The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China

Drought monitoring and assessment are of great importance due to the costly damage caused by drought. Datasets, drought indexes and drought relationships are three critical areas of drought research. Satellite-retrieved soil moisture (SM) products derived from the European Space Agency Climate Change Initiative (ESA CCI) show application potential in drought monitoring. However, the products are missing certain data in some areas. The model-assimilated SM product derived from the Global Land Data Assimilation System (GLDAS) was used to supplement these missing data. The main goals of this paper are to characterize agricultural drought after the utility and applicability of the combined SM product and the monthly scaled soil water deficit index (SWDI) have been evaluated and to investigate the relationships among meteorological, agricultural and vegetation droughts. First, we provided a long series of highly accurate SM products through simple calculations. The drought index, SWDI, was extended to a monthly scale for long-term drought analysis by using the combined SM product. The probability of detection (POD) between the SWDI and in situ drought records performed fairly well. Half of the 566 stations had PODs higher than 0.9, and one-third of these stations had POD values equal to 1. Through correlation analysis and grey incidence analysis (GIA) between the standardized precipitation index (SPI) and SWDI, we found that the propagation time from meteorological drought to agricultural drought was shorter under drier conditions than wetter conditions, and at the regional scale, the response time ranged from 1 month to 2.5 months. Correlation analysis between the SWDI and vegetation condition index (VCI) indicated that there was no delay effect from agricultural to vegetation drought on a monthly scale in most parts of China except in several provinces distributed in the South; additionally, there was a significant time lag in forests, while grassland and agriculture were more inclined to have no time lag or the response time was less than 1 month.