HESS Hydrology and Earth System Sciences HESS Hydrol. Earth Syst. Sci. 1607-7938 Copernicus Publications Göttingen, Germany 10.5194/hess-19-2409-2015 Multi-objective parameter optimization of common land model using adaptive surrogate modeling Gong W. https://orcid.org/0000-0003-3622-7090 1 2 Duan Q. https://orcid.org/0000-0001-9955-1512 1 2 Li J. 1 2 Wang C. 1 2 Di Z. 1 2 Dai Y. 1 2 Ye A. https://orcid.org/0000-0002-5272-134X 1 2 Miao C. https://orcid.org/0000-0001-6413-7020 1 2 College of Global Change and Earth System Science (GCESS), Beijing Normal University, Beijing 100875, China Joint Center for Global Change Studies, Beijing 100875, China 21 05 2015 19 5 2409 2425 Copyright: © 2015 W. Gong et al. 2015 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://hess.copernicus.org/articles/19/2409/2015/hess-19-2409-2015.html The full text article is available as a PDF file from https://hess.copernicus.org/articles/19/2409/2015/hess-19-2409-2015.pdf

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 ~10<sup>5</sup>–10<sup>6</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.

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