Modelling Assessment of Sandy Beaches Erosion in Thailand
This paper focuses on the spatial and temporal aspects of rising sea levels and sandy beach erosion in Thailand. The major scientific challenge tackled in this paper was to distinguish the relevance and contribution of sea level rise (including storms) to beach erosion. The S...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://repository.li.mahidol.ac.th/handle/123456789/53968 |
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| Institution: | Mahidol University |
| Language: | English |
| Summary: | This paper focuses on the spatial and temporal aspects of rising sea levels and sandy beach erosion in Thailand. The major scientific challenge tackled in this paper was to distinguish the relevance and contribution of sea level rise (including storms) to beach erosion. The Simulator of Climate Change Risks and Adaptation Initiatives (SimCLIM) and its’ impact model (CoastCLIM) with two representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5) was utilized to forecast changes in sea level and shoreline between the years 1940-2100. Input parameters underlying the modified Brunn Rule were applied (e.g., coastal and storm characteristics). Moreover, sand loss and forced people migration were estimated using fundamental equations. The sea level is predicted to rise by 147.90 cm and the coastline will be eroded around 517.09 m by 2100, compared to levels in 1995. This level of erosion could lead to a decrease of the coastal sandy area by about 2.69 km2and a population of 873 people, over the same period. In scientific terms, this paper quantifies the contribution and relevance of sea-level rise (SLR) to sandy beach erosion compared to other factors, including ad-hoc short-term impacts from stochastic storminess. The results also showed that 8.02 and 23.26 percent of erosion was attributed to storms and sea-level rise, respectively. Nevertheless, limited multi-century data of residual movement in Thailand could create uncertainties in distinguishing relative contributions. These results could be beneficial to national-scale data and the adaptation planning processes in Thailand. |
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