资源环境科技发展态势分析平台

Model parameter calibration is a fundamentally important stage that must be completed before applying a model to address practical problems. In this study, we describe an automatic calibration framework that combines sensitivity analysis (SA) and an adaptive surrogate modeling-based optimization (ASMO) algorithm. We use this framework to calibrate catchment-specific sensitive parameters for streamflow simulation in the variable infiltration capacity (VIC) model with a 0.25 degrees spatial resolution over 10 major river basins of China from 1960 to 1979. We found that three parameters-the infiltration parameter (B) and two of the soil depth parameters (D-1, D-2)-are highly sensitive in most basins, while other parameter sensitivities are strongly related to the dynamic environment of the basin. Compared with directly calibrating the seven parameters recommended for the default calibration procedure, our framework not only reduced the computing time by two thirds through opting out of insensitive parameters (type I error) but also improved the Nash-Sutcliffe model efficiency coefficient (NSE) for optimized results when it identified a missing sensitive parameter (type II error) in the case study river basins. Results show that the SA-based ASMO framework is an effective and efficient model-optimization technique for matching simulated streamflow with observations across China. The NSE for monthly streamflow ranged from 0.75 to 0.97 and from 0.71 to 0.97 during the validation and calibration periods, respectively. The calibrated parameters can be applied directly in streamflow simulations across China, and the proposed calibration framework holds important implications for relevant simulation studies in other regions.