Human Settlement Pressure Drives Slow-Moving Landslide Exposure

Joaquin V. Ferrer*, Guilherme Samprogna Mohor, Olivier Dewitte, Tomáš Pánek, Cristina Reyes-Carmona, Alexander L. Handwerger, Marcel Hürlimann, Lisa Köhler, Alexandra Urgilez Vinueza, More Authors

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

A rapidly growing population across mountain regions is pressuring expansion onto steeper slopes, leading to increased exposure of people and their assets to slow-moving landslides. These moving hillslopes can inflict damage to buildings and infrastructure, accelerate with urban alterations, and catastrophically fail with climatic and weather extremes. Yet, systematic estimates of slow-moving landslide exposure and their drivers have been elusive. Here, we present a new global database of 7,764 large (A ≥ 0.1 km2) slow-moving landslides across nine IPCC regions. Using high-resolution human settlement footprint data, we identify 563 inhabited landslides. We estimate that 9% of reported slow-moving landslides are inhabited, in a given basin, and have 12% of their areas occupied by human settlements, on average. We find the density of settlements on unstable slopes decreases in basins more affected by slow-moving landslides, but varies across regions with greater flood exposure. Across most regions, urbanization can be a relevant driver of slow-moving landslide exposure, while steepness and flood exposure have regionally varying influences. In East Asia, slow-moving landslide exposure increases with urbanization, gentler slopes, and less flood exposure. Our findings quantify how disparate knowledge creates uncertainty that undermines an assessment of the drivers of slow-moving landslide exposure in mountain regions, facing a future of rising risk, such as Central Asia, Northeast Africa, and the Tibetan Plateau.

Original languageEnglish
Article numbere2024EF004830
Number of pages17
JournalEarth's Future
Volume12
Issue number9
DOIs
Publication statusPublished - 2024

Keywords

  • Bayesian inference
  • landslide exposure
  • natural hazards
  • slow-moving landslides

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