Abstract:
In the cold regions because of harsh climates, there exists no or an inadequate number of monitoring stations. It is indeed a challenge to generate the hydrographs of ungauged basins with scanty information from limited gauged basins. As a result, it has important implications for existing water resources systems as well as for future water resources planning and management since high elevation mountains are all important sources of water to the billions in the lowlands. The Mo Chhu and Po Chhu catchments in Bhutan are used in this study to assess the regionalization of hydrological model parameters from one catchment to the neighbouring catchment having similar characteristics using ABCD hydrological model incorporating snow melt parameter. The Mo Chhu catchment was considered as the gauged catchment and its hydrological parameters were simulated and then transferred to the neighbouring Pho Chhu catchment. For the corresponding watersheds, precipitation, streamflow and temperature daily data were collected for the 11 years from 2006~2017 from the National Centre for Hydrology and Meteorology from Bhutan and checked by visual comparison, single and double mass curve analysis and annual water balance to ensure data reliability, consistency and to identify suitable data periods for model calibration and validation. For the model performance evaluation, Root Mean Square Error (RMSE), Pearson correlation coefficient (r) and Coefficient of determination (R2) were used as the objective functions. The Pearson correlation values for calibration and validation of Mo Chhu basin are 0.84 and 0.88, respectively. When the same model parameters were transferred to Pho Chhu basin, Pearson value for validation was found to be 0.82. Comparing and analysing the results of ABCD model with and without snow parameter "m", it can be concluded that the model with snow parameter performs better due to major contribution to basin flow from snowmelt. The incorporation of snow processes in the monthly ABCD model has thus significantly improved model performance in snow-covered areas in Bhutan.
