The Tohoku region, Northeast Japan, was hit by a gigantic earthquake which occurred in the Pacific close to Tohoku, and subsequently by a giant tsunami. These hazards have caused huge damage on the eastern coast Japan. The earthquake's magnitude was 9.0, the strongest ever recorded in Japan. The tsunami was also historical as its run-up height reached over 39 m. As of early May, 2011, over 24 thousand people were reported as dead or missing. Moreover, serious accidents at the Fukushima Nuclear Power Plants No.1 were caused by the effects of the tsunami. Therefore, the damage faced by Japanese people can be seen as a giant composite disaster. Although Japan, and the northeast of Japan in particular, has over a long time period increased its preparedness against earthquakes and tsunamis, huge damage still occurred. This paper considers why this tragedy occurred, and what unrecognized factors contributed to the high vulnerability of the area. To assist in answering such questions, this paper presents a timely report of the features of the earthquake and tsunami, the damage they caused, and the early efforts for recovery and reconstruction.

We evaluated the past impacts of urbanization and climate change on groundwater-in particular, aquifer temperature-in the Sendai plain, Japan, and further compared with the probable changes due to changing climate in the future. A series of simulations were performed and matched with the observed temperaturedepth profiles as a preliminary step for parameter calibration. The magnitude of ground surface warming estimated from subsurface temperature spans 0.9-1.3 • C, which is consistent with the calibrated ground surface warming rates surrounding various observation wells (0.021-0.015 • C/year) during the last 60 years. We estimate that approximately 75% of the ground surface temperature change can be attributed to the effect of past urbanization. For the climate predictions, climate variables produced by the UK Hadley Centre's Climate Model (HadCM3) under the A2, A1B and B1 scenarios were spatially downscaled by the transfer function method. Downscaled monthly data were used in a water budget analysis to account for the variation in recharge and were further applied in a heat transport equation together with the estimated ground surface warming rates in 2080. Anticipated groundwater recharge under the projected climate in 2080 would decrease by 1-26% compared to the 2007 estimates, despite the projected 7-28% increase in precipitation, due to a higher degree of evapotranspiration resulting from a 2.5-3.9 • C increase in surface air temperature. The overall results from the three scenarios predict a 1.8-3.7 • C subsurface temperature change by 2080, which is notably greater than the previous effect of urbanization and climate change on aquifer temperature in the Sendai plain.

Abstract.To evaluate the frequency and distribution of landslides hazards over Japan, this study uses a probabilistic model based on multiple logistic regression analysis. Study particular concerns several important physical parameters such as hydraulic parameters, geographical parameters and the geological parameters which are considered to be influential in the occurrence of landslides. Sensitivity analysis confirmed that hydrological parameter (hydraulic gradient) is the most influential factor in the occurrence of landslides. Therefore, the hydraulic gradient is used as the main hydraulic parameter; dynamic factor which includes the effect of heavy rainfall and their return period. Using the constructed spatial data-sets, a multiple logistic regression model is applied and landslide hazard probability maps are produced showing the spatial-temporal distribution of landslide hazard probability over Japan. To represent the landslide hazard in different temporal scales, extreme precipitation in 5 years, 30 years, and 100 years return periods are used for the evaluation. The results show that the highest landslide hazard probability exists in the mountain ranges on the western side of Japan (Japan Sea side), including the Hida and Kiso, Iide and the Asahi mountainous range, the south side of Chugoku mountainous range, the south side of Kyusu mountainous and the Dewa mountainous range and the Hokuriku region. The developed landslide hazard probability maps in this study will assist authorities, policy makers and decision makers, who are responsible for infrastructural planning and development, as they can identify landslide-susceptible areas and thus decrease landslide damage through proper preparation.

This study uses a probabilistic model based on multiple logistic regression analysis to evaluate landslide occurrence on natural terrain due to snowmelt in Japan. The evaluation concerns several physical parameters such as hydraulic parameters, geographical parameters and the geological parameters which are considered to be influential in the occurrence of landslides. Hydraulic gradient is the hydraulic parameter, which includes the effect of snowmelt. To estimate snowmelt and associated infiltration in light, average and heavy snow years, a widely used Snow Water Equivalent model (SWE) is used. Using the constructed spatial data-sets, a multiple logistic regression model is applied and landslide susceptibility maps are produced showing the spatial-temporal distribution of landslide hazard probabilities over Japan using 1km×1km resolution grid cells. The results show that, over 95% landslide hazard probability exists in the mountain ranges on the western side of Japan (the Japan Sea side), the Hokuriku region, and the Tohoku region. The developed landslide hazard probability map is verified using the past landslide events in the Aizu region of Fukushima prefecture, located in a landslide prone region. Verification proved that, areas identified as high risk areas (having over 90% landslide hazard probability in numerical modeling) show 87% agreement with observed landslides in the Aizu region. In addition, the economical damage due to landslides is discussed taking road damages by landslides into account. The developed landslide hazard probability maps and economical damage maps will assist authorities, policy makers and decision makers, who are responsible for infrastructural planning and development, as they can identify landslide-susceptible areas and thus decrease landslide damage through proper preparation.

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