High Flood Risk Research Articles

Measuring community resilience inequality to inland flooding using location aware big data

Understanding community resilience is critical for effective resource allocation and planning. However, classical resilience measures often fall short in capturing the diverse characteristics of each stage: preparation, resistance, recovery, and adaptability. We propose a multi-stage resilience assessment framework integrating morphological indicators to measure community resilience. Applying this framework to evaluate disparities in community resilience after the inland flooding in Changsha, we find that morphological indicators provide a more accurate description of resilience priority and ability compared to speed indicators. The analysis reveals stronger complementarity and lower redundancy among resilience indicators. Moreover, we establish a strong connection between resilience, driving factors, and government actions. Neighborhoods with high flood risk and socioeconomic status encounter the severest impact and highest recovery priority, leading to substantial government engineering measures. Conversely, vulnerable communities facing high flood risk and low socioeconomic status display the lowest resistance and recovery ability, emphasizing the necessity for tailored initiatives like increasing green infrastructure and constructing flood buffer zones. Additionally, low river flood risk communities with high socioeconomic status experience minimal flood impact, underscoring the significance of prioritizing low-lying regions susceptible to surface water flooding. The study informs government decision-making, emphasizing differentiated strategies for enhancing community resilience across regions and stages.

Flood risk assessment for Indian sub-continental river basins

Abstract. Floods are among India's most frequently occurring natural disasters, which disrupt all aspects of socio-economic well-being. A large population is affected by floods, which leave their footprints through human mortality, migration, and damage to agriculture and infrastructure, during almost every summer monsoon season in India. Despite the massive imprints of floods, sub-basin level flood risk assessment is still in its infancy and requires advancements. Using hydrological and hydrodynamic models, we reconstructed sub-basin level observed floods for the 1901–2020 period. Our modeling framework includes the influence of 51 major reservoirs that affect flow variability and flood inundation. Sub-basins in the Ganga and Brahmaputra river basins witnessed substantial flood inundation extent during the worst flood in the observational record. Major floods in the sub-basins of the Ganga and Brahmaputra occur during the late summer monsoon season (August–September). While the Beas, Brahmani, upper Satluj, upper Godavari, middle and lower Krishna, and Vashishti are among the sub-basins influenced by the presence of dams on downstream flood dynamics, the Beas, Brahmani, Ravi, and lower Satluj sub-basins are the most impacted by floods and the presence of dams. Bhagirathi, Gandak, Kosi, lower Brahmaputra, and Ghaghara are India's sub-basins with the highest flood risk. Our findings have implications for flood risk assessment and mitigation in India.

Flood Prediction Using Artificial Neural Networks: A Case Study in Temerloh, Pahang

Flood is one of the natural disasters that causes damage to properties, and sometimes loss of lives. Floods in Malaysia happen every year, especially in East Coast Peninsular Malaysia, due to the Northeast Monsoon and climate change, which may lead to heavy rainfall throughout the end of the year. Temerloh is one of the districts in Pahang that frequently encounters flood events, especially between November and January every year. Even though there are multiple efforts in flood mitigation and preparation, the damage to citizens and properties every year has cost thousands of Ringgits and the time taken to clean the damages caused by floods. Despite this, research on flood prediction in the state needs to be done using machine learning techniques. Due to this, this research explored the hydrological and meteorological factors that caused the flood in Temerloh and developed a machine-learning model capable of predicting the flood occurrence. The study used a dataset from the National Hydrological Network Management System (SPRHiN), which consists of hydrological data, and weather underground for the meteorological data in the location. The correlation analysis found that stream flow and water level are highly correlated to floods, with correlation coefficients (r values) of 0.83 and 0.76, respectively, while the temperature is inversely related to floods with a -0.28 correlation value. A lower temperature has a higher chance of rain and subsequent flooding. The results show that the model, by using an artificial neural network (ANN), has produced an accuracy of 0.9909 and a good performance of the area under the receiver operating characteristic curve (ROC) curve (AUC) at 0.888. The model also shows a low error with the mean squared error (MSE) of 0.009 and the root-mean-squared error (RMSE) of 0.096. The R2 value of 0.768 and F1 value of 0.875 indicate that the model has high precision and recall. Afterwards, a flood monitoring dashboard was created to visualize the data interactively. This research is vital in understanding the flood factors in Pahang and would offer academic insight for future research in floods. In addition, the flood monitoring dashboard will significantly assist governments and authorities in focusing the flood management efforts in areas at high risk of flood and be used to aid the state's future development.

Flood risk assessment and adaptation under changing climate for the agricultural system in the Ghanaian White Volta Basin

In the context of river basins, the threat of climate change has been extensively studied. However, many of these studies centred on hazard analysis while neglecting the need for comprehensive risk assessments that account for exposure and vulnerability. Hazard analysis alone is not adequate for making adaptive decisions. Thus, to effectively manage flood risk, it is essential to understand the elements that contribute to vulnerability and exposure in addition to hazard analysis. This study aims to assess flood risk (in space and time until the year 2100) for the agricultural system, in the White Volta Basin in northern Ghana. Employing the impact chain methodology, a mix of quantitative and qualitative data and techniques were used to assess hazard, exposure, and vulnerability. Multi-model climate change data (RCP 8.5) from CORDEX and observation data from the Ghana Meteorological Agency were used for hazard analysis. Data on exposure, vulnerability, and adaptation were collected through structured interviews. Results indicate that flood hazard will increase by 79.1% with high spatial variability of wet periods but the flood risk of the catchment will increase by 19.3% by the end of the twenty-first century. The highest flood risk is found in the Upper East region, followed by North East, Northern, Savannah, and Upper West for all four analysed periods. Adaptive capacity, sensitivity, and exposure factors are driven by poverty, ineffective institutional governance, and a lack of livelihood alternatives. We conclude that the region is highly susceptible and vulnerable to floods, and that shifting from isolated hazard analysis to a comprehensive assessment that considers exposure and vulnerability reveals the underlying root causes of the risk. Also, the impact chain is useful in generating insight into flood risk for policymakers and researchers. We recommend the need to enhance local capacity and foster social transformation in the region.

“Not enough”: A qualitative analysis of community perceptions of neighborhood government flood management plans using the case of Houston, Texas

Disasters, including floods, have increased in intensity over recent decades due to climate change and urbanization. Some local governments have attempted to engage communities in resilience efforts, referred to as Community-Based Disaster Resilience Management (CBDRM). However, it is unclear to what extent governments can effectively involve local communities in flood management plans, and how much flood management is perceived to be a “shared responsibility,” especially in low-income and minority areas. One city attempting to engage low-income and minority communities is Houston, Texas, which provides an opportunity to study how these efforts play out in the community in real time. Specifically, in this study, we examine Kashmere Gardens, a target neighborhood of the Houston initiative. We used a purposive and snowball sampling method and a grounded theory methodological approach to conduct qualitative in-depth interviews with 22 Kashmere Gardens residents. From this process, we found several themes, including affordability and convenience, neglect and distrust toward government, a lack of awareness concerning government flood management plans, and a sense of personal efficacy in the face of feelings of government neglect. The results of this study suggest that clearer communication from the city and more direct engagement with the community through on the ground programming may be necessary to develop better community trust and partnership in high flood risk areas. In terms of policy, utilizing funds to organize transportation or place meetings physically in the neighborhood, and employing more community organizers from within the community who have local knowledge, may foster better engagement to develop trust.

A novel flood risk management approach based on future climate and land use change scenarios

Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools – particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

Flooding in the Yellow River Basin, China: Spatiotemporal patterns, drivers and future tendency

Study regionThe Yellow River Basin (YRB) in China. Study focusThis study is devoted to simulating flood changes with return periods of 10, 20, 30, 50, 100 and 200 years under three Representative Concentration Pathways (RCPs) using CaMa-Flood models, and examining the drivers. New hydrological insights for the regionIn the historical period (1961–2005), the middle and lower YRB and specifically the Wei River Basin (WRB) (a tributary of the middle YRB) are dominated by higher flood risks with once-in-200-year flood magnitude of 9989.63 m3/s. Under climate change scenarios, the relative change in the once-in-100-year flood magnitude increased from 125.0% (RCP2.6) to 204.9% (RCP8.5) during the historical period (1961–2005) to the long-term period (2076–2092). The historical flood-prone regions were more susceptible to increased flood magnitude. Meanwhile, the area with increased flood risks ranged from 73.6% to 95.2% in the YRB. Increased flood magnitude was typically related to lengthened return interval of floods under the RCP2.6 and RCP6.0 scenarios, while decreased flood magnitude was commonly linked to increased return intervals under the RCP8.5 scenarios. Notably, we observed higher correlations between 7-day maximum precipitation and floods compared to those between daily maximum precipitation, soil moisture, and floods. Co-occurrences between flood events and hydrometeorological extremes were higher in the upper and middle YRB.

Discussion on the coupling relationship between flood risk and population vulnerability from climate justice

Abstract Facing the increasingly uncertain climate change, people are paying more and more attention to climate justice in urbanization. Climate change has intensified the vulnerability of cities and may have a greater impact on vulnerable groups. Therefore, this research constructed a framework to explore the coupling relationship between flood risk and population vulnerability from the perspective of climate justice. In this framework, the indicators of population vulnerability and population resilience were built. Then, the flood risk was identified through the relationship of inundation potential. Furthermore, the coupling coordination degree model was used to calculate the coupling between population vulnerability and flood risk, and the coupling between population resilience and flood risk. Finally, the driving factors of urbanization contributing to such coupling were analyzed through the Tobit model. The specific conclusions are (i) the study area shows vulnerable groups more likely to live in areas with high flood risk, and (ii) vulnerable groups are susceptible to the impact of population density and development intensity, while relatively wealthy groups are susceptible to the impact of the level of economic development and urban built environment. The results contribute to a better understanding of spatial inequalities in flood risk and population vulnerability, and climate injustice.

An Assessment of Flood Risk in the Gambia: A Data-Driven Approach to Flood Risk Management and Prevention

Coastal African countries, including Gambia, are increasingly vulnerable to a range of natural disasters, notably flooding, which poses significant challenges to sustainable development and safety. This study set out to comprehensively assess the flood risk in Gambia using a data-driven approach, aiming to enhance flood risk management and prevention strategies. Utilizing geospatial data and Geographic Information System (GIS) techniques, the research involved mapping and categorizing flood risk zones across various regions. The analysis focused on the identification and categorization of flood risk zones, assessment of the vulnerability of key infrastructures, health facilities, road networks, and settlements to flooding. The findings reveal that a total of 2,721 square kilometers of land is at flood risk categorized as either high or very high. This constitutes 26.89 percent of the country’s of terrestrial area. A total of 25 critical health infrastructure were in areas of this high flood risk categorization – ranging from general hospitals to clinics. Up to 24.5% of the nation's road network was found to be in this flood risk zone. Of the country’s five administrative regions, the West Coast Region which is home to most of its urban centers was most at risk of flooding. Here, of the 353 settlements in this region, 172 of them fell under the high to very high-risk flood zone categorization, constituting 48.7% of the settlements at a significant level of flood vulnerability. This study enhances the understanding of flood hazards in Gambia, providing essential perspectives for decision makers, city planners, and emergency response teams. This research highlights the need for using data-driven methods in identifying and managing environmental risks. It also advocates for the adoption of long-term strategic planning, infrastructure enhancements, and community-based activities to reduce the effects of floods.

How local governments avoid floodplain development through consistent implementation of routine municipal ordinances, plans, and programs

Abstract Avoiding floodplain development is critical for limiting flood damage, yet there is little empirical evidence of how local governments effectively avoid floodplain development. We conduct a mixed-methods study to explain how local floodplain management influences floodplain development in New Jersey, a state with high development pressure and flood risk. We find that 85% of towns developed relatively little in the floodplain from 2001 to 2019, and they achieved this with commonplace land use management tools and modest levels of local government capacity. One hundred twenty-six New Jersey towns put none of their new housing in the floodplain 2001–2019. Our findings run counter to common reports of rampant floodplain development requiring legal innovation and capacity-building campaigns and suggest alternative approaches for promoting floodplain avoidance. We find multiple paths to floodplain avoidance, weak support for previously identified drivers, and strong evidence that the keys to avoidance include having a few high-quality tools that are well-implemented, requiring consistency, coordination, and commitment of local officials. The multiple paths and importance of maximum, rather than average, quality might explain the mixed evidence in prior research connecting floodplain management actions and development outcomes. A lack of attention to towns that limit floodplain development impedes our ability to learn from and disseminate their successes. Contrary to our expectations, we show that floodplain avoidance can be and is achieved through routine municipal practices. Our findings underscore the importance of sustained commitment to development management as a core tool for limiting flood risk.

Floodplain mapping of an ungauged river: A case study on Seti River in Pokhara, Nepal

Settlements and infrastructures along the banks of Seti River in Pokhara, Nepal are at high risk of flood. Floodplain mapping for ungauged Seti River is not straightforward like the one for gauged rivers. Main goal of this study was to prepare floodplain maps along the ungauged Seti River in Pokhara, as a case study, using one-dimensional HEC-RAS model. First, catchment area ratio (CAR) method was applied to find annual flow in ungauged Seti River based on flow data of gauged Mardi station. Once the annual maximum flow was estimated for sufficient time length (i.e., 42 years), peak flood was predicted using Gumbel method for various reaches of Seti River within Pokhara. Thus, estimated peak floods were also compared with the peak floods predicted using Gumbel method based on the annual flow data of Tanahu station. As the specific discharge observed to be comparable with each other (difference ≤ 2.68%), CAR method found be a reliable one that is useful for ungauged river. Then, Cowan’s approach was applied to estimate Manning’s roughness coefficient (n) and used it for calibration of HEC-RAS model. Cowan approach found to be a best alternative for ungauged river as the comparison of modelled flow depth with measured flow depth yielded only 3.82% difference. Finally, 1D hydraulic modelling was performed using calibrated HEC-RAS model with available 12.5 m resolution DEM terrain data. Floodplain maps were prepared based on the HEC-RAS simulation results coupled with Google Earth map. The flood inundation area within Pokhara was found to be 2.76, 3.05, and 3.59 Km2 for the peak flood of 20, 50, and 100 years return periods, respectively. Moreover, Laltin Bazar and Gaighat areas were identified to be at high risk of flood such that these areas found to be inundated with 20 or greater years return period floods, compared to Ramghat area which was observed to be flooded with the peak flood of 50 or more years return period. Floodplain maps of this study could be used for preparing flood hazard maps, planning infrastructures, and flood management.

A Weighted Overlay Analysis for Assessing Urban Flood Risks in Arid Lands: A Case Study of Riyadh, Saudi Arabia

Urban flooding poses a significant problem in arid regions, where unusual rainfall events can lead to disastrous consequences. This study used geographic information systems (GIS) to identify and predict potential flood risk zones in Riyadh City, Saudi Arabia. A comprehensive flood risk map was created by integrating various datasets, including digital elevation models (DEM), lithology, precipitation patterns, slope, and drainage density. Through a weighted overlay analysis, Riyadh was segmented into zones with different levels of flood susceptibility, ranging from low to very high risk. The study area spans 905 km2. Of this total area, 32% is at a low risk of flooding, while 31%, 24%, and 12% of the area are at a moderate, high, and very high risk of flooding, respectively. The findings of the study were validated through field surveys conducted before and after recent rainfall events. These validation efforts corroborated the high-risk areas predicted by the model, thereby ensuring the reliability of the map for future urban planning and risk mitigation strategies.

Geo-spatial assessment of pluvial floods in city district Lahore, Pakistan.

This study is an effort of geo-spatial assessment of pluvial floods in District Lahore, Pakistan, caused by urban expansion and the growing frequency and intensity of high-intensity rainfall events. The use of geospatial techniques such as watershed modeling, maximum likelihood image classification, and weighted overlay analysis based on secondary data has enabled the researchers to assess the extent and severity of pluvial floods in the study area. The study's findings highlight the high risk of pluvial floods in the central part of the study area, which is dominated by built-up land and concrete roads. The increase in the area of built-up land from 34.913 km2 in 2018 to 37.442 km2 in 2022 has further intensified the risk of pluvial floods. The findings of this study can assist policymakers in developing effective strategies to reduce the risks associated with pluvial floods. Alongside, it also highlights the importance of geospatial techniques to better understand and address the complex challenges of urbanization and climate change. Flood risk zone-specific strategies are recommended to reduce the risk of pluvial floods.

Financial constraints and short-term planning are linked to flood risk adaptation gaps in US cities

Adaptation is critical in reducing the inevitable impact of climate change. Here we study cities’ adaptation to elevated flood risk by introducing a linguistic measure of adaptation extracted from financial disclosures of 431 US cities over 2013–2020. While cities with a higher flood risk have higher adaptation, more than half of high-risk cities have below-average adaptation levels. We explore three factors associated with this adaptation gap, defined as a city’s adaptation being lower than predicted based on flood risk. We do not find that Republican cities are more likely to have an adaptation gap. Instead, our results point to the importance of financial constraints: cities with one standard deviation smaller unrestricted-fund-to-expense ratio are 6.6% more likely to have an adaptation gap. We also provide evidence on the importance of long-term planning: cities with a planning horizon shorter by one year are 4% more likely to have an adaptation gap.

Flood vulnerability assessment in the flood prone area of Khyber Pakhtunkhwa, Pakistan

Floods are among the most destructive natural disasters, causing extensive damage to human lives, property, and the environment. Pakistan is susceptible to natural calamities, such as floods, resulting in millions of people being impacted yearly. It has been demonstrated that flood severity is rising and may continue to escalate in the coming years because of climate change-induced changes in monsoon precipitation in the country. Given the country’s exposure to flooding, it is essential to assess the vulnerability to floods to prepare for and mitigate their impact in Pakistan. This study provides a new conceptual framework for assessing flood risk and vulnerability in Charsadda, a flood-prone district in Pakistan. It evaluates the vulnerability of settlements to floods based on four indicators: population density, the average gross domestic product (GDP) of land, the distance between settlements and rivers, and land use and cover (LULC). The analytical hierarchy process (AHP) technique was integrated with the geographical information system (GIS) to assess the level of vulnerability to floods in the study area. The results reveal a higher degree of vulnerability to floods in the region. The spatial pattern of vulnerable areas reveals a significant connection between high-risk flood areas and densely populated areas during different flood seasons. The results further reveal that more than 60% of the area is arable land and is highly susceptible to flood. The population and their land-use setup show high and extremely high values of vulnerability in the normalized threshold of 0.3–0.4, respectively. The study provides an in-depth and comprehensive analysis of the chosen indicators, evaluation methods, and results, making this a valuable contribution to the field of flood vulnerability assessment. The findings of this study also include thematic maps and related information to the stakeholders for effective vulnerability management in the study area.

Computer vision based first floor elevation estimation from mobile LiDAR data

First Floor Elevation (FFE) of a house is crucial information for flood management and for accurately assessing the flood exposure risk of a property. However, the lack of reliable FFE data on a large geographic scale significantly limits efforts to mitigate flood risk, such as decision on elevating a property. The traditional method of collecting elevation data of a house relies on time-consuming and labor-intensive on-site inspections conducted by licensed surveyors or engineers. In this paper, we propose an automated and scalable method for extracting FFE from mobile LiDAR point cloud data. The fine-tuned yolov5 model is employed to detect doors, windows, and garage doors on the intensity-based projection of the point cloud, achieving an mAP@0.5:0.95 of 0.689. Subsequently, FFE is estimated using detected objects. We evaluated the Median Absolute Error (MAE) metric for the estimated FFE in Manville, Ventnor, and Longport, which resulted in values of 0.2 ft, 0.27 ft, and 0.24 ft, respectively. The availability of FFE data has the potential to provide valuable guidance for setting flood insurance premiums and facilitating benefit-cost analyses of buyout programs targeting residential buildings with a high flood risk.

Spatial constraints or spatial dynamics? The spatial spillover effect of networks of flood regulation service flows on land-use degree.

In the face of frequent floods under climate and environmental changes, it is particularly important to measure the supply and demand of flood regulation services. Using the Hainan Island as an illustrative case, this study constructs a spatial spillover model to examine the spatial correlation mode and evolution of regional land-use degree through the network of ecosystem service flow. The research results show that forests, grasslands, and reservoirs function as the primary suppliers of flood regulation services, with forests contributing significantly to the regulation of floods. High flood risk was identified in the eastern, northern, and western regions of the Hainan Island, corresponding to increased demand for flood regulation services in croplands, towns, and rural settlements within these areas. The flow of flood regulation services within the Hainan Island was found to be directed from the center to the surrounding areas, with medium and high service flows predominantly concentrated in the northern and surrounding regions. The degree of land use on the Hainan Island demonstrated an influence on socio-economic development. Additionally, the flow network of ecological services was identified as a crucial factor in spatial spillovers, reflecting the level of interaction between county units.

Pluvial flood risk assessment for 2021–2050 under climate change scenarios in the Metropolitan City of Venice

Pluvial flood is a natural hazard occurring from extreme rainfall events that affect millions of people around the world, causing damages to their properties and lives. The magnitude of projected climate risks indicates the urgency of putting in place actions to increase climate resilience. Through this study, we develop a Machine Learning (ML) model to predict pluvial flood risk under Representative Concentration Pathways (RCP) 4.5 and 8.5 for future scenarios of precipitation for the period 2021–2050, considering different triggering factors and precipitation patterns. The analysis is focused on the case study area of the Metropolitan City of Venice (MCV) and considers 212 historical pluvial flood events occurred in the timeframe 1995–2020. The methodology developed implements spatio-temporal constraints in the ML model to improve pluvial flood risk prediction under future scenarios of climate change. Accordingly, a cross-validation approach was applied to frame a model able to predict pluvial flood at any time and space. This was complemented with historical pluvial flood data and the selection of nine triggering factors representative of territorial features that contribute to pluvial flood events. Logistic Regression was the most reliable model, with the highest AUC score, providing robust result both in the validation and test set. Maximum cumulative rainfall of 14 days was the most important feature contributing to pluvial flood occurrence. The final output is represented by a suite of risk maps of the flood-prone areas in the MCV for each quarter of the year for the period 1995–2020 based on historical data, and risk maps for each quarter of the period 2021–2050 under RCP4.5 and 8.5 of future precipitation scenarios. Overall, the results underline a consistent increase in extreme events (i.e., very high and extremely high risk of pluvial flooding) under the more catastrophic scenario RCP8.5 for future decades compared to the baseline.

Flood mapping of the lower Mejerda Valley (Tunisia) using Sentinel-1 SAR: geological and geomorphological controls on flood hazard

Understanding the influence of the hydrological regime changes, encompassing both the duration and spatial extent of floods, is explored, through this paper, in the context of the complex interactions of the geological, geomorphological and climate change conditions. Integrating these parameters and their combined influence becomes paramount for effective flood risk management and disaster prevention, as highlighted in the broader context of the study’s conclusions. Over the past decade, despite prevailing drought conditions in North African countries, the Mejerda Valley in Tunisia has experienced several major flood events. The valley geomorphological history is presented, within the landscape evolution. This study explores the use of Sentinel-1 radar data for detecting and managing floods in the region, with a particular emphasis on the vulnerability of the Lower Mejerda Valley. Sentinel-1 radar data, owing to their continuous monitoring capabilities independent of weather conditions, prove highly effective in tracking the temporal evolution of floods and accurately mapping the extent of affected areas. The methodology utilized in this study has proven to be highly effective in accurately identifying areas susceptible to flooding, both spatially and temporally. In generating classified flood images, water objects are highlighted by applying a specific formula. The study delves into significant flood events in the Mejerda Valley, including occurrences in 2015, 2017, 2019, and 2020, examining their profound impacts on various regions. For instance, the floods of 2015 recorded precipitation levels reaching up to 141 mm, contributing to the overall understanding of flood evolution in the region. In conclusion, the Mejerda Delta’s relief is primarily due to tectonism, influencing both Mio-Pliocene and Quaternary processes, shaping the current delta configuration. The Valley’s topography results from extensive geomorphological evolution, with the eastward shoreline advance leading to the formation of sebkhas and lagoons, indicators of high flood risk, supported by detailed flood event analysis. Moreover, The climate change and anthropic activity are non-negligible parameters influencing flood occurrence and intensity.

High urban flood risk and no shelter access disproportionally impacts vulnerable communities in the USA

Vulnerable communities are disproportionately placed in low-lying, flood-prone neighborhoods, with deficient infrastructure and limited access to shelter. Here we present a methodology to study the risk of urban floods in tandem with access to shelter to reduce the risk of flooding to communities and prevent a natural hazard from turning into a human disaster. We integrate access to national emergency shelters into a national risk index for riverine floods in eight U.S. cities at the block group level using clustering techniques. The results show shelters are more accessible for inner-city residents regardless of the risk level, and communities with high risk of flood and low access to shelter are disproportionately home to the underserved populations of Asians and the elderly. The outcomes delineate the disparity in equity related to urban floods and support plans and policy needs by identifying and prioritizing areas to improve emergency responses and resource allocations.