Urban Water Management and Urban Flooding

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 55661

Special Issue Editor

DHI Water - Environment, Horsholm, Denmark
Interests: urban water systems; sewerage; drainage and surface water; hydrological cycle; urban flooding; urban water impacts on receiving waters

Special Issue Information

Dear Colleagues,

Around the world many cities are growing, increasing the pressure on aging urban infrastructure. In addition, the predictions of future climate changes show that extreme rainfall events will increase in number and strength. Many cities struggle with urban flooding problems today, and the conditions mentioned aggravate the urban flood problems further. Hence, there is a need for methods supporting the timely, sustainable and financially sound management of flood risk in cities.

The aim of this Special Issue is to present state-of-the-art research and applications within urban water management and urban flooding, i.e., to bring forward the most recent research not just dealing with water management and modeling itself, but also presenting novel research within flood damage, resilience, green solutions, health risks associated with urban flooding, and other societal and financial aspects.

We are encouraging papers that demonstrate new research with an added value to traditional research within urban water and flood management, e.g., by the use of AI, crowd sourcing, and IoT.

Dr. Ole Mark
Guest Editor

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Keywords

  • artificial intelligence
  • urban water management
  • hydrological cycle
  • urban flooding
  • health risk
  • green solution

Published Papers (11 papers)

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28 pages, 39408 KiB  
Article
Multi-Scale Target-Specified Sub-Model Approach for Fast Large-Scale High-Resolution 2D Urban Flood Modelling
by Guohan Zhao, Thomas Balstrøm, Ole Mark and Marina B. Jensen
Water 2021, 13(3), 259; https://doi.org/10.3390/w13030259 - 21 Jan 2021
Cited by 12 | Viewed by 4074
Abstract
The accuracy of two-dimensional hydrodynamic models (2D models) is improved when high-resolution Digital Elevation Models (DEMs) are used. However, the entailed high spatial discretisation results in excessive computational expenses, thus prohibiting their implementation in real-time forecasting especially at a large scale. This paper [...] Read more.
The accuracy of two-dimensional hydrodynamic models (2D models) is improved when high-resolution Digital Elevation Models (DEMs) are used. However, the entailed high spatial discretisation results in excessive computational expenses, thus prohibiting their implementation in real-time forecasting especially at a large scale. This paper presents a sub-model approach that adapts 1D static models to tailor high-resolution 2D model grids relevant to specified targets, such that the tailor-made 2D hydrodynamic sub-models yield fast processing without significant loss of accuracy via a GIS-based multi-scale simulation framework. To validate the proposed approach, model experiments were first designed to separately test the impact of two outcomes (i.e., the reduced computational domains and the optimised boundary conditions) towards final 2D prediction results. Then, the robustness of the sub-model approach was evaluated by selecting four focus areas with distinct catchment terrain morphologies as well as distinct rainfall return periods of 1–100 years. The sub-model approach resulted in a 45–553 times faster processing with a 99% reduction in the number of computational cells for all four cases; the goodness of fit regarding predicted flood extents was above 0.88 of F2, flood depths yield Root Mean Square Errors (RMSE) below 1.5 cm and the discrepancies of u- and v-directional velocities at selected points were less than 0.015 ms−1. As such, this approach reduces the 2D models’ computing expenses significantly, thus paving the way for large-scale high-resolution 2D real-time forecasting. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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19 pages, 43485 KiB  
Article
Urban Flood Modeling Using 2D Shallow-Water Equations in Ouagadougou, Burkina Faso
by Gnenakantanhan Coulibaly, Babacar Leye, Fowe Tazen, Lawani Adjadi Mounirou and Harouna Karambiri
Water 2020, 12(8), 2120; https://doi.org/10.3390/w12082120 - 26 Jul 2020
Cited by 11 | Viewed by 4106
Abstract
Appropriate methods and tools accessibility for bi-dimensional flow simulation leads to their weak use for floods assessment and forecasting in West African countries, particularly in urban areas where huge losses of life and property are recorded. To mitigate flood risks or to elaborate [...] Read more.
Appropriate methods and tools accessibility for bi-dimensional flow simulation leads to their weak use for floods assessment and forecasting in West African countries, particularly in urban areas where huge losses of life and property are recorded. To mitigate flood risks or to elaborate flood adaptation strategies, there is a need for scientific information on flood events. This paper focuses on a numerical tool developed for urban inundation extent simulation due to extreme tropical rainfall in Ouagadougou city. Two-dimensional (2D) shallow-water equations are solved using a finite volume method with a Harten, Lax, Van Leer (HLL) numerical fluxes approach. The Digital Elevation Model provided by NASA’s Shuttle Radar Topography Mission (SRTM) was used as the main input of the model. The results have shown the capability of the numerical tool developed to simulate flow depths in natural watercourses. The sensitivity of the model to rainfall intensity and soil roughness coefficient was highlighted through flood spatial extent and water depth at the outlet of the watershed. The performance of the model was assessed through the simulation of two flood events, with satisfactory values of the Nash–Sutcliffe criterion of 0.61 and 0.69. The study is expected to be useful for flood managers and decision makers in assessing flood hazard and vulnerability. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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18 pages, 4023 KiB  
Article
Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia
by Augusto Rafael Garrido-Arévalo, Luis Mauricio Agudelo-Otálora, Nelson Obregón-Neira, Victor Garrido-Arévalo, Edgar Eduardo Quiñones-Bolaños, Parisa Naraei, Mehrab Mehrvar and Ciro Fernando Bustillo-Lecompte
Water 2020, 12(7), 1973; https://doi.org/10.3390/w12071973 - 12 Jul 2020
Cited by 2 | Viewed by 2950
Abstract
An assessment of the rainfall station distribution in the mountainous area of the Regional Autonomous Corporation of Cundinamarca (CAR, for its acronym in Spanish), Colombia, was conducted by applying concepts from information entropy and artificial neural networks (ANNs). This study was divided into [...] Read more.
An assessment of the rainfall station distribution in the mountainous area of the Regional Autonomous Corporation of Cundinamarca (CAR, for its acronym in Spanish), Colombia, was conducted by applying concepts from information entropy and artificial neural networks (ANNs). This study was divided into two phases: first, a classification of the meteorological stations using two-dimensional self-organizing maps; second, the evaluation of the performance of the ANN by applying concepts of information entropy. Three scenarios were raised for the classification of the meteorological stations by adjusting the number of neurons in the output layer. A high number of neurons in the output layer were obtained, causing the model to over-fit while emphasizing differences amid patterns. When comparing the results of the scenarios, the permanence of certain characteristics and features was found in the system, validating the model classification. Subsequently, the results of the first scenario were used to evaluate the entropy of the historical series. Finally, the results show that the area of study presents a lack of information due to the uncertainty associated with the probabilistic arrangement, which can be corrected with the developed model. Consequently, some recommendations for the redesign of the rainfall are provided. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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14 pages, 5875 KiB  
Communication
A Framework for Planning and Evaluating the Role of Urban Stream Restoration for Improving Transportation Resilience to Extreme Rainfall Events
by Barbara A. Doll, J. Jack Kurki-Fox and Daniel E. Line
Water 2020, 12(6), 1620; https://doi.org/10.3390/w12061620 - 06 Jun 2020
Cited by 3 | Viewed by 3746
Abstract
Recent extreme rainfall events produced severe flooding across North Carolina’s Coastal Plain, revealing deep vulnerabilities in many communities. Climate change is expected to exacerbate these problems by further increasing rainfall intensity and the frequency of extreme rainfall events. Due to the risks posed [...] Read more.
Recent extreme rainfall events produced severe flooding across North Carolina’s Coastal Plain, revealing deep vulnerabilities in many communities. Climate change is expected to exacerbate these problems by further increasing rainfall intensity and the frequency of extreme rainfall events. Due to the risks posed by these changing rainfall patterns, a shift in the approach to infrastructure planning and management is needed for many floodprone communities, particularly in regard to managing streams and floodplains in urban areas. This study proposes a framework for systematically evaluating stream restoration in combination with engineered improvements to culvert and bridge crossings to identify and optimize options for mitigating extreme events in urban areas. To illustrate the methodology, extensive hydraulic modeling was conducted to test four different strategies for reducing flooding along a channelized and armored stream, Big Ditch, located in Goldsboro, North Carolina, USA. The results indicate that neither floodplain restoration nor infrastructure modification alone could alleviate flooding along Big Ditch. Rather, a combination approach would be required to mitigate flooding, which could result in substantial benefits for storms in excess of the 100-year event. The results suggest that shifting to a multi-faceted approach to improve resiliency to extreme events could improve public safety and reduce future damages due to flooding. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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16 pages, 9028 KiB  
Article
Inundation Map Prediction with Rainfall Return Period and Machine Learning
by Hyun Il Kim and Kun Yeun Han
Water 2020, 12(6), 1552; https://doi.org/10.3390/w12061552 - 29 May 2020
Cited by 7 | Viewed by 2599
Abstract
To date, various methods of flood prediction using numerical analysis or machine learning have been studied. However, a methodology for simultaneously predicting the rainfall return period and an inundation map for observed rainfall has not been presented. Simultaneous prediction of the return period [...] Read more.
To date, various methods of flood prediction using numerical analysis or machine learning have been studied. However, a methodology for simultaneously predicting the rainfall return period and an inundation map for observed rainfall has not been presented. Simultaneous prediction of the return period and inundation map would be a useful technique for responding to floods in real-time and could provide an expected inundation area by return period. In this study, return period estimation for observed rainfall was performed via PNN (probabilistic neural network). SVR (support vector regression) and a SOM (self-organizing map) were used to predict flood volume and inundation maps. The study area was the Gangnam area, which has experienced extensive urbanization. The database for training SVR and SOM was constructed by one- and two-dimensional flood analysis with consideration of 120 probable rainfall events. The probable rainfall events were composed with 2–100 year return periods and 1–3 hour durations. The SVR technique was used to predict flood volume according to the rainfall return period, and the SOM was used to cluster various expected flood patterns to be used for predicting inundation maps. The prediction results were compared with the simulation results of a two-dimensional flood analysis model. The highest fitness of the predicted flood maps in the study area was calculated at 85.94%. The proposed method was found to constitute a practical methodology that could be helpful in improving urban flood response capabilities. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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18 pages, 6335 KiB  
Article
Assessment of Flood Risk Exposure for the Foshan-Zhongshan Region in Guangdong Province, China
by Qi Zhang, Wei Jian and Edmond Yat Man Lo
Water 2020, 12(4), 1159; https://doi.org/10.3390/w12041159 - 18 Apr 2020
Cited by 3 | Viewed by 3692
Abstract
Floods have caused 20% of the worldwide economic losses resulting from catastrophe events over 2008 to 2018. In China, the annual flood economic losses have exceeded CNY 100 billion from 1990 to 2010, which is equivalent to 1% to 3% of China’s Gross [...] Read more.
Floods have caused 20% of the worldwide economic losses resulting from catastrophe events over 2008 to 2018. In China, the annual flood economic losses have exceeded CNY 100 billion from 1990 to 2010, which is equivalent to 1% to 3% of China’s Gross Domestic Product (GDP). This paper presents a rainfall-runoff model coupled with an inundation estimation to assess the flood risk for a basin within the Foshan-Zhongshan area of the Pearl River Delta (PRD) region in China. A Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model was constructed for the crisscrossing river network in the study basin where the West and North Rivers meet, using publicly accessible meteorological, hydrological and topographical datasets. The developed model was used to analyze two recent flood events, that in July 2017 with large upstream river inflows, and in June 2018 with high local rainfall. Results were further used to develop the needed river rating curves within the basin. Two synthetic events that consider more severe meteorological and hydrological conditions were also analyzed. These results demonstrate the capability of the proposed model for quick assessment of potential flood inundation and the GDP exposure at risk within the economically important PRD region. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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21 pages, 8351 KiB  
Article
Impact Assessment of Urban Flood on Traffic Disruption using Rainfall–Depth–Vehicle Speed Relationship
by Kyung-Su Choo, Dong-Ho Kang and Byung-Sik Kim
Water 2020, 12(4), 926; https://doi.org/10.3390/w12040926 - 25 Mar 2020
Cited by 24 | Viewed by 5074
Abstract
The transportation network enables movement of people and goods and is the basis of economic activity. Recently, short-term locally heavy rains occur frequently in urban areas, causing serious obstacles to road flooding and increasing economic and social effects. Therefore, in advanced weather countries, [...] Read more.
The transportation network enables movement of people and goods and is the basis of economic activity. Recently, short-term locally heavy rains occur frequently in urban areas, causing serious obstacles to road flooding and increasing economic and social effects. Therefore, in advanced weather countries, many studies have been conducted on realistic and reliable impact forecasting by analyzing socioeconomic impacts, not just information transmission as weather forecasts. In this paper, we use the Spatial Runoff Assessment Tool (S-RAT) and Flood Inundation model (FLO-2D model) to calculate the flooding level in urban areas caused by rainfall and use the flooding rate. In addition, the rainfall–flood depth curve and the Flood–Vehicle Speed curve were presented during the analysis, and the traffic disruption map was prepared using this. The results of this study were compared with previous studies and verified by rainfall events in 2011. As a result of the verification, the result was similar to the actual flooding, and when the same rainfall occurred within the range of the target area, it was confirmed that there were sections that could not be passed and sections that could be passed smoothly. Therefore, the results suggested in this study will be helpful for the driver’s route selection by using the urban flood damage analysis and vehicle driving speed analysis. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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14 pages, 4972 KiB  
Article
A Rapid Assessment Method to Identify Potential Groundwater Flooding Hotspots as Sea Levels Rise in Coastal Cities
by Ellen Plane, Kristina Hill and Christine May
Water 2019, 11(11), 2228; https://doi.org/10.3390/w11112228 - 25 Oct 2019
Cited by 26 | Viewed by 12762
Abstract
Sea level rise (SLR) will cause shallow unconfined coastal aquifers to rise. Rising groundwater can emerge as surface flooding and impact buried infrastructure, soil behavior, human health, and nearshore ecosystems. Higher groundwater can also reduce infiltration rates for stormwater, adding to surface flooding [...] Read more.
Sea level rise (SLR) will cause shallow unconfined coastal aquifers to rise. Rising groundwater can emerge as surface flooding and impact buried infrastructure, soil behavior, human health, and nearshore ecosystems. Higher groundwater can also reduce infiltration rates for stormwater, adding to surface flooding problems. Levees and seawalls may not prevent these impacts. Pumping may accelerate land subsidence rates, thereby exacerbating flooding problems associated with SLR. Public agencies at all jurisdiction levels will need information regarding where groundwater impacts are likely to occur for development and infrastructure planning, as extreme precipitation events combine with SLR to drive more frequent flooding. We used empirical depth-to-water data and a digital elevation model of the San Francisco Bay Area to construct an interpolated surface of estimated minimum depth-to-water for 489 square kilometers along the San Francisco Bay shoreline. This rapid assessment approach identified key locations where more rigorous data collection and dynamic modeling is needed to identify risks and prevent impacts to health, buildings, and infrastructure, and develop adaptation strategies for SLR. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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38 pages, 8471 KiB  
Article
Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks
by Benjamin D. Bowes, Jeffrey M. Sadler, Mohamed M. Morsy, Madhur Behl and Jonathan L. Goodall
Water 2019, 11(5), 1098; https://doi.org/10.3390/w11051098 - 25 May 2019
Cited by 98 | Viewed by 8242
Abstract
Many coastal cities are facing frequent flooding from storm events that are made worse by sea level rise and climate change. The groundwater table level in these low relief coastal cities is an important, but often overlooked, factor in the recurrent flooding these [...] Read more.
Many coastal cities are facing frequent flooding from storm events that are made worse by sea level rise and climate change. The groundwater table level in these low relief coastal cities is an important, but often overlooked, factor in the recurrent flooding these locations face. Infiltration of stormwater and water intrusion due to tidal forcing can cause already shallow groundwater tables to quickly rise toward the land surface. This decreases available storage which increases runoff, stormwater system loads, and flooding. Groundwater table forecasts, which could help inform the modeling and management of coastal flooding, are generally unavailable. This study explores two machine learning models, Long Short-term Memory (LSTM) networks and Recurrent Neural Networks (RNN), to model and forecast groundwater table response to storm events in the flood prone coastal city of Norfolk, Virginia. To determine the effect of training data type on model accuracy, two types of datasets (i) the continuous time series and (ii) a dataset of only storm events, created from observed groundwater table, rainfall, and sea level data from 2010–2018 are used to train and test the models. Additionally, a real-time groundwater table forecasting scenario was carried out to compare the models’ abilities to predict groundwater table levels given forecast rainfall and sea level as input data. When modeling the groundwater table with observed data, LSTM networks were found to have more predictive skill than RNNs (root mean squared error (RMSE) of 0.09 m versus 0.14 m, respectively). The real-time forecast scenario showed that models trained only on storm event data outperformed models trained on the continuous time series data (RMSE of 0.07 m versus 0.66 m, respectively) and that LSTM outperformed RNN models. Because models trained with the continuous time series data had much higher RMSE values, they were not suitable for predicting the groundwater table in the real-time scenario when using forecast input data. These results demonstrate the first use of LSTM networks to create hourly forecasts of groundwater table in a coastal city and show they are well suited for creating operational forecasts in real-time. As groundwater table levels increase due to sea level rise, forecasts of groundwater table will become an increasingly valuable part of coastal flood modeling and management. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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18 pages, 2313 KiB  
Article
Effects of Urban Forms on Separate Drainage Systems: A Virtual City Perspective
by Ning Jia, Robert Sitzenfrei, Wolfgang Rauch, Shan Liang and Yi Liu
Water 2019, 11(4), 758; https://doi.org/10.3390/w11040758 - 11 Apr 2019
Cited by 13 | Viewed by 4482
Abstract
The development of urban drainage systems is challenged by rapid urbanization; however, little attention is paid to the urban form and its effects on these systems. This study develops an integrated city-drainage model that configures typical urban forms and their associated drainage infrastructures, [...] Read more.
The development of urban drainage systems is challenged by rapid urbanization; however, little attention is paid to the urban form and its effects on these systems. This study develops an integrated city-drainage model that configures typical urban forms and their associated drainage infrastructures, specifically domestic wastewater and rainwater systems, to analyze the relationship between them. Three typical types of urban forms were investigated: the square, the star, and the strip. Virtual cities were designed first, with the corresponding drainage systems generated automatically and then linked to a model herein called the Storm Water Management Model (SWMM). Evaluation was based on 200 random configurations of wastewater/rainwater systems with different structures or attributes. The results show that urban forms play more important roles on three dimensions of performance, namely economic efficiency, effectiveness, and adaptability, of the rainwater systems than of the wastewater systems. Cost is positively correlated to the effectiveness of rainwater systems among the different urban forms, while adaptability is negatively correlated to the other two performance dimensions. Regardless of the form, it is difficult for a city to make its drainage systems simultaneously cost-effective, efficient, and adaptable based on the virtual cities we investigated. This study could inspire the urban planning of both built-up and to-be-built areas to become more sustainable with their drainage infrastructure by recognizing the pros and cons of different macroscale urban forms. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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13 pages, 3483 KiB  
Technical Note
Monitoring the Hydraulic Performance of Sewers Using Fibre Optic Distributed Temperature Sensing
by Cedric Kechavarzi, Philip Keenan, Xiaomin Xu and Yi Rui
Water 2020, 12(9), 2451; https://doi.org/10.3390/w12092451 - 31 Aug 2020
Cited by 5 | Viewed by 2578
Abstract
The hydraulic performance of sewers is a major public concern in industrialised countries. In this study, fibre optic distributed temperature sensing (DTS) is used to monitor the discharge of wastewater for three months to assess the performance of a long underground foul sewer [...] Read more.
The hydraulic performance of sewers is a major public concern in industrialised countries. In this study, fibre optic distributed temperature sensing (DTS) is used to monitor the discharge of wastewater for three months to assess the performance of a long underground foul sewer in a village in the UK. DTS cables were installed in the invert of sewer pipes to obtain distributed temperature change data along the sewer network. DTS generates a series of two-dimensional data sets (temperature against distance) that can be visualised in waterfall plots to help identify anomalies. The spatial and temperature resolutions are 2 m and 0.2–0.3 °C, respectively. The monitoring data clearly identify high-temperature plumes, which represent the flow of household wastewater in the sewer. Based on the analysis of the waterfall plots, it is found that the flow velocity is about 0.14 m/s under normal conditions. When continuous moderate rain or heavy rain occurs, water backs up from the water treatment plant to upstream distances of up to 400 m and the water flow velocity in the sewer decreases sharply to about 0.03 m/s, which demonstrates the ability of the DTS to localise anomalies in the sewer network. Full article
(This article belongs to the Special Issue Urban Water Management and Urban Flooding)
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