Assessing the Vulnerability of Agriculture Systems to Climate Change in Coastal Areas: A Novel Index

This study proposes a novel index to evaluate agricultural vulnerability to climate change in coastal areas, using the case of Andhra Pradesh, the state with the second longest coastline in India. Field data was collected from more than 1000 farmers (involved in over 50 varieties of crops) in 22 riverine and coastal case study areas. Data was collected through site visits, surveys and five workshops conducted between November 2018 and June 2019. Based on the collected data sets, a new Agricultural Coastal Vulnerability Index (AGCVI) was developed and applied to the 22 sites located in two districts (Krishna and Guntur) of Coastal Andhra Pradesh. The analysis revealed that the areas with three crop seasons (Kharif, Rabi and Zaid) per year are highly vulnerable to climate change. On the other hand, sites with one crop season (Kharif) per annum are the least vulnerable to climate change. Moreover, grains (particularly rice), flowers and fruit crops are more susceptible to climate change and its induced impacts. Rice is no longer a profitable crop in the case study areas partly as a result of unfavourable weather conditions, inadequate insurance provision and lack of government support for farmers. Cumulatively, all these circumstances impact farmers’ incomes and socio-cultural practices: this is leading to a marriage crisis, with a reduction in the desirability of matrimony to farmers. These findings provide valuable information that can support climate and agriculture policies, as well as sustainable cropping patterns among farmers’ communities in coastal areas of India in the future

Spatial mapping and analysis of forest fire risk areas in Sri Lanka – Understanding environmental significance

This study presents the first attempt in Sri Lanka to generate a forest fire risk map covering the entire country using a GIS-based forest fire index (FFI) model. The model utilized seven parameters: land use, temperature, slope, proximity to roads and settlements, elevation, and aspect. All these parameters were derived using GIS techniques with ArcGIS10.4 and QGIS3.16. Data from Remote Sensing sources, particularly the MODIS hotspot real-world dataset, were employed to gather fire count information for the year 2020. Validation was conducted through the merging hotspot technique and kernel density estimation (KDE). The research findings highlight the districts in the Central and Uva provinces, such as NuwaraEliya (10.3 km2), Kandy (2.74 km2), and Badulla (10.41 km2), as having a “very low risk" of forest fire potential. Conversely, districts like Hambanthota (0.1 km2), Kaluthara (0.04 km2), and Kurunegala (0.2 km2) exhibit a “very high risk" of forest fire potential, although it is negligible compared country's total area. Overall, the study suggests that Sri Lanka is not currently facing a significant threat of forest fires and is a “medium risk" of forest fires as 49.49% of land falls under this category. These results are of immense value to relevant authorities, including the Ministry of Wildlife and Forest Resources Conservation, in formulating effective strategies to manage and mitigate forest fire risks in the country

Appraisal of climate change and cyclone trends in Indian coastal states: a systematic approach towards climate action

Abstract: Indian coastal regions have often been affected by frequent climate-induced natural disasters such as cyclones, floods, droughts and other related hazards in recent decades. Existing literature was not sufficient to fully understand these event trends from diverse perspectives in a systematised manner at current scenarios. Therefore, a systematic approach has been employed to assess the climate change and cyclone trends of nine Indian coastal states by using various geographical information system (GIS) tools for 2006–2020. The results showed that 61 cyclones occurred in nine coastal states from 2006 to 2020; the highest numbers were recorded in Odisha (20), West Bengal (14) and Andhra Pradesh (11). Accordingly, these three coastal states emerged as the most vulnerable for high-intensity cyclones. The results also identified that the highest average temperature (29.3 °C) was recorded at Tamil Nadu and Gujarat, and the lowest temperature (26.7 °C) was recorded in West Bengal and Odisha. Most of the coastal states showed fluctuations in temperatures during the study period. At the same time, Kerala and Karnataka states recorded the highest average rainfall (2341 mm and 2261 mm) and highest relative humidity (78.11% and 76.57%). Conversely, the Gujarat and West Bengal states recorded the lowest relative humidity at 59.65% and 70.78%. Based on these results, the current study generated GIS vulnerability maps for climate change and cyclone activity, allowing one to rank each state’s vulnerability. Cumulatively, these results and maps assist in understanding the driving mechanisms of climate change, cyclones and will contribute towards more effective and efficient sustainable disaster management in the future

Analysis of multi-temporal shoreline changes due to a harbor using remote sensing data and GIS techniques

Abstract Coastal landforms are continuously shaped by natural and human-induced forces, exacerbating the associated coastal hazards and risks. Changes in the shoreline are a critical concern for sustainable coastal zone management. However, a limited amount of research has been carried out on the coastal belt of Sri Lanka. Thus, this study investigates the spatiotemporal evolution of the shoreline dynamics on the Oluvil coastline in the Ampara district in Sri Lanka for a two-decade period from 1991 to 2021, where the economically significant Oluvil Harbor exists by utilizing remote sensing and geographic information system (GIS) techniques. Shorelines for each year were delineated using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager images. The Normalized Difference Water Index (NDWI) was applied as a spectral value index approach to differentiate land masses from water bodies. Subsequently, the Digital Shoreline Analysis System (DSAS) tool was used to assess shoreline changes, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR). The results reveal that the Oluvil coast has undergone both accretion and erosion over the years, primarily due to harbor construction. The highest SCE values were calculated within the Oluvil harbor region, reaching 523.8 m. The highest NSM ranges were recorded as −317.1 to −81.3 m in the Oluvil area and 156.3–317.5 m in the harbor and its closest point in the southern direction. The maximum rate of EPR was observed to range from 3 m/year to 10.7 m/year towards the south of the harbor, and from −10.7 m/year to −3.0 m/year towards the north of the harbor. The results of the LRR analysis revealed that the rates of erosion anomaly range from −3 m/year to −10 m/year towards the north of the harbor, while the beach advances at a rate of 3 m/year to 14.3 m/year towards the south of the harbor. The study area has undergone erosion of 40 ha and accretion of 84.44 ha. These findings can serve as valuable input data for sustainable coastal zone management along the Oluvil coast in Sri Lanka, safeguarding the coastal habitats by mitigating further anthropogenic vulnerabilities