Members’ Publication

As climate change intensifies coastal hazards globally, understanding and quantifying community vulnerability is crucial for effective adaptation strategies. This study examines the application of indicators in planning adaptive capacity strategies to reduce community vulnerability to tsunami hazards (THs). Using a case study approach in Mehuin, a coastal community in southern Chile susceptible to tsunamis, we identified and validated specific indicators through both quantitative and qualitative methods. Secondary data sources were used to initially identify indicators, which were then validated through 12 in-depth interviews with community representatives and local emergency management actors. The Analytic Hierarchy Process was employed to determine the relative weights of indicators in adaptive capacity planning. A total of 25 specific indicators were identified for Mehuin, grouped into 7 key issues: human exposure, physical exposure, geographical factors, socioeconomic conditions, psychological factors, governance, and planning capacities. The most influential indicators for adaptive capacity planning were found to be preparedness and planning-related, including the existence of emergency plans (14.33% weight), quality of urban/regional plans (14.20%), and building codes (10.79%). Socioeconomic factors like collective action (9.06%) and social networks (5.61%) were also significant. The least influential factors were building structure (0.16%) and housing density (0.36%). The indicators provide insights into community vulnerabilities and can inform targeted strategies to enhance adaptive capacity. This study concludes that local-scale indicators are crucial for identifying community-specific vulnerabilities and priorities in tsunami preparedness. However, their application requires careful contextual consideration. The findings demonstrate the importance of integrating community perspectives in vulnerability assessments and adaptive planning for coastal hazards. This research advances evidence-based approaches for disaster risk reduction and sustainable development in coastal regions, aligning with UN Sustainable Development Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action).

Groundwater depletion in arid regions poses a significant threat to agricultural sustainability and rural livelihoods. This study employs geospatial analysis and economic modeling to address groundwater depletion in the arid Barind region of Northwest Bangladesh, where 84% of the rural population depends on agriculture. Using remote sensing and GIS, we developed an elevation map revealing areas up to 60 m above sea level, exacerbating evaporation and aquifer dryness. Field data collected through Participatory Rural Appraisal tools showed farmers exhibiting “ignorant myopic” behavior, prioritizing short-term profits over resource conservation. To address this, an Increasing Block Tariff (IBT) water pricing model was developed, dividing water usage into three blocks based on irrigation hours: 1–275 h, 276–550 h, and 551+ h. The proposed IBT model significantly increases water prices across the three blocks: 117 BDT/hour for the first block (from current 100–110 BDT/hour), 120 BDT/hour for the second block, and 138 BDT/hour for the third block. A demand function (y = −0.1178x + 241.8) was formulated to evaluate the model’s impact. The results show potential reductions in groundwater consumption: 59 h in the first block, 26 h in the second block, and 158 h in the third block. These reductions align with the principles of integrated water resource management (IWRM): social equity, economic efficiency, and environmental integration. The model incorporates economic externalities (e.g., well lifting costs) and environmental externalities (e.g., crop pattern shifts), with total costs reaching 92,709,049 BDT for environmental factors. This research provides a framework for sustainable groundwater management in arid regions, potentially reducing overextraction while maintaining agricultural productivity. The proposed IBT model offers a locally driven solution to balance resource conservation with the livelihood needs of farming communities in the Barind tract. By combining remote sensing, GIS, and economic modeling, this research provides a framework for sustainable groundwater management in arid regions, demonstrating the power of geospatial technologies in addressing complex water resource challenges.

Access to land is very important for socially and economically disadvantaged people, from thecountries where agriculture is the main source of employment to improve their livelihoods. Itbrings an opportunity for producing their livelihoods and, at the same time, it affects incentivesto make investments and the ability to access financial markets. Furthermore, socially &economically disadvantaged people’s access to land contributes in reducing poverty andimproving economic growth of a country like Bangladesh. Land reform is a tool beingemployed by the governments around the world to contribute in this respect. Theimplementation of land reform programs takes place through either of three major approaches;state-led, community-based, and market-assisted. The limitations and weaknesses with theseapproaches hinder the success of the programs. The paper focuses on how land reformapproaches can contribute to benefit socially and economically disadvantaged people of Bangladesh.

Bangladesh is among the countries with the highest concentrations of particulate matter and other air pollutants, according to the World Health Organization. The Department of Environment of Bangladesh has installed air monitoring systems in Dhaka, Chattogram, Khulna, Rajshahi, Barishal, Gazipur, and Narayanganj to observe daily gaseous pollutants and particulate matter (PM) concentrations in those cities. This study analyzed the concentration of gaseous and particulate pollutants from 2018 to 2023 in these urban areas of Bangladesh. Ambient air concentrations of PM2.5, PM10, carbon monoxide (CO), oxides of nitrogen (NOx) and ozone (O3) were monitored in the dry and wet seasons for these cities. Temporal variability including hourly, day of the week, monthly, and seasonal variations of particulate matter and gaseous pollutants (except seasonal variation) were assessed. Both PM2.5 and PM10 exceeded the Bangladesh National Ambient Air Quality Standard (BNAAQS) and the World Health Organization (WHO) air quality guidelines during the observed period for all the observed regions. PM2.5 mean concentration was maximum in Narayanganj i.e., 109.7 µg/m3 (313% of the limit of the air quality standard), and PM10 mean concentration was maximum in Narayanganj i.e., 203.3 µg/m3 (407%). Among the observed cities, Khulna had the better air quality although it was not satisfactory at all. However, the gaseous pollutants were within permissible limits. The temporal patterns suggested that vehicles, brick kilns, and industries were responsible for the poor air quality in Bangladesh. The air quality during the period in which COVID-19 was most prevalent (2020–2021) was compared to the two years prior and post to that interval. In general, air quality improved during the COVID period, but in the post-COVID period, they returned to concentrations similar to the pre-COVID period. After identifying possible pollutant sources, these results will assist decision-makers in taking action and implementing policies to control air pollution in cities.

All over the world, unplanned land conversion, especially in urban areas, has become a burning issue, resulting in an increase in surface temperature. The Rajshahi district of Bangladesh is not an exception, where greenery and water bodies are decreasing because of massive infrastructural development. Thus, the study aims to highlight the relationship between land surface temperature and land cover by using GIS and remote sensing approaches. The results of the study reveal that the land surface temperature ranges from 26.18°C to 38.91°C and the temperature difference between urban and rural areas is 1–2°C. However, there is no comfort zone (temperature between 16°C and 25°C) in the year of 2019. More than 81% of the land had a temperature between 26°C and 32°C and about 18% has been delineated as a very hot zone (more than 32°C), within which 50% of the land is in the Rajshahi City Corporation (RCC). Also, a linear regression analysis was conducted to know how temperature is related to different indices where land surface temperature (LST) was negatively correlated with normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), while an affirmative relationship was found between LST and normalized difference built-up index (NDBI). Thus, the study helps to impede the rising temperature not only for the study area but also for other cities by providing an indicator to the concerned development authorities.