Flash droughts identification in a typical arid region: A case study in the Loess Plateau, China

The flash drought (FD) is a kind of agricultural drought distinguished from the traditional drought by its short duration and rapid increasing intensity. In recent years, the flash drought (FD) has been widely reported around the world and has resulted in large socioeconomic losses. The accurate identification of FD is a tough task due to the difficulty in the precise capture of the characteristics of FDs. Among many FD identification methods, the anomaly method (AM), is proven to be capable of the accurate recognition of the FDs through the detections in the hydrometeorological anomalies. The AM classifies the FD into two categories, which are the heat wave flash drought (HWFD) and precipitation deficit flash drought (PDFD), according to different formation mechanisms. The Loess Plateau (LP), which is in northwestern China and is the largest loess region of China and a well-known region with severe soil erosion and water shortages, is selected as the study area. In this study, the AM is applied for the identification of FD during the growing season (May to October) in the LP with weekly hydrometeorological data from 1979 to 2014. Our findings show that the AM can well capture the characteristics of the FDs in the LP. The spatial patterns of the frequency of occurrence (FOC) of the FDs show that the southwestern LP is in the high FOC of the HWFD, while the PDFD is more likely to occur in the north and the middle of the LP. The monthly characteristics of the FDs show that the HWFD is more likely to occur on August, September, and October with high temperature and soil moisture deficit, while the PDFD occurs more on the early summer with less precipitation. In addition, we find that the average number of weeks under FD per year over the entire LP presents an increasing trend, which may arouse the water shortage in the future. The findings of this study are promising to provide scientific support for identification and prediction of FD, which can help mitigate the potential losses of FD.