Projection of climate change-induced sea-level rise for the coasts of Tamil Nadu and Puducherry, India using SimCLIM: a first step towards planning adaptation policies

Ramachandran, A.; Khan, A. Saleem; Palanivelu, K.; Prasannavenkatesh, Ramachandran; Jayanthi, N.

doi:10.1007/s11852-017-0532-6

Cited by 37 publications

(6 citation statements)

References 24 publications

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“…SimCLIM is an integrated software modelling system that users can flexibly customize for their own purposes to carry out assessments of climate change and its impacts and adaptation at local to global scales (Warrick et al, 2005;Mcleod et al, 2010). There is a wide range of applications by SimCLIM in studying the impacts of present climate variability and future change on various fields, for example, water resources management (Warrick, 2009), agriculture systems (Harrison et al, 2016), crop yield (Bao et al, 2015;Amin et al, 2018a;Trisurat et al, 2018), sea level rise (Wang et al, 2016;Ramachandran et al, 2017), ecosystem (Meyer et al, 2018), and so on. For climate change projections, outputs from a large range of complex General Circulation Models (GCMs) and the IPCC AR5 GHG emissions scenarios Representative Concentration Pathways (RCPs) are incorporated in SimCLIM.…”

Section: Model Descriptionmentioning

confidence: 99%

Climate Change Projections of Temperature Over the Coastal Area of China Using SimCLIM

Wang

Piao

Hou

et al. 2021

Front. Environ. Sci.

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Facing the western Pacific Ocean and backed by the Eurasian continent, the coastal area of China (hereafter as CAC) is sensitive and vulnerable to climate change due to the compound effects of land-ocean-atmosphere, and thus is prone to suffer huge climate-related disaster losses because of its large population density and fast developed economy in the context of global warming. Here in this study the near- (2040), mid- (2070), and long-future (2100) mean, minimum, and maximum temperature (Tmean, Tmin, and Tmax) projections based on the statistic downscaling climate prediction model (SimCLIM) integrated with 44 General Circulation Models (GCMs) of CMIP5 under three representative concentration pathway (RCP4.5, RCP6.0, and RCP8.5) scenarios are evaluated over CAC and its sub-regions. Multi-model ensemble of the selected GCMs demonstrated that there was a dominating and consistent warming trend of Tmean, Tmin, and Tmax in the Chinese coastal area in the future. Under RCP4.5, RCP6.0, and RCP8.5 scenarios, the annual temperature increase was respectively projected to be in the range of 0.8–1.2°C for 2040, 1.5–2.7°C for 2070, and 1.6–4.4°C for 2100 over the entire CAC. Moreover, a spatial differentiation of temperature changes both on the sub-regional and meteorological station scales was also revealed, generally showing an increment with “high south and low north” for annual average Tmean but “high north and low south” for Tmin and Tmax. An obvious lower increase of Tmean in the hotter months like July and August in the south and a significant sharper increment of Tmin and Tmax in the colder months such as January, February, and December in the north were expected in the future. Results derived from this study are anticipated to provide insights into future temperature changes and also assist in the development of target climate change mitigation and adaptation measures in the coastal area of China.

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Section: Model Descriptionmentioning

confidence: 99%

Climate Change Projections of Temperature Over the Coastal Area of China Using SimCLIM

Wang

Piao

Hou

et al. 2021

Front. Environ. Sci.

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“…Studies indicated that the Bay of Bengal's SLR is the highest globally at10 mm yr −1 (Eriscson et al, 2006); ranges from 4 to 7.8 mm/year (Alam & Ahmed, 2010). Studies highlighted that the overall projected SLR would be from 0.34 to 1.45 cm/year (IPCC, 2014) and indicate a rise of 0.6 cm/year (Ramachandran et al, 2017). The land change lindicated that mangroves loss would be 22,286 ha in 2020–2050.…”

Section: Discussionmentioning

confidence: 99%

Are the Sundarbans, the World's largest mangroves region under threat?—An ecosystem‐based geospatial approach to assess changes past, present, and future in relation to natural and human‐induced factors

et al. 2022

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The World's most extensive mangrove region, the Sundarbans ecosystem, is highly biodiverse but severely stressed. Past, present, and future potential changes in Sundarbans mangroves have been assessed from multi-temporal satellite data of 1980, 2000, and 2020, elevation data, and topographical maps using geospatial techniques, digital shoreline analysis system, land change modelling, and ground truth verification. The mangrove loss was 16,025 ha between 1980 and 2020 and is predicted to be 22,286 ha between 2020 and 2050. Major changes were observed in mangroves, waterbodies, mudflats, agriculture, and aquaculture. The shoreline change by endpoint rate indicated the erosion rate by 5.81 m yr À1 in 2000-2020 compared to À0.90 m yr À1 in 1980-2000. The weighted linear regression indicated the average erosion and accretion rate of À5.96 m-yr À1 and 4.92 m yr À1 , respectively, with erosion in 76% of transects and accretion in the remaining 24% between 1980 and 2020.A sea-level rise by 1 m will inundate 17,486 ha of mangroves. The finding revealed the dynamic nature of mangroves, past and expected future loss, the severely eroding transects, hotspots, and the consequences of mangrove loss on depending the population. As fringe mangroves will be at greater risk, speedy measures are needed to stabilize the highly eroding regions.climate change vulnerability, geographical information system (GIS), natural and anthropogenic factors, sea-level rise, shoreline change, Sundarban mangroves | INTRODUCTIONMangroves are carbon-rich ecosystems that thrive in intertidal coastal saline zones or brackishwater. They protect the coast from hurricanes, tsunamis, and shoreline erosion, serve as breeding grounds for many shrimps and finfishes, offer medicine, firewood, food, and building construction materials for the local population (Dasgupta et al., 2019;Giri et al., 2007;Menéndez et al., 2020). Despite their importance for maintaining biodiversity, coastal ecology, and supporting local livelihoods, mangroves are greatly threatened across the globe (Friess et al., 2019). The development of satellite, remote sensing, and geospatial analysis software have helped researchers and environmentalists to assess mangrove dynamics and deforestation. Due to timeseries data generation capability, remote sensing science has become

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“…SimCLIM is an "open-framework" piece of software that can be used to study climate change impacts over time and space (Figure 4). The SimCLIM risk assessment software tools can be used for site-specific climatic data analysis (Bao et al, 2015;Ramachandran et al, 2017).…”

Section: Punya Murthy Khristodas Et Almentioning

confidence: 99%

Assessment of observed temperature trend patterns of Bhubaneswar city, India with special prominence on future projections using SimCLIM climate model and farmer’s perception

2024

Global NEST: The International Journal

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<p>Temperature dynamics is a widely recognized indicator of the global warming phenomenon. Changes in temperature patterns in Bhubaneswar, India, were assessed by examining the monthly minimum and maximum temperature data for 60 years (1956–2015) sourced from the Indian Meteorological Department. SimCLIM climate change risk assessment software was used for projecting the temperature regime for four different representative concentration pathways. Further, a survey of 112 farmers was conducted to understand their perceptions of temperature variations in and around Bhubaneswar city using a multi-stage sampling technique. Mann–Kendall statistics and linear regression were used to analyze the monthly temperature data and trend detection. The study reveals a change of +4%,-4.44%, and +1.09% in the mean monthly maximum, minimum, and annual temperature. The results of the future projection show a temperature change of 0.81°C for RCP 2.6, 1.12°C for RCP 4.5, 1.03°C for RCP 6.0, and 1.54°C for RCP 8.5 for the year 2050. Confirming the analysis findings, most of the interviewed farmers also perceived increasing temperatures and decreasing precipitation in and around the city. The study outcome of temperature trend analysis and future projections will be helpful for farmers and policymakers in formulating adaptation strategies to climate change.</p>

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Projection of climate change-induced sea-level rise for the coasts of Tamil Nadu and Puducherry, India using SimCLIM: a first step towards planning adaptation policies

Cited by 37 publications

References 24 publications

Climate Change Projections of Temperature Over the Coastal Area of China Using SimCLIM

Climate Change Projections of Temperature Over the Coastal Area of China Using SimCLIM

Are the Sundarbans, the World's largest mangroves region under threat?—An ecosystem‐based geospatial approach to assess changes past, present, and future in relation to natural and human‐induced factors

Assessment of observed temperature trend patterns of Bhubaneswar city, India with special prominence on future projections using SimCLIM climate model and farmer’s perception

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