Efficient Model Calibration Using Submodels

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES
P. T. M. Vermeulen, G. M. C. M. Janssen, T. Kroon
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引用次数: 0

Abstract

Groundwater models tend to become increasingly detailed to accommodate increasing data availability and higher accuracy demands from stakeholders. As runtimes increase almost quadratically with the number of model cells, this makes the models ever more computationally demanding. This high computational demand introduces challenges for the history-matching (calibration) process as this is an algorithmic process that needs hundreds or thousands of model-runs to obtain the model sensitivities needed to estimate parameters. Model runs may take hours or days to complete which in fact, is often a reason to discard the history-matching all together. As a solution, we present a practical approach to use sub-modeling in combination with parallelization for automatic history-matching. Therefore a large model is subdivided into smaller models to carry out the sensitivity simulations. With a realistic case the method is elaborated, after which the method is demonstrated in the history-matching of the transient Dutch National Groundwater Flow model. In this manner the model, which consisted of over 12 million model cells, could be optimized using 416 sub-models and altogether 2,188 parameters in 1 week. This would take years to complete in a conventional way.
使用子模型进行高效模型校准
地下水模型往往会变得越来越详细,以满足利益相关者日益增长的数据可用性和更高的精度要求。由于运行时间与模型单元数几乎成二次方增长,这使得模型的计算要求越来越高。这种高计算要求给历史匹配(校准)过程带来了挑战,因为这是一个算法过程,需要成百上千次的模型运行才能获得估计参数所需的模型敏感性。模型运行可能需要数小时或数天才能完成,事实上,这往往是完全放弃历史匹配的一个原因。作为一种解决方案,我们提出了一种实用的方法,利用子建模与并行化相结合来实现自动历史匹配。因此,我们将一个大型模型细分为多个小型模型,以进行敏感性模拟。通过一个实际案例对该方法进行了阐述,随后在荷兰国家地下水流瞬态模型的历史匹配中对该方法进行了演示。通过这种方法,可以在一周内使用 416 个子模型和总共 2188 个参数对包含 1200 多万个模型单元的模型进行优化。如果采用传统方法,则需要数年时间才能完成。
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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