Due to climate change, the shape of future extreme precipitation likeliness is uncertain. Global climate models are not intended to capture local extremes, and different climate models provide different results. This is problematic for public works departments, transportation agencies, and other groups responsible for water resources infrastructure, because extreme event estimates drive engineering design. However, using appropriate datasets and techniques, future extreme precipitation can be estimated.

Wood is developing a web application for dynamically analyzing downscaled global climate model (GCM) future precipitation and current NOAA Atlas-14 precipitation-frequency estimates, producing future climate precipitation-frequency estimates that are relevant at a local scale. Based on the LOCA CMIP5 downscaling, this application covers the full extent of Atlas-14 within the lower 48 states. Analysis is dynamic – time periods, climate model groups, statistical distribution types can be adjusted as necessary – and estimates are provided for both the RCP 4.5 (“intermediate”) and RCP 8.5 (“no action”) climate modeling scenarios. Resulting “future climate” precipitation-frequency curves can help inform long-term planning of water resources infrastructure.