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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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Resource type: Composite 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.
DOI: 10.4211/hs.5148675c6a5841e686a3b6aec67a38ee
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Sharing Status: Published
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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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Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
36.9694°
East Longitude
-76.1771°
South Latitude
36.8208°
West Longitude
-76.3335°

Content

References

Related Resources

The content of this resource serves as the data for: 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
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

Sadler, J. (2020). Data and models for exploring real-time control of stormwater systems for mitigating flood risk due to sea level rise, HydroShare, https://doi.org/10.4211/hs.5148675c6a5841e686a3b6aec67a38ee

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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