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General Circulation Model Downscaling Using Interpolation—Machine Learning Model Combination—Case Study: Thailand

Author

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  • Chotirose Prathom

    (Data Science Consortium, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Paskorn Champrasert

    (OASYS Research Group, Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

Climate change, a global problem, is now impacting human life and nature in many sectors. To reduce the severity of the impacts, General Circulation Models (GCMs) are used for predicting future climate. The prediction output of a GCM requires a downscaling process to increase its spatial resolution before projecting on local area. In order to downscale the output to a higher spatial resolution (less than 20 km), a statistical method is typically considered. By using this method, a large amount of historical observed data, up to 30 years, is essential. In some areas, the historical data is insufficient. Hence, the statistical method may not be suitable to downscale the output on the area which lacks the required data. Hence, this research aims to explore a high spatial resolution downscaling process that is able to provide a valid and high accuracy result in the Thailand area with a limitation in quantity of historical data. In this research, a combination of an interpolation and machine learning model called `IDW-ANN’ is proposed for downscaling the data under the condition. The prediction of temperature and precipitation from a GCM, IPSL-CM6A-LR in CMIP6 is downscaled by the proposed combination into a 1 km spatial resolution. After the performance evaluation, the IDW-ANN downscaling process showed good accuracy (RMSE, MAE, and R 2 ) and valid downscaled results. The future climate situation in Thailand, in particular temperature, and precipitation level, in 2040 and 2100 under two scenarios of SSPs (SSP1-2.6 and SSP3-7.0) is also projected at 1 km resolution by using IDW-ANN. From the projection, the level of precipitation sums, and temperature seem to be increased in most of Thailand in all future scenarios.

Suggested Citation

  • Chotirose Prathom & Paskorn Champrasert, 2023. "General Circulation Model Downscaling Using Interpolation—Machine Learning Model Combination—Case Study: Thailand," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9668-:d:1172810
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    Citations

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    Cited by:

    1. Pornnapa Panyadee & Paskorn Champrasert, 2024. "Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand," Sustainability, MDPI, vol. 16(11), pages 1-19, May.
    2. Aida Hosseini Baghanam & Vahid Nourani & Ehsan Norouzi & Amirreza Tabataba Vakili & Hüseyin Gökçekuş, 2023. "Application of Wavelet Transform for Bias Correction and Predictor Screening of Climate Data," Sustainability, MDPI, vol. 15(21), pages 1-19, October.

    More about this item

    Keywords

    climate change; downscaling; machine learning; CMIP6; Thailand;
    All these keywords.

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