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

Moraes, Maiconn Vinicius de; Pampuch, Luana Albertani; Bortolozo, Cassiano Antonio; Mendes, Tatiana Sussel Gonçalves; Andrade, Marcio Roberto Magalhães de; Metodiev, Daniel; Pryer, Tristan

doi:10.4236/ijg.2023.1410048

IJG

2023

DOI: 10.4236/ijg.2023.1410048

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

Maiconn Vinicius de Moraes,

Luana Albertani Pampuch,

Cassiano Antonio Bortolozo

et al.

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Cited by 3 publications

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The SNAKE System: CEMADEN’s Landslide Early Warning System (LEWS) Mechanism

Andrade,

Bortolozo,

Carvalho

et al. 2023

IJG

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The SNAKE System: CEMADEN’s Landslide Early Warning System (LEWS) Mechanism

Andrade,

Bortolozo,

Carvalho

et al. 2023

IJG

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ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

Bortolozo,

Pampuch,

Andrade

et al. 2024

IJG

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A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators' experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale.

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Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides

Moraes,

Filho,

Mendes

et al. 2024

IJG

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

Cited by 3 publications

References 23 publications

The SNAKE System: CEMADEN’s Landslide Early Warning System (LEWS) Mechanism

The SNAKE System: CEMADEN’s Landslide Early Warning System (LEWS) Mechanism

ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides

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