Projected Changes in California’s Precipitation Intensity-Duration-Frequency Curves

Traditionally, infrastructure design and rainfall-triggered landslide models rely on the notion of stationarity, which assumes that the statistics of hydroclimatic extremes (e.g., rainfall, streamflow, etc.) do not change significantly over time. However, during the last century, we have observed a warming climate with more intense precipitation extremes in some regions, likely due to increases in the water holding capacity of the atmosphere. Consequently, infrastructure and natural slopes will likely face more severe climatic conditions, with potential human and socioeconomic consequences. Here, we outline a framework for quantifying climate change impacts on natural and man-made infrastructure using bias-corrected multi-model simulations of historical and projected precipitation extremes. The approach evaluates changes in rainfall intensity-duration- frequency (IDF) curves and their uncertainty bounds using a non-stationary model based on Bayesian inference. We show that highly populated areas across California may experience extreme precipitation that is more intense and twice as frequent, relative to historical records, despite the expectation of unchanged annual mean precipitation. Since IDF curves are widely used for infrastructure design and risk assessment, the proposed framework offers an avenue for assessing infrastructure resilience and landslide hazard in a warming climate.