Building a landslide hazard indicator with machine learning and land surface models. (July 2020) - Stanley, T.A.; Kirschbaum, D.B.; Sobieszczyk, S.; Jasinski, M.F.; Borak, J.S.; Slaughter, S.L.
Abstract: 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 seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate. Highlights: Landslides in the Pacific Northwest followed a strong seasonal cycle along the coast. Land data assimilation systems provide long, consistent records of hydrologic variables. Machine learning can fill the gaps in historical records of landslide activity.