Bayesian reconstruction of sea level and hydroclimates from coastal landform inversion: application to Santa Cruz (US) and Gulf of Corinth

Abstract. Quantifying Quaternary sea-level changes and hydroclimatic conditions is an important challenge, given their intricate relation with paleo-climate, ice-sheets, and geodynamics. The world's coastlines provide an enormous geomorphologic archive, from which forward landscape evolution modeling studies have shown their potential to unravel paleo sea levels, albeit at the cost of assumptions on the genesis of these landforms. We take a next step, by applying a Bayesian approach to jointly invert the geometries of multiple coastal terrace sequences to paleo sea- and lake-level variations and extract past hydroclimatic conditions. Using a Markov chain Monte Carlo sampling method, we first test our approach on synthetic marine terrace profiles as proof of concept and then benchmark our model on an observed marine terrace sequence in Santa Cruz (US). We successfully reproduce observed sequence morphologies and simultaneously obtain probabilistic estimates for past sea-level variations, as well as for other model parameters, such as uplift and erosion rates. When applied to the semi-isolated Gulf of Corinth (Greece), our method allows the geomorphic Rosetta stone to be deciphered at an unprecedented resolution, revealing the connectivity between the Lake/Gulf of Corinth and the open sea for different hydroclimatic conditions. Eustatic sea levels and changing sill depths drive marine and transitional phases during interglacial and interstadial periods, whereas wetter and drier hydroclimates, respectively, over- and underfill Lake Corinth during interstadial and glacial periods.