Preprints
https://doi.org/10.5194/hess-2021-599
https://doi.org/10.5194/hess-2021-599

  13 Dec 2021

13 Dec 2021

Review status: this preprint is currently under review for the journal HESS.

Model Comparisons Between Canonical Vine Copulas and Meta-Gaussian for Agricultural Drought Forecasting over China

Haijiang Wu1,2, Xiaoling Su1,2, Vijay P. Singh3,4, Te Zhang2, and Jixia Qi2 Haijiang Wu et al.
  • 1Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling, Shaanxi, 712100, China
  • 2College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
  • 3Department of Biological and Agricultural Engineering & Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843-2117, USA
  • 4National Water and Energy Center, UAE University, Al Ain, UAE

Abstract. Agricultural drought is caused by reduced soil moisture and precipitation and affects the growth of crops and vegetation, and in turn agricultural production and food security. For developing measures for drought mitigation, reliable agricultural drought forecasting is essential. In this study, we developed an agricultural drought forecasting model based on canonical vine copulas under three-dimensions (3C-vine model), in which the antecedent meteorological drought and agricultural drought persistence were utilized as predictors. Besides, the meta-Gaussian (MG) model was selected as a reference model to evaluate the forecast skill. The agricultural drought in August of 2018 was selected as a case study, and the spatial patterns of 1–3-month lead forecasts of agricultural drought utilizing the 3C-vine model resembled the corresponding observations, indicating the predictive ability of the model. The performance metrics (NSE, R2, and RMSE) showed that the 3C-vine model outperformed the MG model for August under diverse lead times. Also, the 3C-vine model exhibited excellent forecast skills in capturing the extreme agricultural drought over different selected typical regions. This study may help with drought early warning, drought mitigation, and water resources scheduling.

Haijiang Wu et al.

Status: open (until 07 Feb 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-599', Anonymous Referee #1, 28 Dec 2021 reply
    • AC1: 'Reply on RC1', Haijiang Wu, 08 Jan 2022 reply

Haijiang Wu et al.

Haijiang Wu et al.

Viewed

Total article views: 294 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
241 48 5 294 2 3
  • HTML: 241
  • PDF: 48
  • XML: 5
  • Total: 294
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 13 Dec 2021)
Cumulative views and downloads (calculated since 13 Dec 2021)

Viewed (geographical distribution)

Total article views: 290 (including HTML, PDF, and XML) Thereof 290 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Jan 2022
Download
Short summary
Agricultural drought forecasting lies at the core of overall drought risk management and is critical for food security and early warning. We attempted to compare forecast performance of agricultural drought between canonical vine copulas (3C-vine) and MG models under three-dimensional scenarios over China. Based on performance metrics and selected typical agricultural drought events, the 3C-vine model exhibited excellent forecast skills under the 1–3-month lead times in comparison with MG model.