Assessment of cyclone risk and case study of Gaja cyclone using GIS techniques and machine learning algorithms in coastal zone of Tamil Nadu, India

Cyclones can cause devastating impacts, including strong winds, heavy rainfall, storm surges, and flooding. The aftermath includes infrastructure damage, loss of life, displacement of communities, and ecological disruptions. Timely response and recovery efforts are crucial to minimize the socio-economic and environmental consequences of cyclones. To accelerate the time-consuming risk assessment process, particularly in geographically diverse regions, a blend of multi-criteria decision-making and machine learning models was utilized. This novel approach swiftly assessed cyclone risk and the impact of the Gaja cyclone in Nagapattinam, India. The method involved assigning weights to distinct criteria, unveiling notable vulnerability aspects like elevation, slope, proximity to the coast, distance from cyclone tracts, Lu/Lc, population density, proximity to cyclone shelters, household density, accessibility to healthcare facilities, NDVI, and levels of awareness. Daddavari, Ettugudi, Kodikarai, Vedharanyam, Velankanni, and Thirupoondi face high/extreme cyclone risk. Nagore, Nagapattinam, Pillai, Enangudi, and Sannanllur have low/no threat. To further enhance the precision of the study, machine learning algorithms like SVM, SAM, and MLC were deployed. These models were instrumental in generating pre- and post-cyclone land use maps. The influence of Gaja cyclones effects shows decreasing of agriculture land from 34% to 30%, aquaculture increase 1%, barren land decrease from 8% to 6%, Built-up land decrease from 15% to 13%, land with scrub and salt pan also decrease from 21% to 17% and 10%-8%. Mostly effect of Gaja cyclone is dramatic increase of water body from 8% to 21%. Conducting cyclone risk zone analysis and pre/post-cyclone Land Use Land Cover (LULC) detection in Nagapattinam offers valuable insights for disaster preparedness, infrastructure planning, and climate resilience. This study can enhance understanding of vulnerability and aid in formulating strategies to mitigate cyclone impacts, ensuring sustainable development in the region.

Influence of the Interdecadal Pacific Oscillation on Super Cyclone Activities over the Bay of Bengal during the Primary Cyclone Season

An obvious interdecadal change can be measured in the super cyclones (SCs, categories 4 and 5) that occur from October to November over the Bay of Bengal (BoB). This change may be modulated by the interdecadal Pacific oscillation (IPO). A La Niña-like difference between the 1977–1998 (IP1) and 1999–2014 (IP2) periods forced a local Hadley circulation in the eastern tropical Indian Ocean by strengthening the Walker circulation, which caused plummeting upper-level temperatures and ultimately created favorable thermodynamic conditions to enhance the cyclone intensity. Meanwhile, an equatorial downwelling Kelvin wave caused by heating and westerly wind differences entered the BoB rim along the coast and aptly intensified the cyclone, such that the downwelling Kevin wave and Rossby wave generated by its reflection deepened the thermocline in the BoB. The favorable atmospheric and oceanic conditions in IP2 jointly and preferentially cause far more SC activities from October to November over the BoB compared to IP1.

Major Floods and Tropical Cyclones over Bangladesh: Clustering from ENSO Timescales

The present study analyzed major floods and tropical cyclones (TCs) over Bangladesh on El Niño-Southern Oscillation (ENSO) timescales. The geographical location, low and almost flat topography have introduced Bangladesh as one of the most vulnerable countries of the world. Bangladesh is highly vulnerable to the extreme hazard events like floods and cyclones which are impacted by ENSO. ENSO is mainly a tropical event, but its impact is global. El Niño (La Niña) represents the warm (cold) phase of the ENSO cycle. Rainfall and cyclonic disturbances data have been used for the period of 70 years (1948–2017) and compared with the corresponding observations of the Southern Oscillation Index. Result shows that major flood events occurred during the monsoon period, and most of them are during the La Niña condition, consistent with the historical archives of flood events in Bangladesh. Synoptic conditions of these events are well matched during La Niña condition. On the other hand, the major TC cases are in the period of either pre-monsoon or post-monsoon season. The pre-monsoon cases are under neutral (developing La Niña) or El Niño and the post-monsoon cases are under La Niña, consistent with climatology studies that La Niña is favorable to have more intense TCs over the Bay of Bengal.

Effects of Tropical Cyclones on Sea Surface Salinity in the Bay of Bengal Based on SMAP and Argo Data

This paper uses the Argo sea surface salinity (SSSArgo) before and after the passage of 25 tropical cyclones (TCs) in the Bay of Bengal from 2015 to 2019 to evaluate the sea surface salinity (SSS) of the Soil Moisture Active Passive (SMAP) remote sensing satellite (SSSSMAP). First, SSSArgo data were used to evaluate the accuracy of the 8-day SMAP SSS data, and the correlations and biases between SSSSMAP and SSSArgo were calculated. The results show good correlations between SSSSMAP and SSSArgo before and after TCs (before: SSSSMAP = 1.09SSSArgo−3.08 (R2 = 0.69); after: SSSSMAP = 1.11SSSArgo−3.61 (R2 = 0.65)). A stronger negative bias (−0.23) and larger root-mean-square error (RMSE, 0.95) between the SSSSMAP and SSSArgo were observed before the passage of 25 TCs, which were compared to the bias (−0.13) and RMSE (0.75) after the passage of 25 TCs. Then, two specific TCs were selected from 25 TCs to analyze the impact of TCs on the SSS. The results show the significant SSS increase up to the maximum 5.92 psu after TC Kyant (2016), which was mainly owing to vertical mixing and strong Ekman pumping caused by TC and high-salinity waters in the deep layer that were transported to the sea surface. The SSSSMAP agreed well with SSSArgo in both coastal and offshore waters before and after TC Roanu (2016) and TC Kyant (2016) in the Bay of Bengal.

Dissolved oxygen responses to tropical cyclones "Wind Pump" on pre-existing cyclonic and anticyclonic eddies in the Bay of Bengal

This study examines how dissolved oxygen (DO) responds to tropical cyclones (TCs) "Wind Pump" in a pre-existing cyclonic and an anticyclonic eddy over the Bay of Bengal (BoB) based on Argo and satellite data. Both TCs induced DO temporal decreases in the subsurface waters (Deep Depression BOB 04 with a pre-existing cyclonic eddy and cyclonic storm Roanu with a pre-existing anticyclonic eddy) owing mainly to the storm-induced upwelling. The deeper oxycline caused by the pre-existing anticyclonic eddy relieved the storm-induced shallow of oxycline during Roanu. On the contrary, the pre-existing cyclonic eddy shoaled the oxycline, intensifying the storm-induced shallow of oxycline during BOB 04. Furthermore, the pre-existing cyclonic eddy induced a long time of DO decrease after BOB 04. This study suggests that the subsurface DO concentrations in the BoB are affected mainly by storm-induced physical processes, and the mesoscale eddies also play an important role.