Data and scripts associated with the manuscript "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling"

Jiang, Peishi; Son, Kyongho; Mudunuru, Maruti K.; Chen, Xingyuan

doi:10.15485/1873128

Title: Data and scripts associated with the manuscript "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling"

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Abstract

This package contains the data and scripts used in "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling" (Jiang et al., 2022). The ARW_SWAT.zip file contains the SWAT simulation and the sensitivity analysis result in the American River Watershed. The Portage_SWAT.zip file contains the SWAT simulation and the sensitivity analysis result in the Portage River Watershed. The src folder contains the source code of sensitivity analysis and plotting functions. The notebooks folder contains the Jupyter notebooks for performing sensitivity analysis and other post analyses.Global Sensitivity Analysis (GSA) often is applied to assess the sensitivity of model outputs to their inputs using ensemble simulations. However, increasing model complexity and the associated computational cost have limited the use of most GSA approaches for process-based watershed models. We propose to use Mutual Information (MI) as a computationally efficient GSA method for watershed modeling. Such MI computed from several hundred realizations usually can capture nonlinear relationships between inputs and outputs of interest. We perform MI-based watershed sensitivity analyses in studies of the Portage River Watershed in Ohio and the American River Watershed in Washington. In these studies, MI is used to evaluate the sensitivity of river discharges simulated by the Soil and Watermore »« less

Authors:

; Son, Kyongho;

;

ESS-DIVE

Publication Date:: Fri Dec 31 23:00:00 EST 2021

Research Org.:: Environmental System Science Data Infrastructure for a Virtual Ecosystem; River Corridor and Watershed Biogeochemistry SFA

Sponsoring Org.:: U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Subject:: 54 ENVIRONMENTAL SCIENCES; discharge; evapotranspiration; mutual information; sensitivity analysis; watershed modeling

OSTI Identifier:: 1873128

DOI:: https://doi.org/10.15485/1873128

Citation Formats


                    Jiang, Peishi, Son, Kyongho, Mudunuru, Maruti K., and Chen, Xingyuan. Data and scripts associated with the manuscript "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling".  United States: N. p., 2021. 
        Web.  doi:10.15485/1873128.

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                    Jiang, Peishi, Son, Kyongho, Mudunuru, Maruti K., & Chen, Xingyuan. Data and scripts associated with the manuscript "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling".  United States.  doi:https://doi.org/10.15485/1873128

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                    Jiang, Peishi, Son, Kyongho, Mudunuru, Maruti K., and Chen, Xingyuan. 2021.  
"Data and scripts associated with the manuscript "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling"".  United States.  doi:https://doi.org/10.15485/1873128.  https://www.osti.gov/servlets/purl/1873128. Pub date:Fri Dec 31 23:00:00 EST 2021

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@article{osti_1873128,

  title        = {Data and scripts associated with the manuscript "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling"},

  author       = {Jiang, Peishi and Son, Kyongho and Mudunuru, Maruti K. and Chen, Xingyuan},

  abstractNote = {This package contains the data and scripts used in "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling" (Jiang et al., 2022). The ARW_SWAT.zip file contains the SWAT simulation and the sensitivity analysis result in the American River Watershed. The Portage_SWAT.zip file contains the SWAT simulation and the sensitivity analysis result in the Portage River Watershed. The src folder contains the source code of sensitivity analysis and plotting functions. The notebooks folder contains the Jupyter notebooks for performing sensitivity analysis and other post analyses.Global Sensitivity Analysis (GSA) often is applied to assess the sensitivity of model outputs to their inputs using ensemble simulations. However, increasing model complexity and the associated computational cost have limited the use of most GSA approaches for process-based watershed models. We propose to use Mutual Information (MI) as a computationally efficient GSA method for watershed modeling. Such MI computed from several hundred realizations usually can capture nonlinear relationships between inputs and outputs of interest. We perform MI-based watershed sensitivity analyses in studies of the Portage River Watershed in Ohio and the American River Watershed in Washington. In these studies, MI is used to evaluate the sensitivity of river discharges simulated by the Soil and Water Assessment Tool to no less than 20 parameters for each watershed. Our MI-based sensitivity analyses achieved convergence with about 300~500 realizations, a small fraction of the ensemble size (i.e., several thousand) required by the Sobol method. Nevertheless, MI yields similar sensitivity ranking compared to the Sobol method, especially for sensitive parameters. Our study thus sheds new light on the use of MI as an affordable GSA method for computationally intensive models such as the hyper-resolution, watershed hydrobiogeochemical models.},

  doi          = {10.15485/1873128},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Dec 31 23:00:00 EST 2021},

  month        = {Fri Dec 31 23:00:00 EST 2021}

}

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DOI: https://doi.org/10.15485/1873128

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