Foreword

Volume 8, Issue 2 of the Journal of South Carolina Water Resources (JSCWR) includes four articles. Three articles focus on the crucial factors of public perceptions and communications across various stakeholder groups. The fourth article examines the hydrologic modeling of a coastal forest watershed. Additionally, an informative guest commentary about the US Environmental Protection Agency (EPA) Water Quality Exchange (WQX) and Water Quality Portal (WQP) was contributed by authors from the EPA and South Carolina Department of Health and Environmental Control (SCDHEC)

The Implementation of Low Impact Development (LID) Stormwater Practices along the South Carolina Coast

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

The Carolinas Coastal Ocean Observing and Prediction System: An Infrastructure for Communications and Data Management for Real-Time Environmental Monitoring

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

Integration of Environmental Information Systems

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

Making the most of Information from Environmental Monitoring Systems

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

Study of the Science, Economics, and Perceptions Related to Implementation of Traditional and Innovative Stormwater Best Management Practices in Coastal South Carolina

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

The development of a spatially explicit model to estimate

A spatially explicit model of raccoon (Procyon lotor) distribution for the U.S. Department of Energy’s (DOE) Savannah River Site (SRS) in west-central South Carolina was developed using data from a raccoon radio-telemetry study and visualized within a Geographic Information System (GIS). An inductive approach was employed to develop three sub-models using the ecological requirements of raccoons studied in the following habitats: (1) man-made reservoirs, (2) bottomland hardwood/ riverine systems, and (3) isolated wetland systems. Logistic regression was used to derive probabilistic resource selection functions using habitat compositional data and landscape metrics. The final distribution model provides a spatially explicit probability (likelihood of being in an area) surface for male raccoons. The model is a stand-alone tool consisting of algorithms independent of the specific GIS data layers to which they were derived. The model was then used to predict contaminant burdens in raccoons inhabiting a riverine system contaminated with radiocaesium (137Cs). The predicted 137Cs burdens were less than if one would assume homogeneous use of the contaminated areas. This modelling effort provides a template for DOE managed lands and other large government facilities to establish a framework for site-specific ecological assessments that use wildlife species as endpoints

Facility Attractiveness and Social Vulnerability Impacts on Spatial Accessibility to Opioid Treatment Programs in South Carolina

Opioid dependence and opioid-related mortality have been increasing in recent years in the United States. Available and accessible treatments may result in a reduction of opioid-related mortality. This work describes the geographic variation of spatial accessibility to opioid treatment programs (OTPs) and identifies areas with poor access to care in South Carolina. The study develops a new index of access that builds on the two-step floating catchment area (2SFCA) method, and has three dimensions: a facility attractiveness index, defined by services rendered incorporated into the Huff Model; a facility catchment area, defined as a function of facility attractiveness to account for variable catchment size; and a Social Vulnerability Index (SVI) to account for nonspatial factors that mitigate or compound the impacts of spatial access to care. Results of the study indicate a significant variation in access to OTPs statewide. Spatial access to OTPs is low across the entire state except for in a limited number of metropolitan areas. The majority of the population with low access (85%) live in areas with a moderate-to-high levels of social vulnerability. This research provides more realistic estimates of access to care and aims to assist policymakers in better targeting disadvantaged areas for OTP program expansion and resource allocation

A Spatially Explicit Model of the Wild Hog for Ecological Risk Assessment Activities at the Department of Energy\u27s Savannah River Site

In North America, wild hogs (Sus scrofa) are both sought after as prime game and despised due to their detrimental impacts to the environment from their digging and rooting behavior. They are also a potentially useful indicator species for environmental health for both ecological- and human-based risk assessments. An inductive approach was used to develop probabilistic resource selection models using logistic regression to quantify the likelihood of hogs being in any area of the Department of Energy’s 805 km2 Savannah River Site (SRS) in west-central South Carolina. These models were derived by using available SRS hog hunt data from 1993–2000 and a Geographic Information System database describing the habitat structure of the SRS. The model’s significant parameters indicated that wild hogs preferred hardwoods and avoided pine and shrubby areas. Further, landscape metric analyses revealed that hogs preferred areas with large complex patch areas and low size variation. These resource selection models were then utilized to better estimate exposure of wild hogs to radionuclides and metals in a disturbed riparian ecosystem on the SRS using two different possible diets based on food availability. Contaminant exposure can be better estimated using these resource selection models than has been previously possible, because past practices did not consider home range and habitat utilization probability in heterogeneously contaminated habitats. Had these models not been used, risk calculations would assume that contaminated areas were utilized 100% of the time, thus overestimating exposure by a factor of up to 25