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Physically based distributed hydrological models are widely used forhydrological simulations in various environments. As with conceptual models,they are limited in data-sparse basins by the lack of streamflow data forcalibration. Short periods of observational data (less than 1 year) may beobtained from fragmentary historical records of previously existing gaugingstations or from temporary gauging during field surveys, which might be ofvalue for model calibration. However, unlike lumped conceptual models, suchan approach has not been explored sufficiently for physically baseddistributed models. This study explored how the use of limited continuousdaily streamflow data might support the application of a physically baseddistributed model in data-sparse basins. The influence of the length of theobservation period on the calibration of the widely applied soil and waterassessment tool model was evaluated in four Chinese basins with differingclimatic and geophysical characteristics. The evaluations were conducted bycomparing calibrations based on short periods of data with calibrations basedon data from a 3-year period, which were treated as benchmark calibrations ofthe four basins, respectively. To ensure the differences in the modelsimulations solely come from differences in the calibration data, thegeneralized likelihood uncertainty analysis scheme was employed for theautomatic calibration and uncertainty analysis. In the four basins, contraryto the common understanding of the need for observations over a period ofseveral years, data records with lengths of less than 1 year were shown tocalibrate the model effectively, i.e., performances similar to the benchmarkcalibrations were achieved. The models of the wet Jinjiang and Donghe basinscould be effectively calibrated using a shorter data record (1 month),compared with the dry Heihe and upstream Yalongjiang basins (6 months). Eventhough the four basins are very different, when using 1-year or 6-month(covering a whole dry season or rainy season) data, the results show thatdata from wet seasons and wet years are generally more reliable than datafrom dry seasons and dry years, especially for the two dry basins. Theresults demonstrated that this idea could be a promising approach to theproblem of calibration of physically based distributed hydrological models indata-sparse basins, and findings from the discussion in this study arevaluable for assessing the effectiveness of short-period data for modelcalibration in real-world applications

Physically based distributed hydrological model calibration based on a short period of streamflow data: case studies in four Chinese basins