Hydrological simulation applying global meteorological datasets and terrain elevations to local-scale snowy basins in Japan

Although regional water resources issues need to be solved using hydrological models that can accurately reproduce phenomena, difficulties exist in many regions and countries owing to the lack of quantitative observed data and computers. In this case, global metrological datasets and terrain elevations are available for analying hydrological processes in such ungauged basins. When using a regional model in hydrological analysis, the forced use of global datasets requires usually assimilation and bias correction, most often with high computational cost. Since the accuracy of global datasets has been improving in recent years, a global dataset was dared to be applied in local-scale hydrological analysis without bias correction in this study. The result is compared with that of hydrological analysis using a ground dataset.

The hydrological model consisting of the Diskin–Nazimov infiltration model and the storage–discharge relationships developed for mountainous basins (Fujimura et al., 2011) was used in this study because of its simple structure that uses small datasets that accurately estimate runoff phenomena at the local scale, which can help solve the regional water issues in water resource management or flood control design. Global Satellite Mapping of Precipitation (GSMaP) and Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) are used in this study as global metrological datasets for precipitation and temperature, respectively. Multi-Error-Removed Improved-Terrain DEM (MERIT DEM) provided by Yamazaki et al. (2017) is used as the global digital elevation model. The hydrological analysis is carried out for a period of 21 years at daily time steps for four snowy mountainous basins with areas from 103 to 331 km² in the Hokkaido region of Japan, using both the global dataset and the gauge-based dataset. Each simulation was assessed using the average daily runoff relative error (ADRE).

The results show that, when using the ground-based dataset, the ADRE range is from 26.6% to 47.2% and the average is 35.5%, and when using the global dataset it is from 44.0% to 76.7% and the average is 60.4%. The use of a global dataset reduces the accuracy of the analysis, but not considerably.