Performance of rainfall threshold for flood identification from ground- and satellite-based (sub) daily data

Abstract

Great effort has been made over the last few decades to develop and improve methods for monitoring hydrological disasters. Among them, rainfall thresholds, defined as the minimum rainfall conditions that are likely to trigger hydrological disasters, are the most popular tool used to study the relationship between rainfall and hydrological disaster occurrences, highlighted due to their straightforward approach for application in different regions. Some factors make it difficult to determine rainfall thresholds, such as the quality of rainfall and disasters data, and the large distances between the rain gauges and the disaster. To overcome these limitations, the use of satellite-based precipitation products is a way out to characterize rainfall events that trigger disasters. Accordingly, this thesis aims to present an improved method of using a threshold of peak rainfall intensity for robust floods evaluation and warnings by applying probabilistic-based methods and taking into consideration the antecedent conditions. Moreover, this study assesses the quality of satellite-based precipitation products to create rainfall thresholds for floods monitoring. São Paulo State was selected as the study area because is a typical hot spot frequented by landslides and floods. In addition, São Paulo is the richest state in Brazil, with the largest number of floods, flash floods, and sub-daily rainfall data made available by public agencies. Results show that the use of tolerance levels and the delineating of an intermediate threshold by incorporating an exponential curve that relates rainfall intensity and Antecedent Precipitation Index (API) helped reduce significantly many uncertainties in the hydrological disasters monitoring. Moreover, the use of satellite-based products showed to be less accurate compared to rain gauges to characterize extreme rainfall events and delineating the thresholds, tending to underestimate the ground-based precipitation mainly for sub-daily scales. Although underestimating the ground-based data, the satellite-based products can be applied as an alternative source to develop warning systems, especially in areas with a lower density of rain gauges. Overall, the results found in this study showed to be helpful for decision-making by the implementation of flood monitoring and early warning systems, and, consequently to contribute to the development of more robust and/or complex flood models able to minimize the hydrological impacts, whether using ground-based or satellite-based data.