Urban Flood Management Research Articles

Assessment of the impact of urban water system scheduling on urban flooding by using coupled hydrological and hydrodynamic model in Fuzhou City, China

With climate change and urbanization development, urban areas are facing more serious floods. As a result, hydrological and hydrodynamic models have recently shown a broad application prospect in urban flood simulating and forecasting. For the area with rich inland rivers, urban water resources can be effectively regulated and redistributed through river networks and hydraulic structures scheduling. However, the lack of research on the effect of scheduling becomes a major limitation in model applications. Based on a coupled hydrodynamics model, the current study simulates the flooding response to the combined rainstorm and scheduling scenarios and analyzes the river overflow at the community scale. The result indicated that three local regions in the Jin'an study area are inundated easily. The locations near Qinting Lake were more sensitive to the water regulation rules than others. In the model of control on Qinting Lake, section A is more sensitive to the schedule control than section B, while for section A, the water level increased by 1.44% under the return period (RP) (10 a), and the rate changed to 2.64% under the RP (100 a). The differences in inundation from various scenarios are relatively small. In the mode of joint discharge rules under RP (50 a), the water level changed by 4.77% in section A and 1.24% in section B. The simulation at the community scale considers the overflow process, and the results indicated that the total inundation area decreased by 12.8 ha under joint schedules. The significant effects to alleviate urban inundation mainly come from the decreased flood overflow from the channel, but not from the flooding nodes. This study provides promising references for urban flood management.

Extracting historical flood locations from news media data by the named entity recognition (NER) model to assess urban flood susceptibility

Flood susceptibility assessment for identifying flood-prone areas plays a significant role in flood hazard mitigation. Machine learning is an optional assessment method because of its high objectivity and computational efficiency, but how to get enough and accurate information of historical flood locations to train the machine learning models has been a key problem. In recent years, news media data from both news websites and social media accounts has emerged as a promising source for natural science studies. However, the application of news media data in urban flood susceptibility assessment is still inadequate. This study proposed an approach to fill this gap. Firstly, flood locations were extracted from news media data based on a named entity recognition (NER) model. Then, a frequency or distance-based data quality control method was employed to improve the representativeness of the extracted flooded locations. Finally, flood conditioning factors with information of historical flood locations were input into a Support Vector Machine (SVM) model for flood susceptibility assessment. We took the central city of Dalian, China as a case study. The T-test results show that there was no significant difference between the distributions of most flood conditioning factors at the flood locations from the news media data and the official planning report. In the obtained flood susceptibility map, the high flood susceptibility areas got a recall of 90% compared with the high flood hazard areas in the planning report. Performing data quality control in the frequency-based method can improve the precision of the flood susceptibility map by up to 5%, while the distance-based method is ineffective. This study provides an example and offers the value of applying new data sources and modern deep learning techniques for urban flood management.

Review of Urban Flood Resilience: Insights from Scientometric and Systematic Analysis.

In recent decades, climate change is exacerbating meteorological disasters around the world, causing more serious urban flood disaster losses. Many solutions in related research have been proposed to enhance urban adaptation to climate change, including urban flooding simulations, risk reduction and urban flood-resistance capacity. In this paper we provide a thorough review of urban flood-resilience using scientometric and systematic analysis. Using Cite Space and VOS viewer, we conducted a scientometric analysis to quantitively analyze related papers from the Web of Science Core Collection from 1999 to 2021 with urban flood resilience as the keyword. We systematically summarize the relationship of urban flood resilience, including co-citation analysis of keywords, authors, research institutions, countries, and research trends. The scientometric results show that four stages can be distinguished to indicate the evolution of different keywords in urban flood management from 1999, and urban flood resilience has become a research hotspot with a significant increase globally since 2015. The research methods and progress of urban flood resilience in these four related fields are systematically analyzed, including climate change, urban planning, urban system adaptation and urban flood-simulation models. Climate change has been of high interest in urban flood-resilience research. Urban planning and the adaptation of urban systems differ in terms of human involvement and local policies, while more dynamic factors need to be jointly described. Models are mostly evaluated with indicators, and comprehensive resilience studies based on traditional models are needed for multi-level and higher performance models. Consequently, more studies about urban flood resilience based on local policies and dynamics within global urban areas combined with fine simulation are needed in the future, improving the concept of resilience as applied to urban flood-risk-management and assessment.

Adaptive precipitation nowcasting using deep learning and ensemble modeling

Accurate rainfall nowcasting is necessary for real-time flood management in urban areas. In this paper, some deep neural networks (DNNs) are developed for rainfall nowcasting with lead-time of 5 min. To increase the accuracy of the DNNs, their outputs are fused with the predictions of some numerical weather prediction (NWP) models using three ensemble models (i.e., bagging, random forest, and adaboost). The bias of NWPs is corrected using the quantile mapping technique. The ensemble models are compared with the DNNs and NWP models in terms of the accuracy of predictions. To reduce the run time of the proposed rainfall nowcasting models to be applicable for real-time urban flood management, they are executed using a parallel computing algorithm on the central processing unit (CPU) and graphics processing unit (GPU). To evaluate the efficiency of the proposed models, they were applied to the eastern drainage catchment (EDC) of Tehran city in Iran. More than 29,000 observed rainfall data were used for training and cross validating the proposed models. The results illustrated that the accuracy, coefficient of determination, of the ensemble models is at least 10% better than the DNNs in most rainfall events.

Inundation Resilience Analysis of Metro-Network from a Complex System Perspective Using the Grid Hydrodynamic Model and FBWM Approach: A Case Study of Wuhan

The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional response solutions merged multiple sources of data and knowledge to support decision-making. An obvious drawback is that original data sources for evaluations are often stationary, inaccurate, and subjective, owing to the complexity and uncertainty of the metro station’s actual physical environment. Meanwhile, the flood propagation path inside the whole metro station network was prone to be neglected. This paper presents a comprehensive approach to analyzing the resilience of metro networks to solve these problems. Firstly, we designed a simplified weighted and directed metro network module containing six characteristics by a topological approach while considering the slope direction between sites. Subsequently, to estimate the devastating effects and details of the flood hazard on the metro system, a 100-year rainfall–flood scenario simulation was conducted using high-precision DEM and a grid hydrodynamic model to identify the initially above-ground inundated stations (nodes). We developed a dynamic node breakdown algorithm to calculate the inundation sequence of the nodes in the weighted and directed network of the metro. Finally, we analyzed the resilience of the metro network in terms of toughness strength and organization recovery capacity, respectively. The fuzzy best–worst method (FBWM) was developed to obtain the weight of each assessment metric and determine the toughness strength of each node and the entire network. The results were as follows. (1) A simplified three-dimensional metro network based on a complex system perspective was established through a topological approach to explore the resilience of urban subways. (2) A grid hydrodynamic model was developed to accurately and efficiently identify the initially flooded nodes, and a dynamic breakdown algorithm realistically performed the flooding process of the subway network. (3) The node toughness strength was obtained automatically by a nonlinear FBWM method under the constraint of the minimum error to sustain the resilience assessment of the metro network. The research has considerable implications for managing underground flooding and enhancing the resilience of the metro network.

A modelling-chain linking climate science and decision-makers for future urban flood management in West Africa

Intensification of the hydrological cycle resulting from climate change in West Africa poses significant risks for the region’s rapidly urbanising cities, but limited research on flood risk has been undertaken at the urban domain scale. Furthermore, conventional climate models are unable to realistically represent the type of intense storms which dominate the West African monsoon. This paper presents a decision-first framing of climate research in co-production of a climate-hydrology-flooding modelling chain, linking scientists working on state-of-the-art regional climate science with decision-makers involved in city planning for future urban flood management in the city of Ouagadougou, Burkina Faso. The realistic convection-permitting model over Africa (CP4A) is applied at the urban scale for the first time and data suggest significant intensification of high-impact weather events and demonstrate the importance of considering the spatio-temporal scales in CP4A. Hydrological modelling and hydraulic modelling indicate increases in peak flows and flood extents in Ouagadougou in response to climate change which will be further exacerbated by future urbanisation. Advances in decision-makers’ capability for using climate information within Ouagadougou were observed, and key recommendations applicable to other regional urban areas are made. This study provides proof of concept that a decision-first modelling-chain provides a methodology for co-producing climate information that can, to some extent, bridge the usability gap between what scientists think is useful and what decision-makers need.

Understanding the Use of Heterogenous Data in Tackling Urban Flooding: An Integrative Literature Review

Data-driven approaches to urban flooding management require a comprehensive understanding of how heterogenous data are leveraged in tackling this problem. In this paper, we conduct an integrative review of related studies, and this is structured based on two angles: tasks and data. From the selected 69 articles on this topic, diverse tasks in tackling urban flooding are identified and categorized into eight categories, and heterogeneous data are summarized by their content type and source into eight categories. The links between tasks and data are identified by synthesizing what data are used to support the tasks in the studies. The task–data links are a many-to-many relationship in the sense that one particular data category supports multiple tasks, and one particular task uses data from multiple categories. The future research opportunities are also discussed based on our observations. This paper serves a signpost for researchers who wish to gain an overview of the heterogenous data and their use in this field and lays a foundation for studies that aim to develop a data-driven approach to tackle urban flooding.

Urban Flood Relief Management in COVID-19 Pandemic

Introduction: Urban floods were addressed as a separate disaster after the historical 2005 Mumbai floods. Urban flood peaks are 2-8 times and volume 6 times when compared with rural floods. We are now handling multiple disasters simultaneously due to the Covid-19 Pandemic. The river plains of north India are prone to floods in the monsoon season and geographical location of Prayagraj doubles the damage because it faces wrath from two sides. Very few researches have been conducted on urban floods and evidence needs to be generated from the field. Methodology: This qualitative research was planned with an objective to identify the difficulties faced in operating an urban flood relief camp during superimposed burden of COVID-19 Pandemic and to suggest remedial measures from the public health aspect. We conducted in-depth interviews of nodal officers, health staff and beneficiaries of the identified camps. Informed consent was taken from participant after explaining them about the research. Results: The findings from the interviews were categorized into 3phases of flood relief i.e. before the floods, during floods and lastly post flood. The most crucial work before floods is to spread awareness about do’s and don’ts in detail. Next was identification of the local people actually affected by flood. The space and facilities at few centers was low for the population load. Urban flood management needs a major overhauling of public health infrastructure to handle such disasters in future. Conclusion: The officials were working hard to make the homeless feel as if they are on a picnic. The database of beneficiaries should be strengthened and should also include students and labourers, anyone who is a flood victim and not only local flood victims.

Artificial Intelligence Sponge City Wisdom Management Model

Sponge city is currently a new urban rain flood management concept. It refers to the natural disaster that adapts to environmental changes and the natural disasters that is brought by rainwater. It is also known as the sponge, and the alias can also be called as “water elastic city.” The purpose of this article is, based on artificial intelligence, to study the analysis of wisdom management models in sponges. This paper first introduces the artificial intelligence algorithm and sponge city, then compares the traditional sponge city and the wisdom sponge city, then creates a LSTM neural network model, introducing artificial intelligence into sponge city intelligent dynamics in the analysis, and finally compares the rainfall data analysis to the ground. The experimental results show that, at different time points, the training results of rainfall data have shown significant regularity. The entire rainfall process exhibits a rise-decline-rise-increase trend of repetition. The maximum rainfall appeared at the 18th hour, and 7 obvious peaks occurred in 7, 11, 14, 16, 18, 21, and 23 hours.

Exploring the resilience of local detailed plans in the context of car parking at three study areas in the City of Oulu, Finland

ABSTRACT In Finland, based on the Land Use and Building Act, the city has a monopoly in urban planning and design. Through the planning monopoly, the city manages the urban planning process and thus consciously develops the urban structure. Based on the Act, the number of parking spaces stipulated for the property in the local detailed plan and building permit must be provided in connection with the construction work. The obligation of providing a number of parking spaces stipulated in local detailed plans affects the society in many ways. The provision of parking spaces is a key economic factor in construction costs. Car parking is also linked to changes in people’s mobility habits and car ownership. Occupancy rates for obligation parking spaces and other parking spaces vary over time and location. Car parking also impacts urban flood management. Rainwater volumes in condense urban structure can be significant and require heavy technical stormwater management solutions. Urban design is a key action of a city’s competence in terms of preparing for future changes. The number of parking spaces and stormwater management are key tools in urban planning that can be used to define resilience in the northern context of urban planning.

Rethinking urban storm water management through resilience – The case for using green infrastructure in our warming world

Urban flooding is one of the greatest threats to life and property, further exacerbated by the impacts of a warming climate. Balancing the increasing demand for liveable space with infrastructure for urban stormwater management presents a planning conundrum. Traditional thinking and design in urban stormwater is reaching limits in adequacy, or have foreboding implementation costs. We argue that a shift in design philosophy from resisting flooding to resilience can reframe this challenge towards viable solutions. We also propose that a simpler way of assessing urban flood resilience is needed to make the shift away from a single event design approach. To that end a Simple Urban Flood Resilience Index (SUFRI) is developed as the basis and the tool for rethinking urban flood management in a future climate. SUFRI offers a “simple” framework for urban flood management as it is based only on hydrologic and hydraulic modelling. We use SUFRI to evaluate the use of green infrastructure in combatting the threat of urban flooding in warmer climatic conditions. Results show that green infrastructure remains effective during more frequent storms (~ 80th percentile) that can occur in warmer climatic conditions and retrofitting or converting a smaller than expected percentage of the urban landscape (up to 5 %) can yield appreciable benefits. Overall, we show that the flexibility and adaptability needed to address the highly variable urban flood response in warmer climates is implicit within resilience based flood management approaches.

Case study: Diagnosing China's prevailing urban flooding—Causes, challenges, and solutions

AbstractAccompanying its rapid urban development, prevalent urban flooding incidents have been occurring in China in the past two decades with increasing frequency. Through a comparison with generally accepted urban flooding management practices, we (the authors) identified that a lack of surface water runoff management considerations (i.e., missing a major drainage system in urban stormwater system) and inadequate local mitigation are the primary causes of urban flooding problems in China, which in turn is rooted in the planning stage of a city's development. Following identification of the primary causes for the problems, we propose the development of master drainage plans (MDPs) for the concerned cities or regions as the first effective step to mitigate the serious urban flooding problems facing many cities today. An MDP would provide necessary elements to develop a major drainage system for the city's stormwater system and allow for an accurate calculation of parameters necessary for installation of adequate local mitigations. Lessons learned in this study should be useful for China's administrative agencies and professionals while they try to find solutions to their urban flooding problems and beneficial to other nations facing rapid urban development in the future or with existing urban flooding threats.

Sustainable integrated urban flood management strategies for planning of smart cities: a review

Sustainable integrated urban flood management strategies for planning of smart cities: a review

Assessing the impacts of land use-land cover changes on direct surface runoff: a remote sensing approach in Khulna City.

The increased risks of storm flood occurrences in large cities are the result of land use changes due to rapid urbanization. This study examines the influence of land use changes in Khulna City Corporation (KCC) area on surface runoff over a period of 15 years, from 2005 to 2020. Land use-land cover (LULC) maps for 2005, 2010, 2015, and 2020 were created employing support vector machine (SVM)-based supervised image classification using time-series satellite data, and the surface runoff was determined using Soil Conservation Service-Curve Number model. The major land use change drivers of surface runoff were determined through a correlation analysis. Surface runoff was observed to follow a similar trend as that of impervious urban areas, which went up by 5.44% from 2005 to 2020 (17.00 mm increment in average runoff) and the opposite trend was found in vegetation land cover, which declined by 13.34% in areal extent throughout the study period. In comparison with other types of land use, surface runoff changes were most significantly associated with the changes in urban impervious areas and vegetation land use-land cover (LULC) class. In fast-growing cities across the world, and especially in developing nations, the results of this study may serve as a guide for urban storm flood management and urban planning efforts.

A unified formula for discharge capacity of street inlets for urban flood management

Street inlets play a key role in intercepting surface runoff into underground drainage systems, which can reduce the hazard degree during urban flood events. An accurate calculation of the discharge capacity of street inlets is vitally important in numerically modelling urban floods and managing flood resilience. A series of laboratory experiments were combined with a theoretical analysis in this study to investigate the conveyance characteristics of a typical street grate inlet. A large-scale laboratory platform was constructed, consisting of an upper-layer model street and an underground measuring system installed with a V-notch weir structure. A model street inlet was used to connect the upper and underground layers, which had a complete drainage structure including: an inlet grate, a drain box, and a connection tube. A total of 78 experimental runs were conducted using this laboratory platform, and the discharges intercepted by the grate inlet were measured for a range of incoming water depths and flow velocities, where the approaching Froude number varied from 0.05 to 0.89. These experimental results showed that the drainage pattern of the street inlet was converted from weir flow to orifice flow after the drain box was fully pressurized. In addition, a power function was derived between the relative drainage velocity through the inlet and the approaching Froude number, based on the method of dimensional analysis, and a unified formula was proposed to calculate the discharge capacity of street inlets. The corresponding coefficients in the formula were calibrated using the laboratory measurements. The unified discharge capacity formula was verified using other experimental results obtained from previous studies, and the results indicated that the calculated results using the unified formula agreed well with these measurements, with the coefficients of R2 > 0.90. Therefore, the unified formula for discharge capacity of street inlets obtained from the study reported herein has a high predictive accuracy, which can be used in numerical modelling and risk management of urban floods.

Risk Assessment and Prediction of Rainstorm and Flood Disaster Based on Henan Province, China

To reasonably evaluate and predict the loss of rainstorm and flood disaster, this study is based on the rainfall data and rainstorm and flood disaster data of 18 cities in Henan Province from 2010 to 2020, using GIS technology and weighted comprehensive evaluation method to analyze the risk of rainstorm and flood disaster factors in various regions. The four risk factors of hazard risk, hazard-pregnant environment sensitivity, hazard-bearing body vulnerability, and disaster resilience were analyzed in compartment analysis. At the same time, a new rainstorm and flood disaster prediction model was constructed in combination with the hybrid PSO-SVR algorithm. The research results show that there are many rivers in Henan Province, the terrain tends to be higher in the west and lower in the east, and most areas are low plains, making most cities in Henan Province at a moderate risk level. For the more developed cities such as Zhengzhou, Luoyang, and Nanyang, the hazard risk, sensitivity, vulnerability, and disaster resistance are high, and they are prone to heavy rains and floods. For the economically underdeveloped, the terrain is high or hills, such as Sanmenxia City; Xinyang City and other places have low hazard risk and are not prone to rainstorms and floods. By constructing a hybrid PSO-SVR model, selecting two representative cities of Zhengzhou and Luoyang, and predicting the daily rainfall, the number of disasters, and the direct economic loss, the calculated RMSE and MAPE values are both less than GA-SVR, the traditional SVR, and BPNN models, which have verified the superiority of the model proposed in this study and the practical value it brings. To further verify the prediction accuracy of the hybrid model, the average value of RMSE and MAPE of other 16 cities are calculated, and the result is still smaller than other three models, and the study can provide some decision-making references for the urban rainstorm and flood management.

Revealing social vulnerability profiles for urban flood management: the case of Ouagadougou (Burkina Faso)

The inhabitants of the city of Ouagadougou, the capital of Burkina Faso, suffer significant damage every year due to floods, which constitute a major risk for the country. To mitigate the impact of these phenomena, a good understanding of the criteria involved in the vulnerability of populations is necessary. This article sets out to analyse the populations of Ouagadougou’s vulnerability to floods, with the objective of drawing up and spatially representing the social vulnerability profile of populations. It is part of phase 2 of the Raincell Africa project, which aims to understand the social vulnerability of Sahelian populations to the risks of urban flooding. The study is based on a quantitative approach developed by means of a socio-demographic survey of 2137 households in the city of Ouagadougou. Statistical processing of the survey data using factor analysis and Hierarchical Agglomerative Clustering revealed eight vulnerability profiles in the city. The spatial distribution of these profiles indicates greater vulnerability in the margins of the city, characterized by unplanned settlements. This high exposure of informal settlements to flooding is linked to socio-demographic characteristics such as the level of education, risky behaviours in terms of urbanization and the populations’ false perception of danger. These elements must necessarily be taken into account for better flood management.

A comprehensive analysis method of spatial prioritization for urban flood management based on source tracking

Flooding is one of the most frequent and disastrous natural hazards that can have catastrophic impacts on human society and urban infrastructure. Increasing the incidence of urban flooding highlights the need for reliable flood mitigation strategies that can effectively minimize losses owing to disasters. The strategic placement of flood mitigation measures is undoubtedly the key to achieving maximum effectiveness of flood reduction schemes. The physical characteristics of the site and knowledge of regional relevance play an important role in determining the location of appropriate flood mitigation measures. In the case of limited resources, it is crucial to prioritize areas for installation of flood mitigation measures. Therefore, an indicator-based method was developed in this study to identify and prioritize sites to concentrate limited resources where they are needed the most. Based on the source tracking method, a tracer-aided urban flood model was developed to study the impact of direct and indirect connections between urban systems, explore their relative importance in the network, and prioritize related risks to improve resource allocation efficiency. Longkungou drainage district of Haikou City was selected as the study area to determine the prioritization of catchments for flood mitigation. The results indicated that high prioritization sites are in the upstream of the urban areas with prioritization decreasing downstream, and the prioritization of interventions presents a spatial trend of “high south and low north”. This study demonstrates the novelty of a tracer-aided urban flood model in flood management and highlights the spatial connectivity between flood source areas and flood hazard areas. The prioritization scheme of urban catchment flood mitigation measures proposed in this study provides valuable guidance for policy makers and urban planners who wish to exploit the approach for flood mitigation.

Developing an algorithm for urban flood management with the aim of reducing damage and costs using the concept of conditional value at risk

Developing an algorithm for urban flood management with the aim of reducing damage and costs using the concept of conditional value at risk

Investigation of Urban Flood Management Principles within the Scope of Buffalo Bayou Watershed Rehabilitation Project

In this study, the 20-year watershed rehabilitation master plan, which was prepared after the severe Allison storm in 2001 in the Buffalo Bayou watershed in the city of Houston, is examined in detail. For this purpose, a literature review is conducted on the concepts of watershed and watershed management. Then, the existing situation of the study area is presented, and the flood control strategies and rehabilitation practices proposed within the scope of the master plan are investigated. The environmental and social outcomes of the rehabilitation project are evaluated in the conclusion. In Turkey, floods and overflows are one of the most important disasters that cause material and moral damages. Central and local governments need to develop sustainable approaches for the protection of watersheds and the preparation of management plans. In this context, the Buffalo Bayou watershed rehabilitation project can be considered as a resource in taking supportive steps for sustainable watershed management in Turkey. This project and similar good practices will contribute to solve the social, economic and environmental problems in watersheds of Turkey.