Parallel Computing Approach for Rapid Estimation of Tsunami Hazard and Population Exposure in Peru

Abstract

Peru faces a significant tsunami hazard due to its location along the Pacific Ring of Fire. Historical megathrust earthquakes and their resulting tsunamis have caused severe damage, highlighting the need for improved warning systems. This study investigates potential tsunami impacts along the central Peruvian coast—including the regions of Ica, Callao, Lima, and Ancash—using numerical simulations. To enable rapid and efficient simulations, we developed gWave-CPU, a parallelized version of the TUNAMI-N2 model created at Tohoku University. Using 90-m-resolution topographic and bathymetric data in combination with census data, we assessed population exposure under two seismic scenarios: a plausible event based on interseismic coupling and a historical scenario simulating the 1746 tsunami. Under the historical scenario, the exposed population was estimated at 320,128, with the highest concentrations in Callao and Lima. Numerical simulations of four hours of tsunami propagation and inundation were conducted using our parallelized implementation, reducing computation time to 68 minutes—a 26.3-fold speedup compared to the conventional model. The results demonstrate that tsunami inundation and population exposure in this region can be efficiently estimated using the proposed approach, providing a valuable contribution to tsunami hazard assessment, management, and emergency preparedness along Peru’s central coast.