Flood Risk Research Articles

A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.

A fuzzy analytical hierarchy process -GIS approach to flood susceptibility mapping in NEOM, Saudi Arabia

The Kingdom of Saudi Arabia is undergoing massive and rapid urbanization as part of Vision 2030. This includes development projects along Saudi Arabia’s coastline across the Red Sea. Coastal areas, especially the ones along Saudi’s western regions are susceptible to natural disasters such as flooding. NEOM, a futuristic city currently being developed in the northwest of Saudi Arabia, exemplifies a potential flooding hazard due to its geographic location and proposed urbanization plans. This research aims to enhance flood hazard assessment in NEOM by applying the Fuzzy Analytical Hierarchy Process (FAHP) in combination with Geographic Information System (GIS). Acknowledging traditional limitations related to data availability and parameter selection consensus, the study carefully selects parameters such as drainage density, elevation, slope, rainfall, land use/land cover (LULC), soil type, normalized difference vegetation index (NDVI), and topographic wetness index (TWI). The 30 m DEM was used to derive Drainage Density, Slope, and TWI while LULC data helped assess land cover changes. Rainfall data and soil type information are integrated to evaluate their impact on flood susceptibility. NDVI is employed to analyze vegetation cover. Utilizing ArcGIS Pro’s weighted overlay model, the criteria were combined to generate the final flood susceptibility map. The research outcomes manifest in a flood susceptibility map categorizing areas into seven distinct susceptibility classes, ranging from ‘very low’ to ‘very high.’ A quantitative breakdown in a summary table provides insights into the proportional distribution of flood risk. Results indicate a significant portion of NEOM falls within varying degrees of moderate susceptibility range with relatively limited distribution of flood susceptibility on the extremes, equating to areas with ‘low to moderate’ susceptibility is 4,322.8 km2, areas with ‘moderate’ susceptibility is 5,109.69 km2, areas with ‘moderate to high’ is 4,081.39 km2. The flood susceptibility map developed in this study can shed insights on potential optimum areas for flood mitigation measures (i.e., optimum locations for establishing stormwater collection points).

Governing green change to improve resilience by assessing urban risks for localizing nature based solutions in fast sprawling Dehradun, India

Considering the increasing disaster risks, vulnerability and reduced resilience across Himalayas due to urban sprawling, our study attempts to understand the crucial environmental concerns in the valley town of Dehradun. We assessed the long-term risks due to the rise in population density, changing land use patterns, land surface temperature, groundwater shortage, air pollution concerns, vegetation loss and concretization using remote sensing tools. Results reveal the deleterious impact of urban sprawling affecting different wards of the city, with the burgeoning population pressure in the central part of the city (200 people per hectare (pph)) while, peripheral areas have lesser pressure (4 pph) but projected to rise. Loss of vegetation for development across the city has resulted in insufficient and unequal distribution of green spaces. The builtup area increased from 23.63 to 59.78 % after the city became the state capital has led to the increased heat islands and surface temperature (21.23–25.53 °C). Depleting groundwater levels in erstwhile water sufficient wards of Raipur (01), Shastradhara (02) and Adhoiwala N (28) is alarming. Increasing air pollutants (P.M.10, SO2 and NO2) and the risk of urban floods are severe for 10 wards. Dehradun city seems to be at tipping point about to cross its bearing capacity, any additional pressure may have serious repercussions for city residents. City municipal corporation along with diverse stakeholder groups need to mainstream Nature based Solutions, designing and implementing ward specific customized solutions for addressing societal challenges according to Sendai Framework for Disaster Risk Reduction and the UN SDG 11.

Evaluation of key flood risk drivers under climate change using a bottom-up approach

Our understanding of the key drivers of change in flood risk due to climate change remains incomplete. Here, to understand and quantify the key drivers of change in flood risk, we present a framework to undertake a ‘bottom-up’ (‘scenario-neutral’) climate change impact study on flood risk using an event-based flood model that considers non-stationarity in rainfall extremes and catchment wetness. A key advantage of this approach is that by using an event-based model, which explicitly represents key flood drivers, we can directly understand how changes in these drivers will influence changes in flood peaks. The utility of this modelling framework is demonstrated by applying it to one temperate and one tropical catchment in Australia, focusing on the sensitivity of frequent (1 in 5 annual exceedance probability, AEP) and rare (1 in 50 AEP) flood peaks to changes in thermodynamic and dynamic drivers of rainfall extremes, and changes in catchment processes, which are quantified by changes in rainfall losses.Response surfaces of the catchment sensitivity are first produced and demonstrate that increases in rainfall intensity drive increases in the flood peak. Smaller flood peaks in drier catchments are most sensitive to changes in rainfall losses, with the modulating impact of changes in losses decreasing as the runoff ratio increases. Climate model projections of these drivers are then superimposed on these surfaces to identify plausible shifts in flood risk under climate change. Several climate model ensemble members imply a decrease in future flood peaks, most notably 14 % of ensemble members suggest future decreases for the 1 in 5 AEP event for the temperate catchment. Despite large agreement on the direction of change, we found large variability in the expected magnitude of change for both frequent and rare events with flood peaks projected to increase between 0 % and 45 % for the tropical catchment and between −10 % to more than 50 % for the temperate catchment under RCP8.5 in 2085, highlighting the deep uncertainty associated with projecting flood risk under climate change. We believe this study acts as proof of concept for how a bottom-up climate change assessment can be undertaken within an event-based flood modelling framework, and provides insights to help us better understand and communicate the key drivers of changes to flood risk into the future.

Assessment of urban inundation and prediction of combined flood disaster in the middle reaches of Yellow river basin under extreme precipitation

Watershed Cities often face the dual flood risks of upstream floods and urban waterlogging. This compound disaster is caused by the interaction of multiple factors (topography, storms, upstream water and urban drainage, etc.), and is particularly affected by extreme rainfall events and the large and small drainage carrying capacity. To explore the combined effects of extreme rainfall and drainage systems, this study applied the Chicago rainfall hyetograph method and constructed design under different return periods. A comprehensive model combining the verified SWMM and LISFLOOD hydraulic models was utilised to build one-dimensional and two-dimensional coupled watershed urban flood models, simulating inundation depth and duration. By considering urban rainfall, runoff, topography, and drainage system characteristics, the flood processes under different extreme rainfall scenarios were simulated, and the node overflow and inundation depth were obtained. A flood risk assessment of watershed cities was conducted by combining methods such as the HR threshold method, evaluation level, and threshold division. In this study, Huyi District of Xi’an City, China, was used as the research area. The main research findings show: (1) the precipitation design values for different return periods were calculated based on the proposed method, with amount for a 100-year event being 87.196 mm, suitable for composite flood simulation; (2) applying the SWMM-LISFLOOD coupled model constructed for midstream urban areas ensured the accurate simulation of composite flood processes. Under different extreme rainfall return period scenarios, the percentage of flood inundation depth exceeding 1 m caused by multiple disaster factors increased from 0.73 % to 5.19 %; (3) extreme rainfall leads to different levels of internal flooding risk. When the return period of extreme rainfall is 5 years, the area inundated by composite floods in the research area is 67.21 km2. In comparison, when the return period of extreme rainfall is 50 years, the area inundated by composite floods in the research area significantly increases to a total inundation area of 286.85 km2. This study is expected to provide scientific references for flood prevention planning, composite disaster risk management, and urban resilience building in watershed urban areas while significantly impacting modern economic growth, social stability, and the safety of watershed cities.

FLOOD SUSCEPTIBILITY MAPPING USING MACHINE LEARNING IN KENING RIVER, SUB WATERSHED OF BENGAWAN SOLO, TUBAN

Floods are natural occurrences with the potential to cause damage to ecosystems and pose significant threats to human life, resulting in the destruction of property, infrastructure, and socioeconomic challenges. In recent times, flooding in the Sub-Watershed of Bengawan Solo has been linked to the overflowing Kening River in Tuban County. Aim: This study aims to produce a flood susceptibility map to mitigate the frequency of flood occurrences as well as facilitate effective planning for flood disaster risk management. Methodology and results: Flood data is collected from 2016 to 2023 through field surveys, Sentinel-1 satellite imagery, and data from the Development Planning Agency, Tuban County. Integrating remote sensing data from satellite imagery (PlanetScope, Sentinel-2), geographic information systems (GIS), and spatial modeling techniques, a flood susceptibility map is developed for the Kening River catchment. The occurrence of floods in the Kening River area is associated with various factors (11 variables) assessed through the frequency ratio approach, including profil curvature, LS factor, aspect, rainfall, river distance, road distance, building density 100 m, road density 100 m, vegetation type, normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI). The results show flood susceptibility maps utilizing frequency ratio (FR) and convolutional neural network (CNN) techniques. The flood susceptibility map obtained through the CNN method demonstrates a notably high AUC value. The model development generated a validation AUC value of 0.857 for training and 0.856 for testing. Conclusion, significance and impact study: This research provides an understanding into the factors that influence the occurrence of floods in the Kening River catchment area. It also emphasizes the benefit of advanced machine learning approaches in mapping the susceptibility of floods. Furthermore, this study has the potential to be helpful in guiding regional policy decisions and result in enhanced flood risk management measures in Tuban County.

Physics-based flood vulnerability assessment for steel portal framed industrial buildings

Steel portal framed industrial buildings have been widely used as warehouses, supermarkets, manufacturing workshops and storage facilities around the world. As such, these buildings make up a significant portion of the building stock. Despite this, there remains a lack of the physics-based flood vulnerability models necessary for undertaking detailed community flood risk management activities (e.g., quantitative risk assessment, cost-benefit analysis of risk reduction and resilience enhancement measures at the building level). To this end, this study developed a physics-based vulnerability assessment method for steel portal framed industrial buildings subjected to hydrostatic and hydrodynamic flood actions. Physical damage induced by flood forces to the building envelope (wall siding and fenestration) and the structural framing (steel portal frames and end wall frames) was considered. Monte Carlo simulations incorporate various flood load cases, probability models of demands and capacities as well as major failure mechanisms of different structural and non-structural components to conduct the damage assessment. The flood vulnerabilities for a prototype industrial building link the physical damage of individual building subassemblies to monetary loss ratios considering various inundation depths, flow velocities and flow directions.

Evolution of wetlands in the northern part of the Po Delta: The experience of unmanaged realignments

AbstractIt is increasingly recognised that nature‐based solutions in coastal ecosystems provide significant services compared to hard engineering measures. Several restoration projects are now being conducted worldwide to protect coastlines against flood risk and improve the ecosystems' quality. In the case of river deltas, managed realignment through levee breaching is becoming a recognised strategy for coastal wetland restoration. This study focusses on the formation of new wetlands in the Po River Delta after several natural dyke failures that occurred during the last century, when a large part of the land was abandoned and became wetlands. An historical review was performed to quantify the variation of the marsh extent in time developed after natural levee breaching in the easternmost lagoons of the delta and to understand the processes that determined their evolution. The analyses were based on orthophotos from the 1950s to the present‐day integrated with tidal records, water and sediment discharge records, and GPS survey for vegetation distribution. The review indicated that, between the 1950s and 1980s, anthropisation and natural processes caused a strong decline in marsh extent, thus leading to the recent shape of the lagoons. Several breaches and inlets were developed because of a combination of human intervention and erosive processes, and new tidal systems were born. Three main depositional areas connecting the main river branches were identified in three separated lagoons of the delta. These lagoons presented a ‘crevasse splay’ type of deposition which allowed the development of new tidal flats and, in certain cases, of large freshwater marsh systems within less than 8 years after the breach. The newly formed wetland systems (>100 ha) demonstrate the ability of the Po River Delta to build new wetlands, also during periods of human‐induced sediment starvation and high rates of subsidence, suggesting that further levee breaching should be exploited to favour marsh recovery.

Morphometric analysis in the sub basins of the Kali River using Geographic Information System, Karnataka, India

Morphometric analysis serves as a crucial tool for quantitatively characterizing drainage basins and exploring the intricate relationships between hydraulic parameters and geomorphological characteristics. This study presents a comprehensive morphometric analysis of the Kali Subbasin in Karnataka, covering an area of 1175.45 km². Utilizing remote sensing data and GIS tools, we quantified various morphometric parameters, including stream order, stream length, bifurcation ratio, drainage density, stream frequency, form factor, and circularity ratio, to understand the basin's geomorphological and hydrological characteristics. The study area is classified as a seventh-order basin displaying a dendritic drainage pattern with a drainage density of 2.853 km/km². Notably, the bifurcation ratio ranges from 4.396 to 3, indicating low to moderate structural control within the basin. The subbasin's elongated shape, with an elongation ratio of 0.604 and a circularity ratio of 0.298, indicates moderate peak flow and high susceptibility to erosion. Relief parameters showed significant elevation variations, contributing to intense erosion and heightened flood risks in lower-lying areas. The study identifies the basin's transition to a mature geomorphic stage, evidenced by an escalating stream length ratio from lower to higher orders. These findings underscore the importance of tailored watershed management strategies, including floodplain zoning, retention basins, afforestation, and terracing, to mitigate flooding and soil erosion. The study's insights are crucial for developing effective, localized water resource management plans and enhancing the resilience of the Kali Subbasin to future hydrological challenges.

Residential natural hazard risk and mental health effects.

Mental health effects are frequently reported following natural disasters. However, little is known about effects of living in a hazard-prone region on mental health. We analyzed data from 9,312 Gulf Long-term Follow-up Study participants who completed standardized mental health questionnaires including the Patient Health Questionnaire-9 (depression=score≥10), Generalized Anxiety Disorder Questionnaire-7 (anxiety=score≥10), and Primary Care PTSD Screen (PTSD=score≥3). Geocoded residential addresses were linked to census-tract level natural hazard risk scores estimated using the National Risk Index (NRI). We considered an overall risk score representing 18 natural hazards, and individual scores for hurricanes, heatwaves, coastal flooding and riverine flooding. Log binomial regression estimated prevalence ratios (PR) and 95% confidence intervals (CI) for associations between risk scores (quartiles) and mental health outcomes. Increasing hurricane and coastal flooding scores were associated with all mental health outcomes in a suggestive exposure-response manner. Associations were strongest for PTSD, with PRs for the highest vs. lowest quartile of hurricane and coastal flooding risks of 2.29(1.74-3.01) and 1.59(1.23-2.05), respectively. High heatwave risk was associated with anxiety (PR=1.25(1.12-1.38)) and depression (PR=1.19(1.04-1.36)) and suggestively with PTSD (PR=1.20(0.94-1.52)). Results suggest that living in areas prone to natural disasters is one factor associated with poor mental health status.

Mitigating the levee effect – Spatial planning approaches to address residual risk

The levee effect poses a challenge for flood risk management. While there is research on the consequences of the levee effect in general, a detailed understanding of policies addressing residual risk is lacking. The aim of this research is to understand how coercive and cooperative policy designs are applied in spatial planning provisions dealing with residual flood risk and which context conditions influence planning authorities when deciding on a certain policy design. We applied a qualitative research approach, including expert interviews and examining relevant documents. The results show that coercive planning policies can regulate the use of areas with residual risk either by limiting the extension of building land or by prescribing flood adaptation of buildings. However, the formulation of such regulations is connected to the spatial context, the actors involved, past flood events and risk awareness. Cooperative planning policies address residual risk either as recommendations or by providing local authorities scope for decision-making when it comes to land use planning in residual risk areas. The interview partners also discussed possibilities that go beyond spatial planning, such as emergency management. The research shows the variety of policies that can offer ways of dealing with flooding beyond the expected level.

Developing an Audit Framework for Local Flood Risk Management Strategies: Is Increasing Surface Water Flood Risk in England Being Adequately Managed?

Developing an Audit Framework for Local Flood Risk Management Strategies: Is Increasing Surface Water Flood Risk in England Being Adequately Managed?

Proposal of a method to analyze children’s flood risk exposure and risk perception using GPS tracking data and questionnaire survey

BackgroundClimate change has made the relationship between disaster and sustainable development more critical. Especially in developing countries, disasters frequently devastate local communities and hinder progress. Children are at a higher risk during catastrophes; however, their knowledge of disaster risk is limited. Education for disaster risk reduction can bring the necessary changes for a safe society. School disaster prevention education can raise awareness through lectures and games, but interactive dialogues between students and teachers can be more effective. Also, GPS tracking can be a valuable tool for understanding people’s behavior during disasters.MethodologyThis study proposes a method to analyze the relationship between students’ daily behavior and their experiences of flood damage and preparedness for disaster risk. It is a simple method using questionnaires and GPS loggers and can be applied in mountainous areas in developing countries where equipment is inadequate. The proposed method will be used to understand the current state of disaster risk among children and identify disaster risk reduction issues. The study area lies in Paluntar Municipality, Gorkha district, Nepal. Forty-five students from two schools in the region participated in the study.ResultsThe results showed that students who commute to school in areas inundated by heavy rainfall in recent years were not adequately prepared for disasters, even though they had experienced flood damage in the past and were significantly more concerned about future harm. Our field observations and interviews of teacher-student relationships showed that students were very attentive to their teachers’ instruction. Teachers understand the dangers of heavy rainfall and how to prepare for it, but students need to learn how to deal with it. In other words, the school staff’s disaster education to the students effectively reduces disasters. Early warning is needed when severe floods are expected during the rainy season.

A comprehensive approach for assessing social flood vulnerability and social flood risk: The case of Denmark

Flood risk assessment approaches have traditionally been dominated by measures of economic damage. However, the importance of understanding the social impacts of flooding are increasingly being acknowledged. Social vulnerability indices have been constructed in diverse geographical contexts to understand relative susceptibility of different social groups to flood hazards. However, integrated assessments of social vulnerability, exposure, and hazard information are lacking. Here, we construct a national social vulnerability index (SVI) for Denmark and combine this with direct and indirect social exposure data and coastal flood hazard data to construct a national social flood risk index (SFRI). Results show the spatial distribution of social flood vulnerability and social flood risk in Denmark. Our findings illustrate that including social data in flood risk assessment could significantly change our understanding of flood risk on a national scale. Methodologically, our work introduces a comprehensive flood risk modelling approach that explicitly considers the social impacts of flooding in all model components. The application of this model in Denmark reveals that the social impacts of flooding extend far beyond flooded areas, thus highlighting the importance of explicitly considering direct and indirect social exposure in addition to social vulnerability in flood risk assessment. By introducing a comprehensive, socially specific approach to flood risk assessment that is usable within existing risk management frameworks such as the EU Floods Directive, our work aims to mainstream social wellbeing, resilience, and justice as central considerations in decision making on flood risk management.

Agent-Based Modeling for Household Decision-Making in Adoption of Private Flood Mitigation Measures: The Upper Kan Catchment Case Study

Residential areas in developing arid and semi-arid countries are highly vulnerable to flooding, and water shortages have forced their inhabitants to live close to rivers. While climate change is expected to cause more extreme weather conditions in the future, adopting private loss-reduction measures can diminish flood risk. Although the number of flood models has grown significantly for developing arid and semi-arid lands, these models suffer from being incapable of performing micro-scale flood risk analysis and including household behaviors. This research work presents a novel socio-economic simulation model in the framework of flood risk management (FRM) to couple household adaptive responses with flood risk analysis. Agent-based modeling (ABM) embeds human behaviors in a flood-simulating environment. The focus of this research is the upper Kan catchment in Iran with a long history of severe flash flooding. Our results show the ability of the developed framework to address household participation in FRM activities through private precautionary measures. Moreover, the results indicate the importance of presenting such micro-level behaviors in flood modeling for a more realistic flood risk assessment. It is also demonstrated that household adaptation in a continuous way can lead to less flood risks in the region, even under climate change and the future economy. Finally, the results reveal that the remaining and diminished regional flood risks are influenced by the behavioral framework through which the individuals make decisions in adopting flood-loss-reduction measures. A similar pattern is, however, observed in household contributions over time among the discussed behavioral approaches.

Optimization of Manning's roughness coefficient using 1-dimensional hydrodynamic modelling in the perennial river system: A case of lower Narmada Basin, India.

This research bears significant implications for river management, flood forecasting, and ecosystem preservation in the Lower Narmada Basin. A more precise estimation of Manning's Roughness Coefficeint (n) will enhance the accuracy of hydraulic models and facilitate informed decision-making regarding flood risk management, water resource allocation, and environmental conservation efforts. Ultimately, this study aspires to contribute to the sustainable management of perennial river systems in India and beyond by offering a robust methodology for optimizing Manning's n tailored to the complex hydrological dynamics of the Lower Narmada Basin. Through a synthesis of empirical evidence and computational modelling, it seeks to empower stakeholders with actionable insights toward preserving and enhancing these invaluable natural resources. Using the new HEC-RAS v 6.0, a one-dimensional hydrodynamic model was developed to predict overbank discharge at different points along the basin. The study analyzes water levels, stream discharges, and river stage, optimizing Manning's n and required flood risk management. The model predicted a strong output agreement with R2, NSE, and RMSE for the 2020 eventas 0.83, 0.81, and 0.36, respectively, with an optimum Manning's n of 0.03. The lower Narmada Basin part near the coastal zone (validation point) appears inundated frequently. The paper aims to provide insights into optimizing Manning's coefficient, which can ultimately lead to better water flow predictions and more efficient water management in the region.

Hydrological Performance Assessment of Low-Impact Development Practices: A Vegetated Swale Case Study

The increase in rainfall intensity caused by climate change, combined with high levels of urban soil sealing and the limited capacity of drainage systems, is significantly increasing flooding risk. Integrated stormwater management is a key factor in dealing with the consequences of climate change by mitigating flood risk based on nature-based solutions. An experimental site was designed to assess the hydrological response of vegetated swales depending on different catchment surfaces, and to evaluate the impact of spatial variability of the hydraulic conductivity (K). A hydrological model was developed on EPA SWMM and calibrated based on measured data in two experimental swales with different active surfaces (N6—50 m2 and N11—100 m2). The model validation was assessed with NSE scores higher than 0.7. The simulations considered various factors, such as the water level in the swales, the actual rainfall, the evapotranspiration, the swale geometry, the catchment area (Sa), and the hydraulic conductivities of the natural silty soil, to assess, for the heaviest rainfall event, the best swale morphological characteristics. The study examined the combined impact of K (8) and Sa (6) on swale storage capacity, designed with a 250 mm depth. The simulations showed that the 250 mm overflow limit was exceeded for N10 (90 m2) when K ≤ 2 × 10−6 m/s, and for N11 (100 m2) when K ≤ 4 × 10−6 m/s. These results provide valuable information on the optimal storage capacity based on the swales’ geometrical and physical properties.

Assessing the Impact of Climate Change on Glacial Lake Outburst Flood (GLOF) in Eastern Hindu Kush Region Using Integrated Geo-Statistical and Spatial Hydrological Approach

Glacier retreat, a major impact of climate change that continues to occur in many parts of the world, continues to increase the risk of glacial lake outburst floods (GLOFs) in northern Pakistan. The rapid melting of glaciers in the mountains of Northern Pakistan, including the Hindu Kush, the Himalayas and the Karakoram, the rapid melting of glacier has led to the formation of 3044 glacial lakes, with 33 identified as particularly vulnerable to GLOFs. This study uses remote sensing and geographic information systems (GIS) methods for mapping and representing GLOFs. Based on the observational data of lake area, volume, and depth, empirical equations are developed through statistical methods. Only two lakes, Chitral-GL2 and Swat-G31, are classified as lakes with high potential for GLOF. Through modeling techniques using HEC-RAS and HEC-GeoRAS spatial hydrological models integrated with GIS remote sensing, the spatial extent and depth of inundations under different lake volumes are assessed. The analysis reveals that a total area of 20.56 km2 is susceptible to submersion by GLOFs, with Chitral-GL2 flooding area of 14.80 km2 and Swat-GL31 5.79 km2. Different land types are impacted by critical water depths, with built-up and agricultural lands 2.7 km2 totally, and barren lands 8.93 km2 under different flood depths ranging from less than 5 m to over 15 m.

Vulnerability, Resilience and Adaptation of Lagos Coastal Communities to Flooding

Lagos has been identified as one of the 50 cities most vulnerable to extreme sea levels. The state also ranked 30th among 136 port cities in terms of population exposure to flooding under a past climate scenario (2005) and 15th under a future climate scenario (2070s). The state faces significant environmental strains as a result of its geo-economic status. Some of the problems are wetland loss, pollution, population pressure, restricted access to drinkable water, and flooding. All these factors have contributed to the instability of Lagos ecosystems, but the impact of flooding is particularly significant because Lagos is surrounded by aquatic ecosystems and its low elevation and topography increase its susceptibility to flooding. The method adopted in this review involved the use of PICO (Population, Intervention, Control, and Outcomes) criteria to synthesize the research questions and objectives. Thereafter, PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines was employed for the study selection criteria, search strategies and data extraction methods. A broad search strategy involving databases (Google scholar, Science Direct), pertinent keywords and search filters was used to identify relevant articles and minimize selection bias. After the search, PICO criteria was again used to select the studies to be considered in the review. The selected text were downloaded and the essential scientific information were extracted and analyzed in the study. The impacts of flooding are numerous. Flooding is a key factors that has prevented Africa’s growing urban population from escaping poverty, and it also impedes the achievement of some SDGs. This is because many African cities lack the resources and infrastructures needed to withstand extreme weather conditions. Surviving in flood-prone cities like Lagos is a daunting task because flood affects livelihood, human health, and can even cause death. Specifically, the 2020 flood incidence in Nigeria affected about 40% of the local government areas, and 97% of the states, displacing over 120,000 persons and killing 68 persons aside from properties and farmlands destroyed. Women and children are the most vulnerable. The adoption of advanced flood risk management strategies could help in flood containment and management in the state.

Global application of a regional frequency analysis to extreme sea levels

Abstract. Coastal regions face increasing threats from rising sea levels and extreme weather events, highlighting the urgent need for accurate assessments of coastal flood risk. This study presents a novel approach to estimating global extreme sea level (ESL) exceedance probabilities using a regional frequency analysis (RFA) approach. The research combines observed and modelled hindcast data to produce a high-resolution (∼1 km) dataset of ESL exceedance probabilities, including wave setup, along the entire global coastline (excluding Antarctica). The methodology presented in this paper is an extension of the regional framework of Sweet et al. (2022), with innovations introduced to incorporate wave setup and apply the method globally. Water level records from tide gauges and a global reanalysis of tide and surge levels are integrated with a global ocean wave reanalysis. Subsequently, these data are regionalised, normalised, and aggregated and then fit with a generalised Pareto distribution. The regional distributions are downscaled to the local scale using the tidal range at every location along the global coastline obtained from a global tide model. The results show 8 cm of positive bias at the 1-in-10-year return level when compared to individual tide gauges. The RFA approach offers several advantages over traditional methods, particularly in regions with limited observational data. It overcomes the challenge of short and incomplete observational records by substituting long historical records with a collection of shorter but spatially distributed records. These spatially distributed data not only retain the volume of information but also address the issue of sparse tide gauge coverage in less populated areas and developing nations. The RFA process is illustrated using Cyclone Yasi (2011) as a case study, demonstrating how the approach can improve the characterisation of ESLs in regions prone to tropical cyclone activity. In conclusion, this study provides a valuable resource for quantifying the global coastal flood risk, offering an innovative global methodology that can contribute to preparing for – and mitigating against – coastal flooding.