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

Andrea Salomé Viteri López,Carlos Augusto Morales Rodriguez

doi:10.3390/atmos11050473

Andrea Salomé Viteri López, Carlos Augusto Morales Rodriguez

Open Access

https://doi.org/10.3390/atmos11050473

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Journal: Atmosphere	Publication Date: May 7, 2020
Citations: 8	License type: CC BY 4.0

Affiliation: Universidade de São Paulo

Abstract

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 São 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 São 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 Tietê River and in the southeast with probabilities above 50%.

Highlights

More people are affected by flash floods than any other type of natural disaster [1,2,3].In recent years, the most populated cities have been affected by floods and flash floods that directly or indirectly impact the socioeconomic development of the population
São Paulo, known as the most populated city in Brazil, has frequent flash floods caused by several factors that are sometimes related to the hydrographic basin, local catchments, and local effects like the topography and soil coverage
This study aims to estimate the probability of occurrence of flash floods in São Paulo city considering its watersheds

Summary

Introduction

More people are affected by flash floods than any other type of natural disaster [1,2,3]. The most populated cities have been affected by floods and flash floods that directly or indirectly impact the socioeconomic development of the population. São Paulo, known as the most populated city in Brazil, has frequent flash floods caused by several factors that are sometimes related to the hydrographic basin, local catchments, and local effects like the topography and soil coverage. Flash flood forecasting algorithms are commonly based on hydrological or hydraulic models [4,5,6,7], numerical simulation models [3,8,9,10], and Machine Learning algorithms such as the Artificial Neural

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