Research and application of the parallel computing method for the grid-based Xin'anjiang model

• Published in: Hydrology Research Vol. 54; no. 4; pp. 591 - 605
• Main Authors: Liu, Qian, Wan, Dingsheng, Yu, Yufeng, Zhang, Yangming
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.04.2023
• Subjects: Algorithms; Computational efficiency; Computer applications; Computing time; Decomposition; Direction; distributed hydrologic model; Efficiency; Flood forecasting; Floods; grid-based xin'anjiang model; Hydrology; Mathematical models; parallel computing; River discharge; Runoff; Simulation; Task scheduling; Watersheds
• ISSN: 0029-1277; 1998-9563; 2224-7955
• DOI: 10.2166/nh.2023.002

Abstract The grid-based Xin'anjiang model (GXM) has been widely applied to flood forecasting. However, when the model warm-up period is long and the amount of input data is large, the computational efficiency of the GXM is obviously low. Therefore, a GXM parallel algorithm based on grid flow direction division is proposed from the perspective of spatial parallelism, which realizes the parallel computing of the GXM by extracting the parallel routing sequence of the watershed grids. To solve data skew, a Directed Acyclic Graph (DAG) scheduling algorithm based on dynamic priority is proposed for task scheduling. The proposed GXM parallel algorithm is verified in the Qianhe River watershed of Shaanxi Province and the Tunxi watershed of Anhui Province. The results show that the GXM parallel algorithm based on grid flow direction division has good flood forecasting accuracy and higher computational efficiency than the traditional serial computing method. In addition, the DAG scheduling algorithm can effectively improve the parallel efficiency of the GXM.