A Framework for Automatic Calibration of SWMM Considering Input Uncertainty
- 1State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China
- 2Department of Civil Engineering, University of Bristol, BS8 1TR, UK
- 3School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China
- 4School of Civil Engineering, Southeast University, Nanjing, 211189, China
- 1State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China
- 2Department of Civil Engineering, University of Bristol, BS8 1TR, UK
- 3School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China
- 4School of Civil Engineering, Southeast University, Nanjing, 211189, China
Abstract. A new Bayesian framework for automatic calibration of SWMM, which simultaneously considers both parameter uncertainty and input uncertainty, was developed. The framework coupled an optimization algorithm DREAM and the Storm Water Management Model (SWMM) by newly developed API functions used to obtain and adjust parameter values of the model. Besides, a rainfall error model was integrated into the framework to consider systematic rainfall errors. A case study in Guangzhou, China was conducted to demonstrate the use of the framework. The calibration capability of the framework was tested and the impacts of rainfall uncertainty on model parameter estimations and simulated runoff boundaries were identified in the study area. the results show that calibration considering both parameter uncertainty and rainfall uncertainty captures peak flow much better and is more robust in terms of the Nash Sutcliffe index than that only considering parameter uncertainty.
Xichao Gao et al.


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RC1: 'Good identification of problem area, uninspiring execution', Edward Tiernan, 04 Nov 2020
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AC1: 'Reply on RC1', Xichao Gao, 04 Mar 2021
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AC1: 'Reply on RC1', Xichao Gao, 04 Mar 2021
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RC2: 'review', Anonymous Referee #2, 07 Dec 2020
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AC2: 'Reply on RC2', Xichao Gao, 04 Mar 2021
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AC2: 'Reply on RC2', Xichao Gao, 04 Mar 2021


-
RC1: 'Good identification of problem area, uninspiring execution', Edward Tiernan, 04 Nov 2020
-
AC1: 'Reply on RC1', Xichao Gao, 04 Mar 2021
-
AC1: 'Reply on RC1', Xichao Gao, 04 Mar 2021
-
RC2: 'review', Anonymous Referee #2, 07 Dec 2020
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AC2: 'Reply on RC2', Xichao Gao, 04 Mar 2021
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AC2: 'Reply on RC2', Xichao Gao, 04 Mar 2021
Xichao Gao et al.
Xichao Gao et al.
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