Simple Empirical Method for Estimating Lava-Effusion Rate Using
Total Page:16
File Type:pdf, Size:1020Kb
Kaneko et al. Earth, Planets and Space (2021) 73:37 https://doi.org/10.1186/s40623-021-01372-w EXPRESS LETTER Open Access Simple empirical method for estimating lava-efusion rate using nighttime Himawari-8 1.6-µm infrared images Takayuki Kaneko1* , Atsushi Yasuda1 and Toshitsugu Fujii2 Abstract The efusion 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 fow direction or formation of new efus- ing vents. Establishment of a continuous and rapid estimation method has been an issue in volcano research as well as disaster prevention planning. For efusive 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 efusion rate using data from the 2017 Nishinoshima activ- ity. Our analysis confrmed that there was a high positive correlation between these two parameters. Based on the 6 3 1 linear-regression equation obtained here (Y 0.47X, where Y is an efusion rate of 10 m day− and X is an R1.6Mx 6 2 1 1 = of 10 W m− sr− m− ), we can estimate the lava-efusion rate from the observation data of Himawari-8 via a simple calculation. Data from the 2015 Raung activity—an efusive eruption of low-viscosity lava—were arranged along the 6 3 1 extension of this regression line, which suggests that the relationship is applicable up to a level of ~ 2 10 m day− . We applied this method to the December 2019 Nishinoshima activity and obtained an efusion rate of× 0.50 106 3 1 × m day− for the initial stage. We also calculated the efusion rate for the same period based on a topographic 6 3 1 method, and verifed that the obtained value, 0.48 10 m day− , agreed with the estimation using the Himawari-8 data. Further, for Nishinoshima, we simulated the extent× of hazard areas from the initial lava fow and compared cases using the efusion rate obtained here and the value corresponding to the average efusion rate for the 2013–2015 eruptions. The former distribution was close to the actual distribution, while the latter was much smaller. By combin- ing this efusion-rate estimation method with real-time observations by Himawari-8 and lava-fow simulation soft- ware, we can build a rapid and precise prediction system for volcano hazard areas. Keywords: Himawari-8, Volcanoes, Efusion rate, Lava, Remote sensing, Infrared Introduction length of lava fows (Walker 1973) and is also a critical It has been a vital issue to develop a method for continu- input parameter that infuences the results of lava-fow ous and quick estimation of the discharge rate of lava, to simulations (e.g., Ishihara et al. 1990; Miyamoto and promote volcano research and disaster prevention plan- Sasaki 1998). Tere are two ways to describe the dis- ning. Te discharge rate is one of the prime parameters charge rate for efusive eruptions (Wadge 1981): the that afects various aspects of eruptive activities. In efu- “eruption rate” is the average discharge rate throughout sive eruptions, the discharge rate is closely related to the the activity period, and the “efusion rate” is the instan- taneous discharge rate, which changes over the course of *Correspondence: [email protected] the activity. Notably, knowing the efusion rate is essen- 1 Earthquake Research Institute, The University of Tokyo, 1-1-1 Yayoi, tial because it is closely related to the migration process Bunkyo-ku, Tokyo 113-0032, Japan of magma underground (Wadge 1981), as well as sur- Full list of author information is available at the end of the article face phenomena, including short-term events such as © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. Kaneko et al. Earth, Planets and Space (2021) 73:37 Page 2 of 10 the growth pattern of lava domes (e.g., exogenous versus constant in a temperature range of ~ 330–1230 °C. Unlike endogenous growth; Nakada et al. 1995), in addition to wildfres, however, a certain amount of the area of an changes in the fow direction of lava or the formation of active lava surface is considered to be below 330 °C, new efusing vents (Kaneko et al. 2019b). which can potentially cause some errors (Harris 2013). Despite the importance of the efusion rate, its meas- We focused on the thermal anomaly in the 1.6-µm urement is not straightforward. To obtain precise infor- shortwave infrared (SWIR) images from the Himawari-8 mation on variations in the efusion rate, we need to satellite for the estimation. It is reported that in the efu- make very complex topographic measurements over sive activity, thermal anomalies in the 1.6-µm satellite short intervals (e.g., Harris et al. 2007; Maeno et al. 2016; images show a temporal variation similar to that for the Nakada et al. 1999; Swanson et al. 1987). One method lava-efusion rate estimated by the topographic method— estimates the discharge rate based on a heat budget a positive correlation— in the 1991–1994 Unzen (Kaneko between heat supplied to the active fow unit by advec- et al. 2002; Kaneko and Wooster 1999; Wooster and tion of lava and the heat loss from the fow surfaces Kaneko 1998), the 2015 Raung (Kaneko et al. 2019a), and (Coppola et al. 2013, 2019; Harris et al. 1997, 2007; Har- the 2017 Nishinoshima (Kaneko et al. 2019b) activities. If ris and Bologa 2009; Pieri and Bologa 1986; Wright et al. we can obtain a regression equation with sufcient accu- 2001). Te problems with these methods are that various racy between these two parameters, the efusion rate can assumptions are required to estimate the discharge rate be estimated by the satellite observation. from low-resolution thermal infrared (TIR, e.g., 11 µm) Te method using a regression equation can simply or mid-wave infrared (MIR, e.g., 4.0 µm) satellite images, calculate efusion rates without assuming particular tem- as discussed below, and the obtained discharge rate is a peratures or unconfrmed relationships. Especially, when time-averaged value over a given period (time-averaged similar activities continue in a volcano, once that activi- discharge rate) rather than an instantaneous value, that ty’s regression equation is obtained, we can estimate efu- is, the efusion rate (Coppola et al. 2013, 2019; Harris sion rate from the spectral radiance showing a thermal et al. 2007; Harris and Bologa 2009; Wright et al. 2001). In anomaly rather accurately, because diferences in various the method proposed by Harris et al. (1997, 2007), Har- factors, such as the magma temperature, the rheological ris and Bologa (2009), and Wright et al. (2001), surface properties, the content of crystals, the content and shape temperature of the active lava is regarded as uniform (Te of bubbles (Llewellin and Manga 2005), or the emissivity efective radiation temperature). As Te is unknown, given (Rogic et al. 2019) are considered to be minimized. a reasonable minimum–maximum temperature range As the 1.6-µm band is preferentially sensitive to (e.g., 100–500 °C), for several values of Te within this high-temperature materials, such as incandescent lava range, areas of active lava are calculated from the radi- (Wooster and Kaneko 1998; Wooster and Rothery 1997), ance values in the TIR images. Ten, time-averaged dis- the anomaly is thought to refect the efusion of high- charge rates are calculated from the relationship between temperature lava near the vent, which is closely related time-averaged discharge rates and the area of active lava to the instantaneous efusion rate (Kaneko et al. 2002, (Pieri and Bologa 1986). In this method, however, the 2019a). By using the 1.6-µm band of Himawari-8, thus, surface temperature (Te) of the active lava is assumed we can obtain high-density temporal variations in the to be uniform, which is unrealistic for typical lava fows lava-efusion rate, including short-term events, by tak- (Dragoni and Tallarico 2009). Te assumption of a range ing advantage of the high repetition rate of Himawari-8 of Te values also produces a wide range of volume fuxes observations. Such observational data cannot be obtained (time-averaged discharge rates). Further, the empirical by other methods and are essential for understanding parameters relating to the rheological properties, the eruptive processes. Te time-averaged discharge rate, magma temperature, and the slope inclination need to determined by the method using TIR or MIR images, is be considered on a case-by-case basis (Harris et al. 2007; not ideal for observing short-term phenomena (~ 1 h). Harris and Bologa2009; Wright et al.