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Avalanches are often released at the dormant stratovolcano Mt. Fuji, which is the highest mountain of Japan (3776 m a.s.l.). These avalanches exhibit different flow types from dry-snow avalanches in winter to slush flows triggered by heavy rainfall in late winter to early spring. Avalanches from different flanks represent a major natural hazard as they can reach large dimensions with run-out distances up to 4 km, destroy parts of the forest, and sometimes damage infrastructure. To monitor the volcanic activity of Mt. Fuji, a permanent and dense seismic network is installed around the volcano. The small distance between the seismic sensors and the volcano flank ( < 10 km) allowed us to detect numerous avalanche events from the seismic recordings and locate them in time and space. We present the detailed analysis of three avalanche or slush flow periods in the winters of 2014, 2016, and 2018. The largest events (size class 4-5) are detected by the seismic network at maximum distances of about 15 km, and medium-size events (size class 3-4) within a radius of 9 km. To localize the seismic events, we used the automated approach of amplitude source location (ASL) based on the decay of the seismic amplitudes with distance from the moving flow. The recorded amplitudes at each station have to be corrected by the site amplification factors, which are estimated by the coda method using data from local earthquakes. Our results show the feasibility of tracking the flow path of avalanches and slush flows with considerable precision (on the order of magnitude of 100 m) and thus estimating information such as the approximate run-out distance and the average front speed of the flows, which are usually poorly known. To estimate the precision of the seismic tracking, we analyzed aerial photos of the release area and determined the flow path and run-out distance, estimated the release volume from the meteorological records, and conducted numerical simulations with Titan2D to reconstruct the dynamics of the flow. The precision as a function of time is deduced from the comparison with the numerical simulations, showing mean location errors ranging between 85 and 271 m. The average front speeds estimated seismically, which ranged from 27 to 51 m s −1 , are consistent with the numerically predicted speeds. In addition, we deduced two scaling relationships based on seismic parameters to quantify the size of the mass flow events. Our results are indispensable for assessing avalanche risk in the Mt. Fuji region as seismic records are often the only available dataset for this natural hazard. The approach presented here could be applied in the development of an early-detection and location system for avalanches based on seismic sensors.Published by Copernicus Publications on behalf of the European Geosciences Union. 990 C. Pérez-Guillén et al.: Seismic avalanche tracking on Mt. Fuji

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Abstract. Forecasting avalanche danger at a regional scale is a largely data-driven yet also experience-based decision-making process by human experts. In the case of public avalanche forecasts, this assessment process terminates in an expert judgment concerning summarizing avalanche conditions by using one of five danger levels. This strong simplification of the continuous, multi-dimensional nature of avalanche hazard allows for efficient communication but inevitably leads to a loss of information when summarizing the severity of avalanche hazard. Intending to overcome the discrepancy between determining the final target output in higher resolution while maintaining the well-established standard of assessing and communicating avalanche hazard using the avalanche danger scale, avalanche forecasters at the national avalanche warning service in Switzerland used an approach that combines absolute and relative judgments. First, forecasters make an absolute judgment using the five-level danger scale. In a second step, a relative judgment is made by specifying a sub-level describing the avalanche conditions relative to the chosen danger level. This approach takes into account the human ability to reliably estimate only a certain number of classes. Here, we analyze these (yet unpublished) sub-levels, comparing them with data representing the three contributing factors of avalanche hazard: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. We analyze both data used in operational avalanche forecasting and data independent of the forecast, going back 5 years. Using a sequential analysis, we first establish which data are suitable and in which part of the danger scale they belong by comparing their distributions at consecutive danger levels. In a second step, integrating these findings, we compare the frequency of locations with poor snowpack stability and the number and size of avalanches with the forecast sub-level. Overall, we find good agreement: a higher sub-level is generally related to more locations with poor snowpack stability and more avalanches of larger size. These results suggest that on average avalanche forecasters can make avalanche danger assessments with higher resolution than the five-level danger scale. Our findings are specific to the current forecast set-up in Switzerland. However, we believe that avalanche warning services making a hazard assessment using a similar temporal and spatial scale as currently used in Switzerland should also be able to refine their assessments if (1) relevant data are sufficiently available in time and space and (2) a similar approach combining absolute and relative judgment is used. The sub-levels show a rank-order correlation with data related to the three contributing factors of avalanche hazard. Hence, they increase the predictive value of the forecast, opening the discussion on how this information could be provided to forecast users.