Frequency Analysis of Future Maximum Fresh Snow Depth using Multiple Regression Model with Interaction

Abstract

As the frequency of snowfall inducing life loss and property damages increases, many researches to calculate future maximum fresh snow depth and trend have been implemented. In this study, 400-year pre-industrial climate control simulated RCP 2.6, 4.5, 6.0, 8.5 climate change scenario for Korean Peninsula was applied to develop multiple regression models for the estimation of future probable maximum fresh snow depth. Precipitation, minimum, maximum, and average temperature was used as the independent variables, and the multiplication of precipitation and average temperature was inserted in the multiple regression model to consider the interaction between the two variables. Total 74 gauging stations data was applied to varify the developed model. The calculated probable maximum fresh snow depths for 10, 30, 50, and 100 years were compared. The model efficiency of the multiple regression model with interaction was superior to multiple linear regression model. The probable maximum fresh snow depth tend to decrease over the entire Korean Peninsula without the significant changes. The spatial distribution of the locations with highest maximum fresh snow depths remains the same. The results shows the overall variation trend rather than the exact snow depth. Using the developed model, the maximum fresh snow depth could be calculated using the precipitation and temperatures if the input variables are predicted accurately.

Sensitivity Analysis of Weather Variables on Offsite Consequence Analysis Tools in South Korea and the United States

“…In this study, the independent variables were the chemicals used, air temperature, wind speed, humidity, atmospheric stability, surface roughness, and the OCA tools used, while the dependent variable was the impact distance. Therefore, the regression equation [ 33 ] derived for multiple regression analysis was as follows: …”

Reply on RC1

“…Research has been conducted using statistical and machine learning techniques that can consider the nonlinear relationship of factors and SR, which is the ratio of snowfall depth to the amount of liquid-equivalent precipitation (Byun et al, 2008). Because snow cover occurs as a complex nonlinear combination of factors caused by meteorological and geographic conditions, the nonlinear relationship between temperature, precipitation, relative humidity, and geographic factors that affect snow cover should be considered (Park et al, 2016).…”