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At least 1,801 records · Page 100Linked to original sources

USGS2016

Examination of flood characteristics at selected streamgages in the Meramec River Basin, eastern Missouri, December 2015–January 2016

Overview Heavy rainfall resulted in major flooding in the Meramec River Basin in eastern Missouri during late December 2015 through early January 2016. Cumulative rainfall from December 14 to 29, 2015, ranged from 7.6 to 12.3 inches at selected precipitation stations in the basin with flooding driven by the heaviest precipitation (3.9–9.7 inches) between December 27 and 29, 2015. Financial losses from flooding included damage to homes and other structures, damage to roads, and debris removal. Eight of 11 counties in the basin were declared a Federal Disaster Area. The U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers and St. Louis Metropolitan Sewer District, operates multiple streamgages along the Meramec River and its primary tributaries including the Bourbeuse River and Big River. The period of record for streamflow at streamgages in the basin included in this report ranges from 24 to 102 years. Instrumentation in a streamgage shelter automatically makes observations of stage using a variety of methods (submersible pressure transducer, non-submersible pressure transducer, or non-contact radar). These observations are recorded autonomously at a predetermined programmed frequency (typically either 15 or 30 minutes) dependent on drainage-area size and concomitant flashiness of the stream. Although stage data are important, streamflow data are equally or more important for streamflow forecasting, water-quality constituent loads computation, flood-frequency analysis, and flood mitigation planning. Streamflows are computed from recorded stage data using an empirically determined relation between stage and streamflow termed a “rating.” Development and verification of the rating requires periodic onsite discrete measurements of streamflow throughout time and over the range of stages to define local hydraulic conditions. The purpose of this report is to examine characteristics of flooding that occurred in the Meramec River Basin in December 2015–January 2016 including peak stages, peak streamflows, and the flood-frequency statistics associated with the peak flows. A comparison between the December 2015–January 2016 flood and a similar flood in December 1982 in the Meramec River Basin also is included.

Missouri↗

USGS2016

Statistical analysis and mapping of water levels in the Biscayne aquifer, water conservation areas, and Everglades National Park, Miami-Dade County, Florida, 2000–2009

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000–2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000–2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990–1999 and 2000–2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974–2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990–1999 and 2000–2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990–1999 and 2000–2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000–2009 than during 1990–1999; and that inland water levels were generally lower during 2000–2009 than during 1990–1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990–1999. Mean October water levels during 2000–2009 were generally higher than during 1990–1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

Florida↗

USGS2020

Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas

The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, assessed statistical relations between hydraulic properties of streams at approximate bankfull conditions for different ecological regions (ecoregions) in Texas. Data from more than 103,000 records of measured discharge and ancillary hydraulic properties were assembled from summaries of discharge measurements for 424 U.S. Geological Survey streamgages in Texas. The data were subsequently subsetted at each streamgage for a streamgage-specific discharge interval centered on the estimated median annual peak discharge (0.5 annual exceedance probability) obtained from previously published regional regression equations in Texas in conjunction with the streamgage-specific sample median annual peak discharge for the period of record for each streamgage. Discharge measurements at gaged locations representing bankfull conditions (approximated from a discharge interval centered on the estimated median annual peak discharge at a given site) and associated watershed properties were subjected to rigorous statistical analysis. For most discharge measurements (where discharge is symbolically represented as Q ), the following hydraulic properties are available: cross-section area ( A ), water-surface top width ( B ), and reported mean velocity ( V ). Statewide summary statistics were computed by using these four hydraulic properties ( Q , A , B , and V ) and the following five watershed properties: (1) watershed area (contributing drainage area), (2) a multiple of main-channel slope (1,000 times main-channel slope), (3) mean annual precipitation, (4) drainage density, and (5) sinuosity ratio. From the initial set of 424 streamgages, summary statistics were computed for 372 selected streamgages in Texas and constitute the subsetted measurements dataset described in this report. Eight of the 10 ecoregions in Texas are represented in the statewide summary statistics. The resulting statistical relations, expressed as regression equations, can be used to estimate cross-section area, water-surface top width, discharge, and mean velocity of streams in different Texas ecoregions, at approximate bankfull conditions. In the regression equations, watershed properties were the independent variables for applicable watersheds, and predictions from the equations might be useful for estimating the four hydraulic properties at ungaged or unmonitored locations from selected characteristics measured at both the ungaged locations and gaged locations. Four regression equations to estimate the four hydraulic properties were identified as the preferred equations from this study. The four preferred equations use watershed area, mean annual precipitation, and aggregated ecoregion (treated as a categorical variable) to estimate the hydraulic properties, and justification is provided for this preference. For the four equations, the proportions of variance explained by the regression equations as measured by Nash-Sutcliffe efficiency are about 71 percent for cross-section area, 36 percent for top width, 76 percent for discharge, and 25 percent for mean velocity. Residual standard error (RSEs) of the regression equations are 0.252 log10 square feet for cross-section area, 0.319 log10 feet for top width, 0.247 log10 cubic feet per second for discharge, and 0.190 log10 feet per second for mean velocity, and the corresponding standard deviations of response are 0.465 log10 square feet, 0.397 log10 feet, 0.507 log10 cubic feet per second, and 0.220 log10 feet per second, respectively. The residual standard errors are less than the standard deviations as anticipated but show that the uncertainty reduction (percent change) for cross-section area is about −46 percent, about −20 percent for top width, about −51 percent for discharge, and about −14 percent for mean velocity.

Texas↗

USGS1986

Water resources scientific information center

The Water Resources Scientific Information Center (WRSIC) acquires, abstracts and indexes the major water resources related literature of the world, and makes information available to the water resources community and the public. A component of the Water Resources Division of the US Geological Survey, the Center maintains a searchable computerized bibliographic data base, and publishers a monthly journal of abstracts. Through its services, the Center is able to provide reliable scientific and technical information about the most recent water resources developments, as well as long-term trends and changes. WRSIC was established in 1966 by the Secretary of the Interior to further the objectives of the Water Resources Research Act of 1964--legislation that encouraged research in water resources and the prevention of needless duplication of research efforts. It was determined the WRSIC should be the national center for information on water resources, covering research reports, scientific journals, and other water resources literature of the world. WRSIC would evaluate all water resources literature, catalog selected articles, and make the information available in publications or by computer access. In this way WRSIC would increase the availability and awareness of water related scientific and technical information. (Lantz-PTT)

Open-File Report↗

USGS2013

Flood-inundation maps for the Iroquois River at Rensselaer, Indiana

Digital flood-inundation maps for a 4.0-mile reach of the Iroquois River at Rensselaer, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 05522500, Iroquois River at Rensselaer, Ind. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the Internet at (http://waterdata.usgs.gov/in/nwis/uv?site_no=05522500). In addition, the National Weather Service (NWS) forecasts flood hydrographs at the Rensselaer streamgage. That forecasted peak-stage information, also available on the Internet (http://water.weather.gov/ahps/), may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. For this study, flood profiles were computed for the Iroquois River reach by means of a one-dimensional step-backwater model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the most current (June 27, 2012) stage-discharge relations at USGS streamgage 05522500, Iroquois River at Rensselaer, Ind., and high-water marks from the flood of July 2003. The calibrated hydraulic model was then used to determine nine water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from Light Detection and Ranging (LiDAR) data) in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage at Rensselaer, Ind., and forecasted stream stages from the NWS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

Indiana↗

USGS2012

Trends in selected streamflow statistics at 19 long-term streamflow-gaging stations indicative of outflows from Texas to Arkansas, Louisiana, Galveston Bay, and the Gulf of Mexico, 1922-2009

Trends in selected streamflow statistics during 1922-2009 were evaluated at 19 long-term streamflow-gaging stations considered indicative of outflows from Texas to Arkansas, Louisiana, Galveston Bay, and the Gulf of Mexico. The U.S. Geological Survey, in cooperation with the Texas Water Development Board, evaluated streamflow data from streamflow-gaging stations with more than 50 years of record that were active as of 2009. The outflows into Arkansas and Louisiana were represented by 3 streamflow-gaging stations, and outflows into the Gulf of Mexico, including Galveston Bay, were represented by 16 streamflow-gaging stations. Monotonic trend analyses were done using the following three streamflow statistics generated from daily mean values of streamflow: (1) annual mean daily discharge, (2) annual maximum daily discharge, and (3) annual minimum daily discharge. The trend analyses were based on the nonparametric Kendall's Tau test, which is useful for the detection of monotonic upward or downward trends with time. A total of 69 trend analyses by Kendall's Tau were computed - 19 periods of streamflow multiplied by the 3 streamflow statistics plus 12 additional trend analyses because the periods of record for 2 streamflow-gaging stations were divided into periods representing pre- and post-reservoir impoundment. Unless otherwise described, each trend analysis used the entire period of record for each streamflow-gaging station. The monotonic trend analysis detected 11 statistically significant downward trends, 37 instances of no trend, and 21 statistically significant upward trends. One general region studied, which seemingly has relatively more upward trends for many of the streamflow statistics analyzed, includes the rivers and associated creeks and bayous to Galveston Bay in the Houston metropolitan area. Lastly, the most western river basins considered (the Nueces and Rio Grande) had statistically significant downward trends for many of the streamflow statistics analyzed.

Arkansas, Colorado, Louisiana, Oklahoma, New Mexic↗

USGS2026

The electric vehicle “EV”-crane—A next-generation system for fluvial sediment and water-quality sampling using an electric cart with integrated crane and accessories

The U.S. Geological Survey (USGS), with support from the Federal Interagency Sedimentation Project (FISP), developed the electric vehicle (EV)-crane, a modified electric cart equipped with a rigid swivel-mounted crane and integrated accessory holders. The EV-crane was developed to improve the efficiency and safety of fluvial sediment and water-quality sampling from bridges. USGS field staff have relied on foldable cranes on a four-wheeled base or nonstandardized platforms, such as utility terrain vehicle (UTV) mounted cranes, which highlight the need for a safer, standardized, and more adaptable solution. This redesigned platform replaces the foldable cranes on a four-wheeled base with a compact, powered system that reduces physical strain, enhances operator safety, and streamlines field operations on bridges. Dedicated holders for samplers, a controller, batteries, counterweights, and a field computer or tablet create a versatile, organized workspace that supports a wide range of sampling and heavy-lift applications. The EV-crane described here is version 1 of the prototype. Evaluation of the design is ongoing, and the findings summarized here are from initial qualitative user feedback and early quantitative testing. Potential plans may include additional testing and refinement to inform subsequent design iterations.

Open-File Report↗

USGS1995

Digital aeromagnetic anomaly data from eastern-most Guyana

The Center for Inter-American Mineral Resource Investigations (CIMRI) supported distribution and analysis of geoscientific and mineral resource related information concerning Latin America. CIMRI staff digitized aeromagnetic data for eastern-most Guyana as part of a preliminary regional assessment of minerals in the Guyana Shield, South America. The data were digitized from 145 aeromagnetic contour maps at a scale of 1:50,000 and merged into a single digital data set. The data were used to examine the Precambrian shield, greenstone belts, and other tectonic boundaries as well as explore ideas concerning mineral deposits within the area. A subset of these digital data were presented to the Guyanan government during early 1995 (Pierce, 1994). This Open-File report, consisting of this text and seven (7) 3.5" IBM-PC compatible ASCII magnetic disks, makes the digital data available to the public. Information regarding the source of data and subsequent processing is included below. The data were collected in Guyana by two contractors at different times. The first data were collected from 1962 to 1963. These data are several aeromagnetic surveys covering parts of 12 quadrangles funded by the United Nations and flown by Aero Service Corporation. The second and more extensive data set was collected from 1971 to 1972 by the Canadian International Development Agency flown by Terra Surveys Ltd. under a contract with the Geological Survey of Guyana. The Guyana Government published the data as contour maps that are available in Georgetown through the Guyana Government. Coverage extends from about 2°45*N to 8°30*N latitude and from 60°0'W to 57°0'W longitude (see Figure 1.). The contour maps were digitized at points where the magnetic contours intersect the flight lines. The data files include XYZ ASCII files, XYZ binary files, ASCII grids, and binary "standard USGS" grids. There are four grids consisting of the following data types: unprotected raw data grid unprotected residual or International Geomagnetic Reference Field (IGRF) removed grid UTM projected residual (IGRF removed) grid UTM projected residual with a second order surface removed These data files were transferred to 3.5" 1.44 megabyte floppy disks readable on IBM-compatible personal computers. These data are also available from the Department of Commerce National Geophysical Data Center.

Open-File Report↗

USGS2013

Flood-inundation maps for the East Fork White River at Columbus, Indiana

Digital flood-inundation maps for a 5.4-mile reach of the East Fork White River at Columbus, Indiana, from where the Flatrock and Driftwood Rivers combine to make up East Fork White River to just upstream of the confluence of Clifty Creek with the East Fork White River, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation, depict estimates of the areal extent of flooding corresponding to selected water levels (stages) at USGS streamgage 03364000, East Fork White River at Columbus, Indiana. Current conditions at the USGS streamgage may be obtained on the Internet from the USGS National Water Information System (http://waterdata.usgs.gov/in/nwis/uv/?site_no=03364000&agency_cd=USGS&). The National Weather Service (NWS) forecasts flood hydrographs for the East Fork White River at Columbus, Indiana at their Advanced Hydrologic Prediction Service (AHPS) flood warning system Website (http://water.weather.gov/ahps/), that may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at USGS streamgage 03364000, East Fork White River at Columbus, Indiana. The calibrated hydraulic model was then used to determine 15 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from bankfull to approximately the highest recorded water level at the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from Light Detection and Ranging (LiDAR) data), having a 0.37-ft vertical accuracy and a 1.02 ft horizontal accuracy), in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage at Columbus, Indiana, and forecasted stream stages from the NWS will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post-flood recovery efforts.

Indiana↗

USGS2010

Dissolved-Solids Load in Henrys Fork Upstream from the Confluence with Antelope Wash, Wyoming, Water Years 1970-2009

Annual dissolved-solids load at the mouth of Henrys Fork was estimated by using data from U.S. Geological Survey streamflow-gaging station 09229500, Henrys Fork near Manila, Utah. The annual dissolved-solids load for water years 1970-2009 ranged from 18,300 tons in 1977 to 123,300 tons in 1983. Annual streamflows for this period ranged from 14,100 acre-feet in 1977 to 197,500 acre-feet in 1983. The 25-percent trimmed mean dissolved-solids load for water years 1970-2009 was 44,300 tons per year at Henrys Fork near Manila, Utah. Previous simulations using a SPAtially Referenced Regression On Watershed attributes (SPARROW) model for dissolved solids specific to water year 1991 conditions in the Upper Colorado River Basin predicted an annual dissolved-solids load of 25,000 tons for the Henrys Fork Basin upstream from Antelope Wash. On the basis of computed dissolved-solids load data from Henrys Fork near Manila, Utah, together with estimated annual dissolved-solids load from Antelope Wash and Peoples Canal, this prediction was adjusted to 37,200 tons. As determined by simulations with the Upper Colorado River Basin SPARROW model, approximately 56 percent (14,000 tons per year) of the dissolved-solids load at Henrys Fork upstream from Antelope Wash is associated with the 21,500 acres of irrigated agricultural lands in the upper Henrys Fork Basin.

Scientific Investigations Report↗

USGS2015

Hydraulic, geomorphic, and trout habitat conditions of the Lake Fork of the Gunnison River in Hinsdale County, Lake City, Colorado, Water Years 2010-2011

Channel rehabilitation, or reconfiguration, to mitigate a variety of riverine problems has become a common practice in the western United States. However, additional work to monitor and assess the channel response to, and the effectiveness of, these modifications over longer periods of time (decadal or longer) is still needed. The Lake Fork of the Gunnison River has been an area of active channel modification to accommodate the needs of the Lake City community since the 1950s. The Lake Fork Valley Conservancy District began a planning process to assess restoration options for a reach of the Lake Fork in Lake City to enhance hydraulic and ecologic characteristics of the reach. Geomorphic channel form is affected by land-use changes within the basin and geologic controls within the reach. The historic channel was defined as a dynamic, braided channel with an active flood plain. This can result in a natural tendency for the channel to braid. A braided channel can affect channel stability of reconfigured reaches when a single-thread meandering channel is imposed on the stream. The U.S. Geological Survey, in cooperation with the Colorado Water Conservation Board and Colorado River Water Conservation District, began a study in 2010 to quantify existing hydraulic and habitat conditions for a reach of the Lake Fork of the Gunnison River in Lake City, Colorado. The purpose of this report is to quantify existing Lake Fork hydraulic and habitat conditions and establish a baseline against which post-reconfiguration conditions can be compared. This report (1) quantifies the existing hydraulic and geomorphic conditions in a 1.1-kilometer section of the Lake Fork at Lake City that has been proposed as a location for future channel-rehabilitation efforts, (2) characterizes the habitat suitability of the reach for two trout species based on physical conditions within the stream, and (3) characterizes the current riparian canopy density. The FaSTMECH computational flow-model within MD_SWMS was selected to characterize the effects of streamflow on hydraulic and habitat-suitability conditions for a study reach of the Lake Fork. Habitat suitability was evaluated for cutthroat ( Oncorhynchus clarkii ) and brown trout ( Salmo trutta morpha fario ) fry, juveniles, and adults. Microscale (point locations) and mesoscale (reach features) habitats were assessed using the combination of field observations, measurements, and hydraulic simulations within the study reach of the Lake Fork. Microscale trout habitat, presented as weighted usable area, generally increased as streamflow increased for both trout species and all life stages. Areas of suitable microscale habitat occur along the banks for flows of 900 cubic feet per second (ft 3 /s) and less. Out-of-bank areas became more substantial contributors to overall habitat availability for flows of 1,300 ft 3 /s or more when compared to other features. Adult habitat, for both trout species, was the most abundant habitat type for nearly all streamflows. In general, the upper reach provided 2–3 times more available habitat than the lower reach for both trout species. Mesoscale trout habitat of the Lake Fork was assessed based on the conditions present in the 150 ft 3 /s flow simulation as well as field observation. Both the upper and lower reach is primarily characterized as riffle/run habitat. The presence of pool habitat was limited throughout both reaches and occurred along the channel margins. For both reaches, the pool habitat was less than 5 percent of the total wetted area, a percentage that is substantially lower than the recommendations for sustainable populations of 40–70 percent. Areas of cover were adjacent to potential drift feeding areas in the lower reach, and often occurred within the same pool habitat. This may favor energy expenditure ratios of both fish species, wherein little energy is needed to acquire adequate food sources. Sediment mobility is an important process for flushing fine sediments from within the gravel frameworks. Evaluations of channel and flow characteristics at cross-section locations 2–8 show a range of streambed mobility. In general, boundary shear stress and streambed mobility increase with increases in streamflow. Within the cross sections, the greatest boundary shear stress occurs towards the center of the channel. Reach-scale assessment of sediment mobility in the lower reach shows increased streambed mobility. This is due in part to smaller grain sizes in the lower reach, but may also reflect the greater extent of channel alterations, specifically the temporary berms constructed by CDOT in the late 1980s and 1990s, present in this reach.

Colorado↗

USGS2018

Flood-inundation maps for the Patoka River in and near Jasper, southwestern Indiana

Digital flood-inundation maps for a 9.5-mile reach of the Patoka River in and near the city of Jasper, southwestern Indiana (Ind.), from the streamgage near County Road North 175 East, downstream to State Road 162, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site at https://water.usgs.gov/osw/flood_inundation/ , depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Patoka River at Jasper, Ind. (station number 03375500). The Patoka streamgage is located at the upstream end of the 9.5-mile river reach. Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/ , although flood forecasts and stages for action and minor, moderate, and major flood stages are not currently (2017) available at this site (JPRI3). Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at the Patoka River at Jasper, Ind., streamgage and the documented high-water marks from the flood of April 30, 2017. The calibrated hydraulic model was then used to compute five water-surface profiles for flood stages referenced to the streamgage datum ranging from 15 feet (ft), or near bankfull, to 19 ft. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging [lidar] data having a 0.98 ft vertical accuracy and 4.9 ft horizontal resolution) to delineate the area flooded at each water level. The availability of these flood-inundation maps, along with real-time stage from the USGS streamgage at the Patoka River at Jasper, Ind., will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.

Indiana↗

USGS2019

Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma developed by using streamflow data through 2017

The U.S. Geological Survey (USGS), in cooperation with the Oklahoma Department of Transportation, updated peak-streamflow regression equations for estimating flows with annual exceedance probabilities from 50 to 0.2 percent for the State of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the State by use of a generalized least-squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for stream basins with drainage areas less than 2,510 square miles that are not affected by regulation. The standard model error ranged from 31.28 to 49.32 percent for the different annual exceedance probabilities that were computed. Annual-maximum peak flows observed at 212 USGS streamgages through water year 2017 were used for the regression analysis, excluding the Oklahoma Panhandle region. The USGS StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 212 streamgages in and near Oklahoma are provided in this report. This report contains descriptions of the methods that can be used to estimate peak streamflows at ungaged sites by using estimates from streamgages on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater-retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.

Arkansas, Kansas, Missouri, Oklahoma, Texas↗

USGS2018

A practical primer on geostatistics

Introduction The Challenge —Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer. The Aim of Geostatistics —The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps. Geostatistical Methods —Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods. Differences with Time Series —On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has significant methodological implications. Historical Remarks —As a discipline, geostatistics was firmly established in the 1960s by the French engineer Georges Matheron, who was interested in the appraisal of ore reserves in mining. Geostatistics did not develop overnight. Like other disciplines, it has built on previous results, many of which were formulated with different objectives in various fields. Pioneers —Seminal ideas conceptually related to what today we call geostatistics or spatial statistics are found in the work of several pioneers, including: 1940s: A.N. Kolmogorov in turbulent flow and N. Wiener in stochastic processing; 1950s: D. Krige in mining; 1960s: B. Mathern in forestry and L.S. Gandin in meteorology Calculations —Serious applications of geostatistics require the use of digital computers. Although for most geostatistical techniques rudimentary implementation from scratch is fairly straightforward, coding programs from scratch is recommended only as part of a practice that may help users to gain a better grasp of the formulations. Software —For professional work, the reader should employ software packages that have been thoroughly tested to handle any sampling scheme, that run as efficiently as possible, and that offer graphic capabilities for the analysis and display of results. This primer employs primarily the package Stanford Geomodeling Software (SGeMS) - recently developed at the Energy Resources Engineering Department at Stanford University - as a way to show how to obtain results practically. This applied side of the primer should not be interpreted as the notes being a manual for the use of SGeMS. The main objective of the primer is to help the reader gain an understanding of the fundamental concepts and tools in geostatistics. Organization of the Primer —The chapters of greatest importance are those covering kriging and simulation. All other materials are peripheral and are included for better comprehension of these main geostatistical modeling tools. The choice of kriging versus simulation is often a big puzzle to the uninitiated, let alone the different variants of both of them. Chapters 14, 18, and 19 are intended to shed light on those subjects. The critical aspect of assessing and modeling spatial correlation is covered in chapter 7. Chapters 2 and 3 review relevant concepts in classical statistics. Course Objectives —This course offers stochastic solutions to common problems in the characterization of complex geological systems. At the end of the course, participants should have: an understanding of the theoretical foundations of geostatistics; a good grasp of its possibilities and limitations; and reasonable familiarity with the SGeMS software, thus opening the possibility of practically applying geostatistics.

Open-File Report↗

USGS2025

Evaluating Three-Dimensional Elevation Program lidar consistency and accuracy at scale using cloud-native, open-source methods

The U.S. Geological Survey three-dimensional elevation program (3DEP) has significantly expanded national lidar coverage, necessitating scalable, reproducible methods for assessing data quality across diverse terrains and acquisition conditions. This study introduces a cloud-native, open-source workflow designed to evaluate the geometric accuracy and consistency of 3DEP lidar data sets at a national scale. Leveraging tools such as the Point Data Abstraction Library, Open3D, and Amazon Web Services infrastructure, the workflow integrates global navigation satellite system‐surveyed ground control points and terrestrial laser scanning data to validate airborne lidar collections. Two case studies demonstrate the application of this process. In Puerto Rico, the process identified vertical biases and inconsistencies in vegetated areas, while in Iowa and Arizona, the process confirmed high vertical accuracy with minimal bias. The results underscore the effectiveness of combining cloud computing with open-source tools to perform large-scale lidar data quality assessments. This process offers a reproducible, efficient solution for nationwide validation of 3DEP data sets, supporting enhanced decision-making in geospatial applications.

Photogrammetric Engineering and Remote Sensing↗

USGS2006

Geophysical studies of the Crump Geyser known geothermal resource area, Oregon, in 1975

The U.S. Geological Survey (USGS) conducted geophysical studies in support of the resource appraisal of the Crump Geyser Known Geothermal Resource Area (KGRA). This area was designated as a KGRA by the USGS, and this designation became effective on December 24, 1970. The land classification standards for a KGRA were established by the Geothermal Steam Act of 1970 (Public Law 91-581). Federal lands so classified required competitive leasing for the development of geothermal resources. The author presented an administrative report of USGS geophysical studies entitled 'Geophysical background of the Crump Geyser area, Oregon, KGRA' to a USGS resource committee on June 17, 1975. This report, which essentially was a description of geophysical data and a preliminary interpretation without discussion of resource appraisal, is in Appendix 1. Reduction of sheets or plates in the original administrative report to page-size figures, which are listed and appended to the back of the text in Appendix 1, did not seem to significantly degrade legibility. Bold print in the text indicates where minor changes were made. A colored page-size index and tectonic map, which also show regional geology not shown in figure 2, was substituted for original figure 1. Detailed descriptions for the geologic units referenced in the text and shown on figures 1 and 2 were separately defined by Walker and Repenning (1965) and presumably were discussed in other reports to the committee. Heavy dashed lines on figures 1 and 2 indicate the approximate KGRA boundary. One of the principal results of the geophysical studies was to obtain a gravity map (Appendix 1, fig. 10; Plouff, and Conradi, 1975, pl. 9), which reflects the fault-bounded steepness of the west edge of sediments and locates the maximum thickness of valley sediments at about 10 kilometers south of Crump Geyser. Based on the indicated regional-gravity profile and density-contrast assumptions for the two-dimensional profile, the maximum sediment thickness was estimated at 820 meters. A three-dimensional gravity model would have yielded a greater thickness. Audiomagnotelluric measurements were not made as far south as the location of the gravity low, as determined in the field, due to a lack of communication at that time. A boat was borrowed to collect gravity measurements along the edge of Crump Lake, but the attempt was curtailed by harsh, snowy weather on May 21, 1975, which shortly followed days of hot temperature. Most of the geophysical data and illustrations in Appendix 1 have been published (Gregory and Martinez, 1975; Plouff, 1975; and Plouff and Conradi, 1975), and Donald Plouff (1986) discussed a gravity interpretation of Warner Valley at the Fall 1986 American Geophysical Union meeting in San Francisco. Further interpretation of possible subsurface geologic sources of geophysical anomalies was not discussed in Appendix 1. For example, how were apparent resistivity lows (Appendix 1, figs. 3-6) centered near Crump Geyser affected by a well and other manmade electrically conductive or magnetic objects? What is the geologic significance of the 15-milligal eastward decrease across Warner Valley? The explanation that the two-dimensional gravity model (Appendix 1, fig. 14) was based on an inverse iterative method suggested by Bott (1960) was not included. Inasmuch as there was no local subsurface rock density distribution information to further constrain the gravity model, the three-dimensional methodology suggested by Plouff (1976) was not attempted. Inasmuch as the associated publication by Plouff (1975), which released the gravity data, is difficult to obtain and not in digital format, that report is reproduced in Appendix 2. Two figures of the publication are appended to the back of the text. A later formula for the theoretical value of gravity for the given latitudes at sea level (International Association of Geodesy, 1971) should be used to re-compute gravity anomalies. To merge t

Open-File Report↗

USGS2007

Wave-driven spatial and temporal variability in sea-floor sediment mobility in the Monterey Bay, Cordell Bank, and Gulf of the Farallones National Marine Sanctuaries

Wind and wave patterns affect many aspects of continental shelves and shorelines geomorphic evolution. Although our understanding of the processes controlling sediment suspension on continental shelves has improved over the past decade, our ability to predict sediment mobility over large spatial and temporal scales remains limited. The deployment of robust operational buoys along the U.S. West Coast in the early 1980s provides large quantities of high-resolution oceanographic and meteorologic data. By 2006, these data sets were long enough to clearly identify long-term trends and compute statistically significant probability estimates of wave and wind behavior during annual and interannual climatic cycles (that is, El Niño and La Niña). Wave-induced sediment mobility on the shelf and upper slope off central California was modeled using synthesized oceanographic and meteorologic data as boundary input for the Delft SWAN model, sea-floor grain-size data provided by the usSEABED database, and regional bathymetry. Differences in waves (heights, periods, and directions) and winds (speeds and directions) between El Niño and La Niña months cause temporal and spatial variations in peak wave-induced bed shear stresses. These variations, in conjunction with spatially heterogeneous unconsolidated sea-floor sedimentary cover, result in predicted sediment mobility widely varying in both time and space. These findings indicate that these factors have significant consequences for both geological and biological processes.

California↗

USGS2012

Base flow (1966-2009) and streamflow gain and loss (2010) of the Brazos River from the New Mexico-Texas State line to Waco, Texas

During 2010–11, the U.S. Geological Survey (USGS), in cooperation with the Texas Water Development Board, used hydrograph separation to quantify historical base flow at 11 USGS streamflow-gaging stations between water years 1966–2009 and streamflow gains and losses from two sets of synoptic measurements of streamflow and specific conductance (the first in June 2010, followed by another set in October 2010) in the upper Brazos River Basin from the New Mexico–Texas State line to Waco, Texas. The following subbasins compose the study area: Salt Fork Brazos River Basin, Double Mountain Fork Brazos River Basin, Clear Fork Brazos River Basin, North Bosque River Basin, and the Brazos River Basin (main stem) (including its tributaries). Base-flow analysis was done using historical streamflow data from 11 USGS streamflow-gaging stations in the upper Brazos River Basin to compute yearly base-flow indexes (base flow divided by total streamflow) for each station. The base-flow index was used to indicate the fraction of flow consisting of base flow on an annual basis for the period of record evaluated at each streamflow-gaging station. At nine stations there were long-term streamflow data from water years 1966–2009 (October 1965 through September 2009); at two stations slightly shorter periods of record (water years 1967–2009 and 1969–2009) were available. The median base-flow indexes were 0.16 and 0.15 at USGS streamflow-gaging stations 08082000 Salt Fork Brazos River near Aspermont, Tex., and 08080500 Double Mountain Fork Brazos River near Aspermont, Tex., respectively. The amount of the total streamflow consisting of base flow was larger at sites in the Clear Fork Brazos River Basin compared to sites in the Salt Fork Brazos River Basin or Double Mountain Fork Brazos River Basin; at USGS streamflow-gaging stations 08084000 Clear Fork Brazos River at Nugent, Tex., and at 08085500 Clear Fork Brazos River at Fort Griffin, Tex., the median base-flow indexes were 0.28 and 0.23, respectively. The largest median base-flow index for any station was 0.35 at USGS streamflow-gaging station 08091500 Paluxy River at Glen Rose, Tex. The second largest base-flow index was 0.30 at USGS streamflow-gaging station 08095000 North Bosque River near Clifton, Tex. Median base-flow indexes on the main stem of the Brazos River upstream from Possum Kingdom Lake were 0.22 at USGS streamflow-gaging station 08082500 Brazos River at Seymour, Tex., and 0.24 at USGS streamflow-gaging station 08088000 Brazos River near South Bend, Tex. The base-flow indexes for stations between Possum Kingdom Lake and Lake Granbury were 0.19 and 0.27 at USGS streamflow-gaging stations 08089000 Brazos River near Palo Pinto, Tex., and 08090800 Brazos River near Dennis, Tex., respectively. A median base-flow index of 0.19 was also measured at USGS streamflow-gaging station 08091000 Brazos River near Glen Rose, Tex., located between Lake Granbury and Lake Whitney. A Mann-Kendall trend analysis test was performed on annual base-flow index values from each of the 11 streamflow records that were analyzed. Upward trends in base-flow index values indicating increasing flows during the study period were found for USGS streamflow-gaging stations 08080500 Double Mountain Fork Brazos River near Aspermont, Tex., 08089000 Brazos River near Palo Pinto, Tex., and 08090800 Brazos River near Dennis, Tex. Flows at these three streamflow-gaging stations are regulated by reservoir releases, and additional analyses are needed before these streamflow trends can be characterized as indicative of changes in base flow over time. Streamflow was measured at 66 sites from June 6–9, 2010, and at 68 sites from October 16–19, 2010, to identify reaches in the upper Brazos River Basin that were gaining or losing streamflow. Gaining reaches were identified in each of the five subbasins. The gaining reach in the Salt Fork Brazos River Basin began at USGS streamflow-gaging station 08080940 Salt Fork Brazos River at State Highway 208 near Clairemont, Tex. (site SF–6), upstream from where Duck Creek flows into the Salt Fork Brazos River and continued downstream past USGS streamflow-gaging station 08082000 Salt Fork Brazos River near Aspermont, Tex. (site SF–9), to the outlet of the basin. In the Double Mountain Fork Brazos River Basin, a gaining reach from near Post, Tex., downstream to the outlet of the basin was identified. Two gaining reaches were identified in the Clear Fork Brazos River Basin—one from near Roby, Tex., downstream to near Noodle, Tex., and second from Hawley, Tex., downstream to Nugent, Tex. Most of the North Bosque River was characterized as gaining streamflow. Streamflow gains were identified in the main stem of the Brazos River from where the Brazos River main stem forms at the confluence of the Salt Fork Brazos River and Double Mountain Fork Brazos River near Knox City, Tex., downstream to near Seymour, Tex.

New Mexico, Texas↗

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