The closure of Glen Canyon Dam in 1963 resulted in drastic changes to water clarity, temperature, and flow of the Colorado River in Glen, Marble, and Grand Canyons. The Colorado River is now much clearer, water temperature is less variable throughout the year, and the river is much colder in the summer months. The flow—regulated by the dam—is now less variable annually, but has larger daily fluctuations than during pre-dam times. All of these changes have resulted in a different fish community and different food resources for fish than existed before the dam was built. Recent monitoring of water clarity, by measuring turbidity, has helped scientists and river managers understand modern water-clarity patterns in the dam-regulated Colorado River. These data were then used to estimate pre-dam turbidity in the Colorado River in order to make comparisons of pre-dam and dam-regulated conditions, which are useful for assessing biological changes in the river over time. Prior to dam construction, the large sediment load resulted in low water clarity almost all of the time, a condition which was more favorable for the native fish community.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163053","usgsCitation":"Voichick, N., Kennedy, T.A., Topping, D.J., Griffiths, R.E., and Fry, K.L., 2016, Water clarity of the Colorado River—Implications for food webs and fish communities: U.S. Geological Survey Fact Sheet 2016–5053, 4 p., https://dx.doi.org/10.3133/fs20163053.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-067865","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":330598,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3053/fs20163053.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3053"},{"id":330597,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3053/coverthb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Nevada Utah, New Mexico","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.52392578125,\n 39.87601941962116\n ],\n [\n -104.62280273437499,\n 39.06184913429154\n ],\n [\n -106.226806640625,\n 38.487994609214795\n ],\n [\n -107.962646484375,\n 38.039438891821746\n ],\n [\n -108.06152343749999,\n 37.31775185163688\n ],\n [\n -108.6328125,\n 36.359374956015856\n ],\n [\n -109.16015624999999,\n 35.24561909420681\n ],\n [\n -109.127197265625,\n 32.90726224488304\n ],\n [\n -109.13818359375,\n 31.484893386890164\n ],\n [\n -111.126708984375,\n 31.316101383495624\n ],\n [\n -114.80712890625,\n 32.47269502206151\n ],\n [\n -114.697265625,\n 32.731840896865684\n ],\n [\n -114.93896484374999,\n 34.415973384481866\n ],\n [\n -114.97192382812499,\n 35.594785665487244\n ],\n [\n -114.78515624999999,\n 36.85325222344018\n ],\n [\n -114.32373046875,\n 37.49229399862877\n ],\n [\n -113.477783203125,\n 37.49229399862877\n ],\n [\n -112.19238281249999,\n 37.60552821745789\n ],\n [\n -111.236572265625,\n 38.09998264736481\n ],\n [\n -110.0830078125,\n 38.7283759182398\n ],\n [\n -109.281005859375,\n 39.554883059924016\n ],\n [\n -108.182373046875,\n 39.9434364619742\n ],\n [\n -106.3916015625,\n 39.93501296038254\n ],\n [\n -104.52392578125,\n 39.87601941962116\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GCMRC Staff, Southwest Biological Science Center
U.S. Geological Survey
Grand Canyon Monitoring and Research Center
2255 N. Gemini Drive
Flagstaff, AZ 86001
http://www.gcmrc.gov/
The upper Santa Cruz Basin is an important groundwater basin containing the regional aquifer for the city of Nogales, Arizona. This report provides data and interpretations of data aimed at better understanding the bedrock morphology and structure of the upper Santa Cruz Basin study area which encompasses the Rio Rico and Nogales 1:24,000-scale U.S. Geological Survey quadrangles. Data used in this report include the Arizona Aeromagnetic and Gravity Maps and Data referred to here as the 1996 Patagonia Aeromagnetic survey, Bouguer gravity anomaly data, and conductivity-depth transforms (CDTs) from the 1998 Santa Cruz transient electromagnetic survey (whose data are included in appendixes 1 and 2 of this report).
Analyses based on magnetic gradients worked well to identify the range-front faults along the Mt. Benedict horst block, the location of possibly fault-controlled canyons to the west of Mt. Benedict, the edges of buried lava flows, and numerous other concealed faults and contacts. Applying the 1996 Patagonia aeromagnetic survey data using the horizontal gradient method produced results that were most closely correlated with the observed geology.
The 1996 Patagonia aeromagnetic survey was used to estimate depth to bedrock in the upper Santa Cruz Basin study area. Three different depth estimation methods were applied to the data: Euler deconvolution, horizontal gradient magnitude, and analytic signal. The final depth to bedrock map was produced by choosing the maximum depth from each of the three methods at a given location and combining all maximum depths. In locations of rocks with a known reversed natural remanent magnetic field, gravity based depth estimates from Gettings and Houser (1997) were used.
The depth to bedrock map was supported by modeling aeromagnetic anomaly data along six profiles. These cross sectional models demonstrated that by using the depth to bedrock map generated in this study, known and concealed faults, measured and estimated magnetic susceptibilities of rocks found in the study area, and estimated natural remanent magnetic intensities and directions, reasonable geologic models can be built. This indicates that the depth to bedrock map is reason-able and geologically possible.
Finally, CDTs derived from the 1998 Santa Cruz Basin transient electromagnetic survey were used to help identify basin structure and some physical properties of the basin fill in the study area. The CDTs also helped to confirm depth to bedrock estimates in the Santa Cruz Basin, in particular a region of elevated bedrock in the area of Potrero Canyon, and a deep basin in the location of the Arizona State Highway 82 microbasin. The CDTs identified many concealed faults in the study area and possibly indicate deep water-saturated clay-rich sediments in the west-central portion of the study area. These sediments grade to more sand-rich saturated sediments to the south with relatively thick, possibly unsaturated, sediments at the surface. Also, the CDTs may indicate deep saturated clay-rich sediments in the Highway 82 microbasin and in the Mount Benedict horst block from Proto Canyon south to the international border.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161152","usgsCitation":"Bultman, M.W., and Page, W.R., 2016, Bedrock morphology and structure, upper Santa Cruz Basin, south-central Arizona, with transient electromagnetic survey data: U.S. Geological Survey Open-File Report 2016–1152, 49 p., https://dx.doi.org/10.3133/ofr20161152.","productDescription":"Report: viii, 49 p.; 2 Plates: 36.00 x 37.00 inches and 38.00 x 36.50 inches; 2 Appendixes; Read Me","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-060430","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":330512,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1152/ofr20161152_Appendix1.zip","text":"Appendix 1. Santa Cruz Transient Electromagnetic Survey Conductivity-Depth Transforms (CDT) Plots","size":"11.7 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2016-1152 Appendix 1"},{"id":330439,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1152/coverthb.jpg"},{"id":330513,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1152/ofr20161152_Appendix2.zip","text":"Appendix 2. Santa Cruz Transient Electromagnetic Survey Data","size":"45.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2016-1152 Appendix 2"},{"id":330440,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1152/ofr20161152.pdf","text":"Report","size":"8.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1152 Report"},{"id":330441,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2016/1152/ofr2011152_Readme.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1152 Read Me"},{"id":330514,"rank":6,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2016/1152/ofr20161152_plate_1.pdf","text":"Plate 1 Map showing potential field boundaries plotted over upper Santa Cruz Basin study area geology","size":"135 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1152 Plate 1"},{"id":330515,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2016/1152/ofr20161152_plate_2.pdf","text":"Plate 2 Map showing conductivity-depth transforms plotted over upper Santa Cruz Basin study area geology","size":"101 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1152 Plate 2"}],"country":"United States","state":"Arizona","otherGeospatial":"Upper Santa Cruz Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.74356079101562,\n 31.33604401284106\n ],\n [\n -110.74356079101562,\n 31.77837995377096\n ],\n [\n -111.18026733398438,\n 31.77837995377096\n ],\n [\n -111.181640625,\n 31.36653633110671\n ],\n [\n -111.07452392578125,\n 31.33252503230784\n ],\n [\n -110.74356079101562,\n 31.33604401284106\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Geosciences and Environmental Change Science Center
Box 25046, Mail Stop 980
Denver, CO 80225
Nutrients, particularly nitrogen and phosphorus, are a leading cause of water-quality impairment in Kansas and the Nation. Indian Creek is one of the most urban drainage basins in Johnson County, Kansas, and environmental and biological conditions are affected by contaminants from point and other urban sources. The Johnson County Douglas L. Smith Middle Basin (hereinafter Middle Basin) wastewater treatment facility (WWTF) is the largest point-source discharge on Indian Creek. A second facility, the Tomahawk Creek WWTF, discharges into Indian Creek approximately 11.6 kilometers downstream from the Middle Basin WWTF. To better characterize the spatiotemporal variability of nutrients in Indian Creek, the U.S. Geological Survey, in cooperation with the Kansas Department of Health and Environment and Johnson County Wastewater, collected high-resolution spatial and temporal (a large number of samples collected over the entire reach or at single locations over a long period of time) inorganic nutrient (nitrate plus nitrite and orthophosphorus) data using a combination of discrete samples and sensor-measured data during 2012 through 2015.
Nutrient patterns observed in Indian Creek along the upstream-downstream gradient during wastewater effluent dominated streamflow conditions were largely affected by the WWTFs and by travel time of the parcels of water. Nitrate plus nitrite concentrations in the Middle Basin WWTF effluent and at downstream sites varied by as much as 6 milligrams per liter over a 24-hour period. The cyclical variability in the Middle Basin WWTF effluent generated a nitrate plus nitrite pulse that could be tracked for approximately 11.5 kilometers downstream in Indian Creek, until the effect was masked by the Tomahawk Creek WWTF effluent discharge. All longitudinal surveys showed the same general patterns along the upstream-downstream gradient, though streamflows, wastewater effluent contributions to streamflow, and nutrient concentrations spanned a wide range. Differences in orthophosphorus and nitrate plus nitrite patterns were clear along the upstream-downstream gradient in Indian Creek, and orthophosphorus concentrations were not as variable as nitrate plus nitrite concentrations. In general, nitrate plus nitrite concentrations decreased downstream from the Middle Basin WWTF to minima near the confluence with Tomahawk Creek, increased downstream from the Tomahawk Creek WWTF, and then varied little within the study reach. Orthophosphorus concentrations generally decreased downstream from the Middle Basin WWTF.
Despite the marked variability in nitrate plus nitrite concentrations caused by the Middle Basin WWTF effluent discharges, decreases in nitrate plus nitrite concentrations were discernable along the study reach between the two WWTFs. Decreases in nitrate plus nitrite concentrations along study reach were less variable than the cyclical variability typically measured, reiterating the effect of the Middle Basin WWTF effluent discharges on the spatiotemporal variability of nitrate plus nitrite in Indian Creek. Although decreases and rates of change in nitrate plus nitrite concentration were similar between the upper and lower reaches of Indian Creek, relations with initial nitrate plus nitrite concentrations and seasonal patterns were different between the upper (from College to the Marty study sites) and lower reaches (from Marty to the Mission Farms study sites) and did not reflect patterns observed for the overall reach. Quantifying the decreases in nitrate plus nitrite concentration caused by dilution and other in-stream processes were beyond the scope of this study, and were limited by available data. The data that are available suggest that dilution and other in-stream processes play a role in decreasing nitrate plus nitrite concentrations downstream from the Middle Basin WWTF in Indian Creek.
Analysis of the spatiotemporal variability of nutrients focused on below-normal and normal streamflow conditions, when streamflow and nutrient conditions in Indian Creek were largely controlled by WWTF effluent flows and nutrient removal processes. Spatial and temporal data indicate there are decreases in nutrient concentrations along the upstream-downstream gradient in Indian Creek, but quantifying decreases is complicated by the variability in nutrient concentrations caused by the WWTFs. During below-normal and normal streamflow conditions, Indian Creek nutrient concentrations downstream from the Middle Basin WWTF primarily reflect effluent concentrations in the hours or days before depending on relative distance downstream.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165147","collaboration":"Prepared in cooperation with the Kansas Department of Health and Environment and Johnson County Wastewater","usgsCitation":"Foster, G.M, Graham, J.L., Williams, T.J., and King, L.R., 2016, Spatiotemporal variability of inorganic nutrients during wastewater effluent dominated streamflow conditions in Indian Creek, Johnson County, Kansas, 2012–15: U.S. Geological Survey Scientific Investigations Report 2016–5147, 37 p., https://dx.doi.org/10.3133/sir20165147.","productDescription":"Report: v, 36 p.; Appendixes 1-4; Data Releases","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077541","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":438525,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7445JN8","text":"USGS data release","linkHelpText":"Water-quality data from four Indian Creek sites, Johnson County, Kansas, July 22-25, 2014 and August 21-27, 2015"},{"id":438524,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77W69CP","text":"USGS data release","linkHelpText":"Spatial water-quality data for Indian Creek, Johnson County, Kansas, May 23, 2013, July 23, 2014, July 30, 2015, and August 26, 2015"},{"id":330584,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F77W69CP","text":"USGS data release - Spatial water-quality data for Indian Creek, Johnson County, Kansas, May 23, 2013, July 23, 2014, July 30, 2015, and August 26, 2015"},{"id":330581,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5147/sir20165147_appendix.pdf","text":"Appendixes 1–4","size":"698 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5147 Appendixes 1–4"},{"id":330585,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7445JN8","text":"USGS data release - Water-quality data from four Indian Creek sites, Johnson County, Kansas, July 22-25, 2014 and August 21-27, 2015"},{"id":330579,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5147/coverthb.jpg"},{"id":330580,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5147/sir20165147.pdf","text":"Report","size":"3.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5147"}],"country":"United States","state":"Kansas","county":"Johnson County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-94.6075,39.0437],[-94.6075,39.0399],[-94.6082,38.8463],[-94.6084,38.8341],[-94.6102,38.7376],[-95.0572,38.7395],[-95.0558,38.9816],[-95.0477,38.9778],[-95.0383,38.9771],[-95.0312,38.9773],[-95.0292,38.9813],[-95.0271,38.9881],[-95.0249,38.9962],[-95.0189,38.9987],[-95.0135,38.9991],[-95.0077,38.998],[-94.9946,38.9976],[-94.9899,38.997],[-94.9841,38.995],[-94.9789,38.9926],[-94.9755,38.9885],[-94.9704,38.9851],[-94.9645,38.9832],[-94.9575,38.982],[-94.9527,38.9828],[-94.9479,38.9845],[-94.9448,38.9871],[-94.9423,38.9898],[-94.9386,38.9933],[-94.9367,38.9964],[-94.9335,38.9995],[-94.9264,38.9998],[-94.9217,38.9996],[-94.9176,38.9977],[-94.9209,38.9919],[-94.923,38.9856],[-94.9207,38.9837],[-94.9164,38.9859],[-94.9115,38.9889],[-94.9078,38.9924],[-94.9014,39.0022],[-94.8989,39.0053],[-94.8945,39.0102],[-94.8919,39.0155],[-94.891,39.021],[-94.8875,39.0313],[-94.8824,39.0379],[-94.8768,39.0441],[-94.8681,39.052],[-94.8631,39.0564],[-94.8488,39.0578],[-94.8318,39.0546],[-94.8131,39.0486],[-94.8038,39.0456],[-94.7197,39.0435],[-94.6693,39.0433],[-94.6075,39.0437]]]},\"properties\":{\"name\":\"Johnson\",\"state\":\"KS\"}}]}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
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Lawrence, KS 66049
Consistent and reliable sediment data are needed by Federal, State, and local government agencies responsible for monitoring water quality, planning river restoration, quantifying sediment budgets, and evaluating the effectiveness of sediment reduction strategies. Heightened concerns about excessive sediment in rivers and the challenge to reduce costs and eliminate data gaps has guided Federal and State interests in pursuing alternative methods for measuring suspended and bedload sediment. Simple and dependable data collection and estimation techniques are needed to generate hydraulic and water-quality information for areas where data are unavailable or difficult to collect.
The U.S. Geological Survey, in cooperation with the Minnesota Pollution Control Agency and the Minnesota Department of Natural Resources, completed a study to evaluate the use of dimensionless sediment rating curves (DSRCs) to accurately predict suspended-sediment concentrations (SSCs), bedload, and annual sediment loads for selected rivers and streams in Minnesota based on data collected during 2007 through 2013. This study included the application of DSRC models developed for a small group of streams located in the San Juan River Basin near Pagosa Springs in southwestern Colorado to rivers in Minnesota. Regionally based DSRC models for Minnesota also were developed and compared to DSRC models from Pagosa Springs, Colorado, to evaluate which model provided more accurate predictions of SSCs and bedload in Minnesota.
Multiple measures of goodness-of-fit were developed to assess the effectiveness of DSRC models in predicting SSC and bedload for rivers in Minnesota. More than 600 dimensionless ratio values of SSC, bedload, and streamflow were evaluated and delineated according to Pfankuch stream stability categories of “good/fair” and “poor” to develop four Minnesota-based DSRC models. The basis for Pagosa Springs and Minnesota DSRC model effectiveness was founded on measures of goodness-of-fit that included proximity of the model(s) fitted line to the 95-percent confidence intervals of the site-specific model, Nash-Sutcliffe Efficiency values, model biases, and deviation of annual sediment loads from each model to the annual sediment loads calculated from measured data.
Composite plots comparing Pagosa Springs DSRCs, Minnesota DSRCs, site-specific regression models, and measured data indicated that regionally developed DSRCs (Minnesota DSRC models) more closely approximated measured data for nearly every site. Pagosa Springs DSRC models had markedly larger exponents (slopes) when compared to the Minnesota DSRC models and the site-specific regression models, and over-represent SSC and bedload at streamflows exceeding bankfull. The Nash-Sutcliffe Efficiency values for the Minnesota DSRC model for suspended-sediment concentrations closely matched Nash-Sutcliffe Efficiency values of the site-specific regression models for 12 out of 16 sites. Nash-Sutcliffe Efficiency values associated with Minnesota DSRCs were greater than those associated with Pagosa Springs DSRCs for every site except the Whitewater River near Beaver, Minnesota site. Pagosa Springs DSRC models were less accurate than the mean of the measured data at predicting SSC values for one-half of the sites for good/fair stability sites and one-half of the sites for poor stability sites. Relative model biases were calculated and determined to be substantial (greater than 5 percent) for Pagosa Springs and Minnesota models, with Minnesota models having a lower mean model bias. For predicted annual suspended-sediment loads (SSL), the Minnesota DSRC models for good/fair and poor stream stability sites more closely approximated the annual SSLs calculated from the measured data as compared to the Pagosa Springs DSRC model.
Results of data analyses indicate that DSRC models developed using data collected in Minnesota were more effective at compensating for differences in individual stream characteristics across a variety of basin sizes and flow regimes than DSRC models developed using data collected for Pagosa Springs, Colorado. Minnesota DSRC models retained a substantial portion of the unique sediment signatures for most rivers, although deviations were observed for streams with limited sediment supply and for rivers in southeastern Minnesota, which had markedly larger regression exponents. Compared to Pagosa Springs DSRC models, Minnesota DSRC models had regression slopes that more closely matched the slopes of site-specific regression models, had greater Nash-Sutcliffe Efficiency values, had lower model biases, and approximated measured annual sediment loads more closely. The results presented in this report indicate that regionally based DSRCs can be used to estimate reasonably accurate values of SSC and bedload.
Practitioners are cautioned that DSRC reliability is dependent on representative measures of bankfull streamflow, SSC, and bedload. It is, therefore, important that samples of SSC and bedload, which will be used for estimating SSC and bedload at the bankfull streamflow, are collected over a range of conditions that includes the ascending and descending limbs of the event hydrograph. The use of DSRC models may have substantial limitations for certain conditions. For example, DSRC models should not be used to predict SSC and sediment loads for extreme streamflows, such as those that exceed twice the bankfull streamflow value because this constitutes conditions beyond the realm of current (2016) empirical modeling capability. Also, if relations between SSC and streamflow and between bedload and streamflow are not statistically significant, DSRC models should not be used to predict SSC or bedload, as this could result in large errors. For streams that do not violate these conditions, DSRC estimates of SSC and bedload can be used for stream restoration planning and design, and for estimating annual sediment loads for streams where little or no sediment data are available.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165146","collaboration":"Prepared in cooperation with the Minnesota Pollution Control Agency and the Minnesota Department of Natural Resources","usgsCitation":"Ellison, C.A., Groten, J.T., Lorenz, D.L., and Koller, K.S., 2016, Application of dimensionless sediment rating curves to predict suspended-sediment concentrations, bedload, and annual sediment loads for rivers in Minnesota (ver. 1.1, January 2020): U.S. Geological Survey Scientific Investigations Report 2016–5146, 68 p., https://dx.doi.org/10.3133/sir20165146.","productDescription":"Report: ix, 68 p.; 3 Appendix Tables","startPage":"1","endPage":"68","numberOfPages":"82","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078848","costCenters":[{"id":392,"text":"Minnesota Water Science 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\"}}]}","contact":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
Information on low-flow characteristics of streams is essential for the management of water resources. This report provides equations for estimating the 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years and the harmonic-mean flow at ungaged, unregulated stream sites in Indiana. These equations were developed using the low-flow statistics and basin characteristics for 108 continuous-record streamgages in Indiana with at least 10 years of daily mean streamflow data through the 2011 climate year (April 1 through March 31). The equations were developed in cooperation with the Indiana Department of Environmental Management.
Regression techniques were used to develop the equations for estimating low-flow frequency statistics and the harmonic-mean flows on the basis of drainage-basin characteristics. A geographic information system was used to measure basin characteristics for selected streamgages. A final set of 25 basin characteristics measured at all the streamgages were evaluated to choose the best predictors of the low-flow statistics.
Logistic-regression equations applicable statewide are presented for estimating the probability that selected low-flow frequency statistics equal zero. These equations use the explanatory variables total drainage area, average transmissivity of the full thickness of the unconsolidated deposits within 1,000 feet of the stream network, and latitude of the basin outlet. The percentage of the streamgage low-flow statistics correctly classified as zero or nonzero using the logistic-regression equations ranged from 86.1 to 88.9 percent.
Generalized-least-squares regression equations applicable statewide for estimating nonzero low-flow frequency statistics use total drainage area, the average hydraulic conductivity of the top 70 feet of unconsolidated deposits, the slope of the basin, and the index of permeability and thickness of the Quaternary surficial sediments as explanatory variables. The average standard error of prediction of these regression equations ranges from 55.7 to 61.5 percent.
Regional weighted-least-squares regression equations were developed for estimating the harmonic-mean flows by dividing the State into three low-flow regions. The Northern region uses total drainage area and the average transmissivity of the entire thickness of unconsolidated deposits as explanatory variables. The Central region uses total drainage area, the average hydraulic conductivity of the entire thickness of unconsolidated deposits, and the index of permeability and thickness of the Quaternary surficial sediments. The Southern region uses total drainage area and the percent of the basin covered by forest. The average standard error of prediction for these equations ranges from 39.3 to 66.7 percent.
The regional regression equations are applicable only to stream sites with low flows unaffected by regulation and to stream sites with drainage basin characteristic values within specified limits. Caution is advised when applying the equations for basins with characteristics near the applicable limits and for basins with karst drainage features and for urbanized basins. Extrapolations near and beyond the applicable basin characteristic limits will have unknown errors that may be large. Equations are presented for use in estimating the 90-percent prediction interval of the low-flow statistics estimated by use of the regression equations at a given stream site.
The regression equations are to be incorporated into the U.S. Geological Survey StreamStats Web-based application for Indiana. StreamStats allows users to select a stream site on a map and automatically measure the needed basin characteristics and compute the estimated low-flow statistics and associated prediction intervals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165102","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management","usgsCitation":"Martin, G.R., Fowler, K.K., and Arihood, L.D., 2016, Estimating selected low-flow frequency statistics and harmonic-mean flows for ungaged, unregulated streams in Indiana (ver 1.1, October 2016): U.S. Geological Survey Scientific Investigations Report 2016–5102, 45 p., https://dx.doi.org/10.3133/sir20165102.","productDescription":"Report: vii, 45 p.; Metadata; Read Me File; Spatial Data","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066895","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":327994,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2016/5102/readme.pdf","text":"Additional information about SIR 2016–5102 - read me 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\"}}]}","edition":"Version 1.0: Originally posted September 6, 2016; Version 1.1: October 24, 2016","contact":"Director, Indiana-Kentucky Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd
Indianapolis, IN 46278
In January 2014, a storage tank leaked, spilling a large quantity of 4-methylcyclohexane methanol into the Elk River in West Virginia and contaminating the water supply for more than 300,000 people. In response, the West Virginia Legislature passed Senate Bill 373, which requires the West Virginia Department of Health and Human Resources (WVDHHR) to assess the susceptibility and vulnerability of public surface-water-influenced groundwater supply sources (SWIGS) and surface-water intakes statewide. In response to this mandate for reassessing SWIGS statewide, the U.S. Geological Survey (USGS), in cooperation with the WVDHHR, Bureau of Public Health, Office of Environmental Health Services, compiled available data and summarized the results of previous groundwater studies to provide the WVDHHR with data that could be used as part of the process for assessing and determining SWIGS.
\nExisting geologic, hydrologic, well-construction, water-quality, and other related data and information from previous U.S. Geological Survey (USGS) hydrogeologic studies and the USGS National Water Information System (NWIS) database, in conjunction with data from the West Virginia Bureau for Public Health (WVBPH) Department of Health and Human Resources (WVDHHR) and the West Virginia Department of Environmental Protection database and files, were collected, compiled, and analyzed to help the WVDHHR to better assess public groundwater supply wells that may meet the definition of a surface-water-influenced- groundwater supply (SWIGS).
\nIn this study, measures of intrinsic susceptibility, which are characterized by the physical properties that affect the ease with which water moves through the unsaturated zone and, subsequently, into the saturated zone within an aquifer, showed that karst limestone aquifers are the aquifers most intrinsically susceptible to contamination within the State of West Virginia. Karst limestone aquifers are present within Cambrian- and Ordovician-age formations within West Virginia’s eastern panhandle and in Mississippian-age limestones within the Greenbrier River valley. Solution development within these limestone aquifers allows rapid recharge and flow of groundwater within the aquifer, both of which allow surface contaminants to easily enter the aquifer and travel long distances in a short period of time.
\nAlluvial aquifers bordering the Ohio River in western West Virginia are also potentially highly susceptible to contamination because these alluvial aquifers can receive significant recharge from the adjacent Ohio River. Any potential contaminants that may be present in the river have the potential to enter the aquifer and contaminate wells completed within the sand and gravel alluvial sediments within which the wells are completed. These same alluvial sediments, however, help to retard the movement of bacteria and other potentially pathogenic organisms, such as Cryptosporidia and Giardia lamblia, into the aquifer. As a result, samples from alluvial aquifers bordering the Ohio River and elsewhere within the State do not commonly test positive for indicator bacteria, such as total coliform, fecal coliform, or Escherichia coli (E. coli). The alluvial sediments do not, however, provide assimilative capacity with respect to water soluble compounds such as nitrate and certain volatile and semi-volatile organic compounds. Therefore, the Ohio River alluvial aquifers are highly susceptible to organic compounds present in the river or on the land surface near a well. These aquifers are also susceptible to nitrate contamination from fertilizers, pesticides, and manure, which are commonly used on the fertile agricultural soils present on terraces along the Ohio River.
\nAbandoned-coal-mine aquifers, which are typically used as a source of groundwater in southern West Virginia, are moderately susceptible to contamination. The vast network of voids from mine entries provide vast storage for groundwater in abandoned mine aquifers, and fracturing of overburden strata, which is common in areas of past or current mining, can allow rapid infiltration of contaminants to the aquifer. Where streams cross over below-drainage underground coal mines, there is an increased potential for contamination of coal-mine aquifers. As a result, above-drainage underground coal mines, those mines that are present at an elevation above local tributary drainage, are probably less susceptible to contamination than are below-drainage underground coal mines. Public groundwater supplies in abandoned coal mines need to be evaluated on a case-by-case basis to assess the potential for recharge of contaminated surface water to enter below-drainage underground coal-mine aquifers and to assess potential hydraulic conductivity to nearby surface-water bodies, such as lakes, ponds, rivers, or streams.
\nFractured-rock aquifers compose an additional major type of aquifer within the State of West Virginia. Owing to their low permeability and their typically small groundwater capture areas, fractured-rock aquifers within the State of West Virginia typically have low susceptibility to contamination. However, there are exceptions, and wells completed in fractured-rock aquifers that are in close proximity to streams may be adversely affected by induced recharge from the stream. Where such systems are present, frequent bacterial testing of the source water can be used to ascertain the potential for microbial contamination of the aquifer.
\nIntrinsic susceptibility alone does not fully predict whether or not a well is vulnerable to contamination, only that the hydrogeologic terrain is suitable for rapid transport of pathogenic organisms or chemical compounds to and within the aquifer. However, contaminants may or may not be present in the recharge water to an individual well or well field. Therefore, an assessment of potential contaminant sources, such as nearby gas wells, landfills, underground storage tanks, above ground storage tanks, major transportation corridors, surface or underground coal mines, and flood plains, is needed to assess vulnerability. The assessments need to be conducted on a case-by-case basis or, as has been done in this study, by collecting and compiling the number of potential contaminant sources that may be present in the source-water-protection area for an individual public groundwater supply source.
\nGroundwater public-supply systems in areas of high intrinsic susceptibility and with a large number of potential contaminant sources within the recharge or source-water-protection area of individual wells or well fields are potentially vulnerable to contamination and probably warrant further evaluation as potential SWIGS. However, measures can be taken to educate the local population and initiate safety protocols and protective strategies to appropriately manage contaminant sources to prevent release of contaminants to the aquifer, therefore, reducing vulnerability of these systems to contamination. However, each public groundwater supply source needs to be assessed on an individual basis. Data presented in this report can be used to categorize and prioritize wells and springs that have a high potential for intrinsic susceptibility or vulnerability to contamination.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165048","collaboration":"Prepared in cooperation with the West Virginia Department of Health and Human Resources, Bureau of Public Health, Office of Environmental Health Services","usgsCitation":"Kozar, M.D., and Paybins, K.S., 2016, Assessment of hydrogeologic terrains, well-construction characteristics, groundwater hydraulics, and water-quality and microbial data for determination of surface-water-influenced groundwater supplies in West Virginia (ver. 1.1, October 2016): U.S. Geological Survey Scientific Investigations Report 2016–5048, 55 p., https://dx.doi.org/10.3133/sir20165048.","productDescription":"Report: vii, 54 p.; 2 Figures; 3 Appendixes","numberOfPages":"67","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-065870","costCenters":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":325448,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_figure3A.pdf","text":"Figure 3A -","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Major Geologic Formations in West Virginia"},{"id":325450,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix1.xlsx","text":"Appendix 1 - ","size":"168 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"Description of 324 wells in West Virginia sampled as part of the U.S. Geological Survey and West Virginia Department of Environmental Protection statewide Ambient Groundwater Quality Monitoring Network"},{"id":325449,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_figure3B.pdf","text":"Figure 3B -","size":"745 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Major geologic formations in the study area of the Blue Ridge Physiographic Province USGS National Water Quality Assessment study in Virginia and North Carolina"},{"id":325446,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5048/coverthb2.jpg"},{"id":325452,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix3.xlsx","text":"Appendix 3 - ","size":"111 KB","linkHelpText":" Permit data for public groundwater supplies in West Virginia with accompanying counts of number of potential sources of contamination within the respective source-water-protection area for each public groundwater supply source"},{"id":325447,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048.pdf","text":"Report","size":"25.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5048"},{"id":325451,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix2.xlsx","text":"Appendix 2 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Virginia\",\"nation\":\"USA \"}}]}","edition":"Version 1.0: Originally posted August 30, 2016; Version 1.1: October 24, 2016","contact":"Director, West Virginia Water Science Center
U.S. Geological Survey
11 Dunbar Street
Charleston, WV 25301
http://wv.usgs.gov
Treated effluent discharged from municipal wastewater-treatment plants to the Assabet River in central Massachusetts includes phosphorus, which leads to increased growth of nuisance aquatic plants that decrease the river’s water quality and aesthetics in impounded reaches during the growing season. To improve the river’s water quality and aesthetics, the U.S. Environmental Protection Agency approved a total maximum daily load for phosphorus in 2004 that directed the wastewater-treatment plants to reduce the amount of total phosphorus discharged to the river by 2012. The permitted total phosphorus monthly average of 0.75 milligrams per liter during the aquatic plant growing season (April 1 through October 31) was reduced by the total maximum daily load to a target of 0.1 milligrams per liter by 2012, and the nongrowing-season limit was unchanged at 1.0 milligrams per liter.
From October 2008 through April 2014, the U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, measured streamflow and collected weekly flow-proportional, composite samples of water from the Assabet River for analysis of concentrations of total phosphorus and orthophosphate. Streamflow and concentration data were used to estimate total phosphorus and orthophosphate loads in the river. The purpose of this monitoring effort was to evaluate phosphorus concentrations and loads in the river before, during, and after the wastewater-treatment-plant upgrades and to assess the effects of seasonal differences in permitted discharges. The locations of water-quality-monitoring stations, with respect to the Hudson and Ben Smith impoundments, enabled examination of effects of phosphorus entering and leaving the impoundments.
Annual median concentrations of total phosphorus in wastewater-treatment plants were reduced by more than 80 percent with the plant upgrades. Measured instream annual median concentrations of total phosphorus in the Assabet River decreased by about 38 to 50 percent at three of the four monitoring stations following the wastewater-treatment-plant upgrades. At the station farthest upstream, the median total phosphorus concentration remained unchanged throughout the study; this may be attributed to the site location and potential resuspension of particulate organic matter during periods of increased streamflow. Annual median loads from the wastewater-treatment plants were reduced by up to 91 percent following the upgrades, instream annual median total phosphorus loads at the three downstream stations decreased by 71 to 76 percent, and instream orthophosphate loads at the three downstream stations decreased by 79 to 87 percent.
Seasonal fluctuations (growing versus nongrowing) of total phosphorus and orthophosphate were observed instream before the upgrades. However, after the upgrades, fluctuations in phosphorus released from the treatment plants were slight and seasonal changes were typically not observed instream.
Annual loads entering and leaving the two impoundments were inconclusive in determining whether the impoundments were sources or sinks of total phosphorus during the study. Total phosphorus loads entering the Hudson impoundment were consistently greater than those leaving; however, there was uncertainty about the loads at the monitoring station upstream from this impoundment. At the Ben Smith impoundment, total phosphorus and orthophosphate loads downstream were slightly greater than those upstream from the impoundment, but the differences may reflect additions from tributaries and overland runoff.
Estimated instream total phosphorus concentrations and loads indicated that the decreases in total phosphorus in wastewater-treatment-plant discharges were accompanied by reductions measured in the Assabet River. A statistical analysis which incorporates the effect of varying flow conditions demonstrated significant reductions in total phosphorus concentrations after the wastewater-treatment-plant upgrades at three of the four instream monitoring stations. No significant change was observed at the most upstream location, the Assabet River at Port Street at Hudson, Massachusetts (station number 01096835), which may have been affected by flow-related resuspension of particulate phosphorus.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165063","isbn":"978-1-4113-4059-6","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Savoie, J.G., DeSimone, L.A., Mullaney, J.R., Zimmerman, M.J., and Waldron, M.C., 2016, Changes in phosphorus concentrations and loads in the Assabet River, Massachusetts, October 2008 through April 2014: U.S. Geological Survey Scientific Investigations Report 2016–5063, 40 p., https://dx.doi.org/10.3133/sir20165063.","productDescription":"Report: ix, 40 p.; Data Release","numberOfPages":"54","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-056167","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":438530,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72R3PR3","text":"USGS data release","linkHelpText":"Streamflow and total phosphorus and orthophosphate data for samples collected in and near the Assabet River, Massachusetts, October 2008 through April 2014"},{"id":329393,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F72R3PR3","text":"USGS data release","description":"USGS data release","linkHelpText":"Streamflow and Total Phosphorus and Orthophosphate Data for Samples Collected In and Near the Assabet River, Massachusetts, October 2008 Through April 2014"},{"id":329392,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5063/sir20165063.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5063"},{"id":329391,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5063/coverthb3.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Assabet River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.67068481445312,\n 42.245801966774025\n ],\n [\n -71.67068481445312,\n 42.476148570254516\n ],\n [\n -71.36032104492188,\n 42.476148570254516\n ],\n [\n -71.36032104492188,\n 42.245801966774025\n ],\n [\n -71.67068481445312,\n 42.245801966774025\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Or visit our Web site at:
http://newengland.water.usgs.gov
Water levels declined from 2003 to 2011 in many lakes in Ramsey and Washington Counties in the northeast Twin Cities Metropolitan Area, Minnesota; however, water levels in other northeast Twin Cities Metropolitan Area lakes increased during the same period. Groundwater and surface-water exchanges can be important in determining lake levels where these exchanges are an important component of the water budget of a lake. An understanding of groundwater and surface-water exchanges in the northeast Twin Cities Metropolitan Area has been limited by the lack of hydrologic data. The U.S. Geological Survey, in cooperation with the Metropolitan Council and Minnesota Department of Health, completed a field and statistical study assessing lake-water levels and regional and local groundwater and surface-water exchanges near northeast Twin Cities Metropolitan Area lakes. This report documents the analysis of collected hydrologic, water-quality, and geophysical data; and existing hydrologic and geologic data to (1) assess the effect of physical setting and climate on lake-level fluctuations of selected lakes, (2) estimate potential percentages of surface-water contributions to well water across the northeast Twin Cities Metropolitan Area, (3) estimate general ages for waters extracted from the wells, and (4) assess groundwater inflow to lakes and lake-water outflow to aquifers downgradient from White Bear Lake.
Statistical analyses of lake levels during short-term (2002–10) and long-term (1925–2014) periods were completed to help understand lake-level changes across the northeast Twin Cities Metropolitan Area. Comparison of 2002–10 lake levels to several landscape and geologic characteristics explained variability in lake-level changes for 96 northeast Twin Cities Metropolitan Area lakes. Application of several statistical methods determined that (1) closed-basin lakes (without an active outlet) had larger lake-level declines than flow-through lakes with an outlet; (2) closed-basin lake-level changes reflected groundwater-level changes in the Quaternary, Prairie du Chien, and Jordan aquifers; (3) the installation of outlet-control structures, such as culverts and weirs, resulted in smaller multiyear lake-level changes than lakes without outlet-control structures; (4) water levels in lakes primarily overlying Superior Lobe deposits were significantly more variable than lakes primarily overlying Des Moines Lobe deposits; (5) lake-level declines were larger with increasing mean lake-level elevation; and (6) the frequency of some of these characteristics varies by landscape position. Flow-through lakes and lakes with outlet-control structures were more common in watersheds with more than 50 percent urban development compared to watersheds with less than 50 percent urban development. A comparison of two 35-year periods during 1925–2014 revealed that variability of annual mean lake levels in flow-through lakes increased when annual precipitation totals were more variable, whereas variability of annual mean lake levels in closed-basin lakes had the opposite pattern, being more variable when annual precipitation totals were less variable.
Oxygen-18/oxygen-16 and hydrogen-2/hydrogen-1 ratios for water samples from 40 wells indicated the well water was a mixture of surface water and groundwater in 31 wells, whereas ratios from water sampled from 9 other wells indicated that water from these wells receive no surface-water contribution. Of the 31 wells with a mixture of surface water and groundwater, 11 were downgradient from White Bear Lake, likely receiving water from deeper parts of the lake.
Age dating of water samples from wells indicated that the age of water in the Prairie du Chien and Jordan aquifers can vary widely across the northeast Twin Cities Metropolitan Area. Estimated ages of recharge for 9 of the 40 wells sampled for chlorofluorocarbon concentrations ranged widely from the early 1940s to mid-1970s. The wide range in estimated ages of recharge may have resulted from the wide range in the open-interval lengths and depths for the wells.
Results from stable isotope analyses of water samples, lake-sediment coring, continuous seismic-reflection profiling, and water-level and flow monitoring indicated that there is groundwater inflow from nearshore sites and lake-water outflow from deep-water sites in White Bear Lake. Continuous seismic-reflection profiling indicated that deep sections of White Bear, Pleasant, Turtle, and Big Marine Lakes have few trapped gases and little organic material, which indicates where groundwater and lake-water exchanges are more likely. Water-level differences between White Bear Lake and piezometer and seepage measurements in deep waters of the lake indicate that groundwater and lake-water exchange is happening in deep waters, predominantly downgradient from the lake and into the lake sediment. Seepage fluxes measured in the nearshore sites of White Bear Lake generally were higher than seepage fluxes measured in the deep-water sites, which indicates that groundwater-inflow rates at most of the nearshore sites are higher than lake-water outflow from the deep-water sites.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015 (Scientific Investigations Report 2016–5139)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165139A","collaboration":"Prepared in cooperation with the Metropolitan Council and Minnesota Department of Health","usgsCitation":"Jones, P.M., Trost, J.J., Diekoff, A.L., Rosenberry, D.O., White, E.A., Erickson, M.L., Morel, D.L., and Heck, J.M., 2016, Statistical analysis of lake levels and field study of groundwater and surface-water exchanges in the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015: U.S. Geological Survey Scientific Investigations Report 2016–5139–A, 86 p., https://dx.doi.org/10.3133/sir20165139A.","productDescription":"Report: x, 86 p.; 2 Tables; Appendix Tables","numberOfPages":"100","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076833","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":329656,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5139/a/sir20165139A_appendixtables.xlsx","text":"Appendix Tables 1–1 to 1–3","size":"151 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5139 Appendix Tables 1–1 to 1–3"},{"id":329654,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5139/a/sir20165139A_table7.xlsx","text":"Table 7","size":"39 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5139 Table 7"},{"id":329655,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5139/a/sir20165139A_table9.xlsx","text":"Table 9","size":"23.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5139 Table 9"},{"id":329649,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5139/a/coverthb.jpg"},{"id":329650,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5139/a/sir20165139A.pdf","text":"Report","size":"22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5139"}],"country":"United States","state":"Minnesota","otherGeospatial":"Twin Cities Metropolitan Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.55957031249999,\n 45.29614310895674\n ],\n [\n -93.56231689453125,\n 45.464946600971466\n ],\n [\n -92.67791748046875,\n 45.464946600971466\n ],\n [\n -92.64907836914062,\n 45.4524242413431\n ],\n [\n -92.65045166015625,\n 45.427371178989084\n ],\n [\n -92.64770507812499,\n 45.40230699238177\n ],\n [\n -92.6806640625,\n 45.37337295384522\n ],\n [\n 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U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
State and local water-resource managers need modeling tools to help them manage and protect water-supply resources for both human consumption and ecological needs. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, has developed a decision-support tool to estimate the effects of reservoirs on natural streamflow. The Massachusetts Reservoir Simulation Tool is a model that simulates the daily water balance of a reservoir. The reservoir simulation tool provides estimates of daily outflows from reservoirs and compares the frequency, duration, and magnitude of the volume of outflows from reservoirs with estimates of the unaltered streamflow that would occur if no dam were present. This tool will help environmental managers understand the complex interactions and tradeoffs between water withdrawals, reservoir operational practices, and reservoir outflows needed for aquatic habitats.
A sensitivity analysis of the daily water balance equation was performed to identify physical and operational features of reservoirs that could have the greatest effect on reservoir outflows. For the purpose of this report, uncontrolled releases of water (spills or spillage) over the reservoir spillway were considered to be a proxy for reservoir outflows directly below the dam. The ratio of average withdrawals to the average inflows had the largest effect on spillage patterns, with the highest withdrawals leading to the lowest spillage. The size of the surface area relative to the drainage area of the reservoir also had an effect on spillage; reservoirs with large surface areas have high evaporation rates during the summer, which can contribute to frequent and long periods without spillage, even in the absence of water withdrawals. Other reservoir characteristics, such as variability of inflows, groundwater interactions, and seasonal demand patterns, had low to moderate effects on the frequency, duration, and magnitude of spillage. The reservoir simulation tool was used to simulate 35 single- and multiple-reservoir systems in Massachusetts over a 44-year period (water years 1961 to 2004) under two water-use scenarios. The no-pumping scenario assumes no water withdrawal pumping, and the pumping scenario incorporates average annual pumping rates from 2000 to 2004. By comparing the results of the two scenarios, the total streamflow alteration can be parsed into the portion of streamflow alteration caused by the presence of a reservoir and the additional streamflow alteration caused by the level of water use of the system.
For each reservoir system, the following metrics were computed to characterize the frequency, duration, and magnitude of reservoir outflow volumes compared with unaltered streamflow conditions: (1) the median number of days per year in which the reservoir did not spill, (2) the median duration of the longest consecutive period of no-spill days per year, and (3) the lowest annual flow duration exceedance probability at which the outflows are significantly different from estimated unaltered streamflow at the 95-percent confidence level. Most reservoirs in the study do not spill during the summer months even under no-pumping conditions. The median number of days during which there was no spillage was less than 365 for all reservoirs in the study, indicating that, even under reported pumping conditions, the reservoirs refill to full volume and spill at least once during nondrought years, typically in the spring.
Thirteen multiple-reservoir systems consisting of two or three hydrologically connected reservoirs were included in the study. Because operating rules used to manage multiple-reservoir systems are not available, these systems were simulated under two pumping scenarios, one in which water transfers between reservoirs are minimal and one in which reservoirs continually transferred water to intermediate or terminal reservoirs. These two scenarios provided upper and lower estimates of spillage under average pumping conditions from 2000 to 2004.
For sites with insufficient data to simulate daily water balances, a proxy method to estimate the three spillage metrics was developed. A series of 4,000 Monte Carlo simulations of the reservoir water balance were run. In each simulation, streamflow, physical reservoir characteristics, and daily climate inputs were randomly varied. Tobit regression equations that quantify the relation between streamflow alteration and physical and operational characteristics of reservoirs were developed from the results of the Monte Carlo simulations and can be used to estimate each of the three spillage metrics using only the withdrawal ratio and the ratio of the surface area to the drainage area, which are available statewide for all reservoirs.
A graphical user-interface for the Massachusetts Reservoir Simulation Tool was developed in a Microsoft Access environment. The simulation tool contains information for 70 reservoirs in Massachusetts and allows for simulation of additional scenarios than the ones considered in this report, including controlled releases, dam seepage and leakage, demand management plans, and alternative water withdrawal and transfer rules.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Or visit our Web site at:
http://newengland.water.usgs.gov/
To assist resource managers and planners in developing informed strategies to address nitrogen loading to coastal water bodies of Long Island, New York, the U.S. Geological Survey and the New York State Department of Environmental Conservation initiated a program to delineate a comprehensive dataset of groundwater recharge areas (or areas contributing groundwater), travel times, and outflows to streams and saline embayments on Long Island. A four-layer regional three-dimensional finite-difference groundwater-flow model of hydrologic conditions from 1968 to 1983 was used to provide delineations of 48 groundwater watersheds on Long Island. Sixteen particle starting points were evenly spaced within each of the 4,000- by 4,000-foot model cells that receive water-table recharge and tracked using forward particle-tracking analysis modeling software to outflow zones. For each particle, simulated travel times were grouped by age as follows: less than or equal to 10 years, greater than 10 years and less than or equal to 100 years, greater than 100 years and less than or equal to 1,000 years, and greater than 1,000 years; and simulated ending zones were grouped into 48 receiving water bodies, based on the New York State Department of Environmental Conservation Waterbody Inventory/Priority Waterbodies List. Areal delineation of travel time zones and groundwater contributing areas were generated and a table was prepared presenting the sum of groundwater outflow for each area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165138","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Misut, P.E., and Monti, Jack, Jr., 2016, Delineation of areas contributing groundwater to selected receiving surface water bodies for long-term average hydrologic conditions from 1968 to 1983 for Long Island, New York:U.S. Geological Survey Scientific Investigations Report 2016–5138, 22 p., https://dx.doi.org/10.3133/sir20165138.","productDescription":"Report: iv, 22 p.; Figures: 1-5; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[],"links":[{"id":329253,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5138/coverthb.jpg"},{"id":329257,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7TB151D ","text":"USGS data release","description":"USGS data release ","linkHelpText":"MODFLOW-2005 and MODPATH6 models "},{"id":329254,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5138/sir20165138.pdf","text":"Report","size":"5.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5138"},{"id":329255,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5138/sir20165138_figs1-5.zip","text":"Figures 1-5 ","size":"10.2 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5138","linkHelpText":"- Large-format versions of figures in report"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.25,\n 40.5\n ],\n [\n -73.25,\n 40.9\n ],\n [\n -74.25,\n 40.9\n ],\n [\n -74.25,\n 40.5\n ],\n [\n -73.25,\n 40.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180
http://ny.water.usgs.gov
The U.S. Geological Survey, in cooperation with the Connecticut Department of Energy and Environmental Protection, investigated the characteristics of the bedrock aquifer in the Tylerville section of Haddam, Connecticut, from June to August 2014. As part of this investigation, geophysical logs were collected from six water-supply wells and were analyzed to (1) identify well construction, (2) determine the rock type and orientation of the foliation and layering of the rock, (3) characterize the depth and orientation of fractures, (4) evaluate fluid properties of the water in the well, and (5) determine the relative transmissivity and head of discrete fractures or fracture zones. The logs included the following: caliper, electromagnetic induction, gamma, acoustic and (or) optical televiewer, heat-pulse flowmeter under ambient and pumped conditions, hydraulic head data, fluid electrical conductivity and temperature under postpumping conditions, and borehole-radar reflection collected in single-hole mode. In a seventh borehole, a former water-supply well, only caliper, fluid electrical conductivty, and temperature logs were collected, because of a constriction in the borehole.
This report includes a description of the methods used to collect and process the borehole geophysical data, the description of the data collected in each of the wells, and a comparison of the results collected in all of the wells. The data are presented in plots of the borehole geophysical logs, tables, and figures. Collectively these data provide valuable characterizations that can be used to improve or inform site conceptual models of groundwater flow in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1020","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Johnson, C.D., Kiel, K.F., Joesten, P.K., and Pappas, K.L., 2016, Characterization of fractures and flow zones in a contaminated crystalline-rock aquifer in the Tylerville section of Haddam, Connecticut: U.S. Geological Survey Data Series 1020, 40 p., https://dx.doi.org/10.3133/ds1020.","productDescription":"Report: viii, 40 p.; Appendixes: 1-7","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067211","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":329127,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix2.zip","text":"Appendix 2","size":"7.27 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 76–BR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329131,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix6.zip","text":"Appendix 6","size":"19.9 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 130–LMR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329132,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix7.zip","text":"Appendix 7","size":"658 KB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 95–BR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329133,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendixes_1-7.zip","text":"Appendixes 1–7","size":"76.9 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Seven Boreholes in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329128,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix3.zip","text":"Appendix 3","size":"5.84 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 85–BR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329130,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix5.zip","text":"Appendix 5","size":"18 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 77–LMR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329129,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix4.zip","text":"Appendix 4","size":"10.7 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 79/81–BR in the Tylerville Study Area, Haddam, Connecticut, 2014"},{"id":329124,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1020/coverthb.jpg"},{"id":329125,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1020/ds1020.pdf","text":"Report","size":"8.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1020"},{"id":329126,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1020/appendix/ds1020_appendix1.zip","text":"Appendix 1","size":"14.4 MB","linkFileType":{"id":6,"text":"zip"},"description":"DS 1020","linkHelpText":"- Borehole-Geophysical Logs From Borehole 1640–SR in the Tylerville Study Area, Haddam, Connecticut, 2014"}],"country":"United States","state":"Connecticut","county":"Middlesex County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.48126983642578,\n 41.43944494429659\n ],\n [\n -72.48126983642578,\n 41.45745861169602\n ],\n [\n -72.46135711669922,\n 41.45745861169602\n ],\n [\n -72.46135711669922,\n 41.43944494429659\n ],\n [\n -72.48126983642578,\n 41.43944494429659\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Branch of Geophysics
Office of Groundwater
U.S. Geological Survey
11 Sherman Place, Unit 5015
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http://water.usgs.gov/ogw/bgas
The U.S. Geological Survey, in cooperation with the U.S. Department of Energy, used paleomagnetic data from 18 coreholes to construct three cross sections of subsurface basalt flows in the southern part of the Idaho National Laboratory (INL). These cross sections, containing descriptions of the subsurface horizontal and vertical distribution of basalt flows and sediment layers, will be used in geological studies, and to construct numerical models of groundwater flow and contaminant transport.
Subsurface cross sections were used to correlate surface vents to their subsurface flows intersected by coreholes, to correlate subsurface flows between coreholes, and to identify possible subsurface vent locations of subsurface flows. Correlations were identified by average paleomagnetic inclinations of flows, and depth from land surface in coreholes, normalized to the North American Datum of 1927. Paleomagnetic data were combined, in some cases, with other data, such as radiometric ages of flows. Possible vent locations of buried basalt flows were identified by determining the location of the maximum thickness of flows penetrated by more than one corehole.
Flows from the surface volcanic vents Quaking Aspen Butte, Vent 5206, Mid Butte, Lavatoo Butte, Crater Butte, Pond Butte, Vent 5350, Vent 5252, Tin Cup Butte, Vent 4959, Vent 5119, and AEC Butte are found in coreholes, and were correlated to the surface vents by matching their paleomagnetic inclinations, and in some cases, their stratigraphic positions.
Some subsurface basalt flows that do not correlate to surface vents, do correlate over several coreholes, and may correlate to buried vents. Subsurface flows which correlate across several coreholes, but not to a surface vent include the D3 flow, the Big Lost flow, the CFA buried vent flow, the Early, Middle, and Late Basal Brunhes flows, the South Late Matuyama flow, the Matuyama flow, and the Jaramillo flow. The location of vents buried in the subsurface by younger basalt flows can be inferred if their flows are penetrated by several coreholes, by tracing the flows in the subsurface, and determining where the greatest thickness occurs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165131","collaboration":"DOE/ID-22240Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov
Bridge-scour equations presented in the Federal Highway Administration Hydraulic Engineering Circular No. 18 reflect the current state-of-the practice for predicting scour at bridges. Although these laboratory-derived equations provide an important resource for assessing scour potential, there is a measure of uncertainty when applying these equations to field conditions. The uncertainty and limitations have been acknowledged by laboratory researchers and confirmed in field investigations.
Because of the uncertainty associated with bridge-scour equations, HEC-18 recommends that engineers evaluate the computed scour depths obtained from the equations and modify the resulting data if they appear unreasonable. Perhaps the best way to evaluate the reasonableness of predicted scour is to compare it to field measurements of historic scour. Historic field data show scour depths resulting from high flows and provide a reference for evaluating predicted scour. It is rare, however, that such data are available at or near a site of interest, making the evaluation of predicted scour as compared to field data difficult if not impossible. Realizing the value of historic scour measurements, the U.S. Geological Survey (USGS), in cooperation with the South Carolina Department of Transportation (SCDOT), conducted a series of three field investigations to collect historic scour data with the goal of understanding regional trends of scour at riverine bridges in South Carolina.
Historic scour measurements, including measurements of clear-water abutment, contraction, and pier scour, as well as live-bed contraction and pier scour, were made at more than 200 bridges. These field investigations provided valuable insights into regional scour trends and yielded regional bridge-scour envelope curves that can be used as supplementary tools for assessing all components of scour at riverine bridges in South Carolina.
The application and limitations of these envelope curves were documented in four reports. Because each report addresses different components of bridge scour, it was recognized that there was a need to develop an integrated procedure for applying the envelope curves to help assess scour potential at riverine bridges in South Carolina. The result of that effort is detailed in Benedict and others (2016) and summarized in this fact sheet.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163065","collaboration":"Prepared in cooperation with the South Carolina Department of Transportation","usgsCitation":"Benedict, S.T., Feaster, T.D., and Caldwell, A.W., 2016, Assessing potential scour using the South Carolina bridge-scour envelope curves: U.S. Geological Survey Fact Sheet 2016-3065, 2 p., https://dx.doi.org/10.3133/fs20163065.","productDescription":"2 p. 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Carolina\",\"nation\":\"USA \"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road, Suite 129
Columbia, SC 29210
http://sc.water.usgs.gov/
Multiple sampling trips during calendar years 2013 through 2015 were coordinated to provide measurements of interdependent benthic processes that potentially affect contaminant transport in Upper Klamath Lake (UKL), Oregon. The measurements were motivated by recognition that such internal processes (for example, solute benthic flux, bioturbation and solute efflux by benthic invertebrates, and physical groundwater-surface water interactions) were not integrated into existing management models for UKL. Up until 2013, all of the benthic-flux studies generally had been limited spatially to a number of sites in the northern part of UKL and limited temporally to 2–3 samplings per year. All of the benthic invertebrate studies also had been limited to the northern part of the lake; however, intensive temporal (weekly) studies had previously been completed independent of benthic-flux studies. Therefore, knowledge of both the spatial and temporal variability in benthic flux and benthic invertebrate distributions for the entire lake was lacking. To address these limitations, we completed a lakewide spatial study during 2013 and a coordinated temporal study with weekly sampling of benthic flux and benthic invertebrates during 2014. Field design of the spatially focused study in 2013 involved 21 sites sampled three times as the summer cyanobacterial bloom developed (that is, May 23, June 13, and July 3, 2013). Results of the 27-week, temporally focused study of one site in 2014 were summarized and partitioned into three periods (referred to herein as pre-bloom, bloom and post-bloom periods), each period involving 9 weeks of profiler deployments, water column and benthic sampling. Partitioning of the pre-bloom, bloom, and post-bloom periods were based on water-column chlorophyll concentrations and involved the following date intervals, respectively: April 15 through June 10, June 17 through August 13, and August 20 through October 16, 2014.
To examine dissolved-solute (0.2-micrometer [μm] filtered) benthic flux, sets of nonmetallic pore-water profilers (U.S. Patent 8,051,727 B1) were deployed. In 2013, the deployment of profilers at 21 UKL sites occurred at the beginning of the annual cyanobacterial bloom of Aphanizomenon flos–aquae (AFA), in the middle of the bloom period, and at the peak of the bloom. Coordinated benthic invertebrate collections also were made. Based on results from 2013, weekly deployments of profilers and collection of benthic invertebrate samples from late spring to early autumn were used to estimate temporal trends in solute flux and benthic invertebrate densities. Estimates of nutrient efflux by benthic invertebrates were determined in the spring and autumn from 2011 through 2013 and three times (spring, summer, and autumn) in 2015. This work extends UKL studies that began in 2006 to quantify the importance of benthic solute sources in the lake. In 2015, piezometers and thermistor sets were deployed to quantify potential groundwater exchange with the lake water column.
Analysis of the 2013 soluble reactive phosphorus (SRP) benthic flux indicated no effect of location (lake region), habitat, or sampling period, and the average lakewide flux values were consistent with earlier studies that had been confined to the northern region of UKL and adjacent wetlands. The 2014 study therefore focused on estimating temporal trends at a site within Ball Bay. During both 2013 and 2014 field studies, fluxes of macronutrients (soluble reactive phosphorus (SRP) and ammonia) and micronutrients (iron [Fe] and manganese [Mn]) were consistently positive and increased prior to the initial AFA bloom, varied or lagged with water-column chlorophyll during the summer bloom period, then decreased after the cyanobacterial blooms, only to rebound toward pre-bloom conditions in the final weeks of sampling. These four solutes exhibited benthic loads greater than maximum riverine loads estimated during the spring and early summers of 2013 and 2014. However, consistently detectable concentrations for all four solutes provide no evidence that they consistently serve as the limiting nutrient for primary production in the lake. In contrast to the four solutes (SRP, ammonia, Fe, and Mn), benthic fluxes of dissolved arsenic (As) were both negative and positive (that is, the lakebed currently serves as both a source and a sink for dissolved As, depending on season). In a further contrast with SRP, ammonia, dissolved Fe, and Mn, dissolved-As riverine loads to UKL were of similar magnitude to benthic loads. A negative relationship between dissolved-As flux and water-column As over the 2014 temporal study provides a potential advantage for the management of water-quality in contrast to solutes, like SRP or ammonia, with consistently positive flux.
The mean total benthic invertebrate density during 2013 was 12,610 individuals per square meter (n=63). Although benthic invertebrate density did not change over the study period, it was higher in littoral habitats than open-lake or trench habitats and higher in the northern region compared to the central or southern regions of UKL. Mean total benthic invertebrate density during 2014 was 19,726 individuals m−2 (n=27). Density during the pre-bloom and bloom periods of April 15 to August 13, 2014 (the first two thirds of the 2014 sampling period), were similar to 2013. However, benthic invertebrate density more than doubled during the latter one-third of the study, that is, the post-bloom period between August 20 to October 16, 2014. Oligochaeta, Chironomidae and Hirudinea represented well over 90 percent of the benthic fauna; Oligochaeta were twice as abundant as Chironomidae or Hirudinea, the latter two of which were similar in density.
Benthic invertebrates may enhance dissolved-nutrient (or toxicant) transport across the sediment-water interface by (1) modifying diffusion-layer thicknesses and permeability through bioturbation, (2) enhancing advective flow across the interface through bioirrigation, and (3) excreting or expelling dissolved or particulate solutes directly into the overlying water column (Boudreau and Jorgensen, 2001). We evaluated SRP efflux via excretion for approximately 15 different major taxa in UKL. Once these measures were scaled, it was evident that benthic invertebrates potentially contribute approximately 1.5 times the amount of SRP to the water column of Upper Klamath Lake as diffusive SRP flux alone, measured in profiler deployments.
Sets of piezometers and temperature loggers were deployed in UKL to obtain estimates of vertical advective solute flux. The pressure transducer installations, within the piezometers, did not perform as designed, rendering the head gradient data unreliable. However, in terms of future research, this field work did demonstrate the feasibility of collecting vertical gradient data with piezometer deployments. Advective flux estimates herein are based solely on heat-flow modeling based on temperature data from four lake sites, without use of transducer data. Given the magnitudes (both positive or negative) of the heat-transfer fluxes for SRP, relative to diffusive-flux and macroinvertebrate efflux measurements (all positive but spanning the same orders of magnitude), further examination of solute advective flux is recommended as a potential transport process to integrate into existing water-quality (for example, Total Maximum Daily Load [TMDL]) models.
As a complement to the biogeochemical focus of this study, initial analyses of suspended-particle (floc) characteristics and settling velocities from the water column were derived near the surface and lakebed at two UKL sites. To better understand changing particle characteristics during the AFA-bloom period, suspended particles were examined in 2015 using a LabSFLOC (LF), which is a Laboratory Spectral Flocculation Characteristics version of an In-Situ Settling Velocity instrument (INSSEV-LF). Particle characteristics and settling velocities were analyzed from the water column near the surface (sample dp_10) and lakebed (sample dp_90) at two lake sites (open-lake site ML and littoral site LS01). The term “floc” refers herein to suspended particles that may aggregate or disaggregate to change in size, composition, and settling velocity.
During pre-bloom (May) conditions, where maximum suspended particulate matter concentration (SPMC) was 140 milligrams per liter (mg L−1) was now observed at site LS01 in close proximity to the bed, where Dmean peaked at 305 μm, and the corresponding Wsmean was 3.9 millimeters per second (mm s−1). The high near-bed SPMC (828 mg L−1) experienced during post-bloom October 2015 at LS01 formed a benthic nepheloid layer (BNL) above the lake’s bed. Numerous low density, fast settling macrofloc-sized organic aggregates (D >160 μm) were observed (some up to 1 mm in size) near bed at LS01 both during the bloom and post-bloom conditions; many of these flocs displayed fibrous organic structures. In terms of mass settling fluxes, the post-bloom BNL produced a total MSF of 4,139 milligrams per square meter per second (mg m−2 s−1) (92.1 percent of MSF credited to the macrofloc-sized organic aggregates/cyanobacterial colonies); that was nearly three times the corresponding near-bed settling flux observed during the July 2015 bloom and 360 times greater than the pre-bloom conditions from May 2015 (98.8 percent and 14 percent of MSF credited to the macrofloc-sized fractions for those respective months). Such changes in the near-bed settling flux demonstrate the highly significant seasonal effects that the AFA bloom has on the floc depositional fluxes in UKL and highlights the importance of seasonal monitoring of these conditions in order to correctly parameterize the wide range in depositional characteristics and floc properties measured throughout UKL.
Collectively, floc populations observed within UKL demonstrated a wide range in settling velocity (Ws) for a given particle size, D. Similarly, a given settling velocity was not associated with a specific particle size. This variability in particle characteristics and properties indicates the influence of varying floc effective density and its effect on mass and mass settling fluxes (MSF). The use of instruments, such as the INSSEV-LF, enables measuring the variability of settling velocity and its relation to particle density and size.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161175","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Kuwabara, J.S., Topping, B.R., Carter, J.L., Carlson, R.A., Parchaso, F., Fend, S.V., Stauffer-Olsen, N., Manning, A.J., Land, J.M., 2016, Benthic processes affecting contaminant transport in Upper Klamath Lake, Oregon (ver. 1.1, October 2016): U.S. Geological Survey Open-File Report 2016–1175, 103 p., https://dx.doi.org/10.3133/ofr20161175. ","productDescription":"Report: viii, 103 p.; 2 Tables","numberOfPages":"115","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":329222,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1175/coverthb.jpg"},{"id":329223,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1175/ofr20161175.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1175"},{"id":329224,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1175/ofr20161175_table4.xlsx","text":"Table 4","size":"96 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1175 Table 4"},{"id":329425,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2016/1175/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1175 Version History"},{"id":329424,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1175/ofr20161175_table19.xlsx","text":"Table 19","size":"18 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1175 Table 19"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.09793090820311,\n 42.231567925608616\n ],\n [\n -122.09793090820311,\n 42.70464124398721\n ],\n [\n -121.79992675781249,\n 42.70464124398721\n ],\n [\n -121.79992675781249,\n 42.231567925608616\n ],\n [\n -122.09793090820311,\n 42.231567925608616\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted September 30, 2016; Version 1.1: October 11, 2016","contact":"NRP staff
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U.S. Geological Survey
345 Middlefield Road, MS-435
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National Research Program
A hydrogeologic framework was constructed to represent the altitudes and thicknesses of hydrogeologic units within the Ozark Plateaus aquifer system as part of a regional groundwater-flow model supported by the U.S. Geological Survey Water Availability and Use Science Program. The Ozark Plateaus aquifer system study area is nearly 70,000 square miles and includes parts of Arkansas, Kansas, Missouri, and Oklahoma. Nine hydrogeologic units were selected for delineation within the aquifer system and include the Western Interior Plains confining system, the Springfield Plateau aquifer, the Ozark confining unit, the Ozark aquifer, which was divided into the upper, middle, and lower Ozark aquifers to better capture the spatial variation in the hydrologic properties, the St. Francois confining unit, the St. Francois aquifer, and the basement confining unit. Geophysical and well-cutting logs, along with lithologic descriptions by well drillers, were compiled and interpreted to create hydrologic altitudes for each unit. The final compiled dataset included more than 23,000 individual altitude points (excluding synthetic points) representing the nine hydrogeologic units within the Ozark Plateaus aquifer system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165130","collaboration":"Water Availability and Use Science Program","usgsCitation":"Westerman, D.A., Gillip, J.A., Richards, J.M., Hays, P.D., Clark, B.R., 2016, Altitudes and thicknesses of hydrogeologic units of the Ozark Plateaus aquifer system in Arkansas, Kansas, Missouri, and Oklahoma: U.S. Geological Survey Scientific Investigations Report 2016–5130, 32 p., https://dx.doi.org/10.3133/sir20165130. 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U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
This is the eighth annual report highlighting U.S. Geological Survey (USGS) science and decision-support activities conducted for the Wyoming Landscape Conservation Initiative (WLCI). The activities address specific management needs identified by WLCI partner agencies. In 2015, USGS scientists continued 24 WLCI projects in 5 categories: (1) acquiring and analyzing resource-condition data to form a foundation for understanding and monitoring landscape conditions and projecting changes; (2) using new technologies to improve the scope and accuracy of landscape-scale monitoring and assessments, and applying them to monitor indicators of ecosystem conditions and the effectiveness of on-the-ground habitat projects; (3) conducting research to elucidate the mechanisms that drive wildlife and habitat responses to changing land uses; (4) managing and making accessible the large number of databases, maps, and other products being developed; and (5) coordinating efforts among WLCI partners, helping them to use USGS-developed decision-support tools, and integrating WLCI outcomes with future habitat enhancement and research projects. Of the 24 projects, 21 were ongoing, including those that entered new phases or more in-depth lines of inquiry, 2 were new, and 1 was completed.
A highlight of 2015 was the WLCI science conference sponsored by the USGS, Bureau of Land Management, and National Park Service in coordination with the Wyoming chapter of The Wildlife Society. Of 260 participants, 41 were USGS professionals representing 13 USGS science centers, field offices, and Cooperative Wildlife Research Units. Major themes of USGS presentations included using new technologies for developing more efficient research protocols for modeling and monitoring natural resources, researching effects of energy development and other land uses on wildlife species and habitats of concern, and modeling species distributions, population trends, habitat use, and effects of land-use changes. There was also a special session on the effectiveness of Wyoming’s Sage-Grouse Executive Order. Combined, USGS presentations provided WLCI partners with a wealth of information and conservation tools.
The project completed in 2015 yielded an index of important agricultural lands in the WLCI region. The index improves upon existing measures of agricultural productivity and provides planners and managers with additional values to consider when making decisions about land use and conservation actions. The two new projects include an analysis of satellite imagery to quantify sagebrush productivity and mortality, and an evaluation of how groundwater and small streams interact in the upper Green River Basin. Initiated in response to concern among WLCI partners that large areas of sagebrush appear to have died recently, the sagebrush study objectives are to assess effects of these mortality events on overall sagebrush ecosystem productivity, evaluate the feasibility of using satellite imagery to detect patterns in sagebrush mortality over time, and identify factors driving these mortality events. The groundwater-streamflow interaction study is being conducted by hydrologists and fish ecologists to better understand how groundwater-streamflow interactions are affected by energy-resource development and how native fish communities are affected by these factors. Expected outcomes of both new projects will provide WLCI partners with additional information and decision-support tools.
Highlights of ongoing science foundation activities included simulations of nine alternative build-out scenarios for oil and gas development and an associated online fact sheet that explains how the simulations were conducted, with an applied example for the Atlantic Rim. Also completed in 2015 was an update of the USGS online inventory of mineral resources data, and publication of a USGS uranium resource survey for the WLCI region. Combined, the outcomes of this work provide decisionmakers and managers with important baseline information for existing and (or) future planning and monitoring efforts.
Terrestrial monitoring activities in 2015 emphasized the use of satellite data in combination with other technologies and field data to monitor, assess, and (or) forecast distribution patterns and (or) trends in sagebrush ecosystems, seasonal and migration stopover habitats used by mule deer and elk, and semi-arid aspen woodlands. Several professional papers detailing new monitoring models and results have been published. Combined, this and related work will help managers understand distribution patterns and trends among priority habitats, identify areas in need of restoration or conservation, and monitor the effectiveness of habitat-management actions.
Aquatic monitoring activities entailed not only the new groundwater-streamflow interaction study already mentioned, but also continued monitoring with streamgages paired with nearby wells in the Green River Basin to assess groundwater effects on streamflow and surface water temperatures. A map that portrays groundwater levels and general direction of flow in the Green River Basin was published as well. Overall, outcomes of USGS hydrological research and monitoring will inform WLCI partners about water resources in the WLCI region and help to explain fish-community responses to energy-resource development.
In 2015, USGS terrestrial wildlife ecologists continued to make crucial strides towards better understanding wildlife species responses to energy-resource development and other land-use changes. This body of research includes six taxa that require or heavily depend on sagebrush habitats: sage-grouse, pygmy rabbits, 3 songbird species, and mule deer. Native fish communities are also being evaluated. Approaches include modeling and mapping wildlife species distributions, abundances, and trends; using satellite and other technologies to track wildlife seasonal movements; conducting successive phases of research that build on the knowledge gained through prior phases to reveal the specific factors or thresholds that drive population- or individual-level responses to changes; and conducting population viability analyses. Additionally, wildlife habitat association models for pygmy rabbit and sage-grouse were combined with the oil and gas build-out scenarios to project species responses to alternative energy development scenarios. Outcomes of the wildlife response research are helping decisionmakers and managers identify specific factors that contribute to species population trends, the potential for spatial overlap between important wildlife habitats and proposed energy-resource development, locations of priority habitats for restoration and conservation, and more.
Data and WLCI Web site management highlights of 2015 included not only ongoing software upgrades, but also an update of the datasets displayed in two of the online products developed for the WLCI effort: (1) a map of 15,532 oil and natural gas well pad scars and other features associated with oil and gas extraction, and (2) a map of oil and gas, oil shale, uranium, and solar energy production, both for southwestern Wyoming. In addition, a map viewer was developed for a previously published map of coal and wind production in relation to sage-grouse distribution and core management areas in southwestern Wyoming. Combined, these maps place valuable decision-support tools in the hands of WLCI partners.
The USGS coordination efforts on behalf of the WLCI in 2015 included significant work on planning and executing the WLCI science conference. They also included ongoing efforts to support Local Project Development Teams and the WLCI Coordination Team (CT) with developing conservation priorities and strategies, identifying priority areas for future conservation actions, supporting the evaluation and ranking of conservation projects, and evaluating the ways in which proposed habitat projects relate to WLCI priorities. In 2015, the USGS also assisted the WLCI CT with updating the WLCI Conservation Action Plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161141","usgsCitation":"Bowen, Z.H., Aldridge, C.L., Anderson, P.J., Assal, T.J., Bartos, T.T., Chalfoun, A.D., Chong, G.W., Dematatis, M.K., Eddy-Miller, C.A., Garman, S.L., Germaine, S.S., Homer, C.G., Huber, C.C., Kauffman, M.J., Manier, D.J., Melcher, C.P., Miller, K.A., Norkin, Tamar, Sanders, L.E., Walters, A.W., Wilson, A.B., and Wyckoff, T.B., 2016, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2015 annual report: U.S. Geological Survey Open-File Report 2016–1141, 59 p., https://dx.doi.org/10.3133/ofr20161141.","productDescription":"viii, 59 p.","onlineOnly":"Y","ipdsId":"IP-075182","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":329020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1141/coverthb.jpg"},{"id":329021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1141/ofr20161141.pdf","text":"Report","size":"36.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1141"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.07177734375,\n 43.40504748787035\n ],\n [\n -111.0498046875,\n 41\n ],\n [\n -105.99609375,\n 41\n ],\n [\n -105.99609375,\n 41.80407814427234\n ],\n [\n -105.35888671875,\n 41.82045509614034\n ],\n [\n -105.380859375,\n 42.47209690919285\n ],\n [\n -106.5673828125,\n 42.52069952914966\n ],\n [\n -107.20458984375,\n 42.48830197960227\n ],\n [\n -107.70996093749999,\n 42.53689200787315\n ],\n [\n -108.5009765625,\n 42.779275360241904\n ],\n [\n -108.80859375,\n 42.98857645832184\n ],\n [\n -109.22607421875,\n 43.229195113965005\n ],\n [\n -109.3798828125,\n 43.42100882994726\n ],\n [\n -109.79736328125,\n 43.5326204268101\n ],\n [\n -110.41259765625,\n 43.56447158721811\n ],\n [\n -111.07177734375,\n 43.40504748787035\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"
Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Assessments of streamflow (discharge) parameters, water quality, physical habitat, and biological communities were completed between May and September 2014 as part of a monitoring program in the Big Wood River watershed of south-central Idaho. The sampling was conducted by the U.S. Geological Survey in cooperation with Blaine County, Trout Unlimited, the Nature Conservancy, and the Wood River Land Trust to help identify the status of aquatic resources at selected locations in the watershed. Information in this report provides a basis with which to evaluate and monitor the long-term health of the Big Wood River and its major tributaries. Sampling sites were co-located with existing U.S. Geological Survey streamgaging stations: three on the main stem Big Wood River and four on the North Fork Big Wood River (North Fork), Warm Springs Creek (Warm Sp), Trail Creek (Trail Ck), and East Fork Big Wood River (East Fork) tributaries.
The analytical results and quality-assurance information for water quality, physical habitat, and biological community samples collected at study sites during 2 weeks in September 2014 are summarized. Water-quality data include concentrations of major nutrients, suspended sediment, dissolved oxygen, and fecal-coliform bacteria. To assess the potential effects of nutrient enrichment on algal growth, concentrations of periphyton biomass (chlorophyll-a and ash free dry weight) in riffle habitats were determined at each site. Physical habitat parameters include stream channel morphology, habitat volume, instream structure, substrate composition, and riparian vegetative cover. Biological data include taxa richness, abundance, and stream-health indicator metrics for macroinvertebrate and fish communities. Statistical summaries of the water-quality, habitat, and biological data are provided along with discussion of how these findings relate to the health of aquatic resources in the Big Wood River watershed.
Seasonal discharge patterns using statistical summaries of daily discharge from selected sites are reported for water years 2012–15. Results showed that annual average daily mean discharge increased from the Big Wood River near Ketchum, ID (BW Ketchum) downstream to the Big Wood River at Hailey, ID (BW Hailey), but decreased by nearly 50 percent from BW Hailey downstream to Big Wood River at Stanton Crossing near Bellevue, ID (BW Stanton). Annual variability in daily mean discharge among main-stem sites was highest at BW Stanton, suggesting that this part of the river may be subject to some level of flow alteration.
Hydrologic alterations resulting in flow reduction can contribute to higher water temperature, especially during the summer months when conditions are often most stressful to fish and other stream organisms. Daily water temperature and water temperature trends from June to September 2014 are reported for select tributary and main-stem sites on the Big Wood River and can be used to assess the potential for biological impairment based on aquatic life temperature criteria for cold-water streams. The State of Idaho maximum temperature criteria for protection of cold-water aquatic life of 22 °C was exceeded at Warm Sp and BW Stanton during summer 2014, but at none of the other main-stem or tributary sites. The 13 °C critical temperature criterion for salmonid spawning was exceeded in early July 2014 at BW Ketchum and BW Hailey near the end of the rainbow trout critical spawning and rearing period. Temperature exceedances were most frequent at BW Stanton, where exceedances for rainbow trout and brown trout occurred from May through early July 2014 during most of the critical spawning and rearing period.
Water quality and habitat availability did not seem to be limiting for fish or other aquatic organisms at most sites in the Big Wood River watershed. Water quality assessments in September 2014 determined no exceedances of total maximum daily load target levels. The availability and quality of habitat was limited at BW Stanton, where shallow-water habitat conditions prevailed.
Macroinvertebrate community diversity was high at all sites except for BW Stanton, where low community diversity was attributed to low species richness and high abundances of a few tolerant taxa. Presence of low species diversity and high macroinvertebrate tolerance values at BW Stanton indicates that benthic community condition and stream health were reduced at that location.
Fish surveys done in September 2014 did not indicate any significant reductions in native fish communities in the Big Wood River or its tributaries. Native rainbow trout (Oncorhynchus mykiss) and Wood River sculpin (Cottus leiopomus) were the dominant fish species in the drainage and were found at all tributary and main-stem sites. Non-native brown (Salmo trutta) and brook trout (Salvelinus fontinalis) were limited to lower drainage sites on the Big Wood River (BW Hailey and BW Stanton), and occurred in relatively low numbers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165128","collaboration":"Prepared in cooperation with Blaine County, Trout Unlimited, The Nature Conservancy, and the Wood River Land Trust","usgsCitation":"MacCoy, D.E., and Short, T.M., 2016, Aquatic biological communities and associated habitats at selected sites in the Big Wood River watershed, south-central Idaho, 2014: U.S. Geological Survey Scientific Investigations Report 2016–5128, 37 p., https://dx.doi.org/10.3133/sir20165128.","productDescription":"vi, 37 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-061251","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":329075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5128/sir20165128.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5128"},{"id":329074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5128/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Wood River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.58465576171875,\n 43.2622061978402\n ],\n [\n -114.58465576171875,\n 43.81471121600004\n ],\n [\n -114.01473999023438,\n 43.81471121600004\n ],\n [\n -114.01473999023438,\n 43.2622061978402\n ],\n [\n -114.58465576171875,\n 43.2622061978402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov
The city of Sioux Falls is the fastest growing community in South Dakota. In response to this continued growth and planning for future development, Sioux Falls requires a sustainable supply of municipal water. Planning and managing sustainable groundwater supplies requires a thorough understanding of local groundwater resources. The Big Sioux aquifer consists of glacial outwash sands and gravels and is hydraulically connected to the Big Sioux River, which provided about 90 percent of the city’s source-water production in 2015. Managing sustainable groundwater supplies also requires an understanding of groundwater availability. An effective mechanism to inform water management decisions is the development and utilization of a groundwater-flow model. A groundwater-flow model provides a quantitative framework for synthesizing field information and conceptualizing hydrogeologic processes. These groundwater-flow models can support decision making processes by mapping and characterizing the aquifer. Accordingly, the city of Sioux Falls partnered with the U.S. Geological Survey to construct a groundwater-flow model. Model inputs will include data from advanced geophysical techniques, specifically airborne electromagnetic methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163075","collaboration":"Prepared in cooperation with the City of Sioux Falls","usgsCitation":"Valder, J.F., Delzer, G.C., Carter, J.M., Smith, B.D., and Smith, D.V., 2016, Construction of a groundwater-flow model for the Big Sioux aquifer using airborne electromagnetic methods, Sioux Falls, South Dakota: U.S. Geological Survey Fact Sheet 2016–3075, 4 p., https://dx.doi.org/10.3133/fs20163075.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-079092","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":328947,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3075/fs20163075.pdf","text":"Fact Sheet","size":"4.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3075"},{"id":328946,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3075/coverthb.jpg"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.79023742675781,\n 43.56098868633536\n ],\n [\n -96.79023742675781,\n 43.823133635349556\n ],\n [\n -96.69136047363281,\n 43.823133635349556\n ],\n [\n -96.69136047363281,\n 43.56098868633536\n ],\n [\n -96.79023742675781,\n 43.56098868633536\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Dakota Water Science Center
U.S. Geological Survey
1608 Mountain View Road
Rapid City, South Dakota 57702
Warm Mineral Springs, located in southern Sarasota County, Florida, is a warm, highly mineralized, inland spring. Since 1946, a bathing spa has been in operation at the spring, attracting vacationers and health enthusiasts. During the winter months, the warm water attracts manatees to the adjoining spring run and provides vital habitat for these mammals. Well-preserved late Pleistocene to early Holocene-age human and animal bones, artifacts, and plant remains have been found in and around the spring, and indicate the surrounding sinkhole formed more than 12,000 years ago. The spring is a multiuse resource of hydrologic importance, ecological and archeological significance, and economic value to the community.
The pool of Warm Mineral Springs has a circular shape that reflects its origin as a sinkhole. The pool measures about 240 feet in diameter at the surface and has a maximum depth of about 205 feet. The sinkhole developed in the sand, clay, and dolostone of the Arcadia Formation of the Miocene-age to Oligocene-age Hawthorn Group. Underlying the Hawthorn Group are Oligocene-age to Eocene-age limestones and dolostones, including the Suwannee Limestone, Ocala Limestone, and Avon Park Formation. Mineralized groundwater, under artesian pressure in the underlying aquifers, fills the remnant sink, and the overflow discharges into Warm Mineral Springs Creek, to Salt Creek, and subsequently into the Myakka River. Aquifers described in the vicinity of Warm Mineral Springs include the surficial aquifer system, the intermediate aquifer system within the Hawthorn Group, and the Upper Floridan aquifer in the Suwannee Limestone, Ocala Limestone, and Avon Park Formation. The Hawthorn Group acts as an upper confining unit of the Upper Floridan aquifer.
Groundwater flow paths are inferred from the configuration of the potentiometric surface of the Upper Floridan aquifer for September 2010. Groundwater flow models indicate the downward flow of water into the Upper Floridan aquifer in inland areas, and upward flow toward the surface in coastal areas, such as at Warm Mineral Springs. Warm Mineral Springs is located in a discharge area. Changes in water use in the region have affected the potentiometric surface of the Upper Floridan aquifer. Historical increase in groundwater withdrawals resulted in a 10- to 20-foot regional decline in the potentiometric surface of the Upper Floridan aquifer by May 1975 relative to predevelopment levels and remained at approximately that level in May 2007 in the area of Warm Mineral Springs. Discharge measurements at Warm Mineral Springs (1942–2014) decreased from about 11–12 cubic feet per second in the 1940s to about 6–9 cubic feet per second in the 1970s and remained at about that level for the remainder of the period of record. Similarity of changes in regional water use and discharge at Warm Mineral Springs indicates that basin-scale changes to the groundwater system have affected discharge at Warm Mineral Springs. Water temperature had no significant trend in temperature over the period of record, 1943–2015, and outliers were identified in the data that might indicate inconsistencies in measurement methods or locations.
Within the regional groundwater basin, Warm Mineral Springs is influenced by deep Upper Floridan aquifer flow paths that discharge toward the coast. Associated with these flow paths, the groundwater temperatures increase with depth and toward the coast. Multiple lines of evidence indicate that a source of warm groundwater to Warm Mineral Springs is likely the permeable zone of the Avon Park Formation within the Upper Floridan aquifer at a depth of about 1,400 to 1,600 feet, or deeper sources. The permeable zone contains saline groundwater with water temperatures of at least 95 degrees Fahrenheit.
The water quality of Warm Mineral Springs, when compared with other springs in Florida had the highest temperature and the greatest mineralized content. Warm Mineral Springs water is characterized by a slight-green color, with varying water clarity, low dissolved oxygen (indicative of deep groundwater), and a hydrogen sulfide odor. Water-quality samples detected ammonium-nitrogen and nitrates, but at low concentrations. The drinking water standard for nitrate adopted by the U.S. Environmental Protection Agency is 10 milligrams per liter, measured as nitrogen. Water samples collected at spring vents by divers on April 29, 2015, had concentrations of 0.9 milligram per liter nitrate-nitrogen at vent A and 0.04–0.05 milligram per liter at vents B, C, and D. Typically, the water clarity is highest in the morning (about 30 feet Secchi depth) and often decreases throughout the day.
Analysis of existing data provided some insight into the hydrologic processes affecting Warm Mineral Springs; however, data have been sparsely and discontinuously collected since the 1940s. Continuous monitoring of hydrologic characteristics such as discharge, water temperature, specific conductance, and water-quality indicators, such as nitrate and turbidity (water clarity), would be valuable for monitoring and development of models of spring discharge and water quality. In addition, water samples could be analyzed for isotopic tracers, such as strontium, and the results used to identify and quantify the sources of groundwater that discharge at Warm Mineral Springs. Groundwater flow/transport models could be used to evaluate the sensitivity of the quality and quantity of water flowing from Warm Mineral Springs to changes in climate, aquifer levels, and water use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161166","collaboration":"Prepared in cooperation with the City of North Port and Sarasota County ","usgsCitation":"Metz, P.A., 2016, Discharge, water temperature, and water quality of Warm Mineral Springs, Sarasota County, Florida: A retrospective analysis: U.S. Geological Survey Open-File Report 2016–1166, 31 p., https://dx.doi.org/10.3133/ofr20161166.","productDescription":"vi, 31 p.","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-066216","costCenters":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"links":[{"id":328877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1166/coverthb2.jpg"},{"id":328878,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1166/ofr20161166.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1166"}],"country":"United States","state":"Florida","county":"Sarasota County","otherGeospatial":"Warm Mineral Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.26177334785461,\n 27.058867814427533\n ],\n [\n -82.26177334785461,\n 27.060654482992454\n ],\n [\n -82.25942373275757,\n 27.060654482992454\n ],\n [\n -82.25942373275757,\n 27.058867814427533\n ],\n [\n -82.26177334785461,\n 27.058867814427533\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
(813) 498-5000
Or visit the Caribbean-Florida Water Science Center Web page at
http://fl.water.usgs.gov
Resource nationalism encompasses a broad range of political and economic actions taken by Governments to regulate the extraction of natural resources within their borders. Policies such as increased tariffs or export restrictions can have far-reaching economic effects on international trade. As the Governments of several developing countries consider enacting nationalistic policies, an examination of the 2014 mineral export ban in Indonesia provides an instructive example of the possible impacts of resource nationalism. Significant changes in the production and trade of unprocessed (that is, ores and concentrates) and processed (that is, refined metal) aluminum, copper, and nickel before and after the export ban form the basis of this study.
The U.S. Geological Survey (USGS) National Minerals Information Center (NMIC) tracks production and trade of mineral commodities between producer and consumer countries. Materials flow studies clarify the effects of an export ban on different mineral commodities by assessing changes in production, processing capacity, and trade. Using extensive data collection and monitoring procedures, the USGS NMIC investigated the effects of resource nationalism on the flow of mineral commodities from Indonesia to the global economy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163072","usgsCitation":"Lederer, G.W., 2016, Resource nationalism in Indonesia—Effects of the 2014 mineral export ban: U.S. Geological Survey Fact Sheet 2016-3072, 6 p., https://dx.doi.org/10.3133/fs20163072.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-077240","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":328797,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3072/fs20163072.pdf","text":"Report","size":"302 KB","description":"FS 2016-3072"},{"id":328796,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3072/coverthb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n 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U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Or visit the USGS Minerals Information Web site at: http://minerals.usgs.gov/minerals/
","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2016-09-27","noUsgsAuthors":false,"publicationDate":"2016-09-27","publicationStatus":"PW","scienceBaseUri":"57f7c63be4b0bc0bec09c84c","contributors":{"authors":[{"text":"Lederer, Graham W. glederer@usgs.gov","contributorId":174731,"corporation":false,"usgs":true,"family":"Lederer","given":"Graham W.","email":"glederer@usgs.gov","affiliations":[],"preferred":false,"id":649129,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70175519,"text":"sir20165116 - 2016 - Simulating groundwater flow in karst aquifers with distributed parameter models—Comparison of porous-equivalent media and hybrid flow approaches","interactions":[],"lastModifiedDate":"2016-09-22T15:54:17","indexId":"sir20165116","displayToPublicDate":"2016-09-22T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5116","title":"Simulating groundwater flow in karst aquifers with distributed parameter models—Comparison of porous-equivalent media and hybrid flow approaches","docAbstract":"Understanding karst aquifers, for purposes of their management and protection, poses unique challenges. Karst aquifers are characterized by groundwater flow through conduits (tertiary porosity), and (or) layers with interconnected pores (secondary porosity) and through intergranular porosity (primary or matrix porosity). Since the late 1960s, advances have been made in the development of numerical computer codes and the use of mathematical model applications towards the understanding of dual (primary [matrix] and secondary [fractures and conduits]) porosity groundwater flow processes, as well as characterization and management of karst aquifers. The Floridan aquifer system (FAS) in Florida and parts of Alabama, Georgia, and South Carolina is composed of a thick sequence of predominantly carbonate rocks. Karst features are present over much of its area, especially in Florida where more than 30 first-magnitude springs occur, numerous sinkholes and submerged conduits have been mapped, and numerous circular lakes within sinkhole depressions are present. Different types of mathematical models have been applied for simulation of the FAS. Most of these models are distributed parameter models based on the assumption that, like a sponge, water flows through connected pores within the aquifer system and can be simulated with the same mathematical methods applied to flow through sand and gravel aquifers; these models are usually referred to as porous-equivalent media models. The partial differential equation solved for groundwater flow is the potential flow equation of fluid mechanics, which is used when flow is dominated by potential energy and has been applied for many fluid problems in which kinetic energy terms are dropped from the differential equation solved. In many groundwater model codes (basic MODFLOW), it is assumed that the water has a constant temperature and density and that flow is laminar, such that kinetic energy has minimal impact on flow. Some models have been developed that incorporate the submerged conduits as a one-dimensional pipe network within the aquifer rather than as discrete, extremely transmissive features in a porous-equivalent medium; these submerged conduit models are usually referred to as hybrid models and may include the capability to simulate both laminar and turbulent flow in the one-dimensional pipe network. Comparisons of the application of a porous-equivalent media model with and without turbulence (MODFLOW-Conduit Flow Process mode 2 and basic MODFLOW, respectively) and a hybrid (MODFLOW-Conduit Flow Process mode 1) model to the Woodville Karst Plain near Tallahassee, Florida, indicated that for annual, monthly, or seasonal average hydrologic conditions, all methods met calibration criteria (matched observed groundwater levels and average flows). Thus, the increased effort required, such as the collection of data on conduit location, to develop a hybrid model and its increased computational burden, is not necessary for simulation of average hydrologic conditions (non-laminar flow effects on simulated head and spring discharge were minimal). However, simulation of a large storm event in the Woodville Karst Plain with daily stress periods indicated that turbulence is important for matching daily springflow hydrographs. Thus, if matching streamflow hydrographs over a storm event is required, the simulation of non-laminar flow and the location of conduits are required. The main challenge in application of the methods and approaches for developing hybrid models relates to the difficulty of mapping conduit networks or having high-quality datasets to calibrate these models. Additionally, hybrid models have long simulation times, which can preclude the use of parameter estimation for calibration. Simulation of contaminant transport that does not account for preferential flow through conduits or extremely permeable zones in any approach is ill-advised. Simulation results in other karst aquifers or other parts of the FAS may differ from the comparison demonstrated herein.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165116","collaboration":"A product of the Water Use and Availability Science Program","usgsCitation":"Kuniansky, E.L., 2016, Simulating groundwater flow in karst aquifers with distributed parameter models—Comparison of porous-equivalent media and hybrid flow approaches: U.S. Geological Survey Scientific Investigations Report 2016–5116, 14 p., https://dx.doi.org/10.3133/sir20165116.","productDescription":"Report: v, 14 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-071317","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":328727,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5116/sir20165116.pdf","text":"Report","size":"3.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5116"},{"id":328833,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7PK0D87","text":"USGS data release - MODFLOW and MODFLOW Conduit Flow Process data sets for simulation experiments of the Woodville Karst Plain, near Tallahassee, Florida with three different approaches and different stress periods","description":"SIR 2016–5116 Data Release"},{"id":328726,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5116/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Woodville Karst Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.4903564453125,\n 30.04532159026885\n ],\n [\n -84.4903564453125,\n 30.456368670179007\n ],\n [\n -84.06875610351562,\n 30.456368670179007\n ],\n [\n -84.06875610351562,\n 30.04532159026885\n ],\n [\n -84.4903564453125,\n 30.04532159026885\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Caribbean-Florida Water Science Center-Florida
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559–6302
Populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, are experiencing long-term declines in abundance. Upper Klamath Lake populations are decreasing because adult mortality, which is relatively low, is not being balanced by recruitment of young adult suckers into known adult spawning aggregations. Previous sampling for juvenile suckers indicated that most juvenile sucker mortality in Upper Klamath Lake likely occurs within the first year of life. The importance of juvenile sucker mortality to the dynamics of Clear Lake Reservoir populations is less clear, and factors other than juvenile mortality (such as access to spawning habitat) play a substantial role. For example, production of age-0 juvenile suckers, as determined by fin ray annuli and fin development, has not been detected since 2013 in Clear Lake Reservoir, whereas it is detected annually in Upper Klamath Lake.
\nWe initiated a long-term juvenile sucker monitoring program in 2015 designed to track cohorts through seasons and among years in both Upper Klamath Lake and Clear Lake Reservoir. Specifically, our goals are to track annual variability in age-0 sucker production, juvenile sucker survival, growth, and condition. In this first year of the monitoring program, we assessed assumptions that sampled fish were representative of populations of suckers in each lake. The size, age, and species composition of suckers were similar between randomly determined sites and fixed sites in each lake. We captured a wide size and age range of suckers using similar gear, indicating our gear did not exclude older and larger fish. We identified improvements that could be made in the monitoring program including increasing the number of randomly determined sample sites in both lakes, evaluation of gear-size selectivity, and validation of aging methods for juvenile Lost River and shortnose suckers.
\nDiffering age composition of juvenile suckers between lakes in our 2015 catches and as reported in previous studies indicate that juvenile suckers are produced in relatively larger numbers each year in Upper Klamath Lake than in Clear Lake Reservoir. Most (96.6 percent) of suckers captured in Upper Klamath Lake in 2015 were age-0, whereas age-0 or age-1 suckers were not captured in Clear Lake Reservoir. Despite ample effort, age-0 suckers have not been captured in Clear Lake Reservoir since 2013. Estimated ages of suckers captured in 2015 in Clear Lake Reservoir ranged from 2 to 6 years. Low flow during spawning seasons in the only known spawning tributary to Clear Lake Reservoir (Willow Creek) appears to explain the lack of age-0 sucker production in recent years.
\nJuvenile sucker mortality is relatively higher in Upper Klamath Lake than in Clear Lake Reservoir. We compared data collected in 2015 to previously published catch rates to produce an index of annual juvenile sucker survival for these species. We calculated indices of annual apparent survival of juvenile sucker ages 0–5 years old in Clear Lake Reservoir to be between 0.37 (±0.86 standard error [SE]) and 0.44 (±0.84 SE). This is the first time indices of annual apparent survival for Lost River and shortnose suckers have been calculated. This estimate has the limitation of being non-species specific because not all individuals were identified to species in previous years, and suckers that were identified included both taxa. In contrast, catch rates decreased by 89 percent for juvenile Lost River suckers and decreased 50 percent for juvenile shortnose suckers in Upper Klamath Lake between August and September 2015. Very low catch rates of age-1 and older suckers in Upper Klamath Lake indicate that annual juvenile sucker survival rates are near zero.
\nCondition of suckers was assessed in 2015 based on age-0 sucker growth rates in Upper Klamath Lake and the prevalence of externally observable afflictions on suckers from both lakes. Age-0 Lost River suckers grew an average (± standard deviation [SD]) of 0.72 (±0.01) millimeters [mm] standard length [SL] per day, and age-0 shortnose suckers grew an average of 0.57 (±0.04) mm SL per day in 2015. This growth rate was similar to growth rates reported for these species in Upper Klamath Lake in previous years. Opercular deformities, skin hemorrhages, black-spot causing parasites, and Lernaea spp. parasitism were the most common afflictions observed on suckers. Observed afflictions were primarily on suckers from Upper Klamath Lake, with the exception of Lernaea spp., which occurred more frequently on suckers from Clear Lake Reservoir. Opercular deformities and black-spot causing parasites were each observed on 5 percent of age-0 suckers from Upper Klamath Lake. Petechial hemorrhaging of the skin was observed on 43 percent of age-0 Lost River suckers, 38 percent of age-0 suckers of undetermined taxa, and only 24 percent of age-0 shortnose suckers from Upper Klamath Lake. Petechial hemorrhaging of the skin was only observed on a single shortnose sucker from Clear Lake Reservoir. Within Upper Klamath Lake, the prevalence of these hemorrhages was exactly twice as high as was reported in 2014.
\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161164","usgsCitation":"Burdick, S.M., Ostberg, C.O., Hereford, M.E., and Hoy, M.S., 2016, Juvenile sucker cohort tracking data summary and assessment of monitoring program, 2015: U.S. Geological Survey Open-File Report 2016–1164, 30 p., https://dx.doi.org/10.3133/ofr20161164.","productDescription":"Report: iv, 30 p.","startPage":"1","endPage":"30","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-079633","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":328862,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1164/coverthb.jpg"},{"id":328863,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1164/ofr20161164.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1164"}],"country":"United States","state":"Oregon","county":"Klamath County","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.29980468749999,\n 43.06086137134326\n ],\n [\n -121.13525390625,\n 42.706659563510385\n ],\n [\n -120.750732421875,\n 41.68932225997044\n ],\n [\n -121.761474609375,\n 41.492120839687786\n ],\n [\n -122.58544921875,\n 41.3025710943056\n ],\n [\n -123.50830078125,\n 40.48873742102282\n ],\n [\n -124.288330078125,\n 40.85537053192496\n ],\n [\n -123.870849609375,\n 42.00848901572399\n ],\n [\n -123.431396484375,\n 41.902277040963696\n ],\n [\n -123.277587890625,\n 42.04113400940809\n ],\n [\n -122.29980468749999,\n 43.06086137134326\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"
Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
An investigation into the magnitude and frequency of floods in Washington State computed the annual exceedance probability (AEP) statistics for 648 U.S. Geological Survey unregulated streamgages in and near the borders of Washington using the recorded annual peak flows through water year 2014. This is an updated report from a previous report published in 1998 that used annual peak flows through the water year 1996. New in this report, a regional skew coefficient was developed for the Pacific Northwest region that includes areas in Oregon, Washington, Idaho and western Montana within the Columbia River drainage basin south of the United States-Canada border, the coastal areas of Oregon and western Washington, and watersheds draining into Puget Sound, Washington. The skew coefficient is an important term in the Log Pearson Type III equation used to define the distribution of the log-transformed annual peaks. The Expected Moments Algorithm was used to fit historical and censored peak-flow data to the log Pearson Type III distribution. A Multiple Grubb-Beck test was employed to censor low outliers of annual peak flows to improve on the frequency distribution. This investigation also includes a section on observed trends in annual peak flows that showed significant trends (p-value < 0.05) in 21 of 83 long-term sites, but with small magnitude Kendall tau values suggesting a limited monotonic trend in the time series of annual peaks. Most of the sites with a significant trend in western Washington were positive and all the sites with significant trends (three sites) in eastern Washington were negative.
Multivariate regression analysis with measured basin characteristics and the AEP statistics at long-term, unregulated, and un-urbanized (defined as drainage basins with less than 5 percent impervious land cover for this investigation) streamgages within Washington and some in Idaho and Oregon that are near the Washington border was used to develop equations to estimate AEP statistics at ungaged basins. Washington was divided into four regions to improve the accuracy of the regression equations; a set of equations for eight selected AEPs and for each region were constructed. Selected AEP statistics included the annual peak flows that equaled or exceeded 50, 20, 10, 4, 2, 1, 0.5 and 0.2 percent of the time equivalent to peak flows for peaks with a 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively. Annual precipitation and drainage area were the significant basin characteristics in the regression equations for all four regression regions in Washington and forest cover was significant for the two regression regions in eastern Washington. Average standard error of prediction for the regional regression equations ranged from 70.19 to 125.72 percent for Regression Regions 1 and 2 on the eastern side of the Cascade Mountains and from 43.22 to 58.04 percent for Regression Regions 3 and 4 on the western side of the Cascade Mountains. The pseudo coefficient of determination (where a value of 100 signifies a perfect regression model) ranged from 68.39 to 90.68 for Regression Regions 1 and 2, and 92.35 to 95.44 for Regions 3 and 4.
The calculated AEP statistics for the streamgages and the regional regression equations are expected to be incorporated into StreamStats after the publication of this report. StreamStats is the interactive Web-based map tool created by the U.S. Geological Survey to allow the user to choose a streamgage and obtain published statistics or choose ungaged locations where the program automatically applies the regional regression equations and computes the estimates of the AEP statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165118","collaboration":"Prepared in cooperation with the Washington State Department of Transportation and the Washington State Department of Ecology","usgsCitation":"Mastin, M.C., Konrad, C.P., Veilleux, A.G., and Tecca, A.E., 2016, Magnitude, frequency, and trends of floods at gaged and ungaged sites in Washington, based on data through water year 2014 (ver 1.2, November 2017): U.S. Geological Survey Scientific Investigations Report 2016–5118, 70 p., https://dx.doi.org/10.3133/sir20165118.","productDescription":"Report: vi, 69 p.; 3 Tables","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075256","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":329272,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5118/versionHist.txt","size":"3 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KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5118 Flood Q Tools"},{"id":328748,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5118/sir20165118_table8.xlsx","text":"Table 8","size":"458 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5118 Table 8"},{"id":328744,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5118/coverthb2.jpg"}],"country":"United 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\"}}]}","edition":"Version 1.0: Originally post September 20, 2016; Version 1.1: October 4, 2016; Version 1.2: November 2017","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
A statewide study was led to develop regression equations for estimating three selected spring and three selected fall low-flow frequency statistics for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include spring (April through June) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years and fall (October through December) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years. Estimates of the three selected spring statistics are provided for 241 U.S. Geological Survey continuous-record streamgages, and estimates of the three selected fall statistics are provided for 238 of these streamgages, using data through June 2014. Because only 9 years of fall streamflow record were available, three streamgages included in the development of the spring regression equations were not included in the development of the fall regression equations. Because of regulation, diversion, or urbanization, 30 of the 241 streamgages were not included in the development of the regression equations. The study area includes Iowa and adjacent areas within 50 miles of the Iowa border. Because trend analyses indicated statistically significant positive trends when considering the period of record for most of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. Geographic information system software was used to measure 63 selected basin characteristics for each of the 211streamgages used to develop the regional regression equations. The study area was divided into three low-flow regions that were defined in a previous study for the development of regional regression equations.
Because several streamgages included in the development of regional regression equations have estimates of zero flow calculated from observed streamflow for selected spring and fall low-flow frequency statistics, the final equations for the three low-flow regions were developed using two types of regression analyses—left-censored and generalized-least-squares regression analyses. A total of 211 streamgages were included in the development of nine spring regression equations—three equations for each of the three low-flow regions. A total of 208 streamgages were included in the development of nine fall regression equations—three equations for each of the three low-flow regions. A censoring threshold was used to develop 15 left-censored regression equations to estimate the three fall low-flow frequency statistics for each of the three low-flow regions and to estimate the three spring low-flow frequency statistics for the southern and northwest regions. For the northeast region, generalized-least-squares regression was used to develop three equations to estimate the three spring low-flow frequency statistics. For the northeast region, average standard errors of prediction range from 32.4 to 48.4 percent for the spring equations and average standard errors of estimate range from 56.4 to 73.8 percent for the fall equations. For the northwest region, average standard errors of estimate range from 58.9 to 62.1 percent for the spring equations and from 83.2 to 109.4 percent for the fall equations. For the southern region, average standard errors of estimate range from 43.2 to 64.0 percent for the spring equations and from 78.1 to 78.7 percent for the fall equations.
The regression equations are applicable only to stream sites in Iowa with low flows not substantially affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. The regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system application. StreamStats allows users to click on any ungaged stream site and compute estimates of the six selected spring and fall low-flow statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged site are provided. StreamStats also allows users to click on any Iowa streamgage to obtain computed estimates for the six selected spring and fall low-flow statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165111","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Eash, D.A., Barnes, K.K., and O’Shea, P.S., 2016, Methods for estimating selected spring and fall low-flow frequency statistics for ungaged stream sites in Iowa, based on data through June 2014 (ver. 1.1, October 2016): U.S. Geological Survey Scientific Investigations Report 2016–5111, 32 p., https://dx.doi.org/10.3133/sir20165111.","productDescription":"Report: vi, 32 p.; Appendix Table 1–1","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070749","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":328721,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5111/sir20165111.pdf","text":"Report","size":"2.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5111"},{"id":328722,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5111/sir20165111_appendix_table1-1.xlsx","text":"Appendix Table 1–1","size":"91.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5111 Appendix Table 1–1"},{"id":329298,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2016/5111/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016–5111 Version History"},{"id":328720,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5111/coverthb.jpg"}],"country":"United States","state":"Iowa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.218017578125,\n 39.86758762451019\n ],\n [\n -97.218017578125,\n 44.06390660801779\n ],\n [\n -89.461669921875,\n 44.06390660801779\n ],\n [\n -89.461669921875,\n 39.86758762451019\n ],\n [\n -97.218017578125,\n 39.86758762451019\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted September 19, 2016; Version 1.1: October 5, 2016","contact":"Director, Iowa Water Science Center
U.S. Geological Survey
P.O. Box 1230
Iowa City, IA 52244
Sediment budgets of the Lower Missouri River were developed in a study led by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers. The scope of the study included the development of a long-term (post-impoundment, 1968–2014) average annual sediment budget and selected annual, monthly, and daily sediment budgets for a reach and period that adequate data were available. Included in the analyses were 31 main-stem and tributary stations of the Lower Missouri River and two Mississippi River stations—the Mississippi River below Grafton, Illinois, and the Mississippi River at St. Louis, Missouri.
Long-term average annual suspended-sediment loads of Missouri River main-stem stations ranged from 0.33 million tons at the Missouri River at Yankton, South Dakota, station to 71.2 million tons at Missouri River at Hermann, Mo., station. Gaged tributary gains accounted for 9–36 percent of the local reach budgets and cumulative gaged tributary contributions accounted for 84 percent of the long-term average suspended-sediment load of the Missouri River at Hermann, Mo., station. Although the sediment budgets for seven defined main-stem reaches generally were incomplete—missing bedload, reach storage, and ungaged tributary contributions—the budget residuals (net result of sediment inputs and outputs) for six of the seven reaches ranged from -7.0 to 1.7 million tons, or from -9.2 to 4.0 percent of the reach output suspended-sediment load, and were within the 10 percent reported measurement error of annual suspended-sediment loads for large rivers. The remaining reach, downstream from Gavin’s Point Dam, extended from Yankton, S. Dak., to Sioux City, Iowa, and had a budget residual of -9.8 million tons, which was -88 percent of the suspended-sediment load at Sioux City.
The Lower Missouri River reach from Omaha, Nebraska, to Nebraska City, Nebr., had periods of concurrent sediment data for each primary budget component with which to analyze and determine a suspended-sediment budget for selected annual, monthly, and daily time increments. The temporal changes in the cumulative annual budget residuals were poorly correlated with the comparatively steady 1968–2011 annual stage trends at the Missouri River at Nebraska City, Nebr., station. An accurate total sediment budget is developed by having concurrent data available for all primary suspended and bedload components for a reach of interest throughout a period. Such a complete budget, with concurrent record for suspended-sediment load and bedload components, is unavailable for any reach and period in the Lower Missouri River. The primary data gaps are in bedload data, and also in suspended-sediment gains and losses including ungaged tributary inputs and sediment storage. Bedload data gaps in the Missouri River Basin are much more prevalent than suspended-sediment data gaps, and the first step in the development of reach bedload budgets is the establishment of a standardized bedload monitoring program at main-stem stations.
The temporal changes in flow-adjusted suspended-sediment concentrations analyzed at main-stem Missouri River stations indicated an overall downward change in concentrations between 1968 and 2014. Temporary declines in flow-adjusted suspended-sediment concentrations during and following large floods were evident but generally returned to near pre-flood values within about 6 months.
Data uncertainties associated with the development of a sediment budget include uncertainties associated with the collection of suspended-sediment and bedload data and the computation of suspended-sediment loads. These uncertainties vary depending on the frequency of data collection, the variability of conditions being represented by the discrete samples, and the statistical approach to suspended-sediment load computations. The coefficients of variation of suspended-sediment loads of Missouri River tributary stations for 1968–2014 were greater, 75.0 percent, than the main-stem stations, 47.1 percent. The lower coefficient of variation at main-stem stations compared to tributaries, primarily is the result of the lower variability in streamflow and sediment discharge identified at main-stem stations. To obtain similar accuracy between suspended-sediment loads at main-stem and tributary stations, a longer period of record is required of the tributary stations. During 1968–2014, however, the Missouri River main-stem station record was much more complete (87 percent) than the tributary station record (28 percent).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165097","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Heimann, D.C., 2016, Generalized sediment budgets of the Lower Missouri River, 1968–2014: U.S. Geological Survey Scientific Investigations Report 2016–5097, 51 p., https://dx.doi.org/10.3133/sir20165097.","productDescription":"Report: vii, 51 p.; Tables 1-9","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073678","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":328577,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_tables.zip","text":"Tables 1–9","size":"8.88 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016–5097 Tables 1–9"},{"id":328568,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_1.xlsx","text":"Table 1","size":"599 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 1"},{"id":328569,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_2.xlsx","text":"Table 2","size":"6.15 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 2"},{"id":328570,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_3.xlsx","text":"Table 3","size":"9.11 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 3"},{"id":328565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5097/coverthb.jpg"},{"id":328566,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097.pdf","text":"Report","size":"5.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5097"},{"id":328574,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_7.xlsx","text":"Table 7","size":"496 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 7"},{"id":328571,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_4.xlsx","text":"Table 4","size":"496 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 4"},{"id":328575,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_8.xlsx","text":"Table 8","size":"46.4 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 8"},{"id":328572,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_5.xlsx","text":"Table 5","size":"499 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 5"},{"id":328573,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_6.xlsx","text":"Table 6","size":"493 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 6"},{"id":328576,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5097/sir20165097_table_9.xlsx","text":"Table 9","size":"586 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5097 Table 9"}],"country":"United States","otherGeospatial":"Missouri River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.120849609375,\n 38.81617117607388\n ],\n [\n -91.318359375,\n 38.89103282648846\n ],\n [\n -92.933349609375,\n 40.59727063442027\n ],\n [\n -95.361328125,\n 43.51668853502909\n ],\n [\n -97.72338867187499,\n 45.91294412737392\n ],\n [\n -99.54711914062499,\n 47.57652571374621\n ],\n [\n -104.0185546875,\n 48.99463598353408\n ],\n [\n -105.029296875,\n 49.468124067331644\n ],\n [\n -113.203125,\n 49.56797785892715\n ],\n [\n -112.587890625,\n 44.98034238084973\n ],\n [\n -107.09472656249999,\n 40.9964840143779\n ],\n [\n -102.06298828125,\n 38.35888785866677\n ],\n [\n -94.59228515625,\n 37.10776507118514\n ],\n [\n -91.64794921875,\n 37.142803443716836\n ],\n [\n -90.9173583984375,\n 38.36750215395045\n ],\n [\n -90.120849609375,\n 38.81617117607388\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401