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 (P₂) and 90 day (P₉₀) cumulative precipitation, and 2 day water level maximum (W₂) were identified as significant predictors from the p-value of their model coefficients, but required an interaction term P₂*W₂ 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 W₂. 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 ~15. Subtracting recent SLR from W₂ reduced the probability of some recent events, suggesting that SLR already is increasing the rate of SSOs.

在高降水量事件中，沿海废水和雨水系统可能不堪重负，尤其是当高潮浪潮加重了阻塞溢洪道和排水道的情况时。海平面上升（SLR）增加了此类复合洪水事件的风险，引发了下水道下水道（SSO），从而将废水释放到当地环境中。建立了逻辑回归模型以更好地预测佛罗里达州南部Pinellas县的这种风险。使用客观和主观标准从2000年至2017年的累积降水量和沿海水位中选择模型变量。从p值可以看出2天（P ₂）和90天（P ₉₀）的累积降水量和2天最大水位（W ₂）是重要的预测指标。值等于其模型系数的值，但对于模型逼真度需要交互项P ₂ * W ₂。该模型正确地预测了从2000年到2017年的所有6个已识别的SSO。SLR由添加到W _2的最大0.5 m的值范围表示。对于0.5 m的SLR，SSO天数增加了42-52倍，单个事件的数量增加了约15倍。从W _2中减去最近的SLR减少了一些最近事件的可能性，这表明SLR已经在提高SSO的发生率。