Assessing BMP Implementation for Agricultural Pollution Control in a Coastal Zone Watershed

There has been continuing concern regarding the impairment of water quality in the Caloosahatchee Watershed, SW Florida as a result of pollutants emanating primarily from agricultural lands as well as nutrient-laden discharges from Lake Okeechobee. Nutrients, bacteria, and low dissolved oxygen are some of the current threats to water quality in the river and estuarine region, and the adjoining Charlotte Harbor. Currently, efforts are underway to establish Basin Management Action Plans (BMAPs) which involve the coordination of local stakeholders to determine what actions (including BMP implementation) should be pursued to permit the water bodies to meet their designated Total Maximum Daily Load. Modeling studies, using the Hydrologic Simulation Program Fortran (HSPF), have also been conducted in the watershed to identify and evaluate pollutant sources. There is, however, a need to understand the current extent of implementation and effectiveness of the Caloosahatchee River Watershed agricultural BMPs, particularly considering site conditions and management interventions in the watershed. This study was aimed at determining the extent of BMP implementation within the watershed, and updating and extending existing BMP capabilities within the HSPF model for current and planned interventions. By spring 2010 enrollment in BMP programs covered 719 km2 (177,669 acres, ≈ 50% of agricultural land use area). Recent enrollments have increased coverage to over 90% in the citrus growing areas. Common BMPs include nutrient management, vegetated filter strips, and rotational grazing, as well as structural BMPs such as culverts and risers. Some of these BMPs have been implemented. In addition, there has been some implementation outside of the BMP program. Further work is needed to determine definitively the extent of BMP implementation in the watershed. Commonly used BMPs were added to the HSPF model, along with literature-based effectiveness data where available, thus better equipping the model for BMP evaluations in the watershed.