Quantitative analysis of agricultural drought propagation process in the Yangtze River Basin by using cross wavelet analysis and spatial autocorrelation

Abstract It is important to understand the propagation of an agricultural drought, which is crucial for early warning. Recent studies have partly revealed this hidden process and regarded it as another critical feature of drought, but the relevant studies are still limited. Here, we propose a quantitative method to explore the full propagation process of agricultural drought by using cross-wavelets combined with multiple drought indices and spatial autocorrelation methods. The Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), Standardized Soil Moisture Index (SSI) and Vegetation Health Index (VHI) were adopted to characterize meteorological, hydrological, soil moisture and vegetation droughts, respectively. The propagation time of agricultural drought was investigated by the cross wavelet analysis. The spatial relationship of those droughts was examined by spatial autocorrelation method. Results demonstrated that the propagation time was within one month from meteorological to hydrological drought, and within two months from hydrological to soil moisture drought, and between two to three months from hydrological to vegetation drought in most areas of Yangtze River Basin, respectively. It was also found the meteorological and hydrological droughts, hydrological and soil moisture droughts, hydrological and vegetation droughts were all characterized by statistical linkages on both long and short time scales. The global Moran's Index of SPI, SRI and SSI were higher than 0.7 and the local Moran's Index were mainly High-High and Low-Low clustering, indicating those subtype droughts were closely associated with the neighboring regions. This study clearly revealed the full propagation of agricultural drought in Yangtze River Basin both from spatial and temporal perspective for the first time, which provides valuable knowledge for understanding and predicting agricultural drought.