Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016WR019715 |
A new process sensitivity index to identify important system processes under process model and parametric uncertainty | |
Dai, Heng1; Ye, Ming2; Walker, Anthony P.3,4; Chen, Xingyuan1 | |
2017-04-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:4 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. For demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond. Plain Language Summary If we have only one model, we always know how to identify the important factors of the models. However, if there are multiple models, it is not always clear how to identify the important factors. The factors important to one model may not be important to another model. It is necessary to develop a method that can identify important factors not for a single model but for multiple models. This study aims at resolving this problem by developing a mathematically rigorous method to provide a single summary measure for identifying important factors in the face of competing models. This is called multi-model process sensitivity analysis, and the mathematical measure is called process sensitivity index. The new index is demonstrated using a numerical example of groundwater reactive transport modeling with two recharge models and two geology models. The multimodel process sensitivity analysis has a wide range of applications in hydrologic and environmental modeling. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000403682600053 |
WOS关键词 | MULTIMODEL BAYESIAN-ANALYSIS ; DOMINANT PROCESSES CONCEPT ; GLOBAL SENSITIVITY ; COLLOCATION METHOD ; TRANSPORT ; IDENTIFICATION ; LIKELIHOOD ; FRAMEWORK ; FLOW ; SIMPLIFICATION |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/20415 |
专题 | 资源环境科学 |
作者单位 | 1.Pacific Northwest Natl Lab, Richland, WA USA; 2.Florida State Univ, Dept Comp Sci, Tallahassee, FL 32306 USA; 3.Oak Ridge Natl Lab, Div Environm Sci, POB 2008, Oak Ridge, TN 37831 USA; 4.Oak Ridge Natl Lab, Climate Change Sci Inst, Oak Ridge, TN USA |
推荐引用方式 GB/T 7714 | Dai, Heng,Ye, Ming,Walker, Anthony P.,et al. A new process sensitivity index to identify important system processes under process model and parametric uncertainty[J]. WATER RESOURCES RESEARCH,2017,53(4). |
APA | Dai, Heng,Ye, Ming,Walker, Anthony P.,&Chen, Xingyuan.(2017).A new process sensitivity index to identify important system processes under process model and parametric uncertainty.WATER RESOURCES RESEARCH,53(4). |
MLA | Dai, Heng,et al."A new process sensitivity index to identify important system processes under process model and parametric uncertainty".WATER RESOURCES RESEARCH 53.4(2017). |
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