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Real-world hydrologic assessment of a fully-distributed hydrological model in a parallel computing environment

机译:并行计算环境中全分布式水文模型的真实世界水文评估

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A major challenge in the use of fully-distributed hydrologic models has been the lack of computational capabilities for high-resolution, long-term simulations in large river basins. In this study, we present the parallel model implementation and real-world hydrologic assessment of the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS). Our parallelization approach is based on the decomposition of a complex watershed using the channel network as a directed graph. The resulting sub-basin partitioning divides effort among processors and handles hydrologic exchanges across boundaries. Through numerical experiments in a set of nested basins, we quantify parallel performance relative to serial runs for a range of processors, simulation complexities and lengths, and sub-basin partitioning methods, while accounting for inter-run variability on a parallel computing system. In contrast to serial simulations, the parallel model speed-up depends on the variability of hydrologic processes. Load balancing significantly improves parallel speed-up with proportionally faster runs as simulation complexity (domain resolution and channel network extent) increases. The best strategy for large river basins is to combine a balanced partitioning with an extended channel network, with potential savings through a lower TIN resolution. Based on these advances, a wider range of applications for fully-distributed hydrologic models are now possible. This is illustrated through a set of ensemble forecasts that account for precipitation uncertainty derived from a statistical downscaling model.
机译:使用全分布式水文模型的主要挑战是缺乏大型河流流域高分辨率,长期模拟的计算能力。在这项研究中,我们介绍了基于不规则三角网(TIN)的实时综合盆地模拟器(tRIBS)的并行模型实现和真实世界的水文评估。我们的并行化方法基于使用信道网络作为有向图的复杂分水岭的分解。所形成的子流域划分将处理者之间的工作分担并处理跨边界的水文交换。通过在一组嵌套盆地中进行的数值实验,我们针对一系列处理器,仿真复杂性和长度以及子盆地划分方法,量化了与串行运行相关的并行性能,同时考虑了并行计算系统上的运行间差异。与串行模拟相比,并行模型的加速取决于水文过程的可变性。随着模拟复杂度(域分辨率和信道网络范围)的增加,负载平衡可显着提高运行速度的同时并行运行的速度。大型流域的最佳策略是将平衡分区与扩展的通道网络相结合,并通过较低的TIN分辨率来节省成本。基于这些进展,全分布式水文模型的广泛应用现已成为可能。这是通过一组集合预报来说明的,这些集合说明了从统计缩减模型得出的降水不确定性。

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