No abstract

The sea level rise (SLR) in the Sundarbans areas is higher than the global-average rate of sea rise, and many studies assume that most of the dry land of the Sundarbans will be inundated by the end of the twenty-first century. This study aims to analyze the amount of dry land that can potentially be inundated by SLR in Sundarbans and the impact under different land cover conditions. Four SLR scenarios, a digital elevation data grid, and net subsidence data are used to map areas that will be potentially inundated by 2100. Results for the low (35 cm), mid (52 cm), high (70 cm), and extreme (147 cm) SLR scenarios indicate that the Sundarbans landmass area will be flooded up to 40 km2 (1%), 72 km2 (1.8%), 136 km2 (3.4%), and 918 km2 (23%), respectively, under the current net subsidence rate of −2.4 mm/year by 2100. Except for the extreme scenarios, the low, mid, and high SLR will result in riverbank and beach areas to be covered by water. The potential inundation areas of different vegetation cover classes that already exist today (2020) will be nominal for the low, mid, and high SLR scenarios. We also analyzed the sensitivity of the results through station-based SLR data, which fits with the low (35 cm) SLR scenarios under the −2.4 mm/year subsidence rate. This study concluded that the inundation aspect of SLR will not directly affect the Sundarbans; however, indirectly related threats and anthropogenic disturbances can be major drivers of the Sundarbans’ degradation by the end of the twenty-first century. This work discusses reasonable inundation scenarios integrating SLR and subsidence with a custom land-cover map that includes three forest-density categories. The study’s findings contribute to forest management planning and support the UN goals of the Bangladesh Delta Plan.

Abstract. The study aims to compare land use land cover (LULC) change between Bangladesh and Indian Sundarbans from 1975 to 2020 using Landsat Satellite images. We performed supervised maximum likelihood (ML) to classify the study area at four time periods over 45 years (1975, 1990, 2005, and 2020). The classification was assigned to five classes: dense forest, moderate forest, sparse forest, barren land, and water body. Accuracy assessment of the classified images was completed with 250 control points for each year. The findings of our study revealed that the dense forest cover of Bangladesh and Indian parts was 54% and 31%, respectively, whereas, for the whole Sundarbans, it was 45% in 1975. However, the dense forest of Bangladesh and Indian Sundarbans decreased by an annual rate of 1.20% and 1.60%, respectively, from 1975 to 2020. From 1990 to 2005, Bangladesh Sundarbans slightly increased the dense forest cover by an annual rate of 0.68%, while the Indian Sundarbans decreased by an annual rate of 0.63%. The moderate dense forest of Bangladesh and Indian Sundarbans increased by giving almost the same annual rate of 3.62% and 3.59% from 1975 to 2020, whereas the increasing rate of the sparse forest was much higher for Bangladesh (8.36%) Sundarbans than Indian (3.36%) parts. The water bodies of Bangladesh and Indian Sundarbans increased by giving an annual rate of 0.48% and 0.71%, respectively, from 1975 to 2020. Our study found that most of the barren lands were located near the boundary between forest and human settlement of Indian Sundarbans compared to Bangladesh. The findings of the comparative assessment between these two countries can support sustainable forest management and planning by considering the best policy options.