Search Results (14)

Background: Bangladesh’s wetlands support fish genetic resources, biodiversity, and food security but face obstacles like habitat degradation, overfishing, and climate change. This research looks at the diversity, abundance, IUCN conservation status, and stakeholder views for sustainable wetland management in three major wetlands: the Sundarbans mangrove (brackish water), the Sylhet floodplain (freshwater), and the Meghna River basin (estuary). Methods: To assess ecosystem health and vulnerability, we assess fish biodiversity and conservation status using Margalef’s Species Richness Index and Shannon–Weaver Diversity Index. We also used structured questionnaires to interview and gain stakeholders’ perceptions. Results: A total of 165 fish species were identified and categorized based on the IUCN Red List. The Sundarbans exhibited the highest species richness and diversity. The proportion of vulnerable species was highest in Sylhet (15%), followed by the Sundarbans (12%) and Meghna (9%), while 54% of fish species in Sylhet, 36% in the Sundarbans, and 26% in Meghna were not threatened. This study emphasizes integrated wetland management solutions that support SDGs 2 (Zero Hunger), 14 (Life Below Water), and 15 (Life on Land) as outlined by the Food and Agriculture Organization (FAO). Conclusion: Findings provide a foundation for policymakers, researchers, and conservationists to develop sustainable wetland management frameworks that safeguard fish genetic resources, livelihoods, and ecological balance. Full article

This study assesses future climate change impacts on the hydrologic response of the Upper Meghna River Basin (UMRB), a major river system in Bangladesh. Separate SWAT (Soil and Water Assessment Tool) hydrologic models were developed for the three major sub-basins of the UMRB, i.e., Barak, Meghalaya, and Tripura, considering their unique geographical, hydrological, and land-use characteristics. To evaluate the efficiency of multi-site modeling in providing better model performance, the SWAT models were calibrated at both single and multiple locations. Those models were then simulated to estimate future flows using climate projection data from thirteen CMIP6 General Circulation Models (GCMs) under moderate and extreme emission scenarios, SSP2-4.5, and SSP5-8.5. The results revealed that the annual maximum flow will keep increasing gradually with time. The outlets of the Meghalaya sub-basin will experience a more significant rise in future flow in the upcoming decades compared to the Barak and Tripura sub-basins. Results showed that dry season flows with increases of up to 31–50% would be less affected compared to the wet periods, which could experience increases of up to 47–66%) across the sub-basins by the end of the 21st century under extreme emission projections. Besides an increasing trend in the mean flow, future flows at several outlets also exhibited an escalation in the intensity and frequency of extreme flood events. Full article

The Ganges–Brahmaputra estuary, located in the northern Bay of Bengal, is situated within the largest delta in the world. This river basin features a complex river system, a dense population, and significant variation in watershed vegetation cover. Human activities have significantly impacted the concentration of total suspended matter (TSM) in the estuary and the ecological environment of the adjacent bay. In this study, we utilised the Landsat series of satellite remote sensing data from 1990 to 2020 for TSM retrieval. We applied an atmospheric correction algorithm based on the general purpose exact Rayleigh scattering look-up-table (LUT) and the shortwave-infrared (SWIR) bands extrapolation to Landsat L1 products to obtain high-precision remote sensing reflectance. In conjunction with the normalised difference vegetation index (NDVI), precipitation, and discharge data, we analysed the variation and influencing mechanisms of TSM in the Ganges–Brahmaputra estuary and its surrounding areas. We revealed notable seasonal variation in TSM in the estuary, with higher concentrations during the wet season (May–October) compared to the dry season (the rest of the year). Over the period from 1990 to 2020, the NDVI in the watershed exhibited a significant upward trend. The outer estuarine regions of the Hooghly River and Meghna River displayed significant decreases in TSM, whereas the Baleswar River, which flows through mangrove areas, showed no significant trend in TSM. The declining trend in TSM was mainly attributed to land-use changes and anthropogenic activities, including the construction of embankments, dams, and mangrove conservation efforts, rather than to runoff and precipitation. Surface sediment concentration and chlorophyll in the northern Bay of Bengal exhibited slight increases, which means the limited influence of terrestrial inputs on long-term change in surface sediment concentration and chlorophyll in the northern Bay of Bengal. This study emphasises the impact of human activities on the river–estuary–coast continuum and sheds light on future sustainable management. Full article

The present study aimed to delve into the local ecological knowledge of fisheries in the Meghna River Basin (MRB) of Bangladesh by exploring the insights and perspectives of local communities. A survey was administered among six fishing communities from five districts along the MRB between August 2015 and January 2016 to accumulate data for this study. The study sites were selected meticulously based on three crucial criteria: upstream river, coastal area, and fish sanctuaries, which covered three major rivers, namely the Meghna, Andharmanik, and Payra. The study employed participatory rural appraisal (PRA) tools, including 120 individual interviews using purposive sampling, 25 focus group discussions, and 36 key informant interviews. The study identified the ten most frequently caught fish species along with their temporal and spatial variation as reported by the respondents. Nine of these species fell into the least concern category, which indicate their stable population status. Meanwhile, six out of ten species cited as highly caught in the previous one to two decades belong to the threatened or near-threatened category. Findings also reveal that fishers are able to recognize important microhabitats of the study area and their significance for fish species. In addition, fishers identified the negative drivers of ecosystem degradation as well as suggested several management measures to address these challenges. The results of this study underscore the critical role of engaging with local communities and integrating their ecological knowledge into initiatives for the sustainable exploitation and conservation of aquatic resources in the MRB. Full article

Rice landraces, including Asian rice (Oryza sativa L.) and African rice (Oryza glaberrima Steud.), provide important genetic resources for rice breeding to address challenges related to food security. Due to climate change and farm destruction, rice landraces require urgent conservation action. Recognition of the geographical distributions of rice landraces will promote further collecting efforts. Here we modelled the potential distributions of eight rice landrace subgroups using 8351 occurrence records combined with environmental predictors with Maximum Entropy (MaxEnt) algorithm. The results showed they were predicted in eight sub-regions, including the Indus, Ganges, Meghna, Mekong, Yangtze, Pearl, Niger, and Senegal river basins. We then further revealed the changes in suitable areas of rice landraces under future climate change. Suitable areas showed an upward trend in most of study areas, while sub-regions of North and Central China and West Coast of West Africa displayed an unsuitable trend indicating rice landraces are more likely to disappear from fields in these areas. The above changes were mainly determined by changing global temperature and precipitation. Those increasingly unsuitable areas should receive high priority in further collections. Overall, these results provide valuable references for further collecting efforts of rice landraces, while shedding light on global biodiversity conservation. Full article

Elevated arsenic concentrations in groundwater in the Ganga–Brahmaputra–Meghna (GBM) river basin of India has created an alarming situation. Considering that India is one of the largest consumers of groundwater for a variety of uses such as drinking, irrigation, and industry, it is imperative to determine arsenic occurrence and hazard for sustainable groundwater management. The current study focused on the evaluation of arsenic occurrence and groundwater arsenic hazard for the Ganga basin employing Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models. Furthermore, arsenic hazard maps were prepared using a Kriging interpolation method and with overlay analysis in the GIS platform based on the available secondary datasets. Both models generated satisfactory results with minimum differences. The highest hazard likelihood has been displayed around and along the Ganges River. Most of the Uttar Pradesh and Bihar; and parts of Rajasthan, Chhattisgarh, Jharkhand, Madhya Pradesh, and eastern and western regions of West Bengal show a high arsenic hazard. More discrete results were rendered by the AHP model. Validation of arsenic hazard maps was performed through evaluating the Area Under Receiver Operating Characteristics metric (AUROC), where AUC values for both models ranged from 0.7 to 0.8. Furthermore, the final output was also validated against the primary arsenic data generated through field sampling for the districts of two states, viz Bihar (2019) and Uttar Pradesh (2021). Both models showed good accuracy in the spatial prediction of arsenic hazard. Full article

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. Full article

The Ganges-Brahmaputra-Meghna (GBM) river basin is the world’s third largest. Literature show that changes in precipitation have a significant impact on climate, agriculture, and the environment in the GBM. Two satellite-based precipitation products, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Multi-Source Weighted-Ensemble Precipitation (MSWEP), were used to analyze and compare precipitation trends over the GBM as a whole and within 34 pre-defined hydrological sub-basins separately for the period 1983–2019. A non-parametric Modified Mann-Kendall test was applied to determine significant trends in monsoon (June–September) and pre-monsoon (March–May) precipitation. The results show an inconsistency between the two precipitation products. Namely, the MSWEP pre-monsoon precipitation trend has significantly increased (Z-value = 2.236, p = 0.025), and the PERSIANN-CDR monsoon precipitation trend has significantly decreased (Z-value = −33.071, p < 0.000). However, both products strongly indicate that precipitation has recently declined in the pre-monsoon and monsoon seasons in the eastern and southern regions of the GBM river basin, agreeing with several previous studies. Further work is needed to identify the reasons behind inconsistent decreasing and increasing precipitation trends in the GBM river basin. Full article

The Meghna river estuary has had substantial importance in supporting the coastal community’s livelihood for decades, but the pressure on it is immense due to many anthropogenic drivers. The present study aimed to assess its ecosystem services (ES) according to the framework of the standard international classification of ecosystem services (CICES). This study also identified the drivers, pressure, state, impact, and responses (DPSIR), and the factors responsible for ES changes in the Meghna river estuarine ecosystems. By merging both quantitative and qualitative data, a total of 19 sub-categories of ES were found, among which eight are provisioning, seven are regulatory, and four are cultural services, according to the CICES framework. From the results, it can be concluded that food provisioning from the Hilsha fishery, the national fish as a part of cultural heritage, and nursery habitat functions were the top provisional, regulatory, and cultural services to the society in the study areas and beyond. However, several threats and stressors of both anthropogenic and natural origins were identified as drivers of ES changes, such as overexploitation, destructive and illegal fishing, heavy river bed siltation, and natural hazards such as extreme cyclonic events, floods, and sea levels rising. This study underlines the urgency of research and policy attention to address the challenges, and of transforming management regimes to an ecosystem-based approach, which is part of nature-based solutions according to the International Union for Conservation of Nature (IUCN), which refers to the present case, and particularly to fishery co-management. Creating alternative income sources and raising community awareness regarding the importance of maintaining the healthy condition of the river basins, and comprehensive compliance with the rules and regulations are proposed in order to ensure these estuarine ecosystems’ sustainability. Full article

This article illustrates a multi-technique frontier approach for the provenance study of silt-size sediments. The mineralogical composition of low-density and heavy-mineral fractions of four samples of fine to very coarse silt deposited on the Bengal shelf was analyzed separately for six different grain-size classes by combining grain counting under an optical microscope, Raman spectroscopy, and X-ray diffraction. The geochemical composition was determined on both bulk-sediment samples and on their <5-μm classes. Such a “multiple-window” approach allowed capturing the full mineralogical information contained in each sample, as well as the size-dependent intra-sample variability of all compositional parameters. The comparison between grain-size distributions obtained by different methods highlighted a notable fallacy of laser granulometry, which markedly overestimated the size of the finest mode represented by fine silt and clay. As a test case, we chose to investigate sediments of the Bengal shelf, where detritus is fed from the Meghna estuary, formed by the joint Ganga and Brahmaputra Rivers and representing the largest single entry point of sediment in the world’s oceans. The studied samples show the typical fingerprint of orogenic detritus produced by focused erosion of collision orogens. Bengal shelf silt is characterized by a feldspatho-quartzose (F-Q) composition with a Q/F ratio decreasing from 3.0 to 1.7 with increasing grain size, plagioclase prevailing over K-feldspar, and rich transparent-heavy-mineral assemblages including mainly amphibole with epidote, and minor garnet and pyroxene. Such a detrital signature compares very closely with Brahmaputra suspended load, but mineralogical and geochemical parameters, including the anomalous decrease of the Q/F ratio with increasing grain size, consistently indicate more significant Ganga contribution for cohesive fine silt. The accurate quantitative characterization of different size fractions of Bengal shelf sediments represents an essential step to allow comparison of compositional signatures characterizing different segments of this huge source-to-sink system, from fluvial and deltaic sediments of the Himalayan foreland basin and Bengal shelf to the Bengal Fan. Full article

Understanding seasonal precipitation input into river basins is important for linking large-scale climate drivers with societal water resources and the occurrence of hydrologic hazards such as floods and riverbank erosion. Using satellite data at 0.25-degree resolution, spatial patterns of monsoon (June-July-August-September) precipitation variability between 1983 and 2015 within the Ganges–Brahmaputra–Meghna (GBM) river basin are analyzed with Principal Component (PC) analysis and the first three modes (PC1, PC2 and PC3) are related to global atmospheric-oceanic fields. PC1 explains 88.7% of the variance in monsoonal precipitation and resembles climatology with the center of action over Bangladesh. The eigenvector coefficients show a downward trend consistent with studies reporting a recent decline in monsoon rainfall, but little interannual variability. PC2 explains 2.9% of the variance and shows rainfall maxima to the far western and eastern portions of the basin. PC2 has an apparent decadal cycle and surface and upper-air atmospheric height fields suggest the pattern could be forced by tropical South Atlantic heating and a Rossby wave train stemming from the North Atlantic, consistent with previous studies. Finally, PC3 explains 1.5% of the variance and has high spatial variability. The distribution of precipitation is somewhat zonal, with highest values at the southern border and at the Himalayan ridge. There is strong interannual variability associated with PC3, related to the El Nino/Southern Oscillation (ENSO). Next, we perform a hydroclimatological downscaling, as precipitation attributed to the three PCs was averaged over the Pfafstetter level-04 sub-basins obtained from the World Wildlife Fund (Gland, Switzerland). While PC1 was the principal contributor of rainfall for all sub-basins, PC2 contributed the most to rainfall in the western Ganges sub-basin (4524) and PC3 contributed the most to the rainfall in the northern Brahmaputra (4529). Monsoon rainfall within these two sub-basins were the only ones to show a significant relationship (negative) with ENSO, whereas four of the eight sub-basins had a significant relationship (positive) with sea surface temperature (SST) anomalies in the tropical South Atlantic. This work demonstrates a geographic dependence on climate teleconnections in the GBM that deserves further study. Full article

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 basins, thus providing reliable rainfall information and effective water resource management for sustainable development. Full article

Quantitative flood frequency investigation in a large estuary is somewhat challenging by numerical modelling, because the model optimization depends on the appropriate physical and hydrodynamic properties of the estuarine river. This study attempts to solve the bathymetry configurations of the Meghna River estuary and the assimilation of flow data, which exposed an important role in water level prediction. Upstream flow rates and nonlinear semidiurnal tides have an impact on the instability of the flow in this estuarine river. A large amount of flow accumulates in the upstream confluence against or in favor of tides during the rainy season from the adjacent river basins and significantly moves in the Bay of Bengal. The aim of this study is to predict water levels in the un-gauged regions of the Meghna River estuary. A numerical technique was developed using Mike21 flexible mesh, comprising shallow water hydrodynamic components in the estuary. Subsequently, log-normal distribution was employed to analyze the flood magnitudes among the ungauged stations of the estuary. The calibration results comprised with the observed water levels adequately. In conclusion, these water level prediction results can be applied to alleviate the coastal land from extreme flooding and to design hydraulic structures in the narrow streams. Full article