A stochastic approach to simulate realistic continuous snow depth time series

We propose a stochastic approach for simulation of realistic continuous snow depth time series using a snow depth estimation model and a stochastic weather generation model. The snow depth estimation model consists of three steps: defining the precipitation type, estimating the snow ratio, and estimating the decreased snow depth. First, air temperature and relative humidity are used as indices to determine the type of precipitation when precipitation occurs. When the type is identified as snow, the snow ratio is estimated, converting the precipitation depth into depth of fresh snow. Here, the snow ratio is estimated through sigmoidal relationship with the air temperature. Lastly, the amount of decreased snow depth was estimated using a novel temperature index snowmelt equation that can consider depth-dependent melting of snowpack. The model was applied to four meteorological stations of Korea and yielded high Nash Sutcliffe efficiency values which ranged between 0.745 and 0.875 for calibration, and ranged between 0.432 and 0.753 for validation. This calibrated snow depth estimation model was then applied to synthetic weather data (precipitation, temperature, and relative humidity) that was generated by stochastic weather generation model to simulate continuous snow depth time series. The simulated snow depth data accurately reproduced standard and extreme value statistics of the observed data, the latter of which were consistent with the current Korean Building Code. This model can be widely used not only in the field of snow related risk analysis, but also in data simulation for ungauged areas and future trend study using the climate projection.