Abstract: In the light of hydrological model parameter calibration issue, to accelerate hydrological model calculation efficiency significantly, taking SCE-UA algorithm and the Xin'anjiang model as the study objectives, based on the GPU hardware and Compute Unified Device Architecture (CUDA) software platforms, the two key scientific problems, the parallelization of the SCE-UA algorithm and its program implementation, and the performance analysis of the Graphics Processing Unit (GPU)-based parallel SCE-UA algorithm are studied by using integrated approaches of time-space complexity analysis, algorithm parallelism mining, deep code-level performance tuning, and numerical experiment and simulation, etc. The research contents are as follows: (1) to construct CUDA and GPU-based parallel computing software and hardware platforms and to carry on configuration and tuning; (2) to develop the parallel SCE-UA algorithm and its program implementation; (3) to study the performance characteristic of the GPU-based parallel SCE-UA algorithm. The developed method can improve computational efficiency significantly and the expected achievements of the study will rich the knowledge of hydrological simulation, optimization method, computer science and technology, and be of significance for hydrological simulation and forecast and flood control fast response.