DEVELOPMENT OF DECISION SUPPORT SYSTEMS FOR ESTIMATING SALINITY INTRUSION EFFECTS DUE TO CLIMATE CHANGE ON THE SOUTH CAROLINA AND GEORGIA COAST

The ability of water-resource managers to adapt to future climatic change is especially challenging in coastal regions of the world. The balance between the hydrological flow conditions within a coastal drainage basin and sea level governs the characteristics and frequency of salinity intrusions into coastal rivers. There are many municipal water intakes along the Georgia and South Carolina coast that are proximal to the saltwater-freshwater interface of tidal rivers. An increase in the extent of saltwater intrusion along the coast due to climate changes could threaten freshwater intakes for the cities of Myrtle Beach, Georgetown, and Beaufort in South Carolina and Savannah in Georgia. During the Southeast’s record-breaking drought from 1998 to 2002, salinity intrusions inundated one of the coastal municipal freshwater intakes, limiting water supplies during the height of the tourist season. For long range planning purposes, water-resource managers need estimates of the change in the frequency, duration, and magnitude of salinity intrusion near their water intakes that may occur as a result of climate change.

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 gage locations along the Atlantic Intracoastal Waterway and Waccamaw River near Myrtle Beach, SC, and Savannah River near Savannah, GA, data-mining techniques were applied to over twenty years of hourly streamflow, coastal water-quality, and water-level data. Artificial neural network (ANN) models were trained to learn the specific variable interactions that cause salinity intrusions. Streamflows into the estuarine systems are input to the models 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 salinity dynamic behaviors in both systems.User-defined hydrologic and coastal water-level inputs, for example from down-scaling of regional climate models, can be simulated in the salinity intrusion models to evaluate various climate-change scenarios. The models for the two systems are deployed in a decision support system (DSS) and disseminated as an spreadsheet application to facilitate the use of the models for management decisions by a variety of coastal water-resource managers. Preliminary model results near a municipal freshwater intake indicate that a sea-level rise of 1 foot (ft, 30.5 centimeters [cm]) would double the daily frequency of water with a specific conductance value of 2,000 microsiemens per centimeter over a seven year simulation, and a 2 ft (61 cm) sea-level rise would quadruple the frequency. Water-resource managers can use this information to plan mitigation efforts to adapt to potential effects from climate change. Efforts could include timing of withdrawal on outgoing tides, increased storage of raw water, timing increased releases of regulated streamflow, or the blending of higher conductance surface water with lower conductance water from an alternative source such as groundwater.

Acronyms:

ANN Artificial Neural Networks

DSS Decision Support Systems