Assessment Techniques

**119**

**Chapter 5**

USA

**Abstract**

A GIS-Based Approach for

*Philip W. Bellamy and Hyun Jung Cho*

management to more sustainable land development planning.

Halifax River, coastal watershed

**1. Introduction**

**Keywords:** surface runoff, runoff coefficient, stormwater, Indian River Lagoon,

Algae blooms within coastal estuarine systems have been a threat to vital key ecosystem components causing the degradation of ecological integrity. With non-point source pollution being a primary concern, using geographic information system (GIS) approaches to assess the impacts is effective for stormwater management. Therefore, with the use of land use/land cover (LC/LU), soil, and elevation data, the Potential Runoff Coefficient (PRC) and runoff depth were calculated for the IRL and Halifax River watershed. The analysis consisted of manipulating the geospatial data to derive the potential runoff coefficients and runoff depths.

Determining Potential Runoff

Coefficient and Runoff Depth for

the Indian River Lagoon, Florida,

The Indian River Lagoon system (IRL), spanning ~40% of Florida's east coast, is one of the nation's biggest and most biodiverse estuaries. In 2011, a super algal bloom event occurred in the IRL with total nitrogen and phosphorus levels that exceeded historical levels. Scientists suspect that nonpoint source pollution through surface runoff may have had a significant impact on the recent recurring algal blooms. Digital Elevation Model, land cover/land use, and soil data were used to calculate a runoff coefficient for the IRL drainage basin. Rainfall data were used to calculate runoff depth for the study area between the years of 2006–2016. When the monthly runoff depth data for 2011 were compared to a previous study on the 2011 super algal bloom in the lagoon, areas with high runoff visually matched the areas with higher chlorophyll *a* concentrations. Land development was a significant variable for determining runoff depth (*p* < 0.0001), and although used to derive runoff depths, the influence of precipitation was marginally significant (*p* = 0.06). Significant spatial autocorrelation indicated local trends between land development and runoff depth (*p* < 0.0001). Outputs will aid with decisions on stormwater
