Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis

By Jianduo Li, Qingyun Duan, Wei Gong, Aizhong Ye, Yongjiu Dai, Chiyuan Miao, Zhenhua Di, Charles H. Tong, Yunwei Sun. 2013

89 citations recorded by the source corpus.

Abstract. Proper specification of model parameters is critical to the performance of land surface models (LSMs). Due to high dimensionality and parameter interaction, estimating parameters of an LSM is a challenging task. Sensitivity analysis (SA) is a tool that can screen out the most influential parameters on model outputs. In this study, we conducted parameter screening for six output fluxes for the Common Land Model: sensible heat, latent heat, upward longwave radiation, net radiation, soil temperature and soil moisture. A total of 40 adjustable parameters were considered. Five qualitative SA methods, including local, sum-of-trees, multivariate adaptive regression splines, delta test and Morris methods, were compared. The proper sampling design and sufficient sample size necessary to effectively screen out the sensitive parameters were examined. We found that there are 2–8 sensitive parameters, depending on the output type, and about 400 samples are adequate to reliably identify the most sensitive parameters. We also employed a revised Sobol' sensitivity method to quantify the importance of all parameters. The total effects of the parameters were used to assess the contribution of each parameter to the total variances of the model outputs. The results confirmed that global SA methods can generally identify the most sensitive parameters effectively, while local SA methods result in type I errors (i.e., sensitive parameters labeled as insensitive) or type II errors (i.e., insensitive parameters labeled as sensitive). Finally, we evaluated and confirmed the screening results for their consistency with the physical interpretation of the model parameters.

Subfields: Atmospheric Science, Civil and Structural Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/6b1d4c0f808f07829e9e648df678e1473e2a0b05

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Wei Gong
2. Qingyun Duan

In Civil and Structural Engineering

1. Che-sheng Zhan
2. Ming Ye
3. Wei Gong
4. Xiaomeng Song
5. Qingyun Duan
6. Zhenhua Di
7. Chonggang Xu
8. Aizhong Ye

In Global and Planetary Change

1. Ming Ye
2. Wei Gong
3. Qingyun Duan
4. Chonggang Xu
5. Chiyuan Miao
6. Jasper A. Vrugt

Citations in the corpus

• Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, methodology): “…require much hypothesis generation or prior knowledge of the actual response relationship, such as MARS (multivariate adaptive regression splines) (Li et al., 2013; Zhan et al., 2013; Gan et al., 2014), SVM (- support vector machine) (Song et al., 2012a), GP (Gaussian processes) (Gan et al.,…”
• An Intercomparison of Sampling Methods for Uncertainty Quantification of Environmental Dynamic Models (2015, methodology): “In previous research, we have evaluated various sampling methods for its impact on parameter screening effectiveness (Li et al., 2013; Gan et al., 2014; Di et al., 2014).”