VARIABILITY IN ATMOSPHERIC CONDITIONS AMONG WIDESPREAD LANDSLIDE EVENTS IN CALIFORNIA

Conditions leading to rainfall-induced landslides are often described in terms of season antecedent and within-storm rainfall, and triggering thresholds are typically described using rainfall intensity and duration. In California, previous studies have identified a range of antecedent and intensity-duration thresholds for landslide events, creating ambiguity around what operational thresholds should be applied in different areas across the state. Given that atmospheric processes control rainfall intensity, duration, and spatial extent, we hypothesize that the identification of common atmospheric processes across storm events and climatic or geomorphic regions may improve predictability of landslide events or interpretation of past events. We investigated atmospheric conditions associated with 11 widespread landslide events to identify common atmospheric conditions across events as well as to highlight differences among events. Our analysis includes atmospheric variables spanning the synoptic scale (e.g., water vapor transport and geopotential height fields) to mesoscale (e.g., frontal characteristics and radar reflectivity), as well as season antecedent and within-storm precipitation. Though several common factors are present across the storm events that produced widespread landslides, we find considerable variability in the characteristics of these storms. For example, all events featured enhanced moisture transport in the form of an atmospheric river, though the magnitude, orientation, and other characteristics of the moisture plumes varies across events. Analysis of radar reflectivity and other variables reveals a variety of mesoscale features across events that produced high-intensity precipitation that likely acted as a trigger for landslide events, such as isolated thunderstorms, mesoscale vortices, and narrow cold frontal rainbands. This presentation will discuss these similarities and differences in atmospheric conditions as well as and the benefits and challenges this information presents for forecasting landslides, developing early warning systems, or attributing past and future landslide events to atmospheric conditions.