Simple rainfall indices for forecasting hazardous events of hydrologic and geologic nature

Oswaldo Augusto Filho (), Paulo Sérgio Silva Júnior and Cahio Guimarães Seabra Eiras
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Oswaldo Augusto Filho: University of São Paulo
Paulo Sérgio Silva Júnior: University of São Paulo
Cahio Guimarães Seabra Eiras: University of São Paulo

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 101, issue 3, No 4, 689-709

Abstract: Abstract Heavy rains are the main natural trigger agent of floods and slope movements responsible for significant economic and social losses in many regions of the Brazilian territory. The affected municipalities are generally scarce in technical and economic resources to invest in mitigation actions. This work aimed to define readily available rainfall indices to predict the occurrence of these dangerous events. The main pluviometric parameters used were the daily rainfall and the mobile cycle coefficient (MCC). MCC is defined as the ratio between the total amount of rainfall accumulated over a certain period and the accumulated rainfall considered normal for this period. The analyses were based on a spatial database containing daily rainfall, flood and landslides events that occurred from 1965 to 2016 in the São Carlos municipality (São Paulo, Brazil). The structuring of this database and the subsequent spatial analyses were performed using a geographic information system software. The results indicated good potential for the combined use of MCC and daily rainfall indices to predict floods in the study area. The correlations with landslides presented some incongruities that can be mainly explained by not considering the accumulated rainfall before the triggering of landslides and the small number of events available. The simplicity and easy access to these rainfall indices favor their use to subsidize Civil Defense preventive measures, while more detailed studies are not available.

Keywords: Daily rainfall; Floods; Geographic information system (GIS); Mobile cycle coefficient (MCC); Rainfall maps; Landslides (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11069-020-03890-4

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