This study evaluated the rainfall historical simulations of 15 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in replicating annual and seasonal rainfall climatology, their temporal variability and trends in Bangladesh for the period 1979–2014, considering ERA5 (ECMWF Reanalysis 5th Generation) reanalysis as the reference dataset. Shannon's Entropy decision‐analysis was employed for GCMs' rating based on eight statistical indicators and a comprehensive rating metric for the final grading of the GCMs. The majority of the CMIP6 GCMs accurately reproduced the spatial feature of ERA5 rainfall. However, the GCMs underestimated annual rainfall by an average of 190.5 mm, with the highest underestimation in monsoon (131.76 mm) and least in winter (3.52 mm) seasons. Most GCMs also underestimated rainfall variability for all seasons except winter. Besides, the GCMs showed an increasing trend in pre‐monsoon and a decreasing trend in post‐monsoon rainfall like ERA5, but an opposite (negative) to ERA5 trend (positive) in monsoon season rainfall. The ensemble mean of the GCMs showed higher skill in reconstructing rainfall climatology, temporal variability and trends than the individual GCMs. The study identified MPI‐ESM1‐2‐LR, MPI‐ESM1‐2‐HR, and GFDL‐ESM4 as the most effective GCMs in reproducing precipitation over Bangladesh. The selected models' simulation can be used for climate change impact assessment in Bangladesh after bias minimization.

The successful social implementation of a rainwater-harvesting tank can save millions of people in coastal Bangladesh from drinking saline water and health risks. However, previous studies have shown that several potentially effective, innovative disaster-preventive technologies failed to disseminate even after proactive promotional campaigns. People at risk worry about adopting innovative preventive measures because of the uncertainties attached to the new technology, such as its merits, cost, maintenance, durability, social acceptance, etc. Instead of mass media, people rely on social networks to obtain trusted, verified, and personal information. Hearing plays an important role, through which information diffuses from pioneer adopters to late adopters or potential adopters across settlements, starting from the village to district to region. Unlike conventional studies, limited to understanding the regional dimension of diffusion, this study investigated how the information diffuses from pioneer adopters to potential adopters at both the macro-level (e.g., districts, subdistricts, and towns) and micro-level (e.g., villages and neighborhoods). This study was based on field surveys through interviewing 196 innovative rainwater-tank adopters from 30 villages and communities in two subdistricts in coastal Bangladesh. We found that the macro-level pioneer adopters played a critical role in diffusing awareness knowledge, through which people in new villages, neighborhoods, and sub-districts, where mass media and change agents failed to reach, became aware of the existence of the innovative measure. However, macro-adapters alone failed to disseminate the innovation further, as the local communities intend to pay to heed the suggestions and experiences of the local (micro) pioneer adopters to understand the principle and how-to knowledge of the innovation. Information is diffused in the villages and neighborhoods through local pioneer adopters through direct, intimate personal contacts.

Bangladesh, being a geographically vulnerable country experiences a variety of severe weather phenomena and most of them occur during the pre-monsoon season (March-May). This study investigates the occurrence of the pre-monsoon extreme weather events in Sunamganj and their impacts on the rice yield. Sunamganj is one of most vulnerable areas of Bangladesh that experiences almost all types of extreme weather phenomena during pre-monsoon and therefore the assessment of the effect of these hazards on rice yield have been conducted in Dakshin Sreepur union, Tahirpur upazila, Sunamganj. This study has been accomplished by blending the findings of climate data analysis with field investigation. About 13 extreme climate indices related to temperature and rainfall created by the Expert Team on Climate Change Detection Indices (ETCCDI) were considered to evaluate the correlation between rice yield and extreme weather events in the particular region. The correlation shows that extreme events are not the only factor influencing the rice yields; rather, a few related factors such as socio-economic context, physiographic settings, river bed siltation, construction and management of dams are also affecting the rice production in the study area. As a result, the solutions derived from farmers are important in reducing the detrimental impact of extreme events on rice yield for effective food production. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 11(1), 2022: 111-130

This study attempts to simulate heatwave events over Bangladesh using the WRF-ARW Model that could be used to implement a best practice strategy for predicting heatwaves in Bangladesh. In this research, 17 heatwave events were identified from the period between 2007 and 2022. The thresholds for heatwave events were considered when the maximum temperature from a weather station was above the 95th percentile for three consecutive days. After carefully identifying the events, all the events were simulated with the WRF-ARW model. Simulated temperature, relative humidity, and wind vector (direction and speed) were analyzed for this research. The synoptic conditions were synthesized for all the heatwave events. It was found that the propagation of warm temperatures corresponded to the movement of wind and was aptly supported by the presence of humidity in the region of interest. This information could be used at the operational level for predicting heatwaves. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(1), 2023, P 9-28