Preprints
https://doi.org/10.5194/egusphere-2022-90
https://doi.org/10.5194/egusphere-2022-90
 
07 Apr 2022
07 Apr 2022

An Algorithm for Deriving the Topology of Below-ground Urban Stormwater Networks

Taher Chegini and Hong-Yi Li Taher Chegini and Hong-Yi Li
  • University of Houston

Abstract. Below-ground Urban stormwater networks (BUSNs) are critical for removing excess rainfall from impervious areas and preventing or mitigating urban flooding. However, available BUSN data are sparse, preventing modeling and analyzing urban hydrologic processes at regional and larger scales. We thus propose a novel algorithm for estimating BUSNs from existing, extensively available land surface data such as street network, topography, land use/land cover, etc. The rationale underpinning this algorithm are the causal relationships between the topology of BUSNs and urban surface features that we derive based on the Graph theory concepts. We implement this algorithm using web services for data retrieval and high-performance computing techniques for big-data analyses. Lastly, we validate this algorithm at a small portion of Los Angeles and Seattle, and the metropolitan areas of Houston and Baltimore in the U.S., where real BUSN data are available to the public. Results show that our algorithm can effectively capture 60–75 % of the topology of real BUSN data, depending on the supporting data quality. This algorithm has promising potential to support large-scale urban hydrologic modeling and future urban drainage system planning.

Journal article(s) based on this preprint

22 Aug 2022
An algorithm for deriving the topology of belowground urban stormwater networks
Taher Chegini and Hong-Yi Li
Hydrol. Earth Syst. Sci., 26, 4279–4300, https://doi.org/10.5194/hess-26-4279-2022,https://doi.org/10.5194/hess-26-4279-2022, 2022
Short summary

Taher Chegini and Hong-Yi Li

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-90', Anonymous Referee #1, 02 May 2022
    • AC1: 'Reply on RC1', Hongyi Li, 29 Jun 2022
  • RC2: 'Comment on egusphere-2022-90', Anonymous Referee #2, 05 May 2022
    • AC2: 'Reply on RC2', Hongyi Li, 29 Jun 2022
  • RC3: 'Comment on egusphere-2022-90', Anonymous Referee #3, 24 May 2022
    • AC3: 'Reply on RC3', Hongyi Li, 29 Jun 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-90', Anonymous Referee #1, 02 May 2022
    • AC1: 'Reply on RC1', Hongyi Li, 29 Jun 2022
  • RC2: 'Comment on egusphere-2022-90', Anonymous Referee #2, 05 May 2022
    • AC2: 'Reply on RC2', Hongyi Li, 29 Jun 2022
  • RC3: 'Comment on egusphere-2022-90', Anonymous Referee #3, 24 May 2022
    • AC3: 'Reply on RC3', Hongyi Li, 29 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (04 Jul 2022) by Fuqiang Tian
AR by Hongyi Li on behalf of the Authors (12 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (16 Jul 2022) by Fuqiang Tian
RR by Anonymous Referee #3 (22 Jul 2022)
RR by Anonymous Referee #2 (03 Aug 2022)
ED: Publish subject to technical corrections (04 Aug 2022) by Fuqiang Tian

Journal article(s) based on this preprint

22 Aug 2022
An algorithm for deriving the topology of belowground urban stormwater networks
Taher Chegini and Hong-Yi Li
Hydrol. Earth Syst. Sci., 26, 4279–4300, https://doi.org/10.5194/hess-26-4279-2022,https://doi.org/10.5194/hess-26-4279-2022, 2022
Short summary

Taher Chegini and Hong-Yi Li

Taher Chegini and Hong-Yi Li

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
Below-ground urban stormwater networks (BUSNs) play a critical and irreplaceable role in preventing or mitigating urban floods. But they are often not available for urban flood modeling at the regional or larger scales. We develop a novel algorithm to estimate existing BUSNs using ubiquitously available above-ground data at large scales based on the Graph theory. The algorithm has been validated in different urban areas and thus is well transferable.