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Uncertainty Analysis of Monthly Precipitation in GCMs Using Multiple Bias Correction Methods under Different RCPs

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  • Young Hoon Song

    (Faculty of Civil Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea)

  • Eun-Sung Chung

    (Faculty of Civil Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea)

  • Mohammed Sanusi Shiru

    (Faculty of Civil Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea
    Department of Environmental Sciences, Faculty of Science, Federal University Dutse, Dutse P.M.B 7156, Nigeria)

Abstract

This study quantified the uncertainties in historical and future average monthly precipitation based on different bias correction methods, General Circulation Models (GCMs), Representative Concentration Pathways (RCPs), projection periods, and locations within the study area (i.e., the coastal and inland areas of South Korea). The GCMs were downscaled using deep learning, random forest, and nine quantile mapping bias correction methods for 22 gauge stations in South Korea. Data from the Korean Meteorology Administration (1970–2005) were used as the reference data in this study. Two statistical measures, the standard deviation and interquartile range, were used to quantify the uncertainties. The probability distribution density was used to assess the similarity/variation in rainfall distributions. For the historical period, the uncertainty in the selection of bias correction methods was greater than that in the selection of GCMs, whereas the opposite pattern was observed for the projection period. The projection period had the lowest level of uncertainty in the selection of RCP scenarios, and for the future, the uncertainly related to the time period was slightly lower than that for the other sources but was much greater than that for the RCP selection. In addition, it was clear that the level of uncertainty of inland areas is much lower than that of coastal areas. The uncertainty in the selection of the GCMs was slightly greater than that in the selection of the bias correction method. Therefore, the uncertainty in the selection of coastal areas was intermediate between the selection of bias correction methods and GCMs. This paper contributes to an improved understanding of the uncertainties in climate change projections arising from various sources.

Suggested Citation

  • Young Hoon Song & Eun-Sung Chung & Mohammed Sanusi Shiru, 2020. "Uncertainty Analysis of Monthly Precipitation in GCMs Using Multiple Bias Correction Methods under Different RCPs," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7508-:d:412366
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    References listed on IDEAS

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    1. Mohammed Sanusi Shiru & Shamsuddin Shahid & Noraliani Alias & Eun-Sung Chung, 2018. "Trend Analysis of Droughts during Crop Growing Seasons of Nigeria," Sustainability, MDPI, vol. 10(3), pages 1-13, March.
    2. Morteza Mohsenipour & Shamsuddin Shahid & Eun-sung Chung & Xiao-jun Wang, 2018. "Changing Pattern of Droughts during Cropping Seasons of Bangladesh," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1555-1568, March.
    3. Patricia Jitta Abdulai & Eun-Sung Chung, 2019. "Uncertainty Assessment in Drought Severities for the Cheongmicheon Watershed Using Multiple GCMs and the Reliability Ensemble Averaging Method," Sustainability, MDPI, vol. 11(16), pages 1-20, August.
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    1. Jin Hyuck Kim & Jang Hyun Sung & Shamsuddin Shahid & Eun-Sung Chung, 2022. "Future Hydrological Drought Analysis Considering Agricultural Water Withdrawal Under SSP Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 2913-2930, July.
    2. Young Hoon Song & Eun-Sung Chung & Shamsuddin Shahid, 2022. "The New Bias Correction Method for Daily Extremes Precipitation over South Korea using CMIP6 GCMs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5977-5997, December.
    3. Jin Hyuck Kim & Jang Hyun Sung & Eun-Sung Chung & Sang Ug Kim & Minwoo Son & Mohammed Sanusi Shiru, 2021. "Comparison of Projection in Meteorological and Hydrological Droughts in the Cheongmicheon Watershed for RCP4.5 and SSP2-4.5," Sustainability, MDPI, vol. 13(4), pages 1-22, February.

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