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Discussion papers
https://doi.org/10.5194/nhess-2019-327
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-327
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 22 Oct 2019

Submitted as: research article | 22 Oct 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Bias correction of gauge-based gridded product to improve extreme precipitation analysis in the Yarlung Tsangpo-Brahmaputra River Basin

Xian Luo1,2, Xuemei Fan1, Yungang Li1,2, and Xuan Ji1,2 Xian Luo et al.
  • 1Institute of International Rivers and Eco-security, Yunnan University, Kunming, China
  • 2Yunnan Key Laboratory of International Rivers and Transboundary Eco-security, Kunming, China

Abstract. Critical gaps in the amount, quality, consistency, availability, and spatial distribution of rainfall data limit extreme precipitation analysis, and the application of gridded precipitation data are challenging because of their considerable biases. This study corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB) using two linear and two nonlinear methods, and assessed their influence on extreme precipitation indices. The results showed that the original APHRODITE data tended to underestimate precipitation during the summer monsoon season, especially in the topographically complex Himalayan belt. Bias correction using complementary rainfall observations to add spatial coverage in data-sparse regions greatly improved the performance of extreme precipitation analysis. Although all methods could correct mean precipitation, their ability to correct the wet-day frequency and coefficient of variation were substantially different, leading to considerable differences in extreme precipitation indices. Generally, higher-skill bias-corrected APHRODITE data are expected to perform better than those corrected by lower-skill approaches. This study would provide reference for using gridded precipitation data in extreme precipitation analysis and selecting bias-corrected method for rainfall products in data-sparse regions.

Xian Luo et al.
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Short summary
In this study, we corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) in the Yarlung Tsangpo-Brahmaputra River Basin using both linear and nonlinear methods, and their influences on resulting extreme precipitation analysis were assessed. Results showed that all methods were able to correct mean precipitation, but their ability to correct wet-day frequency and coefficient of variation were markedly different.
In this study, we corrected Asian Precipitation Highly Resolved Observational Data Integration...
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