Preprints
https://doi.org/10.5194/hess-2022-78
https://doi.org/10.5194/hess-2022-78
 
01 Apr 2022
01 Apr 2022
Status: a revised version of this preprint is currently under review for the journal HESS.

Estimating propagation probability from meteorological to ecological droughts using a hybrid machine learning-Copula method

Tianliang Jiang1,2, Xiaoling Su1,2, Gengxi Zhang3, Te Zhang1,2, and Haijiang Wu1,2 Tianliang Jiang et al.
  • 1College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, Shaanxi, 712100, China
  • 2Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A & F University, Yangling, Shaanxi, 712100, China
  • 3College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225012, China

Abstract. The impact of droughts on vegetation is essentially manifested as the transition of water shortage from the meteorological to ecological stages. Therefore, understanding the mechanism of drought propagation from meteorological to ecological drought is crucial for ecological conservation. This study proposes a method for calculating the probability of meteorological drought to trigger ecological drought at different magnitudes in Northwestern China. In this approach, meteorological and ecological drought events during 1982–2020 are identified using the three-dimensional identification method; the propagated drought events are extracted according to a certain spatio-temporal overlap rule; and propagation probability is calculated by coupling machine learning model and C-vine copula. The results indicate that: (1) 46 drought events are successfully paired by 130 meteorological and 184 ecological drought events during 1982–2020; ecological drought exhibits a longer duration, but smaller affected area and severity than meteorological drought; (2) Quadratic Discriminant Analysis (QDA) classifier performs the best among the 11 commonly used machine learning models which is combined with four-dimensional C-vine copula to construct drought propagation probability model; (3) the hybrid method considers more drought characteristics and more detailed propagation process which addresses the limited applicability of the traditional method to regions with large spatial extent.

Tianliang Jiang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-78', Anonymous Referee #1, 29 Apr 2022
    • AC1: 'Reply on RC1', Tianliang Jiang, 31 Aug 2022
    • AC2: 'Reply on RC2', Tianliang Jiang, 31 Aug 2022
    • AC4: 'Reply on RC1 (latest version)', Tianliang Jiang, 10 Sep 2022
  • CC1: 'Comment on hess-2022-78', Shanhu Jiang, 01 Aug 2022
    • AC3: 'Reply on CC1', Tianliang Jiang, 31 Aug 2022
  • RC2: 'Comment on hess-2022-78', Anonymous Referee #2, 01 Aug 2022
    • AC2: 'Reply on RC2', Tianliang Jiang, 31 Aug 2022
    • AC5: 'Reply on RC2 (latest version)', Tianliang Jiang, 10 Sep 2022

Tianliang Jiang et al.

Tianliang Jiang et al.

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Short summary
A hybrid method is developed for calculating propagation probability of meteorological to ecological drought at different levels. Drought events are identified from a three-dimensional perspective. Spatial and temporal overlap rule is developed for extracting propagated drought events.