Comparison between canonical vine copulas and a meta-Gaussian model for forecasting agricultural drought over China

Abstract. Agricultural drought mainly stems from reduced soil moisture and precipitation, it causes adverse impacts on the growth of crops vegetation, thereby affecting agricultural production food security. In order to develop mitigation measures, reliable forecasting is essential. this study, we developed an model based canonical vine copulas in three dimensions (3C-vine model) which antecedent meteorological persistence were utilized as predictors. Furthermore, a meta-Gaussian (MG) was selected reference evaluate forecast skill. The China August 2018 typical case spatial patterns 1- 3-month lead forecasts utilizing 3C-vine resembled corresponding observations, indicating good predictive ability model. performance metrics – Nash–Sutcliffe efficiency (NSE), coefficient determination (R2), root-mean-square error (RMSE) showed that outperformed MG with respect for diverse times. Moreover, exhibited excellent skill capturing extreme over different regions. This study may help guide early warning, mitigation, water resource scheduling.