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At least 955 records · Page 53Linked to original sources
USGS1999

Applications and issues of GIS as tool for civil engineering modeling

A tool that has proliferated within civil engineering in recent years is geographic information systems (GIS). The goal of a tool is to supplement ability and knowledge that already exists, not to serve as a replacement for that which is lacking. To secure the benefits and avoid misuse of a burgeoning tool, engineers must understand the limitations, alternatives, and context of the tool. The common benefits of using GIS as a supplement to engineering modeling are summarized. Several brief case studies of GIS modeling applications are taken from popular civil engineering literature to demonstrate the wide use and varied implementation of GIS across the discipline. Drawing from the case studies, limitations regarding traditional GIS data models find the implementation of civil engineering models within current GIS are identified and countered by discussing the direction of the next generation of GIS. The paper concludes by highlighting the potential for the misuse of GIS in the context of engineering modeling and suggests that this potential can be reduced through education and awareness. The goal of this paper is to promote awareness of the issues related to GIS-based modeling and to assist in the formulation of questions regarding the application of current GIS. The technology has experienced much publicity of late, with many engineers being perhaps too excited about the usefulness of current GIS. An undoubtedly beneficial side effect of this, however, is that engineers are becoming more aware of GIS and, hopefully, the associated subtleties. Civil engineers must stay informed of GIS issues and progress, but more importantly, civil engineers must inform the GIS community to direct the technology development optimally.

Journal of Computing in Civil Engineering↗
USGS2022

Assessment of streamflow trends in the eastern Dakotas, water years 1960–2019

Hydrologic extremes, whether periods of drought or flooding, are occurring more frequently with greater severity and can have substantial economic impacts. Along with flooding, the timing and volume of streamflow also is changing across the United States. The focus of this report is to characterize a unique trend in mean annual streamflow occurring in eastern North and South Dakota, hereafter referred to as the eastern Dakotas, that is not being observed anywhere else in the conterminous United States. Streamflow records for 1,853 U.S. Geological Survey streamgages obtained from the U.S. Geological Survey National Water Information System database with a continuous record of mean annual streamflow during water years 1960–2019 were included in this study. Using a Kendall tau statistical test ( p -value less than or equal to 0.10), 573 streamgages had a statistically significant upward trend in mean annual streamflow and are primarily located in the Midwest and northeastern United States. Of the streamgages, 182 had a statistically significant downward trend and are located primarily in the western and southeastern States. Several sites had increases in streamflow between 100 and 500 percent. Most of the streamgages with the highest increases in mean annual streamflow are along the same rivers in the eastern Dakotas, regardless of basin size. A comparison of mean annual streamflow of the last decade (2010–19) to the first decade (1960–69) of the study period shows that the largest increases in annual streamflow volumes in the United States also are in the eastern Dakotas. Among all 1,853 streamgages in the United States, the Sheyenne River near Warwick, North Dakota (U.S. Geological Survey station 05056000), has the greatest percent change, with an increase of 486 percent. Several factors may be contributing to increasing trends in streamflow in the eastern Dakotas and may include, in part, precipitation changes owing to climatic variation within the region, geologic makeup of the subsurface, and land-use changes. A better understanding of these research areas will help producers, resource managers, and infrastructure engineers to make more informed environmental and economic decisions.

North Dakota, South Dakota↗
USGS2021

What is the effect of poaching activity on wildlife species?

Poaching is a pervasive threat to wildlife, yet quantifying the direct effect of poaching on wildlife is rarely possible because both wildlife and threat data are infrequently collected concurrently. In this study, we used poaching data collected through the Management Information System (MIST) and wildlife camera trap data collected by the Tropical Ecology Assessment and Monitoring (TEAM) network from 2014 to 2017 in Volcanoes National Park, Rwanda. We implemented co-occurrence multi-season occupancy models that accounted for imperfect detection to investigate the effect of poaching on initial occupancy, colonization, and extinction of 5 mammal species. Specifically, we focused on 2 species of conservation concern (mountain gorilla ( Gorilla beringei beringei ) and golden money ( Cercopithecus mitis kandti )), and 3 species targeted by poachers (black-fronted duiker ( Cephalophus nigrifrons ), bushbuck ( Tragelaphus scriptus ), and African buffalo ( Syncerus caffer )). We found that the probability of local extinction was highest in sites with poaching activity for golden monkey and bushbuck. In addition, the probability of initial occupancy for golden monkey was highest in sites without poaching activity. We only found weak evidence of effects of poaching on parameters governing the occupancy dynamics of the other species. All species showed evidence of poaching presence affecting the probability of detection of the wildlife species. This is the first study to our knowledge to combine direct threat observations from ranger-based monitoring data with camera trap wildlife observations to quantify the effect of poaching on wildlife. Given the widespread collection of ranger-based monitoring and camera trap data, our approach is broadly applicable to numerous protected areas and has the potential to significantly improve conservation management. Specifically, the relationship between poaching activity and wildlife population dynamics (this paper) can be combined with information on the relationship between ranger patrols and poaching activity (Moore et al. 2017) to develop models useful for making wise decisions about ranger patrol deployment.

Volcanoes National Park↗
USGS2018

Validating the use of object-based image analysis to map commonly-recognized landform features in the United States

The U.S. Geological Survey (USGS) National Geospatial Program (NGP) seeks to i) create semantically-accessible terrain features from the pixel-based 3D Elevation Program (3DEP) data, and ii) enhance the usability of the USGS Geographic Names Information System (GNIS) by associating boundaries with GNIS features whose spatial representation is currently limited to 2D point locations. Geographic object-based image analysis (GEOBIA) was determined to be a promising method to approach both goals. An existing GEOBIA workflow was modified and the resulting segmented objects and terrain categories tested for a strategically chosen physiographic province in the mid-western US, the Ozark Plateaus. The chi-squared test of independence confirmed that there is significant overall spatial association between terrain categories of the GEOBIA and GNIS feature classes. Contingency table analysis also suggests strong category-specific associations between select GNIS and GEOBIA classes. However, 3D visual analysis revealed that GEOBIA objects resembled segmented regions more than they did individual landform objects, with their boundaries often failing to correspond to match what people would likely perceive as landforms. Still, objects derived through GEOBIA can provide initial baseline landscape divisions that can improve the efficiency of more specialized feature extraction methods.

Cartography and Geographic Information Science↗
USGS2012

Mapping plant species ranges in the Hawaiian Islands: developing a methodology and associated GIS layers

This report documents a methodology for projecting the geographic ranges of plant species in the Hawaiian Islands. The methodology consists primarily of the creation of several geographic information system (GIS) data layers depicting attributes related to the geographic ranges of plant species. The most important spatial-data layer generated here is an objectively defined classification of climate as it pertains to the distribution of plant species. By examining previous zonal-vegetation classifications in light of spatially detailed climate data, broad zones of climate relevant to contemporary concepts of vegetation in the Hawaiian Islands can be explicitly defined. Other spatial-data layers presented here include the following: substrate age, as large areas of the island of Hawai'i, in particular, are covered by very young lava flows inimical to the growth of many plant species; biogeographic regions of the larger islands that are composites of multiple volcanoes, as many of their species are restricted to a given topographically isolated mountain or a specified group of them; and human impact, which can reduce the range of many species relative to where they formerly were found. Other factors influencing the geographic ranges of species that are discussed here but not developed further, owing to limitations in rendering them spatially, include topography, soils, and disturbance. A method is described for analyzing these layers in a GIS, in conjunction with a database of species distributions, to project the ranges of plant species, which include both the potential range prior to human disturbance and the projected present range. Examples of range maps for several species are given as case studies that demonstrate different spatial characteristics of range. Several potential applications of species-range maps are discussed, including facilitating field surveys, informing restoration efforts, studying range size and rarity, studying biodiversity, managing invasive species, and planning of conservation efforts.

Hawai'i↗
USGS2018

Soil property and class maps of the conterminous United States at 100-meter spatial resolution

With growing concern for the depletion of soil resources, conventional soil maps need to be updated and provided at finer and finer resolutions to be able to support spatially explicit human–landscape models. Three US soil point datasets—the National Cooperative Soil Survey Characterization Database, the National Soil Information System, and the Rapid Carbon Assessment dataset—were combined with a stack of over 200 environmental datasets and gSSURGO polygon maps to generate complete coverage gridded predictions at 100-m spatial resolution of six soil properties (percentage of organic C, total N, bulk density, pH, and percentage of sand and clay) and two US soil taxonomic classes (291 great groups [GGs] and 78 modified particle size classes [mPSCs]) for the conterminous United States. Models were built using parallelized random forest and gradient boosting algorithms as implemented in the ranger and xgboost packages for R. Soil property predictions were generated at seven standard soil depths (0, 5, 15, 30, 60, 100, and 200 cm). Prediction probability maps for US soil taxonomic classifications were also generated. Cross validation results indicated an out-of-bag classification accuracy of 60% for GGs and 66% for mPSCs; for soil properties, RMSE for leave-location-out cross-validation was 0.74 ( R 2 = 0.68), 17.8 wt% ( R 2 = 0.57), 12 wt% ( R 2 = 0.46), 3.63 wt% ( R 2 = 0.41), 0.2 g cm −3 ( R 2 = 0.42), and 0.27 wt% ( R 2 = 0.39) for pH, percent sand and clay, weight percentage of organic C, bulk density, and weight percentage of total N, respectively. Nine independent validation datasets were used to assess prediction accuracies for soil class models, and results ranged between 24 and 58% and between 24 and 93% for GG and mPSC prediction accuracies, respectively. Although mapping accuracies were variable and likely lower than gSSURGO in some areas, this modeling approach can enable easier integration of soil information with spatially explicit models compared with multicomponent map units.

Soil Science Society of America Journal↗
USGS1998

Satellite telemetry: A new tool for wildlife research and management

The U.S. Fish and Wildlife Service and the Alaska Department of Fish and Game have cooperated since 1984 to develop and evaluate satellite telemetry as a means of overcoming the high costs and logistical problems of conventional VHF (very high frequency) radiotelemetry systems. Detailed locational and behavioral data on caribou ( Rangifer tarandus ), polar bears ( Ursus maritimus ), and other large mammals in Alaska have been obtained using the Argos Data Collection and Location System (DCLS). The Argos system, a cooperative project of the Centre National d'Études Spatiales of France, the National Oceanic and Atmospheric Administration, and the National Aeronautics and Space Administration, is designed to acquire environmental data on a routine basis from anywhere on earth. Transmitters weighing 1.6-2.0 kg and functioning approximately 12-18 months operated on a frequency of 401.650 MHz. Signals from the transmitters were received by Argos DCLS instruments aboard two Tiros-N weather satellites in sun-synchronous, nearpolar orbits. Data from the satellites were received at tracking stations, transferred to processing centers in Maryland and France, and made available to users via computer tape, printouts, or telephone links. During 1985 and 1986, more than 25,000 locations and an additional 28,000 sets of sensor data (transmitter temperature and short-term and long-term indices of animal activity) were acquired for caribou and polar bears. Locations were calculated from the Doppler shift in the transmitted signal as the satellite approached and then moved away from the transmitter. The mean locational error for transmitters at known locations (n - 1,265) was 829 m; 90% of the calculated locations were within 1,700 m of the true location. Caribou transmitters provided a mean of 3.1 (+5.0. SD) locations per day during 6h of daily operation, and polar bear transmitters provided 1.7 (+6.9SD) locations during 12h of operation every third day. During the first 6 months of operation, the UHF (ultra-high frequency) signal failed on three of 32 caribou transmitters and 10 of 36 polar bear transmitters. A geographic information system (GIS) incorporating other databases (e.g., land cover, elevation, slope, aspect, hydrology, ice distribution) was used to analyze and display detailed locational and behavioral data collected via satellite. Examples of GIS applications to research projects using satellite telemetry and examples of detailed movement patterns of caribou and polar bears are presented. This report includes documentation for computer software packages for processing Argos data and presents developments, as of March 1987, in transmitter design, data retrieval using a local user terminal, computer software, and sensor development and calibration.

Resource Publication↗
USGS2012

Archive of side scan sonar and swath bathymetry data collected during USGS cruise 10CCT03 offshore of the Gulf Islands National Seashore, Mississippi, from East Ship Island, Mississippi, to Dauphin Island, Alabama, April 2010

In April of 2010, the U.S. Geological Survey (USGS) conducted a geophysical survey from the east end of East Ship Island, Miss., extending to the middle of Dauphin Island, Ala. (fig. 1). This survey had a dual purpose: (1) to interlink previously conducted nearshore geophysical surveys (shoreline to ~2 km) with those of offshore surveys (~2 to ~9 km) in the area, and (2) to extend the geophysical survey to include a portion of the Dauphin Island nearshore zone. The efforts were part of the USGS Gulf of Mexico Science Coordination partnership with the U.S. Army Corps of Engineers (USACE) to assist the Mississippi Coastal Improvements Program (MsCIP) and the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazards Susceptibility Project by mapping the shallow geological stratigraphic framework of the Mississippi Barrier Island Complex. These geophysical surveys will provide the data necessary for scientists to define, interpret, and provide baseline bathymetry and seafloor habitat for this area and to aid scientists in predicting future geomorpholocial changes of the islands with respect to climate change, storm impact, and sea-level rise. Furthermore, these data will provide information for barrier island restoration feasibility, particularly in Camille Cut, and efforts for the preservation of historical Fort Massachusetts. For more information refer to http://ngom.usgs.gov/gomsc/mscip/. This report serves as an archive of the processed multibeam bathymetry and side scan sonar (SSS) data. Data products herein include gridded and interpolated digital depth surfaces, seabed surface backscatter imagery, and x,y,z data products for both multibeam bathymetry and side scan sonar imagery. Additional files include trackline maps, navigation files, geograpahic information system (GIS) files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Scanned images of the handwritten FACS logs and digital FACS logs are also provided as PDF files. Refer to the Acronyms page for description of acronyms and abbreviations used in this report or hold the cursor over an acronym for a pop-up explanation. The USGS St. Petersburg Coastal and Marine Science Center assigns a unique identifier to each cruise or field activity. For example, 10CCT03 tells us the data were collected in 2010 for the Coastal Change and Transport (CCT) study and the data were collected during the third (03) field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the field activity ID. Data were collected aboard the U.S. Army Corps of Engineers (USACE) SV Irvington , a 56-foot (ft) Kvichak Marine Industries, Inc., catamaran (fig. 2). Side scan sonar and multibeam bathymetry data were collected simultaneously along the tracklines. The side scan sonar towfish was towed off the starboard side just slightly behind the vessel, close to the seafloor. The multibeam transducer was attached to a retractable strut-arm lowered between the catamaran hulls. Navigation was acquired with an Applanix POS MV and differentially corrected using the broadcast signal from a local National Geodetic Survey (NGS) Continuously Operating Reference Station (CORS) beacon. See the digital FACS equipment log for details about the acquisition equipment used. Raw datasets were stored digitally and processed using HYPACK Inc., HYSWEEP software at the USACE Mobile, Ala., District office. For more information on processing refer to the Equipment and Processing page. Chirp seismic data were also collected during this survey and are archived separately.

Gulf Islands National Seashore↗
USGS2018

Assessment of water resources and the potential effects from oil and gas development in the Bureau of Land Management Tri-County planning area, Sierra, Doña Ana, and Otero Counties, New Mexico

The U.S. Geological Survey (USGS), in cooperation with the Bureau of Land Management, conducted a study to assess the water resources and potential effects on the water resources from oil and gas development in the Tri-County planning area, Sierra, Doña Ana, and Otero Counties, New Mexico. Publicly available data were used to assess these resources and effects and to identify data gaps in the Tri-County planning area. The Tri-County planning area includes approximately 9.3 million acres and is within the eastern extent of the Basin and Range Province, which consists of mountain ranges and low elevation basins. Three specific areas of interest within the Tri-County planning area are the Jornada del Muerto, Tularosa Basin, and Otero Mesa, which is adjacent to the Salt Basin. Surface-water resources are limited in the Tri-County planning area, with the Rio Grande as the main perennial river flowing from north to south through Sierra and Doña Ana Counties. The Tularosa Creek is an important surface-water resource in the Tularosa Basin. The Sacramento River, which flows southeast out of the Sacramento Mountains, is an important source of recharge to aquifers in the Salt Basin. Groundwater resources vary in aquifer type, depth to water, and water quality. For example, the Jornada del Muerto, Tularosa Basin, and Salt Basin each have shallow and deep aquifer systems, and water can range from freshwater, with less than 1,000 milligrams per liter (mg/L) of total dissolved solids, to brine, with greater than 35,000 mg/L of total dissolved solids. Water quality in the Tri-County planning area is affected by the dissolution of salt deposits and evaporation which are common in arid regions such as southern New Mexico. The potential for oil and gas development exists in several areas within the Tri-County area. As many as 81 new conventional wells and 25 coalbed natural gas wells could be developed by 2035. Conventional oil and gas well construction in the Tri-County planning area is expected to require 1.53 acre-feet (acre-ft) (500,000 gallons) of water per well, similar to requirements in the nearby Permian Basin of New Mexico, while construction of unconventional wells is expected to require 7.3 acre-ft of water per well. Produced waters in the Permian Basin have high total dissolved solids, in the brackish to brine range. Data gaps identified in this study include the limited detailed data on surface-water resources, the lack of groundwater data in areas of interest, and the lack of water chemistry data related to oil and gas development issues. Surface waters in the Tri-County planning area are sparse; some streams are perennial, and most are ephemeral. A more detailed study of the ephemeral channels and their interaction with groundwater could provide a better understanding of the importance of these surface-water resources. Groundwater data used in this study are from the USGS National Water Information System, which does not have continuous water-level depth data at many of the sites in the Tri-County planning area. On Otero Mesa, no recurrent groundwater-level data are available at any one site. The water-quality data compiled in this study provide a good overview of the general chemistry of groundwater in the Tri-County planning area. To fully understand the groundwater resources, it would be helpful to have more wells in specific areas of interest for groundwater-level and water-quality measurements.

New Mexico↗
USGS2002

A bibliography of terrain modeling (geomorphometry), the quantitative representation of topography: Supplement 4.0

Terrain modeling, the practice of ground-surface quantification, is an amalgam of Earth science, mathematics, engineering, and computer science. The discipline is known variously as geomorphometry (or simply morphometry), terrain analysis, and quantitative geomorphology. It continues to grow through myriad applications to hydrology, geohazards mapping, tectonics, sea-floor and planetary exploration, and other fields. Dating nominally to the co-founders of academic geography, Alexander von Humboldt (1808, 1817) and Carl Ritter (1826, 1828), the field was revolutionized late in the 20th Century by the computer manipulation of spatial arrays of terrain heights, or digital elevation models (DEMs), which can quantify and portray ground-surface form over large areas (Maune, 2001). Morphometric procedures are implemented routinely by commercial geographic information systems (GIS) as well as specialized software (Harvey and Eash, 1996; Köthe and others, 1996; ESRI, 1997; Drzewiecki et al., 1999; Dikau and Saurer, 1999; Djokic and Maidment, 2000; Wilson and Gallant, 2000; Breuer, 2001; Guth, 2001; Eastman, 2002). The new Earth Surface edition of the Journal of Geophysical Research, specializing in surficial processes, is the latest of many publication venues for terrain modeling. This is the fourth update of a bibliography and introduction to terrain modeling (Pike, 1993, 1995, 1996, 1999) designed to collect the diverse, scattered literature on surface measurement as a resource for the research community. The use of DEMs in science and technology continues to accelerate and diversify (Pike, 2000a). New work appears so frequently that a sampling must suffice to represent the vast literature. This report adds 1636 entries to the 4374 in the four earlier publications1. Forty-eight additional entries correct dead Internet links and other errors found in the prior listings. Chronicling the history of terrain modeling, many entries in this report predate the 1999 supplement. Coverage is representative from about 1800 through early–mid 2002. Papers increasingly are published exclusively or in duplicate on the Internet's World Wide Web; the dates given here for Web addresses (URLs) that lack a print publication indicate a Web site's last update or my last access of it. The bibliography is arranged alphabetically and thus is not readily summarized. This introduction cites about 500 entries, a third of them grouped under 24 morphometric topics, as a guide to the listing's contents. Continuing the practice of previous bibliographies in the series to provide more information on a few applications (see summary of past topics in Pike, 2000a), this report elaborates further on topographic data, putative new parameters, tectonic geomorphology/neo-orometry, biogeography, ice-cap morphometry, results from the Mars Global DEM, landslide-hazard mapping, terrain modeling as physics, Hack's law, and broad-scale computer visualization. The literature of some of these subjects is large, and none of the summaries is intended to more than introduce the topic and comment on some of the current contributions of terrain modeling. Closing the essay is a discussion of pre-1900 papers that trace the evolution of ridge-line and watercourse quantification by descriptive geometry, as well as comments on some new books and an on-line bulletin board.

Open-File Report↗
USGS2005

Hydrogeologic framework and estimates of ground-water volumes in Tertiary and upper Cretaceous hydrogeologic units in the Powder River basin, Wyoming

The Powder River Basin in Wyoming and Montana is an important source of energy resources for the United States. Coalbed methane gas is contained in Tertiary and upper Cretaceous hydrogeologic units in the Powder River Basin. This gas is released when water pressure in coalbeds is lowered, usually by pumping ground water. Issues related to disposal and uses of by-product water from coalbed methane production have developed, in part, due to uncertainties in hydrologic properties. One hydrologic property of primary interest is the amount of water contained in Tertiary and upper Cretaceous hydrogeologic units in the Powder River Basin. The U.S. Geological Survey, in cooperation with the Bureau of Land Management, conducted a study to describe the hydrogeologic framework and to estimate ground-water volumes in different facies of Tertiary and upper Cretaceous hydrogeologic units in the Powder River Basin in Wyoming. A geographic information system was used to compile and utilize hydrogeologic maps, to describe the hydrogeologic framework, and to estimate the volume of ground water in Tertiary and upper Cretaceous hydrogeologic units in the Powder River structural basin in Wyoming. Maps of the altitudes of potentiometric surfaces, altitudes of the tops and bottoms of hydrogeologic units, thicknesses of hydrogeologic units, percent sand of hydrogeologic units, and outcrop boundaries for the following hydrogeologic units were used: Tongue River-Wasatch aquifer, Lebo confining unit, Tullock aquifer, Upper Hell Creek confining unit, and the Fox Hills-Lower Hell Creek aquifer. Literature porosity values of 30 percent for sand and 35 percent for non-sand facies were used to calculate the volume of total ground water in each hydrogeologic unit. Literature specific yield values of 26 percent for sand and 10 percent for non-sand facies, and literature specific storage values of 0.0001 ft-1 (1/foot) for sand facies and 0.00001 ft-1 for non-sand facies, were used to calculate a second volume of ground water for each hydrogeologic unit. Significant figure considerations limited estimates of ground-water volumes to two significant digits. A total ground-water volume of 2.0x1014 ft3 (cubic feet) was calculated using porosity values, and a total ground-water volume of 3.6x1013 ft3 was calculated using specific yield and specific storage values. These results are consistent with retention properties, which would have some of the total water being retained in the sediments. Sensitivity analysis shows that the estimates of ground-water volume are most sensitive to porosity. The estimates also are sensitive to confined thickness and saturated thickness. Better spatial information for hydrogeologic units could help refine the ground-water volume estimates.

Scientific Investigations Report↗
USGS2023

Seismic source characterization in central and eastern United States

This report documents earthquake geology studies conducted in the central and eastern United States (CEUS) between 2018 and 2023. The overarching goal of the earthquake geology studies was to identify and characterize fault sources in the New Madrid seismic zone, the eastern Tennessee seismic zone, and the Charleston seismic zone. These studies primarily relied on the interpretation of high-resolution topography, morphotectonic analysis of 10-meter (m) digital elevation models, field reconnaissance, and integration with new and existing subsurface datasets. Results from each study are placed into a seismic hazard framework. The first two chapters focus on the New Madrid seismic zone, which is the most seismically active region in the CEUS, to better characterize the southern extent of the Reelfoot fault in the Obion River valley, and identify and characterize proposed faults along the margins of Crowleys Ridge in the center of the Mississippi River embayment. Crowleys Ridge is a ~320-kilometer (km) long landform that has been proposed to be formed by intermittent faulting or erosion from the Mississippi River. Legacy seismic reflection data documented Eocene, Mesozoic, and older strata offset by faults on the margins of the ridge (Van Arsdale et al., 1999). A morphotectonic analysis of the topography along Crowleys Ridge indicated southward increases in catchment hypsometric integral and slope. Neotectonic mapping on high-resolution lidar data revealed a series of subparallel linear fault and fold scarps along the margins of Crowleys Ridge. These scarps offset previously mapped fluvial and alluvial surfaces that were <56 thousand years ago (ka) (Rittenour et al., 2007). Integration of new seismic reflection and airborne electromagnetic data, legacy seismic data, with the landscape morphotectonic analysis and neotectonic mapping supports the interpretation that Crowleys Ridge is bound by faults that have been active in the late Quaternary but do not experience modern seismicity. By placing these newly characterized faults into the regional framework, the Crowleys Ridge faults can be interpreted as thrust faults associated with stepovers in the overall dextral fault system, similar to the Reelfoot fault. The results of these studies indicate that seismicity has migrated eastward during the late Quaternary, with the Reelfoot fault accommodating much of the modern-day strain. The Reelfoot fault ruptured in the 1811-1812 New Madrid earthquake sequence (Fuller, 1912) and has a record of past large earthquakes (Tuttle et al., 2002; 2019). Whereas the northern extent of the fault intersects the Mississippi River and has well-expressed surface deformation, questions remained regarding the southern limit of seismicity, deformation, and overall fault length. Analysis of <25 ka river terraces along the Obion River valley in western Tennessee revealed subtle fold scarps, with increased magnitudes of deformation on progressively older terraces, indicative of a longer record of fault movement than has been documented by paleoseismic and paleoliquefaction studies alone (e.g., Kelson et al., 1996; Gold et al., 2019; Tuttle et al., 2019). The third chapter integrates surface and subsurface data to assess the landscape record of surface deformation in the eastern Tennessee seismic zone (ETSZ), which is the second most seismically active region in the CEUS. Seismicity in the ETSZ is deep (5-26 km) and focused in the Proterozoic rock below the Paleozoic detachment. Although the region has experienced moderate (≤ Mw 4.8) seismicity in the instrumental record, the lack of a large historical earthquake and limited paleoseismic and paleoliquefaction evidence of prehistoric large ground shaking events have made seismic hazard characterization of the region challenging. Two models have emerged to predict the style and orientation of active surface deformation in the ETSZ. One suggests that surface rupturing earthquakes would exploit pre-existing faults along the regional structural grain (Cox et al., 2022). The second model advocates for primarily strike-slip motion on east-west or north-south faults, following analyses of recent seismicity (Chapman et al., 1997; Dunn and Chapman, 2006; Daniels and Peng, 2022) and the modern stress field (Levandowski et al., 2018). In our analysis, neotectonic mapping of high-resolution lidar data reveals a concentration of lineaments with east-west orientations, with some corresponding to previously mapped east-west faults that crosscut the northeast-southwest regional structural grain from the Paleozoic orogeny. A morphotectonic analysis of catchments and river segments within the same lithology indicates subtle differences that may be indicative of a longer-term tectonic uplift signal. These changes in morphotectonic metrics spatially correspond to newly mapped lineaments and previously mapped east-west trending faults. Within a regional framework, we suggest that diffuse surface deformation associated with deep seismicity is accommodated on a network on east-west faults. However, further work is needed to better understand the potential late Pleistocene fault activity of these lineaments, and we propose that the ETSZ is still best characterized as an area source in seismic hazard models. The final chapter presents preliminary work to create a geographic information system (GIS) database of recent studies analyzing surface and subsurface datasets and interpretations in the Charleston seismic zone, South Carolina. This GIS database will serve as a foundation for future work to analyze new quality level 1 (QL1; <0.5 m resolution) lidar data over the 1886 magnitude (M) 7 Charleston epicentral region to identify possible fault source sources responsible for the 1886 or other surface-rupturing events. This chapter briefly summarizes three new subsurface datasets, including seismic reflection (Pratt et al., 2022; Liberty, 2022) and seismicity data (Chapman et al., 2016), and one new surface dataset (Marple and Hurd, 2020), and their interpretations of potentially active lineaments and faults in the region.

central and eastern United States↗
USGS2008

PFReports: A program for systematic checking of annual peaks in NWISWeb

The accuracy, characterization, and completeness of the U.S. Geological Survey (USGS) peak-flow data drive the determination of flood-frequency estimates that are used daily to design water and transportation infrastructure, delineate flood-plain boundaries, and regulate development and utilization of lands throughout the Nation and are essential to understanding the implications of climate change on flooding. Indeed, this high-profile database reflects and highlights the quality of USGS water-data collection programs. Its extension and improvement are essential to efforts to strengthen USGS networks and science leadership and is worthy of the attention of Water Science Center (WSC) hydrographers. This document describes a computer program, PFReports, and its output that facilitates efficient and robust review and correction of data in the USGS Peak Flow File (PFF) hosted as part of NWISWeb (the USGS public Web interface to much of the data stored and managed within the National Water Information System or NWIS). Checks embedded in the program are recommended as part of a more comprehensive assessment of peak flow data that will eventually include examination of possible regional changes, seasonal changes, and decadal variations in magnitude, timing, and frequency. Just as important as the comprehensive assessment, cleaning up the database will increase the likelihood of improved WSC regional flood-frequency equations. As an example of the value of cleaning up the PFF, data for 26,921 sites in the PFF were obtained. Of those sites, 17,542 sites had peak streamflow values and daily values. For the 17,542 sites, 1,097 peaks were identified that were less than the daily value for the day on which the peak occurred. Of the 26,921 sites, 11,643 had peak streamflow values, concurrent daily values, and at least 10 peaks. At the 11,643 sites, 2,205 peaks were identified as potential outliers in a regression of peak streamflows on daily values. Previous efforts to identify problems with the PFF were time consuming, laborious, and often ineffective. This new suite of checks represents an effort to automate identification of specific problems without plotting or printing large amounts of data that may not have problems. In addition, the results of the checks of the peak flow files are delivered through the World Wide Web with links to individual reports so that WSCs can focus on specific problems in an organized and standardized fashion. Over the years, technical reviews, regional-flood studies, and user inquiries have identified many minor and some major problems in the PFF. However, the cumbersome nature of the PFF editor and a lack of analytical tools have hampered efforts at quality assurance/quality control (QA/QC) and subsequently to make needed revisions to the database. This document is organized to provide information regarding PFReports, especially those tests involving regression and to provide an overview of the review procedures for utilizing the output. It also may be used as a reference for the data qualification codes and abbreviations for the tests. Results of the checks for all peak flow files (March 2008) are available at http://nd.water.usgs.gov/internal/pfreports/.

Open-File Report↗
USGS2006

User's manual for the Graphical Constituent Loading Analysis System (GCLAS)

This manual describes the Graphical Constituent Loading Analysis System (GCLAS), an interactive cross-platform program for computing the mass (load) and average concentration of a constituent that is transported in stream water over a period of time. GCLAS computes loads as a function of an equal-interval streamflow time series and an equal- or unequal-interval time series of constituent concentrations. The constituent-concentration time series may be composed of measured concentrations or a combination of measured and estimated concentrations. GCLAS is not intended for use in situations where concentration data (or an appropriate surrogate) are collected infrequently or where an appreciable amount of the concentration values are censored. It is assumed that the constituent-concentration time series used by GCLAS adequately represents the true time-varying concentration. Commonly, measured constituent concentrations are collected at a frequency that is less than ideal (from a load-computation standpoint), so estimated concentrations must be inserted in the time series to better approximate the expected chemograph. GCLAS provides tools to facilitate estimation and entry of instantaneous concentrations for that purpose. Water-quality samples collected for load computation frequently are collected in a single vertical or at single point in a stream cross section. Several factors, some of which may vary as a function of time and (or) streamflow, can affect whether the sample concentrations are representative of the mean concentration in the cross section. GCLAS provides tools to aid the analyst in assessing whether concentrations in samples collected in a single vertical or at single point in a stream cross section exhibit systematic bias with respect to the mean concentrations. In cases where bias is evident, the analyst can construct coefficient relations in GCLAS to reduce or eliminate the observed bias. GCLAS can export load and concentration data in formats suitable for entry into the U.S. Geological Survey's National Water Information System. GCLAS can also import and export data in formats that are compatible with various commonly used spreadsheet and statistics programs.

Techniques and Methods↗
USGS2023

Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Results

Reliable estimates of the magnitude and frequency of floods are an important part of the framework for hydraulic-structure design and flood-plain management in Georgia, South Carolina, and North Carolina. Annual peak flows measured at U.S. Geological Survey streamgages are used to compute flood‑frequency estimates at those streamgages. However, flood‑frequency estimates also are needed at ungaged stream locations. A process known as regionalization was used to develop regression equations to estimate the magnitude and frequency of floods at ungaged locations. A multistate approach was used to update estimates of the magnitude and frequency of floods in rural, ungaged basins in Georgia, South Carolina, and North Carolina. Annual peak-flow data through September 2017 were analyzed for 965 streamgages with 10 or more years of data on rural streams in Georgia, South Carolina, North Carolina, and adjacent parts of Alabama, Florida, Tennessee, and Virginia. Flood‑frequency estimates of the 50‑, 20‑, 10‑, 4‑, 2‑, 1‑, 0.5‑, and 0.2‑percent annual exceedance probability streamflows, which correspond to flood-recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively, were computed for the 965 streamgages following national guidelines. As part of the computation of flood‑frequency estimates for the streamgages, an updated value for the regional skew coefficient (0.048) was developed using a Bayesian generalized least squares regression model. The new regional skew has a mean square error or average variance of prediction of 0.092. Additionally, basin characteristics for these stations were computed using a geographical information system. Exploratory analyses on the 965 streamgages confirmed the five hydrologic regions for Georgia, South Carolina, and North Carolina defined in a previous rural flood‑frequency study. From the 965 streamgages, streamgages with 30 or more years of record were used to complete a peak-flow trend analysis. Of the 965 streamgages, 164 streamgages were found to be redundant and were excluded from the regional regression analyses. Data from the remaining 801 streamgages (292 in Georgia, 75 in South Carolina, 303 in North Carolina, 15 in Alabama, 12 in Florida, 39 in Tennessee, and 65 in Virginia) were used in a regional regression analysis relating basin characteristics to flood‑frequency estimates. This analysis, based on generalized least squares regression, was used to develop a set of predictive equations to estimate the 50‑, 20‑, 10‑, 4‑, 2‑, 1‑, 0.5‑, and 0.2‑percent annual exceedance probability streamflows for rural, ungaged basins in Georgia, South Carolina, and North Carolina. The final set of predictive equations are all functions of drainage area and percentage of the drainage basin within each of the five hydrologic regions. Average errors of prediction for these regression equations range from 35.8 to 44.4 percent. Flood‑frequency estimates also were computed for 72 regulated (for example, a streamgage where flow is altered by a dam or weir) streamgages in Georgia, South Carolina, and North Carolina with 20 or more years of post-regulation record using data through water year 2019. The water year is the annual period from October 1 through September 30 and is designated by the year in which the period ends. Of the 72 regulated streamgages, 18 had pre-regulated periods of record that also were analyzed as part of this study. Flow adjustments were applied to historic peaks and large floods from the pre-regulated period, if available, for use in the post-regulation frequency analysis. Estimates of large floods provide valuable information in frequency analysis and, thus, were included in the post-regulation frequency analysis.

Georgia, North Carolina, South Carolina↗
USGS2003

Main-channel slopes of selected streams in Iowa for estimation of flood-frequency discharges

This report describes a statewide study conducted to develop main-channel slope (MCS) curves for 138 selected streams in Iowa with drainage areas greater than 100 square miles. MCS values determined from the curves can be used in regression equations for estimating floodfrequency discharges. Multivariable regression equations previously developed for two of the three hydrologic regions defined for Iowa require the measurement of MCS. Main-channel slope is a difficult measurement to obtain for large streams using 1:24,000-scale topographic maps. The curves developed in this report provide a simplified method for determining MCS values for sites located along large streams in Iowa within hydrologic Regions 2 and 3. The curves were developed using MCS values quantified for 2,058 selected sites along 138 selected streams in Iowa. A geographic information system (GIS) technique and 1:24,000-scale topographic data were used to quantify MCS values for the stream sites. The sites were selected at about 5-mile intervals along the streams. River miles were quantified for each stream site using a GIS program. Data points for river-mile and MCS values were plotted and a best-fit curve was developed for each stream. An adjustment was applied to all 138 curves to compensate for differences in MCS values between manual measurements and GIS quantifications. The multivariable equations for Regions 2 and 3 were developed using manual measurements of MCS. A comparison of manual measurements and GIS quantifications of MCS indicates that manual measurements typically produce greater values of MCS compared to GIS quantifications. Median differences between manual measurements and GIS quantifications of MCS are 14.8 and 17.7 percent for Regions 2 and 3, respectively. Comparisons of percentage differences between flood-frequency discharges calculated using MCS values of manual measurements and GIS quantifications indicate that use of GIS values of MCS for Region 3 substantially underestimate flood discharges. Mean and median percentage differences for 2- to 500-year recurrence- interval flood discharges ranged from 5.0 to 5.3 and 4.3 to 4.5 percent, respectively, for Region 2 and ranged from 18.3 to 27.1 and 12.3 to 17.3 percent for Region 3. The MCS curves developed from GIS quantifications were adjusted by 14.8 percent for streams located in Region 2 and by 17.7 percent for streams located in Region 3. Comparisons of percentage differences between flood discharges calculated using MCS values of manual measurements and adjusted-GIS quantifications for Regions 2 and 3 indicate that the flood-discharge estimates are comparable. For Region 2, mean percentage differences for 2- to 500-year recurrence- interval flood discharges ranged between 0.6 and 0.8 percent and median differences were 0.0 percent. For Region 3, mean and median differences ranged between 5.4 to 8.4 and 0.0 to 0.3 percent, respectively. A list of selected stream sites presented with each curve provides information about the sites including river miles, drainage areas, the location of U.S. Geological Survey streamflowgaging stations, and the location of streams crossing hydrologic region boundaries or the Des Moines Lobe landform region boundary. Two examples are presented for determining river-mile and MCS values, and two techniques are presented for computing flood-frequency discharges.

Iowa↗
USGS2000

Altitude, depth, and thickness of the Galena-Platteville Bedrock Unit in the subcrop area of Illinois and Wisconsin

The Galena-Platteville bedrock unit is a carbonate deposit of Ordovician age, composed of the Galena and Platteville Groups in Illinois and the Sinnippee Group in Wisconsin. It is the uppermost bedrock unit (subcrop) in most of northern Illinois and southern and eastern Wisconsin. The subcrop area is shaded in figure 1 of sheet 1 (Batten and others, 1997). The unit is predominately dolomite, with limestone in some areas, and has a weathered surface. Across the subcrop area, the hydrologic characteristics of the bedrock unit vary substantially. The bedrock unit may be either a confining unit or an aquifer. In areas where the Galena-Platteville bedrock unit is an aquifer, the unit is a dependable source of water for many private wells and some municipal-water-supply systems. Ground water in the Galena-Platteville bedrock aquifer is susceptible to contamination because the bedrock unit is near land surface in much of the study area, and the fractures in the unit allow rapid movement of water providing limited capacity to attenuate contaminants. The subcrop (study) area covers approximately 7,850 square miles in northern Illinois and Wisconsin. In the study area, volatile organic compounds and other contaminants have been detected in the aquifer at various sites (Mills, 1993; Kay and others, 1994). Many sources of contaminants, including landfills and industrial facilities, are known or suspected. In order to determine the possible effects of contamination on the ground-water supply, an understanding of the regional hydrogeologic framework of the Galena-Platteville bedrock unit is needed. Published and unpublished map and point data describing the geologic properties of the Galena-Platteville bedrock unit are available from many sources. The U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (USEPA), has selected and compiled a large portion of the available data to create computer data bases and maps. The objective of this effort is to compile and publish these data in a series of reports (U.S. Geological Survey Water-Resources Investigations Reports (WRIR) 974054-A, WRIR 97-4054-B, WRIR 97-4054-C). This is the third in that series of reports. The report describes the altitude, thickness, and depth from land surface of the subcrop area of the Galena-Platteville bedrock unit. The report series will enable investigators involved in site-specific studies within the subcrop area to understand the regional geologic framework of the unit and to find additional reference sources. This report consists of four sheets that show the altitude (sheet 1), depth from land surface (sheet 2), total thickness (sheet 3), and location of altitude data (sheet 4) of the lithologic units that constitute the Galena-Platteville bedrock unit within the subcrop area. The sheets also show major known geologic features within the Galena-Platteville study area in Illinois and Wisconsin. A geographic information system (GIS) was used to generate data layers (coverages) from point data and from published and unpublished contour maps at various scales and detail. Standard GIS procedures were used to change the coverages into the maps shown on the sheets presented in this report. A list of references for the data used to prepare the maps is provided.

Illinois, Wisconsin↗
USGS2017

Evaluation of the streamgage network for estimating streamflow statistics at ungaged sites in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York

The current (2015) streamgage network in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York was evaluated in order to design a network that would meet the hydrologic needs of many partners and serve a variety of purposes and interests, including estimation of streamflow statistics at ungaged sites. This study was done by the U.S. Geological Survey, in cooperation with the Pennsylvania Department of Environmental Protection and the Susquehanna River Basin Commission. The study area includes the Commonwealth of Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York. For this study, 229 streamgages were identified as reference streamgages that could be used to represent ungaged watersheds. Criteria for a reference streamgage are a minimum of 10 years of continuous record, minimally altered streamflow, and a drainage area less than 1,500 square miles. Some of the reference streamgages have been discontinued but provide historical hydrologic information valuable in the determination of streamflow characteristics of ungaged watersheds. Watersheds in the study area not adequately represented by a reference streamgage were identified by examining a range of basin characteristics, the extent of geographic coverage, and the strength of estimated streamflow correlations between gaged and ungaged sites. Basin characteristics were determined for the reference streamgage watersheds and the 1,662 12-digit hydrologic unit code (HUC12) subwatersheds in Pennsylvania and the Susquehanna River Basin using a geographic information system (GIS) spatial analysis and nationally available GIS datasets. Basin characteristics selected for this study include drainage area, mean basin elevation, mean basin slope, percentage of urbanized area, percentage of forested area, percentage of carbonate bedrock, mean annual precipitation, and soil thickness. A GIS spatial analysis was used to identify HUC12 subwatersheds outside the range of basin characteristics of the reference streamgages. There were 320 HUC12 subwatersheds, or 19 percent of the study area, with basin characteristics outside the range represented by the reference streamgage watersheds. A GIS spatial analysis was used to identify geographic gaps in the streamgage network. For each streamgage, a watershed area, called the gage statistical area (GSA), was delineated. The GSA shows the drainage area within a specific drainage-area ratio of the streamgage for transfer of streamflow statistics from that streamgage to ungaged sites on the valid statistical reach of the GSA for a streamgage. In Pennsylvania, a drainage-area ratio of 0.33–3 times the drainage area of the ungaged site was found to perform as well as, if not better than, more traditional ratios such as 0.5–1.5 (or 2) for transfer of selected streamflow statistics. A total of 1,102 HUC12 subwatersheds, or 66 percent of the study area, are outside the GSA for a reference streamgage. The USGS Baseline Streamflow Estimator (BaSE) program was used to determine how well HUC12 subwatersheds outside the streamgage GSAs are represented by the reference streamgage network in Pennsylvania, based on estimated streamflow correlation. The centroid of each HUC12 subwatershed was run through the BaSE program to determine the reference streamgage with the highest estimated streamflow correlation. There were 929 HUC12 subwatersheds in Pennsylvania, or 56 percent of the State, with an estimated correlation coefficient less than 0.96. The results from the basin characteristic, geographic, and streamflow correlation analyses were combined to identify 1,405 HUC12 subwatersheds in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York that lack a representative reference, based on at least one identified gap. Of the 1,405 HUC12 subwatersheds, 139 exhibited all three gaps, indicating a 8-percent gap in the reference streamgage network. Streamgages in areas with similar hydrologic characteristics and in close proximity to one another can potentially provide similar information (termed streamgages with high substitution potential). Streamgages were considered to have a high substitution potential with a nearby streamgage(s) if (1) the streamflow correlation coefficient was equal to or greater than 0.96, (2) the streamgages had 10 years of concurrent record, and (3) the streamgages are in the same watershed within the GSA of the streamgage. Seventy-four current (2015) streamgages with high substitution potential with at least one other streamgage were identified in the study area. Although these identified streamgages have a high substitution potential, they provide valuable streamflow information to a stakeholder. Selected primary uses of these streamgages were identified to determine the overall need for an individual streamgage.

New York, Pennsylvania↗