Abstract
The Generalized Likelihood Uncertainty Estimation (GLUE) method has been thrived for decades, huge number of applications in the field of hydrological model have proved its effectiveness in uncertainty and parameter estimation. However, for many years, the poor computational efficiency of GLUE hampers its further applications. A feasible way to solve this problem is the integration of modern CPU-GPU hybrid high performance computer cluster technology to accelerate the traditional GLUE method. In this study, we developed a CPU-GPU hybrid computer cluster-based highly parallel large-scale GLUE method to improve its computational efficiency. The Intel Xeon multi-core CPU and NVIDIA Tesla many-core GPU were adopted in this study. The source code was developed by using the MPICH2, C++ with OpenMP 2.0, and CUDA 6.5. The parallel GLUE method was tested by a widely-used hydrological model (the Xinanjiang model) to conduct performance and scalability investigation. Comparison results indicated that the parallel GLUE method outperformed the traditional serial method and have good application prospect on super computer clusters such as the ORNL Summit and Sierra of the TOP500 super computers around the world.
Highlights
1.1 Generalized Likelihood Uncertainty Estimation (GLUE)-based hydrological model uncertainty and parameter estimationThe uncertainty in hydrological processes affects the prediction accuracy of the hydrological models (Dong et al, 2015; Kan et al, 2015; Kan et al, 2016; Kan et al., 2017; Kan et al, 2018; Lei et al, 2016; Li et al, 2016; Li et al, 2014; Zuo et al., 2016)
The GLUE method was proposed by Beven in 1992 (Beven and Binley, 1992) for studying the equifinality phenomenon of hydrological model, and it has been widely applied in hydrology studies
Total execution time of serial and parallel GLUE methods is demonstrated in figure 7
Summary
1.1 GLUE-based hydrological model uncertainty and parameter estimationThe uncertainty in hydrological processes affects the prediction accuracy of the hydrological models (Dong et al, 2015; Kan et al, 2015; Kan et al, 2016; Kan et al., 2017; Kan et al, 2018; Lei et al, 2016; Li et al, 2016; Li et al, 2014; Zuo et al., 2016). 1.1 GLUE-based hydrological model uncertainty and parameter estimation. Uncertainty and parameter estimation have become a necessary procedure in the application of hydrological models (Li, 2005). Hydrologists have carried on comprehensive studies on uncertainty analysis and parameter estimation on hydrological model and have obtained significant achievements. The Generalized Likelihood Uncertainty Estimation, GLUE, has become the most widely applied model uncertainty analysis and parameter estimation method. The GLUE method was proposed by Beven in 1992 (Beven and Binley, 1992) for studying the equifinality phenomenon of hydrological model, and it has been widely applied in hydrology studies. Li (Li and Liang, 2006; Shu et al, 2008) used three different typical watersheds as examples and adopted the GLUE method to study the parameter uncertainty issue of the Xinanjiang model, which assessed and conformed the applicability of the GLUE method.
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