The 2011 Tohoku Tsunami in Japan occurred as the result of an earthquake in the Pacific Ocean. The seismic event generated enormous waves that flooded approximately 561 square kilometers of Japanese coastline, resulting in at least 15,891 deaths and the meltdown of the Fukushima Nuclear Power Plant. Japan’s early warning systems alerted many citizens about the incoming disaster—but tracking and forecasting tsunamis associated with such events are an ongoing area of investigation.
Most current tsunami forecasting systems require data about the initial earthquake that sets the tsunami event into motion. The magnitude, amount of tectonic slip, and location of the epicenter are all vital information for accurate predictions. Here, however, Maeda et al. test a new method of tsunami estimation that does not rely on earthquake source data. Instead, their approach involves measuring the tsunami waves themselves as they flow over an array of sensors mounted to the ocean floor deep in the Pacific.
The scientists constructed their model to mirror a real-life tsunami detection network, called S-net, which is currently being constructed around the Japan Trench. Then, to check the model’s accuracy (and thus the real technology’s potential), the team replicated the 2011 Tohoku Tsunami conditions and analyzed the data.
According to the team’s results, if the S-net detection system had existed in 2011, it could have predicted the wave height and flow velocity long before the tsunami reached land. Unlike earthquakes, tsunamis propagate slowly: it took about 30 minutes for the first waves to reach Japan’s coastline but only a minute for the earthquake to arrive. The simulated version of S-net was able to outline the size and shape of the simulated 2011 tsunami within 1000 seconds after the earthquake started. The scientists say that if the sensors work as well in the real world as they do in the models, S-net could provide governments and emergency responders with valuable real-time data about a tsunami, without incorporating complicated seismic variables that require immense computing power and are often hard to detect. (Geophysical Research Letters, doi:10.1002/2015GL065588, 2015)
—David Shultz, Freelance Writer
Citation: Shultz, D. (2016), Tsunami forecast system could provide early warnings in Japan, Eos, 97, doi:10.1o29/2016EO045081. Published on 4 February 2016.