D. B. Kirschbaum
Steven Sobieszczyk
M. F. Jasinski
J. S. Borak
Stephen L. Slaughter
T. A. Stanley
2020
<p><span>The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the </span>seasonality<span> of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.</span></p>
application/pdf
10.1016/j.envsoft.2020.104692
en
Elsevier
Building a landslide hazard indicator with machine learning and land surface models
article