 |
| Titel |
Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis |
| VerfasserIn |
J. Li, Q. Y. Duan, W. Gong, A. Ye, Y. Dai, C. Miao, Z. Di, C. Tong, Y. Sun |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 8 ; Nr. 17, no. 8 (2013-08-21), S.3279-3293 |
| Datensatznummer |
250085915
|
| Publikation (Nr.) |
 copernicus.org/hess-17-3279-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
| 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|