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Evaluating the necessity of post-processing techniques on d4PDF data for extreme climate assessment

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Abstract

The occurrence and severity of extreme precipitation events have been increasing globally. Although numerous projections have been proposed and developed for evaluating the climate change impacts, most models suffer from significant bias error due to the coarse resolution of the climate datasets, which affects the accuracy of the climate change assessment. Therefore, in this study, post-processing techniques (interpolation and bias correction methods) were adopted on the database for Policy Decision Making for Future Climate Change (d4PDF) model for extreme climatic flood events simulation in the Chao Phraya River Basin, Thailand, under + 4-K future climate simulation. Due to the limited number of the rain gages, the gradient plus inverse distance squared interpolation method (combination of multiple linear regression and distance weighting methods) was applied in this study. In the bias correction methods, the additional setting of monthly and seasonal periods was adjusted. The proposed bias correction approach deployed gamma distribution combined with generalized Pareto distribution setting with the seasonal period for the rainy season datasets, whereas only the gamma setting was applied with the monthly period during the dry season. The outcomes revealed that the proposed method could react to extreme rainfall events, expand dry days during dry season, and intensify rainfall amount during rainy season. The post-processing d4PDF trends of six sea surface temperature (SST) patterns (consists of 90 ensemble members) of two periods (near future: 2051–2070 and far future: 2091–2110) recorded the highest and lowest amounts of annual rainfalls of 4,450 mm/year in mid-stream of Nan River and 710 mm/year in the lower CPRB, respectively. Notably, the significant variances noted in the rainfall patterns among ensembles, demanding further investigation in future climate change, impact studies. The findings of the study provided novel insights on the importance of proper post-processing techniques for improving the robustness of d4PDF in climate change impacts assessment.

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Data availability

The raw data that support the findings of this study are obtained from the Pollution Control Department, Thailand. Restrictions apply to the availability of these data, which were used under permission for this study. Data are available from the authors upon reasonable request and with the permission of Pollution Control Department, Thailand.

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Acknowledgements

The observed data were provided by the Thai Meteorological Department. We would like to extend our gratitude to Mr. David Stenzel for his kind comments and valuable advice. Co-author Luksanaree Maneechot thanks her scholarship donor, Japan Ministry of Education, Culture, Sports, Science and Technology (Monbukagakusho: MEXT) for funding this research as a part of her PhD work. The authors would like to express their gratitude to the Kyoto University of Advanced Science for the technical and financial support in this study.

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Authors and Affiliations

  1. Climate Action for Sustainability Office, Sustainability Engineering, Department of Corporate Engineering, Charoen Pokphand Foods PCL, Bangkok, Thailand

    Luksanaree Maneechot

  2. Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, Kyoto, Japan

    Luksanaree Maneechot, Yong Jie Wong, Yoshihisa Shimizu & Khagendra Pralhad Bharambe

  3. Department of Bioenvironmental Design, Faculty of Bioenvironmental Sciences, Kyoto University of Advanced Science, Kameoka, 606-8501, Japan

    Yong Jie Wong & Teerawat Ram-Indra

  4. Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, 611-0011, Japan

    Sophal Try

  5. Socio and Eco Environment Risk Management, Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan

    Khagendra Pralhad Bharambe

  6. Engineering Department, Gulf Energy Development Public Company Limited, Bangkok, Thailand

    Patinya Hanittinan

  7. School of Engineering, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, VIC, 3216, Australia

    Muhammad Usman

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  1. Luksanaree Maneechot
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Contributions

Luksanaree Maneechot: conceptualization, methodology, formal analysis, data curation, software, and writing—original draft preparation. Yong Jie Wong: methodology, formal analysis, data curation, software, and writing—original draft preparation. Sophal Try: formal analysis, methodology, software, and writing—original draft preparation. Yoshihisa Shimizu: supervision, funding acquisition, and writing—review and editing. Khagendra Pralhad Bharambe: formal analysis, methodology, and writing—review and editing. Patinya Hanittinan: methodology and writing—original draft preparation. Teerawat Ram-Indra: methodology and writing—original draft preparation. Muhammad Usman: data curation, software, and writing—review and editing.

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Correspondence to Yong Jie Wong.

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Maneechot, L., Wong, Y.J., Try, S. et al. Evaluating the necessity of post-processing techniques on d4PDF data for extreme climate assessment. Environ Sci Pollut Res 30, 102531–102546 (2023). https://doi.org/10.1007/s11356-023-29572-9

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