2954","contributorId":258157,"corporation":false,"usgs":false,"family":"Assal","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":18142,"text":"Kent State University","active":true,"usgs":false}],"preferred":false,"id":819091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elgersma, Kenneth 0000-0001-9012-8590","orcid":"https://orcid.org/0000-0001-9012-8590","contributorId":260896,"corporation":false,"usgs":false,"family":"Elgersma","given":"Kenneth","email":"","affiliations":[{"id":34268,"text":"University of Northern Iowa","active":true,"usgs":false}],"preferred":false,"id":819092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wen, Ai","contributorId":260897,"corporation":false,"usgs":false,"family":"Wen","given":"Ai","email":"","affiliations":[{"id":34268,"text":"University of Northern Iowa","active":true,"usgs":false}],"preferred":false,"id":819093,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Welty, Justin L. 0000-0001-7829-7324 jwelty@usgs.gov","orcid":"https://orcid.org/0000-0001-7829-7324","contributorId":4206,"corporation":false,"usgs":true,"family":"Welty","given":"Justin","email":"jwelty@usgs.gov","middleInitial":"L.","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":819094,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arkle, Robert S. 0000-0003-3021-1389","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":218006,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":819095,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Iovanna, Rich","contributorId":207528,"corporation":false,"usgs":false,"family":"Iovanna","given":"Rich","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":819096,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237289,"text":"70237289 - 2021 - Review: “Jacob’s Zoo”— How using Jacob’s method for aquifer testing leads to more intuitive understanding of aquifer characteristics","interactions":[],"lastModifiedDate":"2022-10-06T14:28:43.014932","indexId":"70237289","displayToPublicDate":"2021-06-25T09:25:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Review: “Jacob’s Zoo”— How using Jacob’s method for aquifer testing leads to more intuitive understanding of aquifer characteristics","docAbstract":"The interpretation of aquifer responses to pumping tests is an important tool for assessing aquifer geometry and properties, which are critical in the assessment of water resources or in environmental remediation. However, the responses of aquifers, measured by time-drawdown relationships in monitoring wells, are nonunique solutions that are affected by many factors. Jacob’s Zoo is a collection of graphical interpretations that allows students and practitioners to develop an intuitive feel for how natural hydrogeological systems work, and develop a set of skills that provide a better understanding of aquifer properties far beyond interpretation of pumping tests. Jacob’s Zoo, based on the work of Jacob (1950), fosters a deeper understanding, although few practitioners realize the full utility of the method. Jacob CE (1950) Flow of groundwater, In: Rouse H (ed) Engineering Hydraulics, Wiley, New York. P 321–386.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-021-02363-7","usgsCitation":"Pfannkuch, H., Mooers, H.D., Siegel, D.I., Quinn, J.J., Rosenberry, D.O., and Alexander, S.C., 2021, Review: “Jacob’s Zoo”— How using Jacob’s method for aquifer testing leads to more intuitive understanding of aquifer characteristics: Hydrogeology Journal, v. 29, p. 2001-2015, https://doi.org/10.1007/s10040-021-02363-7.","productDescription":"15 p.","startPage":"2001","endPage":"2015","ipdsId":"IP-121619","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467235,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1863219","text":"External Repository"},{"id":408034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","noUsgsAuthors":false,"publicationDate":"2021-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Pfannkuch, Hans-Olaf","contributorId":297386,"corporation":false,"usgs":false,"family":"Pfannkuch","given":"Hans-Olaf","email":"","affiliations":[{"id":64391,"text":"University of Minnesota - Emeritus","active":true,"usgs":false}],"preferred":false,"id":853992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mooers, Howard D. 0000-0001-7160-1135","orcid":"https://orcid.org/0000-0001-7160-1135","contributorId":297387,"corporation":false,"usgs":false,"family":"Mooers","given":"Howard","email":"","middleInitial":"D.","affiliations":[{"id":18006,"text":"University of Minnesota Duluth","active":true,"usgs":false}],"preferred":false,"id":853993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Siegel, Donald I.","contributorId":178130,"corporation":false,"usgs":false,"family":"Siegel","given":"Donald","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":853994,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quinn, John J.","contributorId":297388,"corporation":false,"usgs":false,"family":"Quinn","given":"John","email":"","middleInitial":"J.","affiliations":[{"id":17946,"text":"Argonne National Laboratory","active":true,"usgs":false}],"preferred":false,"id":853995,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":853996,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alexander, Scott C.","contributorId":173842,"corporation":false,"usgs":false,"family":"Alexander","given":"Scott","email":"","middleInitial":"C.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854013,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221663,"text":"70221663 - 2021 - HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation","interactions":[],"lastModifiedDate":"2021-06-28T13:13:22.787252","indexId":"70221663","displayToPublicDate":"2021-06-25T08:10:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation","docAbstract":"Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, warning systems for shallow landslides have been informed by rainfall intensity-duration thresholds. More recent advances have introduced the concept of hydrometeorological thresholds that are informed not only by rainfall, but also by subsurface hydrological measurements. Previously, hydrometeorological thresholds have been shown to improve capabilities for forecasting shallow landslides, and they may ultimately be adapted to more generalized landslide forecasting. We present HydroMet, a code developed in Python by the U.S. Geological Survey, which allows users to guide the automated estimation of hydrometeorological thresholds for a site or area of interest, with the flexibility to select preferred threshold variables for the antecedent hydrologic conditions and the triggering meteorological conditions. Users can import hydrologic time-series data, including rainfall, soil-water content, and pore-water pressure, along with the times of known landslide occurrences, and then conduct objective optimization of warning thresholds using receiver operating characteristics. HydroMet presents many additional options, including selecting the threshold formula, the timescale of possible threshold variables, and the skill statistics used for optimization. Users can develop dual-stage thresholds for watch and warning alerts, with a lower, risk-averse threshold to avoid missed alarms and a less conservative threshold to minimize false alarms. Users may also choose to split their inventory data into calibration and evaluation subsets to independently evaluate the performance of optimized thresholds. We present output and applications of HydroMet using monitoring data from landslide-prone areas in the U.S. to demonstrate its utility and ability to produce thresholds with limited missed and false alarms for informing the next generation of reliable landslide warning systems.
","language":"English","publisher":"MDPI","doi":"10.3390/w13131752","usgsCitation":"Conrad, J.L., Morphew, M.D., Baum, R.L., and Mirus, B.B., 2021, HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation: Water, v. 13, no. 3, 1752, 17 p., https://doi.org/10.3390/w13131752.","productDescription":"1752, 17 p.","ipdsId":"IP-129944","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":451750,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13131752","text":"Publisher Index Page"},{"id":386788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Conrad, Jacob L. 0000-0001-8112-5355","orcid":"https://orcid.org/0000-0001-8112-5355","contributorId":260658,"corporation":false,"usgs":true,"family":"Conrad","given":"Jacob","email":"","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":818377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morphew, Michael D. 0000-0003-0072-1652","orcid":"https://orcid.org/0000-0003-0072-1652","contributorId":207959,"corporation":false,"usgs":false,"family":"Morphew","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":37668,"text":"USGS, Student- Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":818378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":818379,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":818380,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221596,"text":"sir20215058 - 2021 - Two-dimensional hydraulic analyses of Joachim Creek, De Soto, Missouri","interactions":[],"lastModifiedDate":"2021-06-25T12:11:23.622239","indexId":"sir20215058","displayToPublicDate":"2021-06-24T14:51:10","publicationYear":"2021","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":"2021-5058","displayTitle":"Two-Dimensional Hydraulic Analyses of Joachim Creek, De Soto, Missouri","title":"Two-dimensional hydraulic analyses of Joachim Creek, De Soto, Missouri","docAbstract":"A two-dimensional hydraulic model; water-surface profiles; and digital maps of water-surface elevation, velocities, and water depths were developed for a 6.7-mile reach of Joachim Creek within and near the city of De Soto, Missouri. Water-surface profiles were generated for the 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probability (10-, 25-, 50-, 100-, and 500-year recurrence interval) flows. Digital maps of water-surface elevation, water depth, and velocity were generated for the 1- and 0.2-percent annual exceedance probability flows. Water-surface elevations and inundation extents of generated profiles and maps were substantially lower than similar products produced for the 2019 flood-insurance study that included the study reach. The differences in water-surface elevations can be attributed to differences in input streamflows and hydraulic simulation techniques.
The water-surface elevations generated for the 1- and 0.2-percent annual exceedance probability flows were used to assess the vulnerability and inundation depths of 231 selected structures within the city of De Soto. Results indicate that 157 to 177 of the 231 structures were affected at the 1-percent annual exceedance probability flow, depending on the adjacent grade elevation used for reference. Between 185 and 198 structures were affected at the 0.2-percent annual exceedance probability flow, depending on grade elevation. Inundation depths at the affected structures were 0.02 to 9.28 feet (ft), depending on the flow and adjacent grade reference.
Flood elevations were computed for Joachim Creek using a two-dimensional, finite-volume numerical modeling application for river hydraulics. The hydraulic model was calibrated using high-water marks from the April 18, 2013, flood and the maximum measured streamflow at the U.S. Geological Survey streamgage Joachim Creek at De Soto, Mo. (station 07019500), on September 8, 2018. The calibrated model was then used to compute the hydraulic conditions associated with the 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probability flows. The simulated water-surface elevations and digital elevation model (derived from light detection and ranging data having a 0.60-ft vertical accuracy and a 1.97-ft horizontal resolution) were used to generate products including water-surface profiles and maps of inundated area, water depth, and velocities using model postprocessing software.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215058","collaboration":"Prepared in cooperation with the City of De Soto, Missouri","usgsCitation":"Hix, K.D., Rydlund, P.H., and Heimann, D.C., 2021, Two-dimensional hydraulic analyses of Joachim Creek, De Soto, Missouri: U.S. Geological Survey Scientific Investigations Report 2021–5058, 28 p., https://doi.org/10.3133/sir20215058.","productDescription":"Report: viii, 28 p.; Appendix; 2 Data Releases; Dataset","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-124332","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386714,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":386713,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92MQYE7","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial data and model archive associated with the two-dimensional hydraulic analysis of Joachim Creek, De Soto, Missouri"},{"id":386712,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ACXXI7","text":"USGS data release","description":"USGS data release","linkHelpText":"PeakFQ software input files and selected output files for selected long-term streamgages near Jefferson County, Missouri, through water year 2019"},{"id":386711,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5058/sir20215058_table1.1.csv","text":"Table 1.1 (.csv format)","size":"18.2 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2021–5058 Appendix 1.1","linkHelpText":"— Summary of water-surface elevations and depths at selected structures in the city of De Soto, Missouri, for 1- and 0.2-percent annual exceedance probability streamflows"},{"id":386710,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5058/sir20215058_table1.1.xlsx","text":"Table 1.1 (.xlsx format)","size":"34.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2021–5058 Appendix 1.1","linkHelpText":"— Summary of water-surface elevations and depths at selected structures in the city of De Soto, Missouri, for 1- and 0.2-percent annual exceedance probability streamflows"},{"id":386709,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5058/sir20215058.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5058"},{"id":386708,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5058/coverthb.jpg"}],"country":"United States","state":"Missouri","county":"Jefferson County","otherGeospatial":"Joachim 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Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Increasing demand for the limited water resources of the United States continues to put pressure on resource management agencies to balance the competing needs of ecosystem health with municipal, agricultural, and other uses. To meet these needs, the U.S. Geological Survey conducted a multiyear study to evaluate water resources in the upper Rio Grande Basin in the southwestern United States. The upper Rio Grande Basin extends from south-central Colorado, through New Mexico, into west Texas near Fort Quitman, including parts of Chihuahua, Mexico. The upper Rio Grande Basin consists of a sequence of alluvial basins that formed in the Rio Grande rift approximately 30 million years ago.
This report describes the hydrogeology of the upper Rio Grande Basin and how the groundwater resources in the basin have changed from 1980 to 2015. The hydrogeologic framework includes the horizontal delineation of the alluvial basins within the upper Rio Grande Basin from the headwaters in Colorado to Fort Quitman, Texas, including part of Mexico. Groundwater-level measurements from existing State and Federal data were used to construct groundwater-level altitude and groundwater-level change maps.
Of the 2,699 wells with groundwater-level data used in this study, 1,055 wells had data for only a single 5-year period, 703 wells had data for 50 percent or more of the 35 years of the study, and only 57 wells have 5-year groundwater-level data for the entire study period. The median decline in water levels in the upper Rio Grande Basin was 0.13 foot (ft) per 5-year period, and declines were measured in 53 percent of the 703 wells that contained data for 50 percent or more of the study period. Rates of groundwater-level decline greater than 1 ft per 5-year period were measured in 17 percent of the wells, greater than 2 ft per 5-year period, in 3 percent of the wells, and greater than 3 ft per 5-year period, in 1 percent of the wells. Overall, groundwater levels rose in 6 percent of the 703 wells that contained data for 50 percent or more of the study period, and in 4 percent of the wells, groundwater levels rose by 1 ft or more per 5-year period.
Groundwater-level changes in wells with consecutive 5-year measurement periods exhibited the most variability in the Española, Middle Rio Grande, and Mesilla/Conejos-Médanos alluvial basins. The largest declines in groundwater-level altitudes in individual wells were observed in the Española alluvial basin during 1995–2000, in the Palomas alluvial basin during 2010–2015, and in the Jornada del Muerto alluvial basin during 2005–10. The largest rises in groundwater-level altitudes in individual wells were observed in the Española alluvial basin during 2005–10, in the Middle Rio Grande alluvial basin during 1995–2000, and in the Mesilla/Conejos-Médanos alluvial basin during 1980–85.
Changes in groundwater storage throughout the study period varied by alluvial basin, likely based largely on changes in groundwater withdrawals because of increased demands during drier periods and population growth. All alluvial basins except the Tularosa-Hueco alluvial basin were evaluated for changes in groundwater storage from 1980 to 2015. Extremely limited data availability in 2010–15 for the Tularosa-Hueco alluvial basin led to this 5-year period being dropped from the groundwater-level change map and storage analysis for this basin.
In the San Luis Valley in southern Colorado, efforts to reverse groundwater depletion in the unconfined aquifer recovered approximately 250,000 acre-feet in storage between late 2013 and early 2018, following the implementation of a “pay-to-pump” groundwater program. However, severe drought that persists in the upper Rio Grande Basin, particularly in southern Colorado, has undone some of the conservation efforts. Within the Española alluvial basin, groundwater storage varied because municipal demand increased the demand on groundwater resources and conservation efforts were implemented. A groundwater-flow model evaluated for the Española alluvial basin indicated declines in groundwater storage from 1947 through 1982. Groundwater storage decreased in the Española alluvial basin in 1980–85, 1985–90, 1990–95, 1995–2000, and 2005–10 and increased in 2000–05 and 2010–15 leading to groundwater storage in 2015 about even with that in 1985.
Based on gridded groundwater-level altitudes, groundwater storage decreased in the Middle Rio Grande Basin from 1980 to 2015, except for during the 1980–85, 2000–05, and 2010–15 periods with an overall cumulative storage decrease from 1980 to 2015. Groundwater-flow models evaluated for the Middle Rio Grande alluvial basin showed groundwater storage in the Middle Rio Grande alluvial basin has been reduced since the mid-1950s through the end of the study period except for a brief recovery (reduction in storage outflow) in the mid-1980s. Simulated groundwater storage has also decreased in parts of the Palomas and Mesilla/Conejos-Médanos alluvial basins, and the northern part of the Conejos-Médanos alluvial basin starting in 1995 (excluding 2005 and 2007) and in the Tularosa-Hueco alluvial basin from the early 1940s to the end of the study period. Groundwater storage increased in the Mesilla/Conejos-Médanos alluvial basin during 1980–85 and slightly during 1990–95 and then decreased in the other 5-year periods. Groundwater storage in the Tularosa-Hueco alluvial basin increased from 1985 to 1990, but otherwise decreased, leading to an overall net groundwater-level decline in this part of the basin from 1980 to 2010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215035","programNote":"Water Availability and Use Science Program","usgsCitation":"Houston, N.A., Thomas, J.V., Foster, L.K., Pedraza, D.E., and Welborn, T.L., 2021, Hydrogeologic framework and groundwater characterization in selected alluvial basins in the upper Rio Grande basin, Colorado, New Mexico, and Texas, United States, and Chihuahua, Mexico, 1980 to 2015: U.S. Geological Survey Scientific Investigations Report 2021–5035, 71 p., https://doi.org/10.3133/sir20215035.","productDescription":"Report: viii, 71 p.; Data Release","numberOfPages":"71","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094878","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":436289,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJH17","text":"USGS data release","linkHelpText":"Electrical Resistivity Tomography (ERT) and Horizontal-to-Vertical Spectral Ratio (HVSR) Data Collected Within and Near Ellsworth Air Force Base, South Dakota, from 2014 to 2019"},{"id":386627,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7N58KBS","text":"USGS data release","linkHelpText":"Hydrogeologic, geologic, and water-level data for the groundwater component of the upper Rio Grande Focus Area Study, Colorado, New Mexico, and Texas, United States and Chihuahua, Mexico 2017"},{"id":386626,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5035/sir20215035.pdf","text":"Report","size":"22.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5035"},{"id":386625,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5035/coverthb.jpg"}],"country":"Mexico, United States","state":"Colorado, New Mexico, Texas, Chihuahua","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.5341796875,\n 36.94989178681327\n ],\n [\n -107.9736328125,\n 35.67514743608467\n ],\n [\n -107.75390625,\n 33.358061612778876\n ],\n [\n -107.9736328125,\n 31.728167146023935\n ],\n [\n -107.6220703125,\n 30.524413269923986\n ],\n [\n -106.787109375,\n 29.611670115197377\n ],\n [\n -105.9521484375,\n 29.38217507514529\n ],\n [\n -105.3369140625,\n 30.372875188118016\n ],\n [\n -105.6884765625,\n 31.615965936476076\n ],\n [\n -105.4248046875,\n 33.063924198120645\n ],\n [\n -104.80957031249999,\n 35.53222622770337\n ],\n [\n -104.853515625,\n 38.09998264736481\n ],\n [\n -106.5673828125,\n 38.75408327579141\n ],\n [\n -107.314453125,\n 37.96152331396614\n ],\n [\n -107.5341796875,\n 36.94989178681327\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501