[A-TT30] Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions

[ATT30-06] Estimation of past and future snow depth over Japan using machine learning techniques

★Invited Papers

*Noriko N Ishizaki¹, Hidetaka Sasaki¹, Alessandro Damiani¹ (1.National Institute for Environmental Studies)

Keywords:snow depth, climate scenarios, machine learning, spatial distribution

Snow is not only important as a water resource and for the water cycle, but also for alpine ecosystems and organisms that prefer cold regions. Therefore, it is important to understand the regional distribution of snow cover and future changes for its conservation. However, snow cover is redistributed by wind and affected by slight topographical undulations, making it difficult to observe spatial distribution of snow depth. Therefore, attempts have been made to estimate snow cover depth from other meteorological variables. In this study, machine learning was used to estimate snow cover depth using historical observations and topographical factors. Regional climate model NHRCM with a grid spacing of 20 km were used as predictors for machine learning, and the method was repeated 22 times, learning for 21 years and applying to one year. Machine learning estimates successfully reproduced both the maximum snow depth and the duration of snow cover compared to the Degree-day method using daily temperature and precipitation (Fig. 1). Sensitivities to the predictors are also investigated, and methods for application to past and future scenarios are discussed.