Application Of Cluster Computing On Hydrological Process Simulation At River Basin Scale

Most of current large-scale watershed simulation models are built on the concept of distributed hydrologic model, which divides the watershed into small units with similar attributes in underlying surface. In a given watershed area, the number of the units will increase in exponential speed when the resolution standard grows. This is a big challenge for computation capability. On the other side, in the practical applications of watershed simulation, such as flood forecast, the sooner the computation result comes out, the better for people to handle emergencies. To meet the requirement for a high computing capability brought up by these features, a simple way is to raise the computing capability of current hardware, however, it costs much while the extent to which the computing speed can be elevated is limited. Therefore, cluster computing method, which is effective and cheap, can be adopted into the computation in large-scale watershed simulation model. This thesis discusses how to develop a cluster computing system for the hydrological process simulation at large river basin scale.This cluster computing system has a hierarchical structure with four layers. The bottom layer is Modeling Layer, which aims to develop some basic hydrologic models, such as the runoff yield & confluence model and sediment yield model, while other models can be added into framework if needed. The third layer from top to bottom is Computing Layer, which aims to define a coding method for river network, assign and reassign tasks to maintain the Workload Balance, and use adaptive control to optimize the computing efficiency. The second layer is Control Layer, which aims to synchronize configurations and data in computing nodes, supervise the computing processes, and provide reports for analysis and optimization. The top layer is Application Layer, which shows an interface to a GIS platform. Through this interface, the tributaries that have been computed in a certain node are dynamically displayed in the current map. The interface also provides a way to find out real-time information, such as the runoff yield and sediment yield process, on a certain tributary. This cluster computing system is realized at hardware level, data level and software level. In hardware level, a cluster is built in master-slave pattern, and connects to database server through 100M Switch Ethernet. In data level, static data such as topography data are collectively stored in database server with stable Oracle Database, and dynamic data, such as rainfall data are distributively stored in local computer with flexible Access Database. In software level, the system is developed in two modules separately, and the modules are connected by multi-thread programming while communicated by a specific information standard.This thesis also uses the cluster computing system to simulate the runoff process of the sandy and grit area of Yellow River from September 1st to September 21st. The result shows that the speed-up reaches 8 and parallel efficiency reaches 33% when the computing nodes is 24, the computing performance is excellent in all ways, and simulated results are well inosculated the observed results.