Towards Deep Learning Models for Global Coastal Sea Level Prediction

Accurate sea level prediction is crucial for coastal communities and infrastructure. Unfortunately, classical approaches can be computationally expensive and inaccurate, especially in data-sparse regions. Recently, scientists have started to explore to which degree deep learning models could help address these problems. Combined with long historical tidal gauge records, these data-driven approaches offer the potential to improve the accuracy and spatial resolution compared to classical modeling schemes, while at the same time being computationally far cheaper to operate.

Yet, deep learning for sea level modeling is still in its infancy: existing studies typically struggle to accurately predict extreme events, differ in their strategies of (pre-)processing input and output data, and focus on individual gauges or small regions. In this contribution, we take inspiration from recent successes in global riverine hydrologic modeling, where deep learning models greatly improved the accuracy of predictions in ungauged regions. Translating these findings to a coastal floods context, we present our work towards globally applicable deep learning models of sea level and storm surge. Specifically, we focus on these models’ ability to make predictions in places that lack historical gauge measurements.

Our framework utilizes state-of-the-art deep learning architectures to capture complex spatial dependencies between atmospheric drivers and the ocean state. To ensure robust performance, we integrate a diverse set of input features, including ERA5 atmospheric reanalysis (wind and pressure), FES tidal predictions, and high-resolution static geospatial data such as bathymetry and land-sea masks. Furthermore, we explore the utility of pre-trained geospatial embedding data to encode local station properties.

We compare the data-driven predictions with established hydrodynamic model baselines, such as the ocean model run by Environment and Climate Change Canada (ECCC) and the Global Tide and Surge Model (GTSM). Our findings indicate that deep learning approaches can exceed the performance of physics-based models across standard metrics. Furthermore, validation against real-world extreme events confirms the model's superior ability to identify high-impact storm surges.