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USGS2014

Mesohabitats, fish assemblage composition, and mesohabitat use of the Rio Grande silvery minnow over a range of seasonal flow regimes in the Rio Grande/Rio Bravo del Norte, in and near Big Bend National Park, Texas, 2010-11

In 2010–11, the U.S. Geological Survey (USGS), in cooperation with the U.S. Fish and Wildlife Service, evaluated the physical characteristics and fish assemblage composition of mapped river mesohabitats at four sites on the Rio Grande/Rio Bravo del Norte (hereinafter Rio Grande) in and near Big Bend National Park, Texas. The four sites used for the river habitat study were colocated with sites where the U.S. Fish and Wildlife Service has implemented an experimental reintroduction of the Rio Grande silvery minnow (Hybognathus amarus), a federally listed endangered species, into part of the historical range of this species. The four sites from upstream to downstream are USGS station 08374340 Rio Grande at Contrabando Canyon near Lajitas, Tex. (hereinafter the Contrabando site), USGS station 290956103363600 Rio Grande at Santa Elena Canyon, Big Bend National Park, Tex. (hereinafter the Santa Elena site), USGS station 291046102573900 Rio Grande near Ranger Station at Rio Grande Village, Tex. (hereinafter the Rio Grande Village site), and USGS station 292354102491100 Rio Grande above Stillwell Crossing near Big Bend National Park, Tex. (hereinafter the Stillwell Crossing site). In-channel river habitat was mapped at the mesohabitat scale over a range of seasonal streamflows. A late summer (August–September 2010) high-flow regime, an early spring (April–May 2010) intermediate flow regime, and a late spring (May 2011) low-flow regime were the seasonal flows used in the study. River habitat was mapped in the field by using a geographic information system and a Global Positioning System unit to characterize the sites at the mesohabitat scale. Physical characteristics of a subset of mesohabitats in a reach of the Rio Grande at each site were measured during each flow regime and included depth, velocity, type and size of the substrate, and percent embeddedness. Selected water-quality properties (dissolved oxygen, pH, specific conductance, and temperature) of a subset of mesohabitats were also measured. The fish assemblage composition at the four sites was determined during the three flow regimes, and fish were collected by seining in each mesohabitat where physical characteristic data were measured, except during some periods of high flow when electrofishing was done to supplement seining. The total number and number of types of mesohabitats were larger during low flows compared to intermediate flows, and larger during intermediate flows compared to high flows. Decreases in streamflow typically led to increases in channel complexity in terms of the number of different types and total number of mesohabitats present. The total wetted area increased and the number of mesohabitat types generally decreased as streamflow increased. At all four sites, the smallest depths and velocities were generally measured during low flow and the largest depths and velocities at high flow. Specific conductance was relatively consistent between the Contrabando and Santa Elena sites, the two most upstream sites. Specific conductance decreased appreciably between the Santa Elena site and the Rio Grande Village, and decreased slightly between the Rio Grande Village site and the Stillwell Crossing site. Specific-conductance values within and among mesohabitat types at a given site were relatively consistent. The pH values measured within and among mesohabitat types also were relatively consistent at all four sites. Median dissolved oxygen concentrations were relatively consistent between the Contrabando and Santa Elena sites (8.34 and 8.54 milligrams per liter [mg/L], respectively) but decreased along the stretch of river between the Santa Elena and Rio Grande Village sites to 7.31 mg/L, possibly because of small dissolved oxygen concentrations associated with contributions from springs between the Santa Elena and Rio Grande Village sites. Dissolved oxygen concentrations increased substantially between the Rio Grande Village and Stillwell Crossing sites to 10.06 mg/L. Mesohabitat water temperatures were generally highest in mesohabitats commonly associated with shallow water depths and low velocities (forewaters, backwaters, and embayments). Of the 21 species of fish collected during the three flow regimes, red shiner ( Cyprinella lutrensis ) was the most abundant species overall, accounting for about 35 percent of all fish collected. Another minnow, the endemic Tamaulipas shiner ( Notropis braytoni ), was second in overall abundance. A nonnative species, the common carp ( Cyprinus carpio ), was the third most abundant species overall. No statistically significant differences in fish-species richness were found among the different mesohabitat types. Median fish-species richness and maximum fish-species richness values were larger, and fish-species richness was more variable in runs, pools, forewaters, and backwaters during low flow compared to the fish-species richness values calculated for intermediate and high flows. Fish density in backwater mesohabitats was significantly different from fish densities in run mesohabitats, but fish densities were not significantly different among the other mesohabitat types. Of the 39 Rio Grande silvery minnow individuals collected at the four study sites, 21 (more than half) were collected at the Santa Elena site, 12 at the Contrabando site, and 3 each at the Rio Grande Village and Stillwell Crossing sites. Rio Grande silvery minnow fish-species densities followed the same order as abundance of this species at the sites; fish-species densities ranged from 0.95 fish per 100 square meters (m 2 ) at the Santa Elena site to 0.11–0.47 fish per 100 m 2 at the other three sites. The Rio Grande silvery minnow was most common in pools and runs during low- and intermediate-flow regimes. This species was less commonly collected in backwaters, embayments, and rapids, and none were collected in forewaters or submerged channel bars. The Tamaulipas shiner has similar life-history characteristics compared to the Rio Grande silvery minnow, including similar feeding habits and habitat use. Tamaulipas shiner was most common in backwater, run, and riffle mesohabitats (in decreasing order) during low and intermediate flow and was less common in submerged channel bar, pool, forewater, rapid, and embayment mesohabitats (in decreasing order) during the same flows. The overall relative percent density (composite of all three flow regimes) of Rio Grande silvery minnow was largest in rapid and pool mesohabitats and for Tamaulipas shiner was largest in backwater mesohabitats. There were no statistically significant differences between the stream velocities associated with seine hauls of the Rio Grande silvery minnow and Tamaulipas shiner. Stream velocities associated with the seine hauls that included Rio Grande silvery minnow indicate that this species is predominantly found in low-velocity mesohabitats. Velocities associated with seine hauls that included the Tamaulipas shiner represented a much broader overall range of velocities than those associated with Rio Grande silvery minnow collections. No statistically significant differences were found between the depths for seine hauls that included Rio Grande silvery minnow or Tamaulipas shiner. The Rio Grande silvery minnow was more commonly collected in seine hauls from mesohabitats dominated by cobble substrates and less frequently collected in mesohabitats with substrates dominated by fine-sized silt and clay particles, gravels, and sands, in that order. In contrast, the Tamaulipas shiner was broadly distributed among mesohabitats characterized as having gravel, cobble, and silt and clay.

Texas↗

USGS2014

Methods for estimating the magnitude and frequency of floods for urban and small, rural streams in Georgia, South Carolina, and North Carolina, 2011

Reliable estimates of the magnitude and frequency of floods are essential for the design of transportation and water-conveyance structures, flood-insurance studies, and flood-plain management. Such estimates are particularly important in densely populated urban areas. In order to increase the number of streamflow-gaging stations (streamgages) available for analysis, expand the geographical coverage that would allow for application of regional regression equations across State boundaries, and build on a previous flood-frequency investigation of rural U.S Geological Survey streamgages in the Southeast United States, a multistate approach was used to update methods for determining the magnitude and frequency of floods in urban and small, rural streams that are not substantially affected by regulation or tidal fluctuations in Georgia, South Carolina, and North Carolina. The at-site flood-frequency analysis of annual peak-flow data for urban and small, rural streams (through September 30, 2011) included 116 urban streamgages and 32 small, rural streamgages, defined in this report as basins draining less than 1 square mile. The regional regression analysis included annual peak-flow data from an additional 338 rural streamgages previously included in U.S. Geological Survey flood-frequency reports and 2 additional rural streamgages in North Carolina that were not included in the previous Southeast rural flood-frequency investigation for a total of 488 streamgages included in the urban and small, rural regression analysis. The at-site flood-frequency analyses for the urban and small, rural streamgages included the expected moments algorithm, which is a modification of the Bulletin 17B log-Pearson type III method for fitting the statistical distribution to the logarithms of the annual peak flows. Where applicable, the flood-frequency analysis also included low-outlier and historic information. Additionally, the application of a generalized Grubbs-Becks test allowed for the detection of multiple potentially influential low outliers. Streamgage basin characteristics were determined using geographical information system techniques. Initial ordinary least squares regression simulations reduced the number of basin characteristics on the basis of such factors as statistical significance, coefficient of determination, Mallow’s Cp statistic, and ease of measurement of the explanatory variable. Application of generalized least squares regression techniques produced final predictive (regression) equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability flows for urban and small, rural ungaged basins for three hydrologic regions (HR1, Piedmont–Ridge and Valley; HR3, Sand Hills; and HR4, Coastal Plain), which previously had been defined from exploratory regression analysis in the Southeast rural flood-frequency investigation. Because of the limited availability of urban streamgages in the Coastal Plain of Georgia, South Carolina, and North Carolina, additional urban streamgages in Florida and New Jersey were used in the regression analysis for this region. Including the urban streamgages in New Jersey allowed for the expansion of the applicability of the predictive equations in the Coastal Plain from 3.5 to 53.5 square miles. Average standard error of prediction for the predictive equations, which is a measure of the average accuracy of the regression equations when predicting flood estimates for ungaged sites, range from 25.0 percent for the 10-percent annual exceedance probability regression equation for the Piedmont–Ridge and Valley region to 73.3 percent for the 0.2-percent annual exceedance probability regression equation for the Sand Hills region.

Georgia, North Carolina, South Carolina↗

USGS2014

Groundwater and surface-water interaction and potential for underground water storage in the Buena Vista-Salida Basin, Chaffee County, Colorado, 2011

By 2030, the population of the Arkansas Headwaters Region, which includes all of Chaffee and Lake Counties and parts of Custer, Fremont, and Park Counties, Colorado, is forecast to increase about 73 percent. As the region’s population increases, it is anticipated that groundwater will be used to meet much of the increased demand. In September 2009, the U.S. Geological Survey, in cooperation with the Upper Arkansas Water Conservancy District and with support from the Colorado Water Conservation Board; Chaffee, Custer, and Fremont Counties; Buena Vista, Cañon City, Poncha Springs, and Salida; and Round Mountain Water and Sanitation District, began a 3-year study of groundwater and surface-water conditions in the Buena Vista-Salida Basin. This report presents results from the study of the Buena Vista-Salida Basin including synoptic gain-loss measurements and water budgets of Cottonwood, Chalk, and Browns Creeks, changes in groundwater storage, estimates of specific yield, transmissivity and hydraulic conductivity from aquifer tests and slug tests, an evaluation of areas with potential for underground water storage, and estimates of stream-accretion response-time factors for hypothetical recharge and selected streams in the basin. The four synoptic measurements of flow of Cottonwood, Chalk, and Browns Creeks, suggest quantifiable groundwater gains and losses in selected segments in all three perennial streams. The synoptic measurements of flow of Cottonwood and Browns Creeks suggest a seasonal variability, where positive later-irrigation season values in these creeks suggest groundwater discharge, possibly as infiltrated irrigation water. The overall sum of gains and losses on Chalk Creek does not indicate a seasonal variability but indicates a gaining stream in April and August/September. Gains and losses in the measured upper segments of Chalk Creek likely are affected by the Chalk Cliffs Rearing Unit (fish hatchery). Monthly water budgets were estimated for selected segments of five perennial streams (Cottonwood, North Cottonwood, Chalk, and Browns Creeks, and South Arkansas River) in the Buena Vista-Salida Basin for calendar year 2011. Differences between reported diversions and estimated crop irrigation requirements were used to estimate groundwater recharge in the areas irrigated by water supplied from the diversions. The amount of groundwater recharge in all the basins varied monthly; however, the greatest amount of recharge was during June and July for Cottonwood, North Cottonwood, and Chalk Creeks and South Arkansas River. The greatest amount of recharge in 2011 in Browns Creek occurred in July and August. The large seasonal fluctuations of groundwater near irrigated areas in the Buena Vista-Salida Basin indicate that the increased groundwater storage resulting from infiltration of surface-water diversions has dissipated by the following spring. Areas within the Buena Vista-Salida Basin with the potential for underground storage were identified using geographic information system data, including topographic, geologic, and hydrologic data, excluding the mountainous areas that border the Buena Vista-Salida Basin and igneous and metamorphic rock outcrop areas. The areas that met the selection criteria for underground water storage are located on terrace deposits near the Arkansas River and adjacent to its major tributaries. The selected areas also contain much of the irrigated land within the basin; consequently, irrigation ditches and canals could provide a means of conveying water to potential recharge sites.

Colorado↗

USGS2014

Aquifers of Arkansas: protection, management, and hydrologic and geochemical characteristics of groundwater resources in Arkansas

Sixteen aquifers in Arkansas that currently serve or have served as sources of water supply are described with respect to existing groundwater protection and management programs, geology, hydrologic characteristics, water use, water levels, deductive analysis, projections of hydrologic conditions, and water quality. State and Federal protection and management programs are described according to regulatory oversight, management strategies, and ambient groundwater-monitoring programs that currently (2013) are in place for assessing and protecting groundwater resources throughout the State. Physical attributes, groundwater geochemistry, and groundwater quality are described for each of the 16 aquifers of the State. Information in regard to the hydrology and geochemistry of each of the aquifers is summarized from about 550 historical and recent publications. Additionally, more than 8,000 sites with groundwater-quality data were obtained from the U.S. Geological Survey National Water Information System and the Arkansas Department of Environmental Quality databases and entered into a spatial database to investigate distribution and trends in chemical constituents for each of the aquifers. The 16 aquifers of the State were divided into two major physiographic regions of the State: the Coastal Plain Province (referred to as Coastal Plain) of eastern and southern Arkansas, which includes 11 of the 16 aquifers, and the Interior Highlands Division (referred to as Interior Highlands) of western Arkansas, which includes the remaining 5 aquifers. The 11 aquifers in the Coastal Plain consist of various geologic units that are Cenozoic in age and consist primarily of Cretaceous, Tertiary, and Quaternary sands, gravels, silts, and clays. Groundwater in the Coastal Plain represents one of the most valuable natural resources in the State, driving the economic engines of agriculture, while also supplying abundant water for commercial, industrial, and public-supply use. In terms of age from youngest to oldest, the aquifers of the Coastal Plain include Quaternary alluvial aquifers, including the Mississippi River Valley alluvial aquifer (the most important aquifer in Arkansas in terms of volume of use and economic benefits), the Jackson Group (a regional confining unit that served for decades as an important source of domestic supply), and the Cockfield, Sparta, Cane River, Carrizo, Wilcox, Nacatoch, Ozan, Tokio, and Trinity aquifers. The Mississippi River Valley alluvial aquifer accounts for approximately 94 percent of all groundwater used in the State, and the aquifer is used primarily for irrigation purposes. The Sparta aquifer is the second most important aquifer in terms of use, and the aquifer was used in the past dominantly as a source of public and industrial supply, although increasing irrigation use is occurring because of critically declining water levels in the Mississippi River Valley alluvial aquifer. Other aquifers of the Coastal Plain generally are used as important local sources of domestic, industrial, and public supply, in addition to other minor uses. Water quality generally is good for all aquifers of the Coastal Plain, except for elevated iron concentrations and localized areas of high salinity. The high salinity results from intrusion from underlying formations, evapotranspiration processes in areas of low recharge, and inadequate flushing in downgradient areas of residual salinity from deposition in marine environments. Trends in the spatial distribution of individual chemical constituents are related to position along the flow path for most aquifers of the Coastal Plain. These trends include elevated iron and nitrate concentrations with lower pH values and dissolved solids in groundwater from the outcrop areas, transitioning to lower iron and nitrate (related to changes in redox) and higher pH and dissolved solids (dominantly from the dissolution of carbonate minerals) in groundwater downgradient from outcrop areas. Groundwater generally trended from a calcium- to a sodium-bicarbonate water type with increasing cation exchange along the flow path. The Interior Highlands of western Arkansas has less reported groundwater use than other areas of the State, reflecting a combination of factors. These factors include prevalent and increasing use of surface water, less intensive agricultural uses, lower population and industry densities, lesser potential yield of the resource, and lack of detailed reporting. The overall low yields of aquifers of the Interior Highlands result in domestic supply as the dominant use, with minor industrial, public, and commercial-supply use. Where greater volumes are required for growth of population and industry, surface water is the greatest supplier of water needs in the Interior Highlands. The various aquifers of the Interior Highlands generally occur in shallow, fractured, well-indurated, structurally modified bedrock of this mountainous region of the State, as compared to the relatively flat-lying, unconsolidated sediments of the Coastal Plain. In terms of age from youngest to oldest, the aquifers of the Interior Highlands include: the Arkansas River Valley alluvial aquifer, the Ouachita Mountains aquifer, the Western Interior Plains confining system, the Springfield Plateau aquifer, and the Ozark aquifer. Spatial trends in groundwater geochemistry in the Interior Highlands differ greatly from trends noted for aquifers of the Coastal Plain. In the Coastal Plain, the prevalence of long regional flow paths results in regionally predictable and mappable geochemical changes along the flow paths. In the Interior Highlands, short, topographically controlled flow paths (from hilltops to valleys) within small watersheds represent the predominant groundwater-flow system. As such, dense data coverage from numerous wells would be required to effectively characterize these groundwater basins and define small-scale geochemical changes along any given flow path for aquifers of the Interior Highlands. Changes in geochemistry generally were related to rock type and residence time along individual flow paths. Dominant changes in geochemistry for the Ouachita Mountains aquifer and the Western Interior Plains confining system are attributed to rock/water interaction and changes in redox zonation along the flow path. In these areas, groundwater evolves along flow paths from a calcium- to a sodium-bicarbonate water type with increasing reducing conditions resulting in denitrification, elevated iron and manganese concentrations, and production of methane in the more geochemically evolved and strongest reducing conditions. In the Ozark and Springfield Plateau aquifers, rapid influx of surface-derived contaminants, especially nitrogen, coupled with few to no attenuation processes was attributed to the karst landscape developed on Mississippian- and Ordovician-age carbonate rocks of the Ozark Plateaus. Increasing nitrate concentrations are related to increasing agricultural land use, and areas of mature karst development result in higher nitrate concentrations than areas with less karst features.

Arkasas↗

USGS2014

Estimation of regional flow-duration curves for Indiana and Illinois

Flow-duration curves (FDCs) of daily streamflow are useful for many applications in water resources planning and management but must be estimated at ungaged sites. One common technique for estimating FDCs at ungaged sites in a given region is to use equations obtained by linear regression of FDC quantiles against multiple basin characteristics that can be computed by means of a geographic information system (GIS) computer program. In this study, such regional regression equations for estimating FDC quantiles were computed at the 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 98, 99, 99.5, 99.8, and 99.9-percent exceedance probabilities for rural, unregulated streams in Indiana and Illinois with temporally stationary records, using data through September 30, 2007. The approach used accounts for censored values below 0.01 cubic feet per second, which are observed at exceedance probabilities as low as 70 percent (that is, occurring at least 30 percent of the time). The basin characteristics used are suitable for computation by the USGS Web-based application, StreamStats, and are available for all U.S. Environmental Protection Agency (EPA) Region V states and the larger Great Lakes area, with some specific local exceptions. Indiana and Illinois were each divided into three regions, and a different set of equations for estimating FDC quantiles was computed for each region. The error of estimation of the FDC quantiles, measured as the mean square residual in log space converted to a percentage of the quantile, varies somewhat among regions and varies strongly with exceedance probability, with a minimum error of 10 to 20 percent at an exceedance probability of 5 or 10 percent, but rises to 17 to 38 percent at the high-flow end of the FDCs (the 0.1-percent quantile) and 100 to 745 percent at the low-flow end. For comparison, errors of estimation also were computed for FDC quantiles estimated by linear regression on drainage area alone and by using the drainage-area ratio (DAR) method. Three criteria, the nearest basin centroid and two others termed “strict” and “broad”, were used to select index stations for the DAR method. The “strict” and “broad” criteria put conditions on the basin centroid distance and the range of their drainage-area ratios, and the errors were averaged for all index station pairs satisfying each criterion. The use of the simpler DAR method usually resulted in higher errors of estimation compared to the linear regression equations with multiple basin characteristics, except occasionally in the case of the DAR method with the strict index station selection criterion, a criterion that is rarely possible to satisfy in practice. An example application of the estimated equations to one gaged and a few ungaged locations in a watershed in the study area is included to illustrate the steps required. These steps are the computation of the basin characteristics and, using those characteristics together with the estimated equations, the computation of the FDC quantiles and their uncertainties.

Illinois, Indiana↗

USGS2014

An initial abstraction and constant loss model, and methods for estimating unit hydrographs, peak streamflows, and flood volumes for urban basins in Missouri

Streamflow data, basin characteristics, and rainfall data from 39 streamflow-gaging stations for urban areas in and adjacent to Missouri were used by the U.S. Geological Survey in cooperation with the Metropolitan Sewer District of St. Louis to develop an initial abstraction and constant loss model (a time-distributed basin-loss model) and a gamma unit hydrograph (GUH) for urban areas in Missouri. Study-specific methods to determine peak streamflow and flood volume for a given rainfall event also were developed. Distinct basin characteristics were evaluated and selected for use on the basis of their theoretical relation to flow, results from previous studies, and the ability to reliably measure the basin characteristic using digital datasets and geographic information system (GIS) technology. The key basin characteristics determined or computed for each of the 39 basins upstream from the streamflow-gaging stations were drainage area, percent impervious area, main-channel slope based on the 10- and 85-percent length method, percentage of the basin area in storage (lakes, ponds, reservoirs, wetlands), the composite Natural Resources Conservation Service curve number estimated from a combination of the soil type data and land-use characteristics, and the streamflow variability index developed for the recently completed study of low-flow regression in Missouri. Characteristics of spatial and temporal rainfall distribution came from the next generation weather radar (NEXRAD) network. Procedures were developed for this study to convert the variable radar sweep rate into a 5-minute total rainfall hyetograph using data from the radar bin at the centroid of a given basin. Additional characteristics determined for each storm on the basin included the 5-day and 14-day antecedent rainfall, estimated from the mean of daily rainfall values from various rain gages in the area. The database of observed rainfall and runoff events for the 39 basins upstream from the streamflow-gaging stations was analyzed to compute the optimal storm-specific initial abstraction and constant loss values, as well as the time to peak, peak streamflow, and shape factor values of the GUH. The optimal storm-specific values were used to develop a regional regression equation for initial abstraction; conversely, the constant loss was estimated not by regression but from either a generalized or specific regional mean value. The optimal storm-specific values of GUH time to peak, GUH peak streamflow, and GUH shape factor were used to develop regression equations for the GUH. The regression equations for the GUH initially were tested alone, and then were combined with the appropriate regional regression equation for initial abstraction and both the generalized regional and specific regional mean constant loss values. For the GUH regression equations, the interquartile range was substantially smaller than the range spanned by the minimum and maximum values, which indicates most of the errors have much smaller variation, and the minimum and maximum values may be extreme outliers. The central tendency of the regressed errors for peak streamflow and runoff hydrograph volume were both approximately zero, which implies a generally unbiased estimation of these values. The mean and median of the regressed errors for time to peak streamflow were both small but greater than zero, which implies the GUH regression equations create a hydrograph that has a peak that is later in time than observed. Specifically, the regressed times indicate an offset of about 10 minutes, on average, from observed. The mean and median of the regressed errors for widths of the runoff hydrograph at 50 and 75 percent were less than zero, which implies the GUH tends to slightly underestimate these widths compared to the observed. The appropriate regional initial abstraction regression equation was combined with both the generalized and the specific regional mean constant loss values and the GUH regression equations. Both the generalized regional mean constant loss and specific regional mean constant loss forms of the basin-loss model worked equally well to model the observed runoff hydrograph based on the error analysis, and neither model seems to make a consistently better approximation. Both initial abstraction and constant loss models combined with the GUH regression equations were further validated using several storms available after the start of the project in early 2011 with similar but consistently higher error results. If these methods are used in an urban area in Missouri other than those examined in this study, advice to the user is given to consider using the generalized regional mean values. If these methods are used in an urban area that is a subbasin of one of the basins in this study, advice to the user is given to consider using the specific regional mean values. The rainfall-runoff pairs from the storm-specific GUH analysis were further analyzed against various basin and rainfall characteristics to develop equations to estimate the peak streamflow and flood volume based on a quantity of rainfall on the basin.

Missouri↗

USGS2016

Montana StreamStats

About this volume Montana StreamStats is a Web-based geographic information system ( http://water.usgs.gov/osw/streamstats/ ) application that provides users with access to basin and streamflow characteristics for gaged and ungaged streams in Montana. Montana StreamStats was developed by the U.S. Geological Survey (USGS) in cooperation with the Montana Departments of Transportation, Environmental Quality, and Natural Resources and Conservation. The USGS Scientific Investigations Report consists of seven independent but complementary chapters dealing with various aspects of this effort. Chapter A describes the Montana StreamStats application, the basin and streamflow datasets, and provides a brief overview of the streamflow characteristics and regression equations used in the study. Chapters B through E document the datasets, methods, and results of analyses to determine streamflow characteristics, such as peak-flow frequencies, low-flow frequencies, and monthly and annual characteristics, for USGS streamflow-gaging stations in and near Montana. The StreamStats analytical toolsets that allow users to delineate drainage basins and solve regression equations to estimate streamflow characteristics at ungaged sites in Montana are described in Chapters F and G.

Montana↗

USGS2016

Montana StreamStats—A method for retrieving basin and streamflow characteristics in Montana: Chapter A in Montana StreamStats

The U.S. Geological Survey (USGS) provides streamflow characteristics and other related information needed by water-resource managers to protect people and property from floods, plan and manage water-resource activities, and protect water quality. Streamflow characteristics provided by the USGS, such as peak-flow and low-flow frequencies for streamflow-gaging stations, are frequently used by engineers, flood forecasters, land managers, biologists, and others to guide their everyday decisions. In addition to providing streamflow characteristics at streamflow-gaging stations, the USGS also develops regional regression equations and drainage area-adjustment methods for estimating streamflow characteristics at locations on ungaged streams. Regional regression equations can be complex and often require users to determine several basin characteristics, which are physical and climatic characteristics of the stream and its drainage basin. Obtaining these basin characteristics for streamflow-gaging stations and ungaged sites traditionally has been time consuming and subjective, and led to inconsistent results. StreamStats is a Web-based geographic information system application that was created by the USGS to provide users with access to an assortment of analytical tools that are useful for water-resource planning and management. StreamStats allows users to easily obtain streamflow and basin characteristics for USGS streamflow-gaging stations and user-selected locations on ungaged streams. The USGS, in cooperation with Montana Department of Transportation, Montana Department of Environmental Quality, and Montana Department of Natural Resources and Conservation, completed a study to develop a StreamStats application for Montana, compute streamflow characteristics at streamflow-gaging stations, and develop regional regression equations to estimate streamflow characteristics at ungaged sites. Chapter A of this Scientific Investigations Report describes the Montana StreamStats application and the datasets, streamflow-gaging stations, streamflow characteristics, and regression equations (as described fully in Chapters B through G of this report) that are used for development of the StreamStats application for Montana.

Montana↗

USGS2015

Physical characteristics and fish assemblage composition at site and mesohabitat scales over a range of streamflows in the Middle Rio Grande, New Mexico, winter 2011-12, summer 2012

In winter 2011–12 and summer 2012, the U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers, Albuquerque District and the U.S. Fish and Wildlife Service New Mexico Fish and Wildlife Conservation Office in Albuquerque, New Mexico, evaluated the physical characteristics and fish assemblage composition of available mesohabitats over a range of streamflows at 15 sites on the Middle Rio Grande in New Mexico. The fish assemblage of the Middle Rio Grande includes several minnow species adapted to hydrologically variable but seasonably predictable rivers, including the Hybognathus amarus (Rio Grande silvery minnow), a federally listed endangered species. Gaining a better understanding of habitat usage by the Rio Grande silvery minnow was the impetus for studying physical characteristics and fish assemblages in the Middle Rio Grande during different streamflow conditions. Data were collected at all 15 sites during winter 2011–12 (moderate streamflow), and a subset was collected at the 13 most downstream sites in summer 2012 (low streamflow). Sites were grouped into four river reaches separated by diversion dams listed in downstream order (names of the diversion dams are followed by short names of the sites nearest each dam in parentheses, listed in downstream order): (1) Cochiti (Peña Blanca), (2) Angostura (Bernalillo, La Orilla, Barelas, Los Padillas), (3) Isleta (Los Lunas I, Los Lunas II, Abeytas, La Joya, Rio Salado), and (4) San Acacia (Lemitar, Arroyo del Tajo, San Pedro, Bosque del Apache I, and Bosque del Apache II). Stream habitat was mapped in the field by using a geographic information system in conjunction with a Global Positioning System. Fish assemblage composition was determined during both streamflow regimes, and fish were collected by seining in each mesohabitat where physical characteristic data (depth, velocity, dominant substrate type and size, and percent embeddedness) and water-quality properties (temperature, dissolved oxygen, specific conductance, and pH; during summer 2012 only) were measured. Nineteen species of fish were collected among the 15 sites and four reaches over both sampling periods; 10 of these 19 species are introduced. Fish-species richness (total number of fish species collected at each site during each sampling event) among sites that were sampled during both sampling periods ranged from 6 at Rio Salado to 12 at La Orilla. Fish were most abundant at the Lemitar site (1,786 individuals) and least abundant at the San Pedro site (275 individuals). The native Cyprinella lutrensis (red shiner) was the most abundant species collected among all of the sites, accounting for about 42 percent of fish collected. Fish-species richness and catch per unit effort (CPUE) were higher (or equivalent) at all sites during summer 2012 compared to winter 2011–12. The relations between fish assemblage composition (that is, total abundance, which refers to the number of individuals of each species that were collected) and selected environmental variables (physical characteristic data collected at the mesohabitat scale [depth, velocity, and substrate particle size], and mesohabitat types) were explored by using canonical correspondence analysis. Environmental variables explained 8 percent (p=0.48) of the variability in the Middle Rio Grande fish assemblage during winter 2011–12, and Rio Grande silvery minnow were weakly associated with sand substrates, relatively moderate velocities (qualitative descriptors are derived from synthetic gradients extracted from CCAs), and relatively shallow depths. Environmental variables explained 14 percent (p < 0.01) of the variability in the Middle Rio Grande fish assemblage during summer 2012, when Rio Grande silvery minnow were associated with run mesohabitats, relatively high velocities, sand substrates, and relatively moderate depths. The mean fish-species richness was greater in summer 2012 than in winter 2011–12 for each mesohabitat type, and the overall fish-species richness across all mesohabitat types was 0.62 during winter 2011–12, compared to 1.49 during summer 2012. The highest mean CPUE during winter 2011–12 was in isolated pools (54.3 fish per 100 square meters [m 2 ]), whereas the lowest was in flats (18.9 fish per 100 m 2 ). Ranges in CPUE were higher in summer 2012 relative to winter 2011–12 in each mesohabitat type sampled. As in winter 2011–12, the highest mean CPUE during summer 2012 was in isolated pools (233 fish per 100 m 2 ), whereas the lowest was in flats (29.6 fish per 100 m 2 ). Overall mean CPUE per mesohabitat across all mesohabitat types was 29.1 fish per 100 m 2 during winter 2011–12 compared to 85.3 fish per 100 m 2 during summer 2012. Four species of minnows (red shiner, Rio Grande silvery minnow, Pimephales promelas [fathead minnow], and Platygobio gracilis [flathead chub]) were selected to compare preferred mesohabitat characteristics because all are small-bodied minnows and because more than 200 individuals of each of these species were collected. Red shiner were collected across the largest range of depths in both winter 2011–12 (0.02–4.31 feet [ft]) and summer 2012 (0.05–3.4 ft), as well as the largest range of velocities (0.0–4.31 feet per second [ft/s]) during winter 2011–12 among the four minnow species of interest. Rio Grande silvery minnow occurred in the narrowest range of depths (0.30–2.1 ft) during summer 2012, as well as the narrowest range of velocities in both winter 2011–12 (0.0–3.18 ft/s) and summer 2012 (0.02–1.51 ft/s). Water-quality properties were only collected during summer 2012, when low-streamflow conditions existed and water-quality properties were thought to be potentially most limiting to aquatic life. Area-weighted mean water temperatures tended to be higher at the sites that were sampled in August 2012 (25.57 degrees Celsius [°C]) compared to June 2012 (24.61 °C). The highest area-weighted mean water temperature at a given site (29.03 °C) was measured at the Lemitar site on August 7, 2012, coincident with the lowest measured discharge (4.13 cubic feet per second [ft 3 /s]). Area-weighted mean dissolved oxygen concentrations tended to be lower in August (7.46 milligrams per liter [mg/L]) compared to June (8.33 mg/L). The highest area-weighted mean dissolved oxygen concentration (9.13 mg/L) was measured at the Lemitar site on August 7, 2012, and the lowest area-weighted mean dissolved oxygen concentration (6.23 mg/L) was measured at the Los Padillas site on August 10, 2012. Area-weighted specific conductance in the sites upstream from La Joya did not exceed 400 microsiemens per centimeter (μS/cm) at 25 °C, whereas the area-weighted mean specific conductance at La Joya (837 μs/cm at 25 °C), Rio Salado (857 μs/cm at 25 °C), and Lemitar (1,300 μs/cm at 25 °C) were all well above the average of the area-weighted means for the 10 remaining sites (433 μs/cm at 25 °C). Lower area-weighted mean pH values were measured at the 3 sites in and near Albuquerque (La Orilla, Barelas, and Los Padillas—7.98, 8.08, and 7.81, respectively) compared to any of the 10 remaining sites, which had an overall mean pH of 8.44.

New Mexico↗

USGS2015

Peak flow regression equations For small, ungaged streams in Maine: Comparing map-based to field-based variables

Regression equations to estimate peak streamflows with 1- to 500-year recurrence intervals (annual exceedance probabilities from 99 to 0.2 percent, respectively) were developed for small, ungaged streams in Maine. Equations presented here are the best available equations for estimating peak flows at ungaged basins in Maine with drainage areas from 0.3 to 12 square miles (mi 2 ). Previously developed equations continue to be the best available equations for estimating peak flows for basin areas greater than 12 mi 2 . New equations presented here are based on streamflow records at 40 U.S. Geological Survey streamgages with a minimum of 10 years of recorded peak flows between 1963 and 2012. Ordinary least-squares regression techniques were used to determine the best explanatory variables for the regression equations. Traditional map-based explanatory variables were compared to variables requiring field measurements. Two field-based variables—culvert rust lines and bankfull channel widths—either were not commonly found or did not explain enough of the variability in the peak flows to warrant inclusion in the equations. The best explanatory variables were drainage area and percent basin wetlands; values for these variables were determined with a geographic information system. Generalized least-squares regression was used with these two variables to determine the equation coefficients and estimates of accuracy for the final equations.

Maine↗

USGS2015

Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics

Nitrate-concentration data are used in conjunction with land-use and land-cover data to estimate median nitrate concentrations in groundwater underlying the New Jersey (NJ) Highlands Region. Sources of data on nitrate in 19,670 groundwater samples are from the U.S. Geological Survey (USGS) National Water Information System (NWIS) and the NJ Private Well Testing Act (PWTA). In a study conducted by the USGS, in cooperation with the New Jersey Department of Environmental Protection, logistic regression was used to relate measured nitrate concentrations to five explanatory variables (percent urban and agricultural land use, septic-system density, total length of streams, and number of known contaminated sites) quantified in 610-meter-square grid cells. A method for calculating the median concentrations of nitrate from a series of logistic regression models was developed. Two calibration and two validation procedures showed that the logistic-regression-based method can estimate groundwater-nitrate concentrations in the Highlands Region accurately to within 0.1 milligram per liter as nitrogen (mg/L as N). Limitations of the logistic-regression-based method include the inability to select a logistic model with exactly 0.5 probability of exceeding the threshold value and lack of an algorithm to directly calculate the median value. Quantile regression was evaluated as a suitable alternative and was slightly less accurate than the logistic-regression method in estimating median groundwater nitrate concentrations in the Highlands Region. Multiple-linear regression with log-transformed nitrate-concentration data and the same five explanatory values was less accurate than either logistic or quantile regression in estimating median nitrate concentrations. On the basis of 4,516 2000 x 2000 foot grid cells that contain wells with data stored in NWIS and the PWTA database, the estimated median nitrate concentration for the entire Highlands Region is about 1.25 mg/L as N, and estimated median concentrations range from about 1.05 to 1.78 mg/L as N among 11 smaller administratively defined areas within the Highlands Region that vary in percentages of urban land use, agricultural land use, and septic-system density. The Kaplan-Meier method of estimating summary statistics from left-censored data was applied in order to include nondetects (left-censored data) in median nitrate-concentration calculations. Median concentrations also were determined using three alternative methods of handling nondetects. Treatment of the 23 percent of samples that were nondetects had little effect on estimated median nitrate concentrations because method detection limits were mostly less than median values.

New Jersey↗

USGS2015

Flood-inundation maps for the Scioto River at La Rue, Ohio

Digital flood-inundation maps for a 3-mile (mi) reach of the Scioto River that extends about 1/2 mi upstream and 1/2 mi downstream of the corporate boundary for La Rue, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Village of La Rue, Marion County Commissioners, Montgomery Township, and Marion County Scioto River Conservancy. The flood-inundation maps show estimates of the areal extent and depth of flooding correspond ing to selected water levels (stages) at the USGS streamgage on the Scioto River at La Rue (station number 03217500). The maps can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_ inundation/ . Near-real-time stages at this streamgage can be obtained from the USGS National Water Information System at http://waterdata.usgs.gov/oh/nwis/uv/?site_no=03217500 or the National Weather Service (NWS) Advanced Hydro - logic Prediction Service at http://water.weather.gov/ahps2/ hydrograph.php?wfo=cle&gage=LARO1 , which also forecasts flood hydrographs at this site.

Ohio↗

USGS2015

Estimating natural recharge in San Gorgonio Pass watersheds, California, 1913–2012

A daily precipitation-runoff model was developed to estimate spatially and temporally distributed recharge for groundwater basins in the San Gorgonio Pass area, southern California. The recharge estimates are needed to define transient boundary conditions for a groundwater-flow model being developed to evaluate the effects of pumping and climate on the long-term availability of groundwater. The area defined for estimating recharge is referred to as the San Gorgonio Pass watershed model (SGPWM) and includes three watersheds: San Timoteo Creek, Potrero Creek, and San Gorgonio River. The SGPWM was developed by using the U.S. Geological Survey INFILtration version 3.0 (INFILv3) model code used in previous studies of recharge in the southern California region, including the San Gorgonio Pass area. The SGPWM uses a 150-meter gridded discretization of the area of interest in order to account for spatial variability in climate and watershed characteristics. The high degree of spatial variability in climate and watershed characteristics in the San Gorgonio Pass area is caused, in part, by the high relief and rugged topography of the area. Daily climate data developed from a network of monitoring sites and published average monthly precipitation maps were used to develop the climate inputs for the SGPWM. Geographic Information System (GIS) data defining land surface altitude, vegetation, soils, surficial geology, and land cover were used to define input parameters representing the physical characteristics of the land surface, root zone, and shallow subsurface underlying the root zone. Model parameterization was based on a previous INFILv3 model developed for an area including the upper parts of the San Timoteo Creek and Potrero Creek drainages and the western part of the San Gorgonio River watershed. The previous INFILv3 model was calibrated by using available streamflow records from the model area. The SGPWM uses an updated INFILv3 version to represent shallow groundwater flow better beneath the root zone that contributes to lateral, downslope seepage rather than deep recharge. The SGPWM calibration was tested by using available streamflow records in the San Gorgonio Pass region. The SGPWM was used to simulate a 100-year water budget, including recharge and runoff, for water years 1913 through 2012. Results indicated that most recharge came from episodic infiltration of surface-water runoff in the larger stream channels. Results also indicated periods of great variability in recharge and runoff in response to variability in precipitation. More recharge was simulated for the area of the groundwater basin underlying the more permeable alluvial fill of the valley floor compared to recharge in the neighboring upland areas of the less permeable mountain blocks. The greater recharge was in response to the episodic streamflow that discharged from the mountain block areas and quickly infiltrated the permeable alluvial fill of the groundwater basin. Although precipitation at the higher altitudes of the mountain block was more than double precipitation at the lower altitudes of the valley floor, recharge for inter-channel areas of the mountain block was limited by the lower permeability bedrock underlying the thin soil cover, and most of the recharge in the mountain block was limited to the main stream channels underlain by alluvial fill.

California↗

USGS2015

Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle

This report presents the results of a cooperative study by the U.S. Geological Survey and the Oklahoma Department of Transportation to estimate the magnitude and frequency of peak streamflows from regional regression equations for ungaged stream sites in and near the Oklahoma Panhandle. These methods relate basin characteristics (physiographic and climatic attributes) to selected peak streamflow frequency statistics with the 50-, 20-, 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities. These relations were developed based on data from 32 selected streamflow-gaging stations in the Oklahoma Panhandle and in neighboring parts of Colorado, Kansas, New Mexico, and Texas. The basin characteristics for the selected streamflow-gaging stations were determined by using a geographic information system and the Oklahoma StreamStats application. Peak-streamflow frequency statistics were computed from annual peak-streamflow records from the irrigated period of record from water year 1978 through water year 2014. Generalized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.

Colorado, Kansas, New Mexico, Oklahoma, Texas↗

USGS2016

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

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. Existing 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). In 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. Alluvial 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. Abandoned-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. Fractured-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. Intrinsic 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. Groundwater 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.

West Virginia↗

USGS2016

Mineral resources of the Sagebrush Focal Areas of Idaho, Montana, Nevada, Oregon, Utah, and Wyoming

Scientific Investigations Report 2016–5089 and accompanying data releases are the products of the U.S. Geological Survey (USGS) Sagebrush Mineral-Resource Assessment (SaMiRA). The assessment was done at the request of the Bureau of Land Management (BLM) to evaluate the mineral-resource potential of some 10 million acres of Federal and adjacent lands in Idaho, Montana, Nevada, Oregon, Utah, and Wyoming. The need for this assessment arose from the decision by the Secretary of the Interior to pursue the protection of large tracts of contiguous habitat for the greater sage-grouse ( Centrocercus urophasianus ) in the Western United States. One component of the Department of the Interior plan to protect the habitat areas includes withdrawing selected lands from future exploration and development of mineral and energy resources, including copper, gold, silver, rare earth elements, and other commodities used in the U.S. economy. The assessment evaluates the potential for locatable minerals such as gold, copper, and lithium and describes the nature and occurrence of leaseable and salable minerals for seven Sagebrush Focal Areas and additional lands in Nevada (“Nevada additions”) delineated by BLM. Supporting data are available in a series of USGS data releases describing mineral occurrences (the USGS Mineral Deposit Database or “USMIN”), oil and gas production and well status, previous mineral-resource assessments that covered parts of the areas studied, and a compilation of mineral-use cases based on data provided by BLM, as well as results of the locatable mineral-resource assessment in a geographic information system. The present assessment of mineral-resource potential will contribute to a better understanding of the economic and environmental trade-offs that would result from closing approximately 1 0 million acres of Federal lands to mineral entry.

Idaho, Montana, Nevada, Oregon, Utah, Wyoming↗

USGS2016

Estimating selected low-flow frequency statistics and harmonic-mean flows for ungaged, unregulated streams in Indiana

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.

Indiana↗

USGS2017

Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in North Georgia

The U.S. Geological Survey, in cooperation with the Georgia Department of Natural Resources, Environmental Protection Division, developed regional regression equations for estimating selected low-flow frequency and mean annual flow statistics for ungaged streams in north Georgia that are not substantially affected by regulation, diversions, or urbanization. Selected low-flow frequency statistics and basin characteristics for 56 streamgage locations within north Georgia and 75 miles beyond the State’s borders in Alabama, Tennessee, North Carolina, and South Carolina were combined to form the final dataset used in the regional regression analysis. Because some of the streamgages in the study recorded zero flow, the final regression equations were developed using weighted left-censored regression analysis to analyze the flow data in an unbiased manner, with weights based on the number of years of record. The set of equations includes the annual minimum 1- and 7-day average streamflow with the 10-year recurrence interval (referred to as 1Q10 and 7Q10), monthly 7Q10, and mean annual flow. The final regional regression equations are functions of drainage area, mean annual precipitation, and relief ratio for the selected low-flow frequency statistics and drainage area and mean annual precipitation for mean annual flow. The average standard error of estimate was 13.7 percent for the mean annual flow regression equation and ranged from 26.1 to 91.6 percent for the selected low-flow frequency equations. The equations, which are based on data from streams with little to no flow alterations, can be used to provide estimates of the natural flows for selected ungaged stream locations in the area of Georgia north of the Fall Line. The regression equations are not to be used to estimate flows for streams that have been altered by the effects of major dams, surface-water withdrawals, groundwater withdrawals (pumping wells), diversions, or wastewater discharges. The regression equations should be used only for ungaged sites with drainage areas between 1.67 and 576 square miles, mean annual precipitation between 47.6 and 81.6 inches, and relief ratios between 0.146 and 0.607; these are the ranges of the explanatory variables used to develop the equations. An attempt was made to develop regional regression equations for the area of Georgia south of the Fall Line by using the same approach used during this study for north Georgia; however, the equations resulted with high average standard errors of estimates and poorly predicted flows below 0.5 cubic foot per second, which may be attributed to the karst topography common in that area. The final regression equations developed from this study are planned to be incorporated into the U.S. Geological Survey StreamStats program. StreamStats is a Web-based geographic information system that provides users with access to an assortment of analytical tools useful for water-resources planning and management, and for engineering design applications, such as the design of bridges. The StreamStats program provides streamflow statistics and basin characteristics for U.S. Geological Survey streamgage locations and ungaged sites of interest. StreamStats also can compute basin characteristics and provide estimates of streamflow statistics for ungaged sites when users select the location of a site along any stream in Georgia.

Georgia↗