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Landslides Triggered by the May 2017 Extreme Rainfall Event in the East Coast Northeast of Brazil.

Authors :
Espinoza, Nikolai S.
dos Santos, Carlos A. C.
Silva, Madson T.
Gomes, Helber B.
Ferreira, Rosaria R.
da Silva, Maria L.
Santos e Silva, Cláudio M.
de Oliveira, Cristiano P.
Medeiros, João
Giovannettone, Jason
Amaro, Venerando E.
Santos, Celso A. G.
Mishra, Manoranjan
Source :
Atmosphere. Oct2021, Vol. 12 Issue 10, p1261. 1p.
Publication Year :
2021

Abstract

Given the increasing occurrence of landslides on the East Coast Northeast of Brazil (ECNEB), it is essential to understand its conditions and triggering factors because meteorological anomalies triggered by a landslide will threaten life and property in the region. In this sense, this research aimed to diagnose the meteorological conditions that triggered landslides in the ECNEB in May 2017, evaluate the terrain's intrinsic conditions using elevation, slope, and susceptibility parameters and determine critical precipitation thresholds for the city with the highest number of landslide risk areas in the region. A dynamic downscaling experiment was carried out using the Regional Climate Model (RegCM) to verify the ability of this model to represent rainfall over the ECNEB. The results from the intrinsic factors showed that the ECNEB is highly susceptible to landslides with various high-risk sectors for landslides to the population. The extreme rainfall event was associated with the convergence of humidity at low levels over the ocean, which contributed to landslides in the ECNEB, mainly in the State of Pernambuco, where 67 landslides were registered. The RegCM numerical simulation underestimated the high daily rainfall signal seen on the Tropical Rainfall Measuring Mission satellite. It is suggested that sensitivity tests can be performed using other physical parameters to find the best model configuration for the ECNEB. This work recommends that exploring the relationship between precipitation and landslides will provide objective criteria for assessing risk areas by contributing to the predictability of disasters in this region. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20734433
Volume :
12
Issue :
10
Database :
Academic Search Index
Journal :
Atmosphere
Publication Type :
Academic Journal
Accession number :
153219971
Full Text :
https://doi.org/10.3390/atmos12101261