Natural Hazards

, Volume 92, Issue 1, pp 155–172 | Cite as

Evaluation of CHIRPS and its application for drought monitoring over the Haihe River Basin, China

  • Feng Gao
  • Yuhu Zhang
  • Xiulin Ren
  • Yunjun Yao
  • Zengchao Hao
  • Wanyuan Cai
Original Paper
  • 81 Downloads

Abstract

Climate Hazards Group Infrared Precipitation with Stations data (CHIRPS) rainfall dataset was early evaluated and compared with 29 meteorological stations over the Haihe River basin in China, for the period 1981–2015. Seven statistical and categorical metrics were applied to evaluate the performance of CHIRPS with gauge measurements at multi-time scales (monthly, seasonally and annually). Using the Standardized Precipitation Index (SPI) as the drought indicator, the applicability of this new long-term satellite precipitation product for drought monitoring was investigated in this study. Results indicate that the good performances were performed at multiple temporal scales (monthly, seasonally and annually). Although it tends to overestimate the higher precipitation in this region, CHIRPS demonstrated good agreement (R2 > 0.70) with gauge observations at monthly scale and greater agreements (R2 > 0.78) at seasonal and annual scales. Meanwhile, CHIRPS performed a good score of BIAS and lower error in a majority of months at multi-time scales. Because of its good performance at multi-time scales and the advantages of high spatial resolution and long-time record, CHIRPS was applied to derive the SPI over the Haihe River basin. It is evaluated and compared with stations observations to derive SPI at time scale of 1, 3 and 6 months. The results indicate that it performed good ability to monitor drought (R2 > 0.70) and successfully captured the historical drought years (1981, 1999, 2001 and 2012). Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and drought monitoring in this region.

Keywords

CHIRPS Precipitation Drought monitoring Standardized Precipitation Index (SPI) 

Notes

Acknowledgements

Support for this work by National Key Research and Development Program of China (2017YFC0406002) and Clean Development Mechanism (CDM) Fund Grant Program of China (2014092, 2014108). We are grateful to thanks for the producers of CHIRPS and such people which supplied valuable comments and constructive suggestions that helped us improving the manuscript of this paper.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Feng Gao
    • 1
  • Yuhu Zhang
    • 1
  • Xiulin Ren
    • 1
  • Yunjun Yao
    • 2
  • Zengchao Hao
    • 3
  • Wanyuan Cai
    • 4
  1. 1.College of Resources Environment and TourismCapital Normal UniversityBeijingChina
  2. 2.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  3. 3.College of Water SciencesBeijing Normal UniversityBeijingChina
  4. 4.Institute of Remote Sensing and Geographic Information Systems, School of Earth and Space SciencePeking UniversityBeijingChina

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