The Meghna River basin is a transboundary basin that lies in Bangladesh (~40%) and India (~60%). Due to its terrain structure, the Bangladesh portion of the basin experiences frequent floods that cause severe human and economic losses. Bangladesh, as the downstream nation in the basin, faces challenges in receiving hydro-meteorological and water use data from India for effective water resource management. To address such issue, satellite rainfall products are recognized as an alternative. However, they are affected by biases and, thus, must be calibrated and verified using ground observations. This research compares the performance of four widely available gauge-adjusted satellite rainfall products (GSRPs) against ground rainfall observations in the Meghna basin within Bangladesh. Further biases in the GSRPs are then identified. The GSRPs have both similarities and differences in terms of producing biases. To maximize the usage of the GSRPs and to further improve their accuracy, several bias correction and merging techniques are applied to correct them. Correction factors and merging weights are calculated at the local gauge stations and are spatially distributed by adopting an interpolation method to improve the GSRPs, both inside and outside Bangladesh. Of the four bias correction methods, modified linear correction (MLC) has performed better, and partially removed the GSRPs' systematic biases. In addition, of the three merging techniques, inverse error-variance weighting (IEVW) has provided better results than the individual GSRPs and removed significantly more biases than the MLC correction method for three of the five validation stations, whereas the two other stations that experienced heavy rainfall events, showed better results for the MLC method. Hence, the combined use of IEVW merging and MLC correction is explored. The combined method has provided the best results, thus creating an improved dataset. The applicability of this dataset is then investigated using a hydrological model to simulated streamflows at two critical locations. The results show that the dataset reproduces the hydrological responses of the basin well, as compared with the observed streamflows. Together, these results indicate that the improved dataset can overcome the limitations of poor data availability in the basin and can serve as a reference rainfall dataset for wide range of applications (e.g., flood modelling and forecasting, irrigation planning, damage and risk assessment, and climate change adaptation planning). In addition, the proposed methodology of creating a reference rainfall dataset based on the GSRPs could also be applicable to other poorly-gauged and inaccessible transboundary river
Abstract.A case study of Bangladesh presents a methodological possibility based on a global approach for assessing river flood risk and its changes considering flood hazard, exposure, basic vulnerability and coping capacity. This study consists of two parts in the issue of flood change: hazard assessment (Part 1) and risk assessment (Part 2). In Part 1, a hazard modeling technology was introduced and applied to the Ganges, Brahmaputra and Meghna (GBM) basin to quantify the change of 50-and 100-year flood hazards in Bangladesh under the present and future climates. Part 2 focuses on estimating nationwide flood risk in terms of affected people and rice crop damage due to a 50-year flood hazard identified in Part 1, and quantifying flood risk changes between the presence and absence of existing water infrastructure (i.e., embankments). To assess flood risk in terms of rice crop damage, rice paddy fields were extracted and flood stage-damage curves were created for maximum risk scenarios as a demonstration of risk change in the present and future climates. The preliminary results in Bangladesh show that a tendency of flood risk change strongly depends on the temporal and spatial dynamics of exposure and vulnerability such as distributed population and effectiveness of water infrastructure, which suggests that the proposed methodology is applicable anywhere in the world.
Flood vulnerability is estimated by Flood Damage Functions (FDFs), which are crucial for integrated flood risk assessment for developing sustainable flood management, mitigation, and adaptation strategies under global change. However, the FDFs, either empirical or synthetic, are not available in Bangladesh. Therefore, this paper focused on developing the synthetic type of FDFs for agriculture and rural households through the data of a well–structured questionnaire survey conducted in two pilot sub–districts of northeastern Bangladesh in the Meghna River basin. Multiple regression analyses were performed on the collected data, and the best performing models were selected to establish FDFs. The FDF for agriculture (~196 samples) was developed concerning damage to Boro rice, whereas the FDFs for households (~165 samples) were developed concerning damage to the buildings and household property of three house types (Mud, Brick, and Concrete), separately. The results revealed that there were no yield losses when the water levels were lower than 25 cm (~rice tiller height), and the yield losses were ~100% when the water levels were 70–75 cm deep (~rice grain height). Mud houses and their household property were found the most flood–vulnerable and likely to experience total damage when the water levels exceeded 150 cm above the plinth level, whereas the damage to Brick and Concrete houses and their household property was found likely to remain partial even when the water levels exceeded 150 cm above the plinth level. The developed FDFs can be used to assess potential flood risk in the study area for sustainable and effective management of flood disasters and build back better under global change in the future.
After publication of the paper [1], it was found that one of the contributing authors, Nikolaos Mastrantonas, was not included in the original version of the article.[...]
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.