Rainfall-Induced Landslides forecast using local precipitation and global climate indexes
We analyse RIL events between 1950 and 2002 to investigate the role played by climate variability, using the 'El Nino-Southern Oscillation' (ENSO), the Antarctic Oscillation (AAO) and local precipitation as predictors, through logistic and probabilistic (Logit and Probit) modelling. From t...
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ftunivctemuco:oai:repositoriodigital.uct.cl:10925/3897 2023-05-15T13:58:13+02:00 Rainfall-Induced Landslides forecast using local precipitation and global climate indexes Fustos, I Abarca del Rio, R. Moreno Yaeger, P. Somos Valenzuela, M. 2021-04-30T17:04:13Z http://repositoriodigital.uct.cl/handle/10925/3897 https://doi.org/10.1007/s11069-020-03913-0 en eng SPRINGER NATURAL HAZARDS,Vol.102,115-131,2020 http://repositoriodigital.uct.cl/handle/10925/3897 doi:10.1007/s11069-020-03913-0 NATURAL HAZARDS Rainfall-Induced Landslides logistic regression ENSO-AAO variability Article 2021 ftunivctemuco https://doi.org/10.1007/s11069-020-03913-0 2021-05-01T23:51:28Z We analyse RIL events between 1950 and 2002 to investigate the role played by climate variability, using the 'El Nino-Southern Oscillation' (ENSO), the Antarctic Oscillation (AAO) and local precipitation as predictors, through logistic and probabilistic (Logit and Probit) modelling. From the probabilistic regression analysis, it is clear that rain plays a major role, since its weight in the regression is almost 50%. However, we show that integrating South Pacific climate variability represented by ENSO/AAO significantly increases predictability, reaching over 87%. Moreover, sensitivity and specificity analyses confirm that although local rainfall is the main triggering factor, adding the two macroclimate variables increases the ability to predict true positive and negative occurrences by almost 80%. This confirms the need to integrate macroclimatic variables to make assertive local predictions. Surprisingly, and contrary to what might have been expected considering ENSO's recognized role in regional climate variability, the integration of AAO variability significantly improves RIL prediction capacity, while on average ENSO can be considered a second-order predictor. These results, obtained through a simple logistic regression methodology (Logit and/or Probit), can contribute to better risk management in the middle-latitude zones of Chile. The methodology can be extended to other areas of the world that do not have high-density hydrometeorological information to support preventive decision-making through logistic RIL forecasting. Article in Journal/Newspaper Antarc* Antarctic Repositorio Académico de la Universidad Católica de Temuco (UCT) Antarctic Pacific The Antarctic Natural Hazards 102 1 115 131 |
institution |
Open Polar |
collection |
Repositorio Académico de la Universidad Católica de Temuco (UCT) |
op_collection_id |
ftunivctemuco |
language |
English |
topic |
Rainfall-Induced Landslides logistic regression ENSO-AAO variability |
spellingShingle |
Rainfall-Induced Landslides logistic regression ENSO-AAO variability Fustos, I Abarca del Rio, R. Moreno Yaeger, P. Somos Valenzuela, M. Rainfall-Induced Landslides forecast using local precipitation and global climate indexes |
topic_facet |
Rainfall-Induced Landslides logistic regression ENSO-AAO variability |
description |
We analyse RIL events between 1950 and 2002 to investigate the role played by climate variability, using the 'El Nino-Southern Oscillation' (ENSO), the Antarctic Oscillation (AAO) and local precipitation as predictors, through logistic and probabilistic (Logit and Probit) modelling. From the probabilistic regression analysis, it is clear that rain plays a major role, since its weight in the regression is almost 50%. However, we show that integrating South Pacific climate variability represented by ENSO/AAO significantly increases predictability, reaching over 87%. Moreover, sensitivity and specificity analyses confirm that although local rainfall is the main triggering factor, adding the two macroclimate variables increases the ability to predict true positive and negative occurrences by almost 80%. This confirms the need to integrate macroclimatic variables to make assertive local predictions. Surprisingly, and contrary to what might have been expected considering ENSO's recognized role in regional climate variability, the integration of AAO variability significantly improves RIL prediction capacity, while on average ENSO can be considered a second-order predictor. These results, obtained through a simple logistic regression methodology (Logit and/or Probit), can contribute to better risk management in the middle-latitude zones of Chile. The methodology can be extended to other areas of the world that do not have high-density hydrometeorological information to support preventive decision-making through logistic RIL forecasting. |
format |
Article in Journal/Newspaper |
author |
Fustos, I Abarca del Rio, R. Moreno Yaeger, P. Somos Valenzuela, M. |
author_facet |
Fustos, I Abarca del Rio, R. Moreno Yaeger, P. Somos Valenzuela, M. |
author_sort |
Fustos, I |
title |
Rainfall-Induced Landslides forecast using local precipitation and global climate indexes |
title_short |
Rainfall-Induced Landslides forecast using local precipitation and global climate indexes |
title_full |
Rainfall-Induced Landslides forecast using local precipitation and global climate indexes |
title_fullStr |
Rainfall-Induced Landslides forecast using local precipitation and global climate indexes |
title_full_unstemmed |
Rainfall-Induced Landslides forecast using local precipitation and global climate indexes |
title_sort |
rainfall-induced landslides forecast using local precipitation and global climate indexes |
publisher |
SPRINGER |
publishDate |
2021 |
url |
http://repositoriodigital.uct.cl/handle/10925/3897 https://doi.org/10.1007/s11069-020-03913-0 |
geographic |
Antarctic Pacific The Antarctic |
geographic_facet |
Antarctic Pacific The Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_source |
NATURAL HAZARDS |
op_relation |
NATURAL HAZARDS,Vol.102,115-131,2020 http://repositoriodigital.uct.cl/handle/10925/3897 doi:10.1007/s11069-020-03913-0 |
op_doi |
https://doi.org/10.1007/s11069-020-03913-0 |
container_title |
Natural Hazards |
container_volume |
102 |
container_issue |
1 |
container_start_page |
115 |
op_container_end_page |
131 |
_version_ |
1766266388567031808 |