Simulations of future climate suggest profiles of temperature and precipitation may differ significantly from those in the past. These changes in climate will likely lead to changes in the hydrologic cycle. As such, natural resource managers are in need of tools that can provide estimates of key components of the hydrologic cycle, uncertainty associated with the estimates, and limitations associated with the climate forcing data used to estimate these components. To help address this need, the U.S. Geological Survey Monthly Water Balance Model Futures Portal (https://my.usgs.gov/mows/) provides a user friendly interface to deliver hydrologic and meteorological variables for monthly historic and potential future climatic conditions across the continental United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173002","collaboration":"Prepared in cooperation with the U.S. Department of the Interior South Central Climate Science Center and the U.S. Environmental Protection Agency","usgsCitation":"Bock, A.R., 2017, The U.S. Geological Survey Monthly Water Balance Model Futures Portal: U.S. Geological Survey Fact Sheet 2017–3002, 6 p., https://doi.org/10.3133/fs20173002.","productDescription":"6 p.","onlineOnly":"Y","ipdsId":"IP-073900","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":336957,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3002/fs20173002.pdf","text":"Report","size":"1.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3002"},{"id":336153,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3002/coverthb2.jpg"}],"contact":"Director, USGS Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225-0046
Tule Springs Fossil Beds National Monument (TUSK) preserves 22,650 acres of the upper Las Vegas Wash in the northern Las Vegas Valley (Nevada, USA). TUSK is home to extensive and stratigraphically complex groundwater discharge (GWD) deposits, called the Las Vegas Formation, which represent springs and desert wetlands that covered much of the valley during the late Quaternary. The GWD deposits record hydrologic changes that occurred here in a dynamic and temporally congruent response to abrupt climatic oscillations over the last ~300 ka (thousands of years). The deposits also entomb the Tule Springs Local Fauna (TSLF), one of the most significant late Pleistocene (Rancholabrean) vertebrate assemblages in the American Southwest. The TSLF is both prolific and diverse, and includes a large mammal assemblage dominated by Mammuthus columbi and Camelops hesternus. Two (and possibly three) distinct species of Equus, two species of Bison, Panthera atrox, Smilodon fatalis, Canis dirus, Megalonyx jeffersonii, and Nothrotheriops shastensis are also present, and newly recognized faunal components include micromammals, amphibians, snakes, and birds. Invertebrates, plant macrofossils, and pollen also occur in the deposits and provide important and complementary paleoenvironmental information. This field compendium highlights the faunal assemblage in the classic stratigraphic sequences of the Las Vegas Formation within TUSK, emphasizes the significant hydrologic changes that occurred in the area during the recent geologic past, and examines the subsequent and repeated effect of rapid climate change on the local desert wetland ecosystem.
Numerous studies utilizing remote sensing imagery and other methods have documented that thermokarst lakes are undergoing varied hydrological transitions in response to recent climate changes, from surface area expansion to drainage and evaporative desiccation. Here, we provide a synthesis of hydrological conditions for 376 lakes of mainly thermokarst origin across high-latitude North America. We assemble surface water isotope compositions measured during the past decade at five lake-rich landscapes including Arctic Coastal Plain (Alaska), Yukon Flats (Alaska), Old Crow Flats (Yukon), northwestern Hudson Bay Lowlands (Manitoba), and Nunavik (Quebec). These landscapes represent the broad range of thermokarst environments by spanning gradients in meteorological, permafrost, and vegetation conditions. An isotope framework was established based on flux-weighted long-term averages of meteorological conditions for each lake to quantify water balance metrics. The isotope composition of source water and evaporation-to-inflow ratio for each lake were determined, and the results demonstrated a substantial array of regional and subregional diversity of lake hydrological conditions. Controls on lake water balance and how these vary among the five landscapes and with differing environmental drivers are assessed. Findings reveal that lakes in the Hudson Bay Lowlands are most vulnerable to evaporative desiccation, whereas those in Nunavik are most resilient. However, we also identify the complexity in predicting hydrological responses of these thermokarst landscapes to future climate change.
","language":"English","publisher":"NRC Research Press","doi":"10.1139/AS-2016-0019","usgsCitation":"MacDonald, L.A., Wolfe, B.B., Turner, K.W., Anderson, L., Arp, C.D., Birks, J., Bouchard, F., Edwards, T.W., Farquharson, N., Hall, R.I., McDonald, I., Narancic, B., Ouimet, C., Pienitz, R., Tondu, J., and White, H., 2017, A synthesis of thermokarst lake water balance in high-latitude regions of North America from isotope tracers: Arctic Science, v. 3, no. 2, p. 118-149, https://doi.org/10.1139/AS-2016-0019.","productDescription":"32 p.","startPage":"118","endPage":"149","ipdsId":"IP-076403","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470008,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/as-2016-0019","text":"Publisher Index 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W.","contributorId":195380,"corporation":false,"usgs":false,"family":"Turner","given":"Kevin","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":706912,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Lesleigh 0000-0002-5264-089X land@usgs.gov","orcid":"https://orcid.org/0000-0002-5264-089X","contributorId":436,"corporation":false,"usgs":true,"family":"Anderson","given":"Lesleigh","email":"land@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":706909,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":706913,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Birks, Jean","contributorId":87856,"corporation":false,"usgs":true,"family":"Birks","given":"Jean","email":"","affiliations":[],"preferred":false,"id":706914,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bouchard, Frederic","contributorId":194639,"corporation":false,"usgs":false,"family":"Bouchard","given":"Frederic","email":"","affiliations":[],"preferred":false,"id":706915,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Edwards, Thomas W.D. 0000-0002-0773-0909 tce@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-0909","contributorId":195384,"corporation":false,"usgs":false,"family":"Edwards","given":"Thomas","email":"tce@usgs.gov","middleInitial":"W.D.","affiliations":[],"preferred":false,"id":706916,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Farquharson, Nicole","contributorId":195385,"corporation":false,"usgs":false,"family":"Farquharson","given":"Nicole","email":"","affiliations":[],"preferred":false,"id":706917,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hall, Roland I.","contributorId":168744,"corporation":false,"usgs":false,"family":"Hall","given":"Roland","email":"","middleInitial":"I.","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":706918,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McDonald, Ian","contributorId":195387,"corporation":false,"usgs":false,"family":"McDonald","given":"Ian","email":"","affiliations":[],"preferred":false,"id":706919,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Narancic, Biljana","contributorId":195388,"corporation":false,"usgs":false,"family":"Narancic","given":"Biljana","email":"","affiliations":[],"preferred":false,"id":706920,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ouimet, Chantal","contributorId":195389,"corporation":false,"usgs":false,"family":"Ouimet","given":"Chantal","email":"","affiliations":[],"preferred":false,"id":706921,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pienitz, Reinhard","contributorId":195390,"corporation":false,"usgs":false,"family":"Pienitz","given":"Reinhard","email":"","affiliations":[],"preferred":false,"id":706922,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tondu, Jana","contributorId":195391,"corporation":false,"usgs":false,"family":"Tondu","given":"Jana","email":"","affiliations":[],"preferred":false,"id":706923,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"White, Hilary","contributorId":195392,"corporation":false,"usgs":false,"family":"White","given":"Hilary","email":"","affiliations":[],"preferred":false,"id":706924,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70191915,"text":"70191915 - 2017 - Developing multi-model ensemble projections of ecologically relevant climate variables for Puerto Rico and the US Caribbean","interactions":[],"lastModifiedDate":"2020-12-11T21:09:52.907025","indexId":"70191915","displayToPublicDate":"2017-03-13T15:02:52","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":7461,"text":"Final Project Memorandum","active":true,"publicationSubtype":{"id":9}},"seriesNumber":"557-271","title":"Developing multi-model ensemble projections of ecologically relevant climate variables for Puerto Rico and the US Caribbean","docAbstract":"The global increases in surface air temperature are the most widespread and direct consequence of anthropogenic climate change. However, while 21st century temperatures are projected to increase in the Caribbean, the low variability and high average temperatures suggest that impacts on ecosystems and water resources are more likely through changes to the availability, timing, and pattern of moisture. The lack of local-scale climate model information that can resolve the complex topography and small scale climate features hinders the development of robust adaptation strategies. The goal of this project was to develop a suite of local-scale climate projections using dynamic downscaling to aid the development of adaptation strategies in Puerto Rico and the U.S. Virgin Islands (USVI). This project began by engaging the ecologists, hydrologists, and conservation biologists in the region to determine the most valuable types of information to aid research and decision making. The final product provides projections of future climate at a 2km horizontal resolution based on three global climate models and two regional climate models for a scenario with high greenhouse gas emissions. Results from the projections suggest that for Puerto Rico, annual temperature would increase between 1°C and 1.3°C by mid-century with larger temperature increases located in the interior portion of the island. Precipitation totals decrease for much of the island with island average decline between 12% and 19%, with some potentially large localized decreases exceeding 30%. The projected changes for the USVI are dominated by the surrounding ocean environment. The resulting projections will be provided to stakeholders in the region via the USGS and the CLCC.","language":"English","publisher":"Southeast Climate Adaptation Science Center","usgsCitation":"Terando, A., 2017, Developing multi-model ensemble projections of ecologically relevant climate variables for Puerto Rico and the US Caribbean: Final Project Memorandum 557-271, 20 p.","productDescription":"20 p.","ipdsId":"IP-085236","costCenters":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":381228,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":346905,"type":{"id":15,"text":"Index Page"},"url":"https://secasc.ncsu.edu/wp-content/uploads/sites/14/2020/01/020-Final-Memo-Terando.pdf"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"US Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.181884765625,\n 18.07275691457901\n ],\n [\n -65.203857421875,\n 18.3858049312974\n ],\n [\n -66.588134765625,\n 18.646245142670608\n ],\n [\n -67.291259765625,\n 18.594188856740413\n ],\n [\n -67.39013671875,\n 18.15629140283545\n ],\n [\n -67.060546875,\n 17.78007412664325\n ],\n [\n -65.599365234375,\n 17.895114303749143\n ],\n [\n -65.181884765625,\n 18.07275691457901\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.43756103515625,\n 17.712060974461494\n ],\n [\n -64.64630126953125,\n 18.367559302479318\n ],\n [\n -64.7479248046875,\n 18.404048629104647\n ],\n [\n -64.9017333984375,\n 18.474399059267128\n ],\n [\n -65.0665283203125,\n 18.432713391700858\n ],\n [\n -65.1214599609375,\n 18.34931174429646\n ],\n [\n -65.07202148437499,\n 17.63616972425169\n ],\n [\n -64.76165771484375,\n 17.589048722297875\n ],\n [\n -64.43756103515625,\n 17.712060974461494\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Terando, Adam 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":197511,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":713675,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70184404,"text":"ofr20171025 - 2017 - Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia","interactions":[],"lastModifiedDate":"2017-03-14T09:45:46","indexId":"ofr20171025","displayToPublicDate":"2017-03-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1025","title":"Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia","docAbstract":"Between 1997 and 2011, Mongolia established three specially protected areas in the north-central part of the country to protect various high-value resources. These areas are jointly referred to as the Ulaan Taiga Specially Protected Areas. In accordance with the goals of the draft general management plan, this report identifies options for initiating an inventory and monitoring program for the three protected areas. Together, the three areas comprise over 1.5 million hectares of mountainous terrain west of Lake Hovsgol and bordering the Darkhad Valley. The area supports numerous rare ungulates, endangered fish, and over 40 species of threatened plants. Illegal mining, illegal logging, and poaching pose the most immediate threats to resources. As a first step, a review of published literature would inform natural resource management at the Ulaan Taiga Specially Protected Areas because it would inform other inventories.
Vegetation classification and mapping also would inform other inventory efforts because the process incorporates geographic analysis to identify environmental gradients, fine-scale sampling that captures species composition and structure, and landscape-scale results that represent the variety and extent of habitats for various organisms. Mapping using satellite imagery reduces the cost per hectare.
Following a determination of existing knowledge, field surveys of vertebrates and vascular plants would serve to build species lists and fill in gaps in existing knowledge. For abiotic resources, a focus on monitoring air quality, evaluating and monitoring water quality, and assembling and storing weather data would provide information for correlating resource response status with changing environmental conditions.
Finally, we identify datasets that, if incorporated into a geographic information system, would inform resource management. They include political boundaries, infrastructure, topography, surficial geology, hydrology, fire history, and soils.
In terms of tracking high-value resources, vegetation monitoring at the plot scale would provide a basis for detecting change in such characteristics as plant species composition, vegetation structure, and productivity that are associated with landscape-scale factors such as climate change or biotic interactions. Continued population monitoring of rare ungulates, particularly argali or wild sheep (Ovis ammon), would provide information on how populations are responding to natural and anthropogenic stressors. Siberian taimen (Hucho taimen) also is an important monitoring target given ongoing threats of poaching and climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171025","usgsCitation":"Moore, P.E., Meyer, J.B., and Chow, L.S., 2017, Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia: U.S. Geological Survey Open-File Report 2017–1025, 35 p., https://doi.org/10.3133/ofr20171025.","productDescription":"viii, 35 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-082861","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":337345,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1025/coverthb.jpg"},{"id":337346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1025/ofr20171025.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1025"}],"country":"Mongolia","otherGeospatial":"Ulaan Taiga Specially Protected Areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 97.55859375,\n 49.89463439573421\n ],\n [\n 102.48046875,\n 49.89463439573421\n ],\n [\n 102.48046875,\n 52.24125614966341\n ],\n [\n 97.55859375,\n 52.24125614966341\n ],\n [\n 97.55859375,\n 49.89463439573421\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://www.werc.usgs.gov/
This paper presents a conceptual framework to operationalize flow–ecology relationships into decision-support systems of practical use to water-resource managers, who are commonly tasked with balancing multiple competing socioeconomic and environmental priorities. We illustrate this framework with a case study, whereby fish community responses to various water-management scenarios were predicted in a partially regulated river system at a local watershed scale. This case study simulates management scenarios based on interactive effects of dam operation protocols, withdrawals for municipal water supply, effluent discharges from wastewater treatment, and inter-basin water transfers. Modeled streamflow was integrated with flow–ecology relationships relating hydrologic departure from reference conditions to fish species richness, stratified by trophic, reproductive, and habitat characteristics. Adding a hypothetical new water-withdrawal site was predicted to increase the frequency of low-flow conditions with adverse effects for several fish groups. Imposition of new reservoir release requirements was predicted to enhance flow and fish species richness immediately downstream of the reservoir, but these effects were dissipated further downstream. The framework presented here can be used to translate flow–ecology relationships into evidence-based management by developing decision-support systems for conservation of riverine biodiversity while optimizing water availability for human use.
","language":"English","publisher":"MDPI","doi":"10.3390/w9030196","usgsCitation":"Cartwright, J.M., Caldwell, C., Nebiker, S., and Knight, R., 2017, Putting flow-ecology relationships into practice: A decision-support system to assess fish community response to water-management scenarios: Water, v. 9, no. 3, p. 1-18, https://doi.org/10.3390/w9030196.","productDescription":"Article 196; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-076084","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":470021,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w9030196","text":"Publisher Index Page"},{"id":337171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-08","publicationStatus":"PW","scienceBaseUri":"58c277d5e4b014cc3a3e76a9","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":681522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caldwell, Casey","contributorId":187734,"corporation":false,"usgs":false,"family":"Caldwell","given":"Casey","email":"","affiliations":[],"preferred":false,"id":681523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nebiker, Steven","contributorId":187735,"corporation":false,"usgs":false,"family":"Nebiker","given":"Steven","email":"","affiliations":[],"preferred":false,"id":681524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, Rodney 0000-0001-9588-0167 rrknight@usgs.gov","orcid":"https://orcid.org/0000-0001-9588-0167","contributorId":152422,"corporation":false,"usgs":true,"family":"Knight","given":"Rodney","email":"rrknight@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":681525,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70184389,"text":"70184389 - 2017 - Importance of the 2014 Colorado River Delta pulse flow for migratory songbirds: Insights from foraging behavior","interactions":[],"lastModifiedDate":"2018-08-06T13:07:37","indexId":"70184389","displayToPublicDate":"2017-03-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Importance of the 2014 Colorado River Delta pulse flow for migratory songbirds: Insights from foraging behavior","docAbstract":"The Lower Colorado River provides critical riparian areas in an otherwise arid region and is an important stopover site for migrating landbirds. In order to reverse ongoing habitat degradation due to drought and human-altered hydrology, a pulse flow was released from Morelos Dam in spring of 2014, which brought surface flow to dry stretches of the Colorado River in Mexico. To assess the potential effects of habitat modification resulting from the pulse flow, we used foraging behavior of spring migrants from past and current studies to assess the relative importance of different riparian habitats. We observed foraging birds in 2000 and 2014 at five riparian sites along the Lower Colorado River in Mexico to quantify prey attack rates, prey attack maneuvers, vegetation use patterns, and degree of preference for fully leafed-out or flowering plants. Prey attack rate was highest in mesquite (Prosopis spp.) in 2000 and in willow (Salix gooddingii) in 2014; correspondingly, migrants predominantly used mesquite in 2000 and willow in 2014 and showed a preference for willows in flower or fruit in 2014. Wilson’s warbler (Cardellina pusilla) used relatively more low-energy foraging maneuvers in willow than in tamarisk (Tamarix spp.) or mesquite. Those patterns in foraging behavior suggest native riparian vegetation, and especially willow, are important resources for spring migrants along the lower Colorado River. Willow is a relatively short-lived tree dependent on spring floods for dispersal and establishment and thus spring migrants are likely to benefit from controlled pulse flows.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2016.06.001","usgsCitation":"Darrah, A., Greeney, H.F., and van Riper, C., 2017, Importance of the 2014 Colorado River Delta pulse flow for migratory songbirds: Insights from foraging behavior: Ecological Engineering, v. 106, no. B, p. 784-790, https://doi.org/10.1016/j.ecoleng.2016.06.001.","productDescription":"7 p.","startPage":"784","endPage":"790","ipdsId":"IP-071691","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":470022,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoleng.2016.06.001","text":"Publisher Index Page"},{"id":337066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.1202392578125,\n 31.765537409484374\n ],\n [\n -113.70849609375,\n 31.765537409484374\n ],\n [\n -113.70849609375,\n 32.78265637602964\n ],\n [\n -115.1202392578125,\n 32.78265637602964\n ],\n [\n -115.1202392578125,\n 31.765537409484374\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"B","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58c12633e4b014cc3a3d344c","contributors":{"authors":[{"text":"Darrah, Abigail J.","contributorId":187674,"corporation":false,"usgs":false,"family":"Darrah","given":"Abigail J.","affiliations":[{"id":12625,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA","active":true,"usgs":false},{"id":35720,"text":"Audubon Mississippi, Coastal Bird Stewardship ProgramMoss PointUSA","active":true,"usgs":false}],"preferred":false,"id":681275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greeney, Harold F.","contributorId":187675,"corporation":false,"usgs":false,"family":"Greeney","given":"Harold","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":681276,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":681274,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70184207,"text":"sim3377 - 2017 - Predicted pH at the domestic and public supply drinking water depths, Central Valley, California","interactions":[],"lastModifiedDate":"2018-09-18T08:43:53","indexId":"sim3377","displayToPublicDate":"2017-03-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3377","title":"Predicted pH at the domestic and public supply drinking water depths, Central Valley, California","docAbstract":"This scientific investigations map is a product of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project modeling and mapping team. The prediction grids depicted in this map are of continuous pH and are intended to provide an understanding of groundwater-quality conditions at the domestic and public supply drinking water zones in the groundwater of the Central Valley of California. The chemical quality of groundwater and the fate of many contaminants is often influenced by pH in all aquifers. These grids are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to pH. In this work, the median well depth categorized as domestic supply was 30 meters below land surface, and the median well depth categorized as public supply is 100 meters below land surface. Prediction grids were created using prediction modeling methods, specifically boosted regression trees (BRT) with a Gaussian error distribution within a statistical learning framework within the computing framework of R (http://www.r-project.org/). The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. The response variable was measured pH from 1,337 wells and was compiled from two sources: USGS National Water Information System (NWIS) database (all data are publicly available from the USGS: http://waterdata.usgs.gov/ca/nwis/nwis) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water quality data are publicly available from the SWRCB: http://www.waterboards.ca.gov/gama/geotracker_gama.shtml). Only wells with measured pH and well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 was used. A total of 1,003 wells (training dataset) were used to train the BRT model, and 334 wells (hold-out dataset) were used to validate the prediction model. The training r-squared was 0.70, and the root-mean-square error (RMSE) in standard pH units was 0.26. The hold-out r-squared was 0.43, and RMSE in standard pH units was 0.37. Predictor variables consisting of more than 60 variables from 7 sources were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. In this work, wells were attributed to predictor variable values in ArcGIS using a 500-meter buffer.
Faunt, C.C., ed., 2009, Groundwater availability in the Central Valley aquifer, California: U.S. Geological Survey Professional Paper 1776, 225 p., accessed at https://pubs.usgs.gov/pp/1766/.
Faunt, C.C., Belitz, K., and Hanson, R.T., 2010, Development of a three-dimensional model of sedimentary texture in valley-fill deposits of Central Valley, California, USA: Hydrogeology Journal, v. 18, no. 3, p. 625–649, https://doi.org/10.1007/s10040-009-0539-7.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3377","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., Gronberg, J.M., 2017, Predicted pH at the domestic and public supply drinking water depths, Central Valley, California: U.S. Geological Survey Scientific Investigations Map 3377, 1 sheet, scale 1:2,400,000, https://doi.org/10.3133/sim3377.","productDescription":"Sheet: 19.00 x 21.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-079912","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336887,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX77K4","text":"USGS data release","description":"USGS data release","linkHelpText":"Ascii grids of predicted pH in depth zones used by domestic and public drinking water supply depths, Central Valley, California."},{"id":336878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3377/coverthb.jpg"},{"id":336879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3377/sim3377.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3377"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.22290039062499,\n 40.75557964275589\n ],\n [\n -122.958984375,\n 40.38839687388361\n ],\n [\n -122.574462890625,\n 39.32579941789298\n ],\n [\n -122.08007812499999,\n 38.07404145941957\n ],\n [\n -120.7177734375,\n 36.77409249464195\n ],\n [\n -119.83886718750001,\n 35.33529320309328\n ],\n [\n -119.267578125,\n 34.912962495216966\n ],\n [\n -118.740234375,\n 35.110921809704756\n ],\n [\n -118.740234375,\n 35.8356283888737\n ],\n [\n -118.91601562499999,\n 36.359374956015856\n ],\n [\n -119.84985351562499,\n 37.32648861334206\n ],\n [\n -120.82763671875,\n 38.24680876017446\n ],\n [\n -121.39892578125,\n 39.2492708462234\n ],\n [\n -122.1240234375,\n 40.53050177574321\n ],\n [\n -122.22290039062499,\n 40.75557964275589\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
Telephone number: (916) 278-3000
http://ca.water.usgs.gov/
The U.S. Geological Survey, in cooperation with the Puerto Rico Department of Natural and Environmental Resources, completed a study to determine whether a relation exists between the extent of forest cover and the magnitude of base flow at two sets of paired drainage basins in the highlands of the municipalities of Adjuntas and Utuado within the mountainous interior of Puerto Rico. One set of paired basins includes the Río Guaónica and Río Tanamá, both tributaries of the Río Grande de Arecibo. The other set includes two smaller basins in the drainage basin of the Río Coabey, which is a tributary of the Río Tanamá. The paired basins in each set have similar rainfall patterns, geologic substrate, and aspect; the principal difference identified in the study is the extent of forest cover and related land uses such as the cultivation of shade and sun coffee. Data describing the hydrology, hydrogeology, and streamflow were used in the analysis. The principal objective of the study was to compare base flow per unit area among basins having different areal extents of forest cover and land uses such as shade coffee and sun coffee cultivation.
Within the mountainous interior of Puerto Rico, a substantial amount of the annual rainfall (45 to 39 percent in the Rio Guaónica and Rio Tanamá, respectively) can migrate to the subsurface and later emerge as base flow in streams. The magnitude of base flow within the two sets of paired basins varies seasonally. Minimum base flows occur during the annual dry season (generally from January to March), and maximum base flows occur during the wet season (generally from August to October). During the dry season or periods of below-normal rainfall, base flow is either the primary or the sole component of streamflow. Daily mean base flow ranged from 3.2 to 20.5 cubic feet per second (ft3 /s) at the Rio Guaónica Basin, and from 4.2 to 23.0 ft3 /s at the Rio Tanamá Basin. The daily mean base flows during 2010 ranged from 0.28 to 0.98 ft3 /s at Tributary 1 and from 0.22 to 0.58 ft3 /s at Tributary 2 of the Rio Coabey. The normalized daily base flow at the Río Guaónica and Río Tanamá Basin during 2010 ranged from 1.3 to 8.1 cubic feet per second per square mile (ft3 /s)/mi2 and from 1.1 to 6.1 (ft3 /s)/mi2 , respectively. The normalized daily base flow for the basins of Tributary 1 and Tributary 2 of Río Coabey during 2010 ranged from 1.0 to 3.6 (ft3 /s)/mi2 and from 1.5 to 3.9 (ft3 /s)/mi2 , respectively.
The normalized mean annual base flow is similar within the larger paired basins of Río Tanamá (2.74 [ft3 /s]/mi2 ) and Río Guaónica (3.15 [ft3 /s]/mi2 ). The mean annual base flow per unit area for both of these basins is about 79 percent of the mean annual streamflow. In the large paired basins, the proportion of Type I land use (forest patches, shade and mixed shade/sun coffee with associated cash crops) is substantially higher in Rio Guaónica Basin (81 percent) than in the Rio Tanamá Basin (59 percent), and the base flow per unit area is also higher. In the small paired basins of Rio Coabey, the proportion of Type I land use is much higher at Tributary 1 (52 percent) than at Tributary 2 (15 percent), but, in contrast to the large basins, the mean annual base flow per unit area is lower (2.22 and 2.62 [ft3 /s]/mi2 , respectively). There is no consistent relation between land use and normalized base flow between the two sets of paired basins in the study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165142","collaboration":"Prepared in cooperation with the Puerto Rico Department of Natural and Environmental Resources","usgsCitation":"Rodríguez-Martínez, Jesús, and Santiago, Marilyn, 2017, The effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico, 2010: U.S. Geological Survey Scientific Investigations Report 2016–5142, 19 p., https://doi.org/10.3133/sir20165142.","productDescription":"Report: vii, 19 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061550","costCenters":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true}],"links":[{"id":438424,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7N58JG5","text":"USGS data release","linkHelpText":"Hydrologic data for the effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico"},{"id":336264,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7N58JG5","text":"USGS data release ","description":"USGS data release","linkHelpText":"Hydrologic data for the effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico"},{"id":336240,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5142/coverthb.jpg"},{"id":336241,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5142/sir20165142.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5142"}],"otherGeospatial":"Puerto Rico","contact":"Director, Caribbean-Florida Water Science Center
4446 Pet Lane
Suite 108
Lutz, FL 33559
https://pr.water.usgs.gov
Datum conversions from the National Geodetic Vertical Datum of 1929 to the North American Vertical Datum of 1988 among inland and coastal gages throughout the hydrologic regions of New England, the Mid-Atlantic, and the South Atlantic-Gulf have implications among river and storm surge forecasting, general commerce, and water-control operations. The process of data conversions may involve the application of a recovered National Geodetic Vertical Datum of 1929–North American Vertical Datum of 1988 offset, a simplistic datum transformation using VDatum or VERTCON software, or a survey, depending on a gaging network datum evaluation, anticipated uncertainties for data use among the cooperative water community, and methods used to derive the conversion. Datum transformations from National Geodetic Vertical Datum of 1929 to North American Vertical Datum of 1988 using VERTCON purport errors of ± 0.13 foot at the 95 percent confidence level among modeled points, claiming more consistency along the east coast. Survey methods involving differential and trigonometric leveling, along with observations using Global Navigation Satellite System technology, afford a variety of approaches to establish or perpetuate a datum during a survey. Uncertainties among leveling approaches are generally < 0.1 foot, and and Global Navigation Satellite System approaches may be categorized with uncertainties of ≤0.1 foot for a Level I quality category and ≥0.1 foot for Level II or III quality categories (defined by the U.S. Geological Survey) by observation and review of experienced practice. The conversion process is initiated with an evaluation of the inland and coastal gage network datum, beginning with altitude datum components and the history of those components queried through the U.S. Geological Survey Groundwater Site Inventory database. Subsequent edits to the Groundwater Site Inventory database may be required and a consensus reached among the U.S. Geological Survey Water Science Centers to identify the outstanding workload categorized as in-office datum transformations or offset applications versus out-of-office survey efforts. Datum conversions or datum establishment for the inland or coastal gaging network should meet datum uncertainty requirements among other Federal agencies. Datum uncertainty requirements are ±0.25 foot for U.S. Army Corps of Engineers water-control or construction projects and ±0.16 foot for Federal Emergency Management Agency field surveys and checkpoint surveys used for mapping. River level forecasts generally are defined as ± 0.10 foot among the National Oceanic and Atmospheric Administration–National Weather Service. Collaboration and communication among the cooperative water community is necessary during a datum conversion or datum change. Datum notification time-change requirements set by the National Oceanic and Atmospheric Administration–National Weather Service vary from 30 to 120 days, depending on datum conversion or datum-change case scenarios. Notification times associated with these case scenarios may be useful to the National Oceanic and Atmospheric Administration–National Weather Service and U.S. Army Corps of Engineers, because their daily operations are time sensitive, unlike the notification time change requirements of other entities that make up the cooperative water community. At the time of this writing, a future geopotential datum resulting from Gravity for the Redefinition of the American Vertical Datum is anticipated in 2022. A future version of VDatum and VERTCON is anticipated to provide a transformation among North American Vertical Datum of 1988 elevations to the new geopotential datum.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B8","usgsCitation":"Rydlund, P.H., Jr., and Noll, M.L., 2017, Vertical datum conversion process for the inland and coastal gage network located in the New England, Mid-Atlantic, and South Atlantic-Gulf hydrologic regions (ver. 1.1, July 2017) U.S. Geological Survey Techniques and Methods, book 11, chap. B8, 29 p., https://doi.org/10.3133/tm11B8.","productDescription":"ix, 29 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-078726","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":399692,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_105491.htm"},{"id":344212,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/11/b08/versionHist.txt"},{"id":336266,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b08/coverthb2.jpg"},{"id":336267,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b08/tm11B8.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 11-8B"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.6484375,\n 30.183121842195515\n ],\n [\n -89.23095703125,\n 30.278044377800153\n ],\n [\n -88.39599609375,\n 30.221101852485987\n ],\n [\n -87.07763671875,\n 30.35391637229704\n ],\n [\n -86.06689453125,\n 30.164126343161097\n ],\n [\n -85.23193359375,\n 29.49698759653577\n ],\n [\n -84.08935546875,\n 30.012030680358613\n ],\n [\n -83.60595703125,\n 29.630771207229\n ],\n [\n -82.77099609375,\n 28.671310915880834\n ],\n [\n -82.99072265625,\n 27.858503954841247\n ],\n [\n -81.93603515625,\n 26.017297563851745\n ],\n [\n -81.32080078125,\n 25.105497373014686\n ],\n [\n -80.15625,\n 25.045792240303445\n ],\n [\n -79.8486328125,\n 26.70635985763354\n ],\n [\n -80.39794921875,\n 28.013801376380712\n ],\n [\n -81.1669921875,\n 30.939924331023445\n ],\n [\n -80.22216796875,\n 32.194208672875384\n ],\n [\n -77.93701171875,\n 33.687781758439364\n ],\n [\n -75.322265625,\n 35.08395557927643\n ],\n [\n -75.1025390625,\n 35.51434313431818\n ],\n [\n -75.7177734375,\n 36.87962060502676\n ],\n [\n -73.6962890625,\n 40.36328834091583\n ],\n [\n -69.85107421874999,\n 41.178653972331674\n ],\n [\n -69.49951171875,\n 41.83682786072714\n ],\n [\n -70.51025390625,\n 42.24478535602799\n ],\n [\n -70.42236328125,\n 43.197167282501276\n ],\n [\n -69.6533203125,\n 43.644025847699496\n ],\n [\n -66.81884765625,\n 44.74673324024678\n ],\n [\n -67.7197265625,\n 45.72152152227954\n ],\n [\n -67.7197265625,\n 47.100044694025215\n ],\n [\n -68.26904296875,\n 47.35371061951363\n ],\n [\n -69.10400390625,\n 47.517200697839414\n ],\n [\n -69.9609375,\n 46.89023157359399\n ],\n [\n -70.51025390625,\n 45.767522962149876\n ],\n [\n -71.25732421875,\n 45.336701909968134\n ],\n [\n -71.71875,\n 45.02695045318546\n ],\n [\n -73.41064453125,\n 45.01141864227728\n ],\n [\n -74.5751953125,\n 44.63739123445585\n ],\n [\n -76.97021484375,\n 43.13306116240612\n ],\n [\n -78.3544921875,\n 42.114523952464246\n ],\n [\n -79.189453125,\n 39.791654835253425\n ],\n [\n -80.26611328125,\n 37.82280243352756\n ],\n [\n -80.88134765625,\n 36.65079252503471\n ],\n [\n -82.41943359375,\n 35.28150065789119\n ],\n [\n -89.49462890625,\n 35.02999636902566\n ],\n [\n -90.41748046874999,\n 32.47269502206151\n ],\n [\n -90.10986328125,\n 31.034108344903512\n ],\n [\n -89.6484375,\n 30.183121842195515\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted March 2017; Version 1.1: July 2017","publicComments":"This report is Chapter 8 in Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data.","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road, MS 100
Rolla, MO 65401
https://mo.water.usgs.gov/
Intensification of the global hydrological cycle, ranging from larger individual precipitation events to more extreme multiyear droughts, has the potential to cause widespread alterations in ecosystem structure and function. With evidence that the incidence of extreme precipitation years (defined statistically from historical precipitation records) is increasing, there is a clear need to identify ecosystems that are most vulnerable to these changes and understand why some ecosystems are more sensitive to extremes than others. To date, opportunistic studies of naturally occurring extreme precipitation years, combined with results from a relatively small number of experiments, have provided limited mechanistic understanding of differences in ecosystem sensitivity, suggesting that new approaches are needed. Coordinated distributed experiments (CDEs) arrayed across multiple ecosystem types and focused on water can enhance our understanding of differential ecosystem sensitivity to precipitation extremes, but there are many design challenges to overcome (e.g., cost, comparability, standardization). Here, we evaluate contemporary experimental approaches for manipulating precipitation under field conditions to inform the design of ‘Drought-Net’, a relatively low-cost CDE that simulates extreme precipitation years. A common method for imposing both dry and wet years is to alter each ambient precipitation event. We endorse this approach for imposing extreme precipitation years because it simultaneously alters other precipitation characteristics (i.e., event size) consistent with natural precipitation patterns. However, we do not advocate applying identical treatment levels at all sites – a common approach to standardization in CDEs. This is because precipitation variability varies >fivefold globally resulting in a wide range of ecosystem-specific thresholds for defining extreme precipitation years. For CDEs focused on precipitation extremes, treatments should be based on each site's past climatic characteristics. This approach, though not often used by ecologists, allows ecological responses to be directly compared across disparate ecosystems and climates, facilitating process-level understanding of ecosystem sensitivity to precipitation extremes.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13504","usgsCitation":"Knapp, A., Avolio, M.L., Beier, C., Carroll, C.J., Collins, S., Dukes, J.S., Fraser, L.H., Griffin-Nolan, R.J., Hoover, D.L., Jentsch, A., Loik, M.E., Phillips, R.P., Post, A.K., Sala, O.E., Slette, I.J., Yahdjian, L., and Smith, M.D., 2017, Pushing precipitation to the extremes in distributed experiments: Recommendations for simulating wet and dry years: Global Change Biology, v. 23, no. 5, p. 1774-1782, https://doi.org/10.1111/gcb.13504.","productDescription":"9 p.","startPage":"1774","endPage":"1782","ipdsId":"IP-079614","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470023,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gcb.13504","text":"External Repository"},{"id":336943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4efe4b014cc3a3ba47a","contributors":{"authors":[{"text":"Knapp, Alan K.","contributorId":139807,"corporation":false,"usgs":false,"family":"Knapp","given":"Alan K.","affiliations":[{"id":13277,"text":"Graduate Degree Program in Ecology and Department of Biology, Colorado State University, Ft. Collins, CO","active":true,"usgs":false}],"preferred":false,"id":680953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Avolio, Meghan L.","contributorId":187573,"corporation":false,"usgs":false,"family":"Avolio","given":"Meghan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":680954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beier, Claus","contributorId":187574,"corporation":false,"usgs":false,"family":"Beier","given":"Claus","email":"","affiliations":[],"preferred":false,"id":680955,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carroll, Charles J. W.","contributorId":187575,"corporation":false,"usgs":false,"family":"Carroll","given":"Charles","email":"","middleInitial":"J. W.","affiliations":[],"preferred":false,"id":680956,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Scott L.","contributorId":71307,"corporation":false,"usgs":false,"family":"Collins","given":"Scott L.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":680957,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dukes, Jeffrey S.","contributorId":187576,"corporation":false,"usgs":false,"family":"Dukes","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":680958,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fraser, Lauchlan H.","contributorId":187577,"corporation":false,"usgs":false,"family":"Fraser","given":"Lauchlan","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":680959,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Griffin-Nolan, Robert J.","contributorId":187578,"corporation":false,"usgs":false,"family":"Griffin-Nolan","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680960,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hoover, David L. dlhoover@usgs.gov","contributorId":5843,"corporation":false,"usgs":true,"family":"Hoover","given":"David","email":"dlhoover@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":680952,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jentsch, Anke","contributorId":187579,"corporation":false,"usgs":false,"family":"Jentsch","given":"Anke","email":"","affiliations":[],"preferred":false,"id":680961,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Loik, Michael E.","contributorId":187580,"corporation":false,"usgs":false,"family":"Loik","given":"Michael","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":680962,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Phillips, Richard P.","contributorId":187581,"corporation":false,"usgs":false,"family":"Phillips","given":"Richard","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":680963,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Post, Alison K.","contributorId":187582,"corporation":false,"usgs":false,"family":"Post","given":"Alison","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":680964,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sala, Osvaldo E.","contributorId":139047,"corporation":false,"usgs":false,"family":"Sala","given":"Osvaldo","email":"","middleInitial":"E.","affiliations":[{"id":12629,"text":"Arizona State University, Tempe, AZ (DETAIL TO BE ADDED)","active":true,"usgs":false}],"preferred":false,"id":680965,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Slette, Ingrid J.","contributorId":187583,"corporation":false,"usgs":false,"family":"Slette","given":"Ingrid","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680966,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Yahdjian, Laura","contributorId":187584,"corporation":false,"usgs":false,"family":"Yahdjian","given":"Laura","email":"","affiliations":[],"preferred":false,"id":680967,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Smith, Melinda D.","contributorId":187585,"corporation":false,"usgs":false,"family":"Smith","given":"Melinda","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":680968,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70184286,"text":"70184286 - 2017 - Prediction and visualization of redox conditions in the groundwater of Central Valley, California","interactions":[],"lastModifiedDate":"2018-09-25T11:31:39","indexId":"70184286","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Prediction and visualization of redox conditions in the groundwater of Central Valley, California","docAbstract":"Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions.
Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.01.014","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., and Gronberg, J.M., 2017, Prediction and visualization of redox conditions in the groundwater of Central Valley, California: Journal of Hydrology, v. 546, p. 341-356, https://doi.org/10.1016/j.jhydrol.2017.01.014.","productDescription":"16 p.","startPage":"341","endPage":"356","ipdsId":"IP-075668","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","volume":"546","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4f0e4b014cc3a3ba483","contributors":{"authors":[{"text":"Rosecrans, Celia Z. 0000-0003-1456-4360 crosecrans@usgs.gov","orcid":"https://orcid.org/0000-0003-1456-4360","contributorId":187542,"corporation":false,"usgs":true,"family":"Rosecrans","given":"Celia","email":"crosecrans@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":680860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":680862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gronberg, JoAnn M. 0000-0003-4822-7434 jmgronbe@usgs.gov","orcid":"https://orcid.org/0000-0003-4822-7434","contributorId":3548,"corporation":false,"usgs":true,"family":"Gronberg","given":"JoAnn","email":"jmgronbe@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":680861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193819,"text":"70193819 - 2017 - Hydrological and geophysical investigation of streamflow losses and restoration strategies in an abandoned mine lands setting","interactions":[],"lastModifiedDate":"2020-03-10T06:52:56","indexId":"70193819","displayToPublicDate":"2017-03-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1574,"text":"Environmental & Engineering Geoscience","printIssn":"1078-7275","active":true,"publicationSubtype":{"id":10}},"title":"Hydrological and geophysical investigation of streamflow losses and restoration strategies in an abandoned mine lands setting","docAbstract":"Longitudinal discharge and water-quality campaigns (seepage runs) were combined with surface-geophysical surveys, hyporheic-temperature profiling, and watershed-scale hydrological monitoring to evaluate the locations, magnitude, and impact of streamwater losses from the West Creek subbasin of the West West Branch Schuylkill River into the underground Oak Hill Mine complex that extends beneath the watershed divide. Abandoned mine drainage (AMD), containing iron and other contaminants, from the Oak Hill Boreholes to the West Branch Schuylkill River was sustained during low-flow conditions and correlated to streamflow lost through the West Creek streambed. During high-flow conditions, streamflow was transmitted throughout West Creek; however, during low-flow conditions, all streamflow from the perennial headwaters was lost within the 300-to-600-m \"upper reach\" where an 1889 mine map indicated steeply dipping coalbeds underlie the channel. During low-flow conditions, the channel within the \"intermediate reach\" 700-to-1650-m downstream gained groundwater seepage with higher pH and specific conductance than upstream; however, all streamflow 1650-to-2050-m downstream was lost to underlying mines. Electrical resistivity and electromagnetic conductivity surveys indicated conductive zones beneath the upper reach, where flow loss occurred, and through the intermediate reach, where gains and losses occurred. Temperature probes at 0.06-to-0.10-m depth within the hyporheic zone of the intermediate reach indicated potential downward fluxes as high as 2.1x10-5 m/s. Cumulative streamflow lost from West Creek during seepage runs averaged 53.4 L/s, which equates to 19.3 percent of the daily average discharge of AMD from the Oak Hill Boreholes and a downward flux of 1.70x10-5 m/s across the 2.1-km-by-1.5-m West Creek stream-channel area.","language":"English","publisher":"Association of Environmental & Engineering Geologists","doi":"10.2113/gseegeosci.23.4.243","usgsCitation":"Cravotta, C., Sherrod, L., Galeone, D.G., Lehman, W.G., Ackman, T.E., and Kramer, A., 2017, Hydrological and geophysical investigation of streamflow losses and restoration strategies in an abandoned mine lands setting: Environmental & Engineering Geoscience, v. 23, no. 4, p. 243-273, https://doi.org/10.2113/gseegeosci.23.4.243.","productDescription":"31 p.","startPage":"243","endPage":"273","ipdsId":"IP-082023","costCenters":[{"id":532,"text":"Pennsylvania Water Science 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\"}}]}","volume":"23","issue":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07e928e4b09af898c8cbfd","contributors":{"authors":[{"text":"Cravotta, Charles A. III 0000-0003-3116-4684 cravotta@usgs.gov","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":196993,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","email":"cravotta@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":720602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherrod, Laura","contributorId":200005,"corporation":false,"usgs":false,"family":"Sherrod","given":"Laura","email":"","affiliations":[{"id":35676,"text":"Kutztown University Department of Physical Sciences, P.O. Box 730, Kutztown, PA 19530","active":true,"usgs":false}],"preferred":false,"id":720603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galeone, Daniel G. 0000-0002-8007-9278 dgaleone@usgs.gov","orcid":"https://orcid.org/0000-0002-8007-9278","contributorId":2301,"corporation":false,"usgs":true,"family":"Galeone","given":"Daniel","email":"dgaleone@usgs.gov","middleInitial":"G.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":720606,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lehman, Wayne G.","contributorId":200006,"corporation":false,"usgs":false,"family":"Lehman","given":"Wayne","email":"","middleInitial":"G.","affiliations":[{"id":35677,"text":"Schuylkill Conservation District, 1206 AG Center Dr, Pottsville, PA 17901","active":true,"usgs":false}],"preferred":false,"id":720604,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ackman, Terry E.","contributorId":200007,"corporation":false,"usgs":false,"family":"Ackman","given":"Terry","email":"","middleInitial":"E.","affiliations":[{"id":35678,"text":"M T Water Management, Inc., 438 Old Clairton Rd., Jefferson Hills, PA 15025","active":true,"usgs":false}],"preferred":false,"id":720605,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kramer, Alexa","contributorId":200008,"corporation":false,"usgs":false,"family":"Kramer","given":"Alexa","email":"","affiliations":[{"id":35679,"text":"Schuylkill Headwaters Association, Inc., 1206 AG Center Dr, Pottsville, PA","active":true,"usgs":false}],"preferred":false,"id":720607,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191671,"text":"70191671 - 2017 - New insights into nitrate dynamics in a karst groundwater system gained from in situ high-frequency optical sensor measurements","interactions":[],"lastModifiedDate":"2017-10-24T14:04:19","indexId":"70191671","displayToPublicDate":"2017-03-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"New insights into nitrate dynamics in a karst groundwater system gained from in situ high-frequency optical sensor measurements","docAbstract":"Understanding nitrate dynamics in groundwater systems as a function of climatic conditions, especially during contrasting patterns of drought and wet cycles, is limited by a lack of temporal and spatial data. Nitrate sensors have the capability for making accurate, high-frequency measurements of nitrate in situ, but have not yet been evaluated for long-term use in groundwater wells. We measured in situ nitrate continuously in two groundwater monitoring wells —one rural and one urban—located in the recharge zone of a productive karst aquifer in central Texas in order to resolve changes that occur over both short-term (hourly to daily) and long-term (monthly to yearly) periods. Nitrate concentrations, measured as nitrate-nitrogen in milligrams per liter (mg/L), during drought conditions showed little or no temporal change as groundwater levels declined. During aquifer recharge, extremely rapid changes in concentration occurred at both wells as documented by hourly data. At both sites, nitrate concentrations were affected by recharging surface water as evidenced by nitrate concentrations in groundwater recharge (0.8–1.3 mg/L) that were similar to previously reported values for regional recharging streams. Groundwater nitrate concentrations responded differently at urban and rural sites during groundwater recharge. Concentrations at the rural well (approximately 1.0 mg/L) increased as a result of higher nitrate concentrations in groundwater recharge relative to ambient nitrate concentrations in groundwater, whereas concentrations at the urban well (approximately 2.7 mg/L) decreased as a result of the dilution of higher ambient nitrate concentrations relative to those in groundwater recharge. Notably, nitrate concentrations decreased to as low as 0.8 mg/L at the urban site during recharge but postrecharge concentrations exceeded 3.0 mg/L. A return to higher nitrate concentrations postrecharge indicates mobilization of a localized source of elevated nitrate within the urbanized area of the aquifer. Changes in specific conductance were observed at both sites during groundwater recharge, and a significant correlation between specific conductance and nitrate (correlation coefficient [R] = 0.455) was evident at the urban site where large (3-fold) changes in nitrate occurred. Nitrate concentrations and specific conductance measured during a depth profile indicated that the water column was generally homogeneous as expected for this karst environment, but changes were observed in the most productive zone of the aquifer that might indicate some heterogeneity within the complex network of flow paths. Resolving the timing and magnitude of changes and characterizing fine-scale vertical differences would not be possible using conventional sampling techniques. The patterns observed in situ provided new insight into the dynamic nature of nitrate in a karst groundwater system.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2016.12.038","usgsCitation":"Opsahl, S.P., Musgrove, M., and Slattery, R.N., 2017, New insights into nitrate dynamics in a karst groundwater system gained from in situ high-frequency optical sensor measurements: Journal of Hydrology, v. 546, p. 179-188, https://doi.org/10.1016/j.jhydrol.2016.12.038.","productDescription":"10 p.","startPage":"179","endPage":"188","ipdsId":"IP-067710","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":347247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Edwards Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.469970703125,\n 29.11857441491087\n ],\n [\n -97.55584716796875,\n 29.11857441491087\n ],\n [\n -97.55584716796875,\n 30.458144351018078\n ],\n [\n -100.469970703125,\n 30.458144351018078\n ],\n [\n -100.469970703125,\n 29.11857441491087\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"546","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f05123e4b0220bbd9a1d9f","contributors":{"authors":[{"text":"Opsahl, Stephen P. 0000-0002-4774-0415 sopsahl@usgs.gov","orcid":"https://orcid.org/0000-0002-4774-0415","contributorId":4713,"corporation":false,"usgs":true,"family":"Opsahl","given":"Stephen","email":"sopsahl@usgs.gov","middleInitial":"P.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":713011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Musgrove, MaryLynn 0000-0003-1607-3864 mmusgrov@usgs.gov","orcid":"https://orcid.org/0000-0003-1607-3864","contributorId":197013,"corporation":false,"usgs":true,"family":"Musgrove","given":"MaryLynn","email":"mmusgrov@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":false,"id":713012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Slattery, Richard N. 0000-0002-9141-9776 rnslatte@usgs.gov","orcid":"https://orcid.org/0000-0002-9141-9776","contributorId":2471,"corporation":false,"usgs":true,"family":"Slattery","given":"Richard","email":"rnslatte@usgs.gov","middleInitial":"N.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":713013,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70182714,"text":"70182714 - 2017 - Geochemistry and hydrology of perched groundwater springs: assessing elevated uranium concentrations at Pigeon Spring relative to nearby Pigeon Mine, Arizona (USA)","interactions":[],"lastModifiedDate":"2018-08-07T12:40:26","indexId":"70182714","displayToPublicDate":"2017-02-27T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry and hydrology of perched groundwater springs: assessing elevated uranium concentrations at Pigeon Spring relative to nearby Pigeon Mine, Arizona (USA)","docAbstract":"The processes that affect water chemistry as the water flows from recharge areas through breccia-pipe uranium deposits in the Grand Canyon region of the southwestern United States are not well understood. Pigeon Spring had elevated uranium in 1982 (44 μg/L), compared to other perched springs (2.7–18 μg/L), prior to mining operations at the nearby Pigeon Mine. Perched groundwater springs in an area around the Pigeon Mine were sampled between 2009 and 2015 and compared with material from the Pigeon Mine to better understand the geochemistry and hydrology of the area. Two general groups of perched groundwater springs were identified from this study; one group is characterized by calcium sulfate type water, low uranium activity ratio 234U/238U (UAR) values, and a mixture of water with some component of modern water, and the other group by calcium-magnesium sulfate type water, higher UAR values, and radiocarbon ages indicating recharge on the order of several thousand years ago. Multivariate statistical principal components analysis of Pigeon Mine and spring samples indicate Cu, Pb, As, Mn, and Cd concentrations distinguished mining-related leachates from perched groundwater springs. The groundwater potentiometric surface indicates that perched groundwater at Pigeon Mine would likely flow toward the northwest away from Pigeon Spring. The geochemical analysis of the water, sediment and rock samples collected from the Snake Gulch area indicate that the elevated uranium at Pigeon Spring is likely related to a natural source of uranium upgradient from the spring and not likely related to the Pigeon Mine.
","language":"English","publisher":"Springer","publisherLocation":"Berlin","doi":"10.1007/s10040-016-1494-8","usgsCitation":"Beisner, K.R., Paretti, N.V., Tillman, F.D., Naftz, D.L., Bills, D.J., Walton-Day, K., and Gallegos, T.J., 2017, Geochemistry and hydrology of perched groundwater springs: assessing elevated uranium concentrations at Pigeon Spring relative to nearby Pigeon Mine, Arizona (USA): Hydrogeology Journal, v. 25, no. 2, p. 539-556, https://doi.org/10.1007/s10040-016-1494-8.","productDescription":"18 p.","startPage":"539","endPage":"556","ipdsId":"IP-076753","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":470055,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-016-1494-8","text":"Publisher Index Page"},{"id":336246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Pigeon Spring","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.641667,\n 36.916667\n ],\n [\n -112.175,\n 36.916667\n ],\n [\n -112.175,\n 36.566667\n ],\n [\n -112.641667,\n 36.566667\n ],\n [\n -112.641667,\n 36.916667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-23","publicationStatus":"PW","scienceBaseUri":"58b548bce4b01ccd54fddfa2","chorus":{"doi":"10.1007/s10040-016-1494-8","url":"http://dx.doi.org/10.1007/s10040-016-1494-8","publisher":"Springer Nature","authors":"Beisner Kimberly R., Paretti Nicholas V., Tillman Fred D., Naftz David L., Bills Donald J., Walton-Day Katie, Gallegos Tanya J.","journalName":"Hydrogeology Journal","publicationDate":"11/23/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"11/23/2016"},"contributors":{"authors":[{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paretti, Nicholas V. 0000-0003-2178-4820 nparetti@usgs.gov","orcid":"https://orcid.org/0000-0003-2178-4820","contributorId":173412,"corporation":false,"usgs":true,"family":"Paretti","given":"Nicholas","email":"nparetti@usgs.gov","middleInitial":"V.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bills, Donald J. 0000-0001-8955-3370 djbills@usgs.gov","orcid":"https://orcid.org/0000-0001-8955-3370","contributorId":177439,"corporation":false,"usgs":true,"family":"Bills","given":"Donald","email":"djbills@usgs.gov","middleInitial":"J.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673396,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":673397,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gallegos, Tanya J. 0000-0003-3350-6473 tgallegos@usgs.gov","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":2206,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya","email":"tgallegos@usgs.gov","middleInitial":"J.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":673398,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70180976,"text":"sir20175012 - 2017 - Hydrology and water quality in 13 watersheds in Gwinnett County, Georgia, 2001–15","interactions":[],"lastModifiedDate":"2017-02-24T08:35:22","indexId":"sir20175012","displayToPublicDate":"2017-02-23T17:00:00","publicationYear":"2017","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":"2017-5012","title":"Hydrology and water quality in 13 watersheds in Gwinnett County, Georgia, 2001–15","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with Gwinnett County Department of Water Resources, established a Long-Term Trend Monitoring (LTTM) program in 1996. The LTTM program is a comprehensive, long-term, water-quantity and water-quality monitoring program designed to document and analyze the hydrologic and water-quality conditions of selected watersheds in Gwinnett County, Georgia. Water-quality monitoring initially began in six watersheds and currently [2016] includes 13 watersheds.
As part of the LTTM program, streamflow, precipitation, water temperature, specific conductance, and turbidity were measured every 15 minutes for water years 2001–15 at 12 of the 13 watershed monitoring stations and for water years 2010–15 at the other watershed. In addition, discrete water-quality samples were collected seasonally from May through October (summer) and November through April (winter), including one base-flow and three stormflow event composite samples, during the study period. Samples were analyzed for nutrients (nitrogen and phosphorus), total organic carbon, trace elements (total lead and total zinc), total dissolved solids, and total suspended sediment (total suspended solids and suspended-sediment concentrations). The sampling scheme was designed to identify variations in water quality both hydrologically and seasonally.
The 13 watersheds were characterized for basin slope, population density, land use for 2012, and the percentage of impervious area from 2000 to 2014. Several droughts occurred during the study period—water years 2002, 2007–08, and 2011–12. Watersheds with the highest percentage of impervious areas had the highest runoff ratios, which is the portion of precipitation that occurs as runoff. Watershed base-flow indexes, the ratio of base-flow runoff to total runoff, were inversely correlated with watershed impervious area.
Flood-frequency estimates were computed for 13 streamgages in the study area that have 10 or more years of annual peak flow data through water year 2015, using the expected moments algorithm to fit a Pearson Type III distribution to logarithms of annual peak flows. Kendall’s tau nonparametric test was used to determine the statistical significance of trends in the annual peak flows, with none of the 13 streamgages exhibiting significant trends.
A comparison of base-flow and stormflow water-quality samples indicates that turbidity and concentrations of total ammonia plus organic nitrogen, total nitrogen, total phosphorus, total organic carbon, total lead, total zinc, total suspended solids, and suspended-sediment concentrations increased with increasing discharge at all watersheds. Specific conductance decreased during stormflow at all watersheds, and total dissolved solids concentrations decreased during stormflow at a few of the watersheds. Total suspended solids and suspended-sediment concentrations typically were two orders of magnitude higher in stormflow samples, turbidities were about 1.5 orders of magnitude higher, total phosphorus and total zinc were about one order of magnitude higher, and total ammonia plus organic nitrogen, total nitrogen, total organic carbon, and total lead were about twofold higher than in base-flow samples.
Seasonality and long-term trends were identified for the period water years 2001–15 for 10 constituents—total nitrogen, total nitrate plus nitrite, total phosphorus, dissolved phosphorus, total organic carbon, total suspended solids, suspended-sediment concentration, total lead, total zinc, and total dissolved solids. Seasonal patterns were present in most watersheds for all constituents except total dissolved solids, and the watersheds had fairly similar patterns of higher concentrations in the summer and lower concentrations in the winter. A linear long-term trend analysis of residual concentrations from the flow-only load estimation model (without time-trend terms) identified significant trends in 67 of the 130 constituent-watershed combinations. Seventy percent of the significant trends were negative. Total organic carbon and total dissolved solids had predominantly positive trends. Total phosphorus, total suspended solids, suspended-sediment concentration, total lead, and total zinc had only negative trends. The other three constituents exhibited fewer trends, both positive and negative.
Streamwater loads were estimated annually for the 13-year period water years 2003–15 for the same 10 constituents in the trend analysis. Loads were estimated using a regression-model-based approach developed by the USGS for the Gwinnett County LTTM program that accommodates the use of storm-event composited samples. Concentrations were modeled as a function of discharge, base flow, time, season, and turbidity to improve model predictions and reduce errors in load estimates. Total suspended solids annual loads have been identified in Gwinnett County’s Watershed Protection Plan for target performance criterion.
Although the amount of annual runoff was the primary factor in variations in annual loads, climatic conditions (classified as dry, average, or wet) affected annual loads beyond what was attributed to climatic-related variations in annual runoff. Significant negative trends in loads were estimated for the combined area of the watersheds for all constituents except dissolved phosphorus, total organic carbon, and total dissolved solids. The trend analysis indicated that total suspended solids and suspended-sediment concentration loads in the study area were decreasing by 57,000 and 87,000 pounds per day per year, respectively.
Variations in constituent yields between watersheds appeared to be related to various watershed characteristics. Suspended sediment (as either total suspended solids or suspended-sediment concentrations), along with constituents transported predominately in solid phase (total phosphorus, total organic carbon, total lead, and total zinc), and total dissolved solids typically had higher yields from watersheds that had high percentages of impervious areas or high basin slope. High total nitrogen yields were also associated with watersheds with high percentages of impervious areas. Low total nitrogen, total suspended solids, total lead, and total zinc yields appeared to be associated with watersheds that had a low percentage of high-density development.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175012","collaboration":"Prepared in cooperation with the Gwinnett County Department of Water Resources","usgsCitation":"Aulenbach, B.T., Joiner, J.K., and Painter, J.A., 2017, Hydrology and water quality in 13 watersheds in Gwinnett County, Georgia, 2001–15: U.S. Geological Survey Scientific Investigations Report 2017– 5012, 82 p., https://doi.org/10.3133/sir20175012. ","productDescription":"Report: x, 82 p.; Data release","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-077790","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":336056,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5012/coverthb.jpg"},{"id":336057,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5012/sir20175012.pdf","text":"Report","size":"5.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5012"},{"id":336058,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7639MXG","text":"USGS data release","description":"USGS data release","linkHelpText":"Stream water-quality summary statistics and outliers, streamwater load models and yield estimates, and peak flow modeling parameters for 13 watersheds in Gwinnett County, Georgia"}],"country":"United States","state":"Georgia","county":"Gwinnett 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South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Or visit the South Atlantic Water Science Center website at
http://www.usgs.gov/water/southatlantic/
The U.S. Geological Survey (USGS), in cooperation with the Texas Water Development Board and the Galveston Bay Estuary Program, collected streamflow and water-quality data at USGS streamflow-gaging stations in the lower Trinity River watershed from May 2014 to December 2015 to characterize and improve the current understanding of the quantity and quality of freshwater inflow entering Galveston Bay from the Trinity River. Continuous streamflow records at four USGS streamflow-gaging stations were compared to quantify differences in streamflow magnitude between upstream and downstream reaches of the lower Trinity River. Water-quality conditions were characterized from discrete nutrient and sediment samples collected over a range of hydrologic conditions at USGS streamflow-gaging station 08067252 Trinity River at Wallisville, Tex. (hereinafter referred to as the “Wallisville site”), approximately 4 river miles upstream from where the Trinity River enters Galveston Bay.
Based on streamflow records, annual mean outflow from Livingston Dam into the lower Trinity River was 2,240 cubic feet per second (ft3/s) in 2014 and 22,400 ft3/s in 2015, the second lowest and the highest, respectively, during the entire period of record (1966–2015). During this study, only about 54 percent of the total volume measured at upstream sites was accounted for at the Wallisville site as the Trinity River enters Galveston Bay. This difference in water volumes between upstream sites and the Wallisville site indicates that at high flows a large part of the volume released from Lake Livingston does not reach Galveston Bay through the main channel of the Trinity River. These findings indicate that water likely flows into wetlands and water bodies surrounding the main channel of the Trinity River before reaching the Wallisville site and is being stored or discharged through other channels that flow directly into Galveston Bay.
To characterize suspended-sediment concentrations and loads in Trinity River inflow to Galveston Bay, a regression model was developed to estimate suspended-sediment concentrations by using acoustic backscatter data as a surrogate. The model yielded an adjusted coefficient of determination value of 0.92 and a root mean square error of 1.65 milligrams per liter (mg/L). The mean absolute percentage error between measured and estimated suspended-sediment concentration was 35 percent. During this study, estimated suspended-sediment concentrations ranged from 2 to 701 mg/L, with a mean of 97 mg/L. Suspended-sediment concentrations varied in response to changes in discharge, with peak suspended-sediment concentrations occurring 1 to 2 days before the peak discharge for each event. The total suspended-sediment load at the Wallisville site during May 2014–December 2015 was approximately 2,200,000 tons, with a minimum monthly suspended-sediment load of 100 tons in October 2014 and a maximum monthly load of 441,000 tons in November 2015.
Results from nutrient samples collected at the Wallisville site indicate that total nitrogen and total phosphorus concentrations fluctuated at a similar rate, with the highest nutrient concentrations occurring during periods of high flow corresponding to releases from Lake Livingston. The mean concentrations of total nitrogen and total phosphorus were approximately 75 percent higher during high flow releases than during periods of low flow, overshadowing variations in nutrient concentrations caused by seasonality at the Wallisville site.
Results from the study indicate nutrient delivery to Galveston Bay from the main channel of the Trinity River is likely controlled primarily by high-flow releases from Lake Livingston. For most samples collected at the Wallisville site, organic nitrogen was the predominant form of nitrogen; however, when discharge increased because of releases from Lake Livingston, the percentage of organic nitrogen typically decreased and the percentage of nitrate increased. The concentrations of total phosphorus also increased during high-flow events, likely as a result of suspended sediment within Lake Livingston releases and mobilization of sediment particles in the river channel and flood plain during these periods of high flow. The predominant source of phosphorous to Galveston Bay from the Trinity River is in particulate form closely tied to suspended-sediment concentrations. The changes in nutrient concentration and composition caused by releases from Lake Livingston during this study indicate the reservoir may play an important role in the delivery of nutrients into Galveston Bay. Further study is required to better understand the processes in Lake Livingston influencing the characteristics of nutrient and sediment inflow to Galveston Bay. With phosphorous concentrations correlated to suspended-sediment concentrations (coefficient of determination value of 0.75) and with the concentrations of nutrients changing as the discharge changes, the diversion of water and suspended sediment into surrounding wetlands and channels outside of the main channel of the Trinity River may play a large role in regulating nutrient inputs into Galveston Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165177","isbn":"978-1-4113-4107-4","collaboration":"Prepared in cooperation with the Texas Water Development Board and the Galveston Bay Estuary Program ","usgsCitation":"Lucena, Zulimar, and Lee, M.T., 2017, Characterization of streamflow, suspended sediment, and nutrients entering Galveston Bay from the Trinity River, Texas, May 2014–December 2015: U.S. Geological Survey Scientific Investigations Report 2016–5177, 38 p., https://doi.org/10.3133/sir20165177.\n","productDescription":"vii, 37 p.","numberOfPages":"49","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-077707","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":335588,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5177/sir20165177.pdf","text":"Report","size":"11.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5177"},{"id":335587,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5177/coverthb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Galveston Bay, Trinity River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.15533447265625,\n 29.7596087873038\n ],\n [\n -94.58953857421875,\n 29.7596087873038\n ],\n [\n -94.58953857421875,\n 30.982318643027536\n ],\n [\n -95.15533447265625,\n 30.982318643027536\n ],\n [\n -95.15533447265625,\n 29.7596087873038\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, X 78754
https://tx.usgs.gov/
Management actions are based upon predictable responses. To form expected responses to restoration actions, I estimated habitat relationships and trends (2002–2015) for four pond-breeding amphibians on a wildlife refuge (Montana, USA) where changes to restore historical hydrology to the system greatly expanded (≥8 times) the flooded area of the primary breeding site for western toads (Anaxyrus boreas). Additional restoration actions are planned for the near future, including removing ponds that provide amphibian habitat. Multi-season occupancy models based on data from 15 ponds sampled during 7 years revealed that the number of breeding subpopulations increased modestly for Columbia spotted frogs (Rana luteiventris) and was stationary for long-toed salamanders (Ambystoma macrodactylum) and Pacific treefrogs (Pseudacris regilla). For these three species, pond depth was the characteristic that was associated most frequently with occupancy or changes in colonization and extinction. In contrast, a large decrease in colonization by western toads explained the decline from eight occupied ponds in 2002 to two ponds in 2015. This decline occurred despite an increase in wetland area and the colonization of a newly created pond. These changes highlight the challenges of managing for multiple species and how management responses can be unpredictable, possibly reducing the efficacy of targeted actions.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-016-2979-0","usgsCitation":"Hossack, B.R., 2017, Amphibian dynamics in constructed ponds on a wildlife refuge: developing expected responses to hydrological restoration: Hydrobiologia, v. 790, no. 1, p. 23-33, https://doi.org/10.1007/s10750-016-2979-0.","productDescription":"11 p.","startPage":"23","endPage":"33","ipdsId":"IP-070337","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":335893,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"790","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-09","publicationStatus":"PW","scienceBaseUri":"58ad5fc1e4b01ccd54f8b517","chorus":{"doi":"10.1007/s10750-016-2979-0","url":"http://dx.doi.org/10.1007/s10750-016-2979-0","publisher":"Springer Nature","authors":"Hossack Blake R.","journalName":"Hydrobiologia","publicationDate":"9/9/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"9/9/2016"},"contributors":{"authors":[{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":652472,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70178681,"text":"sir20165166 - 2017 - Flood-inundation maps for the Big Blue River at Shelbyville, Indiana","interactions":[],"lastModifiedDate":"2017-03-09T11:07:30","indexId":"sir20165166","displayToPublicDate":"2017-02-16T11:00:00","publicationYear":"2017","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":"2016-5166","title":"Flood-inundation maps for the Big Blue River at Shelbyville, Indiana","docAbstract":"Digital flood-inundation maps for a 4.1-mile reach of the Big Blue River at Shelbyville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The floodinundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at https://water. usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Big Blue River at Shelbyville, Ind. (station number 03361500). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata. usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ ahps/, which also forecasts flood hydrographs at this site (SBVI3). Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at the Big Blue River at Shelbyville, Ind., streamgage. The calibrated hydraulic model was then used to compute 12 water-surface profiles for flood stages referenced to the streamgage datum and ranging from 9.0 feet, or near bankfull, to 19.4 feet, the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging [lidar] data having a 0.98-foot vertical accuracy and 4.9-foot horizontal resolution) to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage at the Big Blue River at Shelbyville, Ind., and forecasted stream stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165166","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Fowler, K.K., 2017, Flood-inundation maps for the Big Blue River at Shelbyville, Indiana: U.S. Geological Survey Scientific Investigations Report 2016–5166, 11 p., https://doi.org/10.3133/sir20165166.","productDescription":"Report: vi, 11 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077203","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":335209,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7WH2N48","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":" Big Blue River at Shelbyville, Indiana, flood-inundation geospatial datasets"},{"id":335207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5166/coverthb.jpg"},{"id":335208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5166/sir20165166.pdf","text":"Report","size":"1.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5166"}],"country":"United States","state":"Indiana","city":"Shelbyville","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.8248519897461,\n 39.504305605954634\n ],\n [\n -85.7457160949707,\n 39.504305605954634\n ],\n [\n -85.7457160949707,\n 39.5546183524477\n ],\n [\n -85.8248519897461,\n 39.5546183524477\n ],\n [\n -85.8248519897461,\n 39.504305605954634\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278–1996
Great Lakes tributaries are known to deliver waterborne pathogens from a host of sources. To examine the hydrologic, land cover, and seasonal patterns of waterborne pathogens (i.e. protozoa (2), pathogenic bacteria (4) human viruses, (8) and bovine viruses (8)) eight rivers were monitored in the Great Lakes Basin over 29 months from February 2011 to June 2013. Sampling locations represented a wide variety of land cover classes from urban to agriculture to forest. A custom automated pathogen sampler was deployed at eight sampling locations which provided unattended, flow-weighted, large-volume (120–1630 L) sampling. Human and bovine viruses and pathogenic bacteria were detected by real-time qPCR in 16%, 14%, and 1.4% of 290 samples collected while protozoa were never detected. The most frequently detected pathogens were: bovine polyomavirus (11%), and human adenovirus C, D, F (9%). Human and bovine viruses were present in 16.9% and 14.8% of runoff-event samples (n = 189) resulting from precipitation and snowmelt, and 13.9% and 12.9% of low-flow samples (n = 101), respectively, indicating multiple delivery mechanisms could be influential. Data indicated human and bovine virus prevalence was different depending on land cover within the watershed. Occurrence, concentration, and flux of human viruses were greatest in samples from the three sampling locations with greater than 25% urban influence than those with less than 25% urban influence. Similarly, occurrence, concentration, and flux of bovine viruses were greatest in samples from the two sampling locations with greater than 50 cattle/km2 than those with less than 50 cattle/km2. In seasonal analysis, human and bovine viruses occurred more frequently in spring and winter seasons than during the fall and summer. Concentration, occurrence, and flux in the context of hydrologic condition, seasonality, and land use must be considered for each watershed individually to develop effective watershed management strategies for pathogen reduction.
","language":"English","publisher":"International Water Association","publisherLocation":"Oxford","doi":"10.1016/j.watres.2017.01.060","usgsCitation":"Lenaker, P.L., Corsi, S., Borchardt, M.A., Spencer, S.K., Baldwin, A.K., and Lutz, M.A., 2017, Hydrologic, land cover, and seasonal patterns of waterborne pathogens in Great Lakes tributaries: Water Research, v. 113, p. 11-21, https://doi.org/10.1016/j.watres.2017.01.060.","productDescription":"11 p.","startPage":"11","endPage":"21","ipdsId":"IP-079348","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":470067,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1016/j.watres.2017.01.060","text":"External Repository"},{"id":335540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes tributaries","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.97802734375,\n 39.825413103424786\n ],\n [\n -81.5625,\n 39.825413103424786\n ],\n [\n -81.5625,\n 47.27922900257082\n ],\n [\n -89.97802734375,\n 47.27922900257082\n ],\n [\n -89.97802734375,\n 39.825413103424786\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"113","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a576bae4b057081a24ed16","contributors":{"authors":[{"text":"Lenaker, Peter L. 0000-0002-9469-6285 plenaker@usgs.gov","orcid":"https://orcid.org/0000-0002-9469-6285","contributorId":5572,"corporation":false,"usgs":true,"family":"Lenaker","given":"Peter","email":"plenaker@usgs.gov","middleInitial":"L.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":669265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spencer, Susan K.","contributorId":181738,"corporation":false,"usgs":false,"family":"Spencer","given":"Susan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":669266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669268,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lutz, Michelle A. malutz@usgs.gov","contributorId":131020,"corporation":false,"usgs":true,"family":"Lutz","given":"Michelle","email":"malutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":669267,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178565,"text":"70178565 - 2017 - Preferential flow, diffuse flow, and perching in an interbedded fractured-rock unsaturated zone","interactions":[],"lastModifiedDate":"2017-02-24T10:34:07","indexId":"70178565","displayToPublicDate":"2017-02-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Preferential flow, diffuse flow, and perching in an interbedded fractured-rock unsaturated zone","docAbstract":"Layers of strong geologic contrast within the unsaturated zone can control recharge and contaminant transport to underlying aquifers. Slow diffuse flow in certain geologic layers, and rapid preferential flow in others, complicates the prediction of vertical and lateral fluxes. A simple model is presented, designed to use limited geological site information to predict these critical subsurface processes in response to a sustained infiltration source. The model is developed and tested using site-specific information from the Idaho National Laboratory in the Eastern Snake River Plain (ESRP), USA, where there are natural and anthropogenic sources of high-volume infiltration from floods, spills, leaks, wastewater disposal, retention ponds, and hydrologic field experiments. The thick unsaturated zone overlying the ESRP aquifer is a good example of a sharply stratified unsaturated zone. Sedimentary interbeds are interspersed between massive and fractured basalt units. The combination of surficial sediments, basalts, and interbeds determines the water fluxes through the variably saturated subsurface. Interbeds are generally less conductive, sometimes causing perched water to collect above them. The model successfully predicts the volume and extent of perching and approximates vertical travel times during events that generate high fluxes from the land surface. These developments are applicable to sites having a thick, geologically complex unsaturated zone of substantial thickness in which preferential and diffuse flow, and perching of percolated water, are important to contaminant transport or aquifer recharge.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-016-1496-6","usgsCitation":"Nimmo, J.R., Creasey, K.M., Perkins, K., and Mirus, B.B., 2017, Preferential flow, diffuse flow, and perching in an interbedded fractured-rock unsaturated zone: Hydrogeology Journal, v. 25, no. 2, p. 421-444, https://doi.org/10.1007/s10040-016-1496-6.","productDescription":"24 p.","startPage":"421","endPage":"444","ipdsId":"IP-065100","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":335550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Eastern Snake River Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.333333,\n 44.083333\n ],\n [\n -112.333333,\n 44.083333\n ],\n [\n -112.333333,\n 43.25\n ],\n [\n -113.333333,\n 43.25\n ],\n [\n -113.333333,\n 44.083333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-26","publicationStatus":"PW","scienceBaseUri":"58a576bee4b057081a24ed30","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":654384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Creasey, Kaitlyn M kcreasey@usgs.gov","contributorId":5799,"corporation":false,"usgs":true,"family":"Creasey","given":"Kaitlyn","email":"kcreasey@usgs.gov","middleInitial":"M","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":654385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perkins, Kimberlie 0000-0001-8349-447X kperkins@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-447X","contributorId":138544,"corporation":false,"usgs":true,"family":"Perkins","given":"Kimberlie","email":"kperkins@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":654386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":654387,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}