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.