Search Results (14)

The Ganges–Brahmaputra–Meghna (GBM) delta, characterized by complex topography and hydrological conditions, is highly susceptible to recurrent flooding, particularly in its coastal regions where tidal dynamics hinder floodwater discharge. This study integrates Synthetic Aperture Radar (SAR) imagery with machine learning (ML) techniques to assess near real-time flood inundation patterns associated with extreme weather events, including recent cyclones between 2017 to 2024 (namely, Mora, Titli, Fani, Amphan, Yaas, Sitrang, Midhili, and Remal) as well as intense monsoonal rainfall during the same period, across a large spatial scale, to support disaster risk management efforts. Three machine learning algorithms, namely, random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were applied to flood extent data derived from SAR imagery to enhance flood detection accuracy. Among these, the SVM algorithm demonstrated the highest classification accuracy (75%) and exhibited superior robustness in delineating flood-affected areas. The analysis reveals that both cyclone intensity and rainfall magnitude significantly influence flood extent, with the western coastal zone (e.g., Morrelganj and Kaliganj) being most consistently affected. The peak inundation extent was observed during the 2023 monsoon (10,333 sq. km), while interannual variability in rainfall intensity directly influenced the spatial extent of flood-affected zones. In parallel, eight major cyclones, including Amphan (2020) and Remal (2024), triggered substantial flooding, with the most severe inundation recorded during Cyclone Remal with an area of 9243 sq. km. Morrelganj and Chakaria were consistently identified as flood hotspots during both monsoonal and cyclonic events. Comparative analysis indicates that cyclones result in larger areas with low-level inundation (19,085 sq. km) compared to monsoons (13,829 sq. km). However, monsoon events result in a larger area impacted by frequent inundation, underscoring the critical role of rainfall intensity. These findings underscore the utility of SAR-ML integration in operational flood monitoring and highlight the urgent need for localized, event-specific flood risk management strategies to enhance flood resilience in the GBM delta. 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 sustainability index (SI) is a relatively new concept for measuring the performance of water resource systems over long time periods. The purpose of its definition is to provide an indication of the integral behavior of the system with regard to possible undesired consequences if a misbalance in available and required waters occurs. Therefore, the tidal river management (TRM) approach has been implemented for the past three decades (from 1990 to 2020) within the polder system in Southwest Bangladesh to achieve water sustainability. TRM plan and watershed management plan (WMP) have commonalities as both are aimed at ensuring the sustainable use of watershed resources with the management of land, water, and the wider ecosystem of the watershed in an integrated way. The TRM plan focuses mostly on coastal regions, whereas the WMP focuses on both coastal and non-coastal regions. According to this, the aim of this study was to explore the application of the sustainability index of tidal river management (SITRM) in measuring the sustainability of tidal river management in the coastal area of the Lower Ganges–Brahmaputra–Meghna (GBM) delta. In order to quantify the sustainability of tidal river management, this research first provided the components and indicators of SITRM for the coastal region. The study follows a 5-point Likert scale for opinion survey of key informants and comprises households’ survey of farmers. In addition, it includes Landsat satellite images from Earth Explorer of the United States Geological Survey (USGS) and direct field observation to collect information regarding the indicators of SITRM. The study measures the index value of SITRM for identifying the water sustainability of Beel East Khukshia-TRM. The index value was 71.8 out of 100, showing good tidal river management for the Hari–Teka–Bhadra catchment. To achieve water sustainability and aid stakeholders and water managers in decision making, it may be possible to include the SITRM framework in tidal river management projects. In addition, the SITRM is more capable of facing drainage congestion, waterlogging, and climate change issues than watershed sustainability index (WSI), Canadian water sustainability index (CWSI), West Java water sustainability index (WJWSI), and water poverty index (WPI). Therefore, water professionals and policymakers can apply SITRM to assess the resilience of specific TRM schemes for greater sustainability in different coastal regions of the world. Full article

The Sundarban is the world’s largest contiguous mangrove forest and stores around 26.62 Tg of blue carbon. The present study reviewed the factors causing a decline in its blue carbon content and poses a challenge in enhancing the carbon stock of this region. This review emphasized that recurrent tropical cyclones, soil erosion, freshwater scarcity, reduced sediment load into the delta, nutrient deficiency, salt-stress-induced changes in species composition, mangrove clearing, and anthropogenic pollution are the fundamental drivers which can potentially reduce the total blue carbon stock of this region. The southern end of the Ganges–Brahmaputra–Meghna Delta that shelters this forest has stopped its natural progradation due to inadequate sediment flow from the upper reaches. Growing population pressure from the north of the Sundarban Biosphere Reserve and severe erosion in the southern end accentuated by regional sea-level rise has left minimal options to enhance the blue carbon stock by extending the forest premises. This study collated the scholarly observations of the past decades from this region, indicating a carbon sequestration potential deterioration. By collecting the existing knowledge base, this review indicated the aspects that require immediate attention to stop this ecosystem’s draining of the valuable carbon sequestered and, at the same time, enhance the carbon stock, if possible. This review provided some key recommendations that can help sustain the blue carbon stock of the Indian Sundarban. This review stressed that characterizing the spatial variability of blue carbon with more sampling points, catering to the damaged trees after tropical cyclones, estuarine rejuvenation in the upper reaches, maintaining species diversity through afforestation programs, arresting coastal erosion through increasing sediment flow, and combating marine pollution have become urgent needs of the hour. The observations synthesized in this study can be helpful for academics, policy managers, and decision makers willing to uphold the sustainability of the blue carbon stock of this crucial ecosystem. Full article

The Ganges–Brahmaputra–Meghna Delta (GBMD) located in the head of the Bay of Bengal is regularly affected by severe tropical cyclones frequently. The GBMD covers the Bangladesh coast, which is one of the most vulnerable areas in the world due to cyclone-induced storm surges. More than 30% of the total country’s population lives on the Bangladesh coast. Hence, it is crucial to understand the underlying processes that modulate the storm surge height in the GBMD. A barotropic numerical 3D model setup is established by using Delft3D and SWAN to investigate a cyclone-induced storm surge event. The model is calibrated and validated for Cyclone Sidr in 2007 and applied to six idealized cyclonic scenarios. Numerical experiments with different coupling configurations are performed to distinguish the contribution of wind, tides, waves, and wave–current interactions (WCI) on the storm surge height. Results show that the wind-driven setup is the dominant contributor to the storm surge height during cyclonic events. Based on the tidal phase and wind direction, the interaction between tide and wind can increase or decrease the magnitude of the storm surge height. Finally, considering the wind-driven wave may increase the surge height up to 0.3 m along the coastline through a strong wave setup. Full article

The Ganges-Brahmaputra-Meghna delta, located in the southern part of Bangladesh, is periodically exposed to severe tropical cyclones. It is estimated that two-fifths of the world’s total impact from tropical-cyclone-induced storm surges occur in this region, and these cause fatalities and economic losses every year. A barotropic numerical 3D model is used to investigate wave dynamics during a cyclone-induced storm-surge event. The model is calibrated and validated for Cyclone Sidr (2007) and applied to ten idealized cyclonic scenarios. Numerical experiments with different coupling configurations are performed to understand wave–current interactions on significant wave heights. Results show that the water level is the dominant factor in significant wave height modulation when the wave propagates into shallower regions from the deeper ocean, whereas the current modulates the deep ocean wave height. The WCI causes higher significant wave heights in shallower waters close to the coast compared with the deep ocean. Wave energy dissipation related to whitecapping processes plays a greater role in reducing the wave height nearshore than the dissipation due to depth-induced breaking and bottom friction in the GBMD during a cyclone-induced storm-surge event. Full article

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere. Full article

The United Nations Sustainable Development Goals (SDGs) and their corresponding targets are significantly interconnected, with many interactions, synergies, and trade-offs between individual goals across multiple temporal and spatial scales. This paper proposes a framework for the Integrated Assessment Modelling (IAM) of a complex deltaic socio-ecological system in order to analyze such SDG interactions. We focused on the Sundarban Biosphere Reserve (SBR), India, within the Ganges-Brahmaputra-Meghna Delta. It is densely populated with 4.4 million people (2011), high levels of poverty, and a strong dependence on rural livelihoods. It is adjacent to the growing megacity of Kolkata. The area also includes the Indian portion of the world’s largest mangrove forest––the Sundarbans––hosting the iconic Bengal Tiger. Like all deltaic systems, this area is subject to multiple drivers of environmental change operating across scales. The IAM framework is designed to investigate socio-environmental change under a range of explorative and/or normative scenarios and explore associated policy impacts, considering a broad range of subthematic SDG indicators. The following elements were explicitly considered: (1) agriculture; (2) aquaculture; (3) mangroves; (4) fisheries; and (5) multidimensional poverty. Key questions that can be addressed include the implications of changing monsoon patterns, trade-offs between agriculture and aquaculture, or the future of the Sundarbans’ mangroves under sea-level rise and different management strategies. The novel, high-resolution analysis of SDG interactions allowed by the IAM will provide stakeholders and policy makers the opportunity to prioritize and explore the SDG targets that are most relevant to the SBR and provide a foundation for further integrated analysis. Full article

Mangrove forests play a global role in providing ecosystem goods and services in addition to acting as carbon sinks, and are particularly vulnerable to climate change effects such as rising sea levels and increased salinity. For this reason, accurate long-term monitoring of mangrove ecosystems is vital. However, these ecosystems are extremely dynamic and data frequency is often reduced by cloud cover. The Continuous Change Detection and Classification (CCDC) method has the potential to overcome this by utilising every available observation on a per-pixel basis to build stable season-trend models of the underlying phenology. These models can then be used for land cover classification and to determine greening and browning trends. To demonstrate the utility of this approach, CCDC was applied to a 30-year time series of Landsat data covering an area of mangrove forest known as the Sundarbans. Spanning the delta formed by the confluence of the Ganges, Brahmaputra and Meghna river systems, the Sundarbans is the largest contiguous mangrove forest in the world. CCDC achieved an overall classification accuracy of 94.5% with a 99% confidence of being between 94.2% and 94.8%. Results showed that while mangrove extent in the Sundarbans has remained stable, around 25% of the area experienced an overall negative trend, probably due to the effect of die-back on Heritiera fomes. In addition, dates and magnitudes of change derived from CCDC were used to investigate damage and recovery from a major cyclone; 11% of the Sundarbans was found to have been affected by Cyclone Sidr in 2007, 47.6% of which had not recovered by mid-2018. The results indicate that while the Sundarbans forest is resilient to cyclone events, the long-term degrading effects of climate change could reduce this resilience to critical levels. The proposed methodology, while computationally expensive, also offers means by which the full Landsat archive can be analyzed and interpreted and should be considered for global application to mangrove monitoring. Full article

Salinity intrusion through the estuaries in low-lying tide-dominated deltas is a serious threat that is expected to worsen in changing climatic conditions. This research makes a comparative analysis on the impact of salinity intrusion due to a reduced upstream discharge, a sea level rise, and cyclonic conditions to find which one of these event dominates the salinity intrusion. A calibrated and validated salinity model (Delft3D) and storm surge model (Delft Dashboard) are used to simulate the surface water salinity for different climatic conditions. Results show that the effects of the reduced upstream discharge, a sea level rise, and cyclones cause different levels of impacts in the Ganges-Brahmaputra-Meghna (GBM) delta along the Bangladesh coast. Reduced upstream discharge causes an increased saltwater intrusion in the entire region. A rising sea level causes increased salinity in the shallower coast. The cyclonic impact on saltwater intrusion is confined within the landfall zone. These outcomes suggest that, for a tide dominated delta, if a sea level rise (SLR) or cyclone occurred, the impact would be conditional and local. However, if the upstream discharge reduces, the impact would be gradual and along the entire coast. Full article

Coastal people, especially those living within deltaic areas, encounter major climatic concerns which affect their livelihoods. To cope with this problem, different types of planned adaptation strategies have been implemented guided by laws, policies and programs. However, these guiding documents sometimes fall short of addressing the needs of climate-affected people, especially in natural resource-dependent societies in Asia and Africa. Based on this premise, this paper sought to evaluate the effectiveness of existing policy documents which affect the lives of people living in one large delta (Ganges-Brahmaputra-Meghna in Bangladesh), two medium-sized deltas (Indian Bengal delta—part of the Ganges-Brahmaputra-Meghna and Mahanadi in India), and a small-sized delta (Volta in Ghana). The study followed a mixed methods research design, which included desktop analyses of policies, laws and programs, a questionnaire survey conducted among individuals who played various roles in the policy and legal development processes at national and local levels and focus group discussions at the community level in the three countries. National laws, policies and programs were assessed in the context of climate change adaptation through three lenses: human rights, natural resource management and disaster response. Findings of this paper reveal that the existing documents have some strengths to promote adaptation, although they have some major limitations that cause concerns among the delta communities. Full article

We examine the similarities and differences of specific deltaic areas in parallel, under the project DEltas, vulnerability and Climate Change: Migration and Adaptation (DECCMA). The main reason for studying Deltas is their potential vulnerability to climate change and sea level rise, which generates important challenges for livelihoods. We provide insights into the current socioeconomic and biophysical states of the Volta Delta (Ghana), Mahanadi Delta (India) and Ganges-Brahmaputra-Meghna (India and Bangladesh). Hybrid methods of input-output (IO) construction are used to develop environmentally extended IO models for comparing the economic characteristics of these delta regions with the rest of the country. The main sources of data for regionalization were country level census data, statistics and economic surveys and data on consumption, trade, agricultural production and fishing harvests. The Leontief demand-driven model is used to analyze land use in the agricultural sector of the Delta and to track the links with final demand. In addition, the Hypothetical Extraction Method is used to evaluate the importance of the hypothetical disappearance of a sector (e.g., agriculture). The results show that, in the case of the Indian deltas, more than 60% of the cropland and pasture land is devoted to satisfying demands from regions outside the delta. While in the case of the Bangladeshi and Ghanaian deltas, close to 70% of the area harvested is linked to internal demand. The results also indicate that the services, trade and transportation sectors represent 50% of the GDP in the deltas. Still, agriculture, an activity directly exposed to climate change, plays a relevant role in the deltas’ economies—we have estimated that the complete disappearance of this activity would entail GDP losses ranging from 18 to 32%. Full article

As part of the contribution to flood disaster risk reduction, it is important to identify and characterize flood areas, locations, and durations. Multiple satellite-based flood mapping and monitoring are an imperative process and the fundamental part of risk assessment in disaster risk management. In this paper, the MODIS-derived synchronized floodwater index (SfWi) was used to detect the maximum extent of a nationwide flood based on annual time-series data of 2015 in order to maximize the application of optical satellite data. The selected three major rivers—i.e., Ganges, Brahmaputra, and Meghna (GBM), transboundary rivers running through the great floodplain delta lying between Bangladesh and eastern India—show that a propensity of flood risk was revealed by the temporal and spatial dynamics of the maximum flood extent during the 2015 monsoon season. Resultant flood maps showed that SfWi-indicated flood areas were small but more accurate than those derived from the single use of the MODIS-derived water index. The return period of SfWi-indicated maximum flood extent was confirmed to be about 20 years based on historical flood records. Full article

The recently endorsed Sustainable Development Goals (SDGs) agenda unanimously agrees on the need to focus on inclusive development, the importance of eradicating extreme poverty and managing often complex human well-being impacts of rapid urban growth. Sustainable and inclusive urbanisation will accelerate progress towards the SDGs and contribute to eradicating extreme poverty. In tropical delta regions, such as the Ganges Brahmaputra Meghna delta region, urban growth and resulting intra-urban inequalities are accelerated by the impact of environmental and climate change. In this context, the present study uses the 2010 Household Income and Expenditure Survey to analyse the extent of wealth-based inequalities in human well-being in the urban delta region and the determinants of selected welfare measures. The results suggest that the extent of intra-urban inequalities is greatest in educational attainment and access to postnatal healthcare and relatively low in the occurrence of gastric disease. The paper concludes by providing policy recommendations to reduce increasing wealth inequalities in urban areas, thus contributing to sustainable development of the region. Full article