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Data and models for exploring real-time control of stormwater systems for mitigating flood risk due to sea level rise
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| Type: | Resource | |
| Storage: | The size of this resource is 867.0 MB | |
| Created: | Jan 24, 2020 at 10:56 p.m. | |
| Last updated: | Mar 18, 2020 at 9:38 p.m. (Metadata update) | |
| Published date: | Mar 18, 2020 at 9:38 p.m. | |
| DOI: | 10.4211/hs.5148675c6a5841e686a3b6aec67a38ee | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 1874 |
| Downloads: | 352 |
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Abstract
This resource contains data and models that were used to produce results for a paper published in the Journal of Hydrology. The models are for a neighborhood in Norfolk, Virginia USA that suffers from frequent coastal flooding. The paper describes the use of active stormwater controls to mitigate this problem which will worsen with sea level rise. The particular type of control approach explored was model predictive control (MPC) and the Stormwater Management Model (SWMM) was used to simulate the stormwater system. The swmm_mpc Python package (https://github.com/UVAdMIST/swmm_mpc) was used to simulate MPC in the SWMM model. MPC was simulated for a number of sea level rise scenarios and the amount of flooding was compared to the system with no controls. The Python script that ran swmm_mpc for the sea level rise scenarios is "models/runs/hgv11.py." The results were compiled and plotted with scripts in the "models/results/" directory.
The citation to the Journal of Hydrology paper is
Jeffrey M. Sadler, Jonathan L. Goodall, Madhur Behl, Benjamin D. Bowes, Mohamed M. Morsy, Exploring real-time control of stormwater systems for mitigating flood risk due to sea level rise, Journal of Hydrology, Volume 583, 2020, 124571, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2020.124571.
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| This resource is referenced by | https://doi.org/10.1016/j.jhydrol.2020.124571 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| National Science Foundation | CRISP Type 2: dMIST: Data-driven Management for Interdependent Stormwater and Transportation Systems | 1735587 |
| National Science Foundation | SCC-IRG Track 1: Overcoming Social and Technical Barriers for the Broad Adoption of Smart Stormwater System | 1737432 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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