Abstract

Search and rescue (SAR) refers to every operation aiming to find someone presumed lost, sick, or injured in remote or hard-to-access areas. This study presents the design of an operational system that supports maritime SAR emergencies by combining information from global hydrodynamic models (GHM) and a local hydrodynamic model (LHM) implemented in FVCOM. The output of these hydrodynamic models is used as input in a multiple particle drift estimator (MPDE) to estimate the trajectories of the floating elements derived from accidents in the ocean. The MPDE also includes trajectory estimates using the empirical LEEWAY formulation. The modeling system is validated with data collected during a SAR emergency that occurred on 2 September 2011, where a C-212 aircraft from the Chilean Air Force destined to the Juan Fernandez Archipelago crashed in the ocean between the islands of Santa Clara and Robinson Crusoe. Trajectories were assessed in terms of the commonly used NCLS (normalized cumulative Lagrangian separation) performance indicator and a modified version, NCLSmod, which considers both the movement and orientation of the trajectories. The LHM was executed in three scenarios: forced only with tide, forced with tide and wind combined, and forced only with wind. The performance of the different models varied in response to the ocean-atmosphere conditions and their local variations at the time of the accident. In times of calm wind, models with tidal influence performed better, while wind-forced models performed better when winds were greater than 7 km h(-1). The use of FVCOM (LHM) solved the coastal circulation and accounted for bathymetric effects in the Juan Fernandez Archipelago area. This resulted in an improved variability and distribution of the modeled trajectories compared to the observed drifter trajectories. This work is the first study related to cases of maritime SAR emergencies in Chile, and provides a fast tool to estimate search areas based on an ensemble of particle drift and trajectory forecasts using multiple publicly available data sources.