DOI: 10.5194/hess-19-2409-2015
Scopus记录号: 2-s2.0-84930216359
论文题名: Multi-objective parameter optimization of common land model using adaptive surrogate modeling
作者: Gong W ; , Duan Q ; , Li J ; , Wang C ; , Di Z ; , Dai Y ; , Ye A ; , Miao C
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期: 5 起始页码: 2409
结束页码: 2425
语种: 英语
Scopus关键词: Climate models
; Dynamic models
; Fuel additives
; Optimization
; Uncertainty analysis
; Complex dynamic models
; Curse of dimensionality
; Effectiveness and efficiencies
; Multi-objective optimization problem
; Multi-objective parameter optimizations
; Parameter specification
; Single objective optimization
; Uncertainty quantifications
; Multiobjective optimization
; carbon cycle
; climate modeling
; common land
; energy balance
; land surface
; optimization
; surrogate method
; water budget
英文摘要: 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 ∼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. © Author(s) 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78517
Appears in Collections: 气候变化事实与影响
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作者单位: College of Global Change and Earth System Science (GCESS), Beijing Normal University, Beijing, China; Joint Center for Global Change Studies, Beijing, China
Recommended Citation:
Gong W,, Duan Q,, Li J,et al. Multi-objective parameter optimization of common land model using adaptive surrogate modeling[J]. Hydrology and Earth System Sciences,2015-01-01,19(5)