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On bias correction in drought frequency analysis based on climate models

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  • Yog Aryal

    (University of Wyoming)

  • Jianting Zhu

    (University of Wyoming)

Abstract

Assessment of future drought characteristics based on climate models is difficult as climate models usually have bias in simulating precipitation frequency and intensity. In this study, we examine the significance of bias correction in the context of drought frequency and scenario analysis using output from climate models. In particular, we use three bias correction techniques with different emphases and complexities to investigate how they affect the results of drought frequency and severity based on climate models. The characteristics of drought are investigated using regional climate model (RCM) output from the North American Regional Climate Change Assessment Program (NARCCAP). The Standardized Precipitation Index (SPI) is used to compare and forecast drought characteristics at different timescales. Systematic biases in the RCM precipitation output are corrected against the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data and the bias-corrected RCM historical simulations. Preserving mean and standard deviation of NARR precipitation is essential in drought frequency analysis. The results demonstrate that bias correction significantly decreases the RCM errors in reproducing drought frequency derived from the NARR data. Different timescales of input precipitation in the bias corrections show similar results. The relative changes in drought frequency in future scenario compared to historical scenario are similar whether both scenarios are bias corrected or both are not bias corrected.

Suggested Citation

  • Yog Aryal & Jianting Zhu, 2017. "On bias correction in drought frequency analysis based on climate models," Climatic Change, Springer, vol. 140(3), pages 361-374, February.
    • Handle: RePEc:spr:climat:v:140:y:2017:i:3:d:10.1007_s10584-016-1862-3
    DOI: 10.1007/s10584-016-1862-3
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    References listed on IDEAS

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    1. Jianting Zhu & William Forsee & Rina Schumer & Mahesh Gautam, 2013. "Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models," Climatic Change, Springer, vol. 118(2), pages 469-485, May.
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    4. Chong-yu Xu, 1999. "Climate Change and Hydrologic Models: A Review of Existing Gaps and Recent Research Developments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(5), pages 369-382, October.
    5. Anonymous, 1966. "World Meteorological Organization," International Organization, Cambridge University Press, vol. 20(4), pages 842-844, October.
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    1. Phuong Nguyen-Ngoc-Bich & Manh-Hung Le & Tan Phan-Van & Thanh Ngo-Duc & Tuan Tran-Bui-Anh & Long Trinh-Tuan & Huong Ngo-Thi-Thanh & Dat Pham-Tien & Fredolin T. Tangang & Liew Juneng & Faye Cruz & Jing, 2025. "How does bias correction impact simulated drought characteristics by Regional Climate Models?," Climatic Change, Springer, vol. 178(4), pages 1-22, April.
    2. D. Carvalho & S. C. Pereira & R. Silva & A. Rocha, 2022. "Aridity and desertification in the Mediterranean under EURO-CORDEX future climate change scenarios," Climatic Change, Springer, vol. 174(3), pages 1-24, October.

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