Articles | Volume 26, issue 14
https://doi.org/10.5194/hess-26-3847-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-3847-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jul 2022
Research article |
| 22 Jul 2022
| 22 Jul 2022
Comparison between canonical vine copulas and a meta-Gaussian model for forecasting agricultural drought over China
Haijiang Wu
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi Province, China
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, Shaanxi Province, China
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi Province, China
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, Shaanxi Province, China
Vijay P. Singh
Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843-2117, USA
Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843-2117, USA
National Water and Energy Center, UAE University, Al Ain, UAE
Te Zhang
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, Shaanxi Province, China
Jixia Qi
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, Shaanxi Province, China
Shengzhi Huang
State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi'an University of Technology, Xi'an 710048, Shaanxi Province, China
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Short summary
Agricultural drought forecasting lies at the core of overall drought risk management and is critical for food security and drought early warning. Using three-dimensional scenarios, we attempted to compare the agricultural drought forecast performance of a canonical vine copula (3C-vine) model and meta-Gaussian (MG) model over China. The findings show that the 3C-vine model exhibits more skill than the MG model when using 1– to 3-month lead times for forecasting agricultural drought.
Agricultural drought forecasting lies at the core of overall drought risk management and is...
