Playa wetlands in the Great Plains, USA support a wide variety of plant species not found elsewhere in this agriculturally-dominated region due to the ephemeral presence of standing water and hydric soils within playas. If longer dry periods occur due to climate change or if changes in surrounding land use alter sediment accumulation rates and water storage capacity in playas, plant communities could experience decreased diversity, with lasting effects on ecosystem services provided by playas in the Great Plains and at a continental-level in North America. We quantified potential changes in playa wetland plant community composition associated with predicted changes in precipitation and land use in the Great Plains through the end of the 21st century. We conducted two six-month greenhouse experiments mimicking field conditions using intact mesocosms collected from playas in Nebraska and Texas. In the precipitation experiment, treatments derived from historical precipitation observations and three future moderate emissions (CMIP5 RCP4.5) downscaled climate projections were applied to mesocosms. For the land use experiment, treatments were simulated by nitrogen (N) applications to soil ranging from 0 to 100 mg-N L-1 with each precipitation event under historical rainfall patterns, representing increasing and decreasing area in agricultural use in playa watersheds. Plant communities tended to shift toward more native species under projected future climate conditions, but as N runoff increased, native species richness decreased. Agricultural land-use surrounding playas may have a greater effect on wetland plant communities than future alterations to hydrology based on climate change in the Great Plains; thus, efforts to reduce nutrient runoff into playas would likely mitigate loss in ecosystem function in the coming decades.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envexpbot.2020.104039","usgsCitation":"Owen, R.K., Webb, E.B., Haukos, D.A., and Goyne, K.W., 2020, Projected climate and land use changes drive plant community composition in agricultural wetlands: Environmental and Experimental Botany, v. 175, p. 1-12, https://doi.org/10.1016/j.envexpbot.2020.104039.","productDescription":"104039, 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-111000","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envexpbot.2020.104039","text":"Publisher Index Page"},{"id":395691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska, Texas","otherGeospatial":"Rainwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.01953125,\n 40.01078714046552\n ],\n [\n -96.51489257812499,\n 40.01078714046552\n ],\n [\n -96.51489257812499,\n 41.77950486590359\n ],\n [\n -100.01953125,\n 41.77950486590359\n ],\n [\n -100.01953125,\n 40.01078714046552\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.07373046875,\n 32.02670629333614\n ],\n [\n -103.040771484375,\n 31.44741029142872\n ],\n [\n -98.668212890625,\n 31.475524020001806\n ],\n [\n -98.7451171875,\n 34.15272698011818\n ],\n [\n -98.85498046875,\n 34.19817309627726\n ],\n [\n -99.041748046875,\n 34.252676117101515\n ],\n [\n -99.151611328125,\n 34.20725938207231\n ],\n [\n -99.283447265625,\n 34.42503613021332\n ],\n [\n -99.42626953125,\n 34.46127728843705\n ],\n [\n -99.42626953125,\n 34.38877925439021\n ],\n [\n -99.60205078124999,\n 34.42503613021332\n ],\n [\n -99.66796875,\n 34.38877925439021\n ],\n [\n -99.986572265625,\n 34.58799745550482\n ],\n [\n -99.97558593749999,\n 36.518465989675875\n ],\n [\n -103.062744140625,\n 36.50963615733049\n ],\n [\n -103.07373046875,\n 32.02670629333614\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"175","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Owen, Rachel K.","contributorId":273204,"corporation":false,"usgs":false,"family":"Owen","given":"Rachel","email":"","middleInitial":"K.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":833945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goyne, Keith W.","contributorId":204931,"corporation":false,"usgs":false,"family":"Goyne","given":"Keith","email":"","middleInitial":"W.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":833948,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215652,"text":"70215652 - 2020 - Estimation of vital population rates to assess the relative health of mussel assemblages in the Upper Mississippi River","interactions":[],"lastModifiedDate":"2020-10-27T12:49:45.812712","indexId":"70215652","displayToPublicDate":"2020-06-30T07:43:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Estimation of vital population rates to assess the relative health of mussel assemblages in the Upper Mississippi River","docAbstract":"The study was conducted in the Northern Atlantic Coastal Plain aquifer system, in the eastern USA.
Groundwater pH and redox conditions are fundamental chemical characteristics controlling the distribution of many contaminants of concern for drinking water or the ecological health of receiving waters. In this study, pH and redox conditions were modeled and mapped in a complex, layered aquifer system. Machine-learning methods (boosted regression trees) were applied to data from 3000 to 5000 wells. Predicted pH and the probability of anoxic conditions, defined by three thresholds of dissolved oxygen (0.5, 1, and 2 mg/L), were mapped at the 1-km2 scale for each of 10 regional aquifer layers.
Maps depict the extent of acidic groundwater and oxic conditions in the shallow, unconfined surficial aquifer and in unconfined, recharge-proximal areas of underlying aquifers, in contrast to alkaline and anoxic groundwater elsewhere. Geographic patterns and influential predictors–including elevation, overlying confining-units thickness, and simulated groundwater age and flux–are consistent with prior understanding of the processes controlling pH and redox in the aquifer system. The model-based maps support robust estimates of aquifer proportions, either areal or volumetric, likely to contain groundwater of a specified quality or be vulnerable to specific pH- or redox-sensitive contaminants. The machine-learning methods were an effective tool to map groundwater quality at the regional scale.
Kidron (2018) uses a straw man argument in an attempt to debunk eight putative hydrological‐related paradigms he believes to be “common among hydrologists, ecologists, or microbiologists that investigate biocrusts.” These paradigms relate to the roles of physical crusts and vascular plants in biocrust development, the major drivers (climate, porosity, hydrophobicity, and exopolysaccharides) of hydrology (infiltration and runoff), and the effect of mosses on hydrology and therefore vascular plants. We see two major problems with his arguments. First, they assume that the paradigms in question are generally accepted by biocrust researchers. Second, they are based on Kidron's (2018) world view of biocrusts, which has largely been informed by his own studies from a single, distinctly unique area of sand dunes at the Nizzana Research Site in the Negev Desert, Israel. This narrow focus and the selective use of published material disqualify his arguments. Our collective experience, based on more than 250 person years of biocrust research, and more than 700 scientific publications on biocrusts from all continents including Antarctica, indicates that, far from the straw man arguments proposed by Kidron (2018), there is no evidence to support the existence of a unifying theory that captures the global effects of biocrusts on hydrology. Our collective works demonstrate that, contrary to claims by Kidron (2018), the hydrological effects of biocrusts are strongly nuanced, varying with, but not limited to, differences in ecological context, landscape position, site condition, crust type and composition, climatic zone, soil texture and porosity, surface morphology, and spatial scale (reviewed in Weber, Büdel, & Belnap, 2016). Below, we critically analyse each of Kidron's (2018) paradigms, providing rigorous empirical evidence to show that none represent commonly held views among the biocrust research community.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.2215","usgsCitation":"Felde, V.J., Rodriguez-Caballero, E., Chamizo, S., Rossi, F., Uteau, D., Peth, S., Keck, H., de Philippis, R., Belnap, J., and Eldridge, D.J., 2020, Comment on 'Kidron (2018): Biocrust research: A critical view on eight common hydrological‐related paradigms and dubious theses. Ecohydrology, e2061': Ecohydrology, v. 13, no. 6, e2215, 6 p., https://doi.org/10.1002/eco.2215.","productDescription":"e2215, 6 p.","ipdsId":"IP-118462","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":456283,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2215","text":"Publisher Index Page"},{"id":377108,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Felde, Vincent J. M. N. L. 0000-0002-1018-2376","orcid":"https://orcid.org/0000-0002-1018-2376","contributorId":237005,"corporation":false,"usgs":false,"family":"Felde","given":"Vincent","email":"","middleInitial":"J. M. N. L.","affiliations":[],"preferred":false,"id":794882,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rodriguez-Caballero, Emilio 0000-0002-5934-3214","orcid":"https://orcid.org/0000-0002-5934-3214","contributorId":205639,"corporation":false,"usgs":false,"family":"Rodriguez-Caballero","given":"Emilio","email":"","affiliations":[{"id":37132,"text":"Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":794883,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chamizo, Sonia 0000-0002-2980-1683","orcid":"https://orcid.org/0000-0002-2980-1683","contributorId":174264,"corporation":false,"usgs":false,"family":"Chamizo","given":"Sonia","email":"","affiliations":[{"id":27406,"text":"Department of Agronomy, University of Almeria, 04120 Almeria, Spain","active":true,"usgs":false}],"preferred":false,"id":794884,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rossi, Federico 0000-0001-8367-6847","orcid":"https://orcid.org/0000-0001-8367-6847","contributorId":237006,"corporation":false,"usgs":false,"family":"Rossi","given":"Federico","email":"","affiliations":[],"preferred":false,"id":794885,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Uteau, Daniel 0000-0003-1499-4344","orcid":"https://orcid.org/0000-0003-1499-4344","contributorId":237007,"corporation":false,"usgs":false,"family":"Uteau","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":794886,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peth, Stephen 0000-0001-9799-212X","orcid":"https://orcid.org/0000-0001-9799-212X","contributorId":237008,"corporation":false,"usgs":false,"family":"Peth","given":"Stephen","email":"","affiliations":[],"preferred":false,"id":794887,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Keck, Hannes 0000-0001-7592-2833","orcid":"https://orcid.org/0000-0001-7592-2833","contributorId":237009,"corporation":false,"usgs":false,"family":"Keck","given":"Hannes","email":"","affiliations":[],"preferred":false,"id":794888,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"de Philippis, Roberto 0000-0001-7398-3536","orcid":"https://orcid.org/0000-0001-7398-3536","contributorId":237010,"corporation":false,"usgs":false,"family":"de Philippis","given":"Roberto","email":"","affiliations":[],"preferred":false,"id":794889,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":794890,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Eldridge, David J. 0000-0002-2191-486X","orcid":"https://orcid.org/0000-0002-2191-486X","contributorId":207298,"corporation":false,"usgs":false,"family":"Eldridge","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":37514,"text":"Center for Ecosystem Science, University of New South Wales, Sydney, NSW 2052, Australia","active":true,"usgs":false}],"preferred":false,"id":794891,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70210783,"text":"70210783 - 2020 - Structural impacts, carbon losses, and regeneration in mangrove wetlands after two hurricanes on St. John, U.S. Virgin Islands","interactions":[],"lastModifiedDate":"2020-12-30T13:06:42.999769","indexId":"70210783","displayToPublicDate":"2020-06-18T08:59:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Structural impacts, carbon losses, and regeneration in mangrove wetlands after two hurricanes on St. John, U.S. Virgin Islands","docAbstract":"Hurricanes Irma and Maria ravaged the mangroves of St. John, U.S. Virgin Islands, in 2017. Basal area losses were large (63–100%) and storm losses of carbon associated with aboveground biomass amounted to 11.9–43.5 Mg C/ha. Carbon biomass of dead standing trees increased 8.1–18.3 Mg C/ha among sites, and carbon in coarse woody debris on the forest floor increased 1.9–18.2 Mg C/ha, with effects varying by mangrove typology. While St. John has only ~45 ha of mangroves, they exist as isolated basins, salt ponds, and fringe mangroves; the latter sometimes support diverse marine communities. Salt pond and fringe mangroves had proportionately more organic carbon (46.3 Mg C/ha) than inorganic carbon (1.1 Mg C/ha) in soils than isolated basins. Soil organic carbon was also appreciable in isolated basins (30.8 Mg C/ha) but was matched by inorganic C (36.7 Mg C/ha), possibly due to adjacent land use history (e.g., road construction), previous storm overwash, or geomorphology. Soil nitrogen stocks were low across all typologies. Mangroves had limited regeneration 26 months after the storms, and recovery on St. John may be hindered by pre-storm hydrologic change in some stands, and potential genetic bottlenecks and lack of propagule sources for expedient recovery in all stands.","language":"English","publisher":"Springer","doi":"10.1007/s13157-020-01313-5","usgsCitation":"Krauss, K., From, A., Rogers, C., Whelan, K.R., Grimes, K.W., Dobbs, R., and Kelley, T., 2020, Structural impacts, carbon losses, and regeneration in mangrove wetlands after two hurricanes on St. John, U.S. Virgin Islands: Wetlands, v. 40, p. 2397-2412, https://doi.org/10.1007/s13157-020-01313-5.","productDescription":"16 p.","startPage":"2397","endPage":"2412","ipdsId":"IP-116442","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":436925,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3IYOT","text":"USGS data release","linkHelpText":"Forest structure, regeneration, and soil data to support mangrove forest damage assessment on St. John, U.S. Virgin Islands, from Hurricane Irma (2018-2019)"},{"id":375852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"U.S. Virgin Islands","otherGeospatial":"St Johns","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.64836120605469,\n 18.345075428248094\n ],\n [\n -64.71942901611328,\n 18.37505327646064\n ],\n [\n -64.75341796875,\n 18.377985612444007\n ],\n [\n -64.76268768310547,\n 18.370491765846573\n ],\n [\n -64.76062774658203,\n 18.359087461734603\n ],\n [\n -64.79290008544922,\n 18.358761613402578\n ],\n [\n -64.80560302734375,\n 18.33562481050443\n ],\n [\n -64.79393005371094,\n 18.309877404250322\n ],\n [\n -64.77161407470703,\n 18.306617965766893\n ],\n [\n -64.74723815917969,\n 18.31900350555467\n ],\n [\n -64.72835540771484,\n 18.308899579147358\n ],\n [\n -64.71427917480469,\n 18.30270655860875\n ],\n [\n -64.69814300537108,\n 18.2939055695918\n ],\n [\n -64.64836120605469,\n 18.345075428248094\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":791395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"From, Andrew 0000-0002-6543-2627","orcid":"https://orcid.org/0000-0002-6543-2627","contributorId":221941,"corporation":false,"usgs":true,"family":"From","given":"Andrew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":791396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rogers, Caroline 0000-0001-9056-6961","orcid":"https://orcid.org/0000-0001-9056-6961","contributorId":222443,"corporation":false,"usgs":true,"family":"Rogers","given":"Caroline","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":791397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whelan, Kevin R.T.","contributorId":225171,"corporation":false,"usgs":false,"family":"Whelan","given":"Kevin","email":"","middleInitial":"R.T.","affiliations":[{"id":41065,"text":"3U.S. National Park Service, Miami, FL 33157 USA","active":true,"usgs":false}],"preferred":false,"id":791398,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grimes, Kristen W.","contributorId":225506,"corporation":false,"usgs":false,"family":"Grimes","given":"Kristen","email":"","middleInitial":"W.","affiliations":[{"id":41149,"text":"University of the Virgin Islands","active":true,"usgs":false}],"preferred":false,"id":791399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dobbs, Robert C. 0000-0002-9079-7249 rdobbs@usgs.gov","orcid":"https://orcid.org/0000-0002-9079-7249","contributorId":200300,"corporation":false,"usgs":false,"family":"Dobbs","given":"Robert C.","email":"rdobbs@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":791400,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kelley, Thomas","contributorId":225507,"corporation":false,"usgs":false,"family":"Kelley","given":"Thomas","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":791401,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70208133,"text":"sir20195144 - 2020 - Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016","interactions":[],"lastModifiedDate":"2020-06-17T14:21:21.015204","indexId":"sir20195144","displayToPublicDate":"2020-06-17T07:36:04","publicationYear":"2020","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":"2019-5144","displayTitle":"Small Basin Annual Yield and Percentage of Snowmelt Runoff in North Dakota, 1931–2016","title":"Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016","docAbstract":"The North Dakota hydrology manual prepared by the U.S. Department of Agriculture, Soil Conservation Service, presents methodologies primarily used for developing hydrology for onfarm conservation practices, watershed projects, Resource Conservation and Development project measures, and river basin studies. The manual includes data necessary for determining hydrologic factors and developing a design discharge for a given site and intended purpose. The U.S. Geological Survey, in cooperation with the North Dakota Natural Resources Conservation Service, developed methods to reproduce and update the annual yield maps for chapter 7 of the North Dakota hydrology manual. Annual yields, in acre-feet per square mile, for the 50- and 80-percent exceedance probabilities and expected percentage of snowmelt runoff isolines were estimated using U.S. Geological Survey streamflow data from 1931 to 2016 for 71 selected streamgages with drainage areas of 505 square miles or less. An application of a modified Maintenance of Variance Extension Type III was used to estimate missing annual streamflow volumes. An alternate expected percentage of snowmelt runoff isolines was estimated using High Plains Climatic Center precipitation and snowmelt data from 1931 to 2016 for 85 selected sites. The final expected percentage of snowmelt runoff isolines was estimated using streamflow data instead of precipitation and snowfall depth data. A snowmelt runoff seasonal period of March–May produced better isoline slopes than a November–May runoff seasonal period. Slopes of the expected percentage of snowmelt runoff isolines were sensitive to amounts of missing record. Suitable isoline slopes appeared when the missing record was set to 50 percent (43 years) and 66 percent (57 years) for the 86-year period of 1931–2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195144","collaboration":"Prepared in cooperation with the Natural Resources Conservation Service—North Dakota","usgsCitation":"Williams-Sether, T., and Wheeling, S.L., 2020, Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016: U.S. Geological Survey Scientific Investigations Report 2019–5144, 37 p., https://doi.org/10.3133/sir20195144.","productDescription":"Report: vii, 38 p.; Dataset; 2 Appendixes","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-104356","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":375620,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144.pdf","text":"Report","size":"6.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5144"},{"id":375416,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5144/coverthb.jpg"},{"id":375418,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144_appendix_1.xlsx","text":"Appendix 1","size":"136 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5144 Appendix 1","linkHelpText":"—Table 1.1. Example data and computations for U.S. Geological Survey station 05056100"},{"id":375419,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144_appendix_2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2019–5144 Appendix 2","linkHelpText":"—R Code Script and Supporting Data for the Modified Maintenance of Variance Extension Type III, MOVE.3, Application"},{"id":375420,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System","description":"USGS Data Release","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"North 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Dakota\",\"nation\":\"USA \"}}]}","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
This report presents a novel conceptual framework and approach for conducting a geologically based environmental assessment, or geoenvironmental assessment, of undiscovered uranium resources within an area likely to contain uranium deposits. The framework is based on a source-to-receptor model that prioritizes the most likely contaminant sources, contaminant pathways, and affected environmental media for three common uranium extraction methods—open pit or underground mining with milling and in situ recovery (ISR). Data on regional geology, hydrology, and climate, as well as historical uranium mining and milling records are used to estimate the probable amounts of waste rock, tailings, wastewater, surface land disturbance, and subsurface aquifer disturbance for likely mining methods. Constituents of concern that might take the form of leachates, dust, radon, and sediments formed by chemical and physical weathering are also identified in the geoenvironmental assessment. Finally, areas where constituents of concern are likely to occur and persist in air, land, surface water, and groundwater are indicated by the potential for dispersion of dust by wind, accumulation of radon because of air stagnation, dispersion of sediments and wastewater by runoff, and infiltration of wastewater or leachates with consideration of the likely mobility of contaminants in surface water and groundwater. The geoenvironmental assessment output can be summarized in the following primary products: (1) a descriptive geoenvironmental model; (2) maps and statistics of variables that indicate the potential for constituents of concern to occur and persist in air, land, surface water, and groundwater within a tract that is geologically permissive for the occurrence of uranium; and (3) tables providing estimated or indicated quantities of waste rock, tailings, wastewater, dust, and radon emissions that could be associated with undiscovered uranium resources, if extracted, for each permissive tract. The uranium geoenvironmental assessment could help natural resource managers to prioritize and (or) identify (1) important potential contaminant pathways, (2) management practices required depending on the types of constituents that could be of concern, (3) areas for response in the event of accidental release, and (4) future directions for study. Furthermore, indicators of rock and water volumes potentially associated with an undiscovered uranium deposit may be evaluated to make quantitative comparisons of water required for uranium production or potential waste products generated during uranium extraction from areas permissive for uranium resource occurrence throughout the United States.
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Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.
","language":"English","doi":"10.5194/hess-24-2545-2020","usgsCitation":"Helbig, N., Moeser, C.D., Teich, M., Vincent, L., Lejeune, Y., Sicart, J., and Monnet, J., 2020, Snow processes in mountain forests: Interception modeling for coarse-scale applications: Hydrology and Earth System Sciences, v. 24, p. 2545-2560, https://doi.org/10.5194/hess-24-2545-2020.","productDescription":"16 p.","startPage":"2545","endPage":"2560","ipdsId":"IP-111174","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":456397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-24-2545-2020","text":"Publisher Index Page"},{"id":375684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France, United States","state":"Utah","otherGeospatial":"French Alps, Rocky Mountains","volume":"24","noUsgsAuthors":false,"publicationDate":"2020-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Helbig, N. 0000-0002-8663-7306","orcid":"https://orcid.org/0000-0002-8663-7306","contributorId":225392,"corporation":false,"usgs":false,"family":"Helbig","given":"N.","email":"","affiliations":[{"id":41093,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland","active":true,"usgs":false}],"preferred":false,"id":791020,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791021,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teich, M. 0000-0002-8850-9279","orcid":"https://orcid.org/0000-0002-8850-9279","contributorId":225393,"corporation":false,"usgs":false,"family":"Teich","given":"M.","email":"","affiliations":[{"id":41094,"text":"Austrian Research Centre for Forests (BFW), Innsbruck, Austria","active":true,"usgs":false}],"preferred":false,"id":791022,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vincent, L.","contributorId":225394,"corporation":false,"usgs":false,"family":"Vincent","given":"L.","email":"","affiliations":[{"id":41095,"text":"University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791023,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lejeune, Y.","contributorId":225395,"corporation":false,"usgs":false,"family":"Lejeune","given":"Y.","email":"","affiliations":[{"id":41095,"text":"University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791024,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sicart, J.-E.","contributorId":225396,"corporation":false,"usgs":false,"family":"Sicart","given":"J.-E.","email":"","affiliations":[{"id":41096,"text":"Université Grenoble Alpes, CNRS, IRD, Grenoble INP, Institut des Géosciences de l’Environnement (IGE) - UMR 5001,","active":true,"usgs":false}],"preferred":false,"id":791025,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monnet, J.-M.","contributorId":225397,"corporation":false,"usgs":false,"family":"Monnet","given":"J.-M.","email":"","affiliations":[{"id":41097,"text":"Univ. Grenoble Alpes, Irstea, LESSEM, 38000 Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791026,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70210718,"text":"70210718 - 2020 - Regional patterns in hydrologic response, a new three-component metric for hydrograph analysis and implications for ecohydrology, Northwest Volcanic Aquifer Study Area, USA","interactions":[],"lastModifiedDate":"2020-08-06T19:30:06.543616","indexId":"70210718","displayToPublicDate":"2020-06-12T09:48:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Regional patterns in hydrologic response, a new three-component metric for hydrograph analysis and implications for ecohydrology, Northwest Volcanic Aquifer Study Area, USA","docAbstract":"Oregon, California, Idaho, Nevada and Utah
Spatial patterns of hydrologic response were examined for the Northwest Volcanic Aquifer Study Area (NVASA). The utility of established hydrograph-separation methods for assessing hydrologic response in permeable volcanic terranes was assessed and a new three-component metric for hydrograph analysis was developed. The new metric, which partitions streamflow into subcomponents defined by the timescales of hydrologic response (e.g., fast-runoff, intermediate-interflow and slow-baseflow), was used to gain a fundamental understanding of the regional hydrology, investigate sub-regional differences, influencing factors, and ecohydrological implications.
The combined effects of NVASA’s physiography, climate and geology create a strongly coupled surface-groundwater system that produces copious baseflow and limited quantities of runoff and interflow. Patterns of hydrologic response are influenced by the type and rate of precipitation and permeability of the underlying geology. Under variable precipitation conditions the hydrologic response of volcanic terranes with similar permeability and subsurface-storage capacity can be significantly different. From a water management and ecohydrology perspective, understanding regional patterns of hydrologic response and sub-regional differences is fundamental. Results indicate that minimum-flow methods provide the most conservative estimate of baseflow and may be the most robust for filtering out snowmelt bias in baseflow estimates. Baseflow contributes ∼75% of the perennial streamflow across the NVASA and represents a critical component of the regional water supply that provides critical cold-water habitat.
Ecological studies indicate that impervious cover (IC) greater than approximately 5%–20% may have adverse effects on receiving-stream ecology. It is difficult to separate the effects of runoff quality from other effects of urbanization on receiving streams. This study presents the results of a numerical experiment to assess the effects of increasing IC on water quality using the Stochastic Empirical Loading and Dilution Model (SELDM). Hydrologic and physiographic variables representative of southern New England were used to simulate receiving water quality in a basin with IC ranging from 0.1% to 30%. Simulation results mirror the results of ecological studies; event mean concentrations (EMCs) of total phosphorus (TP) increase proportionally to the logarithms of imperviousness for a given risk percentile. Simulation results indicated that commonly used stormwater treatment methods may be insufficient for mitigating the effects of imperviousness. Therefore, disconnection, rather than treatment, may be needed to protect water quality, and efforts to preserve undeveloped stream basins may be more effective than efforts to remediate conditions in highly developed basins. Results also indicate that commonly used water-quality criteria may be too restrictive for stormwater because TP EMCs frequently exceed these criteria, even in minimally developed basins.
Prior to emergence as fry, salmonid embryos incubating within gravel nests called “redds” are vulnerable to substrate mobilization and lowering of the streambed, a process termed “streambed scour,” during floods. Water managers regulating discharge in salmonid-bearing rivers need information about the magnitude of discharge during which the scour of substrate surrounding salmonid redds occurs. The time when scour occurs, however, is difficult to measure and usually poorly constrained. The South Fork Tolt River in western Washington supplies the City of Seattle with hydroelectric power and about 40 percent of its municipal water needs, while providing spawning habitat for two salmonid species listed under the Endangered Species Act: Chinook salmon (Oncorhynchus tshawytscha) and steelhead trout (O. mykiss). The U.S. Geological Survey, in cooperation with Seattle City Light and Seattle Public Utilities, began a study in 2015 using accelerometer scour monitors (ASM) to characterize the timing of and hydrologic conditions associated with streambed scour at the depth of incubating salmonid embryos in the South Fork Tolt River. Prior to this study, operational thresholds for peak discharge on the South Fork Tolt River were 350 cubic feet per second (cfs) in the upper part of the river and 550 cfs in the lower part of the river as measured at USGS streamgages 12148000 and 12148300, respectively. These thresholds were developed from the peak discharge associated with observations of the flattening of redd structure and not from direct measurement of scour at the depth of egg pockets within redds. Accelerometer scour monitors were deployed at the level of salmonid egg pockets in spawning habitat of the South Fork Tolt River to record the temporal pattern of streambed scour at the depth of incubating salmon eggs during fall and winter flood seasons of water years (WY) 2016 and 2017. Thirteen of 48 ASMs deployed during the WY 2016 flood season recorded scour attributed to high streamflow when discharge measured at USGS streamgage 12148300 (the lower river streamgage used as an index gage) was between 969 and 1,360 cfs. Local discharge at individual scour sites varied depending on the timing of tributary inputs and downstream transport of water. During the subsequent flood season in WY 2017, peak discharge at the index gage reached 809 cfs. None of the 38 ASMs deployed recorded scour attributed to streamflow alone, although 10 ASMs recorded localized bed movement attributed to spawning activity of fish. Most scour at the depth of redds measured during WY 2016 occurred at or before peak flood discharge consistent with previous redd scour studies. The lack of scour measured in WY 2017 when peak discharge (809 cfs) was less than the minimum discharge when scour occurred in WY 2016 (969 cfs) suggests minimal to no scour of egg pockets in salmonid redds when discharge is less than 809 cfs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205044","collaboration":"Prepared in cooperation with Seattle City Light and Seattle Public Utilities","usgsCitation":"Gendaszek, A.S., Ablow, E., and Marks, D., 2020, Streambed scour of salmon (Oncorhynchus spp.) and steelhead (Oncorhynchus mykiss) redds in the South Fork Tolt River, King County, Washington: U.S. Geological Survey Scientific Investigations Report 2020–5044, 20 p., https://doi.org/10.3133/sir20205044.","productDescription":"iv, 20 p.","onlineOnly":"Y","ipdsId":"IP-110809","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":375381,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5044/coverthb.jpg"},{"id":375382,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5044/sir20205044.pdf","text":"Report","size":"3 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Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Drought risk management involves three pillars: drought early warning, drought vulnerability and risk assessment, and drought preparedness, mitigation, and response. This book collects in one place a description of all the key components of the first pillar, and describes strategies for fitting these pieces together. The best modern drought early warning systems incorporate and integrate a broad array of environmental information sources: weather station observations, satellite imagery, land surface and crop model simulations, and weather and climate model forecasts, and analyze this information in context-relevant ways that take into account exposure and vulnerability. Drought Early Warning and Forecasting: Theory and Practice assembles a comprehensive overview of these components, providing examples drawn from the Famine Early Warning Systems Network and the United States Drought Monitor. This book simultaneously addresses the physical, social, and information management aspects of drought early warning, and informs readers about the tools, techniques, and conceptual models required to effectively identify, predict, and communicate potential drought-related disasters.
This book is a key text for postgraduate scientists and graduate and advanced undergraduate students in hydrology, geography, earth sciences, meteorology, climatology, and environmental sciences programs. Professionals dealing with disaster management and drought forecasting will also find this book beneficial to their work.
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The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Cape May and Supawna Meadows National Wildlife Refuges in New Jersey. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of 13 marsh management units within the refuges and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that would be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that would maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $785,000, but that further expenditures may yield diminishing return on investment. Management actions in optimal portfolios at total costs less than $785,000 included applying sediment to the marsh surface (thin layer deposition) in seven marsh management units, controlling the invasive reed Phragmites australis in four marsh management units, remediating hydrologic alterations in two marsh management units, and planting native vegetation in one marsh management unit. The management benefits were derived from expected improvements in the capacity for marsh elevation to keep pace with sea-level rise, increases in numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds, and increased cover of native vegetation. The prototype presented here provides a framework for decision making at the Cape May and Supawna Meadows National Wildlife Refuges that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201055","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Braudis, B., and Hanlon, H., 2020, Optimization of tidal marsh management at the Cape May and Supawna Meadows National Wildlife Refuges, New Jersey, through use of structured decision making: U.S. Geological Survey Open-File Report 2020–1055, 41 p., https://doi.org/10.3133/ofr20201055.","productDescription":"vii, 41 p.","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101980","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":375304,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1055/ofr20201055.pdf","text":"Report","size":"3.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1055"},{"id":375303,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1055/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Cape May, Supawna Meadows National Wildlife Refuges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.377197265625,\n 39.690280594818034\n ],\n [\n -75.1025390625,\n 39.95185892663005\n ],\n [\n -75.41015624999999,\n 39.9602803542957\n ],\n [\n -75.618896484375,\n 39.58029027440865\n ],\n [\n -75.3662109375,\n 39.2407625100131\n ],\n [\n -75.0146484375,\n 38.788345355085625\n ],\n [\n -74.42138671875,\n 39.07037913108751\n ],\n [\n -74.410400390625,\n 39.605688178320804\n ],\n [\n -74.77294921875,\n 39.36827914916014\n ],\n [\n -75.16845703124999,\n 39.40224434029275\n ],\n [\n -75.377197265625,\n 39.690280594818034\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
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Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake’s watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near-stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near-stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake-specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2187","usgsCitation":"Stachelek, J., Weng, W., Carey, C.C., Kemanian, A.R., Cobourn, K.M., Wagner, T., Weathers, K., and Soranno, P.A., 2020, Granular measures of agricultural land use influence lake nitrogen and phosphorus differently at macroscales: Ecological Applications, v. 30, no. 8, e02187, 13 p., https://doi.org/10.1002/eap.2187.","productDescription":"e02187, 13 p.","ipdsId":"IP-114603","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":456515,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.2187","text":"External Repository"},{"id":395692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, New Hampshire, New York, Ohio, Pennsylvania, 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York","active":true,"usgs":false}],"preferred":false,"id":833989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carey, C. 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C.","affiliations":[{"id":56760,"text":"Carey Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":833993,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Soranno, P. A.","contributorId":275324,"corporation":false,"usgs":false,"family":"Soranno","given":"P.","email":"","middleInitial":"A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833994,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209091,"text":"sir20205026 - 2020 - Application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin","interactions":[],"lastModifiedDate":"2020-09-01T12:26:51.639849","indexId":"sir20205026","displayToPublicDate":"2020-06-01T14:36:39","publicationYear":"2020","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":"2020-5026","displayTitle":"Application of the Precipitation-Runoff Modeling System (PRMS) To Simulate Near-Native Streamflow in the Upper Rio Grande Basin","title":"Application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin","docAbstract":"The U.S. Geological Survey’s Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic response and streamflow. This study developed, calibrated, and assessed a PRMS model that simulates near-native or naturalized streamflow conditions in the Upper Rio Grande Basin. A PRMS model framework of 1,021 hydrologic response units was constructed for the basin. Subbasins within the larger Upper Rio Grande Basin range from snow-dominated northern basins to monsoon driven southern basins. The 1,021 hydrologic response units were grouped into 133 subareas within the basin, and solar radiation and potential evapotranspiration data were used to calibrate corresponding PRMS parameters in each subarea independently. Nine subbasins with streamgages distributed across the basin were identified as “near-native” subbasins, or those basins with low anthropogenic disturbance. Model parameters that affect streamflow were calibrated for the near-native subbasins, and the calibrated parameters were distributed to the remaining hydrologic response units on the basis of terrain, soil, and vegetation conditions linked to a distribution and weighting algorithm developed for this study. The parameter distribution method was validated in three of the nine near-native subbasins. Calibration results demonstrated that the PRMS model developed in this study with distributed model parameters for the entire Upper Rio Grande Basin was successful in applying local information to improve model performance over the National Hydrologic Model, and that the new model is appropriate to use to simulate near-native conditions throughout the basin. The result is a model that can simulate naturalized flow and other variables that affect the water budget (including soil moisture, evapotranspiration, recharge) at the daily time step for current and future climate conditions, and that can also be used in conjunction with other models developed for the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205026","collaboration":"U.S. Geological Survey National Water Census and Water Availability and Use Science Program","usgsCitation":"Chavarria, S.B., Moeser, C.D., and Douglas-Mankin, K.R., 2020, Application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin: U.S. Geological Survey Scientific Investigations Report 2020–5026, 38 p., https://doi.org/10.3133/sir20205026.","productDescription":"Report: vi, 38 p.; Data Release","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-111974","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":436948,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ML93QB","text":"USGS data release","linkHelpText":"Hydrologic simulations using projected climate data as input to the Precipitation-Runoff Modeling System (PRMS) in the Upper Rio Grande Basin"},{"id":375137,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YOPYW7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input and output data for the application of the Precipitation-Runoff Modeling System (PRMS) to simulate near-native streamflow in the Upper Rio Grande Basin"},{"id":375136,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5026/sir20205026.pdf","text":"Report","size":"15.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5026"},{"id":375135,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5026/coverthb.jpg"}],"country":"United States","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 -106.74316406249999,\n 31.466153715024294\n ],\n [\n -106.0400390625,\n 31.052933985705163\n ],\n [\n -105.380859375,\n 30.90222470517144\n ],\n [\n -105.0732421875,\n 31.12819929911196\n ],\n [\n -105.5126953125,\n 32.175612478499325\n ],\n [\n -105.2490234375,\n 32.80574473290688\n ],\n [\n -105.732421875,\n 33.211116472416855\n ],\n [\n -105.16113281249999,\n 33.797408767572485\n ],\n [\n -104.8974609375,\n 34.66935854524543\n ],\n [\n -105.380859375,\n 35.460669951495305\n ],\n [\n -104.5458984375,\n 36.80928470205937\n ],\n [\n -104.94140625,\n 38.03078569382294\n ],\n [\n -106.34765625,\n 38.54816542304656\n ],\n [\n -107.314453125,\n 37.92686760148135\n ],\n [\n -106.8310546875,\n 37.33522435930639\n ],\n [\n -108.06152343749999,\n 35.99578538642032\n ],\n [\n -107.75390625,\n 34.488447837809304\n ],\n [\n -108.19335937499999,\n 33.61461929233378\n ],\n [\n -108.984375,\n 32.65787573695528\n ],\n [\n -108.80859375,\n 31.541089879585808\n ],\n [\n -108.45703125,\n 31.27855085894653\n ],\n [\n -107.7978515625,\n 32.287132632616384\n ],\n [\n -107.05078125,\n 32.39851580247402\n ],\n [\n -106.74316406249999,\n 31.466153715024294\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
A study was conducted by the U.S. Geological Survey, in cooperation with the U.S. Air Force, to describe the regional hydrostratigraphy of shallow aquifers and confining units underlying Joint Base McGuire-Dix-Lakehurst (JBMDL) and vicinity, New Jersey, in the context of contamination of groundwater and surface water by per- and polyfluoroalkyl substances (PFAS) potentially originating from JBMDL sources. The aquifers studied are two that crop out within JBMDL boundaries—the Kirkwood-Cohansey aquifer system and the Vincentown aquifer—and another aquifer near JBMDL that does not crop out at land surface—the Piney Point aquifer. The unconfined portion of the Vincentown aquifer and portions of the Kirkwood-Cohansey aquifer system that overlie the unconfined portion of the Vincentown aquifer are consolidated into, and described as, a single, separate unconfined aquifer system. Regionally extensive clay subunits that potentially create semiconfined hydrologic conditions within the mostly unconfined Kirkwood-Cohansey aquifer system also are identified. Two confining units were studied—the Manasquan-Shark River confining unit underlying the Kirkwood-Cohansey aquifer system, which includes the basal confining sediment in the Kirkwood Formation, and the Navesink-Hornerstown confining unit underlying the Vincentown aquifer. The hydrostratigraphic units are defined using available borehole geophysical logs, lithologic logs, and (or) drillers’ logs from 131 wells and are presented in a series of 8 aquifer structure maps and 12 cross sections. The framework positions JBMDL into a regional hydrostratigraphic structure for which higher-resolution delineation of the shallow aquifers can be constructed to determine potential pathways of PFAS contamination in groundwater to off-site drinking water wells in areas adjacent to JBMDL.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191134","collaboration":"Prepared in cooperation with the U.S. Air Force","usgsCitation":"Fiore, A.R., 2020, Regional hydrostratigraphic framework of Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, in the context of perfluoroalkyl substances contamination of groundwater and surface water: U.S. Geological Survey Open-File Report 2019–1134, 42 p., https://doi.org/10.3133/ofr20191134.","productDescription":"Report: viii, 42 p.; 12 Plates: 30 x 24 inches; 2 Tables","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-107327","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":375120,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate06.pdf","text":"Plate 6","size":"1.87 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of the top of the confined portion of the Vincentown aquifer, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375125,"rank":13,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate11.pdf","text":"Plate 11","size":"533 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Sections F-F’ through I-I’, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375113,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1134/coverthb.jpg"},{"id":375114,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134.pdf","text":"Report","size":"9.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1134"},{"id":375115,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate01.pdf","text":"Plate 1","size":"1.21 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of well locations and outcrop areas of hydrostratigraphic units, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375116,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate02.pdf","text":"Plate 2","size":"1.42 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of the bottom of the Kirkwood-Cohansey aquifer system, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375117,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate03.pdf","text":"Plate 3","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of the top of semiconfining subunits within the Kirkwood-Cohansey aquifer system, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375122,"rank":10,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate08.pdf","text":"Plate 8","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of the bottom of the unconfined portion of the Vincentown aquifer, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375123,"rank":11,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate09.pdf","text":"Plate 9","size":"2.04 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of the bottom of the Navesink-Hornerstown confining unit, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375124,"rank":12,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate10.pdf","text":"Plate 10","size":"588 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Sections A-A’ through E-E’, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375127,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_table03.xlsx","text":"Table 3","size":"23.2 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Wells used to develop a hydrostratigraphic framework, and interpreted aquifer structure points, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey (Preferred method to view file)"},{"id":375128,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_table03.csv","text":"Table 3","size":"10.2 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Wells used to develop a hydrostratigraphic framework, and interpreted aquifer structure points, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375118,"rank":6,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate04.pdf","text":"Plate 4","size":"1.48 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of the thickness of semiconfining subunits within the Kirkwood-Cohansey aquifer system, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375119,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate05.pdf","text":"Plate 5","size":"1.18 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of the top of the Piney Point aquifer, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375121,"rank":9,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate07.pdf","text":"Plate 7","size":"755 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of the thickness of the confined portion of the Vincentown aquifer, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"},{"id":375126,"rank":14,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1134/ofr20191134_plate12.pdf","text":"Plate 12","size":"409 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Sections J-J’ through L-L’, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey"}],"country":"United States","state":"New Jersey","otherGeospatial":"Joint Base McGuire-Dix-Lakehurst","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.74822998046875,\n 39.886557705928475\n ],\n [\n -74.25796508789062,\n 39.886557705928475\n ],\n [\n -74.25796508789062,\n 40.11799004890473\n ],\n [\n -74.74822998046875,\n 40.11799004890473\n ],\n [\n -74.74822998046875,\n 39.886557705928475\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Most wetlands have been subject to changes in flooding regimes by climate change and human activities, resulting in widespread alteration of wetland plants at different organizational levels. However, scaling the responses of wetland plants to changes in flooding regimes is still challenging, because flooding could indirectly affect wetland plants through affecting environment factors (e.g. soil properties). During the non-flooding period, we investigated leaf N and P stoichiometry at three organizational levels (intra-species, inter-species, inter-community) along a flooding duration gradient in a lakeshore meadow of Poyang Lake floodplain, China. At the intra-species level, leaf N and P stoichiometry showed species-specific responses to flooding duration. At the inter-species level, leaf N or P contents or N:P ratio showed no significant response to flooding duration. At the inter-community level, leaf N and P contents significantly increased with flooding duration, while leaf N:P ratio decreased. At each organizational level, leaf N and P stoichiometry showed poor correlation with soil N and P stoichiometry. Moreover, intra-specific responses of leaf N and P contents to flooding duration and soil nutrient content increased with mean flooding duration of species distribution, which was the index of species hydrological niche. Intraspecific variation had lower contribution than species turnover to variations in community leaf nutrient stoichiometry. In all, flooding duration affected leaf N and P stoichiometry mainly through direct pathway at the intra-species and inter-community level, rather than the indirect pathway via soil nutrient stoichiometry. Therefore, our results have implications for scaling up from environmental conditions to ecosystem processes via wetland plant communities.
Climate change is the defining environmental problem for our generation. The effects of climate change are increasingly evident and are anticipated to profoundly affect our ability to conserve fish habitats and fish assemblages. Reservoirs are important structures for coping with projected shifts in water supply, but they also provide refuge for riverine fishes and retain distinct fish assemblages that support diverse fisheries. The effects of climate change on reservoirs are unique among aquatic systems because reservoirs have distinctive habitat characteristics due to their terrestrial origin and strong linkage to catchments. This article reviews (1) the projected effects of rising temperature and shifting precipitation on reservoir fish habitats, and (2) adaptation strategies to cope with the anticipated effects. Climate warming impacts to reservoirs may include higher water temperatures and shifts in hydrology that can result in reduced water levels in summer and fall, altered water residence cycles, disconnection from upstream riverine habitats and backwaters, increased stratification, eutrophication, anoxia, and a general shift in biotic assemblages including plants, invertebrates, and fishes. What is needed to adapt to these changes is a perspective that focuses on maintaining ecosystem functionality rather than on retaining a certain species composition. To that end, various strategies organized into planning, monitoring, and managing compartments are identified.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/23308249.2020.1767035","usgsCitation":"Miranda, L.E., Coppola, G., and Boxrucker, J., 2020, Reservoir fish habitats: A perspective on coping with climate change: Reviews in Fisheries Science & Aquaculture, v. 20, no. 4, p. 478-498, https://doi.org/10.1080/23308249.2020.1767035.","productDescription":"21 p.","startPage":"478","endPage":"498","ipdsId":"IP-115752","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456668,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/23308249.2020.1767035","text":"Publisher Index Page"},{"id":395773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-05-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coppola, G.","contributorId":265335,"corporation":false,"usgs":false,"family":"Coppola","given":"G.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":834267,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boxrucker, J.","contributorId":275763,"corporation":false,"usgs":false,"family":"Boxrucker","given":"J.","affiliations":[{"id":56890,"text":"Reservoir Fisheries Habitat Partnership","active":true,"usgs":false}],"preferred":false,"id":834268,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211965,"text":"70211965 - 2020 - Short- and long-term responses of riparian cottonwoods (Populus spp.) to flow diversion: Analysis of tree-ring radial growth and stable carbon isotopes","interactions":[],"lastModifiedDate":"2020-08-12T21:02:36.901702","indexId":"70211965","displayToPublicDate":"2020-05-20T15:57:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Short- and long-term responses of riparian cottonwoods (Populus spp.) to flow diversion: Analysis of tree-ring radial growth and stable carbon isotopes","title":"Short- and long-term responses of riparian cottonwoods (Populus spp.) to flow diversion: Analysis of tree-ring radial growth and stable carbon isotopes","docAbstract":"Long duration tree-ring records with annual precision allow for the reconstruction of past growing conditions. Investigations limited to the most common tree-ring proxy of ring width can be difficult to interpret, however, because radial growth is affected by multiple environmental processes. Furthermore, studies of living trees may miss important effects of drought on tree survival and forest changes. Stable carbon isotopes can help distinguish drought from other environmental factors that influence tree-ring width and forest stand condition. We quantified tree-ring radial expansion and stable carbon isotope ratios (δ13C) in riparian cottonwoods (Populus angustifolia and P. angustifolia x P. trichocarpa) along Snake Creek in Nevada, USA. We investigated how hydrological drought affected tree growth and death at annual to half-century scales in a partially dewatered reach (DW) compared to reference reaches immediately upstream and downstream. A gradual decline in tree-ring basal area increment (BAI) began at DW concurrent to streamflow diversion in 1961. BAI at DW diverged from one reference reach immediately but not from the other until nearly 50 years later. In contrast, tree-ring δ13C had a rapid and sustained increase following diversion at DW only, providing the stronger and clearer drought signal. BAI and δ13C were not significantly correlated prior to diversion; after diversion they both reflected drought and were correlated for DW trees only. Cluster analyses distinguished all trees in DW from those in reference reaches based on δ13C, but BAI patterns left trees intermixed across reaches. Branch and tree mortality were also highest and canopy vigor was lowest in DW. Results indicate that water scarcity strongly limited cottonwood photosynthesis following flow diversion, thus reducing carbon assimilation, basal growth and survival. The dieback was not sudden, but occurred over decades as carbon deficits mounted and depleted streamflow left trees increasingly vulnerable to local meteorological drought.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.139523","usgsCitation":"Schook, D.M., Friedman, J.M., Stricker, C.A., Csank, A.Z., and Cooper, D.J., 2020, Short- and long-term responses of riparian cottonwoods (Populus spp.) to flow diversion: Analysis of tree-ring radial growth and stable carbon isotopes: Science of the Total Environment, v. 735, 139523, 11 p., https://doi.org/10.1016/j.scitotenv.2020.139523.","productDescription":"139523, 11 p.","ipdsId":"IP-117602","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":456676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.139523","text":"Publisher Index Page"},{"id":377443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Great Basin Nation Park, Snake Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.41436767578124,\n 38.791556581282244\n ],\n [\n -114.09576416015624,\n 38.791556581282244\n ],\n [\n -114.09576416015624,\n 39.07784203269269\n ],\n [\n -114.41436767578124,\n 39.07784203269269\n ],\n [\n -114.41436767578124,\n 38.791556581282244\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"735","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schook, Derek M.","contributorId":178325,"corporation":false,"usgs":false,"family":"Schook","given":"Derek","email":"","middleInitial":"M.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":795997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":795998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":795999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Csank, Adam Z.","contributorId":238091,"corporation":false,"usgs":false,"family":"Csank","given":"Adam","email":"","middleInitial":"Z.","affiliations":[{"id":37455,"text":"University of Nevada","active":true,"usgs":false}],"preferred":false,"id":796000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooper, David J.","contributorId":196510,"corporation":false,"usgs":false,"family":"Cooper","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":13017,"text":"Department of Forest and Rangeland Stewardship, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":796001,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}