ASSESSMENT OF THE IMPACT OF URBAN FLOODING ON LANDZUN CORRIDOR OF BIDA, NIGER STATE, NIGERIA

Urban flooding is a growing concern in many cities around the world; due to the increasing intensity and frequency of extreme weather events, such as heavy rainfall and storms. This study assessed the impact of urban flooding on infrastructure, public health, and the economy in the Landzun corridor of Bida, Niger State. Descriptive research design was employed for the research. The research used data from primary and secondary sources. Primary data were sourced through a structured questionnaire administered to 118 respondents who were randomly selected along the Landzun corridor, a very busy route in Bida. The secondary data were from related literature from journals and textbooks. Business owners accounted for 50.85% of the respondents. A total of 54.23% of the respondents experienced annual flooding. However, the major identified factors aiding flooding in Landzun corridor were poor drainage (62.71%), insufficient drainage facilities (28.81%) and disposal of waste along drainage channels (44.07%). The physical effects of the flooding resulted to collapsed building parts of 42.37% of the respondents and 24.58% had their properties destroyed during flooding. The health effects on the respondents included Malaria fever (36.44%), cholera (32.20%) and mental health (38.98%). 39.83% admitted that the cost of repairs of their damaged properties ranged between ₦101,000 and ₦200,000. As support, 33.05% of the respondents got infrastructural provisions from government. The respondents consequently suggested flood management strategies through community engagement (28.81%), the provision of more drainage facilities (53.40%) and enforcement of urban planning practices through zoning regulations (13.56%). The study concluded that the major causes of flooding were insufficient drainage facilities, dumping of refuse along drainage and indiscriminate siting of buildings on waterways. The study, therefore, recommends stakeholders collaboration to make informed decisions like building indigenous sustainable embarkments along landzun flood plains, flood awareness programmes, enforcement of urban planning regulations, and recycling of waste to reduce the impact of flooding on the Landzun corridor of Bida.

This study examined the environmental consequences of urban flood on the growth and development of Ado-Ekiti, Nigeria. Urban flood is any overland flow of water, over urban street, sufficient to cause significant damage to lives and property, traffic obstructions, nuisance and health hazards. Data for this study were collected from primary sources, through the administration of three hundred (300) questionnaires on respondents in the study area. Results from this study revealed that high intensity of rainfall, unturned road, dumping of refuse on drainage channels, poor construction of drainage channels and poor town planning practices are the main causes of urban flood problems in the study area. This study therefore recommends adequate drainage system, proper land use planning, construction of embankments, proper refuse disposal and environmental enlightenment programmes as a panacea to urban flood problems. Key words: Environment; Consequences; Urban-flood; Developments

Resilience benefit assessment for multi-scale urban flood control programs

Urban areas are gradually being affected by climate change. It is difficult to avoid urban flooding caused by heavy rainfall. Especially road flooding occurs 2-3 times a year in urban areas in the summer of Taiwan, when the regional weather is convective rainfall strong, it is difficult for general weather forecasting models to predict the amount of rainfall in the city in a short period of time. Rainfall areas in urban areas are prone to road flooding. Therefore, the intensity management value (>50dBz) of the radar reflectivity around the city is used to estimate the rainfall and urban flood warning, and the IoT water level monitoring instrument can monitor the water level in the urban rainwater sewer and set the urban flood warning based on the management value. The local low-lying areas of the city can also use CCTV images to identify flooding situation as a tool through AI's CCN deep learning technology and CCTV's flooding big data database that according to CNN's learning, training, and testing, after the completion, CCTV inspection and flood image recognition can be used for urban disaster prevention and relief. Finally, the monitoring data related to urban flooding is collected and displayed through the urban smart flood prevention platform, which provides efficient data collection, increases the response time for disaster relief, and quickly eliminates road flooding in the city. This study takes the urban smart flood prevention platform in New Taipei City, Taiwan as an example.

Synthetic design storms are often used to plan new drainage systems or assess flood impacts on infrastructure. To simulate extreme rainfall events under climate change, design storms can be modified to match a different return frequency of extreme rainfall events as well as a modified temporal distribution of rainfall intensities. However, the same magnitude of change to the rainfall intensities is often applied in space. Several hydrological applications are limited by this. Climate change impacts on urban pluvial floods, for example, require the use of 2D design storms (rainfall fields) at sub-kilometer and sub-hourly scales. Recent kilometer scale climate models, also known as convection-permitting climate models (CPM), provide rainfall outputs at a high spatial resolution, although rainfall simulations are still restricted to a limited number of climate scenarios and time periods. We nevertheless explored the potential use of rainfall data obtained from these models for hydrological flood impact studies by introducing a method of spatial quantile mapping (SQM). To demonstrate the new methodology, we extracted high-resolution rainfall simulations from a CPM for four domains representing different urban areas in Switzerland. Extreme storms that are plausible under the present climate conditions were simulated with a 2D stochastic rainfall model. Based on the CPM-informed stochastically generated rainfall fields, we modified the design storms to fit the future climate scenario using three different methods: the SQM, a uniform quantile mapping, and a uniform adjustment based on a rainfall–temperature relationship. Throughout all storms, the temporal distribution of rainfall was the same. Using a flood model, we assessed the impact of different rainfall adjustment methods on urban flooding. Significant differences were found in the flood water depths and areas between the three methods. In general, the SQM method results in a higher flood impact than the storms that were modified otherwise. The results suggest that spatial storm profiles may need to be re-adjusted when assessing flood impacts.

Climate change causes erratic rainfall and often results in flooding of urban areas. Floods are hydrometeorological disasters that occur in various regions of Indonesia. Flood vulnerability in urban areas has increased over the past 30 years. Kauman Village is included in the Asri water catchment area, which has an Asri primary channel downstream of the urban area of Nganjuk District. However, from 2019 to 2022, the urban area of Nganjuk District, including Kauman Village, was affected by flooding. Urban floods inundated office areas, schools, housing, and public facilities such as the Nganjuk District General Hospital. The factor indicated as the cause of flooding is the clogging of the drainage channel with rubbish. Therefore, field research and mathematical calculations were conducted to evaluate the discharge capacity of drainage channels in the village. Based on the research, it was found that the existing drainage channel discharge in the research area could not accommodate the planned discharge for the 10-year return period. In addition, there are 33 channels that are unable to accommodate the planned discharge because the channeldimensions are too small, some channels are slightly damaged, sedimentation occurs, and they are blocked by rubbish. Drainage channels that do not function optimally affect urban flooding. Therefore, several efforts have been made to reduce the risk of flooding by changing the dimensions of drainage channels, normalizing drainage channels, and getting used to maintain drainage channels and not throwing rubbish in drainage channels.

The interest in urban flood risk is growing steadily over the last decades. Still, in the Netherlands no data is available to quantify urban flood risk. In this paper an estimation of urban flood frequencies is made in a detailed analysis of an urban catchment. Calculation results from a theoretical model are compared with data from a complaint register. The analysis in the case study shows that insufficient system maintenance condition is an important potential cause of urban flooding. The estimated flood frequency caused by severe rainfall is 4 events in 7 years or 0.6 per year, while the flood frequency caused by maintenance problems is 13 in 4 years or 3.3 per year. This includes 2 flood events that are caused by heavy rainfall and 11 events that are related to maintenance problems. The number of locations that suffer flooding caused by severe rainfall is more than 3 or 4 per event, while the number of locations that suffer flooding caused by maintenance problems is not more than 2 per event. It is expected that these numbers are representative for the rest of the Netherlands. Further research and data collection will very this assumption.

Cities are vulnerable to local floods due to heavy rainfall. Urban flooding causes damage to buildings and contents, and also disturbs daily city activities as it entails drainage, transportation, and electricity interruptions. Urban flooding is expected to increase as climate change drives heavier rainfall events. Population and assets densification, as well as infrastructure aging, increasingly hamper cities from tackling pluvial flooding. Climate adaptation measures can help cities to face the challenge of heavier weather and urban flooding. Examples of those measures are: smart drainage maintenance and emergency responses, urban climate-proofing and retrofitting, and provision of real-time flooding information to citizens and government officials, among others. To plan and perform such measures it is required to know, and even predict before a heavy storm is onset, where, when, and why urban flooding occurs. This knowledge is not always available though. Required knowledge to design and implement adaptation measures against urban flooding is insufficient in cases such as Amsterdam and Rotterdam. In these cities, urban drainage models are limited to certain districts or uncalibrated; they cannot validly predict where or when the drainage system will surcharge or flood, and thus, they cannot be used for flood damage modeling. Moreover, urban flooding may not only depend on hydraulic parameters of underground drainage systems; other physical and socioeconomic characteristics of the urban fabric may also influence the flooding likelihood at a particular urban location. Urban flooding can be better understood by using non-hydraulic and unconventional sources of information. Available public data, curated by statistics, cadastral, or municipal call-center services, can provide insights about urban flooding damage. Using mainstream technology, such as web, traffic, and smart-phone cameras, can also afford for valuable data about urban flooding impacts, which contributes to the development of climate adaptation measures in lowland cities. This dissertation aimed to determine the potential of such alternative data sources in better explaining urban flooding incidents. Employed methods combined techniques from geographic information systems, graph theory, community ecology, and computer vision. The exploration done in this research follows three main steps: testing previously proposed models, exploring currently available data sources, and evaluating the usefulness of attainable and affordable technology to gather key, nonexistent data about the timing, location, and extent of urban flooding incidents.

Driven by climate change and rapid urbanization, pluvial flooding is increasingly endangering urban environments, prompting the widespread use of coupled hydrological–hydrodynamic models that enable more accurate urban flood simulations and enhanced pluvial flood forecasting. The simulation method for urban river flooding caused by heavy rainfall has garnered growing attention. However, existing studies primarily concentrate on prediction using hydrodynamic models or machine learning models, and there remains a dearth of a comprehensive prediction framework that couples both models to simulate the temporal evolution of river flood changes. This research proposes a novel framework for simulating urban pluvial river flooding by integrating physically based models with deep learning approaches. The sample set is enhanced through data augmentation and Generative Adversarial Networks, and scheduling control signals are incorporated into the encoder–decoder architecture to enable urban pluvial river flooding forecasting. The results demonstrate strong model performance, provided that the model’s structural complexity is aligned with the available training data. After incorporating scheduling information, the simulated water level process exhibits a “double-peak” pattern, where the first peak is noticeably lower than that under non-scheduling conditions. The current research introduces an innovative method for simulating and analyzing large-scale urban flooding, offering valuable perspectives for urban planning and flood mitigation strategies.

Urban flooding has become an urgent environmental issue in rapidly developing cities like Cebu City, Philippines, where accelerated urbanization has led to increased impervious surfaces and limited natural water absorption. Traditional flood management approaches, such as concrete drainage systems, are proving insufficient amid the growing impacts of climate change. This study investigates the effectiveness of green infrastructure (GI) in urban flood control, with a focus on Barangay Lorega. GI includes sustainable, nature-based solutions such as permeable pavements, green roofs, rain gardens, and constructed wetlands that offer both ecological and social benefits. Employing a descriptive-quantitative research design, the study combined survey data and stakeholder interviews to evaluate GI’s flood mitigation capacity, cost-effectiveness, and social relevance. Statistical results revealed no significant differences in flood control effectiveness based on demographic factors such as sex, age, civil status, or education. However, regression analysis showed that community engagement and awareness significantly influenced perceived effectiveness, while GI implementation demonstrated a modest yet meaningful impact. These findings suggest that while GI alone may not drastically transform urban flood resilience, it plays a valuable role when combined with public participation and informed governance. This study contributes to the growing body of knowledge on urban resilience and sustainable flood management, particularly in tropical, high-density settings. It offers insights for local policymakers, urban planners, and community stakeholders seeking to integrate green infrastructure into comprehensive climate-adaptive urban planning.

Urban flooding has become an urgent environmental issue in rapidly developing cities like Cebu City, Philippines, where accelerated urbanization has led to increased impervious surfaces and limited natural water absorption. Traditional flood management approaches, such as concrete drainage systems, are proving insufficient amid the growing impacts of climate change. This study investigates the effectiveness of green infrastructure (GI) in urban flood control, with a focus on Barangay Lorega. GI includes sustainable, nature-based solutions such as permeable pavements, green roofs, rain gardens, and constructed wetlands that offer both ecological and social benefits. Employing a descriptive-quantitative research design, the study combined survey data and stakeholder interviews to evaluate GI’s flood mitigation capacity, cost-effectiveness, and social relevance. Statistical results revealed no significant differences in flood control effectiveness based on demographic factors such as sex, age, civil status, or education. However, regression analysis showed that community engagement and awareness significantly influenced perceived effectiveness, while GI implementation demonstrated a modest yet meaningful impact. These findings suggest that while GI alone may not drastically transform urban flood resilience, it plays a valuable role when combined with public participation and informed governance. This study contributes to the growing body of knowledge on urban resilience and sustainable flood management, particularly in tropical, high-density settings. It offers insights for local policymakers, urban planners, and community stakeholders seeking to integrate green infrastructure into comprehensive climate-adaptive urban planning.

With the intensification of global climate change, extreme precipitation events are occurring more frequently, making the monitoring and management of urban flooding a critical global issue. Urban surveillance camera sensor networks, characterized by their large-scale deployment, rapid data transmission, and low cost, have emerged as a key complement to traditional remote sensing techniques. These networks offer new opportunities for high-spatiotemporal-resolution urban flood monitoring, enabling real-time, localized observations that satellite and aerial systems may not capture. However, in low-light environments—such as during nighttime or heavy rainfall—the image features of flooded areas become more complex and variable, posing significant challenges for accurate flood detection and timely warnings. To address these challenges, this study develops an imaging model tailored to flooded areas under low-light conditions and proposes an invariant feature extraction model for flooding areas within surveillance videos. By using extracted image features (i.e., brightness and invariant features of flooded areas) as inputs, a deep learning-based flood segmentation model is built on the U-Net architecture. A new low-light surveillance flood image dataset, named UWs, is constructed for training and testing the model. The experimental results demonstrate the efficacy of the proposed method, achieving an mRecall of 0.88, an mF1_score of 0.91, and an mIoU score of 0.85. These results significantly outperform the comparison algorithms, including LRASPP, DeepLabv3+ with MobileNet and ResNet backbones, and the classic DeepLabv3+, with improvements of 4.9%, 3.0%, and 4.4% in mRecall, mF1_score, and mIoU, respectively, compared to Res-UNet. Additionally, the method maintains its strong performance in real-world tests, and it is also effective for daytime flood monitoring, showcasing its robustness for all-weather applications. The findings of this study provide solid support for the development of an all-weather urban surveillance camera flood monitoring network, with significant practical value for enhancing urban emergency management and disaster reduction efforts.

Flooding in urban areas can be caused by heavy rainfall, improper planning or component failures. Few studies have addressed quantitative contributions of different causes to urban flood probability. In this article, we apply probabilistic fault tree analysis for the first time to assess the probability of urban flooding as a result of a range of causes. We rank the causes according to their relative contributions. To quantify the occurrence of flood incidents for individual causes we use data from municipal call centres complemented with rainfall data and hydrodynamic model simulations. Results show that component failures and human errors contribute more to flood probability than sewer overloading by heavy rainfall. This applies not only to flooding in public areas but also to flooding in buildings. Fault tree analysis has proved useful in identifying relative contributions of failure mechanisms and providing quantitative data for risk management.

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.

Urban flooding can cause significant infrastructure and property damage to cities, loss of human life, disruption of human activities, and other problems and negative consequences on people and the local government administration. The objective of this research work is to investigate the relation between urban flood occurrence and potentially flood-triggering factors. The analysis is performed in the western part of Athens Basin (Attica, Greece), where over the past decades several flood events caused human losses and damages to properties and infrastructure. Flood impact is measured by the number of citizen calls for help to the emergency line of the fire service, while potentially influencing factors are several geomorphological characteristics of the area and hydrometeorological indices regarding storms, which were determined with the aid of GIS techniques. The analysis is based on the investigation on binary logistic regression and generalized linear regression models that are used to build relationships between the potentially flood-influencing factors and the flood occurrence/impact for three events that were selected for reasons of comparison. The entire analysis highlights the variations attributed to the consideration of different factors, events, as well as to the different cell size of the grid used in the analysis. Results indicate that, the binary logistic regression model performed for flood occurrence achieves higher predictability, compared to the ability of the model used to describe flood impact.