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

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 spatiotemporal overlap rule, and propagation probability is calculated by coupling the machine learning model and C-vine copula. The results indicate that (1) 46 drought events are successfully paired with 130 meteorological and 184 ecological drought events during 1982–2020, and ecological drought exhibits a longer duration but smaller affected area and severity than meteorological drought; (2) a quadratic discriminant analysis (QDA) classifier performs the best among the 11 commonly used machine learning models which are combined with four-dimensional C-vine copula to construct the drought propagation probability model; and (3) the hybrid method considers more drought characteristics and a more detailed propagation process which addresses the limited applicability of the traditional method to regions with large spatial extent.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Estimating propagation probability from meteorological to ecological droughts using a hybrid machine learning copula method ; volume:27 ; number:2 ; year:2023 ; pages:559-576 ; extent:18 ; Hydrology and earth system sciences ; 27, Heft 2 (2023), 559-576 (gesamt 18)

Contributors, Places and Time

• Creator: Jiang, Tianliang ; Su, Xiaoling ; Zhang, Gengxi ; Zhang, Te ; Wu, Haijiang

Further information

• DOI: 10.5194/hess-27-559-2023
• URN: urn:nbn:de:101:1-2023033007555507636810
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 11:01 AM CEST