Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach
Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, whic...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2013-01-01
|
| Series: | Water Science and Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674237015302210 |
| _version_ | 1819011539524386816 |
|---|---|
| author | Xiao-meng Song Fan-zhe Kong Che-sheng Zhan Ji-wei Han Xin-hua Zhang |
| author_facet | Xiao-meng Song Fan-zhe Kong Che-sheng Zhan Ji-wei Han Xin-hua Zhang |
| author_sort | Xiao-meng Song |
| collection | DOAJ |
| description | Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1) a screening method (Morris) for qualitative ranking of parameters, and (2) a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol). First, the Morris screening method was used to qualitatively identify the parameters' sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM) were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model. |
| first_indexed | 2024-12-21T01:29:46Z |
| format | Article |
| id | doaj.art-c99ac803e77a4d969b99769a64dc7295 |
| institution | Directory Open Access Journal |
| issn | 1674-2370 |
| language | English |
| last_indexed | 2024-12-21T01:29:46Z |
| publishDate | 2013-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Water Science and Engineering |
| spelling | doaj.art-c99ac803e77a4d969b99769a64dc72952022-12-21T19:20:25ZengElsevierWater Science and Engineering1674-23702013-01-016111710.3882/j.issn.1674-2370.2013.01.001Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approachXiao-meng Song0Fan-zhe Kong1Che-sheng Zhan2Ji-wei Han3Xin-hua Zhang4Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, P. R. ChinaSchool of Resource and Earth Science, China University of Mining and Technology, Xuzhou 221116, P. R. ChinaKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P. R. ChinaSchool of Resource and Earth Science, China University of Mining and Technology, Xuzhou 221116, P. R. ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, P. R. ChinaParameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1) a screening method (Morris) for qualitative ranking of parameters, and (2) a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol). First, the Morris screening method was used to qualitatively identify the parameters' sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM) were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.http://www.sciencedirect.com/science/article/pii/S1674237015302210Xin'anjiang modelglobal sensitivity analysisparameter identificationmeta-modeling approachresponse surface model |
| spellingShingle | Xiao-meng Song Fan-zhe Kong Che-sheng Zhan Ji-wei Han Xin-hua Zhang Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach Water Science and Engineering Xin'anjiang model global sensitivity analysis parameter identification meta-modeling approach response surface model |
| title | Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach |
| title_full | Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach |
| title_fullStr | Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach |
| title_full_unstemmed | Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach |
| title_short | Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach |
| title_sort | parameter identification and global sensitivity analysis of xin anjiang model using meta modeling approach |
| topic | Xin'anjiang model global sensitivity analysis parameter identification meta-modeling approach response surface model |
| url | http://www.sciencedirect.com/science/article/pii/S1674237015302210 |
| work_keys_str_mv | AT xiaomengsong parameteridentificationandglobalsensitivityanalysisofxinanjiangmodelusingmetamodelingapproach AT fanzhekong parameteridentificationandglobalsensitivityanalysisofxinanjiangmodelusingmetamodelingapproach AT cheshengzhan parameteridentificationandglobalsensitivityanalysisofxinanjiangmodelusingmetamodelingapproach AT jiweihan parameteridentificationandglobalsensitivityanalysisofxinanjiangmodelusingmetamodelingapproach AT xinhuazhang parameteridentificationandglobalsensitivityanalysisofxinanjiangmodelusingmetamodelingapproach |