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Increasing spatial and temporal resolution of numerical models continues topropel progress in hydrological sciences, but, at the same time, it hasstrained the ability of modern automatic calibration methods to producerealistic model parameter combinations for these models. This paper presents anew reliable and fast automatic calibration framework to address this issue. Inessence, the proposed framework, adopting a divide and conquer strategy, firstpartitions the parameters into groups of different resolutions based on theirsensitivity or importance, in which the most sensitive parameters areprioritized with highest resolution in parameter search space, while the leastsensitive ones are explored with the coarsest resolution at beginning. This isfollowed by an optimization based iterative calibration procedure consisting ofa series of sub-tasks or runs. Between consecutive runs, the setupconfiguration is heterogeneous with parameter search ranges and resolutionsvarying among groups. At the completion of each sub-task, the parameter rangeswithin each group are systematically refined from their previously estimatedranges which are initially based on a priori information. Parameters attainstable convergence progressively with each run. A comparison of this newcalibration framework with a traditional optimization-based approach wasperformed using a quasi-synthetic double-model setup experiment to calibrate134 parameters and two well-known distributed hydrological models: the VariableInfiltration Capacity (VIC) model and the Distributed Hydrology Soil VegetationModel (DHSVM). The results demonstrate statistically that the proposedframework can better mitigate equifinality problem, yields more realistic modelparameter estimates, and is computationally more efficient.

A calibration framework for high-resolution hydrological models using a multiresolution and heterogeneous strategy