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

Increasing spatial and temporal resolution of numerical models continues to propel progress in hydrological sciences, but, at the same time, it has strained the ability of modern automatic calibration methods to produce realistic model parameter combinations for these models. This paper presents a new reliable and fast automatic calibration framework to address this issue. In essence, the proposed framework, adopting a divide and conquer strategy, first partitions the parameters into groups of different resolutions based on their sensitivity or importance, in which the most sensitive parameters are prioritized with the highest resolution in the parameter search space, while the least sensitive ones are initially explored with the coarsest resolution. This is followed by an optimization-based iterative calibration procedure consisting of a series of subtasks or runs. Between consecutive runs, the setup configuration is heterogeneous with parameter search ranges and resolutions varying among groups. At the completion of each subtask, the parameter ranges within each group are systematically refined from their previously estimated ranges, which are initially based on a priori information. Parameters attain stable convergence progressively with each run. A comparison of this new calibration framework with a traditional optimization-based approach was performed using a quasi-synthetic double-model setup experiment to calibrate 134 parameters and two well-known distributed hydrological models: the Variable Infiltration Capacity (VIC) model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The results demonstrate statistically that the proposed framework can better mitigate equifinality problem, yields more realistic model parameter estimates, and is computationally more efficient. Key Points A novel framework is developed for automatic calibration of computationally demanding hydrological models with a large number of parameters The framework alleviates equifinality, and calibrated parameters achieve more reasonable values that better correspond to physical reality The framework leads to faster improvement and smoother convergence to optimal objective values and is tested with 134 calibration parameters