Publication Date:
2023-01-20
Description:
Seasonal variations in landslide activity remain understudied compared to recent advances in landslide early warning at hourly to daily timescales. Here, we learn the seasonal pattern of monthly landslide activity in the Pacific Northwest from five heterogeneous landslide inventories with differing spatial and temporal coverage and reporting protocols combined in a Bayesian multi‐level model. We find that landslide activity is distinctly seasonal, with credible increases in landslide intensity, inter‐annual variability, and probability marking the onset of the landslide season in November. Peaks in landslide probability in January and intensity in February lag the annual peak in mean monthly precipitation and landslide activity is more variable in winter than in summer, when landslides are rare. For a given monthly rainfall, landslide intensity at the season peak in February is up to 10 times higher than at the onset in November, underlining the importance of antecedent seasonal hillslope conditions.
Description:
Plain Language Summary:
Better knowing when landslides are likely over the course of the year can reduce landslide risk by improving emergency preparedness. One research challenge is that catalogs of past landslides rarely cover the same areas or time periods, and have been collected in different ways. Here, we use statistical models to estimate monthly landslide activity in the Pacific Northwest. The models are able to combine five different landslide catalogs to make best use of all available information. We find a seasonal pattern in both the average number of landslides in a month and the probability of having any landslides. The landslide season begins in November, when the average number and the probability of landslides increase. The probability of landslides peaks in January and the average number in February, lagging behind winter rainfall peaks by one to two months. While landslides are least likely in summer, their activity is more variable in winter, with some winters bringing hundreds of landslides, and some very few. At the landslide season peak in February, a comparable amount of rain leads to many more landslides than at the onset in November, likely because already wet hillslopes are more prone to failure.
Description:
Key Points:
Bayesian inference learns the seasonal pattern of landslide activity in the Pacific Northwest from five combined heterogeneous inventories.
Landsliding is distinctly seasonal with highest probability (intensity) in January (February), lagging the annual precipitation peak.
Landslide intensity for a given monthly rainfall during peak season in February is up to 10 times higher than at the onset in November.
Description:
DFG RTG NatRiskChange
Description:
https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4
Keywords:
ddc:551.3
;
landslide
;
seasonality
;
Pacific Northwest
;
Bayesian multi‐level models
;
logistic regression
;
negative binomial regression
Language:
English
Type:
doc-type:article
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