Statistical prediction of agricultural drought severity in China based on dry or hot events

Extreme climatic events such as drought and high temperatures are frequently occurring under the global climate change scenarios causing severe threats to crop yield. Several studies have used individual events, e.g., meteorological drought (dry event), high temperatures (hot event), or compound dry–hot events as predictors of agricultural drought severity. However, only a few studies considered the situation when at least one dry event or hot event appeared. Moreover, the ability to depict drought phenomena, in terms of duration and severity, with the same drought index for different time scales was often inconsistent. This study adopted the meta-Gaussian model (MG) to predict agricultural drought severity for the crops and vegetation growing seasons (from April to September) from the year 1948 to 2014 in China by using two predictors, namely, standardized dry or hot events index (SDHEI) and joint soil moisture deficit index (JSDI). The SDHEI was built based on monthly precipitation and daily temperature, and the JSDI was calculated by combining 1-, 3-, 6-, 9-, and 12-month standardized soil moisture index (SSI) based on monthly root zone soil moisture. The prediction performance of the MG was assessed by employing the Brier skill score (BSS), Nash–Sutcliffe efficiency coefficient (NSE), and root mean square error (RMSE). The severe agricultural drought in the months of April–September of 2010 in China was used as a case study. A visual comparison of this event indicated that the lead JSDI predictions for 1–3 months showed a resembled behavioral pattern with corresponding historical observations for most of the areas of China; however, it had some disagreements in the spatial extent and severity of the agricultural drought in some of the regions. The MG had a great performance with BSS > 0.4, NSE > 0.6, and RMSE