Natural Hazards Research最新文献

Bangladesh is highly vulnerable to the adverse impacts of extreme sea levels (ESLs) because of its geographical location with low-lying coast. In addition, high discharge of huge rain water to the nearby coast may influence the ESL. The effects of different meteorological forcings like atmospheric pressure, wind-induced wave, and surges to ESL have been investigated intensively around Bangladesh. However, the role of surface rainfall to the ESL along the coast of Bangladesh remains unknown. In this study, the role of surface rainfall to the ESL was investigated for Cox's Bazar area along the eastern coast of Bangladesh. The ESL events were selected by applying the threshold of 500 ​mm height. The variations in SLA during seven days in prior to the ESL was predicted by multivariate regression using selected climatic variables of rainfall, sea level pressure, and wind. It was revealed that the prediction of ESL considering the contribution of rainfall outperforms the predictions without rainfall. The significant contribution of rainfall for prediction of ESL at Cox's Bazar, reflecting the hilly landscape at Cox's Bazar where a clear response of high surface runoff is expected. The findings suggest that the rainfall have an important effect to the ESL change along the eastern coast of Bangladesh. Therefore, incorporating rainfall effect is essential for better prediction of the ESLs which helps coastal management and reduction of hazards.

Social vulnerability assessment is a dynamic process, which varies from place to place. In the present study, the social vulnerability index (SVI) of Malda district has been prepared because of several impacts of flood inundation. The flood inundation layer has been generated using multi-temporal remote sensing data. The flood inundation layer is prepared from real-time Synthetic Aperture Radar (SAR) data. For social vulnerability assessment, the most efficient indicators are household composition, age & sex composition, and underprivileged population (SC& ST). Economic and educational data has been collected from the Census of India Handbook 2011. All these data are combined with the district's village database on the GIS platform. The weightage overlay analysis method is applied to generate the social vulnerability index of the study area, where the multi-influencing factor (MIF) technique has been used for determining the influencing factors. The social vulnerability index has categories into Very High (4%), High (37%), Moderate (32%) and Low (27%). The social vulnerability index is being further intersected with the flood inundation layer to build a database for the most vulnerable area of this district. It has been observed that 70 villages are in Very High zones, 662 villages are in High, 578 villages are in Moderate and 479 villages are in Low zones. This study will help the disaster manager and stakeholders about the vulnerable situation of the study area and also depict the importance of geospatial techniques in disaster management.

Liquefaction can cause significant damage to the built environment; therefore, assessing the liquefaction hazard in a seismically active region is essential to minimize the risk. This study attempted to evaluate the liquefaction potential of the south-central coastal areas of Bangladesh by calculating the liquefaction potential index (LPI) considering a scenario earthquake of M_w ​= ​7.5 having a peak ground acceleration of 0.15g. For calculating LPI, both standard penetration test blow count (SPT-N) and shear wave velocity (V_s) data have been used in this study. The results show that the study area's LPI values vary from 0 to 37. A liquefaction hazard map is prepared for the area using the calculated LPI values from V_s data shows about 8% of the study area is very highly susceptible to liquefaction hazard, whereas 62% of the area falls under high hazard-prone area while about 28% and 2% area of the study have respectively low (0<LPI ≤5) and very low (LPI ​= ​0) liquefaction potentiality. In addition, after analyzing the study area's fluctuating groundwater level (GWL) during the last 20 years, it has been observed that the GWL is likely to rise, thereby intensifying the potentiality of liquefaction hazards in the future. The outcome of this study will help engineers, urban planners, and policymakers to prepare a risk-sensitive land use plan and to develop a robust earthquake emergency response plan to reduce seismic risk.

Natural hazards often pose a considerable amount of social vulnerability which is the function of exposure, sensitivity and adaptation. Livelihood vulnerability assessment (LVA) benefits site-specific resilience building and disaster management. There are two popular indices of LVA – (1) the livelihood vulnerability index (LVI) of the Intergovernmental Panel on Climate Change (IPCC, 2007) and another method by (2) Hahn et al. (2009). The study intends to reveal the index that is more suitable to address the flood-induced livelihood vulnerability of the rural communities of the Mayurakshi river basin, India. To this end, the nature of exposure, adaptive capacity and sensitivity involving 35 parameters are measured mainly based on the primary data collected from a questionnaire survey executed over 2382 households spreading over 43 villages from five community development (C.D.) blocks. Moreover, the annual flood reports, district census reports, topographical maps, and satellite images are used as secondary data. The result shows that as per Hahn et al.’s LVI, Khargram (LVI- 0.41) is the most vulnerable block while Nabagram (LVI- 0.35) is the least vulnerable block. However, according to IPCC-LVI, Bharatpur-I has the highest LVI (0.02) and Burwan has the lowest LVI (-0.09). It is observed that exposure exhibits a strong positive correlation with IPCC-LVI and adaptive capacity also maintains a similar correlation with Hahn et al.’s LVI. Interestingly, Kandi is the most exposed block (score: 0.59) with a high adaptive capacity (score: 0.47) resulting in its exclusion from the high LVI category of both methods. This comparative performance assessment underscores the significance of the work before the decision-makers in preparing microscale disaster management plans.

The country wide lockdown implemented during 27th April to 14^th June 2021 in order to prevent the spread of COVID-19 during the second wave in India. Effect of the restricted lockdown resulted in improved air quality. This study focuses on analyzing the spatio-temporal distribution analysis of major air pollutant concentration over Bangalore city in India. The inverse distance weighting (IDW) method is implemented for the spatial analysis in order to quantify the distribution of the pollutant concentrations at each location in the Urban city of Bangalore. The research considers the distinct periods of pre-lockdown and lockdown during the second wave of COVID-19 pandemic in 2021 to investigate the impact of reduced human activities on air quality over the city. The study mainly utilizes the air pollution data collected from Central Pollution Control Board (CPCB) monitoring stations across Bangalore, including measurements of pollutants such as PM_2.5, PM₁₀, O₃, NO₂, SO₂, and CO. The IDW method is implemented to create the high-resolution pollution concentration maps for both the pre-lockdown and lockdown periods. This spatial distribution provides valuable insights into the variations in the pollution levels though out the Bangalore city. The comparative analysis of the concentration maps reveals significant changes in air pollution levels between the two periods; similarly, the temporal weekly average analysis also witnessed negative anomalies during the lockdown weeks. The results indicate substantial reductions in pollutant concentrations during the second wave COVID-19 lockdown, attributed to decreased vehicular emissions, industrial activities, and construction operations. The pre-lockdown period serves as a baseline for assessing the improvements in air quality during the lockdown. The spatio-temporal modeling approach enhances our understanding of the distribution patterns of air pollutants across the Bangalore metropolitan city. The findings underscore the potential benefits of implementing sustainable strategies to maintain improved air quality even after the pandemic subsides.

Somalia has experienced extreme flash floods in recent years across the arid regions causing tremendous loss of lives and properties. However, the flood magnitude, depth, and frequency of occurrence are not yet quantified. This is mainly due to scarce datasets in the area. . In this study, integration of observed and Climate Hazards Infrared Precipitation (CHIRP) satellite rainfall products and remote sensing raster data were used to improve hydrological model simulation outputs. The Hydrologic Engineering Center namely HEC-HMS and HEC-RAS models were used to simulate the rainfall-runoff processes and flood inundation, respectively. The land use, soil, slope and Digital Elevation map (DEM) were used to set-up the models and generate outputs. The HEC-HMS model calibration results depict that the model is able to reproduce the observed streamflow The simulated flows generated by the model predicted good agreement with the observed flow with values of 0.79, 0.74, 0.78, and 0.78 evaluated through the Nash and Sutcliffe Efficiency (NSE), Runoff Volume Error (RVE), coefficient of determination (R²), and percentage error of peak flow (PEPF), respectively. The HEC-RAS model result indicates that the maximum flood depth and velocity were obtained at the floodplain area. The peak flood at 50, 100, and 200-year return period using General Extreme Value (GEV) distribution revealed 384m³s^-1, 409m³s^-1, and 434m³s^-1, respectively. The 100-year peak flood discharge in a specific part of the river revealed a flood depth of 7.53m. The provision of Levees as mitigation measures revealed reduction of the flood extent by 35% and suggested as possible flood protection measures for the study area.

The incidence of flooding is set to rise due to climate impacts in the coming years. Nigeria is one of the countries increasingly experiencing flooding. Its urban areas are expected to suffer more from the impacts of flooding due to the concentration of economic activities therein and projected population growth. To sustainably manage flood risks, there is a growing call to incorporate indigenous knowledge and practices in contemporary flood risk management. This work engaged with experts working in the field of flood risk management in public institutions to understand if indigenous knowledge and methods could positively inform modern flood risk management in Port Harcourt, a major flood prone Nigerian city. It finds that the applicability of indigenous knowledge in contemporary flood management in the research location is limited given the poorly managed transformation, growth, and evolution the city has experienced over time. However, some practice rooted in Indigenous knowledge and practices like planting of certain trees and mangrove species still have utility today. The government is encouraged to halt reclamations and conversions of wetlands and instead, seek ways of restoring and bringing back these important ecosystems given their natural role in flood mitigation and control. Developing urban forests can also play an integral role in managing rainwater runoff while improving the overall environmental quality.

This study is modeling flood susceptibility in Jhelum River Basin by employing Geo-Morphometric ranking approach. The study area is located in the Monsoon region and exposed to heavy rainfall events. Every year monsoon rainfall and melting of snow and glaciers together generate flash floods in the upper catchment and then riverine flood in the low-lying areas. Watershed modeling approach is used to delineate the Jhelum watershed and its sub-watershed. Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) is utilized as input data in ArcMap software environment. Then Geo-Morphometric ranking model is applied to rank the susceptibility of each sub-watershed to flood. The analysis showed that geo-morphometric characteristics vary from basin to basin. There are 44 sub-basins which cover area less than 1000 ​km². The area of sub-basins ranges from 47.68 ​km² to 7667.77 ​km². Based on our results, area of sub-basin directly impacts flood susceptibility. Geo-morphometric parameters such as gradient, relief and geology of basin such as type of bed rock govern the drainage pattern. The results of the study can assist line agencies and concerned government departments to design location specific flood risk reduction strategy.

Karnataka state situated in the southern part of India is surrounded by a long coast line and western ghat mountain regions on the western side. The rainfall distribution shows a large variability in different parts of the state. North interior part in the state receives less rainfall as compared to the south interior region, which receives moderate rainfall. Although coastal Karnataka witnesses a high rainfall and is one of the most important regions in the country, in recent years the state has faced drought because of less rainfall which may be attributed to the regional climate change. Because of the decreasing trend in rainfall almost all the important sectors like agriculture and water resources have been affected a lot in Karnataka. In this work, assessment of the spatial and temporal distribution of monsoon rainfall is presented in terms of the climatology, variability, trend etc. The time series analysis also indicated the increasing trend of rainfall as a whole but the trend in recent decades is very less as compared to the long term. The El-Niño Southern Oscillation (ENSO) and the regional rainfall pattern relation is also explored. The increase in heavy rainfall in recent times is observed from the analysis. This study can be useful in addressing regional climate change, understanding the local and large scale variability impacts and in can be a good input for preparing the policy formulation and pro-active disaster management.