The successful application of hydrological models relies on careful calibration and\nuncertainty analysis. However, there are many different calibration/uncertainty analysis algorithms,\nand each could be run with different objective functions. In this paper, we highlight the fact\nthat each combination of optimization algorithm-objective functions may lead to a different set of\noptimum parameters, while having the same performance; this makes the interpretation of dominant\nhydrological processes in a watershed highly uncertain. We used three different optimization\nalgorithms (SUFI-2, GLUE, and PSO), and eight different objective functions (R2, bR2, NSE, MNS,\nRSR, SSQR, KGE, and PBIAS) in a SWAT model to calibrate the monthly discharges in two watersheds\nin Iran. The results show that all three algorithms, using the same objective function, produced\nacceptable calibration results; however, with significantly different parameter ranges. Similarly,\nan algorithm using different objective functions also produced acceptable calibration results, but with\ndifferent parameter ranges. The different calibrated parameter ranges consequently resulted in\nsignificantly different water resource estimates. Hence, the parameters and the outputs that they\nproduce in a calibrated model are ââ?¬Å?conditionedââ?¬Â on the choices of the optimization algorithm and\nobjective function. This adds another level of non-negligible uncertainty to watershed models, calling\nfor more attention and investigation in this area.
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