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Title: A new process sensitivity index to identify important system processes under process model and parametric uncertainty

Abstract

Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averaging methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters.more » The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [1]
  1. Pacific Northwest National Laboratory, Richland Washington USA
  2. Department of Scientific Computing, Florida State University, Tallahassee Florida USA
  3. Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge Tennessee USA
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1358473
Report Number(s):
PNNL-SA-120642
Journal ID: ISSN 0043-1397; KP1702030
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 53; Journal Issue: 4; Journal ID: ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English

Citation Formats

Dai, Heng, Ye, Ming, Walker, Anthony P., and Chen, Xingyuan. A new process sensitivity index to identify important system processes under process model and parametric uncertainty. United States: N. p., 2017. Web. doi:10.1002/2016WR019715.
Dai, Heng, Ye, Ming, Walker, Anthony P., & Chen, Xingyuan. A new process sensitivity index to identify important system processes under process model and parametric uncertainty. United States. doi:10.1002/2016WR019715.
Dai, Heng, Ye, Ming, Walker, Anthony P., and Chen, Xingyuan. Sat . "A new process sensitivity index to identify important system processes under process model and parametric uncertainty". United States. doi:10.1002/2016WR019715.
@article{osti_1358473,
title = {A new process sensitivity index to identify important system processes under process model and parametric uncertainty},
author = {Dai, Heng and Ye, Ming and Walker, Anthony P. and Chen, Xingyuan},
abstractNote = {Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averaging methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations 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.},
doi = {10.1002/2016WR019715},
journal = {Water Resources Research},
issn = {0043-1397},
number = 4,
volume = 53,
place = {United States},
year = {2017},
month = {4}
}

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