Forecasting sea cliff retreat in Southern California using process-based models and artificial neural networks

Modeling coastal geomorphic change over multi-decadal time and regional spatial scales (i.e. >20 km alongshore) is in high demand due to rising global sea levels and heavily populated coastal zones, but is challenging for several reasons: adequate geomorphic and oceanographic data often does not exist over the entire study area or time period; models can be too computationally expensive; and model uncertainty is high. In the absence of rich datasets and unlimited computer processing power, researchers are forced to leverage existing data, however sparse, and find analytical methods that minimize computation time without sacrificing (too much) model reliability. Machine learning techniques, such as artificial neural networks, can assimilate and efficiently extrapolate geomorphic model behavior over large areas. They can also facilitate ensemble model forecasts over a broad range of parameter space, which is useful when a paucity of observational data inhibits the constraint of model parameters. Here, we assimilate the behavior of two established process-based sea cliff erosion and retreat models into a neural network to forecast the impacts of sea level rise on sea cliff retreat in Southern California (~400 km) through the 21^st century. Using inputs such as historical cliff retreat rates, mean wave power, and whether or not a beach is present, the neural network independently reproduces modeled sea cliff retreat as a function of sea level rise with a high degree of confidence (R² > 0.9, mean squared error < 0.1 m yr^-1). Results will continuously improve as more model scenarios are assimilated into the neural network, and more field data (i.e., cliff composition and rock hardness) becomes available to tune the cliff retreat models. Preliminary results suggest that sea level rise rates of 2 to 20 mm yr^-1 during the next century could accelerate historical cliff retreat rates in Southern California by an average of 0.10 – 0.56 m yr^-1.