Effectiveness of Urban Distributed Runoff Model for Discharge and Water Depth Calculation in Urban Drainage Pipe Networks

Author:

Zhou Yang1,Leng Yiling1,Wang Pengyu1,Zhang Shanghong1,Zhu Yulong1ORCID,Yi Yujun2

Affiliation:

1. North China Electric Power University

2. Beijing Normal University

Abstract

Abstract Effective urban land-use re-planning and the strategic arrangement of drainage pipe networks can significantly enhance a city's capacity to mitigate flood risks. Aimed at reducing the potential risks of urban flooding, this paper presents a straightforward and efficient approach to make urban land-use re-planning and drainage pipe network layout (ULP-DPNL). To achieve this goal, an urban distributed runoff model (UDRM) is firstly developed to quantify the discharge and water depth within urban drainage pipe networks under varying rainfall intensities and land-use scenarios. The Nash efficiency coefficient of UDRM is greater than 0.9 with high computational efficiency, affirming its potential benefit in predicting urban flooding. Then five different flood recurrence intervals are adopted to predict drainage congestion under both current and re-planned land-use typologies. The findings reveal that the re-planned land-use strategies could effectively diminish flood risk upstream of the drainage pipe network across all five flood recurrence intervals. However, in the case of extreme rainfall events (a 100-year flood recurrence), the re-planned land-use approach fell short of fulfilling the requirements necessary for flood disaster mitigation. In these instances, the adoption of larger-diameter drainage pipes becomes an essential requisite to satisfy drainage needs. Accordingly, the proposed UDRM-based ULP-DPNL approach effectively amalgamates land-use information with pipeline data to provide constructive recommendations for pipeline modification and land-use optimization in combating urban floods. Therefore, this methodology warrants further promotion in the field of urban re-planning.

Publisher

Research Square Platform LLC

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