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| Titel |
Multi-objective parameter optimization of common land model using adaptive surrogate modeling |
| VerfasserIn |
W. Gong, Q. Duan, J. Li, C. Wang, Z. Di, Y. Dai, A. Ye, C. Miao |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 5 ; Nr. 19, no. 5 (2015-05-21), S.2409-2425 |
| Datensatznummer |
250120718
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| Publikation (Nr.) |
 copernicus.org/hess-19-2409-2015.pdf |
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| Zusammenfassung |
| 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
~105–106). 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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