Sagebrush (Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km2 (1.1%) across the study area under the A1B scenario and 41.15 km2 (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km2 (4.1%) under A1B and 50.8 km2 (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km2 (3.8%) under A1B and 40.59 km2 (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse (Centrocercus urophasianus) habitat models to evaluate the effects of potential climate-induced habitat change. Under the 2050 IPCC A1B scenario, 11.6% of currently identified nesting habitat was lost, and 0.002% of new potential habitat was gained, with 4% of summer habitat lost and 0.039% gained. Our results demonstrate the successful ability of remote sensing based sagebrush components, when coupled with precipitation, to forecast future component response using IPCC precipitation scenarios. Our approach also enables future quantification of greater sage-grouse habitat under different precipitation scenarios, and provides additional capability to identify regional precipitation influence on sagebrush component response.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2015.03.002","usgsCitation":"Homer, C.G., Xian, G.Z., Aldridge, C.L., Meyer, D.K., Loveland, T.R., and O’Donnell, M.S., 2015, Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: learning from past climate patterns and Landsat imagery to predict the future: Ecological Indicators, v. 55, p. 131-145, https://doi.org/10.1016/j.ecolind.2015.03.002.","productDescription":"15 p.","startPage":"131","endPage":"145","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061116","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472146,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2015.03.002","text":"Publisher Index Page"},{"id":299699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.96240234375,\n 41.48080459927738\n ],\n [\n -110.00473022460938,\n 41.6770148220322\n ],\n [\n -109.69573974609375,\n 42.56926437219384\n ],\n [\n -108.65341186523436,\n 42.37883631647602\n ],\n [\n -108.96240234375,\n 41.48080459927738\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"552f7d9ae4b0b22a158031c7","contributors":{"authors":[{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":544946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":544947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":544948,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyer, Debra K. 0000-0002-8841-697X dkmeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":3145,"corporation":false,"usgs":true,"family":"Meyer","given":"Debra","email":"dkmeyer@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":544950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140256,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":544949,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":3351,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":544951,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70145793,"text":"ofr20151069 - 2015 - Physical habitat monitoring strategy (PHAMS) for reach-scale restoration effectiveness monitoring","interactions":[],"lastModifiedDate":"2015-05-06T12:29:35","indexId":"ofr20151069","displayToPublicDate":"2015-04-14T16:30:00","publicationYear":"2015","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":"2015-1069","title":"Physical habitat monitoring strategy (PHAMS) for reach-scale restoration effectiveness monitoring","docAbstract":"Habitat restoration efforts by the Confederated Tribes of the Umatilla Indian Reservation (CTUIR) have shifted from the site scale (1-10 meters) to the reach scale (100-1,000 meters). This shift was in response to the growing scientific emphasis on process-based restoration and to support from the 2007 Accords Agreement with the Bonneville Power Administration. With the increased size of restoration projects, the CTUIR and other agencies are in need of applicable monitoring methods for assessing large-scale changes in river and floodplain habitats following restoration. The goal of the Physical Habitat Monitoring Strategy is to outline methods that are useful for capturing reach-scale changes in surface and groundwater hydrology, geomorphology, hydrologic connectivity, and riparian vegetation at restoration projects. The Physical Habitat Monitoring Strategy aims to avoid duplication with existing regional effectiveness monitoring protocols by identifying complimentary reach-scale metrics and methods that may improve the ability of CTUIR and others to detect instream and riparian changes at large restoration projects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151069","collaboration":"Prepared in cooperation with the Confederated Tribes of the Umatilla Indian Reservation, Department of Natural Resources, and Northwest Marine Fisheries Service","usgsCitation":"Jones, K.L., O’Daniel, S.J., Beechie, T.J., Zakrajsek, John, and Webster, J.G., 2015, Physical habitat monitoring strategy (PHAMS) for reach-scale restoration effectiveness monitoring: U.S. Geological Survey Open-File Report 2015-1069, 58 p., https://dx.doi.org/10.3133/ofr20151069.","productDescription":"vi, 58 p.","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055704","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":299682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151069.jpg"},{"id":299681,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1069/pdf/ofr2015-1069.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2015-1069 Report"},{"id":299680,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1069/"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Umatilla Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.44335937499999,\n 43.739352079154706\n ],\n [\n -119.44335937499999,\n 47.040182144806664\n ],\n [\n -116.54296874999999,\n 47.040182144806664\n ],\n [\n -116.54296874999999,\n 43.739352079154706\n ],\n [\n -119.44335937499999,\n 43.739352079154706\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
Summary
\n1. Extreme hydrologic events are becoming more common with changing climate. Although the impacts of winter and spring floods on lotic ecosystems have been well studied, the effects of summer floods are less well known.
\n2. The Upper Esopus Creek Basin in the Catskill Mountains, NY, experienced severe flooding from Tropical Storm Irene on 28 August 2011, and peak discharges exceeded the 0.01 annual exceedance probability (>100 year flood) in some reaches. Three years of fish community data from pre-flood surveys at nine sites were compared to data from 2 years of post-flood surveys to evaluate changes in fish communities and populations of brown trout (Salmo trutta) and rainbow trout (Oncorhynchus mykiss).
\n3. Basinwide, fish assemblages were not strongly impacted and appeared highly resilient to the effects of the flood. Total density and biomass of fish communities were greater at most sites 10-11 months after the flood than 1 month prior to the flood while richness and diversity were generally unchanged. Community composition did not differ significantly between years or between the pre-and post-flood periods.
\n4. Although the density of mature brown trout was low at most sites (mean density = 146 fish ha-1), young-of-the-year brown trout reached their highest density (mean = 2312 fish ha-1) during 2012. In contrast, rainbow trout densities declined substantially during the 5-year study and the 2012 year class was small (mean density = 222 fish ha-1).
\n5. Late summer floods may be less damaging to stream fish communities than winter or spring floods as spawning activity is negligible and early life stages of many species are generally larger and less susceptible to displacement and mortality. Additionally, post-flood conditions may be advantageous for brown trout recruitment.
","language":"English","publisher":"John Wiley & Sons Ltd.","publisherLocation":"Oxford, England","doi":"10.1111/fwb.12577","collaboration":"New York State Energy Research & Development Authority; Cornell Cooperative Extension of Ulster County; US Geological Survey","usgsCitation":"George, S.D., Baldigo, B.P., Smith, A., and Robinson, G., 2015, Effects of extreme floods on trout populations and fish communities in a Catskill Mountain river: Freshwater Biology, v. 60, no. 12, p. 2511-2522, https://doi.org/10.1111/fwb.12577.","productDescription":"12 p.","startPage":"2511","endPage":"2522","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052350","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":472149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/fwb.12577","text":"Publisher Index Page"},{"id":299785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":299783,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1111/fwb.12577/full"}],"volume":"60","issue":"12","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-13","publicationStatus":"PW","scienceBaseUri":"55362338e4b0b22a15807a8e","chorus":{"doi":"10.1111/fwb.12577","url":"http://dx.doi.org/10.1111/fwb.12577","publisher":"Wiley-Blackwell","authors":"George S. D., Baldigo B. P., Smith A. J., Robinson G. R.","journalName":"Freshwater Biology","publicationDate":"4/13/2015","auditedOn":"7/24/2015"},"contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":545306,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":545307,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Alexander J.","contributorId":140345,"corporation":false,"usgs":false,"family":"Smith","given":"Alexander J.","affiliations":[{"id":13464,"text":"Environmental Analyst, NY State Dept of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":545308,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robinson, George","contributorId":140346,"corporation":false,"usgs":false,"family":"Robinson","given":"George","email":"","affiliations":[{"id":13465,"text":"Assoc. Professor, State University of New York at Albany","active":true,"usgs":false}],"preferred":false,"id":545309,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70141222,"text":"sir20155016 - 2015 - Evaluation of mean-monthly streamflow-regression equations for Colorado, 2014","interactions":[],"lastModifiedDate":"2015-04-09T09:22:23","indexId":"sir20155016","displayToPublicDate":"2015-04-09T10:15:00","publicationYear":"2015","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":"2015-5016","title":"Evaluation of mean-monthly streamflow-regression equations for Colorado, 2014","docAbstract":"The U.S. Geological Survey, in cooperation with the Colorado Water Conservation Board, evaluated the predictive uncertainty of mean-monthly streamflow-regression equations representative of natural streamflow conditions in Colorado. This study evaluates the predictive uncertainty of mean-monthly streamflow-regression equations developed in a 2009 U.S. Geological Survey study using streamflow data collected over the entire period of record at each streamgage through calendar year 2013. The study area for this report is limited to the Mountain, Northwest, Rio Grande, and Southwest hydrologic regions of Colorado.
\nData collected from the beginning of the period of record through calendar year 2013 were used to evaluate the mean-monthly streamflow equations using the same basin characteristics as in the 2009 study. U.S. Geological Survey and Colorado Division of Water Resources streamgages with at least 10 years of streamflow record and identified as representative of natural streamflow conditions were selected for this study. During the streamgage selection process, a total of 432 streamgages, composed of 278 from the 2009 study and 154 new streamgages, were identified.
\nThe updated standard error of prediction and adjusted coefficient of determination values that correspond to the mean-monthly streamflow equations developed in the 2009 study are in close agreement with the results of this study. The old streamgages performed slightly better than the new streamgages, with approximately 88 and 85 percent of the data within the prediction intervals, respectively. This result was expected because the streamgages used to develop the regression equations should yield a better performance than the new streamgages.
\nFor all hydrologic regions, approximately 87 percent of the data are within the 95-percent prediction intervals. The explanation for why fewer than 95 percent of the data are within the prediction intervals is that the data do not conform perfectly to the regression assumptions required to accurately estimate performance metrics. The equations for the Rio Grande hydrologic region had the best fit with the parametric prediction-interval assumptions, with approximately 91.8 percent of the data within the prediction interval (average 12 months). The Mountain, Northwest, and Southwest hydrologic regions had 87.8, 84.9, and 83.5 percent of the data contained within the prediction interval, respectively.
\nMonthly adjusted coefficient of determination values were computed and have the same general pattern for all four hydrologic regions. The largest values usually occur in March or April, and the lowest values usually occur in August or September. Only the Rio Grande hydrologic region deviates from this seasonal pattern, exhibiting a decrease in adjusted coefficient of determination values in August and September, with the lowest values occurring in the winter months (December, January, and February). Generally, the adjusted coefficient of determination values for this report are just slightly less (0.76 compared to 0.79) than the values computed in the 2009 study. The similarity of values, even when tested with data not used to originally develop the mean-monthly streamflow-regression equations, provides confidence that the predictive uncertainty of mean-monthly regression equations in the 2009 study are accurate. The fact that the results for the two datasets are very similar provides assurance that when these equations are applied to locations not used to develop the equations, the standard error of prediction and adjusted-coefficient of determination error metrics should be similar to those established in the 2009 study for locations with natural streamflow.
\nThe median absolute differences between the observed and computed mean-monthly streamflow for Mountain, Northwest, and Southwest hydrologic regions are fairly uniform throughout the year, with the exception of late summer and early fall (July, August, and September), when each hydrologic region exhibits a substantial increase in median absolute percent difference. The greatest difference occurs in the Northwest hydrologic region, and the smallest difference occurs in the Mountain hydrologic region. The Rio Grande hydrologic region shows seasonal variation in median absolute percent difference with March, April, August, and September having a median absolute difference near or below 40 percent, and the remaining months of the year having a median absolute difference near or above 50 percent. In the Mountain, Northwest, and Southwest hydrologic regions, the mean-monthly streamflow equations perform the best during spring (March, April, and May). However, in the Rio Grande hydrologic region, the mean-monthly streamflow equations perform the best during late summer and early fall (August and September).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155016","collaboration":"Colorado Water Conservation Board","usgsCitation":"Kohn, M.S., Stevens, M.R., Bock, A.R., and Char, S.J., 2015, Evaluation of mean-monthly streamflow-regression equations for Colorado, 2014: U.S. Geological Survey Scientific Investigations Report 2015-5016, vii, 53, https://doi.org/10.3133/sir20155016.","productDescription":"vii, 53","startPage":"53","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2013-01-01","temporalEnd":"2013-12-31","ipdsId":"IP-057631","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":299533,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155016.jpg"},{"id":299532,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5016/pdf/sir2015-5016.pdf","text":"Report","size":"5.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":299521,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2015/5016/"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.45654296875,\n 36.12012758978146\n ],\n [\n -110.45654296875,\n 41.78769700539063\n ],\n [\n -104.853515625,\n 41.78769700539063\n ],\n [\n -104.853515625,\n 36.12012758978146\n ],\n [\n -110.45654296875,\n 36.12012758978146\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5527949ae4b026915857c838","contributors":{"authors":[{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":544455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Michael R. 0000-0002-9476-6335 mrsteven@usgs.gov","orcid":"https://orcid.org/0000-0002-9476-6335","contributorId":769,"corporation":false,"usgs":true,"family":"Stevens","given":"Michael","email":"mrsteven@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":544456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":544457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Char, Stephen J. sjchar@usgs.gov","contributorId":3982,"corporation":false,"usgs":true,"family":"Char","given":"Stephen","email":"sjchar@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":544458,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70154856,"text":"70154856 - 2015 - A chronicle of a killer alga in the west: Ecology, assessment, and management of Prymnesium parvum blooms","interactions":[],"lastModifiedDate":"2022-11-22T17:35:37.333821","indexId":"70154856","displayToPublicDate":"2015-04-08T11:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A chronicle of a killer alga in the west: Ecology, assessment, and management of Prymnesium parvum blooms","title":"A chronicle of a killer alga in the west: Ecology, assessment, and management of Prymnesium parvum blooms","docAbstract":"Since the mid-1980s, fish-killing blooms of Prymnesium parvum spread throughout the USA. In the south central USA, P. parvum blooms have commonly spanned hundreds of kilometers. There is much evidence that physiological stress brought on by inorganic nutrient limitation enhances toxicity. Other factors influence toxin production as well, such as stress experienced at low salinity and temperature. A better understanding of toxin production by P. parvum remains elusive and the identities and functions of chemicals produced are unclear. This limits our understanding of factors that facilitated the spread of P. parvum blooms. In the south central USA, not only is there evidence that the spread of blooms was controlled, in part, by migration limitation. But there are also observations that suggest changed environmental conditions, primarily salinity, facilitated the spread of blooms. Other factors that might have played a role include altered hydrology and nutrient loading. Changes in water hardness, herbicide use, system pH, and the presence of toxin-resistant and/or P. parvum-inhibiting plankton may also have played a role. Management of P. parvum in natural systems has yet to be attempted, but may be guided by successes achieved in small impoundments and mesocosm experiments that employed various chemical and hydraulic control approaches.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-015-2273-6","usgsCitation":"Roelke, D.L., Barkoh, A., Brooks, B.W., Grover, J.P., Hambright, K.D., LaClaire, J.W., Moeller, P.D., and Patino, R., 2015, A chronicle of a killer alga in the west: Ecology, assessment, and management of Prymnesium parvum blooms: Hydrobiologia, v. 764, p. 29-50, https://doi.org/10.1007/s10750-015-2273-6.","productDescription":"22 p.","startPage":"29","endPage":"50","numberOfPages":"22","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061394","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":305644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"764","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-08","publicationStatus":"PW","scienceBaseUri":"57f7ef48e4b0bc0bec09f00f","contributors":{"authors":[{"text":"Roelke, D. L.","contributorId":28342,"corporation":false,"usgs":true,"family":"Roelke","given":"D.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":564562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barkoh, Aaron","contributorId":145542,"corporation":false,"usgs":false,"family":"Barkoh","given":"Aaron","email":"","affiliations":[],"preferred":false,"id":564563,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brooks, Bryan W. 0000-0002-6277-9852","orcid":"https://orcid.org/0000-0002-6277-9852","contributorId":198868,"corporation":false,"usgs":false,"family":"Brooks","given":"Bryan","email":"","middleInitial":"W.","affiliations":[{"id":35352,"text":"Department of Environmental Science, Baylor University, Waco, TX, USA","active":true,"usgs":false}],"preferred":false,"id":564564,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grover, J. P.","contributorId":20453,"corporation":false,"usgs":true,"family":"Grover","given":"J.","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":564565,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hambright, K. D.","contributorId":25793,"corporation":false,"usgs":true,"family":"Hambright","given":"K.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":564566,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"LaClaire, John W. II","contributorId":145543,"corporation":false,"usgs":false,"family":"LaClaire","given":"John","suffix":"II","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":564567,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moeller, Peter D. R.","contributorId":145544,"corporation":false,"usgs":false,"family":"Moeller","given":"Peter","email":"","middleInitial":"D. R.","affiliations":[],"preferred":false,"id":564568,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564276,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70139404,"text":"sir20145224 - 2015 - Hydrogeologic framework, hydrology, and refined conceptual model of groundwater flow for Coastal Plain aquifers at the Standard Chlorine of Delaware, Inc. Superfund Site, New Castle County, Delaware, 2005-12","interactions":[],"lastModifiedDate":"2018-03-21T15:43:13","indexId":"sir20145224","displayToPublicDate":"2015-04-08T10:30:00","publicationYear":"2015","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":"2014-5224","title":"Hydrogeologic framework, hydrology, and refined conceptual model of groundwater flow for Coastal Plain aquifers at the Standard Chlorine of Delaware, Inc. Superfund Site, New Castle County, Delaware, 2005-12","docAbstract":"From 1966 to 2002, activities at the Standard Chlorine of Delaware chemical facility in New Castle County, Delaware resulted in the contamination of groundwater, soils, and wetland sediment. In 2005, the U.S. Geological Survey (USGS), in partnership with the U.S. Environmental Protection Agency, Region 3, and the Delaware Department of Natural Resources and Environmental Control began a multi-year investigation of the hydrogeologic framework and hydrology of the confined aquifer system. The goals of the ongoing study at the site (the Potomac Aquifer Study) are to determine the hydraulic connection between the Columbia and Potomac aquifers, determine the direction of groundwater flow in the Potomac aquifer, and identify factors affecting the fate of contaminated groundwater. This report describes progress made towards these goals based on available data collected through September 2012.
\nThe regional hydrogeologic framework indicates that the site is underlain by Coastal Plain sediments of the Columbia, Merchantville, and Potomac Formations. Two primary aquifers underlying the site, the Columbia and the upper Potomac, are separated by the Merchantville Formation confining unit. Local groundwater flow in the surficial (Columbia) aquifer is controlled by topography and generally flows northward and discharges to nearby surface water. Regional flow within the Potomac aquifer is towards the southeast, and is strongly influenced by major water withdrawals locally. Previous investigations at the site indicated that contaminants, primarily benzene and chlorinated benzene compounds, were present in the Columbia aquifer in most locations; however, there were only limited detections in the upper Potomac aquifer as of 2004. From 2005 through 2012, the USGS designed a monitoring network, assisted with exploratory drilling, collected data at monitoring wells, conducted geophysical surveys, evaluated water-level responses in wells during pumping of a production well, and evaluated major aquifer withdrawals. Data collected through these efforts were used to refine the local conceptual flow system. The refined conceptual flow system for the site includes: (a) identification of gaps in confining units in the study area, (b) identification and correlation of multiple water-bearing sand intervals within the upper Potomac Formation, (c) connections between groundwater and surface water, (d) connections between shallow and deeper groundwater, (e) new water-level (or potentiometric surface) maps and inferred flow directions, and (f) identification of major local pumping well influences. The implications of the revised conceptual flow system on the occurrence and movement of site contaminants are that the resulting detection of contaminants in the upper Potomac aquifer at specific well locations can be attributed primarily to either advective lateral transport, direct vertical contaminant transport, or a combination of vertical and lateral movement resulting from changes in water withdrawal rates over time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145224","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Brayton, M.J., Cruz, R.M., Myers, L., Degnan, J.R., and Raffensperger, J.P., 2015, Hydrogeologic framework, hydrology, and refined conceptual model of groundwater flow for Coastal Plain aquifers at the Standard Chlorine of Delaware, Inc. Superfund Site, New Castle County, Delaware, 2005-12: U.S. Geological Survey Scientific Investigations Report 2014-5224, vii, 61 p., https://doi.org/10.3133/sir20145224.","productDescription":"vii, 61 p.","numberOfPages":"74","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2005-01-01","temporalEnd":"2012-09-30","ipdsId":"IP-059549","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":299486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145224.jpg"},{"id":299484,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5224/"},{"id":299485,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5224/pdf/sir2014-5224.pdf","text":"Report","size":"3.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"projection":"State Plane Delaware Projection","datum":"North American Datum of 1983","country":"United States","state":"Delaware","county":"New Castle County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.60430526733398,\n 39.63187001350982\n ],\n [\n -75.65872192382812,\n 39.637422462817\n ],\n [\n -75.70489883422852,\n 39.60886226158157\n ],\n [\n -75.71365356445311,\n 39.59464387992515\n ],\n [\n -75.65108299255371,\n 39.55554482419571\n ],\n [\n -75.59306144714355,\n 39.571623755318214\n ],\n [\n -75.58670997619629,\n 39.579231826349016\n ],\n [\n -75.58653831481934,\n 39.5808856443388\n ],\n [\n -75.59864044189453,\n 39.58935257415456\n ],\n [\n -75.60842514038085,\n 39.59484229602979\n ],\n [\n -75.61426162719727,\n 39.60463011823322\n ],\n [\n -75.61134338378906,\n 39.6109782362526\n ],\n [\n -75.61408996582031,\n 39.617325772242175\n ],\n [\n -75.61031341552734,\n 39.625788248139436\n ],\n [\n -75.60636520385742,\n 39.6265815522009\n ],\n [\n -75.60430526733398,\n 39.63187001350982\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5526431de4b026915857c634","contributors":{"authors":[{"text":"Brayton, Michael J. mbrayton@usgs.gov","contributorId":2993,"corporation":false,"usgs":true,"family":"Brayton","given":"Michael","email":"mbrayton@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":539388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cruz, Roberto M. 0000-0003-1235-3295 rmcruz@usgs.gov","orcid":"https://orcid.org/0000-0003-1235-3295","contributorId":5757,"corporation":false,"usgs":true,"family":"Cruz","given":"Roberto","email":"rmcruz@usgs.gov","middleInitial":"M.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":539389,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Myers, Luke lmyers@usgs.gov","contributorId":5758,"corporation":false,"usgs":true,"family":"Myers","given":"Luke","email":"lmyers@usgs.gov","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":539390,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Degnan, James R. 0000-0002-5665-9010 jrdegnan@usgs.gov","orcid":"https://orcid.org/0000-0002-5665-9010","contributorId":498,"corporation":false,"usgs":true,"family":"Degnan","given":"James","email":"jrdegnan@usgs.gov","middleInitial":"R.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":539391,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Raffensperger, Jeff P. 0000-0001-9275-6646 jpraffen@usgs.gov","orcid":"https://orcid.org/0000-0001-9275-6646","contributorId":199119,"corporation":false,"usgs":true,"family":"Raffensperger","given":"Jeff","email":"jpraffen@usgs.gov","middleInitial":"P.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":539392,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70141848,"text":"sir20155024 - 2015 - Hydrologic effects of potential changes in climate, water use, and land cover in the Upper Scioto River Basin, Ohio","interactions":[],"lastModifiedDate":"2015-04-15T08:44:40","indexId":"sir20155024","displayToPublicDate":"2015-04-08T10:00:00","publicationYear":"2015","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":"2015-5024","title":"Hydrologic effects of potential changes in climate, water use, and land cover in the Upper Scioto River Basin, Ohio","docAbstract":"This report presents the results of a study to provide information on the hydrologic effects of potential 21st-century changes in climate, water use, and land cover in the Upper Scioto River Basin, Ohio (from Circleville, Ohio, to the headwaters). A precipitation-runoff model, calibrated on the basis of historical climate and streamflow data, was used to simulate the effects of climate change on streamflows and reservoir water levels at several locations in the basin. Two levels of simulations were done. The first level of simulation (level 1) accounted only for anticipated 21st-century changes in climate and operations of three City of Columbus upground reservoirs located in northwest Delaware County, Ohio. The second level of simulation (level 2) accounted for development-driven changes in land cover and water use in addition to changes in climate and reservoir operations.
\nA statistical change-factor approach was used to construct future climate time series that were used in the precipitation-runoff model to compute time series of future streamflows and reservoir water levels. Monthly change factors were computed by determining differences or fractional changes between baseline historical climate time series and future climate time series consisting of outputs from selected global climate models that were included in the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3 (CMIP3). Eight sets of change factors were determined on the basis of outputs from four global climate models, each of which was run under two greenhouse-gas scenarios (the “A1b” and “A2” scenarios from the Intergovernmental Panel on Climate Change’s 4th assessment). The 4 global climate models whose data were used in this study were selected to represent a wide range of potential climate outcomes as compared to the entire range of potential climate outcomes associated with the 16 global climate models represented in the CMIP3 multimodel dataset.
\nFuture land-cover and water-use data were estimated for use in the level-2 precipitation-runoff simulations to account for development-driven changes in land cover and water use. Future land-cover characteristics were estimated for selected future years based on population projections and zoning plans for communities in the basin. Future water-use data for major water suppliers and wastewater-treatment facilities were estimated from current per capita water use, population projections for 2035, and population projections for 2090 assuming full build-out. A statistical change-factor-based approach was used to estimate future water-use characteristics by major water suppliers and wastewater-treatment facilities on the basis of reference-period historical water uses. Annual change factors that were determined for future years other than 2035 and 2090 (when the change factors could be explicitly computed) were estimated by interpolating or extrapolating linearly in time. Water uses by entities other than major water suppliers and wastewater-treatment facilities were assumed to remain unchanged because of uncertainty about if and (or) how they might change.
\nResults from the level-1 simulations were analyzed primarily to facilitate evaluation of climate-driven temporal changes in annual, seasonal, and monthly streamflow and water-level characteristics, as well as in maximum and minimum 7-, 30-, and 180-day average streamflow and reservoir water levels. Results from the level-2 simulations were analyzed to help evaluate and contrast (relative to level-1 results) the effects of the added development-related factors on maximums and minimum 7-, 30-, and 180-day average streamflows and reservoir water levels and duration characteristics of 7- and 30-day average streamflows and reservoir water levels. Results for 12 stream locations and 5 reservoirs in the Upper Scioto River Basin are presented primarily as a series of plots.
\nAlthough it is beyond the scope of this study to address results in detail for each model-output location, selected results are discussed to illustrate potential uses and interpretations of the graph products provided in this report. In addition, general trends and patterns in streamflow and water-level characteristics are identified where possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155024","collaboration":"Prepared in cooperation with the Mid-Ohio Regional Planning Commission; the Ohio Water Development Authority; the City of Columbus, Ohio; and Del-Co Water Company","usgsCitation":"Ebner, A.D., Koltun, G., and Ostheimer, C., 2015, Hydrologic effects of potential changes in climate, water use, and land cover in the Upper Scioto River Basin, Ohio: U.S. Geological Survey Scientific Investigations Report 2015-5024, Report: vii, 34 p.; Appendixes A-G; Downloads Directory, https://doi.org/10.3133/sir20155024.","productDescription":"Report: vii, 34 p.; Appendixes A-G; Downloads Directory","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060946","costCenters":[{"id":513,"text":"Ohio Water Science 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quantile."},{"id":299478,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5024/appendix/sir2015-5024_appendixd.pdf","text":"Appendix D","size":"905 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Appendix D","linkHelpText":"Plots of maximum and minimum 7-, 30-, and 180-day average streamflows and water levels as a function of plotting year."},{"id":299481,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5024/appendix/sir2015-5024_appendixg.pdf","text":"Appendix G","size":"226 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Appendix G","linkHelpText":"Plots of simulated level-2 30-day running average streamflows and water levels as a function of exceedance quantile."},{"id":299482,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5024/downloads","text":"Downloads Directory","size":"5.44 MB","description":"Downloads Directory","linkHelpText":"Contains Appendixes A-G ZIP 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time."},{"id":299477,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5024/appendix/sir2015-5024_appendixc.pdf","text":"Appendix C","size":"1.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Appendix C","linkHelpText":"Boxplots of the medians of site-, month-, and emission-specific level-1 ensemble mean streamflows and water levels as a function of epoch."}],"projection":"Universal Transverse Mercator projection, Zone 17","datum":"North American Datum of 1983","country":"United States","state":"Ohio","otherGeospatial":"Upper Scioto River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.72602081298828,\n 40.80497409762779\n ],\n [\n -83.00823211669922,\n 40.804454347291006\n ],\n [\n -83.111572265625,\n 40.73997376331186\n ],\n [\n -83.91014099121094,\n 40.7508982634657\n ],\n [\n 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F. 0000-0003-0255-2960 gfkoltun@usgs.gov","orcid":"https://orcid.org/0000-0003-0255-2960","contributorId":1852,"corporation":false,"usgs":true,"family":"Koltun","given":"G. F.","email":"gfkoltun@usgs.gov","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":false,"id":544328,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ostheimer, Chad J. ostheime@usgs.gov","contributorId":127446,"corporation":false,"usgs":true,"family":"Ostheimer","given":"Chad J.","email":"ostheime@usgs.gov","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":false,"id":544329,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70145259,"text":"70145259 - 2015 - RRAWFLOW: Rainfall-Response Aquifer and Watershed Flow Model (v1.15)","interactions":[],"lastModifiedDate":"2017-10-12T20:04:28","indexId":"70145259","displayToPublicDate":"2015-04-07T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"RRAWFLOW: Rainfall-Response Aquifer and Watershed Flow Model (v1.15)","docAbstract":"The Rainfall-Response Aquifer and Watershed Flow Model (RRAWFLOW) is a lumped-parameter model that simulates streamflow, spring flow, groundwater level, or solute transport for a measurement point in response to a system input of precipitation, recharge, or solute injection. I introduce the first version of RRAWFLOW available for download and public use and describe additional options. The open-source code is written in the R language and is available at http://sd.water.usgs.gov/projects/RRAWFLOW/RRAWFLOW.html along with an example model of streamflow. RRAWFLOW includes a time-series process to estimate recharge from precipitation and simulates the response to recharge by convolution, i.e., the unit-hydrograph approach. Gamma functions are used for estimation of parametric impulse-response functions (IRFs); a combination of two gamma functions results in a double-peaked IRF. A spline fit to a set of control points is introduced as a new method for estimation of nonparametric IRFs. Several options are included to simulate time-variant systems. For many applications, lumped models simulate the system response with equal accuracy to that of distributed models, but moreover, the ease of model construction and calibration of lumped models makes them a good choice for many applications (e.g., estimating missing periods in a hydrologic record). RRAWFLOW provides professional hydrologists and students with an accessible and versatile tool for lumped-parameter modeling.
","language":"English","publisher":"European Geosciences Union","publisherLocation":"Katlenburg-Lindau, Germany","doi":"10.5194/gmd-8-865-2015","usgsCitation":"Long, A.J., 2015, RRAWFLOW: Rainfall-Response Aquifer and Watershed Flow Model (v1.15): Geoscientific Model Development, v. 8, p. 865-880, https://doi.org/10.5194/gmd-8-865-2015.","productDescription":"16 p.","startPage":"865","endPage":"880","numberOfPages":"16","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056483","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":472158,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-8-865-2015","text":"Publisher Index Page"},{"id":299446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-30","publicationStatus":"PW","scienceBaseUri":"5524f19ce4b027f0aee3d45d","contributors":{"authors":[{"text":"Long, Andrew J. 0000-0001-7385-8081 ajlong@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-8081","contributorId":989,"corporation":false,"usgs":true,"family":"Long","given":"Andrew","email":"ajlong@usgs.gov","middleInitial":"J.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":544130,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70144431,"text":"sir20155038 - 2015 - Potential groundwater recharge for the State of Minnesota using the Soil-Water-Balance model, 1996-2010","interactions":[],"lastModifiedDate":"2015-04-06T15:06:47","indexId":"sir20155038","displayToPublicDate":"2015-04-06T15:00:00","publicationYear":"2015","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":"2015-5038","title":"Potential groundwater recharge for the State of Minnesota using the Soil-Water-Balance model, 1996-2010","docAbstract":"Groundwater recharge is one of the most difficult components of a water budget to ascertain, yet is an important boundary condition necessary for the quantification of water resources. In Minnesota, improved estimates of recharge are necessary because approximately 75 percent of drinking water and 90 percent of agricultural irrigation water in Minnesota are supplied from groundwater. The water that is withdrawn must be supplied by some combination of (1) increased recharge, (2) decreased discharge to streams, lakes, and other surface-water bodies, and (3) removal of water that was stored in the system. Recent pressure on groundwater resources has highlighted the need to provide more accurate recharge estimates for various tools that can assess the sustainability of long-term water use. As part of this effort, the U.S. Geological Survey, in cooperation with the Minnesota Pollution Control Agency, used the Soil-Water-Balance model to calculate gridded estimates of potential groundwater recharge across Minnesota for 1996‒2010 at a 1-kilometer (0.621-mile) resolution. The potential groundwater recharge estimates calculated for Minnesota from the Soil-Water Balance model included gridded values (1-kilometer resolution) of annual mean estimates (that is, the means for individual years from 1996 through 2010) and mean annual estimates (that is, the mean for the 15-year period 1996−2010).
\nThe Soil-Water-Balance model uses a modified Thornthwaite-Mather soil-water-balance approach, with components of the soil-water balance calculated on a daily basis. A key advantage of this approach includes the use of commonly available geographic information system data layers that incorporate land cover, soil properties, and daily meteorological data to produce temporally and spatially variable gridded estimates of potential recharge. The Soil-Water-Balance model was calibrated by using a combination of parameter estimation techniques, making manual adjustments of model parameters, and using parameter values from previously published Soil-Water-Balance models. Each calibration simulation compared the potential recharge estimate from the model against base-flow estimates derived from three separate hydrograph separation techniques. A total of 35 Minnesota watersheds were selected for the model calibration.
\nMeteorological data necessary for the model included daily precipitation, minimum daily temperature, and maximum daily temperature. All of the meteorological data were provided by the Daymet dataset, which included daily continuous surfaces of key climatological data. Land-cover data were provided by the 2001 and 2006 National Land Cover Database: the 2001 classification was used from 1994 through 2003, and the 2006 classification was used from 2004 through 2010. Soil data used in the model included hydrologic soils group and the available soil-water capacity. These soil data were obtained from the Natural Resources Conservation Service Soil Survey Geographic (SSURGO) database and the State Soil Geographic (STATSGO) database.
\nThe statewide mean annual potential recharge rate from 1996–2010 was 4.9 inches per year. Potential recharge estimates increased from west to east across Minnesota. The mean annual potential recharge estimates across Minnesota at a 1-km resolution for the overall simulation period (1996–2010) ranged from less than 0.1 to 17.8 inches per year. Some of the lowest potential recharge rates for the simulation period were in the Red River of the North Basin of northwestern Minnesota, and generally were between 1.0 and 1.5 inches per year. The highest potential recharge rates were in northeastern Minnesota and the Anoka Sand Plain in central Minnesota. Eighty-eight percent of the potential recharge rates (by grid cell) were between 2 and 8 inches per year from 1996–2010. Only about 3 percent of all the potential recharge estimates (by grid cell) were less than 2 inches per year, and 9 percent of estimates were greater than 8 inches per year.
\nOn an annual basis, however, potential recharge rates were as high as 27.2 inches per year. The highest annual mean recharge estimate across the State was for 2010, and the lowest mean recharge estimate was for 2003. Although precipitation variability partially explained the annual differences in potential recharge estimates, precipitation alone did not account for these differences, and other factors such as antecedent moisture conditions likely were important. Also, because precipitation gradients across the State can vary from year to year, the dominant land-cover class and hydrologic soil group combinations for a particular region had a large effect on the resulting potential recharge value. During 1996–2010, April had the greatest monthly mean potential recharge compared to all other months, accounting for a mean of 30 percent of annual potential recharge in this single month.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155038","collaboration":"Prepared in cooperation with the Minnesota Pollution Control Agency","usgsCitation":"Smith, E.A., and Westenbroek, S.M., 2015, Potential groundwater recharge for the State of Minnesota using the Soil-Water-Balance model, 1996-2010: U.S. Geological Survey Scientific Investigations Report 2015-5038, vii, 85 p., https://doi.org/10.3133/sir20155038.","productDescription":"vii, 85 p.","startPage":"85","numberOfPages":"98","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"1996-01-01","temporalEnd":"2010-12-31","ipdsId":"IP-034584","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":299402,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20155038.jpg"},{"id":299399,"type":{"id":15,"text":"Index 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easmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":1405,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","email":"easmith@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":544121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Westenbroek, Stephen M. 0000-0002-6284-8643 smwesten@usgs.gov","orcid":"https://orcid.org/0000-0002-6284-8643","contributorId":2210,"corporation":false,"usgs":true,"family":"Westenbroek","given":"Stephen","email":"smwesten@usgs.gov","middleInitial":"M.","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":544123,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70145260,"text":"70145260 - 2015 - Conceptual models of the formation of acid-rock drainage at road cuts in Tennessee","interactions":[],"lastModifiedDate":"2015-11-23T15:30:52","indexId":"70145260","displayToPublicDate":"2015-04-03T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"Conceptual models of the formation of acid-rock drainage at road cuts in Tennessee","docAbstract":"Pyrite and other minerals containing sulfur and trace metals occur in several rock formations throughout Middle and East Tennessee. Pyrite (FeS2) weathers in the presence of oxygen and water to form iron hydroxides and sulfuric acid. The weathering and interaction of the acid on the rocks and other minerals at road cuts can result in drainage with low pH (< 4) and high concentrations of trace metals. Acid-rock drainage can cause environmental problems and damage transportation infrastructure. The formation and remediation of acid-drainage from roads cuts has not been researched as thoroughly as acid-mine drainage. The U.S Geological Survey, in cooperation with the Tennessee Department of Transportation, is conducting an investigation to better understand the geologic, hydrologic, and biogeochemical factors that control acid formation at road cuts. Road cuts with the potential for acid-rock drainage were identifed and evaluated in Middle and East Tennessee. The pyrite-bearing formations evaluated were the Chattanooga Shale (Devonian black shale), the Fentress Formation (coal-bearing), and the Precambrian Anakeesta Formation and similar Precambrian rocks. Conceptual models of the formation and transport of acid-rock drainage (ARD) from road cuts were developed based on the results of a literature review, site reconnaissance, and the initial rock and water sampling. The formation of ARD requires a combination of hydrologic, geochemical, and microbial interactions which affect drainage from the site, acidity of the water, and trace metal concentrations. The basic modes of ARD formation from road cuts are; 1 - seeps and springs from pyrite-bearing formations and 2 - runoff over the face of a road cut in a pyrite-bearing formation. Depending on site conditions at road cuts, the basic modes of ARD formation can be altered and the additional modes of ARD formation are; 3 - runoff over and through piles of pyrite-bearing material, either from construction or breakdown material weathered from shale, and 4 - the deposition of secondary-sulfate minerals can store trace metals and, during rainfall, result in increased acidity and higher concentrations of trace metals in storm runoff. Understanding the factors that control ARD formation and transport are key to addressing the problems associated with the movement of ARD from the road cuts to the environment. The investigation will provide the Tennessee Department of Transportation with a regional characterization of ARD and provide insights into the geochemical and biochemical attributes for the control and remediation of ARD from road cuts.
","largerWorkTitle":"Proceedings of the 2015 Tennessee Water Resources Symposium","conferenceTitle":"2015 Tennessee Water Resources Symposium","conferenceDate":"April 1-3, 2015","conferenceLocation":"Montgomery Bell State Park Burns, Tennessee","language":"English","publisher":"Tennessee Section of the American Water Resources Association","collaboration":"Tenn. Department of Transportation","usgsCitation":"Bradley, M., Worland, S., and Byl, T., 2015, Conceptual models of the formation of acid-rock drainage at road cuts in Tennessee, in Proceedings of the 2015 Tennessee Water Resources Symposium, Montgomery Bell State Park Burns, Tennessee, April 1-3, 2015, p. 2C-8-2C-9.","productDescription":"1 p.","startPage":"2C-8","endPage":"2C-9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062621","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"links":[{"id":311666,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":299403,"type":{"id":15,"text":"Index Page"},"url":"https://tnawra.er.usgs.gov/Library/Proceedings24th.pdf"}],"country":"United States","state":"Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.748046875,\n 36.677230602346214\n ],\n [\n -87.56103515625,\n 36.075742215627\n ],\n [\n -87.593994140625,\n 35.53222622770337\n ],\n [\n -87.220458984375,\n 35.0120020431607\n ],\n [\n -86.81396484375,\n 35.110921809704756\n ],\n [\n -86.484375,\n 35.29943548054545\n ],\n [\n -85.98999023437499,\n 35.951329861522666\n ],\n [\n -86.28662109375,\n 36.5184659896759\n ],\n [\n -86.572265625,\n 36.659606226479696\n ],\n [\n -86.748046875,\n 36.677230602346214\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.836669921875,\n 36.60670888641815\n ],\n [\n -84.74853515625,\n 36.27085020723905\n ],\n [\n -85.23193359375,\n 35.7019167328534\n ],\n [\n -85.462646484375,\n 35.22767235493586\n ],\n [\n -85.484619140625,\n 35.02099970111467\n ],\n [\n -85.1220703125,\n 34.94899072578227\n ],\n [\n -84.891357421875,\n 35.003003395276714\n ],\n [\n -84.3310546875,\n 35.755428369259626\n ],\n [\n -83.726806640625,\n 36.33282808737917\n ],\n [\n -83.5400390625,\n 36.56260003738548\n ],\n [\n -83.836669921875,\n 36.60670888641815\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.2216796875,\n 36.58024660149866\n ],\n [\n -84.649658203125,\n 35.16482750605027\n ],\n [\n -84.385986328125,\n 35.038992046780784\n ],\n [\n -84.19921875,\n 35.27253175660236\n ],\n [\n -83.8037109375,\n 35.48751102385376\n ],\n [\n -83.16650390625,\n 35.69299463209881\n ],\n [\n -82.5732421875,\n 36.04021586880111\n ],\n [\n -82.3974609375,\n 36.13787471840729\n ],\n [\n -81.990966796875,\n 36.19995805932895\n ],\n [\n -81.63940429687499,\n 36.62434536776987\n ],\n [\n -82.2216796875,\n 36.58024660149866\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"565446bde4b071e7ea53d4a6","contributors":{"authors":[{"text":"Bradley, Mike 0000-0002-2979-265X mbradley@usgs.gov","orcid":"https://orcid.org/0000-0002-2979-265X","contributorId":582,"corporation":false,"usgs":true,"family":"Bradley","given":"Mike","email":"mbradley@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":544131,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Worland, Scott","contributorId":150038,"corporation":false,"usgs":false,"family":"Worland","given":"Scott","affiliations":[],"preferred":false,"id":580500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byl, Tom","contributorId":150039,"corporation":false,"usgs":false,"family":"Byl","given":"Tom","affiliations":[],"preferred":false,"id":580501,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70144294,"text":"ofr20151058 - 2015 - An evaluation of the accuracy of modeled and computed streamflow time-series data for the Ohio River at Hannibal Lock and Dam and at a location upstream from Sardis, Ohio","interactions":[],"lastModifiedDate":"2015-04-09T08:31:36","indexId":"ofr20151058","displayToPublicDate":"2015-04-02T11:00:00","publicationYear":"2015","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":"2015-1058","title":"An evaluation of the accuracy of modeled and computed streamflow time-series data for the Ohio River at Hannibal Lock and Dam and at a location upstream from Sardis, Ohio","docAbstract":"Between July 2013 and June 2014, the U.S. Geological Survey (USGS) made 10 streamflow measurements on the Ohio River about 1.5 miles (mi) downstream from the Hannibal Lock and Dam (near Hannibal, Ohio) and 11 streamflow measurements near the USGS Sardis gage (station number 03114306) located approximately 2.4 mi upstream from Sardis, Ohio. The measurement results were used to assess the accuracy of modeled or computed instantaneous streamflow time series created and supplied by the USGS, U.S. Army Corps of Engineers (USACE), and National Weather Service (NWS) for the Ohio River at Hannibal Lock and Dam and (or) at the USGS streamgage. Hydraulic or hydrologic models were used to create the modeled time series; index-velocity methods or gate-opening ratings coupled with hydropower operation data were used to create the computed time series. The time step of the various instantaneous streamflow time series ranged from 15 minutes to 24 hours (once-daily values at 12:00 Coordinated Universal Time [UTC]). The 15-minute time-series data, computed by the USGS for the Sardis gage, also were downsampled to 1-hour and 24-hour time steps to permit more direct comparisons with other streamflow time series.
\nTo facilitate comparisons between measurement results and time-series data, streamflows corresponding to the times of the streamflow measurements were computed from the time-series data by time-based linear interpolation. Prior to doing interpolations, measurement times for the Hannibal Lock and Dam location were adjusted for traveltime to account for the fact that the streamflow measurements were made about 1.5 mi downstream from the location corresponding to the modeled/computed time-series data. Measured and interpolated streamflows were tabulated along with residuals (the difference between measured and interpolated streamflows) and selected summary statistics.
\nOverall, streamflows interpolated from the USGS computed 15-minute time-series data (hereafter referred to as the USGS 15-minute time-series data) had the smallest root-mean-square error (RMSE) (3,939 cubic feet per second [ft3/s]) and the second smallest mean absolute residual (2,636 ft3/s), whereas streamflows interpolated from the USACE 12 UTC time series had the largest RMSE (14,590 ft3/s) and the largest mean absolute residual (10,800 ft3/s). The larger RMSEs for streamflows interpolated from the USACE 12 UTC time series likely resulted in part from the coarser time step of that time series. Streamflows interpolated from the USGS downsampled 1-hour time series had the second smallest RMSE (4,025 ft3/s) and the smallest mean absolute residual (2,600 ft3/s). Somewhat surprisingly, streamflows interpolated from the NWS 6-hour model time series had the third smallest RMSE (4,483 ft3/s) and mean absolute residual (4,050 ft3/s) in spite of being determined from a time series with a coarser time step than the USACE 1-hour modeled and computed time series.
\nMeasured streamflows at the Sardis gage and at the Hannibal Lock and Dam measurement location were plotted versus residuals (expressed as a percentage of the measured streamflows) of corresponding interpolated time-series streamflow values. Results for each of the time series exhibited some anomaly, possibly indicating the need and (or) potential for improvement in the streamflow computational/modeling processes.
\nStreamflow hydrographs were plotted for modeled/computed time series for the Ohio River near the USGS Sardis gage and the Ohio River at the Hannibal Lock and Dam. In general, the time series at these two locations compared well. Some notable differences include the exclusive presence of short periods of negative streamflows in the USGS 15-minute time-series data for the gage on the Ohio River above Sardis, Ohio, and the occurrence of several peak streamflows in the USACE gate/hydropower time series for the Hannibal Lock and Dam that were appreciably larger than corresponding peaks in the other time series, including those modeled/computed for the downstream Sardis gage
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151058","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Koltun, G., 2015, An evaluation of the accuracy of modeled and computed streamflow time-series data for the Ohio River at Hannibal Lock and Dam and at a location upstream from Sardis, Ohio: U.S. Geological Survey Open-File Report 2015-1058, viii, 23 p., https://doi.org/10.3133/ofr20151058.","productDescription":"viii, 23 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063449","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":299300,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151058.jpg"},{"id":299296,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1058/"},{"id":299297,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1058/pdf/ofr2015-1058.pdf","text":"Report","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Ohio","otherGeospatial":"Ohio River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.96099853515624,\n 39.57817336212527\n ],\n [\n -80.96099853515624,\n 39.68182601089365\n ],\n [\n -80.82092285156249,\n 39.68182601089365\n ],\n [\n -80.82092285156249,\n 39.57817336212527\n ],\n [\n -80.96099853515624,\n 39.57817336212527\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551e5a1be4b027f0aee3b86b","contributors":{"authors":[{"text":"Koltun, G. F. 0000-0003-0255-2960 gfkoltun@usgs.gov","orcid":"https://orcid.org/0000-0003-0255-2960","contributorId":1852,"corporation":false,"usgs":true,"family":"Koltun","given":"G. F.","email":"gfkoltun@usgs.gov","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":false,"id":543454,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70147071,"text":"70147071 - 2015 - Targeting climate diversity in conservation planning to build resilience to climate change","interactions":[],"lastModifiedDate":"2018-09-18T10:34:24","indexId":"70147071","displayToPublicDate":"2015-04-01T13:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Targeting climate diversity in conservation planning to build resilience to climate change","docAbstract":"Climate change is raising challenging concerns for systematic conservation planning. Are methods based on the current spatial patterns of biodiversity effective given long-term climate change? Some conservation scientists argue that planning should focus on protecting the abiotic diversity in the landscape, which drives patterns of biological diversity, rather than focusing on the distribution of focal species, which shift in response to climate change. Climate is one important abiotic driver of biodiversity patterns, as different climates host different biological communities and genetic pools. We propose conservation networks that capture the full range of climatic diversity in a region will improve the resilience of biotic communities to climate change compared to networks that do not. In this study we used historical and future hydro-climate projections from the high resolution Basin Characterization Model to explore the utility of directly targeting climatic diversity in planning. Using the spatial planning tool, Marxan, we designed conservation networks to capture the diversity of climate types, at the regional and sub-regional scale, and compared them to networks we designed to capture the diversity of vegetation types. By focusing on the Conservation Lands Network (CLN) of the San Francisco Bay Area as a real-world case study, we compared the potential resilience of networks by examining two factors: the range of climate space captured, and climatic stability to 18 future climates, reflecting different emission scenarios and global climate models. We found that the climate-based network planned at the sub-regional scale captured a greater range of climate space and showed higher climatic stability than the vegetation and regional based-networks. At the same time, differences among network scenarios are small relative to the variance in climate stability across global climate models. Across different projected futures, topographically heterogeneous areas consistently show greater climate stability than homogenous areas. The analysis suggests that utilizing high-resolution climate and hydrological data in conservation planning improves the likely resilience of biodiversity to climate change. We used these analyses to suggest new conservation priorities for the San Francisco Bay Area.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1890/ES14-00313.1","usgsCitation":"Heller, N.E., Kreitler, J.R., Ackerly, D., Weiss, S., Recinos, A., Branciforte, R., Flint, L.E., Flint, A.L., and Micheli, E., 2015, Targeting climate diversity in conservation planning to build resilience to climate change: Ecosphere, v. 6, no. 4, p. 1-20, https://doi.org/10.1890/ES14-00313.1.","productDescription":"20 p.","startPage":"1","endPage":"20","numberOfPages":"20","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058616","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":472162,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1890/es14-00313.1","text":"External Repository"},{"id":299894,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-24","publicationStatus":"PW","scienceBaseUri":"553f5dbbe4b0a658d7938cfc","contributors":{"authors":[{"text":"Heller, Nicole E.","contributorId":140429,"corporation":false,"usgs":false,"family":"Heller","given":"Nicole","email":"","middleInitial":"E.","affiliations":[{"id":13495,"text":"Dwight Center for Conservation Science at Pepperwood Preserve","active":true,"usgs":false}],"preferred":false,"id":545619,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreitler, Jason R. 0000-0002-0243-5281 jkreitler@usgs.gov","orcid":"https://orcid.org/0000-0002-0243-5281","contributorId":4050,"corporation":false,"usgs":true,"family":"Kreitler","given":"Jason","email":"jkreitler@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":545618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ackerly, David","contributorId":139541,"corporation":false,"usgs":false,"family":"Ackerly","given":"David","affiliations":[{"id":7102,"text":"University of California, Berkeley, Dept. of Civil & Envir. Engineering","active":true,"usgs":false}],"preferred":false,"id":545620,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weiss, Stuart","contributorId":7590,"corporation":false,"usgs":true,"family":"Weiss","given":"Stuart","email":"","affiliations":[],"preferred":false,"id":545621,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Recinos, Amanda","contributorId":140430,"corporation":false,"usgs":false,"family":"Recinos","given":"Amanda","email":"","affiliations":[{"id":13496,"text":"GreenInfo Network","active":true,"usgs":false}],"preferred":false,"id":545622,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Branciforte, Ryan","contributorId":140431,"corporation":false,"usgs":false,"family":"Branciforte","given":"Ryan","email":"","affiliations":[{"id":13497,"text":"Bay Area Open Space Council","active":true,"usgs":false}],"preferred":false,"id":545623,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":545624,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":545625,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Micheli, Elisabeth","contributorId":105615,"corporation":false,"usgs":true,"family":"Micheli","given":"Elisabeth","email":"","affiliations":[],"preferred":false,"id":545626,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70145179,"text":"70145179 - 2015 - Soil respiration patterns and controls in limestone cedar glades","interactions":[],"lastModifiedDate":"2015-04-06T11:35:12","indexId":"70145179","displayToPublicDate":"2015-04-01T12:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3089,"text":"Plant and Soil","active":true,"publicationSubtype":{"id":10}},"title":"Soil respiration patterns and controls in limestone cedar glades","docAbstract":"Aims
\nDrivers of soil respiration (Rs) in rock outcrop ecosystems remain poorly understood. We investigated these drivers in limestone cedar glades, known for their concentrations of endemic plant species and for seasonal hydrologic extremes (xeric and saturated conditions), and compared our findings to those in temperate grasslands and semi-arid ecosystems.
\nMethods
\nWe measured Rs, soil temperature (Ts), volumetric soil water content (SWC), soil organic matter (SOM), soil depth, and vegetation cover monthly over 16 mo and analyzed effects of these variables on Rs.
\nResults
\nSeasonally, Rs primarily tracked Ts(r2=0.77; P < 0.01); however Rs was depressed during a summer drought. SOM was highly variable spatially, and incorporating SOM effects into the Rs model dramativally improved model performance. Both shallow soil and sparse vegetation cover were also associated with lower Rs.
\nConclusions
\nSoil depth, SOM, and vegetation cover were important drivers of Rs in limestone cedar glades. Seasonal Rs patterns reflected those for mesic temperate grasslands more than for semi-arid ecosystems, in that Rs primarily tracked temperature for most of the year.
","language":"English","publisher":"Kluwer Academic Publishers","publisherLocation":"Dordrecht","doi":"10.1007/s11104-014-2348-6","collaboration":"National Park Service (Stones River National Battlefield), Tennessee State University","usgsCitation":"Cartwright, J.M., and Hui, D., 2015, Soil respiration patterns and controls in limestone cedar glades: Plant and Soil, v. 389, no. 1-2, p. 157-169, https://doi.org/10.1007/s11104-014-2348-6.","productDescription":"13","startPage":"157","endPage":"169","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055589","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"links":[{"id":472163,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11104-014-2348-6","text":"Publisher Index Page"},{"id":299380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"389","issue":"1-2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2014-12-07","publicationStatus":"PW","scienceBaseUri":"5523ae44e4b027f0aee3d14e","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":544024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hui, Dafeng","contributorId":140059,"corporation":false,"usgs":false,"family":"Hui","given":"Dafeng","email":"","affiliations":[{"id":13370,"text":"Tennessee State University","active":true,"usgs":false}],"preferred":false,"id":544025,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70143983,"text":"ofr20151056 - 2015 - Hydrologic conditions in Massachusetts during water year 2014","interactions":[],"lastModifiedDate":"2015-04-01T10:01:52","indexId":"ofr20151056","displayToPublicDate":"2015-04-01T12:00:00","publicationYear":"2015","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":"2015-1056","title":"Hydrologic conditions in Massachusetts during water year 2014","docAbstract":"Hydrologic data and conditions throughout Massachusetts during water year 2014 (October 1, 2013, to September 30, 2014) are presented in this report. Stream discharge and groundwater levels during water year 2014 varied geographically across the State. The data are described as being above, below, or near normal in relation to long-term averages for the period of record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151056","usgsCitation":"Verdi, R.J., 2015, Hydrologic conditions in Massachusetts during water year 2014: U.S. Geological Survey Open-File Report 2015-1056, iii, 9 p., https://doi.org/10.3133/ofr20151056.","productDescription":"iii, 9 p.","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2013-10-01","temporalEnd":"2014-09-30","ipdsId":"IP-063076","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"links":[{"id":299138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20151056.jpg"},{"id":299135,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2015/1056/"},{"id":299136,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1056/pdf/ofr2015-1056.pdf","text":"Report","size":"736 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.2733154296875,\n 42.74701217318067\n ],\n [\n -73.50677490234375,\n 42.087012670088214\n ],\n [\n -73.49853515625,\n 42.049292638686836\n ],\n [\n -72.81463623046875,\n 42.03603433407788\n ],\n [\n -72.8173828125,\n 41.99828401778616\n ],\n [\n -72.76657104492188,\n 42.0064481470799\n ],\n [\n -72.75009155273438,\n 42.037054301883806\n ],\n [\n -72.61001586914062,\n 42.03195429915656\n ],\n [\n -72.60726928710938,\n 42.02481360781777\n ],\n [\n -72.57568359375,\n 42.02685388718981\n ],\n [\n -72.53036499023438,\n 42.03603433407788\n ],\n [\n -71.79977416992188,\n 42.02379344357796\n ],\n [\n -71.79977416992188,\n 42.00848901572399\n ],\n [\n -71.38092041015625,\n 42.01767211439039\n ],\n [\n -71.3836669921875,\n 41.89307729913167\n ],\n [\n -71.33697509765625,\n 41.898188430430444\n ],\n [\n -71.34658813476562,\n 41.81943165932007\n ],\n [\n -71.3287353515625,\n 41.781552998900345\n ],\n [\n -71.26007080078125,\n 41.75287318430239\n ],\n [\n -71.19827270507812,\n 41.67496335351134\n ],\n [\n -71.13372802734375,\n 41.66060124302088\n ],\n [\n -71.12548828125,\n 41.498292501398545\n ],\n [\n -70.784912109375,\n 41.290189955885644\n ],\n [\n -69.95956420898438,\n 41.20138969929042\n ],\n [\n -69.89227294921875,\n 41.775408403663285\n ],\n [\n -70.00213623046875,\n 42.0615286181226\n ],\n [\n -70.16693115234375,\n 42.108411365705855\n ],\n [\n -70.6256103515625,\n 42.10229818948117\n ],\n [\n -70.70594787597656,\n 42.21071942977117\n ],\n [\n -70.76019287109375,\n 42.26054098165532\n ],\n [\n -70.86456298828124,\n 42.34585783931204\n ],\n [\n -70.86868286132812,\n 42.46247316292525\n ],\n [\n -70.6475830078125,\n 42.57533232110996\n ],\n [\n -70.56381225585938,\n 42.62991729384455\n ],\n [\n -70.56243896484375,\n 42.66527089792358\n ],\n [\n -70.61737060546875,\n 42.70464124398721\n ],\n [\n -70.6585693359375,\n 42.69959515809203\n ],\n [\n -70.697021484375,\n 42.67031977251906\n ],\n [\n -70.76156616210938,\n 42.696567309696974\n ],\n [\n -70.7958984375,\n 42.789354160502775\n ],\n [\n -70.806884765625,\n 42.87495770751784\n ],\n [\n -70.84808349609375,\n 42.86338298693097\n ],\n [\n -70.88241577148438,\n 42.88149898076182\n ],\n [\n -70.90507507324219,\n 42.88803956056295\n ],\n [\n -70.93391418457031,\n 42.88602714832883\n ],\n [\n -70.96549987792969,\n 42.870931963933735\n ],\n [\n -71.03416442871094,\n 42.85985981506279\n ],\n [\n -71.04995727539062,\n 42.848785680348875\n ],\n [\n -71.06575012207031,\n 42.80799561516835\n ],\n [\n -71.13235473632812,\n 42.82209892875648\n ],\n [\n -71.16737365722656,\n 42.81051429991593\n ],\n [\n -71.18728637695312,\n 42.79187360418141\n ],\n [\n -71.18247985839844,\n 42.73843944000847\n ],\n [\n -71.22505187988281,\n 42.74751641468717\n ],\n [\n -71.2470245361328,\n 42.744995166137286\n ],\n [\n -71.29440307617188,\n 42.69858589169842\n ],\n [\n -73.2733154296875,\n 42.74701217318067\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551a65aee4b032384278342a","contributors":{"authors":[{"text":"Verdi, Richard J. 0000-0002-7093-9203 rverdi@usgs.gov","orcid":"https://orcid.org/0000-0002-7093-9203","contributorId":1098,"corporation":false,"usgs":true,"family":"Verdi","given":"Richard","email":"rverdi@usgs.gov","middleInitial":"J.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":543218,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70142978,"text":"70142978 - 2015 - Modeled intermittency risk for small streams in the Upper Colorado River Basin under climate change","interactions":[],"lastModifiedDate":"2016-04-12T13:52:55","indexId":"70142978","displayToPublicDate":"2015-04-01T11:00:00","publicationYear":"2015","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":"Modeled intermittency risk for small streams in the Upper Colorado River Basin under climate change","docAbstract":"Longer, drier summers projected for arid and semi-arid regions of western North America under climate change are likely to have enormous consequences for water resources and river-dependent ecosystems. Many climate change scenarios for this region involve decreases in mean annual streamflow, late summer precipitation and late-summer streamflow in the coming decades. Intermittent streams are already common in this region, and it is likely that minimum flows will decrease and some perennial streams will shift to intermittent flow under climate-driven changes in timing and magnitude of precipitation and runoff, combined with increases in temperature. To understand current intermittency among streams and analyze the potential for streams to shift from perennial to intermittent under a warmer climate, we analyzed historic flow records from streams in the Upper Colorado River Basin (UCRB). Approximately two-thirds of 115 gaged stream reaches included in our analysis are currently perennial and the rest have some degree of intermittency. Dry years with combinations of high temperatures and low precipitation were associated with more zero-flow days. Mean annual flow was positively related to minimum flows, suggesting that potential future declines in mean annual flows will correspond with declines in minimum flows. The most important landscape variables for predicting low flow metrics were precipitation, percent snow, potential evapotranspiration, soils, and drainage area. Perennial streams in the UCRB that have high minimum-flow variability and low mean flows are likely to be most susceptible to increasing streamflow intermittency in the future.
","language":"English","publisher":"European Geophysical Society","publisherLocation":"New York, NY","doi":"10.1016/j.jhydrol.2015.02.025","usgsCitation":"Reynolds, L., Shafroth, P.B., and Poff, N.L., 2015, Modeled intermittency risk for small streams in the Upper Colorado River Basin under climate change: Journal of Hydrology, v. 523, p. 768-780, https://doi.org/10.1016/j.jhydrol.2015.02.025.","productDescription":"13 p.","startPage":"768","endPage":"780","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059776","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":298560,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551d089ee4b0256c24f42155","contributors":{"authors":[{"text":"Reynolds, Lindsay V.","contributorId":102732,"corporation":false,"usgs":true,"family":"Reynolds","given":"Lindsay V.","affiliations":[],"preferred":false,"id":542374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":542373,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poff, N. 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Historical (1977-1989) and current (2010-2011) fish assemblages were analyzed by rarefaction analysis (species richness), nonmetric multidimensional scaling (composition/variability), multiresponse permutation procedures (composition), and paired t-test (variability). Trends in hydrological conditions (1970s-2010s) were examined by Kendall tau and quantile regression, and associations between streamfiow and specific conductance (salinity) by generalized linear models. Since the 1970s, species richness and variability of fish assemblages decreased in the Rio Grande below the confluence with the Rio Conchos (Mexico), a major tributary, but not above it. There was increased representation of lower-flow/higher-salinity tolerant species, thus making fish communities below the confluence taxonomically and functionally more homogeneous to those above it. Unlike findings elsewhere, this biotic homogenization was due primarily to changes in the relative abundances of native species. While Rio Conchos discharge was > 2-fold higher than Rio Grande discharge above their confluence, Rio Conchos discharge decreased during the study period causing Rio Grande discharge below the confluence to also decrease. Rio Conchos salinity is lower than Rio Grande salinity above their confluence and, as Rio Conchos discharge decreased, it caused Rio Grande salinity below the confluence to increase (reduced dilution). Trends in discharge did not correspond to trends in precipitation except at extreme-high (90th quantile) levels. In conclusion, decreasing discharge from the Rio Conchos has led to decreasing flow and increasing salinity in the Rio Grande below the confluence. This spatially uneven desertification and salinization of the Rio Grande has in turn led to a region-wide homogenization of hydrological conditions and of taxonomic and functional attributes of fish assemblages.
","language":"English","publisher":"Elsevier Pub. Co.","publisherLocation":"Amsterdam","doi":"10.1016/j.scitotenv.2014.12.079","usgsCitation":"Miyazono, S., Patino, R., and Taylor, C., 2015, Desertification, salinization, and biotic homogenization in a dryland river ecosystem: Science of the Total Environment, v. 511, p. 444-453, https://doi.org/10.1016/j.scitotenv.2014.12.079.","productDescription":"10 p.","startPage":"444","endPage":"453","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059894","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":302362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"511","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"558e77b2e4b0b6d21dd65946","contributors":{"authors":[{"text":"Miyazono, S.","contributorId":79310,"corporation":false,"usgs":true,"family":"Miyazono","given":"S.","affiliations":[],"preferred":false,"id":556942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":556904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, C.M.","contributorId":64707,"corporation":false,"usgs":true,"family":"Taylor","given":"C.M.","email":"","affiliations":[],"preferred":false,"id":556943,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70144855,"text":"sir20145209 - 2015 - The Everglades Depth Estimation Network (EDEN) surface-water model, version 2","interactions":[],"lastModifiedDate":"2015-04-01T09:14:54","indexId":"sir20145209","displayToPublicDate":"2015-04-01T10:00:00","publicationYear":"2015","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":"2014-5209","title":"The Everglades Depth Estimation Network (EDEN) surface-water model, version 2","docAbstract":"The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models that generate daily water-level and water-depth data, and applications that compute derived hydrologic data across the freshwater part of the greater Everglades landscape. The U.S. Geological Survey Greater Everglades Priority Ecosystems Science provides support for EDEN in order for EDEN to provide quality-assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan.
\nThe EDEN surface-water model, version 2 (V2), interpolates water-level data from a network of 240 gages to generate gridded daily water-level surfaces for the freshwater domain of the Everglades. When these spatiotemporal continuous surfaces are combined with EDEN’s digital elevation model of ground surface, derived hydrologic data provide scientists and water managers working in the Everglades with data necessary to analyze ecological and biotic responses to hydrologic changes in the Everglades. Derived datasets include water depth, recession rates, days since last dry, water-surface slopes, and hydroperiod. The V2 model includes enhancements from the previous model (version 1; V1) to accommodate changes in the water-level gage network, adjustments to water-level data, improved understanding of the flow dynamics (particularly near canals), and installation of an elevation benchmark network. Enhancements to the V2 model included
\nModel performance statistics show a general improvement in the V2 model as compared to the V1 model. Overall, the root mean squared error (RMSE) was reduced by 2.42 centimeters (cm) to 4.68 cm. In Water Conservation Area 3A North and Water Conservation Area 3B, the RMSE was reduced by 10.88 and 9.15 cm, respectively. In addition to evaluating model performance statistics, 2-cm water-level maps were generated and evaluated for irregular contours that would indicate a potential problem either with data input or water-level estimates.
\nThree applications of the EDEN-modeled water surfaces and other EDEN datasets are presented in the report to show how scientists and resource managers are using EDEN datasets to analyze biological and ecological responses to hydrologic changes in the Everglades. The biological responses of two important Everglades species, alligators and wading birds, to changes in hydrology are described. The effects of hydrology on fire dynamics in the Everglades are also discussed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145209","collaboration":"Prepared as part of the U.S. Geological Survey Greater Everglades Priority Ecosystem Science and in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Telis, P., Xie, Z., Liu, Z., Li, Y., and Conrads, P., 2015, The Everglades Depth Estimation Network (EDEN) surface-water model, version 2: U.S. Geological Survey Scientific Investigations Report 2014-5209, Report: viii, 42 p. ; 3 Appendices, https://doi.org/10.3133/sir20145209.","productDescription":"Report: viii, 42 p. ; 3 Appendices","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-050914","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"links":[{"id":299244,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145209.jpg"},{"id":299239,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5209/"},{"id":299240,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5209/pdf/sir2014-5209.pdf","text":"Report","size":"27.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":299241,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5209/appendix/sir2014-5209_appendix1.xlsx","text":"Appendix 1","size":"58.3 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Appendix 1","linkHelpText":"This is an electronic copy of Appendix 1. Water-level gages used to develop the EDEN surface-water model, version 2."},{"id":299242,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5209/appendix/sir2014-5209_appendix2.xlsx","text":"Appendix 2","size":"14.3 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Appendix 2","linkHelpText":"This is an electronic copy of Appendix 2. Network of benchmarks in greater Everglades used to evaluate EDEN surface-water model."},{"id":299243,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2014/5209/appendix/sir2014-5209_appendix3.xlsx","text":"Appendix 3","size":"39.6 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Appendix 3","linkHelpText":"This is an electronic copy of Appendix 3. Water-level measurements at elevation benchmarks and differences between the modeled surfaces for the EDEN surface-water model, versions 1 and 2."}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.93603515625,\n 25.12539261151203\n ],\n [\n -81.93603515625,\n 26.41155054662258\n ],\n [\n -80.00244140625,\n 26.41155054662258\n ],\n [\n -80.00244140625,\n 25.12539261151203\n ],\n [\n -81.93603515625,\n 25.12539261151203\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"551d08a0e4b0256c24f42159","contributors":{"authors":[{"text":"Telis, Pamela A. patelis@usgs.gov","contributorId":140030,"corporation":false,"usgs":true,"family":"Telis","given":"Pamela A.","email":"patelis@usgs.gov","affiliations":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"preferred":false,"id":543825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xie, Zhixiao","contributorId":40336,"corporation":false,"usgs":true,"family":"Xie","given":"Zhixiao","email":"","affiliations":[],"preferred":false,"id":543826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liu, Zhongwei","contributorId":34245,"corporation":false,"usgs":true,"family":"Liu","given":"Zhongwei","email":"","affiliations":[],"preferred":false,"id":543827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Yingru","contributorId":140031,"corporation":false,"usgs":false,"family":"Li","given":"Yingru","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":543828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conrads, Paul 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":764,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":543829,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70145178,"text":"70145178 - 2015 - Females exceed males in mercury concentrations of burbot Lota lota","interactions":[],"lastModifiedDate":"2018-08-09T12:44:28","indexId":"70145178","displayToPublicDate":"2015-04-01T10:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":887,"text":"Archives of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Females exceed males in mercury concentrations of burbot Lota lota","docAbstract":"Examination of differences in contaminant concentrations between sexes of fish, across several fish species, may reveal clues for important behavioral and physiological differences between the sexes. We determined whole-fish total mercury (Hg) concentrations of 25 male and 25 female adult burbot Lota lota captured in Lake Erie during summer 2011, and of 14 male and 18 female adult burbot captured in Great Slave Lake (Northwest Territories, Canada) during winter 2013. On average, females were 22 % greater in Hg concentration than males. This difference was probably not due to a greater feeding rate by females, because results from previous studies based on polychlorinated biphenyl (PCB) determinations of these same burbot indicated that males fed at a substantially greater rate than females. Based on our determinations of Hg concentrations in the gonads and somatic tissue of five ripe females and five ripe males, this difference was not attributable to changes in Hg concentration immediately after spawning due to release of gametes. Further, bioenergetics modeling results from previous studies indicated that growth dilution would not explain any portion of this observed difference in Hg concentrations between the sexes. We, therefore, conclude that this difference was most likely due to a substantially faster rate of Hg elimination by males compared with females. Male burbot exhibit among the greatest gonadosomatic indices (GSIs) of all male fishes, with their testes accounting for between 10 and 15 % of their body weight when the fish are in ripe condition. Androgens have been linked to enhanced Hg elimination rates in other vertebrates. If androgen production is positively related to GSI, then male burbot would be expected to have among the greatest androgen levels of all fishes. Thus, we hypothesize that male burbot eliminate Hg from their bodies faster than most other male fishes, and this explains the greater Hg concentration in females compared with males.
","language":"English","publisher":"Springer","publisherLocation":"New York, NY","doi":"10.1007/s00244-015-0131-1","usgsCitation":"Madenjian, C.P., Stapanian, M.A., Cott, P.A., Krabbenhoft, D.P., Edwards, W., Ogilvie, L.M., Mychek-Londer, J., and DeWild, J.F., 2015, Females exceed males in mercury concentrations of burbot Lota lota: Archives of Environmental Contamination and Toxicology, v. 68, no. 4, p. 678-688, https://doi.org/10.1007/s00244-015-0131-1.","productDescription":"11 p.","startPage":"678","endPage":"688","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059536","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":299374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-01-28","publicationStatus":"PW","scienceBaseUri":"5523ae35e4b027f0aee3d12c","contributors":{"authors":[{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":544016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stapanian, Martin A. 0000-0001-8173-4273 mstapanian@usgs.gov","orcid":"https://orcid.org/0000-0001-8173-4273","contributorId":3425,"corporation":false,"usgs":true,"family":"Stapanian","given":"Martin","email":"mstapanian@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":544017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cott, Peter A.","contributorId":64160,"corporation":false,"usgs":true,"family":"Cott","given":"Peter","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":544018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - 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2015 - Simulated high-latitude soil thermal dynamics during the past four decades","interactions":[],"lastModifiedDate":"2016-03-24T12:01:23","indexId":"70169236","displayToPublicDate":"2015-04-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1350,"text":"Cryosphere Discussions","active":true,"publicationSubtype":{"id":10}},"title":"Simulated high-latitude soil thermal dynamics during the past four decades","docAbstract":"Soil temperature (Ts ) change is a key indicator of the dynamics of permafrost. On seasonal and inter-annual time scales, the variability of Ts determines the active layer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing T 5 s not only drives permafrost thaw/retreat, but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960–2000, to characterize the warming rate of Ts 10 in permafrost regions. There is a large spread of Ts trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 ◦C yr−1 . Most models show smaller increase in Ts with increasing depth. Air temperature (Ta ) and longwave downward radiation (LWDR) are the main drivers of Ts trends, but their relative contributions differ 15 amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in Ts trends, while trends of Ta only explain 5 % of the differences in Ts trends. Uncertain climate forcing contributes a larger uncertainty in Ts trends (0.021 ± 0.008 ◦C yr−1 , mean ± SD) than the uncertainty of model structure (0.012 ± 0.001 ◦C yr−1 ), diagnosed from the range of response between different mod- 20 els, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active layer thickness (ALT) is less than 3 m loss rate is found to be significantly correlated with the magnitude of the trends of Ts at 1 m depth across the models (R = −0.85, P = 0.003), but not with the initial total near-surface permafrost area (R = −0.30, P = 0.438). The sensitivity of the total boreal near-surface permafrost area to T 25 s at 1 m, is estimated to be of −2.80 ± 0.67 million km2 ◦C −1 . Finally, by using two long-term LWDR datasets and relationships between trends of LWDR and Ts across models, we infer an observationconstrained total boreal near-surface permafrost area decrease comprised between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr−1 from 1960 to 2000. This corresponds to 9– 18 % degradation of the current permafrost area.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/tc-10-179-2016","usgsCitation":"Peng, S., Ciais, P., Wang, T., Gouttevin, I., McGuire, A., Lawrence, D., Burke, E., Chen, X., Delire, C., Koven, C., MacDougall, A., Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T.J., Decharme, B., Hajima, T., Ji, D., Lettenmaier, D., Miller, P., Moore, J., Smith, B., and Sueyoshi, T., 2015, Simulated high-latitude soil thermal dynamics during the past four decades: Cryosphere Discussions, v. 9, p. 2301-2337, https://doi.org/10.5194/tc-10-179-2016.","productDescription":"37 p.","startPage":"2301","endPage":"2337","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063588","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":472178,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-10-179-2016","text":"Publisher Index Page"},{"id":319364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-20","publicationStatus":"PW","scienceBaseUri":"56f50fd2e4b0f59b85e1ebbb","contributors":{"authors":[{"text":"Peng, S.","contributorId":68688,"corporation":false,"usgs":true,"family":"Peng","given":"S.","email":"","affiliations":[],"preferred":false,"id":623658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ciais, P.","contributorId":39604,"corporation":false,"usgs":true,"family":"Ciais","given":"P.","affiliations":[],"preferred":false,"id":623659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, T.","contributorId":53707,"corporation":false,"usgs":true,"family":"Wang","given":"T.","affiliations":[],"preferred":false,"id":623660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gouttevin, I.","contributorId":167818,"corporation":false,"usgs":false,"family":"Gouttevin","given":"I.","affiliations":[],"preferred":false,"id":623661,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGuire, A. 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Analysis included bulk quality parameters, trace organic and inorganic constituents, and organic matter characterization. The flowback sample contained salts (TDS = 22,500 mg/L), metals (e.g., iron at 81.4 mg/L) and high concentration of dissolved organic matter (DOC = 590 mgC/L). The organic matter comprised fracturing fluid additives such as surfactants (e.g., linear alkyl ethoxylates) and high levels of acetic acid (an additives' degradation product), indicating the anthropogenic impact on this wastewater. Based on the water quality results and preliminary treatability tests, the removal of suspended solids and iron by aeration/precipitation (and/or filtration) followed by disinfection was identified as appropriate for flowback recycling in future fracturing operations. In addition to these treatments, a biological treatment (to remove dissolved organic matter) followed by reverse osmosis desalination was determined to be necessary to attain water quality standards appropriate for other water reuse options (e.g., crop irrigation). The study provides a framework for evaluating site-specific hydraulic fracturing wastewaters, proposing a suite of analytical methods for characterization, and a process for guiding the choice of a tailored treatment approach.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2015.01.043","usgsCitation":"Lester, Y., Ferrer, I., Thurman, E.M., Sitterley, K.A., Korak, J.A., Aiken, G.R., and Linden, K.G., 2015, Characterization of hydraulic fracturing flowback water in Colorado: Implications for water treatment: Science of the Total Environment, v. 512-513, p. 637-644, https://doi.org/10.1016/j.scitotenv.2015.01.043.","productDescription":"8 p.","startPage":"637","endPage":"644","ipdsId":"IP-062886","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":343870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","volume":"512-513","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5969d82de4b0d1f9f060a1a1","contributors":{"authors":[{"text":"Lester, Yaal","contributorId":194687,"corporation":false,"usgs":false,"family":"Lester","given":"Yaal","email":"","affiliations":[],"preferred":false,"id":705041,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferrer, Imma","contributorId":68606,"corporation":false,"usgs":true,"family":"Ferrer","given":"Imma","affiliations":[],"preferred":false,"id":705042,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thurman, E. 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Under natural conditions in this flat landscape, water flowed slowly downstream as broad, shallow sheet flow. The ecosystem is markedly different now, altered by nutrient pollution and construction of canals, levees, and water control structures designed for flood control and water supply. These alterations have resulted in a 50 % reduction of the ecosystem’s spatial extent and significant changes in ecological function in the remaining portion. One of the world’s largest restoration programs is underway to restore some of the historic hydrologic and ecological functions of the Everglades, via a multi-billion dollar Comprehensive Everglades Restoration Plan. This plan, finalized in 2000, did not explicitly consider climate change effects, yet today we realize that sea level rise and future changes in rainfall (RF), temperature, and evapotranspiration (ET) may have system-wide impacts. This series of papers describes results of a workshop where a regional hydrologic model was used to simulate the hydrology expected in 2060 with climate changes including increased temperature, ET, and sea level, and either an increase or decrease in RF. Ecologists with expertise in various areas of the ecosystem evaluated the hydrologic outputs, drew conclusions about potential ecosystem responses, and identified research needs where projections of response had high uncertainty. Resource managers participated in the workshop, and they present lessons learned regarding how the new information might be used to guide Everglades restoration in the context of climate change.
","language":"English","publisher":"Springer","doi":"10.1007/s00267-014-0439-z","usgsCitation":"Aumen, N.G., Havens, K.E., Best, G.R., and Berry, L., 2015, Predicting ecological responses of the Florida Everglades to possible future climate scenarios: Introduction: Environmental Management, v. 55, no. 4, p. 741-748, https://doi.org/10.1007/s00267-014-0439-z.","productDescription":"8 p.","startPage":"741","endPage":"748","ipdsId":"IP-051181","costCenters":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"links":[{"id":346566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Everglades ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.69958496093749,\n 25.06569718553588\n ],\n [\n -79.903564453125,\n 25.06569718553588\n ],\n [\n -79.903564453125,\n 27.508271413876017\n ],\n [\n -82.69958496093749,\n 27.508271413876017\n ],\n [\n -82.69958496093749,\n 25.06569718553588\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-06","publicationStatus":"PW","scienceBaseUri":"59e1d09ae4b05fe04cd117c0","contributors":{"authors":[{"text":"Aumen, Nicholas G. 0000-0002-5277-2630 naumen@usgs.gov","orcid":"https://orcid.org/0000-0002-5277-2630","contributorId":5418,"corporation":false,"usgs":true,"family":"Aumen","given":"Nicholas","email":"naumen@usgs.gov","middleInitial":"G.","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":13415,"text":"Everglades National Park","active":true,"usgs":false}],"preferred":true,"id":712352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Havens, Karl E","contributorId":197036,"corporation":false,"usgs":false,"family":"Havens","given":"Karl","email":"","middleInitial":"E","affiliations":[],"preferred":false,"id":712353,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Best, G. Ronnie ronnie_best@usgs.gov","contributorId":4282,"corporation":false,"usgs":true,"family":"Best","given":"G.","email":"ronnie_best@usgs.gov","middleInitial":"Ronnie","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":712354,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berry, Leonard","contributorId":119091,"corporation":false,"usgs":true,"family":"Berry","given":"Leonard","email":"","affiliations":[],"preferred":false,"id":712355,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70155853,"text":"70155853 - 2015 - Hydrologic remediation for the Deepwater Horizon incident drove ancillary primary production increase in coastal swamps","interactions":[],"lastModifiedDate":"2019-12-11T09:34:58","indexId":"70155853","displayToPublicDate":"2015-03-30T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic remediation for the Deepwater Horizon incident drove ancillary primary production increase in coastal swamps","docAbstract":"As coastal wetlands subside worldwide, there is an urgency to understand the hydrologic drivers and dynamics of plant production and peat accretion. One incidental test of the effects of high rates of discharge on forested wetland production occurred in response to the 2010 Deepwater Horizon incident, in which all diversions in Louisiana were operated at or near their maximum discharge level for an extended period to keep offshore oil from threatened coastal wetlands. Davis Pond Diversion was operated at six times the normal discharge levels for almost 4 months, so that Taxodium distichum swamps downstream of the diversion experienced greater inundation and lower salinity. After this remediation event in 2010, above-ground litter production increased by 2.7 times of production levels in 2007–2011. Biomass of the leaf and reproductive tissues of several species increased; wood litter was minimal and did not change during this period. Root production decreased in 2010 but subsequently returned to pre-remediation values in 2011. Both litter and root production remained high in the second growing season after hydrologic remediation. Annual tree growth (circumference increment) was not significantly altered by the remediation. The potential of freshwater pulses for regulating tidal swamp production is further supported by observations of higher T. distichum growth in lower salinity and/or pulsed environments across the U.S. Gulf Coast. Usage of freshwater pulses to manage altered estuaries deserves further consideration, particularly because the timing and duration of such pulses could influence both primary production and peat accretion.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1625","usgsCitation":"Middleton, B.A., Johnson, D., and Roberts, B., 2015, Hydrologic remediation for the Deepwater Horizon incident drove ancillary primary production increase in coastal swamps: Ecohydrology, v. 8, no. 5, p. 838-850, https://doi.org/10.1002/eco.1625.","productDescription":"12 p.","startPage":"838","endPage":"850","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045601","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":488714,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.1625","text":"Publisher Index Page"},{"id":306616,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Davis Pond Diversion Outlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.5218505859375,\n 29.104176683949984\n ],\n [\n -89.176025390625,\n 29.104176683949984\n ],\n [\n -89.176025390625,\n 30.130875412002318\n ],\n [\n -90.5218505859375,\n 30.130875412002318\n ],\n [\n -90.5218505859375,\n 29.104176683949984\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-30","publicationStatus":"PW","scienceBaseUri":"55cc6e29e4b08400b1fe0fd4","contributors":{"authors":[{"text":"Middleton, Beth A. 0000-0002-1220-2326 middletonb@usgs.gov","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":2029,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","email":"middletonb@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":566607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Darren 0000-0002-0502-6045 johnsond@usgs.gov","orcid":"https://orcid.org/0000-0002-0502-6045","contributorId":3663,"corporation":false,"usgs":true,"family":"Johnson","given":"Darren","email":"johnsond@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":566608,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, Brian J","contributorId":146207,"corporation":false,"usgs":false,"family":"Roberts","given":"Brian J","affiliations":[{"id":16627,"text":"Louisiana Universities Marine Consortium (LUMCON)","active":true,"usgs":false}],"preferred":false,"id":566609,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70160373,"text":"70160373 - 2015 - Distribution of invasive and native riparian woody plants across the western USA in relation to climate, river flow, floodplain geometry and patterns of introduction","interactions":[],"lastModifiedDate":"2015-12-18T14:59:26","indexId":"70160373","displayToPublicDate":"2015-03-28T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Distribution of invasive and native riparian woody plants across the western USA in relation to climate, river flow, floodplain geometry and patterns of introduction","docAbstract":"Management of riparian plant invasions across the landscape requires understanding the combined influence of climate, hydrology, geologic constraints and patterns of introduction. We measured abundance of nine riparian woody taxa at 456 stream gages across the western USA. We constructed conditional inference recursive binary partitioning models to discriminate the influence of eleven environmental variables on plant occurrence and abundance, focusing on the two most abundant non-native taxa, Tamarix spp. and Elaeagnus angustifolia, and their native competitor Populus deltoides. River reaches in this study were distributed along a composite gradient from cooler, wetter higher-elevation reaches with higher stream power and earlier snowmelt flood peaks to warmer, drier lower-elevation reaches with lower power and later peaks. Plant distributions were strongly related to climate, hydrologic and geomorphic factors, and introduction history. The strongest associations were with temperature and then precipitation. Among hydrologic and geomorphic variables, stream power, peak flow timing and 10-yr flood magnitude had stronger associations than did peak flow predictability, low-flow magnitude, mean annual flow and channel confinement. Nearby intentional planting of Elaeagnus was the best predictor of its occurrence, but planting of Tamarix was rare. Higher temperatures were associated with greater abundance of Tamarix relative to P. deltoides, and greater abundance of P. deltoides relative toElaeagnus. Populus deltoides abundance was more strongly related to peak flow timing than was that of Elaeagnus or Tamarix. Higher stream power and larger 10-yr floods were associated with greater abundance of P. deltoides and Tamarix relative to Elaeagnus. Therefore, increases in temperature could increase abundance of Tamarix and decrease that of Elaeagnus relative to P. deltoides, changes in peak flow timing caused by climate change or dam operations could increase abundance of both invasive taxa, and dam-induced reductions in flood peaks could increase abundance of Elaeagnus relative to Tamarix and P. deltoides.
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Whether a lake is large or small, deep or shallow, open or closed, the movement of water to a lake and the circulation patterns of water within a lake control how and where sediment and contaminants are deposited. Particle-associated contaminants may stay close to the input source of contamination or be transported by currents to bathymetric lows. A complex morphology of the lake bottom or shoreline can also affect how contaminants will be distributed. Dissolved contaminants may be widely dispersed in smaller lakes, but may be diluted in large lakes away from the source. Although dissolved contaminants may not be deposited in lake sediments, the impact of dissolved contaminants (such as nitrogen) may be reflected by the ecosystem. For instance, increased phosphorus and nitrogen may increase organic content or algal biomass, and contribute to eutrophication of the lake over time. Changes in oxidation-reduction potential at the sediment-water interface may either release some contaminants to the water column or conversely deposit other contaminants to the sediment depending on the compound’s chemical characteristics. Changes in land use generally affect the hydrology of the watershed surrounding a lake, providing more runoff if soil binding vegetation is removed or if more impervious cover (roads and buildings) is increased. Groundwater inputs may change if pumping of the aquifer connected to the lake occurs. Even if groundwater is only a small portion of the volume of water entering a lake, if contaminant concentrations in the aquifer are high compared to surface water inputs, the mass of contaminants from groundwater may be as, or more, important than surface water contributions.
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","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Environmental Contaminants","language":"English","publisher":"Springer Netherlands","doi":"10.1007/978-94-017-9541-8_1","usgsCitation":"Blais, J., Rosen, M.R., and John Smol, 2015, Using natural archives to track sources and long-term trends of pollution: an introduction, chap. of Environmental Contaminants, p. 1-3, https://doi.org/10.1007/978-94-017-9541-8_1.","productDescription":"3 p.","startPage":"1","endPage":"3","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058003","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":311402,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-28","publicationStatus":"PW","scienceBaseUri":"564b0c69e4b0ebfbef0d3196","contributors":{"authors":[{"text":"Blais, Jules","contributorId":140070,"corporation":false,"usgs":false,"family":"Blais","given":"Jules","email":"","affiliations":[{"id":13374,"text":"University of Ottawa, Canada","active":true,"usgs":false}],"preferred":false,"id":544155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":544154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"John Smol","contributorId":140071,"corporation":false,"usgs":false,"family":"John Smol","affiliations":[{"id":13375,"text":"Queens University, Canada","active":true,"usgs":false}],"preferred":false,"id":544156,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}