An empirical method to forecast the effect of storm intensity on shallow landslide abundance

Abstract

alii, 2009), and involve failure of soil and weathered bedrock layers. They are commonly fractions of a meter to several meters deep, rarely exceeding ~10 m in width or length. A fraction of these failures mobilize as debris flows, mixtures of soil, water and rock that sweep down steep valleys at high velocities and destroy homes and infrastructure with little warning. Varved sediments in the Santa Barbara basin, southern California, may contain the geologic record of the largest storms over the past millennium (sCHimmelman et alii, 2003). Layers interpreted as storm deposits have a periodicity of several hundred years, not unlike earthquake recurrence intervals for the San Andreas Fault. These deposits are substantially thicker than those associated with storms of January 1969, the most recent historic events to generate widespread landslides in southern California. If layer thickness scales with storm intensity, these layers imply that southern California has experienced storms that are massive compared to our recent historical experience. Unlike seismic hazard maps available for some large faults, we cannot begin to quantify the magnitude of landslide hazards that will accompany such a storm. Put simply, we cannot answer the question of how many shallow landslides California's largest storms would trigger We are beginning to understand more about what such massive storms might look like. Recent work (RalPH et alii, 2006; neiman et alii, 2008) illustrates that some of California’s largest storms are atmosABSTRACT We hypothesize that the number of shallow landslides a storm triggers in a landscape increases with rainfall intensity, duration and the number of unstable model cells for a given shallow landslide susceptibility model of that landscape. For selected areas in California, USA, we use digital maps of historic shallow landslides with adjacent rainfall records to construct a relation between rainfall intensity and the fraction of unstable model cells that actually failed in historic storms. We find that this fraction increases as a power law with the 6-hour rainfall intensity for sites in southern California. We use this relation to forecast shallow landslide abundance for a dynamic numerical simulation storm for California, representing the most extreme historic storms known to have impacted the state.