Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.153
IF5.153
IF 5-year value: 5.460
IF 5-year
5.460
CiteScore value: 7.8
CiteScore
7.8
SNIP value: 1.623
SNIP1.623
IPP value: 4.91
IPP4.91
SJR value: 2.092
SJR2.092
Scimago H <br class='widget-line-break'>index value: 123
Scimago H
index
123
h5-index value: 65
h5-index65
Preprints
https://doi.org/10.5194/hess-2020-367
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-367
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Aug 2020

17 Aug 2020

Review status
This preprint is currently under review for the journal HESS.

A Framework for Automatic Calibration of SWMM Considering Input Uncertainty

Xichao Gao1,2, Zhiyong Yang1, Dawei Han2, Guoru Huang3, and Qian Zhu4 Xichao Gao et al.
  • 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.

Interactive discussion

Status: open (until 03 Nov 2020)
Status: open (until 03 Nov 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Xichao Gao et al.

Xichao Gao et al.

Viewed

Total article views: 185 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
130 54 1 185 1 0
  • HTML: 130
  • PDF: 54
  • XML: 1
  • Total: 185
  • BibTeX: 1
  • EndNote: 0
Views and downloads (calculated since 17 Aug 2020)
Cumulative views and downloads (calculated since 17 Aug 2020)

Viewed (geographical distribution)

Total article views: 169 (including HTML, PDF, and XML) Thereof 166 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 28 Sep 2020
Publications Copernicus
Download
Short summary
Input errors and parameter errors are two main sources of uncertainties in hydrological model calibration. We developed a new Bayesian framework for automatic calibration of the Storm Water Management Model (SWMM), simultaneously considering parameter and input uncertainties and verified the framework with a case study. The results shows that calibration considering both parameter and input uncertainties captures peak flow much better that only considering parameter uncertainty.
Input errors and parameter errors are two main sources of uncertainties in hydrological model...
Citation