Flash Flood Forecasting in Sao Paulo Using a Binary Logistic Regression Model

This study presents a flash flood forecasting model that uses a binary logistic regression method to determine the occurrence of flash flood events in different watersheds in the city of Sao Paulo, Brazil. This study is based on two years (2015-2016) of rain estimates from a dual-polarization S-band Doppler weather radar (SPOL) and flood locations observed by the Climate Emergency Management Center (CGE) of Sao Paulo City Hall. The logistic regression model is based on daily accumulated precipitation, a maximum precipitation rate, and daily rainfall duration. The model presented a probability of detection (POD) of 46% (71%) on average for flood events (conditional), while, for events without flash flood, it reached 98% probability. Despite the low averaged POD for flash flood occurrence, the model demonstrated a good performance for watersheds located in the east of the city near the Tiete River and in the southeast with probabilities above 50%.