Physics-anchored fusion for coastal sea level prediction with multi-source geodetic observations

Abstract

Abstract Accurate short-term tide forecasting is essential for flood early warning, port operations, coastal infrastructure protection, and adaptation planning under accelerating sea-level rise and intensifying storms. Traditional methods remain limited: harmonic analysis neglects surge contributions, while hydrodynamic models are computationally prohibitive for real-time applications. To address these gaps, we propose TideFusionNet, a lightweight, physics-anchored multi-source network that integrates tide-gauge records, hourly meteorology from ECMWF Reanalysis v5 (ERA5), harmonic constituents, satellite sea level anomaly (SLA) data, surface water and ocean topography (SWOT) snapshots, and static digital elevation model (DEM) data. A cross-source attention mechanism, anchored by gauge observations, ensures consistent information fusion, while spectral–phase regularization stabilizes tidal-band energy. Evaluations using one year of observations from Sippican Harbor (Buzzards Bay) demonstrate that TideFusionNet achieves near-zero bias, tight identity-line scatter, and smoothly increasing errors from 12 to 48 h horizons. Ablation experiments reveal a clear information hierarchy: harmonics and meteorology provide the largest contributions, satellites and DEM yield complementary gains, and the freshness–proximity gate effectively regulates SWOT inputs. Under typical missing-source conditions, the forecast skill degrades gracefully, confirming the robustness of the architecture. This study demonstrates how a compact, physics-anchored framework can deliver real-time, physics-constrained tide forecasts. The framework can support operational flood warning, port scheduling, infrastructure risk assessment, and local coastal digital twins by providing fast, interpretable, and physics-constrained sea-level forecasts.