Effects of different types of drought on vegetation in Huang-Huai-Hai River Basin, China

Due to climate change and human activities, drought has become one of the most complex natural disasters in the Huang-Huai-Hai River basin, China. The purpose of the current research is to analyze the response of vegetation to drought in the Huang-Huai-Hai River basin by calculating the lag time correlation coefficient between the enhanced vegetation index (EVI) and drought indices. First, the water storage deficit index (WSDI) calculated from the total water storage anomalies (TWSA) was used as the hydrological drought index. The meteorological drought index we used was the self-calibrating Palmer drought severity index (scPDSI) provided by the Climate Research Unit (CRU). Then, the relationship between hydrological and meteorological drought was obtained based on the wavelet coherent change. Finally, the response of different vegetation types to drought was analyzed based on the lag time correlation coefficient. The results showed that: the average time of hydrological drought lagged meteorological drought in the three first-level basins of the Huang-Huai-Hai River basin, respectively, was 50 days (Yellow River basin), 43 days (Huaihe River basin), and 35 days (Haihe River basin); the lag time correlation coefficient of the different time scales (1-12 months) can reflect the sensitivity of vegetation to drought in different vegetation types and regions. The forest was the least sensitive to drought in the Huang-Huai-Hai River basin, followed by cropland, while grassland was the most sensitive to drought. It was discovered that hydrological drought had a huge influence on vegetation rather than meteorological drought by comparing the maximum lag time correlation coefficients of EVI with drought indices (scPDSI and SPEI). This study stresses the importance of taking into account the effects of various types of drought on the vegetation in the Huang-Huai-Hai River basin. This will help us better understand how drought affects vegetation.