摘要
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.
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.
作者
Maiconn Vinicius de Moraes
Luana Albertani Pampuch
Cassiano Antonio Bortolozo
Tatiana Sussel Gonçalves Mendes
Marcio Roberto Magalhães de Andrade
Daniel Metodiev
Tristan Pryer
Maiconn Vinicius de Moraes;Luana Albertani Pampuch;Cassiano Antonio Bortolozo;Tatiana Sussel Gonçalves Mendes;Marcio Roberto Magalhães de Andrade;Daniel Metodiev;Tristan Pryer(Environmental Engineering Department, Institute of Science and Technology, Sã,o Paulo State University, Sã,o José dos Campos, Brazil;Cemaden—National Center for Monitoring and Early Warning of Natural Disasters, General Coordination of Research and Development, Sã,o José dos Campos, Brazil;Department of Mathematical Sciences, University of Bath, Bath, UK)
