Parallel simulation of hydrological model based on container and automated deployment tools

Abstract

The increasing use of fine spatial and temporal data in hydrological modeling has resulted in a prohibitively computational demand and run time. The serial computing method adopted by most modeling routines has largely hindered the application of high-fidelity distributed hydrological models. This study proposes a parallel simulation method for hydrological models based on container and automated deployment tools, i.e., Docker and Kubernetes, to achieve parallel simulation of hydrological models on a container cluster. Specifically, the Soil and Water Assessment Tool (SWAT) is used as an example to verify the efficiency of the proposed method by comparing it with the traditional serial simulation method. The experimental results show that the combined application of Kubernetes and Docker can significantly improve the simulation efficiency of the SWAT model. A maximum speedup of 3.6 times was achieved on a Docker cluster consisting of 10 virtual worker nodes. Due to the remarkable encapsulation ability of the container technology, the proposed parallelization method can also be applied to other environment models in the future, and the simulation efficiency can be further improved by extending the Docker cluster.