07 Apr 2022
07 Apr 2022
Status: this preprint is open for discussion.

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.

Taher Chegini and Hong-Yi Li

Status: open (until 07 Jun 2022)

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 reply
  • RC2: 'Comment on egusphere-2022-90', Anonymous Referee #2, 05 May 2022 reply

Taher Chegini and Hong-Yi Li

Taher Chegini and Hong-Yi Li


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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.