Vivek Ganesh

Climate change is a major driver of increased flood risk, which is causing economic meltdown in many parts of the world. Globally, economic losses incurred by floods are estimated at around 453 billion USD. In the Asian region, India experienced the third highest economic loss of 4.2 billion USD due to flooding. Mumbai, India’s financial capital, faces climate change threats due to rising sea level, increased rainfall, and intense cyclones, posing risks to infrastructures, economy, and population, especially in low-lying areas. The Mithi river which overflows during monsoon season, plays a crucial role in carrying storm water to the sea in Mumbai. As it flows through an international airport, major industrial complexes and densely populated residentials, these areas became more vulnerable to flooding. This study demonstrates the domino effects of climate change on Mithi River watershed by utilising CMIP6 13 GCM ensembled daily mean precipitation model data for the near future 2030 under shared socio-economic pathways (SSP) 245 and 585 scenarios. Using the Hydrodynamic model GeoHECRAS, the flood inundation depth and extent were estimated. Under both projections, July 25-26, 2030, observed maximum rainfall and exhibited maximum streamflow with a peak discharge of 51.2 m3/sec (SSP245) and 38.5 m3/sec (SSP585). A quantitative risk assessment conducted based on the domino effects triggered by flooding to determine the projected impacted population and economic losses. The annual projected impacted population under both scenarios is observed as SSP245: Very High (0.24M), High (0.74M), Moderate (0.80M), Low (2.90M), and SSP585: Very High (0.68M), High (0.70M), Moderate (0.86M), Low (2.45M). The annual expected amount of urban property damaged due to this effect will range from $157 billion to $535 billion, with a projected affected GDP of more than $84 billion. This cascading effect is likely to disrupt Mumbai's million-dollar trade, affecting global financial flows. This study will be useful to understand the domino effect and raising the flood risk awareness for the development of sustainable policies.Keywords: Domino effects, CMIP6, economic loss, hydrodynamic, flood depth and extent

Asia’s largest lake and the world’s foremost tropical lagoon, Chilika, stands as a testament to ecological diversity. The lake is diverse in biodiversity and is a sanctuary for over 400 distinct brackish and freshwater species. However, the lake is confronted by ceaseless pressures from a confluence of natural forces and anthropogenic activities. These challenges threaten to unleash ecological transformations that could reshape this ecological marvel. This study examined the spatial–temporal variation in the lake for the years 1988 to 2017 using archival remote sensing data. The Normalized Difference Water Index (NDWI) derived from Landsat 5-TM and Landsat 8-OLI was used to understand the expansion and contraction happening in the extent of the lake. To determine the water spread area, from each NDWI image, the minimum (Min.) pixel values, maximum (Max.) pixel values, and mean pixel values were extracted, and a yearly composite for was created the aforementioned years.

One of the societal outcomes envisaged for the United Nations Decade of Ocean Science for Sustainable Development (2021‐2030) is a safe ocean where the safety of operations at sea and the coast are ensured. The Indian coast is prone to tropical cyclones, and the recent Ockhi, Gaja, Nilam, and Nivar cyclones devastated coastal Tamil Nadu in southern India, particularly the fishing community. This brought into focus the need to develop tools for the safety of fishermen at sea. A user-friendly application was developed named “Thoondil” (meaning fishing rod in the local language) by the National Centre for Coastal Research and made operational by the Department of Fisheries, Government of Tamil Nadu. It comprises a web-GIS-based dashboard for the state administrators and an android application for the fishermen. The salient features of the mobile application include the compass, weather, rescue plan, offline maps that provide the route to the nearest ports, incidence reporting, weather details, and potential fishing zone. The Thoondil dashboard provides information about the users and travel details, and helps to get information about the fishermen at sea at any point of time. A two-way communication between the administrators and the fishing community is enabled. The system is available in vernacular language with more than 15,000 downloads in a couple of months. Based on user interactions, it has evolved as a pan-India application and currently replicated in Kozhikode, Kerala. The use of the app has reduced risk to the fishing community, especially during hazards and has also contributed to the resilience of the coastal fishing communities.

Changing trend in coastal ecosystem can be quantified by performing time series analysis. Time series analysis performed using long term remote sensing data will help us to identify the dynamic changes happening in the coastal ecosystem and its surrounding regions. In the present study we performed time series analysis on northern sector of Chilika Lake and its nearby regions of Odisha, which is situated in the east coast of India using three decades of freely available Landsat archive data. In order to detect dynamic changes trend parameters were calculated by using available data sets from Landsat Thematic Mapper (TM) and Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) for the observation period from 1988 to 2017. Two multi-spectral indices i.e. NDVI and EVI were generated from the available data sets and the trend analyses were performed using Theil-Sen (T-S) regression method by identifying robust trend parameters (slope and pvalue). The average mean values for NDVI and EVI were recorded at 0.4 and 0.2. Significant positive trend was observed in both vegetation indices (NDVI and EVI) with a mean slope value of 0.004 and 0.003 and pvalue of 0.03 and 0.02 respectively.

Changing trend in coastal ecosystem can be quantified by performing time series analysis. Time series analysis performed using long term remote sensing data will help us to identify the dynamic changes happening in the coastal ecosystem and its surrounding regions. In the present study we performed time series analysis on northern sector of Chilika Lake and its nearby regions of Odisha, which is situated in the east coast of India using three decades of freely available Landsat archive data. In order to detect dynamic changes trend parameters were calculated by using available data sets from Landsat Thematic Mapper (TM) and Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) for the observation period from 1988 to 2017. Two multi-spectral indices i.e. NDVI and EVI were generated from the available data sets and the trend analyses were performed using Theil-Sen (T-S) regression method by identifying robust trend parameters (slope and pvalue). The average mean values for NDVI and EVI were recorded at 0.4 and 0.2. Significant positive trend was observed in both vegetation indices (NDVI and EVI) with a mean slope value of 0.004 and 0.003 and pvalue of 0.03 and 0.02 respectively.