Using logistic regression to model the risk of sewer overflows triggered by compound flooding with application to sea level rise

Coastal wastewater and storm water systems can be overwhelmed during high precipitation events, particularly when compounded by high storm surge that blocks spillways and drainage ways. Sea level rise (SLR) brings increased risk of such compound flooding events, triggering sanitary sewer overflows (SSO) which release waste water into the local environment. A logistic regression model was developed to better predict this risk in southern Pinellas County, FL. Model variables were selected from 2000 to 2017 cumulative precipitation and coastal water levels using both objective and subjective criteria. The 2 day (P-2) and 90 day (P-90) cumulative precipitation, and 2 day water level maximum (W-2) were identified as significant predictors from the p-value of their model coefficients, but required an interaction term P-2*W-2 for model fidelity. The model correctly hindcasted all 6 identified SSOs from 2000 to 2017. SLR was represented by a range of values up to 0.5 m added to W-2. For a SLR of 0.5 m the number of SSO days increased by a factor of 42-52 and the number of individual events increased by a factor of similar to 15. Subtracting recent SLR from W-2 reduced the probability of some recent events, suggesting that SLR already is increasing the rate of SSOs.

Automated summary

The study found that sea level rise could increase the occurrence of sanitary sewer overflows, and developed a logistic regression model to better predict this risk. By simulating precipitation and coastal water levels, the significant predictors for sanitary sewer overflows were identified.