Adaptive Statistical Spatial Downscaling of Precipitation Supported by High-Resolution Atmospheric Simulation Data for Mountainous Areas of Nepal

In complex terrain regions, it is very challenging to obtain high accuracy and resolution precipitation data that are required in land hydrological studies. In this study, an adaptive precipitation downscaling method is proposed based on the statistical downscaling model MicroMet. A key input parameter in the MicroMet is the precipitation adjustment factor (PAF) that shows the elevation dependence of precipitation. Its value is estimated conventionally based on station observations and suffers sparse stations in high altitudes. This study proposes to estimate the PAF value and its spatial variability with precipitation data from high-resolution atmospheric simulations and tests the idea in Nepal of South Himalayas, where rainfall stations are relatively dense. The result shows that MicroMet performs the best with the PAF value estimated from the simulation data at the scale of approximately 1.5 degrees. Not only the value at this scale is qualitatively consistent with early knowledge obtained from intensive observations, but also the downscaling performance with this value is better than or comparable to that with the PAF estimated from dense station data. Finally, it is shown that the PAF estimation, although critical, cannot replace the importance of increasing input station density for downscaling.