Urban Pluvial Flooding Research Articles

Climate change impact assessment on pluvial flooding using a distribution-based bias correction of regional climate model simulations

The expected intensification of extreme precipitation events under climate change likely results in more frequent and intense pluvial floods worldwide. To study the climate change impact on urban pluvial flooding, fine-scale climate model simulations are needed. The number of such simulations is, however, still limited and not widely available, thus entailing the downscaling of the model outputs. One of the commonly used methods to meet this demand is statistical downscaling, where the statistical properties of large(r)‐scale climate simulations are used to derive local climate variables. This study focuses on applying a distribution-based bias correction method on regional climate model (RCM) simulations to explore how climate change affects extreme precipitation and urban flood events at the end of this century (2071–2100). A 1D-2D hydrodynamic model, implemented in Infoworks ICM, is used to simulate pluvial flood events for several return periods for a case study in the city of Antwerp in Belgium. The results show that the statistical downscaling approach can effectively decrease the bias in the model simulations and offer strong scaling relations to derive high-resolution extreme precipitation time series. The analyses also reveal that climate change may cause an increase of 16%, 31%, 47%, 63%, 73% and 84% in the flood volume for 2-, 5-, 10-, 20-, 30- and 50-year return periods, respectively. This projected increase in the flood volume enlarges the inundated area by 32%, 49%, 56%, 58%, 58% and 59% for the respective return periods. The flood frequency is also projected to almost double in the future, so that a 5-year flood event in the historical period will most likely be a 2-year event in the future period.

Predicting flood events in Kathmandu Metropolitan City under climate change and urbanisation

Urbanisation and climate change collectively impose the threat of urban flood. The impervious transformation and changes in local climatic conditions increase the risk of frequent pluvial flooding in Kathmandu. Therefore, this study aims to assess the integrated impact of urbanisation and climate change on pluvial flooding in Kathmandu Metropolitan City, using the Personal Computer Storm Water Management Model. The future daily rainfall from three Regional Climate Models under two Representative Concentration Pathways (RCPs) and the observed daily data has been disaggregated to hourly series using temporal rainfall disaggregation. The result shows that the combined impact on pluvial flood is likely to intensify with relatively more contribution from climate change. The CNRM-CM5-CSIRO-CCAM under the RCP 4.5 scenario projects the maximum increase (60–90%) in flood volume for current (75%) and extreme (90%) imperviousness, under a 2-year and 20-year return period (RP) with the extent of the flood area increasing more than the depth. For a 2-year RP, areas with a depth of 0.10–0.25 m are likely to expand while those from 0.25 to 0.40 m in depth are projected to have more velocity, and these trends are expected to be magnified for higher RPs. The study shows that even though urbanisation contributes less to urban pluvial flood, it can be a catalyst for exaggerating other factors and implementing management measures. For instance, the application of small-scale rainwater harvesting, and overflow storage reduced the runoff, thereby reducing the flood volume by 20–35%. Furthermore, the findings highlight the need for planning management strategies such as evaluation and upgrading of conventional drainage systems with low impact measures (rainwater harvesting and green roofs); technical and financial assistance to urban dwellers in adopting the management measures or a combination of them based on the location, requirements, and availability of information and resources to reduce the effects of pluvial flood in Kathmandu.

A review on urban pluvial floods: Characteristics, mechanisms, data, and research methods

A review on urban pluvial floods: Characteristics, mechanisms, data, and research methods

A Methodological Approach to Municipal Pluvial Flood Risk Assessment Based on a Small City Case Study

Urban flooding caused by heavy rainfall confronts cities worldwide with new challenges. Urban flash floods lead to considerable dangers and risks. In cities and urban areas, the vulnerability to pluvial flooding is particularly high. In order to be able to respond to heavy rainfall events with adaptation strategies and measures in the course of urban development, the spatial hazards, vulnerabilities and risks must first be determined and evaluated. This article shows a new, universally applicable methodical approach of a municipal pluvial flood risk assessment for small and medium-sized cities. We follow the common approaches to risk and vulnerability analyses and take into account current research approaches to heavy rainfall and urban pluvial flooding. Based on the intersection of the hazard with the vulnerability, the pluvial flood risk is determined. The aim of the present pluvial flood risk assessment was to identify particularly affected areas in the event of heavy rainfall in the small German city of Olfen. The research procedure and the results have been coordinated with the city’s administration within the framework of a real laboratory. In the course of the science–policy cooperation, it was ensured that the results could be applied appropriately in urban developments.

Data‐driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks

AbstractComputational complexity has been the bottleneck for applying physically based simulations in large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessment. To overcome the issue of long computational time and accelerate the prediction process, this paper proposes that the prediction of maximum water depth can be considered an image‐to‐image translation problem in which water depth rasters are generated using the information learned from data instead of by conducting simulations. The proposed data‐driven urban pluvial flood approach is based on a deep convolutional neural network trained using flood simulation data obtained from three catchments and 18 hyetographs. Multiple tests to assess the accuracy and validity of the proposed approach were conducted with both design and real hyetographs. The results show that flood prediction based on neural networks use only 0.5% of the time compared with that of physically based models, with promising accuracy and generalizability. The proposed neural network can also potentially be applied to different but relevant problems, including flood analysis for flood‐safe urban layout planning.

Investigating the influence of three-dimensional building configuration on urban pluvial flooding using random forest algorithm

Urban pluvial flooding has emerged as a serious threat to environmental conditions and human lives. Identifying its key drivers is crucial for the mitigation of flood risks. Although previous studies have demonstrated that pluvial flooding is caused by both natural (e.g., topography) and anthropogenic factors (e.g., land cover condition), much less effort has been devoted to investigating the potential influence of three-dimensional building configuration on pluvial flooding. To shed some light on this topic, we first analyzed the linear relationship between the density of flooding hotspots and different potential drivers in a highly-urbanized city using Pearson correlation analysis. Next, we designed two random forest-based models to quantify the importance of various building metrics. The first model considers only common drivers, while the second one also includes different types of building metrics. Results indicate that the density of buildings, building congestion degree, and building coverage ratio have exerted considerable influence on the occurrence of pluvial flooding. For example, the root relative squared error of our enhanced model (28.36%) is lower than that of the baseline model (32.58%). Our findings are expected to provide practical guidance for the mitigation of pluvial flood risks from the perspective of three-dimensional urban planning. Moreover, this methodological framework can be further applied to the analysis of flooding in many other regions.

Spatial heterogeneity of controlling factors’ impact on urban pluvial flooding in Cincinnati, US

Urban pluvial flooding has become a common threat for urban areas. Intensified rainfall, increasing imperviousness, and insufficient drainage capacity contribute to the increase of pluvial flood risk. The strategy of risk mitigation relies on the understanding of these controlling factors of urban pluvial flooding. However, spatial heterogeneity of the impacts of controlling factors has received limited attention. This study analyzes how topography, rainfall, impervious surfaces affect urban pluvial flooding and how the effects vary in space. A case study in the city of Cincinnati, US based on four storm events in recent years is conducted. The impact of topography is measured by a depression-based empirical parameter, Topographic Control Index (TCI), which is generated based on depression ponding volume, contributing area, and upstream slope. Rainfall depth estimated from kriging interpolation method is used to measure how the rainfall affect the urban pluvial flooding. The impervious area ratio of the contributing area of each depression as well as that of observed flooded locations are calculated to analyze the influence of impervious area. The results showed that TCI value and rainfall intensity are spatially correlated with the presence of flooded locations, and flooded locations can be classified into two types: rainfall control or topography control. The imperviousness of land surface does not show a significant correlation with pluvial flood due to the relatively small spatial variation of impervious area in a well-developed city. The framework in this study could identify the spatial distribution of dominant controlling factors for pluvial flooding, which is critical for the effective planning of mitigation measures.

Mitigation of Urban Pluvial Flooding: What Drives Residents’ Willingness to Implement Green or Grey Stormwater Infrastructures on Their Property?

As a consequence of climate change, the impact of pluvial flooding is expected to increase in the next decades. Despite citizens’ poor knowledge, several types of stormwater infrastructure can be implemented to mitigate the impact of future events. This paper focuses on the implementation of green and grey stormwater interventions (i.e., with or without vegetation) on private properties. Framed by the Protection Motivation Theory, a survey-based case study analysis, carried out in a pluvial flooding-prone area of the Veneto Region (Italy), highlights the main factors driving people’s willingness to implement these interventions. The analysis shows that the implementation of grey stormwater infrastructures is driven by the perceived threat and the amount of past pluvial flooding damage (i.e., the direct experience as a proxy of prior knowledge) while the implementation of green stormwater infrastructures is driven also by additional factors (awareness of these interventions, age and education level of the citizens). Based on these results, lack of knowledge on innovative stormwater interventions represents a critical barrier to their implementation on private properties, and it confirms the need for specific dissemination and information activities.

Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community

AbstractMitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective flood prediction for prompt action. Typically, physics‐based 1‐D pipe/2‐D overland flow models are used to simulate urban pluvial flooding. Because these models require significant computational resources and have long run times, they are often unsuitable for real‐time flood prediction at a street scale. This study explores the potential of a machine learning method, Random Forest (RF), to serve as a surrogate model for urban flood predictions. The surrogate model was trained to relate topographic and environmental features to hourly water depths simulated by a high‐resolution 1‐D/2‐D physics‐based model at 16,914 road segments in the coastal city of Norfolk, Virginia, USA. Two training scenarios for the RF model were explored: (i) training on only the most flood‐prone street segments in the study area and (ii) training on all 16,914 street segments in the study area. The RF model yielded high predictive skill, especially for the scenario when the model was trained on only the most flood‐prone streets. The results also showed that the surrogate model reduced the computational run time of the physics‐based model by a factor of 3,000, making real‐time decision support more feasible compared to using the full physics‐based model. We concluded that machine learning surrogate models strategically trained on high‐resolution and high‐fidelity physics‐based models have the potential to significantly advance the ability to support decision making in real‐time flood management within urban communities.

Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzhen city, China

Urban pluvial flooding is a threatening natural hazard in urban areas all over the world, especially in recent years given its increasing frequency of occurrence. In order to prevent flood occurrence and mitigate the subsequent aftermath, urban water managers aim to predict precipitation characteristics, including peak intensity, arrival time and duration, so that they can further warn inhabitants in risky areas and take emergency actions when forecasting a pluvial flood. Previous studies that dealt with the prediction of urban pluvial flooding are mainly based on hydrological or hydraulic models, requiring a large volume of data for simulation accuracy. These methods are computationally expensive. Using a rainfall threshold to predict flooding based on a data-driven approach can decrease the computational complexity to a great extent. In order to prepare cities for frequent pluvial flood events – especially in the future climate – this paper uses a rainfall threshold for classifying flood vs. non-flood events, based on machine learning (ML) approaches, applied to a case study of Shenzhen city in China. In doing so, ML models can determine several rainfall threshold lines projected in a plane spanned by two principal components, which provides a binary result (flood or no flood). Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, can classify flooding and non-flooding by different combinations of multiple-resolution rainfall intensities, greatly raising the accuracy to 96.5% and lowering the false alert rate to 25%. Compared to the conventional model, the critical indices of accuracy and true positive rate (TPR) were 5%-15% higher in ML models. Such models are applicable to other urban catchments as well. The results are expected to be used to assist early warning systems and provide rational information for contingency and emergency planning.

Urban Floods: Linking the Overloading of a Storm Water Sewer System to Precipitation Parameters

The lack and inefficiency of urban drainage systems, as well as extreme precipitation, can lead to system overloading and, therefore, an urban pluvial flood. The study brings insights into this phenomenon from the perspective of the statistical relationship between precipitation and flooding parameters. The paper investigates the possibility of predicting sewer overloading based on the characteristics of the upcoming rain event using the Storm Water Management Model (SWMM) and statistical methods. Additionally, it examines the influence of precipitation resolution on the model sensitivity regarding floods. The study is set in a small urban catchment in Dresden (Germany) with a separated stormwater sewer system (SWSS). The flood-event-based calibrated model runs with observed and designed heavy rain events of various sums, durations, and intensities. Afterward, the analysis focuses on precipitation and model overloading parameters (total flood volume, maximum flooding time and flow rate, and maximum nodal water depth) with pairwise correlation and multi-linear regression (MLR). The results indicate that it is possible to define a certain threshold (or range) for a few precipitation characteristics, which could lead to an urban flood, and fitting MLR can noticeably improve the predictability of the SWSS overloading parameters. The study concludes that design and observed rain events should be considered separately and that the resolution of the precipitation data (1/5/10 min) does not play a significant role in SWSS overloading.

The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden

Abstract. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17 %–44 % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh−1. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.

Linking a Storm Water Management Model to a Novel Two-Dimensional Model for Urban Pluvial Flood Modeling

This article describes a new method of urban pluvial flood modeling by coupling the 1D storm water management model (SWMM) and the 2D flood inundation model (ECNU Flood-Urban). The SWMM modeling results (the overflow of the manholes) are used as the input boundary condition of the ECNU Flood-Urban model to simulate the rainfall–runoff processes in an urban environment. The analysis is applied to the central business district of East Nanjing Road in downtown Shanghai, considering 5-, 10-, 20-, 50-, and 100-year return period rainfall scenarios. The results show that node overflow, water depth, and inundation area increase proportionately with the growing return periods. Water depths are mostly predicted to be shallow and surface flows generally occur in the urban road network due to its low-lying nature. The simulation result of the coupled model proves to be reliable and suggests that urban surface water flooding could be accurately simulated by using this methodology. Adaptation measures (upgrading of the urban drainage system) can then be targeted at specific locations with significant overflow and flooding.

A review of flood modeling methods for urban pluvial flood application

Pluvial flood has been increasingly understood as a major threat that has presented a significant risk for many cities worldwide. Regarding flood risk management, flood modeling enables to understand, assess and forecast flood conditions and their impact. Likewise, several hydrodynamic models have been developed and their application has been spread. With respect to effective flood modeling, particularly in urbanized floodplains, the choice of an appropriate method, considering contextual requirements, is challenging. This paper gives an overview of prevailing flood modeling approaches in view of their potentials and limitations for modeling pluvial flood in urban settings. The existing methods are categorized into: rapid flood spreading, one-dimensional sewer, overland flow (1D and 2D), sewer-surface coupling approaches (1D–1D and 1D–2D). Each of these techniques is described, by taking aspects influencing the selection of a proper flood modeling method for a particular application into account. This paper would help urban flood managers, and potential users undertake effective flood modeling tasks, balancing between their needs, model complexity and requirements of both input data and time.

Cover Image, Volume 7, Issue 3

AbstractThe front cover image is based on the Overview Nature‐based solutions for urban pluvial flood risk management, Zhan Tian et al., https://doi.org/10.1002/wat2.1421 image

Urban pluvial flood risk assessment – data resolution and spatial scale when developing screening approaches on the microscale

Abstract. Urban development models typically provide simulated building areas in an aggregated form. When using such outputs to parametrize pluvial flood risk simulations in an urban setting, we need to identify ways to characterize imperviousness and flood exposure. We develop data-driven approaches for establishing this link, and we focus on the data resolutions and spatial scales that should be considered. We use regression models linking aggregated building areas to total imperviousness and models that link aggregated building areas and simulated flood areas to flood damage. The data resolutions used for training regression models are demonstrated to have a strong impact on identifiability, with too fine data resolutions preventing the identification of the link between building areas and hydrology and too coarse resolutions leading to uncertain parameter estimates. The optimal data resolution for modeling imperviousness was identified to be 400 m in our case study, while an aggregation of the data to at least 1000 m resolution is required when modeling flood damage. In addition, regression models for flood damage are more robust when considering building data with coarser resolutions of 200 m than with finer resolutions. The results suggest that aggregated building data can be used to derive realistic estimations of flood risk in screening simulations.

Nature‐based solutions for urban pluvial flood risk management

AbstractUrban pluvial flooding now occurs more frequently than it has in past decades, mainly due to an increasing number of extreme precipitation events occurring in the context of a changing climate. To limit the evolving risks of urban pluvial flooding in a more environmentally friendly manner, the research community has recently paid increasing attention to Nature‐Based Solutions (NBS), which are based on new green technologies. To meet the urgent demand for a comprehensive review of the most recent literature, this review conducts a systematic survey of the literature to characterize various NBS adopted in different regions of the world and to elaborate on the benefits and limitations of such NBS. The review highlights the role of NBS in urban flood risk management under ongoing climate change and rapid urbanization. It shows that NBS could effectively mitigate urban flooding caused by high‐frequency precipitation events, with additional economic, ecological, and social benefits. However, NBS are less effective at helping cope with pluvial flooding caused by extreme precipitation events over a short period of time, while gray infrastructures also have limitations as a mitigation measure against extreme pluvial flooding. We thus recommend identifying flood risk management strategies by evaluating the performance of alternative combinations of NBS with gray infrastructures in preventing pluvial flooding in the cities. Finally, recent advances made in the applications of NBS are presented with suggestions (e.g., long‐term monitoring) to improve urban flood adaptive management.This article is categorized under: Engineering Water > Planning Water Engineering Water > Sustainable Engineering of Water Science of Water > Water Extremes

The potential of proxy water level measurements for calibrating urban pluvial flood models

Urban pluvial flood models need to be calibrated with data from actual flood events in order to validate and improve model performance. Due to the lack of conventional sensor solutions, alternative sources of data such as citizen science, social media, and surveillance cameras have been proposed in literature. Some of the methods proposed boast high scalability but without an on-site survey, they can only provide proxy measurements for physical flooding variables (such as water level). In this study, the potential value of such proxy measurements was evaluated by calibrating an urban pluvial flood model with data from experimental flood events conducted in a 25 × 25 m facility, monitored with surveillance cameras and conventional sensors in parallel. Both ideal proxy data and actual image-based proxy measurements with noise were tested, and the effects of measurement location and measurement noise were investigated separately. The results with error-free proxy data confirm the theoretic potential of such measurements, as in half of the calibration configurations tested, ideal proxy data increases model performance by at least 70% compared to sensor data. However, image-based proxy data can contain complex correlated errors, which have a complex and predominantly negative effect on performance.

The Effect of Nonstationarity in Rainfall on Urban Flooding Based on Coupling SWMM and MIKE21

The characteristics of urban pluvial flooding are altering all over the world due to environmental change. In this paper, generalized additive models for location, scale and shape (GAMLSS) was employed to analyze nonstationary frequency of extreme precipitation at subdaily scale. Nonstationary precipitation return periods were estimated using the expected waiting time (EWT) interpretation. A model coupling SWMM and MIKE21 was established, and calibrated and verified by three historical urban floods. Then, it was utilized to simulate urban floods under stationary and nonstationary rainfall conditions with different return periods. The simulated results illustrated that rainfall depth under nonstationarity was greater than that of stationarity when return period was less than 10 years, but the results reversed when the return period was over 20 years. The main variation of rainfall depth occurred within 6 h. The deviation of the maximum water depth was less than 10% for five return levels, and the difference in the longest inundation lengths was 1.2 h for 50-year return period under two assumptions. It may indicate that slight differences of urban flooding were detected between stationary and nonstationary conditions in the study region, which suggested a further study about urban flood under nonstationarity.

Influence of urban forms on surface flow in urban pluvial flooding

This paper presents a systematic analysis of the influence of nine urban characteristics (distance between buildings, mean building size, building coverage, etc.) on surface flow in case of pluvial flooding. Time dependent stored volumes, outflow discharges and mean water depths were computed for a set of 2000 synthetic urban forms, considering various terrain slopes and return periods of the rainfall. An efficient porosity-based surface flow model was used to compute the 2D flow variables. Statistical analysis of the relationship between the flow and urban variables highlights that the flooding severity is mostly influenced by the building coverage.