Many at-risk species lack standardized surveys across their range or quantitative data capable of detecting demographic trends. As a result, extinction risk assessments often rely on ordinal categories of risk based on explicit criteria or expert elicitation. This study demonstrates a Bayesian approach to assessing extinction risk based on this common data structure, using three freshwater mussel species being considered for listing under the US Endangered Species Act. The probability that a population is classified under each risk category was modeled as a function of projected landscape change using ordered probit regression, assuming observed categories reflect a latent, continuous probability of persistence. All three species were more likely than not (mean probability >0.5) to be classified as extirpated or low condition throughout their range based on effects of urban development and hydrologic alteration. Spatial variation in estimates revealed strongholds and high-risk areas relevant to conservation decision making. Projected change in probabilities of each risk category based on multiple land-use and climate models was generally small relative to high baseline risk resulting from past landscape changes. Assessing extinction risk based on probabilities of ordinal condition as a function of landscape patterns may provide a flexible and robust approach for many at-risk taxa by adjusting species' demographic criteria to match relative risk categories, following standardized criteria, or using expert elicitation for data-deficient species. This approach provides decision makers with a useful measure of uncertainty around ordinal classifications and provides a framework for estimating future risk based on projections of anthropogenic stressors.
Degradation of peatlands via drainage is increasing globally and destabilizing peat carbon (C) stores. The effects of drainage on the timing and magnitude of lateral C losses from degraded peatlands remains understudied. We measured spatial and temporal variability in lateral C exports from three drained peat islands in the Sacramento‐San Joaquin Delta in California across the 2017 and 2018 water years using measurements of dissolved inorganic C (DIC), dissolved organic C (DOC), and suspended particulate organic C (POC) concentration combined with discharge. These measurements were supplemented with stable isotope data (δ13C‐DIC, δ13C‐POC, δ15N‐PON, and δ2H‐H2O values) to provide insight into hydrological and biogeochemical controls on lateral C exports from drained peatlands. Drainage DOC and DIC concentrations were seasonally variable with the highest values in the winter rainy season, when discharge was also elevated. Seasonal differences in the mobilization of dissolved C appeared to result from changing water sources and water table levels. Peat island drainage C contributions to surrounding waterways were also greatest during the winter. Although temporal variability in C cycling processes and trends were generally similar across islands, baseline drainage DIC, DOC, and POC concentrations were spatially variable, likely a result of sub‐island‐scale differences in soil organic matter content and hydrology. This spatial variability complicates system‐wide assessments of C budgets. Net lateral C exports were water year dependent and comparable to previously published vertical C emission rates for this system. This work highlights the importance of including lateral C exports from drained peatlands in local and regional C budgets.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JG005883","usgsCitation":"Richardson, C.M., Fackrell, J.K., Kraus, T.E., Young, M.B., and Paytan, A., 2020, Lateral carbon exports from drained peatlands: An understudied carbon pathway in the Sacramento-San Joaquin Delta, California: Journal of Geophysical Research: Biogeosciences, v. 125, no. 12, e2020JG005883, 21 p., https://doi.org/10.1029/2020JG005883.","productDescription":"e2020JG005883, 21 p.","ipdsId":"IP-119742","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":467269,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/37323","text":"External Repository"},{"id":381943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.200927734375,\n 37.98100996893789\n ],\n [\n -121.68731689453125,\n 37.98100996893789\n ],\n [\n -121.68731689453125,\n 38.225235239076824\n ],\n [\n -122.200927734375,\n 38.225235239076824\n ],\n [\n -122.200927734375,\n 37.98100996893789\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Christina M. 0000-0003-0597-8836","orcid":"https://orcid.org/0000-0003-0597-8836","contributorId":147438,"corporation":false,"usgs":false,"family":"Richardson","given":"Christina","email":"","middleInitial":"M.","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":807604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fackrell, Joseph K. 0000-0001-8148-3734","orcid":"https://orcid.org/0000-0001-8148-3734","contributorId":225515,"corporation":false,"usgs":true,"family":"Fackrell","given":"Joseph","email":"","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Tamara E. 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C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807606,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Megan B. 0000-0002-0229-4108 mbyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-0229-4108","contributorId":3315,"corporation":false,"usgs":true,"family":"Young","given":"Megan","email":"mbyoung@usgs.gov","middleInitial":"B.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":807607,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paytan, Adina 0000-0001-8360-4712","orcid":"https://orcid.org/0000-0001-8360-4712","contributorId":193046,"corporation":false,"usgs":false,"family":"Paytan","given":"Adina","email":"","affiliations":[],"preferred":false,"id":807608,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216662,"text":"70216662 - 2020 - Towards the understanding of hydrogeochemical seismic responses in karst aquifers: A retrospective meta-analysis focused on the Apennines (Italy)","interactions":[],"lastModifiedDate":"2020-11-27T15:19:23.675338","indexId":"70216662","displayToPublicDate":"2020-11-26T06:48:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Towards the understanding of hydrogeochemical seismic responses in karst aquifers: A retrospective meta-analysis focused on the Apennines (Italy)","docAbstract":"Earthquakes are known to affect groundwater properties, yet the mechanisms causing chemical and physical aquifer changes are still unclear. The Apennines mountain belt in Italy presents a rich literature of case studies documenting hydrogeochemical response to seismicity, due to the high frequency of seismic events and the presence of different regional aquifers in the area. In this study, we synthesize published data from the last 30 years in the Apennine region in order to shed light on the main mechanisms causing earthquake induced water changes. The results suggest the geologic and hydrologic setting specific to a given spring play an important role in spring response, as well as the timing of the observed response. In contrast to setting, the main focal mechanisms of earthquake and the distance between epicenter and the analyzed springs seems to present a minor role in defining the response. The analysis of different response variables, moreover, indicates that an important driver of change is the degassing of CO2, especially in thermal springs, whereas a rapid increase in solute concentration due to permeability enhancement is observable in different cold and shallow springs. These findings also leave open the debate regarding whether earthquake precursors can be recognized beyond site-specific responses. Such responses can be understood more comprehensively through the establishment of a regional long-term monitoring system and continuous harmonization of data and sampling strategies, achievable in the Apennine region through the set-up of a monitoring network.
","language":"English","publisher":"MDPI","doi":"10.3390/min10121058","usgsCitation":"Binda, G., Pozzi, A., Michetti, A., Noble, P., and Rosen, M.R., 2020, Towards the understanding of hydrogeochemical seismic responses in karst aquifers: A retrospective meta-analysis focused on the Apennines (Italy): Minerals, v. 10, no. 12, 1058, 28 p., https://doi.org/10.3390/min10121058.","productDescription":"1058, 28 p.","ipdsId":"IP-120991","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":454745,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min10121058","text":"Publisher Index Page"},{"id":380832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Apennines mountain belt","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 7.6025390625,\n 43.67581809328341\n ],\n [\n 8.8330078125,\n 44.38669150215206\n ],\n [\n 10.48095703125,\n 42.94033923363181\n ],\n [\n 13.0078125,\n 41.27780646738183\n ],\n [\n 14.919433593750002,\n 40.17887331434696\n ],\n [\n 15.8203125,\n 39.85915479295669\n ],\n [\n 16.8310546875,\n 40.6306300839918\n ],\n [\n 16.06201171875,\n 41.86956082699455\n ],\n [\n 13.73291015625,\n 43.30919109985686\n ],\n [\n 12.568359375,\n 44.88701247981298\n ],\n [\n 13.7109375,\n 45.767522962149876\n ],\n [\n 13.798828125,\n 46.543749602738565\n ],\n [\n 12.0849609375,\n 47.15984001304432\n ],\n [\n 10.634765625,\n 47.010225655683485\n ],\n [\n 9.667968749999998,\n 46.543749602738565\n ],\n [\n 8.3056640625,\n 46.51351558059737\n ],\n [\n 6.74560546875,\n 45.84410779560204\n ],\n [\n 6.459960937499999,\n 44.793530904744074\n ],\n [\n 7.6025390625,\n 43.67581809328341\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Binda, Gilberto 0000-0001-5530-3939","orcid":"https://orcid.org/0000-0001-5530-3939","contributorId":206790,"corporation":false,"usgs":false,"family":"Binda","given":"Gilberto","email":"","affiliations":[{"id":37402,"text":"Università degli Studi dell’Insubria","active":true,"usgs":false}],"preferred":false,"id":805789,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pozzi, Andrea","contributorId":206791,"corporation":false,"usgs":false,"family":"Pozzi","given":"Andrea","email":"","affiliations":[{"id":37402,"text":"Università degli Studi dell’Insubria","active":true,"usgs":false}],"preferred":false,"id":805790,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michetti, Alessandro 0000-0002-1775-1340","orcid":"https://orcid.org/0000-0002-1775-1340","contributorId":206792,"corporation":false,"usgs":false,"family":"Michetti","given":"Alessandro","email":"","affiliations":[{"id":37402,"text":"Università degli Studi dell’Insubria","active":true,"usgs":false}],"preferred":false,"id":805791,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noble, Paula","contributorId":198953,"corporation":false,"usgs":false,"family":"Noble","given":"Paula","affiliations":[{"id":33648,"text":"Department of Geological Sciences and Engineering, University of Nevada","active":true,"usgs":false}],"preferred":false,"id":805792,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":805793,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216479,"text":"ofr20201116 - 2020 - Multiple-well monitoring site adjacent to the North and South Belridge Oil Fields, Kern County, California","interactions":[],"lastModifiedDate":"2020-11-25T12:52:01.362381","indexId":"ofr20201116","displayToPublicDate":"2020-11-24T12:43:43","publicationYear":"2020","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":"2020-1116","displayTitle":"Multiple-Well Monitoring Site Adjacent to the North and South Belridge Oil Fields, Kern County, California","title":"Multiple-well monitoring site adjacent to the North and South Belridge Oil Fields, Kern County, California","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board, is evaluating several questions about oil and gas development and groundwater resources in California, including (1) the location of groundwater resources; (2) the proximity of oil and gas operations to groundwater and the geologic materials between them; (3) evidence (or no evidence) of fluids from oil and gas sources in groundwater; and (4) the pathways or processes responsible when fluids from oil and gas sources are present in groundwater (U.S. Geological Survey, 2017). As part of this evaluation, the USGS installed a multiple-well monitoring site in the southern San Joaquin Valley groundwater basin adjacent to the North and South Belridge oil fields, about 7 miles southwest of Lost Hills, California. Data collected at the Belridge multiple-well monitoring site (BWSD) provide information about the geology, hydrology, geophysical properties, and geochemistry of the aquifer system, thus enhancing understanding of relations between adjacent groundwater and the North and South Belridge oil fields in an area where there are few groundwater data. This report presents construction information for the BWSD and initial hydrogeologic data collected from the site. A similar site installed to the east of the Lost Hills oil field, 11.5 miles to the north of the BWSD site, was described by Everett and others (2020a).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201116","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Everett, R.R., Brown, A.A., Gillespie, J.M., Kjos, A., and Fenton, N.C., 2020, Multiple-well monitoring site adjacent to the North and South Belridge Oil Fields, Kern County, California: U.S. Geological Survey Open-File Report 2020-1116, 10 p., https://doi.org/10.3133/ofr20201116.","productDescription":"Report: 10 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-112077","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":380658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1116/ofr20201116.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1116"},{"id":380659,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96WITX5","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aquifer test data for the Belridge multiple-well monitoring site (BWSD), Kern County, California"},{"id":380657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1116/coverthb.jpg"}],"country":"United States","state":"California","county":"Kern County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-120.1945,35.788],[-120.1842,35.789],[-120.1655,35.7891],[-120.1474,35.7887],[-120.0816,35.7886],[-119.9688,35.7896],[-119.852,35.7891],[-119.7618,35.7906],[-119.6472,35.7895],[-119.5395,35.79],[-119.4301,35.7905],[-119.3308,35.7899],[-119.2169,35.7906],[-119.1182,35.7903],[-118.9027,35.789],[-118.6504,35.7897],[-118.6441,35.7896],[-118.5885,35.7897],[-118.5233,35.7892],[-118.4785,35.7915],[-118.4706,35.7919],[-118.4502,35.7908],[-118.2716,35.7896],[-118.2562,35.7894],[-118.2387,35.7897],[-118.2137,35.7894],[-118.1956,35.7896],[-118.1632,35.7893],[-118.0839,35.7865],[-118.0697,35.7859],[-118.009,35.7861],[-117.9234,35.7863],[-117.9249,35.7986],[-117.9005,35.7983],[-117.8738,35.7988],[-117.8523,35.7985],[-117.6362,35.7958],[-117.6355,35.7086],[-117.6537,35.7085],[-117.6527,35.6776],[-117.6176,35.6775],[-117.6166,35.6493],[-117.6353,35.6487],[-117.6354,35.6233],[-117.6352,35.5807],[-117.6356,35.5666],[-117.6351,35.5639],[-117.6346,35.4472],[-117.6352,35.3755],[-117.6353,35.3464],[-117.6351,35.3319],[-117.6343,35.3174],[-117.6341,35.3028],[-117.6345,35.2874],[-117.6343,35.2742],[-117.6341,35.2588],[-117.6339,35.2447],[-117.6342,35.2302],[-117.634,35.2157],[-117.6338,35.2011],[-117.6336,35.1861],[-117.6334,35.1707],[-117.6338,35.1562],[-117.6336,35.1417],[-117.6333,35.1271],[-117.6331,35.1126],[-117.6329,35.098],[-117.6352,35.0981],[-117.636,35.0872],[-117.6358,35.0727],[-117.6356,35.0581],[-117.6357,35.0295],[-117.6361,35.015],[-117.6357,34.985],[-117.6351,34.8233],[-117.6519,34.8227],[-117.6704,34.8221],[-117.7757,34.8229],[-118.1408,34.8195],[-118.1493,34.8195],[-118.5995,34.8175],[-118.8946,34.8181],[-118.8945,34.818],[-118.8825,34.791],[-118.9772,34.7902],[-118.9771,34.8126],[-119.2462,34.8147],[-119.2461,34.857],[-119.2797,34.858],[-119.2779,34.8793],[-119.3844,34.8794],[-119.385,34.884],[-119.3849,34.899],[-119.4382,34.8999],[-119.4438,34.8999],[-119.4544,34.8999],[-119.4571,34.9],[-119.4746,34.9004],[-119.4746,34.9005],[-119.4746,34.9136],[-119.474,34.9367],[-119.474,34.9499],[-119.474,34.9576],[-119.474,34.9721],[-119.4746,35.0184],[-119.4746,35.0325],[-119.4745,35.077],[-119.4908,35.077],[-119.4914,35.092],[-119.5004,35.0915],[-119.5088,35.0906],[-119.5628,35.0883],[-119.5583,35.1369],[-119.5566,35.1601],[-119.5549,35.1791],[-119.5769,35.1787],[-119.6095,35.1773],[-119.6675,35.1749],[-119.6675,35.1908],[-119.6675,35.2049],[-119.6688,35.2617],[-119.7397,35.2629],[-119.7572,35.2633],[-119.7746,35.2633],[-119.8113,35.2641],[-119.8122,35.3508],[-119.8815,35.3501],[-119.8824,35.41],[-119.8824,35.4246],[-119.8831,35.4377],[-119.9999,35.4396],[-120.0007,35.4695],[-120.0171,35.469],[-120.0194,35.4835],[-120.0358,35.4834],[-120.0359,35.497],[-120.0523,35.4974],[-120.053,35.5124],[-120.0699,35.5128],[-120.0711,35.5268],[-120.0875,35.5276],[-120.0876,35.6139],[-120.1951,35.6151],[-120.1947,35.7481],[-120.1942,35.7626],[-120.1945,35.788]]]},\"properties\":{\"name\":\"Kern\",\"state\":\"CA\"}}]}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Exceptionally low river flows are predicted to become more frequent and more severe across many global regions as a consequence of climate change. Investigations of trace metal transport dynamics across streamflows reveal stark changes in water chemistry, metal transformation processes, and remediation effectiveness under exceptionally low-flow conditions. High spatial resolution hydrological and water quality datasets indicate that metal-rich groundwater will exert a greater control on stream water chemistry and metal concentrations because of climate change. This is because the proportion of stream water sourced from mined areas and mineralized strata will increase under predicted future low-flow scenarios (from 25% under Q45 flow to 66% under Q99 flow in this study). However, mineral speciation modelling indicates that changes in stream pH and hydraulic conditions at low flow will decrease aqueous metal transport and increase sediment metal concentrations by enhancing metal sorption directly to streambed sediments. Solute transport modelling further demonstrates how increases in the importance of metal-rich diffuse groundwater sources at low flow could minimize the benefits of point source metal contamination treatment. Understanding metal transport dynamics under exceptionally low flows, as well as under high flows, is crucial to evaluate ecosystem service provision and remediation effectiveness in watersheds under future climate change scenarios.
Deeper flows through bedrock in mountain watersheds could be important, but lack of data to characterize bedrock properties limits understanding. To address data scarcity, we combine a previously published integrated hydrologic model of a snow‐dominated, headwater basin of the Colorado River with a new method for dating baseflow age using dissolved gas tracers SF6, CFC‐113, N2, and Ar. The original flow model predicts the majority of groundwater flow through shallow alluvium (<8 m) sitting on top of less permeable bedrock. The water moves too quickly and is unable to reproduce observed SF6 concentrations. To match gas data, bedrock permeability is increased to allow a larger fraction of deeper and older groundwater flow (median 112 m). The updated hydrologic model indicates interannual variability in baseflow age (3–12 years) is controlled by the volume of seasonal interflow and tightly coupled to snow accumulation and monsoon rain. Deeper groundwater flow remains stable (11.7 ± 0.7 years) as a function mean historical recharge to bedrock hydraulic conductivity (R/K). A sensitivity analysis suggests that increasing bedrock K effectively moves this alpine basin away from its original conceptualization of hyperlocalized groundwater flow (high R/K) with groundwater age insensitive to changes in water inputs. Instead, this basin is situated close to the precipitation threshold defining recharge controlled groundwater flow conditions (low R/K) in which groundwater age increases with small reductions in precipitation. Work stresses the need to explore alternative methods characterizing bedrock properties in mountain basins to better quantify deeper groundwater flow and predict their hydrologic response to change.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028161","usgsCitation":"Carroll, R.W., Manning, A.H., Niswonger, R.G., Marchetti, D.W., and Williams, K.H., 2020, Baseflow age distributions and depth of active groundwater flow in a snow‐dominated mountain headwater basin: Water Resources Research, v. 56, no. 12, e2020WR028161, 19 p., https://doi.org/10.1029/2020WR028161.","productDescription":"e2020WR028161, 19 p.","ipdsId":"IP-115011","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":454804,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr028161","text":"Publisher Index Page"},{"id":384624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.039306640625,\n 37.00255267215955\n ],\n [\n -106.138916015625,\n 37.00255267215955\n ],\n [\n -106.138916015625,\n 40.98819156349393\n ],\n [\n -109.039306640625,\n 40.98819156349393\n ],\n [\n -109.039306640625,\n 37.00255267215955\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Carroll, Rosemary W.H. 0000-0002-9302-8074","orcid":"https://orcid.org/0000-0002-9302-8074","contributorId":178784,"corporation":false,"usgs":false,"family":"Carroll","given":"Rosemary","email":"","middleInitial":"W.H.","affiliations":[],"preferred":false,"id":812816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812817,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812818,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marchetti, David W 0000-0002-1246-0798","orcid":"https://orcid.org/0000-0002-1246-0798","contributorId":255716,"corporation":false,"usgs":false,"family":"Marchetti","given":"David","email":"","middleInitial":"W","affiliations":[{"id":38118,"text":"Western Colorado University","active":true,"usgs":false}],"preferred":false,"id":812819,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Kenneth H. 0000-0002-3568-1155","orcid":"https://orcid.org/0000-0002-3568-1155","contributorId":176791,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":812820,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216405,"text":"ofr20201123 - 2020 - Field comparison of five in situ turbidity sensors","interactions":[],"lastModifiedDate":"2020-11-19T15:03:44.391711","indexId":"ofr20201123","displayToPublicDate":"2020-11-17T10:45:04","publicationYear":"2020","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":"2020-1123","displayTitle":"Field Comparison of Five In Situ Turbidity Sensors","title":"Field comparison of five in situ turbidity sensors","docAbstract":"Five commercially available turbidity sensors were field tested by the U.S. Geological Survey Hydrologic Instrumentation Facility for accuracy and data comparability. The tested sensors were the Xylem EXO (EXO), the Hach Solitax sc (Solitax), the In Situ Aqua TROLL sensor installed onto a TROLL 600 sonde (TROLL 600), the Campbell Scientific OBS501 (OBS501), and the Observator ANALITE NEP–5000 (NEP–5000). The sensors were deployed at Pearl River at National Space Technology Laboratories Station, Mississippi (U.S. Geological Survey site 02492620), and were serviced weekly. In addition to the five in situ turbidity sensors, corresponding discrete samples were collected and analyzed during the evaluation on a calibrated Hach 2100N benchtop turbidimeter. The OBS501 malfunctioned early in the evaluation and eventually failed, resulting in few data from the sensor.
During this study, the four remaining sensors (minus the OBS501) changed similarly throughout the field test; however, sensor data from the EXO consistently demonstrated lower results than the Solitax, TROLL 600, and NEP–5000, possibly because of the variation in raw signal processing among manufacturers. Results from a single factor analysis of variance test and a Tukey Honestly Significant Difference test verified the low bias observed in the EXO data and indicated there was a significant difference between the EXO data and data from the Solitax, TROLL 600, and NEP–5000 but an insignificant difference among the data when the Solitax, TROLL 600, and NEP–5000 were compared to each other.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201123","usgsCitation":"Snazelle, T.T., 2020, Field comparison of five in situ turbidity sensors: U.S. Geological Survey Open-File Report 2020–1123, 15 p., https://doi.org/10.3133/ofr20201123.","productDescription":"Report: iv, 15 p.; Data Release; Dataset","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-103944","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":380549,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1123/ofr20201123.pdf","text":"Report","size":"3.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1123"},{"id":380548,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1123/coverthb.jpg"},{"id":380550,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KDERG6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Turbidity data collected by five in situ sensors at USGS site 02492620 Pearl River at NSTL station, Mississippi, from November 2017 to January 2018"},{"id":380551,"rank":4,"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"}],"country":"United States","state":"Mississippi","otherGeospatial":"National Space Technology Laboratories Station","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.0274658203125,\n 30.211608223816906\n ],\n [\n -89.28314208984375,\n 30.211608223816906\n ],\n [\n -89.28314208984375,\n 30.41078179084589\n ],\n [\n -90.0274658203125,\n 30.41078179084589\n ],\n [\n -90.0274658203125,\n 30.211608223816906\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"U.S. Geological Survey
Water Mission Area
12201 Sunrise Valley Drive
Reston, VA 20192
Given the importance of crayfish in stream ecosystems, gaining insight into the role of stream permanence in maintaining predator–prey interactions is critical. Our objectives were to determine the influence of stream permanence and season on crayfish predation and assess the role of stream permanence and crayfish density on the presence of predators, while accounting for imperfect detection. We conducted surveys of crayfish density, mammalian scat, and environmental variables within 10 intermittent and 10 permanent streams in the Ozark Highlands. We used occupancy modeling to assess the relationship between predator presence, crayfish density, and environmental variables. Stream permanence did not play a role in determining relative frequency of occurrence or percent volume of crayfish prey in mammalian diets. However, percent volume and relative frequency of crayfish prey found in scats differed by season, with both highest in spring and summer. The relative frequency and percent volume of fish prey showed a significant interaction of season by stream permanence, which may be the first instance of this observation. Procyon lotor (Raccoon) had the highest detection probability (p = 0.39), whereas Neovison vison (American Mink; p = 0.15) and Lontra canadensis (River Otter; p = 0.03) had low detection probabilities. Further study into predator–prey interactions in the context of hydrology, particularly when related to imperiled groups like freshwater crayfishes, is needed since climate change is expected to alter hydrologic patterns.
This report is an instructional reference document that describes methods developed and used by the U.S. Geological Survey (USGS) New Jersey Water Science Center (NJWSC) to assure the quality and completeness of water-use data as provided by the New Jersey Department of Environmental Protection (NJDEP) Bureau of Water Allocation. These data are owned wholly by the State of New Jersey. The role of the USGS NJWSC is to assure the quality of these data by compiling, reviewing, and checking the datasets before uploading them into the New Jersey Water Transfer Data System (NJWaTr) database on an annual basis. The complete uploaded version of the NJWaTr database serves as the repository for New Jersey’s approved and published water-use data. The State of New Jersey maintains a public-facing version of the NJWaTr database (available online at https://www.nj.gov/dep/njgs/geodata/dgs10-3.htm) that contains monthly water-use data at the municipality and 14-digit Hydrologic Unit Code subwatershed level. The protected version of the NJWaTr database that contains monthly site-specific water-use data is available from the NJDEP upon request.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201085","collaboration":"Prepared in cooperation with New Jersey Department of Environmental Protection","usgsCitation":"Shourds, J.L., 2020, Quality assurance/quality control procedure for New Jersey’s water-use data for the New Jersey Water Transfer Data System (NJWaTr): U.S. Geological Survey Open-File Report 2020–1085, 26 p., https://doi.org/10.3133/ofr20201085.","productDescription":"viii, 26 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112307","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":380203,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1085/coverthb.jpg"},{"id":380204,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1085/ofr20201085.pdf","text":"Report","size":"2.09 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Understanding recent historical and projected trends in precipitation and temperature in the Colorado River Basin, and estimating what the projected changes in these climate parameters may mean for groundwater resources in the region, is important for water managers and policymakers to sustainably manage water resources in the basin. Historical (1896–2019) precipitation and temperature data for the upper and lower Colorado River Basins were analyzed to better understand recent trends in climate data that may affect groundwater resources in the area. Historical data indicate multidecadal-scale cyclical patterns in precipitation in both the upper and lower basins. Although upper basin precipitation had no statistical trend over the recent historical period, the lower basin had a weak negative trend over this period. Multidecadal-scale cyclical patterns in temperature also are observed in historical climate data in both the upper and lower basins, at least until the early 1970s. Beginning at that time, both the upper and lower basins experienced strong, monotonic positive trends in temperature. Basic principles of hydrology indicate that periods of decreasing precipitation as well as increasing temperature would have a negative effect, that is, reduction in groundwater infiltration and hence, reduced recharge of aquifer systems.
Projected climate data from 97 Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble members across the full range of Representative Concentration Pathway (RCPs) from water years 1951 through 2099 were evaluated to understand what current global climate models are projecting about future conditions in the Colorado River Basin, and what this might mean for groundwater systems in the region. Precipitation in the upper basin is projected to increase throughout the rest of the century, rising to 6 percent above the 1951–2015 historical period by mid-century and to 9 percent above the historical period by the end of the century. Temperature in the upper basin also is projected to be above the recent historical median throughout the rest of the century, with steady warming in decadal average temperatures expected until the last quarter of this century. In contrast to projected precipitation in the upper basin, precipitation in the lower basin is projected to be the same as, or slightly less than, the historical period throughout most of the rest of this century. Like projected temperature in the upper basin, temperature in the lower basin also is projected to be above the recent historical median throughout the rest of the century. Comparing median projections for all future decades with median results from all historical decades, future precipitation is expected to be greater than that of the past in the upper basin, though no significant difference is projected for precipitation in the lower basin. Significant increases (p-value<0.05) are expected in temperature in both the upper and lower basins.
To estimate the effects of projected precipitation and temperature on groundwater systems in the region, results from the 97 member CMIP5 climate projection ensemble were used as input in a Soil-Water Balance (SWB) groundwater infiltration model for the Colorado River Basin. SWB simulation results indicate that the upper Colorado River Basin is expected to experience decades of above-historical-average groundwater infiltration through the end of the century. For the lower Colorado River Basin, simulated groundwater infiltration is projected to be consistently less than the recent (1951–2015) historical period for most of the remaining century. A comparison of the distribution of all median simulated groundwater infiltration results between recent historical and future periods indicates projected groundwater infiltration in the upper basin is significantly (p-value<0.05) greater over the combined 2020–2099 future period than the recent (1951–2015) historical period. Moreover, in 41 of 71 (58 percent) possible future decades in this century, groundwater infiltration is projected to be greater than the 75th percentile of historical simulated groundwater infiltration. Projected groundwater infiltration in the lower Colorado River Basin across all future decades is significantly less than in the historical period. Of the 71 future decades in the century, projected groundwater infiltration in the lower basin is expected to be less than the 25th percentile of historical infiltration in 55 (77 percent) of the 10-year periods. Important differences in projected precipitation between the upper (increasing precipitation) and lower (decreasing precipitation) basins largely drive the different responses of simulated groundwater infiltration in the upper (increasing infiltration) and lower (decreasing infiltration) basins. It will be useful to revisit projections in groundwater infiltration in the Colorado River Basin when more up-to-date projections of precipitation become available from the next Coupled Model Intercomparison Project phases or by using climate input developments through Regional Climate Modeling efforts and stochastic weather generators.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205107","collaboration":"Prepared in cooperation with Bureau of Reclamation","usgsCitation":"Tillman, F.D., Gangopadhyay, S., and Pruitt, T., 2020, Trends in recent historical and projected climate data for the Colorado River Basin and potential effects on groundwater availability: U.S. Geological Survey Scientific Investigations Report 2020–5107, 24 p., https://doi.org/10.3133/sir20205107.","productDescription":"Report: vii, 24 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-117191","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":380358,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5107/coverthb.jpg"},{"id":380361,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7ST7MX7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-water balance groundwater recharge model results for the Upper Colorado River Basin (ver. 2.0, April 2017)"},{"id":380359,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5107/sir20205107.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5107"},{"id":380360,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VLU0O6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-water balance groundwater infiltration model results for the Lower Colorado River Basin"}],"country":"Mexico, United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.5,\n 30.088107753367257\n ],\n [\n -108.984375,\n 30.221101852485987\n ],\n [\n -108.21533203125,\n 31.39115752282472\n ],\n [\n -107.16064453125,\n 35.08395557927643\n ],\n [\n -105.35888671875,\n 36.12012758978146\n ],\n [\n -104.6337890625,\n 36.40359962073253\n ],\n [\n -104.96337890625,\n 37.735969208590504\n ],\n [\n -105.3369140625,\n 38.58252615935333\n ],\n [\n -104.83154296875,\n 39.027718840211605\n ],\n [\n -104.87548828125,\n 41.19518982948959\n ],\n [\n -105.05126953124999,\n 41.934976500546604\n ],\n [\n -106.3916015625,\n 43.16512263158296\n ],\n [\n -110.91796875,\n 43.100982876188546\n ],\n [\n -111.09374999999999,\n 40.64730356252251\n ],\n [\n -111.533203125,\n 39.07890809706475\n ],\n [\n -113.04931640625,\n 37.405073750176925\n ],\n [\n -114.14794921875,\n 37.10776507118514\n ],\n [\n -114.54345703125,\n 37.54457732085582\n ],\n [\n -115.46630859375,\n 38.839707613545144\n ],\n [\n -116.806640625,\n 38.805470223177466\n ],\n [\n -116.19140625,\n 37.3002752813443\n ],\n [\n -115.3564453125,\n 36.421282443649496\n ],\n [\n -116.1474609375,\n 35.55010533588552\n ],\n [\n -115.4443359375,\n 32.491230287947594\n ],\n [\n -114.93896484374999,\n 31.3348710339506\n ],\n [\n -114.2578125,\n 31.57853542647338\n ],\n [\n -113.291015625,\n 31.316101383495624\n ],\n [\n -113.04931640625,\n 30.732392734006083\n ],\n [\n -112.7197265625,\n 30.012030680358613\n ],\n [\n -112.47802734375,\n 30.012030680358613\n ],\n [\n -112.5,\n 30.088107753367257\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Arizona Water Science Center
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To meet drinking water regulations, rather than investing in costly treatment plant operations, managers can look for ways to improve source water quality; this requires understanding watershed sources and fates of constituents of concern. Trihalomethanes (THMs) are one of the major classes of regulated disinfection byproducts, formed when a specific fraction of the organic carbon pool—referred to as THM precursors—reacts with chorine and/or bromine during treatment. Understanding the source, fate, timing and duration of the organic compounds that react to form THMs will allow identification of targeted and effective management actions. In this study we evaluated THM precursor contributions from multiple land use categories and hydrologic contexts, including novel data for urban land uses that demonstrate strong potential to release water with high THM formation potential (THMFP; median 618 μg L−1): greater than storm runoff integrated across a mixed-use (1/3 natural, 2/3 agricultural) watershed (median 460 μg L−1), irrigation runoff from agricultural systems (357 μg L−1), or runoff from a natural forested (median 123 μg L−1) and shrubland/grassland (median 259 μg L−1) watersheds. While individual storm events released high THM precursor concentrations over short periods, dry season agricultural irrigation as well as urban landscapes have the potential to release water high in THM precursors for several months. Experimental bioassays and sampling along 333 miles of the California Aqueduct confirmed bioavailability and photooxidation potential of less than 10% for THM precursors, suggesting that rivers with residence times of days to weeks may act as THM precursor conduits, shuttling THM precursors from hundreds of miles away to drinking water intakes with minimal degradation. This finding has considerable implications for water managers, who may therefore consider THM precursor management strategies that target even sources located far upstream.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.140571","usgsCitation":"Eckard, R.S., Bergamaschi, B.A., Pellerin, B., Kraus, T.E., and Hernes, P.J., 2020, Trihalomethane precursors: Land use hot spots, persistence during transport, and management options: Science of the Total Environment, v. 742, 140571, 9 p., https://doi.org/10.1016/j.scitotenv.2020.140571.","productDescription":"140571, 9 p.","ipdsId":"IP-119566","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":420660,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River, Willow Slough Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.17412069648752,\n 38.72735441792287\n ],\n [\n -122.17412069648752,\n 38.49482301341115\n ],\n [\n -121.67887201941832,\n 38.49482301341115\n ],\n [\n -121.67887201941832,\n 38.72735441792287\n ],\n [\n -122.17412069648752,\n 38.72735441792287\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"742","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eckard, Robert S.","contributorId":88863,"corporation":false,"usgs":true,"family":"Eckard","given":"Robert","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":882660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":882661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pellerin, Brian A. 0000-0003-3712-7884","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":204324,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian A.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":882662,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":882663,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hernes, Peter J.","contributorId":139730,"corporation":false,"usgs":false,"family":"Hernes","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":12894,"text":"Department of Land, Air, and Water Resources, University of California, One Shields Avenue, Davis, CA, 95616, USA","active":true,"usgs":false}],"preferred":false,"id":882664,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255619,"text":"70255619 - 2020 - Comparison of groundwater storage changes from GRACE satellites with monitoring and modeling of major U.S. aquifers","interactions":[],"lastModifiedDate":"2024-06-26T12:26:49.454901","indexId":"70255619","displayToPublicDate":"2020-11-05T07:20:17","publicationYear":"2020","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":"Comparison of groundwater storage changes from GRACE satellites with monitoring and modeling of major U.S. aquifers","docAbstract":"GRACE satellite data are widely used to estimate groundwater storage (GWS) changes in aquifers globally; however, comparisons with GW monitoring and modeling data are limited. Here we compared GWS changes from GRACE over 15 yr (2002–2017) in 14 major U.S. aquifers with groundwater-level (GWL) monitoring data in ~23,000 wells and with regional and global hydrologic and land surface models. Results show declining GWS trends from GRACE data in the six southwestern and south-central U.S. aquifers, totaling −90 km3 over 15 yr, related to long-term (5–15 yr) droughts, and exceeding Lake Mead volume by ~2.5×. GWS trends in most remaining aquifers were stable or slightly rising. GRACE-derived GWS changes agree with GWL monitoring data in most aquifers (correlation coefficients, R = 0.52–0.95), showing that GRACE satellites capture groundwater (GW) dynamics. Regional GW models (eight models) generally show similar or greater GWS trends than those from GRACE. Large discrepancies in the Mississippi Embayment aquifer, with modeled GWS decline approximately four times that of GRACE, may reflect uncertainties in model storage parameters, stream capture, pumpage, and/or recharge rates. Global hydrologic models (2003–2014), which include GW pumping, generally overestimate GRACE GWS depletion (total: approximately −172 to −186 km3) in heavily exploited aquifers in southwestern and south-central U.S. by ~2.4× (GRACE: −74 km3), underscoring needed modeling improvements relative to anthropogenic impacts. Global land surface models tend to track GRACE GWS dynamics better than global hydrologic models. Intercomparing remote sensing, monitoring, and modeling data underscores the importance of considering all data sources to constrain GWS uncertainties.
Urbanization substantially alters the landscape in ways that can impact stream hydrology, water chemistry, and the health of aquatic communities. Stormwater best management practices (BMPs) are the primary tools used to mitigate the effects of urban stressors such as increased runoff, decreased baseflow, and increased nutrient and sediment transport. To date, Fairfax County Virginia’s stormwater management program has made substantial investments into the implementation of both structural and nonstructural BMPs aimed at restoring and protecting watersheds. The U.S. Geological Survey (USGS), in cooperation with Fairfax County, Virginia, established a long-term water-resources monitoring program to evaluate the watershed-scale effects of these investments. Monitoring began at 14 stations in 2007 and was expanded to 20 stations in 2013. This report utilized the first 10 years of data collection to (1) assess water quantity and quality, as well as ecological condition; (2) compute annual nutrient and sediment loads; and (3) evaluate trends in streamflow, water quality, and ecological condition. Efforts are underway to link the biotic and abiotic patterns described herein to watershed management practices as well as factors such as land use change, public works infrastructure, and climate.
Hydrologic, chemical, and benthic macroinvertebrate community conditions in the streams monitored were similar to those observed in other studies of urban streams. Multidecadal trends in baseflow indices and runoff ratios at long-term Chesapeake Bay Non-tidal Network streamgages (CB-NTN) indicate a decrease in groundwater recharge and increase in storm runoff as a result of urbanization. Streamflow yields varied spatially with land cover, geology, and soil characteristics, whereas flashiness was positively related to impervious area. Dissolved oxygen typically was lowest in the Coastal Plain and across all Triassic Lowlands streams, and highest in the Piedmont. Dissolved oxygen concentrations generally were above Virginia’s minimum criterion of 4.0 milligrams per liter (mg/L), most violations occurred at Paul Spring Branch in the Coastal Plain during the warmest months of the year owing to increased chemical and biological oxygen demand. Typical pH values of the monitored streams centered on neutrality (pH = 7); however, diurnal fluctuations were most prevalent in the continuous pH data at Flatlick Branch (FLAT; a Triassic Lowlands station), as a result of increased photosynthesis catalyzed by phosphorus-rich geology. Specific conductance (SC) varied spatially owing to geology (highest at Triassic Lowlands stations) and anthropogenic disturbance (watersheds with high impervious land cover). Specific conductance typically was inversely related to streamflow except in winter months following deicing road salt applications, when values increased by several orders of magnitude. A significant increase in SC of about 2 percent per year was observed from the combined trend result of all monitoring stations over the 10-year period. Significant SC increases occurred at nearly all monitoring stations. Increasing trends were observed during winter and nonwinter months, which suggests that salts applied to deice roadways and other impervious surfaces are stored in the environment and released year-round.
Suspended-sediment (SS) concentrations in monthly samples did not vary significantly between most stations, but typically were highest in the spring and lowest in the fall as a result of seasonal differences in streamflow and climate. Suspended-sediment yields ranged from 62 to 1,428 tons per square mile (ton/mi2), with a median of 302 ton/mi2. Annual loads were greatest during the wettest water years (October 1-September 30; 2008, 2011, and 2014), with the greatest interannual variability occurring at Difficult Run above Fox Lake (DIFF) and South Fork Little Difficult Run (SFLIL). Suspended sediment was primarily composed of silts and clays; however, the proportion of sand in suspended sediment was related positively to streamflow. Cross-correlation analyses suggested the dominant sources of SS were streambank erosion and resuspension of in-channel material at DIFF and FLAT; whereas, upland sources and erosion of upper streambanks were more common at Dead Run (DEAD), Long Branch (LONG), and SFLIL.
Median total phosphorus (TP) concentrations ranged from 0.016 to 0.077 mg/L, with a networkwide median of 0.022 mg/L, were highest in the warm season (April-September), and were composed primarily of dissolved phosphorous. Although TP concentrations were relatively low across the network, the highest concentrations were consistently at stations located in the Triassic Lowlands, owing to phosphorous-rich geology, and in the Coastal Plain, owing to the low-phosphorous sorptive capacity of those soils. A significant increase in TP concentration occurred in a few stations, but the combined trend results from all stations demonstrated a significant increase of about 4 percent per year. Networkwide increases were also observed in total dissolved phosphorus, orthophosphate, and total particulate phosphorus. The composition of TP shifted from dissolved to particulate as streamflow increased and for this reason loads primarily were composed of particulate phosphorous. Median annual TP loads were highest at FLAT and DEAD and ranged from 247 to 642 pounds per square mile (lbs/mi2) networkwide. Interannual variability in phosphorous yields was apparent at most stations; the highest loading years were also the wettest years during the study period and coincident with the highest peak annual flows.
Total nitrogen (TN) concentrations typically were low throughout the network with exceptions occurring at stations located in watersheds with a high density of septic infrastructure. Elevated TN concentrations also were observed in some watersheds without a high density of septic systems and may be attributable to geologic and soil properties that limit denitrification as well as other unknown anthropogenic inputs. Total nitrogen typically was dominated by nitrate during baseflows; however, the proportion of particulate nitrogen increased during stormflows. Total nitrogen yields were similar across stations, with medians ranging from about 3,600 to 6,300 lbs/mi2 and were related to annual streamflow volume. Total nitrogen concentrations and flow-normalized concentrations decreased over the 10-year period at 7 stations, with median reductions of about 2.5 percent. Increasing trends were observed at the two stations with the highest median TN concentration (Captain Hickory Run and SFLIL, 3–5 mg/L), both watersheds contain a high density of septic infrastructure. The combined trend results from all stations revealed no trend in TN and a declining trend in nitrate of about 2 percent per year.
Overall, benthic community metrics indicated that streams throughout Fairfax County were initially of poor health; however, many metrics show an improving trend (from poor to fair based on the Fairfax County Index of Biological Integrity [IBI]). Significant increasing trends in IBI occurred at the network-scale and at 4 individual stations; additionally, scores improved by at least 1 qualitative category (for example, poor to fair, fair to good) at 11 of the 14 stations between 2009 (the first year all 14 stations were sampled) and 2017. Changes in all metrics suggest that the biodiversity, function, and condition of streams in Fairfax County are improving, but some of these improvements are driven by increased diversity and percent composition of organisms that are tolerant of the urban environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205061","collaboration":"Prepared in cooperation with Fairfax County, Virginia","usgsCitation":"Porter, A.J., Webber, J.S., Witt, J.W., and Jastram, J.D., 2020, Spatial and temporal patterns in streamflow, water chemistry, and aquatic macroinvertebrates of selected streams in Fairfax County, Virginia, 2007–18: U.S. Geological Survey Scientific Investigations Report 2020–5061, 106 p., https://doi.org/10.3133/sir20205061.","productDescription":"Report: xii, 106 p.; Data Release","numberOfPages":"106","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-113872","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":378258,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95S9RFV","text":"USGS data release","linkHelpText":"Inputs and selected outputs used to assess spatial 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 E. Parham Road
Richmond, VA 23228
The COVID-19 pandemic has led to environmental recovery in some ecosystems from a global “anthropause,” yet such evidence for natural resources with extraction or production value (e.g., fisheries) is limited. This brief report provides a data-driven global snapshot of expert-perceived impacts of COVID-19 on inland fisheries. We distributed an online survey assessing perceptions of inland fishery pressures in June and July 2020 to basin-level inland fishery experts (i.e., identified by the Food and Agriculture Organization of the United Nations across the global North and South); 437 respondents from 79 countries addressed 93 unique hydrological basins, accounting for 82.1% of global inland fish catch. Based on the responses analyzed against extrinsic fish catch and human development index data, pandemic impacts on inland fisheries 1) add gradation to the largely positive environmental narrative of the global pandemic and 2) identify that basins of higher provisioning value are perceived to experience greater fishery pressures but may have limited compensatory capacity to mitigate COVID-19 impacts along with negative pressures already present.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.2014016117","usgsCitation":"Stokes, G.L., Lynch, A., Lowe, B.S., Funge-Smith, S., Valbo-Jorgensen, J., and Smidt, S.J., 2020, COVID-19 pandemic impacts on global inland fisheries: PNAS, v. 117, no. 47, p. 29419-29421, https://doi.org/10.1073/pnas.2014016117.","productDescription":"3 p.","startPage":"29419","endPage":"29421","ipdsId":"IP-120698","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":454904,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2014016117","text":"Publisher Index Page"},{"id":381214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"47","noUsgsAuthors":false,"publicationDate":"2020-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Stokes, Gretchen L. 0000-0003-4202-6527","orcid":"https://orcid.org/0000-0003-4202-6527","contributorId":245640,"corporation":false,"usgs":false,"family":"Stokes","given":"Gretchen","email":"","middleInitial":"L.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":806705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":806691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowe, Benjamin S. 0000-0002-1879-254X","orcid":"https://orcid.org/0000-0002-1879-254X","contributorId":245641,"corporation":false,"usgs":false,"family":"Lowe","given":"Benjamin","email":"","middleInitial":"S.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":806706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Funge-Smith, Simon 0000-0001-9974-5333","orcid":"https://orcid.org/0000-0001-9974-5333","contributorId":245642,"corporation":false,"usgs":false,"family":"Funge-Smith","given":"Simon","email":"","affiliations":[{"id":32888,"text":"Food and Agriculture organization of the United Nations","active":true,"usgs":false}],"preferred":false,"id":806707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Valbo-Jorgensen, John 0000-0002-1992-5682","orcid":"https://orcid.org/0000-0002-1992-5682","contributorId":220485,"corporation":false,"usgs":false,"family":"Valbo-Jorgensen","given":"John","affiliations":[{"id":32888,"text":"Food and Agriculture organization of the United Nations","active":true,"usgs":false}],"preferred":false,"id":806708,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smidt, Samuel J. 0000-0001-7728-2083","orcid":"https://orcid.org/0000-0001-7728-2083","contributorId":192816,"corporation":false,"usgs":false,"family":"Smidt","given":"Samuel","email":"","middleInitial":"J.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":806709,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215672,"text":"70215672 - 2020 - Hydrologic properties of a highly permeable firn aquifer in the Wilkins Ice Shelf, Antarctica","interactions":[],"lastModifiedDate":"2021-01-22T22:18:00.667303","indexId":"70215672","displayToPublicDate":"2020-10-29T15:58:35","publicationYear":"2020","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":"Hydrologic properties of a highly permeable firn aquifer in the Wilkins Ice Shelf, Antarctica","docAbstract":"We present measurements of the density, hydraulic conductivity, and specific discharge of a widespread firn aquifer in Antarctica, within the Wilkins Ice Shelf. At the field site, the aquifer is 16.2 m thick, starting at 13.4 m from the snow surface and transitioning from water‐saturated firn to ice at 29.6 m. Hydraulic conductivity derived from slug tests show a geometric mean value of 1.4 ± 1.2 × 10−4 m s−1, equivalent to permeability of 2.6 ± 2.2 × 10−11 m2. A borehole dilution test indicates an average specific discharge value of 1.9 ± 2.8 × 10−6 m s−1. Ground‐penetrating radar profiles and a groundwater flow model show the aquifer is draining laterally into a large nearby rift. Our findings indicate that the firn aquifer in the vicinity of the field site is likely not in a steady state and its presence likely contributed to past ice shelf instability.
","language":"English","publisher":"Wiley","doi":"10.1029/2020GL089552","usgsCitation":"Montgomery, L., Miege, C., MIller, J., Wallin, B., Miller, O.L., Scambos, T.A., Solomon, D., Forster, R., and Koenig, L., 2020, Hydrologic properties of a highly permeable firn aquifer in the Wilkins Ice Shelf, Antarctica: Geophysical Research Letters, v. 47, e2020GL089552, 10 p., https://doi.org/10.1029/2020GL089552.","productDescription":"e2020GL089552, 10 p.","ipdsId":"IP-119455","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":454923,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020gl089552","text":"External Repository"},{"id":382525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Wilkins Ice Sheet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.54022216796875,\n -71.79883675782347\n ],\n [\n -70.400390625,\n -71.79883675782347\n ],\n [\n -70.400390625,\n -71.54143894204527\n ],\n [\n -71.54022216796875,\n -71.54143894204527\n ],\n [\n -71.54022216796875,\n -71.79883675782347\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2020-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Montgomery, Lynn","contributorId":244036,"corporation":false,"usgs":false,"family":"Montgomery","given":"Lynn","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":803105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miege, C.","contributorId":248303,"corporation":false,"usgs":false,"family":"Miege","given":"C.","email":"","affiliations":[],"preferred":false,"id":808855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MIller, Julie","contributorId":248311,"corporation":false,"usgs":false,"family":"MIller","given":"Julie","email":"","affiliations":[],"preferred":false,"id":808856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wallin, Bruce","contributorId":248312,"corporation":false,"usgs":false,"family":"Wallin","given":"Bruce","email":"","affiliations":[],"preferred":false,"id":808857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scambos, Ted A.","contributorId":57367,"corporation":false,"usgs":true,"family":"Scambos","given":"Ted","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":808858,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Olivia L. 0000-0002-8846-7048","orcid":"https://orcid.org/0000-0002-8846-7048","contributorId":216556,"corporation":false,"usgs":true,"family":"Miller","given":"Olivia","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803106,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Solomon, D Kip","contributorId":146290,"corporation":false,"usgs":false,"family":"Solomon","given":"D Kip","affiliations":[{"id":7215,"text":"University of Utah Dept. of Geography","active":true,"usgs":false}],"preferred":false,"id":808859,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Forster, Richard","contributorId":172149,"corporation":false,"usgs":false,"family":"Forster","given":"Richard","affiliations":[{"id":26993,"text":"University of Utah, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":808860,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Koenig, Lora","contributorId":248313,"corporation":false,"usgs":false,"family":"Koenig","given":"Lora","affiliations":[],"preferred":false,"id":808861,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70267767,"text":"70267767 - 2020 - Ontogenetic shifts in mesohabitat use of young-of-year Rio Grande blue sucker in the Big Bend region of the Rio Grande","interactions":[],"lastModifiedDate":"2025-05-30T16:03:05.271414","indexId":"70267767","displayToPublicDate":"2020-10-28T00:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Ontogenetic shifts in mesohabitat use of young-of-year Rio Grande blue sucker in the Big Bend region of the Rio Grande","docAbstract":"Alteration of flow regimes by anthropogenic activities is one of the primary environmental problems in riverine systems. Understanding how hydrologic conditions can affect ontogenetic habitat shifts of imperiled fishes is important in order to develop conservation and management strategies for each life-history stage. We examined relationships between the abundance of young-of-the-year (YOY) Rio Grande Blue Sucker and various abiotic variables in the Trans-Pecos region of the Rio Grande in Texas, USA. We used open N-mixture modeling to better understand the factors affecting ontogenetic habitat shifts of the imperiled aridland river fish. In addition, we examined differences in Rio Grande Blue Sucker total length among three mesohabitat types (pool, riffle, and run). The results of open N-mixture modeling suggested that as pool area increased, the abundance of YOY Rio Grande Blue Sucker increased. Total length of YOY Rio Grande Blue Sucker also significantly differed among the three mesohabitat types. The total lengths of YOY Rio Grande Blue Sucker in pool habitats were lower than in other mesohabitats, suggesting that YOY Rio Grande Blue Sucker undergo ontogenetic habitat shifts into greater current velocity habitats as they grow. The habitat associations we documented support the growing body of research emphasizing the importance of maintaining sufficient and appropriately timed flows to avoid prolonged low flows that limit habitat availability for native fish species during sensitive life stages in the Rio Grande and other aridland rivers.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10641-020-01038-8","usgsCitation":"Miyazono, S., Pease, A., Fritts, S., and Grabowski, T.B., 2020, Ontogenetic shifts in mesohabitat use of young-of-year Rio Grande blue sucker in the Big Bend region of the Rio Grande: Environmental Biology of Fishes, v. 103, p. 1471-1480, https://doi.org/10.1007/s10641-020-01038-8.","productDescription":"10 p.","startPage":"1471","endPage":"1480","ipdsId":"IP-118286","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Rio Grande in the Big Bend region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.91886105019951,\n 29.728911907450694\n ],\n [\n -103.91886105019951,\n 28.965625411076672\n ],\n [\n -102.76942901752302,\n 28.965625411076672\n ],\n [\n -102.76942901752302,\n 29.728911907450694\n ],\n [\n -103.91886105019951,\n 29.728911907450694\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"103","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Miyazono, Seiji","contributorId":356122,"corporation":false,"usgs":false,"family":"Miyazono","given":"Seiji","affiliations":[{"id":37463,"text":"TTU","active":true,"usgs":false}],"preferred":false,"id":938781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pease, Allison A.","contributorId":356124,"corporation":false,"usgs":false,"family":"Pease","given":"Allison A.","affiliations":[{"id":37463,"text":"TTU","active":true,"usgs":false}],"preferred":false,"id":938782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fritts, Sarah","contributorId":356126,"corporation":false,"usgs":false,"family":"Fritts","given":"Sarah","affiliations":[{"id":84915,"text":"tsu","active":true,"usgs":false}],"preferred":false,"id":938783,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","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":938780,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201829,"text":"tm4A3 - 2020 - Statistical methods in water resources","interactions":[{"subject":{"id":47512,"text":"twri04A3 - 2002 - Statistical methods in water resources","indexId":"twri04A3","publicationYear":"2002","noYear":false,"displayTitle":"Statistical Methods in Water Resources","title":"Statistical methods in water resources"},"predicate":"SUPERSEDED_BY","object":{"id":70201829,"text":"tm4A3 - 2020 - Statistical methods in water resources","indexId":"tm4A3","publicationYear":"2020","noYear":false,"title":"Statistical methods in water resources"},"id":1}],"lastModifiedDate":"2024-08-13T14:02:36.434133","indexId":"tm4A3","displayToPublicDate":"2020-10-27T09:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-A3","displayTitle":"Statistical Methods in Water Resources","title":"Statistical methods in water resources","docAbstract":"This text began as a collection of class notes for a course on applied statistical methods for hydrologists taught at the U.S. Geological Survey (USGS) National Training Center. Course material was formalized and organized into a textbook, first published in 1992 by Elsevier as part of their Studies in Environmental Science series. In 2002, the work was made available online as a USGS report.
The text has now been updated as a USGS Techniques and Methods Report. It is intended to be a text in applied statistics for hydrology, environmental science, environmental engineering, geology, or biology that addresses distinctive features of environmental data. For example, water resources data tend to have many variables with a lower bound of zero, tend to be more skewed than data from many other disciplines, commonly contain censored data (less than values), and assumptions that the data are normally distributed are not appropriate. Computer-intensive methods (bootstrapping and permutation tests) now improve upon and replace the dependence on t-intervals, t-tests, and analysis of variance. A new chapter on sampling design addresses questions such as “How many observations do I need?” The chapter also presents distribution-free methods to help plan sampling efforts. The trends chapter has been updated to include the WRTDS (Weighted Regressions on Time, Discharge, and Season) method for analysis of water-quality data. This new version contains updated graphics and updated guidance on the use of statistical techniques. The text utilizes R, a programming language and open-source software environment, for all exercises and most graphics, and the R code used to generate figures and examples is provided for download.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4A3","usgsCitation":"Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3, 458 p., https://doi.org/10.3133/tm4a3. [Supersedes USGS Techniques of Water-Resources Investigations, book 4, chap. A3, version 1.1.]","productDescription":"Report: xxii, 458 p.; Data Release","numberOfPages":"484","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-089727","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":418371,"rank":5,"type":{"id":12,"text":"Errata"},"url":"https://pubs.usgs.gov/tm/04/a03/Errata_Sheet.pdf","text":"Errata Sheet","size":"136 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Errata Sheet"},{"id":379731,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://store.usgs.gov/product/533012","text":"Print Version Available"},{"id":374999,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JWL6XR","text":"USGS data release","linkHelpText":"Statistical Methods in Water Resources - Supporting Materials"},{"id":375013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/a03/tm4a3.pdf","text":"Report","size":"9.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-A3"},{"id":375000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/a03/coverthb.jpg"}],"publicComments":"Techniques and Methods 4-A3 supersedes Techniques of Water-Resources Investigations, book 4, chapter A3, version 1.1.","contact":"Chief, Analysis and Prediction Branch
Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Dr., Mail Stop 415
Reston, VA 20192
This report provides an overview of existing hydrologic information describing the quality, quantity, and extent of the major surface-water and groundwater resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma. Hydrologic information is provided for five major river systems (Cimarron River, North Canadian River, Canadian River, Washita River, and North Fork Red River), two reservoirs (Foss Reservoir and Canton Lake), and eight aquifers consisting of the alluvial aquifers associated with each of the five major river systems and three major bedrock aquifers (Ogallala aquifer, Elk City aquifer, and Rush Springs aquifer).
Types of information provided about rivers and reservoirs for the Cheyenne and Arapaho Tribal jurisdictional area include diversion sites and amounts of water allocated and diverted for permitted uses in 2015; treated wastewater discharge sites and amounts discharged in 2015; and characteristics describing water-quality field properties, major ions, nutrients, and selected trace elements. Major ions, nutrients, and selected trace elements are compared to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water. Additionally, statistics are provided describing daily, monthly, and annual streamflow characteristics at 12 U.S. Geological Survey streamgages. Streamflow statistics include the magnitudes and frequencies of floods, base-flow characteristics, and long-term streamflow trends.
Types of information provided about the aquifers include amounts of water allocated and pumped for permitted uses in 2015; characteristics of groundwater describing water-quality field properties, major ions, nitrate (measured as nitrogen), and selected trace elements with comparisons to secondary maximum contaminant levels and maximum contaminant levels for finished drinking water; groundwater levels and long-term changes in water levels; and ranges of hydraulic conductivity, aquifer recharge, specific yield, transmissivity, and well yields from reports and groundwater-flow models.
Surface water is used primarily for irrigation and mining and other nonconsumptive uses in the Cheyenne and Arapaho Tribal jurisdictional area, except from the Washita and North Fork Red Rivers, where water is treated for use as a public-water supply. Large concentrations of dissolved solids are the primary limiting factor affecting the use of surface water. Median concentrations of dissolved solids in surface water range from less than 1,000 milligrams per liter (mg/L) in samples from the North Canadian River to greater than 9,000 mg/L in samples from the Cimarron River. Large dissolved solids concentrations are correlated with hard water. Median hardness as calcium carbonate concentrations in surface water ranges from 427 mg/L in samples from Canton Lake to 1,000 mg/L in samples from the Washita River.
In 2015, groundwater was used at more than twice the rate of surface water in the Cheyenne and Arapaho Tribal jurisdictional area. Although the alluvial aquifers are considered reliably good sources of water in the Cheyenne and Arapaho Tribal jurisdictional area, concentrations of nitrate (measured as nitrogen) exceed the maximum contaminant level of 10 mg/L established by the U.S. Environmental Protection Agency for finished drinking water in parts of all of the alluvial aquifers. Water from the three major bedrock aquifers is used for irrigation, mining, public-water supply, and other uses; however, large concentrations of dissolved solids, nitrate (measured as nitrogen), and naturally occurring trace elements such as arsenic and uranium may limit the use of groundwater as a source of public-water supply in some areas. As of 2015, the depletion of groundwater from the major aquifers in west-central Oklahoma is a minor concern to the Oklahoma Water Resources Board. Groundwater levels and other hydrologic information show that recharge rates exceed the rates of water pumped from aquifers, except in areas that may be affected locally by groundwater depletions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205105","collaboration":"Prepared in cooperation with the Cheyenne and Arapaho Tribes of Oklahoma and the Bureau of Indian Affairs","usgsCitation":"Becker, C.J., and Varonka, M.S., 2020, Water resources in the Cheyenne and Arapaho Tribal jurisdictional area, west-central Oklahoma, with an analysis of data gaps through 2015 (ver. 1.1, January 2021): U.S. Geological Survey Scientific Investigations Report 2020–5105, 158 p., 1 app., https://doi.org/10.3133/sir20205105..","productDescription":"xi, 158 p.","numberOfPages":"175","onlineOnly":"Y","ipdsId":"IP-109610","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":382059,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5105/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5105 Version History"},{"id":379749,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5105/sir20205105.pdf","text":"Report","size":"36.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5105"},{"id":379748,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5105/coverthb2.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Cheyenne and Arapaho Tribal Jurisdictional Area","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.3595,35.1163],[-99.4067,35.1161],[-99.409,35.1148],[-99.4129,35.1134],[-99.4163,35.1134],[-99.4196,35.1143],[-99.4219,35.1138],[-99.4258,35.1129],[-99.4281,35.1111],[-99.4337,35.1097],[-99.4393,35.111],[-99.4432,35.1124],[-99.4449,35.116],[-99.7833,35.1161],[-99.7839,35.1151],[-99.7852,35.028],[-99.8897,35.0277],[-100.0008,35.0295],[-100.0014,35.1818],[-100.0015,35.2034],[-100.0013,35.2689],[-100.0012,35.293],[-100.0009,35.4223],[-100.0014,35.4558],[-100.0011,35.6197],[-100.001,35.64],[-100.0015,35.8008],[-100.0015,35.8782],[-100.0015,35.9478],[-100.002,36.0539],[-100.0025,36.1891],[-100.003,36.3134],[-100.0031,36.3348],[-100.0038,36.4998],[-100.0044,36.5849],[-100.0045,36.5917],[-99.6193,36.5916],[-99.605,36.5917],[-99.6043,36.506],[-99.6038,36.3051],[-99.6034,36.2457],[-99.5954,36.2457],[-99.5976,36.1639],[-99.382,36.1645],[-98.9565,36.1587],[-98.7878,36.1613],[-98.7428,36.1625],[-98.7251,36.1634],[-98.6362,36.1636],[-98.634,36.1636],[-98.3177,36.1645],[-98.2122,36.1656],[-98.1057,36.1658],[-97.6759,36.1663],[-97.6765,36.0715],[-97.6763,35.984],[-97.6761,35.8973],[-97.6757,35.7253],[-97.6753,35.5506],[-97.6719,35.5506],[-97.6729,35.4639],[-97.6733,35.3763],[-97.6729,35.335],[-97.6836,35.3351],[-97.6898,35.3338],[-97.6948,35.3339],[-97.7016,35.3353],[-97.7033,35.3353],[-97.7073,35.334],[-97.7118,35.3313],[-97.7181,35.3287],[-97.7231,35.3278],[-97.7288,35.3274],[-97.7367,35.328],[-97.7423,35.3298],[-97.749,35.3326],[-97.7546,35.3354],[-97.7635,35.3414],[-97.7759,35.3434],[-97.7917,35.3408],[-97.829,35.3348],[-97.838,35.3354],[-97.8464,35.3368],[-97.8565,35.341],[-97.8582,35.3428],[-97.8614,35.3551],[-97.8636,35.3588],[-97.8669,35.3615],[-97.8703,35.3629],[-97.8754,35.3625],[-97.8811,35.3608],[-97.8845,35.3572],[-97.8885,35.3545],[-97.8936,35.3513],[-97.9009,35.3514],[-97.9082,35.3528],[-97.9105,35.3533],[-97.915,35.3556],[-97.925,35.3612],[-97.9351,35.3649],[-97.9368,35.3672],[-97.9378,35.3744],[-97.9395,35.3763],[-97.9429,35.3772],[-97.9474,35.3777],[-97.9491,35.3764],[-97.9502,35.3745],[-97.9492,35.3668],[-97.951,35.3596],[-97.9527,35.3555],[-97.955,35.3528],[-97.9562,35.3519],[-97.9663,35.3525],[-97.9697,35.3529],[-97.9736,35.3543],[-97.9843,35.3599],[-97.9994,35.365],[-98.0184,35.3765],[-98.0985,35.3767],[-98.305,35.3744],[-98.3051,35.5437],[-98.3085,35.541],[-98.3119,35.5401],[-98.3142,35.5406],[-98.3193,35.5429],[-98.3193,35.5438],[-98.3187,35.5479],[-98.3181,35.5502],[-98.6199,35.552],[-98.6209,35.4639],[-98.6216,35.2038],[-98.616,35.2038],[-98.6177,35.0994],[-98.621,35.0981],[-98.6255,35.1035],[-98.6294,35.1167],[-98.6355,35.1231],[-98.6406,35.1231],[-98.6429,35.1145],[-98.6451,35.1113],[-98.6496,35.1141],[-98.6513,35.1177],[-98.649,35.1195],[-98.6485,35.1213],[-98.6485,35.1231],[-98.6513,35.125],[-98.6575,35.1236],[-98.6665,35.1209],[-98.6738,35.1187],[-98.6778,35.1082],[-98.6822,35.1101],[-98.6856,35.1078],[-98.6985,35.1115],[-98.7042,35.111],[-98.7109,35.1065],[-98.7132,35.1065],[-98.721,35.1138],[-98.7255,35.1115],[-98.7317,35.1129],[-98.7351,35.1029],[-98.7379,35.102],[-98.7401,35.107],[-98.748,35.1166],[-98.8244,35.1176],[-98.9312,35.1168],[-98.9807,35.1173],[-99.0425,35.1168],[-99.2555,35.1161],[-99.3595,35.1163]]]},\"properties\":{\"name\":\"Beckham\",\"state\":\"OK\"}}]}","edition":"Version 1.0: October 27, 2020; Version 1.1: January 11, 2021","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
No abstract available.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13954","usgsCitation":"Nimmo, J.R., 2020, Imperatives for predicting preferential and diffuse flow in the unsaturated zone: 1. Equal emphasis: Hydrological Processes, v. 34, no. 26, p. 5690-5693, https://doi.org/10.1002/hyp.13954.","productDescription":"4 p.","startPage":"5690","endPage":"5693","ipdsId":"IP-123789","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":405457,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"26","noUsgsAuthors":false,"publicationDate":"2020-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":849509,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70235840,"text":"70235840 - 2020 - Imperatives for predicting preferential and diffuse flow in the unsaturated zone: 2. Disparate formulation","interactions":[],"lastModifiedDate":"2022-08-23T13:55:39.99211","indexId":"70235840","displayToPublicDate":"2020-10-26T08:53:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Imperatives for predicting preferential and diffuse flow in the unsaturated zone: 2. Disparate formulation","docAbstract":"No abstract available.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13957","usgsCitation":"Nimmo, J.R., 2020, Imperatives for predicting preferential and diffuse flow in the unsaturated zone: 2. Disparate formulation: Hydrological Processes, v. 34, no. 26, p. 5694-5698, https://doi.org/10.1002/hyp.13957.","productDescription":"5 p.","startPage":"5694","endPage":"5698","ipdsId":"IP-123788","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":405455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"26","noUsgsAuthors":false,"publicationDate":"2020-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":849508,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70216504,"text":"70216504 - 2020 - Injection‐induced earthquakes near Milan, Kansas, controlled by Karstic Networks","interactions":[],"lastModifiedDate":"2020-11-24T13:38:00.985824","indexId":"70216504","displayToPublicDate":"2020-10-19T07:34:12","publicationYear":"2020","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":"Injection‐induced earthquakes near Milan, Kansas, controlled by Karstic Networks","docAbstract":"Induced earthquakes from waste disposal operations in otherwise tectonically stable regions significantly increases seismic hazard. It remains unclear why injections induce large earthquakes on non‐optimally oriented faults kilometers below the injection horizon, particularly since fluids are not injected under pressure, but rather poured, into the well as observed in the Milan, Kansas area. Here we propose a mechanism for induced earthquakes whereby the karstic lower Arbuckle provides the short‐circuit that establishes a tens of MPa stepwise fluid pressure increase within the basement upon arrival of the hydraulic connection to the free surface and ultimately induce slip on the deeper fault. We investigate this scenario through modeling and mechanical analysis and show that earthquakes near Milan are likely induced by large (and sudden) fluid pressure changes when the karst network links two previously isolated hydrological systems.
Large-domain hydrological models are increasingly needed to support water-resource assessment and management in large river basins. Here, we describe results for the first Brazilian application of the SPAtially Referenced Regression On Watershed attributes (SPARROW) model using a new open-source modeling and interactive decision support system tool (RSPARROW) to quantify the origin, flux, and fate of total nitrogen (TN) in two sub-basins of the Grande River Basin (GRB; 43,000 km2). Land under cultivation for sugar cane, urban land, and point source inputs from wastewater treatment plants was estimated to each contribute approximately 30% of the TN load at the outlet, with pasture land contributing about 10% of the load. Hypothetical assessments of wastewater treatment plant upgrades and the building of new facilities that could treat currently untreated urban runoff suggest that these management actions could potentially reduce loading at the outlet by as much as 20–25%. This study highlights the ability of SPARROW and the RSPARROW mapping tool to assist with the development and evaluation of management actions aimed at reducing nutrient pollution and eutrophication. The freely available RSPARROW modeling tool provides new opportunities to improve understanding of the sources, delivery, and transport of water-quality contaminants in watersheds throughout the world.
","language":"English","publisher":"MDPI","doi":"10.3390/w12102911","usgsCitation":"Miller, M., de Souza, M.L., Alexander, R.B., Gorman Sanisaca, L.E., de Amorim Teixeira, A., and Appling, A.P., 2020, Application of the RSPARROW modeling tool to estimate total nitrogen sources to streams and evaluate source reduction management scenarios in the Grande River Basin, Brazil: Water, v. 12, no. 10, 2911, 20 p., https://doi.org/10.3390/w12102911.","productDescription":"2911, 20 p.","ipdsId":"IP-122604","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":455023,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12102911","text":"Publisher Index Page"},{"id":436752,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FZV0Z0","text":"USGS data release","linkHelpText":"RSPARROW Model Archive Files for the Grande River Basin TN SPARROW Model"},{"id":379649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Grande River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -50.95458984374999,\n -20.324023603422507\n ],\n [\n -49.32861328125,\n -21.46329344189928\n ],\n [\n -48.284912109375,\n -22.451648819126202\n ],\n [\n -46.73583984375,\n -23.29181053244191\n ],\n [\n -45.37353515625,\n -22.61401087437028\n ],\n [\n -44.05517578124999,\n -21.881889807629257\n ],\n [\n -43.5498046875,\n -21.125497636606266\n ],\n [\n -45.736083984375,\n -20.33432561683554\n ],\n [\n -46.35131835937499,\n -20.478481600090554\n ],\n [\n -46.966552734375,\n -20.014645445341355\n ],\n [\n -47.647705078125,\n -19.797717490704724\n ],\n [\n -48.944091796875,\n -19.9526963975442\n ],\n [\n -49.32861328125,\n -19.652934210612436\n ],\n [\n -50.28442382812499,\n -19.425153718960143\n ],\n [\n -50.86669921875,\n -19.756364230752375\n ],\n [\n -50.95458984374999,\n -20.324023603422507\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Souza, Marcelo L","contributorId":243598,"corporation":false,"usgs":false,"family":"de Souza","given":"Marcelo","email":"","middleInitial":"L","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":802666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Richard B 0000-0001-9166-0626","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":243599,"corporation":false,"usgs":false,"family":"Alexander","given":"Richard","email":"","middleInitial":"B","affiliations":[{"id":38108,"text":"NA","active":true,"usgs":false}],"preferred":false,"id":802667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorman Sanisaca, Lillian E. 0000-0003-1711-3864","orcid":"https://orcid.org/0000-0003-1711-3864","contributorId":210381,"corporation":false,"usgs":true,"family":"Gorman Sanisaca","given":"Lillian","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"de Amorim Teixeira, Alexandre","contributorId":243600,"corporation":false,"usgs":false,"family":"de Amorim Teixeira","given":"Alexandre","email":"","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":802669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":802670,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228259,"text":"70228259 - 2020 - Trophic structure of apex fish communities in closed versus leaky lakes of arctic Alaska","interactions":[],"lastModifiedDate":"2022-02-08T17:54:29.288876","indexId":"70228259","displayToPublicDate":"2020-10-15T11:46:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Trophic structure of apex fish communities in closed versus leaky lakes of arctic Alaska","docAbstract":"Despite low species diversity and primary production, trophic structure (e.g., top predator species, predator size) is surprisingly variable among Arctic lakes. We investigated trophic structure in lakes of arctic Alaska containing arctic char Salvelinus alpinus using stomach contents and stable isotope ratios in two geographically-close but hydrologically-distinct lake clusters to investigate how these fish may interact and compete for limited food resources. Aside from different lake connectivity patterns (‘leaky’ versus ‘closed’), differing fish communities (up to five versus only two species) between lake clusters allowed us to test trophic hypotheses including: (1) arctic char are more piscivorous, and thereby grow larger and obtain higher trophic positions, in the presence of other fish species; and, (2) between arctic char size classes, resource polymorphism is more prominent, and thereby trophic niches are narrower and overlap less, in the absence of other predators. Regardless of lake cluster, we observed little direct evidence of arctic char consuming other fishes, but char were larger (mean TL = 468 vs 264 mm) and trophic position was higher (mean TP = 4.0 vs 3.8 for large char) in lakes with other fishes. Further, char demonstrated less intraspecific overlap when other predators were present whereas niche overlap was up to 100% in closed, char only lakes. As hydrologic characteristics (e.g., lake connectivity, water temperatures) will change across the Arctic owing to climate change, our results provide insight regarding potential concomitant changes to fish interactions and increase our understanding of lake trophic structure to guide management and conservation goals.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-020-04776-9","usgsCitation":"Klobucar, S.L., and Budy, P., 2020, Trophic structure of apex fish communities in closed versus leaky lakes of arctic Alaska: Oecologia, v. 194, p. 491-504, https://doi.org/10.1007/s00442-020-04776-9.","productDescription":"14 p.","startPage":"491","endPage":"504","ipdsId":"IP-109849","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Brooks Range, Toolik Field Station","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.69558715820312,\n 68.3996855982224\n ],\n [\n -149.20257568359375,\n 68.3996855982224\n ],\n [\n -149.20257568359375,\n 68.64455609820665\n ],\n [\n -149.69558715820312,\n 68.64455609820665\n ],\n [\n -149.69558715820312,\n 68.3996855982224\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"194","noUsgsAuthors":false,"publicationDate":"2020-10-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Klobucar, Stephen L.","contributorId":274993,"corporation":false,"usgs":false,"family":"Klobucar","given":"Stephen","email":"","middleInitial":"L.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":833550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833549,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216544,"text":"70216544 - 2020 - Climate sensitivity of water use by riparian woodlands at landscape scales","interactions":[],"lastModifiedDate":"2020-12-14T16:56:03.212697","indexId":"70216544","displayToPublicDate":"2020-10-15T10:46:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Climate sensitivity of water use by riparian woodlands at landscape scales","docAbstract":"Semi‐arid riparian woodlands face threats from increasing extractive water demand and climate change in dryland landscapes worldwide. Improved landscape‐scale understanding of riparian woodland water use (evapotranspiration, ET) and its sensitivity to climate variables is needed to strategically manage water resources, as well as to create successful ecosystem conservation and restoration plans for potential climate futures. In this work, we assess the spatial and temporal variability of Cottonwood (Populus fremontii)‐Willow (Salix gooddingii) riparian gallery woodland ET and its relationships to vegetation structure and climate variables for 80 km of the San Pedro River corridor in southeastern Arizona, USA, between 2014 and 2019. We use a novel combination of publicly available remote sensing, climate and hydrological datasets: cloud‐based Landsat thermal remote sensing data products for ET (Google Earth Engine EEFlux), Landsat multispectral imagery and field data‐based calibrations to vegetation structure (leaf‐area index, LAI), and open‐source climate and hydrological data. We show that at landscape scales, daily ET rates (6–10 mm day−1) and growing season ET totals (400–1,400 mm) matched rates of published field data, and modelled reach‐scale average LAI (0.80–1.70) matched lower ranges of published field data. Over 6 years, the spatial variability of total growing season ET (CV = 0.18) exceeded that of temporal variability (CV = 0.10), indicating the importance of reach‐scale vegetation and hydrological conditions for controlling ET dynamics. Responses of ET to climate differed between perennial and intermittent‐flow stream reaches. At perennial‐flow reaches, ET correlated significantly with temperature, whilst at intermittent‐flow sites ET correlated significantly with rainfall and stream discharge. Amongst reaches studied in detail, we found positive but differing logarithmic relationships between LAI and ET. By documenting patterns of high spatial variability of ET at basin scales, these results underscore the importance of accurately accounting for differences in woodland vegetation structure and hydrological conditions for assessing water‐use requirements. Results also suggest that the climate sensitivity of ET may be used as a remote indicator of subsurface water resources relative to vegetation demand, and an indicator for informing conservation management priorities.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13942","usgsCitation":"Mayes, M., Caylor, K.K., Singer, M.B., Stella, J., Roberts, D., and Nagler, P.L., 2020, Climate sensitivity of water use by riparian woodlands at landscape scales: Hydrological Processes, v. 34, no. 25, p. 4884-4903, https://doi.org/10.1002/hyp.13942.","productDescription":"10 p.","startPage":"4884","endPage":"4903","ipdsId":"IP-120214","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455038,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://orca.cardiff.ac.uk/id/eprint/135647/","text":"External Repository"},{"id":380788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"San Pedro River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58563232421875,\n 31.3348710339506\n ],\n [\n -109.79461669921875,\n 31.3348710339506\n ],\n [\n -109.79461669921875,\n 32.15468722002481\n ],\n [\n -110.58563232421875,\n 32.15468722002481\n ],\n [\n -110.58563232421875,\n 31.3348710339506\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"25","noUsgsAuthors":false,"publicationDate":"2020-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Mayes, Marc","contributorId":245241,"corporation":false,"usgs":false,"family":"Mayes","given":"Marc","email":"","affiliations":[],"preferred":false,"id":805665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caylor, Kelly K.","contributorId":245242,"corporation":false,"usgs":false,"family":"Caylor","given":"Kelly","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":805666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singer, Michael B.","contributorId":168369,"corporation":false,"usgs":false,"family":"Singer","given":"Michael","email":"","middleInitial":"B.","affiliations":[{"id":25268,"text":"University of St Andrews, UK","active":true,"usgs":false}],"preferred":false,"id":805667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stella, John C","contributorId":149423,"corporation":false,"usgs":false,"family":"Stella","given":"John C","affiliations":[{"id":17732,"text":"Professor, Dept of Forest & Natural Resources Mgmt, SUNY at ESF","active":true,"usgs":false}],"preferred":false,"id":805668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberts, Dar","contributorId":13721,"corporation":false,"usgs":true,"family":"Roberts","given":"Dar","affiliations":[],"preferred":false,"id":805669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805569,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}