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Multi-objective parameter optimization of common land model using adaptive surrogate modeling

机译：基于自适应代理建模的通用土地模型多目标参数优化

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摘要

Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20 to 100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ~10sup5/sup–10sup6/sup). It makes parameter optimization computationally prohibitive. An uncertainty quantification framework was developed to meet the aforementioned challenges, which include the following steps: (1) using parameter screening to reduce the number of adjustable parameters, (2) using surrogate models to emulate the responses of dynamic models to the variation of adjustable parameters, (3) using an adaptive strategy to improve the efficiency of surrogate modeling-based optimization; (4) using a weighting function to transfer multi-objective optimization to single-objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column application of a LSM – the Common Land Model (CoLM), and evaluate the effectiveness and efficiency of the proposed framework. The result indicate that this framework can efficiently achieve optimal parameters in a more effective way. Moreover, this result implies the possibility of calibrating other large complex dynamic models, such as regional-scale LSMs, atmospheric models and climate models.
机译：参数规范通常会对地表模型（LSM）的性能产生重大影响。但是，由于以下原因，正确估计参数是一项艰巨的任务：（1）LSM通常具有太多可调参数（20到100甚至更多），从而导致参数输入空间中的维数诅咒; （2）LSM通常具有许多涉及水/能源/碳循环的输出变量，因此校准LSM实际上是一个多目标优化问题; （3）区域LSM的运行成本很高，而传统的多目标优化方法需要大量的模型运行（通常〜10 5 –10 6 ）。这使得参数优化在计算上无法实现。开发了不确定性量化框架来应对上述挑战，其中包括以下步骤：（1）使用参数筛选来减少可调整参数的数量，（2）使用替代模型来模拟动态模型对可调整变量的响应参数，（3）采用自适应策略提高基于代理建模的优化效率; （4）使用加权函数将多目标优化转移到单目标优化。在这项研究中，我们演示了LSM单列应用程序的不确定性量化框架-共同土地模型（CoLM），并评估了所提出框架的有效性和效率。结果表明，该框架可以更有效地有效地获得最佳参数。此外，该结果暗示有可能校准其他大型复杂动态模型，例如区域尺度的LSM，大气模型和气候模型。

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