Double-layer parallelization for hydrological model calibration on HPC systems

Abstract

Summary Large-scale problems that demand high precision have remarkably increased the computational time of numerical simulation models. Therefore, the parallelization of models has been widely implemented in recent years. However, computing time remains a major challenge when a large model is calibrated using optimization techniques. To overcome this difficulty, we proposed a double-layer parallel system for hydrological model calibration using high-performance computing (HPC) systems. The lower-layer parallelism is achieved using a hydrological model, the Digital Yellow River Integrated Model, which was parallelized by decomposing river basins. The upper-layer parallelism is achieved by simultaneous hydrological simulations with different parameter combinations in the same generation of the genetic algorithm and is implemented using the job scheduling functions of an HPC system. The proposed system was applied to the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River, to calibrate the model effectively by making full use of the computing resources in the HPC system and to investigate the model’s behavior under various parameter combinations. This approach is applicable to most of the existing hydrology models for many applications.