High Flood Research Articles

A comprehensive study of floodplain analysis utilising HEC-HMS, HEC-RAS, and GIS on the Kosasthalaiyar River sub-basin

ABSTRACT The present study focused on the Kosasthalaiyar River basin in Chennai, Tamil Nadu, India. We analysed the 2015 peak flood and forecasted the feature data in the representative concentration pathways (RCP) 4.5 scenario for various years. We used the scientific data management system (SDSM) software to downscale the Geophysical Fluid Dynamics Laboratory (GFDL) 2.0 general circulation models (GCMs). According to hydrograph analysis, 142.7, 75.31, 461.73, and 248.22 mm runoff can occur in 2030, 2050, 2080, and 2100, respectively. The current study estimates probable peak flows by performing floodplain analysis on the Kosasthalaiyar River sub-basin using the Hydrologic Engineering Centre's Hydrologic Modelling System (HEC-HMS), the Hydrologic Engineering Centre's River Analysis System (HEC-RAS), and geographic information system (GIS) tools. It is possible to observe that the two major peak floods, measuring 581.6 and 110.7 m3/s, respectively, will occur on 28 November 2030 at 10:20 a.m. and 12 December 2050 at 9:20 a.m. Additionally, high floods of 997 and 1,438.4 m3/s can be recorded on 20 December 2080 at 9:50 a.m. and 29 November 2100 at 9:40 a.m., respectively.

Exploring Spatio-Temporal Dynamics of Future Extreme Precipitation, Runoff, and Flood Risk in the Hanjiang River Basin, China

The hydrological cycle is altered by climate change and human activities, amplifying extreme precipitation and heightening the flood risk regionally and globally. It is imperative to explore the future possible alterations in flood risk at the regional scale. Focusing on the Hanjiang river basin (HRB), this study develops a framework for establishing a scientific assessment of spatio-temporal dynamics of future flood risks under multiple future scenarios. In this framework, a GCMs statistical downscaling method based on machine learning is used to project future precipitation, the PLUS model is used to project future land use, the digitwining watershed model (DWM) is used to project future runoff, and the entropy weight method is used to calculate risk. Six extreme precipitation indices are calculated to project the spatio-temporal patterns of future precipitation extremes in the HRB. The results of this study show that the intensity (Rx1day, Rx5day, PRCPTOT, SDII), frequency (R20m), and duration (CWD) of future precipitation extremes will be consistently increasing over the HRB during the 21st century. The high values of extreme precipitation indices in the HRB are primarily located in the southeast and southwest. The future annual average runoff in the upper HRB during the near-term (2023–2042) and mid-term (2043–2062) is projected to decrease in comparison to the baseline period (1995–2014), with the exception of that during the mid-term under the SSP5-8.5 scenario. The high flood risk center in the future will be distributed in the southwestern region of the upper HRB. The proportions of areas with high and medium–high flood risk in the upper HRB will increase significantly. Under the SSP5-8.5 scenario, the area percentage with high flood risk during the future mid-term will reach 24.02%. The findings of this study will facilitate local governments in formulating effective strategic plans for future flood control management.

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.

Exploring a GIS-based analytic hierarchy process for spatial flood risk assessment in Egypt: a case study of the Damietta branch

Floods are the most common and costly disasters worldwide, while spatial flood risk assessment is still challenging due to fewer observations and method limitations. In this study, the flood risk zonation in the Nile districts of the Damietta branch, Egypt, is delineated and assessed by integrating remote sensing with a geographic information system, and an analytical hierarchy process (AHP). Twelve thematic layers (elevation, slope, normalized difference vegetation index, topographic wetness index, modified normalized difference water index, topographic positioning index, stream power index, modified Fournier index, drainage density, distance to the river, sediment transport index, and lithology) are used for producing flood susceptibility zonation (FSZ) and six parameters (total population, distance to hospital, land use/land cover, population density, road density, and distance to road) are utilized for producing flood vulnerability zonation. Multicollinearity analysis is applied to identify highly correlated independent variables. Sensitivity studies have been used to assess the effectiveness of the AHP model. The results indicate that the high and very high flood risk classes cover 21.40% and 8.26% of the area, respectively. In 14.07%, 27.01%, and 29.26% of the research area, respectively, flood risk zones classified as very low, low, and moderate are found. Finally, FSZ is validated using the receiver operating characteristics curve and area under curve (AUC) analysis. A higher AUC value (0.741) in the validation findings demonstrated the validity of this AHP approach. The results of this study will help planners, hydrologists, and managers of water resources manage areas that are susceptible to flooding and reduce potential harm.

Enormous headward and gully erosion in a floodplain area reclaimed for open-cast lignite mining during the July 2021 flood in the Inde River valley (Western Germany)

BackgroundThe July flood 2021 at the mountain front of the Eifel-Ardennes Mountains and their foreland resulted in the flooding of the lignite mining area of Inden in Germany. The mining activities resulted in large-scale anthropogenic changes to the relief and fluvial system, leading to a landscape that is no longer adjusted to the recent process-response system. This paper concentrates on the Inde River, where lignite mining led to the relocation of a 5-km-long river section. The flood event resulted in the temporary avulsion of the Inde River into the former channel and, ultimately, in the flooding of the open-cast lignite mine Inden.ResultsThe flooding of the open-cast lignite mine Inden led to headwall erosion and enormous sediment mobilisation, mobilising more than half a million cubic metres of sediment within a few hours, forming a 700-m-long deeply incised channel cut. Thereby, the underlying bedrock, near-subsurface man-made structures, and former river channels influence the erosional processes to different degrees. Surface erosion is likely to be the decisive process, and subsurface erosion is likely to play a secondary role. In both cases, former channels and mill ditches were likely impacting the course of erosional processes.ConclusionsDuring high flood events open-cast mining sites in floodplains are endangered by enormous erosion and sediment transport within a short period of time (several hours). Understanding such complex erosion and depositional processes in open-cast mining areas could provide a blueprint for geomorphological processes and hazards in these anthropogenically shaped fluvial landscapes. Further, information on historic impact in the area is crucial to estimate potential risks.

Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios

Flash floods are highly destructive, and their frequency and intensity are expected to escalate due to climatic changes. This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. To predict FFS, we evaluated twelve flood-influencing variables: elevation (EL), slope (SL), aspect (AS), drainage density (DD), distance to stream (DS), topography roughness index (TRI), stream power index (SPI), topographic wetness index (TWI), soil permeability (SP), precipitation (PR), land use and land cover (LULC) and normalized difference vegetation index (NDVI). Earth observation data, field surveys, and past flood records were used to create a detailed flood inventory. Among the machine learning models tested, the random forest (RF) algorithm outperformed others, including support vector machine (SVC), logistic regression (LR), and extreme gradient boosting (XGBoost), and was subsequently used for flood susceptibility mapping based on future precipitation projections under two Sixth Coupled model intercomparison project (CMIP6) climate change scenarios: SSP1-2.6 and SSP5-8.5. Our findings indicated that the areas at high to very high risk of flooding are projected to increase significantly under both the SSP1-2.6 and SSP5-8.5 scenarios. Initially, around 38 % of the studied region had high to very high flood susceptibility, but this is expected to rise to 40–42 % over the projected time periods. These spatial delineations of flood-prone areas can provide guidance for developing effective mitigation and adaptation strategies to address the adverse impacts of flash flooding in the hilly river basins of Bangladesh.

Does flood risk affect property prices? Evidence from a property-level flood score

One in six properties in England is exposed to flood risk and around half of those affected properties can be characterised as high risk. In this paper we examine whether the probability of flooding is capitalised in England's property market prices. We use unique property-level data from Rightmove, UK's no.1 property website and the property-level FloodScoreTMby Twinn, Royal HaskoningDHV. The latter is a metric of objective flood risk based on the likelihood of an individual property being flooded due to rainfall, overflowing rivers and tidal surges and is commonly used by UK lenders. By comparing the unconditional averages of our data, we find that properties at risk are sold at an 8.14 % discount compared to non-exposed properties, and the price discount increases to 32.2 % for properties with very high flood risk. By 2080 the flood events are expected to become more frequent and the average flood risk is projected to increase by 8 %. Our empirical model suggests that one percentage point increase in properties' flood risk is associated with a decline of 0.07 % to 0.11 % in both sold and asking property prices. The impact is higher for properties of which flood risk is expected to increase or for regions that have recently experienced a flood event.

Green spaces and flood vulnerability in urbanising regions: a case study in the Philadelphia metropolitan area

ABSTRACT Flooding continues to be one of the most significant natural hazards, disproportionately impacting vulnerable populations in urbanising regions. This study aims to examine the relationship between green space density and flood vulnerability in the Philadelphia Metropolitan Area by integrating social and geophysical data. The research focuses on identifying areas where high flood vulnerability coincides with low green space availability, as these are critical regions for targeted intervention. Using social vulnerability indicators such as income, age, and housing characteristics, combined with geophysical factors like soil permeability and drainage density, the study assesses flood vulnerability across census block groups. Local Indicators of Spatial Association (LISA) and discriminant analysis were employed to detect spatial clusters with elevated flood risks and low green space density. Results reveal that these high-risk clusters are concentrated in densely urbanised areas such as Norristown, where lower-income and elderly populations are overrepresented. These findings reinforce broader environmental justice concerns, as socially vulnerable communities face compounded risks from both inadequate flood protection and limited access to green spaces. The study emphasises the need for integrated flood management, combining green infrastructure with structural interventions, to guide urban planners and policymakers in reducing flood risks and promoting environmental equity.

Flood risk assessment under the shared socioeconomic pathways: a case of electricity bulk supply points in Greater Accra, Ghana

This study evaluates flood susceptibility and risk on Bulk Supply Points in the Greater Accra region (GAR) using a Frequency Ratio model based on 15 flood conditioning factors. The model explores the influence of natural, meteorological and anthropogenic factors on flooding occurrences under the Shared Socioeconomic Pathway (SSP) scenarios and assesses flood risks at Bulk Supply Points (BSPs). Flood susceptibility mapping was conducted for both current and future periods under various SSP scenarios. Results reveal that elevation, slope, soil type, distance from urban areas, and SPI are the most influential factors contributing to flooding susceptibility in the region. The current flood map, about 37% of the total area of GAR categorized under the moderate flood-susceptible zone category followed by about 30% categorized under the low flood-vulnerable zone. However, about 16% was categorized under the very high flood-vulnerable zone. The study projects increasing flood susceptibility under the SSP scenarios with intensification under SSP2 and SSP3 scenarios. For instance, the areas categorized as high and very high flood susceptibility zones are projected to expand to approximately 32% and 26% each by 2055 under SSP3. The study also assesses flood risks at Bulk Supply Points (BSPs), highlighting the escalating susceptibility of power assets to flooding under different scenarios. For instance, in the very high scenario, flooding is estimated to reach 640 h in 2045 and exceed 800 h in 2055—more than double the 2020 baseline. The analysis shows the bulk supply points face increasing flood susceptibility, with risks escalating most sharply under the severe climate change SSP3 and SSP5 scenarios. Over 75% of BSPs are expected to fall in the low- to medium-risk categories across SSPs while more than 50% of BSPs are within medium- to high-risk categories in all scenarios except SSP1, reflecting the impact of climate change. SSP3 and SSP5 stand out with over 60% of BSPs facing high or very high flooding risks by 2055. It indicates moderate resilience with proper adaptation but highlights potential disruptions in critical infrastructure, such as BSPs, during persistent flooding. The findings of the study are expected to inform Ghana’s contributions towards addressing Sustainable Development Goals (SDGs) 7, 11 and 13 in Ghana.

CO2 high-pressure miscible flooding and storage technology and its application in Shengli Oilfield, China

There are various issues for CO2 flooding and storage in Shengli Oilfield, which are characterized by low light hydrocarbon content of oil and high miscible pressure, strong reservoir heterogeneity and low sweep efficiency, gas channeling and difficult whole-process control. Through laboratory experiments, technical research and field practice, the theory and technology of CO2 high pressure miscible flooding and storage are established. By increasing the formation pressure to 1.2 times the minimum miscible pressure, the miscibility of the medium-heavy components can be improved, the production percentage of oil in small pores can be increased, the displacing front developed evenly, and the swept volume expanded. Rapid high-pressure miscibility is realized through advanced pressure flooding and energy replenishment, and technologies of cascade water-alternating-gas (WAG), injection and production coupling and multistage chemical plugging are used for dynamic control of flow resistance, so as to obtain the optimum of oil recovery and CO2 storage factor. The research results have been applied to the Gao89-Fan142 in carbon capture, utilization and storage (CCUS) demonstration site, where the daily oil production of the block has increased from 254.6 t to 358.2 t, and the recovery degree is expected to increase by 11.6 percentage points in 15 years, providing theoretical and technical support for the large-scale development of CCUS.

Flood Susceptibility Mapping Using Analytic Hierarchy Process and Geographic Information System in Baitarani Basin of Odisha, India

Baitarani, the 6th largest river basin in Odisha, eastern India, faces water related challenges in its catchments. As one of the most frequent environmental disasters, floods are consistently cited in hotspots of the eastern region in India. Hence, in this study, flood susceptibility maps were generated for Baitarani River basin by using analytical hierarchy process technique and geographic information system. Eleven thematic maps of topographic ruggedness index, topographic wetness index, stream power index, elevation, lithology, soil, slope, drainage density, distance from drainage network, land use/land cover and normalized difference vegetation index were prepared for flood vulnerability mapping. The themes were assigned suitable weights depending on relative influence of each of the themes on flood risk. The results indicated that 87% of the basin falls under intermediate flood susceptibility zone, while 10% is classified under high flood hazard zone. Areas in the lower elevation catchment, particularly those with flat topography near the coastline, were identified as being highly susceptible to flooding. The impacts of severe flooding in the basin are most pronounced in specific blocks in Jajpur district (Jajpur, Dasarathpur, and Korei blocks), Bhadrak district (Dhamnagar and Bhandaripokhari blocks), and Anandapur block in the Keonjhar district of Odisha. Additionally, changes in land use/land cover during 1995-2020 revealed 8% decline in agricultural land and 13.93% increase in fallow land in the area. The outcomes of this research can help researchers, planners and managers in managing natural resources, find locations that could be vulnerable to flooding, and lessen the damage.

Risk perceptions and preventive preparedness toward anthrax re-emergence within the lens of one health in Northcentral Nigeria.

Anthrax has re-emerged in domestic ruminants in Nigeria with public health concerns. This study assessed stakeholders' perceptions and preventive preparedness toward further resurgence and spread at the human-animal-environment interface. A questionnaire-based cross-sectional study was conducted in Nigeria. Descriptive and analytical statistical analyses were performed at 95% confidence levels. All the 384 recruited stakeholders responded. The majority (96.2%) of animal health practitioners (AHPs) and 56.7% of pastoralists were aware of the recent anthrax re-emergence in Nigeria (p < 0.001). Noteworthy, 88.5% of the AHPs and 32.2% of pastoralists mentioned that anthrax has an environmental component in its transmission to humans and animals. From the environmental perspective, 87.7% of AHPs and 24.0% of pastoralists significantly perceived that soil and aerosol contamination with anthrax spores are highly plausible explanation routes for its re-emergence. Extreme weather events (high rainfall, flooding, winds, and drought) (p = 0.001); grazing of livestock on pastures grown on contaminated soil (p < 0.001), transboundary movement and trade of animals (p = 0.001); introduction of new animals into the herds without quarantine (p = 0.001); and bioterrorism (p < 0.001) were more likely to influence the re-emergence and spread of anthrax. To tackle gaps in knowledge and risk perceptions, and address the socio-economic and anthropogenic drivers, cooperation and collaborations through the lens of the One Health approach are needed. The partnership will promote an integrated disease surveillance system from planning to implementation for the realization of elimination or reduction of the burden of anthrax and other zoonoses in Nigeria and contribute to achieving food safety, food security, and public and ecosystem health.

Analyzing sedimentation patterns in the Naumure Multipurpose Project (NMP) reservoir using 1D HEC-RAS modeling

Naumure Multipurpose Project (NMP) featuring a 169 m high Concrete Face Rock Filled Dam (CFRD) is the proposed reservoir project in the West Rapti River with an installed capacity of 218.34 MW. Most of the rivers in Nepal carry significant sediment loads that will consequently catalyze reservoir sedimentation. This phenomenon prevails as the primary factor in reducing reservoirs useful life, making ineffective for both flood control and hydroelectricity generation. Ultimately, such process of sedimentation has adverse impacts on projects economic feasibility and long-term sustainability. Therefore, the objective of this research was to examine the expected sediment deposition pattern in the NMP reservoir throughout its operational period by employing 1D HEC-RAS model to simulate the sedimentation process. The model was simulated for 10, 20, 30, 40 and 50 years. Yang’s equation as a sediment transport function, Active layer as a bed sorting method and Toffaleti as a fall velocity method were best suited for the river reach. The delta deposition was formed between 11 km and 22 km upstream of the dam region in the Jhimruk river, with the sediment deposition depth reaching peaks of about 23 m, 38 m, 39 m, 41 m and 49 m in 10, 20, 30, 40 and 50 years, respectively. Similarly, the delta deposition was formed between 13 km and 33 km upstream of dam region in the Madi river, with the sediment deposition depth reaching peaks of about 47 m, 62 m, 60 m, 68 m and 75 m in 10, 20, 30, 40 and 50 years respectively. Headcutting of delta deposition occurred between 20 and 30 year due to high flood during low stage of reservoir. Furthermore, the study revealed that about 6.22%, 11.61%, 15.94%, 22.96% and 25.65% of the storage capacity of NMP reservoir will be depleted in 10, 20, 30, 40 and 50 years respectively.

A Study on Flood Hazard Zonation Mapping Based on GIS-driven Approach using Remote Sensing Data and Weighted Overlay Analysis (Woa) Model

Flood hazard mapping is crucial for identifying flood-prone areas and developing effective mitigation strategies. This study utilized a Weighted Overlay Analysis, which is one of the subjective model derived from Multi-Criteria Decision Support System (MCDA), to assess flood hazards in the Auranga watershed, India. Six key flood-inducing factors were selected, including topographic data such as elevation, slope, river distance, and flow length derived from remote sensing data of Digital Elevation Model and classified Land use/Land cover data, along with hydro-meteorological data of precipitation. Each dataset was standardized to a common scale to facilitate comparison and integration. Weights were assigned to the layers like elevation, slope, river distance, flow length, Land use/Land cover, and precipitation according to their significance in contributing to flood risk. These weighted layers were then combined using GIS tools like ArcGIS, resulting in a composite map that delineates areas with varying flood hazard zones. The final flood hazard map identified that approximately 914 sq. km (60.0%) of the study area was at high to very high flood risk, particularly in regions close to rivers, while around 97 sq. km (6.4%) exhibited very low flood hazard. The GIS-Weighted Overlay Analysis method proved effective in flood hazard zone mapping, especially when incorporating a greater number of parameters. This approach is widely recognized in environmental planning and risk assessment, especially in areas vulnerable to natural disasters like floods.

Investigation of quenching phenomena during the relooding phase against the FLECHT-SEASET experiment by using RELAP5/MOD3.3

The reflood model of RELAP5/MOD3.3 (patch 4) was assessed by using the FLECHT-SEASET tests. The tests were conducted to have a better understanding of the postulated loss of coolant accident in a light water reactor (LWR). The best-estimate system analysis code was used to simulate these accident scenarios, especially in Design Basic Accident such as Loss-Of-Coolant-Accident (LOCA). The primary purpose of this report was to assess the accuracy of a computer system analysis code by using RELAP5/MOD3.3 in comparison to actual test results taken from the FLECHT-SEASET tests in which the reflood model was built in. In RELAP5’s simulation cases, the various boundary and initial conditions, such as power supplied and reflooding rate were selected. As a result, the RELAP5 looked to be accurate in predicting the quenching time and rod surface temperature for this particular case. However, the RELAP5 code under-estimated the rod surface temperature in comparing with the experimental data of the FLECHT-SEASET tests. Accordingly, for this high flooding rate and particular reactor power level that the reflooding model in RELAP5 could be possible used for predicting the reflooding phenomena during the LOCA accident.

Flood hazard assessment using machine learning and hydrodynamic modeling: case study in the Vu Ga–Thu Bon basin in Vietnam

ABSTRACT The objective of this study was the construction of a theoretical framework to evaluate flood hazards by integrating machine learning and hydrodynamic modeling in Vietnam's Vu Gia–Thu Bon basin. MIKE FLOOD was used to simulate historical floods in 2017 in order to obtain flood depth and velocity. Support vector machine-stochastic gradient descent (SVM-SGD), decision trees (DT), and dagging (DA) were used to determine flood susceptibility. Flood hazard was constructed by combining the flood depth, velocity, and susceptibility using the analytic hierarchy process technique. The statistical indices AUC, RMSE, MAE, and NASH were used to evaluate the precision of the hydrodynamic and machine learning models. The results showed that hydrodynamic modeling was highly accurate, with a NASH value of 0.86. The proposed models achieved AUC values of 0.98 for SVM-SGD, 0.93 for DT, and 0.92 for DA. The results showed that 7.59% of the flood zones is located in the very low flood hazard zone, 108.2 km2 in the low flood hazard zone, 24.59% in the moderate flood hazard zone, 22.53% in the high flood hazard zone, and 10.01% in the very high flood hazard zone.

Geospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, Nigeria

Flooding remains a major environmental problem in many parts of the world including Nigeria, causing untold losses. This study modelled the flood susceptibility of Calabar Metropolis using frequency ratio and geographical information system (GIS). Direct field observation and key informant approaches were used to obtain 127 previous and current flood locations in the study area from which a database of historical flood occurrence was developed. Using the geostatistical analyst tool within a geographical information environment the flood locations were split into two parts in the ratio of 70:30 percent for the training and testing processes, respectively. Eight flood conditioning factors (elevation, slope, aspect, curvature topographic position index, topographic wetness index, land cover and normalized difference vegetation index) were extracted from SRTM-30m DEM and Landsat 8 data accordingly and used in the geospatial analysis of flood occurrence within the GIS. The frequency ratio model was then developed using the training dataset. The final product was the flood susceptibility map of the study area. The results revealed that 9.3 percent of the study area was considered to have very high flood susceptibility, 19.97 percent was classified as high flood susceptibility while 31.79 percent was under moderate risk area. About 39 percent of the study area was classified at least under low flood susceptibility. The order of contribution of the conditioning factors to the model were topographic position index (TPI), elevation, normalized difference vegetation index (NDVI), land cover, curvature, aspect, slope, and topographic wetness index (TWI). The validation of the model using the receiver operating characteristics (ROC) curve returned an AUC value of 74.81 percent. Hence, the result was acceptable for the prediction of flood locations in the study area. The findings would assist urban planners, environmental managers, risk reduction and management agencies, land developers, policy makers, and indeed the general public.

The changing social vulnerability in Shanghai during 2000–2020 and its implications for risk management

Risk dynamics are driven not only by changes in hazard and exposure, but also the changing social vulnerability along with urban development, aging, renewal, and adaptation planning. However, there has been limited research on the spatial and temporal changes in social vulnerability, particularly at a fine scale in urban settings. To bridge this gap, our study developed a framework to consistently evaluate social vulnerability. Using Shanghai as a case study, the framework was applied to assess the social vulnerability at a township level for the years 2000, 2010, and 2020. The results revealed that the social vulnerability index decreased in 99.56 % of the townships in Shanghai from 2000 to 2020. However, there were notable variations among the four primary sub-indexes as demographic sub-index, particularly regarding an aging population, displayed a strong increase aggravating social vulnerability, in contrast to an overall decline trend of the other three sub-indexes of educational, residential, and economic. Therefore, the social vulnerability reduced along with socioeconomic development while an aging society posed a new challenge. Furthermore, 32 townships were identified with a notable rising demographic sub-index, a high flood hazard, and a low capability of disaster reliefs, demonstrating social vulnerability should be comprehensively addressed aligning with other challenges. The study highlighted the need to consider the dynamics of social vulnerability in disaster risk reduction strategies and the importance of addressing emerging challenges, such as an aging population, in urban vulnerability.

The IoT-Based Early Warning System for Detecting High Tide Floods (ROB-EWS) in Tambak Lorok, Semarang Indonesia

Background and Objectives: The Tambaklorok fishing village area, located on the northern coast of Semarang City, Indonesia, is susceptible to sea level fluctuations, which often lead to tidal flooding and seawater encroaching into residential areas. Consequently, a seawater flood detector (rob) is needed for effective disaster mitigation for affected communities. This study aims to implement a disaster mitigation system by installing an IoT-based Seawater Flood (Rob) Early Warning System to reduce disaster risks and their impacts on the community. Methods: The IoT-based Early Warning System is designed to detect floods by monitoring sea level height parameters. Results: IoT-based devices measure sea level using ultrasonic sensors, with the readings processed by a microprocessor. The data is then periodically analyzed and transmitted to an application in real-time. This technology allows for real-time information dissemination through web applications and alerts in the form of sirens. These warnings enable residents to evacuate and move to safer areas. The siren alerts are categorized into two levels: Alert 1 indicates that seawater is approaching the land boundary, while Alert 2 signals that water has begun to enter residential areas. Additionally, designated evacuation zones will guide affected communities to safer areas. Conclusion: The installation of an IoT-based Early Warning System delivers real-time information to help mitigate the impact of rising sea levels (rob) in the Tambak Lorok fishing village community.

Enhancing Grid Resiliency in High Fire or Flood Risk Areas: Integrating Protective Relay Settings, Broken Conductor Detection, and Grid Hardening for Climate-Induced Event Preparedness

Enhancing Grid Resiliency in High Fire or Flood Risk Areas: Integrating Protective Relay Settings, Broken Conductor Detection, and Grid Hardening for Climate-Induced Event Preparedness