Geology topics

Daniel M. Wagner

Publications and source records attributed to Daniel M. Wagner.

16 recordsLinked to original sources

USGS2026

Estimation of magnitude and frequency of floods for rural, unregulated streams in and near Virginia and West Virginia

Magnitude and frequency of annual peak streamflows were computed for 813 streamgages on rural, unregulated streams with annual peak streamflow data from 1791 through the 2021 water years in and near Virginia and West Virginia. The study was done in cooperation with the Federal Emergency Management Agency, the West Virginia Department of Transportation, and the Virginia Department of Transportation. Regression equations were developed for estimating flood frequency and magnitude. Twelve regions with homogeneous flood characteristics were identified. Generalized least squares regression equations relating logarithmic-transformed drainage area and peak streamflow were developed for the 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, 0.005, and 0.002 annual exceedance probabilities (AEPs). Drainage area was the only significant variable for all equations. The range of drainage areas used to develop the equations differed for each region; the smallest drainage area in any region was 0.21 square miles (mi 2 ) and the largest drainage area in any region is 2,966 mi 2 . Pseudo coefficient of determination (pseudo- R 2 ) values for regression equations ranged from 0.481 to 0.995 for all regions and AEPs. Performance metrics and diagnostic plots indicated that equations for 11 of the 12 regions showed generally good performance, with pseudo- R 2 values ranging from 0.762 to 0.968 for the 0.01 AEP. The overall average change in at-site 0.01 AEP annual peak streamflows at individual streamgages was 0.5 percent compared to the most recent 2011 Virginia study and 2.3 percent compared to the most recent 2010 West Virginia study. Changes from the previous studies for estimates from regional equations for the 0.01 AEP, solved specifically for a 50 mi 2 basin, ranged from a 30 percent increase to a 45 percent decrease in areas where the previous regions overlapped with the current regions by 750 mi 2 or more. New regional skews were developed using Bayesian weighted least-squares/Bayesian generalized least-squares regression for two skew regions that included the study area. A constant regional skew of 0.50 was computed for streams in Virginia, West Virginia, and Maryland that drain to the Atlantic Ocean. A constant regional skew of 0.048 was computed for streams that drain to the Gulf of America, including streams in Kentucky and Tennessee, most of West Virginia, far southwestern Virginia, and part of western Maryland. About 12 percent of the 418 streamgages with 30 or more gaged peaks had statistically significant (p-value [significance level] less than or equal to 0.05) trends, with 40 of these exhibiting positive trends and 11 exhibiting negative trends. Streamgages with 30 percent or greater development were excluded from regression analyses. A regulation index was developed that accounted for storage and drainage area of dams and drainage area at the streamgage; a value of 0.0040 or more for the regulation index indicates regulated peak streamflow. Frequency analyses were done at 86 streamgages on regulated streams. Regression procedures developed in this study are applicable only to rural, unregulated streams within Virginia and West Virginia with drainage basins that (1) are within the range of drainage areas used to develop the equations for each region, (2) included less than 30 percent of developed area, and (3) had a regulation index less than 0.0040.

Kentucky, Maryland, North Carolina, Ohio, Pennsylv↗

USGS2026

Estimating the magnitude and frequency of floods at ungaged locations on urban streams in Tennessee and parts of Alabama, Georgia, Mississippi, North Carolina, and South Carolina, using data through the 2022 water year

In 2024, the U.S. Geological Survey, in cooperation with the Tennessee Department of Transportation, updated the methods for predicting the magnitude and frequency of floods at ungaged locations on streams in urban areas in Tennessee. The study area included 136 streamgages in urban areas in Tennessee, Mississippi, Alabama, Georgia, South Carolina, and North Carolina that had at least 10 percent developed imperviousness in their basins as indicated by data from the 2011 National Land Cover Database. Regression equations were developed to predict streamflows corresponding to the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEPs) and were incorporated into the StreamStats application. In generalized least-squares regression, the base-10 logarithm of drainage area, the percentages of the streamgage basins in developed land use, and the percentages of the streamgage basins in the Piedmont and Ridge and Valley Level 3 ecoregions were statistically significant in explaining the variability in annual peak streamflows in the study area. Drainage areas ranged from 0.164 to 93.4 square miles, the percentage of the streamgage basins in developed land use ranged from 26 to 100 percent, and the percentage of the streamgage basins in Piedmont and Ridge and Valley Level 3 ecoregions ranged from 0 to 100 percent. Pseudo R-squared values for the regression equations ranged from 0.86, or 86 percent, for the 50- and 20-percent AEPs (2- and 5-year floods) to 0.71, or 71 percent, for the 0.2-percent AEP (500-year flood). The average variance of prediction (in log base-10 units) ranged from 0.023 for the 20- and 10-percent AEPs to 0.05 for the 0.2-percent AEP. The average variance of prediction can be reported as a percentage of the predicted value, known as the standard error of prediction, which ranged from 35.8 percent for the 20-percent AEP (5-year flood) to 55.4 percent for the 0.2-percent AEP (500-year flood). Methods are presented for estimating annual peak streamflows for gaged locations, ungaged locations on gaged streams, and locations on ungaged streams.

Alabama, Georgia, Mississippi, North Carolina, Sou↗

USGS2025

Wet antecedent soil moisture increases atmospheric river streamflow magnitudes non-linearly

Atmospheric rivers (ARs) drive most riverine floods on the United States (U.S.) West Coast. However, estimating flood risk based solely on AR intensity and duration is challenging because precipitation phase, antecedent conditions, and physical watershed characteristics (e.g., slope and soil depth) can influence the magnitude of floods. Here, we analyze how antecedent soil moisture (ASM) conditions contribute to variability in streamflow during AR events and how that changes across climatic regimes and physiography in 122 U.S. West Coast watersheds. We identify a robust non-linear relationship between streamflow and ASM during ARs in 89% of watersheds. The inflection point in this relationship represents a watershed-specific critical ASM threshold, above which event maximum streamflow is, on average, two to four and a half times larger. Wet ASM conditions amplify the hydrologic impacts of more frequent but weaker, lower moisture transport AR events, while dry ASM conditions attenuate the hydrologic impacts that stronger, higher moisture transport AR events could otherwise cause. Our research shows that watersheds prone to ASM-amplified streamflows have higher evaporation ratios, lower cold-season precipitation, lower snow-to-rain ratios, and shallower, clay-rich soils. Higher evaporation and lower precipitation lead to greater ASM variability during the cold season, increasing streamflow during wet periods and buffering streamflow during dry periods. Lower snow fraction and shallower soils limit the antecedent water storage capacity of a watershed, contributing to greater sensitivity of streamflow peaks to ASM variability. Incorporating ASM thresholds into hydrologic models in these regions prone to AR-amplified streamflow could improve forecasts and decrease uncertainty.

California, Nevada, Oregon, Washington↗

USGS2024

Magnitude and frequency of floods in the Coastal Plain region of Louisiana, 2016

To improve flood-frequency estimates for rural streams in the Coastal Plain region of Louisiana, generalized least-squares regression techniques were used to relate corresponding annual exceedance probability streamflows for 211 streamgages in the region to a suite of explanatory variables that include physical, climatic, pedologic, and land-use characteristics of the streamgage drainage area. The resulting generalized least-squares models can be used to estimate selected annual exceedance probability streamflows for rural ungaged locations in the Coastal Plain region of Louisiana. For the 211 streamgages in the Coastal Plain region of Louisiana and surrounding States, annual peak-streamflow data available through the 2016 water year were used in this study. Two unique flood regions, the Mississippi Alluvial Plain and Coastal Plain, were identified as separate hydrologic regions based on statistical evaluation and significance of categorical variables representing the regions regressed against the 1-percent annual exceedance probability streamflow (the 100-year flood). Regional regression equations for estimating annual exceedance probability streamflow for the Mississippi Alluvial Plain region have been previously published; therefore, the purpose of this study was to generate updated regional regression equations for the Coastal Plain region of Louisiana. The final regression models used drainage area and channel slope as explanatory variables based on performance metrics.

Arkansas, Louisiana, Mississippi, Texas↗

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↗

USGS2023

Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020

Accurate estimates of flood magnitude and frequency are needed to (1) optimize the design and location of infrastructure, including dams, culverts, bridges, industrial buildings, and highways, and (2) inform flood-zoning and flood-insurance studies. The U.S. Geological Survey (USGS), in cooperation with the State of Hawaiʻi Department of Transportation, estimated flood magnitudes for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEP) for unregulated streamgages in Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, using data through water year 2020. Regression equations were developed to estimate flood magnitude and associated frequency at ungaged streams. This study improves upon a previous USGS flood-frequency report (Oki and others, 2010) by including more peak-flow data, implementing new statistical methods in flood-frequency analysis, and using updated techniques to estimate the regional-skewness coefficient (regional skew). Flood magnitude and frequency at 238 streamgages were estimated—following national guidelines established in Bulletin 17C (England and others, 2019)—by fitting annual peak-flow data to the Log-Pearson Type III distribution using the expected moments algorithm and the PeakFQ flood-frequency software. Potentially influential low outliers in the data were identified and removed using the Multiple Grubbs-Beck Test. An updated regional skew for Hawaiʻi was estimated using the Bayesian weighted least squares/Bayesian generalized least squares method. The updated regional skew employs a constant model for the five islands in the study area and has a value of −0.157 (mean square error of 0.212). Multiple linear regression techniques were used to develop regression equations that relate basin and climatic characteristics to peak flows at streamgages. The regression equations can be applied to estimate flood magnitude and frequency at ungaged sites. The study area was split into 10 regions—2 regions per island, generally following a leeward/windward division—containing from 9 to 49 streamgages each. The final regression equations for each region were determined with generalized least-squares analysis using the USGS weighted-multiple-linear regression (WREG) program. The standard error of prediction at the 1-percent AEP for the regression equations ranged from 18 to 164 percent; the pseudo coefficient of determination (pseudo-R2) at the 1-percent AEP ranged from 46 to 100 percent. The regression equations performed well for all regions except leeward Molokaʻi and southern Island of Hawaiʻi; for all other regions, the pseudo-R2 values ranged from about 75 to 100 percent. Compared to the regression equations developed by Oki and others (2010), the regression equations in this study generally showed modest improvements, although the magnitude of differences varied for each region. Peak-flow estimates at the 238 streamgages included in this study are improved by weighting the at-site statistics computed with PeakFQ and the predicted flows based on the regression equations. Results of this study—including the final peak-flow estimates at streamgages and the regional regression equations—are implemented in the USGS StreamStats web application (U.S. Geological Survey, 2023, StreamStats: https://streamstats.usgs.gov/ss/ ). StreamStats provides a consistent approach for obtaining peak-flow estimates at streamgages and for applying the regional regression equations for estimating peak flows at ungaged locations.

Hawaii↗

USGS2021

Methods for estimating regional skewness of annual peak flows in parts of eastern New York and Pennsylvania, based on data through water year 2013

Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP–III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the skew of the annual peak flows for a given streamgage and a regional skew. This report presents an estimate of regional skew for a study area encompassing parts of eastern New York and Pennsylvania. A total of 232 candidate U.S. Geological Survey streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length ( P RL ) of at least 36 years, 183 streamgages were selected for use in the study. Flood frequencies for candidate streamgages were analyzed by employing the expected moments algorithm, which extends the method of moments so that it can accommodate interval, censored, and historical/paleo flow data, as well as the multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Ten basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 1 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model that had a regional skew coefficient of 0.32 and an average variance of prediction at a new streamgage ( AVP new , which corresponds to the mean square error [MSE] of 0.11) was selected. The AVP new corresponds to an effective record length of 68 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.

New York, Pennsylvania↗

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↗

USGS2020

Methods for estimating regional skewness of annual peak flows in parts of the Great Lakes and Ohio River Basins, based on data through water year 2013

Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP−III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the station skew for a given streamgage and a regional skew. This report generates an estimate of regional skew for a study area encompassing most of the Great Lakes Basin (hydrologic unit 04) and part of the Ohio River Basin (hydrologic unit 05). A total of 551 candidate streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length greater than 36 years, 368 streamgages were selected for use in the study. Flood frequencies for candidate streamgages were analyzed by employing the Expected Moments Algorithm, which extends the method of moments so that it can accommodate interval, censored, and historic/paleo flow data, as well as the Multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Twelve basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 5 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model having a regional skew coefficient of 0.086 and an average variance of prediction ( AVP new ) (which corresponds to the mean square error [MSE]) of 0.13 at a new streamgage was selected. The AVP new corresponds to an effective record length of 54 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.

Great Lakes and Ohio River Basins↗

USGS2019

Interpretation of dye tracing data collected November 13–December 2, 2017, at the Savoy Experimental Watershed as part of the Advanced Groundwater Field Techniques in Karst Terrains course, Savoy, Arkansas

The first course on the use of advanced groundwater field techniques for karst aquifers was conducted November 13–17, 2017, at the University of Arkansas Savoy Experimental Watershed (SEW), which is located on pastures for beef livestock research conducted by the Department of Animal Sciences at the University of Arkansas at Savoy, Arkansas. The SEW is an interdisciplinary, collaborative, long-term research site for the study of animal-waste management in a mantled karst setting. The course focused on advanced field activities appropriate for karst aquifer studies: dye tracing, groundwater/surface-water interactions, geophysical methods, and geochemistry. This report summarizes the data collected and interpreted from the dye tracing part of the November 2017 course, other USGS field courses, and past dye tracing investigations conducted by University of Arkansas students.

Arkansas↗

USGS2019

Erosion monitoring along selected bank locations of the Coosa River in Alabama using terrestrial light detection and ranging (T–lidar) technology, 2014–17

The Alabama Power Company operates a series of dams on the Coosa River in east central Alabama. Seven dams impound the river to form six reservoirs: Weiss Lake, H Neely Henry Lake, Logan Martin Lake, Lay Lake, Lake Mitchell, and Lake Jordan. Streamflow below these reservoirs is primarily controlled by power generation at the dams, and there is ongoing concern about the stability of selected stream banks downstream from the dams. During relicensing in the early 2000s, the Alabama Power Company and stakeholders identified particular areas of concern to monitor and document the extent of erosion. The U.S. Geological Survey, in cooperation with the Alabama Power Company, conducted a 3-year monitoring program, from 2014 to 2017, of the geomorphic conditions of six selected reaches along the Coosa River. The six reaches included two downstream from H Neely Henry Dam near Gadsden, two downstream from Logan Martin Dam near Vincent, and two downstream from Walter Bouldin Dam near Wetumpka, Alabama. The geomorphic monitoring was conducted using boat- and tripod-mounted terrestrial light detection and ranging technology. Site LM–108, an island in the Coosa River downstream from Logan Martin Dam, exhibited the greatest amount of normalized erosion, 2.05 cubic meters per square meter of area, likely because this site experiences head-on flow from the river. Bank retreat at the upstream end of the island (LM–108) was estimated at 2.9 meters for the study period. The remaining five reaches were exposed to shear flow from the river; the greatest amount of normalized erosion, 0.467 cubic meter per square meter of area, was exhibited by site WB–106 on the right bank downstream from Walter Bouldin Dam. Results of the comparisons of terrestrial light detection and ranging scans indicated that intervals between scans that exhibited the greatest amounts of erosion generally corresponded to periods of above-median flow, and that intervals between scans that exhibited the least amounts of erosion, or deposition, generally corresponded to periods of below-median flow. Relatively smaller surface areas could be surveyed at some sites because inundation or dense vegetation obscured parts of the banks, suggesting that, in future investigations, it may be preferable to conduct scans during periods of leaf-off and low flow to avoid bias introduced by parts of the banks of interest being inundated or obscured by vegetation.

Alabama↗

USGS2016

Methods for estimating annual exceedance probability discharges for streams in Arkansas, based on data through water year 2013

In 2013, the U.S. Geological Survey initiated a study to update regional skew, annual exceedance probability discharges, and regional regression equations used to estimate annual exceedance probability discharges for ungaged locations on streams in the study area with the use of recent geospatial data, new analytical methods, and available annual peak-discharge data through the 2013 water year. An analysis of regional skew using Bayesian weighted least-squares/Bayesian generalized-least squares regression was performed for Arkansas, Louisiana, and parts of Missouri and Oklahoma. The newly developed constant regional skew of -0.17 was used in the computation of annual exceedance probability discharges for 281 streamgages used in the regional regression analysis. Based on analysis of covariance, four flood regions were identified for use in the generation of regional regression models. Thirty-nine basin characteristics were considered as potential explanatory variables, and ordinary least-squares regression techniques were used to determine the optimum combinations of basin characteristics for each of the four regions. Basin characteristics in candidate models were evaluated based on multicollinearity with other basin characteristics (variance inflation factor < 2.5) and statistical significance at the 95-percent confidence level ( p ≤ 0.05). Generalized least-squares regression was used to develop the final regression models for each flood region. Average standard errors of prediction of the generalized least-squares models ranged from 32.76 to 59.53 percent, with the largest range in flood region D. Pseudo coefficients of determination of the generalized least-squares models ranged from 90.29 to 97.28 percent, with the largest range also in flood region D. The regional regression equations apply only to locations on streams in Arkansas where annual peak discharges are not substantially affected by regulation, diversion, channelization, backwater, or urbanization. The applicability and accuracy of the regional regression equations depend on the basin characteristics measured for an ungaged location on a stream being within range of those used to develop the equations.

Arkansas↗

USGS2014

Trends in precipitation, streamflow, reservoir pool elevations, and reservoir releases in Arkansas and selected sites in Louisiana, Missouri, and Oklahoma, 1951–2011

The U.S. Geological Survey (USGS) and the U.S. Army Corps of Engineers (USACE) conducted a statistical analysis of trends in precipitation, streamflow, reservoir pool elevations, and reservoir releases in Arkansas and selected sites in Louisiana, Missouri, and Oklahoma for the period 1951–2011. The Mann-Kendall test was used to test for trends in annual and seasonal precipitation, annual and seasonal streamflows of 42 continuous-record USGS streamflow-gaging stations, annual pool elevations and releases from 16 USACE reservoirs, and annual releases from 11 dams on the Arkansas River. A statistically significant (p≤0.10) upward trend was observed in annual precipitation for the State, with a Sen slope of approximately 0.10 inch per year. Autumn and winter were the only seasons that had statistically significant trends in precipitation. Five of six physiographic sections and six of seven 4-digit hydrologic unit code (HUC) regions in Arkansas had statistically significant upward trends in autumn precipitation, with Sen slopes of approximately 0.06 to 0.10 inch per year. Sixteen sites had statistically significant upward trends in the annual mean daily streamflow and were located on streams that drained regions with statistically significant upward trends in annual precipitation. Expected annual rates of change corresponding to statistically significant trends in annual mean daily streamflows, which ranged from 0.32 to 0.88 percent, were greater than those corresponding to regions with statistically significant upward trends in annual precipitation, which ranged from 0.19 to 0.28 percent, suggesting that the observed trends in regional annual precipitation do not fully account for the observed trends in annual mean daily streamflows. Trends in annual maximum daily streamflows were similar to trends in the annual mean daily streamflows but were only statistically significant at seven sites. There were more statistically significant trends (28 of 42 sites) in the annual minimum daily streamflows than in the annual means or maximums. Statistically significant trends in the annual minimum daily streamflows were upward at 18 sites and downward at 10 sites. Despite autumn being the only season that had statistically significant upward trends in seasonal precipitation, statistically significant upward trends in seasonal mean streamflows occurred in every season but spring. Trends in the annual mean, maximum, and minimum daily pool elevations of USACE reservoirs were consistent between metrics for reservoirs in the White, Arkansas, and Ouachita River watersheds, while trends varied between metrics at DeQueen Lake, Millwood Lake, and Lake Chicot. Most of the statistically significant trends in pool elevation metrics were upward and gradual—Sen slopes were less than 0.37 foot per year—and were likely the result of changes in reservoir regulation plans. Trends in the annual mean and maximum daily releases from USACE reservoirs were generally upward in all HUC regions. There were few statistically significant trends in the annual mean daily releases because the reservoirs are operated to maintain a regulation stage at a downstream site according to guidelines set forth in the regulation plans of the reservoirs. The annual number of low-flow days was both increasing and decreasing for reservoirs in northern Arkansas and southern Missouri and generally increasing for reservoirs in southern Arkansas.

Arkansas;Louisiana;Missouri;Oklahoma↗

USGS2013

Two-dimensional simulation of the June 11, 2010, flood of the Little Missouri River at Albert Pike Recreational Area, Ouachita National Forest, Arkansas

In the early morning hours of June 11, 2010, substantial flooding occurred at Albert Pike Recreation Area in the Ouachita National Forest of west-central Arkansas, killing 20 campers. The U.S. Forest Service needed information concerning the extent and depth of flood inundation, the water velocity, and flow paths throughout Albert Pike Recreation Area for the flood and for streamflows corresponding to annual exceedence probabilities of 1 and 2 percent. The two-dimensional flow model Fst2DH, part of the Federal Highway Administration’s Finite Element Surface-water Modeling System, and the graphical user interface Surface-water Modeling System (SMS) were used to perform a steady-state simulation of the flood in a 1.5-mile reach of the Little Missouri River at Albert Pike Recreation Area. Peak streamflows of the Little Missouri River and tributary Brier Creek served as inputs to the simulation, which was calibrated to the surveyed elevations of high-water marks left by the flood and then used to predict flooding that would result from streamflows corresponding to annual exceedence probabilities of 1 and 2 percent. The simulated extent of the June 11, 2010, flood matched the observed extent of flooding at Albert Pike Recreation Area. The mean depth of inundation in the camp areas was 8.5 feet in Area D, 7.4 feet in Area C, 3.8 feet in Areas A, B, and the Day Use Area, and 12.5 feet in Lowry’s Camp Albert Pike. The mean water velocity was 7.2 feet per second in Area D, 7.6 feet per second in Area C, 7.2 feet per second in Areas A, B, and the Day Use Area, and 7.6 feet per second in Lowry’s Camp Albert Pike. A sensitivity analysis indicated that varying the streamflow of the Little Missouri River had the greatest effect on simulated water-surface elevation, while varying the streamflow of tributary Brier Creek had the least effect. Simulated water-surface elevations were lower than those modeled by the U.S. Forest Service using the standard-step method, but the comparison between the two was favorable with a mean absolute difference of 0.58 feet in Area C and 0.32 feet in Area D. Results of a HEC-RAS model of the Little Missouri River watershed upstream from the U.S. Geological Survey streamflow-gaging station near Langley showed no difference in mean depth in the areas in common between the models, and a difference in mean velocity of only 0.5 foot per second. Predictions of flooding that would result from streamflows corresponding to annual exceedence probabilities of 1 and 2 percent indicated that the extent of inundation of the June 11, 2010, flood exceeded that of the 1 percent flood, and that for both the 1 and 2 percent floods, all of Areas C and D, and parts of Areas A, B, and the Day Use Area were inundated. Predicted water-surface elevations for the 1 and 2 percent floods were approximately 1 foot lower than those predicted by the U.S. Forest Service using a standard-step model.

Arkansas↗

USGS2011

Flood of June 11, 2010, in the Upper Little Missouri River watershed, Arkansas

Catastrophic flash flooding occurred in the early morning hours of June 11, 2010, in the upper Little Missouri River and tributary streams in southwest Arkansas. The flooding, which resulted in 20 fatalities and substantial property damage, was caused by as much as 4.7 inches of rain falling in the upper Little Missouri River watershed in 3 hours. The 4.7 inches of rain in 3 hours corresponds to estimated annual exceedance probability of approximately 2 percent for a 3-hour duration storm. The maximum total estimated rainfall in the upper Missouri River watershed was 5.3 inches in 6 hours. Peak streamflows and other hydraulic properties were determined at five ungaged locations and one gaged location in the upper Little Missouri River watershed.The peak streamflow for the Little Missouri River at Albert Pike, Arkansas was 40,100 cubic feet per second, estimated to have occurred between 4:00 AM and 4:30 AM the morning of June 11, 2010. The peak streamflow resulted in average water depths in the nearby floodplain (Area C of the Albert Pike Campground) of 7 feet flowing at velocities potentially as great as 11 feet per second. Peak streamflow 9.1 miles downstream on the Little Missouri at the U.S. Geological Survey streamgage near Langley, Arkansas was 70,800 cubic feet per second, which corresponds to an estimated annual exceedance probability of less than 1 percent.

Arkansas↗

USGS2010

Bed-Sediment Sampling and Analysis for Physical and Chemical Properties of the Lower Mississippi River near Memphis, Tennessee

In February 2010, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, Memphis District, investigated the presence of inorganic elements and organic compounds in bed sediments of the lower Mississippi River. Selected sites were located in the navigation channel near river miles 737, 773, and 790 near Memphis, Tennessee. Bed-sediment samples were collected using a Shipek grab sampler mounted to a boom crane with a motorized winch. Samples then were processed and shipped to the U.S. Geological Survey Sediment Laboratory in Rolla, Missouri, the USGS National Water Quality Laboratory in Denver, Colorado, and to TestAmerica Laboratory, Inc. in West Sacramento, California. Samples were analyzed for grain size, inorganic elements (including mercury), and organic compounds. Chemical results were tabulated and listed with sediment-quality guidelines and presented with the physical property results. All of the bed material samples collected during this investigation yielded concentrations that were less than the Consensus-Based Probable Effect Concentration guidelines. The physical properties were tabulated and listed using a standard U.S. Geological Survey scale of sizes by class for sediment analysis. All of the samples collected during this investigation indicated a percent composition mostly comprised of sand, ranging from less than 0.125 millimeters to less than 2 millimeters.

Open-File Report↗