Urban Flood Hazard Research Articles

Global Megacities and Frequent Floods: Correlation between Urban Expansion Patterns and Urban Flood Hazards

With climate change causing increased extreme weather events, megacities worldwide are experiencing unprecedentedly devastating floods and recurring flood damage. Investigating global megacities’ increased disposition to flooding will aid in developing sustainable flood-risk-management frameworks. Many studies have been conducted on the association between land-cover types and flood consequences, but few on investigating urban expansion patterns’ correlation with flood hazard and risk. This study examines the correlation between urban expansion patterns and increased flood hazards. Twelve megacities throughout the world were selected for this study. After exploring the possibility of the megacities having experienced flooding, we qualified their patterns of urban expansion and their potential to influence the elements of flood risk. Our results revealed that edge expansion and leapfrogging patterns had a strong positive correlation with statistical significance with flood hazard, while infilling had a weak positive correlation that showed no statistical significance with flood hazard. Further, we found that the megacities have all experienced devastating floods in the past two decades. Flood risk frameworks need to account for the impact of these patterns, and future urban planning designs and policies need to incorporate flood risk frameworks that account for patterns of urban expansion.

Application of physically-based and experimentally calibrated method for flood hazard assessment: Case study of Zaio, Morocco

This study aims to assess the urban flood hazard in Zaio, Morocco by utilizing a rainfall-runoff transformation and hydrodynamic modelling. We utilized a statistical adjustment and The Soil Conservation Service–Curve Number (SCS-CN) method to estimate the 100-year return period flood hydrograph, which itself served as input for the Iber numerical model. Flood hazard assessment for human adults was calculated using a physically-based and experimentally calibrated method, a method that takes into consideration all physical forces acting on a human body during a flood, i.e., drag and buoyancy, and thereby facilitate a more precise assessment of urban flood hazard.

Developing reliable urban flood hazard mapping from LiDAR data

Digital surface models (DSMs) are crucial in providing accurate urban flood hazard maps. The ubiquitous availability of LiDAR data (where accessible) makes constructing geometrically sound DSMs feasible. However, little attention has been paid to developing approaches for producing geometrically consistent DSMs. Herein is described an application-driven procedure for creating a geometrically robust DSM (DSM1). Two further DSMs were created, one for portraying streets using breaklines as ancillary information (DSM2) and the other through direct interpolation of LiDAR data (DSM3). The geometrical correctness and vertical accuracy of these DSMs were examined qualitatively and quantitatively by plotting longitudinal profiles and cross-sections onto major runoff pathways and determining statistical error. The effect of these DSMs’ geometric consistency on flood hazard maps was also evaluated. For this, hydraulic outputs from DSM1 were used as a benchmark to compare hydraulic outputs from DSM2 and DSM3. This comparison was conducted at two spatial resolutions: i) at the total area flooded using the F statistic; and ii) at the pixel level by employing global indices and category-level indices extracted from a confusion matrix. Our findings revealed that: 1) DSM1 defined the most geometrically coherent configurations for runoff pathways; 2) in urban areas with the most densely packed streets and buildings, DSM2 and particularly DSM3 featured the most unrealistic geometric representations of the urban domain, displaying fake water flow barriers and lower than real runoff pathway cross-sections; and 3) the geometric quality of the DSMs created had a significant impact on flood hazard maps reliability (i.e., the disagreement in flood hazard categories between DSM2 and DSM3 and DSM1 varied from 28% to 82%). These findings can be very valuable in achieving further reductions and better flood risk management.

Urban flood susceptibility mapping based on social media data in Chengdu city, China

Increase in urban flood hazards has become a major threat to cities, causing considerable losses of life and in the economy. To improve pre-disaster strategies and to mitigate potential losses, it is important to make urban flood susceptibility assessments and to carry out spatiotemporal analyses. In this study, we used standard deviation ellipse (SDE) to analyze the spatial pattern of urban floods and find the area of interest (AOI) based upon related social media data that were collected in Chengdu city, China. We used the social media data as the response variable and selected 10 urban flood-influencing factors as independent variables. We estimated the susceptibility model using the Naïve Bayes (NB) method. The results show that the urban flood events are concentrated in the northeast-central part of Chengdu city, especially around the city center. Results of the susceptibility model were checked by the Receiver Operating Characteristic (ROC) curve, showing that the area under the curve (AUC) was equal to 0.8299. This validation result confirmed that the susceptibility model can predict urban flood with a satisfactory accuracy. The urban flood susceptibility map in the city center area provides a realistic reference for flood monitoring and early warning.

Spatial characteristics and driving factors of urban flooding in Chinese megacities

Changes in hydrological processes caused by rapid urbanization lead to the growing incidence of urban flooding, which is a major challenge to urban sustainability. Urban floods have seriously threatened the natural environment and human life. Understanding the spatial patterns and influencing factors of urban flooding has important implications for mitigating urban flood hazards. Previous studies have demonstrated the impact of natural and human factors on urban flooding, but a comprehensive understanding of the mechanisms of urban flooding requires appropriate scales and evidence from multiple cities. In this study, 1201 flood records from 2015 to 2020 in 9 megacities (including Beijing, Tianjin, Shanghai, Xi’an, Nanjing, Wuhan, Guangzhou, Shenzhen, Shenyang) in China were used to investigate the spatial characteristics and driving factors of urban flooding. A combination of multiple stepwise regression and boosted regression tree (BRT) models was used to reveal the flood driving mechanism in megacities. The results indicated that flood events in 9 cities presented an aggregation effect, but the spatial characteristics and kernel density were different between coastal and inland cities. At all scales (1 km, 3 km, 5 km), physical geomorphic features (2D and 3D landscapes) have a high contribution to urban flooding, especially patch density, density of buildings and building shape coefficients. The relative contributions of the 2D and 3D landscape pattern factors increased by 43.8 % and 36.2 % as the grid scale increased from 1 km to 5 km, respectively. However, the relative contributions of topography, meteorology and drainage capacity factors decreased by 47.6 %, 39.0 % and 33.2 %, respectively. At a large scale (5 km), the correlation of driving factors for urban flooding was stronger, and the dominant factors were more obvious. At a small scale (1 km), the contribution of driving factors is relatively average. The scale effect significantly affects urban flooding. which suggests that an appropriate scale can more accurately capture the dominant drivers of urban flooding. Therefore, a novel method that integrates the stepwise regression model and BRT model were presented to quantify the complex relationship between urban flooding and driving factors under various analysis scales. The methods and results proposed by our study provided important insights and perspectives for urban flood risk mitigation and urban planning through multiscale analysis of flood driving mechanisms.

Hydrological impacts of land use - land cover change on urban flood hazard: A case study of the Jukskei River in Alexandra Township, Johannesburg, South Africa.

Flooding in urban areas is a major natural disaster causing damage to infrastructure, properties and loss of life. In urban areas the major causes behind the changing hydrological processes (i.e., floods) include topography, increase in precipitation due to climate change and change in land-use/land-cover (LULC) over time. The objective of this study is to evaluate the spatial and temporal LULC change impacts on flooding along the Jukskei River in Alexandra Township, Johannesburg, South Africa. The LULC images of 1987 MSS and 2015 OLI derived from Landsat satellite were pre-processed and classified using a supervised classification method. The analysis of LULC revealed that, there is an increase in built-up area from 934,2 ha to 1277,2 ha and reduction in intact and sparse vegetation from 190,5 ha to 62,4 ha and 380,8 ha to 142,1 ha, respectively, between the years 1987 and 2015. The flood depth map, velocity map and flood depth-velocity for different return periods and LULC scenarios have been developed by using an integrated approach of the Hydrological Engineering Centre-River Analysis System (HEC-RAS) and the HEC-GeoRAS with the geographic information system (GIS) and remote sensing data. From the analysis, it is observed that there is an increase in flood depth and flood velocity from 2,3 m to 3,0 m and 1,4 m/s to 3,4 m/s, whereas the depth-velocity for the last 28-years increased by 3,4 m2/s from 2,9 m2/s to 6,3 m2/s for the 1987 LULC and the 2015 LULC conditions, respectively. The flood hazard maps generated in this study can be used by local authorities and municipalities for flood disaster management.

A multi-criteria risk-based approach for optimal planning of SuDS solutions in urban flood management

ABSTRACT This paper presents a multi-criteria risk-based approach for managing urban flood hazards by using a combination of conventional measures and contemporary Sustainable Drainage Systems (SuDS). A multi-objective optimisation model coupled with a simulation model of UDS in the SWMM software is developed with the three objectives of minimising total costs, the risk of flooding and pollution discharged into receiving waters. K-means clustering technique is used to group the optimal solutions. A few optimal solutions and individual SuDS solutions are then ranked together by using the compromise programming (CP) method. The methodology is demonstrated by its application on a case study of the Golestan city UDS in Iran. The results obtained show there are indirect correlations between non-dominated solutions that minimise the risk of either flooding or pollution. The results also show the selected optimal solutions can provide cost-effective strategies that reduce both flood and pollution risks by at least 27% and 50%, respectively.

Peri-urban growth into natural hazard-prone areas: mapping exposure transformation of the built environment in Nairobi and Nyeri, Kenya, from 1948 to today

Kenya experiences massive urban growth, also into natural hazard-prone areas, exposing settlements and the natural environment to riverine and pluvial floods and other natural hazards. While Nairobi as the capital and principal city has been extensively analysed regarding urban growth and flood hazard in some central parts, awareness of growing peri-urban areas has not been studied as much. The results are of interest to other locations in Kenya and worldwide, too, since the current research and disaster risk practice focus is still too much on megacities and city centres. Therefore, the study compares urban growth into hazard areas in urban rims of Nairobi and Nyeri, Kenya. A change assessment from 1948 to 2020 is conducted by aerial images, declassified satellite images, and recent data. Urban growth rates are 10- to 26-fold, while growth into flood exposed areas ranges from 2- to 100-fold. This study reveals unused opportunities for expanding existing land-use change analysis back to the 1940s in data-scarce environments.

Grid edge classification method to enhance levee resolution in dual-grid flood inundation models

Dual-grid models address the computational bottlenecks of large-scale (>103 km2) urban flood modeling with solution updates on a coarse grid that are informed by topographic data on a fine grid. However, dual-grid models may poorly resolve levees, leading to loss of accuracy. Here we present a grid edge classification method whereby specific edges of the coarse grid are flagged to gather nearby topographic data from the fine grid and create a contiguous physical barrier. The method relies on levee location data in a polyline format, and does not require levee height data since that information is stored on the fine grid. Using a 6804 km2 model of the Los Angeles Metropolitan Region with 3 m topographic data and 987 km of levees, the proposed method is implemented and evaluated. Simulations using coarse grids of 15, 30 and 60 m capture flood extent consistent with fine-grid models based on a critical success index (CSI) of 90, 87 and 82%, respectively. Edge classification improves CSI up to 7 percentage points over a model with unclassified coarse grid edges, and reduces the false alarm ratio up to 10 percentage points. Differences in model performance across the study area are noted, including lower accuracy on urbanized alluvial fans. With compute costs that scale with the coarse grid, dual-grid models can efficiently realize more accurate large-scale models of urban flood hazards.

Simulation of flood hazard, prioritization of critical sub-catchments, and resilience study in an urban setting using PCSWMM: a case study

Abstract Due to the dual pressure of rapid urbanization and climate change, urban flooding has become more common. Thus, for effective planning and mitigation strategies, it is of paramount interest to quantify the generated runoff and prioritize the urban critical sub-catchments. The present study investigates flood inundation in Hyderabad urban setting (zone-XII, zone-IV&V) using the Personal Computer Storm Water Management Model (PCSWMM) and prioritizes the critical sub-catchments using the compromise programming method (CPM) and PCSWMM. In addition, the system resilience is examined by integrating PCSWMM with GIS. The model simulation is performed for a 264 h (11 days) rainfall event that occurred in October 2020. The outcomes from the simulation are found to be satisfactory and in agreement with the field water logging points (WLPs). The inundation map results are validated with social media markers (SMMs). The critical sub-catchments are prioritized based on PCSWMM by runoff results and CPM by considering WLPs, slope and impervious percentage of sub-catchments as input criteria. The Integrated 1D-2D PCSWMM is used to examine the inundation velocity and depth. An urban flood hazard (UFH) map is generated to identify optimal low impact developments (LIDs). Subsequently, the present study showed how storage can improve the catchment capability and resilience of urban settings to tackle the excess stormwater.

A spatial accessibility assessment of urban tourist attractions emergency response in Shanghai

Tourism is particularly vulnerable to natural hazards. This paper presents a scenario-based study that assesses the spatial accessibility of urban tourist attractions' emergency response to urban pluvial flooding in Shanghai's city center, China, through the combination of flood hazard analysis and GIS-based network analysis. Emergency coverages and response times in various traffic conditions are quantified to assess tourist attractions' spatial accessibility using the water level conditions in the years of 2010, 2030, and 2050, normal flood-free, 100-year, and 1000-year flood scenarios. The findings indicate that the scope and depth of flooding, road traffic conditions, and the number and location of emergency medical service (EMS) jointly determine the scope and response time of urban flood hazard medical emergency services. Under flood scenarios, part of the road network is paralyzed, traffic is interrupted, and emergency vehicles cannot pass, resulting in a significantly smaller range of EMS space accessibility than usual. Under the 1000-year flood scenario, up to 22% of tourist attractions cannot obtain emergency services within 15 min. The method proposed in this paper can provide a decision-making basis for the emergency management of tourism under urban flood hazards.

Evaluating urban flood hazard index (UFHI) of Dehradun city using GIS and multi-criteria decision analysis

Evaluating urban flood hazard index (UFHI) of Dehradun city using GIS and multi-criteria decision analysis

Application of the WRF model rainfall product for the localized flood hazard modeling in a data-scarce environment

Urban flood hazard model needs rainfall with high spatial and temporal resolutions for flood hazard analysis to better simulate flood dynamics in complex urban environments. However, in many developing countries, such high-quality data are scarce. Data that exist are also spatially biased toward airports and urban areas in general, where these locations may not represent flood-prone areas. One way to gain insight into the rainfall data and its spatial patterns is through numerical weather prediction models. As their performance improves, these might serve as alternative rainfall data sources for producing optimal design storms required for flood hazard modeling in data-scarce areas. To gain such insight, we developed Weather Research and Forecasting (WRF) design storms based on the spatial distribution of high-intensity rainfall events simulated at high spatial and temporal resolutions. Firstly, three known storm events (i.e., 25 June 2012, 13 April 2016, and 16 April 2016) that caused the flood hazard in the study area are simulated using the WRF model. Secondly, the potential gridcell events that are able to trigger the localized flood hazard in the catchment are selected and translated to the WRF design storm form using a quantile expression. Finally, three different WRF design storms per event are constructed: Lower, median, and upper quantiles. The results are compared with the design storms of 2- and 10-year return periods constructed based on the alternating-block method to evaluate differences from a flood hazard assessment point of view. The method is tested in the case of Kampala city, Uganda. The comparison of the design storms indicates that the WRF model design storms properties are in good agreement with the alternating-block design storms. Mainly, the differences between the produced flood characteristics (e.g., hydrographs and the number of flood gird cells) when using WRF lower quantiles (WRFLs) versus 2-year and WRF upper quantiles (WRFUs) versus 10-year alternating-block storms are very minimal. The calculated aggregated performance statistics (F scores) for the simulated flood extent of WRF design storms benchmarked with the alternating-block storms also produced a higher score of 0.9 for both WRF lower quantiles versus 2-year and WRF upper quantile versus 10-year alternating-block storm. The result suggested that the WRF design storms can be considered an added value for flood hazard assessment as they are closer to real systems causing rainfall. However, more research is needed on which area can be considered as a representative area in the catchment. The result has practical application for flood risk assessment, which is the core of integrated flood management.

Evaluating urban flood risk using hybrid method of TOPSIS and machine learning

With the growth of cities, urban flooding has increasingly become an issue for regional and national governments. The destructive effects of floods are magnified in cities. Accurate models of urban flood susceptibility are required to mitigate this hazard mitigation and build resilience in cities. In this paper, we evaluate flood riskin Jiroft city, Iran, using a combination of machine learning and decision-making methods. Flood hazard maps were created using three state-of-the-art machine learning methods (support vector machine, random forest, and boosted regression tree). The metadata supporting our analysis comprises 218 flood inundation points and a variety of derived factors: slope aspect, elevation, slope angle, rainfall, distance to streets, distance to rivers, land use/land cover, distance to urban drainages, urban drainage density, and curve number. We then employed the TOPSIS decision-making tool for urban flood vulnerability analysis, which is based on socio-economic factors such as building density, population density, building history, and socio-economic conditions. Finally, we derived an urban flood risk map for Jiroft based on flood hazard and vulnerability maps. Of the three models tested, the random forest model yielded the most accurate map. The results indicate that urban drainage density and distance to urban drainages are the most important factors in urban flood hazard modeling. As might be expected, areas with a high or very high population density are most vulnerable to flooding. These results show that flood risk mapping provide insights for priority planning in flood risk management, especially in areas with limited hydrological data.

Assessment of climate change impacts and urban flood management schemes in central Shanghai

Increasing urbanization combined with climate change has resulted in the increased risk of flooding in cities. Thus, assessing the effect of future climate change on urban flooding and adopting effective flood management measures is becoming increasingly important. In this study, an urban rainstorm model and a scenario simulation method were applied to central Shanghai, China. We first examined the risks of flooding in the study area under the impact of future climate change using a simulation with different rainfall return periods. We then evaluated the benefits of traditional drainage system adaptation measures and Low Impact Development (LID) practices on alleviating the current urban flood hazard. Finally, we explored the effectiveness of using an integrated flood management scheme to adapt to climate change. The results show that the urban flood volume increases non-linearly with increasing rainfall intensity under climate change. The maximum flooding area also increases accordingly, and is much more sensitive to smaller rainfall events. Both traditional drainage system adaptation measures and LID practices can effectively alleviate flooding, while an integrated flood management scheme that emphasizes the combination of the two methods is shown to be the most effective in reducing flooding under future climate change.

Low Impact Development Measures Spatial Arrangement for Urban Flood Mitigation: An Exploratory Optimal Framework based on Source Tracking

Urban areas are vulnerable to flooding as a result of climate change and rapid urbanization and thus flood losses are becoming increasingly severe. Low impact development (LID) measures are a storm management technique designed for controlling runoff in urban areas, which is critical for solving urban flood hazard. Therefore, this study developed an exploratory simulation–optimization framework for the spatial arrangement of LID measures. The proposed framework begins by applying a numerical model to simulate hydrological and hydrodynamic processes during a storm event, and the urban flood model coupled with the source tracking method was then used to identify the flood source areas. Next, based on source tracking data, the LID investment in each catchment was determined using the inundation volume contribution ratio of the flood source area (where most of the investment is required) to the flood hazard area (where most of the flooding occurs). Finally, the resiliency and sustainability of different LID scenarios were evaluated using several different storm events in order to provide suggestions for flooding prediction and the decision-making process. The results of this study emphasized the importance of flood source control. Furthermore, to quantitatively evaluate the impact of inundation volume transport between catchments on the effectiveness of LID measures, a regional relevance index (RI) was proposed to analyze the spatial connectivity between different regions. The simulation–optimization framework was applied to Haikou City, China, wherein the results indicated that LID measures in a spatial arrangement based on the source tracking method are a robust and resilient solution to flood mitigation. This study demonstrates the novelty of combining the source tracking method and highlights the spatial connectivity between flood source areas and flood hazard areas.

Climate Justice Planning in Global South: Applying a Coupled Nature–Human Flood Risk Assessment Framework in a Case for Ho Chi Minh City, Vietnam

Developing countries in the global south that contribute less to climate change have suffered greater from its impacts, such as extreme climatic events and disasters compared to developed countries, causing climate justice concerns globally. Ho Chi Minh City has experienced increased intensity and frequency of climate change-induced urban floods, causing socio-economic damage that disturbs their livelihoods while urban populations continue to grow. This study aims to establish a citywide flood risk map to inform risk management in the city and address climate justice locally. This study applied a flood risk assessment framework integrating a coupled nature–human approach and examined the spatial distribution of urban flood hazard and urban flood vulnerability. A flood hazard map was generated using selected morphological and hydro-meteorological indicators. A flood vulnerability map was generated based on a literature review and a social survey weighed by experts’ priorities using the Fuzzy Delphi Method and Analytic Network Process. Vulnerability indicators including demographic characteristics, infrastructure, and land use patterns were used to generate a flood vulnerability map. The results illustrate that almost the entire central and northeastern parts of the city are at high flood risk, whereas the western part is at low flood risk. The findings have implications in urban planning via identifying risk hot spots in order to prioritize resources for mitigating hazards and enhancing community resilience to urban floods.

A GIS-Based Hydrological Modeling Approach for Rapid Urban Flood Hazard Assessment

Urban floods are detrimental to societies, and flood mapping techniques provide essential support for decision-making on the better management of flood risks. This study presents a GIS-based flood characterization methodology for the rapid and efficient identification of urban flood-prone areas, which is especially relevant for large-scale flood hazards and emergency assessments for data-scarce studies. The results suggested that optimal flood mapping was achieved by adopting the median values of the thresholds for local depression extraction, the topographic wetness index (TWI) and aggregation analyses. This study showed the constraints of the depression extraction and TWI methods and proposed a methodology to improve the performance. A new performance indicator was further introduced to improve the evaluation ability of hazard mapping. It was shown that the developed methodology has a much lower demand on the data and computation efforts in comparison to the traditional two-dimensional models and, meanwhile, provides relatively accurate and robust assessments of flood hazards.

GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan

Urban flooding has been an alarming issue in the past around the globe, particularly in South Asia. Pakistan is no exception from this situation where urban floods with associated damages are frequently occurring phenomena. In Pakistan, rapid urbanization is the key factor for urban flooding, which is not taken into account. This study aims to identify flood sensitivity and coping capacity while assessing urban flood resilience and move a step toward the initialization of resilience, specifically for Peshawar city and generally for other cities of Pakistan. To achieve this aim, an attempt has been made to propose an integrated approach named the “urban flood resilience model (UFResi-M),” which is based on geographical information system(GIS), remote sensing (RS), and the theory of analytical hierarchy process (AHP). The UFResi-M incorporates four main factors—urban flood hazard, exposure, susceptibility, and coping capacity into two parts, i.e., sensitivity and coping capacity. The first part consists of three factors—IH, IE, and IS—that represent sensitivity, while the second part represents coping capacity (ICc). All four indicators were weighted through AHP to obtain product value for each indicator. The result showed that in the Westzone of the study area, the northwestern and central parts have very high resilience, whereas the southern and southwestern parts have very low resilience. Similarly, in the East zone of the study area, the northwest and southwest parts have very high resilience, while the northern and western parts have very low resilience. The likelihood of the proposed model was also determined using the receiver operating characteristic (ROC) curve method; the area under the curve acquired for the model was 0.904. The outcomes of these integrated assessments can help in tracking community performance and can provide a tool to decision makers to integrate the resilience aspect into urban flood management, urban development, and urban planning.

Application of entropy weighting method for urban flood hazard mapping

Application of entropy weighting method for urban flood hazard mapping