Crisis hazard assessment for snow-related lahars from an unforeseen new vent eruption: the 2018 eruption of Kusatsu-Shirane volcano, Japan

Two-thirds of the 111 active volcanoes in Japan are covered with snow for several months during winter and demonstrate high hazard and risk potentials associated with snow-related lahars during and after eruptions. On 23 January 2018, a sudden phreatic eruption occurred at the ski field on Kusatsu-Shirane (Mt. Motoshirane) volcano, Japan. This new vent eruption from the snow-clad pyroclastic cone required forecasting of future snow-related lahars and crisis hazards zonation of downslope areas including Kusatsu town, a popular tourist site for skiing and hot springs. In order to achieve a prompt hazard assessment for snow-related lahars, a multidisciplinary approach was carried out involving characterization of proximal tephra deposits, snow surveys, and numerical lahar flow simulations using the Titan2D model. To determine the input parameters for the flow model, the consideration of snow water equivalent (SWE) immediately after the eruption (on 29 January) and in the post-eruptive period (on 12 March), was significant. In the case of Kusatsu-Shirane volcano during the winter of 2018, linear relationships between altitude and SWE, obtained at different elevations, were used to estimate the snow volume around the new vents. Several scenarios incorporating snow and snowmelt (water), with or without the occurrence of a new eruption, were simulated for the prediction of future lahars. Three lahar scenarios were simulated, including A) rain-on-snow triggered, B) ice/snow slurry, and C) full snowmelt triggered by a new eruption, and indicated the flow paths (inundation areas) and travel distances. These were useful for lahar hazard zonation and identification of potential high-risk areas. Since the input parameters required for the Titan2D flow model can be relatively easily determined, the model was suitable for the 2018 eruption at Motoshirane where historical and geological lahar records are not available for calibration. The procedure used in the study will enable rapid lahar prediction and hazard zonation at snow-clad volcanoes. Further consideration for simulating a cohesive-type flow, which was predicted by the primary deposits containing large amounts of clay minerals and could not be expressed in the Titan2D flow model, is necessary.

Flood hazard zonation of upper kolodyne using GIS and multi criteria decision analysis in Mizoram, India

Kolodyne is the largest river in Mizoram. The river originates in Myanmar where it flows in a southerly direction and enters Mizoram where it is called Chhimtuipui river and it becomes the international border between India and Myanmar. The Kolodyne river meets several rivers in Mizoram before it enters Chin State in Myanmar again. The upper Kolodyne river has caused destructive floods recently, however, attempts to delineate the flood hazard zones have not been carried out. This river is a source of livelihood for many families in the region and it had wrecked havoc in the past monsoon seasons with the loss of lives and property. The potential flood hazard zonation of the upper Kolodyne watershed using geographic information systems and multi-criteria decision analysis has revealed that about 40% of the total watershed fall in the high and very high potential zones and flood control measures are needed to be updated.

Landslide Hazard Zonation along the MH SH-73 at Kelghar Ghat, Satara, Maharashtra

Road transportation is the most common victim of landslide in the world. The present study investigates the landslide hazard zonation along the MH SH-73 at Kelghar ghat between Medha and Mahabaleshwar hill station of Maharashtra. Remote Sensing and GIS were used for the landslide hazard zonation of this section. The ghat section was buffered 100 m on both side to define the extent of study area based on the field investigation. The study incorporated predefined important landslide causative factors, viz. slope, rainfall, relief, lithology, soil depth, soil erosion, soil texture, land use / land cover, drainage distance, drainage density, lineament distance, lineament density, aspect, temperature, landslide inventory and in this approach fifteen thematic layers were prepared in GIS platform. The weight and score were assigned to each thematic layer based on heuristic approach on their relative importance in causing landslide. Multi-criteria model in ArcGIS 10.5 software were used for the mapping landslide hazard zones and it were classified into six zones: very high (1.3 %), high (1.7 %), moderate (3.4 %), low (7.6 %) very low (8.4 %) and no risk zone (77.7 %). The final result of this research can help for proper mitigation and adaptation measures for engineers, planners and administrators for this ghat section. Keywords: GIS, Landslide Hazard Zonation, Remote Sensing, Susceptibility, Sahyadri, Kelghar.