Coastal Resilience Assessment Based On The Proposed Climate Change Resilience Model And Remote Sensing Techniques

In recent years,globally more focuses are on the development of integrated approach to the climate change resilience,since it is in line with central theme of sustainability science.However,a conceptual framework for assessing climate change resilience is still in the process of being established hindering the implementations and development of effective policies.Therefore,conceptualization and operationalization of climate change resilience is required to provide a foundational development.Firstly,the study formulated a theoretical model titled Climate Change Resilience of Place(C-CROP)model,a geo-based model,based on the principles of climate change resilience with an approach to the incorporation of nature-based solution(NBS).C-CROP model views climate change resilience as a socio-ecological construct that is the output of the balance between adaptive capacity and vulnerability of the place reflecting;1)socio-ecological system must be embedded within the vulnerability to be resilient,2)each system possesses some inherent adaptive capacity and vulnerability,thus intrinsic resilience reflected by the sustainable networks of various adaptive capacity of ecosystem and social system in the face of vulnerability,and 3)each system’s transformative capacity over extended period of time can add positive edits to the adaptive capacity of that system at a given point in time through incorporation of NBS(as a part of climate change adaptation).Secondly,in order to address the first dimension of C-CROP model i.e.,assessment of coastal vulnerability,Coastal Vulnerability Index(CVI)was conducted in the study area(central coast of Bangladesh).The index was calculated using nine parameters including geomorphology,shoreline change rate,coastal slope,rate of sea level change,mean tide range,bathymetry,salinity concentration in ground water,storm surge height and coastal protection through mangrove afforestation.The result reveled that low lying intertidal and supratidal flats,very high to moderate erosion rate,shallow bathymetry,high to moderate salinity level,high storm surge inundation susceptibility and very low level of mangrove protection appears to be the responsible factor for very high to highly vulnerable situation.Thirdly,the second dimension of C-CROP model i.e.,intrinsic resilience of socio-ecological system was assessed.As part of innovation points,this is the first-ever attempt to imply operational framework of C-CROP model.Remote Sensing based earth observation,census,and ancillary data were in the center of the investigation while Principal Component Analysis(PCA)was employed to select and weigh bottom level indicators.20 adaptive capacity indicators and 17 vulnerability indicators were selected in this regard.Using PCA,37 indicators are reduced to 5 adaptive capacity and 3 vulnerability principal components which explain 73.81% and 79.17% variance in the data respectively.Quantification and mapping of intrinsic resilience through geospatial approach using Google Earth Engine(GEE)provide useful data that show how intrinsic resilience is spatially distributed in the most vulnerable hotspot in the climate change context.The study found that only 26.16% area of the central coast of Bangladesh was found resilient in the context of climate change,the majority of the area of the central coast was found non-resilient.Fourthly,as per C-CROP model,Nature-based Solution(NBS)is proposed to increase the transformative capacity,so as to increase resilience of socio-ecological system in the climate change context.How NBS works to promote climate change resilience,i.e.,effectiveness of proposed NBS(mangrove afforestation)is quantified.The main innovation of this part of the dissertation is to spatially establishing the effectiveness of the NBS.Alongside this,the study critically assessed and estimates stable lands(agricultural lands)and their socioeconomic benefits as part of the effectiveness of the NBS.As part of the NBS derived benefits and services,in terms of climate change context,the study estimates the sequestrated carbon in mangrove forest which is the direct contribution of NBS in indirect benefits of ecosystem services.For this part,the study considered entire coast of Bangladesh except Sundarbans Reserve Forest(which is a natural mangrove forest)and also rolled back to 1962 to work on a 60 years’ time series data.The study used,declassified CORONA satellite imagery to assess the NBS intervention in the country which is the first ever attempt in the remote sensing-based ecosystem work in Bangladesh.Again,the concept of NBS is in development stage,and very few or no works have been done so far in measuring and labelling the effectiveness of the NBS,which is why the study can innovatively contribute to the scientific community to show the effectiveness of the NBS in three domains(i.e.,Societal benefits,economic benefits,and ecological benefits).The integration of applied remote sensing technology into geo-based modelling of climate change resilience with the approach to the incorporation of NBS can provide crucial information and make valuable contributions in guiding resource allocations towards resilience-building that finally will complement the Sustainable Development Goals(SDGs).Despite the multifaceted nature of resilience,and more than five decades of disagreement over the use of the term among researchers and scholars,it is found a useful concept that offers a more systematic and cross-cutting approach to the climate change risk reduction and adaptation;thus,this study can be a pioneer for modelling climate change resilience in changing climatic scenario.