SEF KAHG KAWG Flank instability? If this region (~0.5 km3) generates a sector collapse (a big assumption!), then the volume the collapse will be up to 107 m3. （24 ka Kurofu： 4x109 m3, 1783 Lava flow： 1.7x108 m3 ） ur model assuming model derived t ation (7)) and As / w · l. However, as 3.2, slide width, w, is not well con- cal considerations (in theory it could ally infinite slope). Two end‐members de widths: a lower bound arises from sting w / t, so that As / t · l, (this is d end‐member) and an approximate dth suggests w / l, so that As / l2. our model thus predicts scaling rang- 9 (for w / t) and t / As 0.27 (for w / l), scatter of t versus As observed by may be explained via the mechani- nge in landslide widths, w, which ge in mechanically permissible areas and 20 kPa. When comparing with the model results using the w / l scaling (Figure 15), and a power of 1.32, the cohesion is found to be in the range of 0.6–200 kPa. [32] The very large range of obtained permissible cohe- sions (more than 5 orders of magnitude, 10−3 to 200 kPa) versus the very narrow range of permissible power ex- ponents (d ranging by a few percent between 1.32 and 1.38) indicates that the value of the power exponent is not very sensitive to the specific cohesion value of the slope material, and also not very sensitive to the width of the slide, but is instead mostly sensitive to the aspect ratio t/l which really dictates the scaling between volume and surface area. This clearly supports, and partially explains, the observations that landslides from diverse conditions follow similar power laws. 14. Analytical model (based on w = t) superimposed on the observational field data. 8 of 10 Scaling laws between the area and volume of landslides (Klair et al., JGR, 2011; Blahut et al., Landslides, 2019) classification of remotely sensed imagery (e.g. Fiorucci et al. 2011) and their derivatives such as high resolution digital elevation models (e.g. Břežný and Pánek 2017) and Synthetic Aperture Radar (e.g. Strozzi et al. 2018). The rapid preparation of more reliable and representative land- slide inventories covering increasingly extensive regions is being facili- tated by these advancing technologies coupled with data mining from media reports (e.g. Battistini et al. 2013) or social networks (De Longueville et al. 2010; Yin et al. 2012). Unfortunately, the mapping of submarine landslides is limited by financial constraints and the time- consuming nature of seafloor investigations. Therefore, there is a ten- dency for submarine landslide inventories to come from regions of high economic importance, including both continental margins (Chaytor et al. 2009; Katz et al. 2015) and intraplate volcanic islands (e.g. Gee et al. 2001). In this regard, it is notable that much of the information about giant landslides on volcanic islands comes from economically prosperous archipelagos such as the Canary Islands and the Hawaiian Islands. To some extent, the global distribution of giant landslides on volcanic islands outlined here may be distorted by the fact that so much research focuses on these islands. In the future, it is expected that an increasing number of giant landslides will be recognised in more re- mote, less prosperous islands such as those in the Subantarctic. Conclusions In this report, a global database of giant landslides on volcanic islands has been presented. One hundred and eighty-two records are listed: seventy- five are hosted in the Atlantic Ocean, sixty-seven are hosted in the Pacific Fig. 4 Volume-area relationships calculated using the outlined database and from other literature sources V = αAγ. (1) Giant landslides on volcanic islands, α = 0.26, γ = 1.29 (Blahůt et al., this study); (2) Martian landslides, α = 2.53, γ = 1.23 (Crosta et al. 2018); (3) Martian landslides, α = 0.20, γ = 1.43 (Legros 2002); (4) all landslides, α = 0.15, γ = 1.33 (Larsen et al. 2010); (5) bedrock landslides, α = 0.19, IPL/WCoE activities
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