Annual Flood Research Articles

Sylvia Doe and the 100-Year Flood by Robert Beatty (review)

Sylvia Doe and the 100-Year Flood by Robert Beatty (review)

An Impact-Based Flood Forecasting System for Citizen Empowerment

This work addresses the critical issue of flooding, a significant natural hazard, consistently ranked highest in the 2023 World Risk Index. The annual onslaught of tropical cyclones and the associated abnormal rainfall threaten lives, and destroy crops and property, thereby causing great economic damage, demanding urgent and science-based decision-making. We introduce an impact-based flood forecasting system as a proactive anticipatory action (i.e., AA) measure, linking climate services and disaster risk management to mitigate extreme weather impacts. Developed by the University of the Philippines Resilience Institute, the National Operational Assessment of Hazards (NOAH) Center, and Gerry Bagtasa of the Institute of Environmental Science and Meteorology, this system advances disaster resilience efforts, leveraging science-based forecasts to prioritize vulnerable barangays (villages). Unlike traditional early warning systems, the proposed automated system predicts flooding one day in advance and assesses exposure levels based on population distribution. By utilizing global weather forecast models locally calibrated for Philippine conditions and 100-year rain return flood hazard maps for the Philippines, the system forecasts river inundated areas, enabling local government units (LGUs) and humanitarian organizations to prioritize preparations for communities and allocate resources during flooding events effectively. This system enhances the efficiency of disaster response planning, fostering a proactive and impactful strategy to address the recurring threat of flooding in the country. The system also underscores the role of open data in disaster resilience, exemplified by NOAH’s system to disseminating big data, which aligns with open governance principles. By encouraging transparency and stakeholder collaboration, data exchange is promoted, providing actionable information that fosters collaboration between the LGUs, humanitarian organizations, and other stakeholders. Cognizant of the importance of community engagement in disaster resilience, the newly developed impact-based flood forecasting system encourages community involvement by providing easily accessible information, which can be used to validate the forecasts based on local knowledge and experience. This research contributes to the urgent call for anticipatory action in the face of escalating extreme weather events globally.

Assessment of the extent of community preparedness to flood risk in Jigawa State, Nigeria

Jigawa State faces annual flood disasters that have resulted in significant loss of life and property, putting many communities at risk. This study aim to evaluate the preparedness for flood risk in Jigawa State, Nigeria. A sample of 666 respondents was selected for a self-administered questionnaire, with 601 fully completed. The collected data were analyzed descriptively to determine the preparedness index. (PI). From the result of the findings, a majority (77.7%) of the respondents agreed that the community is aware of flood risks. There was also a provision of a neighborhood directory (97.8%), local shelter (75.9%) and emergency contacts (55.4%) during floods. The PI (92.6%, 98.6% 84.1% and 85.1% respectively), are above the 65% threshold and hence considered ‘prepared’ in this regard; but, ‘somewhat prepared’ (PI of 61.7%) regarding awareness of flood disaster risk management strategies; ‘and unprepared’ (PI of 55.5%, 50.5%, 50.5%, 50.8% and 54.3% respectively) in terms of community organization of public education and training of flood risk reduction (66.6%); engagement on rehearsals/emergency drills (51.6%); good community-based warning system (51.4%); and a standalone system for property protection measures during floods (52.4%); as well as an active recovery plan (62.9%). It was recommended that proper embankment protection and water regulation outlets be established. Additionally, real-time flood forecasting and effective early warning systems should be developed. Communities should be assessed to identify flood risk zones before construction activities begin, or alternatively, resettlement to safer areas should be considered for those living in flood-prone regions.

Triple exposure: the geographic correlation between flood risk, climate skepticism, and social vulnerability in the United States

Abstract This study investigates the geographic correlation between flood risk, climate skepticism, and social vulnerability across the United States. Our results reveal a systematic underestimation of flood risk in the Federal Emergency Management Agency’s (FEMA) Flood Insurance Rate Maps, especially in Appalachia, parts of New England, and the Northwest. These three regions face two additional risks: high levels of social vulnerability and skepticism about climate change. Nationally, there is a statisically significant correlation (0.19, p < 0.005) between flood risk and climate change skepticism, which increases (0.28, p < 0.005) in regions with high FEMA undercounts and elevated flood risk. Climate change skepticism manifests as distrust in science, an underestimation of property and community risk, and a resistance to mitigation and adaptation efforts. Indicators of social vulnerability, such as poverty rates, physical disabilities, unemployment, households in mobile homes, and lack of vehicle access, are especially pronounced in Appalachia. Addressing this geographically-embedded triple exposure—flood risk, social vulnerability, climate change skepticism— requires strategies to enhance local resilience. These include revising the 100-year floodplain categorization in FEMA’s National Flood Insurance Program to better reflect climate change, conducting public education campaigns in vulnerable communities, and scaling-up financial assistance for flood mitigation and adaptation projects.

Measuring rising heat and flood risk along the belt-and-road initiative

China’s global infrastructure financing flagship, the Belt and Road Initiative (BRI) encompasses countries hosting over 60% of the global population and one-third of worldwide GDP. It is based mainly on long-term loans that will mature decades into the future, and timely repayments are only possible if they remain commercially viable. But despite its vast global scope, little is known about the climate risks that could imperil the operations of BRI projects over the next few decades, and, consequently, threaten their long-term sustainability. We narrow this gap by estimating the impacts of future climate change on 217 BRI projects across 70 countries and 9 sectors in two dimensions. First, the effects of increased heat stress on human physical work capacity are calculated using the wet bulb globe temperature and an assessment of the workload for each selected BRI project. Second, the potential structural damages from more frequent flooding incidents are measured by return period (RP), where a shorter RP signals heightened risk. Both have direct impacts on human productivity and infrastructural integrity, which are essential to maintaining the operational viability and financial stability of BRI projects. We compared projected changes on both measures for the mid- and late-twentieth centuries (2041–2060 and 2081–2100) to the historical baseline (1981–2010). We found that BRI projects face escalating vulnerability to climatic risks on both counts. The results underscore a broad variance across different future carbon emission scenarios measured under the Shared Socioeconomic Pathways (including SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). BRI’s aggregated climatic risks are substantially elevated under a high carbon emission scenario compared to a low emission scenario. By the end of the twentieth century, labor workability under SSP3-7.0 (31%) could potentially decrease three times more compared to SSP1-2.6 (10%). Under an intermediate-emission scenario (SSP2-4.5), floods with a historical return period of 10 years could have a return period of 5 years in the future. Significantly hampering the utilization and economic return generation potential of infrastructure projects. In addition, regional geography contributes to risk heterogeneity, with 100-year floods occurring every 15 years in South Asia and every 24 years in Sub-Saharan Africa. Such climate risk implications, potentially overlooked by development financiers, represent significant risks to the sustenance of the BRI, estimated to be worth $1 trillion.

Assessment of Climate Change Impact on Flood Hazard Zones

AbstractThere have been many destructive pluvial and fluvial floods in Poland and the projection of increasing flood hazards in the future is a reason of considerable concern. The maps of river hazard zones are changing over time, and understanding these changes is of primary importance for flood risk reduction and climate change adaptation. This article aims to assess the impact of climate change on the spatial extent and depth classes of flood hazard zones for a selected reach of the River Warta in the western part of Poland. To this end, we integrated the Soil & Water Assessment Tool (SWAT) hydrological model of the Warta River Basin with the 1D hydraulic model HEC-RAS of the selected reach. The climate change effect was quantified based on the coupled model simulations forced with bias-corrected projections from the EURO-CORDEX project. Flood hazard maps were developed for two townships along the River Warta (Oborniki and Wronki), three greenhouse gas concentration scenarios (one for the baseline scenario in the reference period, 1971–2000; one for RCP 4.5 and one for RCP 8.5, for the time horizon 2021–2050) and for three return periods (10-, 100- and 500-year floods). Based on the ensemble mean, the increase in the flooded area projected in the future is more pronounced for RCP8.5 than for RCP4.5. This unique combination of software and data enabled the transformation of climate change impact into the land surface part of the hydrological cycle and assessment of changes in flood hazard and opens the way to assess the potential increases in the economic losses in the future.

Understanding bi-modal seas, shingle beach response and flood risk at Hayling island, UK

Beach management through nourishment and annual recycling has been applied for over 37 years to manage flood and coastal erosion risk to 1700 properties at Eastoke, Hayling Island, UK. This form of natural flood risk management has proved successful in avoiding the annual flooding that blighted this area previously. However there have been unexpected storm events which have caused beach erosion and localised flooding. Using long-term nearshore wave datasets and state-of-the-art statistical methods, new multivariate extreme wave conditions were derived. The conditions were applied to simulate beach profile response, estimated bimodal overtopping rates and resulting flood inundation, then validated against recent storm events. The Eastoke beach was found to meet its original design criteria of a 0.5% AEP standard of protection for unimodal wave conditions. However, it experiences greater roll back erosion, wave overwash and therefore flood inundation under certain bimodal wave conditions, causing uncertainty around the future standard of protection.It remains unclear if the unexpected beach response at Eastoke resulted from increased storminess due to climate change, additional loading by mixed sea/swell events, increasing bimodal events, or the assumptions within the original bivariate analysis, leading to incorrect AEP attribution.Communicating these issues to practitioners and a non-specialist audience is challenging as we tend to oversimplify, despite every beach and storm being unique. Better understanding of mixed beaches both in situ and through parametric and numerical models would reduce uncertainty, to ensure communities are resilient to climate change.

Extracting Wetlands in Coastal Louisiana from the Operational VIIRS and GOES-R Flood Products

Visible Infrared Imaging Radiometer Suite (VIIRS) and Advanced Baseline Imager (GOES-R ABI) flood products have been widely used by the National Weather Service (NWS) for river flood monitoring, and by the Federal Emergency Management Agency (FEMA) for rescue and relief efforts. Some water bodies, like wetlands, are detected as water but not marked as permanent or normal water, which may result in their misclassification as floodwaters by VIIRS and GOES-R flood products. These water bodies generally do not cause significant property damage or fatalities, but they can complicate the identification of truly hazardous floods. This study utilizes the severe Louisiana flood event caused by Hurricane Ida to demonstrate how to differentiate wetlands from real-hazard flooding. Since Hurricane Ida made landfall in 2021, and there was no major flood event in 2022, VIIRS and ABI flood data from 2021 and 2022 were selected. The difference in annual total flooding days between 2021 and 2022 was calculated and combined with long-time flood frequency to distinguish non-hazard floodwaters due to wetlands identified from real-hazard floods caused by the hurricane. The results were compared with the wetlands from the change detection analysis. The confusion matrix analysis indicated an accuracy of 91.58%, precision of 89.97%, and F1-score of 76.63% for the VIIRS flood products. For the GOES-R ABI flood products, the confusion matrix analysis yielded an accuracy of 86.88%, precision of 97.49%, and F1-score of 75.21%. The accuracy and F1-score values for the GOES-R ABI flood products are slightly lower than those for the VIIRS flood products, possibly due to their lower spatial resolution, but still within a feasible range.

Flood frequency analysis in the lower Burhi Dehing River in Assam, India using Gumbel Extreme Value and log Pearson Type III methods

Flood is a widespread climate-related hazard with the potential to occur in almost any geographical location where fluvial processes are active and can occur due to the involvement of multiple factors. Flood frequency analysis (FFA) is considered a valuable hydrologic tool to know the magnitude and frequency of the recurrence of floods. In this research, we have examined the Lower Burhi Dehing River (LBDR) in Assam, India, which frequently experiences severe flooding due to its meandering course induced by heavy monsoon rainfall. Therefore, the present research aims to analyze past flood events and current trends and predict future flood frequencies using Gumbel’s extreme value distribution and Log Pearson Type III Method. In this particular investigation, 25 years of annual maximum peak discharge data from 1972 to 1997 was employed to examine the discharge records of LBDR. This research estimated the return periods of different flood magnitudes, quantifying the likelihood of future floods with varying degrees of severity. In this regard, the return periods are estimated at 2, 5, 10, 20, 50, 100, and 200 years. The study reveals that the largest flood event occurred in 1972, with a discharge of 1134.4 m3/s, and the smallest flood event occurred in 1997, with a discharge of 214.65 m3/s. The 2-year flood is a relatively common event with a 50% probability of occurring in any given year, characterized by a moderate discharge of 1384.90 m3/s and minor impacts of 0.3665. The 5-year flood, occurring with a 20% chance annually, brings a significantly higher water level of 2008.81 m3/s and moderate-to-severe impacts of 1.4999, potentially causing widespread flooding. In the 10-year and 50-year return period, which has a probability of returning 10% and 2% chance annually, the discharge will reach 2421.89 m3/s and 3331.01 m3/s, respectively. The outcome of this research will sustainably guide policymakers and environmental planners to mitigate flood hazards.

Flood Risks Analysis in Upton Upon Severn: Assessing the Baseline Conditions and Future Scenarios for 100 and 1000-year Flood Events with a 2050 Climate Change Perspective.

This paper investigates the flood risks in Upton upon Severn, a town in Worcestershire, England, which is prone to fluvial flooding from the River Severn. Historical events, such as the significant floods of 1947 and 2007, highlight the town’s vulnerability and underscore the importance of analysing current and future flood risks. This research uses a one-dimensional hydraulic model, using Flood Modeller and QGIS software, to assess baseline flood conditions and predict the potential impacts of severe and extreme flood events, including 1 in 100-year and 1 in 1000-year flood scenarios. Additionally, it examines the projected effects of climate change by incorporating a 20% increase in flood risk estimates for the year 2050. The model results reveal that the baseline scenario poses limited flood risks, with only 17 properties affected, and key infrastructure like the A4104 road remains passable. However, under the 1 in 100-year event, approximately 336 properties are at risk, with significant road blockages and a flooded area of 5.09 Km2. The 1 in 1000-year flood scenario predicts a peak flow of 1807 m3/s, threatening more properties and covering a larger area of land. When climate change is factored into the 1 in 100-year scenario, the risks become similar to those of the 1 in 1000-year event, with 500 properties and key infrastructure, including the fire station and primary school, at risk and the A4104 road becoming impassable. These findings provide vital insights for flood risk management, especially given the possible effects of climate change, and stress the need for long-term solutions to flooding.

Reconstructing 169 years of historical atmospheric and hydrologic conditions in the American River Watershed through the Watershed Environmental Hydrology Hydro-Climate Model system

The risk of more frequent and intensified extreme events is expected to increase with climate change. In semi-arid regions, particularly, both increased flooding and prolonged droughts pose a threat as the water resources system must be prepared for both types of extremes. To understand how future extremes may be altered, however, past extremes must be understood. For this reason, this study employs three physically based models to simulate the atmospheric, snow, and hydrologic conditions in the American River Watershed: the Weather Research and Forecasting Model and the Watershed Environmental Hydrology Hydro-Climate Model with the addition of a SNOW Module. Each model is individually calibrated and validated against either gauge data or data provided by the Parameter-Elevation Regressions on Independent Slopes Model for two 10-year periods before a full 169-year daily reconstruction is performed for the period from 1852 through 2020. To analyze the model performance under hydrologic extremes, three flood years (1997, 2006, and 2017) and two drought periods (1987–1992 and 2012–2016) are further analyzed. The reconstruction outputs are found to successfully simulate historical conditions at both daily and monthly scales. Results highlight the importance of fully accounting for the role of temperature in snow processes and the subsequent flow conditions. By understanding how the models perform under historical extreme conditions, future extreme events can be compared, and necessary adaptation plans can be conceived to ensure that the watershed is prepared for future wet and dry conditions. Additionally, the created reconstruction dataset is comprehensive, simulating conditions across the hydroclimate, at a finer spatial and temporal scale than is typically available from observation data alone. The dataset can be further analyzed to assess changes in the trends and the potential trajectory of extreme events within the American River Watershed.

Application of GRACE-based satellite estimates in the assessment of flood potential: A case study of Gangetic-Brahmaputra basin, India.

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson's lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods.

Changes in Magnitude and Shifts in Timing of the Latvian River Annual Flood Peaks

Changes in Magnitude and Shifts in Timing of the Latvian River Annual Flood Peaks

Multifaceted economic impacts of a 500-year flood on gateway communities of Yellowstone National Park

Disasters can have dramatic implications for gateway communities of National Parks, especially local economies that rely on park tourist visitation as their primary source of income. In June 2022, a 500-year flood event impacted Yellowstone National Park (YNP) and gateway businesses, both directly through infrastructure damages (property loss) and indirectly through park closures and post-disaster marketing. We used a combination of approaches to document and explore the relative importance of these diverse sources of economic impacts. First, structured surveys were used to evaluate economic impacts, from the perspective of individual business owners and community leaders in six gateway communities surrounding YNP. Average revenue loss was 48 % during peak tourist season across all communities but averaged 75 % for communities inaccessible to the park. Direct infrastructure damages to businesses were localized; however, even businesses sustaining property losses suggested more severe economic impacts were the result of loss of visitors from park closures and damaging media coverage. Using annual visitation and economic input-output model data, we developed a statistical model to estimate regional economic impacts from the flood. The model corroborated the results of our survey and suggested the flood led to a total loss of $156 million in visitor spending in gateway areas in 2022, exceeding economic losses from the COVID-19 pandemic in 2020. We present some guidelines for building economic resilience in gateway communities, including diversifying sources of income, building social capital, embracing tourist adaptations, and developing coordinated park-gateway-regional messaging and marketing to the media post-disasters.

A novel ANN-CA and MCDA integrated framework for predicting urban expansion and its implications on future flood risk, Accra Metropolis

Urban development in African countries significantly impacts environmental sustainability and city resilience, particularly in flood risk management. The Accra Metropolis, in particular, faces increasingly prevalent annual floods that disproportionately affect the urban poor living in informal settlements, resulting in significant loss of life and disruption of livelihoods. This study developed a GIS-based flood risk mapping framework that integrates artificial neural network (ANN) and cellular automata (CA) modelling with multi-criteria decision analysis. This hybrid approach was employed to predict flood scenarios for the Metropolis in 2032 and 2042. The framework incorporated six hydro-geomorphological indicators influencing extreme events: LULC, slope, elevation, drainage density, soil type, and proximity to rivers. The study analysed LULC projections, revealing a trend of substantial urban expansion with an estimated 10.9% increase in built-up areas within the next 20 years. This growth is expected to significantly heighten future flood risk in both the central upstream and downstream portions of the Metropolis, particularly in low-lying informal settlements close to the Odaw River and Korle Lagoon. Model performance was validated by ROC curve analysis, with AUC values of 0.927 and 0.922 for 2032 and 2042, respectively, which resonates with the records of previous flood distribution studies of the area. This study highlights the crucial need for sustainable urban development, improved infrastructure, and proactive flood mitigation measures in Accra to assist urban planners, policymakers, and stakeholders in managing flood risks effectively.

Social dimensions of climate‐induced flooding in Jakarta (Indonesia): The role of non‐point source pollution

AbstractBecause of its low‐lying location, urbanization, and inadequate infrastructure, Jakarta (Indonesia) has experienced an increase in annual flooding events, rising from an average of five significant floods per year in the 1990s to over 20 annually (2010–2020). With climate change exacerbating extreme weather events, Jakarta encounters escalating risks of flooding. Although the recurrent flooding is exacerbated by non‐point source (NPS) of pollution such as urban runoff and agricultural discharge that contribute to 40% of total pollutants leading to flood‐related issues in Jakarta, none has investigated this research gap. To reflect its novelty, this work explores the implications of climate change on the annual flooding in Jakarta by focusing on NPS and analyzes their impacts from social perspectives. This work also underscores the implications of flooding on livelihoods, health, and social cohesion in Jakarta. Focus group discussion with affected residents was used to shed light on the coping strategies employed in response to recurrent floods, ranging from community‐based initiatives to reliance on informal networks. The empirical findings show that the implications of flooding extend beyond physical damages. Displacement of communities, loss of livelihoods, disruption of essential services, and increased health risks are among the social impacts experienced by local residents. Vulnerable populations, including low‐income communities residing in informal settlements, bear their consequences. Economic losses from flooding amount to USD 500 million annually, impacting over 1 million residents. However, recent interventions have led to a 15% reduction in peak flood levels and a 20% reduction in flood duration in affected areas. Community resilience has also improved, with a 25% increase in flood insurance coverage and a 20% rise in community response initiatives. Overall, this study highlights that climate change exacerbates annual flooding in Jakarta, significantly impacting vulnerable communities through NPS pollution. Addressing the challenges requires integrated approaches combining effective pollution control, resilient infrastructure, and community engagement to mitigate social and long‐term environmental impacts.Practitioner Points Climate‐induced flooding disproportionately affects vulnerable communities in Jakarta. Non‐point source pollution from urban runoff contributes to the severity of flooding in Jakarta. Waterborne diseases, disruption of livelihoods, and reduced access to clean water are major concerns identified in the study. The study highlights the importance of community‐based adaptation strategies to mitigate the impact of flooding and pollution.

Identification and characterization of dominant seepage channels in oil reservoirs after polymer flooding based on streamline numerical simulation

Reservoir heterogeneity, water-oil mobility ratio, cementation degree, and strong injection and production significantly influence long-term waterflooding and polymer flooding processes in oil reservoir development, leading to the formation of low-resistance dominant seepage channels in the lower part of thick oil layers without internal laminations. Understanding the spatial distribution and evolution of dominant seepage channels in oil reservoirs during waterflooding and polymer flooding is crucial for improving reservoir development and recovery rates. It uses a streamlined numerical simulation method to quantitatively show the spatial and temporal distribution of dominant seepage channels in the oil reservoir. A model and chart were created to demonstrate the relationship between permeability and throat radius, capturing changes in reservoir permeabilities over time. The study developed a comprehensive mathematical model to identify dominant seepage channels, considering parameters such as inter-well flow ratio, injection efficiency, permeability change value, and water saturation. Research findings indicate a 14.8 % increase in reservoir permeability after polymer flooding, with slight changes in porosity and a 20.3 % decrease in clay content. The established mathematical model and identification criteria were used to comprehensively quantitatively evaluate flow channels, describing the distribution characteristics and evolution process of dominant seepage channels in the experimental area over four periods: pre-polymer flooding, low water-saturation period during polymer flooding, post-polymer injection, and subsequent 10 years of water flooding. Both polymer flooding and subsequent water flooding have significantly altered the distribution pattern of predominant fluid pathways: 254 well layers now demonstrate robust predominant fluid pathways (19 %); another 272 well layers feature moderately predominant fluid pathways (22 %); while an additional 440 well layers display weaker predominant fluid pathways (approximately 35 %). There has been a noticeable increase in both weak and moderate predominant fluid pathways, which are now more widespread in the area. However, localized occurrences of strong predominant fluid pathways persist with no significant change in their distribution characteristics. Analysis at four different time points shows a clear trend of increasing intensity as we move from polymer to subsequent water flooding in this decade. Additionally, examination along an axis from upper left to lower right reveals a linear progression that indicates clear connections among different categories of dominant seepage channel. The verification results show a strong correlation between the calculated dominant seepage channel distribution and actual water-injection profile test results, indicating that the method effectively identifies dominant seepage channels in oil reservoirs after polymer flooding and has the potential to enhance oil recovery further.

A data-driven framework for enhancing coastal flood resilience in resource-crunched developing nations

A comprehensive Flood Resilient Scenario Model ‘FReSMo’ employs a data-driven, evidence-based approach for assessing climate-induced flood risk and validating the efficacy of mangroves (as context-specific adaptation measure) in reducing residential building damage. Based on an improvised Source-Pathway-Receptor-Consequence-Evidence concept, FReSMo employs a three-step analysis. First, hazard mapping estimates coastal flood extents for various return period under different Shared Socioeconomic Pathways. Second, the model maps the exposure of residential buildings to these flood extents by projecting built-up area for 2050 using the FUTURES model, based on physiographic, socio-demographic, and economic parameters. Finally, a data-driven probabilistic damage model is applied to estimate the built-up area damage for a 100-year coastal flood event (SSP-2.6). The pre-and post-adaptation damage estimate demonstrates the efficacity of NBS (mangroves) in reducing coastal flood risk. A 100 m mangrove patch on the Sagar coastline reduced the building damage cost by 70% for a 48-hr and 75% for a 24-hr flood. Considering a plantation cost for 6.2 km2, the total benefit, despite persistent losses, was a 222% return on investment. FReSMo transcends conventional risk assessment frameworks by offering a comprehensive approach for evaluating the cost-effectiveness of adaptation investments in developing countries, making it an invaluable tool in the face of climate change.

Analysis of Hydraulic Characteristics and Acquisition of Flood Management Data Using D-HSI in Natural Streams

Natural disaster prevention requires the maintenance of the proper function of river flow during droughts and heavy rains caused by abnormal weather as well as the minimization of damage via rapid and accurate predictions. This study analyzed hydraulic characteristics by utilizing D-LiDAR (Drone LiDAR) topographic data and D-HSI (Drone Hyperspectral Image) data to quickly and accurately collect and analyze related data. The HEC-RAS model was used to compare and analyze the results obtained via the D-LiDAR topographic data of this study, the roughness coefficient of the river master plan, and the results of applying parameters by adding NDVI. As a result, in the case of a 100-year frequency flood discharge, the average velocity decreased by an average of 2.23 m/s when applying the resistance coefficient by area, based on NDVI. Moreover, the flood level increased by an average of 0.30 m, and the cross-sectional area of ​​the flow capacity increased by an average of 20.17 m2. These results confirms that vegetation has a significant influence on the flow characteristics of natural streams. Hence, the data construction and analysis method used in this study can provide useful information regarding the rapid measurement and analysis of river flow characteristics during floods as well as and the development of prediction techniques and warning-related systems for drought and flood prevention.

A Comparative Study on 2015 and 2023 Chennai Flooding: A Multifactorial Perspective

Floods are highly destructive natural disasters. Climate change and urbanization greatly impact their severity and frequency. Understanding flood causes in urban areas is essential due to significant economic and social impacts. Hydrological data and satellite imagery are critical for assessing and managing flood effects. This study uses satellite images, climate anomalies, reservoir data, and cyclonic activity to examine the 2015 and 2023 floods in Chennai, Kanchipuram, and Thiruvallur districts, Tamil Nadu. Synthetic-aperture radar (SAR) satellite data were used to delineate flood extents, and this information was integrated with reservoir data to understand the hydrological dynamics of floods. The classification and regression tree (CART) model delineates flood zones in Chennai, Kanchipuram, and Thiruvallur during the flood years. The study region is highly susceptible to climatic events such as monsoons and cyclones, leading to recurrent flooding. The region’s reservoirs discharged floodwaters exceeding 35,000 cubic meters per second in 2015 and 15,000 cubic meters per second in 2023. Further, the study examines the roles of the Indian Ocean Dipole (IOD), which reached its peak values of 0.33 and 3.96 (positive IOD), and El Niño in causing floods here. The complex network of waterways and large reservoirs poses challenges for flood management. This research offers valuable insights for improving the region’s flood preparedness, response strategies, and overall disaster management.