Supporting Coastal Resiliency by Investigating Tidal Reach and Inter-Connected Factors in Coastal Georgia

Increasing our understanding of the tidal dynamics, the extent of tidal reach, and storm surge impacts on near-coastal areas of Georgia and South Carolina rivers is a significant research opportunity. It has the potential to yield benefits to sustainable planning, ecosystem protection, and risk management for regulators and state agencies, local municipalities, coastal residents, and other regional stakeholders. This study leveraged existing United States Geological Survey (USGS) water level data for the Savannah River, added additional water level gauges in key areas for less than one year, and analyzed these combined large data sets with modified wavelet analysis and Fourier analysis. One significant outcome of the research included confirmation of river mile 45, historically referred to as Ebenezer Landing, as the head of tide. We also provide information on the dynamics of wave propagation through the near-coastal area of the Savannah River, give indication of critical areas of concern for flooding resulting from interactions between the interconnected factors affecting elevated upstream flows and storm tides, and discuss relevance of study results for various stakeholders

Understanding Hydrologic Variation Through Time-Series Analysis

2010 S.C. Water Resources Conference - Science and Policy Challenges for a Sustainable Futur

High Density Lagrangian Sampling for Pathogen Source Identification

Proceedings of the 2013 Georgia Water Resources Conference, April 10-11, 2013, Athens, Georgia.In compliance to the Clean Water Act, each US state compiles a list of water bodies not meeting regulatory criteria. The most common impairment in US water bodies is elevated pathogens measured by fecal indicator bacteria (FIB). Reasons for this prevalence probably include the true magnitude of pathogen contamination, monitoring bias from human health concern, inaccuracy of FIB monitoring compared to other parameters, and difficulty estimating background condition. In practice, identification and citation of impairment is extensive, while development of plans that identify the source with certainty and implement high probability remediation lags behind. The difficulty in confidently identifying sources of impairment is an impediment to the protection of water bodies and increases the cost of remediation due to the need for casting a wider net of lower probability solutions. With a high proportion of resources directed to pathogen contamination, it is important to confidently identify sources. Increased confidence will improve efficacy of remediation and ability to secure funding. To achieve these objectives, we designed a study method to investigate Rocky Creek, a pathogen impaired stream in Augusta, GA. This method applied a Lagrangian FIB sampling approach to reduce confounding variability and a high sampling density to identify high contribution watershed areas. We then layered typical pathogen sources (e.g. septic, pet waste, sewer, wildlife) and alternative sources (e.g. sediment, instream growth) along with their GIS data over the FIB data. In this way, we were able to target remediation efforts on the convergence of sources and regions and thereby decrease the scale of remediation efforts.Sponsored by: Georgia Environmental Protection Division; U.S. Department of Agriculture, Natural Resources Conservation Service; Georgia Institute of Technology, Georgia Water Resources Institute; The University of Georgia, Water Resources Faculty.This book was published by Warnell School of Forestry and Natural Resources, The University of Georgia, Athens, Georgia 30602-2152. The views and statements advanced in this publication are solely those of the authors and do not represent official views or policies of The University of Georgia, the Georgia Water Research Institute as authorized by the Water Research Institutes Authorization Act of 1990 (P.L. 101-307) or the other conference sponsors

Changing Our Perspective to Increase Our Understanding of Basic Aquatic Ecosystem Function

Proceedings of the 2013 Georgia Water Resources Conference, April 10-11, 2013, Athens, Georgia.Aquatic ecosystems are dynamic mixtures of physical, chemical, biological, geological, and meteorological elements. Understanding how that mixture produces the observed water quality at a given location is one of our greatest challenges. To a large degree, our understanding has been limited by the availability of tools and by our research approach. Advances within the last two decades have allowed us to go beyond synoptic sampling (data collection from many sites without regard to travel time) to multiple site, continuous sampling efforts (high frequency data from multiple fixed locations). While those data are important for assessing regulatory water quality, fixed position sampling (Eulerian perspective) falls short of providing a true understanding of aquatic ecosystem function because of the significant spatiotemporal gaps between data collection sites. Continuous data from multiple locations increases data resolution but connecting those data within the context of advective transport requires simulation; this results in far more simulated than measured data. Continuous measurements while following the same parcel of water as it is advectively transported (Lagrangian perspective) is another important approach to understanding aquatic ecosystem function. This approach allows for better spatiotemporal resolution and can lead to better understanding of ecosystem function. The Lagrangian perspective is however limited by the costs and time associated with conducting this type of data collection effort and data sets may be limited in the range of seasonal and stochastic conditions. For six years, Southeastern Natural Sciences Academy has been collecting water quality data with Eulerian data collection methods throughout the Middle and Lower Savannah River Basins. In June 2012, we launched our first Lagrangian research expedition along 233 kilometers (145 miles) of the Middle Savannah River Basin. The goal of this paper is to discuss some of the differences between our Eulerian and Lagrangian data sets and the challenges that lie ahead.Sponsored by: Georgia Environmental Protection Division; U.S. Department of Agriculture, Natural Resources Conservation Service; Georgia Institute of Technology, Georgia Water Resources Institute; The University of Georgia, Water Resources Faculty.This book was published by Warnell School of Forestry and Natural Resources, The University of Georgia, Athens, Georgia 30602-2152. The views and statements advanced in this publication are solely those of the authors and do not represent official views

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research