Articles | Volume 28, issue 1
https://doi.org/10.5194/hess-28-21-2024
https://doi.org/10.5194/hess-28-21-2024
Research article
 | 
02 Jan 2024
Research article |  | 02 Jan 2024

A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+

Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Čerkasova, and Jungang Gao

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Cited articles

Abbas, S., Xuan, Y., and Bailey, R.: Assessing Climate Change Impact on Water Resources in Water Demand Scenarios Using SWAT-MODFLOW-WEAP, Hydrology, 9, 164, https://doi.org/10.3390/hydrology9100164, 2022. 
Arnold, J., Srinivasan, R., Muttiah, R., and Williams, J.: Large area hydrologic modeling and assessment part I: Model development, J. Am. Water Resour. Assoc., 34, 73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998. 
Arnold, J., Kiniry, J., Srinivasan, R., Williams, J., Haney, E., and Neitsch, S.: Soil & Water Assessment Tool: Input/output documentation, version 2012, 2013 TR-439, Texas Water Resources Institute, https://swat.tamu.edu/media/69296/swat-io-documentation-2012.pdf (last access: 16 June 2023), 2013. 
Arnold, J., White, M., Allen, P., Gassman, P., and Bieger, K.: Conceptual Framework of connectivity for a national agroecosystem model based on transport processes and management practices. J. Am. Water Resour. Assoc., 57, 154–169, https://doi.org/10.1111/1752-1688.12890, 2020. 
Bahremand, A. and De Smedt, F.: Predictive Analysis and Simulation Uncertainty of a Distributed Hydrological Model, Water Resour. Manage., 24, 2869–2880, https://doi.org/10.1007/s11269-010-9584-1, 2010. 
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Research highlights.

1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.

2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.

3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.

4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.

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