A Novel Approach in Determining Changes in Consumptive Use

2008 S.C. Water Resources Conference - Addressing Water Challenges Facing the State and Regio

Development of Interential Sensors for Real-time Quality Control of Water-level Data for the Everglades Depth Estimation Network

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

Neural Network Analysis of the Effects of Rainfall Variability and Groundwater Usage on the Hydrologic System in a Karst Terrain, Central Florida, USA

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Data Mining to Predict Climate and Groundwater Use Impacts on the Hydrology of Central Florida

2012 S.C. Water Resources Conference - Exploring Opportunities for Collaborative Water Research, Policy and Managemen

Development of Decision Support Systems for Estimating Salinity Instrusion Effects due to Climate Change on the South Carolina and Georgia Coast

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

Using Inferential Sensors for Quality Control of the Everglades Depth Estimation Network (EDEN)

2012 S.C. Water Resources Conference - Exploring Opportunities for Collaborative Water Research, Policy and Managemen

Predicting the Impact of Climate Change on Salinity Intrusions in Coastal South Carolina and Georgia

Proceedings of the 2013 Georgia Water Resources Conference, April 10-11, 2013, Athens, Georgia.This paper summarizes findings from Water Research Foundation Project 4285, which was sponsored the Foundation and Beaufort-Jasper Water and Sewer Authority (Roehl et al. 2012). The project’s thesis is as follows. Coastal fresh water intakes are at risk due to sea-level rise (SLR) and climate change. Because of past storms and droughts, long-term historical data already contains information about how a hydrologic system will respond. A predictive model that is accurate across a site’s full range of historical forcing can be used to assess risk.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

Simulation of Salinity in the Tidal Marshes in the Vicinity of the Savannah National Wildlife Refuge Using Artificial Neural Networks

Proceedings of the 2007 Georgia Water Resources Conference, March 27-29, 2007, Athens, Georgia.The Savannah Harbor is one of the busiest ports on the East Coast of the United States and is located downstream from the Savannah National Wildlife Refuge, which is one of the Nation’s largest freshwater tidal marshes. The Georgia Ports Authority and the U.S. Army Corps of Engineers funded hydrodynamic and ecological studies to evaluate the potential effects of a proposed deepening of Savannah Harbor as part of the Environmental Impact Statement. These studies included a three-dimensional (3D) model of the Savannah River estuary system, which was developed to simulate changes in water levels and interstitial (or pore-water) salinity in the system in response to geometry changes as a result of the deepening of Savannah Harbor, and a marsh-succession model that predicts plant distribution in the tidal marshes in response to changes in the water-level and interstitial salinity conditions in the marsh. Beginning in May 2001, the U.S. Geological Survey entered into cooperative agreements with the Georgia Ports Authority to develop empirical models to simulate the water level and salinity of the rivers and tidal marshes in the vicinity of the Savannah National Wildlife Refuge and to link the 3D hydrodynamic river-estuary model and the marsh-succession model. Understanding freshwater inflows, tidal water levels, and specific conductance in the rivers and marshes is critical to enhancing the predictive capabilities of a successful marsh succession model. Data-mining techniques, including artificial neural network (ANN) models, were applied to address various needs of the ecology study and to integrate the riverine predictions from the 3D model to the marsh-succession model. ANN models were developed to simulate riverine water levels and specific conductance in the vicinity of the tidal marshes for the full range of historical conditions using data from the river gaging networks. ANN models also were developed to simulate the marsh water levels and interstitial salinities using data from the marsh gaging networks. Using the marsh ANN models, the continuous marsh network was indcasted to be concurrent with the long-term riverine network. The hindcasted data allow ecologists to compute hydrologic parameters—such as hydroperiods and exposure frequency—to help analyze historical vegetation data.Sponsored and Organized by: U.S. Geological Survey, Georgia Department of Natural Resources, Natural Resources Conservation Service, The University of Georgia, Georgia State University, Georgia Institute of TechnologyThis book was published by the Institute of Ecology, The University of Georgia, Athens, Georgia 30602-2202. 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 U.S. Geological Survey, the Georgia Water Research Institute as authorized by the Water Resources Research Act of 1990 (P.L. 101-397) or the other conference sponsors