The use of PIT tags has rapidly proliferated since their introduction, and new mobile detection methods have been developed. However, the presence of ghost tags (i.e., PIT tags left in the system after a fish dies) creates uncertainty about the status (live or dead) of tags detected. Herein, we describe our raft-based mobile PIT tag antenna system, which was used to evaluate the movements of “seeded tags” (i.e., PIT tags that we placed in the river as ghost tag analogs) and their interactions with habitat features. We deployed 5,000 seeded tags in the San Juan River, a large sand-bed river in the southwestern USA. Total distances moved by PIT tags ranged from 0.8 to 4,124 m, but 75% of movements were less than 100 m. Flow conditions causing the smallest to largest movements were (1) base flows, (2) spring runoff flows, (3) flash flood flows, and (4) a combination of spring runoff and flash flood flows. Based on Ivlev's electivity index, tags were more likely to be detected in riffles than in runs. These findings will help to classify mobile PIT tag detections as ghost tags or live fish, a critical data gap limiting the accurate estimation of demographic rates, population status metrics, and descriptions of the habitat use of fishes.
The U.S. Army Corps of Engineers (USACE), Nashville District, is conducting ongoing water-supply analyses of USACE reservoirs in the Cumberland River watershed to identify areas where potential water-resources issues may arise in the future. To assist the USACE in their efforts, the U.S. Geological Survey, in cooperation with the USACE, collected and analyzed water-use data to estimate public-supply, self-supplied industrial, irrigation, and thermoelectric water use for 2010 and to project water demand to 2040 for the Cumberland River watershed area.
Estimates of water use for public supply were projected in 10-year increments through 2040 and were based on 2010 public water-supply data and population projections for 2020 to 2040. Additionally, estimates of consumptive use, wastewater releases, and thermoelectric power and industrial return flows were calculated. All estimates are presented for the entire watershed and for the 10 reservoir catchment areas (RCAs) within the watershed.
Estimated water withdrawals in the Cumberland River watershed during 2010 averaged 3,456.23 million gallons per day (Mgal/d) of freshwater for offstream use. Return flow was estimated to be 3,370.08 Mgal/d, or 98 percent of the water withdrawn during 2010. Total consumptive use accounts for the remaining 2 percent, or 86.2 Mgal/d. Estimates of water withdrawals by source indicate that withdrawals from surface water during 2010 accounted for more than 99 percent of the total withdrawals, or 3,437.90 Mgal/d. Total groundwater withdrawals during 2010 were 18.33 Mgal/d, or less than 1 percent of the total withdrawals.
During 2010, withdrawals by category were estimated as follows: thermoelectric power, 3,051.12 Mgal/d; public supply, 360.00 Mgal/d; industrial, 31.5 Mgal/d; and irrigation, 13.6 Mgal/d. Return flows were estimated as thermoelectric power, 3,051.06 Mgal/d, and industrial and public supply, 319.02 Mgal/d. Consumptive use was estimated as thermoelectric power, 0.06 Mgal/d; industrial and public supply, 72.5 Mgal/d; and irrigation, 13.6 Mgal/d.
By 2040, the public supply of raw and (or) finished water to meet demand for the 10 RCAs is projected to increase 48 percent to 532.51 Mgal/d. This projected increase includes an increase from 51.5 to 72.5 Mgal/d, or 41 percent, in the Barkley RCA. The combined total water demand for the Cheatham, J. Percy Priest, and Old Hickory RCAs is projected to increase from 224.08 to 359.58 Mgal/d, or 61 percent. The combined total water demand for the Center Hill, Cordell Hull, and Dale Hollow RCAs is projected to increase from 31.7 to 43.0 Mgal/d, or 36 percent. The combined total water demand for the Martins Fork, Laurel, and Wolf Creek RCAs is projected to increase from 52.8 to 57.4 Mgal/d, or 9 percent. The only RCA in the watershed with a projected decrease in water demand is Martins Fork.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185130","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Nashville District","usgsCitation":"Robinson, J.A., 2019, Estimated use of water in the Cumberland River watershed in 2010 and projections of public-supply water use to 2040: U.S. Geological Survey Scientific Investigations Report 2018–5130, 62 p., https://doi.org/10.3133/sir20185130.","productDescription":"Report: viii, 62 p.; Data Release","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-044987","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":367657,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7M043KK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Public Supply Water Use in the Cumberland River Watershed in 2010 and Projections of Public-supply Water Use to 2040"},{"id":367656,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5130/sir20185130.pdf","text":"Report","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5131"},{"id":367655,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5130/coverthb.jpg"}],"country":"United States","state":"Kentucky, Tennessee, Virginia","otherGeospatial":"Cumberland River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.472900390625,\n 37.23907530202184\n ],\n [\n -87.725830078125,\n 36.36822190085111\n ],\n [\n -86.50634765625,\n 35.737595151747826\n ],\n [\n -82.55126953124999,\n 36.74768773190056\n ],\n [\n -82.562255859375,\n 36.99377838872517\n ],\n [\n -83.60595703125,\n 36.83566824724438\n ],\n [\n -84.462890625,\n 37.57070524233116\n ],\n [\n -85.166015625,\n 37.54457732085582\n ],\n [\n -85.572509765625,\n 36.677230602346214\n ],\n [\n -88.472900390625,\n 37.23907530202184\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Although streamflow is widely recognized as a controlling factor in stream health, empirical relations between indicators of anthropogenic modification of streamflow and ecological indicators have been elusive. The objective of this report is to build upon specific findings reported in recent publications by providing a library of empirical models that describe the relations between streamflow modification and indicators of biological integrity. Biological monitoring data from 812 streams and rivers across the United States were matched with sites where daily streamflow was also monitored by the U.S. Geological Survey. Of these sites, 118 were sampled by the U.S. Geological Survey along gradients of streamflow modification within 3 regional focus studies. The integrity of invertebrate and fish communities was expressed as a binary variable, “impaired” or “unimpaired,” signifying whether or not the composition and structure of the biological community was statistically reduced relative to regional reference sites. Streamflow modification at each gaged site was quantified with 509 streamflow statistics scaled to express the ratio of observed streamflow conditions to site-specific expected conditions in the absence of human influences on watershed hydrology. For each region, generalized additive modeling was used to examine relations between each indicator of streamflow modification and indicators of biological integrity (response variable). In every region examined, statistically defensible and ecologically realistic relations were found between indicators of streamflow modification and indicators of biological integrity. These findings can aid practitioners and managers seeking to (1) propose empirically based hypotheses about the specific components of streamflow regimes that are critical to aquatic communities, which can subsequently be explored in detail in a region or river basin of interest; and (2) predict biological responses to anthropogenic modification of specific components of the streamflow regime.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191088","usgsCitation":"Carlisle, D.M., Grantham, T.E., Eng, K., Wolock, D.M., 2019, Regional-scale associations between indicators of biological integrity and indicators of streamflow modification: U.S. Geological Survey Open-File Report 2019–1088, 10 p., https://doi.org/10.3133/ofr20191088.\n","productDescription":"iv, 10 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097828","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":367467,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O2ZV0M","linkHelpText":"Regional-scale Model Predictions of the Relation Between Biological Integrity and Streamflow Modification"},{"id":367452,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1088/ofr20191088.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1088"},{"id":367451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1088/coverthb.jpg"}],"contact":"Director, USGS Kansas Water Science Center
1217 Biltmore Drive
Lawrence, KS 66049
785-842-9909
Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
While machine learning techniques have been increasingly applied to land cover classification problems, these techniques have not focused on separating exposed bare rock from soil covered areas. Therefore, we built a convolutional neural network (CNN) to differentiate exposed bare rock (rock) from soil cover (other). We made a training dataset by mapping exposed rock at eight test sites across the Sierra Nevada Mountains (California, USA) using USDA’s 0.6 m National Aerial Inventory Program (NAIP) orthoimagery. These areas were then used to train and test the CNN. The resulting machine learning approach classifies bare rock in NAIP orthoimagery with a 0.95
The challenges of providing safe and sustainable water supplies for human and ecological uses and protecting lives and property during water emergencies are well recognized. The U.S. Geological Survey (USGS) plays an essential role in meeting these challenges through its observational networks and renowned water science and research activities (National Academies of Science, Engineering, and Medicine, 2018). Substantial advances in water science, together with emerging breakthroughs in technical and computational capabilities, have led the USGS to develop a Next Generation Water Observing System (NGWOS). The NGWOS will provide real-time data on water quantity and quality in more affordable and rapid ways than previously possible, and in more locations. The data will be served through a modernized USGS National Water Information System that will be coupled to advanced modeling tools to inform daily water operations, decision-making during water emergencies (like floods, droughts, and contaminant spills), assessments of past trends in water quantity and quality, and forecasts of future water availability.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193046","usgsCitation":"Eberts, S.M., Wagner, C.R., and Woodside, M.D., 2019, Water priorities for the Nation—The U.S. Geological Survey Next Generation Water Observing System: U.S. Geological Survey Fact Sheet 2019–3046, 2 p., https://doi.org/10.3133/fs20193046.","productDescription":"2 p.","onlineOnly":"Y","ipdsId":"IP-109916","costCenters":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":366749,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3046/coverthb.jpg"},{"id":367487,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3046/fs20193046.pdf","text":"Report","size":"2.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2019-3046"}],"contact":"U.S. Geological Survey
Water Resources Mission Area
Groundwater and Streamflow Information Program
3916 Sunset Ridge Road
Raleigh, North Carolina 26707
Information concerning the availability, use, and quality of water in Lincoln Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 7.76 million gallons per day (Mgal/d) of water were withdrawn in Lincoln Parish: 7.69 Mgal/d from groundwater sources and 0.07 Mgal/d from surface-water sources. Withdrawals for public-supply use accounted for about 89 percent (6.88 Mgal/d) of the total water withdrawn. Other categories of use included industrial, general irrigation, livestock, and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicate that water withdrawals peaked in 2000 at 11.01 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193019","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2019, Water resources of Lincoln Parish, Louisiana: U.S. Geological Survey Fact Sheet 2019–3019, 6 p., https://doi.org/10.3133/fs20193019.","productDescription":"Report: 6 p; Data Release","onlineOnly":"N","ipdsId":"IP-081706","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":367462,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3019/coverthb.jpg"},{"id":367464,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":367463,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3019/fs20193019.pdf","text":"Report","size":"871 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3019"}],"country":"United States","state":"Louisiana","county":"Lincoln Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.8807,32.5853],[-92.8808,32.5898],[-92.8809,32.5953],[-92.881,32.6003],[-92.875,32.6026],[-92.8723,32.6022],[-92.8669,32.6068],[-92.8655,32.6178],[-92.8645,32.626],[-92.8609,32.6374],[-92.8638,32.6497],[-92.8667,32.6592],[-92.8647,32.6661],[-92.8686,32.6729],[-92.866,32.6788],[-92.8623,32.6871],[-92.8548,32.6926],[-92.8484,32.7009],[-92.8409,32.7106],[-92.835,32.7134],[-92.8284,32.7125],[-92.8287,32.7603],[-92.8014,32.7602],[-92.7757,32.76],[-92.7256,32.7597],[-92.6361,32.7597],[-92.6312,32.7593],[-92.6279,32.7575],[-92.6191,32.7548],[-92.6147,32.7526],[-92.6099,32.7549],[-92.6044,32.7555],[-92.5968,32.7551],[-92.5913,32.7528],[-92.5852,32.7488],[-92.5787,32.748],[-92.5722,32.7489],[-92.5672,32.7453],[-92.5572,32.7331],[-92.5517,32.7268],[-92.5472,32.7205],[-92.5418,32.7187],[-92.5374,32.7206],[-92.5342,32.7224],[-92.5271,32.7202],[-92.5233,32.723],[-92.5195,32.7239],[-92.5188,32.6725],[-92.4736,32.6715],[-92.4153,32.672],[-92.4132,32.5845],[-92.4155,32.4952],[-92.6231,32.497],[-92.6228,32.4747],[-92.6231,32.4537],[-92.7768,32.4548],[-92.8078,32.4545],[-92.8795,32.4541],[-92.8779,32.5202],[-92.8807,32.5853]]]},\"properties\":{\"name\":\"Lincoln\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Winn Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 2.74 million gallons per day (Mgal/d) of water were withdrawn in Winn Parish: 2.69 Mgal/d from groundwater sources and 0.05 Mgal/d from surface-water sources. Withdrawals for public supply accounted for about 71 percent (1.95 Mgal/d) of the total water withdrawn, and industrial use accounted for about 19 percent (0.51 Mgal/d). Other categories of use included rural domestic, livestock, and general irrigation. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2000 at about 3.81 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193022","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2019, Water resources of Winn Parish, Louisiana: U.S. Geological Survey Fact Sheet 2019–3022, 6 p., https://doi.org/10.3133/fs20193022.","productDescription":"Report: 6 p; Data Release","onlineOnly":"N","ipdsId":"IP-081708","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":367459,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3022/coverthb.jpg"},{"id":367460,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3022/fs20193022.pdf","text":"Report","size":"857 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3022"},{"id":367461,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release ","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"}],"country":"United States","state":"Louisiana ","otherGeospatial":"Winn Parish ","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.3114,32.1483],[-92.3129,31.9276],[-92.3126,31.8966],[-92.3136,31.8934],[-92.3201,31.8893],[-92.3216,31.8847],[-92.3194,31.8792],[-92.3221,31.8719],[-92.3241,31.8605],[-92.3295,31.8577],[-92.3354,31.8563],[-92.3375,31.854],[-92.3348,31.8499],[-92.3315,31.8463],[-92.3352,31.8394],[-92.34,31.8307],[-92.3383,31.8239],[-92.3404,31.8207],[-92.3409,31.8157],[-92.3489,31.8092],[-92.3532,31.8083],[-92.3586,31.8073],[-92.359,31.8014],[-92.3628,31.7968],[-92.4043,31.797],[-92.4156,31.7969],[-92.428,31.7972],[-92.4662,31.7969],[-92.6193,31.7978],[-92.6196,31.7836],[-92.6194,31.7686],[-92.6202,31.7101],[-92.6541,31.7098],[-92.6708,31.7096],[-92.8248,31.7102],[-92.9647,31.7098],[-92.9734,31.7147],[-92.9657,31.7354],[-92.9693,31.7473],[-92.973,31.7512],[-92.9733,31.7523],[-92.9742,31.7562],[-92.9742,31.7582],[-92.9735,31.7606],[-92.9723,31.7637],[-92.967,31.7759],[-92.9633,31.7796],[-92.9547,31.7816],[-92.9538,31.7884],[-92.9528,31.7939],[-92.9518,31.7967],[-92.9464,31.8004],[-92.9465,31.8045],[-92.9476,31.8081],[-92.9418,31.8151],[-92.9489,31.8186],[-92.9517,31.8231],[-92.9512,31.8254],[-92.9491,31.8273],[-92.9469,31.8268],[-92.9409,31.8224],[-92.9382,31.8238],[-92.9389,31.8288],[-92.9465,31.8351],[-92.9498,31.8387],[-92.9494,31.8441],[-92.9468,31.8492],[-92.9506,31.8523],[-92.9528,31.8569],[-92.9464,31.8574],[-92.9415,31.8543],[-92.9366,31.8534],[-92.9263,31.8548],[-92.9178,31.8528],[-92.9101,31.851],[-92.9068,31.8553],[-92.904,31.8547],[-92.8992,31.8598],[-92.9067,31.8698],[-92.9025,31.8757],[-92.9033,31.8926],[-92.8943,31.9014],[-92.8971,31.91],[-92.8951,31.9174],[-92.893,31.9197],[-92.8871,31.9243],[-92.8836,31.9378],[-92.8745,31.9427],[-92.8735,31.9459],[-92.8833,31.9535],[-92.8888,31.9562],[-92.8894,31.9612],[-92.8878,31.964],[-92.8852,31.9667],[-92.8836,31.9672],[-92.883,31.9682],[-92.8837,31.9727],[-92.8832,31.9804],[-92.8924,31.9812],[-92.8962,31.9816],[-92.9011,31.9825],[-92.9083,31.9902],[-92.9045,31.9929],[-92.8965,31.9967],[-92.8906,32.0018],[-92.8892,32.0118],[-92.8893,32.0191],[-92.8873,32.0269],[-92.8934,32.0364],[-92.8931,32.0478],[-92.8998,32.0628],[-92.9084,32.0722],[-92.9131,32.0781],[-92.9078,32.0868],[-92.9079,32.0914],[-92.9129,32.0982],[-92.9143,32.1008],[-92.9162,32.104],[-92.9218,32.1117],[-92.9262,32.118],[-92.935,32.1257],[-92.9363,32.138],[-92.938,32.1425],[-92.9402,32.1457],[-92.9407,32.148],[-92.9376,32.148],[-92.8207,32.149],[-92.812,32.1491],[-92.3114,32.1483]]]},\"properties\":{\"name\":\"Winn\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Franklin Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 41.79 million gallons per day (Mgal/d) of water were withdrawn in Franklin Parish: 37.73 Mgal/d from groundwater sources and 4.06 Mgal/d from surface-water sources. Withdrawals for agricultural use—composed of general irrigation, rice irrigation, aquaculture, and livestock—accounted for about 89 percent (37.16 Mgal/d) of the total water withdrawn. Public-supply use accounted for about 3 percent (1.07 Mgal/d); industry accounted for about 7 percent (2.92 Mgal/d); and rural domestic use accounted for about 2 percent (0.64 Mgal/d). Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2005 at more than 50 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193021","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2019, Water resources of Franklin Parish, Louisiana: U.S. Geological Survey Fact Sheet 2019–3021, 6 p., https://doi.org/10.3133/fs20193021.","productDescription":"Report: 6 p; Data Release","onlineOnly":"N","ipdsId":"IP-081698","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":367456,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3021/coverthb.jpg"},{"id":367457,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3021/fs20193021.pdf","text":"Report","size":"0.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3021"},{"id":367458,"rank":3,"type":{"id":30,"text":"Data 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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Madison Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, 50.66 million gallons per day (Mgal/d) of water were withdrawn in Madison Parish: 44.37 Mgal/d from groundwater sources and 6.30 Mgal/d from surface-water sources. Withdrawals for agricultural use—composed of general irrigation, rice irrigation, livestock, and aquaculture—accounted for about 96 percent (48.86 Mgal/d) of the total water withdrawn. Other categories of use included public supply and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2014. The relatively large increase in water use from 2005 to 2010 is largely attributable to a change in the methods used for estimation of general irrigation land usage. General irrigation withdrawals from groundwater increased from 11.13 Mgal/d in 2005 to 28.28 Mgal/d in 2010.
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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Environmental DNA (eDNA) analysis is increasingly being used to detect the presence and relative abundance of rare species, especially invasive or imperiled aquatic species. The rapid progress in the eDNA field has resulted in numerous studies impacting conservation and management actions. However, standardization of eDNA methods and reporting across the field is yet to be fully established, with one area being the calculation and interpretation of assay limit of detection (LOD) and limit of quantification (LOQ).
Here, we propose establishing consistent methods for determining and reporting of LOD and LOQ for single‐species quantitative PCR (qPCR) eDNA studies.
We utilize datasets from multiple cooperating laboratories to demonstrate both a discrete threshold approach and a curve‐fitting modeling approach for determining LODs and LOQs for eDNA qPCR assays. We also provide details of an R script developed and applied for the modeling method.
Ultimately, standardization of how LOD and LOQ are determined, interpreted, and reported for eDNA assays will allow for more informed interpretation of assay results, more meaningful interlaboratory comparisons of experiments, and enhanced capacity for assessing the relative technical quality and performance of different eDNA qPCR assays.
Geoelectric field time series can be estimated by convolving estimates of Earth‐surface impedance, such as those obtained from magnetotelluric survey measurements, with historical records of geomagnetic variation obtained at magnetic observatories. This straightforward procedure permits the mapping of geoelectric field variation during magnetic storms. Statistical analysis of the time series allows extrapolation to extreme‐value amplitudes, such as might be realized during an intense magnetic storm in the future. The development of these products is illustrated for the Mid‐Atlantic United States, using impedances obtained from EarthScope survey data and geomagnetic variation records obtained at the Fredericksburg observatory operated by the U.S. Geological Survey. For this region, 100‐year geoelectric exceedance amplitudes have a range of almost three orders of magnitude (from 0.04 V/km at a site in southern Pennsylvania to 24.29 V/km at a site in central Virginia), and they have significant geographic granularity, which is due to site‐to‐site differences in surface impedance (and subsurface electrical conductivity structure). Maps of 100‐year exceedance amplitudes resemble those of geoelectric amplitudes for the March 1989 magnetic storm, and, in that sense, the March 1989 storm resembles what might be loosely called a “100‐year” event.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geomagnetically induced currents from the sun to the power grid","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Geophysical Union","doi":"10.1002/9781119434412.ch6","usgsCitation":"Love, J.J., Bedrosian, P.A., Kelbert, A., and Lucas, G.M., 2019, Empirical estimation of natural geoelectric hazards, chap. 6 of Geomagnetically induced currents from the sun to the power grid, p. 95-105, https://doi.org/10.1002/9781119434412.ch6.","productDescription":"11 p.","startPage":"95","endPage":"105","ipdsId":"IP-103190","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":369882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":761901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":761902,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":761903,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lucas, Greg M. 0000-0003-1331-1863","orcid":"https://orcid.org/0000-0003-1331-1863","contributorId":202808,"corporation":false,"usgs":true,"family":"Lucas","given":"Greg","email":"","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":761904,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207301,"text":"70207301 - 2019 - Monitoring plans for Louisiana’s system-wide assessment and monitoring program (SWAMP). Version IV","interactions":[],"lastModifiedDate":"2019-12-16T12:34:16","indexId":"70207301","displayToPublicDate":"2019-09-16T11:54:07","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":"Monitoring plans for Louisiana’s system-wide assessment and monitoring program (SWAMP). Version IV","docAbstract":"The System-Wide Assessment and Monitoring Program (SWAMP) has been envisioned as a long-term monitoring program to ensure a comprehensive network of coastal data collection activities is in place to support the development, implementation, and adaptive management of the coastal protection and restoration program within coastal Louisiana. The Coastwide Reference Monitoring System (CRMS) and Barrier Island Comprehensive Monitoring (BICM) programs have been implemented under SWAMP, while other aspects of system dynamics, including offshore and inland water-body boundary conditions, nontidal freshwater habitats, riverine conditions, risk status, and protection performance, are not presently the subject of CPRA-coordinated (Coastal Protection and Restoration Authority) monitoring. In order to implement these additional aspects of SWAMP, CPRA partnered with The Water Institute of the Gulf and others to develop 1) a programmatic monitoring plan for evaluating the effectiveness of the coastal protection and restoration program on a coastwide scale, and 2) basinwide monitoring plans that will incorporate the elements of the programmatic plan with specific data collection activities designed to capture effects within the basin. Monitoring plans were developed for Barataria Basin, Pontchartrain Region (includes Breton Sound, Pontchartrain and Mississippi River Delta Basins), and the western basins (Calcasieu-Sabine, Mermentau, Teche-Vermilion, Atchafalaya, and Terrebonne) for both the natural and human systems using a process to identify the monitoring variables, objectives, and sampling design. The monitoring variables and objectives identified fall under the general categories of weather and climate, biotic integrity, water quality, hydrology, physical terrain, population and demographics, housing and community characteristics, economy and employment, ecosystem dependency, residential properties protection, and critical infrastructure and essential services protection. A rigorous statistical analysis, examination of modeling needs, and thorough reviews of previous planning and monitoring efforts were conducted to develop the sampling designs for the natural and human system monitoring plans. The plan relies heavily on the use of existing data, thus, coordination with other agencies (e.g., LDEQ , LDWF) and CPRA’s existing monitoring programs (e.g., BICM, CRMS) is critical to the plan’s success. Implementation of the plans will require development of quality control and quality assurance protocols, specific standardized operating procedures for each of the data collection efforts, a data management plan, and a reporting framework to contribute to decision making and reducing uncertainty in management actions.
","language":"English","publisher":"Water Institute of the Gulf","usgsCitation":"Hemmerling, S., Baustian, M., Bienn, H., Dausman, A., Grace, A., Grimley, L., McInnis, A., Vingiello, M., Vu, H., Sable, S., Gentile, B., Lafargue, P., Hijuelos, A., Piazza, S., Stagg, C., Raynie, R., Haywood, E., and Khalid, S., 2019, Monitoring plans for Louisiana’s system-wide assessment and monitoring program (SWAMP). Version IV: Cooperator Report, xiii, 235 p.","productDescription":"xiii, 235 p.","ipdsId":"IP-109812","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":370311,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cims.coastal.louisiana.gov/RecordDetail.aspx?Root=0&sid=23567"},{"id":370312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.71337890625,\n 30.282788098216884\n ],\n [\n -93.702392578125,\n 30.12612436422458\n ],\n [\n -93.75732421875,\n 30.0262995822237\n ],\n [\n -93.91113281249999,\n 29.79298413547051\n ],\n [\n -93.8177490234375,\n 29.6880527498568\n ],\n [\n -93.22448730468749,\n 29.754839972510933\n ],\n [\n 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0000-0003-0922-6754","orcid":"https://orcid.org/0000-0003-0922-6754","contributorId":201525,"corporation":false,"usgs":true,"family":"Hijuelos","given":"Ann","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":777626,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Piazza, Sarai 0000-0001-6962-9008 piazzas@usgs.gov","orcid":"https://orcid.org/0000-0001-6962-9008","contributorId":169024,"corporation":false,"usgs":true,"family":"Piazza","given":"Sarai","email":"piazzas@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":777627,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":206064,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":777628,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Raynie, Richard C","contributorId":140898,"corporation":false,"usgs":false,"family":"Raynie","given":"Richard C","affiliations":[{"id":13608,"text":"Louisiana Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":777629,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Haywood, Edward","contributorId":194452,"corporation":false,"usgs":false,"family":"Haywood","given":"Edward","email":"","affiliations":[],"preferred":false,"id":777630,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Khalid, Syed","contributorId":221283,"corporation":false,"usgs":false,"family":"Khalid","given":"Syed","email":"","affiliations":[],"preferred":false,"id":777631,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70207519,"text":"70207519 - 2019 - The first 3D conductivity model of the contiguous US: Reflections on geologic structure and application to induction hazards","interactions":[],"lastModifiedDate":"2019-12-23T07:54:17","indexId":"70207519","displayToPublicDate":"2019-09-16T07:47:49","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"8","title":"The first 3D conductivity model of the contiguous US: Reflections on geologic structure and application to induction hazards","docAbstract":"Estimation of ground level geoelectric fields has been identified by the National Space Weather Action Plan as a key component of assessment and mitigation of space weather impacts on critical infrastructure. Estimates of spatially and temporally variable electric fields are used to generate statistically based hazard maps and show promise toward monitoring and responding to geomagnetic disturbances in near real‐time. One approach to geoelectric field estimation is to employ three‐dimensional (3D) Earth conductivity models. These data‐constrained conductivity models are the results of regional magnetotelluric inversions based primarily on NSF’s Earthscope USArray impedances, which to date cover ~60% of the contiguous United States. Here, we present the first‐ever composite conductivity model of the contiguous United States and describe its compilation from 3D regional conductivity models, a global mantle conductivity model, offshore bathymetry, and sediment thickness data. We discuss structures within the conductivity model and how they relate to the complex geologic tapestry of the continent. Finally, we discuss the utility of this synthesis model for estimation and mitigation of geomagnetically induced currents.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geomagnetically induced currents from the Sun to the power grid","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Geophysical Union","doi":"10.1002/9781119434412.ch8","usgsCitation":"Kelbert, A., Bedrosian, P.A., and Murphy, B., 2019, The first 3D conductivity model of the contiguous US: Reflections on geologic structure and application to induction hazards, chap. 8 of Geomagnetically induced currents from the Sun to the power grid, p. 127-151, https://doi.org/10.1002/9781119434412.ch8.","productDescription":"25 p.","startPage":"127","endPage":"151","ipdsId":"IP-100625","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":370629,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.47656249999999,\n 32.24997445586331\n ],\n [\n -95.2734375,\n 23.885837699862005\n ],\n [\n -85.78125,\n 29.84064389983441\n ],\n [\n -79.8046875,\n 23.563987128451217\n ],\n [\n -79.8046875,\n 29.22889003019423\n ],\n [\n -74.8828125,\n 34.016241889667015\n ],\n [\n -66.09375,\n 42.293564192170095\n ],\n [\n -67.8515625,\n 47.27922900257082\n ],\n [\n -81.9140625,\n 42.293564192170095\n ],\n [\n -83.671875,\n 46.800059446787316\n ],\n [\n -93.1640625,\n 48.922499263758255\n ],\n [\n -122.6953125,\n 50.064191736659104\n ],\n [\n -125.5078125,\n 48.922499263758255\n ],\n [\n -126.5625,\n 40.17887331434696\n ],\n [\n -118.47656249999999,\n 32.24997445586331\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":778346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":778347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Benjamin S. 0000-0001-7636-3711","orcid":"https://orcid.org/0000-0001-7636-3711","contributorId":221483,"corporation":false,"usgs":false,"family":"Murphy","given":"Benjamin S.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":778348,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208839,"text":"70208839 - 2019 - Moving at the speed of flight: Dabbling duck-movement rates and the relationship with electronic tracking interval","interactions":[],"lastModifiedDate":"2020-03-03T07:37:16","indexId":"70208839","displayToPublicDate":"2019-09-16T07:33:37","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3777,"text":"Wildlife Research","active":true,"publicationSubtype":{"id":10}},"title":"Moving at the speed of flight: Dabbling duck-movement rates and the relationship with electronic tracking interval","docAbstract":"Context. Effective wildlife management requires information on habitat and resource needs, which can be estimated with movement information and modelling energetics. One necessary component of avian models is flight speeds at multiple temporal scales. Technology has limited the ability to accurately assess flight speeds, leading to estimates of questionable accuracy, many of which have not been updated in almost a century.\n\nAims. We aimed to update flight speeds of ducks, and differentiate between migratory and non-migratory flight speeds, a detail that was unclear in previous estimates. We also analysed the difference in speeds of migratory and non-migratory flights, and quantified how data collected at different temporal intervals affected estimates of flight speed.\n\nMethods. We tracked six California dabbling duck species with high spatio-temporal resolution GPS–GSM transmitters, calculated speeds of different flight types, and modelled how estimates varied by flight and data interval (30 min to 6 h).\n\nKey results. Median migratory speeds were faster (but non-significant) for the larger mallard (Anas platyrhynchos; 82.5 km h–1), northern pintail (Anas acuta; 79.0 km h–1) and gadwall (Mareca strepera; 70.6 km h–1), than the smaller-bodied northern shoveler (Spatula clypeata; 65.7 km h–1), cinnamon teal (Spatula cyanoptera; 63.5 km h–1) and American wigeon (Mareca Americana; 52 km h–1). Migratory flights were faster than non-migratory flights for all species and speeds were consistently slower with an increasing data interval.\n\nImplications. The need to balance time and energy requirements may drive different speeds for migratory and non-migratory flights. Lower speeds at longer intervals are likely to be due to a greater proportion of ‘loafing’ time included in flighted segments, demonstrating that data acquired at different intervals provide a means to evaluate and estimate behaviours that influence speed estimation. Shorter-interval data should be the most accurate, but longer-interval data may be easier to collect over lengthier timeframes, so it may be expedient to trade-off a degree of accuracy in broad-scale studies for the larger dataset. Our updated flight speeds for dabbling duck species can be used to parameterise and validate energetics models, guide management decisions regarding optimal habitat distribution, and, ultimately, improve conservation management of wetlands for waterfowl.","language":"English","publisher":"CSIRO","doi":"10.1071/WR19028","usgsCitation":"McDuie, F., Casazza, M.L., Keiter, D.A., Overton, C.T., Herzog, M.P., Feldheim, C.L., and Ackerman, J., 2019, Moving at the speed of flight: Dabbling duck-movement rates and the relationship with electronic tracking interval: Wildlife Research, v. 46, no. 6, p. 533-543, https://doi.org/10.1071/WR19028.","productDescription":"11 p.","startPage":"533","endPage":"543","ipdsId":"IP-102666","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":459806,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr19028","text":"Publisher Index Page"},{"id":372828,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McDuie, Fiona 0000-0002-1948-5613","orcid":"https://orcid.org/0000-0002-1948-5613","contributorId":222936,"corporation":false,"usgs":true,"family":"McDuie","given":"Fiona","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keiter, David A.","contributorId":176521,"corporation":false,"usgs":false,"family":"Keiter","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":783586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Overton, Cory T. 0000-0002-5060-7447 coverton@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-7447","contributorId":3262,"corporation":false,"usgs":true,"family":"Overton","given":"Cory","email":"coverton@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783587,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783588,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Feldheim, Cliff L.","contributorId":206561,"corporation":false,"usgs":false,"family":"Feldheim","given":"Cliff","email":"","middleInitial":"L.","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":783589,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":783590,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70205360,"text":"70205360 - 2019 - Characterization and evaluation of controls on post-fire streamflow response across western U.S. watersheds","interactions":[],"lastModifiedDate":"2019-09-16T09:14:57","indexId":"70205360","displayToPublicDate":"2019-09-15T23:03:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Characterization and evaluation of controls on post-fire streamflow response across western U.S. watersheds","docAbstract":"This research investigates the impact of wildfires on watershed flow regimes, specifically focusing on evaluation of fire events within specified hydroclimatic regions in the western United States, and evaluating the impact of climate and geophysical variables on response. Eighty-two watersheds were identified with at least 10 years of continuous pre-fire daily streamflow records and 5 years of continuous post-fire daily flow records. Percent change in annual runoff ratio, low flows, high flows, peak flows, number of zero flow days, baseflow index, and Richards–Baker flashiness index were calculated for each watershed using pre- and post-fire periods. Independent variables were identified for each watershed and fire event, including topographic, vegetation, climate, burn severity, percent area burned, and soils data. \n\nResults show that low flows, high flows, and peak flows increase in the first 2 years following a wildfire and decrease over time. Relative response was used to scale response variables with the respective percent area of watershed burned in order to compare regional differences in watershed response. To account for variability in precipitation events, runoff ratio was used to compare runoff directly to PRISM precipitation estimates. To account for regional differences in climate patterns, watersheds were divided into nine regions, or clusters, through k-means clustering using climate data, and regression models were produced for watersheds grouped by total area burned. Watersheds in Cluster 9 (eastern California, western Nevada, Oregon) demonstrate a small negative response to observed flow regimes after fire. Cluster 8 watersheds (coastal California) display the greatest flow responses, typically within the first year following wildfire. Most other watersheds show a positive mean relative response. In addition, simple regression models show low correlation between percent watershed burned and streamflow response, implying that other watershed factors strongly influence response. \n\nSpearman correlation identified NDVI, aridity index, percent of a watershed's precipitation that falls as rain, and slope as being positively correlated with post-fire streamflow response. This metric also suggested a negative correlation between response and the soil erodibility factor, watershed area, and percent low burn severity. Regression models identified only moderate burn severity and watershed area as being consistently positively/negatively correlated, respectively, with response. The random forest model identified only slope and percent area burned as significant watershed parameters controlling response. \n\nResults will help inform post-fire runoff management decisions by helping to identify expected changes to flow regimes, as well as facilitate parameterization for model application in burned watersheds.","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-22-1221-2018","usgsCitation":"Saxe, S., Hogue, T.S., and Hay, L., 2019, Characterization and evaluation of controls on post-fire streamflow response across western U.S. watersheds: Hydrology and Earth System Sciences, v. 22, no. 2, p. 1221-1237, https://doi.org/10.5194/hess-22-1221-2018.","productDescription":"17 p.","startPage":"1221","endPage":"1237","numberOfPages":"17","ipdsId":"IP-090164","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":459812,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-22-1221-2018","text":"Publisher Index Page"},{"id":367422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"22","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Saxe, Samuel 0000-0003-1151-8908","orcid":"https://orcid.org/0000-0003-1151-8908","contributorId":215753,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":770931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":770932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hay, Lauren E. 0000-0003-3763-4595","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":211478,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":770930,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205904,"text":"70205904 - 2019 - A process-based model of pre-eruption seismicity patterns and its use for eruption forecasting at dormant stratovolcanoes","interactions":[],"lastModifiedDate":"2019-10-28T14:27:48","indexId":"70205904","displayToPublicDate":"2019-09-15T12:48:21","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"A process-based model of pre-eruption seismicity patterns and its use for eruption forecasting at dormant stratovolcanoes","docAbstract":"Using long-term data sets, the eruptive size, timing and style can typically be successfully forecast for dormant volcanoes using current monitoring data and knowledge of precursory eruptive patterns. The USAID-USGS Volcano Disaster Assistance Program (VDAP) utilizes seismic data over multiple eruption cycles and across volcano types to successfully forecast eruption size, style and onset times, as well as changes within ongoing eruptions. From millions of seismic event observations, we show that seismic data, a proxy for rate-dependent strain changes in the magmatic system, combined with a process-based conceptual geologic model enables eruption forecasting, especially for stratovolcanoes dormant >20 years. We show that this model often works for some open systems, ones that sufficiently reseal to plug the conduit, and also where a deep intrusion occurs after phreatic or phreatomagmatic explosions have begun. This model is divided into four seismicity and geologic stages. We describe each of the four stages of seismicity in detail and how they relate to the ongoing geologic process of that stage: Stage 1. Deep intrusion and seismicity occurring at 10 to 40 + km depth below the volcanic edifice; Stage 2. Distal VT seismicity induced at distances from 2 to >30 km laterally from the summit by magma intruding into and inflating the upper-crustal magma storage region; Stage 3. Vent-clearing seismicity associated with the initial vent creation and clearing phase of eruptive activity. Stage 4. Repetitive event seismicity associated with the final ascent of magma from 2 km to the surface. We present 36 examples of this seismic progression from 26 volcanoes. We believe that this progression generally correlates with the magma flux and the eventual explosivity and is invaluable for forecasting eruptions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2019.03.004","usgsCitation":"White, R.A., and McCausland, W.A., 2019, A process-based model of pre-eruption seismicity patterns and its use for eruption forecasting at dormant stratovolcanoes: Journal of Volcanology and Geothermal Research, v. 382, p. 267-297, https://doi.org/10.1016/j.jvolgeores.2019.03.004.","productDescription":"31 p.","startPage":"267","endPage":"297","ipdsId":"IP-082435","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":459815,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2019.03.004","text":"Publisher Index Page"},{"id":368169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"382","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Randall A. 0000-0003-4074-8577 rwhite@usgs.gov","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":1993,"corporation":false,"usgs":true,"family":"White","given":"Randall","email":"rwhite@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":772819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCausland, Wendy A. 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":204380,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":772820,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205521,"text":"70205521 - 2019 - Relations of dissolved-oxygen variability, selected field constituents, and metabolism estimates to land use and nutrients in high-gradient Boston Mountain streams, Arkansas","interactions":[],"lastModifiedDate":"2020-09-02T12:43:52.51217","indexId":"70205521","displayToPublicDate":"2019-09-14T12:26:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Relations of dissolved-oxygen variability, selected field constituents, and metabolism estimates to land use and nutrients in high-gradient Boston Mountain streams, Arkansas","docAbstract":"Continuous monitoring data can be extremely useful for assessing water quality conditions particularly for variables that exhibit dynamic diel swings such as dissolved oxygen. As a means of evaluating dissolved oxygen criteria used by the Arkansas Department of Environmental Quality (ADEQ) for assessing this stream class, we compared continuous dissolved oxygen (DO) data collected at five small- to moderate-sized (watersheds 10-100 mi2), high-gradient streams in the Boston Mountains distributed across a land-use and nutrient condition gradient. The current DO criteria employed by ADEQ for Boston Mountains streams >10 mi2 consists of both an exceedance rate and a magnitude, in which, streams may be considered 'impaired' if greater than 10% of DO measurements during a period of record are < 6 mg/L. The 10% exceedance rate, however, is a commonly used “default” value that requires independent testing for different ecoregion stream classifications. Our findings for the five Boston Mountain streams fit a general pattern established for other aquatic systems (e.g. larger streams, low-gradient streams, and lakes) where increasing land-use intensity generally results in increased nutrient concentrations, which can lead to stream eutrophication and increased DO variability. DO concentrations were < 6 mg/L for fewer than 4% of measurements at the two sites identified “a priori” as least disturbed by nutrient and land-use indices, while concentrations at the three sites identified as moderately and most disturbed were < 6 mg/L for 20 to 33% of measurements. These findings demonstrate that the 10% exceedance rate currently employed by ADEQ was effective at identifying various degrees of DO impairment in Boston Mountain streams. Our analysis also demonstrated that continuous pH and specific conductance data and estimates of stream metabolism were helpful for associating DO variability to anthropogenic or natural origins. Considerations that were useful for examining these relationships and evaluating ADEQ’s DO criteria should be applicable to DO studies in other locations where stream and geologic characteristics are like those of the Boston Mountains.
","language":"English","publisher":"Springer","doi":"10.1007/s10661-019-7737-0","usgsCitation":"Justus, B., Driver, L., Green, J., and Wentz, N., 2019, Relations of dissolved-oxygen variability, selected field constituents, and metabolism estimates to land use and nutrients in high-gradient Boston Mountain streams, Arkansas: Environmental Monitoring and Assessment, v. 10, no. 191, 632, 18 p., https://doi.org/10.1007/s10661-019-7737-0.","productDescription":"632, 18 p.","ipdsId":"IP-082531","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":367635,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Big Creek stream, Illinois Bayou stream, South Fork Little Red stream, Town Branch stream, White River 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\"}}]}","volume":"10","issue":"191","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Justus, Billy 0000-0002-3458-9656 bjustus@usgs.gov","orcid":"https://orcid.org/0000-0002-3458-9656","contributorId":202148,"corporation":false,"usgs":true,"family":"Justus","given":"Billy","email":"bjustus@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driver, Lucas 0000-0003-2549-1849","orcid":"https://orcid.org/0000-0003-2549-1849","contributorId":219176,"corporation":false,"usgs":true,"family":"Driver","given":"Lucas","email":"","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, J.J.","contributorId":219175,"corporation":false,"usgs":false,"family":"Green","given":"J.J.","email":"","affiliations":[{"id":39966,"text":"Arkansas Dept Env. 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Quality","active":true,"usgs":false}],"preferred":false,"id":771496,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205385,"text":"70205385 - 2019 - Physically based estimation of rainfall thresholds triggering shallow landslides in volcanic slopes of southern Italy","interactions":[],"lastModifiedDate":"2019-09-17T08:50:30","indexId":"70205385","displayToPublicDate":"2019-09-14T08:49:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Physically based estimation of rainfall thresholds triggering shallow landslides in volcanic slopes of southern Italy","docAbstract":"On the 4th and 5th of March 2005, about 100 rainfall-induced landslides occurred along volcanic slopes of Camaldoli Hill in Naples, Italy. These started as soil slips in the upper substratum of incoherent and welded volcaniclastic deposits, then evolved downslope according to debris avalanche and debris flow mechanisms. This specific case of slope instability on complex volcaniclastic deposits remains poorly characterized and understood, although similar shallow landsliding phenomena have largely been studied in other peri-volcanic areas of the Campania region underlain by carbonate bedrock. Considering the landslide hazard in this urbanized area, this study focused on quantitatively advancing the understanding of the predisposing factors and hydrological conditions contributing to the initial landslide triggering. Borehole drilling, trial pits, dynamic penetrometer tests, topographic surveys, and infiltration tests were conducted on a slope sector of Camaldoli Hill to develop a geological framework model. Undisturbed soil samples were collected for laboratory testing to further characterize hydraulic and geotechnical properties of the soil units identified. In situ soil pressure head monitoring probes were also installed. A numerical model of two-dimensional variably saturated subsurface water flow was parameterized for the monitored hillslope using field and laboratory data. Based on the observed soil pressure head dynamics, the model was calibrated by adjusting the evapotranspiration parameters. This physically based hydrologic model was combined with an infinite-slope stability analysis to reconstruct the critical unsaturated/saturated conditions leading to slope failure. This coupled hydromechanical numerical model was then used to determine intensity–duration (I-D) thresholds for landslide initiation over a range of plausible rainfall intensities and topographic slope angles for the region. The proposed approach can be conceived as a practicable method for defining a warning criterion in urbanized areas threatened by rainfall-induced shallow landslides, given the unavailability of a consistent inventory of past landslide events that prevents a rigorous empirical analysis.","language":"English","publisher":"MDPI","doi":"10.3390/w11091915","usgsCitation":"Fusco, F., De Vita, P., Mirus, B.B., Baum, R.L., Allocca, V., Tufano, R., and Calcaterra, D., 2019, Physically based estimation of rainfall thresholds triggering shallow landslides in volcanic slopes of southern Italy: Water, v. 11, no. 9, Article 1915, https://doi.org/10.3390/w11091915.","productDescription":"Article 1915","ipdsId":"IP-102857","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":459825,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w11091915","text":"Publisher Index Page"},{"id":367450,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[15.52038,38.23116],[15.16024,37.44405],[15.3099,37.13422],[15.09999,36.61999],[14.33523,36.99663],[13.82673,37.10453],[12.431,37.61295],[12.57094,38.12638],[13.74116,38.03497],[14.76125,38.14387],[15.52038,38.23116]]],[[[9.21001,41.20999],[9.80998,40.50001],[9.66952,39.17738],[9.21482,39.24047],[8.80694,38.90662],[8.4283,39.17185],[8.38825,40.37831],[8.16,40.95001],[8.70999,40.89998],[9.21001,41.20999]]],[[[12.37649,46.76756],[13.80648,46.50931],[13.69811,46.01678],[13.93763,45.59102],[13.14161,45.73669],[12.32858,45.38178],[12.38387,44.88537],[12.26145,44.60048],[12.58924,44.09137],[13.52691,43.58773],[14.02982,42.76101],[15.14257,41.95514],[15.92619,41.96132],[16.1699,41.74029],[15.88935,41.54108],[16.785,41.17961],[17.51917,40.87714],[18.37669,40.35562],[18.48025,40.16887],[18.29339,39.81077],[17.73838,40.27767],[16.8696,40.44223],[16.44874,39.7954],[17.17149,39.4247],[17.05284,38.90287],[16.63509,38.84357],[16.10096,37.9859],[15.68409,37.90885],[15.68796,38.21459],[15.89198,38.75094],[16.10933,38.96455],[15.71881,39.54407],[15.41361,40.04836],[14.9985,40.17295],[14.70327,40.60455],[14.06067,40.78635],[13.62799,41.18829],[12.88808,41.25309],[12.10668,41.70453],[11.19191,42.35543],[10.51195,42.93146],[10.20003,43.92001],[9.70249,44.03628],[8.88895,44.36634],[8.42856,44.23123],[7.85077,43.76715],[7.43518,43.69384],[7.5496,44.1279],[7.00756,44.25477],[6.74996,45.02852],[7.09665,45.3331],[6.80236,45.70858],[6.84359,45.99115],[7.27385,45.77695],[7.75599,45.82449],[8.31663,46.16364],[8.48995,46.00515],[8.96631,46.03693],[9.18288,46.44021],[9.92284,46.3149],[10.36338,46.48357],[10.4427,46.89355],[11.04856,46.75136],[11.16483,46.94158],[12.15309,47.11539],[12.37649,46.76756]]]]},\"properties\":{\"name\":\"Italy\"}}]}","volume":"11","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Fusco, F. 0000-0002-6271-2228","orcid":"https://orcid.org/0000-0002-6271-2228","contributorId":219005,"corporation":false,"usgs":false,"family":"Fusco","given":"F.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":770977,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Vita, P.","contributorId":219006,"corporation":false,"usgs":false,"family":"De Vita","given":"P.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":770978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":770979,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":770980,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allocca, V.","contributorId":149077,"corporation":false,"usgs":false,"family":"Allocca","given":"V.","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":770981,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tufano, R.","contributorId":219007,"corporation":false,"usgs":false,"family":"Tufano","given":"R.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":770982,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Calcaterra, D. 0000-0002-3480-3667","orcid":"https://orcid.org/0000-0002-3480-3667","contributorId":219008,"corporation":false,"usgs":false,"family":"Calcaterra","given":"D.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":770983,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70205868,"text":"70205868 - 2019 - A multi-indicator spatial similarity approach for evaluating ecological restoration scenarios","interactions":[],"lastModifiedDate":"2019-11-13T13:47:55","indexId":"70205868","displayToPublicDate":"2019-09-13T17:24:13","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A multi-indicator spatial similarity approach for evaluating ecological restoration scenarios","docAbstract":"The greater Everglades region in Florida (USA) is an area of wetlands that has been altered and reduced to 50% of its original area and faces multiple threats. Spatial landscape analysis can help guide a large and complex ecosystem restoration process, involving billions of dollars and multiple groups of stakeholders.
To guide Everglades restoration efforts, we evaluated ecological performance of different hydrologic restoration scenarios using a novel technique, the structural similarity index (SSIM), which quantitatively compares similarity between pairs of gridded maps in terms of mean, variance, and covariance.
Using the SSIM, we evaluated system-wide performance of apple snails, American alligators, Great egrets, and long- and short-hydroperiod vegetation types under multiple restoration scenarios that varied in water management strategies, amounts of water storage, removal of levees and canals (decompartmentalization), and seepage control barriers. We then compared species and habitat responses under each restoration scenario to a target scenario simulating the historical, natural system.
The SSIM approach provides a reliable means of scenario comparison, accounting for both the local magnitude and spatial structure of the underlying data. Our results demonstrated that decompartmentalization benefits the indicator species. In general, scenarios with increased water storage were closer to the target scenario.
This spatial comparison technique is useful for evaluating restoration efforts at multiple spatial scales, ranging from the entire ecosystem down to individual compartments or sub-compartments. The results can be used to inform management and restoration efforts and to guide policy for the greater Everglades area.