An analytical framework was designed to estimate water use associated with continuous oil and gas (COG) development in support of the U.S. Geological Survey Water Availability and Use Science Program. This framework was developed to better understand the relation between the production of COG resources for energy and the amount of water needed to sustain this type of energy development in the United States. The total mean undiscovered, technically recoverable volume of COG has increased, highlighting the continued need to develop approaches to better characterize water use associated with COG development.
The analytical framework can be used to estimate water use associated with COG development for three water-use components—direct, indirect, and ancillary water use—that are related to the life cycle of COG development. Direct water use is defined as water used in a wellbore to complete a well, including the water used for drilling, cementing, stimulating, and maintaining the well during production. Indirect water use is the water used at or near the well site, including water used for dust abatement, for cleaning equipment, and for crew and staff use. Ancillary water use is all other water used during the life cycle of COG development that is not categorized as direct or indirect, such as additional local or regional water use resulting from a change (for example, population) related to COG development. The analytical framework includes the data inputs, the processes involved in estimating the water-use coefficients and analyzing their uncertainties, and the outputs. The analytical framework was developed as an R script, which contains the statistical models used to estimate water-use components.
The availability of data across COG reservoirs in the United States is variable and presents challenges for estimating water use for extracting COG from their reservoirs; thus, the R script can be modified for the types of data available within a COG reservoir, the extent and resolution of data available for each water-use component, and the desired output of the water-use assessment. The script was written so that the units of the data in the script were standardized. Water-use estimates were simulated for the mean and 10th, 50th, and 90th percentiles of the data distributions. Uncertainties were quantified with confidence intervals for the estimated coefficients. Uncertainty for estimated or simulated data can be calculated with the R script by providing a range of representative values that are within the appropriate confidence intervals of the mean of the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195100","collaboration":"Water Availability and Use Science Program","usgsCitation":"Valder, J.F., McShane, R.R., Barnhart, T.B., Wheeling, S.L., Carter, J.M., Macek-Rowland, K.M., Delzer, G.C., and Thamke, J.N., 2019, Analytical framework to estimate water use associated with continuous oil and gas development: U.S. Geological Survey Scientific Investigations Report 2019–5100, 19 p., https://doi.org/10.3133/sir20195100.","productDescription":"Report: vi, 19 p.; Appendix; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106622","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":367751,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5100/coverthb.jpg"},{"id":367753,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5100/COGWaterUseTool.zip","text":"COG Water Use Tool","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2019–5100 Appendix"},{"id":367754,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPKRLW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data to estimate water use associated with continuous oil and gas development, Williston Basin, United States, 1980–2017"},{"id":367752,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5100/sir20195100.pdf","text":"Report ","size":"2.13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5100"}],"country":"United States ","otherGeospatial":"Lower 48 states ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.09765625,\n 48.980216985374994\n ],\n [\n -123.26660156249998,\n 49.03786794532644\n ],\n [\n -124.73876953125,\n 48.356249029540734\n ],\n [\n -124.1015625,\n 45.85941212790755\n ],\n [\n -124.67285156250001,\n 42.58544425738491\n ],\n [\n -124.45312499999998,\n 39.198205348894795\n ],\n [\n -120.498046875,\n 33.97980872872457\n ],\n [\n -117.158203125,\n 32.54681317351514\n ],\n [\n -114.5654296875,\n 32.69486597787505\n ],\n [\n -114.6533203125,\n 32.47269502206151\n ],\n [\n -110.7861328125,\n 31.31610138349565\n ],\n [\n -108.1494140625,\n 31.42866311735861\n ],\n [\n -108.2373046875,\n 31.87755764334002\n ],\n [\n -106.435546875,\n 31.87755764334002\n ],\n [\n -104.80957031249998,\n 30.524413269923986\n ],\n [\n -104.2822265625,\n 29.267232865200878\n ],\n [\n -103.1396484375,\n 29.036960648558267\n ],\n [\n -102.5244140625,\n 29.878755346037977\n ],\n [\n -101.162109375,\n 29.6880527498568\n ],\n [\n -100.1513671875,\n 28.22697003891834\n ],\n [\n -98.9208984375,\n 26.194876675795218\n ],\n [\n -97.119140625,\n 25.839449402063184\n ],\n [\n -96.2841796875,\n 28.613459424004414\n ],\n [\n -93.91113281249998,\n 29.726222319395503\n ],\n [\n -90.966796875,\n 29.267232865200878\n ],\n [\n -88.9892578125,\n 29.22889003019423\n ],\n [\n -89.384765625,\n 30.183121842195515\n ],\n [\n -87.01171875,\n 30.372875188118016\n ],\n [\n -85.20996093749998,\n 29.53522956294847\n ],\n [\n -84.24316406249998,\n 29.99300228455108\n ],\n [\n -82.6611328125,\n 28.497660832963472\n ],\n [\n -82.8369140625,\n 27.68352808378776\n ],\n [\n -81.9140625,\n 26.352497858154024\n ],\n [\n -81.1669921875,\n 25.522614647623292\n ],\n [\n -80.5078125,\n 25.20494115356912\n ],\n [\n -80.0244140625,\n 26.86328062676624\n ],\n [\n -81.6064453125,\n 30.637912028341123\n ],\n [\n -80.5078125,\n 32.509761735919426\n ],\n [\n -77.82714843749998,\n 34.161818161230386\n ],\n [\n -76.201171875,\n 34.994003757575776\n ],\n [\n -75.89355468749998,\n 36.527294814546245\n ],\n [\n -74.3994140625,\n 39.67337039176558\n ],\n [\n -69.697265625,\n 41.27780646738183\n ],\n [\n -68.37890625,\n 44.465151013519616\n ],\n [\n -66.7529296875,\n 44.68427737181225\n ],\n [\n -67.412109375,\n 45.583289756006316\n ],\n [\n -67.67578124999998,\n 45.79816953017265\n ],\n [\n -67.8076171875,\n 47.12995075666307\n ],\n [\n -68.115234375,\n 47.42808726171425\n ],\n [\n -68.9501953125,\n 47.18971246448421\n ],\n [\n -69.2578125,\n 47.45780853075031\n ],\n [\n -70.048828125,\n 46.31658418182218\n ],\n [\n -71.103515625,\n 45.24395342262324\n ],\n [\n -74.92675781249998,\n 44.96479793033101\n ],\n [\n -76.81640625,\n 43.70759350405294\n ],\n [\n -79.40917968749998,\n 43.35713822211053\n ],\n [\n -79.3212890625,\n 42.58544425738491\n ],\n [\n -82.7490234375,\n 41.73852846935917\n ],\n [\n -82.96875,\n 42.19596877629178\n ],\n [\n -82.265625,\n 43.644025847699496\n ],\n [\n -82.6171875,\n 45.36758436884978\n ],\n [\n -84.3310546875,\n 46.46813299215554\n ],\n [\n -88.4619140625,\n 48.3416461723746\n ],\n [\n -95.09765625,\n 48.980216985374994\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
Since water-year (WY) 2011, pool levels at Fall Creek Lake, Oregon, are temporarily lowered to an elevation near historical streambed each fall, creating free-flowing channel conditions that facilitate downstream passage of juvenile spring Chinook salmon. These drawdown operations have also mobilized substantial quantities of predominantly fine (<2 mm) reservoir sediment as well as some coarser gravels. To assess the potential impact of reservoir sediment erosion and transport on downstream reach morphology and habitats, linkages between reservoir sedimentation in Fall Creek Lake and drawdown-related reservoir erosion are inferred from geomorphic mapping and volumetric change analyses developed from high resolution aerial photographs and digital elevation models of the empty reservoir. Recent and historical drawdown operations have helped maintain a thalweg in much of Fall Creek Lake, constraining most coarse-grained sediment transport and re-deposition, whereas fine-grained deposition has mainly occurred on the former floodplain and lowermost reservoir reaches. Fine-grained sediment deposits are thickest and bury pre-dam morphology immediately upstream of the dam where they are accessible to fluvial erosion during streambed drawdown operations. Farther from the dam, where pre-dam morphology has not been buried, erosion is limited to sediment accumulation in the reservoir thalweg and minor tributary and ‘drawdown’ channels. In former floodplain regions of the reservoir not adjacent to the thalweg, thicker sediment deposits are inaccessible to fluvial erosion at full streambed drawdown. Altogether, these findings highlight controls on patterns and processes of reservoir erosion during drawdowns. This understanding of long-term sedimentation and streambed-drawdown erosion at Fall Creek Lake allows better evaluation and anticipation of the timing, magnitude, and sediment characteristics delivered to downstream reaches.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD 2019 Conference","conferenceDate":"June 24-28, 2019","conferenceLocation":"Reno, NV","language":"English","publisher":"Federal Interagency Sedimentation Conference (FISC) and Federal Interagency Hydrologic Modeling Conference (FIHMC)","usgsCitation":"Keith, M.K., and Stratton, L., 2019, Linking sedimentation and erosion patterns with reservoir morphology and dam operations during streambed drawdowns in a flood-control reservoir in the Oregon Cascades, in Proceedings of SEDHYD 2019, v. 3, Reno, NV, June 24-28, 2019, 11 p.","productDescription":"11 p.","ipdsId":"IP-104720","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":369932,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":369931,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2019/#sedhyd-2019-proceedings"}],"country":"United States","state":"Oregon","otherGeospatial":"Fall Creek Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.76088714599608,\n 43.92336814487696\n ],\n [\n -122.65205383300781,\n 43.92336814487696\n ],\n [\n -122.65205383300781,\n 43.97922818610027\n ],\n [\n -122.76088714599608,\n 43.97922818610027\n ],\n [\n -122.76088714599608,\n 43.92336814487696\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keith, Mackenzie K. 0000-0002-7239-0576 mkeith@usgs.gov","orcid":"https://orcid.org/0000-0002-7239-0576","contributorId":196963,"corporation":false,"usgs":true,"family":"Keith","given":"Mackenzie","email":"mkeith@usgs.gov","middleInitial":"K.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stratton, Laurel E. 0000-0001-8567-8619","orcid":"https://orcid.org/0000-0001-8567-8619","contributorId":215056,"corporation":false,"usgs":true,"family":"Stratton","given":"Laurel E.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761526,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205422,"text":"ofr20191100 - 2019 - Anderson Ranch wetlands hydrologic characterization in Taos County, New Mexico","interactions":[],"lastModifiedDate":"2019-10-03T14:08:15","indexId":"ofr20191100","displayToPublicDate":"2019-09-30T15:57:27","publicationYear":"2019","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":"2019-1100","displayTitle":"Anderson Ranch Wetlands Hydrologic Characterization in Taos County, New Mexico","title":"Anderson Ranch wetlands hydrologic characterization in Taos County, New Mexico","docAbstract":"The Anderson Ranch property (study area), located in Taos County, north-central New Mexico, was transferred from Chevron Mining, Inc. (CMI) to the Bureau of Land Management (BLM) as part of a Natural Resource Damage Assessment and Restoration (NRDAR) court-ordered settlement. The study area supports freshwater emergent wetlands and freshwater ponds. The settlement states that CMI will provide the land and a monetary settlement to support the restoration of the wetlands on the property. To best manage the study area, the BLM requires an understanding of potential effects of climate variability and groundwater withdrawals on the wetland function. This study, completed by the U.S. Geological Survey in cooperation with the BLM, provides an initial hydrologic characterization of the study area, which included literature review, collection of groundwater-level and aqueous-chemistry data, completion of a vegetation survey, and preliminary data analysis. The data compiled, collected, and analyzed as part of this study indicate that the wetlands within the study area are groundwater fed and that the water maintaining the wetlands is modern. Surface-water levels in the pond and groundwater levels in the surrounding wetland fluctuate seasonally. The hydraulic gradient in the study area is from northeast to southwest. Evapotranspiration is a main driver of water demand within the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191100","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Galanter, A.E., Shephard, Z.M., and Herrera-Olivas, P., 2019, Anderson Ranch wetlands hydrologic characterization in Taos County, New Mexico: U.S. Geological Survey Open-File Report 2019–1100, 42 p., https://doi.org/10.3133/ofr20191100. ","productDescription":"iii, 42 p. ","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-109765","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":367755,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1100/coverthb.jpg"},{"id":367756,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1100/ofr20191100.pdf","text":"Slide Presentation","size":"9.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1100"}],"country":"United States ","state":"New Mexico ","county":"Taos ","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-106.0068,36.9967],[-106,36.9967],[-105.8744,36.9972],[-105.8554,36.9972],[-105.7574,36.997],[-105.7188,36.9969],[-105.2198,36.9961],[-105.2205,36.9911],[-105.232,36.9871],[-105.2361,36.9835],[-105.2361,36.9798],[-105.2402,36.9762],[-105.2345,36.9712],[-105.2352,36.9626],[-105.2266,36.9553],[-105.2227,36.9426],[-105.2205,36.9303],[-105.2223,36.9249],[-105.2137,36.9203],[-105.2121,36.9135],[-105.2052,36.9076],[-105.2052,36.9044],[-105.2129,36.8922],[-105.2165,36.8777],[-105.2176,36.8768],[-105.2194,36.875],[-105.2218,36.865],[-105.2276,36.8601],[-105.23,36.8479],[-105.2289,36.8406],[-105.2244,36.836],[-105.2233,36.8292],[-105.2239,36.8233],[-105.228,36.8215],[-105.228,36.8148],[-105.2321,36.8107],[-105.2316,36.8066],[-105.227,36.8039],[-105.2264,36.8016],[-105.2329,36.7935],[-105.229,36.7771],[-105.2279,36.7712],[-105.2268,36.7635],[-105.2246,36.7599],[-105.2241,36.7508],[-105.2235,36.7494],[-105.2253,36.7431],[-105.2254,36.7372],[-105.2249,36.7327],[-105.2249,36.7313],[-105.2301,36.7259],[-105.2313,36.7241],[-105.2318,36.7232],[-105.2336,36.7227],[-105.2347,36.7223],[-105.2365,36.7223],[-105.2393,36.7223],[-105.2428,36.7228],[-105.2492,36.7129],[-105.252,36.7129],[-105.2577,36.7174],[-105.2669,36.717],[-105.2744,36.7198],[-105.2813,36.7198],[-105.2875,36.7258],[-105.3065,36.725],[-105.3139,36.7241],[-105.3186,36.711],[-105.3233,36.6997],[-105.3274,36.6988],[-105.3348,36.7025],[-105.3446,36.6966],[-105.3532,36.6958],[-105.3608,36.6872],[-105.3677,36.6804],[-105.3683,36.6741],[-105.3661,36.67],[-105.3627,36.6645],[-105.3645,36.6582],[-105.3605,36.6518],[-105.3577,36.6477],[-105.3548,36.6432],[-105.3514,36.6391],[-105.3538,36.6341],[-105.3549,36.6305],[-105.3572,36.6282],[-105.3601,36.6273],[-105.3647,36.6242],[-105.3688,36.6201],[-105.3711,36.6152],[-105.3718,36.607],[-105.3695,36.602],[-105.3638,36.5979],[-105.357,36.5892],[-105.3542,36.5856],[-105.3525,36.5792],[-105.352,36.5742],[-105.3509,36.567],[-105.3509,36.5624],[-105.3613,36.557],[-105.3756,36.5558],[-105.3768,36.554],[-105.3762,36.5531],[-105.3706,36.5453],[-105.3614,36.5403],[-105.3592,36.5389],[-105.3535,36.5316],[-105.3506,36.5289],[-105.349,36.5252],[-105.3479,36.5171],[-105.3468,36.5121],[-105.3365,36.5084],[-105.3354,36.5057],[-105.3303,36.4993],[-105.3269,36.4952],[-105.3224,36.4924],[-105.3252,36.4893],[-105.3316,36.487],[-105.3373,36.4826],[-105.3402,36.4794],[-105.3414,36.4726],[-105.3437,36.4681],[-105.3455,36.464],[-105.3473,36.4577],[-105.3479,36.4504],[-105.3428,36.4468],[-105.3389,36.439],[-105.3412,36.4331],[-105.3401,36.4245],[-105.3413,36.4155],[-105.3322,36.4113],[-105.3294,36.4077],[-105.3289,36.404],[-105.3266,36.399],[-105.3267,36.3941],[-105.3267,36.3913],[-105.3267,36.39],[-105.3285,36.38],[-105.3308,36.3764],[-105.3354,36.3714],[-105.336,36.3692],[-105.3309,36.3682],[-105.3309,36.3669],[-105.3304,36.3632],[-105.3287,36.3587],[-105.3299,36.3542],[-105.3299,36.3519],[-105.3294,36.3496],[-105.3294,36.3487],[-105.3282,36.3442],[-105.3335,36.3333],[-105.3353,36.3224],[-105.3355,36.3011],[-105.3267,36.2768],[-105.332,36.2748],[-105.3325,36.272],[-105.3355,36.2712],[-105.3357,36.2709],[-105.3388,36.2697],[-105.3432,36.2704],[-105.3484,36.2714],[-105.3599,36.2694],[-105.3683,36.2627],[-105.3772,36.2557],[-105.4094,36.2378],[-105.4012,36.2318],[-105.397,36.2203],[-105.3953,36.2123],[-105.3966,36.1995],[-105.4024,36.1875],[-105.4202,36.1712],[-105.4157,36.156],[-105.4219,36.1485],[-105.4206,36.1314],[-105.4297,36.1191],[-105.4346,36.1089],[-105.4339,36.0999],[-105.432,36.0892],[-105.4405,36.0833],[-105.4514,36.08],[-105.4513,36.0782],[-105.4449,36.0677],[-105.4475,36.0576],[-105.4506,36.0538],[-105.461,36.0443],[-105.4754,36.0426],[-105.484,36.0394],[-105.495,36.0379],[-105.504,36.0239],[-105.5104,36.0199],[-105.5274,36.0122],[-105.6165,36.0497],[-105.7375,36.1004],[-105.7625,36.1299],[-105.7949,36.1644],[-105.8074,36.1781],[-105.8153,36.1862],[-105.8233,36.1949],[-105.8529,36.2253],[-105.8574,36.2299],[-105.8796,36.2371],[-105.9241,36.2517],[-106.0148,36.2826],[-106.0508,36.2948],[-106.0554,36.2957],[-106.052,36.3011],[-106.0394,36.3202],[-106.0366,36.3247],[-106.0348,36.327],[-106.0331,36.3284],[-106.0286,36.3297],[-106.0229,36.3297],[-106.0166,36.3306],[-106.008,36.3324],[-105.9971,36.3361],[-105.9811,36.3433],[-105.9806,36.3435],[-105.9715,36.3446],[-105.9626,36.3518],[-105.9539,36.3669],[-105.9508,36.378],[-105.951,36.3854],[-105.953,36.403],[-105.9546,36.4217],[-105.9512,36.435],[-105.9429,36.4508],[-105.9378,36.4634],[-105.9396,36.4857],[-105.942,36.4996],[-105.945,36.5086],[-105.9456,36.5093],[-105.9484,36.527],[-105.9506,36.5523],[-105.9568,36.581],[-105.9642,36.5993],[-105.9657,36.6179],[-105.9722,36.6393],[-105.9728,36.6483],[-105.9844,36.6456],[-105.9838,36.6612],[-105.967,36.6599],[-105.97,36.6827],[-105.9796,36.7135],[-105.9765,36.7263],[-105.979,36.741],[-105.9818,36.7511],[-105.9851,36.8087],[-105.9734,36.8321],[-105.9705,36.8568],[-105.9687,36.8773],[-105.9733,36.9002],[-105.9809,36.9148],[-105.981,36.9151],[-105.9817,36.9173],[-105.9861,36.9261],[-105.9869,36.9345],[-105.9873,36.936],[-105.9885,36.9392],[-106.0037,36.976],[-106.0057,36.9837],[-106.0068,36.9967]]]},\"properties\":{\"name\":\"Taos\",\"state\":\"NM\"}}]}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE, Suite B
Albuquerque, NM 87113
This study explores the feasibility and utility of integrating environmental DNA (eDNA) assessments of species occurrences into the United States (U.S.) Geological Survey’s national streamgage network. We used an existing network of five gages in southwest Idaho to explore the type of information that could be gained as well as the associated costs and limitations. Hydrologic technicians were trained in eDNA sampling protocols and they collected samples during routine monthly visits to streamgages over an entire water year (2016). We analyzed the eDNA in the filtered water samples to determine the presence of two fish species: bull trout and rainbow trout. We then modeled the spatiotemporal distribution of each species using discharge and temperature data. To assess the influence of the spatial distribution of the gages on the biological information obtained, we also collected eDNA samples from locations between the gages three times during the water year. We found eDNA monitoring at the five gages provided meaningful information about the distribution of both species, especially when detection probabilities accounted for variations in temperature and discharge. Sampling between the gages provided additional information about bull trout distribution — the rarer of the two species. Our study suggests the integration of eDNA sampling into a streamgage network is feasible and could provide a novel and powerful source of biological information for riverine ecosystems in the U.S.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12800","usgsCitation":"Pilliod, D.S., Laramie, M., McCoy, D., and Maclean, S., 2019, Integration of eDNA-based biological monitoring within the US Geological Survey’s national streamgage network: Journal of the American Water Resources Association, v. 55, no. 6, p. 1505-1518, https://doi.org/10.1111/1752-1688.12800.","productDescription":"14 p.","startPage":"1505","endPage":"1518","numberOfPages":"14","ipdsId":"IP-104039","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":459688,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12800","text":"Publisher Index Page"},{"id":367934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Nebraska ","otherGeospatial":"Bruneau–Jarbidge Rivers watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.26281738281249,\n 41.32732632036622\n ],\n [\n -114.87854003906249,\n 41.32732632036622\n ],\n [\n -114.87854003906249,\n 42.46399280017058\n ],\n [\n -116.26281738281249,\n 42.46399280017058\n ],\n [\n -116.26281738281249,\n 41.32732632036622\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":216342,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":772287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laramie, Matthew 0000-0001-7820-2583 mlaramie@usgs.gov","orcid":"https://orcid.org/0000-0001-7820-2583","contributorId":152532,"corporation":false,"usgs":true,"family":"Laramie","given":"Matthew","email":"mlaramie@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":772288,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCoy, Dorene","contributorId":219452,"corporation":false,"usgs":false,"family":"McCoy","given":"Dorene","email":"","affiliations":[{"id":39997,"text":"Idaho Water Science Center (retired)","active":true,"usgs":false}],"preferred":false,"id":772289,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maclean, Scott","contributorId":219453,"corporation":false,"usgs":false,"family":"Maclean","given":"Scott","email":"","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":772290,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205084,"text":"sir20195095 - 2019 - Water resources on Guam—Potential impacts of and adaptive response to climate change","interactions":[],"lastModifiedDate":"2019-12-30T11:39:08","indexId":"sir20195095","displayToPublicDate":"2019-09-30T12:48:06","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5095","displayTitle":"Water resources on Guam—Potential impacts of and adaptive response to climate change","title":"Water resources on Guam—Potential impacts of and adaptive response to climate change","docAbstract":"The goals of this joint U.S. Geological Survey, University of Hawaiʻi, University of Guam, University of Texas, and East-West Center study were to (1) provide basic understanding about water resources for U.S. Department of Defense installations on Guam and (2) assess the resulting effect of sea-level rise and a changing climate on freshwater availability, on the basis of historic information, sea-level rise projections, and global-climate model temperature and rainfall projections. Downscaled regional climate models, informed by a multimodel ensemble of global climate models provided projections of future climate conditions for Guam. These projected climate conditions provided input to surface-water and groundwater models developed for Guam’s hydrology. Guam’s water resources in a future climate condition (2080–99) are projected to diminish relative to the recent climate condition. Projected average temperature increases, and average rainfall decreases will lead to reduced streamflow in southern Guam and reduced groundwater recharge to the Northern Guam Lens Aquifer (NGLA). Projected average temperatures in southern Guam will increase about 5.8 °F (3.22 °C), overall rainfall will decrease about 7 percent, and streamflow will consequently decrease 18 percent in important areas of southern Guam. Similarly, across the NGLA, future groundwater recharge will be 19 percent less than estimated recharge from 2012. Reduced future streamflow will decrease water availability from the Fena Valley Reservoir; however, the reservoir is expected to be able to supply water at recent demand rates without lowering the reservoir level to the elevation of the water-supply intakes throughout the simulated period of a future climate. A twelve-year simulation indicates that the reservoir can supply about twice the 2018 demand without lowering the reservoir level to the water-supply intakes. By following mitigation strategies to increase reservoir water availability, the withdrawal rate can be increased by 1.7 percent if the water-supply intakes are lowered 5 ft, by 3.5 percent if the spillway height is raised 5 ft, and by 5.3 percent if both strategies are combined. Higher sea level and reduced future recharge will decrease water availability from the NGLA. An index of composite chloride concentration from production wells increases to 300 milligrams per liter (mg/L) for future climate conditions and at 2010 withdrawal rates, compared with 130 mg/L under historic climate conditions. Most of this increase is due to reduced recharge as higher (+3.2 ft) sea level only has a small role in increasing withdrawn water salinity. A redistributed withdrawal scenario in which the composite chloride concentration is 290 mg/L offers only slight improvement. Should future droughts reduce recharge proportionally to the decreases observed during historic droughts, the composite concentration would be about 900 mg/L, and more than 70 percent of Guam’s production wells would produce water with a composite concentration greater than 500 mg/L. Potential mitigation strategies for increasing the potable yield of the NGLA in a future climate include reducing depths of deep production wells and reducing the withdrawal rates in selected wells projected to have higher chloride concentrations. Simulations show both strategies are effective in lowering the composite concentration of the withdrawn water.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195095","collaboration":"Prepared in cooperation with the Strategic Environmental Research and Development Program, U.S. Department of Defense","usgsCitation":"Gingerich, S.B., Johnson, A.G., Rosa, S.N., Marineau, M.D., Wright, S.A., Hay, L.E., Widlansky, M.J., Jenson, J.W., Wong, C.I., Banner, J.L., Keener, V.W., and Finucane, M.L., 2019, Water resources on Guam—Potential impacts of and adaptive response to climate change: U.S. Geological Survey Scientific Investigations Report 2019–5095, 55 p., https://doi.org/10.3133/sir20195095.","productDescription":"Report: viii, 55 p.: 3 Data Releases ","numberOfPages":"55","onlineOnly":"Y","ipdsId":"IP-099440","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":367769,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A64801","linkHelpText":"Mean annual water-budget components for Guam for historic (1990–2009) and future (2080–2099) climate conditions"},{"id":367768,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5095/sir20195095.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5095"},{"id":367770,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U34ACT","linkHelpText":"SUTRA model used to evaluate the freshwater flow system for a future (2080–2099) climate on Guam"},{"id":367771,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90S1CSX","linkHelpText":"Southern Guam watershed model and Fena Valley Reservoir water-balance model input files for historic (1990–2099) climate conditions"},{"id":367767,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5095/coverthb.jpg"}],"country":"Guam ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.53613281249997,\n 13.090179355733738\n ],\n [\n 145.01953124999997,\n 13.090179355733738\n ],\n [\n 145.01953124999997,\n 13.870080100685891\n ],\n [\n 144.53613281249997,\n 13.870080100685891\n ],\n [\n 144.53613281249997,\n 13.090179355733738\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Since the early 1900s, most of the groundwater withdrawals in the Houston-Galveston region, Texas, have been from the three primary aquifers that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers. Withdrawals from these aquifers are used for municipal supply, commercial and industrial use, and irrigation. This report, prepared by the U.S. Geological Survey in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District, is one in an annual series of reports depicting the status of groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers in the Houston-Galveston region. This report contains regional-scale maps depicting approximate 2019 groundwater-level altitudes (represented by measurements made during December 2018 through March 2019) and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers.
In 2019, groundwater-level-altitude contours for the Chicot aquifer ranged from 200 feet (ft) below the North American Vertical Datum of 1988 (hereinafter referred to as “datum”) to 200 ft above datum. The 1977–2019 groundwater-level-change contours for the Chicot aquifer depict a large area of decline in groundwater-level altitudes (100 ft) in northwestern Harris County. The largest rise in groundwater-level altitudes in the Chicot aquifer from 1977 to 2019 (200 ft) was in southeastern Harris County.
In 2019, groundwater-level-altitude contours for the Evangeline aquifer ranged from 300 ft below datum to 200 ft above datum. The 1977–2019 groundwater-level-change contours for the Evangeline aquifer depict broad areas where groundwater-level altitudes either declined or rose. The largest decline in groundwater-level altitudes (280 ft) was in southern Montgomery and northern Harris Counties. The largest rise in groundwater-level altitudes in the Evangeline aquifer from 1977 to 2019 (240 ft) was in southeastern Harris County.
In 2019, groundwater-level-altitude contours for the Jasper aquifer ranged from 200 ft below datum to 250 ft above datum. The 2000–19 groundwater-level-change contours for the Jasper aquifer depict groundwater-level declines throughout most of the study area where groundwater-level-altitude data from the Jasper aquifer were collected, with the largest decline (200 ft) in southern Montgomery County.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195089","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District, City of Houston, Fort Bend Subsidence District, Lone Star Groundwater Conservation District, and Brazoria County Groundwater Conservation District","usgsCitation":"Braun, C.L., Ramage, J.K., and Shah, S.D., 2019, Status of groundwater-level altitudes and long-term groundwater-level changes in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston region, Texas, 2019: U.S. Geological Survey Scientific Investigations Report 2019–5089, 18 p., https://doi.org/10.3133/sir20195089.","productDescription":"Report: vi, 18 p.; 2 Data Releases","onlineOnly":"N","ipdsId":"IP-108529 ","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":367799,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LKT49P","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Depth to Groundwater Measured from Wells Completed in the Chicot, Evangeline, and Jasper Aquifers, Houston-Galveston Region, Texas, 2019"},{"id":367796,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5089/coverthb.jpg"},{"id":367797,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5089/sir20195089.pdf","text":"Report","size":"14.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5089"},{"id":367798,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/doi:10.5066/P91CKWVC","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Groundwater-level altitudes and long-term groundwater-level Changes in the Chicot, Evangeline, and Jasper aquifers, Houston-Galveston Region, Texas, 2019"}],"country":"United States","state":"Texas","otherGeospatial":"Chicot Aquifer, Evangeline Aquifer, Jasper Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.13336181640625,\n 29.03215782622282\n ],\n [\n -94.2022705078125,\n 29.57345707301757\n ],\n [\n -94.9822998046875,\n 30.398937557618677\n ],\n [\n -95.635986328125,\n 30.836214626064844\n ],\n [\n -95.888671875,\n 30.850363469502362\n ],\n [\n -96.61102294921875,\n 30.14512718337613\n ],\n [\n -96.27319335937499,\n 29.506549442788593\n ],\n [\n -95.130615234375,\n 29.020149792758527\n ],\n [\n -95.13336181640625,\n 29.03215782622282\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
The production of Klamath River fall Chinook salmon (Oncorhynchus tshawytscha) in northern California and southern Oregon is thought to be limited by poor survival during freshwater juvenile life stages, in part a result of Ceratonova shasta—a highly infectious disease that can lead to high fish mortality. Higher flushing river flows are thought to affect the concentration of C. shasta spores, and in turn, juvenile salmon infection and mortality. The Stream Salmonid Simulator (S3) model was built to simulate the spatiotemporal dynamics of the growth, movement, and survival of juvenile salmon from spawning through migration to the Pacific Ocean in response to river flow, habitat availability, water temperature, and C. shasta spore concentrations. The S3 model has been calibrated to juvenile fall Chinook salmon abundances at a trap site within the Klamath River, and was specifically designed to provide objective predictions of juvenile salmon abundance and survival in relation to proposed flow management alternatives and resulting fish infection and mortality by C. shasta. Infection by C. shasta in the Klamath River is location specific, occurring in a “disease zone” with high spore concentrations. The spatial extent of this disease zone (from river kilometer 289.6 to 212.9) has been incorporated in the S3 model for the Klamath River, enabling the assessment of disease effects on fish at specific spatial locations such as the trap sampling sites, and for fish that were or were not exposed to the disease zone as they emigrate the Klamath River to the Pacific Ocean.
Given the information gained from field observations on spore concentrations in relation to river flow, deliberations by resource managers resulted in the incorporation of springtime flushing flows in a Proposed Action (PA) scenario developed in part to lower spore concentrations within the disease zone. A Historical (HI) scenario based on the observed flows, temperatures, and spore concentrations from 2004 to 2016 was used to compare and contrast the potential benefits to juvenile salmon from PA flows in relation to the HI conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191099","collaboration":"Prepared in cooperation with the National Oceanic and Atmospheric Administration, National Marine Fisheries Service","usgsCitation":"Plumb, J.M., Perry, R.W., Som, N.A., Alexander, J., and Hetrick, N.J., 2019, Using the stream salmonid simulator (S3) to assess juvenile Chinook salmon (Oncorhynchus tshawytscha) production under historical and proposed action flows in the Klamath River, California: U.S. Geological Survey Open-File\nReport 2019-1099, 43 p., https://doi.org/10.3133/ofr20191099.","productDescription":"vi, 43 p.","onlineOnly":"Y","ipdsId":"IP-107092","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":367843,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1099/coverthb.jpg"},{"id":367844,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1099/ofr20191099.pdf","text":"Report","size":"3.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1099"}],"country":"United States","state":"California","otherGeospatial":"Klamath River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.5247802734375,\n 41.38917324986403\n ],\n [\n -122.23114013671875,\n 41.38917324986403\n ],\n [\n -122.23114013671875,\n 41.92475971933975\n ],\n [\n -123.5247802734375,\n 41.92475971933975\n ],\n [\n -123.5247802734375,\n 41.38917324986403\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
In this report, we describe application of the Stream Salmonid Simulator (S3) to Chinook salmon (Oncorhynchus tshawytscha) in the Klamath River between Keno Dam in southern Oregon and the ocean in northern California. S3 is a deterministic life-stage-structured population model that tracks daily growth, movement, and survival of juvenile salmon. It can track different source populations or species, such as major tributary populations that enter a river like the Klamath River. A key theme of the model is that river flow affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily time series of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit affect survival and growth within each habitat unit and movement of fish among habitat units.
The physical template of the Klamath River was formed by classifying the river into 2,635 mesohabitat units composed of runs, riffles, and pools. This template enabled modeling of the unimpounded Klamath River between the Keno Dam (the uppermost of four dams) and Iron Gate Dam (the lowermost dam) to address dam-removal scenarios. However, in this report, our focus was on parameterizing and calibrating the model under existing conditions, which included 1,706 discrete habitat units over the 312-kilometer (km) section of river between Iron Gate Dam and the ocean. For each habitat unit, we developed a time series of daily flow, water temperature, and amount of available habitat (weighted usable habitat area [WUA]) for spawners, fry, and parr. WUA time series were constructed using habitat suitability criteria for Chinook salmon applied to eight two-dimensional (2-D) hydrodynamic models that represented the geomorphic variability in habitat across the Klamath River. Results from the 2-D models were then extrapolated to unmodeled habitat units by scaling WUA curves for changes in habitat unit length and width. These variables were then used to drive population dynamics such as egg development and survival and juvenile movement, growth, and survival.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191107","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Bureau of Reclamation","usgsCitation":"Perry, R.W., Plumb, J.M., Jones, E.C., Som, N.A., Hardy, T.B., and Hetrick, N.J., 2019, Application of the Stream Salmonid Simulator (S3) to Klamath River fall Chinook salmon (Oncorhynchus tshawytscha), California—Parameterization and calibration: U.S. Geological Survey Open-File Report 2019–1107, 89 p., https://doi.org/10.3133/ofr20191107.","productDescription":"Report: viii, 89p.; Appendix 1","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106890","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":367791,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1107/ofr20191107.pdf","text":"Report","size":"5.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1107"},{"id":367792,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1107/ofr20191107_a1.pdf","text":"Appendix 1","size":"241 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1107 Appendix 1"},{"id":367790,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1107/coverthb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Keno Dam, Klamath River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.4091796875,\n 41.17038447781618\n ],\n [\n -120.66284179687498,\n 41.17038447781618\n ],\n [\n -120.66284179687498,\n 42.4234565179383\n ],\n [\n -124.4091796875,\n 42.4234565179383\n ],\n [\n -124.4091796875,\n 41.17038447781618\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Guam receives 85 to 116 inches of rain a year, two-thirds of which has historically fallen during the wet season. On average, three tropical storms and one typhoon pass within 80 nautical miles of Guam each year, generally during the rainy season. Both drought and flooding can impact freshwater supply and the associated infrastructure. Department of Defense (DoD) installations and non-military populations on Guam share freshwater resources, which will be impacted by changes in demographics, freshwater demand, and climate. This DoD Strategic Environmental Research and Development Program (SERDP) funded study evaluated potential climate impacts on freshwater supplies in Guam, and identified methods of increasing the water distribution system’s resilience.
","language":"English","publisher":"PacificRISA","usgsCitation":"Gingerich, S., Keener, V., and Finucane, M.L., 2019, Freshwater availability in Guam with projected changes in climate: Cooperator Report, 4 p.","productDescription":"4 p.","ipdsId":"IP-099441","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":375313,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375312,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.pacificrisa.org/wp-content/uploads/2019/10/Pacific-RISA-Guam-climate-summary_Sept-2019.pdf"}],"country":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.1845703125,\n 12.961735843534306\n ],\n [\n 145.30517578125,\n 12.961735843534306\n ],\n [\n 145.30517578125,\n 13.859413869074032\n ],\n [\n 144.1845703125,\n 13.859413869074032\n ],\n [\n 144.1845703125,\n 12.961735843534306\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gingerich, Stephen 0000-0002-4381-0746 sbginger@usgs.gov","orcid":"https://orcid.org/0000-0002-4381-0746","contributorId":220301,"corporation":false,"usgs":true,"family":"Gingerich","given":"Stephen","email":"sbginger@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":774683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keener, Victoria","contributorId":212170,"corporation":false,"usgs":false,"family":"Keener","given":"Victoria","affiliations":[{"id":38447,"text":"East-West Center, Honolulu, Hawai`i","active":true,"usgs":false}],"preferred":false,"id":774684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finucane, Melissa L.","contributorId":140152,"corporation":false,"usgs":false,"family":"Finucane","given":"Melissa","email":"","middleInitial":"L.","affiliations":[{"id":13398,"text":"East-West Center","active":true,"usgs":false}],"preferred":false,"id":774685,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70219449,"text":"70219449 - 2019 - Modeling long-term effects of fuel treatments on fuel loads and fire regimes in the Great Basin","interactions":[],"lastModifiedDate":"2021-04-08T13:28:54.447157","indexId":"70219449","displayToPublicDate":"2019-09-30T08:21:50","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":251,"text":"Final Report","active":false,"publicationSubtype":{"id":4}},"title":"Modeling long-term effects of fuel treatments on fuel loads and fire regimes in the Great Basin","docAbstract":"The principal motivation for this study is that sagebrush-steppe ecosystems are undergoing significant state changes, and land managers are challenged with optimizing their resources for both short- and long-term use. Yet, limited knowledge is available regarding how the sagebrush-steppe will respond to environmental changes related to precipitation and temperature regimes, and disturbance such as fire. Furthermore, there is a lack of understanding on how fuels reduction and other fuel management activities will impact these ecosystems over the long-term. We addressed these challenges by adapting and testing a vegetation dynamics model, the Ecosystem Demography v2.2 model (EDv2.2), for the sagebrush-steppe. Vegetation dynamics models can provide estimations of ecosystem productivity in their natural and disturbance states, and thus serve as a tool to understand and predict potential changes in various processes and properties of vegetation communities. Yet, there is no vegetation dynamics model that is well-developed for the sagebrush-steppe, and thus significant effort is needed to test EDv2.2 for its application. As part of our efforts to develop the EDv2.2 model into a useful tool for the sagebrush-steppe, we developed a sagebrush plant functional type (PFT) as part of this study, and then performed sensitivity analyses, model calibration, and finally model evaluation. Furthermore, we developed several model scenarios under natural (undisturbed) and disturbed (fire) environments. We compared our model outputs with ground-based data (field and eddy covariance) and remote sensing observations. The results of our project include a sagebrush PFT that can be used in both future EDv2.2 modeling efforts and other vegetation dynamic models. Our results from the model sensitivity analysis indicate that specific leaf area (SLA), stomatal slope (STO_S), cuticular conductance (CUT_C), and carboxylase rate constant (VM0) are sensitive parameters to vegetation productivity in the model (based on gross primary production, GPP), and future modeling efforts will benefit from both lab and field studies of these parameters and sensitivity analyses. Through calibration, we found that the EDv2.2 model estimates of GPP were modeled well at our lowest elevation field site in Reynolds Creek Experimental Watershed (RCEW), which is dominated by Wyoming big sagebrush. On the contrary, we found poorer results at higher elevation site shrub sites. These sites are characterized by either low sagebrush or mountain big sagebrush, and have more forb cover than the low elevation site. In this project we also implemented the fire model in EDv2.2 to explore how shrub and C3 grasses respond to fire by analyzing post-fire GPP. We ran both point and regional model runs with fire introduced. In most fire scenarios, fire substantially reduced shrub GPP and it took several decades for shrub GPP to return to pre-fire conditions. Grass GPP responded more quickly in post-fire conditions. While these processes are representative of what other studies have found, significant efforts to improve the fire processes in EDv2.2 are needed. For example, nuances associated with the fire subroutine in the model (running periodic fire events versus instantaneous fires and fire intensity) will need to be expanded. Another significant contribution to our knowledge gap is that additional PFTs to represent the sagebrush-steppe (e.g. annual grasses such cheatgrass) are needed for EDv2.2. Regardless, this project made significant advances in PFT development and model testing. Moreover, the EDv2.2 provides a useful framework to conceptualize vegetation dynamics, project future conditions, and consider fire as a disturbance. With additional parameterizations, PFTs, and fire routines, EDv2.2 will evolve as a tool for which to better understand future ecosystem dynamics of the sagebrush-steppe.","language":"English","publisher":"Joint Fire Science Program","usgsCitation":"Glenn, N.F., Flores, A.N., Shinneman, D.J., and Pilliod, D., 2019, Modeling long-term effects of fuel treatments on fuel loads and fire regimes in the Great Basin: Final Report, iii, 29 p.","productDescription":"iii, 29 p.","ipdsId":"IP-112685","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":384934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384901,"type":{"id":15,"text":"Index Page"},"url":"https://www.nrfirescience.org/resource/20381"}],"country":"United States","state":"Idaho","otherGeospatial":"Reynolds Creek Experimental Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.98242187499999,\n 42.89206418807337\n ],\n [\n -115.98266601562499,\n 42.89206418807337\n ],\n [\n -115.98266601562499,\n 43.61221676817573\n ],\n [\n -116.98242187499999,\n 43.61221676817573\n ],\n [\n -116.98242187499999,\n 42.89206418807337\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Glenn, Nancy F.","contributorId":195241,"corporation":false,"usgs":false,"family":"Glenn","given":"Nancy","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":813604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flores, Alejandro N","contributorId":256965,"corporation":false,"usgs":false,"family":"Flores","given":"Alejandro","email":"","middleInitial":"N","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":813605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813603,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813606,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206457,"text":"70206457 - 2019 - Guam's water resources","interactions":[],"lastModifiedDate":"2020-06-03T15:18:34.952527","indexId":"70206457","displayToPublicDate":"2019-09-30T08:19:19","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5883,"text":"Cooperator Report","active":true,"publicationSubtype":{"id":1}},"title":"Guam's water resources","docAbstract":"How do climate and humans impact freshwater resources, and how can we plan for change?
","language":"English","publisher":"PacificRISA","usgsCitation":"Gingerich, S., Keener, V., and Finucane, M.L., 2019, Guam's water resources: Cooperator Report, 2 p.","productDescription":"2 p.","ipdsId":"IP-099442","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":375311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375310,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.pacificrisa.org/wp-content/uploads/2019/10/Pacific-RISA-Guam-water-resources-handout_Sept-2019.pdf"}],"country":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.1845703125,\n 12.961735843534306\n ],\n [\n 145.30517578125,\n 12.961735843534306\n ],\n [\n 145.30517578125,\n 13.859413869074032\n ],\n [\n 144.1845703125,\n 13.859413869074032\n ],\n [\n 144.1845703125,\n 12.961735843534306\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gingerich, Stephen","contributorId":220300,"corporation":false,"usgs":true,"family":"Gingerich","given":"Stephen","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":774680,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keener, Victoria","contributorId":212170,"corporation":false,"usgs":false,"family":"Keener","given":"Victoria","affiliations":[{"id":38447,"text":"East-West Center, Honolulu, Hawai`i","active":true,"usgs":false}],"preferred":false,"id":774681,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finucane, Melissa L.","contributorId":140152,"corporation":false,"usgs":false,"family":"Finucane","given":"Melissa","email":"","middleInitial":"L.","affiliations":[{"id":13398,"text":"East-West Center","active":true,"usgs":false}],"preferred":false,"id":774682,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221765,"text":"70221765 - 2019 - Discovering blind geothermal systems in the Great Basin Region: An integrated geologic and geophysical approach for establishing geothermal play fairways: All phases","interactions":[],"lastModifiedDate":"2021-07-02T13:12:02.411942","indexId":"70221765","displayToPublicDate":"2019-09-30T07:51:29","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Discovering blind geothermal systems in the Great Basin Region: An integrated geologic and geophysical approach for establishing geothermal play fairways: All phases","docAbstract":"Most geothermal resources in the Great Basin region of the western USA are blind, and thus the discovery of new commercial-grade systems requires synthesis of favorable characteristics for geothermal activity. The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity to identify promising areas for new development. This project integrated multiple datasets to apply the play fairway concept and assess geothermal potential in a large region of the Great Basin in Nevada. It is therefore referred to as the Nevada play fairway project. This project was a strong collaborative effort between several organizations, led by the Nevada Bureau of Mines and Geology at the University of Nevada, Reno, but with key support from the U.S. Geological Survey, ATLAS Geosciences, Inc,, Hi-Q Geophysical, Inc., Lawrence Berkeley National Laboratory, Utah Geological Survey, and Innovative Geothermal Ltd. In Budget Period 1 of this project, available data for nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new detailed geothermal potential map of 96,000 km2 from west-central to eastern Nevada (Figure 1). These parameters were grouped into subsets and individually weighted (Figure 2) to delineate rankings for local permeability, intermediate permeability, regional permeability, and thermal potential, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). This initial work was aimed at reducing the risks in regional exploration and therefore facilitating discovery of new commercial-grade systems in blind settings, as well as in areas with surface expressions of geothermal activity. Budget Period 2 of the project involved detailed analysis of some of the most promising areas identified in Phase 1. Twenty-four highly prospective areas, including both known undeveloped systems and previously undiscovered potential blind systems, were identified for further analysis (Figures 3 and 4). After reconnaissance of these areas, five of the most promising sites were selected for detailed studies. Multiple techniques were employed in the detailed studies, including geologic mapping, shallow temperature surveys, gravity surveys, Lidar, geochemical studies, seismic reflection analysis, and 3D modeling. The goal of the detailed studies was to identify specific areas with the highest likelihood for high permeability and thermal fluids, such that drill sites could be targeted. Three main sets of predictive maps were generated for each detailed study area: 1) play fairway maps, 2) play fairway error maps, and 3) direct evidence maps. Local- and intermediate-scale permeability models were revised to reflect results of the detailed geologic, geophysical, and geochemical analyses. Budget Period 3 of the project involved more detailed geophysical analyses and temperature-gradient (TG) drilling in southeastern Gabbs Valley and northern Granite Springs Valley (Figure 4), deemed the two most promising sites, with the goal of providing preliminary validation of the play fairway methodology. In southeastern Gabbs Valley, the collocation of a favorable structural setting (displacement transfer zone and fault intersections), Quaternary faults, intersecting and terminating gravity gradients, magnetic low, shallow (2 m) temperature anomaly, low resistivity anomaly, and promising geothermometry from nearby water wells provided evidence for a blind system. Drilling of six TG holes defines an apparent geothermal system at this locality with temperatures as high as 124°C at 152 m. This system is blind, with no surface hot springs, fumaroles, or paleo-geothermal deposits. For northern Granite Springs Valley, a favorable structural setting (termination of a major Quaternary normal fault), terminating gravity gradient, magnetic gradient, newly discovered sinter deposits, nearby warm water wells, previously drilled TG holes in the vicinity, and promising geothermometry suggest a hidden system. Drilling of six new TG holes yields temperatures of ~96°C at ~250 m, suggesting the presence of a geothermal system. Major lessons learned in the course of this project include: 1) initially identified sites commonly include multiple favorable structural settings at a finer scale; 2) promising sites in Cenozoic basins cannot be recognized without detailed geophysical surveys; and 3) play fairway analysis should be refined as the exploration program vectors into the most promising sites and finer-scale data are acquired. In addition to producing copious amounts of data, this project resulted in 16 published papers, 10 abstracts, more than 40 presentations across the U.S. and abroad (including several keynote addresses), 2 Masters theses, and 7 media reports.
Electricity from wind energy is a major contributor to the strategy to reduce greenhouse gas emissions from fossil fuel use and thus reduce the negative impacts of climate change. Wind energy, like all power sources, can have adverse impacts on wildlife. After nearly 25 years of focused research, these impacts are much better understood, although uncertainty remains. In this report, we summarize positive impacts of replacing fossil fuels with wind energy, while describing what we have learned and what remains uncertain about negative ecological impacts of the construction and operation of land-based and offshore wind energy on wildlife and wildlife habitat in the U.S. Finally, we propose research on ways to minimize these impacts. TO SUMMARIZE: 1 Environmental and other benefits of wind energy include near-zero greenhouse gas emissions, reductions of other common air pollutants, and little or no water use associated with producing electricity from wind energy. Various scenarios for meeting U.S. carbon emission reduction goals indicate that a four- to five-fold expansion of land-based wind energy from the current 97 gigawatts (GW) by the year 2050 is needed to minimize temperature increases and reduce the risk of climate change to people and wildlife. 2 Collision fatalities of birds and bats are the most visible and measurable impacts of wind energy production. Current estimates suggest most bird species, especially songbirds, are at low risk of population-level impacts. Raptors as a group appear more vulnerable to collisions. Population-level impacts on migratory tree bats are a concern, and better information on population sizes is needed to evaluate potential impacts to these species. Although recorded fatalities of cave-dwelling bat species are typically low at most wind energy facilities, additional mortality from collisions is a concern given major declines in these species due to white-nose syndrome (WNS). Assessments of regional and cumulative fatality impacts for birds and bats have been hampered by the lack of data from areas with a high proportion of the nation’s installed wind energy capacity. Efforts to expand data accessibility from all regions are underway, and this greater access to data along with improvements in statistical estimators should lead to improved impact assessments. 3 Habitat impacts of wind energy development are difficult to assess. An individual wind energy facility may encompass thousands of acres, but only a small percentage of the landscape within the project area is directly transformed. If a project is sited in previously undisturbed habitat, there is concern for indirect impacts, such as displacement of sensitive species. Studies to date indicate displacement of some species, but the long-term population impacts are unknown. 4 Offshore wind energy development in the U.S. is just beginning. Studies at offshore wind facilities in Europe indicate some bird and marine mammal species are displaced from project areas, but substantial uncertainty exists regarding the individual or population-level impacts of this displacement. Bird and bat collisions with offshore turbines are thought to be less common than at terrestrial facilities, but currently the tools to measure fatalities at offshore wind energy facilities are not available. The wind energy industry, state and federal agencies, conservation groups, academia, and scientific organizations have collaborated for nearly 25 years to conduct the research needed to improve our understanding of risk to wildlife and to avoid and minimize that risk. Efforts to reduce the uncertainty about wildlife risk must keep up withthe pace and scale of the need to reduce carbon emissions. This will require focusing our research priorities and increasing the rate at which we incorporate research results into the development and validation of best practices for siting and operating wind energy facilities.
","language":"English","publisher":"Ecological Society of America","usgsCitation":"Allison, T., Diffendorfer, J., Baerwald, E., Julie Beston, David Drake, Hale, A., Cris Hein, Huso, M.M., Loss, S., Lovich, J.E., Strickland, D., Kate Williams, and Virginia Winder, 2019, Impacts to wildlife of wind energy siting and operation in the United States: Issues in Ecology, no. 21, p. 1-24.","productDescription":"24 p.","startPage":"1","endPage":"24","ipdsId":"IP-082737","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":368388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368387,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.esa.org/wp-content/uploads/2019/09/Issues-in-Ecology_Fall-2019.pdf"}],"issue":"21","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Allison, Taber","contributorId":215930,"corporation":false,"usgs":false,"family":"Allison","given":"Taber","email":"","affiliations":[{"id":39329,"text":"American Wind Wildlife Institute","active":true,"usgs":false}],"preferred":false,"id":773360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":773359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baerwald, Erin","contributorId":219854,"corporation":false,"usgs":false,"family":"Baerwald","given":"Erin","email":"","affiliations":[{"id":16660,"text":"University of Calgary","active":true,"usgs":false}],"preferred":false,"id":773361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Julie Beston","contributorId":174816,"corporation":false,"usgs":false,"family":"Julie Beston","affiliations":[{"id":27515,"text":"UW Stout","active":true,"usgs":false}],"preferred":false,"id":773362,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"David Drake","contributorId":219855,"corporation":false,"usgs":false,"family":"David Drake","affiliations":[{"id":29843,"text":"Univ of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":773363,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hale, Amanda","contributorId":219856,"corporation":false,"usgs":false,"family":"Hale","given":"Amanda","email":"","affiliations":[{"id":25471,"text":"Texas Christian University","active":true,"usgs":false}],"preferred":false,"id":773364,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cris Hein","contributorId":219857,"corporation":false,"usgs":false,"family":"Cris Hein","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":773365,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Huso, Manuela M. 0000-0003-4687-6625 mhuso@usgs.gov","orcid":"https://orcid.org/0000-0003-4687-6625","contributorId":150012,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela","email":"mhuso@usgs.gov","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":773366,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Loss, Scott","contributorId":167554,"corporation":false,"usgs":false,"family":"Loss","given":"Scott","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":773367,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":773368,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Strickland, Dale","contributorId":219858,"corporation":false,"usgs":false,"family":"Strickland","given":"Dale","email":"","affiliations":[{"id":40080,"text":"WEST Inc","active":true,"usgs":false}],"preferred":false,"id":773369,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kate Williams","contributorId":219859,"corporation":false,"usgs":false,"family":"Kate Williams","affiliations":[{"id":40081,"text":"BRI","active":true,"usgs":false}],"preferred":false,"id":773370,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Virginia Winder","contributorId":219860,"corporation":false,"usgs":false,"family":"Virginia Winder","affiliations":[{"id":40082,"text":"Benedictine University","active":true,"usgs":false}],"preferred":false,"id":773371,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ]}