We provide a detailed characterization of the frequency, intensity and duration of flooding events at a site along the Texas coast. Our analysis demonstrates the suitability of relatively simple wave run-up models to estimate the frequency and intensity of coastal flooding. Our results validate and expand a probabilistic model of coastal flooding driven by wave run-up that can then be used in coastal risk management in response to sea level rise.

Coastal flooding can cause significant damage to coastal ecosystems, infrastructure, and communities and is expected to increase in frequency with the acceleration of sea level rise. In order to respond to it, it is crucial to measure and model their frequency and intensity. Here, we show deep-learning techniques can be successfully used to automatically detect flooding events from complex coastal imagery, opening the way to real-time monitoring and data acquisition for model development.