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

Jiang, Tianliang; Su, Xiaoling; Zhang, Gengxi; Zhang, Te; Wu, Haijiang

doi:https://doi.org/10.5194/hess-27-559-2023

Articles | Volume 27, issue 2

https://doi.org/10.5194/hess-27-559-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-27-559-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 2

Research article

|

27 Jan 2023

Research article |

| 27 Jan 2023

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

Tianliang Jiang, Xiaoling Su, Gengxi Zhang, Te Zhang, and Haijiang Wu

Data sets

NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI), Version 5 E. Vermote https://doi.org/10.7289/V5ZG6QH9

China's multi-period land use/cover change monitoring dataset X. Xu, J. Liu, S. Zhang, R. Li, C. Yan, and S. Wu https://doi.org/10.12078/2018070201

ERA5-Land monthly averaged data from 1981 to present J. Muñoz Sabater https://doi.org/10.24381/cds.68d2bb30

The Global Land Data Assimilation System M. Rodell, P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, K., C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J. K. Entin, J. P. Walker, D. Lohmann, and D. Toll https://doi.org/10.5067/SXAVCZFAQLNO

Short summary

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