Mercury monitoring results from about 300 Morone saxatilis (striped bass) muscle tissue samples collected by the State of Utah from Lake Powell resulted in a Utah/Arizona fish consumption advisory issued in 2012 for approximately the lower 100 kilometers of the reservoir. Chemical, physical, and biological data were collected during two synoptic sampling cruises on Lake Powell during May/June 2014 and August 2015 to test three hypotheses associated with a conceptual model developed to explain the observed geographic concentration gradient of Hg in fish tissue samples. This model proposes that in the transition from a primarily riverine system to a reservoir, there is a change in the concentration and composition of water-column particulate material, increasing in the proportion of organic content moving downstream, as the larger size fractions of the inorganic particulate load are deposited in the upper reservoir. This change alleviates light limitation of phytoplankton production and leads to a higher proportion of autochthonous primary production in the downstream direction. This, in turn, drives increased microbial methylmercury (MeHg) production in the benthos and potentially the water column, in the downstream direction, and results in the observed elevated fish Hg levels in the lower part of the reservoir. The model also proposes that there are differences between the main stem of Lake Powell and side canyons, embayments, or secondary rivers entering the reservoir, in terms of Hg cycling dynamics and bioaccumulations, driven mainly by differences in hydrology. Finally, seasonal differences in Hg dynamics within the reservoir are proposed, based on seasonal dynamics associated with primary production and the physical process of seasonal stratification.
A total of three statistically testable hypotheses were proposed and postulated that measurable differences in key Hg and non-Hg metrics exist between: (1) the upper and lower reservoir; (2) main stem and river arm/side canyon/embayment sites; and (3) early-season (May/June 2014, less stratified) and late-season (August 2015, stratified) conditions. Statistically modeled least square means in combination with the graphical analysis of Hg and non-Hg parameters were used to examine the data collected during the study and test these hypotheses. Data collected during the study are included in a U.S. Geological Survey data release and are available online at https://doi.org/10.5066/F74X560J.
In general, water-column, plankton, and surface sediment samples collected during the synoptic sampling cruises are supportive of the three hypotheses associated with the conceptual model. In support of hypothesis 1 (comparing upper and lower reservoir sites), the least square mean for turbidity was higher in the upper reservoir. In contrast, surface water particulate organic carbon (as a percentage of total particulate mass), particulate MeHg (by mass [in nanograms per gram] and as a percentage of total mercury [THg]), and particulate-dissolved partitioning coefficients for THg and MeHg were higher in the lower reservoir. Plankton THg concentrations also were significantly (probability [p] less than (<) 0.05) higher in the lower reservoir. Surface sediment metrics in support of hypothesis 1 include higher MeHg production potential rates in the lower reservoir. In contrast, there were no statistically significant differences between the upper and lower reservoir for surface sediment percent of MeHg and MeHg concentration, percent MeHg, or methylation rate constants. These spatial trends associated with hypothesis 1 indicate a pathway for enhanced Hg bioavailability in the lower reservoir.
Hypothesis 2, which tested for differences between main stem and river arm/side canyon/embayment sites, was supported by a number of water-column parameters, including particulate THg and MeHg concentrations by mass (in nanograms per gram) and percent particulate MeHg being significantly (p<0.05) higher in the river arms, side canyons, and embayments relative to the main stem channel. Plankton MeHg concentrations (by mass [in nanograms per gram] and volume [in nanograms per liter] and as a percentage of THg) were elevated in river arm/side canyon/embayment sites compared to main stem sites, indicating an enhanced potential for MeHg bioaccumulation at the base of the pelagic food web in river arms, side canyons, and embayments. In contrast, few of the sediment metrics differed between main stem and river arm/side canyon/embayment sampling sites; however, the potential for MeHg degradation in surface sediment was significantly higher in the main stem. The data indicate that river arm/side canyon/embayment sites may experience enhanced Hg bioaccumulation, compared to the main stem, because of higher MeHg levels at the base of the pelagic food web. This conclusion is supported by the elevated Hg detected in striped bass muscle tissue samples collected in the San Juan Arm during this study (2014). Fish collected from the lower reservoir exhibited a distinct Hg isotopic signature that was enriched in delta (δ)202Hg and capital delta (Δ)199Hg relative to fish samples collected from either Good Hope Bay or the San Juan Arm.
Hypothesis 3 tested for differences between early (May/June) high-flow and late (August) low-flow seasons. This test was supported by a range of non-Hg metrics (nitrate, phosphate, chlorophyll a, dissolved oxygen, fluorescent dissolved organic matter, temperature, and pH) that reflect the increase in chlorophyll a, decrease in nutrients, and buildup of stratified conditions in the transition from early- to late-season sampling periods. Significant seasonal differences also were noted for multiple Hg metrics, including (a) water-column filtered and particulate (by mass) MeHg and THg concentrations; (b) plankton MeHg and THg concentration (by mass); and (c) sediment percent MeHg, Hg(II)-methylation rate constant, and microbial ribosomal ribonucleic acid, small subunit 16 (16S rRNA) abundance, all of which were higher during the late-season synoptic sampling. Overall, the surface sediment metrics are consistent with a seasonal shift from the early-season synoptic results, when the availability of Hg(II) exerts a primary control on MeHg production, to the late-season synoptic sampling, when microbial activity is a dominant driver of MeHg production.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181159","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Naftz, D.L., Marvin-DiPasquale, M., Krabbenhoft, D.P., Aiken, G., Boyd, E.S., Conaway, C.H., Ogorek, J., and Anderson, G.M., 2019, Biogeochemical and physical processes controlling mercury methylation and bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015: U.S. Geological Survey Open-File Report 2018–1159, 81 p., https://doi.org/10.3133/ofr20181159.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-095917","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359576,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1159/coverthb.jpg"},{"id":359577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1159/ofr20181159.pdf","text":"Report","size":"9.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1159"},{"id":359578,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74X560J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for Biogeochemical and Physical Processes Controlling Mercury Methylation and Bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014–2015"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Glen Canyon, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.63551330566406,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 36.75594019674357\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle West
Valley City, UT 84119
About one-quarter of the water supply for the Village of Ruidoso, New Mexico, is derived from groundwater pumping along North Fork Eagle Creek in the Eagle Creek Basin near Alto, New Mexico. Because of concerns regarding the effects of groundwater pumping on surface-water hydrology in the Eagle Creek Basin and the effects of the 2012 Little Bear Fire, which resulted in substantial losses of vegetation in the basin, the monitoring of North Fork Eagle Creek for short-term geomorphic change has been required by the U.S. Department of Agriculture Forest Service, Lincoln National Forest, as part of the permitting decision that allows for the continued pumping of the production wells. The monitoring of short-term geomorphic change in North Fork Eagle Creek began in June 2017 with a geomorphic survey of the stream reach located between the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station (USGS site 08387550) and the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station (USGS site 08387600). The 2017 geomorphic survey was conducted by the U.S. Geological Survey (USGS), in cooperation with the Village of Ruidoso, and was the first in a planned series of five annual geomorphic surveys. The results of the 2017 geomorphic survey are summarized and interpreted in this report and are provided in their entirety in its companion data release.
The study reach is 1.86 miles long, and large sections of the reach are characterized by intermittent streamflow. Where water is normally present (including at the upper and lower portions of the reach near the streamflow-gaging stations), the discharge typically remains below 2 cubic feet per second throughout the year. Therefore, if geomorphic change is to occur, it will likely be driven by seasonal high-flow events. Discharge records from streamflow-gaging stations in the Eagle Creek Basin indicated that high-flow events in the basin (with peaks above 50 cubic feet per second) typically occurred during the North American monsoon months of July, August, and September. Additionally, the records appear to indicate that, as expected, overland runoff and “flashy” responses to rainfall have increased in the 5 years since the 2012 Little Bear Fire.
For the 2017 geomorphic survey of North Fork Eagle Creek, cross sections were established and surveyed at 14 locations along the study reach. Cross-section survey results indicated that channel characteristics (including channel width and area) varied widely along the study reach. Also, as part of the survey, woody debris accumulations and pools in the channel of the study reach were identified, cataloged, photographed, and surveyed for location. There were 58 woody debris accumulations and 14 pools found in the study reach. On the basis that debris jams could be a driver of geomorphic change in North Fork Eagle Creek, woody debris accumulations were classified according to their debris jam potential. The burn marks found on some woody debris indicated that the 2012 Little Bear Fire may be a contributing factor to the volume of debris in North Fork Eagle Creek. However, the woody debris present at the time of the survey did not appear to have substantially affected the geomorphic state of the study reach. Further, the structure and composition of the woody debris accumulations indicated that, under high-flow conditions, most woody debris would likely be transported downstream and out of the study reach without causing substantial geomorphic change through further jamming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181187","collaboration":"Prepared in cooperation with the Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., 2019, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2017: U.S. Geological Survey Open-File Report 2018–1187, 28 p., https://doi.org/10.3133/ofr20181187.","productDescription":"Report: v., 28 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","ipdsId":"IP-093851","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":362041,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PR7TX3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data supporting the 2017 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":362039,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1187/coverthb.jpg"},{"id":362040,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1187/ofr20181187.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1187"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.98236083984375,\n 33.02939031998959\n ],\n [\n -104.98260498046875,\n 33.02939031998959\n ],\n [\n -104.98260498046875,\n 33.68549637289138\n ],\n [\n -105.98236083984375,\n 33.68549637289138\n ],\n [\n -105.98236083984375,\n 33.02939031998959\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, NM 87113
Digital flood-inundation maps for a 16.4-mile reach of the Yellow River in Gwinnett County, Georgia, from 0.5 mile upstream from River Drive to Centerville Highway (Georgia State Route 124) were developed to depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at two U.S. Geological Survey (USGS) streamgages in the mapped area. The maps for the 9.0-mile reach from 0.5 mile upstream from River Drive to Stone Mountain Highway (U.S. Route 78) are referenced to the streamgage Yellow River near Snellville, Ga. (station 02206500), and the maps for the 7.4-mile reach from Stone Mountain Highway to Centerville Highway are referenced to the streamgage Yellow River at Ga. 124, near Lithonia, Ga. (02207120). Real-time stage information from these streamgages can be used with these maps to estimate near real-time areas of inundation. The forecasted peak-stage information for the USGS streamgages Yellow River near Snellville, Ga. (02206500), and Yellow River at Ga. 124, near Lithonia, Ga. (02207120), can be used in conjunction with the maps developed for this study to show predicted areas of flood inundation.
A one-dimensional step-backwater model was developed using the U.S. Army Corps of Engineers Hydrologic Engineering Center's River Analysis System (HEC–RAS) software for the Yellow River and was used to compute flood profiles for a 16.4-mile reach of the Yellow River. The hydraulic model was then used to simulate 16 water-surface profiles at 1.0-foot (ft) intervals at the Yellow River near Snellville streamgage and 17 water-surface profiles at 1.0-ft intervals at the Yellow River near Lithonia streamgage. At the Yellow River near Snellville streamgage, the profiles ranged from a stage of 18.0 ft, which is 819.1 ft above the North American Vertical Datum of 1988 (NAVD 88), to a stage of 33.0 ft, which is 834.1 ft above NAVD 88. At the Yellow River near Lithonia streamgage, the profiles ranged from the National Weather Service action stage of 13.0 ft, which is 732.5 ft above NAVD 88, to a stage of 29.0 ft, which is 748.5 ft above NAVD 88. The simulated water-surface profiles were then combined with a geographic information system digital elevation model—derived from light detection and ranging (lidar) data having a 5.0-ft horizontal resolution—to delineate the area flooded at each 1.0-ft interval of stream stage for both streamgages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195009","collaboration":"Prepared in cooperation with Gwinnett County, Georgia","usgsCitation":"Musser, J.W., 2019, Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia: U.S. Geological Survey Scientific Investigations Report 2019–5009, 15 p., https://doi.org/10.3133/sir20195009.","productDescription":"Report: vi, 15 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-100804","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":361975,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KKB3H2","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood inundation and flood depth for the Yellow River in Gwinnett County, Georgia based on water-surface elevation at the U.S. Geological Survey streamgages Yellow River, near Snellville, Georgia (02206500) and Yellow River at Ga. 124, near Lithonia, Georgia (02207120)"},{"id":361973,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5009/coverthb.jpg"},{"id":361974,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5009/sir20195009.pdf","text":"Report","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5009"}],"country":"United States","state":"Georgia","county":"Gwinnett County","otherGeospatial":"Yellow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.1667,\n 33.75\n ],\n [\n -84,\n 33.75\n ],\n [\n -84,\n 33.9167\n ],\n [\n -84.1667,\n 33.9167\n ],\n [\n -84.1667,\n 33.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
Glacial−interglacial variations in CO2 and methane in polar ice cores have been attributed, in part, to changes in global wetland extent, but the wetland distribution before the Last Glacial Maximum (LGM, 21 ka to 18 ka) remains virtually unknown. We present a study of global peatland extent and carbon (C) stocks through the last glacial cycle (130 ka to present) using a newly compiled database of 1,063 detailed stratigraphic records of peat deposits buried by mineral sediments, as well as a global peatland model. Quantitative agreement between modeling and observations shows extensive peat accumulation before the LGM in northern latitudes (>40°N), particularly during warmer periods including the last interglacial (130 ka to 116 ka, MIS 5e) and the interstadial (57 ka to 29 ka, MIS 3). During cooling periods of glacial advance and permafrost formation, the burial of northern peatlands by glaciers and mineral sediments decreased active peatland extent, thickness, and modeled C stocks by 70 to 90% from warmer times. Tropical peatland extent and C stocks show little temporal variation throughout the study period. While the increased burial of northern peats was correlated with cooling periods, the burial of tropical peat was predominately driven by changes in sea level and regional hydrology. Peat burial by mineral sediments represents a mechanism for long-term terrestrial C storage in the Earth system. These results show that northern peatlands accumulate significant C stocks during warmer times, indicating their potential for C sequestration during the warming Anthropocene.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.1813305116","usgsCitation":"Treat, C.C., Kleinen, T., Broothaerts , N., Dalton, A.S., Dommain, R., Douglas, T.A., Drexler, J.Z., Finkelstein, S., Grosse, G., Hope, G., Hutchings, J., Jones, M.C., Kuhry, P., Lacourse, T., Lahteenoja, O., Loisel, J., Notebaert, B., Payne, R., Peteet, D.M., Sannel, A.B., Stelling, J.M., Strauss, J., Swindles, G.T., Talbot, J., Tarnocai, C., Verstraeten, G., Williams , C., Xia, Z., Yu, Z., Valiranta, M., Hattestrand, M., Alexanderson, H., and Brovkin, V., 2019, Widespread global peatland establishment and persistence over the last 130,000 y: PNAS, v. 116, no. 11, p. 4822-4827, https://doi.org/10.1073/pnas.1813305116.","productDescription":"6 p.","startPage":"4822","endPage":"4827","ipdsId":"IP-091584","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":467822,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1813305116","text":"Publisher Index Page"},{"id":373582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Treat, Claire C.","contributorId":96606,"corporation":false,"usgs":true,"family":"Treat","given":"Claire","email":"","middleInitial":"C.","affiliations":[{"id":25501,"text":"University of Eastern Finland","active":true,"usgs":false}],"preferred":false,"id":785865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleinen, Thomas 0000-0001-9550-5164","orcid":"https://orcid.org/0000-0001-9550-5164","contributorId":50427,"corporation":false,"usgs":true,"family":"Kleinen","given":"Thomas","email":"","affiliations":[{"id":32387,"text":"Max Planck Institute for Meteorology, Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":785866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broothaerts , Nils ","contributorId":223665,"corporation":false,"usgs":true,"family":"Broothaerts ","given":"Nils ","affiliations":[],"preferred":false,"id":785867,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalton, April S.","contributorId":223666,"corporation":false,"usgs":true,"family":"Dalton","given":"April","email":"","middleInitial":"S.","affiliations":[{"id":13300,"text":"3Department of Earth Sciences, University of Toronto, Toronto, Ontario M5S 3B1, Canada.","active":true,"usgs":false}],"preferred":false,"id":785868,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dommain, Rene","contributorId":220666,"corporation":false,"usgs":false,"family":"Dommain","given":"Rene","email":"","affiliations":[{"id":40220,"text":"University of Potsdam, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":785869,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Douglas, Thomas A. 0000-0003-1314-1905","orcid":"https://orcid.org/0000-0003-1314-1905","contributorId":64553,"corporation":false,"usgs":false,"family":"Douglas","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":33087,"text":"Cold Regions Research and Engineering Laboratory","active":true,"usgs":false}],"preferred":true,"id":785870,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785871,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Finkelstein, Sarah A","contributorId":217548,"corporation":false,"usgs":true,"family":"Finkelstein","given":"Sarah A","affiliations":[{"id":7044,"text":"University of Toronto","active":true,"usgs":false}],"preferred":false,"id":785872,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":785873,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hope, Geoffrey","contributorId":223668,"corporation":false,"usgs":false,"family":"Hope","given":"Geoffrey","email":"","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":785874,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hutchings, Jack","contributorId":223669,"corporation":false,"usgs":true,"family":"Hutchings","given":"Jack","email":"","affiliations":[{"id":36338,"text":"University of Florida Department of Geological Sciences","active":true,"usgs":false}],"preferred":false,"id":785875,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jones, Miriam C. 0000-0002-6650-7619 miriamjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":4056,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"miriamjones@usgs.gov","middleInitial":"C.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":785876,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kuhry, Peter","contributorId":9513,"corporation":false,"usgs":true,"family":"Kuhry","given":"Peter","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":785877,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lacourse, Terri","contributorId":220254,"corporation":false,"usgs":true,"family":"Lacourse","given":"Terri","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":785878,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Lahteenoja, Outi","contributorId":223670,"corporation":false,"usgs":true,"family":"Lahteenoja","given":"Outi","email":"","affiliations":[{"id":29807,"text":"Arizona State University, School of Life Sciences,Tempe, AZ 85287","active":true,"usgs":false}],"preferred":false,"id":785879,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Loisel, Julie","contributorId":166672,"corporation":false,"usgs":false,"family":"Loisel","given":"Julie","email":"","affiliations":[{"id":18162,"text":"University of Helsinki","active":true,"usgs":false}],"preferred":false,"id":785880,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Notebaert, Bastiaan","contributorId":223671,"corporation":false,"usgs":false,"family":"Notebaert","given":"Bastiaan","email":"","affiliations":[],"preferred":false,"id":785881,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Payne, Richard","contributorId":200868,"corporation":false,"usgs":true,"family":"Payne","given":"Richard","email":"","affiliations":[{"id":35536,"text":"University of York","active":true,"usgs":false}],"preferred":false,"id":785882,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Peteet, Dorothy M. 0000-0003-3029-7506","orcid":"https://orcid.org/0000-0003-3029-7506","contributorId":147523,"corporation":false,"usgs":false,"family":"Peteet","given":"Dorothy","email":"","middleInitial":"M.","affiliations":[{"id":16858,"text":"Goddard Institute","active":true,"usgs":false}],"preferred":false,"id":785883,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Sannel, A. 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","contributorId":223677,"corporation":false,"usgs":false,"family":"Williams ","given":"Christopher J. ","affiliations":[],"preferred":false,"id":785894,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Xia, Zhengyu","contributorId":223678,"corporation":false,"usgs":false,"family":"Xia","given":"Zhengyu","email":"","affiliations":[],"preferred":false,"id":785896,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Yu, Zicheng 0000-0003-2358-2712","orcid":"https://orcid.org/0000-0003-2358-2712","contributorId":147521,"corporation":false,"usgs":false,"family":"Yu","given":"Zicheng","email":"","affiliations":[{"id":16857,"text":"Lehigh Univ.","active":true,"usgs":false}],"preferred":false,"id":785897,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Valiranta, Minna","contributorId":223679,"corporation":false,"usgs":false,"family":"Valiranta","given":"Minna","email":"","affiliations":[{"id":18162,"text":"University of Helsinki","active":true,"usgs":false}],"preferred":false,"id":785898,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Hattestrand, Martina","contributorId":223680,"corporation":false,"usgs":false,"family":"Hattestrand","given":"Martina","email":"","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":785899,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Alexanderson, Helena","contributorId":223681,"corporation":false,"usgs":false,"family":"Alexanderson","given":"Helena","email":"","affiliations":[{"id":13428,"text":"Lund University","active":true,"usgs":false}],"preferred":false,"id":785900,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Brovkin, Victor 0000-0001-6420-3198","orcid":"https://orcid.org/0000-0001-6420-3198","contributorId":223682,"corporation":false,"usgs":true,"family":"Brovkin","given":"Victor","email":"","affiliations":[{"id":32387,"text":"Max Planck Institute for Meteorology, Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":785901,"contributorType":{"id":1,"text":"Authors"},"rank":33}]}} ,{"id":70202553,"text":"70202553 - 2019 - Effects of land use on greenhouse gas flux in playa wetlands and associated watersheds in the High Plains, USA","interactions":[],"lastModifiedDate":"2019-03-11T14:49:39","indexId":"70202553","displayToPublicDate":"2019-03-11T14:49:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5735,"text":"Agricultural Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Effects of land use on greenhouse gas flux in playa wetlands and associated watersheds in the High Plains, USA","docAbstract":"In the High Plains, U.S., native prairie conversion to cropland agriculture has resulted in a loss of service delivery capabilities from most depressional wetlands as a result of sedimentation. Restoring historic hydrological conditions to affected wetlands may rejuvenate some services, however, there may be tradeoffs due to emissions of CH4 and N2O. We evaluated the influence of two predominant conservation programs (Wetlands Reserve Program, WRP and Conservation Reserve Program, CRP) on gas emissions (CO2, CH4, N2O) from 42 playas and uplands in the High Plains of Nebraska. Because playa restoration through the WRP is most prevalent in the Rainwater Basin (RWB), we studied 27 playas/uplands among reference condition, cropland, and WRP land uses. We studied 15 playas/uplands within native grassland, cropland, and CRP land uses in the Western High Plains (WHP) of Nebraska. Emissions were collected bi-weekly from April-October of 2012 and 2013 from four landscape positions extending outward from the wetland center into upland. In RWB playas, CH4 and N2O emissions were similar among land uses but CO2 was 28% higher in cropland than WRP wetlands. Cropland uplands emitted 648% more N2O than reference and WRP uplands. Overall, net CO2-equiv emissions were lower in playas/uplands in WRP, suggesting that benefits of playa restoration may include climate mitigation services as well as increased water storage capacity and biodiversity provisioning. In the WHP, cropland and grassland playas emitted 46 and 23 times more CH4, respectively, than CRP in 2013. Playas in CRP emitted 43% less N2O than cropland playas. In 2013, net emissions for cropland and native grassland playas were 75% and 39% greater, respectively, than CRP playas. In the WHP, the benefits of lower gas emissions must be appropriately weighted against tradeoffs of ecosystem services related to shorter hydroperiods as a result of reduced runoff into playas in CRP.
","language":"English","publisher":"Scientific Research","doi":"10.4236/as.2019.102016","usgsCitation":"Daniel, D.W., Smith, L.M., McMurry, S.T., Tangen, B., Dahl, C.F., Euliss, N., and LaGrange, T., 2019, Effects of land use on greenhouse gas flux in playa wetlands and associated watersheds in the High Plains, USA: Agricultural Sciences, v. 10, no. 2, p. 181-201, https://doi.org/10.4236/as.2019.102016.","productDescription":"21 p.","startPage":"181","endPage":"201","ipdsId":"IP-070774","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467824,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4236/as.2019.102016","text":"Publisher Index Page"},{"id":361981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daniel, Dale W.","contributorId":191880,"corporation":false,"usgs":false,"family":"Daniel","given":"Dale","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":759081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Loren M.","contributorId":196856,"corporation":false,"usgs":false,"family":"Smith","given":"Loren","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":759082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McMurry, Scott T.","contributorId":191876,"corporation":false,"usgs":false,"family":"McMurry","given":"Scott","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":759083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":759080,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dahl, Charles F. cdahl@usgs.gov","contributorId":4052,"corporation":false,"usgs":true,"family":"Dahl","given":"Charles","email":"cdahl@usgs.gov","middleInitial":"F.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":759084,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Euliss, Ned ceuliss@usgs.gov","contributorId":192021,"corporation":false,"usgs":true,"family":"Euliss","given":"Ned","email":"ceuliss@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":759085,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"LaGrange, Ted","contributorId":207141,"corporation":false,"usgs":false,"family":"LaGrange","given":"Ted","email":"","affiliations":[{"id":17640,"text":"Nebraska Game and Parks Commission","active":true,"usgs":false}],"preferred":false,"id":759086,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202202,"text":"sir20195004 - 2019 - Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018","interactions":[],"lastModifiedDate":"2019-10-23T09:27:27","indexId":"sir20195004","displayToPublicDate":"2019-03-11T13:38:08","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5004","displayTitle":"Flood-Inundation Maps of the Meramec River from Eureka to Arnold, Missouri, 2018","title":"Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018","docAbstract":"Libraries of digital flood-inundation maps that spanned a combined 37.2-mile reach of the Meramec River that extended upstream from Eureka, Missouri, to downstream near the confluence of the Meramec and Mississippi Rivers were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers, Metropolitan St. Louis Sewer District, Missouri Department of Transportation, Missouri American Water, Federal Emergency Management Agency Region 7, and the cities of Pacific, Eureka, Wildwood, and Arnold. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the cooperative USGS streamgages for the Meramec River near Eureka, Mo. (USGS station 07019000), the Meramec River at Valley Park, Mo. (USGS station 07019130), the Meramec River at Fenton, Mo. (USGS station 07019210), and the Meramec River at Arnold, Mo. (USGS station 07019300). Near-real-time stage data at these streamgages may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites (listed as NWS sites erkm7, vllm7, fnnm7, and arnm7, respectively).
Flood profiles were computed for the stream reach by means of a calibrated one-dimensional step-backwater hydraulic model. The model was calibrated using a stage-discharge relation at the Meramec River near Eureka, Mo., streamgage (USGS station 07019000) and documented high-water marks from the flood of December 2015 through January 2016.
The calibrated hydraulic model was used to compute water-surface profiles: 1 set for the Meramec River near Eureka, Mo., streamgage (USGS station 07019000); 1 set for the Meramec River at Valley Park, Mo., streamgage (USGS station 07019130); 7 sets for the Meramec River at Fenton, Mo., streamgage (USGS station 07019210) for a range of Mississippi River conditions; and 8 sets for the Meramec River at Arnold, Mo., streamgage (USGS station 07019300) for a range of Mississippi River conditions. The water-surface profiles were produced for stages at 1-foot (ft) intervals referenced to the datum from each streamgage and ranging from the NWS action stage, or near bankfull discharge, to the stage corresponding to the estimated 0.2-percent annual exceedance probability (500-year recurrence interval) flood, as determined at the Meramec River near Eureka, Mo., streamgage (USGS station 07019000). The simulated water-surface profiles then were combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.28-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each simulated 1-ft stage increment. Previously published flood-inundation maps were updated and incorporated in the flood map libraries for USGS stations 07019130 and 07019210 to complete the map sets corresponding to eight Mississippi River conditions.
The availability of these maps, along with internet information regarding current stage from the USGS streamgages and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures and for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195004","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Metropolitan St. Louis Sewer District, Missouri Department of Transportation, Missouri American Water, Federal Emergency Management Agency Region 7, the city of Pacific, the city of Eureka, the city of Wildwood, and the city of Arnold","usgsCitation":"Dietsch, B.J., and Strauch, K.R., 2019, Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5004, 12 p., https://doi.org/10.3133/sir20195004.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-096951","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361924,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5004/sir20195004.pdf","text":"Report","size":"1.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5004"},{"id":361925,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B0XLJL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-Inundation Maps of the Meramec River from Eureka to Arnold, Missouri, 2018"},{"id":361923,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5004/coverthb.jpg"}],"country":"United States","state":"Missouri","city":"Arnold, Eureka","otherGeospatial":"Meramec River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.66398620605469,\n 38.38795699631396\n ],\n [\n -90.33405303955078,\n 38.38795699631396\n ],\n [\n -90.33405303955078,\n 38.565347844885466\n ],\n [\n -90.66398620605469,\n 38.565347844885466\n ],\n [\n -90.66398620605469,\n 38.38795699631396\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
In 2013, the U.S. Geological Survey, in cooperation with the Keweenaw Bay Indian Community, began monitoring the water quality of springs and seeps within the Yellow Dog and Salmon Trout watersheds in Marquette County, Michigan. The objectives of this study were to (1) monitor streamflow and analyze the hydrology of the watersheds and (2) characterize the water quality in the watersheds prior to development of mineral resources within the watershed. Three continuous-record streamgages (U.S. Geological Survey stations 04043238, 04043244, and 04043275) were examined to identify runoff and baseflow components of streamflow and the relative magnitudes of those components. Streamflow at each station was dominated by groundwater discharge with about 70 to 80 percent of the annual streamflow being groundwater-derived baseflow.
From May 2013 to October 2016, 239 water-quality samples were collected at 15 stations within the Yellow Dog and Salmon Trout watersheds. Of the 15 stations sampled, 8 of the stations were springs and 7 of the stations were streams. Samples were analyzed for nutrient, trace metal, and major-ion species at all stations with additional suspended-sediment samples collected at the 7 stream stations. Where applicable, water-quality results were compared to aquatic health guidelines used by the Michigan Department of Environmental Quality. Copper concentrations exceeded the final chronic value five times and the aquatic maximum value once, whereas silver concentrations exceeded the final chronic value twice and the aquatic maximum value once. Results indicate that chloride concentrations may be increasing at some stations, but values are generally low with a median concentration of 0.25 milligram per liter.
Bed-sediment chemistry was evaluated twice for each stream sampling station. Samples were collected in the first and last year of the study and analyzed for trace metals. Sediment chemistry results were compared to consensus-based sediment quality guidelines. None of the metal constituents analyzed exceeded the threshold effect concentration or probable effect concentration thresholds, indicating a healthy aquatic environment in relation to bed-sediment quality.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185152","collaboration":"Prepared in cooperation with the Keweenaw Bay Indian Community","usgsCitation":"Hoard, C.J., and Weaver, T.L., 2019, Water quality and hydrology of the Yellow Dog and Salmon Trout watersheds, Marquette County, Michigan, 2013–16: U.S. Geological Survey Scientific Investigations Report 2018–5152, 24 p., https://doi.org/10.3133/sir20185152.","productDescription":"viii, 24 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098434","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5152/coverthb.jpg"},{"id":361779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5152/sir20185152.pdf","text":"Report","size":"9.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5152"}],"country":"United States","state":"Michigan","county":"Marquette County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.07464599609375,\n 46.64283679198892\n ],\n [\n -87.60017395019531,\n 46.64283679198892\n ],\n [\n -87.60017395019531,\n 46.91884832811514\n ],\n [\n -88.07464599609375,\n 46.91884832811514\n ],\n [\n -88.07464599609375,\n 46.64283679198892\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911
Context
Movement of prey on hydrologically pulsed, spatially heterogeneous wetlands can result in transient, high prey concentrations, when changes in landscape features such as connectivity between flooded areas alternately facilitate and impede prey movement. Predators track and exploit these concentrations, depleting them as they arise.
Objectives
We sought to describe how prey pulses of fish rapidly form and persist on wetland landscapes, while enduring constant consumption by wading birds, without being fully depleted. Specifically, we questioned how is the predator–prey relationship mediated by interactions between animal movement and dynamic landscape connectivity?
Methods
Two models were developed of the predator–prey-landscape system with qualitatively different representations of space, to identify and quantify prey pulsing dynamics that were robust across modeled assumptions. The first included a homogeneous landscape described by simple geometry, and implicit fish movement as wetland volume contracts. The second modeled transverse movement across a heterogeneous landscape, with isolated drying patches.
Results
Both models produced rapid fish prey concentrations as the wetland dried to shallow water depths. These conditions are critical for making prey available to wading birds. Fish were also rapidly depleted by birds, representing daily caloric intake supporting birds. Model 1 provided average estimates across the modeled domain. Model 2 mapped locations of emerging prey hotspots on the landscape through time.
Conclusions
Our models tracked predator, prey, and landscape dynamics in parallel, inducing systems dynamics from empirical observations. Explicit inclusion of dynamic wetland hydrologic connectivity, a key landscape mechanism, allowed for a comprehensive picture of links between landscape dynamics and the adapted predator–prey system.
In 2016, digital flood-inundation maps along the shoreline of Lake Champlain in Addison, Chittenden, Franklin, and Grand Isle Counties in Vermont and northern Clinton County in New York were created by the U.S. Geological Survey (USGS) in cooperation with the International Joint Commission (IJC). This report discusses the creation of updated static digital flood-inundation mapping, in 2018, to include the entire shoreline of Lake Champlain in the United States. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent of flooding corresponding to selected water-surface elevations (stages) at the USGS lake gages on Lake Champlain.
As a result of the record setting floods of May 2011 in Lake Champlain and the Richelieu River, the U.S. and Canadian governments requested that the IJC issue a reference for a study to identify how flood forecasting, preparedness, and mitigation could be improved in the Lake Champlain–Richelieu River Basin. The IJC submitted the Lake Champlain–Richelieu River Plan of Study to the governments of Canada and the United States in 2013. The flood-inundation maps in this study are one aspect of the task work outlined in the IJC 2013 Plan of Study.
Wind and seiche effects (standing oscillating wave with a long wavelength) that can influence flooding along the Lake Champlain shoreline were not represented. The flood-inundation maps reflect 11 stages for Lake Champlain that are static for the entire area of the lake. Near-real-time stages at the USGS gages on Lake Champlain may be obtained from the USGS National Water Information System website at http://waterdata.usgs.gov/ (https://doi.org/10.5066/F7P55KJN) or from the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/.
Updated static flood-inundation boundary extents were created for Lake Champlain in Franklin, Chittenden, Addison, Rutland, and Grand Isle Counties in Vermont and Clinton, Essex, and Washington Counties in New York by using recently acquired (2009, 2012, 2014, and 2015) light detection and ranging (lidar) data. The corresponding flood-inundation maps may be referenced to any of the four active USGS lake gages on Lake Champlain. Of these four active lake gages, USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y.; USGS lake gage 04294500, Lake Champlain at Burlington, Vt.; USGS lake gage 04279085 Lake Champlain north of Whitehall, N.Y.; and USGS lake gage 04294413, Lake Champlain at Port Henry, N.Y., only the Richelieu River (Lake Champlain) at Rouses Point, N.Y., gage also serves as a National Weather Service prediction location. Lake Champlain static flood-inundation map boundary extents corresponding to the May 2011 peak flood stage (103.20 feet [ft], National Geodetic Vertical Datum of 1929 [NGVD 29], as recorded at the USGS Rouses Point lake gage, were compared to the flood-inundation area extents determined from satellite imagery for the May 2011 flood (which incorporated documented high-water marks from the flood of May 2011) and were found to be in good agreement. The May 2011 flood is the highest recorded lake water level (stage) at the Rouses Point, N.Y., and Burlington, Vt., lake gages. Flood stages greater than 101.5 ft (NGVD 29) exceed the “major flood stage” as defined by the National Weather Service for USGS lake gage 04295000.
Updated digital elevation models (DEMs) were created from the recent lidar data for Lake Champlain in Vermont and New York. These DEMs were used in determining the flood-inundation boundary and associated depth grids for 11 flood stages at 0.5-ft or 1-ft intervals from 100.0 to 106.0 ft (NGVD 29) as referenced to the USGS lake gages. In addition, the May 2011 flood-inundation area for elevation 103.20 ft (NGVD 29) (102.77 ft, North American Vertical Datum of 1988) was determined from these updated DEMs.
The availability of these maps, along with online information regarding current stages at the USGS lake gages and forecasted high-flow stages from the National Weather Service at USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y., will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185169","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Flynn, R.H., and Hayes, L., 2019, Flood-inundation maps for Lake Champlain in Vermont and New York: U.S. Geological Survey Scientific Investigations Report 2018–5169, 14 p., https://doi.org/10.3133/sir20185169. [Supersedes USGS Scientific Investigations Report 2016–5060.]","productDescription":"Report: v, 14 p.; Application Site; Data release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101452","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437545,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBDF6S","text":"USGS data release","linkHelpText":"Flood-Inundation Shapefiles and Grids for Lake Champlain in Vermont and New York"},{"id":361774,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html ","linkHelpText":"- Flood Inundation Mapper"},{"id":361771,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5169/coverthb.jpg"},{"id":361772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5169/sir20185169.pdf","text":"Report","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5169"},{"id":361773,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBDF6S ","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood-inundation shapefiles and grids for Lake Champlain in Vermont and New York"}],"country":"United States","state":"New York, Vermont","county":"Addison, Chittenden, Clinton, Franklin, Grand Isle ","otherGeospatial":"Lake Champlain","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -73.7081,43.5785 ], [ -73.7081,45.0891 ], [ -72.8948,45.0891 ], [ -72.8948,43.5785 ], [ -73.7081,43.5785 ] ] ] } } ] }","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
The chaparral-dominated national forests of southern California were in part established to provide water provision services to the surrounding urban populations and irrigation for agriculture. However, water provision in the form of groundwater recharge and surface runoff depends on the climatological conditions of any given year and also landscape-scale disturbances such as fire. Fire is increasing in frequency in southern California and understanding its impacts both immediately postfire and as vegetation recovers, and the interactions between fire and hydrology, are key components to managing federal lands effectively. In this study we focus on nine fires in a study area that encompasses the four southern California national forests (Los Padres, Angeles, San Bernardino, and Cleveland) and use a water balance model to investigate the effects of water provision services post-fire at a regional scale. We found that runoff and recharge increased post-fire, with increases in recharge being greater with recovery times ranging from 2 to 4 y post-fire. Vegetation recovery occurred 2 y post-fire for all basins as indicated by remotely sensed imagery measuring vegetation greenness having returned to or exceeded pre-fire values for the basin. We found that runoff and recharge were more sensitive to the effects of climate than to length of time post-fire. Findings from these modeling tools allow users to anticipate the impact of fire on water provision services in the region and develop management strategies that help reduce the impacts of wildfire.
Peat cores are valuable archives of past environmental change because they accumulate plant organic matter over millennia. While studies have primarily focused on physical, ecological, and some biogeochemical proxies, cores from peatlands have increasingly been used to interpret hydroclimatic change using stable isotope analyses of cellulose preserved in plant remains. Previous studies indicate that the stable oxygen isotope compositions (δ18O) preserved in alpha cellulose extracted from specific plant macrofossils reflect the δ18O values of past peatland water and thereby provide information on long-term changes in hydrology in response to climate. Oxygen isotope analyses of peat cellulose (δ18Ocellulose) have been successfully developed from peat cores that accumulate the same species for millennia. However, to fully exploit the potential of this proxy in species-diverse fens, studies are needed that account for the isotopic variations caused by changes in dominant species composition. This study assesses variation in δ18O values among peatland plant species and how they relate to environmental waters in two fens informally named Horse Trail and Goldfin, located on the leeward (dry) and windward (wet) side, respectively, of the climatic gradient across the Kenai Peninsula, Alaska. Environmental water δ18O values at both fens reflect unmodified δ18O values of mean annual precipitation, although at Goldfin standing pools were slightly influenced by evaporation. Modern plant [mosses and Carex spp. (sedges)] δ18Ocellulose values indicate that all Carex spp. are higher (~2.5‰) than those of mosses, likely driven by their vascular structure and ecophysiological difference from non-vascular mosses. Moss δ18Ocellulose values within each peatland are similar among the species, and differences appear related to evaporation effects on environmental waters within hummocks and hollows. The plant taxa-environmental water δ18O differences are applied to the previously determined Horse Trail Fen untreated bulk δ18O record. Results include significant changes to inferred millennial-to-centennial scale hydroclimatic trends where dominant taxa shift from moss to Carex spp., indicating that modern calibration datasets are necessary for interpreting stable isotopes from fens, containing a mix of vascular and nonvascular plants. Accounting for isotopic offsets through macrofossil analysis and modern plant-water isotope measurements opens new opportunities for hydroclimatic reconstructions from fen peatlands.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2019.00025","usgsCitation":"Jones, M., Anderson, L., Keller, K., Nash, B., Littell, V., Wooller, M.J., and Jolley, C., 2019, An assessment of plant species differences on cellulose oxygen isotopes from two Kenai Peninsula, Alaska peatlands: Implications for hydroclimatic reconstructions: Frontiers in Earth Science, v. 7, 25, 16 p., https://doi.org/10.3389/feart.2019.00025.","productDescription":"25, 16 p.","ipdsId":"IP-102651","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":467843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2019.00025","text":"Publisher Index Page"},{"id":368887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arc Lake, Bear Lake, Bear Mountain Lake, Browse Lake, Headquarters Lake, Horse Trail clearing, Kenai Lake, Lower Ohmer Lake, Portage Lake, Skilak Lake, Summit Lake, Tern Lake, Upper Ohmer Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.578369140625,\n 59.9274956808828\n ],\n [\n -149.04052734375,\n 59.9274956808828\n ],\n [\n -149.04052734375,\n 60.919754532399686\n ],\n [\n -151.578369140625,\n 60.919754532399686\n ],\n [\n -151.578369140625,\n 59.9274956808828\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -173.485107421875,\n 60.10319489936693\n ],\n [\n -171.826171875,\n 60.10319489936693\n ],\n [\n -171.826171875,\n 60.925093815014655\n ],\n [\n -173.485107421875,\n 60.925093815014655\n ],\n [\n -173.485107421875,\n 60.10319489936693\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Miriam 0000-0002-6650-7619","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":201994,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":774422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Lesleigh 0000-0002-5264-089X land@usgs.gov","orcid":"https://orcid.org/0000-0002-5264-089X","contributorId":436,"corporation":false,"usgs":true,"family":"Anderson","given":"Lesleigh","email":"land@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":774423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keller, Katherine 0000-0001-6915-5455","orcid":"https://orcid.org/0000-0001-6915-5455","contributorId":218048,"corporation":false,"usgs":false,"family":"Keller","given":"Katherine","email":"","affiliations":[{"id":39732,"text":"Natural Systems Analysts, Harvard University","active":true,"usgs":false}],"preferred":false,"id":774424,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nash, Bailey 0000-0001-6423-2773 bnash@usgs.gov","orcid":"https://orcid.org/0000-0001-6423-2773","contributorId":220192,"corporation":false,"usgs":true,"family":"Nash","given":"Bailey","email":"bnash@usgs.gov","affiliations":[{"id":40146,"text":"Iowa State University, Ames, IA","active":true,"usgs":false},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":774425,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Littell, Virginia","contributorId":220193,"corporation":false,"usgs":false,"family":"Littell","given":"Virginia","email":"","affiliations":[{"id":40147,"text":"University of Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":774426,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wooller, Matthew J.","contributorId":192799,"corporation":false,"usgs":false,"family":"Wooller","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":774427,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jolley, Chelsea","contributorId":220194,"corporation":false,"usgs":false,"family":"Jolley","given":"Chelsea","email":"","affiliations":[{"id":26916,"text":"Brigham Young University, Provo, UT","active":true,"usgs":false}],"preferred":false,"id":774428,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215498,"text":"70215498 - 2019 - Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations","interactions":[],"lastModifiedDate":"2020-10-21T15:39:49.079734","indexId":"70215498","displayToPublicDate":"2019-03-05T10:36:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7168,"text":"Journal of the American Water Resources Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations","docAbstract":"Representing hydrologic connectivity of non‐floodplain wetlands (NFWs) to downstream waters in process‐based models is an emerging challenge relevant to many research, regulatory, and management activities. We review four case studies that utilize process‐based models developed to simulate NFW hydrology. Models range from a simple, lumped parameter model to a highly complex, fully distributed model. Across case studies, we highlight appropriate application of each model, emphasizing spatial scale, computational demands, process representation, and model limitations. We end with a synthesis of recommended “best modeling practices” to guide model application. These recommendations include: (1) clearly articulate modeling objectives, and revisit and adjust those objectives regularly; (2) develop a conceptualization of NFW connectivity using qualitative observations, empirical data, and process‐based modeling; (3) select a model to represent NFW connectivity by balancing both modeling objectives and available resources; (4) use innovative techniques and data sources to validate and calibrate NFW connectivity simulations; and (5) clearly articulate the limits of the resulting NFW connectivity representation. Our review and synthesis of these case studies highlights modeling approaches that incorporate NFW connectivity, demonstrates tradeoffs in model selection, and ultimately provides actionable guidance for future model application and development.
The earthquake activity in Oklahoma and Kansas that began in 2008 reflects the most widespread instance of induced seismicity observed to date. We develop a reservoir model to calculate the hydrologic conditions associated with the activity of 902 saltwater disposal wells injecting into the Arbuckle aquifer. Estimates of basement fault stressing conditions inform a rate‐and‐state friction earthquake nucleation model to forecast the seismic response to injection. Our model replicates many salient features of the induced earthquake sequence, including the onset of seismicity, the timing of the peak seismicity rate, and the reduction in seismicity following decreased disposal activity. We present evidence for variable time lags between changes in injection and seismicity rates, consistent with the prediction from rate‐and‐state theory that seismicity rate transients occur over timescales inversely proportional to stressing rate. Given the efficacy of the hydromechanical model, as confirmed through a likelihood statistical test, the results of this study support broader integration of earthquake physics within seismic hazard analysis.
Earth System Models (ESMs) are essential tools for understanding and predicting global change, but they cannot explicitly resolve hillslope‐scale terrain structures that fundamentally organize water, energy, and biogeochemical stores and fluxes at subgrid scales. Here we bring together hydrologists, Critical Zone scientists, and ESM developers, to explore how hillslope structures may modulate ESM grid‐level water, energy, and biogeochemical fluxes. In contrast to the one‐dimensional (1‐D), 2‐ to 3‐m deep, and free‐draining soil hydrology in most ESM land models, we hypothesize that 3‐D, lateral ridge‐to‐valley flow through shallow and deep paths and insolation contrasts between sunny and shady slopes are the top two globally quantifiable organizers of water and energy (and vegetation) within an ESM grid cell. We hypothesize that these two processes are likely to impact ESM predictions where (and when) water and/or energy are limiting. We further hypothesize that, if implemented in ESM land models, these processes will increase simulated continental water storage and residence time, buffering terrestrial ecosystems against seasonal and interannual droughts. We explore efficient ways to capture these mechanisms in ESMs and identify critical knowledge gaps preventing us from scaling up hillslope to global processes. One such gap is our extremely limited knowledge of the subsurface, where water is stored (supporting vegetation) and released to stream baseflow (supporting aquatic ecosystems). We conclude with a set of organizing hypotheses and a call for global syntheses activities and model experiments to assess the impact of hillslope hydrology on global change predictions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023903","usgsCitation":"Fan, Y., Clark, M., Lawrence, D.M., Swenson, S., Band, L.E., Brantley, S.L., Brooks, P.D., Dietrich, W.E., Flores, A., Grant, G., Kirchner, J.W., Mackay, D., McDonnell, J., Milly, P.C., Sullivan, P.L., Tague, C., Ajami, H., Chaney, N.W., Hartmann, A., Hazenberg, P., McNamara, J., Pelletier, J., Perket, J., Rouholahnejad-Freund, E., Wagener, T., Zeng, X., Beighley, E., Buzan, J., Huang, M., Livneh, B., Mohanty, B.P., Nijssen, B., Safeeq, M., Shen, C., van Verseveld, W., Volk, J., and Yamazaki, D., 2019, Hillslope hydrology in global change research and earth system modeling: Water Resources Research, v. 55, no. 2, p. 1737-1772, https://doi.org/10.1029/2018WR023903.","productDescription":"36 p.","startPage":"1737","endPage":"1772","ipdsId":"IP-102674","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":467856,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018wr023903","text":"Publisher Index Page"},{"id":361636,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Fan, Ying","contributorId":213846,"corporation":false,"usgs":false,"family":"Fan","given":"Ying","email":"","affiliations":[{"id":38903,"text":"Rutgers Univ.","active":true,"usgs":false}],"preferred":false,"id":758550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Martyn","contributorId":176319,"corporation":false,"usgs":false,"family":"Clark","given":"Martyn","affiliations":[],"preferred":false,"id":758551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, David M.","contributorId":105206,"corporation":false,"usgs":false,"family":"Lawrence","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":7166,"text":"Johns Hopkins University Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":758552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swenson, Sean","contributorId":213847,"corporation":false,"usgs":false,"family":"Swenson","given":"Sean","email":"","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":758553,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Band, L. 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To prepare for and cope with the related risks, stakeholders need to devise plans for sustainable management of river systems, which in turn requires the identification of management-appropriate operational units, such as groups of rivers that share similar environmental and biological characteristics. Ideally, these units are of a manageable size, and are biotically or abiotically distinguishable across a variety of river types. Here, we aim to address this need by presenting a new global river classification framework (GloRiC) to establish a common vocabulary and standardized approach to the development of globally comprehensive and integrated river classifications that can be tailored to different goals and requirements. We define the GloRiC conceptual framework based on five categories of variables: (1) hydrology; (2) physiography and climate; (3) fluvial geomorphology; (4) water chemistry; and (5) aquatic biology. We then apply the framework using hydro-environmental attributes provided by a seamless high-resolution river reach database to create initial instances of three sub-classifications (hydrologic, physio-climatic, and geomorphic) which we ultimately combine into 127 river reach types at the global scale. These supervised classifications utilize a mix of statistical analyses and expert interpretation to identify the classifier variables, the number of classes, and their thresholds. In addition, we also present an unsupervised, multivariable k-means statistical clustering of all river reaches into 30 groups. These first-of-their-kind global river reach classifications at high spatial resolution provide baseline information for a total of 35.9 million kilometers of rivers that have been assessed in this study, and are expected to be particularly useful in remote or data-poor river basins. The GloRiC framework and associated data are primarily designed for broad and rapid applicability in assessments that require stratified analyses of river ecosystem conditions at global and regional scales; smaller-scale applications could follow the same conceptual framework yet use more detailed data sources.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/aad8e9","usgsCitation":"Ouellet Dallaire, C., Lehner, B., Sayre, R., and Thieme, M., 2019, A multidisciplinary framework to derive global river reach classifications at high spatial resolution: Environmental Research Letters, v. 14, no. 2, p. 1-12, https://doi.org/10.1088/1748-9326/aad8e9.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-091251","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":467864,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aad8e9","text":"Publisher Index Page"},{"id":361601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Ouellet Dallaire, Camille","contributorId":213685,"corporation":false,"usgs":false,"family":"Ouellet Dallaire","given":"Camille","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":758282,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lehner, Bernhard","contributorId":213686,"corporation":false,"usgs":false,"family":"Lehner","given":"Bernhard","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":758283,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sayre, Roger 0000-0001-6703-7105 rsayre@usgs.gov","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":191629,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","email":"rsayre@usgs.gov","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":758281,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thieme, Michele","contributorId":213687,"corporation":false,"usgs":false,"family":"Thieme","given":"Michele","affiliations":[{"id":37767,"text":"World Wildlife Fund","active":true,"usgs":false}],"preferred":false,"id":758284,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202362,"text":"70202362 - 2019 - Modeling riparian restoration impacts on the hydrologic cycle at the Babacomari Ranch, SE Arizona, USA","interactions":[],"lastModifiedDate":"2019-02-25T13:49:06","indexId":"70202362","displayToPublicDate":"2019-02-25T13:49:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Modeling riparian restoration impacts on the hydrologic cycle at the Babacomari Ranch, SE Arizona, USA","docAbstract":"This paper describes coupling field experiments with surface and groundwater modeling to investigate rangelands of SE Arizona, USA using erosion-control structures to augment shallow and deep aquifer recharge. We collected field data to describe the physical and hydrological properties before and after gabions (caged riprap) were installed in an ephemeral channel. The modular finite-difference flow model is applied to simulate the amount of increase needed to raise groundwater levels. We used the average increase in infiltration measured in the field and projected on site, assuming all infiltration becomes recharge, to estimate how many gabions would be needed to increase recharge in the larger watershed. A watershed model was then applied and calibrated with discharge and 3D terrain measurements, to simulate flow volumes. Findings were coupled to extrapolate simulations and quantify long-term impacts of riparian restoration. Projected scenarios demonstrate how erosion-control structures could impact all components of the annual water budget. Results support the potential of watershed-wide gabion installation to increase total aquifer recharge, with models portraying increased subsurface connectivity and accentuated lateral flow contributions.
","language":"English","publisher":"MDPI","doi":"10.3390/w11020381","usgsCitation":"Norman, L., Callegary, J.B., Lacher, L., Wilson, N., Fandel, C., Forbes, B.T., and Swetnam, T., 2019, Modeling riparian restoration impacts on the hydrologic cycle at the Babacomari Ranch, SE Arizona, USA: Water, v. 11, no. 2, p. 1-20, https://doi.org/10.3390/w11020381.","productDescription":"Article 381; 20 p.","startPage":"1","endPage":"20","ipdsId":"IP-090323","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":467872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w11020381","text":"Publisher Index Page"},{"id":361505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Babacomari Ranch","volume":"11","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":757998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Callegary, James B. 0000-0003-3604-0517 jcallega@usgs.gov","orcid":"https://orcid.org/0000-0003-3604-0517","contributorId":2171,"corporation":false,"usgs":true,"family":"Callegary","given":"James","email":"jcallega@usgs.gov","middleInitial":"B.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lacher, Laurel","contributorId":213547,"corporation":false,"usgs":false,"family":"Lacher","given":"Laurel","affiliations":[{"id":38785,"text":"Lacher Hydrological Consulting, Tucson, AZ 85719","active":true,"usgs":false}],"preferred":false,"id":758000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Natalie R. 0000-0001-5145-1221","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":202534,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":758001,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fandel, Chloé","contributorId":213548,"corporation":false,"usgs":false,"family":"Fandel","given":"Chloé","affiliations":[{"id":38786,"text":"University of Arizona, Hydrology and Water Resources, Tucson, AZ 85719","active":true,"usgs":false}],"preferred":false,"id":758002,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Forbes, Brandon T. 0000-0003-4051-0593 bforbes@usgs.gov","orcid":"https://orcid.org/0000-0003-4051-0593","contributorId":213549,"corporation":false,"usgs":true,"family":"Forbes","given":"Brandon","email":"bforbes@usgs.gov","middleInitial":"T.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758003,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Swetnam, Tyson","contributorId":213550,"corporation":false,"usgs":false,"family":"Swetnam","given":"Tyson","email":"","affiliations":[{"id":38787,"text":"University of Arizona , BIO5 Institute, Tucson, AZ 85719","active":true,"usgs":false}],"preferred":false,"id":758004,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202234,"text":"ofr20171064 - 2019 - Evaluation of recommended revisions to Bulletin 17B","interactions":[],"lastModifiedDate":"2019-02-22T10:08:21","indexId":"ofr20171064","displayToPublicDate":"2019-02-21T15:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1064","displayTitle":"Evaluation of Recommended Revisions to Bulletin 17B","title":"Evaluation of recommended revisions to Bulletin 17B","docAbstract":"For the past 36 years, Bulletin 17B, published by the Interagency Committee on Water Data in 1982, has guided flood-frequency analyses in the United States. During this period, much has been learned about both hydrology and statistical methods. In keeping with the tradition of periodically updating the Bulletin 17B guidelines in light of advances in our understanding and methods, the Hydrologic Frequency Analysis Work Group (HFAWG) was charged by the Subcommittee on Hydrology (SOH) of the Advisory Committee on Water Information (ACWI) to consider possible updates to Bulletin 17B.
The purpose of this report is to consider the statistical performance of possible revisions to Bulletin 17B procedures. Of particular interest are procedures designed to accommodate more general forms of flood information. The concern is how the proposed procedures would affect the precision, accuracy and robustness of flood-frequency estimates. The investigations reported here focus on techniques for the following:
The proposed changes, which mostly involve generalizing Bulletin 17B’s method-of-moments procedures by using the Expected Moments Algorithm (EMA), are relatively modest, at least in the sense that they would not affect the main features of Bulletin 17B. The proposed methods include the following:
The hydrological literature already provides extensive support for the theory behind the proposed changes. The remaining question is practical: How well do the proposed methods perform under typical and realistic conditions and, specifically, with difficult records occasionally encountered in practice? In order to answer these questions, the HFAWG commissioned the work reported here. The following four major sets of results are provided:
Collectively, these studies provide a reasonably comprehensive, valid, and robust assessment of the properties of the Bulletin 17B methods and proposed alternatives. The experiments and analysis indicate that the flood quantile estimators, proposed as a revision of Bulletin 17B, do the following:
Chief, Analysis and Prediction Branch
Integrated Modeling and Prediction Division
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Reston, VA 20192
Recent studies suggest that about 2 Pg of organic C is stored on floodplains worldwide. The present study indicates the Atchafalaya River, fifth largest river in the United States in terms of discharge, traps 30 mm/y of sediment on average within its floodplain, which is the highest average non‐episodic rate of fluvial deposition on the U.S. Coastal Plain. We installed sediment sampling stations at 23 sites, normally in transect, in the Atchafalaya Basin; these sites represent the range of hydrogeomorphic conditions on the floodplain based on hydrologic connectivity with the river main stem. The rate of sedimentation translates into about 12.5 Tg/y and includes 694 Mg/y of organic C. Highest sedimentation rates are associated with areas of high connectivity to channels and prograding deltaic processes. The δ13C content suggests that 35% of deposited C is derived from river‐suspended sediment compared to litterfall in the Basin. Thus, much of the organic C sequestered is allochthonous material. However, isolated interior sites with limited connectivity to the channel may generate and sequester large amounts of autochthonous C. The substantial trapping of both auto‐ and allochthonous C (392 Mg/y) make this freshwater‐forested floodplain critical in storage of material before reaching the coastal delta and estuary. This C deposition rate (340 g C m‐2y‐1) exceeds all other rates reported in recent Blue Carbon and Tidal Freshwater Forested Wetland studies. Atchafalaya C sequestration occurs in/near areas with tidal influence and like other coastal systems is an important site for trapping mineral and organic sediment and in global C cycling.
","language":"English","publisher":"AGU","doi":"10.1029/2018JG004533","usgsCitation":"Hupp, C.R., Kroes, D.E., Noe, G.E., Schenk, E.R., and Day, R.H., 2019, Sediment trapping and carbon sequestration in floodplains of the lower Atchafalaya Basin, LA: Allochthonous vs. autochthonous carbon sources: Journal of Geophysical Research: Biogeosciences, v. 124, no. 3, p. 663-677, https://doi.org/10.1029/2018JG004533.","productDescription":"15 p.","startPage":"663","endPage":"677","ipdsId":"IP-093039","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467885,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018jg004533","text":"Publisher Index Page"},{"id":361394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Lower Atchafalaya Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92,\n 29\n ],\n [\n -91,\n 29\n ],\n [\n -91,\n 30.5\n ],\n [\n -92,\n 30.5\n ],\n [\n -92,\n 29\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Hupp, Cliff R. 0000-0003-1853-9197 crhupp@usgs.gov","orcid":"https://orcid.org/0000-0003-1853-9197","contributorId":2344,"corporation":false,"usgs":true,"family":"Hupp","given":"Cliff","email":"crhupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":757668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kroes, Daniel E. 0000-0001-9107-9077 dkroes@usgs.gov","orcid":"https://orcid.org/0000-0001-9107-9077","contributorId":213413,"corporation":false,"usgs":true,"family":"Kroes","given":"Daniel","email":"dkroes@usgs.gov","middleInitial":"E.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":757670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schenk, Edward R.","contributorId":202018,"corporation":false,"usgs":false,"family":"Schenk","given":"Edward","email":"","middleInitial":"R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":757671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Day, Richard H. 0000-0002-5959-7054 dayr@usgs.gov","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":2427,"corporation":false,"usgs":true,"family":"Day","given":"Richard","email":"dayr@usgs.gov","middleInitial":"H.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":757672,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215593,"text":"70215593 - 2019 - Spatially distributed denitrification in a karst springshed","interactions":[],"lastModifiedDate":"2020-10-25T17:58:54.311132","indexId":"70215593","displayToPublicDate":"2019-02-19T12:54:13","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7176,"text":"Hydrologic Processes","active":true,"publicationSubtype":{"id":10}},"title":"Spatially distributed denitrification in a karst springshed","docAbstract":"Karst spring measurements assess biogeochemical processes occurring within groundwater contributing areas to springs (springsheds) but can only provide aggregated information. To better understand spatially distributed processes that comprise these aggregated measures, we investigated aquifer denitrification evidence in groundwater wells (n = 16) distributed throughout a springshed in the Upper Floridan aquifer in northern Florida. Aquifer geochemistry, nitrate isotopes, and dissolved gases were compared against similar measurements at the spring outlet to evaluate spatial heterogeneity of denitrification evidence in relation to land surface–aquifer connectivity. Sample locations spanned spatial variation in recharge processes (i.e., diffuse vs. focused recharge) and proximity to sources of denitrification reactants (e.g., wetlands). Although no distinct spatial pattern in denitrification was uncovered, excess dissolved N2 gas measurements were only above detection in the unconfined springshed, with some evidence of a wetland proximity effect. Measured oxidation–reduction potential and dissolved oxygen poorly predicted denitrification, indicating that measured denitrification may be occurring upgradient from sampled wells. Despite dramatic spatial chemical heterogeneity across wells, mean values for recharge nitrate concentrations (0.02 to 5.56 mg N L−1) and excess N2 from aquifer denitrification (below detection to 1.37 mg N L−1) corresponded reasonably with mean spring outlet measurements for initial nitrate (0.78 to 1.36 mg N L−1) and excess N2 (0.15 to 1.04 mg N L−1). Congruence between groundwater and spring measurements indicates that combining sampling at the spring outlet and across the springshed is useful for understanding spatial aquifer denitrification. However, this approach would be improved with a high‐density sampling network with transects of wells along distinct groundwater flow paths.
In order to produce useful hydrologic and aquatic habitat data from the Landsat system, the U.S. Geological Survey has developed the “Dynamic Surface Water Extent” (DSWE) Landsat Science Product. DSWE will provide long-term, high-temporal resolution data on variations in inundation extent. The model used to generate DSWE is composed of five decision-rule based tests that do not require scene-based training. To allow its general application, required inputs are limited to the Landsat at-surface reflectance product and a digital elevation model. Unlike other Landsat-based water products, DSWE includes pixels that are only partially covered by water to increase inundation dynamics information content. Previously published DSWE model development included one wetland-focused test developed through visual inspection of field-collected Everglades spectra. A comparison of that test’s output against Everglades Depth Estimation Network (EDEN) in situ data confirmed the expectation that omission errors were a prime source of inaccuracy in vegetated environments. Further evaluation exposed a tendency toward commission error in coniferous forests. Improvements to the subpixel level “partial surface water” (PSW) component of DSWE was the focus of this research. Spectral mixture models were created from a variety of laboratory and image-derived endmembers. Based on the mixture modeling, a more “aggressive” PSW rule improved accuracy in herbaceous wetlands and reduced errors of commission elsewhere, while a second “conservative” test provides an alternative when commission errors must be minimized. Replication of the EDEN-based experiments using the revised PSW tests yielded a statistically significant increase in mean overall agreement (4%, p = 0.01, n = 50) and a statistically significant decrease (11%, p = 0.009, n = 50) in mean errors of omission. Because the developed spectral mixture models included image-derived vegetation endmembers and laboratory spectra for soil groups found across the US, simulations suggest where the revised DSWE PSW tests perform as they do in the Everglades and where they may prove problematic. Visual comparison of DSWE outputs with an unusual variety of coincidently collected images for locations spread throughout the US support conclusions drawn from Everglades quantitative analyses and highlight DSWE PSW component strengths and weaknesses.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11040374","usgsCitation":"Jones, J., 2019, Improved automated detection of subpixel-scale inundation – Revised Dynamic Surface Water Extent (DSWE) partial surface water tests: Remote Sensing, v. 11, no. 4, p. 1-26, https://doi.org/10.3390/rs11040374.","productDescription":"Article 374; 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-102379","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":467892,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11040374","text":"Publisher Index Page"},{"id":361339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, John 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":757437,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70200151,"text":"sir20185125 - 2019 - Potential for increased inundation in flood-prone regions of southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69","interactions":[],"lastModifiedDate":"2019-02-19T14:54:42","indexId":"sir20185125","displayToPublicDate":"2019-02-19T11:28:48","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5125","displayTitle":"Potential for Increased Inundation in Flood-Prone Regions of Southeast Florida in Response to Climate and Sea-Level Changes in Broward County, Florida, 2060–69","title":"Potential for increased inundation in flood-prone regions of southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69","docAbstract":"The U.S. Geological Survey, in cooperation with Broward County Environmental Planning and Resilience Division, has developed county-scale and local-scale groundwater/surface-water models to study the potential for increased inundation and flooding in eastern Broward County that are due to changes in future climate and sea-level rise. These models were constructed by using MODFLOW 2005, with the surface-water system represented by using the Surface-Water Routing process and a new Urban Runoff process. The local-scale model allowed the use of finer grid resolution in a selected area of the county, whereas the county-scale model provided boundary conditions for the local-scale model and insight into the hydrologic behavior of the larger system. The aquifer layering, properties, and boundaries relied heavily on a previous three-dimensional variable-density solute-transport model of the same area developed by the U.S. Geological Survey. The surface-water system within these new models actively simulates a part of the extensive canal network by using level-pool routing and active structure operations within the Surface-Water Routing process. These models were used to simulate a historical base-case period (1990–99) by using measured data and regional climate model rainfall and potential evapotranspiration output. The simulated flow and water-level results generally captured the behavior of the hydrologic system. A future period (2060–69) was simulated by using regional climate model rainfall and potential evapotranspiration output representing a wetter and drier future and low, intermediate, and high sea-level rise projections. The results were used to evaluate the potential effects on the surface-water drainage system, coastal-structure operation, and wet-season groundwater levels.
Future period simulations using the county-scale model indicate that (1) the effects of the changing climate and sea level are much more evident in eastern and coastal areas of Broward County compared to western areas, with increases in groundwater level nearly equivalent to sea-level rise; (2) coastal groundwater-level increases are distributed farther inland in the wetter future scenarios than in the drier future scenarios; (3) water levels at the westernmost groundwater station locations exhibited little change caused by sea-level rise and showed more dependence on changes in precipitation; (4) there was a reduced west-to-east groundwater gradient with increasing sea-level rise; and (5) increased downstream tidal stage at the S–13 structure resulted in increased reliance on the pump to control upstream inland canal stages. Future simulations using the local-scale model indicate similar behavior as seen in the county-scale model: (1) the coastal areas exhibited the largest impacts in groundwater levels in the future scenarios; (2) the westernmost, interior areas exhibited little change during the future scenarios; and (3) there was an increased reliance on the pump at the S–13 coastal structure but to a lesser extent than indicated in the county-scale model because of the reduced temporal scale of the local-scale model.
Possible adaptation and mitigation strategies were simulated to evaluate the county-scale and local-scale models’ ability to simulate hydrologic changes. Alterations to S–13 pump operations within the county-scale model were tested, and results indicate a reduced effect of sea-level rise inland of the control structure, but the affected area is spatially limited. The concept of using pumps to reduce the local groundwater levels in two neighborhood-sized areas was tested by using the local-scale model. The MODFLOW 2005 Drain package was used to remove groundwater by using drainage elevations set to zero, 1 foot, and 2 feet above average wet-season groundwater levels. Area 1 was well connected to coastal boundaries, and a high rate of groundwater removal was required, whereas the rate of groundwater removal required was greatly reduced in Area 2, which is less connected to tidal boundaries. Water for these scenarios was assumed to be pumped to tide with no downstream effects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185125","collaboration":"Prepared in cooperation with the Broward County Environmental Planning and Resilience Division","usgsCitation":"Decker, J.D., Hughes, J.D., and Swain, E.D., 2019, Potential for increased inundation in flood-prone regions of southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69: U.S. Geological Survey Scientific Investigations Report 2018–5125, 106 p., https://doi.org/10.3133/sir20185125.","productDescription":"Report: viii, 106 p.; Data Release","numberOfPages":"118","onlineOnly":"Y","ipdsId":"IP-066244","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":361163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5125/sir20185125.pdf","text":"Report","size":"10.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5125"},{"id":361162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5125/coverthb.jpg"},{"id":361164,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E6INWZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW 2005 data sets for the simulation of potential increased inundation in flood-prone regions of Southeast Florida in response to climate and sea-level changes in Broward County, Florida, 2060–69"}],"country":"United States","state":"Florida","county":"Broward County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.44326782226562,\n 25.95557515483912\n ],\n [\n -80.07522583007812,\n 25.95557515483912\n ],\n [\n -80.07522583007812,\n 26.331576128197028\n ],\n [\n -80.44326782226562,\n 26.331576128197028\n ],\n [\n -80.44326782226562,\n 25.95557515483912\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Aquatic features critical to watershed hydrology range widely in size from narrow, shallow streams to large, deep lakes. In this study we evaluated wetland, lake, and river systems across the Prairie Pothole Region to explore where pan-sharpened high-resolution (PSHR) imagery, relative to Landsat imagery, could provide additional data on surface water distribution and movement, missed by Landsat. We used the monthly Global Surface Water (GSW) Landsat product as well as surface water derived from Landsat imagery using a matched filtering algorithm (MF Landsat) to help consider how including partially inundated Landsat pixels as water influenced our findings. The PSHR outputs (and MF Landsat) were able to identify ~60–90% more surface water interactions between waterbodies, relative to the GSW Landsat product. However, regardless of Landsat source, by documenting many smaller (<0.2 ha), inundated wetlands, the PSHR outputs modified our interpretation of wetland size distribution across the Prairie Pothole Region.
The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow–outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-12-473-2019","usgsCitation":"Hipsey, M.R., Bruce, L.C., Boon, C., Busch, B., Carey, C.C., Hamilton, D., Hanson, P.C., Read, J.S., de Sousa, E., Weber, M., and Winslow, L., 2019, A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON): Geoscientific Model Development, v. 12, p. 473-523, https://doi.org/10.5194/gmd-12-473-2019.","productDescription":"51 p.","startPage":"473","endPage":"523","ipdsId":"IP-091920","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":467899,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-12-473-2019","text":"Publisher Index 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C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":757427,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":757428,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanson, Paul C.","contributorId":35634,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":757429,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":757422,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"de Sousa, Eduardo","contributorId":213317,"corporation":false,"usgs":false,"family":"de Sousa","given":"Eduardo","email":"","affiliations":[{"id":38735,"text":"UWA School of Agriculture & Environment, The University of Western 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