Temporal and spatial evolution trends of drought in northern Shaanxi of China: 1960–2100

Abstract

Drought prediction and assessment are the basis for addressing climate change and extreme weather. Northern Shaanxi is an important energy base and ecological barrier in China. Mitigating the threat of drought is necessary for sustainable development of the economy and society of the region. There are great uncertainties in drought predication because of climate change. Based on historical meteorological data and a multi-model ensemble mean value, temporal and spatial variation characteristics of drought in northern Shaanxi during the historical time period (1960–2018) and the future time period (2020–2100) were analyzed by detrending, Mann–Kendall trend test (MK), empirical orthogonal function spatial analysis (EOF), and wavelet analysis. The results show that the multi-model ensemble mean value provides a better simulation of precipitation and temperature in northern Shaanxi. The application of the Standardized Precipitation Evapotranspiration Index (SPEI) in the region is superior to the Standardized Precipitation Index (SPI) and self-calibrated Palmer Drought Index (sc-PDSI). The interannual dry and wet changes in the northern Shaanxi region during the historical period (1960–2018) can be divided into two periods. Overall, the drought index showed a significant decrease, and spring drought increased. In the future, precipitation and temperature in the region will increase from south to north, with the largest increase in the west. In the middle and late twenty-first century, due to the significant increase in temperature, the aridification trend in northern Shaanxi may be aggravated, which is consistent with the trend of drought in northern China. The future of spring drought is grim, and summer drought has an increasingly frequent trend. Making full use of water resources and accelerating the construction of the Yellow River diversion project are effective ways to alleviate drought and water shortage in the region.