Simple empirical method for estimating lava-effusion rate using nighttime Himawari-8 1.6-µm infrared images

Abstract

AbstractThe effusion rate of lava is one of the most important eruption parameters, as it is closely related to the migration process of magma underground and on the surface, such as changes in lava flow direction or formation of new effusing vents. Establishment of a continuous and rapid estimation method has been an issue in volcano research as well as disaster prevention planning. For effusive eruptions of low-viscosity lava, we examined the relationship between the nighttime spectral radiance in the 1.6-µm band of the Himawari-8 satellite (R1.6Mx: the pixel value showing the maximum radiance in the heat source area) and the effusion rate using data from the 2017 Nishinoshima activity. Our analysis confirmed that there was a high positive correlation between these two parameters. Based on the linear-regression equation obtained here (Y = 0.47X, where Y is an effusion rate of 106 m3 day−1 and X is an R1.6Mx of 106 W m−2 sr−1 m−1), we can estimate the lava-effusion rate from the observation data of Himawari-8 via a simple calculation. Data from the 2015 Raung activity—an effusive eruption of low-viscosity lava—were arranged along the extension of this regression line, which suggests that the relationship is applicable up to a level of ~ 2 × 106 m3 day−1. We applied this method to the December 2019 Nishinoshima activity and obtained an effusion rate of 0.50 × 106 m3 day−1 for the initial stage. We also calculated the effusion rate for the same period based on a topographic method, and verified that the obtained value, 0.48 × 106 m3 day−1, agreed with the estimation using the Himawari-8 data. Further, for Nishinoshima, we simulated the extent of hazard areas from the initial lava flow and compared cases using the effusion rate obtained here and the value corresponding to the average effusion rate for the 2013–2015 eruptions. The former distribution was close to the actual distribution, while the latter was much smaller. By combining this effusion-rate estimation method with real-time observations by Himawari-8 and lava-flow simulation software, we can build a rapid and precise prediction system for volcano hazard areas.