Preprints
https://doi.org/10.5194/essd-2021-105
https://doi.org/10.5194/essd-2021-105

  22 Apr 2021

22 Apr 2021

Review status: this preprint has been withdrawn by the authors.

Daily standardized precipitation index with multiple time scale for monitoring water deficit across the mainland China from 1961 to 2018

Qianfeng Wang1,3, Rongrong Zhang1, Yanping Qu2, Jingyu Zeng1, Xiaoping Wu1, Xiaozhen Zhou1,4, Binyu Ren1,4, Xiaohan Li5, and Duhui Zhou6 Qianfeng Wang et al.
  • 1Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection/College of Environment and Resource, Fuzhou University, Fuzhou, 350116, China
  • 2Research Center on Flood and Drought Disaster Reduction, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China
  • 3Joint Global Change Research Institute, Pacific Northwest National Laboratory and University of Maryland, College Park, MD 20740, USA
  • 4Key Lab of Spatial Data Mining & Information Sharing, Ministry of Education of China, Fuzhou, 350116, China
  • 5Beijing water science technology institute, Beijing, 100048, China
  • 6Public Policy Department,Chongqing Planning Research Institue, Chongqing 400011, China

Abstract. With the increasing shortage of water resources, drought has become one of the hot issues in the world. The standardized precipitation index (SPI) is one of the widely used drought assessment indicators because of its simple and effective calculation method, but it can only assess drought events more than one month. We developed a new multi-scale daily SPI dataset to make up for the shortcomings of the commonly used SPI and meet the needs of drought types at different time scales. Taking three typical stations in Henan, Yunnan and Fujian Province as examples, the drought events identified by SPI with different scales were consistent with the historical drought events recorded. Meanwhile, we took the 3-month scale SPI of soil and agricultural drought as an example, and analyzed the characteristics of drought events in 484 stations in Chinese mainland. The results showed that most of the drought events the mainland China did not increase significantly, and some parts of the northwestern Xinjiang and Northeast China showed signs of gradual relief. In short, our daily SPI data set is freely available to the public on the website https://doi.org/10.6084/m9.figshare.14135144, and can effectively capture drought events of different scales. It can also meet the needs of drought research in different fields such as meteorology, hydrology, agriculture, social economy, etc.

This preprint has been withdrawn.

Qianfeng Wang et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-105', Anonymous Referee #1, 10 May 2021
  • RC2: 'Comment on essd-2021-105', Anonymous Referee #2, 11 May 2021
  • EC1: 'Comment on essd-2021-105', Qingxiang Li, 19 Jun 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-105', Anonymous Referee #1, 10 May 2021
  • RC2: 'Comment on essd-2021-105', Anonymous Referee #2, 11 May 2021
  • EC1: 'Comment on essd-2021-105', Qingxiang Li, 19 Jun 2021

Qianfeng Wang et al.

Data sets

Muliti-scale daily SPI dataset over the Mainland China from 1961-2018 (version March 2021) Wang, Q., Zhang, R., Qi, J., Zeng, J., Zhang, X., Wu, X., Zhou, X., and Ren, B. https://doi.org/10.6084/m9.figshare.14135144

Qianfeng Wang et al.

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This preprint has been withdrawn.

Short summary
The standardized precision index (SPI), which is commonly used for drought monitoring and assessment, is limited by its temporal resolution and cannot identify flash drought in less than one month. Therefore, we developed a new daily SPI dataset. The results show that the drought events identified by our SPI dataset were consistent with the historical drought events, which is effective and reliable. At the same time, the dataset will be open to the public free of charge.