Whether coupling auxiliary information (except for conventional rainfall–runoff and temperature data) into hydrological models can improve model performance and transferability is still an open question. In this study, we chose a glacier catchment to test the effect of auxiliary information, i.e., distributed forcing input, topography, snow-ice accumulation and melting on model calibration–validation and transferability. First, we applied the point observed precipitation and temperature as forcing data, to test the model performance in calibration–validation and transferability. Second, we took spatial distribution of forcing data into account, and did the same test. Third, the aspect was involved to do an identical experiment. Finally, the snow–ice simulation was used as part of the objective function in calibration, and to conduct the same experiment. Through stepwisely accounting these three pieces of auxiliary information, we found that a model without involving forcing data distribution, local relief, or snow–ice data can also perform well in calibration, but adding forcing data distribution and topography can dramatically increase model validation and transferability. It is also remarkable that including the snow–ice simulation into objective function did not improve model performance and transferability in this study. This may be because the well-gauged hydro-meteorological data are sufficient to constrain a well-designed hydrological model.
Use of auxiliary data of topography, snow and ice to improve model performance in a glacier-dominated catchment in Central Asia
Hongkai Gao, Tianding Han, Youcun Liu, Qiudong Zhao; Use of auxiliary data of topography, snow and ice to improve model performance in a glacier-dominated catchment in Central Asia. Hydrology Research 1 October 2017; 48 (5): 1418–1437. doi: https://doi.org/10.2166/nh.2016.242
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