Physically-based distributed hydrological model calibration based on a short period of streamflow data: case studies in two Chinese basins
Abstract. Physically-based distributed hydrological models are widely used for hydrological simulations in various environments. However, as with conceptual models, they are limited in data-sparse basin by the lack of streamflow data for calibration. Short periods of observational data (less than 1 year) may be obtained from the fragmentary historical records of past-existed gauging stations or from temporary gauging during field surveys, which might be of values for model calibration. This study explored how the use of limited continuous daily streamflow data might support the application of a physically-based distributed model in data-sparse basins. The influence of the length of observation period on the calibration of the widely applied Soil and Water Assessment Tool model was evaluated in two Chinese basins with differing climatic and geophysical characteristics. The evaluations were conducted by comparing calibrations based on short periods of data with calibrations based on data from a 3-year period, which were treated as benchmark calibrations for the two basins. To ensure the differences in the model simulations solely come from differences in the calibration data, the Generalized Likelihood Uncertainty Analysis scheme was employed for the automatic calibration and uncertainty analysis. In both basins, contrary to the common understanding of the need for observations over a period of several years, data records with lengths of less than 1 year were shown to calibrate the model effectively, i.e. performances similar to the benchmark calibrations were achieved. The model of wet Jinjiang Basin could be effectively calibrated using a shorter data record (1 month), compared with the arid Heihe Basin (6 months). Even though the two basins are very different, the results demonstrated that data from the wet season and wetter years performed better that data from the dry season and drier year. The results of this study demonstrated that short periods of observations could be a promising solution to the problem of calibration of physically-based distributed hydrological models in data-sparse basins and further researches similar to this study are required to gain more general understandings about the optimum number of observations needed for calibration when such model are applied to real data-sparse basins.