气候变化领域集成服务门户(CCPortal): Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models

CCPortal

DOI	10.1029/2018MS001573
	Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models
	Huo X.; Gupta H.; Niu G.-Y.; Gong W.; Duan Q.
发表日期	2019
ISSN	19422466
起始页码	2896
结束页码	2909
卷号	11 期号:9
英文摘要	Dynamical environmental systems models are highly parameterized, having large numbers of parameters whose values are uncertain. For spatially distributed continental-scale applications, such models must be run for very large numbers of grid locations. To calibrate such models, it is useful to be able to perform parameter screening, via sensitivity analysis, to identify the most important parameters. However, since this typically requires the models to be run for a large number of sampled parameter combinations, the computational burden can be huge. To make such an investigation computationally feasible, we propose a novel approach to combining spatial sampling with parameter sampling and test it for the Noah-MP land surface model applied across the continental United States, focusing on gross primary production and flux of latent heat simulations for two vegetation types. Our approach uses (a) progressive Latin hypercube sampling to sample at four grid levels and four parameter levels, (b) a recently developed grouping-based sensitivity analysis approach that ranks parameters by importance group rather than individually, and (c) a measure of robustness to grid and parameter sampling variability. The results show that a relatively small grid sample size (i.e., 5% of the total grids) and small parameter sample size (i.e., 5 times the number of parameters) are sufficient to identify the most important parameters, with very high robustness to grid sampling variability and a medium level of robustness to parameter sampling variability. The results ensure a dramatic reduction in computational costs for such studies. ©2019. The Authors.
英文关键词	grouping-based ranking; parameter sensitivity analysis; progressive Latin hypercube sampling; robustness to sampling variability; sample design
语种	英语
scopus关键词	Distributed computer systems; Sensitivity analysis; Spatial distribution; Uncertainty analysis; Computational burden; Environmental systems; Gross primary production; grouping-based ranking; Land surface modeling; Latin hypercube sampling; Parameter sensitivity analysis; Sample designs; Importance sampling; design method; detection method; land surface; net primary production; sampling; sensitivity analysis; vegetation type
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156865
作者单位	State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China; Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, China; Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, United States; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing, China
推荐引用方式 GB/T 7714	Huo X.,Gupta H.,Niu G.-Y.,et al. Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models[J],2019,11(9).
APA	Huo X.,Gupta H.,Niu G.-Y.,Gong W.,&Duan Q..(2019).Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models.Journal of Advances in Modeling Earth Systems,11(9).
MLA	Huo X.,et al."Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models".Journal of Advances in Modeling Earth Systems 11.9(2019).