A study has been conducted with the objective to assess the existing early flood warning dissemination system (EFWDS) in Bangladesh and to suggest the suitable improvements in the same system based on review of literature, interaction with stakeholders of various organisations involved in flood forecasting and dissemination, and analysis of feedback from the flood‐affected people of Dhobaura and Shibalaya subdistricts in Bangladesh. The existing set‐up has been studied to assess the present activities and future expectations. The recommendation for active participation by all related organisations has been made in this study. Two studies have been conducted by surveying the opinion of flood vulnerable communities so that all elements of the EFWDS would provide useful flood warnings to all potential users.

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The relative performance of global climate models (GCMs) of phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977-2005. The multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multicriteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skilful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, a significant improvement in CMIP6 MME compared to CMIP5 MME was noticed in simulating rainfall over Bangladesh at annual and seasonal scales. CMIP6 MME also showed significant reduction in maximum and minimum temperature biases over Bangladesh. However, systematic wet and cold biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlation with observed data compared to CMIP5 GCMs, but higher difference in terms of standard deviations and centered root mean square errors, indicating better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables for different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature compared to CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is incompatible with the climate models used in this research.

Quantitative precipitation estimates from the European Centre for Medium‐Range Weather Forecasts (ECMWF) modelling system and the Tropical Rainfall Measuring Mission (TRMM) may be used for areas where rainfall data are not available or where the number of rainfall stations is inadequate. In this study, ECMWF 40 years re‐analysis (ERA40) and TRMM satellite estimated daily rainfall data have been analysed at three station locations, Dinajpur, Rangpur, and Sylhet, in the Bangladesh territories of Ganges, Brahmaputra and Meghna basins, respectively. Statistical verifications (visual verification, yes/no dichotomous verification and continuous variables verification methods) have been applied to these data. Bangladesh Meteorological Department (BMD) rainfall data are used as the reference data. The continuous variables verification methods show the better accuracy of the TRMM data. The probability of occurrence of TRMM rainfall is close to the BMD‐observed data. It implies that the TRMM data can be used for modelling of design flood in areas where observed rainfall data are not available. However, in the case of the TRMM rainfall data, the chances of missing a flood event are higher compared to the ECMWF data. Yes/no dichotomous verification methods show that ECMWF rainfall data are safe to use for flood‐forecasting‐related planning purposes because rain events are estimated better in ECMWF. The probability of occurrence of ECMWF rainfall shows that these data can also be used to estimate the design flood flow. Both ECMWF and TRMM rainfall data may be used where locally observed rainfall data are not available. Copyright © 2011 Royal Meteorological Society

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Collecting data using an appropriate sampling technique is a challenging task for a researcher to do. The researchers will be unable to collect data from all possible situations, which will preclude them from answering the study’s research questions in their current form. In light of the enormous number and variety of sampling techniques/methods available, the researcher must be knowledgeable about the differences to select the most appropriate sampling technique/method for the specific study under consideration. In this context, this study also looks into the basic concepts in probability sampling, kinds of probability sampling techniques with their advantages and disadvantages. Social science researchers will benefit from this study since it will assist them in choosing the most suitable probability sampling technique(s) for completing their research smoothly and successfully.