Geology topics

Paul Conrads

Publications and source records attributed to Paul Conrads.

At least 37 records · Page 2Linked to original sources

USGS2013

Simulation of salinity intrusion along the Georgia and South Carolina coasts using climate-change scenarios

Potential changes in climate could alter interactions between environmental and societal systems and adversely affect the availability of water resources in many coastal communities. Changes in streamflow patterns in conjunction with sea-level rise may change the salinity-intrusion dynamics of coastal rivers. Several municipal water-supply intakes are located along the Georgia and South Carolina coast that are proximal to the present day saltwater-freshwater interface of tidal rivers. Increases in the extent of salinity intrusion resulting from climate change could threaten the availability of freshwater supplies in the vicinity of these intakes. To effectively manage these supplies, water-resource managers need estimates of potential changes in the frequency, duration, and magnitude of salinity intrusion near their water-supply intakes that may occur as a result of climate change. This study examines potential effects of climate change, including altered streamflow and sea-level rise, on the dynamics of saltwater intrusion near municipal water-supply intakes in two coastal areas. One area consists of the Atlantic Intracoastal Waterway (AIW) and the Waccamaw River near Myrtle Beach along the Grand Strand of the South Carolina Coast, and the second area is on or near the lower Savannah River near Savannah, Georgia. The study evaluated how future sea-level rise and a reduction in streamflows can potentially affect salinity intrusion and threaten municipal water supplies and the biodiversity of freshwater tidal marshes in these two areas. Salinity intrusion occurs as a result of the interaction between three principal forces—streamflow, mean coastal water levels, and tidal range. To analyze and simulate salinity dynamics at critical coastal gaging stations near four municipal water-supply intakes, various data-mining techniques, including artificial neural network (ANN) models, were used to evaluate hourly streamflow, salinity, and coastal water-level data collected over a period exceeding 10 years. The ANN models were trained (calibrated) to learn the specific interactions that cause salinity intrusions, and resulting models were able to accurately simulate historical salinity dynamics in both study areas. Changes in sea level and streamflow quantity and timing can be simulated by the salinity intrusion models to evaluate various climate-change scenarios. The salinity intrusion models for the study areas are deployed in a decision support system to facilitate the use of the models for management decisions by coastal water-resource managers. The report describes the use of the salinity-intrusion models decision support system to evaluate salinity-intrusion dynamics for various climate-change scenarios, including incremental increases in sea level in combination with incremental decreases in streamflow. Operation of municipal water-treatment plants is problematic when the specific-conductance values for source water are greater than 1,000 to 2,000 microsiemens per centimeter (µS/cm). High specific-conductance values contribute to taste problems that require treatment. Data from a gage downstream from a municipal water intake indicate specific conductance exceeded 1,000 µS/cm about 5.4 percent of the time over the 14-year period from August 1995 to August 2008. Simulations of specific conductance at this gaging station that incorporates sea-level rises resulted in a doubling of the exceedances to 11.0 percent for a 1-foot increase and 17.6 percent for a 2-foot increase. The frequency of intrusion of water with specific conductance values of 1,000 µS/cm was less sensitive to incremental reductions in streamflow than to incremental increases in sea level. Simulations of conditions associated with a 10-percent reduction in streamflow, in combination with a 1-foot rise in sea level, increased the percentage of time specific conductance exceeded 1,000 µS/cm at this site from 11.0 to 13.3 percent, and a 20-percent reduction in streamflow increased the percentage of time to 16.6 percent. Precipitation and temperature data from a global circulation model were used, after scale adjustments, as input to a watershed model of the Yadkin-Pee Dee River basin, which flows into the Waccamaw River and Atlantic Intracoastal Waterway study area in South Carolina. The simulated streamflow for historical conditions and projected climate change in the future was used as input for the ANN model in decision support system. Results of simulations incorporating climate-change projections for alterations in streamflow indicate an increase in the frequency of salinity-intrusion events and a shift in the seasonal occurrence of the intrusion events from the summer to the fall.

South Carolina↗

USGS2012

Data visualization, time-series analysis, and mass-balance modeling of hydrologic and water-quality data for the McTier Creek watershed, South Carolina, 2007-2009

The McTier Creek watershed is located in the headwaters of the Edisto River Basin, which is in the Coastal Plain region of South Carolina. The Edisto ecosystem has some of the highest recorded fish-tissue mercury concentrations in the United States. In an effort to advance the understanding of the fate and transport of mercury in stream ecosystems, the U.S. Geological Survey, as part of its National Water-Quality Assessment Program, initiated a field investigation of mercury in the McTier Creek watershed in 2006. The initial efforts of the investigation included the collection of extensive hydrologic and water-quality field data, along with the development of several hydrologic and water-quality models. This series of measured and modeled data forms the primary source of information for this investigation to assess the fate and transport of mercury within the McTier Creek watershed.

South Carolina↗

USGS2012

Comparison of TOPMODEL streamflow simulations using NEXRAD-based and measured rainfall data, McTier Creek watershed, South Carolina

Rainfall is an important forcing function in most watershed models. As part of a previous investigation to assess interactions among hydrologic, geochemical, and ecological processes that affect fish-tissue mercury concentrations in the Edisto River Basin, the topography-based hydrological model (TOPMODEL) was applied in the McTier Creek watershed in Aiken County, South Carolina. Measured rainfall data from six National Weather Service (NWS) Cooperative (COOP) stations surrounding the McTier Creek watershed were used to calibrate the McTier Creek TOPMODEL. Since the 1990s, the next generation weather radar (NEXRAD) has provided rainfall estimates at a finer spatial and temporal resolution than the NWS COOP network. For this investigation, NEXRAD-based rainfall data were generated at the NWS COOP stations and compared with measured rainfall data for the period June 13, 2007, to September 30, 2009. Likewise, these NEXRAD-based rainfall data were used with TOPMODEL to simulate streamflow in the McTier Creek watershed and then compared with the simulations made using measured rainfall data. NEXRAD-based rainfall data for non-zero rainfall days were lower than measured rainfall data at all six NWS COOP locations. The total number of concurrent days for which both measured and NEXRAD-based data were available at the COOP stations ranged from 501 to 833, the number of non-zero days ranged from 139 to 209, and the total difference in rainfall ranged from -1.3 to -21.6 inches. With the calibrated TOPMODEL, simulations using NEXRAD-based rainfall data and those using measured rainfall data produce similar results with respect to matching the timing and shape of the hydrographs. Comparison of the bias, which is the mean of the residuals between observed and simulated streamflow, however, reveals that simulations using NEXRAD-based rainfall tended to underpredict streamflow overall. Given that the total NEXRAD-based rainfall data for the simulation period is lower than the total measured rainfall at the NWS COOP locations, this bias would be expected. Therefore, to better assess the use of NEXRAD-based rainfall estimates as compared to NWS COOP rainfall data on the hydrologic simulations, TOPMODEL was recalibrated and updated simulations were made using the NEXRAD-based rainfall data. Comparisons of observed and simulated streamflow show that the TOPMODEL results using measured rainfall data and NEXRAD-based rainfall are comparable. Nonetheless, TOPMODEL simulations using NEXRAD-based rainfall still tended to underpredict total streamflow volume, although the magnitude of differences were similar to the simulations using measured rainfall. The McTier Creek watershed was subdivided into 12 subwatersheds and NEXRAD-based rainfall data were generated for each subwatershed. Simulations of streamflow were generated for each subwatershed using NEXRAD-based rainfall and compared with subwatershed simulations using measured rainfall data, which unlike the NEXRAD-based rainfall were the same data for all subwatersheds (derived from a weighted average of the six NWS COOP stations surrounding the basin). For the two simulations, subwatershed streamflow were summed and compared to streamflow simulations at two U.S. Geological Survey streamgages. The percentage differences at the gage near Monetta, South Carolina, were the same for simulations using measured rainfall data and NEXRAD-based rainfall. At the gage near New Holland, South Carolina, the percentage differences using the NEXRAD-based rainfall were twice as much as those using the measured rainfall. Single-mass curve comparisons showed an increase in the total volume of rainfall from north to south. Similar comparisons of the measured rainfall at the NWS COOP stations showed similar percentage differences, but the NEXRAD-based rainfall variations occurred over a much smaller distance than the measured rainfall. Nonetheless, it was concluded that in some cases, using NEXRAD-based rainfall data in TOPMODEL streamflow simulations may provide an effective alternative to using measured rainfall data. For this investigation, however, TOPMODEL streamflow simulations using NEXRAD-based rainfall data for both calibration and simulations did not show significant improvements with respect to matching observed streamflow over simulations generated using measured rainfall data.

South Carolina↗

USGS2012

Quantification of water-level variability effect on plant species populations using paleoecological and hydrological time series data

Soil cores provide valuable data on historical changes in vegetation and hydrologic conditions. Empirical models were developed to quantify the effect of meteorological and hydrologic forcing on plant species distributions over a 110-year period in Water Conservation Area 1 (WCA1) in the Florida Everglades, also known as the Arthur R. Marshall Loxahatchee National Wildlife Refuge. Empirical models that predict plant species distributions at sites within WCA1 were developed by linking temporally sparse seed bank data from soil cores with continuous multi-decadal daily meteorological and hydrologic time series data. The meteorological data included rainfall and maximum daily temperatures that spanned the entire study period of 110 years. The hydrologic data included stage data from two gages in WCA1 established in 1954. These stage data were hindcasted to be concurrent with the meteorological data by using correlation models that fit measured stages as a function of the meteorological parameters. The historical plant species data came from seven peat cores from WCA1. Different depths from each core were carbon-dated and assayed for relative percentages of 83 plant species using pollen counts. The oldest dates were more than 1,000 years old; however, only core data that overlapped the study period were used, for a total of 67 assays among the seven cores. Twenty-three of the species had ratios of at least 5 percent for one or more of the 67 assays, hereafter referred to as the "top23". Using the assays as input vectors, the top23 were grouped using the k-means clustering into four plant classes that represented the extent to which the various species have historically appeared together. This reduced the modeling problem to one of predicting the relative ratios of the four plant classes from the hindcasted stage time-series data. A separate empirical model was developed for each class using a multi-layer perceptron artificial neural network, which provides multivariate, nonlinear curve fitting. The models predicted the relative ratios of the classes, and the sums of the predictions are near 1. The coefficient of determination (R2) of the models varied from 0.87 to 0.96, indicating that the relative ratios of the plant classes are predictable, and therefore controllable, from stage forcing. Similar soil cores are available for the Coastal Plain of North Carolina and are planned for the Congaree National Park in South Carolina.

Florida↗

USGS2010

Flood hydrology and methylmercury availability in Coastal Plain rivers

Mercury (Hg) burdens in top-predator fish differ substantially between adjacent South Carolina Coastal Plain river basins with similar wetlands coverage. In the Congaree River, floodwaters frequently originate in the Blue Ridge and Piedmont regions, where wetlands coverage and surface water dissolved methylmercury (MeHg) concentrations are low. Piedmont-driven flood events can lead to downward hydraulic gradients in the Coastal Plain riparian wetland margins, inhibiting MeHg transport from wetland sediments, and decreasing MeHg availability in the Congaree River habitat. In the adjacent Edisto River basin, floodwaters originate only within Coastal Plain sediments, maintaining upward hydraulic gradients even during flood events, promoting MeHg transport to the water column, and enhancing MeHg availability in the Edisto River habitat. These results indicate that flood hydrodynamics contribute to the variability in Hg vulnerability between Coastal Plain rivers and that comprehensive regional assessment of the relationship between flood hydrodynamics and Hg risk in Coastal Plain streams is warranted.

South Carolina↗

USGS2010

Continuous tidal streamflow, water level, and specific conductance data for Union Creek and the Little Back, Middle, and Front Rivers, Savannah River Estuary, November 2008 to March 2009

In the Water Resource Development Act of 1999, the U.S. Congress authorized the deepening of the Savannah Harbor. Additional studies were then identified by the Georgia Ports Authority and other local and regional stakeholders to determine and fully describe the potential environmental effects of deepening the channel. One need that was identified was the validation of a three-dimensional hydrodynamic model developed to evaluate mitigation scenarios for a potential harbor deepening and the effects on the Savannah River estuary. The streamflow in the estuary is very complex due to reversing tidal flows, interconnections of streams and tidal creeks, and the daily flooding and draining of the marshes. The model was calibrated using very limited streamflow data and no continuous streamflow measurements. To better characterize the streamflow dynamics and mass transport of the estuary, two index-velocity sites were instrumented with continuous acoustic velocity, water level, and specific conductance sensors on the Little Back and Middle Rivers for the 5-month period of November 2008 through March 2009. During the same period, a third acoustic velocity meter was installed on the Front River just downstream from U.S. Geological Survey streamgaging station 02198920 (Savannah River at GA 25, at Port Wentworth, Georgia) where water level and specific conductance data were being collected. A fourth index-velocity site was instrumented with continuous acoustic velocity, water level, and specific conductance sensors on Union Creek for a 2-month period starting in November 2008. In addition to monitoring the tidal cycles, streamflow measurements were made at the four index-velocity sites to develop ratings to compute continuous discharge for each site. The maximum flood (incoming) and ebb (outgoing) tides measured on Little Back River were –4,570 and 7,990 cubic feet per second, respectively. On Middle River, the maximum flood and ebb tides measured were –9,630 and 13,600 cubic feet per second, respectively. On Front River, the maximum flood and ebb tides were –34,500 and 43,700 cubic feet per second, respectively; and on Union Creek, the maximum flood and ebb tides were –2,390 and 4,610 cubic feet per second, respectively. During the 5-month instrumentation deployment, computed tidal streamflows on Little Back River ranged from –7,820 to 9,600 cubic feet per second for the flood and ebb tides, respectively. On Middle River, the computed tidal streamflows ranged from –17,500 to 22,500 cubic feet per second for the flood and ebb tides, respectively. The computed tidal streamflows on Front River ranged from –78,900 to 87,200 cubic feet per second, and from –3,850 to 6,130 cubic feet per second on Union Creek for the flood and ebb tides, respectively. The streamgages on the Little Back, Middle, and Front Rivers have continued in operation following the initial 5-month deployment.

Georgia, South Carolina↗

USGS2010

Simulation of streamflow in the McTier Creek watershed, South Carolina

The McTier Creek watershed is located in the Sand Hills ecoregion of South Carolina and is a small catchment within the Edisto River Basin. Two watershed hydrology models were applied to the McTier Creek watershed as part of a larger scientific investigation to expand the understanding of relations among hydrologic, geochemical, and ecological processes that affect fish-tissue mercury concentrations within the Edisto River Basin. The two models are the topography-based hydrological model (TOPMODEL) and the grid-based mercury model (GBMM). TOPMODEL uses the variable-source area concept for simulating streamflow, and GBMM uses a spatially explicit modified curve-number approach for simulating streamflow. The hydrologic output from TOPMODEL can be used explicitly to simulate the transport of mercury in separate applications, whereas the hydrology output from GBMM is used implicitly in the simulation of mercury fate and transport in GBMM. The modeling efforts were a collaboration between the U.S. Geological Survey and the U.S. Environmental Protection Agency, National Exposure Research Laboratory. Calibrations of TOPMODEL and GBMM were done independently while using the same meteorological data and the same period of record of observed data. Two U.S. Geological Survey streamflow-gaging stations were available for comparison of observed daily mean flow with simulated daily mean flow-station 02172300, McTier Creek near Monetta, South Carolina, and station 02172305, McTier Creek near New Holland, South Carolina. The period of record at the Monetta gage covers a broad range of hydrologic conditions, including a drought and a significant wet period. Calibrating the models under these extreme conditions along with the normal flow conditions included in the record enhances the robustness of the two models. Several quantitative assessments of the goodness of fit between model simulations and the observed daily mean flows were done. These included the Nash-Sutcliffe coefficient of model-fit efficiency index, Pearson's correlation coefficient, the root mean square error, the bias, and the mean absolute error. In addition, a number of graphical tools were used to assess how well the models captured the characteristics of the observed data at the Monetta and New Holland streamflow-gaging stations. The graphical tools included temporal plots of simulated and observed daily mean flows, flow-duration curves, single-mass curves, and various residual plots. The results indicated that TOPMODEL and GBMM generally produced simulations that reasonably capture the quantity, variability, and timing of the observed streamflow. For the periods modeled, the total volume of simulated daily mean flows as compared to the total volume of the observed daily mean flow from TOPMODEL was within 1 to 5 percent, and the total volume from GBMM was within 1 to 10 percent. A noticeable characteristic of the simulated hydrographs from both models is the complexity of balancing groundwater recession and flow at the streamgage when flows peak and recede rapidly. However, GBMM results indicate that groundwater recession, which affects the receding limb of the hydrograph, was more difficult to estimate with the spatially explicit curve number approach. Although the purpose of this report is not to directly compare both models, given the characteristics of the McTier Creek watershed and the fact that GBMM uses the spatially explicit curve number approach as compared to the variable-source-area concept in TOPMODEL, GBMM was able to capture the flow characteristics reasonably well.

South Carolina↗

USGS2010

Analysis and simulation of water-level, specific conductance, and total phosphorus dynamics of the Loxahatchee National Wildlife Refuge, Florida, 1995-2006

The Arthur R. Marshall Loxahatchee Wildlife Refuge (Refuge) was established in 1951 through a license agreement between the South Florida Water Management District and the U.S. Fish and Wildlife Service (USFWS) as part of the Migratory Bird Conservation Act. Under the license agreement, the State of Florida owns the land of the Refuge and the USFWS manages the land. Fifty-seven miles of levees and borrow canals surround the Refuge. Water in the canals surrounding the marsh is controlled by inflows and outflows through control structures. The transport of canal water with higher specific conductance and nutrient concentrations to the interior marsh has the potential to alter critical ecosystem functions of the marsh. Data-mining techniques were applied to 12 years (1995-2006) of historical data to systematically synthesize and analyze the dataset to enhance the understanding of the hydrology and water quality of the Refuge. From the analysis, empirical models, including artificial neural network (ANN) models, were developed to answer critical questions related to the relative effects of controlled releases, precipitation, and meteorological forcing on water levels, specific conductance, and phosphorous concentrations of the interior marsh. Data mining is a powerful tool for converting large databases into information to solve complex problems resulting from large numbers of explanatory variables or poorly understood process physics. For the application of the linear regression and ANN models to the Refuge, data-mining methods were applied to maximize the information content in the raw data. Signal processing techniques used in the data analysis and model development included signal decomposition, digital filtering, time derivatives, time delays, and running averages. Inputs to the empirical models included time series, or signals, of inflows and outflows from the control structures, precipitation, and evapotranspiration. For a complex hydrologic system like the Refuge, the statistical accuracy of the models and predictive capability were good. The water-level models have coefficient of determination (R 2 values ranging from 0.90 to 0.98. The R 2 for the specific conductance model is 0.82, and the R2 for the total phosphorus model is 0.51. The accuracy of the models was attributable to the quantity and quality of the available data. To make the models directly available to all stakeholders, an easy-to-use decision support system (DSS) called the Loxahatchee Artificial Neural Network Model (LOXANN) DSS was developed as a spreadsheet application that integrates the historical database, linear regression and ANN models, model controls, streaming graphics, and model output. The LOXANN DSS allows Refuge managers and other users to easily execute the water level, specific conductance, and phosphorous models to evaluate various water-resource management scenarios. The user is able to choose from three options in setting the control-structure flows: as a percentage of historical flow, as a constant flow, or as a user-defined hydrograph. Output from the LOXANN DSS includes tabular time series of predictions of the measured data and predictions of the user-specified conditions. A three-dimensional visualization routine also was developed that displays longitudinal specific conductance conditions. Two scenarios were simulated with the LOXANN DSS. One scenario increased the historical flows at four control structures by 40 percent. The second scenario used a user-defined hydrograph to set the outflow from the Refuge to the weekly average inflow to the Refuge delayed by 2 days. Both scenarios decreased the potential of canal water intruding into the marsh by decreasing the slope of the water level between the canals and the marsh.

Scientific Investigations Report↗

USGS2010

Development of inferential sensors for real-time quality control of water-level data for the Everglades Depth Estimation Network

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level gaging stations, ground-elevation models, and watersurface models designed to provide scientists, engineers, and water-resource managers with current (2000-present) water-depth information for the entire freshwater portion of the greater Everglades. The generation of EDEN waterlevel surfaces is derived from real-time data. Real-time data are automatically checked for outliers using minimum, maximum, and rate-of-change thresholds for each station. Smaller errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the gages. Correcting smaller errors in the data often is time consuming and water-level data may not be finalized for several months. To provide water-level surfaces on a daily basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous waterlevel data. A technology often used for industrial applications is “inferential sensor.” Rather than installing a redundant sensor to measure a process, such as an additional waterlevel gage, an inferential sensor, or virtual sensor, is developed that estimates the processes measured by the physical sensor. The advantage of an inferential sensor is that it provides a redundant signal to the sensor in the field but without exposure to environmental threats. In the event that a gage does malfunction, the inferential sensor provides an estimate for the period of missing data. The inferential sensor also can be used in the quality assurance and quality control of the data. Inferential sensors for gages in the EDEN network are currently (2010) under development. The inferential sensors will be automated so that the real-time EDEN data will continuously be compared to the inferential sensor signal and digital reports of the status of the real-time data will be sent periodically to the appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.

Florida↗

USGS2010

Estimating salinity intrusion effects due to climate change along the Grand Strand of the South Carolina coast

The ability of water-resource managers to adapt to future climatic change is especially challenging in coastal regions of the world. The East Coast of the United States falls into this category given the high number of people living along the Atlantic seaboard and the added strain on resources as populations continue to increase, particularly in the Southeast. Increased temperatures, changes in regional precipitation regimes, and potential increased sea level would have a great impact on existing hydrological systems in the region. Six reservoirs in North Carolina discharge into the Pee Dee River, which flows 160 miles through South Carolina to the coastal communities near Myrtle Beach, SC. During the Southeast’s record-breaking drought from 1998 to 2002, salinity intrusions inundated a coastal municipal freshwater intake, limiting water supplies. Salinity intrusion results from the interaction of three principal forces - streamflow, mean tidal water levels, and tidal range. To analyze, model, and simulate hydrodynamic behaviors at critical coastal streamgages along the Atlantic Intracoastal Waterway (AIW) near Myrtle Beach, SC, data-mining techniques were applied to over 20 years of hourly streamflow, coastal water-quality, and water-level data. Artificial neural network (ANN) models were trained to learn the variable interactions that cause salinity intrusions. Streamflow from the 12,700 square-mile Pee Dee River Basin that flows into the AIW are input to the model as time-delayed variables and accumulated tributary inflows. Tidal inputs to the models were obtained by decomposing tidal water-level data into a “periodic” signal of tidal range and a “chaotic” signal of mean water levels. The ANN models were able to convincingly reproduce historical behaviors and generate alternative scenarios of interest. To evaluate the impact of climate change on salinity intrusion, inputs of streamflows and mean tidal water levels were modified to incorporate estimated changes in precipitation patterns and sea-level rise appropriate for the Southeastern United States. Changes in mean tidal water levels were changed parametrically for various sea-level rise conditions. Preliminary model results at the U.S. Geological Survey Pawleys Island streamgage (station 02110125) near a municipal freshwater intake indicate that a sea-level rise of 1 foot (ft, 30.5 centimeters [cm]) would double the frequency of water with a specific conductance value of 2,000 microsiemens per centimeter close to 4 percent. A 2 ft (61 cm) sea-level rise would quadruple the frequency to 9 percent.

South Carolina↗

USGS2010

Estimating salinity intrusion effects due to climate change on the Lower Savannah River Estuary

The ability of water-resource managers to adapt to future climatic change is especially challenging in coastal regions of the world. The East Coast of the United States falls into this category given the high number of people living along the Atlantic seaboard and the added strain on resources as populations continue to increase, particularly in the Southeast. Increased temperatures, changes in regional precipitation regimes, and potential increased sea level may have a great impact on existing hydrological systems in the region. The Savannah River originates at the confluence of the Seneca and Tugaloo Rivers, near Hartwell, Ga., and forms the state boundary between South Carolina and Georgia. The J. Strom Thurmond Dam and Lake, located 238 miles upstream from the Atlantic Ocean, is responsible for most of the flow regulation that affects the Savannah River from Augusta, Ga., to the coast. The Savannah Harbor experiences semi-diurnal tides of two low and two high tides in a 24.8-hour period with pronounced differences in tidal range between neap and spring tides occurring on a 14-day and 28-day lunar cycle. Salinity intrusion results from the interaction of three principal forces - streamflow, mean tidal water levels, and tidal range. To analyze, model, and simulate hydrodynamic behaviors at critical coastal streamgages in the Lower Savannah River Estuary, data-mining techniques were applied to over 15 years of hourly streamflow, coastal water-quality, and water-level data. Artificial neural network (ANN) models were trained to learn the variable interactions that cause salinity intrusions. Streamflow data from the 9,850 square-mile Savannah River Basin were input into the model as time-delayed variables. Tidal inputs to the models were obtained by decomposing tidal water-level data into a “periodic” signal of tidal range and a “chaotic” signal of mean water levels. The ANN models were able to convincingly reproduce historical behaviors and generate alternative scenarios of interest. Important freshwater resources are located proximal to the freshwater-saltwater interface of the estuary. The Savannah National Wildlife Refuge is located in the upper portion of the Savannah River Estuary. The tidal freshwater marsh is an essential part of the 28,000-acre refuge and is home to a diverse variety of wildlife and plant communities. Two municipal freshwater intakes are located upstream from the refuge. To evaluate the impact of climate change on salinity intrusion on these resources, inputs of streamflows and mean tidal water levels were modified to incorporate estimated changes in precipitation patterns and sea-level rise appropriate for the Southeastern United States. Changes in mean tidal water levels were changed parametrically for various sea-level rise conditions. Preliminary model results at the U.S. Geological Survey (USGS) Interstate-95 streamgage (station 02198840) for a 7½-year simulation show that historical daily salinity concentrations never exceeded 0.5 practical salinity units (psu). A 1-foot sea-level rise (ft, 30.5 centimeters [cm]) would increase the number of days of salinity concentrations greater than 0.5 psu to 47 days. A 2-ft (61 cm) sea-level rise would increase the number of days to 248.

Georgia, South Carolina↗

USGS2010

Potential mitigation approach to minimize salinity intrusion in the Lower Savannah River Estuary due to reduced controlled releases from Lake Thurmond

The Savannah River originates at the confluence of the Seneca and Tugaloo Rivers, near Hartwell, Ga. and forms the State boundary between South Carolina and Georgia. The J. Strom Thurmond Dam and Lake, located 187 miles upstream from the coast, is responsible for most of the flow regulation that affects the Savannah River from Augusta to the coast. The Savannah Harbor experiences semi-diurnal tides of two high and two low tides in a 24.8-hour period with pronounced differences in tidal range between neap and spring tides occurring on a 14-day and 28-day lunar cycle. The Savannah National Wildlife Refuge is located in the Savannah River Estuary. The tidal freshwater marsh is an essential part of the 28,000-acre refuge and is home to a diverse variety of wildlife and plant communities. The Southeastern U.S. experienced severe drought conditions in 2008 and if the conditions had persisted in Georgia and South Carolina, Thurmond Lake could have reached an emergency operation level where outflow from the lake is equal to the inflow to the lake. To decrease the effect of the reduced releases on downstream resources, a stepped approach was proposed to reduce the flow in increments of 500 cubic feet per second (ft 3 /s) intervals. Reduced flows from 3,600 ft 3 /s to 3,100 ft 3 /s and 2,600 ft 3 /s were simulated with two previously developed models of the Lower Savannah River Estuary to evaluate the potential effects on salinity intrusion. The end of the previous drought (2002) was selected as the baseline condition for the simulations with the model. Salinity intrusion coincided with the 28-day cycle semidiurnal tidal cycles. The results show a difference between the model simulations of how the salinity will respond to the decreased flows. The Model-to-Marsh Decision Support System (M2MDSS) salinity response shows a large increase in the magnitude (> 6.0 practical salinity units, psu) and duration (3-4 days) of the salinity intrusion with extended periods (21 days) of tidal freshwater remaining in the system. The Environmental Fluid Dynamic Code (EFDC) model predicts increases in the magnitude of the salinity intrusion but only to 2 and 3 psu and the intrusion duration greater than a week. A potential mitigation to the increased salinity intrusion predicted by the M2MDSS would be to time pulses of increase flows to reduce the magnitude of the intrusion. Seven-day streamflow pulses of 4,500 ft 3 /s were inserted into the constant 3,100 ft 3 /s streamflow condition. The streamflow pulses did substantially decrease the magnitude and duration of the salinity intrusion. The result of the streamflow pulse scenario demonstrates how alternative release patterns from Lake Thurmond could be utilized to mitigate potential salinity changes in the Lower Savannah River Estuary.

Georgia, South Carolina↗

USGS2010

Data mining for water resource management part 2 - methods and approaches to solving contemporary problems

This is the second of two papers that describe how data mining can aid natural-resource managers with the difficult problem of controlling the interactions between hydrologic and man-made systems. Data mining is a new science that assists scientists in converting large databases into knowledge, and is uniquely able to leverage the large amounts of real-time, multivariate data now being collected for hydrologic systems. Part 1 gives a high-level overview of data mining, and describes several applications that have addressed major water resource issues in South Carolina. This Part 2 paper describes how various data mining methods are integrated to produce predictive models for controlling surface- and groundwater hydraulics and quality. The methods include: - signal processing to remove noise and decompose complex signals into simpler components; - time series clustering that optimally groups hundreds of signals into "classes" that behave similarly for data reduction and (or) divide-and-conquer problem solving; - classification which optimally matches new data to behavioral classes; - artificial neural networks which optimally fit multivariate data to create predictive models; - model response surface visualization that greatly aids in understanding data and physical processes; and, - decision support systems that integrate data, models, and graphics into a single package that is easy to use.

South Carolina↗

USGS2009

Continuous Tidal Streamflow and Gage-Height Data for Bass and Cinder Creeks on Kiawah Island, South Carolina, September 2007

A three-dimensional model of Bass and Cinder Creeks on Kiawah Island, South Carolina, was developed to evaluate methodologies for determining fecal coliform total maximum daily loads for shellfish waters. To calibrate the model, two index-velocity sites on the creeks were instrumented with continuous acoustic velocity meters and water-level sensors to compute a 21-day continuous record of tidal streamflows. In addition to monitoring tidal cycles, streamflow measurements were made at the index-velocity sites, and tidal-cycle streamflow measurements were made at the mouth of Bass Creek and on the Stono River to characterize the streamflow dynamics near the ocean boundary of the three-dimensional model at the beginning, September 6, 2007, and end, September 26, 2007, of the index-velocity meter deployment. The maximum floodtide and ebbtide measured on the Stono River by the mouth of Bass Creek for the two measurements were -155,000 and 170,000 cubic feet per second (ft3/s). At the mouth of Bass Creek, the maximum floodtide and ebbtide measurements during the 2 measurement days were +/-10,200 ft3/s. Tidal streamflows for the 21-day deployment on Bass Creek ranged from -2,510 ft3/s for an incoming tide to 4,360 ft3/s for an outgoing tide. On Cinder Creek, the incoming and outgoing tide varied from -2,180 to 2,400 ft3/s during the same period.

South Carolina↗

USGS2009

Estimation of Missing Water-Level Data for the Everglades Depth Estimation Network (EDEN)

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level gaging stations, ground-elevation models, and water-surface elevation models designed to provide scientists, engineers, and water-resource managers with current (2000-2009) water-depth information for the entire freshwater portion of the greater Everglades. The U.S. Geological Survey Greater Everglades Priority Ecosystems Science provides support for EDEN and their goal of providing quality-assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. To increase the accuracy of the daily water-surface elevation model, water-level estimation equations were developed to fill missing data. To minimize the occurrences of no estimation of data due to missing data for an input station, a minimum of three linear regression equations were developed for each station using different input stations. Of the 726 water-level estimation equations developed to fill missing data at 239 stations, more than 60 percent of the equations have coefficients of determination greater than 0.90, and 92 percent have an coefficient of determination greater than 0.70.

Open-File Report↗

USGS2008

The Effects of the Saluda Dam on the Surface-Water and Ground-Water Hydrology of the Congaree National Park Flood Plain, South Carolina

The Congaree National Park was established '... to preserve and protect for the education, inspiration, and enjoyment of present and future generations an outstanding example of a near-virgin, southern hardwood forest situated in the Congaree River flood plain in Richland County, South Carolina' (Public Law 94-545). The resource managers at Congaree National Park are concerned about the timing, frequency, magnitude, and duration of flood-plain inundation of the Congaree River. The dynamics of the Congaree River directly affect ground-water levels in the flood plain, and the delivery of sediments and nutrients is constrained by the duration, extent, and frequency of flooding from the Congaree River. The Congaree River is the southern boundary of the Congaree National Park and is formed by the convergence of the Saluda and Broad Rivers 24 river miles upstream from the park. The streamflow of the Saluda River has been regulated since 1929 by the operation of the Saluda Dam at Lake Murray. The U.S. Geological Survey, in cooperation with the National Park Service, Congaree National Park, studied the interaction between surface water in the Congaree River and ground water in the flood plain to determine the effect Saluda Dam operations have on water levels in the Congaree National Park flood plain. Analysis of peak flows showed the reduction in peak flows after the construction of Lake Murray was more a result of climate variability and the absence of large floods after 1930 than the operation of the Lake Murray dam. Dam operations reduced the recurrence interval of the 2-year to 100-year peak flows by 6.1 to 17.6 percent, respectively. Analysis of the daily gage height of the Congaree River showed that the dam has had the effect of lowering high gage heights (95th percentile) in the first half of the year (December to May) and raising low gage heights (5th percentile) in the second half of the year (June to November). The dam has also had the effect of increasing the 1-, 3-, 7-, 30-, and 90-day minimum gage heights by as much as 23.9 percent and decreasing the 1-, 3-, 7-, 30-, and 90-day maximum gage heights by as much as 7.2 percent. Analysis of the ground-water elevations in the Congaree National Park flood plain shows similar results as the gage-height analysis--the dam has had the effect of lowering high ground-water elevations and increasing low ground-water elevations. Overall, the operation of the dam has had a greater effect on the gage heights within the river banks than gage heights in the flood plain. This result may have a greater effect on the subsurface water levels of the surficial flood-plain aquifer than the frequency and magnitude of inundation of the flood plain.

South Carolina↗

USGS2007

Hydrologic Record Extension of Water-Level Data in the Everglades Depth Estimation Network (EDEN) Using Artificial Neural Network Models, 2000-2006

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level gaging stations, ground-elevation models, and water-surface models designed to provide scientists, engineers, and water-resource managers with current (2000-present) water-depth information for the entire freshwater portion of the greater Everglades. The U.S. Geological Survey Greater Everglades Priority Ecosystem Science provides support for EDEN and the goal of providing quality assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. To increase the accuracy of the water-surface models, 25 real-time water-level gaging stations were added to the network of 253 established water-level gaging stations. To incorporate the data from the newly added stations to the 7-year EDEN database in the greater Everglades, the short-term water-level records (generally less than 1 year) needed to be simulated back in time (hindcasted) to be concurrent with data from the established gaging stations in the database. A three-step modeling approach using artificial neural network models was used to estimate the water levels at the new stations. The artificial neural network models used static variables that represent the gaging station location and percent vegetation in addition to dynamic variables that represent water-level data from the established EDEN gaging stations. The final step of the modeling approach was to simulate the computed error of the initial estimate to increase the accuracy of the final water-level estimate. The three-step modeling approach for estimating water levels at the new EDEN gaging stations produced satisfactory results. The coefficients of determination (R2) for 21 of the 25 estimates were greater than 0.95, and all of the estimates (25 of 25) were greater than 0.82. The model estimates showed good agreement with the measured data. For some new EDEN stations with limited measured data, the record extension (hindcasts) included periods beyond the range of the data used to train the artificial neural network models. The comparison of the hindcasts with long-term water-level data proximal to the new EDEN gaging stations indicated that the water-level estimates were reasonable. The percent model error (root mean square error divided by the range of the measured data) was less than 6 percent, and for the majority of stations (20 of 25), the percent model error was less than 1 percent.

Open-File Report↗

USGS2007

Analysis of salinity intrusion in the Waccamaw River and Atlantic Intracoastal Waterway near Myrtle Beach, South Carolina, 1995-2002

Six reservoirs in North Carolina discharge into the Pee Dee River, which flows 160 miles through South Carolina to the coastal communities near Myrtle Beach, South Carolina. During the Southeast's record-breaking drought from 1998 to 2003, salinity intrusions inundated a coastal municipal freshwater intake, limiting water supplies. To evaluate the effects of regulated flows of the Pee Dee River on salinity intrusion in the Waccamaw River and Atlantic Intracoastal Waterway, the South Carolina Department of Natural Resources and a consortium of stakeholders entered into a cooperative agreement with the U.S. Geological Survey to apply data-mining techniques to the long-term time series to analyze and simulate salinity dynamics near the freshwater intakes along the Grand Strand of South Carolina. Salinity intrusion in tidal rivers results from the interaction of three principal forces—streamflow, mean tidal water levels, and tidal range. To analyze, model, and simulate hydrodynamic behaviors at critical coastal gages, data-mining techniques were applied to over 20 years of hourly streamflow, coastal water-quality, and water-level data. Artificial neural network models were trained to learn the variable interactions that cause salinity intrusions. Streamflow data from the 18,300-square-mile basin were input to the model as time-delayed variables and accumulated tributary inflows. Tidal inputs to the models were obtained by decomposing tidal water-level data into a "periodic" signal of tidal range and a "chaotic" signal of mean water levels. The artificial neural network models were able to convincingly reproduce historical behaviors and generate alternative scenarios of interest. To make the models directly available to all stakeholders along the Pee Dee and Waccamaw Rivers and Atlantic Intracoastal Waterway, an easy-to-use decision support system (DSS) was developed as a spreadsheet application that integrates the historical database, artificial neural network models, model controls, streaming graphics, and model output. An additional feature is a built-in optimizer that dynamically calculates the amount of flow needed to suppress salinity intrusions as tidal ranges and water levels vary over days and months. This DSS greatly reduced the number of long-term simulations needed for stakeholders to determine the minimum flow required to adequately protect the freshwater intakes.

South Carolina↗