A field study was conducted to estimate survival of juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, Oregon, during 2018. The study consisted of releasing three groups of genetically-marked fish into the reservoir, and sampling them monthly. Juveniles were released during April 10–13 (116,708 fish), May 15–18 (31,911 fish), and June 19–20 (11,758 fish). Reservoir sampling began in May and occurred monthly through October, consisting of 5-day events where juvenile Chinook salmon were collected using electrofishing, shoreline traps, and gill nets. Data were analyzed using a staggered release-recovery model and a parentage-based tagging (PBT) N-mixture model. The staggered release-recovery model provided survival estimates from three periods: mid-April to mid-May (SSRRM1); mid-May to mid-June (SSRRM2); and mid-April to mid-June (SSRRM12). Multiple estimates of survival were possible for each period using different combinations of recovery data from the three groups of fish that were released. Survival probability estimates for SSRRM1 ranged from 0.98520 to 0.98954; estimates for SSRRM2 ranged from 0.09338 to 0.62142; and the estimate for cumulative survival from mid-April to mid-June (SSRRM12) were 0.75211. We suspect that issues with release groups in May (R2) and June (R3) led to biased survival results using the staggered release-recovery model. The PBT N-mixture model provided survival estimates from six periods: mid-April to mid-May (SNMIX1); mid-May to mid-June (SNMIX2), mid-June to mid-July (SNMIX3), mid-July to mid-August (SNMIX4), mid-August to mid-September (SNMIX5); and mid-September to mid-October (SNMIX6). Survival estimates from the PBT N-mixture model were lowest for SNMIX6 (0.41620) and highest for SNMIX1 (0.79587). These results differed from those in 2017 when monthly survival increased across months. This suggests that one or more factors could have affected juvenile Chinook salmon survival in Lookout Point Reservoir. One possible factor could be copepods (which were highly prevalent on juvenile Chinook salmon during summer 2018), but environmental factors such as reserveroir elevation, discharge at Lookout Point Dam, and fish distributions within the reservoir differed between study years. Two PBT N-mixture models provided cumulative survival estimates from mid-April to mid-October. Estimates from the two models were 0.061 and 0.039, which suggests that survival of subyearling Chinook salmon in Lookout Point Reservoir was very low in 2018. Additional research is recommended to better understand inter-annual variability of subyearling Chinook salmon in the reservoir and to gain insights into factors that affect their survival.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191097","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and Oregon State University","usgsCitation":"Kock, T.J., Perry, R.W., Hansen, G.S., Haner, P.V., Pope, A.C., Plumb, J.M., Cogliati, K.M., and Hansen, A.C., 2019, Juvenile Chinook salmon (Oncorhynchus tshawytscha) survival in Lookout Point Reservoir, Oregon, 2018: U.S. Geological Survey Open-File Report 2019–1097, 41 p., https://doi.org/10.3133/ofr20191097.","productDescription":"vi, 41 p.","onlineOnly":"Y","ipdsId":"IP-108747","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":366974,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1097/coverthb.jpg"},{"id":366975,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1097/ofr20191097.pdf","text":"Report","size":"6.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1097"}],"country":"United States","state":"Oregon","otherGeospatial":"Lookout Point Reservoir, Middle Fork Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.8165054321289,\n 43.80009302166679\n ],\n [\n -122.55970001220705,\n 43.80009302166679\n ],\n [\n -122.55970001220705,\n 43.93919224634882\n ],\n [\n -122.8165054321289,\n 43.93919224634882\n ],\n [\n -122.8165054321289,\n 43.80009302166679\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
Fresh volcanic eruption deposits tend to be loose, bare, and readily resuspended by wind. Major resuspension events in Patagonia, Iceland, and Alaska have lofted ash clouds with potential to impact aircraft, infrastructure, and downwind communities. However, poor constraints on this resuspension process limit our ability to model this phenomenon. Here, we present laboratory experiments measuring threshold shear velocities and emission rates of resuspended ash under different environmental conditions, including relative humidity of 25–75% and simulated rainfall with subsequent drying. Eruption deposits were replicated using ash collected from two major eruptions: the 18 May 1980 eruption of Mount St. Helens and the 1912 eruption of Novarupta, in Alaska's Valley of Ten Thousand Smokes. Samples were conditioned in a laboratory chamber and prepared with bulk deposit densities of 1,300–1,500 kg/m3. A control sample of dune sand was included for comparison. The deposits were subjected to different wind speeds using a modified PI‐SWERL® instrument. Under a constant relative humidity of 50% and shear velocities 0.4–0.8 m/s, PM10 emission by resuspension ranged from 10 to >100 mg·m−2·s−1. Addition of liquid water equivalent to 5 mm of rainfall had little lasting effect on Mount St. Helens wind erosion potential, while the Valley of Ten Thousand Smokes deposits exhibited lower emissions for at least 12 days. The results indicate that particle resuspension due to wind erosion from ash deposits potentially exceeds that of most desert surfaces and approaches some of the highest emissions ever measured.
Hyperalkaline (pH greater than 12) ponds and groundwater exist at Big Marsh near Lake Calumet, Chicago, Illinois, a site used by the steel industry during the mid-1900s to deposit steel- and iron-making waste, in particular, slag. The hyperalkaline ponds may pose a hazard to human health and the environment. The U.S. Geological Survey (USGS), in cooperation with the Environmental Protection Agency (EPA) and in collaboration with the City of Chicago’s Park District, completed a study to evaluate the hydrologic balance, water quality, and chemical-mass balance of hyperalkaline ponds at Big Marsh and geochemical modeling used to evaluate remediation options for water quality at the site based on data collected in 2016–17.
Synoptic measurements of surface-water and groundwater elevations were used to determine flow directions and to enable a preliminary estimate of the hydrologic balance for the ponds. Water-quality samples also were collected and analyzed for selected constituents including major anions and cations, nutrients, metals, and trace elements. The results of the water-quality analyses were used to develop a geochemical model to evaluate concentrations, factors affecting pH, and the state of equilibrium between surface waters and atmospheric carbon dioxide. The geochemical model was used to evaluate remediation scenarios using riprap, spillways, or active aeration. The results indicate that active aeration will decrease the pH to near 7.5 in about 8 hours, the fastest rate of the scenarios. Passive aeration, such as riprap or spillways, also can be effective at decreasing the pH in about 45 hours, but spatial obstacles limit their implementation. Seasonal variations in temperature also affect the rate of equilibration, where colder temperatures may have a lower pH than warmer temperatures and may affect the timing and frequency of remediation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195078","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency, Brownfields Program, and in collaboration with the City of Chicago’s Park District","usgsCitation":"Gahala, A.M., Seal, R.R., and Piatak, N.M., 2019, Hydrologic balance, water quality, chemical-mass balance, and geochemical modeling of hyperalkaline ponds at Big Marsh, Chicago, Illinois, 2016–17: U.S. Geological Survey Scientific Investigations Report 2019–5078, 31 p., https://doi.org/10.3133/sir20195078.","productDescription":"Report: vi, 31 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-091826","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":366917,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5078/sir20195078.pdf","text":"SIR 2019–5078","size":"3.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5078"},{"id":366918,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VUAQ35","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Water level data from single-well (slug) tests at a monitoring well in Big Marsh, Chicago, Illinois"},{"id":366916,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5078/coverthb.jpg"}],"country":"United States","state":"Illinois","county":"Cook County","city":"Chicago","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-88.1992,42.1555],[-88.1218,42.1561],[-88.0042,42.1557],[-88.0042,42.157],[-87.886,42.1552],[-87.7659,42.155],[-87.7572,42.1548],[-87.753,42.1502],[-87.7447,42.137],[-87.7399,42.1319],[-87.7393,42.1296],[-87.7351,42.125],[-87.7302,42.1218],[-87.729,42.1213],[-87.7272,42.1194],[-87.7261,42.1153],[-87.72,42.1089],[-87.7079,42.0983],[-87.6976,42.0909],[-87.6916,42.0863],[-87.6885,42.0835],[-87.6861,42.0812],[-87.685,42.0784],[-87.6807,42.0766],[-87.6771,42.0729],[-87.6747,42.0692],[-87.6742,42.066],[-87.6729,42.0651],[-87.6731,42.0587],[-87.6704,42.0446],[-87.6674,42.0428],[-87.6681,42.0396],[-87.6669,42.0359],[-87.6657,42.0336],[-87.6646,42.0295],[-87.6617,42.0213],[-87.6589,42.0122],[-87.6577,42.0095],[-87.6535,42.0049],[-87.6523,42.0021],[-87.6506,41.9994],[-87.6494,41.9962],[-87.6509,41.9871],[-87.6498,41.9826],[-87.6467,41.9807],[-87.6449,41.9789],[-87.6443,41.9779],[-87.6419,41.9765],[-87.6419,41.9756],[-87.642,41.972],[-87.6396,41.9692],[-87.6378,41.9669],[-87.6354,41.9651],[-87.6317,41.9646],[-87.6287,41.9636],[-87.6275,41.9622],[-87.6288,41.9604],[-87.6331,41.9587],[-87.6362,41.9592],[-87.6369,41.9578],[-87.6351,41.9533],[-87.6316,41.9473],[-87.6298,41.945],[-87.6292,41.9432],[-87.6293,41.9396],[-87.6281,41.9373],[-87.6263,41.9359],[-87.627,41.9323],[-87.6258,41.9309],[-87.6253,41.9282],[-87.6254,41.9245],[-87.6231,41.9186],[-87.6207,41.9145],[-87.6195,41.9135],[-87.6177,41.914],[-87.6164,41.913],[-87.6183,41.9117],[-87.6209,41.9099],[-87.6215,41.9077],[-87.621,41.9058],[-87.6204,41.9036],[-87.6186,41.9031],[-87.6161,41.9017],[-87.6149,41.9007],[-87.6131,41.8994],[-87.6108,41.8957],[-87.6096,41.8943],[-87.5985,41.8932],[-87.5973,41.8928],[-87.5973,41.8919],[-87.5985,41.8914],[-87.6066,41.8915],[-87.6084,41.8907],[-87.6103,41.8889],[-87.6097,41.8875],[-87.611,41.8848],[-87.6124,41.8821],[-87.6131,41.878],[-87.6127,41.8698],[-87.6109,41.8689],[-87.609,41.8675],[-87.6041,41.8674],[-87.6029,41.8674],[-87.603,41.8629],[-87.6038,41.8579],[-87.6038,41.8561],[-87.6063,41.8552],[-87.6088,41.8539],[-87.6059,41.8457],[-87.6031,41.8384],[-87.5995,41.832],[-87.5954,41.826],[-87.5894,41.8177],[-87.5841,41.8117],[-87.5811,41.8081],[-87.5793,41.8053],[-87.5782,41.8021],[-87.5764,41.7998],[-87.5758,41.7989],[-87.574,41.7984],[-87.5734,41.798],[-87.5728,41.797],[-87.574,41.7962],[-87.5765,41.7944],[-87.576,41.7921],[-87.5748,41.7898],[-87.5742,41.7884],[-87.5743,41.7871],[-87.5743,41.7857],[-87.5737,41.7848],[-87.5719,41.7839],[-87.5694,41.7834],[-87.5676,41.7824],[-87.5689,41.7815],[-87.5713,41.7816],[-87.5732,41.7812],[-87.5745,41.7803],[-87.5745,41.7794],[-87.5739,41.778],[-87.5727,41.7775],[-87.5714,41.7779],[-87.5677,41.7788],[-87.5665,41.7774],[-87.5659,41.7765],[-87.5611,41.7719],[-87.5606,41.7705],[-87.56,41.7691],[-87.5594,41.7687],[-87.5576,41.7668],[-87.5576,41.765],[-87.5528,41.7604],[-87.5504,41.7599],[-87.5479,41.7594],[-87.5461,41.7594],[-87.5449,41.7598],[-87.5412,41.7593],[-87.54,41.7584],[-87.5394,41.7566],[-87.5407,41.7552],[-87.5407,41.7534],[-87.5395,41.7525],[-87.5377,41.7525],[-87.5359,41.7511],[-87.5334,41.7497],[-87.531,41.7483],[-87.5298,41.7469],[-87.5283,41.736],[-87.5277,41.7337],[-87.5272,41.73],[-87.5257,41.7182],[-87.524,41.7135],[-87.5239,41.6941],[-87.5255,41.5516],[-87.5265,41.4712],[-87.5565,41.4712],[-87.6706,41.4715],[-87.7888,41.4723],[-87.7891,41.4855],[-87.7894,41.5],[-87.7922,41.5377],[-87.7923,41.5595],[-87.9071,41.5578],[-87.9106,41.6445],[-88.0299,41.6428],[-88.0308,41.6868],[-88.0013,41.6874],[-87.9883,41.6877],[-87.9674,41.6879],[-87.9482,41.694],[-87.9438,41.7017],[-87.9139,41.7172],[-87.9142,41.7318],[-87.9178,41.8185],[-87.9188,41.9076],[-87.9175,41.9938],[-88.0342,41.9925],[-88.1473,41.9883],[-88.2634,41.9876],[-88.2632,42.0675],[-88.2632,42.0685],[-88.2379,42.0682],[-88.2382,42.155],[-88.1992,42.1555]]]},\"properties\":{\"name\":\"Cook\",\"state\":\"IL\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Santa Rosa is no stranger to earthquakes. This northern California city was damaged several times in the late 19th and early 20th centuries by shaking from earthquakes, culminating in the devastating earthquake of 1906, whose rupture passed 20 miles to the west of the city on the San Andreas Fault. Then in 1969, Santa Rosa was again strongly shaken and buildings were damaged by a pair of nearby, moderate-sized earthquakes on the Rodgers Creek Fault. Since then, scientists have learned how the underlying geology increases shaking damage in Santa Rosa, have mapped where the Rodgers Creek Fault runs beneath the city, and have discovered that this fault is capable of much larger earthquakes. Following the 1969 earthquakes, Santa Rosa rose to the challenge of improving seismic safety; however, continued progress is needed to increase seismic resilience and reduce the impact of future earthquakes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193035","usgsCitation":"Hecker, S., McPhee, D.K., Langenheim, V.E., and Watt, J.T., 2019, Santa Rosa's past and future earthquakes: U.S. Geological Survey Fact Sheet 2019–3035, 4 p., https://doi.org/10.3133/fs20193035. ","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-102642","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":366908,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3035/fs20193035.pdf","text":"Report","size":"5.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2019-3035"},{"id":366907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3035/coverthb.jpg"}],"country":"United States","state":"California","city":"Santa Rosa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.12927246093751,\n 38.11727165830543\n ],\n [\n -122.26409912109375,\n 38.11727165830543\n ],\n [\n -122.26409912109375,\n 38.603993275591684\n ],\n [\n -123.12927246093751,\n 38.603993275591684\n ],\n [\n -123.12927246093751,\n 38.11727165830543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, California 94025
The invasion of non-native fishes has caused a great detriment to many of our native fishes. Since the introduction of invasive carps, such as Silver, Bighead, Common and Grass Carp, managers and researcher have been struggling to remove these species while also hypothesizing the detriment of further invasion. This study developed a food web model of four locations on the Mississippi River and used those models to assess the impacts of two scenarios: carp removal and carp invasion. In the Middle Mississippi River where these invasive carps are already present, the models found that it would take a sustained exploitation of up to 30% of initial biomass over an extended period to remove Grass Carp and up to 90% removal of initial biomass to remove Silver and Bighead Carp. In the locations where Silver, Bighead, and Grass Carp are not yet established (i.e., Pools 4,8, and 13) the invasion of these species could cause declines from 10 to 30% in initial biomass of native fishes as well as already established nonnative invasive species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fooweb.2019.e00120","usgsCitation":"Kramer, N.W., Phelps, Q.E., Pierce, C., and Colvin, M., 2019, A food web modeling assessment of Asian Carp impacts in the Middle and Upper Mississippi River, USA: Food Webs, v. 21, e00120, 9 p., https://doi.org/10.1016/j.fooweb.2019.e00120.","productDescription":"e00120, 9 p.","ipdsId":"IP-103332","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467338,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1330&context=nrem_pubs","text":"External Repository"},{"id":388582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Middle and Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.5770263671875,\n 37.09900294387622\n ],\n [\n -89.35455322265625,\n 37.09900294387622\n ],\n [\n -89.35455322265625,\n 37.57070524233116\n ],\n [\n -89.5770263671875,\n 37.57070524233116\n ],\n [\n -89.5770263671875,\n 37.09900294387622\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.03659057617188,\n 44.31058342934815\n ],\n [\n -91.91024780273438,\n 44.31058342934815\n ],\n [\n -91.91024780273438,\n 44.414654778606874\n ],\n [\n -92.03659057617188,\n 44.414654778606874\n ],\n [\n -92.03659057617188,\n 44.31058342934815\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.2802505493164,\n 43.57516780951105\n ],\n [\n -91.2081527709961,\n 43.57516780951105\n ],\n [\n -91.2081527709961,\n 43.74108839068835\n ],\n [\n -91.2802505493164,\n 43.74108839068835\n ],\n [\n -91.2802505493164,\n 43.57516780951105\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.2581787109375,\n 41.83478149415483\n ],\n [\n -90.076904296875,\n 41.83478149415483\n ],\n [\n -90.076904296875,\n 42.157295553651664\n ],\n [\n -90.2581787109375,\n 42.157295553651664\n ],\n [\n -90.2581787109375,\n 41.83478149415483\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kramer, Nicholas W.","contributorId":264840,"corporation":false,"usgs":false,"family":"Kramer","given":"Nicholas","email":"","middleInitial":"W.","affiliations":[{"id":17621,"text":"Southeast Missouri State University","active":true,"usgs":false}],"preferred":false,"id":822082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phelps, Quinton E.","contributorId":264841,"corporation":false,"usgs":false,"family":"Phelps","given":"Quinton","email":"","middleInitial":"E.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":822083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pierce, Clay 0000-0001-5088-5431 cpierce@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-5431","contributorId":150492,"corporation":false,"usgs":true,"family":"Pierce","given":"Clay","email":"cpierce@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822081,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colvin, Michael E.","contributorId":264842,"corporation":false,"usgs":false,"family":"Colvin","given":"Michael E.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":822084,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223496,"text":"70223496 - 2019 - Invertebrate prey contributions to juvenile Coho Salmon diet from riparian habitats along three Alaska streams: Implications for environmental change","interactions":[],"lastModifiedDate":"2021-08-31T13:39:20.889311","indexId":"70223496","displayToPublicDate":"2019-08-26T08:30:53","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2299,"text":"Journal of Freshwater Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Invertebrate prey contributions to juvenile Coho Salmon diet from riparian habitats along three Alaska streams: Implications for environmental change","docAbstract":"Stream fish rely on a mix of terrestrial and aquatic prey sources. While the importance of terrestrial invertebrates as a food source for stream fish is well documented, the role of aquatic insects that emerge from the stream as winged adult insects (aquatic winged adults) and return to the stream as prey is less understood. In this study we determined the proportion of total diet for stream-rearing juvenile Coho Salmon (Oncorhynchus kisutch) that is derived from terrestrial and aquatic winged adult invertebrates which enter the stream from riparian habitats and consider how those cross-ecosystem prey contributions vary based on riparian habitat type. Study reaches were identified in three streams within the Kenai River watershed of Alaska that were representative of habitats found throughout the region and riparian vegetation was classified into grass/sedge, shrub and tree types using LiDAR. Juvenile Coho Salmon stomach contents were sampled seasonally in study reaches over a two-year period and ingested invertebrates were identified by taxa, life stage and origin. Our results showed that aquatic winged adult prey contributions to juvenile salmon diet were significantly lower in the grass/sedge study reach, and cross-ecosystem invertebrate prey represented a significantly higher proportion of juvenile salmon diet in the tree study reach. Invertebrate prey in the grass/sedge reach were composed primarily of the larval life stage of aquatic winged adults. These results suggest that change in riparian vegetation from tree/shrub to grass/sedge along Kenai streams as projected by regional climate change models, or that results from anthropogenic modification, will likely lead to lower availability of cross-ecosystem prey for stream fish. Management of riparian buffers along streams to preserve or increase occurrence of trees and shrubs is likely to help mitigate impacts of those possible changes.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02705060.2019.1642243","usgsCitation":"Grunblatt, J., Meyer, B., and Wipfli, M.S., 2019, Invertebrate prey contributions to juvenile Coho Salmon diet from riparian habitats along three Alaska streams: Implications for environmental change: Journal of Freshwater Ecology, v. 34, no. 1, p. 617-631, https://doi.org/10.1080/02705060.2019.1642243.","productDescription":"16 p.","startPage":"617","endPage":"631","ipdsId":"IP-103789","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467339,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2019.1642243","text":"Publisher Index Page"},{"id":388688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kenai watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.369384765625,\n 59.77852198502987\n ],\n [\n -148.919677734375,\n 59.77852198502987\n ],\n [\n -148.919677734375,\n 61.312451574838214\n ],\n [\n -152.369384765625,\n 61.312451574838214\n ],\n [\n -152.369384765625,\n 59.77852198502987\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Grunblatt, Jess","contributorId":264907,"corporation":false,"usgs":false,"family":"Grunblatt","given":"Jess","affiliations":[{"id":54579,"text":"uak","active":true,"usgs":false}],"preferred":false,"id":822179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Benjamin E.","contributorId":264908,"corporation":false,"usgs":false,"family":"Meyer","given":"Benjamin E.","affiliations":[{"id":54579,"text":"uak","active":true,"usgs":false}],"preferred":false,"id":822180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":822178,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204839,"text":"sir20195067 - 2019 - Flood-inundation maps for a 23-mile reach of the Medina River at Bandera, Texas, 2018","interactions":[],"lastModifiedDate":"2019-08-26T05:37:05","indexId":"sir20195067","displayToPublicDate":"2019-08-26T05:36:50","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":"2019-5067","displayTitle":"Flood-Inundation Maps for a 23-Mile Reach of the Medina River at Bandera, Texas, 2018","title":"Flood-inundation maps for a 23-mile reach of the Medina River at Bandera, Texas, 2018","docAbstract":"In 2018, the U.S. Geological Survey (USGS), in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, studied floods through the period of record to create a library of flood-inundation maps for the Medina River at Bandera, Texas. Digital flood-inundation maps for a 23-mile reach of the Medina River at and near Bandera, from the confluence with Winans Creek to English Crossing Road, were developed. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to a range of different gage heights (gage height is commonly referred to as “stage,” or the water-surface elevation at a streamflow-gaging station) at USGS streamflow-gaging station 08178880 Medina River at Bandera, Tex. (hereinafter referred to as the “Bandera station”). Water-surface profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The stage-discharge (streamflow) relation effective in 2018 was used to calibrate the model, and stages from four recent flood events were used to independently validate the model. The calibrated hydraulic model was then used to compute 29 water-surface profiles for stages at 1-foot (ft) increments referenced to the station datum and ranging from 10 ft (near bankfull) to 38 ft, which exceeds the major flood stage of the National Weather Service Advanced Hydrologic Prediction Service of 24 ft. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.4-ft vertical accuracy and 1.6-ft horizontal resolution) to delineate the area flooded for stages ranging from 10 to 38 ft.
The digital flood-inundation maps are delivered through the USGS Flood Inundation Mapper application that presents map libraries and provides detailed information on flood-inundation extents and stages for modeled sites. The flood-inundation maps developed in this study, in conjunction with the real-time stage data from the Bandera station, are intended to help guide the public in taking individual safety precautions and provide emergency management personnel with a tool to efficiently manage emergency flood operations and post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195067","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., and Engel, F.L., 2019, Flood-inundation maps for a 23-mile reach of the Medina River at Bandera, Texas, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5067, 15 p., https://doi.org/10.3133/sir20195067.","productDescription":"Report: viii, 15 p.; Fact Sheet: 2 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-104084","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":366755,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/fs20193043","text":"FS 2019–3043","size":"895 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3043","linkHelpText":" Flood Warning Toolset for the Medina River in Bandera County, Texas"},{"id":366756,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WYD6LS","text":"USGS data release ","linkHelpText":"Geospatial and survey data for flood-inundation maps in a 23-mile reach of the Medina River at Bandera, Texas, 2018"},{"id":366666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5067/coverthb.jpg"},{"id":366667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5067/sir20195067.pdf","text":"Report","size":"3.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5067"}],"contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Floods are the most common natural disaster in the United States. The Medina River in Bandera County, Texas, is in the Edwards Plateau, where high-intensity rain rates and steep terrain frequently contribute to severe flash flooding capable of causing loss of life and property. For example, the July 5, 2002, flood claimed a total of 12 lives in the central Texas area. The estimated peak discharge during this flood at U.S. Geological Survey (USGS) streamflow-gaging station 08178880 Medina River at Bandera, Tex., was 159,000 cubic feet per second (corresponding to a stage or gage height of 38.91 feet), causing significant flooding in Bandera near Mud Creek and farther downstream.
In 2018, the USGS, in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, developed a flood early-warning toolset to enhance the communication of flood risk and provide emergency management with additional information to improve flood response and mitigation. This toolset consists of a continuous streamflow-gage monitoring network, a well-calibrated hydraulic model of the Medina River, and a flood-inundation mapper application for the study area. A library of flood-inundation maps tied to the National Weather Service river stage forecast capability is included with the toolset.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193043","usgsCitation":"Engel, F.L., and Choi, N., 2019, Flood warning toolset for the Medina River in Bandera County, Texas: U.S. Geological Survey Fact Sheet 2019–3043, 2 p., https://doi.org/10.3133/fs20193043. ","productDescription":"Report: 2 p.; Companion Files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-110193","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":366754,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195067","text":"SIR 2019–5067","size":"3.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5067","linkHelpText":" Flood-Inundation Maps for a 23-Mile Reach of the Medina River at Bandera, Texas, 2018"},{"id":366753,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3043/fs20193043.pdf","text":"Report","size":"895 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3043"},{"id":366752,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3043/coverthb.jpg"}],"country":"United States","state":"Texas","county":"Bandera County ","otherGeospatial":"Medina River","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-98.9253,29.7842],[-98.7869,29.7168],[-98.8056,29.6968],[-98.9213,29.5665],[-98.9245,29.562],[-98.9282,29.5593],[-98.9318,29.5588],[-98.9429,29.5585],[-98.9513,29.5581],[-98.9607,29.5578],[-98.9633,29.5578],[-98.9676,29.5546],[-98.9712,29.5533],[-98.9765,29.5547],[-98.978,29.5556],[-98.9811,29.5589],[-98.9832,29.5625],[-98.9837,29.5671],[-98.9836,29.5717],[-98.9819,29.5804],[-98.9818,29.5909],[-98.9801,29.5983],[-98.9779,29.606],[-98.9789,29.6102],[-98.9794,29.6129],[-98.982,29.6148],[-98.9909,29.6185],[-99.0103,29.6187],[-99.4132,29.6253],[-99.6033,29.6257],[-99.6031,29.9068],[-99.2839,29.905],[-99.1766,29.8946],[-98.9253,29.7842]]]},\"properties\":{\"name\":\"Bandera\",\"state\":\"TX\"}}]}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11172005","usgsCitation":"Fu, Y., He, H.S., Hawbaker, T., Henne, P., Zhu, Z., and Larsen, D.R., 2019, Evaluating k-nearest neighbor (kNN) imputation models for species-level aboveground forest biomass mapping in northeast China: Remote Sensing, v. 17, no. 11, p. 1-20, https://doi.org/10.3390/rs11172005.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-109980","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":467340,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11172005","text":"Publisher Index Page"},{"id":437358,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MOB5E3","text":"USGS data release","linkHelpText":"Data release for: Evaluating k-nearest neighbor (kNN) imputation models for species-level aboveground forest biomass mapping in northeast China"},{"id":367198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 124.8046875,\n 40.3130432088809\n ],\n [\n 129.90234375,\n 43.32517767999296\n ],\n [\n 131.484375,\n 42.293564192170095\n ],\n [\n 135.17578125,\n 48.45835188280866\n ],\n [\n 130.95703125,\n 47.87214396888731\n ],\n [\n 124.27734374999999,\n 53.54030739150022\n ],\n [\n 120.41015624999999,\n 52.696361078274485\n ],\n [\n 118.47656249999999,\n 49.61070993807422\n ],\n [\n 116.54296874999999,\n 49.724479188712984\n ],\n [\n 115.83984375,\n 47.87214396888731\n ],\n [\n 119.00390625,\n 46.92025531537451\n ],\n [\n 110.91796875,\n 44.465151013519616\n ],\n [\n 118.65234374999999,\n 37.996162679728116\n ],\n [\n 124.8046875,\n 40.3130432088809\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Fu, Yuanyuan","contributorId":218762,"corporation":false,"usgs":false,"family":"Fu","given":"Yuanyuan","email":"","affiliations":[{"id":39903,"text":"School of Geographical Sciences, Northeast Normal University, Changchun, China","active":true,"usgs":false}],"preferred":false,"id":770181,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"He, Hong S","contributorId":218764,"corporation":false,"usgs":false,"family":"He","given":"Hong","email":"","middleInitial":"S","affiliations":[{"id":39904,"text":"University of Missouri, School of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":770182,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":770183,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Henne, Paul D. 0000-0003-1211-5545 phenne@usgs.gov","orcid":"https://orcid.org/0000-0003-1211-5545","contributorId":169166,"corporation":false,"usgs":true,"family":"Henne","given":"Paul D.","email":"phenne@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":770180,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":770184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Larsen, David R. 0000-0001-5861-8952","orcid":"https://orcid.org/0000-0001-5861-8952","contributorId":218763,"corporation":false,"usgs":false,"family":"Larsen","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":36845,"text":"School of Natural Resources, University of Missouri","active":true,"usgs":false}],"preferred":false,"id":770185,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254786,"text":"70254786 - 2019 - Strategic conservation for lesser prairie-chickens among landscapes of varying anthropogenic influence","interactions":[],"lastModifiedDate":"2024-06-07T14:20:41.927474","indexId":"70254786","displayToPublicDate":"2019-08-24T09:10:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Strategic conservation for lesser prairie-chickens among landscapes of varying anthropogenic influence","docAbstract":"For millennia grasslands have provided a myriad of ecosystem services and have been coupled with human resource use. The loss of 46% of grasslands worldwide necessitates the need for conservation that is spatially, temporally, and socioeconomically strategic. In the Southern Great Plains of the United States, conversion of native grasslands to cropland, woody encroachment, and establishment of vertical anthropogenic features have made large intact grasslands rare for lesser prairie-chickens (Tympanuchus pallidicinctus). However, it remains unclear how the spatial distribution of grasslands and anthropogenic features constrain populations and influence conservation. We estimated the distribution of lesser prairie-chickens using data from individuals marked with GPS transmitters in Kansas and Colorado, USA, and empirically derived relationships with anthropogenic structure densities and grassland composition. Our model suggested decreased probability of use in 2-km radius (12.6 km2) landscapes that had greater than two vertical features, two oil wells, 8 km of county roads, and 0.15 km of major roads or transmission lines. Predicted probability of use was greatest in 5-km radius landscapes that were 77% grassland. Based on our model predictions, ~10% of the current expected lesser prairie-chicken distribution was available as habitat. We used our estimated species distribution to provide spatially explicit prescriptions for CRP enrollment and tree removal in locations most likely to benefit lesser prairie-chickens. Spatially incentivized CRP sign up has the potential to provide 4189 km2 of additional habitat and strategic application of tree removal has the potential to restore 1154 km2. Tree removal and CRP enrollment are conservation tools that can align with landowner goals and are much more likely to be effective on privately owned working lands.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2019.108213","usgsCitation":"Sullins, D.S., Haukos, D.A., Lautenbach, J.M., Lautenbach, J., Robinson, S.G., Rice, M.B., Sandercock, B.K., Kraft, J.D., Plumb, R.T., Reitz, J., Hutchinson, J.M., and Hagen, C., 2019, Strategic conservation for lesser prairie-chickens among landscapes of varying anthropogenic influence: Biological Conservation, v. 238, 108213, 10 p., https://doi.org/10.1016/j.biocon.2019.108213.","productDescription":"108213, 10 p.","ipdsId":"IP-105616","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2019.108213","text":"Publisher Index Page"},{"id":429644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.72145468354752,\n 39.86812103477851\n ],\n [\n -103.72145468354752,\n 37.00372049543908\n ],\n [\n -97.7109610420592,\n 37.00372049543908\n ],\n [\n -97.7109610420592,\n 39.86812103477851\n ],\n [\n -103.72145468354752,\n 39.86812103477851\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"238","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sullins, Daniel S.","contributorId":166689,"corporation":false,"usgs":false,"family":"Sullins","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":902538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":902539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lautenbach, Joseph M.","contributorId":172788,"corporation":false,"usgs":false,"family":"Lautenbach","given":"Joseph","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":902540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lautenbach, Jonathan","contributorId":272579,"corporation":false,"usgs":false,"family":"Lautenbach","given":"Jonathan","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":902541,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robinson, Samantha G.","contributorId":172786,"corporation":false,"usgs":false,"family":"Robinson","given":"Samantha","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":902542,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rice, Mindy B.","contributorId":214399,"corporation":false,"usgs":false,"family":"Rice","given":"Mindy","email":"","middleInitial":"B.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":902543,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sandercock, Brett K.","contributorId":95816,"corporation":false,"usgs":true,"family":"Sandercock","given":"Brett","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":902544,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kraft, John D.","contributorId":172789,"corporation":false,"usgs":false,"family":"Kraft","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":902545,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Plumb, Reid T.","contributorId":172787,"corporation":false,"usgs":false,"family":"Plumb","given":"Reid","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":902546,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reitz, Jonathan H.","contributorId":337597,"corporation":false,"usgs":false,"family":"Reitz","given":"Jonathan H.","affiliations":[{"id":40103,"text":"cdpw","active":true,"usgs":false}],"preferred":false,"id":902547,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hutchinson, J. M. Shawn","contributorId":337599,"corporation":false,"usgs":false,"family":"Hutchinson","given":"J.","email":"","middleInitial":"M. Shawn","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":902548,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hagen, Christian A.","contributorId":279696,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":902549,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70223799,"text":"70223799 - 2019 - Influence of climate change and postdelisting management on long-term population viability of the conservation-reliant Kirtland's Warbler","interactions":[],"lastModifiedDate":"2021-09-08T12:39:32.771645","indexId":"70223799","displayToPublicDate":"2019-08-24T07:36:04","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Influence of climate change and postdelisting management on long-term population viability of the conservation-reliant Kirtland's Warbler","docAbstract":"Rapid global climate change is resulting in novel abiotic and biotic conditions and interactions. Identifying management strategies that maximize probability of long-term persistence requires an understanding of the vulnerability of species to environmental changes. We sought to quantify the vulnerability of Kirtland's Warbler (Setophaga kirtlandii), a rare Neotropical migratory songbird that breeds almost exclusively in the Lower Peninsula of Michigan and winters in the Bahamian Archipelago, to projected environmental changes on the breeding and wintering grounds. We developed a population-level simulation model that incorporates the influence of annual environmental conditions on the breeding and wintering grounds, and parameterized the model using empirical relationships. We simulated independent and additive effects of reduced breeding grounds habitat quantity and quality, and wintering grounds habitat quality, on population viability. Our results indicated the Kirtland's Warbler population is stable under current environmental and management conditions. Reduced breeding grounds habitat quantity resulted in reductions of the stable population size, but did not cause extinction under the scenarios we examined. In contrast, projected large reductions in wintering grounds precipitation caused the population to decline, with risk of extinction magnified when breeding habitat quantity or quality also decreased. Our study indicates that probability of long-term persistence for Kirtland's Warbler will depend on climate change impacts to wintering grounds habitat quality and contributes to the growing literature documenting the importance of considering the full annual cycle for understanding population dynamics of migratory species.
Uplift of the Andean Cordillera during the Miocene and Pliocene produced large-scale changes in regional atmospheric circulation that impacted local ecosystems. The Lauca Basin (northern Chilean Altiplano) contains variably fluvial and lacustrine sedimentary sequences spanning the interval from 8.7 to 2.3 Ma. Field samples were collected from paleo-lacustrine sediments in the basin. Sediments were dated using detrital zircon geochronology on volcanic tuffs, yielding an age range between ~5.57 and 5.44 Ma. These new age constraints provided an opportunity to evaluate changes in the Lauca Basin ecosystem across this dynamic Miocene-Pliocene transition. We employed multiple proxies (lithofacies analysis, diatoms, pollen, and oxygen stable isotopes of authigenic carbonates) to interpret ancient lacustrine and terrestrial paleoenvironments. Alternations among mudstone, carbonate, and evaporitic facies indicate lake-level variability through time. The diatom assemblage is characterized by meso- to hypersaline and alkaline-tolerant taxa typical of shallow lakes. The δ18O values ranged from −8.96 to −2.22‰ indicating fluctuations in water balance. Pollen taxa in the outcrop are typical of a transitional stage between seasonal cloud forest and open grassland. Together, these proxies indicate that the Lauca paleolake sediments were deposited under a wetter-than-modern climate with high temporal variability. Our results refine previous studies in the Lauca Basin and are consistent with other regional studies suggesting that the South American summer monsoon at the Miocene-Pliocene transition was more intense than it is at present.
Small ponds—farm ponds, detention ponds, or impoundments below 0.01 km2—serve important human needs throughout most large river basins. Yet the role of small ponds in regional nutrient and sediment budgets is essentially unknown, currently making it impossible to evaluate their management potential to achieve water quality objectives. Here we used new hydrography data sets and found that small ponds, depending on their spatial position within both their local catchments and the larger river network, can dominate the retention of nitrogen, phosphorus, and sediment compared to rivers, lakes, and reservoirs. Over 300,000 small ponds are collectively responsible for 34%, 69%, and 12% of the mean annual retention of nitrogen, phosphorus, and sediment in the Northeastern United States, respectively, with a dominant influence in headwater catchments (54%, 85%, and 50%, respectively). Small ponds play a critical role among the many aquatic features in long‐term nutrient and sediment loading to downstream waters.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL083937","usgsCitation":"Schmadel, N., Harvey, J., Schwarz, G., Alexander, R., Gomez-Velez, J., Scott, D., and Ator, S., 2019, Small ponds in headwater catchments are a dominant influence on regional nutrient and sediment budgets: Geophysical Research Letters, v. 46, no. 16, p. 9669-9677, https://doi.org/10.1029/2019GL083937.","productDescription":"9 p.","startPage":"9669","endPage":"9677","ipdsId":"IP-109711","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":467351,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gl083937","text":"Publisher Index Page"},{"id":367537,"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 \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"46","issue":"16","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmadel, Noah M. 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":771266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":771265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":219106,"corporation":false,"usgs":false,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":false,"id":771267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander, Richard 0000-0001-9166-0626","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":219107,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":771268,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gomez-Velez, Jesus D.","contributorId":219103,"corporation":false,"usgs":false,"family":"Gomez-Velez","given":"Jesus D.","affiliations":[{"id":39962,"text":"Department of Earth & Environmental Science, New Mexico Institute of Mining and Technology, Socorro, New Mexico, USA","active":true,"usgs":false}],"preferred":false,"id":771269,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Durelle","contributorId":219088,"corporation":false,"usgs":false,"family":"Scott","given":"Durelle","affiliations":[{"id":39959,"text":"Virginia Tech.","active":true,"usgs":false}],"preferred":false,"id":771270,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ator, Scott W. 0000-0002-9186-4837","orcid":"https://orcid.org/0000-0002-9186-4837","contributorId":210852,"corporation":false,"usgs":true,"family":"Ator","given":"Scott W.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771271,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70128734,"text":"tm6A52 - 2019 - SUTRA, a model for saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of generalized boundary conditions, a modified implementation of specified pressures and concentrations or temperatures, and the lake capability","interactions":[],"lastModifiedDate":"2019-08-23T09:31:13","indexId":"tm6A52","displayToPublicDate":"2019-08-21T13:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-A52","displayTitle":"SUTRA, a Model for Saturated-Unsaturated, Variable-Density Groundwater Flow with Solute or Energy Transport—Documentation of Generalized Boundary Conditions, a Modified Implementation of Specified Pressures and Concentrations or Temperatures, and the Lake Capability","title":"SUTRA, a model for saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of generalized boundary conditions, a modified implementation of specified pressures and concentrations or temperatures, and the lake capability","docAbstract":"Version 3.0 of the SUTRA groundwater modeling program offers three new capabilities: generalized boundary conditions, a modified implementation of specified pressures and concentrations or temperatures, and lakes. Two new types of “generalized” boundary conditions facilitate simulation of a wide range of hydrologic processes that interact with the groundwater model, such as rivers, drains, and evapotranspiration. For generalized-flow boundary conditions, gain (inflow) or loss (outflow) of fluid mass varies linearly with pressure, subject to optional upper and lower limits on flow and (or) pressure. For generalized-transport boundary conditions, gain or loss of solute mass or energy varies linearly with concentration or temperature, respectively. Two of the original types of SUTRA boundary conditions—specified-pressure and specified-concentration or temperature—have been modified such that user-specified, conductance-like factors (known as GNUP and GNUU in previous versions of SUTRA) are no longer required. The new lake capability works with all types of SUTRA boundary conditions, including the new generalized boundary conditions, to enable simulation of the interaction of groundwater flow and transport with lake water “ponded” on the surface of a three-dimensional model. SUTRA uses the topography of the top surface of the model, or, optionally, user-specified lake-bottom elevations, to identify potential lakes automatically. Increases and decreases in lake stage can cause lakes to coalesce and divide, respectively. The lake capability may be used with saturated or unsaturated flow and solute or energy transport.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Groundwater in Book 6 Modeling Techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A52","collaboration":"Prepared in cooperation with the Strategic Environmental Research and Development Program","usgsCitation":"Provost, A.M., and Voss, C.I., 2019, SUTRA, a model for saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of generalized boundary conditions, a modified implementation of specified pressures and concentrations or temperatures, and the lake capability: U.S. Geological Survey Techniques and Methods, book 6, chap. A52, 62 p., https://doi.org/10.3133/tm6A52.","productDescription":"viii, 62 p.","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-058173","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":437362,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PPEHHM","text":"USGS data release","linkHelpText":"SUTRA 3"},{"id":364789,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a52/tm6a52.pdf","text":"Report","size":"4.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-A52"},{"id":364788,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a52/coverthb.jpg"}],"publicComments":"This report is Chapter 52 of Section A: Groundwater in Book 6 Modeling Techniques","contact":"Director, Earth System Processes Division
U.S. Geological Survey
Mail Stop 411
12201 Sunrise Valley Drive
Reston, VA 20192
Director,
Volcano Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
The U.S. Geological Survey conducted a study using modern methods of molecular analysis aimed at attempting to identify the source(s) of fecal contamination that had been identified in previous studies conducted by the West Virginia Conservation Agency in the Elk Run watershed, Jefferson County, West Virginia. Water samples from multiple sites showing elevated fecal coliform counts were analyzed using molecular markers associated with general mammalian fecal contamination (AllBac), human Bacteroides (HF183), bovine Bacteroides (BoBac), and human polyomavirus (HPyV). Samples were also analyzed by quantitative polymerase chain reaction (qPCR) for human and bovine cytochrome b (mitochondrial DNA marker). A headwater site (Elk Branch at Shenandoah Junction) was found to be severely affected by both human and bovine contamination in May 2017. Although many of the molecular marker levels as well as Escherichia coli numbers had declined by a repeat sampling in June 2017, total coliform bacterial numbers remained high. Examination of the data indicated that this site had probably been affected by two separate contamination events, an influx of bovine contamination close to the time of the May sampling and a human contamination event that had occurred earlier. Samples from all sites contained bovine mitochondrial DNA, whereas only one revealed relatively high levels of human mitochondrial DNA. The Elk Run watershed appears to be widely affected by bovine influences with human influence episodically playing a role. Surface runoff caused by rain events exacerbates both.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191064","usgsCitation":"Schill, W.B., and Iwanowicz, D.D., 2019, Molecular identification of fecal contamination in the Elks Run watershed, Jefferson County, West Virginia, 2016–17: U.S. Geological Survey Open-File Report 2019–1064, 9 p., https://doi.org/10.3133/ofr20191064.","productDescription":"9 p.","onlineOnly":"Y","ipdsId":"IP-092227","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":366675,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1064/ofr20191064.pdf","text":"Report","size":"6.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1064"},{"id":366674,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1064/coverthb.jpg"}],"country":"United States","state":"West Virginia","county":"Jefferson County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-77.7197,39.3253],[-77.7273,39.3199],[-77.734,39.315],[-77.735,39.3133],[-77.7353,39.3129],[-77.7371,39.3101],[-77.7402,39.3052],[-77.7446,39.3003],[-77.7471,39.2963],[-77.7496,39.2909],[-77.7527,39.2864],[-77.7546,39.2824],[-77.7559,39.2765],[-77.7573,39.2719],[-77.7598,39.2674],[-77.7629,39.2639],[-77.7636,39.2632],[-77.7658,39.2612],[-77.7681,39.2589],[-77.7694,39.2576],[-77.7707,39.2554],[-77.771,39.2545],[-77.7713,39.2531],[-77.7709,39.2486],[-77.7703,39.2459],[-77.771,39.2418],[-77.7713,39.2404],[-77.772,39.2387],[-77.7736,39.2364],[-77.7747,39.235],[-77.7755,39.2342],[-77.7776,39.2324],[-77.781,39.2292],[-77.782,39.2283],[-77.7851,39.2248],[-77.7888,39.2194],[-77.7917,39.2127],[-77.7919,39.2122],[-77.7956,39.2045],[-77.7982,39.1974],[-77.7995,39.1923],[-77.8009,39.1875],[-77.8028,39.183],[-77.8036,39.178],[-77.8053,39.1743],[-77.8074,39.1708],[-77.8104,39.1682],[-77.8135,39.1641],[-77.816,39.1605],[-77.8178,39.1574],[-77.8198,39.1516],[-77.8207,39.1492],[-77.8213,39.1468],[-77.8222,39.1429],[-77.8227,39.1409],[-77.8239,39.1371],[-77.8262,39.1353],[-77.8299,39.1343],[-77.8643,39.1567],[-77.9534,39.2147],[-77.995,39.2414],[-78.0315,39.2644],[-78.0349,39.2669],[-78.0332,39.2698],[-78.0284,39.2724],[-78.0266,39.2733],[-78.03,39.2766],[-78.0324,39.2784],[-78.0323,39.2798],[-78.0323,39.2807],[-78.0293,39.2811],[-78.0281,39.2811],[-78.0263,39.2815],[-78.0257,39.2824],[-78.0251,39.2837],[-78.025,39.2855],[-78.025,39.2874],[-78.0256,39.2887],[-78.0249,39.2901],[-78.0219,39.2914],[-78.0213,39.2914],[-78.0201,39.2918],[-78.0177,39.294],[-78.0134,39.299],[-78.0073,39.3057],[-78.0018,39.3106],[-77.9964,39.3127],[-77.9909,39.3181],[-77.9878,39.3208],[-77.9895,39.3249],[-77.9912,39.3267],[-77.9912,39.3276],[-77.9906,39.3285],[-77.9888,39.3285],[-77.9876,39.3289],[-77.9869,39.3307],[-77.9863,39.3321],[-77.9857,39.3339],[-77.9838,39.3352],[-77.9832,39.3357],[-77.9753,39.3437],[-77.9811,39.3492],[-77.981,39.3501],[-77.9792,39.3515],[-77.9751,39.3509],[-77.9704,39.3486],[-77.9674,39.3472],[-77.965,39.3503],[-77.9649,39.3539],[-77.9666,39.3567],[-77.9671,39.3585],[-77.9629,39.3607],[-77.9611,39.362],[-77.9616,39.3639],[-77.9651,39.3671],[-77.9657,39.368],[-77.9639,39.3689],[-77.9579,39.3683],[-77.9555,39.3701],[-77.9506,39.3741],[-77.9476,39.3745],[-77.9441,39.374],[-77.9435,39.374],[-77.9429,39.374],[-77.9423,39.3744],[-77.9422,39.3753],[-77.9428,39.3758],[-77.9475,39.3781],[-77.9474,39.3804],[-77.9445,39.3813],[-77.9325,39.3824],[-77.9307,39.3833],[-77.8613,39.4365],[-77.8563,39.4428],[-77.8496,39.4472],[-77.8465,39.4517],[-77.8464,39.4544],[-77.8445,39.4598],[-77.8402,39.4625],[-77.8407,39.4666],[-77.8394,39.4693],[-77.8381,39.4747],[-77.8326,39.48],[-77.8324,39.4841],[-77.8243,39.4958],[-77.819,39.4956],[-77.8182,39.4954],[-77.8142,39.4944],[-77.8109,39.4932],[-77.8066,39.4918],[-77.8025,39.4903],[-77.8019,39.4902],[-77.7995,39.4901],[-77.796,39.491],[-77.793,39.4928],[-77.7919,39.4934],[-77.7901,39.4953],[-77.7886,39.4961],[-77.7841,39.4992],[-77.7829,39.4996],[-77.7795,39.5006],[-77.7757,39.501],[-77.7711,39.5006],[-77.7687,39.5002],[-77.7676,39.4997],[-77.7658,39.4983],[-77.7651,39.4966],[-77.7656,39.4951],[-77.7667,39.4937],[-77.7677,39.4927],[-77.7703,39.4915],[-77.7738,39.4899],[-77.7763,39.4886],[-77.7793,39.4878],[-77.784,39.486],[-77.7858,39.4856],[-77.79,39.4846],[-77.7943,39.483],[-77.7968,39.4812],[-77.7977,39.4802],[-77.7984,39.4788],[-77.7978,39.4772],[-77.7974,39.4767],[-77.7953,39.4747],[-77.7934,39.4731],[-77.7864,39.4697],[-77.7834,39.4683],[-77.781,39.467],[-77.7792,39.4656],[-77.7788,39.465],[-77.778,39.4634],[-77.778,39.4625],[-77.7803,39.4616],[-77.7821,39.4615],[-77.7848,39.4615],[-77.7875,39.4616],[-77.7899,39.4621],[-77.7932,39.4625],[-77.7947,39.4627],[-77.7954,39.4626],[-77.796,39.4625],[-77.797,39.4615],[-77.7978,39.4607],[-77.7976,39.4597],[-77.7965,39.4574],[-77.7946,39.4561],[-77.7922,39.4535],[-77.7869,39.4511],[-77.7858,39.4493],[-77.7845,39.4484],[-77.7845,39.4475],[-77.7841,39.4464],[-77.7841,39.4457],[-77.7847,39.4449],[-77.7874,39.4434],[-77.7884,39.443],[-77.7916,39.4429],[-77.794,39.4429],[-77.7949,39.443],[-77.7967,39.4429],[-77.7987,39.4424],[-77.8004,39.442],[-77.8011,39.4411],[-77.8015,39.4405],[-77.801,39.4391],[-77.7999,39.4375],[-77.7981,39.4357],[-77.7952,39.4345],[-77.7923,39.4331],[-77.7863,39.4321],[-77.7749,39.4303],[-77.7713,39.4298],[-77.7677,39.4298],[-77.7649,39.4299],[-77.7614,39.4289],[-77.7584,39.4285],[-77.756,39.4277],[-77.754,39.4258],[-77.753,39.425],[-77.7516,39.424],[-77.7513,39.4234],[-77.7503,39.4219],[-77.7492,39.4191],[-77.7486,39.417],[-77.7438,39.4109],[-77.7395,39.404],[-77.7372,39.3977],[-77.736,39.3937],[-77.7358,39.3925],[-77.7352,39.3909],[-77.7354,39.3895],[-77.7356,39.3887],[-77.7359,39.3881],[-77.7371,39.3873],[-77.7384,39.3868],[-77.7408,39.3868],[-77.7431,39.3864],[-77.7461,39.3855],[-77.7475,39.3852],[-77.7491,39.3847],[-77.7503,39.3838],[-77.7503,39.3825],[-77.7504,39.3806],[-77.7499,39.379],[-77.7497,39.3775],[-77.7485,39.3755],[-77.7473,39.3728],[-77.7465,39.3709],[-77.746,39.3701],[-77.7454,39.3679],[-77.7449,39.366],[-77.7444,39.3642],[-77.7444,39.3628],[-77.7445,39.3606],[-77.7449,39.3583],[-77.7458,39.3556],[-77.7483,39.3516],[-77.752,39.3489],[-77.7538,39.3476],[-77.7561,39.3465],[-77.7567,39.3456],[-77.7579,39.3442],[-77.7578,39.3424],[-77.7577,39.3405],[-77.757,39.3395],[-77.7559,39.3381],[-77.7547,39.3372],[-77.7539,39.3362],[-77.7535,39.3359],[-77.7531,39.3357],[-77.7512,39.3349],[-77.7483,39.3334],[-77.7412,39.3315],[-77.7364,39.3301],[-77.731,39.3286],[-77.7294,39.3281],[-77.7197,39.3253]]]},\"properties\":{\"name\":\"Jefferson\",\"state\":\"WV\"}}]}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The Operational Simplified Surface Energy Balance (SSEBop) model uses the principle of satellite psychrometry to produce spatially explicit actual evapotranspiration (ETa) with remotely sensed and weather data. The temperature difference (dT) in the model is a predefined parameter quantifying the difference between surface temperature at bare soil and air temperature at canopy level. Because dT is derived from the average-sky net radiation based primarily on climate data, validation of the dT estimation is critical for assuring a high-quality ETa product. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) data to evaluate the SSEBop dT estimation for the conterminous United States. MODIS data (2008–2017) were processed to compute the 10-year average land surface temperature (LST) and normalized difference vegetation index (NDVI) at 1 km resolution and 8-day interval. The observed dT (dTo) was computed from the LST difference between hot (NDVI < 0.25) and cold (NDVI > 0.7) pixels within each 2° × 2° sampling block. There were enough hot and cold pixels within each block to create dTo timeseries in the West Coast and South-Central regions. The comparison of dTo and modeled dT (dTm) showed high agreement, with a bias of 0.8 K and a correlation coefficient of 0.88 on average. This study concludes that the dTm estimation from the SSEBop model is reliable, which further assures the accuracy of the ETa estimation.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11161947","usgsCitation":"Ji, L., Senay, G.B., Velpuri, N., and Kagone, S., 2019, Evaluating the temperature difference parameter in the SSEBop model with satellite observed land surface temperature data: Remote Sensing, v. 11, no. 6, 1947, 16 p., https://doi.org/10.3390/rs11161947.","productDescription":"1947, 16 p.","ipdsId":"IP-108754","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467358,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11161947","text":"Publisher Index Page"},{"id":387802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":820916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":820917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Velpuri, Naga Manohar 0000-0002-6370-1926","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":216911,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga Manohar ","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":820918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":210980,"corporation":false,"usgs":true,"family":"Kagone","given":"Stefanie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":820919,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204349,"text":"sir20195069 - 2019 - Estimates of long-term mean daily streamflow and annual nutrient and suspended-sediment loads considered for use in regional SPARROW models of the Conterminous United States, 2012 base year","interactions":[],"lastModifiedDate":"2019-12-05T09:57:02","indexId":"sir20195069","displayToPublicDate":"2019-08-19T15:45:00","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":"2019-5069","displayTitle":"Estimates of Long-Term Mean Daily Streamflow and Annual Nutrient and Suspended-Sediment Loads Considered for Use in Regional SPARROW Models of the Conterminous United States, 2012 Base Year","title":"Estimates of long-term mean daily streamflow and annual nutrient and suspended-sediment loads considered for use in regional SPARROW models of the Conterminous United States, 2012 base year","docAbstract":"Streamflow, nutrient, and sediment concentration data needed to estimate long-term mean daily streamflow and annual constituent loads were compiled from Federal, State, Tribal, and regional agencies, universities, and nongovernmental organizations. The streamflow and loads are used to develop Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models help describe the distribution, sources, and transport of streamflow, nutrients, and sediment in streams throughout five regions of the conterminous United States. After the data were screened, approximately 5,200 streamflow, 3,000 sediment, and 3,300 nutrient sites, sampled by 137 agencies and organizations were identified as having suitable data for calculating the long-term mean daily streamflow and annual nutrient and sediment loads required for SPARROW model estimation. These sites are representative of a wide range in terms of watershed size, contaminant source types, and land-use and other important watershed characteristics. The methods used to estimate long-term mean annual loads include the Beale ratio estimator and Fluxmaster regression method with Kalman smoothing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195069","collaboration":" ","usgsCitation":"Saad, D.A., Schwarz, G.E., Argue, D.M., Anning, D.W., Ator, S.W., Hoos, A.B., Preston, S.D., Robertson, D.M., and Wise, D.R., 2019, Estimates of long-term mean daily streamflow and annual nutrient and suspended-sediment loads considered for use in regional SPARROW models of the conterminous United States, 2012 base year: U.S. Geological Survey Scientific Investigations Report 2019–5069, 51 p., https://doi.org/10.3133/sir20195069.","productDescription":"Report: vii, 51 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-081781","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":437366,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DN436B","text":"USGS data release","linkHelpText":"Water-quality and streamflow datasets used for estimating long-term mean daily streamflow and annual loads to be considered for use in regional streamflow, nutrient and sediment SPARROW models, United States, 1999-2014"},{"id":366624,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5069/sir20195069.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5069"},{"id":366634,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DN436B ","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-Quality and Streamflow Datasets Used for Estimating Long-Term Mean Daily Streamflow and Annual Loads to be Considered for Use in Regional Streamflow, Nutrient and Sediment SPARROW Models, United States, 1999-2014"},{"id":366623,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5069/coverthb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
This fact sheet describes water quality and geochemistry of the Little Arkansas River and Equus Beds aquifer during 2001 through 2016 as part of the City of Wichita’s Equus Beds aquifer storage and recovery project in south-central Kansas. The Equus Beds aquifer storage and recovery project was developed to help meet future water demand by pumping water out of the Little Arkansas River (during above-base-flow conditions), treating it using National Primary Drinking Water Regulations as a guideline, and injecting it into the aquifer for later use. Water-quality data were collected and analyzed by the U.S. Geological Survey from 2 Little Arkansas River surface-water sites and 63 Equus Beds groundwater sites, including 38 areal assessment index wells, each of which has a shallow well and a deep well. About 4,700 surface and groundwater samples were collected and analyzed for more than 300 water-quality constituents. About 1,300 groundwater chemistry samples were geochemically modeled. Constituents of concern in the Equus Beds aquifer exceeded their respective Federal criteria throughout the study period and included chloride, sulfate, nitrate plus nitrite, Escherichia coli (E. coli), total coliforms, and dissolved iron and arsenic species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193017","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., Klager, B.J., and Ziegler, A.C., 2019, Water-quality and geochemical variability in the Little Arkansas River and Equus Beds aquifer, south-central Kansas, 2001–16: U.S. Geological Survey Fact Sheet 2019–3017, 6 p., https://doi.org/10.3133/fs20193017.","productDescription":"Report: 6 p.; Companion Files","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-097042","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":364768,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3017/coverthb.jpg"},{"id":364769,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3017/fs20193017.pdf","text":"Report","size":"5.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3017"},{"id":364770,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026.pdf","text":"SIR 2019–5026","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5026","linkHelpText":" – Water-Quality and Geochemical Variability in the Little Arkansas River and Equus Beds Aquifer, South-Central Kansas, 2001–16"},{"id":364797,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026_appendix01.xlsx","text":"SIR 2019–5026 Appendix Tables","size":"236 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5026 Appendix Tables","linkHelpText":"– Table 1.1 through Table 1.14"}],"country":"United States","state":"Kansas","otherGeospatial":"Equus Beds Aquifer, Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.83462524414062,\n 37.884608857503785\n ],\n [\n -97.82844543457031,\n 37.85859141570558\n ],\n [\n -97.76664733886719,\n 37.79296501804014\n ],\n [\n -97.57919311523438,\n 37.66805980224121\n ],\n [\n -97.33749389648438,\n 37.684907136008846\n ],\n [\n -97.33062744140625,\n 37.74248523826606\n ],\n [\n -97.35397338867188,\n 37.859675659210005\n ],\n [\n -97.34230041503906,\n 38.03619406237626\n ],\n [\n -97.3443603515625,\n 38.17829073458205\n ],\n [\n -97.40684509277344,\n 38.17613163876633\n ],\n [\n -97.8826904296875,\n 38.171273439283084\n ],\n [\n -97.89985656738281,\n 38.149137543764894\n ],\n [\n -97.83462524414062,\n 37.884608857503785\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Dr.
Lawrence, KS 66049
Widespread land use change in coastal ecosystems has led to a decline in the amount of habitat available for fish and wildlife, lower production of ecosystem goods and services, and loss of recreational and aesthetic value. This has prompted global efforts to restore the natural hydrologic regimes of developed shorelines, especially resource-rich estuaries, but the resilience of these restored ecosystems in the face of accelerated sea-level rise (SLR) remains uncertain. We implemented a Monitoring-based Simulation of Accretion in Coastal Estuaries (MOSAICS) in R statistical software to address uncertainty in the resilience of modified estuarine habitats, using the Nisqually River Delta in the Pacific Northwest USA as a case study. MOSAICS is a spatially explicit model with a numerical foundation that uses empirical monitoring datasets to forecast habitat change in response to rising tidal levels. Because it accounts for the crucial ecomorphodynamic feedbacks between tidal inundation, vegetative growth, and sediment accretion, MOSAICS can be used to determine whether alternative management scenarios, such as enhanced sediment inputs, will bolster estuarine resilience to SLR. Under moderate SLR (0.62 m), the model predicted that a two-fold increase in mean daily suspended sediment during the rainy season was sufficient to maintain Nisqually’s emergent marshes through 2100, but under high SLR (1.35 m) MOSAICS indicated that greater sediment additions would be necessary to prevent submergence. A comparison between a restored marsh with subsided and high-elevation areas and a relict marsh demonstrated that the subsided restoration area was highly susceptible to SLR. Findings from the MOSAICS model highlight the importance of a site’s initial elevation, capacity for producing above and belowground biomass, and suspended sediment availability when considering management actions in estuaries and other coastal ecosystems.