Geology topics

Anthony J. Gotvald

Publications and source records attributed to Anthony J. Gotvald.

At least 19 recordsLinked to original sources

USGS2023

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

Reliable flood-frequency estimates are important for hydraulic structure design and floodplain management in Georgia, South Carolina, and North Carolina. Annual peak streamflows (hereafter, referred to as peak flows) measured at 965 U.S. Geological Survey streamgages were used to compute flood-frequency estimates with annual exceedance probabilities (AEPs) of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent. These AEPs correspond to flood-recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A subset of these streamgages (801) were used to develop equations to predict the AEP flood flows at ungaged stream locations. This study was completed by the USGS in cooperation with the Georgia, South Carolina, and North Carolina Departments of Transportation and the North Carolina Department of Crime Control and Public Safety, and the results are summarized in this fact sheet. The complete results and the supporting data are presented in the companion scientific investigations report and data release.

Georgia, North Carolina, South Carolina↗

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↗

USGS2021

Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017

The methods of computation and estimates of the magnitude of flood flows were updated for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance levels for 91 streamgages on the main island of Puerto Rico by using annual peak-flow data through 2017. Since the previous flood frequency study in 1994, the U.S. Geological Survey has collected additional peak flows at additional streamgages, and Puerto Rico has experienced numerous flood events. This updated study was performed using longer annual peak-flow datasets from more stations to provide more representative equations to predict flood flows. Screening criteria for these streamgages included 10 or more years of annual peak-flow data, unregulated flow, and less than 10 percent impervious drainage area. The magnitude and frequency of floods at selected streamgages in Puerto Rico were estimated using updated methods outlined in Bulletin 17C. The new procedures include a regional skew analysis that incorporates Bayesian regression techniques, the Expected Moments Algorithm to better represent missing record and estimate parameters of the log-Pearson Type III distribution, and the Multiple Grubbs-Beck test for low outlier detection. Regional regression equations were developed to estimate peak-flow statistics at ungaged locations by using selected basin and climatic characteristics as explanatory variables. These variables were determined from digital spatial datasets and geographic information systems by using the most recent data available. Ordinary least-squares regression techniques were used to filter the basin characteristics and determine two separate regions, region 1 (west) and region 2 (east), based on residuals. A generalized least-squares procedure was used to account for cross-correlation of sites and develop the final set of equations that have drainage area as the only explanatory variable. The average standard errors of prediction ranged from 18.7 to 46.7 percent in region 1 and 33.4 to 57.6 percent in region 2 for all annual exceedance probabilities (AEPs) examined. The updated statistics showed a greater accuracy of prediction when compared to those from the previous study using drainage area as the only explanatory variable for all AEPs examined in region 1 and the 0.01 and 0.002 AEP flows for region 2. When compared to equations developed in the previous study that have drainage area, mean annual rainfall, and (or) depth-to-rock as explanatory variables, the updated statistics show a greater accuracy of prediction in region 1 at AEP flows of 0.02 and lower (that is, higher flows). Those developed for region 2 do not show a greater accuracy of prediction for any AEP flows when compared to the equations having multiple explanatory variables in the previous study. The calculated regression equations, basin characteristics, and at-site statistics will be incorporated into the U.S. Geological Survey web application, StreamStats ( https://streamstats.usgs.gov/ss/ ). This application allows users to select a location on a stream, whether gaged or ungaged, to obtain estimates of basin characteristics and flow statistics.

Puerto Rico↗

USGS2018

Preliminary peak stage and streamflow data for selected U.S. Geological Survey streamgaging stations in North and South Carolina for flooding following Hurricane Florence, September 2018

Hurricane Florence made landfall as a Category 1 hurricane at Wrightsville Beach, North Carolina, shortly after dawn on September 14, 2018. Once over land, the forward motion of the hurricane slowed to about 2 to 3 miles per hour. Over the next several days, the hurricane delivered historic amounts of rainfall across North and South Carolina, causing substantial flooding in many communities across both States. For the Hurricane Florence event, a new record rainfall total of 35.93 inches was set in Elizabethtown, N.C. Many other locations throughout North Carolina set new records for rainfall, exceeding the previous State record for rainfall from a tropical system of 24.06 inches, which was set over a 4-day period in Southport, N.C., during Hurricane Floyd in 1999. In South Carolina, the highest reported total rainfall of 23.63 inches was in Loris, S.C., which was the highest total rainfall in South Carolina from a tropical cyclone, replacing the previous total of 17.45 inches associated with Tropical Storm Beryl in 1994. During the October 2015 flood in South Carolina, a 4-day total rainfall of 26.88 inches was recorded in Mount Pleasant; however, because that total rainfall was a combination of a tropical storm system and another front that was centered over the State, it is not considered the largest rainfall event from a tropical storm. Peak streamflow and stage data at 84 U.S. Geological Survey streamflow gaging stations (referred to hereafter as streamgages) in North and South Carolina with at least 10 years of systematic record and for which the flooding following Hurricane Florence resulted in a peak in the top 5 for the period of record are included in this report. New peak streamflows of record were recorded at 18 sites in North Carolina and 10 sites in South Carolina. Another 49 streamgages recorded peak streamflows in the top 5 for their record (45 in North Carolina and 4 in South Carolina). Peak streamflow data following Hurricane Florence were not available for three additional streamgages prior to the publication of this report. Of those three streamgages, two recorded a new peak stage of record and one recorded the second highest peak stage of record. An additional four stage-only streamgages having at least 10 years of systematic record also had new peak stages (also referred to as gage height) of record. For 11 of the 28 streamgages for which the September 2018 peak streamflow was the peak of record, the October 2016 peak following Hurricane Matthew was the second largest peak, and for another four streamgages the September 1999 peak following Hurricane Floyd was the second largest peak. For the 28 streamgages for which a new peak streamflow of record was recorded, a flood-frequency analysis was done using available systematic record through September 2017 and the peak streamflow from the Hurricane Florence event. Of the 28 streamgages analyzed, the estimated annual exceedance probability for the Hurricane Florence peak streamflow at 9 of the streamgages was less than 0.2 percent, which in terms of recurrence intervals is greater than a 500-year flood event. At three streamgages, the estimated annual exceedance probability was equal to 0.2 percent, and at six streamgages, it was between 0.2 and 1 percent (between a 500- and 100-year recurrence interval, respectively). For the remaining 10 streamgages, the estimated annual exceedance probability was between 1.5 and 7.1 percent, which in terms of recurrence intervals is approximately a 67- to 14-year event, respectively.

North Carolina, South Carolina↗

USGS2017

Characterization of peak streamflows and flood inundation at selected areas in North Carolina following Hurricane Matthew, October 2016

The passage of Hurricane Matthew through central and eastern North Carolina during October 7–9, 2016, brought heavy rainfall, which resulted in major flooding. More than 15 inches of rain was recorded in some areas. More than 600 roads were closed, including Interstates 95 and 40, and nearly 99,000 structures were affected by floodwaters. Immediately following the flooding, the U.S. Geological Survey documented 267 high-water marks, of which 254 were surveyed. North Carolina Emergency Management documented and surveyed 353 high-water marks. Using a subset of these highwater marks, six flood-inundation maps were created for hard-hit communities. Digital datasets of the inundation areas, study reach boundary, and water-depth rasters are available for download. In addition, peak gage-height data, peak streamflow data, and annual exceedance probabilities (in percent) were determined for 24 U.S. Geological Survey streamgages located near the heavily flooded communities.

North Carolina, South Carolina↗

USGS2017

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

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

Georgia↗

USGS2016

Selected low-flow frequency statistics for continuous-record streamgages in Georgia, 2013

This report presents the annual and monthly minimum 1- and 7-day average streamflows with the 10-year recurrence interval (1Q10 and 7Q10) for 197 continuous-record streamgages in Georgia. Streamgages used in the study included active and discontinued stations having a minimum of 10 complete climatic years of record as of September 30, 2013. The 1Q10 and 7Q10 flow statistics were computed for 85 streamgages on unregulated streams with minimal diversions upstream, 43 streamgages on regulated streams, and 69 streamgages known, or considered, to be affected by varying degrees of diversions upstream. Descriptive information for each of these streamgages, including the U.S. Geological Survey (USGS) station number, station name, latitude, longitude, county, drainage area, and period of record analyzed also is presented. Kendall’s tau nonparametric test was used to determine the statistical significance of trends in annual and monthly minimum 1-day and 7-day average flows for the 197 streamgages. Significant negative trends in the minimum annual 1-day and 7-day average streamflow were indicated for 77 of the 197 streamgages. Many of these significant negative trends are due to the period of record ending during one of the recent droughts in Georgia, particularly those streamgages with record through the 2013 water year. Long-term unregulated streamgages with 70 or more years of record indicate significant negative trends in the annual minimum 7-day average flow for central and southern Georgia. Watersheds for some of these streamgages have experienced minimal human impact, thus indicating that the significant negative trends observed in flows at the long-term streamgages may be influenced by changing climatological conditions. A Kendall-tau trend analysis of the annual air temperature and precipitation totals for Georgia indicated no significant trends. A comprehensive analysis of causes of the trends in annual and monthly minimum 1-day and 7-day average flows in central and southern Georgia is outside the scope of this study. Further study is needed to determine some of the causes, including both climatological and human impacts, of the significant negative trends in annual minimum 1-day and 7-day average flows in central and southern Georgia. To assess the changes in the annual 1Q10 and 7Q10 statistics over time for long-term continuous streamgages with significant trends in record, the annual 1Q10 and 7Q10 statistics were computed on a decadal accumulated basis for 39 streamgages having 40 or more years of record that indicated a significant trend. Records from most of the streamgages showed a decline in 7Q10 statistics for the decades of 1980–89, 1990–99, and 2000–09 because of the recent droughts in Georgia. Twenty four of the 39 streamgages had complete records from 1980 to 2010, and records from 23 of these gages exhibited a decline in the 7Q10 statistics during this period, ranging from –6.3 to –76.2 percent with a mean of –27.3 percent. No attempts were made during this study to adjust streamflow records or statistical analyses on the basis of trends. The monthly and annual 1Q10 and 7Q10 flow statistics for the entire period of record analyzed in the study are incorporated into the USGS StreamStatsDB, which is a database accessible to users through the recently released USGS StreamStats application for Georgia. StreamStats is a Web-based geographic information system that provides users with access to an assortment of analytical tools that are useful for water-resources planning and management, and for engineering design applications, such as the design of bridges. StreamStats allows users to easily obtain streamflow statistics, basin characteristics, and other information for user-selected streamgages.

Georgia↗

USGS2016

Flood-Inundation Maps of Selected Areas Affected by the Flood of October 2015 in Central and Coastal South Carolina

Heavy rainfall occurred across South Carolina during October 1–5, 2015, as a result of an upper atmospheric low-pressure system that funneled tropical moisture from Hurricane Joaquin into the State. The storm caused major flooding in the central and coastal parts of South Carolina. Almost 27 inches of rain fell near Mount Pleasant in Charleston County during this period. U.S. Geological Survey (USGS) streamgages recorded peaks of record at 17 locations, and 15 other locations had peaks that ranked in the top 5 for the period of record. During the October 2015 flood event, USGS personnel made about 140 streamflow measurements at 86 locations to verify, update, or extend existing rating curves (which are used to compute streamflow from monitored river stage). Immediately after the storm event, USGS personnel documented 602 high-water marks, noting the location and height of the water above land surface. Later in October, 50 additional high-water marks were documented near bridges for South Carolina Department of Transportation. Using a subset of these high-water marks, 20 flood-inundation maps of 12 communities were created. Digital datasets of the inundation area, modeling boundary, and water depth rasters are all available for download.

South Carolina↗

USGS2015

StreamStats in Georgia: A water-resources web application

Summary Part of the mission of the U.S. Geological Survey (USGS) is to provide information on streamflow in the Nation's streams to help understand the Nation's water resources. Streamflow statistics are used by water managers, engineers, scientists, and others to protect people and property during floods and droughts, and to manage, protect, and enhance water resources. StreamStats is a Web-based Geographic Information System (GIS) application that was created by the USGS, in cooperation with the Environmental Systems Research Institute, Inc., that allows users to easily obtain streamflow statistics, basin characteristics, and descriptive information for USGS streamgages and user-selected ungaged locations on streams (Ries and others, 2008). StreamStats is being implemented on a State-by-State basis to allow for customization of the data development and underlying datasets to address their specific needs, issues, and objectives. The USGS, in cooperation with the Georgia Environmental Protection Division and Georgia Department of Transportation, has implemented StreamStats for Georgia. The Georgia StreamStats Web site is available through the national StreamStats Web-page portal at http://streamstats.usgs.gov. Links are provided on this Web page for individual State applications, instructions for using StreamStats, definitions of basin characteristics and streamflow statistics, and other supporting information.

Georgia↗

USGS2014

Estimating flood magnitude and frequency for urban and small, rural streams in Georgia, South Carolina, and North Carolina, 2011

Reliable estimates of the magnitude and frequency of floods are essential for the design of transportation and water-conveyance structures, flood insurance studies, and flood-plain management. Flood-frequency estimates are particularly important in densely populated urban areas. The U.S. Geological Survey (USGS) used a multistate approach to update methods for determining the magnitude and frequency of floods in urban and small, rural streams that are not substantially affected by regulation or tidal fluctuations in Georgia, South Carolina, and North Carolina (Feaster and others, 2014). The multistate approach has the advantage over a single state approach of increasing the number of streamflow-gaging station (streamgages) available for analysis, expanding the geographical coverage that would allow for application of regional regression equations across state boundaries, and building on a previous flood-frequency investigation of rural streamgages in the Southeastern United States. This investigation was funded as part of a cooperative program of water-resources investigations between the USGS, the South Carolina Department of Transportation, and the North Carolina Department of Transportation. In addition, much of the data and information for the Georgia streamgages was funded through a similar cooperative program with the Georgia Department of Transportation.

Georgia, North Carolina, South Carolina↗

USGS2014

Flood-tracking chart for the Withlacoochee and Little River Basins in south-central Georgia and northern Florida

The U.S. Geological Survey (USGS), in cooperation with other Federal, State, and local agencies, operates a flood-monitoring system in the Withlacoochee and Little River Basins. This system is a network of automated river stage stations (ten are shown on page 2 of this publication) that transmit stage data through satellite telemetry to the USGS in Atlanta, Georgia and the National Weather Service (NWS) in Peachtree City, Georgia. During floods, the public and emergency response agencies use this information to make decisions about road closures, evacuations, and other public safety issues. This Withlacoochee and Little River Basins flood-tracking chart can be used by local citizens and emergency response personnel to record the latest river stage and predicted flood-crest information along the Withlacoochee River, Little River, and Okapilco Creek in south-central Georgia and northern Florida. By comparing the current stage (water-surface level above a datum) and predicted flood crest to the recorded peak stages of previous floods, emergency response personnel and residents can make informed decisions concerning the threat to life and property.

Florida, Georgia↗

USGS2014

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

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

Georgia, North Carolina, South Carolina↗

USGS2014

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

Reliable estimates of the magnitude and frequency of floods are essential for such things as the design of transportation and water-conveyance structures, Flood Insurance Studies, and flood-plain management. The flood-frequency estimates are particularly important in densely populated urban areas. A multistate approach was used to update methods for determining the magnitude and frequency of floods in urban and small, rural streams that are not substantially affected by regulation or tidal fluctuations in Georgia, South Carolina, and North Carolina. The multistate approach has the advantage over a single state approach of increasing the number of stations available for analysis, expanding the geographical coverage that would allow for application of regional regression equations across state boundaries, and building on a previous flood-frequency investigation of rural streamflow-gaging stations (streamgages) in the Southeastern United States. In addition, streamgages from the inner Coastal Plain of New Jersey were included in the analysis. Generalized least-squares regression techniques were used to generate predictive equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability flows for urban and small, rural ungaged basins for three hydrologic regions; the Piedmont-Ridge and Valley, Sand Hills, and Coastal Plain. Incorporation of urban streamgages from New Jersey also allowed for the expansion of the applicability of the predictive equations in the Coastal Plain from 2.1 to 53.5 square miles. Explanatory variables in the regression equations included drainage area (DA) and percent of impervious area (IA) for the Piedmont-Ridge and Valley region; DA and percent of developed land for the Sand Hills; and DA, IA, and 24-hour, 50-year maximum precipitation for the Coastal Plain. An application spreadsheet also was developed that can be used to compute the flood-frequency estimates along with the 95-percent prediction intervals for an ungaged location.

Georgia, North Carolina, South Carolina↗

USGS2013

Monitoring storm tide and flooding from Hurricane Sandy along the Atlantic coast of the United States, October 2012

The U.S. Geological Survey (USGS) deployed a temporary monitoring network of water-level and barometric pressure sensors at 224 locations along the Atlantic coast from Virginia to Maine to continuously record the timing, areal extent, and magnitude of hurricane storm tide and coastal flooding generated by Hurricane Sandy. These records were greatly supplemented by an extensive post-flood high-water mark (HWM) flagging and surveying campaign from November to December 2012 involving more than 950 HWMs. Both efforts were undertaken as part of a coordinated federal emergency response as outlined by the Stafford Act under a directed mission assignment by the Federal Emergency Management Agency (FEMA).

Connecticut, Delaware, Maine, Maryland, Massachuse↗

USGS2012

Monitoring storm tide and flooding from Hurricane Isaac along the Gulf Coast of the United States, August 2012

The U.S. Geological Survey (USGS) deployed a temporary monitoring network of water-level and barometric pressure sensors at 127 locations along the gulf coast from Alabama to Louisiana to record the timing, areal extent, and magnitude of hurricane storm tide and coastal flooding generated by Hurricane Isaac. This deployment was undertaken as part of a coordinated federal emergency response as outlined by the Stafford Act under a directed mission assignment by the Federal Emergency Management Agency. Storm tide, as defined by National Oceanic and Atmospheric Administration (NOAA; National Oceanic and Atmospheric Administration, 2008), is the water-level rise generated by a combination of storm surge and astronomical tide during a coastal storm. Hurricane Isaac initially made landfall on the coast of Louisiana in Plaquemines Parish on August 28, 2012, as a Category 1 hurricane on the Saffir–Simpson Hurricane Wind Scale (National Weather Service, 1974) and then stalled over southern Louisiana for several days, causing prolonged storm-tide impacts. A total of 188 water-level and wave-height sensors were deployed at 127 locations during August 27–28 prior to landfall. More than 90 percent of the sensors and all high-water marks (HWMs) were recovered and surveyed to North American Vertical Datum of 1988 (NAVD 88) within 7 days of the Isaac landfall. Only a handful of sensors in the Plaquemines Parish area of Louisiana could not be retrieved until weeks later due to prolonged flooding in the area. Data collected from this event can be used to evaluate the performance of storm-tide models for maximum and incremental water level and flood extent and the site-specific effects of storm tide on natural and anthropogenic features of the environment.

Alabama, Louisiana, Mississippi↗

USGS2012

Methods for determining magnitude and frequency of floods in California, based on data through water year 2006

Methods for estimating the magnitude and frequency of floods in California that are not substantially affected by regulation or diversions have been updated. Annual peak-flow data through water year 2006 were analyzed for 771 streamflow-gaging stations (streamgages) in California having 10 or more years of data. Flood-frequency estimates were computed for the streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to logarithms of annual peak flows for each streamgage. Low-outlier and historic information were incorporated into the flood-frequency analysis, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low outliers. Special methods for fitting the distribution were developed for streamgages in the desert region in southeastern California. Additionally, basin characteristics for the streamgages were computed by using a geographical information system. Regional regression analysis, using generalized least squares regression, was used to develop a set of equations for estimating flows with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for ungaged basins in California that are outside of the southeastern desert region. Flood-frequency estimates and basin characteristics for 630 streamgages were combined to form the final database used in the regional regression analysis. Five hydrologic regions were developed for the area of California outside of the desert region. The final regional regression equations are functions of drainage area and mean annual precipitation for four of the five regions. In one region, the Sierra Nevada region, the final equations are functions of drainage area, mean basin elevation, and mean annual precipitation. Average standard errors of prediction for the regression equations in all five regions range from 42.7 to 161.9 percent. For the desert region of California, an analysis of 33 streamgages was used to develop regional estimates of all three parameters (mean, standard deviation, and skew) of the log-Pearson Type III distribution. The regional estimates were then used to develop a set of equations for estimating flows with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for ungaged basins. The final regional regression equations are functions of drainage area. Average standard errors of prediction for these regression equations range from 214.2 to 856.2 percent. Annual peak-flow data through water year 2006 were analyzed for eight streamgages in California having 10 or more years of data considered to be affected by urbanization. Flood-frequency estimates were computed for the urban streamgages by fitting a Pearson Type III distribution to logarithms of annual peak flows for each streamgage. Regression analysis could not be used to develop flood-frequency estimation equations for urban streams because of the limited number of sites. Flood-frequency estimates for the eight urban sites were graphically compared to flood-frequency estimates for 630 non-urban sites. The regression equations developed from this study will be incorporated into the U.S. Geological Survey (USGS) StreamStats program. The StreamStats program is a Web-based application that provides streamflow statistics and basin characteristics for USGS streamgages and ungaged sites of interest. StreamStats can also compute basin characteristics and provide estimates of streamflow statistics for ungaged sites when users select the location of a site along any stream in California.

California↗

USGS2011

Magnitude and Frequency of Floods for Urban and Small Rural Streams in Georgia, 2008

A study was conducted that updated methods for estimating the magnitude and frequency of floods in ungaged urban basins in Georgia that are not substantially affected by regulation or tidal fluctuations. Annual peak-flow data for urban streams from September 2008 were analyzed for 50 streamgaging stations (streamgages) in Georgia and 6 streamgages on adjacent urban streams in Florida and South Carolina having 10 or more years of data. Flood-frequency estimates were computed for the 56 urban streamgages by fitting logarithms of annual peak flows for each streamgage to a Pearson Type III distribution. Additionally, basin characteristics for the streamgages were computed by using a geographical information system and computer algorithms. Regional regression analysis, using generalized least-squares regression, was used to develop a set of equations for estimating flows with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for ungaged urban basins in Georgia. In addition to the 56 urban streamgages, 171 rural streamgages were included in the regression analysis to maintain continuity between flood estimates for urban and rural basins as the basin characteristics pertaining to urbanization approach zero. Because 21 of the rural streamgages have drainage areas less than 1 square mile, the set of equations developed for this study can also be used for estimating small ungaged rural streams in Georgia. Flood-frequency estimates and basin characteristics for 227 streamgages were combined to form the final database used in the regional regression analysis. Four hydrologic regions were developed for Georgia. The final equations are functions of drainage area and percentage of impervious area for three of the regions and drainage area, percentage of developed land, and mean basin slope for the fourth region. Average standard errors of prediction for these regression equations range from 20.0 to 74.5 percent.

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USGS2010

Historic flooding in northern Georgia, September 16-22, 2009

A primary mission of the U.S. Geological Survey (USGS) is the measurement and documentation of the magnitude and extent of hydrologic hazards, such as floods, droughts, and hurricane storm surge. USGS personnel were deployed to document historic, widespread flooding that occurred throughout the Atlanta metropolitan area and northwestern Georgia in the early fall of 2009. The floods were created by prolonged rainfall that occurred during September 16?22, 2009, with an especially intense period of rainfall during the late evening of September 20. The National Weather Service (NWS) reported that the southeastern United States had above-normal precipitation from August into early September, resulting in saturated soil conditions making the region extremely flood prone. Precipitation totals were the sixth highest on record for the month of September for the region (National Weather Service, 2010). Lessons learned from this flood include the need for more effective communication of the latest river information by Federal agencies with flood-threatened communities. Communicating the flood threat in an easy, accessible manner would have helped emergency managers and the public greatly during this flood. In response, the USGS developed WaterAlert (http://water.usgs.gov/wateralert/) to send notifications of flood events by way of text and e-mail. Also in development are real-time flood-inundation maps to give the hydrograph spatial context by way of a map-based product.

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