Compared to other natural hazards such as hurricanes or forest fires that annually propagate, large tsunamis are infrequent. As a result, over the last 50 years as digital geophysical instrumentation has matured, local tsunami warning systems that alert the coastlines immediately adjacent to a large event have not been a priority of national or international monitoring agencies. The incidence of return periods of large events are usually measured in many decades to centuries. Thus, local warning systems do not exist in the majority of countries located along subduction zones, including the United States. However, recent events in Indonesia, Chile, and Japan, have shown that despite their comparative rarity, tsunamis can lead to substantial casualties, potentially tallied in the tens to hundreds of thousands of lives, as well as to the total economic collapse of the affected regions.
Compounding the problem are steady increases in population in tsunami-prone areas over the last 25 years. Because evacuation start time is the most important variable in tsunami mortality rates, rapid tsunami information systems that forecast intensities at the local level in the first 5 minutes are essential in providing actionable information to emergency responders and decision-makers to order evacuations in the affected regions as quickly as possible. All of these elements make the development of a rapid and accurate local tsunami warning methodology, and its implementation, a pressing issue which we propose to help solve in this work.
The use of Global Navigation Satellite System (GNSS) displacement data in the near-field is a paradigm-shifting technology thanks to its ability to track the motions of large earthquakes without going off-scale. Real-time, high-data-rate GNSS networks are currently operational in many countries around the Pacific Rim. These networks were originally installed to measure long-term tectonic motions, and over time, were upgraded with higher sample rate receivers and robust telemetry. Because of this, these networks are primed to both record long-term tectonic motions and strong ground motions from nearby great earthquakes, oftentimes with greater spatial density than complementary seismic networks.
We will modernize near-field (local) operational tsunami forecasting and early warning through the addition of GNSS-derived earthquake source products and the seamless connection to already existing NOAA tsunami modeling codes. Extensive testing, both online and offline, will be performed using historical and synthetic earthquake datasets. These tests will help to guide modifications in the software and familiarize practitioners with the strengths and limitations of the different codes. Finally, using lessons learned, we will work with partners in Chile and New Zealand to guide them through the process of an operational GNSS-enabled tsunami early warning system.