To address the lack of information on the spatial and temporal variability of recharge to groundwater systems in Maine, a study was initiated in cooperation with the Maine Geological Survey to use the U.S. Geological Survey Soil-Water-Balance model to evaluate annual average potential recharge across the State over a 25-year period from 1991 to 2015. The Maine Soil-Water-Balance model was calibrated using annual observations of recharge, runoff, and evapotranspiration for 32 calibration watersheds in the State during 2001–12 (902 total observations). Observations of recharge, runoff, and evapotranspiration were developed for each watershed to reduce the possibility of nonunique combinations of model parameters during the calibration. The Maine Soil-Water-Balance model was run using an optional evapotranspiration calculation method that provides more control for calibration than the standard method. The model was calibrated using the Parameter ESTimation software suite.
The overall mean model error (average of all annual residuals for recharge, runoff, and precipitation) was 0.39 inch. The mean of the absolute value of the residuals, or the mean absolute error, was 2.32 inches. The root mean squared error for the calibrated model overall was 3.14 inches. Statistical tests indicated that the model residuals are normally distributed. To determine the potential uncertainty in the median annual potential recharge that results from uncertainty in the parameters as they relate to information contained in the observations, 300 alternate model realizations were run, and the standard deviation of the median potential recharge value at every pixel was calculated.
Simulated 25-year median potential recharge across the State is widely variable; this variability closely follows patterns of precipitation, with additional variability contributed by the patchwork nature of the combinations of land-use class and hydrologic soil group inputs, and distribution of available water capacity in the soil across the State. Overall, the 25-year median annual potential recharge across the State is 7.5 inches, ranging from a low of about 5 inches to over 30 inches. The statewide range in the 25-year minimum values is from just over 2 inches to just over 20 inches. The statewide range in the 25-year maximum potential recharge is between 15 and 48 inches per year.
The model areas with the highest simulated median potential recharge include areas underlain by type A soils (sandy and well drained), particularly those that also have land uses with low or little vegetation (blueberry barrens, developed, open space, scrub/shrub, and cropland, for example). The potential recharge values for these areas are similar to previously published values for comparable soil types.
The 25-year average potential recharge grids were compared to recharge evaluated through groundwater-flow models or other methods in four hydrogeologic settings at six study areas in the State. A key factor in the ability of the Soil-Water-Balance model to reproduce the earlier study results was whether the available water-capacity data were an appropriate match for the hydrologic soil groups. The Maine Soil-Water-Balance model does a good job in representing an accurate potential recharge under circumstances where the surficial mapped soils extend below the surface to the water-table aquifer and where the available water-capacity data are in an appropriate range for the hydrologic soil group. One hydrogeologic setting that was challenging for the model was where a silt and clay layer was below a shallow soil unit that did not have available water-capacity data that were appropriate for the hydrologic soil group. In these cases, typically the available water-capacity data were very low, not accounting for the impedance of water flow provided by the underlying soil. The model also does not simulate well areas where bedrock surfaces are above the water table but below the plant rooting zone.
The data products accompanying this report are intended to be used to provide first-cut estimates of recharge for geographic areas no smaller than the smallest watersheds used in the calibration of the model—or about 1.5 square miles. It is recommended that the grids are used to calculate an area-wide average potential recharge for any given area of study, and an uncertainty around the mean should be calculated from the standard deviation grid at the same time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195125","collaboration":"Prepared in cooperation with the Maine Geological Survey","usgsCitation":"Nielsen, M.G., and Westenbroek, S.M., 2019, Groundwater recharge estimates for Maine using a Soil-Water-Balance model—25-year average, range, and uncertainty, 1991 to 2015: U.S. Geological Survey Scientific Investigations Report 2019–5125, 58 p., https://doi.org/10.3133/sir20195125.","productDescription":"Report: vii, 56 p.; Tables; 2 Data Releases","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106360","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380621,"rank":7,"type":{"id":34,"text":"Image 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Most of the population of the Treasure Valley and the surrounding area of southwestern Idaho and easternmost Oregon depends on groundwater for domestic supply, either from domestic or municipal-supply wells. As of 2017, 41 percent of Idaho’s population was concentrated in Idaho’s portion of the Treasure Valley, and current and projected rapid population growth in the area has caused concern about the long-term sustainability of the groundwater resource. In 2016, the U.S. Geological Survey, in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources, began a project to construct a numerical groundwater-flow model of the westernmost western Snake River Plain (WSRP) aquifer system. As part of this project, a three-dimensional hydrogeologic framework model (3D HFM) of the aquifer system was generated, primarily from lithologic data compiled from 291 well-driller reports.
Four major hydrogeologic units are shown in the 3D HFM: Coarse-grained fluvial and alluvial deposits, Pliocene-Pleistocene and Miocene basalts, fine-grained lacustrine deposits, and granitic and rhyolitic bedrock. Generally, the 3D HFM is in agreement with the geologic history of the WSRP and hydrogeologic frameworks developed by previous authors. The resolution (voxel size) of the 3D HFM is sufficient for the construction of a regional groundwater-flow model.
The major components of inflow (or recharge) to the WSRP aquifer system are seepage from irrigation canals, direct infiltration from precipitation and excess irrigation water, seepage from the Boise and Payette Rivers and Lake Lowell, and subsurface inflow from adjoining uplands. The major components of outflow (or discharge) from the aquifer system are discharge to surface water (rivers, agricultural drains, and streams), groundwater pumping, and direct evapotranspiration from groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195138","collaboration":"Prepared in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources","usgsCitation":"Bartolino, J.R., 2019, Hydrogeologic framework of the Treasure Valley and surrounding area, Idaho and Oregon (ver. 1.1, January 2020): U.S. Geological Survey Scientific Investigations Report 2019–5138, 31 p., https://doi.org/10.3133/sir20195138.","productDescription":"Report: v, 31 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-093399","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":371171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5138/coverthb.jpg"},{"id":371344,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5138/sir20195138_v1.1.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019-5138"},{"id":371345,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CAC0F6","linkHelpText":"Hydrogeologic Framework of the Treasure Valley and Surrounding Area, Idaho and Oregon"},{"id":371346,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5138/sir20195138_versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Scientific Investigations Report 2019-5138"},{"id":399614,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109577.htm"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Treasure Valley and surrounding area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.1097,\n 43.1803\n ],\n [\n -115.86,\n 43.1803\n ],\n [\n -115.86,\n 44.0381\n ],\n [\n -117.1097,\n 44.0381\n ],\n [\n -117.1097,\n 43.1803\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: January 2020; Version 1: December 2019","contact":"Director,
Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
Changing climate conditions can make water management planning and drought preparedness decisions more complicated than ever before. Resource managers can no longer rely solely on historical data and trends to base their actions, and are in need of science that is relevant to their specific needs and can directly inform important planning decisions. Questions remain, however, regarding the most effective and efficient methods for extending scientific knowledge and products into management and decision-making.
This study analyzed two unique cases of water management to better understand how science can be translated into resource management actions and decision-making. In particular, this project sought to understand 1) the characteristics that make science actionable and useful for water resource management and drought preparedness, and 2) the ideal types of scientific knowledge or science products that facilitate the use of science in management and decision-making.
The first case study focused on beaver mimicry, an emerging nature-based solution that increases the presence of wood and woody debris in rivers and streams to mimic the actions of beavers. This technique has been rapidly adopted by natural resource managers as a way to restore riparian areas, increase groundwater infiltration, and slow surface water flow so that more water is available later in the year during hotter and dryer months. The second case study focused on an established research program, Colorado Dust on Snow, that provides water managers with scientific information explaining how the movement of dust particles from the Colorado Plateau influences hydrology and the timing and intensity of snow melt and water runoff into critical water sources. This program has support from and is being used by several water conservation districts in the state.
Understanding how scientific knowledge translates into action and decision-making in these cases is expected to strengthen our knowledge of actionable science in the context of drought and its impacts on ecosystems. The project team gathered qualitative data through stakeholder interviews and will conduct an extensive literature review. Findings from these efforts will also be incorporated into a broader Intermountain West synthesis effort to determine and assess commonalities and differences among socio-ecological aspects of drought adaptation and planning.
Stream stability, flood frequency, and streambed scour potential were evaluated at 20 Alaskan river- and stream-spanning bridges lacking a quantitative scour analysis or having unknown foundation details. Three of the bridges had been assessed shortly before the study described in this report but were re-assessed using different methods or data. Channel instability related to mining may affect scour at one site, while channel instability related to flow distribution changes can be seen at one site. One bridge was closed because of abutment scour prior to the study. Otherwise, channels generally showed stable bed elevations.
Contraction and abutment scour were calculated for all 20 bridges, and pier scour was calculated for the 2 bridges that had piers. Vertical contraction (pressure flow) scour was calculated for one site at which the modeled water surface was higher than the superstructure of the bridge. Hydraulic variables for the scour calculations were derived from one-dimensional and two-dimensional hydraulic models of the 1- and 0.2-percent annual exceedance probability floods (also known as the 100- and 500-year floods, respectively). Scour also was calculated for large recorded floods at two sites.
At many sites, overflow of road approaches relieves the bridge during floods and lessens the potential for scour. Two-dimensional hydraulic models are superior to one-dimensional hydraulic models at distributing flow between bridges, road approaches, and floodplains, and therefore likely produce more reasonable scour values at sites with substantial floodplain flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195110","collaboration":"Prepared in cooperation with the Alaska Department of Transportation and Public Facilities","usgsCitation":"Beebee, R.A., Dworsky, K.L., and Knopp, S.J., 2019, Streambed scour evaluations and conditions at selected bridge Sites in Alaska, 2016–17 (version 1.1, April 2023): U.S. Geological Survey Scientific Investigations Report 2019-5110, 32 p., https://doi.org/10.3133/sir20195110.","productDescription":"Report: vi, 32 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099321","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":399597,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109571.htm"},{"id":370872,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5110/coverthb2.jpg"},{"id":370873,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5110/sir20195110.pdf","text":"Report","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5110"},{"id":415671,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5110/sir20195110_RevisionHistory.txt","description":"SIR 2019-5110 Version History"},{"id":370874,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LUTFHZ","linkHelpText":"Tabular input/output data and model files for 19 hydraulic models for streambed scour evaluations at selected bridge sites, Alaska, 2016–17"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.41259765625,\n 59.01794033995248\n ],\n [\n -144.77783203125,\n 59.01794033995248\n ],\n [\n -144.77783203125,\n 64.97006438589436\n ],\n [\n -155.41259765625,\n 64.97006438589436\n ],\n [\n -155.41259765625,\n 59.01794033995248\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: April 2023; Version 1.0: December 2019","contact":"Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Throughout the western United States, efforts are underway to better understand and preserve migration and movement corridors for mule deer and other big game and to minimize the impacts of development and other land-use change on populations. San Diego County is home to a unique non-migratory subspecies of mule deer, the Southern mule deer (Odocoileus hemionus fuliginatus; herein referred to as “mule deer”). Because it is the only large herbivorous mammal in San Diego, connectivity among mule deer groups is an important indicator of functional connectivity throughout San Diego County urban preserves and has therefore been monitored within central and eastern San Diego County using DNA fingerprinting since 2005. To continue this effort and to assess genetic connectivity in north San Diego County (herein “North County”), we genotyped scat samples from preserves in the area and tissue samples from Marine Corps Base Camp Pendleton (MCBCP). We used non-invasive capture/recapture analyses and pedigree analyses for assessing short-term movement and population clustering analyses to assess gene flow in North County. Additionally, we performed similar analyses on the combined San Diego County dataset, which was composed of the North County dataset collected for this study and a previously collected dataset from central and eastern San Diego County. Using recapture data, we found multiple instances of mule deer crossing roads in urban North County preserves, with several of these events occurring in areas where there are underpasses and culverts known to be used by mule deer. Corroborating previous studies in the region and statewide, pedigree and population structure analyses support the presence of two genetic clusters for mule deer in San Diego County—the “Coastal” and “Inland/Mountain” clusters. Low estimates of effective population size, especially in the Coastal cluster, suggest that to further understand potential vulnerabilities of mule deer in this region, it is important to continue to monitor connectivity, in particular, at the boundary between these two clusters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191138","usgsCitation":"Mitelberg, A., Smith, J.G., and Vandergast, A.G., 2019, DNA Fingerprinting of Southern mule deer (Odocoileus hemionus fuliginatus) in north San Diego County, California (2018–19): U.S. Geological Survey Open-File Report 2019–1138, 25 p., https://doi.org/10.3133/ofr20191138.","productDescription":"vi, 25 p.","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-112707","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":437245,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YXWXA9","text":"USGS data release","linkHelpText":"Microsatellite Genetic Marker Genotypes from Southern Mule Deer (Odocoileus hemionus fuliginatus) Sampled in San Diego County, California"},{"id":370869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1138/ofr20191138.pdf","text":"Report","size":"31 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":370868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1138/coverthb.jpg"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.31201171875001,\n 32.713355353177555\n ],\n [\n -116.05957031249999,\n 32.713355353177555\n ],\n [\n -116.05957031249999,\n 33.25706340236547\n ],\n [\n -117.31201171875001,\n 33.25706340236547\n ],\n [\n -117.31201171875001,\n 32.713355353177555\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This report summarizes a national assessment of flowing waters conducted by the U.S. Geological Survey’s (USGS) National Water-Quality Assessment (NAWQA) Project and addresses several pressing questions about the modification of natural flows in streams and rivers. The assessment is based on the integration, modeling, and synthesis of monitoring data collected by the USGS and the U.S. Environmental Protection Agency at more than 7,000 streams and rivers across the conterminous United States from 1980 to 2014. Key findings include the following. First, flow in many of the Nation’s streams and rivers is different from what it would be under natural conditions. In particular, low flows are more frequent, are of shorter duration, and vary less from one year to the next than they would naturally. In addition, high flows have been reduced in magnitude, are of shorter duration, are less frequent, and vary less from one year to the next than they would naturally. Other characteristics of natural flows also have been modified. Second, over the last 60 years (1955–2014), climatic trends have caused a change of 50 percent or more in one or more streamflow attributes at two-thirds of climate-sensitive streamgaging sites. However, these climate-induced changes have been less influential on streamflow modification than have land and water-management practices. Third, in every region assessed, streamflow modification was associated with reduced ecological health, as indicated by two biological communities—invertebrates and fish. Biological communities were increasingly likely to be impaired (defined as having lost a statistically significant number of species) in streams with flows most different from natural conditions. Finally, several case studies are presented that illustrate viable management strategies for balancing the water needs of people and ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1461","collaboration":"National Water-Quality ProgramNational Water-Quality Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey (USGS), in cooperation with the Oklahoma Department of Transportation, updated peak-streamflow regression equations for estimating flows with annual exceedance probabilities from 50 to 0.2 percent for the State of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the State by use of a generalized least-squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for stream basins with drainage areas less than 2,510 square miles that are not affected by regulation. The standard model error ranged from 31.28 to 49.32 percent for the different annual exceedance probabilities that were computed.
Annual-maximum peak flows observed at 212 USGS streamgages through water year 2017 were used for the regression analysis, excluding the Oklahoma Panhandle region. The USGS StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 212 streamgages in and near Oklahoma are provided in this report.
This report contains descriptions of the methods that can be used to estimate peak streamflows at ungaged sites by using estimates from streamgages on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater-retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195143","collaboration":"Prepared in cooperation with the Oklahoma Department of Transportation","usgsCitation":"Lewis, J.M., Hunter, S.L., and Labriola, L.G., 2019, Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma developed by using streamflow data through 2017 (ver. 1.1, March 2020): U.S. Geological Survey Scientific Investigations Report 2019–5143, 39 p., https://doi.org/10.3133/sir20195143.","productDescription":"Report: v, 39 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-111975","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":373219,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5143/sir20195143_v1.1.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5143"},{"id":370619,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B99TQZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data release of basin characteristics, generalized skew map and peak-streamflow frequency estimates in Oklahoma, 2017"},{"id":373218,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5143/coverthb2.jpg"},{"id":373266,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5143/versionHist.txt","text":"Version History","description":"Version History"},{"id":399618,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109563.htm"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.919921875,\n 36.87962060502676\n ],\n [\n -102.83203125,\n 34.415973384481866\n ],\n [\n -97.91015624999999,\n 33.97980872872457\n ],\n [\n -94.5703125,\n 33.17434155100208\n ],\n [\n -93.515625,\n 33.97980872872457\n ],\n [\n -93.251953125,\n 37.125286284966805\n ],\n [\n -93.7353515625,\n 38.09998264736481\n ],\n [\n -99.8876953125,\n 38.09998264736481\n ],\n [\n -101.953125,\n 37.71859032558816\n ],\n [\n -102.919921875,\n 36.87962060502676\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: March 2020; Version 1.0: December 2019","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
The National Hydrography Dataset Plus High Resolution (NHDPlus HR) is a scalable geospatial hydrography framework built from the High Resolution (1:24,000-scale or better) National Hydrography Dataset (NHD), nationally complete Watershed Boundary Dataset (WBD), and ⅓-arc-second (10-meter ground spacing) 3D Elevation Program (3DEP) digital elevation model (DEM) data. The NHDPlus HR brings modeling and assessment to a local neighborhood level while nesting seamlessly into the national context.
NHDPlus HR is modeled after the highly successful NHDPlus version 2 (NHDPlus V2). Like the NHDPlus V2, the NHDPlus HR includes data for a nationally seamless network of stream reaches, elevation-based catchment areas, flow surfaces, and value-added attributes that enhance stream-network navigation, analysis, and data display. Users will find that the NHDPlus HR, however, provides much greater detail. This User’s Guide is intended to provide necessary information and guidance in the use of NHDPlus HR data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191096","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Moore, R.B., McKay, L.D., Rea, A.H., Bondelid, T.R., Price, C.V., Dewald, T.G., and Johnston, C.M., 2019, User's guide for the national hydrography dataset plus (NHDPlus) high resolution: U.S. Geological Survey Open-File Report 2019–1096, 66 p., https://doi.org/10.3133/ofr20191096.","productDescription":"Report: x, 66 p.; Dataset","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091493","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":399414,"rank":4,"type":{"id":36,"text":"NGMDB Index 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}\n }\n ]\n}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
The U.S. Geological Survey (USGS), in cooperation with The Nature Conservancy, completed a study to estimate and assess trends in streamflow and annual mean concentrations and flux of nutrients (nitrate plus nitrite, total Kjeldahl nitrogen, and total phosphorus) and total suspended solids at three USGS streamgages (hereafter referred to as “study gages”) on the Upper White River at Muncie (USGS station 03347000), near Nora (USGS station 03351000), and near Centerton (USGS station 03354000), Indiana. Water-quality data used in the analyses were collected by several agencies between calendar years 1991 and 2017, and streamflow (discharge) data were collected by the USGS. For most of the water-quality constituents, there were suitable data to facilitate an analysis of the 26-year period extending from calendar years 1991 to 2017 (water years 1992 to 2017); however, shorter analytical periods were necessary for total Kjeldahl nitrogen for the study gages at Muncie and near Centerton and for total suspended solids for the study gage near Centerton.
Temporal trends in streamflows at the study gages for the period extending from water years 1978 to 2017 were assessed using Exploration and Graphics for RivEr Trends (EGRET) and Mann-Kendall and Pettitt tests. With just one exception, the annual maximum and mean daily streamflows and the annual minimum 7-day mean streamflows at the study gages demonstrated upward trends (increasing streamflows) in the EGRET analyses. The exception was the annual 7-day minimum streamflow at the study gage near Nora, which indicated no trend. Mann-Kendall tests also indicated that the average trend for the annual maximum daily, annual mean daily, and annual 7-day minimum streamflow statistics between water years 1978 and 2017 was upward at each of the study gages; however, only the trends in the annual mean daily streamflows at the study gage at Muncie and the annual maximum daily streamflows at the study gages near Nora and near Centerton were statistically significant at a 0.05 probability level. The Pettitt tests indicated that a statistically significant step trend (abrupt change) in annual mean daily streamflows occurred at each of the study gages around water year 2001.
The seasonal distributions of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen concentrations at the study gages were evaluated to identify patterns and other distinguishing characteristics by examining boxplots of concentrations as a function of month of the year. Seasonal distributions of nitrate plus nitrite concentrations and total suspended solids concentrations differed from each other but were generally similar among the three study gages for a given constituent. Median concentrations of nitrate plus nitrite were highest during the January–June months, whereas median concentrations of total suspended solids were highest during June and July. Seasonal distributions of total phosphorus concentrations were similar at the study gages near Nora and near Centerton, but the seasonal distribution was noticeably different at the study gage at Muncie, which had monthly median concentrations that were substantially lower than at the two downstream study gages (near Nora and near Centerton). The seasonal distribution of total Kjeldahl nitrogen concentrations differed in pattern among the three study gages; however, in general, some of the higher monthly median total Kjeldahl nitrogen concentrations at each study gage were associated with the late spring and summer periods.
The Weighted Regressions on Time, Discharge, and Season (WRTDS) method implemented in EGRET was used to estimate water-year annual mean daily concentrations and flux of nutrients and total suspended solids, as well as estimates of concentrations and flux that were “normalized” to remove the effect of year-to-year variation in streamflow. The approximate coefficients of determination for the WRTDS regression models ranged from a high of 0.82 for total phosphorus for the study gage near Centerton to a low of 0.19 for nitrate plus nitrite for the study gage near Nora.
Loads and yields of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen were estimated for analytical periods consisting of the longest periods of concurrent record at the three study gages. Loads of each of the constituents increased sequentially from the most upstream study gage to the most downstream study gage; however, the same was not true for yields. The highest yields of total suspended solids, total phosphorus, and total Kjeldahl nitrogen occurred at the most upstream study gage (at Muncie); however, the highest yield of nitrate plus nitrite occurred at the most downstream study gage (near Centerton).
WRTDS bootstrap tests were used to assess the magnitude, direction, and likelihood of changes in annual flow-normalized mean daily concentrations and flux of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen at the study gages between water years 1997 and 2017. Changes in flow-normalized concentrations and flux of the constituents between water years 1997 and 2017 were mostly downward (decreasing). The exceptions were likely to highly likely upward (increasing) changes in (1) flow-normalized annual mean daily concentration and annual flux for total suspended solids and total phosphorus at the study gage at Muncie, (2) flow-normalized annual mean daily total phosphorus concentration at the study gage near Centerton, (3) flow-normalized annual flux of total phosphorus at the study gage near Centerton, and (4) flow-normalized annual mean daily nitrate plus nitrite concentration at the study gage near Centerton. Although an upward change in flow-normalized nitrate plus nitrite concentrations was likely at the study gage near Centerton, flow-normalized annual flux of nitrate plus nitrite at that study gage was determined to have a highly likely downward change.
EGRET and Exploration and Graphics for RivEr Trends Confidence Intervals (EGRETci) analyses can be used to improve our understanding of how concentrations and flux change as functions of time and streamflow, as well as provide information on how the relations between streamflow and constituent concentrations have changed within the calendar year between any 2 years included in the analyses. Examples of those uses, illustrating changes between calendar years 1992 and 2017, were given for total suspended solids concentrations at the study gage near Nora and for nitrate plus nitrite concentrations at the study gage near Centerton.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195119","collaboration":"Prepared in cooperation with The Nature Conservancy","usgsCitation":"Koltun, G.F., 2019, Trends in streamflow and concentrations and flux of nutrients and total suspended solids in the Upper White River at Muncie, near Nora, and near Centerton, Indiana: U.S. Geological Survey Scientific Investigations Report 2019–5119, 34 p., https://doi.org/10.3133/sir20195119.","productDescription":"Report: viii, 34 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-109722","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":399602,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109513.htm"},{"id":370134,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VN5RKV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen concentration data for the White River at Muncie, near Nora, and near Centerton, Indiana, 1991–2017"},{"id":370133,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5119/sir20195119.pdf","text":"Report","size":"3.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5119"},{"id":370132,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5119/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Morgan County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.8311,\n 39.2633\n ],\n [\n -84.9667,\n 39.2633\n ],\n [\n -84.9667,\n 40.3608\n ],\n [\n -86.8311,\n 40.3608\n ],\n [\n -86.8311,\n 39.2633\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Ste 100
Columbus, OH 43229–1737
In 2005, the U.S. Geological Survey began a cooperative study with the New York City Department of Environmental Protection to characterize the local groundwater-flow system and identify potential sources of seeps on the southern embankment of the Hillview Reservoir in southern Westchester County, New York. The earthen embankment comprises low-permeability glacial clays that were excavated from the site and rest on a veneer of low-permeability glacial deposits that overlie crystalline bedrock. At least two groundwater-flow zones—one shallow and the other deep—overlie the bedrock at the reservoir. As part of the study, slug-test data from 38 screened wells were analyzed to determine the hydraulic conductivity of the sediments in the groundwater-flow zones. Slug-test data were collected from 12 wells at the Hillview Reservoir during August 2007 and from 25 wells at the reservoir and 1 monitoring well south of the reservoir in northern Bronx County in June 2012.
Hydraulic conductivity values at the reservoir ranged from 0.0012 to 2 feet per day. On the southern embankment, hydraulic conductivity ranged from 0.0026 to 1 foot per day for wells screened in the shallow saturated zone; 0.0012 to 2 feet per day for wells screened in the deep saturated zone; and 0.021 to 0.27 foot per day for wells screened in the toe of the southern embankment, where the deep and shallow saturated zones coalesce. A hydraulic conductivity of 0.016 foot per day was determined for a well partially screened in the crystalline-bedrock aquifer, which potentially indicates an interconnection of transmissive fractures near the bedrock surface. The results of four slug-out tests are also included in this report to quality assure the hydraulic conductivity estimates from the slug-in test analysis. The results of the four slug-out tests were within 8 percent of slug-in test results, with an average of less than 2 percent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191102","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Noll, M.L., Chu, A., and Capurso, W.D., 2019, Slug-test analysis of selected wells at an earthen dam site in southern Westchester County, New York: U.S. Geological Survey Open-File Report 2019–1102, 14 p., https://doi.org/10.3133/ofr20191102.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095039","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":399415,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109510.htm"},{"id":369616,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1102/coverthb.jpg"},{"id":369866,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1102/ofr20191102.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1102"},{"id":369619,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J404KW","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Data and analytical type-curve match for selected hydraulic tests in New York State"}],"country":"United States","state":"New York","county":"Westchester County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.87674808502196,\n 40.90551783054535\n ],\n [\n -73.86099815368652,\n 40.90551783054535\n ],\n [\n -73.86099815368652,\n 40.91821491609591\n ],\n [\n -73.87674808502196,\n 40.91821491609591\n ],\n [\n -73.87674808502196,\n 40.90551783054535\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
2045 Route 112, Building 4
Coram, NY 11727
The Yorktown-Eastover aquifer system of the Virginia Eastern Shore consists of upper, middle, and lower confined aquifers overlain by correspondingly named confining units and underlain by the Saint Marys confining unit. Miocene- to Pliocene-age marine-shelf sediments observed in 205 boreholes include medium- to coarse-grained sand and shells that compose the aquifers and fine-grained sand, silt, and clay that compose the confining units. The upper confining unit also includes fine-grained and organic-rich back-barrier and estuarine sediments of Pleistocene age. An overlying surficial aquifer is composed mostly of Pleistocene-age nearshore sand and gravel with smaller amounts of cobbles and boulders.
In addition, Pleistocene-age sediments that fill three buried paleochannels are for the first time explicitly delineated here as distinct hydrogeologic units. Two aquifers are composed of medium- to coarse-grained fluvial sand and gravel, and an intervening confining unit is composed of fine-grained estuarine sand, silt, clay, and organic material. Aquifer and confining-unit sediments are also mixed with reworked marine-shelf sediments eroded from the sides of the paleochannels.
Hydrogeologic units of the Yorktown-Eastover aquifer system generally dip eastward, are as much as several tens of feet thick, and have an undulating configuration possibly resulting from the underlying Chesapeake Bay impact crater. Aquifers and confining units are incised by the three paleochannels along an upward-widening and eastward-lengthening series of structural “windows.” Hydrogeologic units within mainstems and branching tributaries of the paleochannels dip southeastward parallel to slopes of the paleochannels, are as much as several tens of feet thick, and laterally abut the Yorktown-Eastover aquifer system along paleochannel sidewalls. The Yorktown-Eastover aquifer system is thereby hydraulically breached by the paleochannels to alternately create barriers to or conduits for groundwater flow.
Results of previously documented aquifer tests at 58 wells indicate that transmissivity is generally greatest in young, shallow, and coarse-grained nearshore and fluvial sediments of the surficial aquifer and paleochannels. Transmissivity progressively decreases with depth in older, deeper, and finer grained marine-shelf sediments of the Yorktown-Eastover aquifer system, probably because they have undergone compaction as a result of greater overburden pressure over longer periods of time.
Compiled chloride concentrations in samples from 330 wells generally increase downward, with most of the samples collected at altitudes above −300 feet and with most concentrations less than 250 milligrams per liter. The saltwater-transition zone has a broad trough-like shape aligned with the peninsula, being relatively shallow along the coastline and deeper along the central “spine.” Because movement of the saltwater is slow, the configuration largely reflects groundwater flow prior to widespread groundwater withdrawals. Fresh groundwater has leaked downward along deep parts of the saltwater-transition zone and leaked upward along shallower parts to discharge at the coast.
The saltwater-transition zone also exhibits an anomalous ridge across the center of the peninsula. Groundwater levels indicate that the saltwater ridge formed primarily by the Exmore paleochannel acting as a large lateral collector drain. Groundwater levels were lowered, and the position of saltwater-transition zone was elevated, by a flow conduit that intercepted groundwater that otherwise would have flowed toward and discharged along the coastline.
Nearly all freshwater on the Virginia Eastern Shore is supplied by groundwater withdrawals, which have lowered water levels, altered hydraulic gradients, and created a concern for saltwater intrusion. Previous characterizations of groundwater conditions that are relied on to manage groundwater development have been limited by a lack of hydrogeologic information, particularly data on buried paleochannels that are critical to safeguarding the groundwater supply. Using recently available expanded information, the U.S. Geological Survey undertook a study in cooperation with the Virginia Department of Environmental Quality during 2016–19 to develop an improved description of the groundwater system called a “hydrogeologic framework.”
The hydrogeologic framework can aid water-supply planning and development by providing information on broad trends in aquifer configurations, hydraulic properties, and proximity to saltwater to avoid chloride contamination. Digital models to evaluate effects of groundwater withdrawals can also be improved with expanded data and capabilities to evaluate paleochannel hydraulic connections and the potential for saltwater movement.
The hydrogeologic framework is limited by the nonuniform distribution of boreholes and the subjective delineation of aquifers and confining units, including those within paleochannels that are regarded as preliminary. The configuration of the saltwater-transition zone is also regarded as preliminary because of the nonuniform distribution of groundwater samples. Low well-sampling frequency precludes characterizing movement of the saltwater-transition zone. A monitoring strategy of sampling and possibly electromagnetic-induction well logging could be used to detect saltwater movement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195093","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"McFarland, E.R., and Beach, T.A., 2019, Hydrogeologic framework of the Virginia Eastern Shore: U.S. Geological Survey Scientific Investigations Report 2019–5093, 26 p., 13 pl., https://doi.org/10.3133/sir20195093.","productDescription":"Report: viii, 26 p.; 13 Plates: 11.00 x 17.00 inches or smaller; Data Release","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-108409","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":369803,"rank":16,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093.pdf","text":"Report","size":"3.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5093"},{"id":369731,"rank":15,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate13.pdf","text":"Plate 13","size":"352 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Locations and Numbers of Sampled Wells and Altitude of the 250-Milligram-Per-Liter Chloride-Concentration Surface on the Virginia Eastern Shore"},{"id":369730,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate12.pdf","text":"Plate 12","size":"332 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Upper Confining Unit on the Virginia Eastern Shore"},{"id":369725,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate07.pdf","text":"Plate 7","size":"346 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Middle Confining Unit on the Virginia Eastern Shore"},{"id":369724,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate06.pdf","text":"Plate 6","size":"340 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Middle Aquifer on the Virginia Eastern Shore"},{"id":369723,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate05.pdf","text":"Plate 5","size":"332 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Lower Confining Unit on the Virginia Eastern Shore"},{"id":399542,"rank":17,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109487.htm"},{"id":369721,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate03.pdf","text":"Plate 3","size":"323 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Saint Marys Confining Unit on the Virginia Eastern Shore"},{"id":369720,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate02.pdf","text":"Plate 2","size":"336 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Hydrogeologic Section through the Virginia Eastern Shore"},{"id":369719,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate01.pdf","text":"Plate 1","size":"339 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Locations and Numbers of Boreholes on the Virginia Eastern Shore"},{"id":369714,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MPE5SD","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Borehole hydrogeologic-unit top-surface altitudes, aquifer hydraulic properties, and groundwater-sample chloride-concentration data from 1906 through 2016 for the Virginia Eastern Shore"},{"id":369728,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate10.pdf","text":"Plate 10","size":"319 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Confining Unit on the Virginia Eastern Shore"},{"id":369727,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate09.pdf","text":"Plate 9","size":"316 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Lower Aquifer on the Virginia Eastern Shore"},{"id":369726,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate08.pdf","text":"Plate 8","size":"350 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Upper Aquifer on the Virginia Eastern Shore"},{"id":369729,"rank":13,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate11.pdf","text":"Plate 11","size":"321 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Upper Aquifer on the Virginia Eastern Shore"},{"id":369722,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate04.pdf","text":"Plate 4","size":"327 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Lower Aquifer on the Virginia Eastern Shore"},{"id":369709,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5093/coverthb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.058349609375,\n 37.1165261849112\n ],\n 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U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 57th Annual Report of the River Master of the Delaware River. It covers the 2010 River Master report year, the period from December 1, 2009, to November 30, 2010.
During the report year, precipitation in the upper Delaware River Basin was 49.38 inches or 112 percent of the long-term average. Combined storage in Pepacton, Cannonsville, and Neversink Reservoirs remained high much of the year and did not decline below 80 percent of combined capacity until September 2010. A lower basin drought warning was issued by the Delaware River Basin Commission on September 24, 2010. It automatically ended on October 31, 2010, when the reservoir contents rose above drought levels, due in large part to heavy rainfall during the last week of September. River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 81 days during the report year. Interim Excess Release Quantity and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were made during the report year.
The quality of water in the Delaware Estuary between Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at various locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191093","usgsCitation":"Russell, K.L., Ockerman, D., Krejmas, B.E., Paulachok, G.N., and Mason, R.R., Jr., 2019, Report of the River Master of the Delaware River for the period December 1, 2009–November 30, 2010: U.S. Geological Survey Open-File Report 2019–1093, 128 p., https://doi.org/10.3133/ofr20191093.","productDescription":"x, 128 p.","numberOfPages":"142","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-099205","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":369615,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1093/ofr20191093.pdf","text":"Report","size":"3.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1093"},{"id":368735,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1093/coverthb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.278076171875,\n 39.32579941789298\n ],\n [\n -74.608154296875,\n 39.32579941789298\n ],\n [\n -74.608154296875,\n 42.68243539838623\n ],\n [\n -76.278076171875,\n 42.68243539838623\n ],\n [\n -76.278076171875,\n 39.32579941789298\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Deputy Delaware River Master
Office of the Delaware River Master
U.S. Geological Survey
120 Route 209 South
Milford, PA 18337
The elevated salinity of the Pecos River throughout much of its length is of paramount concern to water users and water managers. Dissolved-solids concentrations in the Pecos River exceed 3,000 milligrams per liter in many of its reaches in the study area, from Santa Rosa Lake, New Mexico, to the confluence of the Pecos River with the Rio Grande, Texas. The salinity of the Pecos River increases downstream and affects the availability of useable water in the Pecos River Basin. In this report, “salinity” and “dissolved-solids concentration” are considered synonymous; both terms are used to refer to the total ionic concentration of dissolved minerals in water. The sources of salinity in the Pecos River Basin are natural (geologic) and anthropogenic, including but not limited to groundwater discharge, springs, and irrigation return flows. Previous studies in the Pecos River Basin were project specific and designed to address salinity issues in specific parts of the basin; therefore, in 2015, the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers, New Mexico Interstate Stream Commission, Texas Commission on Environmental Quality, and Texas Water Development Board assessed the major sources of salinity throughout the extent of the basin where elevated salinity in the Pecos River is well documented (that is, in the drainage area of the Pecos River from Santa Rosa Lake to the confluence of the Pecos River and the Rio Grande). The goal was to gain a better understanding of how specific areas might be contributing to the elevated salinity in the Pecos River and how salinity of the Pecos River has changed over time. This assessment includes a literature review and compilation of previously published salinity-related data, which guided the collection of additional water-quality samples and streamflow gain-loss measurements. Differences in water quality of surface-water and groundwater samples, streamflow measurements, and geophysical data were assessed to gain new insights regarding sources of salinity in the Pecos River Basin and a more detailed assessment of potential areas of elevated salinity in the basin. The datasets compiled for this assessment are available in a companion data release.
The literature review identified several potential sources of salinity inputs to the Pecos River in New Mexico and Texas. In New Mexico, sources of salinity inputs included sinkhole springs discharging into El Rito Creek, the Bitter Lake National Wildlife Refuge inflow to the Pecos River, inflow from the Rio Hondo, including the main channel and a restored channel at the Bitter Lake National Wildlife Refuge referred to as the “Rio Hondo spring channel,” the outflow from Lea Lake at Bottomless Lakes State Park, and the Malaga Bend region of the Pecos River. In Texas, sources of salinity inputs included Salt Creek downstream from Red Bluff Reservoir and the area near the Horsehead Crossing ford on the Pecos River.
The compilation of historical water-quality data revealed a lack of consistent sampling of the same constituents at the same sites along the main stem of the Pecos River, which results in data gaps that hinder the ability to effectively analyze long-term changes in water quality that may help with the understanding of how salinity in the Pecos River has changed over time and identifying the sources of salinity in the Pecos River Basin. To help fill these data gaps, water-quality and streamflow data were collected in the study area in February 2015 by the U.S. Geological Survey. Historical water-quality data and newly collected data from February 2015 were evaluated for selected major-ion concentrations, dissolved-solids concentrations, and deuterium, oxygen, and strontium isotopes. Analysis of the data indicated several areas of increasing salinity in the Pecos River. Most notable increases were in two subreaches of the river, between Acme, N. Mex., and Artesia, N. Mex., and between Orla, Tex., and Grandfalls, Tex. Increasing sodium and chloride concentrations from Acme to Artesia coincided with changes in isotopic ratios within the Pecos River Basin. Changes in isotopic ratios in this reach indicate a likely inflow from an isotopically different source of water compared to the water in the main stem of the Pecos River, such as groundwater inflow, inflow from surface-water features distinct from the main stem of the Pecos River, or both. In the subreach between Orla and Grandfalls, an increase in dissolved-solids concentrations was observed along with a shift in isotope values, indicating that neither evaporative processes in Red Bluff Reservoir nor inflow from Salt Creek likely solely influences the salinity of the Pecos River in this subreach. The highest dissolved-solids concentrations in the Pecos River Basin were measured downstream from Grandfalls, where dissolved-solids concentrations are greater than 16,000 milligrams per liter near Iraan, Tex. Changes in isotopic values (deuterium, oxygen, and strontium) indicate mixing of different waters at several areas along the main stem of the Pecos River. The spatial distribution of the areas of interest from the literature review and the water-quality data are available in the companion data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195071","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, New Mexico Interstate Stream Commission, Texas Commission on Environmental Quality, and Texas Water Development Board","usgsCitation":"Houston, N.A., Thomas, J.V., Ging, P.B., Teeple, A.P., Pedraza, D.E., and Wallace, D.S., 2019, Pecos River Basin salinity assessment, Santa Rosa Lake, New Mexico, to the confluence of the Pecos River and the Rio Grande, Texas, 2015: U.S. Geological Survey Scientific Investigations Report 2019–5071, 75 p., https://doi.org/10.3133/sir20195071.","productDescription":"Report: xi, 75 p.; Data Release","numberOfPages":"91","onlineOnly":"Y","ipdsId":"IP-083306","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":369530,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DB800T","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water Quality, Streamflow Gain Loss, Geologic, and Geospatial Data Used in the Pecos River Basin Salinity Assessment from Santa Rosa Lake, New Mexico to the Confluence of the Pecos River and the Rio Grande, Texas, 1900–2015"},{"id":369528,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5071/coverthb.jpg"},{"id":369529,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5071/sir20195071.pdf","text":"Report","size":"9.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5071"}],"country":"United States","state":"Texas, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.39257812499999,\n 29.916852233070173\n ],\n [\n -101.162109375,\n 29.305561325527698\n ],\n [\n -100.1513671875,\n 30.826780904779774\n ],\n [\n -99.97558593749999,\n 32.32427558887655\n ],\n [\n -101.9970703125,\n 33.797408767572485\n ],\n [\n -103.5791015625,\n 34.74161249883172\n ],\n [\n -105.0732421875,\n 35.567980458012094\n ],\n [\n -106.787109375,\n 36.38591277287651\n ],\n [\n -107.22656249999999,\n 35.53222622770337\n ],\n [\n -107.6220703125,\n 34.34343606848294\n ],\n [\n -105.908203125,\n 32.62087018318113\n ],\n [\n -102.39257812499999,\n 29.916852233070173\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The Ouachita Mountains aquifer system potentiometric-surface map is one component of the Hot Springs Bypass Groundwater Monitoring Project. The potentiometric-surface map provides a baseline assessment of shallow groundwater levels and flow directions before the construction of the Arkansas Department of Transportation planned extension of the Highway 270 bypass, east of Hot Springs, Arkansas. The map provides data regarding status of groundwater levels and potential effects on the recharge area in the Hot Springs National Park and to groundwater that supplies water to domestic users near the Highway 270 bypass.
Groundwater levels from 66 wells were measured in July–August 2017. Fifty nine of the 66 groundwater-level altitudes measured, along with select surface-water features and springs, were used to construct the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric surface, a two-dimensional representation, shows groundwater-level altitudes ranging from a maximum of 766 ft above the North American Vertical Datum of 1988 (NAVD 88) to a minimum of 443 ft NAVD 88. The spring altitudes on the potentiometric-surface map range from 534 ft to 927 ft above NAVD 88. The study area, located in the Ouachita Mountains physiographic section of the Ouachita physiographic province, comprises narrow valleys and high ridges of Stanley Shale, Hot Springs Sandstone, Arkansas Novaculite, Missouri Mountain-Polk Creek Shale, and Bigfork Chert. The highest groundwater-level altitudes observed were in the Hot Springs Sandstone, Arkansas Novaculite, and Missouri Mountain-Polk Creek Shale. The springs discharge in outcrop areas of the Stanley Shale, Bigfork Chert, and Arkansas novaculite. The planned Highway 270 bypass will cut across ridges and valleys comprising these formations and, very importantly, across areas with elevations above 660 ft above NAVD 88 that define the hot springs recharge zone.
This potentiometric-surface map defines the status of the shallow groundwater potentiometric surface near the Highway 270 bypass prior to initiation of construction activities. A post-construction potentiometric map is planned. It must be noted that shallow groundwater levels are also subject to climatic effects including changes in amount and timing of precipitation and changes in temperature.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3444","collaboration":"Prepared in cooperation with the Arkansas Department of Transportation and the National Park Service","usgsCitation":"Nottmeier, A.M., and Hays, P.D., 2019, Potentiometric surface of groundwater-level altitudes near the planned Highway 270 bypass, east of Hot Springs, Arkansas, July–August 2017: U.S. Geological Survey Scientific Investigations Map 3444, 13 p., 1 sheet, https://doi.org/10.3133/sim3444.","productDescription":"Pamphlet: v, 13 p.; Sheet: 22 x 28 inches; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-092242","costCenters":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":369331,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TD9WK0","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Datasets of the Potentiometric Surface of Groundwater-Level Altitudes Near the Planned Highway 270 Bypass, East of Hot Springs, Arkansas, July–August 2017"},{"id":369328,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3444/coverthb.jpg"},{"id":369329,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3444/sim3444.pdf","text":"Sheet ","size":"2.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3444 ","linkHelpText":"– Potentiometric-surface map for the Ouachita Mountains aquifer, July–August 2017"},{"id":369330,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3444/sim3444_pamphlet.pdf","text":"Pamphlet","size":"3.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3444 Pamphlet"}],"country":"United States","state":"Arkansas","county":"Garland County","city":"Hot Springs","otherGeospatial":"Highway 270 Bypass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.01935195922852,\n 34.50542493789137\n ],\n [\n -92.94931411743164,\n 34.50542493789137\n ],\n [\n -92.94931411743164,\n 34.5710371883746\n ],\n [\n -93.01935195922852,\n 34.5710371883746\n ],\n [\n -93.01935195922852,\n 34.50542493789137\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a network of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2018 (October 1, 2017, through September 30, 2018), water-quality data were collected at 76 stations: 74 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Among the 76 stations in this report, 4 stations have data presented from additional sampling performed in cooperation with the U.S. Army Corps of Engineers. Summaries of the concentrations of 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 selected pesticide compounds are presented. Most of the stations have been classified based on the physiographic province or primary land use in the watershed monitored by the station. Some stations have been classified based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) they monitor. A summary of hydrologic conditions including peak streamflows, monthly mean streamflows, and 7-day low flows also are presented for representative streamflow-gaging stations in the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1119","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Kay, R.T., 2019, Quality of surface water in Missouri, water year 2018: U.S. Geological Survey Data Series 1119, 25 p., https://doi.org/10.3133/ds1119.","productDescription":"v, 25 p.","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-107435","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":369064,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1119/ds1119.pdf","text":"Report","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1119"},{"id":369063,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1119/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The Full Equations Model Graphical Data Inspector (FEQ–GDI) is a menu-driven utility program that enables users to visualize and check the geometric and hydraulic properties of channel cross sections, selected control structures, and stream profiles in the input files for the Full Equations (FEQ) Model and the Full Equations Utilities (FEQUTL) Model. The FEQ Model is a computer program for the simulation of one-dimensional, unsteady flow in open channels and through control structures using the full, dynamic equations of motion. The input to FEQ Model includes the output from the FEQUTL Model, which computes tables relating the hydraulic properties of channel cross sections and control structures to depth, flow, and (or) other specified parameters. FEQ–GDI can be used to help users quickly detect anomalies in the data that may indicate errors in the input files.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191113","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Ern, J.L., Ortel, T., Ishii, A.L., and Bera, M., 2019, Full Equations Model Graphical Data Inspector (FEQ–GDI) user guide: U.S. Geological Survey Open-File Report 2019–1113, 11 p., https://doi.org/10.3133/ofr20191113.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-111050","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":369032,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1113/coverthb.jpg"},{"id":369033,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1113/ofr20191113.pdf","text":"Report","size":"4.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1113"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
In view of the U.S. Army’s historical reliance and plans to increase demands on groundwater to supply its operations at Fort Irwin National Training Center (NTC), California, coupled with the continuing water-level declines in some developed groundwater basins as a result of pumping, the U.S. Geological Survey (USGS), in cooperation with the U.S. Army, evaluated the water resources, including water quality and potential groundwater supply, of undeveloped basins in the NTC. Previous work in the three developed groundwater basins—Langford, Bicycle, and Irwin—provided information to support water-resources management of those basins. During 2009–12, the USGS installed 41 wells at the NTC; 34 wells were at 14 single- or multiple-well monitoring sites, and 7 wells were long-screen test wells. The majority of the wells were installed in previously undeveloped or minimally developed groundwater basins (Cronise, Red Pass, the Central Corridor area, Superior, Goldstone, and Nelson Basins). During 2012–15, the USGS tested hydrologic properties at 32 wells in 8 basins to help characterize the aquifer system. This report presents data and analyses from core samples; slug tests and single-well aquifer tests; coupled measurements of wellbore flow, water levels, and water-quality constituents; and results from two-dimensional numerical modeling. This information provides a basis for developing and constraining basin-scale hydrogeologic framework and groundwater-flow models to further evaluate water resources in each groundwater basin.
Core samples were tested for vertical saturated hydraulic conductivity, physical properties, and particle-size distribution. Vertical saturated hydraulic conductivities of the cores ranged from less than 0.00001 to 18.13 feet per day, and porosities ranged from 0.15 to 0.56. These physical properties and particle-size analyses indicate the high degree of heterogeneity of the hydrogeologic deposits penetrated by the boreholes. Horizontal hydraulic conductivities estimated from slug tests in 22 monitoring wells in 6 basins (Cronise, Central Corridor area, Goldstone, Langford, Bicycle, and Nelson Basins) ranged from less than 0.1 to 40 feet per day. Results of the aquifer tests at six test wells in the Goldstone, Nelson, and Superior Basins indicate hydraulic conductivities ranged from 0.37 to 66 feet per day; associated transmissivity values ranged from 130 to 28,000 feet squared per day. Wellbore-flow data, collected from the six test wells under unpumped and pumped conditions, generally showed downward movement of water. Flow data collected under unpumped conditions indicate groundwater entered the well through the upper part of the screened interval and exited to aquifer zones in the lower part of the screened interval at rates ranging from 1 to 3 gallons per minute. Flow data collected under pumping conditions show increased flow downward in the test wells, indicating higher yields from deeper aquifers.
Water levels, measured periodically between 2011 and 2015, remained stable during this period in the majority of the wells measured since 2011, except at two monitoring sites in developed basins (Bicycle and Langford). Vertical hydraulic gradients were generally low throughout the NTC, but ranged from –0.0003 to 0.27 during the summer of 2015. Multiple-well monitoring sites in Bicycle, Central Corridor area, Cronise, Goldstone, Nelson, and Superior Basins, had downward vertical gradients.
Groundwater in wells in Nelson and Superior Basins, and wells BLA5, CCT1, and GOLD2 #2, was characterized as sodium-bicarbonate water, whereas groundwater from the remaining wells in Goldstone Basin was characterized as sodium-chloride water and Cronise Basin, and well LL04 was characterized by sodium-sulfate water. Total dissolved solids (TDS) ranged from 285 to 13,400 milligrams per liter (mg/L) TDS and chloride concentrations ranged from 19 to 1,030 mg/L chloride, with lowest concentrations of each in groundwater from Superior and Nelson Basins and highest concentrations in Cronise Basin. Nitrate plus nitrite as nitrogen ranged from less than 0.040 mg/L in groundwater from Cronise and Goldstone Basins to about 20 mg/L in Nelson Basin. Groundwater from wells in Nelson Basin was isotopically light, whereas groundwater samples from wells CRTH1, CRTH2, and LL04 were isotopically heavier and plotted along an evaporative trend line. No measurable tritium was detected in groundwater from 13 wells sampled in 2015, indicating that groundwater was recharged prior to 1952. Measured carbon-14 (14C) activities in groundwater from four wells sampled in 2015 ranged from about 7.9 to 23.5 percent modern carbon and had apparent (uncorrected) ages of 11,970–20,980 years. Arsenic concentrations were above the maximum contaminant level of 10 micrograms per liter in groundwater from all wells, except those in Goldstone Basin and the two deepest wells in Langford Basin (LL04); likewise, fluoride concentrations were above the California maximum contaminant level of 2 mg/L in groundwater from most wells, except those in Goldstone and Superior Basins, the middle well in Langford Basin, middle and deep wells in two locations in Cronise Basin, and two wells in Nelson Basin.
Wellbore flow was simulated for each well by using an integrated-flow analysis tool, AnalyzeHOLE, to evaluate aquifer properties and heterogeneity. Horizontal layers in the model (hydrogeologic units) were defined by lithostratigraphic‐geophysical units, interpreted from lithologic and geophysical logs for each well, and were adjusted during calibration. The saturated hydraulic conductivities derived from the calibrated simulations ranged from less than 0.01 to 60 feet per day in Nelson, Goldstone, and Superior Basins.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Automated data-processing methods allow hydrologists to efficiently incorporate digital well-record datasets into the construction of hydrostratigraphic frameworks for groundwater-flow models. The method selected to construct the hydrostratigraphic framework can affect the extent of geologic heterogeneity that can be included in the model. The detail generated from a hydrostratigraphic framework can affect groundwater simulation results. The effects of detail on model accuracy, groundwater-flow simulations, and particle-tracking simulations are described in this study. This report compares differences in hydrostratigraphic frameworks and results of groundwater models using (1) a method that incorporates more hydrologic judgment at the expense of using limited lithologic data and (2) a method that is more automated and uses all available lithologic data. The study additionally evaluates the effect of model discretization and inclusion of more (or less) geologic detail on simulation results.
Two methods were used to create hydrostratigraphic frameworks of glacial deposits in the St. Joseph River Basin. One method, referred to as the subjective method, manually identifies stratigraphic boundaries using a sample of well logs from State databases and uses two-dimensional kriging to create three model layers of the study area. Indicator kriging is used to define aquifer extent in each layer. The second method, referred to as the objective method, uses three-dimensional kriging to automatically create a detailed heterogeneous model of the study area using all wells logs from the State database. The objective method increases detail in the vertical by greatly increasing the number of computer groundwater model layers from 3 to 30. In Elkhart County, Indiana, a previously published model represents the product of the subjective method, and a newly calibrated model of the same area represents the product of the objective method.
An automated calibration procedure was used with the objective model (derived from the objective method) for Elkhart County. The two most-sensitive parameters for the Elkhart County objective model are horizontal hydraulic conductivity of the sand and the combined sand and gravel/gravel deposits. Vertical hydraulic conductivity of the fine-grained and intermediate-sized deposits could not be estimated, possibly indicating major flow paths are along a continuously connected series of sand and gravel deposits and not through a confining layer.
The statistics measuring model calibration accuracy for the objective model were slightly better than statistics for the subjective model (model derived from the subjective method) of Elkhart County, but the hydraulic conductivities and flow rates for the two models were different. The mean absolute errors between simulated and measured groundwater levels are 2.04 and 2.16 feet for the objective and subjective models, respectively. Simulated seepage losses from and groundwater discharges to measured stream reaches in the objective model were evenly balanced in terms of over and under simulations of measured values; the subjective model tended to overpredict measured groundwater discharge to streams. The overprediction may be related to the 58 percent greater total inflow and outflow through the subjective model. The greater flow rate through the subjective model results from higher horizontal hydraulic conductivities in the subjective model than in the objective model. Horizontal hydraulic conductivity ranged from 23.9 to 111 feet per day in the objective model and generally ranged from 170 to 370 feet per day in the subjective model. The improvement in calibration statistics for the objective model relative to the subjective model may be from increased detail in how the objective model represents the distribution of fine- and coarse-grained deposits. The improvement also could be associated with the difference in methods used to represent the continuity of the confining unit.
The effect of differences in horizontal hydraulic conductivity distributions between the two models for Elkhart County is evident in the groundwater-flow paths simulated by the objective and subjective models. At a withdrawal well location, the flow lines produced by the objective model indicate a wider contributing area than that for the subjective model. The discontinuous confining unit represented in the objective model provided the opportunity for groundwater flow to split into an upper and lower path. The split in flow simulated by the objective model at one location was independently supported by bromide concentrations in groundwater; the subjective model did not duplicate the split in flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195088","collaboration":"U.S. Geological Survey Groundwater Resources Program","usgsCitation":"Arihood, L.D., Lampe, D.C., Bayless, E.R., and Brown, S.E., 2019, Comparison of groundwater-model construction methods, representations of glacial geology, model designs, and groundwater-model flow simulations within Elkhart County, Indiana: U.S. Geological Survey Scientific Investigations Report 2019–5088, 44 p., https://doi.org/10.3133/sir20195088.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-065522 ","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368474,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5088/sir20195088.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5088"},{"id":368475,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QN65RW","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"MODFLOW-2000 model used to illustrate the differences in flow paths and travel times when three-dimensional kriging is used to estimate the hydraulic conductivity distribution as compared to manual determinations of hydraulic conductivity distribution"},{"id":368473,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5088/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Elkhart County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-85.7874,41.7615],[-85.7591,41.7613],[-85.6606,41.7608],[-85.6589,41.699],[-85.6575,41.6122],[-85.6554,41.5251],[-85.6542,41.4733],[-85.6552,41.4384],[-85.7704,41.4377],[-85.8874,41.4379],[-86.0008,41.4375],[-86.059,41.4367],[-86.0594,41.4644],[-86.0593,41.474],[-86.0593,41.479],[-86.0592,41.4935],[-86.0598,41.4999],[-86.0624,41.7619],[-85.932,41.7623],[-85.7874,41.7615]]]},\"properties\":{\"name\":\"Elkhart\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278-1996
Hurricane Matthew, the strongest Atlantic hurricane of the 2016 hurricane season, made land-fall south of McClellanville, S.C., around 1500 Coordinated Universal Time (UTC) on October 8, 2016. Hurricane Matthew affected the States of Florida, Georgia, South Carolina, and North Carolina along the U.S. Atlantic coastline. Numerous barrier islands were breached, and the erosion of beaches and dunes occurred along most of the South Atlantic coast. The U.S. Geological Survey (USGS) fore-casted potential coastal-change effects—including dune erosion and overwash that can threaten coastal resources and infrastructure—to assist with pre-storm management decisions. Following the storm, oblique aerial photography was collected, and lidar topographic survey missions were flown. These two datasets were used to document the changes that resulted from the storm and to validate coastal change forecasts. Comparisons of pre- and post-storm photographs were used to characterize the nature, extent, and spatial variability of hurricane-induced coastal changes. Analyses of pre- and post-storm lidar eleva-tions were used to quantify magnitudes of change in shoreline positions, dune elevations, and beach volumes. Erosion was observed along the coast from Florida to North Carolina; however, the coastal response exhibited extensive spatial variability, as would be expected over such a large region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191095","usgsCitation":"Birchler, J.J., Doran, K.S., Long, J.W., and Stockdon, H.F., 2019, Hurricane Matthew—Predictions, observations, and an analysis of coastal change: U.S. Geological Survey Open-File Report 2019–1095, 37 p., https://doi.org/10.3133/ofr20191095.","productDescription":"x, 37 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103132","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":437304,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BW6CG6","text":"USGS data release","linkHelpText":"Storm-Induced Overwash Extent"},{"id":368353,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1095/ofr20191095.pdf","text":"Report","size":"9.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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\"}}]}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
The spatial and temporal distribution of Pliocene to Holocene Colorado River deposits (southwestern USA and northwestern Mexico) form a primary data set that records the evolution of a continental-scale river system and helps to delineate and quantify the magnitude of regional deformation. We focus in particular on the age and distribution of ancestral Colorado River deposits from field observations, geologic mapping, and subsurface studies in the area downstream from Grand Canyon (Arizona, USA). A new 4.73 ± 0.17 Ma age is reported for a basalt that flowed down Grand Wash to near its confluence with the Colorado River at the eastern end of what is now Lake Mead (Arizona and Nevada). That basalt flow, which caps tributary gravels, another previously dated 4.49 ± 0.46 Ma basalt flow that caps Colorado River gravel nearby, and previously dated speleothems (2.17 ± 0.34 and 3.87 ± 0.1 Ma) in western Grand Canyon allow for the calculation of long-term incision rates. Those rates are ~90 m/Ma in western Grand Canyon and ~18–64 m/Ma in the eastern Lake Mead area. In western Lake Mead and downstream, the base of 4.5–3.5 Ma ancestral Colorado River deposits, called the Bullhead Alluvium, is generally preserved below river level, suggesting little if any bedrock incision since deposition. Paleoprofiles reconstructed using ancestral river deposits indicate that the lower Colorado River established a smooth profile that has been graded to near sea level since ca. 4.5 Ma. Steady incision rates in western Grand Canyon over the past 0.6–4 Ma also suggest that the lower Colorado River has remained in a quasi–steady state for millions of years with respect to bedrock incision. Differential incision between the lower Colorado River corridor and western Grand Canyon is best explained by differential uplift across the Lake Mead region, as the overall 4.5 Ma profile of the Colorado River remains graded to Pliocene sea level, suggesting little regional subsidence or uplift. Cumulative estimates of ca. 4 Ma offsets across faults in the Lake Mead region are similar in magnitude to the differential incision across the area during the same approximate time frame. This suggests that in the past ~4 Ma, vertical deformation in the Lake Mead area has been localized along faults, which may be a surficial response to more deep-seated processes. Together these data sets suggest ~140–370m of uplift in the past 2–4 Ma across the Lake Mead region.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02020.1","usgsCitation":"Crow, R.S., Howard, K.A., Beard, L.S., Pearthree, P., House, K., Karlstrom, K., Lisa Peters, McIntosh, W.C., Cassidy, C., Felger, T.J., and Block, D., 2019, Insights into post-Miocene uplift of the western margin of the Colorado Plateau from the stratigraphic record of the lower Colorado River: Geosphere, v. 15, no. 6, p. 1826-1845, https://doi.org/10.1130/GES02020.1.","productDescription":"20 p.","startPage":"1826","endPage":"1845","ipdsId":"IP-072908","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":459490,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02020.1","text":"Publisher Index Page"},{"id":368729,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California, Nevada","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.97167968750001,\n 31.541089879585808\n ],\n [\n -112.236328125,\n 31.541089879585808\n ],\n [\n -112.236328125,\n 36.94989178681327\n ],\n [\n -115.97167968750001,\n 36.94989178681327\n ],\n [\n -115.97167968750001,\n 31.541089879585808\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Crow, Ryan S. 0000-0002-2403-6361 rcrow@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-6361","contributorId":5792,"corporation":false,"usgs":true,"family":"Crow","given":"Ryan","email":"rcrow@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howard, Keith A. 0000-0002-6462-2947 khoward@usgs.gov","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":3439,"corporation":false,"usgs":true,"family":"Howard","given":"Keith","email":"khoward@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beard, L. Sue 0000-0001-9552-1893 sbeard@usgs.gov","orcid":"https://orcid.org/0000-0001-9552-1893","contributorId":152,"corporation":false,"usgs":true,"family":"Beard","given":"L.","email":"sbeard@usgs.gov","middleInitial":"Sue","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pearthree, Phil","contributorId":218167,"corporation":false,"usgs":false,"family":"Pearthree","given":"Phil","email":"","affiliations":[{"id":39771,"text":"AZGS","active":true,"usgs":false}],"preferred":false,"id":768528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"House, Kyle 0000-0002-0019-8075 khouse@usgs.gov","orcid":"https://orcid.org/0000-0002-0019-8075","contributorId":2293,"corporation":false,"usgs":true,"family":"House","given":"Kyle","email":"khouse@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Karlstrom, Karl","contributorId":218165,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Karl","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":768522,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lisa Peters","contributorId":179357,"corporation":false,"usgs":false,"family":"Lisa Peters","affiliations":[],"preferred":false,"id":768529,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McIntosh, William C.","contributorId":191163,"corporation":false,"usgs":false,"family":"McIntosh","given":"William","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":768526,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cassidy, Colleen 0000-0003-2963-9185","orcid":"https://orcid.org/0000-0003-2963-9185","contributorId":207193,"corporation":false,"usgs":true,"family":"Cassidy","given":"Colleen","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768530,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Felger, Tracey J. 0000-0003-0841-4235 tfelger@usgs.gov","orcid":"https://orcid.org/0000-0003-0841-4235","contributorId":1117,"corporation":false,"usgs":true,"family":"Felger","given":"Tracey","email":"tfelger@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768531,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Block, Debra 0000-0001-7348-3064 dblock@usgs.gov","orcid":"https://orcid.org/0000-0001-7348-3064","contributorId":198448,"corporation":false,"usgs":true,"family":"Block","given":"Debra","email":"dblock@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768527,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70205944,"text":"sir20195108 - 2019 - Spatial and temporal distribution of bacterial indicators and microbial-source tracking within Tumacácori National Historical Park and the upper Santa Cruz River, southern Arizona and northern Mexico, 2015–2016","interactions":[],"lastModifiedDate":"2019-10-15T14:56:51","indexId":"sir20195108","displayToPublicDate":"2019-10-15T09:30:42","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5108","displayTitle":"Spatial and Temporal Distribution of Bacterial Indicators and Microbial-Source Tracking within Tumacácori National Historical Park and the Upper Santa Cruz River, Southern Arizona and Northern Mexico, 2015–2016","title":"Spatial and temporal distribution of bacterial indicators and microbial-source tracking within Tumacácori National Historical Park and the upper Santa Cruz River, southern Arizona and northern Mexico, 2015–2016","docAbstract":"Tumacácori National Historical Park (TUMA) in southern Arizona protects the culturally important Mission San José de Tumacácori, while also managing a part of the ecologically diverse riparian corridor of the Santa Cruz River. The quality of the water flowing through depends solely on upstream watershed activities, and among the water-quality issues concerning TUMA is the microbiological pathogens in the river introduced by human and animal sources that pose a significant human health risk to employees and visitors. The U.S. Geological Survey (USGS) conducted a 3-year study to understand the sources, timing, and distribution of the fecal-indicator bacteria Escherichia coli (E. coli) within TUMA and the upstream watershed.
The information provided in this investigation is a result of a comprehensive approach to quantify the spatial and temporal variability of E. coli and suspended sediment in the Upper Santa Cruz River Watershed. Several types of flow were sampled from base flow to flood flow and at high frequency intervals (rise, peak, and recession) to determine daily variability, as well as seasonal variability. Hydrologic data collection and estimation techniques were used to establish a hydrologic relation with E. coli and suspended sediment. Furthermore, source tracking was used to describe the potential sources of E. coli. Models were developed that are expected to be useful for predicting E. coli concentrations to help TUMA managers understand instantaneous conditions to keep the public and staff informed about potentially harmful water-quality conditions. In addition, the concentration, flux, and source information will provide more accurate data for other surface-water modeling and can be useful in the development of total maximum daily load standards. This will help TUMA describe the water-quality conditions at the park and waters flowing through the park, as well as prioritize and help carry out future best-management actions to address these issues.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Estimates of the magnitudes of annual peak streamflows with annual exceedance probabilities of 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, and 0.002 (equivalent to recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively) were computed for 391 streamgages in Ohio and adjacent states based on data collected through the 2015 water year. The flood-frequency estimates were computed following guidance outlined in Bulletin 17C, developed by the Advisory Committee on Water Information. The Bulletin 17C guidelines retain the basic statistical framework of the superseded Bulletin 17B guidelines; however, the Bulletin 17C guidelines add several enhancements including an improved method of moments approach for fitting the log-Pearson Type III (LPIII) distribution to the flood peaks (called the expected moments algorithm), a generalization of the Grubbs Beck low-outlier test (called the Multiple Grubbs Beck test) that permits identification of multiple potentially influential low floods, and new methods for estimating regional skew and uncertainty.
Equations for estimating flood-frequency characteristics at ungaged sites on rural, unregulated streams in Ohio were developed with a two-step process involving ordinary least-squares and generalized least-squares regression techniques. Data from 333 streamgages with 10 or more years of unregulated record were screened for redundancy and a regression dataset was selected that was composed of flood-frequency and basin-characteristic data for 275 streamgages in Ohio and adjacent states. Two sets of equations were developed—one set, referred to as the “simple model,” uses regression region and drainage area as regressor variables, and a second set, referred to as the “full model,” uses regression region, drainage area, main-channel slope, and the percentage of the watershed covered by water and wetlands as regressor variables.
The average standard errors of prediction ranged from about 40.5 to 46.5 percent for the simple-model equations and from about 37.2 to 40.3 percent for the full-model equations. For sites meeting the rural, unregulated criteria, flood-frequency estimates determined by means of LPIII analyses are reported along with weighted flood-frequency estimates, computed as a function of the LPIII estimates and the regression estimates. For sites with homogenous periods of regulation, flood-frequency estimates determined by means of LPIII analyses are reported. Ninety-five percent confidence limits are reported for all estimates.
Values of regressor variables were determined from digital spatial datasets by means of a geographic information system (GIS). The GIS datasets and the new full-model equations have been incorporated into Ohio’s StreamStats application, a web-based, GIS-backed system designed to facilitate the estimation of streamflow statistics at ungaged locations on streams.
Seasonal patterns in peak flows were assessed for 295 streamgages in Ohio. Annual peak flows occurred most frequently between January and April, with March having the highest frequency of occurrence. The month with the fewest number of annual peaks was October. Peak-of-record flows occurred most frequently in March, followed by January (months in which two of Ohio’s most severe widespread floods in recent history occurred). None of the peak-of-record flows occurred in October and only two occurred in November.
Temporal trend in annual peak flows were assessed for 133 streamgages on unregulated streams in Ohio with 30 or more years of systematic record. Trends were assessed by computing the rank correlation (as measured with the two-sided Kendall’s tau statistic) between time and annual peak flows. Weak but statistically significant trends were indicated at 15 of the 133 streamgages. Of the 15 streamgages with significant trend in annual peak flows, 12 had an upward trend (positive tau) and 3 had a downward trend (negative tau). All 12 streamgages with positive tau values were at latitudes north of 40°33', and streamgages with negative tau values were at latitudes south of 40°33'.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195018","collaboration":"Prepared in cooperation with the Ohio Department of Transportation","usgsCitation":"Koltun, G.F., 2019, Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams: U.S. Geological Survey Scientific Investigations Report 2019–5018, 25 p., https://doi.org/10.3133/sir20195018.","productDescription":"Report: vi, 25 p.; 2 Tables; Appendices 1.1-1.8; Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100946","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368118,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5018/sir20195018.pdf","text":"Report ","size":"6.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5018"},{"id":368119,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5018/sir20195018_table_1.xlsx","text":"Table 1","size":"203 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5018 Table 1","linkHelpText":"– Flood-frequency characteristics of unregulated streamgages."},{"id":368117,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5018/coverthb.jpg"},{"id":368120,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5018/sir20195018_table_2.xlsx","text":"Table 2","size":"33.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5018 Table 2","linkHelpText":"– Flood-frequency characteristics of regulated streamgages."},{"id":368121,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5018/sir20195018_appendix_tables","text":"Appendix tables 1.1 to 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\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Ste 100
Columbus, OH 43229–1737