A comprehensive modeling approach for urban rainfall - runoff simulation
type: CreativeWork
description: Urban flood modeling is a prevalent research topic worldwide, including surface flow, underground drainage flow and especially the inevitable flow connecting with underground infrastructures in highly urbanized areas. A comprehensive modeling approach and effective mitigation measures are necessary for urban risk assessment and management. Herein in this work, surface flow modeling was first investigated based on an experiment; then, the underground modeling system was included by a coupled model proposed in this work; finally, urban rainfall - runoff simulations were carried out accounting for different spatial and temporal resolutions of rain data and further mitigation effect of rain garden. In pure surface flow modeling, depth-dependent roughness and infiltration methods were numerically investigated based on rainfall - runoff experiments, under two conditions in terms of traditional and LID (Low Impact Development measures) surface conditions, different slopes and rainfall intensities. First, observed runoff data has been used to calibrate the model parameters for the depth - dependent roughness and infiltration approaches for both traditional and LID surfaces. The calibrated parameter values then have been applied to validate other two rainfall events. High NSE Coefficient (Nash-Sutcliffe Efficiency) and low SDR (Standard Deviation R) values were obtained, indicating a satisfied agreement between simulated results and observed data for both calibration and validation cases. Then, further comparison of constant and depth - dependent methods clearly pointed out the superiority of the latter methods, as they led to much better evaluation criteria. In addition, the depth-dependent roughness method ensured stability. We also refer to Mügler et al. (2011a) who compared four roughness methods within one case study also proving that the best results were obtained with a water depth-dependent Manning law. The results demonstrated the superiority of the depth - dependen…
abstract: Urban flood modeling is a prevalent research topic worldwide, including surface flow, underground drainage flow and especially the inevitable flow connecting with underground infrastructures in highly urbanized areas. A comprehensive modeling approach and effective mitigation measures are necessary for urban risk assessment and management. Herein in this work, surface flow modeling was first investigated based on an experiment; then, the underground modeling system was included by a coupled model proposed in this work; finally, urban rainfall - runoff simulations were carried out accounting for different spatial and temporal resolutions of rain data and further mitigation effect of rain garden. In pure surface flow modeling, depth-dependent roughness and infiltration methods were numerically investigated based on rainfall - runoff experiments, under two conditions in terms of traditional and LID (Low Impact Development measures) surface conditions, different slopes and rainfall intensities. First, observed runoff data has been used to calibrate the model parameters for the depth - dependent roughness and infiltration approaches for both traditional and LID surfaces. The calibrated parameter values then have been applied to validate other two rainfall events. High NSE Coefficient (Nash-Sutcliffe Efficiency) and low SDR (Standard Deviation R) values were obtained, indicating a satisfied agreement between simulated results and observed data for both calibration and validation cases. Then, further comparison of constant and depth - dependent methods clearly pointed out the superiority of the latter methods, as they led to much better evaluation criteria. In addition, the depth-dependent roughness method ensured stability. We also refer to Mügler et al. (2011a) who compared four roughness methods within one case study also proving that the best results were obtained with a water depth-dependent Manning law. The results demonstrated the superiority of the depth - dependen…
author: Zhang, Yangwei
language: en
license: https://creativecommons.org/licenses/by-nd/4.0/
date published: 2026
date modified: 2026-07-27T21:28:52Z
provider: Technische Universität Berlin
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Technische Universität Berlin

PropertyValue
property ID: DOI
value: 10.14279/depositonce-24499