A Quantitative Framework for Analyzing Spatial Dynamics of Flood Events: A Case Study of Super Cyclone Amphan

Cyclone-induced coastal vulnerability, livelihood challenges and mitigation measures of Matla-Bidya inter-estuarine area, Indian Sundarban

Indian Sundarban is highly susceptible to tropical cyclones and resultant impacts such as storm surge-induced floods, embankment breaching, and saline water intrusion. It affects life and livelihood in severe ways. Mitigation and policy measures are therefore very important, based on information gathered at the grassroots level. Hence, this study is designed to assess inter-village variation in cyclone vulnerability, considering physical vulnerability, social vulnerability, and mitigation capacity. This study also highlights livelihood challenges faced by coastal dwellers. Geospatial and quantitative methods were used to assess the composite vulnerability index (CVI). Remote sensing data and climatic data were integrated to assess physical vulnerability and various socioeconomic data were incorporated to determine the social vulnerability. Moreover, an intensive field survey (2020-2021) was also conducted to understand the livelihood challenges of local people and accordingly suggest mitigation measures to cope with natural hazards. According to this analysis, nearly 18% of the total population living in the southern and eastern parts of the Matla-Bidya inter-estuarine area (MBI) are extremely vulnerable (CVI > 0.544) due to their geographical location and high exposure to coastal hazards. Almost 51% of the total populations inhabited in 46% of the total MBI villages are experiencing high to moderate vulnerability. Conversely, MBI villages in the northern part, where 32% of the total population lives, show low vulnerability (CVI < 0.387) due to less exposure and high resilience. Coastal low-lying villages are often hardest hit by tropical cyclones. Therefore, effective mitigation strategies and coping mechanisms are essentially needed to reduce the adverse impacts of cyclones.

Delineation of urban expansion influences urban heat islands and natural environment using remote sensing and GIS-based in industrial area

Land transformation monitoring is essential for controlling the anthropogenic activities that could cause the degradation of natural environment. This study investigated the urban heat island (UHI) effect at the Asansol and Kulti blocks of Paschim Bardhaman district, India. The increasing land surface temperature (LST) can cause the UHI effect and affect the environmental conditions in the urban area. The vulnerability of the UHI effect was measured quantitatively and qualitatively by using the urban thermal field variation index (UTFVI). The land use and land cover (LULC) dynamics are identified by utilizing the remote sensing and maximum likelihood supervised classification techniques for the years 1990, 2000, 2010, and 2020, respectively. The results indicated a decrease around 19.05 km², 15.47 km², and 9.86 km² for vegetation, agricultural land, and grassland, respectively. Meanwhile, there is an increase of 35.69 km² of the built-up area from the year 1990 to 2020. The highest LST has increased by 11.55 °C, while the lowest LST increased by 8.35 °C from 1990 to 2020. The correlation analyses showed negative relationship between LST and vegetation index, while positive correlation was observed for built-up index. Hotspot maps have identified the spatio-temporal thermal variations in Mohanpur, Lohat, Ramnagar, Madhabpur, and Hansdiha where these cities are mostly affected by the urban expansion and industrialization developments. This study will be helpful to urban planners, stakeholders, and administrators for monitoring the anthropological activities and thus ensuring a sustainable urban development.

Radar and optical remote sensing for near real-time assessments of cyclone impacts on coastal ecosystems

Rapid impact assessment of cyclones on coastal ecosystems is critical for timely rescue and rehabilitation operations in highly human-dominated landscapes. Such assessments should also include damage assessments of vegetation for restoration planning in impacted natural landscapes. Our objective is to develop a remote sensing-based approach combining satellite data derived from optical (Sentinel-2), radar (Sentinel-1), and LiDAR (Global Ecosystem Dynamics Investigation) platforms for rapid assessment of post-cyclone inundation in non-forested areas and vegetation damage in a primarily forested ecosystem. We apply this multi-scalar approach for assessing damages caused by the cyclone Amphan that hit coastal India and Bangladesh in May 2020, severely flooding several districts in the two countries, and causing destruction to the Sundarban mangrove forests. Our analysis shows that at least 6821 sq. km. land across the 39 study districts was inundated even after 10 days after the cyclone. We further calculated the change in forest greenness as the difference in normalized difference vegetation index (NDVI) pre- and post-cyclone. Our findings indicate a <0.2 unit decline in NDVI in 3.45 sq. km. of the forest. Rapid assessment of post-cyclone damage in mangroves is challenging due to limited navigability of waterways, but critical for planning of mitigation and recovery measures. We demonstrate the utility of Otsu method, an automated statistical approach of the Google Earth Engine platform to identify inundated areas within days after a cyclone. Our radar-based inundation analysis advances current practices because it requires minimal user inputs, and is effective in the presence of high cloud cover. Such rapid assessment, when complemented with detailed information on species and vegetation composition, can inform appropriate restoration efforts in severely impacted regions and help decision makers efficiently manage resources for recovery and aid relief. We provide the datasets from this study on an open platform to aid in future research and planning endeavors.

Dynamics and Causes of Sea Level Rise in the Coastal Region of Southwest Bangladesh at Global, Regional, and Local Levels

Global greenhouse gas emissions have caused sea level rise (SLR) at a global and local level since the industrial revolution, mainly through thermal expansion and ice melting. Projections indicate that the acceleration of SLR will increase in the near future. This will affect coastal and deltaic populations worldwide, such as in Bangladesh, where almost half of the population resides in regions lower than 5 m above sea level. This study analyzed three coastal tidal gauges and five deltaic gauge stations, which showed increases in SLR at greater rates than the regional and global averages. This research also used satellite altimetry data to analyze regional and global SLR averages in the recent past and the 21st century. There is a trend towards increasing sea level based on results from three tide gauge stations: Char Changa with 7.6 mm/yr, Hiron Point at 3.1 mm/yr from 1993 to 2019, and 14.5 mm/yr at Cox’s Bazar from 1993 to 2011. Based on the linear trend from these time frames, it is projected that SLR in Char Changa will increase by 228 mm cm from 2020 to 2050, and by 608 mm by 2100, at Hiron Point by 93mm in 2050 and 248 mm by 2100, and at Cox’s Bazar by almost 435.7 mm by 2050, and more than 1162 mm by 2100. Based on an average from satellite altimeters, assuming a linear increase in SLR, the Bay of Bengal shows an increase of 0.4 mm compared to the global trend. Other river delta stations in the study area also show increasing SLR, specifically, at Kalaroa, Benarpota, Kaikhali, Tala Magura, and Elarchari. Kalaroa and Benarpota show the highest, with SLR of >40 mm/yr. It is also observed that increasing SLR trends are far higher than coastal tide gauges, indicating that physical processes in the delta region are affecting SLR, further contributing to either an increase in water volume/SLR or activating land subsidence. This is partly due to the subsidence of the delta as a result of natural and anthropomorphic effects, as well as an increase in Himalayan glacier melting due to global warming. This indicates that Bangladesh coastal areas will soon experience a far greater SLR than the rest of the Bay of Bengal or other global coastal areas.

Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events

The remarkable flexibility and adaptability of both deep learning models and ensemble methods have led to the proliferation for their application in understanding many physical phenomena. Traditionally, these two techniques have largely been treated as independent methodologies in practical applications. This study develops an optimized ensemble deep learning framework wherein these two machine learning techniques are jointly used to achieve synergistic improvements in model accuracy, stability, scalability, and reproducibility, prompting a new wave of applications in the forecasting of dynamics. Unpredictability is considered one of the key features of chaotic dynamics; therefore, forecasting such dynamics of nonlinear systems is a relevant issue in the scientific community. It becomes more challenging when the prediction of extreme events is the focus issue for us. In this circumstance, the proposed optimized ensemble deep learning (OEDL) model based on a best convex combination of feed-forward neural networks, reservoir computing, and long short-term memory can play a key role in advancing predictions of dynamics consisting of extreme events. The combined framework can generate the best out-of-sample performance than the individual deep learners and standard ensemble framework for both numerically simulated and real-world data sets. We exhibit the outstanding performance of the OEDL framework for forecasting extreme events generated from a Liénard-type system, prediction of COVID-19 cases in Brazil, dengue cases in San Juan, and sea surface temperature in the Niño 3.4 region.

Land cover change and its impact on human–elephant conflict: a case from Fashiakhali forest reserve in Bangladesh

Changes in land cover are a major driving force behind habitat change, which significantly impacts the distribution of wildlife and ecological systems. However, there is a substantial lack of information on the effects of land cover changes on wildlife habitat and local conservation. Therefore, it is essential to understand how land cover changes may threaten future land cover trends and wildlife habitat loss, especially in protected areas. Landsat satellite imagery uses a geographic information system and remote sensing techniques to determine the spatiotemporal pattern of land cover change and its impact on the human–elephant conflict in the Fashiakhali Wildlife Sanctuary. We found that within the sanctuary (1994–2005), settlements, agricultural land, and bare land increased by 69.8 ha (2.3%), 991.6 ha (32.3%), and 39.5 ha (1.3%), and forest areas and water areas decreased by 1094.1 ha (35.7%) and 6.9 ha (0.2%), respectively. On the other hand (2005–2015), settlements, agricultural land, and water areas increased by 11.7 ha (0.4%), 264.7 ha (8.6%), and 36.2 ha (1.2%), and forest areas and bare land decreased by 308.9 ha (10.1%) and 3.7 ha (0.1%), respectively. Our findings have shown that increased agriculture and settlements have become a severe threat to the ecological sustainability of elephant habitat, resulting in habitat fragmentation and human encroachment of elephant habitats, as well as extreme pressure and competition on resources.