Short-term agricultural drought prediction based on D-vine copula quantile regression in snow-free unfrozen surface area, China

Abstract

This study aims to propose an agricultural drought prediction model based on D-vine copula quantile regression considering several factors related to agricultural drought occurrence mechanisms in China. The proposed model is applied for short-term agricultural drought prediction (represented by 1-month time scale of the standardized soil moisture index (SSI-1)) considered antecedent soil moisture (represented by SSI-3), real-time rainfall (represented by 1-month time scale of the Standardized Precipitation Index (SPI-1)) and vegetation cover (represented by the Normalized Difference Vegetation Index (NDVI)) based on monthly soil moisture data from the Climate Change Initiative (CCI) program of European Space Agency (ESA), precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and NDVI products from MODIS. The proposed model was employed and evaluated in China and results showed it performed well in snow-free unfrozen surface area such as south-east China. The outcome of this study can contribute to early warning for agricultural drought.

Keywords:

• agricultural drought
• drought prediction
• vine copula
• remote sensing

Disclosure statement

No potential competing interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

This study was supported by the key research and development program of Shaanxi province under grant number No:2020NY-166 and the soft science project of Xi'an Science and Technology Bureau under grant number No.2021-0013.