Subcontractor of a project leading the following tasks: (a) Multi-hazard confluence modeling of the stormwater infrastructure considering the future sea level rise; (b) Assess change in the return periods of sea levels; (c) Assess change in the return periods of runoff.

Infrastructure systems such as natural gas pipelines are experiencing changes in exposure to natural hazards across California. Consequently, infrastructure will likely face more severe climatic conditions in a warming climate, with potential societal and economic consequences. Infrastructure design has historically relied on the notion of stationarity, which assumes that the statistics of hydroclimatic extremes such as rainfall or streamflow do not change over time. The necessity to adapt infrastructure to climate extremes was recognized by California State Legislature in Assembly Bill 2800, which aimed to start the process to ensure the long-term resilience of infrastructure throughout the state. This project explored  quantifying climate change impacts on different climatic hazards that can potentially impact natural gas infrastructure systems such as extreme rainfall, coastal and inland flooding, and wildfires. 

Infrastructure design and risk assessment (e.g., road and bridge design), and rainfall-triggered landslide models have been based on the concept of stationarity, assuming that the statistical characteristics of climate extremes (such as rainfall and streamflow) remain relatively constant over time. However, over the past century, we have witnessed a warming climate, leading to more intense precipitation extremes in certain regions, possibly due to increased atmospheric water holding capacity. As a result, both natural slopes and man-made infrastructure are likely to encounter more severe climatic conditions, with potential implications for human and socioeconomic well-being. In this project, our team developed a framework for updating rainfall Intensity-Duration-Frequency (IDF) curves and their uncertainty using a non-stationary model based on Bayesian inference, following California's Fourth Assessment guidelines.