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

Espinoza, Nikolai S.; Santos, Carlos Antônio Costa dos; Silva, Madson Tavares; Gomes, Heliofábio Barros; Ferreira, Rosária R.; Silva, Maria Leidinice da; Silva, Cláudio Moisés Santos e; Oliveira, Cristiano Prestrelo de; Medeiros, João Telésforo Nóbrega de; Giovannettone, Jason; Amaro, Venerando Eustáquio; Santos, Celso Augusto Guimarães; Mishra, Manoranjan

doi:10.3390/atmos12101261

Cited by 12 publications

(2 citation statements)

References 61 publications

(57 reference statements)

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“…Between May 23 and 30, 2017, a series of floods, landslides, and property destruction affected 12 cities in the State of Pernambuco, resulting in numerous deaths. Jaboatão dos Guararapes was particularly hit, experiencing six landslides [28]. Another significant event occurred from June 13 to 18, 2019 (Figure 2 Wave Disturbances (EWD) [31].…”

Section: Landslide Eventsmentioning

confidence: 99%

Thresholds of Instability: Precipitation, Landslides, and Early Warning Systems in Brazil

Moraes,

Pampuch,

Bortolozo

et al. 2023

IJG

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Rainfall accumulation thresholds are crucial for issuing landslide warnings by identifying when soil saturation from rain could potentially trigger a landslide. Two essential types of thresholds are considered: environmental and operational. The environmental threshold indicates the minimum rainfall level required to potentially initiate a landslide. Conversely, the operational threshold is set lower to enable agencies to issue alerts before reaching environmental thresholds. Establishing these thresholds improves the accuracy of landslide predictions in terms of location and timing. This study introduces an innovative approach for determining these thresholds. Our approach employs cluster analysis and historical landslide data from the Metropolitan Region of Recife, Pernambuco State, Brazil. We applied our defined values to a significant landslide event in 2022, validating their robustness as the foundation for the operational threshold used by Cemaden, Brazil's National Center for Monitoring and Early Warning of Natural Disasters.

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Section: Landslide Eventsmentioning

confidence: 99%

Thresholds of Instability: Precipitation, Landslides, and Early Warning Systems in Brazil

Moraes,

Pampuch,

Bortolozo

et al. 2023

IJG

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“…In Brazil, extreme hydrological events represent a significant threat to the Northeast region since it presents high spatial and temporal variability in its rainfall regime [19]. The state of Pernambuco, which is located in this region, is susceptible to extreme rainfall events and is frequently affected by heavy rains, which cause coastal erosion, landslides, floods, and inundations, especially in the Metropolitana do Recife, Zona da Mata, and Agreste Pernambucano mesoregions [20,21], and by cycles of recurrent droughts, which mainly affect the semiarid region [22]. In the last 20 years, floods, inundations, and landslides were the disasters that most displaced people and caused the death of the population of Pernambuco.…”

Section: Introductionmentioning

confidence: 99%

Climate Indices-Based Analysis of Rainfall Spatiotemporal Variability in Pernambuco State, Brazil

Silva

Santos

Silva

et al. 2022

Water

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In Brazil, the Northeast region, particularly the state of Pernambuco, is prone to extreme hydrological events, especially episodes of heavy rain and long periods of drought. This study examined the spatiotemporal variability of rainfall in Pernambuco and the occurrence of extreme rainfall events. In this study, the following climate indices were used in daily rainfall data from the historical series from 1990 to 2020: (a) the Precipitation Concentration Index (PCI), (b) the Standardized Precipitation Index (SPI), (c) the Precipitation Concentration Degree (PCD), (d) the Precipitation Concentration Period (PCP), and (e) the Rainfall Anomaly Index (RAI), and the non-parametric Mann–Kendall and Sen’s slope tests, for the analysis of trends in the series of precipitation and the studied climate indices. The results obtained indicated that the east of the state presents more distributed precipitation throughout the year, being “moderately seasonal” in the mesoregions Metropolitana do Recife and Zona da Mata; “seasonal” in the Agreste Pernambucano mesoregion; and “strongly seasonal” in the Sertão Pernambucano and Sertão do São Francisco mesoregions. The SPI found that the most severe and extreme droughts occurred in almost all mesoregions during the 1990s, except in the Sertão do São Francisco mesoregion, where they were predominant in the 2010s. Furthermore, based on the RAI index, it was possible to have a greater occurrence of negative anomalies than positive anomalies, especially in the last decade (2010–2020), indicating a possible change in rainfall patterns. However, more in-depth research is required to determine the possible causes of this increased number of negative anomalies. Finally, the trend analysis indicated that only the SPI-12 presented trends in the Metropolitana do Recife, Agreste, and Sertão Pernambucano mesoregions. However, Sen’s slope test indicated that the magnitude of these trends is not significant.

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Extreme Rainfall Analysis in Pernambuco, Northeast Brazil, Using a High‐Resolution Gridded Dataset

Gomes,

da Silva,

Araújo

et al. 2024

Intl Journal of Climatology

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This paper presents a detailed spatio‐temporal analysis of the rainfall in the state of Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by the Expert Team on Climate Change Detection, Monitoring and Indices. To accomplish this, daily rainfall 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using the Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance and magnitude of index trends were assessed using the modified Mann–Kendall and Sen's slope tests. This study also examined whether there existed a significant difference in climate indices among the three regions (Sertão, Agreste and Zona da Mata) within the state. The findings revealed notable significant negative trends in the PRCPTOT, R10mm, R20mm, Rx1day, Rx5day and CWD indices across all regions of Pernambuco, exhibiting a gradient from the coast to the state's interior. Reduction values of up to 15 mm year−1 for PRCPTOT, 0.7 day year−1 for R10mm, 0.2 day year−1 for R20mm, 0.01 mm year−1 for Rx1day, 0.03 mm year−1 for Rx5day, 0.4 day year−1 for CWD were observed. Furthermore, an alarming pattern was also noted for CDD, displaying a higher concentration of significant positive trends in all regions of the state, with estimated increases of up to 1.4 day year−1. Conversely, a balance of trends—both positive and negative—was observed across the entire state for R95p and R99p, with a majority of trends proving non‐significant. SDII exhibited a higher frequency of grid points showing a significant positive trend, particularly notable in the Sertão and Zona da Mata regions, where significant differences in the index values were absent. However, the remaining indices showcased notable regional differences, with values decreasing from the east to the west of the state, except for CDD. This study will assist decision makers, providing detailed long‐term information essential for preventing natural disasters and supporting socioeconomic and environmental policies in the state.

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

Cited by 12 publications

References 61 publications

Thresholds of Instability: Precipitation, Landslides, and Early Warning Systems in Brazil

Thresholds of Instability: Precipitation, Landslides, and Early Warning Systems in Brazil

Climate Indices-Based Analysis of Rainfall Spatiotemporal Variability in Pernambuco State, Brazil

Extreme Rainfall Analysis in Pernambuco, Northeast Brazil, Using a High‐Resolution Gridded Dataset

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