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Model Identifies Septic System Discharge ‘Hotspots’ in Southeast Florida

High-tide flooding submerges vehicles on a street in Miami Beach
High-tide flooding on a street in Miami Beach, September 2015. Credit: NOAA.

Groundwater in southeast Florida’s coastal zone is never far from the land’s surface, posing a risk to the proper function of the more than 112,000 residential septic systems in the region.

High water tables, driven by sea level rise and flooding from coastal storms, can infiltrate septic systems and cause them to leach untreated waste into the environment, degrading habitats and threatening public health. Miami-Dade County estimates that 64 percent of its septic systems are periodically compromised by groundwater infiltration.

Miami’s sea level has risen by more than four inches since 1994 and is projected to continue rising. With future climate change expected to exacerbate septic system failure, NCCOS-funded researchers conducted a study to help Miami-Dade County identify areas most in need of septic-to-sewer conversions to protect people and the environment from wastewater contamination.

By combining a surface-water/groundwater model developed by the U.S. Geological Survey with a particle tracking model, the research team was able to estimate the flow paths and discharge locations of septic effluents. Using the anticipated three-inch rise in regional sea level over the next sixteen years, the study provides estimates of the locations and magnitudes of septic effluent flows in the county through 2040.

Approximately 70 percent of the flows terminate in canals (note, canal water is ultimately transported to Biscayne Bay), 19 percent in well fields, 7 percent directly in Biscayne Bay, and 4 percent on the ocean side and other areas. Further, model results showed that half of the particles reach their endpoints in less than five years, with remaining particles having longer discharge times.

This is the first known attempt to explicitly track septic effluents in the county based on a groundwater flow model.