City Flood Research Articles

Assessing Public Perceptions of Blue–Green Infrastructure in Urban Watersheds: A Case Study of Acari River, Brazil

Accelerated urbanization and frequent flooding in contemporary cities have contributed to the devaluation of rivers within urban landscapes, resulting in a diminished relationship between residents and these waterways. Although there has been a notable increase in frameworks designed to enhance stakeholder engagement in flood risk management, these primarily target decision-makers, with limited focus on processes that ensure community participation in Blue–Green Infrastructure (BGI) initiatives. This paper seeks to address this gap by proposing a conceptual framework for assessing public perceptions of BGI in urban watersheds, particularly in areas facing socioeconomic vulnerabilities. The framework was applied through an online survey directed at inhabitants and experts associated with the Acari River Watershed, Rio de Janeiro, Brazil. The survey comprises five sections aimed at gathering respondent profiles, exploring their interactions with the case study, and eliciting their perceptions of BGI alternatives, supported by graphic representations. BGI alternatives were evaluated based on four principles: usability, reliability, feasibility, and acceptance. The findings indicate that while certain strategies were perceived as reliable, concerns regarding pollution and trust were significant, especially in densely populated areas. This framework is recommended for use in other urban contexts to facilitate effective community engagement in sustainable planning processes, especially in developing countries.

Brief communication: From modelling to reality – flood modelling gaps highlighted by a recent severe storm surge event along the German Baltic Sea coast

Abstract. In October 2023, the Baltic Sea coasts of Germany and Denmark experienced a severe storm surge, predominantly impacting the German state of Schleswig-Holstein and parts of southern Denmark. The surge led to extensive flooding in cities like Flensburg and Schleswig, causing the breaching of at least six (regional) dikes and causing over EUR 200 million in damages in Schleswig-Holstein. By chance, the peak water levels of this storm surge aligned well with those of recent hydrodynamic flood modelling studies of the region. This rare coincidence offers crucial insights for our understanding of flooding impacts, flood management, and modelling. By comparing those studies to the real-world example using extensive media reports, we aim to extract key insights and identify gaps to be tackled in order to improve flood risk modelling in the Baltic Sea region and beyond.

Decadal trends and climatic influences on flash droughts and flash floods in Indian cities

Flash droughts/floods are extreme weather phenomena that are expected to become increasingly frequent and severe with the changing climate. Flash droughts result from a rapid decline in soil moisture, while flash floods occur due to a high extreme rainfall intensity over a short duration. This study analyzes the ERA5 reanalysis data (hourly temperature, soil moisture, and precipitation) from 1992 to 2022 to assess flash drought/flood attribute variations across fourteen Indian cities. Flash drought events are identified based on specific conditions using the obtained Soil Moisture Index (SMI) values. At the same time, we propose a novel approach to attribute flash floods by setting thresholds for precipitation and soil moisture.This study examines the frequency and trends of flash drought and flood events across India's various Köppen-Geiger climatic zones from 1992 to 2022. Jaipur and Dehradun show a statistically significant decrease in flash drought events with magnitudes of −0.0833 events/year and −0.0769 events/year, respectively. Conversely, Hyderabad exhibits a highly significant increase in flash flood events with a magnitude of 1.1851 events/year. Similarly, Bengaluru, Varanasi, and Vishakhapatnam also show substantial increases in flash flood events. These findings underscore the impact of climate change on flash droughts/floods, highlighting the necessity for sustainable strategies.

Small-grid urban flood prediction model using Twitter data and population GPS data - an example of the 2019 Nagano city flood

In this study, a small-grid urban flood prediction model integrating Twitter data and population GPS data was constructed using the 2019 Nagano City flood as an example, and the validity of these two data for the model was determined. Using natural language processing techniques, Twitter data was filtered to extract real-time information relevant to flooding. At the same time, geographic information processing techniques were applied to analyze the population GPS data and obtain the distribution of the local population. Based on these two types of data, we combined with terrain, land use, traffic and infrastructure data related to flooding, a real-time flood prediction model was constructed using the random forest algorithm with a basic unit of a 70 m × 70 m grid. An analysis of the model accuracy showed that, the model that included both GPS and Twitter data showed an improvement in prediction accuracy of about 8% compared to flood prediction models that do not have these data sources. This indicated that the integrated use of Twitter and GPS data allowed us for a more accurate representation of the dynamic characteristics of flood disasters, thereby improving the performance of real-time flood prediction models and increasing real-time awareness of flood events. This approach provided effective flood monitoring methods for disaster management authorities.

Analysis of Short-Term Heavy Rainfall-Based Urban Flood Disaster Risk Assessment Using Integrated Learning Approach

Accurate and timely risk assessment of short-term rainstorm-type flood disasters is very important for ecological environment protection and sustainable socio-economic development. Given the complexity and variability of different geographical environments and climate conditions, a single machine learning model may lead to overfitting issues in flood disaster assessment, limiting the generalization ability of such models. In order to overcome this challenge, this study proposed a short-term rainstorm flood disaster risk assessment framework under the integrated learning model, which is divided into two stages: The first stage uses microwave remote sensing images to extract flood coverage and establish disaster samples, and integrates multi-source heterogeneous data to build a flood disaster risk assessment index system. The second stage, under the constraints of Whale Optimization Algorithm (WOA), optimizes the integration of random forest (RF), support vector machine (SVM), and logistic regression (LR) base models, and then the WRSL-Short-Term Flood Risk Assessment Model is established. The experimental results show that the Area Under Curve (AUC) accuracy of the WRSL-Short-Term Flood Risk Assessment Model is 89.27%, which is 0.95%, 1.77%, 2.07%, 1.86%, and 0.47% higher than RF, SVM, LR, XGBoost, and average weight RF-SVM-LR, respectively. The accuracy evaluation metrics for accuracy, Recall, and F1 Score have improved by 5.84%, 21.50%, and 11.06%, respectively. In this paper, WRSL-Short-Term Flood Risk Assessment Model is used to carry out the risk assessment of flood and waterlogging disasters in Henan Province, and ArcGIS is used to complete the short-term rainstorm city flood and waterlogging risk map. The research results will provide a scientific assessment basis for short-term rainstorm city flood disaster risk assessment and provide technical support for regional flood control and risk management.

Assessing flash flood erosion following storm Daniel in Libya

The eastern Mediterranean basin is witnessing increased storm activity impacting populous urban coastal areas that historically were not prone to catastrophic flooding. In the fall of 2023, Storm Daniel struck the eastern coast of Libya, causing unprecedented flash floods with a tragic death toll and large-scale infrastructure damages. We use Sentinel-1A C-band SAR images to characterize the resulting flash flood erosion and sediment load dynamics across the watersheds and to map damages within coastal cities at their outlets. Our results suggest that sediment loading, resulting from surface erosion, increased the density of turbid streams. The above exacerbated the catastrophic impact of the flash floods in the coastal cities of Derna and Susah, where 66% and 48% of their respective urban surface have experienced moderate-to-high damages. Our findings highlight the increased vulnerability of coastal watersheds in arid areas within the eastern Mediterranean basin due to the forecasted increase in hydroclimatic extremes and call for a transformative coastal management approach to urgently implement nature-based solutions and land-use changes to mitigate these rising risks.

Zoning and regulation of compound flooding in coastal cities coupled with tracer method

Urban inland rivers play a significant role in mitigating flooding. However, the influence of their discharge on urban flooding affected by different flood drivers remains to be fully explored. Taking a catchment area in Haikou City, Hainan Province, China as an example, in light of the urban flooding model coupled with the water tracer method, this study investigates the degree of inundation in each flood zone under different inland river flows. The flood zones are determined by the flooding simulation results of three cases, and then are verified by the zonation based on the tide tracer approach. Finally, the tracer method is used to study the impact of urban inland rivers on flooding and the connection of hydraulic and water volume between rivers and pipelines. The results indicate that the inundation area decreased by 3.71 % as the river flow increased from 15 to 25 m3/s. In the process of river flow rising from 5 to 45 m3/s, The average flooding depth in the rainfall-affected area climbs slowly from 0.53 m to 0.62 m; In the tidal-affected area, the average flooding depth drops from 0.52 m to 0.12 m when the inland river flow increases from 5 to 25 m3/s, and rises from 0.12 m to 0.54 m when the inland river flow continues from 25 to 45 m3/s; The percentage of pipelines grows from 18.5 % to 28.0 %. This study provides valuable scientific insights and support for flood prevention planning and flood control strategies in coastal cities.

The research on narrow-valley city flood control mode based on hydrodynamic-hydrological coupling model

The flood processes and control methods in narrow-valley cities exhibit significant differences compared to other regions. Therefore, precise modeling and simulation of their flood processes is crucial. This study focuses on a typical narrow-valley city located in the Loess Plateau of northern China. A hydrodynamic-hydrological coupling model was constructed to simulate the floods, analyzing flood disaster characteristics as well as the effects and differences of various flood control measures in the narrow-valley city. The results indicate: (1) The hydrodynamic-hydrological coupling model proposed in this paper is highly appropriate for accurately simulating floods in narrow-valley cities. A hydrological model was used to generalize the hydrological process in hilly mountainous areas. While a hydrodynamic model was employed to conduct a detailed simulation of the river terrace, where the main urban area is located. After validation through two actual flood events, the model demonstrated satisfactory simulation performance. (2) The flood characteristics of narrow-valley cities differ from those of plain regions mainly because the terrain restricts the spread of floods once they surpass the embankments. This limitation leads to potentially greater inundation risks in such topographical features. Additionally, the elongated distribution of cities in these areas creates spatial disparities in flooding between upstream and downstream areas. (3) There are significant variations in the effectiveness of different flood control measures. Various flood control approaches were modeled and simulated in this paper. Their flood control capabilities were ranked in descending order as: reservoirs, flood diversion channels, and river channel dredging. (4) The combined flood control effect of multiple measures is notably superior to that of individual measures. Among them, the combination of reservoirs and flood diversion channels is optimal, as it regulates the inflow from a large upstream area. The conclusions drawn in this paper can provide a certain degree of reference and basis for the construction of flood models and flood control systems in narrow-valley cities.

Urban flood prediction using ensemble artificial neural network: an investigation on improving model uncertainty

Reducing the impact of artificial neural networks (ANN) affected by sources of uncertainty is crucial to improving the reliability of the flood prediction model. This study proposes an ensemble artificial neural network (EANN) model to predict the degree of flooding in coastal cities. Combined methods are used to reduce the model’s uncertainty, heuristic neural pathway strength feature selection is used to select inputs, the coupling method is used to optimize network architecture and parameters, and the integration method which paralleling three ANN models with different predicted lead periods ensemble together is used to capture output uncertainty. The EANN model has successfully predicted flooding in the Chinese coastal city of Macao during a typhoon, with convincing accuracy. The study also analyzed the impacts of both long and short training datasets with appropriate time intervals on ANN modeling performance. It was found that the performance of short training datasets, with appropriate time intervals, was similar to or better than models with long training datasets.

Modern reservoirs with dam heights of tens and hundreds of meters above the river coastline with flooding of cities, villages, territories – this is a crime against humanity and nature. It is quite possible to eliminate floods.

Modern reservoirs with dam heights of tens and hundreds of meters above the river coastline with flooding of cities, villages, territories – this is a crime against humanity and nature. It is quite possible to eliminate floods.

GIS-based spatial approaches to refining urban catchment delineation that integrate stormwater network infrastructure

Rapid urbanization and escalating climate change impacts have heightened stormwater-related concerns (e.g., pluvial flooding) in cities. Understanding catchment dynamics and characteristics, including precise catchment mapping, is essential to accurate surface water monitoring and management. Traditionally, topography is the primary data set used to model surface water flow dynamics in undisturbed natural landscapes. However, urban systems also contain stormwater drainage infrastructure, which can alter catchment boundaries and runoff behavior. Acknowledging both natural and built environmental influences, this study introduces three GIS-based approaches to enhance urban catchment mapping: (1) Modifying DEM elevations at inlet locations; (2) Adjusting DEM elevations along pipeline paths; (3) Applying the QGRASS plug-in to systematically incorporate infrastructure data. Our evaluation using the geographical Friedman test (p > 0.05) and Dice Similarity Coefficient (DSC = 0.80) confirms the statistical and spatial consistency among the studying methods. Coupled with onsite flow direction validation, these results support the feasibility and reliability of integrating elements of nature and built infrastructure in urban catchment mapping. The refined mapping approaches explored in this study offer improved and more accurate and efficient urban drainage catchment zoning, beyond using elevation and topographic data alone. Likewise, these methods bolster predictive stormwater management at catchment scales, ultimately strengthening urban stormwater and flooding resilience.

Formulating flood mitigation through public participation, remote sensing analysis, and flood genetic understanding: study case Samarinda City flood January 2020

Samarinda, a city characterized by its unique topography and proximity to major rivers, has been grappling with recurring and escalating flood events. The frequency and severity of these floods have been well-documented in numerous past academic studies, underscoring the pressing need for innovative flood mitigation strategies. In response to this challenge, this study employs a multi-faceted approach by incorporating public participation, remote sensing analysis, and flood genetic understanding. To facilitate a comprehensive flood mitigation strategy, we initiated the collection of real-time reports from the public during flood events. The participation of the local community in this data collection process has provided critical insights into the dynamics of flood occurrence and its impacts. The integration of crowdsourced data has been instrumental in augmenting our understanding of the local flood patterns. In parallel, we harnessed the capabilities of the Sentinel-1 satellite to conduct remote sensing analysis, enabling us to identify and map inundated areas during specific flood events. This approach, utilizing Sentinel-1, has been validated in prior flood studies. Our research further combines these datasets, merging live reports with remote sensing flood mapping to detect and assess affected areas. We introduce a novel categorization system based on flood genetics, dividing the floodprone region into three distinct zones according to hydrogeological condition of each water catchment area. We divided the area into three zones based on its genetic. This model guides us to the innovative approach enriching our flood mitigation strategy based on those valuable insights. The culmination of our study is the formulation of tailored mitigation plans for each of the three identified flood genetic zones. Incorporating public participation, remote sensing, and flood genetic understanding, our study provides a holistic framework for flood mitigation in the flood-prone city of Samarinda. By harnessing the collective intelligence of the community, leveraging cutting-edge technology, and integrating advanced scientific concepts, we present a comprehensive approach to address and mitigate the ongoing flood challenges in the region.

How effective is twitter (X) social media data for urban flood management?

Social media has been an instant source of information for natural disasters, such as urban floods, throughout the world. Ex-post evaluation of the information is considered more important after a flood disaster with devastating consequences on critical infrastructure, the environment, and the well-being of the society. Research proposes an evaluation framework for integrating the disorganized online public opinion on urban flood disaster events towards emotional and conceptual characteristics for better ex-post analysis and public participation. Social media posts on an urban flood were acquired using a search engine, and then sentiment analysis, topic modeling, and spatial–temporal analysis were performed to generate measures of online public opinion about the urban flood crisis. Twitter (X) is the most popular microblogging service presently available. As per the methodology, we analyzed all tweets regarding the 2022 urban flood in the cosmopolitan city (Karachi) of Pakistan from users all over the world. The evaluation results demonstrated that the distribution patterns of post intensities and emotion polarity in response to the floods emphasized crucial aspects with contradictory emotions and highlighted the strategic implications. Experimental results on real datasets show relatively better performance than the baseline and state-of-the-art approaches and achieved the highest 91% score. Online public opinion is a valuable supplement to ex post-disaster evaluation as it helps the project (e.g., flood management) better perform and provides suggestions for future flood mitigation, especially public participation management.

Sensitivity analysis of coastal cities to effects of rainstorm and flood disasters.

Heavy rains and floods cause human, material, and economic damage in cities worldwide. The severity of flooding has intensified due to accelerating urbanization. While much of the existing research on flood hazards emphasizes simulation and assessment, the correlation between indicators has yet to be explored. This study employs the Tree Gaussian Process sensitivity analysis method. Through rigorous sampling and correlation analysis, the model identifies critical determinants. Significantly, factors such as the water supply penetration rate (Var3), water pipeline density in built-up areas (Var4), centralized treatment rate of sewage treatment plants (Var6), agricultural land for forestry (Var13), and urban, village, and industrial and mining land (Var15) stand out as primary influencers on the flood-affected populace. These variables reflect a city's flood management capability and its dedication to resource stewardship and ecological equilibrium, underscoring its critical role in flood risk assessment and strategic mitigation. The study further illuminates that the interplay of these variables can exacerbate flood consequences, suggesting a compounded impact when variables operate in tandem. Recognizing these synergistic effects reveals a more pronounced flood threat than previously estimated, indicating that viewing these factors in silos might underrepresent the risk involved.

Sensitivity analysis of typhoon-induced floods in coastal cities using improved ANP-GIS

This study developed an analytical network process (ANP) incorporated into a geographic information system (GIS; abbreviated as ANP-GIS) to assess flood sensitivity in coastal cities. A new consulting process is proposed to improve the ANP analysis by determining range scores of assessment factors. Compared with the original analytical hierarchy process (AHP) combined with GIS (AHP-GIS), the improved ANP-GIS with new consulting process can effectively consider the interrelationships of assessment factors between the index and sub-index layers. To verify the efficiency of ANP-GIS with new consulting process, both AHP-GIS and ANP-GIS were adopted to assess the exposure of sensitivity to typhoon-induced floods in the coastal cities of Guangdong Province. The results were validated by historical typhoon-induced flood records, revealing that most of the typhoon-induced floods were in the region with the assessed higher and highest flood sensitivities. The percentage of the highest flood sensitivity from the improved ANP-GIS with typhoons was twice that of the AHP-GIS. When considering typhoons, the higher and highest flood sensitivity levels increased from 29% to 48%, as indicated in this study. The assessment results from the improved ANP-GIS were closer to the historical records and better than the performance of AHP-GIS.

Ponds and Wetlands Landscapes of Flood Management in the Cities of the Lower Yellow River Floodplain—The Case of Huaiyang, China

As interest has increased in green infrastructure and nature-based solutions, traditional approaches to managing landscapes have emerged as providing useful models for sustainable water and land management. From local gazetteers, archives, oral histories, maps, and satellite images, we documented the historical landscapes of ponds and wetlands back to the 17th century in the historic city of Huaiyang on the lower Yellow River floodplain. Like neighboring cities, Huaiyang was protected by a ring levee. As the floodplain rapidly aggraded, cities within the levee became topographically lower than the surrounding landscape. In this context, ponds and wetlands were essential for flood and storm water retention in the low-lying city. These seasonal waterbodies alternated between drying and wetting, providing a dynamic and diverse background for native habitats and human uses. CORONA satellite images ca. 1960s show ponds and wetlands shrinking in the dry season to 35.6% of their wet season extent, while the farmed area expanded 5.3 times. The multiple uses of wetlands included dry-season farming, harvesting wetland plants, and fishing, each use adapted to the localized topographic and hydrologic conditions of the wetlands. The late 20th century saw massive transformations for modern agriculture and urbanization. Understanding the historical evolution of this landscape can provide inspiration for developing green infrastructure and resilient designs that preserve cultural diversity and sustainably manage water in an urbanizing landscape.

Mitigating flash flooding in the city: Drain or harvest?

AbstractRainwater harvesting (RWH) is principally based on collecting, storing, and using rainfall which would otherwise be lost as surface runoff. Runoff threatens in several ways: accelerating erosion, intensifying flooding, and reducing groundwater recharge. Therefore, purposely retaining rainfall in the urban water cycle rather than draining has several positive impacts on designing sustainable cities. This work presents a proposal on how to avoid flooding in cities by systematically harvesting, storing rainwater, and using it for multiple purposes. The concept of RWH presented here has the potential to be a radical innovation to solve the social, economic, and environmental challenges associated with flash flooding. Each residence is regarded as a water production unit. Depending on the climatic conditions, people can meet their water needs on a local household basis, or alternatively use piped water as a complement. By infiltrating rainwater, groundwater is locally recharged and downstream wells are more productive. The implementation of this idea involves entrepreneurial agency that challenges existing structures, rather than adapting to them. Clearly, social entrepreneurship and social innovation are expected to catalyse the realization of this social innovation, also in rural areas. It is about mobilizing ideas, capacities, and resources to create a sustainable social transformation.

A Multi-Parameter Flexible Smart Water Gauge for the Accurate Monitoring of Urban Water Levels and Flow Rates

Urban drainage and waterlogging prevention are critical components of urban water management systems, as they help to mitigate the risks of flooding and water damage in cities. The accurate collection of liquid level and flow rate data at the end of these systems is crucial for their effective monitoring and management. However, existing water equipment for this purpose has several shortcomings, including limited accuracy, inflexibility, and difficulty in operation under specific working conditions. A new type of multi-parameter flexible smart water gauge was developed to address these issues. This technology uses underwater simulation robot technology and is designed to overcome the deficiencies of existing water equipment. The flexibility of the gauge allows it to be adapted to different working conditions, ensuring accurate data collection even in challenging environments. The accuracy of the new water gauge was tested through a series of experiments, and the results showed that it was highly accurate in measuring both liquid level and flow rate. This new technology has the potential to be a key tool in smart water conservancy, enabling the more efficient and accurate monitoring of water levels and flow rates. By providing a new solution to the problem of collecting terminal equipment for urban drainage and waterlogging prevention, this technology can help to improve the resilience and sustainability of urban water management systems.

The impact of sea level rise on the compound submergence of coastal cities- a case study of Haidian Island

Coastal cities often have advanced drainage systems connected to the sea, reducing the risk of flooding. In recent years, with climate change, global sea levels have been rising. In 2023, the sea level in the South China Sea rose by 52 mm compared to the long-term average. This study uses a coupled system of a two-dimensional hydrodynamic model and a one-dimensional drainage pipe network model to investigate the impact of rising sea levels on compound flooding in coastal cities under future climate change scenarios. The results indicate that rising sea levels increase the baseline water level of storm surges, significantly increasing the risk of flooding in cities.

Analysis of factors in community willingness to manage floods in East Java during the pandemic.

Floods in the city during the pandemic caused concern for those affected by the disaster and the implementation of activities adjusted government policies. For sustainability, the urban environment in Indonesia is working hard to anticipate flooding in cities. Apart from that, the government, private sector, community leaders, and the media also play an important role.