基于云计算的流域模型校正

M. Humphrey, N. Beekwilder, J. Goodall, M. Ercan
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引用次数: 35

摘要

在面对洪水和干旱等极端事件或对水质的担忧时,了解大型流域和河流流域尺度上的水文系统对社会至关重要。流域建模的一个关键要求是模型校准,即在搜索算法中改变计算模型的参数,以便找到与物理观测现象(如溪流)的最佳匹配。由于通常是在笔记本电脑上进行的,这个校准阶段可能非常耗时,极大地限制了水文学家使用不同模型进行实验的能力。在本文中,我们描述了我们使用云计算,特别是微软Windows Azure的分水岭模型校准系统。以一个典型的分水岭模型为例,在一台商用笔记本电脑上校准需要11.4小时,我们基于云的系统使用16个云核(15.78倍加速)在43.32分钟内校准分水岭模型,使用64个云核(58.13倍加速)在11.76分钟内校准分水岭模型,使用256个云核(135.89倍加速)在5.03分钟内校准分水岭模型。我们相信,这种加速为持续校准的实时交互式模型创建提供了潜力,为流域建模带来了新的范例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Calibration of watershed models using cloud computing
Understanding hydrologic systems at the scale of large watersheds and river basins is critically important to society when faced with extreme events, such as floods and droughts, or with concerns about water quality. A critical requirement of watershed modeling is model calibration, in which the computational model's parameters are varied during a search algorithm in order to find the best match against physically-observed phenomena such as streamflow. Because it is generally performed on a laptop computer, this calibration phase can be very time-consuming, significantly limiting the ability of a hydrologist to experiment with different models. In this paper, we describe our system for watershed model calibration using cloud computing, specifically Microsoft Windows Azure. With a representative watershed model whose calibration takes 11.4 hours on a commodity laptop, our cloud-based system calibrates the watershed model in 43.32 minutes using 16 cloud cores (15.78x speedup), 11.76 minutes using 64 cloud cores (58.13x speedup), and 5.03 minutes using 256 cloud cores (135.89x speedup). We believe that such speed-ups offer the potential toward real-time interactive model creation with continuous calibration, ushering in a new paradigm for watershed modeling.
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