This study produced a geospatial database for use in a decision support system by the Bolivian authorities to investigate further development and investment potentials in sustainable hydropower in Bolivia. The study assessed theoretical hydropower of all 1-kilometer (km) stream segments in the country using multisource satellite data and a hydrologic modeling approach. With the assessment covering the 2 million square kilometer (km2) region influencing Bolivia’s drainage network, the potential hydropower figures are based on theoretical yield assuming that the systems generating the power are 100 percent efficient. There are several factors to consider when determining the real-world or technical power potential of a hydropower system, and these factors can vary depending on local conditions. Since this assessment covers a large area, it was necessary to reduce these variables to the two that can be modeled consistently throughout the region, streamflow or discharge, and elevation drop or head. First, the Shuttle Radar Topography Mission high-resolution 30-meter (m) digital elevation model was used to identify stream segments with greater than 10 km2 of upstream drainage. We applied several preconditioning processes to the 30-m digital elevation model to reduce errors and improve the accuracy of stream delineation and head height estimation. A total of 316,500 1-km stream segments were identified and used in this study to assess the total theoretical hydropower potential of Bolivia. Precipitation observations from a total of 463 stations obtained from the Bolivian Servicio Nacional de Meteorología e Hidrología (Bolivian National Meteorology and Hydrology Service) and the Brazilian Agência Nacional de Águas (Brazilian National Water Agency) were used to validate six different gridded precipitation estimates for Bolivia obtained from various sources. Validation results indicated that gridded precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) reanalysis product (3B43) had the highest accuracies. The coarse-resolution (25-km) TRMM data were disaggregated to 5-km pixels using climatology information obtained from the Climate Hazards Group Infrared Precipitation with Stations dataset. About a 17-percent bias was observed in the disaggregated TRMM estimates, which was corrected using the station observations. The bias-corrected, disaggregated TRMM precipitation estimate was used to compute stream discharge using a regionalization approach. In regionalization approach, required homogeneous regions for Bolivia were derived from precipitation patterns and topographic characteristics using a k-means clustering approach. Using the discharge and head height estimates for each 1-km stream segment, we computed hydropower potential for 316,490 stream segments within Bolivia and that share borders with Bolivia. The total theoretical hydropower potential (TTHP) of these stream segments was found to be 212 gigawatts (GW). Out of this total, 77.4 GW was within protected areas where hydropower projects cannot be developed; hence, the remaining total theoretical hydropower in Bolivia (outside the protected areas) was estimated as 135 GW. Nearly 1,000 1-km stream segments, however, were within the boundaries of existing hydropower projects. The TTHP of these stream segments was nearly 1.4 GW, so the residual TTHP of the streams in Bolivia was estimated as 133 GW. Care should be exercised to understand and interpret the TTHP identified in this study because all the stream segments identified and assessed in this study cannot be harnessed to their full capacity; furthermore, factors such as required environmental flows, efficiency, economics, and feasibility need to be considered to better identify a more real-world hydropower potential. If environmental flow requirements of 20–40 percent are considered, the total theoretical power available reduces by 60–80 percent. In addition, a 0.72 efficiency factor further reduces the estimation by another 28 percent. This study provides the base theoretical hydropower potential for Bolivia, the next step is to identify optimal hydropower plant locations and factor in the principles to appraise a real-world power potential in Bolivia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161156","collaboration":"Prepared in cooperation with the CAF – Development Bank of Latin America","usgsCitation":"Velpuri, N.M., Pervez, M.S., and Cushing, W.M., 2016, Hydropower assessment of Bolivia—A multisource satellite data and hydrologic modeling approach: U.S. Geological Survey Open-File Report 2016–1156, 65 p., https://dx.doi.org/10.3133/ofr20161156.","productDescription":"Report: x, 65 p.; Appendixes: 2-4","numberOfPages":"79","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075626","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":331175,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1156/ofr20161156.pdf","text":"Report","size":"23.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1156"},{"id":331174,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1156/coverthb.jpg"},{"id":331176,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1156/downloads","text":"Appendixes 2–4","description":"OFR 2016–1156 Appendixes 2–4"}],"country":"Bolivia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-62.84647,-22.03499],[-63.98684,-21.99364],[-64.37702,-22.79809],[-64.96489,-22.07586],[-66.27334,-21.83231],[-67.10667,-22.73592],[-67.82818,-22.87292],[-68.21991,-21.49435],[-68.75717,-20.37266],[-68.44223,-19.40507],[-68.96682,-18.98168],[-69.10025,-18.26013],[-69.59042,-17.58001],[-68.95964,-16.5007],[-69.38976,-15.66013],[-69.16035,-15.32397],[-69.33953,-14.9532],[-68.94889,-14.45364],[-68.92922,-13.60268],[-68.88008,-12.89973],[-68.66508,-12.5613],[-69.52968,-10.95173],[-68.78616,-11.03638],[-68.27125,-11.01452],[-68.04819,-10.71206],[-67.1738,-10.30681],[-66.64691,-9.93133],[-65.33844,-9.76199],[-65.44484,-10.51145],[-65.3219,-10.89587],[-65.40228,-11.56627],[-64.31635,-12.46198],[-63.1965,-12.62703],[-62.80306,-13.00065],[-62.12708,-13.19878],[-61.7132,-13.4892],[-61.08412,-13.47938],[-60.5033,-13.77595],[-60.4592,-14.35401],[-60.26433,-14.64598],[-60.25115,-15.07722],[-60.54297,-15.09391],[-60.15839,-16.25828],[-58.24122,-16.29957],[-58.38806,-16.87711],[-58.2808,-17.27171],[-57.73456,-17.55247],[-57.49837,-18.17419],[-57.67601,-18.96184],[-57.95,-19.4],[-57.8538,-19.97],[-58.16639,-20.1767],[-58.18347,-19.8684],[-59.11504,-19.35691],[-60.04356,-19.34275],[-61.78633,-19.63374],[-62.26596,-20.51373],[-62.29118,-21.05163],[-62.68506,-22.24903],[-62.84647,-22.03499]]]},\"properties\":{\"name\":\"Bolivia\"}}]}","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The hydrogeology and hydrologic characteristics of the Ozark Plateaus aquifer system were characterized as part of ongoing U.S. Geological Survey efforts to assess groundwater availability across the Nation. The need for such a study in the Ozark Plateaus physiographic province (Ozark Plateaus) is highlighted by increasing demand on groundwater resources by the 5.3 million people of the Ozark Plateaus, water-level declines in some areas, and potential impacts of climate change on groundwater availability. The subject study integrates knowledge gained through local investigation within a regional perspective to develop a regional conceptual model of groundwater flow in the Ozark Plateaus aquifer system (Ozark system), a key phase of groundwater availability assessment. The Ozark system extends across much of southern Missouri and northwestern and north-central Arkansas and smaller areas of southeastern Kansas and northeastern Oklahoma. The region is one of the major karst landscapes in the United States, and karst aquifers are predominant in the Ozark system. Groundwater flow is ultimately controlled by aquifer and confining unit lithologies and stratigraphic relations, geologic structure, karst development, and the character of surficial lithologies and regolith mantle. The regolith mantle is a defining element of Ozark Plateaus karst, affecting recharge, karst development, and vulnerability to surface-derived contaminants. Karst development is more advanced—as evidenced by larger springs, hydraulic characteristics, and higher well yields—in the Salem Plateau and in the northern part of the Springfield Plateau (generally north of the Arkansas-Missouri border) as compared with the southern part of the Springfield Plateau in Arkansas, largely due to thinner, less extensive regolith and purer carbonate lithology.
Precipitation is the ultimate source of all water to the Ozark system, and the hydrologic budget for the Ozark system includes inputs from recharge, losing-stream sections, and groundwater inflows and losses of water to gaining-stream sections, groundwater withdrawals, and surface-water and groundwater outflows to neighboring systems. Groundwater recharge, estimated by a soil-water-balance model, represents about 24 percent, or 11 inches, of 43.9 inches annual precipitation. Recharge is spatially variable, being greater in the northern Springfield Plateau and Salem Plateau than in the southern Springfield Plateau (generally south of the Arkansas border) because of differences in regolith mantle extent and thickness and carbonate lithology and hydraulic properties. Increased precipitation and decreased agricultural land use during the period 1951 through 2011 increased recharge by approximately 5 percent. Although all Ozark streams have losing, neutral, and gaining sections, they are dominantly gaining and are a net sink for groundwater with nearly 90 percent of groundwater recharge returned to springs and streams. Groundwater pumping is a small but important loss of water in the Ozark system hydrologic budget; water-level declines and local cones of depression have been observed around pumping centers and strong concerns exist over potential effects on stream and spring flow.
Data indicate that societal needs for freshwater resources in the Ozark Plateaus will continue to increase and will do so in the context of changing climate and hydrology. Groundwater will continue to be an important part of supporting these societal needs and also local ecosystems. The unique character and hydrogeologic variability across the Ozark system will control how the system responds to future stress. Groundwater of the Ozark system in the northern study area is more dynamic, has greater storage and larger flux, and has greater potential for further development than in the part of the study area south of the Arkansas-Missouri border. Further south in Arkansas, a line exists, roughly defined as 5 miles south of the Springfield Plateau-Boston Mountains boundary, beyond which further extensive municipal or commercial development appears unlikely under current economic and resource-need conditions. A small part of the Ozark system groundwater budget is currently drafted for use, leaving an apparently large component available for further development and use—particularly in the northern Springfield Plateau and Salem Plateau; however, the effects of increased pumping on groundwater’s role in maintaining ecosystems and ecosystem services are not quantitatively well understood, and the close relation between groundwater and surface water highlights the importance of further quantitative assessment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165137","collaboration":"Prepared in cooperation with the Groundwater Resources Program","usgsCitation":"Hays, P.D., Knierim, K.J., Breaker, Brian, Westerman, D.A., and Clark, B.R., 2016, Hydrogeology and hydrologic conditions of the Ozark Plateaus aquifer system: U.S. Geological Survey Scientific Investigations Report 2016–5137, 61 p., https://dx.doi.org/10.3133/sir20165137. \n\n","productDescription":"Report: vii, 61 p.; Appendixes: 1-2","numberOfPages":"73","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071467","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":331147,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5137/coverthb.jpg"},{"id":331148,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5137/sir20165137.pdf","description":"SIR 2016–5137"},{"id":331149,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5137/downloads","text":"Appendix 1 & 2","description":"SIR 2016–5137 Appendix 1 & 2"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark Plateaus Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.1263427734375,\n 38.81831117374662\n ],\n [\n -90.3076171875,\n 38.89103282648846\n ],\n [\n -90.4559326171875,\n 38.8225909761771\n ],\n [\n -90.692138671875,\n 38.70694605159386\n ],\n [\n -90.8734130859375,\n 38.556757147352215\n ],\n [\n -91.0546875,\n 38.59970036588819\n ],\n [\n -91.2689208984375,\n 38.66835610151506\n ],\n [\n -91.3897705078125,\n 38.71551876930462\n ],\n [\n -91.571044921875,\n 38.68122173079789\n ],\n [\n -91.7083740234375,\n 38.70265930723801\n ],\n [\n -91.9171142578125,\n 38.65119833229951\n ],\n [\n -92.07092285156249,\n 38.57823196583313\n ],\n [\n -92.197265625,\n 38.586820096127674\n ],\n [\n -92.3345947265625,\n 38.6897975322717\n ],\n [\n -92.4884033203125,\n 38.90813299596705\n ],\n [\n -92.57080078125,\n 38.96368010198575\n ],\n [\n -92.74108886718749,\n 38.98503278695909\n ],\n [\n -92.87841796875,\n 38.997841307500714\n ],\n [\n -92.92236328125,\n 39.091699613104595\n ],\n [\n -92.9058837890625,\n 39.17691709496078\n ],\n [\n -92.8619384765625,\n 39.2407625100131\n ],\n [\n -92.9608154296875,\n 39.317300373271024\n ],\n [\n -93.0926513671875,\n 39.38101803294523\n ],\n [\n -93.2025146484375,\n 39.40224434029275\n ],\n [\n -93.350830078125,\n 39.30454987014581\n ],\n [\n -93.482666015625,\n 39.287545585410435\n ],\n [\n -93.70788574218749,\n 39.223742741391305\n ],\n [\n -93.98803710937499,\n 39.15136267949029\n ],\n [\n -95.2349853515625,\n 38.25112269630296\n ],\n [\n -95.2789306640625,\n 36.45221769643571\n ],\n [\n -95.0152587890625,\n 35.30840140169162\n ],\n [\n -94.19128417968749,\n 35.35321610123823\n ],\n [\n -93.834228515625,\n 35.47409160773029\n ],\n [\n -93.482666015625,\n 35.40696093270201\n ],\n [\n -93.16955566406249,\n 35.25459097465022\n ],\n [\n -93.065185546875,\n 35.15584570226544\n ],\n [\n -92.6641845703125,\n 35.08845057036537\n ],\n [\n -92.55432128906249,\n 34.95349314197422\n ],\n [\n -92.427978515625,\n 34.836349990763864\n ],\n [\n -92.1148681640625,\n 34.8183131456094\n ],\n [\n -90.3515625,\n 35.97800618085566\n ],\n [\n -89.35729980468749,\n 37.01132594307015\n ],\n [\n -89.4561767578125,\n 37.25656608611523\n ],\n [\n -89.4287109375,\n 37.37015718405753\n ],\n [\n -89.46716308593749,\n 37.45741810262938\n ],\n [\n -89.5166015625,\n 37.58811876638322\n ],\n [\n -89.56054687499999,\n 37.71859032558816\n ],\n [\n -89.70886230468749,\n 37.82280243352756\n ],\n [\n -89.8187255859375,\n 37.900865092570065\n ],\n [\n -89.9560546875,\n 37.97451499202459\n ],\n [\n -90.164794921875,\n 38.07836562996712\n ],\n [\n -90.2801513671875,\n 38.14751758025121\n ],\n [\n -90.3680419921875,\n 38.268375880204744\n ],\n [\n -90.32409667968749,\n 38.40194908237822\n ],\n [\n -90.252685546875,\n 38.53097889440024\n ],\n [\n -90.2032470703125,\n 38.62974534092597\n ],\n [\n -90.19775390625,\n 38.70694605159386\n ],\n [\n -90.1263427734375,\n 38.81831117374662\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
Results of a flood-hazard analysis conducted by the U.S. Geological Survey, in cooperation with the Argonne National Laboratory, for four headwater streams within the Argonne National Laboratory property indicate that the 1-percent and 0.2-percent annual exceedance probability floods would cause multiple roads to be overtopped. Results indicate that most of the effects on the infrastructure would be from flooding of Freund Brook. Flooding on the Northeast and Southeast Drainage Ways would be limited to overtopping of one road crossing for each of those streams. The Northwest Drainage Way would be the least affected with flooding expected to occur in open grass or forested areas.
The Argonne Site Sustainability Plan outlined the development of hydrologic and hydraulic models and the creation of flood-plain maps of the existing site conditions as a first step in addressing resiliency to possible climate change impacts as required by Executive Order 13653 “Preparing the United States for the Impacts of Climate Change.” The Hydrological Simulation Program-FORTRAN is the hydrologic model used in the study, and the Hydrologic Engineering Center‒River Analysis System (HEC–RAS) is the hydraulic model. The model results were verified by comparing simulated water-surface elevations to observed water-surface elevations measured at a network of five crest-stage gages on the four study streams. The comparison between crest-stage gage and simulated elevations resulted in an average absolute difference of 0.06 feet and a maximum difference of 0.19 feet.
In addition to the flood-hazard model development and mapping, a qualitative stream assessment was conducted to evaluate stream channel and substrate conditions in the study reaches. This information can be used to evaluate erosion potential.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165132","collaboration":"Prepared in cooperation with the Argonne National Laboratory","usgsCitation":"Soong, D.T., Murphy, E.A., Straub, T.D., and Zeeb, H.L., 2016, Flood-hazard analysis of four headwater streams draining the Argonne National Laboratory property, DuPage County, Illinois: U.S. Geological Survey Scientific Investigations Report 2016-5132, 57 p., https://dx.doi.org/10.3133/sir20165132.","productDescription":"vii, 57 p.","numberOfPages":"69","onlineOnly":"Y","ipdsId":"IP-075928","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":331075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5132/sir20165132.pdf","text":"Report","size":"67.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5132"},{"id":331074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5132/coverthb.jpg"}],"country":"United States","state":"Illinois","county":"DuPage County","otherGeospatial":"Sawmill Creek Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.02177429199219,\n 41.6872711837914\n ],\n [\n -88.02177429199219,\n 41.77873679916478\n ],\n [\n -87.9287338256836,\n 41.77873679916478\n ],\n [\n -87.9287338256836,\n 41.6872711837914\n ],\n [\n -88.02177429199219,\n 41.6872711837914\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois-Iowa Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, Illinois 61801
http://il.water.usgs.gov
In 2012, a late season tropical depression developed into a tropical storm and later a hurricane. The hurricane, named “Hurricane Sandy,” gained strength to a Category 3 storm on October 25, 2012, and underwent several transitions on its approach to the mid-Atlantic region of the eastern coast of the United States. By October 28, 2012, Hurricane Sandy had strengthened into the largest hurricane ever recorded in the North Atlantic and was tracking parallel to the east coast of United States, heading toward New Jersey. On October 29, 2012, the storm turned west-northwest and made landfall near Atlantic City, N.J. The high winds and wind-driven storm surge caused massive damage along the entire coastline of New Jersey. Millions of people were left without power or communication networks. Many homes were completely destroyed. Sand dunes were eroded, and the barrier island at Mantoloking was breached, connecting the ocean with Barnegat Bay.
Several days before the storm made landfall in New Jersey, the U.S. Geological Survey (USGS) made a decision to deploy a temporary network of storm-tide sensors and barometric pressure sensors from Virginia to Maine to supplement the existing USGS and National Oceanic and Atmospheric Administration (NOAA) networks of permanent tide monitoring stations. After the storm made landfall, the USGS conducted a sensor data recovery and high-water-mark collection campaign in cooperation with the Federal Emergency Management Agency (FEMA).
Peak storm-tide elevations documented at USGS tide gages, tidal crest-stage gages, temporary storm sensor locations, and high-water-mark sites indicate the area from southern Monmouth County, N.J., north through Raritan Bay, N.J., had the highest peak storm-tide elevations during this storm. The USGS tide gages at Raritan River at South Amboy and Raritan Bay at Keansburg, part of the New Jersey Tide Telemetry System, each recorded peak storm-tide elevations of greater than 13 feet (ft)—more than 5 ft higher than the previously recorded period-of-record maximum. A comparison of peak storm-tide elevations to preliminary FEMA Coastal Flood Insurance Study flood elevations indicated that these areas experienced the highest recurrence intervals along the coast of New Jersey. Analysis showed peak storm-tide elevations exceeded the 100-year FEMA flood elevations in many parts of Middlesex, Union, Essex, Hudson, and Bergen Counties, and peak storm-tide elevations at many locations in Monmouth County exceeded the 500-year recurrence interval.
A level 1 HAZUS (HAZards United States) analysis was done for the counties in New Jersey affected by flooding to estimate total building stock losses. The aggregated total building stock losses estimated by HAZUS for New Jersey, on the basis of the final inundation verified by USGS high-water marks, was almost $19 billion. A comparison of Hurricane Sandy with historic coastal storms showed that peak storm-tide elevations associated with Hurricane Sandy exceeded most of the previously documented elevations associated with the storms of December 1992, March 1962, September 1960, and September 1944 at many coastal communities in New Jersey. This scientific investigation report was prepared in cooperation with FEMA to document flood processes and flood damages resulting from this storm and to assist in future flood mitigation actions in New Jersey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165085","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Suro, T.P., Deetz, Anna, and Hearn, Paul, 2016, Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012: U.S. Geological Survey Scientific Investigations Report 2016–5085, 73 p., https://dx.doi.org/10.3133/sir20165085.","productDescription":"Report: ix, 73 p.; 5 Tables","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-055579","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":330616,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table4.xls","text":"Table 4","size":"45 KB xls","description":"SIR 2016-5085","linkHelpText":"- Description of U.S. Geological Survey sensors temporarily deployed for Hurricane Sandy with peak storm tide elevations, annual exceedance probabilities, and estimated recurrence intervals in New Jersey, October 29–30, 2012 \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330617,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table5.xls","text":"Table 5","size":"144 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations at 169 high-water-mark sites along the coast of New Jersey during Hurricane Sandy, October 29–30, 2012, and the corresponding Federal Emergency Management Agency flood elevations for the 10-, 50-, 100-, and 500-year recurrence intervals \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330618,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table6.xls","text":"Table 6","size":"61 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations for selected historic coastal floods and peak storm-tide elevations during Hurricane Sandy, October 29–30, 2012, at selected U.S. Geological Survey permanent monitoring tide gages in New Jersey"},{"id":330619,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table7.xls","text":"Table 7","size":"74 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations at 82 high-water-mark sites flagged and surveyed after the December 1992 storm in New Jersey, peak storm-tide elevations from the closest high-water-mark sites flagged and surveyed after Hurricane Sandy, October 29–30, 2012, and peak storm-tide elevations from the nearest U.S. Geological Survey tide gage along the coast of New Jersey during Hurricane Sandy, October 29–30, 2012\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330614,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085.pdf","text":"Report","size":"85.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5085"},{"id":330615,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table3.xls","text":"Table 3","size":"77.5 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak-of-record tide elevations and peak storm-tide elevations at U.S. Geological Survey permanent monitoring tide gages in New Jersey, October 29–30, 2012\t\t\t\t\t\t"},{"id":330613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5085/coverthb.jpg"}],"country":"United States","state":"New 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville NJ, 08648
http://nj.usgs.gov/
Despite widespread and ongoing implementation of conservation practices throughout the Chesapeake Bay watershed, water quality continues to be degraded by excess sediment and nutrient inputs. While the Chesapeake Bay Program has developed and maintains a large-scale and long-term monitoring network to detect improvements in water quality throughout the watershed, fewer resources have been allocated for monitoring smaller watersheds, even though water-quality improvements that may result from the implementation of conservation practices are likely to be first detected at smaller watershed scales.
In 2010, the U.S. Geological Survey partnered with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture to initiate water-quality monitoring in four selected small watersheds that were targeted for increased implementation of conservation practices. Smith Creek watershed is an agricultural watershed in the Shenandoah Valley of Virginia that is dominated by cattle and poultry production, and the Upper Chester River watershed is an agricultural watershed on the Eastern Shore of Maryland that is dominated by row-cropping activities. The Conewago Creek watershed is an agricultural watershed in southeastern Pennsylvania that is characterized by mixed agricultural activities. The fourth watershed, Difficult Run, is a suburban watershed in northern Virginia that is dominated by medium density residential development. The objective of this study was to investigate spatial and temporal variations in water chemistry and suspended sediment in these four relatively small watersheds that represent a range of land-use patterns and underlying geology to (1) characterize current water-quality conditions in these watersheds, and (2) identify the dominant sources, sinks, and transport processes in each watershed.
The general study design involved two components. The first included intensive routine water-quality monitoring at an existing streamgage within each study area (including continuous water-quality monitoring as well as discrete water-quality sampling) to develop a detailed understanding of the temporal and hydrologic variability in stream chemistry and sediment transport in each watershed. The second component involved extensive water-quality monitoring at various sites throughout each watershed to develop a detailed understanding of spatial patterns. Both components were used to improve understanding of sources and transport processes affecting stream chemistry, including nutrients and suspended sediments, and their implications for detecting long-term trends related to best management practices. This report summarizes the results of monitoring that was performed from April 2010 through September 2013.
Summaries for each of the four small watersheds are presented below. Each watershed has a more descriptive and detailed section in the report, but these summaries may be particularly useful for some watershed managers and stakeholders desiring slightly less technical detail.
Smith Creek is a 105.39-mi2 watershed within the Shenandoah Valley that drains to the North Fork Shenandoah River. The long-term Smith Creek base-flow index is 72.3 percent, indicating that on average, approximately 72 percent of Smith Creek flow was base flow, which suggests that Smith Creek streamflow is dominated by groundwater discharge rather than stormwater runoff. A series of cluster and principal components analyses demonstrated that the majority of the variability in Smith Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed for turbidity, suspended sediments, total nitrogen, ammonium, orthophosphate, iron, total phosphorus, and the ratio of calcium to magnesium. Statistically significant inverse correlations with flow were observed for specific conductance, magnesium, δ15N of nitrate, pH, bicarbonate, calcium, and δ18O of nitrate. Of particular note, flow and nitrate were not statistically significantly correlated, likely because of the relatively complex concentration-discharge relationship observed in continuous and discrete datasets. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, turbidity, orthophosphate, total phosphorus, suspended-sediment concentration, and silica were higher during the warm season, but pH, dissolved oxygen, and sulfate were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loads in Smith Creek using the continuous water-quality monitors. The mean Smith Creek in-stream sediment load was approximately 6,900 tons per year, with nearly 90 percent of the sediment load over the 3-year study period contributed during the eight largest storm events during that period. The Smith Creek total phosphorus load was approximately 21,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods, although a substantial phosphorus load still occurs during base-flow conditions. The Smith Creek total nitrogen load was approximately 400,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions (as was the case for sediment and total phosphorus) and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Smith Creek watershed revealed how the complex geology and hydrology interacted to result in variable water chemistry. During relatively dry and low base-flow periods, much of the discharge in Smith Creek was contributed by a single dominant spring—Lacey Spring. During wetter base-flow periods, the flows in Smith Creek were largely generated by a mixture of headwater springs and forested mountain tributaries with very different geochemical composition. The headwater springs generally issued from limestone bedrock and were characterized as having relatively high nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The undeveloped, high-gradient, forested mountain sites were generally characterized by low ionic strength waters with low nutrient concentrations. Nitrate isotope data from the limestone springs generally were consistent with manure-derived nitrogen sources (such as cattle and poultry), although the possibility of other mixed sources cannot be excluded. Nitrate isotope data from the undeveloped, high-gradient forested mountain sites were more consistent with nitrogen from undisturbed soils, atmospheric deposition, or nitrogen fixation. Regardless of the nitrogen source, oxygen isotope data indicate that the nitrate was largely a result of nitrification. Land-use data indicate that manure sources of nitrogen dominated watershed nitrogen inputs. Phosphorus sources were less well studied. The presence of a single point-source discharge near the town of New Market contributed the majority of the phosphorus to Smith Creek under base-flow conditions, but nonpoint sources of phosphorus dominated the loading to Smith Creek during stormflow periods.
Implementation of conservation practices increased in the Smith Creek watershed during the study period, and even though a broad range of practice types was implemented, the most common practices included stream fencing (for cattle exclusion), the development of nutrient management plans, conservation crop rotation, and the planting of cover crops. While the implementation of these conservation practices is encouraging, results indicate small increases in nitrate concentrations at the streamgage over the last 29 years, concurrent with small decreases in nitrate fluxes. It will likely be years before the cumulative effect of these practices can be detected in the Smith Creek water quality, and the magnitude of the effect of these conservation practices detected in Smith Creek will depend largely on whether nutrient loading (of manure and commercial fertilizer) is reduced over time.
The Upper Chester River watershed includes the 36-square-mile (mi2) watershed area around several nontidal tributaries that drain into the tidal Chester River. The streamgage is on Chesterville Branch, the largest nontidal tributary (approximately 6.12 mi2) and is the site for continuous water-quality monitoring for this project. The base-flow index at Chesterville Branch is about 72 percent and indicates that, as in most of the Coastal Plain, groundwater is the greatest contributor to streamflow. As such, more than 90 percent of the nitrogen in the stream is in the form of nitrate from groundwater. Continuous and discrete data collected at Chesterville Branch show the effects of streamflow and season on water quality. Significantly positive correlations with flow were observed for ammonium, dissolved and total phosphorus, sediment, and turbidity as runoff carried these constituents from the land surface into Chesterville Branch. Other constituents that increased significantly with flow include potassium, sulfate, iron, and manganese, which are likely contributed from near-stream areas and ponds with high organic-matter content. Total nitrogen, pH, and specific conductance, along with chemical constituents associated with groundwater inputs including nitrate, calcium, ratio of calcium to magnesium, silica, bicarbonate, and sodium, were negatively correlated with flow because concentrations of these constituents were diluted by runoff.
Seasonal differences in water chemistry, which are most likely related to increased biologic effects on the uptake and release of chemicals in the stream and near-stream areas, also were observed. Water temperature, orthophosphate, δ15N of nitrate, bicarbonate, sodium, and the ratio of sodium to chloride were higher during the warm season, and dissolved oxygen, total nitrogen, nitrate, magnesium, sulfate, and manganese were higher during the cool season.
Surrogate-regression models developed by using continuous water-quality data showed that the annual sediment load for the 2013 water year was about 2,600 tons, with more than 90 percent of this sediment contributed during two storms. The total phosphorus load in 2013 was about 13,000 pounds with more than 90 percent contributed during the same two storms as sediment. The load of total nitrogen, 140,000 pounds, accumulated steadily throughout the 2013 water year as nitrate in groundwater continuously discharged into the stream. The same two large storms that contributed 90 percent of the suspended-sediment and total phosphorus load only contributed about 20 percent of the annual total nitrogen load.
Extensive water-quality monitoring of stream base flow throughout the Upper Chester River watershed identified how differences in land use and hydrogeology affected water chemistry. In parts of the watershed with well-drained soil and thick sandy aquifer sediments, concentrations of nitrate and other chemicals associated with fertilizer and lime application increased in streams as agricultural land use increased. More than 90 percent of the nitrogen in streams from these areas was in the form of nitrate, and concentrations ranged from about 5 milligrams per liter (mg/L) to 8 mg/L as nitrogen in the two largest tributaries. Stream nitrate concentrations were about 1 mg/L as nitrogen where soils were more poorly drained, the surficial aquifer sediments were thinner, and forests and wetlands were more widespread than agriculture. Nitrate isotope data were consistent with inorganic fertilizers ± atmospheric deposition and N2 fixation as sources of nitrogen, and with nitrification as the dominant nitrate-forming process. Nitrate reduction was indicated by elevated δ15N and δ18O values in some samples from streams draining watersheds with poorly drained soils. An analysis of land-use data and SPARROW modeling input data attributed almost 90 percent of the nitrogen sources in the Upper Chester River watershed to inorganic fertilizer and fixation of atmospheric nitrogen by legumes, which is in agreement with the isotopic characteristics of nitrate in this watershed. Local sources of manure are limited in this area. Total phosphorus concentrations during base flow ranged from below detection to about 0.2 mg/L. Stream phosphorus concentrations during base flow were generally lower than those measured during storms because most phosphorus transport likely occurs as phosphorus attached to sediment particles during runoff. Because manure is not widely used in this area, the major source of phosphorus is likely fertilizer.
The implementation of conservation practices in the Upper Chester River watershed increased substantially during the study period, with a total implementation of 1,194 U.S. Department of Agriculture-compliant practices. The most frequently used practices were oriented towards nutrient and sediment control, including cover crops, nutrient management planning, conservation crop rotation, conservation tillage, and irrigation management. The current Chesapeake Bay model for this area predicts that implementation of best management practices should result in a 13-percent decrease in overall delivery of nitrogen to the Upper Chester River. Because most nitrogen travels through the groundwater system for years to decades before being discharged to streams, the time period of monitoring was not sufficient to see the effects of these practices on water quality. The magnitude of the effect that may eventually be detected will depend on the degree to which nitrate leaching into the groundwater system is reduced over time. Loadings of phosphorus and sediment are primarily transported during large runoff events and are difficult to control and analyze for trends because of their timing and episodic nature.
Conewago Creek has two primary monitoring locations—one near the middle of the 47-mi2 watershed and the other near the outlet just upstream of the Susquehanna River. The base-flow index was 47.3 percent for 2012–2013, indicating that on average, approximately 53 percent of the streamflow in Conewago Creek exited the watershed as surface flow, which suggests that the stormwater runoff was somewhat greater than groundwater discharge (base flow). A series of cluster and principal components analyses demonstrated that the majority of the variability in the Conewago Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed at both monitoring sites for ammonium, total phosphorus, orthophosphate, iron, and manganese; additionally, at the upstream monitoring station, total nitrogen demonstrated a statistically significant positive correlation with flow. Statistically significant inverse correlations with flow were observed at both sites for water temperature, specific conductance (at the downstream site only), sulfate, chloride, calcium, and magnesium. Statistically significant seasonal patterns were observed for several water-quality constituents. Water temperature, phosphorus (upstream site only), and orthophosphate were higher during the warm season, and nitrate and total nitrogen (upstream site only) were higher during the cool season.
Surrogate regression models were developed to compute sediment and nutrient load in Conewago Creek by using the continuous water-quality monitors and water-quality samples. Conewago Creek sediment load was approximately 9,900 tons in 2012 and approximately 18,900 tons in 2013, with nearly 80 percent of the sediment load in 2013 contributed by the three largest storm events. Annual total nitrogen loads could not be estimated due to poor model performance. The addition of continued monitoring or a continuously recording nitrate sensor could improve estimates of total nitrogen loads. During 2012 and 2013, phosphorus loads in Conewago Creek were approximately 50,000 pounds in each year.
Combining data from one high-flow synoptic sampling with the data from routine sampling revealed how the geology and hydrology interact to result in variable water chemistry throughout the Conewago Creek watershed. The areas above the upstream gage in the headwaters are generally underlain by forested non-carbonate bedrock and are characterized by relatively low nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The more developed, agricultural areas below the upstream site were generally characterized by higher ionic strength waters with higher nutrient and metal concentrations. An analysis of land-use data and SPAtially Referenced Regressions On Watershed (SPARROW) modeling data indicates that manure sources of nitrogen dominate the input of nitrogen to the watershed.
Implementation of conservation practices increased in the Conewago Creek watershed during the study period, and while a broad range of practice types were implemented, the most common practices included residue and tillage management, cover crops, nutrient management, terracing, and stream fencing (for animal exclusion or bank restoration). While the implementation of these conservation practices is encouraging, the cumulative effects of these practices probably will not be detected in Conewago Creek water quality for several years. The magnitude of the effects of these conservation practices on water quality in Conewago Creek will depend largely on the extent to which nutrient loading (septic, manure, and commercial fertilizer) and sediment-producing activities are reduced over time.
The Difficult Run watershed is a 57.82-mi2 watershed that drains to the Potomac River. The long-term Difficult Run base-flow index (from 1936 to 2010) was 57.9, indicating that approximately 58 percent of streamflow exited the watershed as base flow and 42 percent as stormflow; however, with continued development and urbanization of the watershed, the base-flow index has decreased to 50 percent during the last 20 years. This base-flow index was less than those of the other watersheds evaluated in this study, likely because the Difficult Run watershed largely is underlain by crystalline piedmont metamorphic rocks and has a greater proportion of impervious urban land cover. A series of cluster and principal components analyses indicated that most of the variability in Difficult Run water quality could be attributed to hydrologic variability and seasonality. Statistically significant positive correlations with flow were observed for turbidity, dissolved oxygen, suspended sediments, ammonium, orthophosphate, iron, and total phosphorus. Statistically significant inverse correlations with flow were observed for water temperature, pH, specific conductance, bicarbonate, calcium, magnesium, nitrate, δ15N of nitrate, and silica. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, ammonium, orthophosphate, and δ15N of nitrate were higher during the warm season, and dissolved oxygen, nitrate, and manganese were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loading rates. The Difficult Run sediment load was approximately 8,000 tons per year, with greater than 95 percent of the sediment load in the 2013 water year contributed by the seven largest storm events. The total phosphorus load in Difficult Run was approximately 14,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods. The total nitrogen load in Difficult Run is estimated to have been approximately 140,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions than that of phosphorus and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Difficult Run watershed revealed relatively uniform generation of flow per unit of watershed area, as well as spatial variation in water quality that is strongly related to land-use activities. Elevated nitrate concentrations were observed in a subset of monitoring sites that are inversely correlated with population density and positively correlated to the septic system density within each subwatershed. The majority of the elevated nitrate concentrations for these sites are hypothesized to be caused by nitrate leaching from septic systems, more so than homeowner fertilizer usage among these subwatersheds that have lower population densities than other parts of the watershed. Nitrate isotope data, temporal patterns in the water-quality data, mass-balance computations, and a separate land-use analysis all generally indicate that leachate from septic systems was the likely source of the elevated nitrate. Another group of water-quality sites have relatively low nitrogen concentrations, are located in areas that are served by city sewer lines, and have experienced stream restoration activities. A final group of sites drained the areas with the highest imperviousness and had strongly elevated specific conductance, chloride, and sodium, which were likely caused by a combination of road salting and other anthropogenic sources draining these urbanized areas in the watershed. A fourth group of sites represents a mixture of water sources and had water quality similar to that at the Difficult Run streamgage. Analysis of the nitrate isotope data generally indicates a broad range of composition indicative of mixed natural and anthropogenic nitrogen sources. Implementation of conservation practices increased in the Difficult Run watershed during the study period, and while a broad range of practice types was implemented, the most common practices included stream restoration. While the implementation of these conservation practices is encouraging, the cumulative effect of these practices probably will not be detected in Difficult Run water quality for several years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165093","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program","usgsCitation":"Hyer, K.E., Denver, J.M., Langland, M.J., Webber, J.S., Böhlke, J.K., Hively, W.D., and Clune, J.W., 2016, Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013: U.S. Geological Survey Scientific Investigations Report 2016–5093, 211 p., https://dx.doi.org/10.3133/sir20165093.","productDescription":"Report: xix, 211 p.","startPage":"1","endPage":"211","numberOfPages":"236","onlineOnly":"N","ipdsId":"IP-067371","costCenters":[{"id":614,"text":"Virginia Water Science 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U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Alaska has considerable potential for undiscovered mineral resources. This report evaluates potential for undiscovered critical minerals in Alaska. Critical minerals are those for which the United States imports more than half of its total supply and which are largely derived from nations that cannot be considered reliable trading partners. In this report, estimated resource potential and certainty for the state of Alaska are analyzed and mapped for the following six selected mineral deposit groups that may contain one or more critical minerals: (1) rare earth elements-thorium-yttrium-niobium(-uranium-zirconium) [REE-Th-Y-Nb(-U-Zr)] deposits associated with peralkaline to carbonatitic igneous intrusive rocks; (2) placer and paleoplacer gold (Au) deposits that in some places might also produce platinum group elements (PGE), chromium (Cr), tin (Sn), tungsten (W), silver (Ag), or titanium (Ti); (3) platinum group elements(-cobalt-chromium-nickel-titanium-vanadium) [PGE(-Co-Cr-Ni-Ti-V)] deposits associated with mafic to ultramafic intrusive rocks; (4) carbonate-hosted copper(-cobalt-silver-germanium-gallium) [Cu(-Co-Ag-Ge-Ga)] deposits; (5) sandstone-hosted uranium(-vanadium-copper) [U(-V-Cu)] deposits; and (6) tin-tungsten-molybdenum(-tantalum-indium-fluorspar) [Sn-W-Mo(-Ta-In-fluorspar)] deposits associated with specialized granites.
This study used a data-driven, geographic information system (GIS)-implemented method to identify areas that have mineral resource potential in Alaska. This method systematically and simultaneously analyzes geoscience data from multiple geospatially referenced datasets and uses individual subwatersheds (12-digit hydrologic units) as the spatial unit of classification. The final map output uses a red, yellow, green, and gray color scheme to portray estimated relative potential (High, Medium, Low, Unknown) for each of the six groups of mineral deposit types, and it indicates the relative certainty (High, Medium, Low) of that estimate for each 12-digit hydrologic unit through color shading. Accompanying tables describe the data layers employed to score favorability for the presence of each mineral deposit group, the values assigned for specific analysis parameters, and the relative weighting of each data layer that contributes to estimated measures of potential and certainty. Core datasets used include the Alaska Geochemical Database, Version 2.0 (AGDB2); the Alaska Division of Geological & Geophysical Surveys (ADGGS) web-based geochemical database; the digital “Geologic Map of Alaska;” the Alaska Resource Data File (ARDF); and aerial gamma-ray surveys flown as part of the National Uranium Resource Evaluation (NURE) Program by the U.S. Department of Energy.
Maps accompanying this report illustrate the scores for estimated mineral resource potential for the six deposit groups for the state of Alaska. Areas that have known potential, as well as new areas that were not previously known to have potential, for the targeted minerals and deposit groups are identified and described. Numerous areas in Alaska, some of them large, have high potential for one or more of the selected groups of deposit types within Alaska.
Matthew Granitto, Timothy S. Hayes, James V. Jones, III, Susan M. Karl, Keith A. Labay, Jeffrey L. Mauk, Jeanine M. Schmidt, Nora B. Shew, Erin Todd, Bronwen Wang, Melanie B. Werdon, and Douglas B. Yager
Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
Digital flood-inundation maps for a 4.9-mile reach of the Yellow River at Plymouth, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 05516500, Yellow River at Plymouth, Ind. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/in/nwis/uv?site_no=05516500. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood-warning system (http:/water.weather.gov/ahps/). The NWS AHPS forecasts flood hydrographs at many sites that are often collocated with USGS streamgages, including the Yellow River at Plymouth, Ind. NWS AHPS-forecast peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood and forecasts of flood hydrographs at this site.
For this study, flood profiles were computed for the Yellow River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the current stage-discharge relations at the Yellow River streamgage, in combination with the flood-insurance study for Marshall County (issued in 2011). The calibrated hydraulic model was then used to determine eight water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The 1-percent annual exceedance probability flood profile elevation (flood elevation with recurrence intervals within 100 years) is within the calibrated water-surface elevations for comparison. The simulated water-surface profiles were then used with a geographic information system (GIS) digital elevation model (DEM, derived from Light Detection and Ranging [lidar]) in order to delineate the area flooded at each water level.
The availability of these maps, along with Internet information regarding current stage from the USGS streamgage 05516500, Yellow River at Plymouth, Ind., and forecast stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165117","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Menke, C.D., Bunch, A.R., and Kim, M.H., 2016, Flood-inundation maps for the Yellow River at Plymouth, Indiana: U.S. Geological Survey Scientific Investigations Report 2016–5117, 9 p., https://dx.doi.org/10.3133/sir20165117.","productDescription":"Report: vi, 9 p.; Metadata: 2 files; Read Me; Spatial Data: 2 files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078607","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":331047,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5117/coverthb.jpg"},{"id":331048,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117.pdf","text":"Report","size":"2.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5117"},{"id":331049,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117_dep_grd.metadata","text":"Metadata Depth Grids","size":"16.5 KB"},{"id":331050,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117_shapefile.metadata","text":"Metadata Shapefiles","size":"16.7 KB"},{"id":331051,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2016/5117/00Readme.txt","text":"Readme","size":"8.18 KB","linkFileType":{"id":2,"text":"txt"}},{"id":331052,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5117/gis_data/depth_grids.zip","text":"Depth Grids","size":"4.37 MB","linkFileType":{"id":6,"text":"zip"}},{"id":331053,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5117/gis_data/shapefile.zip","text":"Shape File","size":"807 KB","linkFileType":{"id":6,"text":"zip"}}],"country":"United States","state":"Indiana","city":"Plymouth","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 -86.4,\n 41.3\n ],\n [\n -86.4,\n 41.5\n ],\n [\n -86.2,\n 41.5\n ],\n [\n -86.2,\n 41.3\n ],\n [\n -86.4,\n 41.3 ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278
http://in.water.usgs.gov/
http://ky.water.usgs.gov/
Mean water depth and range of interannual water-level fluctuations over wet-dry cycles in precipitation are major drivers of vegetation zone formation in North American prairie potholes. We used harmonic hydrological models, which require only mean interannual water depth and amplitude of water-level fluctuations over a wet–dry cycle, to examine how the vegetation zones in a pothole would respond to small changes in water depth and/or amplitude of water-level fluctuations. Field data from wetlands in Saskatchewan, North Dakota, and South Dakota were used to parameterize harmonic models for four pothole classes. Six scenarios in which small negative or positive changes in either mean water depth, amplitude of interannual fluctuations, or both, were modeled to predict if they would affect the number of zones in each wetland class. The results indicated that, in some cases, even small changes in mean water depth when coupled with a small change in amplitude of water-level fluctuations can shift a prairie pothole wetland from one class to another. Our results suggest that climate change could alter the relative proportion of different wetland classes in the prairie pothole region.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0850-8","usgsCitation":"van der Valk, A., and Mushet, D.M., 2016, Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change: Wetlands, v. 36, no. 2, p. 397-406, https://doi.org/10.1007/s13157-016-0850-8.","productDescription":"10 p.","startPage":"397","endPage":"406","ipdsId":"IP-072077","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470416,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1303&context=eeob_ag_pubs","text":"External Repository"},{"id":331061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"582dd8e9e4b04d580bd3fa87","contributors":{"authors":[{"text":"van der Valk, Arnold","contributorId":145612,"corporation":false,"usgs":false,"family":"van der Valk","given":"Arnold","affiliations":[{"id":15296,"text":"Iowa State University, Ames, IA, USA","active":true,"usgs":false}],"preferred":false,"id":653912,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653911,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70176543,"text":"ofr20161167 - 2016 - Potash—A vital agricultural nutrient sourced from geologic deposits","interactions":[],"lastModifiedDate":"2017-01-10T10:36:20","indexId":"ofr20161167","displayToPublicDate":"2016-11-15T11:45:00","publicationYear":"2016","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":"2016-1167","title":"Potash—A vital agricultural nutrient sourced from geologic deposits","docAbstract":"This report summarizes the primary sources of potash in the United States. Potash is an essential nutrient that, along with phosphorus and nitrogen, is used as fertilizer for growing crops. Plants require sufficient potash to activate enzymes, which in turn catalyze chemical reactions important for water uptake and photosynthesis. When potassium is available in quantities necessary for healthy plant growth, disease resistance and physical quality are improved and crop yield and shelf life are increased. Potash is a water-soluble compound of potassium formed by geologic and hydrologic processes. The principal potash sources discussed are the large, stratiform deposits that formed during retreat and evaporation of intracontinental seas. The Paradox, Delaware, Holbrook, Michigan, and Williston sedimentary basins in the United States are examples where extensive potash beds were deposited. Ancient marine-type potash deposits that are close to the surface can be mined using conventional underground mining methods. In situ solution mining can be used where beds are too deep, making underground mining cost-prohibitive, or where underground mines are converted to in situ solution mines. Quaternary brine is another source of potash that is recovered by solar evaporation in manmade ponds. Groundwater from Pleistocene Lake Bonneville (Wendover, Utah) and the present-day Great Salt Lake in Utah are sources of potashbearing brine. Brine from these sources pumped to solar ponds is evaporated and potash concentrated for harvesting, processing, and refinement. Although there is sufficient potash to meet near-term demand, the large marine-type deposits are either geographically restricted to a few areas or are too deep to easily mine. Other regions lack sources of potash brine from groundwater or surface water. Thus, some areas of the world rely heavily on potash imports. Political, economic, and global population pressures may limit the ability of some countries from securing potash resources in the future. In this context, a historical perspective on U.S. potash production in a global framework is discussed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161167","isbn":"978-1-4113-4101-2","usgsCitation":"Yager, D.B., 2016, Potash—A vital agricultural nutrient sourced from geologic deposits: U.S. Geological Survey Open-File Report 2016-1167, 28 p., https://dx.doi.org/10.3133/ofr20161167.","productDescription":"v, 28 p.","numberOfPages":"38","onlineOnly":"N","ipdsId":"IP-067057","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":330757,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1167/coverthb.jpg"},{"id":330758,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1167/ofr20161167.pdf","text":"Report","size":"4.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1176"}],"contact":"Center Director
USGS Central Mineral and Environmental Resources Science Center
U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225
The integrity of wetlands is of global concern. A common approach to evaluating ecological integrity involves bioassessment procedures that quantify the degree to which communities deviate from historical norms. While helpful, bioassessment provides little information about how altered conditions connect to community response. More detailed information is needed for conservation and restoration. We have illustrated an approach to addressing this challenge using structural equation modeling (SEM) and long-term monitoring data from Rocky Mountain National Park (RMNP). Wetlands in RMNP are threatened by a complex history of anthropogenic disturbance including direct alteration of hydrologic regimes; elimination of elk, wolves, and grizzly bears; reintroduction of elk (absent their primary predators); and the extirpation of beaver. More recently, nonnative moose were introduced to the region and have expanded into the park. Bioassessment suggests that up to half of the park's wetlands are not in reference condition. We developed and evaluated a general hypothesis about how human alterations influence wetland integrity and then develop a specific model using RMNP wetlands. Bioassessment revealed three bioindicators that appear to be highly sensitive to human disturbance (HD): (1) conservatism, (2) degree of invasion, and (3) cover of native forbs. SEM analyses suggest several ways human activities have impacted wetland integrity and the landscape of RMNP. First, degradation is highest where the combined effects of all types of direct HD have been the greatest (i.e., there is a general, overall effect). Second, specific HDs appear to create a “mixed-bag” of complex indirect effects, including reduced invasion and increased conservatism, but also reduced native forb cover. Some of these effects are associated with alterations to hydrologic regimes, while others are associated with altered shrub production. Third, landscape features created by historical beaver activity continue to influence wetland integrity years after beavers have abandoned sites via persistent landforms and reduced biomass of tall shrubs. Our model provides a system-level perspective on wetland integrity and provides a context for future evaluations and investigations. It also suggests scientifically supported natural resource management strategies that can assist in the National Park Service mission of maintaining or, when indicated, restoring ecological integrity “unimpaired for future generations.”
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1548","usgsCitation":"Schweiger, E.W., Grace, J.B., Cooper, D., Bobowski, B., and Britten, M., 2016, Using structural equation modeling to link human activities to wetland ecological integrity: Ecosphere, v. 7, no. 11, p. 1-30, https://doi.org/10.1002/ecs2.1548.","productDescription":"e01548; 30 p.","startPage":"1","endPage":"30","ipdsId":"IP-074267","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470418,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1548","text":"Publisher Index Page"},{"id":331006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountain National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.93292236328124,\n 40.15998434802335\n ],\n [\n -105.93292236328124,\n 40.69625781921317\n ],\n [\n -105.4302978515625,\n 40.69625781921317\n ],\n [\n -105.4302978515625,\n 40.15998434802335\n ],\n [\n -105.93292236328124,\n 40.15998434802335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-10","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bf8","chorus":{"doi":"10.1002/ecs2.1548","url":"http://dx.doi.org/10.1002/ecs2.1548","publisher":"Wiley-Blackwell","authors":"Schweiger E. William, Grace James B., Cooper David, Bobowski Ben, Britten Mike","journalName":"Ecosphere","publicationDate":"11/2016"},"contributors":{"authors":[{"text":"Schweiger, E. William","contributorId":53635,"corporation":false,"usgs":true,"family":"Schweiger","given":"E.","email":"","middleInitial":"William","affiliations":[],"preferred":false,"id":653814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":653815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, David","contributorId":176856,"corporation":false,"usgs":false,"family":"Cooper","given":"David","affiliations":[],"preferred":false,"id":653816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bobowski, Ben","contributorId":176857,"corporation":false,"usgs":false,"family":"Bobowski","given":"Ben","email":"","affiliations":[],"preferred":false,"id":653817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britten, Mike","contributorId":176858,"corporation":false,"usgs":false,"family":"Britten","given":"Mike","email":"","affiliations":[],"preferred":false,"id":653818,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178340,"text":"70178340 - 2016 - Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration","interactions":[],"lastModifiedDate":"2016-11-14T12:17:49","indexId":"70178340","displayToPublicDate":"2016-11-14T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration","docAbstract":"The Delaware River Basin (DRB) encompasses approximately 0.4 % of the area of the United States (U.S.), but supplies water to 5 % of the population. We studied three forested tributaries to quantify the potential climate-driven change in hydrologic budget for two 25-year time periods centered on 2030 and 2060, focusing on sensitivity to the method of estimating potential evapotranspiration (PET) change. Hydrology was simulated using the Water Availability Tool for Environmental Resources (Williamson et al. 2015). Climate-change scenarios for four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) and two Representative Concentration Pathways (RCPs) were used to derive monthly change factors for temperature (T), precipitation (PPT), and PET according to the energy-based method of Priestley and Taylor (1972). Hydrologic simulations indicate a general increase in annual (especially winter) streamflow (Q) as early as 2030 across the DRB, with a larger increase by 2060. This increase in Q is the result of (1) higher winter PPT, which outweighs an annual actual evapotranspiration (AET) increase and (2) (for winter) a major shift away from storage of PPT as snow pack. However, when PET change is evaluated instead using the simpler T-based method of Hamon (1963), the increases in Q are small or even negative. In fact, the change of Q depends as much on PET method as on time period or RCP. This large sensitivity and associated uncertainty underscore the importance of exercising caution in the selection of a PET method for use in climate-change analyses.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-016-1782-2","usgsCitation":"Williamson, T., Nystrom, E.A., and Milly, P., 2016, Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration: Climatic Change, v. 139, no. 2, p. 215-228, https://doi.org/10.1007/s10584-016-1782-2.","productDescription":"14 p.","startPage":"215","endPage":"228","ipdsId":"IP-072262","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":330971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"139","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-16","publicationStatus":"PW","scienceBaseUri":"582adb44e4b0c253bdfff09a","chorus":{"doi":"10.1007/s10584-016-1782-2","url":"http://dx.doi.org/10.1007/s10584-016-1782-2","publisher":"Springer Nature","authors":"Williamson Tanja N., Nystrom Elizabeth A., Milly Paul C. D.","journalName":"Climatic Change","publicationDate":"9/16/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"9/16/2016"},"contributors":{"authors":[{"text":"Williamson, Tanja N. tnwillia@usgs.gov","contributorId":148942,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja N.","email":"tnwillia@usgs.gov","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":false,"id":653642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nystrom, Elizabeth A. 0000-0002-0886-3439 nystrom@usgs.gov","orcid":"https://orcid.org/0000-0002-0886-3439","contributorId":1072,"corporation":false,"usgs":true,"family":"Nystrom","given":"Elizabeth","email":"nystrom@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milly, Paul C.D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":2119,"corporation":false,"usgs":true,"family":"Milly","given":"Paul C.D.","email":"cmilly@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":653644,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176823,"text":"ds1023 - 2016 - Quality of surface water in Missouri, water year 2015","interactions":[],"lastModifiedDate":"2016-11-14T12:56:11","indexId":"ds1023","displayToPublicDate":"2016-11-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1023","title":"Quality of surface water in Missouri, water year 2015","docAbstract":"The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a series of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2015 (October 1, 2014, through September 30, 2015), data were collected at 74 stations—72 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and select pesticide compound summaries are presented for 71 of these stations. The stations primarily have been classified into groups corresponding to the physiography of the State, primary land use, or unique station types. In addition, a summary of hydrologic conditions in the State including peak streamflows, monthly mean streamflows, and 7-day low flows is presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1023","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Barr, M.N., and Heimann, D.C., 2016, Quality of surface water in Missouri, water year 2015: U.S. Geological Survey Data Series 1023, 22 p., https://dx.doi.org/10.3133/ds1023.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-077875","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":330865,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1023/ds1023.pdf","text":"Report","size":"5.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1023"},{"id":330864,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1023/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The Delaware River Basin (DRB) was selected as a Focus Area Study in 2011 by the U.S. Geological Survey (USGS) as part of the USGS National Water Census. The National Water Census is a USGS research program that focuses on national water availability and use and then develops new water accounting tools and assesses water availability at both the regional and national scales. One of the water management needs that the DRB study addressed, and that was identified by stakeholder groups from the DRB, was to improve the integration of state water use and water-supply data and to provide the compiled water use information to basin users. This water use information was also used in the hydrologic modeling and ecological components of the study.
Instream and offstream water use was calculated for 2010 for the DRB based on information received from Delaware, New Jersey, New York, and Pennsylvania. Water withdrawal, interbasin transfers, return flow, and hydroelectric power generation release data were compiled for 11 categories by hydrologic subregion, basin, subbasin, and subwatershed. Data availability varied by state. Site-specific data were used whenever possible to calculate public supply, irrigation (golf courses, nurseries, sod farms, and crops), aquaculture, self-supplied industrial, commercial, mining, thermoelectric, and hydroelectric power withdrawals. Where site-specific data were not available, primarily for crop irrigation, livestock, and domestic use, various techniques were used to estimate water withdrawals.
Total water withdrawals in the Delaware River Basin were calculated to be about 7,130 million gallons per day (Mgal/d) in 2010. Calculations of withdrawals by source indicate that freshwater withdrawals were about 4,130 Mgal/d (58 percent of the total) and the remaining 3,000 Mgal/d (42 percent) were from saline water. Total surface-water withdrawals were calculated to be 6,590 Mgal/d, or 92 percent of the total; about 54 percent (3,590 Mgal/d) of surface water withdrawn was freshwater. Total groundwater withdrawals were calculated to be 545 Mgal/d (8 percent of the total), all of which was freshwater. During 2010, calculated withdrawals by category, in decreasing order, were: thermoelectric power, 4,910 Mgal/d; public supply, 1,490 Mgal/d; self-supplied industrial, 350 Mgal/d; irrigation, 175 Mgal/d; self-supplied domestic, 117 Mgal/d; mining, 41.3 Mgal/d; aquaculture, 19.3 Mgal/d; livestock, 6.72 Mgal/d, and commercial, 5.89 Mgal/d. The amount of instream use for hydroelectric power generation purposes in 2010 was reported to be 273 Mgal/d for the Wallenpaupack Plant and 127 Mgal/d for the Mongaup River system.
Total return flows in the DRB were 2,960 Mgal/d in 2010. Although municipal wastewater-treatment plants accounted for 539 (97 percent) of the return-flow sites, they accounted for about 70 percent of the total return flows in the DRB. There was limited information on return flows from thermoelectric power.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155142","usgsCitation":"Hutson, S.S., Linsey, K.S., Ludlow, R.A., Reyes, Betzaida, and Shourds, J.L., 2016, Estimated use of water in the Delaware River Basin in Delaware, New Jersey, New York, and Pennsylvania, 2010: U.S. Geological Survey Scientific Investigations Report 2015–5142, 76 p., https://dx.doi.org/10.3133/sir20155142.","productDescription":"Report: vii, 76 p.; 2 Appendixes; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-058986","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":438513,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TM787C","text":"USGS data release","linkHelpText":"Estimated Use of Water by Subbasin (HUC8 and HUC12) in the Delaware River Basin, 2010"},{"id":330727,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142_appendix3.xlsx","text":"Appendix 3","size":"269 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5142 Appendix 3","linkHelpText":"- Delaware River Basin Water Use by Subbasin, 2010"},{"id":330724,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5142/coverthb.jpg"},{"id":330725,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142.pdf","text":"Report","size":"57.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5142"},{"id":330726,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5142/sir20155142_appendix2.docx","text":"Appendix 2","size":"2.49 MB docx","description":"SIR 2015-5142 Appendix 2","linkHelpText":"- Hydrologic Subbasins, Watersheds, and Subwatersheds in the Delaware River Basin"},{"id":330728,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7TM787C","text":"USGS data release","description":"USGS data release","linkHelpText":"Estimated Use of Water by Subbasin (HUC8) and Subwatershed (HUC12) in the Delaware River Basin, 2010"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.652099609375,\n 39.18117526158749\n ],\n [\n -74.50927734375,\n 40.195659093364654\n ],\n [\n -74.586181640625,\n 41.36856413680967\n ],\n [\n -74.300537109375,\n 42.09822241118974\n ],\n [\n -74.619140625,\n 42.53689200787315\n ],\n [\n -75.498046875,\n 42.10637370579324\n ],\n [\n -75.9375,\n 41.12074559016745\n ],\n [\n -76.607666015625,\n 40.36328834091583\n ],\n [\n -75.860595703125,\n 39.715638134796336\n ],\n [\n -75.43212890625,\n 38.685509760012\n ],\n [\n -75.069580078125,\n 38.77121637244273\n ],\n [\n -74.652099609375,\n 39.18117526158749\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Coordinator—National Water Census
U.S. Geological Survey
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","tableOfContents":"The salt fraction in permafrost soils/sediments of the McMurdo Dry Valleys (MDV) of Antarctica can be used as a proxy for cold desert geochemical processes and paleoclimate reconstruction. Previous analyses of the salt fraction in MDV permafrost soils have largely been conducted in coastal regions where permafrost soils are variably affected by aqueous processes and mixed inputs from marine and stratospheric sources. We expand upon this work by evaluating permafrost soil/sediments in University Valley, located in the ultraxerous zone where both liquid water transport and marine influences are minimal. We determined the abundances of Cl−, NO3−, ClO4− and ClO3− in dry and ice-cemented soil/sediments, snow and glacier ice, and also characterized Cl− and NO3−isotopically. The data are not consistent with salt deposition in a sublimation till, nor with nuclear weapon testing fall-out, and instead point to a dominantly stratospheric source and to varying degrees of post depositional transformation depending on the substrate, from minimal alteration in bare soils to significant alteration (photodegradation and/or volatilization) in snow and glacier ice. Ionic abundances in the dry permafrost layer indicate limited vertical transport under the current climate conditions, likely due to percolation of snowmelt. Subtle changes in ClO4−/NO3− ratios and NO3− isotopic composition with depth and location may reflect both transport related fractionation and depositional history. Low molar ratios of ClO3−/ClO4− in surface soils compared to deposition and other arid systems suggest significant post depositional loss of ClO3−, possibly due to reduction by iron minerals, which may have important implications for oxy-chlorine species on Mars. Salt accumulation varies with distance along the valley and apparent accumulation times based on multiple methods range from ∼10 to 30 kyr near the glacier to 70–200 kyr near the valley mouth. The relatively young age of the salts and relatively low and homogeneous anion concentrations in the ice-cemented sediments point to either a mechanism of recent salt removal, or to relatively modern permafrost soils (<1 million years). Together, our results show that near surface salts in University Valley serve as an end-member of stratospheric sources not subject to biological processes or extensive remobilization.
","language":"English","publisher":"Geochemical Society","publisherLocation":"New York, NY","doi":"10.1016/j.gca.2016.03.012","usgsCitation":"Jackson, A., Davila, A.F., Böhlke, J., Sturchio, N.C., Sevanthi, R., Estrada, N., Brundrett, M., Lacelle, D., McKay, C.P., Poghosyan, A., Pollard, W., and Zacny, K., 2016, Deposition, accumulation, and alteration of Cl−, NO3−, ClO4− and ClO3− salts in a hyper-arid polar environment: Mass balance and isotopic constraints: Geochimica et Cosmochimica Acta, v. 182, p. 197-215, https://doi.org/10.1016/j.gca.2016.03.012.","productDescription":"18 p.","startPage":"197","endPage":"215","ipdsId":"IP-073229","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":470437,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2016.03.012","text":"Publisher Index Page"},{"id":330687,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, University Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 160.666667,\n -77.845833\n ],\n [\n 160.666667,\n -77.911111\n ],\n [\n 160.779167,\n -77.911111\n ],\n [\n 160.779167,\n -77.845833\n ],\n [\n 160.666667,\n -77.845833\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"182","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"581c4cc1e4b09688d6e90fa7","contributors":{"authors":[{"text":"Jackson, Andrew","contributorId":176588,"corporation":false,"usgs":false,"family":"Jackson","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":652873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davila, Alfonso F.","contributorId":16282,"corporation":false,"usgs":true,"family":"Davila","given":"Alfonso","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":652874,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Böhlke, John Karl 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":1285,"corporation":false,"usgs":true,"family":"Böhlke","given":"John Karl","email":"jkbohlke@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":652875,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sturchio, Neil C.","contributorId":149375,"corporation":false,"usgs":false,"family":"Sturchio","given":"Neil","email":"","middleInitial":"C.","affiliations":[{"id":15289,"text":"University of Illinois, Ven Te Chow Hydrosystems Laboratory","active":true,"usgs":false}],"preferred":false,"id":652876,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sevanthi, Ritesh","contributorId":14301,"corporation":false,"usgs":true,"family":"Sevanthi","given":"Ritesh","affiliations":[],"preferred":false,"id":652877,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Estrada, Nubia","contributorId":176622,"corporation":false,"usgs":false,"family":"Estrada","given":"Nubia","affiliations":[],"preferred":false,"id":652879,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brundrett, Maeghan","contributorId":176623,"corporation":false,"usgs":false,"family":"Brundrett","given":"Maeghan","email":"","affiliations":[],"preferred":false,"id":652880,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lacelle, Denis","contributorId":176624,"corporation":false,"usgs":false,"family":"Lacelle","given":"Denis","email":"","affiliations":[],"preferred":false,"id":652881,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McKay, Christopher P.","contributorId":58156,"corporation":false,"usgs":true,"family":"McKay","given":"Christopher","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":652882,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Poghosyan, Armen","contributorId":176625,"corporation":false,"usgs":false,"family":"Poghosyan","given":"Armen","email":"","affiliations":[],"preferred":false,"id":652883,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pollard, Wayne","contributorId":176626,"corporation":false,"usgs":false,"family":"Pollard","given":"Wayne","email":"","affiliations":[],"preferred":false,"id":652884,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Zacny, Kris","contributorId":176627,"corporation":false,"usgs":false,"family":"Zacny","given":"Kris","email":"","affiliations":[],"preferred":false,"id":652885,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70177969,"text":"70177969 - 2016 - Bounded fractional diffusion in geological media: Definition and Lagrangian approximation","interactions":[],"lastModifiedDate":"2018-08-09T12:27:12","indexId":"70177969","displayToPublicDate":"2016-11-03T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Bounded fractional diffusion in geological media: Definition and Lagrangian approximation","docAbstract":"Spatiotemporal Fractional-Derivative Models (FDMs) have been increasingly used to simulate non-Fickian diffusion, but methods have not been available to define boundary conditions for FDMs in bounded domains. This study defines boundary conditions and then develops a Lagrangian solver to approximate bounded, one-dimensional fractional diffusion. Both the zero-value and non-zero-value Dirichlet, Neumann, and mixed Robin boundary conditions are defined, where the sign of Riemann-Liouville fractional derivative (capturing non-zero-value spatial-nonlocal boundary conditions with directional super-diffusion) remains consistent with the sign of the fractional-diffusive flux term in the FDMs. New Lagrangian schemes are then proposed to track solute particles moving in bounded domains, where the solutions are checked against analytical or Eularian solutions available for simplified FDMs. Numerical experiments show that the particle-tracking algorithm for non-Fickian diffusion differs from Fickian diffusion in relocating the particle position around the reflective boundary, likely due to the non-local and non-symmetric fractional diffusion. For a non-zero-value Neumann or Robin boundary, a source cell with a reflective face can be applied to define the release rate of random-walking particles at the specified flux boundary. Mathematical definitions of physically meaningful nonlocal boundaries combined with bounded Lagrangian solvers in this study may provide the only viable techniques at present to quantify the impact of boundaries on anomalous diffusion, expanding the applicability of FDMs from infinite do mains to those with any size and boundary conditions.","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2016WR019178","usgsCitation":"Zhang, Y., Green, C.T., LaBolle, E.M., Neupauer, R.M., and Sun, H., 2016, Bounded fractional diffusion in geological media: Definition and Lagrangian approximation: Water Resources Research, v. 52, no. 11, p. 8561-8577, https://doi.org/10.1002/2016WR019178.","productDescription":"17 p.","startPage":"8561","endPage":"8577","ipdsId":"IP-075843","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":470444,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr019178","text":"Publisher Index Page"},{"id":330678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-12","publicationStatus":"PW","scienceBaseUri":"581c4cc2e4b09688d6e90fb1","contributors":{"authors":[{"text":"Zhang, Yong","contributorId":19029,"corporation":false,"usgs":true,"family":"Zhang","given":"Yong","affiliations":[],"preferred":false,"id":652794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Green, Christopher T. 0000-0002-6480-8194 ctgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":1343,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"ctgreen@usgs.gov","middleInitial":"T.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":652795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaBolle, Eric M.","contributorId":176579,"corporation":false,"usgs":false,"family":"LaBolle","given":"Eric","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":652796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Neupauer, Roseanna M.","contributorId":176580,"corporation":false,"usgs":false,"family":"Neupauer","given":"Roseanna","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":652797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sun, Hong-Guang 0000-0002-8422-3871","orcid":"https://orcid.org/0000-0002-8422-3871","contributorId":176581,"corporation":false,"usgs":false,"family":"Sun","given":"Hong-Guang","email":"","affiliations":[],"preferred":false,"id":652798,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70177939,"text":"70177939 - 2016 - Migratory bird habitat in relation to tile drainage and poorly drained hydrologic soil groups","interactions":[],"lastModifiedDate":"2017-01-20T11:10:39","indexId":"70177939","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Migratory bird habitat in relation to tile drainage and poorly drained hydrologic soil groups","docAbstract":"The Prairie Pothole Region (PPR) is home to more than 50% of the migratory waterfowl in North America. Although the PPR provides an abundance of temporary and permanent wetlands for nesting and feeding, increases in commodity prices and agricultural drainage practices have led to a trend of wetland drainage. The Northern Shoveler is a migratory dabbling duck species that uses wetland habitats and cultivated croplands in the PPR. Richland County in North Dakota and Roberts County in South Dakota have an abundance of wetlands and croplands and were chosen as the study areas for this research to assess the wetland size and cultivated cropland in relation to hydrologic soil groups for the Northern Shoveler habitat. This study used geographic information system data to analyze Northern Shoveler habitats in association with Natural Resource Conservation Service soil data. Habitats, which are spatially associated with certain hydrologic soil groups, may be at risk of artificial drainage installations because of their proximity to cultivated croplands and soil lacking in natural drainage that may become wet or inundated. Findings indicate that most wetlands that are part of Northern Shoveler habitats were within or adjacent to cultivated croplands. The results also revealed soil hydrologic groups with high runoff potential and low water transmission rates account for most of the soil within the Northern Shoveler‘s wetland and cropland habitats. Habitats near agriculture with high runoff potential are likely to be drained and this has the potential of reducing Northern Shoveler habitat.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"10th International Drainage Symposium Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"10th International Drainage Symposium Conference","conferenceDate":"September 6-9, 2016","conferenceLocation":"Minneapolis, Minnesota","language":"English","publisher":"American Society of Agricultural and Biological Engineers","doi":"10.13031/IDS.20162493338","usgsCitation":"Kastner, B., Christensen, V.G., Williamson, T., and Sanocki, C.A., 2016, Migratory bird habitat in relation to tile drainage and poorly drained hydrologic soil groups, in 10th International Drainage Symposium Conference, Minneapolis, Minnesota, September 6-9, 2016, https://doi.org/10.13031/IDS.20162493338.","ipdsId":"IP-076360","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":333559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-07","publicationStatus":"PW","scienceBaseUri":"58833022e4b0d0023163778e","contributors":{"authors":[{"text":"Kastner, Brandi bkastner@usgs.gov","contributorId":176471,"corporation":false,"usgs":true,"family":"Kastner","given":"Brandi","email":"bkastner@usgs.gov","affiliations":[],"preferred":true,"id":659209,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christensen, Victoria G. 0000-0003-4166-7461 vglenn@usgs.gov","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":2354,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","email":"vglenn@usgs.gov","middleInitial":"G.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":659210,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williamson, Tanja N. tnwillia@usgs.gov","contributorId":452,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja N.","email":"tnwillia@usgs.gov","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":false,"id":659211,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanocki, Christopher A. 0000-0001-6714-5421 sanocki@usgs.gov","orcid":"https://orcid.org/0000-0001-6714-5421","contributorId":3142,"corporation":false,"usgs":true,"family":"Sanocki","given":"Christopher","email":"sanocki@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":659212,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194545,"text":"70194545 - 2016 - A glacier runoff extension to the Precipitation Runoff Modeling System","interactions":[],"lastModifiedDate":"2017-12-05T11:00:44","indexId":"70194545","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"A glacier runoff extension to the Precipitation Runoff Modeling System","docAbstract":"A module to simulate glacier runoff, PRMSglacier, was added to PRMS (Precipitation Runoff Modeling System), a distributed-parameter, physical-process hydrological simulation code. The extension does not require extensive on-glacier measurements or computational expense but still relies on physical principles over empirical relations as much as is feasible while maintaining model usability. PRMSglacier is validated on two basins in Alaska, Wolverine, and Gulkana Glacier basin, which have been studied since 1966 and have a substantial amount of data with which to test model performance over a long period of time covering a wide range of climatic and hydrologic conditions. When error in field measurements is considered, the Nash-Sutcliffe efficiencies of streamflow are 0.87 and 0.86, the absolute bias fractions of the winter mass balance simulations are 0.10 and 0.08, and the absolute bias fractions of the summer mass balances are 0.01 and 0.03, all computed over 42 years for the Wolverine and Gulkana Glacier basins, respectively. Without taking into account measurement error, the values are still within the range achieved by the more computationally expensive codes tested over shorter time periods.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2015JF003789","usgsCitation":"Van Beusekom, A.E., and Viger, R.J., 2016, A glacier runoff extension to the Precipitation Runoff Modeling System: Journal of Geophysical Research F: Earth Surface, v. 121, no. 11, p. 2001-2021, https://doi.org/10.1002/2015JF003789.","productDescription":"21 p.","startPage":"2001","endPage":"2021","ipdsId":"IP-081396","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":438520,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75T3HMV","text":"USGS data release","linkHelpText":"Supporting data for A Glacier Runoff Extension to the Precipitation Runoff Modeling System"},{"id":349679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"121","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-04","publicationStatus":"PW","scienceBaseUri":"5a60fc9be4b06e28e9c24045","contributors":{"authors":[{"text":"Van Beusekom, Ashley E. 0000-0002-6996-978X beusekom@usgs.gov","orcid":"https://orcid.org/0000-0002-6996-978X","contributorId":3992,"corporation":false,"usgs":true,"family":"Van Beusekom","given":"Ashley","email":"beusekom@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":724413,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Viger, Roland J. 0000-0003-2520-714X rviger@usgs.gov","orcid":"https://orcid.org/0000-0003-2520-714X","contributorId":168799,"corporation":false,"usgs":true,"family":"Viger","given":"Roland","email":"rviger@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":724414,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178649,"text":"70178649 - 2016 - Temperature and hydrology affect methane emissions from Prairie Pothole Wetlands","interactions":[],"lastModifiedDate":"2017-04-27T10:07:14","indexId":"70178649","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Temperature and hydrology affect methane emissions from Prairie Pothole Wetlands","docAbstract":"The Prairie Pothole Region (PPR) in central North America consists of millions of depressional wetlands that each have considerable potential to emit methane (CH4). Changes in temperature and hydrology in the PPR from climate change may affect methane fluxes from these wetlands. To assess the potential effects of changes in climate on methane emissions, we examined the relationships between flux rates and temperature or water depth using six years of bi-weekly flux measurements during the snow-free period from six temporarily ponded and six permanently ponded wetlands in North Dakota, USA. Methane flux rates were among the highest reported for freshwater wetlands, and had considerable spatial and temporal variation. Methane flux rates increased with increasing temperature and water depth, and were especially high when conditions were warmer and wetter than average (163 ± 28 mg CH4 m−2 h−1) compared to warmer and drier (37 ± 7 mg CH4 m−2 h−1). Methane emission rates from permanent wetlands were less sensitive to changes in temperature and water depth compared to temporary wetlands, likely due to higher sulfate concentrations in permanent wetlands. While the predicted increase in temperature with climate change will likely increase methane emission rates from PPR wetlands, drier conditions could moderate these increases.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0826-8","usgsCitation":"Bansal, S., Tangen, B., and Finocchiaro, R., 2016, Temperature and hydrology affect methane emissions from Prairie Pothole Wetlands: Wetlands, v. 36, no. s2, p. 371-381, https://doi.org/10.1007/s13157-016-0826-8.","productDescription":"11 p.","startPage":"371","endPage":"381","ipdsId":"IP-073125","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":331417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"s2","noUsgsAuthors":false,"publicationDate":"2016-09-29","publicationStatus":"PW","scienceBaseUri":"584144dee4b04fc80e507392","contributors":{"authors":[{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":654711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":654712,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finocchiaro, Raymond 0000-0002-5514-8729 rfinocchiaro@usgs.gov","orcid":"https://orcid.org/0000-0002-5514-8729","contributorId":167278,"corporation":false,"usgs":true,"family":"Finocchiaro","given":"Raymond","email":"rfinocchiaro@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":654713,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70185051,"text":"70185051 - 2016 - Estimation of time-variable fast flow path chemical concentrations for application in tracer-based hydrograph separation analyses","interactions":[],"lastModifiedDate":"2017-03-13T16:21:41","indexId":"70185051","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Estimation of time-variable fast flow path chemical concentrations for application in tracer-based hydrograph separation analyses","docAbstract":"Mixing models are a commonly used method for hydrograph separation, but can be hindered by the subjective choice of the end-member tracer concentrations. This work tests a new variant of mixing model that uses high-frequency measures of two tracers and streamflow to separate total streamflow into water from slowflow and fastflow sources. The ratio between the concentrations of the two tracers is used to create a time-variable estimate of the concentration of each tracer in the fastflow end-member. Multiple synthetic data sets, and data from two hydrologically diverse streams, are used to test the performance and limitations of the new model (two-tracer ratio-based mixing model: TRaMM). When applied to the synthetic streams under many different scenarios, the TRaMM produces results that were reasonable approximations of the actual values of fastflow discharge (±0.1% of maximum fastflow) and fastflow tracer concentrations (±9.5% and ±16% of maximum fastflow nitrate concentration and specific conductance, respectively). With real stream data, the TRaMM produces high-frequency estimates of slowflow and fastflow discharge that align with expectations for each stream based on their respective hydrologic settings. The use of two tracers with the TRaMM provides an innovative and objective approach for estimating high-frequency fastflow concentrations and contributions of fastflow water to the stream. This provides useful information for tracking chemical movement to streams and allows for better selection and implementation of water quality management strategies.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016WR018797","usgsCitation":"Kronholm, S.C., and Capel, P.D., 2016, Estimation of time-variable fast flow path chemical concentrations for application in tracer-based hydrograph separation analyses: Water Resources Research, v. 52, no. 9, p. 6881-6896, https://doi.org/10.1002/2016WR018797.","productDescription":"16 p.","startPage":"6881","endPage":"6896","ipdsId":"IP-075597","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":470473,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr018797","text":"Publisher Index Page"},{"id":438519,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71R6NMQ","text":"USGS data release","linkHelpText":"Real and synthetic data used to test the Two-tracer Ratio-based Mixing Model (TRaMM)"},{"id":337470,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-10","publicationStatus":"PW","scienceBaseUri":"58c7af9ee4b0849ce9795e8e","contributors":{"authors":[{"text":"Kronholm, Scott C.","contributorId":184190,"corporation":false,"usgs":false,"family":"Kronholm","given":"Scott","email":"","middleInitial":"C.","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":684079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":684078,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178027,"text":"70178027 - 2016 - Analyses of infrequent (quasi-decadal) large groundwater recharge events in the northern Great Basin: Their importance for groundwater availability, use, and management","interactions":[],"lastModifiedDate":"2017-01-11T16:32:00","indexId":"70178027","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Analyses of infrequent (quasi-decadal) large groundwater recharge events in the northern Great Basin: Their importance for groundwater availability, use, and management","docAbstract":"There has been a considerable amount of research linking climatic variability to hydrologic responses in the western United States. Although much effort has been spent to assess and predict changes in surface water resources, little has been done to understand how climatic events and changes affect groundwater resources. This study focuses on characterizing and quantifying the effects of large, multiyear, quasi-decadal groundwater recharge events in the northern Utah portion of the Great Basin for the period 1960–2013. Annual groundwater level data were analyzed with climatic data to characterize climatic conditions and frequency of these large recharge events. Using observed water-level changes and multivariate analysis, five large groundwater recharge events were identified with a frequency of about 11–13 years. These events were generally characterized as having above-average annual precipitation and snow water equivalent and below-average seasonal temperatures, especially during the spring (April through June). Existing groundwater flow models for several basins within the study area were used to quantify changes in groundwater storage from these events. Simulated groundwater storage increases per basin from a single recharge event ranged from about 115 to 205 Mm3. Extrapolating these amounts over the entire northern Great Basin indicates that a single large quasi-decadal recharge event could result in billions of cubic meters of groundwater storage. Understanding the role of these large quasi-decadal recharge events in replenishing aquifers and sustaining water supplies is crucial for long-term groundwater management.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016WR019060","usgsCitation":"Masbruch, M.D., Rumsey, C., Gangopadhyay, S., Susong, D.D., and Pruitt, T., 2016, Analyses of infrequent (quasi-decadal) large groundwater recharge events in the northern Great Basin: Their importance for groundwater availability, use, and management: Water Resources Research, v. 52, no. 10, p. 7819-7836, https://doi.org/10.1002/2016WR019060.","productDescription":"18 p.","startPage":"7819","endPage":"7836","ipdsId":"IP-069809","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":470453,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr019060","text":"Publisher Index Page"},{"id":330630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.08203125,\n 38.47939467327645\n ],\n [\n -114.08203125,\n 42.00032514831621\n ],\n [\n -109.0283203125,\n 42.00032514831621\n ],\n [\n -109.0283203125,\n 38.47939467327645\n ],\n [\n -114.08203125,\n 38.47939467327645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"10","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-11","publicationStatus":"PW","scienceBaseUri":"5819a9c2e4b0bb36a4c9100d","contributors":{"authors":[{"text":"Masbruch, Melissa D. 0000-0001-6568-160X mmasbruch@usgs.gov","orcid":"https://orcid.org/0000-0001-6568-160X","contributorId":1902,"corporation":false,"usgs":true,"family":"Masbruch","given":"Melissa","email":"mmasbruch@usgs.gov","middleInitial":"D.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652542,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rumsey, Christine 0000-0001-7536-750X crumsey@usgs.gov","orcid":"https://orcid.org/0000-0001-7536-750X","contributorId":146240,"corporation":false,"usgs":true,"family":"Rumsey","given":"Christine","email":"crumsey@usgs.gov","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652543,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":652544,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Susong, David D. ddsusong@usgs.gov","contributorId":1040,"corporation":false,"usgs":true,"family":"Susong","given":"David","email":"ddsusong@usgs.gov","middleInitial":"D.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":652545,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pruitt, Tom 0000-0002-3543-1324","orcid":"https://orcid.org/0000-0002-3543-1324","contributorId":173440,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","email":"","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":652546,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70185054,"text":"70185054 - 2016 - Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios","interactions":[],"lastModifiedDate":"2017-03-13T15:11:54","indexId":"70185054","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios","docAbstract":"Complex watershed simulation models are powerful tools that can help scientists and policy-makers address challenging topics, such as land use management and water security. In the Western Lake Erie Basin (WLEB), complex hydrological models have been applied at various scales to help describe relationships between land use and water, nutrient, and sediment dynamics. This manuscript evaluated the capacity of the current Soil and Water Assessment Tool (SWAT2012) to predict hydrological and water quality processes within WLEB at the finest resolution watershed boundary unit (NHDPlus) along with the current conditions and conservation scenarios. The process based SWAT model was capable of the fine-scale computation and complex routing used in this project, as indicated by measured data at five gaging stations. The level of detail required for fine-scale spatial simulation made the use of both hard and soft data necessary in model calibration, alongside other model adaptations. Limitations to the model's predictive capacity were due to a paucity of data in the region at the NHDPlus scale rather than due to SWAT functionality. Results of treatment scenarios demonstrate variable effects of structural practices and nutrient management on sediment and nutrient loss dynamics. Targeting treatment to acres with critical outstanding conservation needs provides the largest return on investment in terms of nutrient loss reduction per dollar spent, relative to treating acres with lower inherent nutrient loss vulnerabilities. Importantly, this research raises considerations about use of models to guide land management decisions at very fine spatial scales. Decision makers using these results should be aware of data limitations that hinder fine-scale model interpretation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.06.202","usgsCitation":"Yen, H., White, M.J., Arnold, J.G., Keitzer, S.C., Johnson, M.V., Atwood, J.D., Daggupati, P., Herbert, M.E., Sowa, S.P., Ludsin, S.A., Robertson, D.M., Srinivasan, R., and Rewa, C.A., 2016, Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios: Science of the Total Environment, v. 569-570, p. 1265-1281, https://doi.org/10.1016/j.scitotenv.2016.06.202.","productDescription":"17 p.","startPage":"1265","endPage":"1281","ipdsId":"IP-075988","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":470472,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.06.202","text":"Publisher Index Page"},{"id":337455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie Basin","volume":"569-570","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58c7af9ee4b0849ce9795e8c","contributors":{"authors":[{"text":"Yen, Haw 0000-0002-5509-8792","orcid":"https://orcid.org/0000-0002-5509-8792","contributorId":169564,"corporation":false,"usgs":false,"family":"Yen","given":"Haw","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":684085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Michael J.","contributorId":172348,"corporation":false,"usgs":false,"family":"White","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":684087,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arnold, Jeffrey G.","contributorId":172345,"corporation":false,"usgs":false,"family":"Arnold","given":"Jeffrey","email":"","middleInitial":"G.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":684097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keitzer, S. Conor 0000-0002-8164-4099","orcid":"https://orcid.org/0000-0002-8164-4099","contributorId":189196,"corporation":false,"usgs":false,"family":"Keitzer","given":"S.","email":"","middleInitial":"Conor","affiliations":[],"preferred":false,"id":684096,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Mari-Vaughn V. 0000-0002-2944-2529","orcid":"https://orcid.org/0000-0002-2944-2529","contributorId":189195,"corporation":false,"usgs":false,"family":"Johnson","given":"Mari-Vaughn","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":684095,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Atwood, Jay D.","contributorId":189194,"corporation":false,"usgs":false,"family":"Atwood","given":"Jay","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":684094,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Daggupati, Prasad 0000-0002-7044-3435","orcid":"https://orcid.org/0000-0002-7044-3435","contributorId":189193,"corporation":false,"usgs":false,"family":"Daggupati","given":"Prasad","email":"","affiliations":[],"preferred":false,"id":684093,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Herbert, Matthew E.","contributorId":189192,"corporation":false,"usgs":false,"family":"Herbert","given":"Matthew","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":684092,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sowa, Scott P. 0000-0002-5425-2591 sowasp@missouri.edu","orcid":"https://orcid.org/0000-0002-5425-2591","contributorId":146672,"corporation":false,"usgs":false,"family":"Sowa","given":"Scott","email":"sowasp@missouri.edu","middleInitial":"P.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":684091,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ludsin, Stuart A. 0000-0002-3866-2216","orcid":"https://orcid.org/0000-0002-3866-2216","contributorId":175425,"corporation":false,"usgs":false,"family":"Ludsin","given":"Stuart","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":684090,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Robertson, Dale M. 0000-0001-6799-0596 dzrobert@usgs.gov","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":150760,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"dzrobert@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684086,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Srinivasan, Raghavan","contributorId":189191,"corporation":false,"usgs":false,"family":"Srinivasan","given":"Raghavan","affiliations":[],"preferred":false,"id":684089,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rewa, Charles A.","contributorId":189190,"corporation":false,"usgs":false,"family":"Rewa","given":"Charles","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":684088,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70178341,"text":"70178341 - 2016 - Prediction of pesticide toxicity in Midwest streams","interactions":[],"lastModifiedDate":"2018-09-26T12:40:43","indexId":"70178341","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Prediction of pesticide toxicity in Midwest streams","docAbstract":"The occurrence of pesticide mixtures is common in stream waters of the United States, and the impact of multiple compounds on aquatic organisms is not well understood. Watershed Regressions for Pesticides (WARP) models were developed to predict Pesticide Toxicity Index (PTI) values in unmonitored streams in the Midwest and are referred to as WARP-PTI models. The PTI is a tool for assessing the relative toxicity of pesticide mixtures to fish, benthic invertebrates, and cladocera in stream water. One hundred stream sites in the Midwest were sampled weekly in May through August 2013, and the highest calculated PTI for each site was used as the WARP-PTI model response variable. Watershed characteristics that represent pesticide sources and transport were used as the WARP-PTI model explanatory variables. Three WARP-PTI models—fish, benthic invertebrates, and cladocera—were developed that include watershed characteristics describing toxicity-weighted agricultural use intensity, land use, agricultural management practices, soil properties, precipitation, and hydrologic properties. The models explained between 41 and 48% of the variability in the measured PTI values. WARP-PTI model evaluation with independent data showed reasonable performance with no clear bias. The models were applied to streams in the Midwest to demonstrate extrapolation for a regional assessment to indicate vulnerable streams and to guide more intensive monitoring.
","language":"English","publisher":"ACSESS","doi":"10.2134/jeq2015.12.0624","usgsCitation":"Shoda, M.E., Stone, W.W., and Nowell, L.H., 2016, Prediction of pesticide toxicity in Midwest streams: Journal of Environmental Quality, v. 45, no. 6, p. 1856-1864, https://doi.org/10.2134/jeq2015.12.0624.","productDescription":"9 p.","startPage":"1856","endPage":"1864","ipdsId":"IP-064521","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":470462,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2134/jeq2015.12.0624","text":"Publisher Index Page"},{"id":330980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Midwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.41552734375,\n 36.65079252503471\n ],\n [\n -98.41552734375,\n 45.336701909968134\n ],\n [\n -81.71630859375,\n 45.336701909968134\n ],\n [\n -81.71630859375,\n 36.65079252503471\n ],\n [\n -98.41552734375,\n 36.65079252503471\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"6","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582adb45e4b0c253bdfff0af","contributors":{"authors":[{"text":"Shoda, Megan E. 0000-0002-5343-9717 meshoda@usgs.gov","orcid":"https://orcid.org/0000-0002-5343-9717","contributorId":4352,"corporation":false,"usgs":true,"family":"Shoda","given":"Megan","email":"meshoda@usgs.gov","middleInitial":"E.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stone, Wesley W. 0000-0003-0239-2063 wwstone@usgs.gov","orcid":"https://orcid.org/0000-0003-0239-2063","contributorId":1496,"corporation":false,"usgs":true,"family":"Stone","given":"Wesley","email":"wwstone@usgs.gov","middleInitial":"W.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nowell, Lisa H. 0000-0001-5417-7264 lhnowell@usgs.gov","orcid":"https://orcid.org/0000-0001-5417-7264","contributorId":490,"corporation":false,"usgs":true,"family":"Nowell","given":"Lisa","email":"lhnowell@usgs.gov","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":653654,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178189,"text":"70178189 - 2016 - Karst","interactions":[],"lastModifiedDate":"2020-08-25T16:59:32.756086","indexId":"70178189","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"89","title":"Karst","docAbstract":"Karst areas present unique hydrologic and hydrogeological characteristics that\nare often challenging to investigate. These characteristics are largely dependent\non the extent of development of solution conduits within the underlying bedrock,\nand the resulting integration of surface and subsurface drainage components\ninto a karst aquifer system. The investigation and characterization of\nkarst aquifers typically require a multidisciplinary approach and the use of\nrelatively specialized methods such as tracer testing, spring discharge monitoring,\nand various hydrograph separation or modeling techniques. Conventional\nmethods of hydrologic or hydrogeologic investigation may be applied successfully\nfor specific purposes; however, proper conceptualization of a given karst\naquifer system is a requirement for effective analysis, modeling, and interpretation\nof karst hydrologic and hydrogeologic data.","language":"English","publisher":"McGraw-Hill","isbn":"9780071835091","usgsCitation":"Taylor, C., and Doctor, D., 2016, Karst, p. 89-1-89-14.","productDescription":"14 p.","startPage":"89-1","endPage":"89-14","ipdsId":"IP-068743","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":330873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330782,"type":{"id":15,"text":"Index Page"},"url":"https://www.mhprofessional.com/9780071835091-usa-handbook-of-applied-hydrology-second-edition"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5822f23ae4b0ef3123a9701c","contributors":{"editors":[{"text":"Singh, Vijay P.","contributorId":176741,"corporation":false,"usgs":false,"family":"Singh","given":"Vijay","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":653370,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Taylor, C.J.","contributorId":22337,"corporation":false,"usgs":true,"family":"Taylor","given":"C.J.","email":"","affiliations":[],"preferred":false,"id":653368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, D.H.","contributorId":94773,"corporation":false,"usgs":true,"family":"Doctor","given":"D.H.","affiliations":[],"preferred":false,"id":653369,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70185042,"text":"70185042 - 2016 - Land–atmosphere feedbacks amplify aridity increase over land under global warming","interactions":[],"lastModifiedDate":"2017-03-14T11:46:26","indexId":"70185042","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Land–atmosphere feedbacks amplify aridity increase over land under global warming","docAbstract":"The response of the terrestrial water cycle to global warming is central to issues including water resources, agriculture and ecosystem health. Recent studies indicate that aridity, defined in terms of atmospheric supply (precipitation, P) and demand (potential evapotranspiration, Ep) of water at the land surface, will increase globally in a warmer world. Recently proposed mechanisms for this response emphasize the driving role of oceanic warming and associated atmospheric processes. Here we show that the aridity response is substantially amplified by land–atmosphere feedbacks associated with the land surface’s response to climate and CO2 change. Using simulations from the Global Land Atmosphere Coupling Experiment (GLACE)-CMIP5 experiment, we show that global aridity is enhanced by the feedbacks of projected soil moisture decrease on land surface temperature, relative humidity and precipitation. The physiological impact of increasing atmospheric CO2 on vegetation exerts a qualitatively similar control on aridity. We reconcile these findings with previously proposed mechanisms by showing that the moist enthalpy change over land is unaffected by the land hydrological response. Thus, although oceanic warming constrains the combined moisture and temperature changes over land, land hydrology modulates the partitioning of this enthalpy increase towards increased aridity.