Characterising Spatial Patterns of Exposure, Sensitivity and Adaptive Capacity to Assess Heatwave Vulnerability of Bangladesh

Characterising Spatial Patterns of Exposure, Sensitivity and Adaptive Capacity to Assess Heatwave Vulnerability of Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Characterising Spatial Patterns of Exposure, Sensitivity and Adaptive Capacity to Assess Heatwave Vulnerability of Bangladesh Sworna Akter, Md Anarul Haque Mondol, Humaira Rahman Logna, Hafizur Rahman, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7752795/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Considering the effects of the climate change, the heat waves are becoming one of the most significant and least researched environmental hazards in Bangladesh, while determination of the areas most vulnerable to increased temperatures and frequent heat waves is necessary for preparing firm climate adaptation plans. This study explores Bangladesh's heatwave vulnerability by developing three composite indices related to heatwave vulnerability, including- exposure, sensitivity, and adaptive capacity, and was analyzed using district-level data. Exposure was measured using land surface temperature (LST) and population density, while sensitivity included the elderly, very young, and female population, illiteracy rate, built-up area, poverty, access to water, unemployment, occupation, and disability. Adaptive capacity was calculated using vegetation cover, water resources, and electricity access (proxied by average radiance of nighttime light). Using both spatial and statistical methods, the data were normalized and aggregated to obtain indices of heatwaves. Findings revealed significant regional disparities, with a high degree of exposure found in regions where high temperatures are combined with considerable population, such as Dhaka, Rajshahi and Chattogram, and the strongest sensitivity in Rangpur and coastal regions where the population is economically vulnerable. The country could be lacking infrastructures that could moderate the effects of the enormous heat in the north and south parts of the country. The statistical validity of differences in the indices of vulnerability across the country was confirmed by the use of Kruskal-Wallis H test. The variance of sensitivity and adaptive capacity was large, with the distribution of the exposure, being less variable. In addition, a heatwave vulnerability triangle was constructed to visualize the regional disparity in the exposure, sensitivity, and adaptive capacity. The sensitivity analysis was used to confirm the strength of the indices, which revealed that LST, vegetation cover, and waterbodies are the strongest indicators that determine the vulnerability of the districts. Overall, the spatial analysis indicates in order to adapt to the heat and build the resilience to heat in Bangladesh, it is critical to prioritize the most vulnerable and least ready areas. Heatwave Vulnerability Exposure Sensitivity Adaptive Capacity Spatial Analysis and Statistical Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Highlights Heatwave vulnerability in Bangladesh measured by exposure, sensitivity, and capacity. Sensitivity and adaptive capacity varied widely, while exposure showed less variation. Regional differences in heatwave vulnerability are significant in Bangladesh. 1. Introduction Heatwaves have been among the most significant climate-related hazards of the past decades, and they have far-reaching impacts on ecosystems and human societies. The IPCC reported an increase in the incidence of heat waves following the end of the twentieth century and projected that this would be sustained throughout the twenty-first century (IPCC, 2014 ; Dosio et al., 2018 ; Ganguly et al., 2009 ). Extreme heat events have increased in frequency and intensity over the past several years in the context of climate change, leading to global warming (Azhar et al., 2017 ; Ho et al., 2015 ; Aubrecht & Özceylan, 2013 ). Tropical and subtropical regions, particularly South Asia, are experiencing such extreme heat conditions more and more, and the impacts are heightened mortality, widespread agricultural losses, and enhanced economic burdens. Bangladesh, as a low-lying, densely populated tropical monsoon climate nation, is most vulnerable to the adverse effects of heatwaves. However, over the past few decades, there has been an increase in the frequency and severity of heatwaves. Although meteorologists tend to refer to heatwaves by temperatures exceeding a certain level, this is not the only way of describing such stratus. What makes a heatwave a hazard is not only the strength of heat but also the circumstances in which it takes place, such as the vulnerabilities, resilience, and adaptation capacities of the concerned communities. Therefore, a heatwave is defined as an unusually high temperature period of an extended duration, which is above climatic norms and endangers human health and livelihoods, and the environment. In Bangladesh, climatic exposure, and the socio-demographic traits of the population determine its severity. These have all worked to enhance the UHI (Urban Heat Island) effect, especially in big cities like Dhaka. This is particularly applicable in large cities, where dense populations and the urban heat island (UHI) effect exacerbate temperatures (Wolf & McGregor, 2013 ; Manoli et al., 2019 ; Li et al., 2020 ). Meteorological heatwaves (short periods of an abnormal high temperature) and the more general seasonal heat conditions have been differentiated in this study. Exposure measure is the mean land surface temperature (LST) in hot summer season (March-June), which captures persistent heat over season instead of one off heat occurrences. Spatial seasonal exposure is identified to heat at a district level, together with sensitivity and capacity of adaptation scores. Heatwave vulnerability is theoretically the outcome of three intangible indices: exposure, sensitivity, and adaptive capacity (Mac & McCauley, 2017 ; Luo et al., 2024 ; Yu et al., 2021 ; Courtney-Wolfman, 2024 ). Variation in exposure, sensitivities, and adaptive capacity explains variation in vulnerability. Exposure is where people, livelihoods, environmental resources and services, infrastructure, or economic, social, or cultural resources are located close to extreme events, resulting in possible future harm, damage, or loss (Raja et al., 2021 ; Handmer et al., 2012 ; Birkmann & Welle, 2015 ). Sensitivity is the physical vulnerability of humans and the environment to be affected by hazardous events because of the lack of resistance (Field et al., 2012 ; Zuhra et al., 2019 ). Adaptive capacity is the ability of a person, household, community, or other social entity to adjust to changes in the environment in order to survive and be sustainable (Lavell, 1999 ; Smit & Wandel, 2006 ; Engle, 2011 ). A three-dimensional integrated assessment framework of exposure, sensitivity, and adaptive capacity is required to comprehend and understand the complexity of heatwave vulnerability. Although the risk of heatwaves is increasingly high in Bangladesh, there are not many studies that consider the issue of vulnerability with a comprehensive, district-level view. The majority of the previous research work is limited to the urban heat island or the use of one indicator, such as temperature or poverty. The integrated analysis of exposure, sensitivity, and adaptive capacity across the country is still lacking. Moreover, no evidence has been statistically confirmed that there are spatial differences in vulnerability in the past work. This study addresses that gap by exploring the heatwave vulnerability based on both satellite remote sensing and census data on a district basis. The paper provides spatial and statistical analysis of exposure, sensitivity, and adaptive capacity. Regional disparities are visualized in the form of a vulnerability triangle. Lastly, the study on heatwave vulnerability in Bangladesh highlights priority areas and provides policy recommendations based on the key findings. 2. Study Area This study focused on the administrative districts of Bangladesh, a low-lying country with a deltaic profile within South Asia, which is broadly separated into eight administrative divisions. It covers an area of about 147,570 km, very prone to risks of floods, cyclones and heat waves. The country has a tropical climate, mainly with three distinctly different seasons: the hot summer season (March to June), the monsoon season (June to October), and the dry, cold winter season (October to March). Temperatures in the hot summer seasons are over 40°C in the western and northwestern regions of the country. The heat-humidity interaction most commonly leads to critical heat stress situations among urban and rural dwellers. The spatial growth rates of the cities geographically underscore the difficulties of urbanization, as Dhaka is growing at a rate of 11.5% per year (Moniruzzaman et al., 2020 ), Rajshahi at 5.0% (Faridatul, 2017 ), and Chittagong at 3.75% (Abdullah et al., 2022 ). These urban cities are prone to heightened urban heat stress due to extensive population growth, rapid urbanization, and high availability of impermeable surfaces, particularly during summer (Dewan et al., 2021 ; Oleson et al., 2015 ). Due to the high density of structures and sparse vegetation, urban regions are hotter than their rural equivalents. The heat island phenomenon is strong in urban areas like Dhaka because of unorganized urbanization and loss of green cover, which increases exposure to heat. Conversely, even with more vegetation cover, rural regions may lack the facilities and infrastructure required to handle extreme heat and display different vulnerability profiles. Table 1 Exposure, Sensitivity and Adaptive capacity Indicators (a) Exposure Indicators Indicator Source Unit Description Land Surface Temperature (LST) USGS, MODIS °C Surface temperature during the pre-monsoon period (Retrieved in May 2023) Population Density BBS, 2011 people/km² Number of people per square kilometer (b) Sensitivity Indicators Indicator Source Unit Description Elderly Population BBS, 2011 people/km² Individuals aged 65 and above Very Young Population BBS, 2011 " Children underage 5 Female Population BBS, 2011 " Proportion of female population Illiteracy Rate BBS, 2011 " Percentage of people unable to read or write Poverty Rate BBS, 2016 " Percentage of population below the poverty line Unemployment BBS, 2011 " Share of the labour force without employment Occupation BBS, 2011 " Proportion of labour in vulnerable sectors Disability BBS, 2011 " Population with physical or mental disabilities Access to Water BBS, 2011 " Population with access to safe water sources Built-up Area ESRI’s Living Atlas, Sentinel-2 (Retrieved in May 2023) km² Area of impervious surfaces indicating urbanization (c) Adaptive Capacity Indicators Indicator Source Unit Description Nighttime Light (NTL) NASA, VIIRS(Retrieved in Nov 2023) nW/cm²/sr Proxy for electrification and infrastructure presence Vegetation ESRI’s Living Atlas, Sentinel-2 (Retrieved in May 2023) km² Area covered by vegetation Waterbody ESRI’s Living Atlas, Sentinel-2 (Retrieved in May 2023) " Area covered by surface water 3. Materials and Methods 3.1 Data and Sources Table 1 represent the exposure, sensitivity and adaptive capacity indicators. Table 1 (a) presents the exposure indicators that are incorporated into the Heatwave Vulnerability. These indicators consist of Land Surface Temperature (LST) and population density. LST is used to measure the level of environmental heat exposure directly to the populations. Population Density captures the demographic aspect because increased population concentrations result in more direct individuals who are vulnerable in heatwaves (Ho et al., 2015 ; Raja et al., 2021 ). Table 1 (b) lists the sensitivity indicators that reflect demographic and socioeconomic factors influencing the ability of a population to withstand heatwave events. Among them are the proportion of elderly population, population below five years, disabled population, female population, poverty rate, illiteracy rate, unemployment, occupation, water availability and built-up area. Children below five years of age and elderly population are physiologically vulnerable age groups that may be more vulnerable to heat stress. The inclusion of female population is based on the fact that gender basis vulnerabilities are well documented and women in South Asia tend to have lower adaptive choices. Socioeconomic disadvantage is indicated by illiteracy and poverty rate, which decrease awareness and capacity to deal with heat risks. Livelihood-related vulnerability is captured by unemployment and unsafe occupation since outdoor and insecure occupations tend to expose them to heat. Disability rate indicates physical illness to extreme heat. During a heatwave, clean water is essential in avoiding the occurrence of heat rashes and dehydration. The presence of impervious surfaces in built-up area reminds the extent of the urban heat island effect, increasing the exposure to it, thus the sensitivity (Dewan et al., 2021 ; Oleson et al., 2015 ). Table (c) shows the indicators of adaptive capacity, which include electricity access (proxied by nighttime light intensity), green cover and waterbody availability. Electricity access (NTL) is measured as a proxy of access to cooling devices and services. Natural shading and evapotranspiration are given by vegetation cover which lowers the ambient heat. Water bodies serve as ecological buffers to moderate the local microclimates and facilitate cooling. With the multifactorial construct of heatwave vulnerability that involves environmental exposure, socio-demographic sensitivity, and infrastructural adaptive capacity, this study takes advantage of various authoritative data platforms that offer both spatial and statistical precision (Li et al., 2024 ; Gao, 2024 ). MODIS Land Surface Temperature (LST) was retrieved through Earth Explorer from the US Geological Survey (USGS). NASA's Terra and Aqua satellites have a sensor called MODIS onboard, which provides high-resolution thermal images and global surface temperature variability data. Seasonal mean daytime LST over the hot summer season (March-June, 2013–2022), was averaged along with district population density, to measure exposure. The start and end of the meteorological heatwave are not identified here, since daily data are required to characterize the hotspots, and threshold-based definitions cannot be derived from the MODIS dataset. Instead, the hot season (March-June) is defined as a time of extended high seasonal temperatures. For that reason, high LST is not considered as heatwave but only a long-term heat stress symptom during the season. To describe the socio-economic and demographic vulnerability of the population, the study utilizes data from the Bangladesh Bureau of Statistics (BBS). The analysis exploits county-level socio-demographic data from the Population and Housing Census 2011 and HIES 2016 to identify the variables such as age, illiteracy, unemployment, poverty, etc. The Satellite imagery of the Sentinel-2 of the Copernicus mission offered by ESRI offers vegetation, built-up areas and waterbodies data for the year 2021 at 10 m spatial resolutions and is a contribution to sensitivity and adaptive capacity. The access to electricity, as a proxy of adaptive infrastructure, is based on NASA VIIRS nighttime lights data of May, 2021. Electrification and resilience potential are at the level of districts, which is denoted by radiance values. The radiance values are measured in nW/cm²/sr. This unit represents the brightness of the light observed on the surface of the earth, which is considered a common proxy of electrification and infrastructural development. Vulnerability was mediated in the context of gendered roles, where women’s domestic roles and men’s outdoor work led to greater exposure. Social context influenced risk, with a strong family network in Bangladesh mitigating the risk faced by elderly persons, but still leaving marginalized groups at increased risk. Barriers such as poor electricity, inadequate ventilation, and overcrowding of housing were identified as compounding heat stress and limiting household ability to use adaptation strategies. These datasets of remote sensing and statistics together provide an empirical, spatially fine-grained study on vulnerability of heatwaves. 3.2 Data Preprocessing The datasets were preprocessed to make them commensurable for the analysis of the Heatwave Vulnerability. It began with the collection of raw data for all the chosen indicators for exposure, sensitivity, and adaptive capacity. Land Surface Temperature (LST) satellite imagery was downloaded from the MODIS Global LST product via the USGS Earth Explorer website. This was then georeferenced in ArcGIS, where projection correction, mosaicking, and any other involved processes were performed for geographical correctness. The max of the LST values was extracted for each district in Bangladesh through the Zonal Statistics tool in ArcGIS, and district-level temperatures were generated for further analysis. Satellite imagery from the Sentinel-2 Land Cover dataset was acquired via ESRI's Living Atlas portal to record land cover information for Bangladesh. In ArcGIS, the data were processed and utilized to classify and calculate the spatial area of built-up area, vegetation, and waterbodies for all the districts. The classified land cover data were then clipped to district boundaries, and zonal statistics were employed to calculate the area of every land cover class by district to provide standardized spatial analysis for further integration into vulnerability assessment. VIIRS Nighttime Lights satellite imagery was retrieved from NASA Earthdata and analyzed on ArcGIS to estimate district-level access to electricity in Bangladesh. Radiance values from the imagery were clipped to the national boundary and averaged within district boundaries using zonal statistics. The vulnerability analysis used Average radiance values obtained as a proxy measure for access to electricity. Whereas socio-demographic indicators were collected from the Population and Housing Census 2011: Zila Report conducted by the Bangladesh Bureau of Statistics (BBS). 3.3 Normalization (Min-Max Scaling) To render the indices of the heatwave vulnerability directly comparable, the data for each of the indicators was first normalized using the Min-Max scaling technique. This normalization process transforms all indicators to the same scale of 0 to 1. The formula is expressed as X norm = (X − X min ) / (X max − X min ) where X is the variable's original value, and X min and X max are the variable's minimum and maximum values across all districts, allowing standardization across various measures. The motivation behind this normalization step is the inherent heterogeneity of data sources and measurement units; without scaling these indicators onto the same range, any aggregation will result in disproportional contributions from variables with larger numeric ranges (Gan et al.,2017; Talukder et al., 2017 ). 3.4 Equal Weighting Following normalization, an equal weight was assigned to each indicator on each of the three indices. Equal weighting is grounded in the lack of decisive evidence or precedent that one indicator would be more significant than another in measuring heatwave vulnerability. Although it's understood that there are certain variables that would likely have a greater impact in specific situations, equality weighting maintains simplicity, objectivity, and methodological clarity. 3.5 Aggregation of Indicators Following weighting, the indicators per index were added together to generate three composite indices. Here’s the formula used for all three-composite indices: Exposure (E), Sensitivity (S), and Adaptive Capacity (AC): I i (C) = ∑ k∈ IC w k x′ ik/ ∑ k∈ IC w k , C∈{E,S,AC} • x′ ik = normalized value (0–1) of indicator k for district i. • I C = the set of indicators for component C(e.g., I E ={LST′, population density′}; I S ={illiteracy′, poverty′, vulnerable age groups′, built-up′, etc.}; I AC ={vegetation′, water bodies′, NTL′} • w k = weight of indicator (use w k =1 for equal-weight averages). The Exposure Index, which captures the extent to which populations are exposed to heatwaves, was calculated as the mean of normalized values of land surface temperature (LST) and population density. These two variables capture both the environmental and demographic determinants of heat exposure. The Sensitivity Index was developed to represent demographic and socio-economic conditions that affect the vulnerability of a population to the impacts of extreme heat. It combines a series of indicators, including the proportion of elderly and very young population, female population percentage, illiteracy percentage, poverty percentage, unemployment percentage, occupational pattern, percentage of disabilities, access to clean water, and built-up area coverage. The Adaptive Capacity Index measures the extent to which a district can adjust to or resist the impacts of heat stress. It includes measures such as mean night-time light radiance (as a proxy for electricity coverage), vegetation cover, and water body area. They are all indirect measures of infrastructure and environmental buffering against heat. 3.6 Kruskal-Wallis H Test In order to determine whether statistically significant differences existed between the heatwave vulnerability indices, that is, Exposure, Sensitivity and Adaptive Capacity, across divisions in Bangladesh, the non-parametric Kruskal-Wallis H was used. This test has been used in comparing the medians of two or more independent groups and applies to ordinal or non-normally distributed continuous data. 3.7 Post Hoc Dunn’s Test In cases when the Kruskal-Wallis H test suggested that differences were statistically significant (p < 0.05), Dunn post hoc with Bonferroni correction was used to estimate which particular pairs of the divisions as well as which division by division were significantly different in heatwave vulnerability indices. Such a pairwise approach allowed distinguishing regional differences in exposure, sensitivity, and adaptive capacity. 3.8 Heatwave Vulnerability Triangle A Heatwave Vulnerability Triangle was created to visually present an overview of how each administrative division was relative in their contribution to each of the indices, including Exposure, Sensitivity, and Adaptive Capacity. This visualization is based on the LVI-IPCC model of conceptualizing vulnerability as depending on these three indices. All the three sides or axes of the triangle were scaled (0 to 1) scores of a component and could be interpreted comparatively. The approach has provided a graphic description of the way in which levels of vulnerability differ structurally across divisions. 3.9 Sensitivity Analysis A leave-one-out sensitivity analysis was used to test the strength of the constructed indices. Under this method, one indicator at a time was dropped out of the index and the changes in the scores of the districts were noted. Three indicators were calculated for each index, which included (i) mean absolute change in index scores across the districts, (ii) maximum absolute change of any district and (iii) the proportion of the districts that changed vulnerability status as a consequence of the omission. The approach enabled us to determine the relative implication of the individual indicators on the Exposure, Sensitivity, and Adaptive Capacity indices. The indicators that had greater changes when omitted were taken as important drivers of the total index, but those that had smaller effects indicated more stability and redundancy in the system. This analysis will ensure that the indices are not overly influenced by one variable, which is known to increase the credibility of the heatwave vulnerability assessment by the study. The overall methodology of this study has been visualized in Fig. 2 . 4. Results and Discussion An insight into the determinants of heatwave vulnerability necessitates a detailed analysis of three indices: exposure, sensitivity, and adaptive capacity. Each dimension has a varying spatial pattern and contributes uniquely to the overall Heatwave Vulnerability of Bangladesh. This study delves into the spatial variations at the district scale of these three dimensions with the help of diverse datasets, geospatial maps, and statistical graphs. 4.1 Characteristics of Exposure to Heatwaves Exposure in this study was measured primarily by two significant indicators: Land Surface Temperature (LST) and population density (Supplementary Table A1). LST was retrieved from MODIS satellite data, and pixel-based and district-based LST maps were generated to determine micro- and macro-level differences in surface temperature in Bangladesh. The pixel-based LST map (Fig. 3 a) revealed intense thermal patterns concentrated in the main urban and semi-urban areas. The mean LST was found to be above 30°C with a standard deviation of 4.92°C (Fig. 4 a). The district-based LST map (Fig. 3 b), also indicated that there are several hotspots observed (Fig. 3 a), where the LST ranges between 23.39°C and 43.07°C. Dhaka, Gazipur, Rajshahi, Narayanganj, and Chattogram are some of the hottest areas of the country, which experience high surface temperature constantly due to urbanization, dense human settlements, and the urban heat island (UHI) effect. These urban places, which possess low vegetation cover and infrastructural concrete, have the tendency to absorb and retain heat, exacerbating surface temperature. So it is obvious that the LST value was higher in built-up areas (Fig. 3 c). A significant negative correlation exists (Pearson correlation coefficient − 0.439) between LST and NDVI (Raja et al., 2021 ). The density of built-up area was the highest in the central part of the city, where the proportion of other land use types was low. Those areas are characterized by a higher level of LST. LST was low for the north-eastern and south-eastern parts of the country, which are mostly covered by vegetation. The normal distribution curve of observed LST (Fig. 4 a) exhibited a right-skewed pattern, indicating that a high number of districts possess moderately high LSTs, while some urban districts experience extreme values. The box plot (Fig. 4 b) also showed high variability in the values of LST between districts, and outliers were also present considerably in the urban areas. This variability was explored further (Fig. 4 c), showing the distribution of LST based on different landcover classes. The mean LST of waterbody, vegetation, and built-up were 30.30°C, 30.19°C, and 30.30°C, respectively, with corresponding standard deviations of 4.54, 4.72, and 4.93 (Fig. 4 c). Population density, the second indicator of exposure, also showed extreme differences between divisions. Dhaka, Chattogram, and Rajshahi divisions had districts with extremely high population density, making them more vulnerable due to the larger number of individuals exposed during heatwave conditions. A graph (Fig. 5 ) of the normal distribution of exposure indicators by division, validates this observation. Dhaka and Rajshahi divisions’ districts are most at risk, which means that these divisions should be prioritized for heat adaptation initiatives. The spatial pattern of exposure for all the districts of Bangladesh has been visually represented (Fig. 9 a), merging land surface temperature (LST) and population density to provide an obvious perception of heatwave exposure (Supplementary Table A4). The map is exhibiting acute regional differences, with maximum exposure concentrated in urbanized districts such as Dhaka, Narayanganj, Gazipur, Rajshahi, and Chattogram. These areas are characterized by high surface heat intensities driven by high built-up densities, low vegetation cover density, and high population densities, all of which boost the urban heat island effect and subject people to high heat intensification. In contrast, northern and northeastern regions like Sunamganj, Netrokona, and Kurigram have reduced exposure rates, and this is likely due to the fact that they have lower population densities as well as the availability of natural cooling factors like wetlands, forests, or open green spaces. The regional trends reflected (Fig. 9 a) confirm that heatwave exposure in Bangladesh is neither evenly distributed nor follows urbanization and ecological change patterns. This regional information is critical to the determination of hotspots where emergency heat adaptation interventions are required, particularly in those highly exposed districts where population vulnerability is already complemented by environmental insufficiency. In general, the findings confirm that the heatwave exposure is not uniformly distributed across the districts of Bangladesh. It is mostly controlled by the combined effects of climatic, demographic, and urbanization factors (Liang et al., 2020 ; Wang et al., 2019 ; Nelson et al., 2009 ). Poorly ventilated and industrially concentrated zones are experiencing both high LST and population density pressure, and thus are priority zones for heatwave management. 4.2 Characteristics of Sensitivity to Heatwaves Sensitivity was quantified on the basis of indicators such as the proportion of vulnerable populations (children under five and elderly over 65), poverty rate, illiteracy rate, and access to health facilities (Supplementary Table A2). These indicators portray a region's demographic and socioeconomic capacity to withstand heat stress. The normal distribution of the sensitivity indicators between divisions (Fig. 6 ) is widespread, with peaks in districts located in the northern and coastal regions. These, viz., Kurigram, Nilphamari, Noakhali, and Cox's Bazar, are often lacking in resilience; infrastructure, i.e., heat-resistant housing, availability of clean water, and public health facilities. These districts also have high infant and elderly population rates, which further increases their sensitivity. Spatial analysis (Fig. 9 b) indicated that the districts of the Rangpur, Dhaka, and Chittagong divisions have the highest sensitivity (Supplementary Table A4). Rangpur is one of the poorest regions of the country, with a high proportion of the population living below the poverty line and with less access to health infrastructure facilities. Though not necessarily with the highest LST, these districts are exposed to greater heat-related health impacts due to vulnerable living conditions. It is also notable that some districts of the Dhaka division also have high exposure and sensitivity, a combination that makes them highly vulnerable. Urban slums with dense populations in the capital area are likely to have low-income families with limited resources with which to adapt to heat stress (Laue et al., 2022 ). Thus, even in locations with more infrastructure, socioeconomic inequality can lead to heightened vulnerability. Cumulatively, the sensitivity indicators reveal socioeconomic vulnerability and demographic susceptibility to be as much, if not more, a factor as climate in heatwave risk. The overlap between high exposure and high sensitivity increases the demand for targeted policy intervention. 4.3 Characteristics of Adaptive Capacity to Heatwaves Adaptive capacity, the final dimension, refers to the ability of a district to respond to heatwave impacts. Adaptive capacity in this study is captured through the aggregation of nighttime light (NTL) radiance intensity as a proxy for access to electricity and vegetation cover and waterbodies (Supplementary Table A3), which are key environmental buffers to heat stress. The NTL average radiance distribution (Fig. 7 ) presents an evident urban-rural gradient with the mean average radiance of 0.82 nW/cm²/sr. Dhaka, Khulna, Sylhet, and Chattogram divisions are of a higher radiance value, reflecting increased availability of electricity and cooling appliances like air conditioners and fans. Such districts are increasingly urbanized and have wider coverage of core facilities. According to the normal adaptive capacity distribution between divisions (Fig. 8 ), districts under Sylhet, Khulna, and parts of Chattogram are performing well, districts under Barishal, Mymensingh, and parts of Rangpur have much lower adaptive capacity. Low electricity coverage, poor road connectivity, and lower investment in public infrastructure are the key constraints of these districts in adapting to heat stress. District-level adaptive capacity (Supplementary Table A4) also bears witness to these disparities. Adaptive capacity is mostly focused on central and southeast Bangladesh, with northwest and southern coastal districts apparently very poorly served (Fig. 9 c). This kind of spatial polarization is troubling with regard to climate justice and equity, since the districts most in need of adaptation are often the least capable of achieving it. The lack of adaptive infrastructure in these low-capacity districts concentrates the population increasingly reliant on regular coping mechanisms or suffers the consequences of heatwaves with fewer remedies. Projects like solar-powered cool centers, emergency health systems, and community-based adaptation networks could fill this gap. These analyses reveals that heating events are not evenly distributed throughout Bangladesh’s districts. Dhaka, Rajshahi and Chattogram have the highest risk because of their raised land surface temperatures and large populations, combined with the urban heat effect. In comparison, areas in Rangpur and alongside the coast are more sensitive, because of poverty, low rates of literacy, less water supply and large numbers of vulnerable age groups. Access to electricity, plenty of greenery and available water is mostly found in cities, leaving many regions in the north and south very limited. 4.4 Statistical Validation of Exposure, Sensitivity and Adaptive Capacity All the three indices were analyzed by the district level scores and analyzed based on the division. The findings showed that there existed no significant differences in exposure to heatwaves across divisions with test statistic (H) = 11.937 and p-value = 0.1026 which means that regional differences are statistically insignificant at the 5 percent level. This indicates that, whereas the spatial patterns of exposure identify some of the urban districts as more exposed, these variations are not statistically significant as measured by aggregation by division. Contrastingly, there was a statistically significant variability of sensitivity to heatwaves by divisions. The Kruskal-Wallis H test made an output of 23.132 with p = 0.0016, which strongly rejected the null hypothesis of equal distributions of sensitivity. This finding makes it certain that certain divisions especially Rangpur, Barishal, and regions of Chattogram are socio-demographically more vulnerable likely because of the add-on impact of poverty, illiteracy, and a greater number of vulnerable age groups in that area. An obvious argument to this finding is this spatial analysis and maps depicted previously that show that northern and coastal areas are more demographically sensitive to extreme-heat event temperatures. There was also a huge variance in adaptive capacity between divisions (test statistic was 19.098 and p-value was 0.0079), which means that there is an infrastructural and ecological imbalance in Bangladesh to deal with the effects of a heatwave. Those divisions, which have better access to electricity (proxied by nighttime intensity of light), vegetation and water bodies including Dhaka and Khulna portrayed a slightly better score of adaptive capacity. On the other side, the district in Barishal, Rangpur and Mymensingh had much less ability to adjust, which confirms the previous findings based on spatial analysis. The post hoc result supports Kruskal-Wallis H test results presented above, which revealed the inter-divisional difference of Exposure (H = 11.937, p = 0.1026), Sensitivity (H = 23.132, p = 0.0016) and Adaptive Capacity (H = 19.098, p = 0.0079). The insignificance in exposure of the divisions could be explained by the fact that land surface temperature and population density as the indicators of heatwave conditions have more even geographic distribution on the territory of the country (Fig. 9 a). Despite there being some visible difference in the Exposure Map with regard to spatial variation in land surface temperature and population density (especially around the southern and central parts), the Kruskal-Wallis tests reveal that such variations are not statistically significant across divisions (p = 0.1026). This validates the conclusion that the climatic exposure is less skewed in Bangladesh perhaps because the entire country has succumbed to this monsoonal climatic condition, whereas the microclimate has fluctuated locally. By comparison, Sensitivity and Adaptive Capacity will depend on more short-distance socio-economic and infrastructural factors which are highly discrepant with administrative boundaries. On the whole, the post hoc test proves the strength of indices of the Heatwave Vulnerability by ensuring that apparent spatial differences are statistically significant in many important combinations. This statistical confirmation substantiates the demand of region orientation in adaptation approaches. Hypothetically, it would be possible to reduce the Barishal and Rangpur risk by investing in education, infrastructure and heat resilient housing. 4.5 Heatwave Vulnerability Triangle of Divisional Heatwave Vulnerability Indices Heatwave Vulnerability Triangle captures complex data sets in an easily understandable graphic with an emphasis on differences and similarities between regional vulnerabilities. Based on the triangle (Fig. 11 ), the most vulnerable area is Dhaka Division since it is characterized by very hot land surface temperatures (LST), and extremely high population density. The high density of population, a high density of built-up properties and low coverage of vegetation aggravate the urban heat island (UHI) effect, increasing thermal exposure to its residents. Nevertheless, although highly affected and moderately sensitive, Dhaka score decently on adaptive capacity, because of the high access to electricity, and high access to sources of critical infrastructure and services. Adaptive Capacity Level Division Conversely, the situation is very different in Rangpur and Barishal Divisions. They have a high level of sensitivity and low versatility degree even though the exposure scores were similar to the central urban areas. The region has had high levels of poverty, a greater percentage of the vulnerable population, poor health infrastructure, and illiteracy rates. Their adaptability is also limited by bad access to electrification and the unexhausted number of urban services along with lesser coverage of vegetation and water bodies. The combination of such high sensitivity and low capacity puts these divisions in a highly vulnerable position of being affected by any moderate-level heat events. Chattogram Division presents a mixed profile: moderately high exposure and sensitivity, with variable adaptive capacity across its districts. Coastal districts like Cox’s Bazar and Noakhali, though rich in natural cooling elements (water bodies and vegetation), often lack sufficient infrastructure to support heatwave resilience, thus requiring attention for targeted interventions. Interestingly, Sylhet and Khulna Divisions display relatively better adaptive capacities, likely due to a combination of urban infrastructure, moderate electrification, and ecological buffers such as forests and water bodies. These characteristics offset some of their sensitivity and exposure, making them more resilient than other rural divisions. Overall, the triangle analysis illustrates a critical imbalance between exposure, sensitivity, and adaptive capacity in many divisions. While exposure is more evenly spread (with some statistically insignificant variation across divisions), sensitivity and adaptive capacity differ significantly, as confirmed by Kruskal-Wallis and Dunn’s tests. These disparities emphasize the importance of tailored policy responses. Divisions like Barishal, Rangpur, and parts of Mymensingh, where high vulnerability shoots from both social fragility and lack of resilience infrastructure, should be prioritized in heatwave adaptation strategies. The triangle serves not just as a diagnostic tool, but as a compelling visual argument for equity-driven adaptation planning. It reinforces the need to focus on climate justice, where the least resilient communities receive the greatest support to cope with intensifying heat extremes. 4.6 Sensitivity Analysis of Indices A leave-one-out sensitivity analysis was performed to determine the strength of the indices and in this analysis, the indicators were taken out one at a time to monitor the changes in the index scores (Table 2 , Fig. 12 ). The findings indicated that there are those indicators that have a stronger impact on the indices as compared to the others. In case of Exposure Index, not only land surface temperature (LST) but also population density played a critical role. Omitting one or the other led to an average absolute change of 0.13 in the scores of the various districts and over half of the districts (59.4%) changed exposure class when LST was not used. This confirms that in Bangladesh exposure is mostly influenced by the interaction of climatic stress (heat intensity) and population pressure (population density) supporting previous results that urbanized and densely populated districts like Dhaka, Rajshahi and Chattogram are the most exposed. Most demographic indicators were relatively small when left out in the Sensitivity Index with negligent changes in districts. However, accumulated builtup area, poverty and availability of water produced visible variations with as much as 9.4 percent of districts changing the sensitivity class. This shows that the age structure as well as gender and disability are local causes of vulnerability, but structural socioeconomic causes, particularly, poverty, urban structure, and access to water, are more likely to define the sensitivity of a district. The results are in line with previous findings that districts in Rangpur and Mymensingh are extremely sensitive because of poverty and poor infrastructure. In the case of the Adaptive Capacity Index, water bodies and vegetation cover were the most dominant factors. Removal of vegetation by an average of 0.13 and reclassified 44.4 percent of districts, whereas removal of waterbodies shifted 66.7 percent of districts. Electrification, represented by nighttime light was also significant, changing 18.1 percent of districts when omitted. The findings highlight the key place of ecological buffers and infrastructure in the development of the resilience. The north-western and southern districts, which are already known to be with low adaptive capacity, are particularly susceptible without vegetation and cooling systems based on water. Table 2 Results of Leave-One-Out Sensitivity Analysis for Indices Index Indicator dropped Mean absolute change Max absolute change Districts changed class (%) Exposure LST 0.13 0.43 59.4 Population Density 0.13 0.44 18.8 Sensitivity Elderly Pop 0.00 0.08 0 Very Young Pop 0.01 0.04 0 Illiteracy Rate 0.00 0.02 0 Builtup Area 0.02 0.05 7.8 Poverty 0.01 0.06 9.4 Access to water 0.02 0.09 3.1 Female Pop 0.01 0.02 0 Unemployment 0.01 0.03 0 Occupation 0.00 0.03 0 Disability 0.00 0.02 0 Adaptive Capacity NTL 0.11 0.28 18.1 Vegetation 0.13 0.33 44.4 Waterbodies 0.07 0.28 66.7 In general, the sensitivity analysis proves that the indices do not rely on the individual variable too much, though particular indicators that have the most significant influence are LST (exposure), poverty, and built-up area (sensitivity), vegetation and waterbodies (adaptive capacity). This confirms the previous observation that the relationship between climate, urbanization, and socioeconomic vulnerability is a determinant of the heatwave vulnerability in Bangladesh. It also highlights the necessity of policies that can protect the natural cooling systems along with the reinforcement of basic infrastructure in order to develop a resilience where it is most vulnerable. The results of this study rely on seasonal mean LST to expose, thus not capturing events that could have severe short heatwaves or the distinction between the daytime and nighttime heat. To a great extent, hot nights need to be considered; however, they have not been addressed directly. Nighttime lights were used as a proxy variable measuring electricity access but this does not reflect the reliability of the grid or access to cooling. Factors playing a role in vulnerability include social factors (e.g. gender roles, elderly isolation, local support networks, etc.), which should be investigated further. 5. Conclusion and Recommendations It is obvious that the effects of heatwaves vary across Bangladesh because the country has different environmental, population, and infrastructure factors in different areas. Cities have been experiencing prolonged exposure to intense solar radiation on a regular basis due to overcrowding, which intensifies land surface heating. Due to the lack of various resources and high-quality services in rural and northern regions, the citizens tend to adjust themselves to hard circumstances. Most of the most vulnerable regions in the country lack the infrastructure that can assist them to cope with the increase in temperatures. These findings show clearly that adaptation to the heat has to be region-specific by encompassing the impacts of the environment, social components and the effect of policies. The empirical evidence made was stronger as these findings were statistically validated by the Kruskal-Wallis H test when it was proven that there is a considerable inter-divisional variability in there, in reference to sensitivity and adaptive capacity. Further, the post hoc investigation of Dunn revealed the existence of specific divisional differences, with Barishal and Rangpur being the most vulnerable ones because of both their extreme sensitivity and weak adaptive potential. Such findings substantiate the fact that although exposure is relatively balanced throughout the nation, social and infrastructural disparities are the primary determinants of heatwave vulnerability. The Heatwave Vulnerability Triangle was used to create a visual representation of the situation that could be interpreted, and which shows that there is an imbalance that must be addressed on an urgent basis as every division has different levels of exposure, sensitivity and adaptive capacity. Societies that are more vulnerable to increased risk of heatwave but possess a weak adaptive capacity need improved infrastructure, increased cool spot and air conditioning and local resilience capacity. Heat is one of the many warming climate challenges that local and national planners can confront using this model. It highlights the importance of climate justice. The least resilient groups need to be provided with the most robust support in dealing with the extreme heat. The sensitivity analysis also provided additional validity of the findings since it demonstrated that climatic and ecological indicators, specifically LST, vegetation and waterbodies, influences the vulnerability results, most of all. This supports the necessity of policy interventions that will involve ecological recovery and infrastructural development to minimize the exposure to extreme heat. Although the analysis gives excellent trends, it is composed of seasonal means of land surface temperature and, therefore, cannot identify short-duration heatwaves or nighttime heat records. Future studies are needed to include the event based heatwave detection and nighttime temperature indicators to enhance the insight in heat-related hazards. Declarations Acknowledgement We acknowledge the Bangladesh Bureau of Statistics (BBS) for providing data used in this research. References Abdullah, F., Myers, J., Basu, D., Tintinger, G., Ueckermann, V., Mathebula, M., ... & Jassat, W. (2022). 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1","display":"","copyAsset":false,"role":"figure","size":744529,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of Bangladesh showing administrative districts\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/b72d74f468662a0cde35900d.jpeg"},{"id":96753535,"identity":"c0142a29-6b26-420a-af2f-e244d1222702","added_by":"auto","created_at":"2025-11-25 17:24:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93354,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Diagram of the Methodology\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/8cbb22b328f10778cd4785c4.png"},{"id":96753538,"identity":"ca428fed-0eaf-43a5-800e-e1c047fb3be8","added_by":"auto","created_at":"2025-11-25 17:24:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":261097,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Pixel-based LST map, (b) District-based LST map, and (c) Land Cover map (Source: Compiled by Author, 2025)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/a94e8b4b4985d69a98dee605.png"},{"id":96753537,"identity":"2463c088-5a55-4380-933e-f9d7b3e72f6f","added_by":"auto","created_at":"2025-11-25 17:24:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123712,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Normal distribution curve of observed LST, (b) Box Plot of LST distribution, and (c) Normal distribution curve of observed LST based on different land cover 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Division\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/64991a0ee566699b188ddf37.png"},{"id":96915739,"identity":"c4e00b0d-59c3-4e9f-a09c-6697dc63b412","added_by":"auto","created_at":"2025-11-27 14:07:35","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":112695,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of NTL (Average Radiance) Intensity\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/13c53d503e791388ad93ec66.jpeg"},{"id":96915535,"identity":"700dfedd-8062-4e70-a471-19b6fb93257d","added_by":"auto","created_at":"2025-11-27 14:07:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":70480,"visible":true,"origin":"","legend":"\u003cp\u003eNormal distribution of Adaptive Capacity Indicators by Division\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/6fd3349a5a0ff73650e9deff.png"},{"id":96914188,"identity":"6a1aece3-ee1b-49c5-8d2c-bf531ef9b1aa","added_by":"auto","created_at":"2025-11-27 14:05:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":85810,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Spatial variation of Exposure, (b) Spatial variation of Sensitivity, and (c) Spatial variation of Adaptive Capacity (Source: Compiled by Author, 2025)\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/3351feda770ce2a424ea9090.png"},{"id":96913981,"identity":"1b8780fb-72a4-45b7-95ae-cad1a4e39a3e","added_by":"auto","created_at":"2025-11-27 14:04:55","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":52494,"visible":true,"origin":"","legend":"\u003cp\u003eBox Plot of Exposure, Sensitivity and Adaptive Capacity by Division\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/d5e0a78a15f62cb068fb269d.png"},{"id":96753541,"identity":"28ff9f1f-e8a3-4584-9669-74c64c85989f","added_by":"auto","created_at":"2025-11-25 17:24:42","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":381205,"visible":true,"origin":"","legend":"\u003cp\u003eThe Vulnerability Triangle of average Exposure, Sensitivity and Adaptive Capacity Level Division\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/112411c2b54e29d35ba4911e.jpeg"},{"id":96915367,"identity":"8c9f6052-46d1-4f46-99d8-fe358d27a20f","added_by":"auto","created_at":"2025-11-27 14:07:11","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":39431,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-One-Out Sensitivity Analysis across Indices\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/8ef9dc810d4237a7dc5c59a7.png"},{"id":97248344,"identity":"f37fef41-4c21-45ce-88b6-245fdea5108c","added_by":"auto","created_at":"2025-12-02 12:54:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2942373,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/f06c105b-cd6c-4180-a881-f68e18e4d0a2.pdf"},{"id":96753536,"identity":"f249801f-6cd6-43fd-aef6-6cbef59a383a","added_by":"auto","created_at":"2025-11-25 17:24:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":50206,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-7752795/v1/42f34522ae5fe9122fe901a2.docx"}],"financialInterests":"","formattedTitle":"Characterising Spatial Patterns of Exposure, Sensitivity and Adaptive Capacity to Assess Heatwave Vulnerability of Bangladesh","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eHeatwave vulnerability in Bangladesh measured by exposure, sensitivity, and capacity.\u003c/li\u003e\n \u003cli\u003eSensitivity and adaptive capacity varied widely, while exposure showed less variation.\u003c/li\u003e\n \u003cli\u003eRegional differences in heatwave vulnerability are significant in Bangladesh.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eHeatwaves have been among the most significant climate-related hazards of the past decades, and they have far-reaching impacts on ecosystems and human societies. The IPCC reported an increase in the incidence of heat waves following the end of the twentieth century and projected that this would be sustained throughout the twenty-first century (IPCC, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dosio et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ganguly et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Extreme heat events have increased in frequency and intensity over the past several years in the context of climate change, leading to global warming (Azhar et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ho et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Aubrecht \u0026amp; \u0026Ouml;zceylan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Tropical and subtropical regions, particularly South Asia, are experiencing such extreme heat conditions more and more, and the impacts are heightened mortality, widespread agricultural losses, and enhanced economic burdens. Bangladesh, as a low-lying, densely populated tropical monsoon climate nation, is most vulnerable to the adverse effects of heatwaves. However, over the past few decades, there has been an increase in the frequency and severity of heatwaves. Although meteorologists tend to refer to heatwaves by temperatures exceeding a certain level, this is not the only way of describing such stratus. What makes a heatwave a hazard is not only the strength of heat but also the circumstances in which it takes place, such as the vulnerabilities, resilience, and adaptation capacities of the concerned communities. Therefore, a heatwave is defined as an unusually high temperature period of an extended duration, which is above climatic norms and endangers human health and livelihoods, and the environment. In Bangladesh, climatic exposure, and the socio-demographic traits of the population determine its severity. These have all worked to enhance the UHI (Urban Heat Island) effect, especially in big cities like Dhaka. This is particularly applicable in large cities, where dense populations and the urban heat island (UHI) effect exacerbate temperatures (Wolf \u0026amp; McGregor, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Manoli et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Meteorological heatwaves (short periods of an abnormal high temperature) and the more general seasonal heat conditions have been differentiated in this study. Exposure measure is the mean land surface temperature (LST) in hot summer season (March-June), which captures persistent heat over season instead of one off heat occurrences. Spatial seasonal exposure is identified to heat at a district level, together with sensitivity and capacity of adaptation scores. Heatwave vulnerability is theoretically the outcome of three intangible indices: exposure, sensitivity, and adaptive capacity (Mac \u0026amp; McCauley, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Courtney-Wolfman, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Variation in exposure, sensitivities, and adaptive capacity explains variation in vulnerability. Exposure is where people, livelihoods, environmental resources and services, infrastructure, or economic, social, or cultural resources are located close to extreme events, resulting in possible future harm, damage, or loss (Raja et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Handmer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Birkmann \u0026amp; Welle, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Sensitivity is the physical vulnerability of humans and the environment to be affected by hazardous events because of the lack of resistance (Field et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zuhra et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Adaptive capacity is the ability of a person, household, community, or other social entity to adjust to changes in the environment in order to survive and be sustainable (Lavell, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Smit \u0026amp; Wandel, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Engle, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). A three-dimensional integrated assessment framework of exposure, sensitivity, and adaptive capacity is required to comprehend and understand the complexity of heatwave vulnerability. Although the risk of heatwaves is increasingly high in Bangladesh, there are not many studies that consider the issue of vulnerability with a comprehensive, district-level view. The majority of the previous research work is limited to the urban heat island or the use of one indicator, such as temperature or poverty. The integrated analysis of exposure, sensitivity, and adaptive capacity across the country is still lacking. Moreover, no evidence has been statistically confirmed that there are spatial differences in vulnerability in the past work. This study addresses that gap by exploring the heatwave vulnerability based on both satellite remote sensing and census data on a district basis. The paper provides spatial and statistical analysis of exposure, sensitivity, and adaptive capacity. Regional disparities are visualized in the form of a vulnerability triangle. Lastly, the study on heatwave vulnerability in Bangladesh highlights priority areas and provides policy recommendations based on the key findings.\u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003eThis study focused on the administrative districts of Bangladesh, a low-lying country with a deltaic profile within South Asia, which is broadly separated into eight administrative divisions. It covers an area of about 147,570 km, very prone to risks of floods, cyclones and heat waves. The country has a tropical climate, mainly with three distinctly different seasons: the hot summer season (March to June), the monsoon season (June to October), and the dry, cold winter season (October to March). Temperatures in the hot summer seasons are over 40\u0026deg;C in the western and northwestern regions of the country. The heat-humidity interaction most commonly leads to critical heat stress situations among urban and rural dwellers. The spatial growth rates of the cities geographically underscore the difficulties of urbanization, as Dhaka is growing at a rate of 11.5% per year (Moniruzzaman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Rajshahi at 5.0% (Faridatul, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Chittagong at 3.75% (Abdullah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These urban cities are prone to heightened urban heat stress due to extensive population growth, rapid urbanization, and high availability of impermeable surfaces, particularly during summer (Dewan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oleson et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Due to the high density of structures and sparse vegetation, urban regions are hotter than their rural equivalents. The heat island phenomenon is strong in urban areas like Dhaka because of unorganized urbanization and loss of green cover, which increases exposure to heat. Conversely, even with more vegetation cover, rural regions may lack the facilities and infrastructure required to handle extreme heat and display different vulnerability profiles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExposure, Sensitivity and Adaptive capacity Indicators\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e(a) Exposure Indicators\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLand Surface Temperature (LST)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSGS, MODIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSurface temperature during the pre-monsoon period\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Retrieved in May 2023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBS, 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epeople/km\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of people per square kilometer\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(b) Sensitivity Indicators\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElderly Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBS, 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epeople/km\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndividuals aged 65 and above\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery Young Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBS, 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChildren underage 5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBS, 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProportion of female population\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliteracy Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBS, 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage of people unable to read or write\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoverty Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBS, 2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage of population below the poverty line\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBS, 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eShare of the labour force without employment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBS, 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProportion of labour in vulnerable sectors\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBS, 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePopulation with physical or mental disabilities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to Water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBBS, 2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePopulation with access to safe water sources\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-up Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESRI\u0026rsquo;s Living Atlas, Sentinel-2 (Retrieved in May 2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ekm\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArea of impervious surfaces indicating urbanization\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(c) Adaptive Capacity Indicators\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNighttime Light (NTL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNASA, VIIRS(Retrieved in Nov 2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003enW/cm\u0026sup2;/sr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProxy for electrification and infrastructure presence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESRI\u0026rsquo;s Living Atlas, Sentinel-2 (Retrieved in May 2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ekm\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArea covered by vegetation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaterbody\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESRI\u0026rsquo;s Living Atlas, Sentinel-2 (Retrieved in May 2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\"\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArea covered by surface water\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3. Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Data and Sources\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e represent the exposure, sensitivity and adaptive capacity indicators. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(a) presents the exposure indicators that are incorporated into the Heatwave Vulnerability. These indicators consist of Land Surface Temperature (LST) and population density. LST is used to measure the level of environmental heat exposure directly to the populations. Population Density captures the demographic aspect because increased population concentrations result in more direct individuals who are vulnerable in heatwaves (Ho et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Raja et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(b) lists the sensitivity indicators that reflect demographic and socioeconomic factors influencing the ability of a population to withstand heatwave events. Among them are the proportion of elderly population, population below five years, disabled population, female population, poverty rate, illiteracy rate, unemployment, occupation, water availability and built-up area. Children below five years of age and elderly population are physiologically vulnerable age groups that may be more vulnerable to heat stress. The inclusion of female population is based on the fact that gender basis vulnerabilities are well documented and women in South Asia tend to have lower adaptive choices. Socioeconomic disadvantage is indicated by illiteracy and poverty rate, which decrease awareness and capacity to deal with heat risks. Livelihood-related vulnerability is captured by unemployment and unsafe occupation since outdoor and insecure occupations tend to expose them to heat. Disability rate indicates physical illness to extreme heat. During a heatwave, clean water is essential in avoiding the occurrence of heat rashes and dehydration. The presence of impervious surfaces in built-up area reminds the extent of the urban heat island effect, increasing the exposure to it, thus the sensitivity (Dewan et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oleson et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Table (c) shows the indicators of adaptive capacity, which include electricity access (proxied by nighttime light intensity), green cover and waterbody availability. Electricity access (NTL) is measured as a proxy of access to cooling devices and services. Natural shading and evapotranspiration are given by vegetation cover which lowers the ambient heat. Water bodies serve as ecological buffers to moderate the local microclimates and facilitate cooling.\u003c/p\u003e\n \u003cp\u003eWith the multifactorial construct of heatwave vulnerability that involves environmental exposure, socio-demographic sensitivity, and infrastructural adaptive capacity, this study takes advantage of various authoritative data platforms that offer both spatial and statistical precision (Li et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gao, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). MODIS Land Surface Temperature (LST) was retrieved through Earth Explorer from the US Geological Survey (USGS). NASA\u0026apos;s Terra and Aqua satellites have a sensor called MODIS onboard, which provides high-resolution thermal images and global surface temperature variability data. Seasonal mean daytime LST over the hot summer season (March-June, 2013\u0026ndash;2022), was averaged along with district population density, to measure exposure. The start and end of the meteorological heatwave are not identified here, since daily data are required to characterize the hotspots, and threshold-based definitions cannot be derived from the MODIS dataset. Instead, the hot season (March-June) is defined as a time of extended high seasonal temperatures. For that reason, high LST is not considered as heatwave but only a long-term heat stress symptom during the season. To describe the socio-economic and demographic vulnerability of the population, the study utilizes data from the Bangladesh Bureau of Statistics (BBS). The analysis exploits county-level socio-demographic data from the Population and Housing Census 2011 and HIES 2016 to identify the variables such as age, illiteracy, unemployment, poverty, etc. The Satellite imagery of the Sentinel-2 of the Copernicus mission offered by ESRI offers vegetation, built-up areas and waterbodies data for the year 2021 at 10 m spatial resolutions and is a contribution to sensitivity and adaptive capacity. The access to electricity, as a proxy of adaptive infrastructure, is based on NASA VIIRS nighttime lights data of May, 2021. Electrification and resilience potential are at the level of districts, which is denoted by radiance values. The radiance values are measured in nW/cm\u0026sup2;/sr. This unit represents the brightness of the light observed on the surface of the earth, which is considered a common proxy of electrification and infrastructural development. Vulnerability was mediated in the context of gendered roles, where women\u0026rsquo;s domestic roles and men\u0026rsquo;s outdoor work led to greater exposure. Social context influenced risk, with a strong family network in Bangladesh mitigating the risk faced by elderly persons, but still leaving marginalized groups at increased risk. Barriers such as poor electricity, inadequate ventilation, and overcrowding of housing were identified as compounding heat stress and limiting household ability to use adaptation strategies. These datasets of remote sensing and statistics together provide an empirical, spatially fine-grained study on vulnerability of heatwaves.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Data Preprocessing\u003c/h2\u003e\n \u003cp\u003eThe datasets were preprocessed to make them commensurable for the analysis of the Heatwave Vulnerability. It began with the collection of raw data for all the chosen indicators for exposure, sensitivity, and adaptive capacity. Land Surface Temperature (LST) satellite imagery was downloaded from the MODIS Global LST product via the USGS Earth Explorer website. This was then georeferenced in ArcGIS, where projection correction, mosaicking, and any other involved processes were performed for geographical correctness. The max of the LST values was extracted for each district in Bangladesh through the Zonal Statistics tool in ArcGIS, and district-level temperatures were generated for further analysis. Satellite imagery from the Sentinel-2 Land Cover dataset was acquired via ESRI\u0026apos;s Living Atlas portal to record land cover information for Bangladesh. In ArcGIS, the data were processed and utilized to classify and calculate the spatial area of built-up area, vegetation, and waterbodies for all the districts. The classified land cover data were then clipped to district boundaries, and zonal statistics were employed to calculate the area of every land cover class by district to provide standardized spatial analysis for further integration into vulnerability assessment. VIIRS Nighttime Lights satellite imagery was retrieved from NASA Earthdata and analyzed on ArcGIS to estimate district-level access to electricity in Bangladesh. Radiance values from the imagery were clipped to the national boundary and averaged within district boundaries using zonal statistics. The vulnerability analysis used Average radiance values obtained as a proxy measure for access to electricity. Whereas socio-demographic indicators were collected from the Population and Housing Census 2011: Zila Report conducted by the Bangladesh Bureau of Statistics (BBS).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Normalization (Min-Max Scaling)\u003c/h2\u003e\n \u003cp\u003eTo render the indices of the heatwave vulnerability directly comparable, the data for each of the indicators was first normalized using the Min-Max scaling technique. This normalization process transforms all indicators to the same scale of 0 to 1. The formula is expressed as\u003c/p\u003e\n \u003cp\u003eX\u003csub\u003enorm\u003c/sub\u003e = (X\u0026thinsp;\u0026minus;\u0026thinsp;X\u003csub\u003emin\u003c/sub\u003e) / (X\u003csub\u003emax\u003c/sub\u003e \u0026minus; X\u003csub\u003emin\u003c/sub\u003e)\u003c/p\u003e\n \u003cp\u003ewhere X is the variable\u0026apos;s original value, and X\u003csub\u003emin\u003c/sub\u003e and X\u003csub\u003emax\u003c/sub\u003e are the variable\u0026apos;s minimum and maximum values across all districts, allowing standardization across various measures. The motivation behind this normalization step is the inherent heterogeneity of data sources and measurement units; without scaling these indicators onto the same range, any aggregation will result in disproportional contributions from variables with larger numeric ranges (Gan et al.,2017; Talukder et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Equal Weighting\u003c/h2\u003e\n \u003cp\u003eFollowing normalization, an equal weight was assigned to each indicator on each of the three indices. Equal weighting is grounded in the lack of decisive evidence or precedent that one indicator would be more significant than another in measuring heatwave vulnerability. Although it\u0026apos;s understood that there are certain variables that would likely have a greater impact in specific situations, equality weighting maintains simplicity, objectivity, and methodological clarity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Aggregation of Indicators\u003c/h2\u003e\n \u003cp\u003eFollowing weighting, the indicators per index were added together to generate three composite indices. Here\u0026rsquo;s the formula used for all three-composite indices: Exposure (E), Sensitivity (S), and Adaptive Capacity (AC):\u003c/p\u003e\n \u003cp\u003eI\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(C)\u003c/sup\u003e = \u0026sum;\u003csub\u003ek\u0026isin; IC\u003c/sub\u003ew\u003csub\u003ek\u003c/sub\u003ex\u0026prime;\u003csub\u003eik/\u003c/sub\u003e\u0026sum;\u003csub\u003ek\u0026isin; IC\u003c/sub\u003ew\u003csub\u003ek\u003c/sub\u003e, C\u0026isin;{E,S,AC}\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u0026bull; x\u0026prime;\u003csub\u003eik\u003c/sub\u003e = normalized value (0\u0026ndash;1) of indicator k for district i.\u003c/p\u003e\n \u003cp\u003e\u0026bull; I\u003csub\u003eC\u003c/sub\u003e = the set of indicators for component C(e.g., I\u003csub\u003eE\u003c/sub\u003e={LST\u0026prime;, population density\u0026prime;}; I\u003csub\u003eS\u003c/sub\u003e={illiteracy\u0026prime;, poverty\u0026prime;, vulnerable age groups\u0026prime;, built-up\u0026prime;, etc.}; I\u003csub\u003eAC\u003c/sub\u003e={vegetation\u0026prime;, water bodies\u0026prime;, NTL\u0026prime;}\u003c/p\u003e\n \u003cp\u003e\u0026bull; w\u003csub\u003ek\u003c/sub\u003e= weight of indicator (use w\u003csub\u003ek\u003c/sub\u003e=1 for equal-weight averages).\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eThe Exposure Index, which captures the extent to which populations are exposed to heatwaves, was calculated as the mean of normalized values of land surface temperature (LST) and population density. These two variables capture both the environmental and demographic determinants of heat exposure. The Sensitivity Index was developed to represent demographic and socio-economic conditions that affect the vulnerability of a population to the impacts of extreme heat. It combines a series of indicators, including the proportion of elderly and very young population, female population percentage, illiteracy percentage, poverty percentage, unemployment percentage, occupational pattern, percentage of disabilities, access to clean water, and built-up area coverage. The Adaptive Capacity Index measures the extent to which a district can adjust to or resist the impacts of heat stress. It includes measures such as mean night-time light radiance (as a proxy for electricity coverage), vegetation cover, and water body area. They are all indirect measures of infrastructure and environmental buffering against heat.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Kruskal-Wallis H Test\u003c/h2\u003e\n \u003cp\u003eIn order to determine whether statistically significant differences existed between the heatwave vulnerability indices, that is, Exposure, Sensitivity and Adaptive Capacity, across divisions in Bangladesh, the non-parametric Kruskal-Wallis H was used. This test has been used in comparing the medians of two or more independent groups and applies to ordinal or non-normally distributed continuous data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Post Hoc Dunn\u0026rsquo;s Test\u003c/h2\u003e\n \u003cp\u003eIn cases when the Kruskal-Wallis H test suggested that differences were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Dunn post hoc with Bonferroni correction was used to estimate which particular pairs of the divisions as well as which division by division were significantly different in heatwave vulnerability indices. Such a pairwise approach allowed distinguishing regional differences in exposure, sensitivity, and adaptive capacity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Heatwave Vulnerability Triangle\u003c/h2\u003e\n \u003cp\u003eA Heatwave Vulnerability Triangle was created to visually present an overview of how each administrative division was relative in their contribution to each of the indices, including Exposure, Sensitivity, and Adaptive Capacity. This visualization is based on the LVI-IPCC model of conceptualizing vulnerability as depending on these three indices. All the three sides or axes of the triangle were scaled (0 to 1) scores of a component and could be interpreted comparatively. The approach has provided a graphic description of the way in which levels of vulnerability differ structurally across divisions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.9 Sensitivity Analysis\u003c/h2\u003e\n \u003cp\u003eA leave-one-out sensitivity analysis was used to test the strength of the constructed indices. Under this method, one indicator at a time was dropped out of the index and the changes in the scores of the districts were noted. Three indicators were calculated for each index, which included (i) mean absolute change in index scores across the districts, (ii) maximum absolute change of any district and (iii) the proportion of the districts that changed vulnerability status as a consequence of the omission. The approach enabled us to determine the relative implication of the individual indicators on the Exposure, Sensitivity, and Adaptive Capacity indices. The indicators that had greater changes when omitted were taken as important drivers of the total index, but those that had smaller effects indicated more stability and redundancy in the system. This analysis will ensure that the indices are not overly influenced by one variable, which is known to increase the credibility of the heatwave vulnerability assessment by the study. The overall methodology of this study has been visualized in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eAn insight into the determinants of heatwave vulnerability necessitates a detailed analysis of three indices: exposure, sensitivity, and adaptive capacity. Each dimension has a varying spatial pattern and contributes uniquely to the overall Heatwave Vulnerability of Bangladesh. This study delves into the spatial variations at the district scale of these three dimensions with the help of diverse datasets, geospatial maps, and statistical graphs.\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Characteristics of Exposure to Heatwaves\u003c/h2\u003e\n \u003cp\u003eExposure in this study was measured primarily by two significant indicators: Land Surface Temperature (LST) and population density (Supplementary Table A1). LST was retrieved from MODIS satellite data, and pixel-based and district-based LST maps were generated to determine micro- and macro-level differences in surface temperature in Bangladesh. The pixel-based LST map (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea) revealed intense thermal patterns concentrated in the main urban and semi-urban areas. The mean LST was found to be above 30\u0026deg;C with a standard deviation of 4.92\u0026deg;C (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). The district-based LST map (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb), also indicated that there are several hotspots observed (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea), where the LST ranges between 23.39\u0026deg;C and 43.07\u0026deg;C. Dhaka, Gazipur, Rajshahi, Narayanganj, and Chattogram are some of the hottest areas of the country, which experience high surface temperature constantly due to urbanization, dense human settlements, and the urban heat island (UHI) effect. These urban places, which possess low vegetation cover and infrastructural concrete, have the tendency to absorb and retain heat, exacerbating surface temperature. So it is obvious that the LST value was higher in built-up areas (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). A significant negative correlation exists (Pearson correlation coefficient \u0026minus;\u0026thinsp;0.439) between LST and NDVI (Raja et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The density of built-up area was the highest in the central part of the city, where the proportion of other land use types was low. Those areas are characterized by a higher level of LST. LST was low for the north-eastern and south-eastern parts of the country, which are mostly covered by vegetation. The normal distribution curve of observed LST (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea) exhibited a right-skewed pattern, indicating that a high number of districts possess moderately high LSTs, while some urban districts experience extreme values. The box plot (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb) also showed high variability in the values of LST between districts, and outliers were also present considerably in the urban areas. This variability was explored further (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec), showing the distribution of LST based on different landcover classes. The mean LST of waterbody, vegetation, and built-up were 30.30\u0026deg;C, 30.19\u0026deg;C, and 30.30\u0026deg;C, respectively, with corresponding standard deviations of 4.54, 4.72, and 4.93 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec). Population density, the second indicator of exposure, also showed extreme differences between divisions. Dhaka, Chattogram, and Rajshahi divisions had districts with extremely high population density, making them more vulnerable due to the larger number of individuals exposed during heatwave conditions. A graph (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) of the normal distribution of exposure indicators by division, validates this observation. Dhaka and Rajshahi divisions\u0026rsquo; districts are most at risk, which means that these divisions should be prioritized for heat adaptation initiatives. The spatial pattern of exposure for all the districts of Bangladesh has been visually represented (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea), merging land surface temperature (LST) and population density to provide an obvious perception of heatwave exposure (Supplementary Table A4). The map is exhibiting acute regional differences, with maximum exposure concentrated in urbanized districts such as Dhaka, Narayanganj, Gazipur, Rajshahi, and Chattogram. These areas are characterized by high surface heat intensities driven by high built-up densities, low vegetation cover density, and high population densities, all of which boost the urban heat island effect and subject people to high heat intensification. In contrast, northern and northeastern regions like Sunamganj, Netrokona, and Kurigram have reduced exposure rates, and this is likely due to the fact that they have lower population densities as well as the availability of natural cooling factors like wetlands, forests, or open green spaces.\u003c/p\u003e\n \u003cp\u003eThe regional trends reflected (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea) confirm that heatwave exposure in Bangladesh is neither evenly distributed nor follows urbanization and ecological change patterns. This regional information is critical to the determination of hotspots where emergency heat adaptation interventions are required, particularly in those highly exposed districts where population vulnerability is already complemented by environmental insufficiency. In general, the findings confirm that the heatwave exposure is not uniformly distributed across the districts of Bangladesh. It is mostly controlled by the combined effects of climatic, demographic, and urbanization factors (Liang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nelson et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Poorly ventilated and industrially concentrated zones are experiencing both high LST and population density pressure, and thus are priority zones for heatwave management.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Characteristics of Sensitivity to Heatwaves\u003c/h2\u003e\n \u003cp\u003eSensitivity was quantified on the basis of indicators such as the proportion of vulnerable populations (children under five and elderly over 65), poverty rate, illiteracy rate, and access to health facilities (Supplementary Table A2). These indicators portray a region\u0026apos;s demographic and socioeconomic capacity to withstand heat stress. The normal distribution of the sensitivity indicators between divisions (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) is widespread, with peaks in districts located in the northern and coastal regions. These, viz., Kurigram, Nilphamari, Noakhali, and Cox\u0026apos;s Bazar, are often lacking in resilience; infrastructure, i.e., heat-resistant housing, availability of clean water, and public health facilities.\u003c/p\u003e\n \u003cp\u003eThese districts also have high infant and elderly population rates, which further increases their sensitivity. Spatial analysis (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eb) indicated that the districts of the Rangpur, Dhaka, and Chittagong divisions have the highest sensitivity (Supplementary Table A4). Rangpur is one of the poorest regions of the country, with a high proportion of the population living below the poverty line and with less access to health infrastructure facilities. Though not necessarily with the highest LST, these districts are exposed to greater heat-related health impacts due to vulnerable living conditions. It is also notable that some districts of the Dhaka division also have high exposure and sensitivity, a combination that makes them highly vulnerable. Urban slums with dense populations in the capital area are likely to have low-income families with limited resources with which to adapt to heat stress (Laue et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, even in locations with more infrastructure, socioeconomic inequality can lead to heightened vulnerability. Cumulatively, the sensitivity indicators reveal socioeconomic vulnerability and demographic susceptibility to be as much, if not more, a factor as climate in heatwave risk. The overlap between high exposure and high sensitivity increases the demand for targeted policy intervention.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Characteristics of Adaptive Capacity to Heatwaves\u003c/h2\u003e\n \u003cp\u003eAdaptive capacity, the final dimension, refers to the ability of a district to respond to heatwave impacts. Adaptive capacity in this study is captured through the aggregation of nighttime light (NTL) radiance intensity as a proxy for access to electricity and vegetation cover and waterbodies (Supplementary Table A3), which are key environmental buffers to heat stress. The NTL average radiance distribution (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) presents an evident urban-rural gradient with the mean average radiance of 0.82 nW/cm\u0026sup2;/sr. Dhaka, Khulna, Sylhet, and Chattogram divisions are of a higher radiance value, reflecting increased availability of electricity and cooling appliances like air conditioners and fans. Such districts are increasingly urbanized and have wider coverage of core facilities. According to the normal adaptive capacity distribution between divisions (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), districts under Sylhet, Khulna, and parts of Chattogram are performing well, districts under Barishal, Mymensingh, and parts of Rangpur have much lower adaptive capacity. Low electricity coverage, poor road connectivity, and lower investment in public infrastructure are the key constraints of these districts in adapting to heat stress. District-level adaptive capacity (Supplementary Table A4) also bears witness to these disparities. Adaptive capacity is mostly focused on central and southeast Bangladesh, with northwest and southern coastal districts apparently very poorly served (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ec). This kind of spatial polarization is troubling with regard to climate justice and equity, since the districts most in need of adaptation are often the least capable of achieving it. The lack of adaptive infrastructure in these low-capacity districts concentrates the population increasingly reliant on regular coping mechanisms or suffers the consequences of heatwaves with fewer remedies. Projects like solar-powered cool centers, emergency health systems, and community-based adaptation networks could fill this gap.\u003c/p\u003e\n \u003cp\u003eThese analyses reveals that heating events are not evenly distributed throughout Bangladesh\u0026rsquo;s districts. Dhaka, Rajshahi and Chattogram have the highest risk because of their raised land surface temperatures and large populations, combined with the urban heat effect. In comparison, areas in Rangpur and alongside the coast are more sensitive, because of poverty, low rates of literacy, less water supply and large numbers of vulnerable age groups. Access to electricity, plenty of greenery and available water is mostly found in cities, leaving many regions in the north and south very limited.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Statistical Validation of Exposure, Sensitivity and Adaptive Capacity\u003c/h2\u003e\n \u003cp\u003eAll the three indices were analyzed by the district level scores and analyzed based on the division. The findings showed that there existed no significant differences in exposure to heatwaves across divisions with test statistic (H)\u0026thinsp;=\u0026thinsp;11.937 and p-value\u0026thinsp;=\u0026thinsp;0.1026 which means that regional differences are statistically insignificant at the 5 percent level. This indicates that, whereas the spatial patterns of exposure identify some of the urban districts as more exposed, these variations are not statistically significant as measured by aggregation by division. Contrastingly, there was a statistically significant variability of sensitivity to heatwaves by divisions. The Kruskal-Wallis H test made an output of 23.132 with p\u0026thinsp;=\u0026thinsp;0.0016, which strongly rejected the null hypothesis of equal distributions of sensitivity. This finding makes it certain that certain divisions especially Rangpur, Barishal, and regions of Chattogram are socio-demographically more vulnerable likely because of the add-on impact of poverty, illiteracy, and a greater number of vulnerable age groups in that area. An obvious argument to this finding is this spatial analysis and maps depicted previously that show that northern and coastal areas are more demographically sensitive to extreme-heat event temperatures.\u003c/p\u003e\n \u003cp\u003eThere was also a huge variance in adaptive capacity between divisions (test statistic was 19.098 and p-value was 0.0079), which means that there is an infrastructural and ecological imbalance in Bangladesh to deal with the effects of a heatwave. Those divisions, which have better access to electricity (proxied by nighttime intensity of light), vegetation and water bodies including Dhaka and Khulna portrayed a slightly better score of adaptive capacity. On the other side, the district in Barishal, Rangpur and Mymensingh had much less ability to adjust, which confirms the previous findings based on spatial analysis. The post hoc result supports Kruskal-Wallis H test results presented above, which revealed the inter-divisional difference of Exposure (H\u0026thinsp;=\u0026thinsp;11.937, p\u0026thinsp;=\u0026thinsp;0.1026), Sensitivity (H\u0026thinsp;=\u0026thinsp;23.132, p\u0026thinsp;=\u0026thinsp;0.0016) and Adaptive Capacity (H\u0026thinsp;=\u0026thinsp;19.098, p\u0026thinsp;=\u0026thinsp;0.0079). The insignificance in exposure of the divisions could be explained by the fact that land surface temperature and population density as the indicators of heatwave conditions have more even geographic distribution on the territory of the country (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea). Despite there being some visible difference in the Exposure Map with regard to spatial variation in land surface temperature and population density (especially around the southern and central parts), the Kruskal-Wallis tests reveal that such variations are not statistically significant across divisions (p\u0026thinsp;=\u0026thinsp;0.1026). This validates the conclusion that the climatic exposure is less skewed in Bangladesh perhaps because the entire country has succumbed to this monsoonal climatic condition, whereas the microclimate has fluctuated locally. By comparison, Sensitivity and Adaptive Capacity will depend on more short-distance socio-economic and infrastructural factors which are highly discrepant with administrative boundaries. On the whole, the post hoc test proves the strength of indices of the Heatwave Vulnerability by ensuring that apparent spatial differences are statistically significant in many important combinations. This statistical confirmation substantiates the demand of region orientation in adaptation approaches. Hypothetically, it would be possible to reduce the Barishal and Rangpur risk by investing in education, infrastructure and heat resilient housing.\u003c/p\u003e\n \u003cp\u003e4.5 Heatwave Vulnerability Triangle of Divisional Heatwave Vulnerability Indices\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003cp\u003eHeatwave Vulnerability Triangle captures complex data sets in an easily understandable graphic with an emphasis on differences and similarities between regional vulnerabilities. Based on the triangle (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e), the most vulnerable area is Dhaka Division since it is characterized by very hot land surface temperatures (LST), and extremely high population density. The high density of population, a high density of built-up properties and low coverage of vegetation aggravate the urban heat island (UHI) effect, increasing thermal exposure to its residents. Nevertheless, although highly affected and moderately sensitive, Dhaka score decently on adaptive capacity, because of the high access to electricity, and high access to sources of critical infrastructure and services.\u003c/p\u003e\n \u003cp\u003eAdaptive Capacity Level Division\u003c/p\u003e\n \u003cp\u003eConversely, the situation is very different in Rangpur and Barishal Divisions. They have a high level of sensitivity and low versatility degree even though the exposure scores were similar to the central urban areas. The region has had high levels of poverty, a greater percentage of the vulnerable population, poor health infrastructure, and illiteracy rates. Their adaptability is also limited by bad access to electrification and the unexhausted number of urban services along with lesser coverage of vegetation and water bodies. The combination of such high sensitivity and low capacity puts these divisions in a highly vulnerable position of being affected by any moderate-level heat events. Chattogram Division presents a mixed profile: moderately high exposure and sensitivity, with variable adaptive capacity across its districts. Coastal districts like Cox\u0026rsquo;s Bazar and Noakhali, though rich in natural cooling elements (water bodies and vegetation), often lack sufficient infrastructure to support heatwave resilience, thus requiring attention for targeted interventions. Interestingly, Sylhet and Khulna Divisions display relatively better adaptive capacities, likely due to a combination of urban infrastructure, moderate electrification, and ecological buffers such as forests and water bodies. These characteristics offset some of their sensitivity and exposure, making them more resilient than other rural divisions.\u003c/p\u003e\n \u003cp\u003eOverall, the triangle analysis illustrates a critical imbalance between exposure, sensitivity, and adaptive capacity in many divisions. While exposure is more evenly spread (with some statistically insignificant variation across divisions), sensitivity and adaptive capacity differ significantly, as confirmed by Kruskal-Wallis and Dunn\u0026rsquo;s tests. These disparities emphasize the importance of tailored policy responses. Divisions like Barishal, Rangpur, and parts of Mymensingh, where high vulnerability shoots from both social fragility and lack of resilience infrastructure, should be prioritized in heatwave adaptation strategies. The triangle serves not just as a diagnostic tool, but as a compelling visual argument for equity-driven adaptation planning. It reinforces the need to focus on climate justice, where the least resilient communities receive the greatest support to cope with intensifying heat extremes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e4.6 Sensitivity Analysis of Indices\u003c/h2\u003e\n \u003cp\u003eA leave-one-out sensitivity analysis was performed to determine the strength of the indices and in this analysis, the indicators were taken out one at a time to monitor the changes in the index scores (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e). The findings indicated that there are those indicators that have a stronger impact on the indices as compared to the others. In case of Exposure Index, not only land surface temperature (LST) but also population density played a critical role. Omitting one or the other led to an average absolute change of 0.13 in the scores of the various districts and over half of the districts (59.4%) changed exposure class when LST was not used. This confirms that in Bangladesh exposure is mostly influenced by the interaction of climatic stress (heat intensity) and population pressure (population density) supporting previous results that urbanized and densely populated districts like Dhaka, Rajshahi and Chattogram are the most exposed.\u003c/p\u003e\n \u003cp\u003eMost demographic indicators were relatively small when left out in the Sensitivity Index with negligent changes in districts. However, accumulated builtup area, poverty and availability of water produced visible variations with as much as 9.4 percent of districts changing the sensitivity class. This shows that the age structure as well as gender and disability are local causes of vulnerability, but structural socioeconomic causes, particularly, poverty, urban structure, and access to water, are more likely to define the sensitivity of a district. The results are in line with previous findings that districts in Rangpur and Mymensingh are extremely sensitive because of poverty and poor infrastructure. In the case of the Adaptive Capacity Index, water bodies and vegetation cover were the most dominant factors. Removal of vegetation by an average of 0.13 and reclassified 44.4 percent of districts, whereas removal of waterbodies shifted 66.7 percent of districts. Electrification, represented by nighttime light was also significant, changing 18.1 percent of districts when omitted. The findings highlight the key place of ecological buffers and infrastructure in the development of the resilience. The north-western and southern districts, which are already known to be with low adaptive capacity, are particularly susceptible without vegetation and cooling systems based on water.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Leave-One-Out Sensitivity Analysis for Indices\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicator dropped\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean absolute change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax absolute change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistricts changed class (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElderly Pop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery Young Pop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliteracy Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuiltup Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoverty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale Pop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAdaptive Capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaterbodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn general, the sensitivity analysis proves that the indices do not rely on the individual variable too much, though particular indicators that have the most significant influence are LST (exposure), poverty, and built-up area (sensitivity), vegetation and waterbodies (adaptive capacity). This confirms the previous observation that the relationship between climate, urbanization, and socioeconomic vulnerability is a determinant of the heatwave vulnerability in Bangladesh. It also highlights the necessity of policies that can protect the natural cooling systems along with the reinforcement of basic infrastructure in order to develop a resilience where it is most vulnerable.\u003c/p\u003e\n \u003cp\u003eThe results of this study rely on seasonal mean LST to expose, thus not capturing events that could have severe short heatwaves or the distinction between the daytime and nighttime heat. To a great extent, hot nights need to be considered; however, they have not been addressed directly. Nighttime lights were used as a proxy variable measuring electricity access but this does not reflect the reliability of the grid or access to cooling. Factors playing a role in vulnerability include social factors (e.g. gender roles, elderly isolation, local support networks, etc.), which should be investigated further.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion and Recommendations","content":"\u003cp\u003eIt is obvious that the effects of heatwaves vary across Bangladesh because the country has different environmental, population, and infrastructure factors in different areas. Cities have been experiencing prolonged exposure to intense solar radiation on a regular basis due to overcrowding, which intensifies land surface heating. Due to the lack of various resources and high-quality services in rural and northern regions, the citizens tend to adjust themselves to hard circumstances. Most of the most vulnerable regions in the country lack the infrastructure that can assist them to cope with the increase in temperatures. These findings show clearly that adaptation to the heat has to be region-specific by encompassing the impacts of the environment, social components and the effect of policies. The empirical evidence made was stronger as these findings were statistically validated by the Kruskal-Wallis H test when it was proven that there is a considerable inter-divisional variability in there, in reference to sensitivity and adaptive capacity. Further, the post hoc investigation of Dunn revealed the existence of specific divisional differences, with Barishal and Rangpur being the most vulnerable ones because of both their extreme sensitivity and weak adaptive potential. Such findings substantiate the fact that although exposure is relatively balanced throughout the nation, social and infrastructural disparities are the primary determinants of heatwave vulnerability.\u003c/p\u003e\u003cp\u003eThe Heatwave Vulnerability Triangle was used to create a visual representation of the situation that could be interpreted, and which shows that there is an imbalance that must be addressed on an urgent basis as every division has different levels of exposure, sensitivity and adaptive capacity. Societies that are more vulnerable to increased risk of heatwave but possess a weak adaptive capacity need improved infrastructure, increased cool spot and air conditioning and local resilience capacity. Heat is one of the many warming climate challenges that local and national planners can confront using this model. It highlights the importance of climate justice. The least resilient groups need to be provided with the most robust support in dealing with the extreme heat. The sensitivity analysis also provided additional validity of the findings since it demonstrated that climatic and ecological indicators, specifically LST, vegetation and waterbodies, influences the vulnerability results, most of all. This supports the necessity of policy interventions that will involve ecological recovery and infrastructural development to minimize the exposure to extreme heat. Although the analysis gives excellent trends, it is composed of seasonal means of land surface temperature and, therefore, cannot identify short-duration heatwaves or nighttime heat records. Future studies are needed to include the event based heatwave detection and nighttime temperature indicators to enhance the insight in heat-related hazards.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Bangladesh Bureau of Statistics (BBS) for providing data used in this research. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdullah, F., Myers, J., Basu, D., Tintinger, G., Ueckermann, V., Mathebula, M., ... \u0026amp; Jassat, W. (2022). Decreased severity of disease during the first global omicron variant covid-19 outbreak in a large hospital in tshwane, south africa. \u003cem\u003eInternational journal of infectious diseases\u003c/em\u003e, \u003cem\u003e116\u003c/em\u003e, 38-42.\u003c/li\u003e\n\u003cli\u003eAbrar, R., Rahman, M. M., Rifat, M. H., \u0026amp; Hossain, M. A. (2022). 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Appraisal of the heat vulnerability index in Punjab: a case study of spatial pattern for exposure, sensitivity, and adaptive capacity in megacity Lahore, Pakistan. \u003cem\u003eInternational journal of biometeorology\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e, 1669-1682.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Heatwave Vulnerability, Exposure, Sensitivity, Adaptive Capacity, Spatial Analysis, and Statistical Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7752795/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7752795/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConsidering the effects of the climate change, the heat waves are becoming one of the most significant and least researched environmental hazards in Bangladesh, while determination of the areas most vulnerable to increased temperatures and frequent heat waves is necessary for preparing firm climate adaptation plans. This study explores Bangladesh's heatwave vulnerability by developing three composite indices related to heatwave vulnerability, including- exposure, sensitivity, and adaptive capacity, and was analyzed using district-level data. Exposure was measured using land surface temperature (LST) and population density, while sensitivity included the elderly, very young, and female population, illiteracy rate, built-up area, poverty, access to water, unemployment, occupation, and disability. Adaptive capacity was calculated using vegetation cover, water resources, and electricity access (proxied by average radiance of nighttime light). Using both spatial and statistical methods, the data were normalized and aggregated to obtain indices of heatwaves. Findings revealed significant regional disparities, with a high degree of exposure found in regions where high temperatures are combined with considerable population, such as Dhaka, Rajshahi and Chattogram, and the strongest sensitivity in Rangpur and coastal regions where the population is economically vulnerable. The country could be lacking infrastructures that could moderate the effects of the enormous heat in the north and south parts of the country. The statistical validity of differences in the indices of vulnerability across the country was confirmed by the use of Kruskal-Wallis H test. The variance of sensitivity and adaptive capacity was large, with the distribution of the exposure, being less variable. In addition, a heatwave vulnerability triangle was constructed to visualize the regional disparity in the exposure, sensitivity, and adaptive capacity. The sensitivity analysis was used to confirm the strength of the indices, which revealed that LST, vegetation cover, and waterbodies are the strongest indicators that determine the vulnerability of the districts. Overall, the spatial analysis indicates in order to adapt to the heat and build the resilience to heat in Bangladesh, it is critical to prioritize the most vulnerable and least ready areas.\u003c/p\u003e","manuscriptTitle":"Characterising Spatial Patterns of Exposure, Sensitivity and Adaptive Capacity to Assess Heatwave Vulnerability of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 17:24:38","doi":"10.21203/rs.3.rs-7752795/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-11-14T12:09:15+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-14T12:01:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Natural Hazards","date":"2025-10-25T15:52:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-01T04:03:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2025-09-30T11:03:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9a67cd98-5c7b-4fa8-80ac-66d5a4765d57","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T15:40:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 17:24:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7752795","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7752795","identity":"rs-7752795","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}