Impact Of Floods Research Articles

2022 Flood Impact in Pakistan: Remote Sensing Assessment of Agricultural and Urban Damage

Pakistan was hit by the world's deadliest flood in June 2022, causing agriculture and infrastructure damage across the country. Remote sensing technology offers a cost-effective and efficient method for flood impact assessment. This study is aimed to assess the impact of flooding on crops and built-up areas. Landsat 9 imagery, European Space Agency-Land Use/Land Cover (ESA-LULC) and Soil Moisture Active Passive (SMAP) data are used to identify and quantify the extent of flood-affected areas, crop damage, and built-up area destruction. The findings indicate that Sindh, a province in Pakistan, suffered the most. This impact destroyed most Kharif season crops, typically cultivated from March to November. Using the SMAP satellite data, it is assessed that the high amount of soil moisture after flood also caused a significant delay in the cultivation of Rabi crops. The findings of this study provide valuable information for decision-makers and stakeholders involved in flood risk management and disaster response.

Integrated Analysis of Rockfalls and Floods in the Jiului Gorge, Romania: Impacts on Road and Rail Traffic

This study examines the impact of rockfalls and floods on road and rail traffic in the Jiului Gorge, Romania, a critical transportation corridor. Using Sentinel-1 radar imagery processed through ESA SNAP and ArcGIS Pro, alongside traffic detection facilitated by YOLO models, we assessed susceptibility to both rockfalls and floods. The primary aim was to enhance public safety for traffic participants by providing accurate hazard mapping. Our study focuses on the area from Bumbești-Jiu to Petroșani, traversing the Southern Carpathians. The results demonstrate the utility of integrating remote sensing with machine learning to improve hazard management and inform more effective traffic planning. These findings contribute to safer, more resilient infrastructure in areas vulnerable to natural hazards.

HANZE v2.1: an improved database of flood impacts in Europe from 1870 to 2020

Abstract. Assessing long-term trends in flood losses and attributing them to climatic and socioeconomic changes requires comprehensive and systematic collection of historical information. Here, we present flood impact data for Europe that are part of the HANZE (Historical Analysis of Natural HaZards) database. The dataset covers riverine, pluvial, coastal, and compound floods that have occurred in 42 European countries between 1870 and 2020. The data were obtained by extensive data collection from more than 800 sources ranging from news reports through government databases to scientific papers. The dataset includes 2521 events characterized by at least one impact statistic: area inundated, fatalities, persons affected. or economic loss. Economic losses are presented both in the original currencies and price levels and with the inflation and exchange rate adjusted to the 2020 value of the euro. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union's Nomenclature of Territorial Units for Statistics (NUTS) level 3. Daily start and end dates, information on causes of the events, notes on data quality issues or associated non-flood impacts, and full bibliography of each record supplement the dataset. Apart from the possibility of downloading the data (https://doi.org/10.5281/zenodo.8410025; Paprotny, 2023a), the database can be viewed, filtered, and visualized online at https://naturalhazards.eu (last access: 4 November 2024). The dataset is designed to be complementary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset values) and be easily usable in statistical and spatial analyses, including multi-hazard studies.

Flood impact assessment in remote areas using machine learning, SAR, and GIS: a case study of Ngabang District, Indonesia

ABSTRACT Flooding in remote regions presents significant challenges due to data scarcity, complicating impact assessment and mitigation efforts. This research delineates an integrated methodology for quantifying flood impacts in such contexts. By leveraging machine-learning algorithms, Sentinel-1 synthetic aperture radar (SAR) imagery was combined with digital elevation model data and river proximity metrics to predict and accurately demarcate flood extents. Geographic information systems overlay techniques were then employed for spatial analysis of the floods’ impacts on population and infrastructural assets. The methodology was applied in a case study in Ngabang District, Indonesia, demonstrating its utility. Analysis using decision tree, random forest (RF), and gradient boosting machine models provided critical insights into flood prediction factors. The RF model was chosen as the best, successfully identified flood-prone regions, achieving an accuracy of 0.94 and a Kappa of 0.87 on the testing data, demonstrating its robustness. The flood map showed significant impacts, affecting 373.81 hectares, 10,706 people, 1,500 buildings, and 15 km of roads. This study highlights the importance of proximity, elevation, SAR imagery, and iterative model improvements in flood prediction, offering valuable insights for flood management and mitigation efforts in data-scarce regions.

Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra

The downtown area of the city of Coimbra, Portugal, is at low altitude and has historically suffered floods that have caused serious economic losses. The present research proposes a mobile augmented reality (MAR) application aimed at visualising the effect of possible scenarios of flooding in an area of higher risk in the city. A realistic 3D model of the city was created, using data extracted with BLosm and processed through Blender, followed by its integration into Unity with Vuforia for AR visualisation. The methodology encompasses the extraction and simplification of 3D models, mapping real-world coordinates in Unity, analysing several datasets, obtaining a model through regression and implementing a workflow to manage interactions between various Unity objects. The MAR application enables users to visualise potential flood impacts on buildings, utilising colour-coded indicators to represent different levels of water contact. The system’s efficacy was evaluated by simulating various use-case scenarios, demonstrating the application’s capability to provide real-time, interactive flood risk assessments. The results underline the potential of integrating AR and machine learning for enhancing urban flood management and prevention.

Impacts of Floods on Sustainable Urban Development in Kenya: The Case of Nairobi and Mombasa

Impacts of Floods on Sustainable Urban Development in Kenya: The Case of Nairobi and Mombasa

Justification of the safe parameters of recreational zones during the reclamation of watered residual quarry spaces

Purpose. To determine the safe parameters of the recreational zones created in the residual space of the quarry taking into account the physical and mechanical properties of waste rocks in a watered state. Methodology. The Bishop Simplified Method is used to determine the influence of the irrigation level of the residual quarry space on the stability of the embankment from different types of mining rocks when creating a recreational area during reclamation works. Findings. The safe parameters of recreational areas during their construction in the watered residual space of the quarry were established taking into account the physical and mechanical properties of embankments made of sand, loam, and crushed rock by determining the stability of their slopes. The obtained results are necessary for the implementation of project works on the development of technological schemes for the reclamation of the residual spaces of construction materials quarry for the recreational direction of post-mining. Originality. The influence of the height of the rock embankment formation on the stable angle of inclination of the watered slope was established, which allowed determining that with an increase in the aggregates embankment height from 20 to 80 m, the safe angle of the slope will decrease from 46 to 26°. It was determined that the lowest FOS indicator is 0.57 when using sand rocks for an embankment height of 80 m at a water content of 40 %. It was established that with partial flooding of the rock embankment by 45–50 % for sandy, loamy and rocky rocks, there is a significant decrease in the coefficient of the reserve of stability by 1.4–1.5 times, in contrast to the absence of water or complete flooding, which confirms the negative impact of partial flooding of embankments and reducing the stability of their slopes. Practical value. It was determined that when forming an embankment 20 m high from loamy rocks, the volume of reclamation works will be 1.34 times less compared to sandy rocks, but 1.02 times larger than rocky rocks. When the height of the embankment increases to 80 m, the volume of reclamation works when replacing loam with sand will increase to 1.87 and 1.12 times when using crushed stone. However, taking into account the market value of materials, when using loam, the cost of construction will decrease by 2.5 times compared to sandy rocks and 3.2 times – to crushed stone, with an embankment height of 20 m. When the embankment height increases to 80 m, the cost of materials will increase by 3.5 and 3.8 times when loamy rocks are replaced by sand or crushed stone, respectively.

Road network disruptions during extreme flooding events and their impact on the access to emergency medical services: A spatiotemporal vulnerability analysis

The impact of extreme floods can be significant, affecting humans, the environment and any type of human-built infrastructure. A further consequence of flooding is restricted access to medical facilities, including constraints to access more dedicated emergency medical services (EMS). To date, there has been a lack of investigation into EMS accessibility dynamics during extreme floods. The objective of this study is to gain a deeper understanding of the spatiotemporal changes in accessibility-based vulnerability related to EMS during a representative extreme flood event, which was simulated over a period of six days. The study assesses the spatial accessibility of EMS centres to populations located within 15-, 30-, 45-, and 60-minute travel time thresholds in Canton Bern, Switzerland, and applies floating catchment area method. The dynamic aspects of EMS access vulnerability during extreme floods were assessed in two different ways. Firstly, the ratios between accessibility change and accessibility under normal conditions in 1 km grid cells were calculated at hourly moments, t. Subsequently, the resulting values were used to calculate the average vulnerability score. Secondly, percentage changes of affected populations were evaluated for different accessibility classes during the flood event and under all-time high static flood conditions. Varied spatial patterns of accessibility were generally observed with respect to the road network and population distributions in the hilly and mountainous landscape. Extending evaluations to consider temporal dynamics revealed a complex pattern of accessibility gains and losses in different regions of the study area, including those where no direct flood impacts occurred on the road network. The application of a static, all-time high flood condition resulted in an overestimation of accessibility limits to EMS centres. While this overestimation was not considered to be critical, the application of a spatiotemporal accessibility-based vulnerability analysis method to EMS is considered to be more holistic. Insights from this study can be used to evaluate the effectiveness of EMS risk management plans with respect to evolving extreme flood scenarios.

A literature review: rainfall thresholds as flash flood monitoring for an early warning system

ABSTRACT Flash floods, increasingly common and impactful, can be better managed through effective early warning systems. This study reviews methods for determining rainfall thresholds using empirical, hydrological, and machine learning approaches. Empirical methods, based on historical data, identify patterns between rainfall and floods but struggle with spatial and temporal variations. Hydrological methods, which consider watershed characteristics, offer greater accuracy but require detailed data. Machine learning, with its capacity for real-time, adaptive analysis of big data, shows promise for improving predictions. Integrating these approaches can enhance early warning systems, making them more effective in the face of climate change and significantly reducing the impact of flash floods.

Can we adapt fairly? Systematic review of health equity implications of climate change in coastal communities in the UK

Abstract As climate change becomes more apparent, it is important to evaluate the equity implications of adaptation interventions and policy measures. Many coastal populations experience high levels of social deprivation, which can be compounded by environmental factors. Coastal hazards, such as erosion and flooding can cause short and long term impacts on physical and mental health. To systematically review the published evidence on the differential impacts of coastal change on health inequalities and review the implications of adaptation responses for health inequality. We systemically reviewed the evidence for the UK only on a) inequalities in coastal flood impact or risk, and b) effectiveness and equity implications of current measures to manage the climate risks to an acceptable level of impact. Interventions included: plans and guidance, insurance, and infrastructure, including natural flood management. We found 8 papers quantifying differentials in current and future impacts of coastal flood risk and 6 papers assessing equity implications of adaptation measures. Coastal flood risk is unevenly distributed. Those owning their own home were more likely to experience flood impacts including increased stress and displacement. There is good evidence that policies for household insurance and property level protection measures have the potential to increase inequalities; other measures (community engagement; planning; defences) may reduce health inequalities, depending on implementation and local context. Adaptation to coastal change requires a range of approaches in the short and longer term, which potentially exacerbate current inequalities. Adaptation responses that rely on individual behaviour change, such as relocation, purchasing insurance, or retrofitting dwellings, may exacerbate inequalities within coastal communities. Climate change challenges organisations to deliver national and local policy responses ensuring that adaptation is effective and equitable.

Assessment of coastal flood risk scenarios on infrastructure in the Keta municipality in Ghana using a GIS approach

Coastal flooding and erosion, caused by climate change-induced sea level rise, pose significant threats to low-lying coastal areas worldwide. The African continent, including Ghana, has experienced severe impacts from these hazards, affecting the socio-economic development of coastal communities. This research focuses on the Keta municipality in Ghana. The Keta municipality is highly vulnerable to coastal flooding and erosion due to its low elevation and the construction of the Akosombo hydroelectric dam. This research aims to predict the impact of coastal flooding on infrastructure in the Keta municipality using different flood scenarios. It also fills the existing knowledge gap in understanding how future flood risk can impact on critical infrastructure and consequently on other vital socio-economic sectors in the coastal community of Keta. The study utilizes a Geographic Information System (GIS)-based approach, combining various datasets, including topographic data, administrative shapefiles, field survey points, and infrastructure data. Two flood scenarios were considered to assess areas at risk: a 2.5-m sea level flood scenario representing potential climate change events and a worst-case 5-m sea level flood scenario. The findings reveal that 3.3 km2 (9 %) of coastal land area, 3.9 km of roads, and 69 structures are at risk under a 2.5m sea level rise scenario, increasing to 7.1 km2 (19.4 %), 13.6 km of roads, and 667 structures under a 5m scenario. The study highlights the urgency of addressing these risks to protect coastal communities' socio-economic development and livelihoods. Recommendations include implementing appropriate coastal management strategies, improving infrastructure resilience, and promoting sustainable land-use practices. By understanding and addressing the future impact of coastal flooding, decision-makers can mitigate climate change's adverse effects on coastal areas and ensure the region's long-term sustainability.

Characterization of hydrometeorological events and flood impacts in the Basque Country

Abstract. In this study, the focus is on the characterization of floods in Basque Autonomous Community, considering damages, hydrometeorological aspects and others factors during the period 2000–2021. The ultimate goal is to contribute to understanding the processes of impact, enhancing awareness, and improving preparedness before flood events. To assess flood impact, damage data from the Spanish Insurance Compensation Consortium areexposureccccccc used as a proxy. For hydro-meteorological characterization and weather context, we use data from the Automatic Weather Station Network and various Euskalmet general and local ad-hoc weather context classifications. Different datasets are conveniently prepared to extract a range of indicators, taking into account: (1) physiographic and socio-economic factors, (2) damages and impact, (3) hydrometeorological aspects, and (4) weather context. Visual data analytics techniques are utilized for analysis, characterization, and presentation of findings. We integrate indicators with diverse temporal resolutions (daily and monthly) and spatial aggregations (primary watersheds and selected hydrological units) to facilitate discussions and derive conclusions. This work establishes a foundation for a semi-automatic procedure for data preparation, indicator extraction, analysis, and results presentation. Although it is a strategy specifically designed for its application in our context, we believe it can serve as inspiration for conducting similar free software based exercises in other parts of the world.

Application of Analytical Hierarchy Process (AHP) and Multi-Criteria Evaluation (MCE) for a case study and scenario assessment of flood risk in the White Volta Basin of the Upper East Region, Ghana

In recent years, Ghana, particularly the inhabitants of the Upper East Region, has experienced profound impact of flooding, largely attributable to the complex interplay of climatic factors. This research embarks on a comprehensive assessment of flood risk zones nestled within the White Volta Basin, situated in the Upper East Region. The study employs advanced cartographic methodologies and uses Geographic Information Systems (GIS) in conjunction with the Analytical Hierarchy Process (AHP) to systematically categorize areas susceptible to inundation. Leveraging geospatial datasets acquired from satellites such as Landsat and Sentinel. Topographic, slope, and Land Use/Land Cover (LULC) maps have been constructed. The empirical findings underscore the susceptibility of specific regions, including the Talensi District, territories within Bawku West, and some segments of the Bolgatanga Municipal area, to escalated flood risk. Additionally, the research underscores the high vulnerability of communities such as Nunku, Tolla, Zaare, Pwalugu, Balungu, Winkongo, Biung, and Tongo to the negative impact of inundation. Significantly, the study unveils a pivotal factor in the perpetuation of flood devastation—namely, the role of water discharge. This intrinsic linkage between discharge rates and flood occurrences underscores the pressing need to address this critical component in mitigation strategies to reduce adverse impacts on the basin’s resident communities. The insights derived from the study offer some level of hope for residents, providing essential knowledge concerning flood-prone areas and optimal timing for agricultural activities to safeguard their cherished livelihoods.

Estimating the economic vulnerability of 2020–2099 storm flooding with sea level rise on Coastal Virginia

Estimating the economic impact of flooding is an essential element of flood risk management. Policy interventions to mitigate the impact of flooding must be balanced against the financial and economic costs of flooding. Using projections of sea level rise for 2040, 2060, and 2080 and estimates of flooding damage that are equaled or exceeded 10-, 50-, 100-, and 500-year period, we investigate the financial costs of flooding across eight planning districts in Coastal Virginia. We briefly discuss the methodology to estimate the average annualized loss (AAL) given discrete events and likelihoods and how these AALs flow into the estimation of the net present value of flooding in Coastal Virginia. The Hampton Roads metropolitan area accounts for approximately 80.7% of estimated nominal losses currently, rising to 88.1% by 2080 due to sea level rise. This vulnerability is linked to poorly planned development, population growth, ground subsidence, and economic inequities. The present value of unmitigated flooding ranges from $20.0 billion to $58.7 billion in 2021 dollars, depending on the discount rate. Discounted economic impacts for unmitigated sea level rise could reach $82.9 billion for Coastal Virginia this century. The concentration of costs in Hampton Roads, which produces 20% of Virginia's GDP, underscores the need for targeted economic and security policies.

Fusing remote and social sensing data for flood impact mapping

AbstractThe absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end‐to‐end system, that ingests data from multiple nontraditional data sources such as remote sensing, social sensing, and geospatial data. We employ state‐of‐the‐art natural language processing and computer vision models to identify flood exposure, ground‐level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real‐world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through various statistical analyses using official ground‐truth data, showcasing its strong performance and explanatory power of integrating multiple data sources. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard‐hit districts and enhancing disaster response.

Environmental shocks and agriculture: implications of floods on labor market outcomes

Abstract Floods often displace people and exacerbate their access to finance, affecting the livelihood of daily wage workers in least-developed countries. In August 2017, Nepal experienced the heaviest rainfall in more than 60 years, severely flooding about 80 per cent of the land in the southern part of the country. Using the two-way fixed effects approach and an event study design, we evaluate the impact of severe flooding on the wages of agricultural workers. We show that the 2017 floods resulted in a 9–10 per cent decrease in cash wages among agricultural households while in-kind wages of agricultural laborers increased significantly after the floods, implying that in-kind wages helped mitigate the adverse effects of floods on cash wages. We also investigate changes in assistance, loan-seeking behavior, loan repayment, and collection behavior as mechanisms leading to the risk-mitigating behavior by farmers.

Divergent contributions of microbes and plants to soil organic carbon in the drawdown area of a large reservoir: Impacts of periodic flooding and drying

The distribution patterns and accumulation mechanisms of plant and microbial residues, along with their potential contributions to soil organic carbon (SOC), remain subjects of considerable debate, particularly within drawdown areas affected by reservoir operation. In this study, surface soil samples (0–10 cm) were collected from three different elevations within the drawdown area of the Three Gorges Reservoir. Amino sugars and lignin phenols served as biomarkers for microbial residues and plant-derived materials, respectively. The results revealed that with increasing duration of flooding, the content of amino sugars increased from 0.26 mg g−1 to 0.64 mg g−1, whereas the content of lignin phenols decreased from 204.09 mg kg−1 to 37.93 mg kg−1. Moreover, as the duration of flooding increased, the contribution of microbial necromass carbon (MNC) to SOC rose from 29% to 47%, while the contribution of plant-derived carbon to SOC gradually declined. Plants biomass and iron minerals influenced the accumulation of lignin phenols, whereas amino sugars were affected by plants biomass, microbial biomass carbon and nitrogen, and clay minerals. The periodic flooding and drying events induced alterations in carbon inputs and environmental characteristics within the drawdown area, resulting in fluctuations in the contributions of plants and MNC to SOC in this region. The findings of this study highlight the critical role played by both plant- and microbial-derived carbon in the retention and turnover of SOC within the terrestrial-aquatic transition zone.

Creating Sustainable Flood Maps Using Machine Learning and Free Remote Sensing Data in Unmapped Areas

This study leverages a Random Forest model to predict flood hazard in Arizona, New Mexico, Colorado, and Utah, focusing on enhancing sustainability in flood management. Utilizing the National Flood Hazard Layer (NFHL), an intricate flood map of Arizona was generated, with the Random Forest Classification algorithm assessing flood hazard for each grid cell. Weather variable predictions from TerraClimate were integrated with NFHL classifications and Digital Elevation Model (DEM) analyses, providing a comprehensive understanding of flood dynamics. The research highlights the model’s capability to predict flood hazard in areas lacking NFHL classifications, thereby supporting sustainable flood management by elucidating weather’s influence on flood hazard. This approach aligns with sustainable development goals by aiding in resilient infrastructure design and informed urban planning, reducing the impact of floods on communities. Despite recognizing constraints such as input data precision and the model’s potential limitations in capturing complex variable interactions, the methodology offers a robust framework for flood hazard evaluation in other regions. Integrating diverse data sources, this study presents a valuable tool for decision-makers, supporting sustainable practices, and enhancing the resilience of vulnerable regions against flood hazards. This integrated approach underscores the potential of advanced modeling techniques in promoting sustainability in environmental hazard management.

An Examination of the Decision of Microentrepreneurs to Remain in Urban Flood-prone Areas in Marikina City, Metro Manila (NCR), Philippines

Despite the impact of urban flooding on businesses, the choice of microentrepreneurs continuing to stay in flood hazard areas runs counterintuitive to the principles of profitability and sustainability albeit both are the most vulnerable aspects of the business sectors. Existing literature has provided substantial knowledge on the factors affecting an entrepreneur’s choice of operating location but does not go deeper into why entrepreneurs decide to remain and operate in frequently flooded urban areas. A multi-stage cluster sampling technique was used in this study to establish an adequate representation of microentrepreneurs in flood-prone areas of Marikina City in Metro Manila, Philippines, using various statistical tools to evaluate decision factors. The research revealed that component factors such as economic considerations, the microentrepreneur’s socio-spatial relations, and experiences and perception of resilience influence the decision to remain in urban flood-prone areas. Moreover, specific sub-factors under each component factor were identified, which can be grouped into ‘pre-existing’ and ‘learned’ categories, introducing a new location decision model attuned to the dynamics that exists between microentrepreneurs and a natural disaster such as urban flooding. The findings of the study may guide policymakers and urban planners in designing targeted intervention measures that translate to more micro enterprise-responsive disaster risk reduction and management plans. Moreover, by treating microenterprises as separate from the traditional occupations, communities might appreciate the crafting of local land use plans which would successfully improve the overall resilience of this sector in urban flood-prone areas.

Model Komunikasi dalam Mitigasi Bencana Banjir di Kabupaten Kerinci dan Sungai Penuh

This article discusses the problems of development communication in flood disaster mitigation in Kerinci Regency and Sungai Penuh City. Kerinci Regency with its mountainous topography and extensive river network often experiences flood disasters, which have worsened in recent years. This study aims to explore an effective communication model in reducing the impact of flooding and improving community preparedness. Using a literature study method, this study highlights the importance of two-way communication between the government and the community, community-based education, and the use of modern technology such as social media and mobile applications. The results of the study indicate that a participatory communication model that actively involves the community and a technology-based early warning system can improve disaster mitigation and community response. This article provides a more inclusive communication approach, involving various stakeholders, to strengthen community resilience to future disasters.