Journal of Geographical Sciences ›› 2019, Vol. 29 ›› Issue (6): 863-876.doi: 10.1007/s11442-019-1633-y

• Special Issue: Water Resources in Beijing-Tianjin-Hebei Region •     Next Articles

Spatio-temporal patterns of drought evolution over the Beijing-Tianjin-Hebei region, China

Jie ZHANG1, Fubao SUN1*(), Wenbin LIU1, Jiahong LIU2, Hong WANG1   

  1. 1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Science and Natural Resources Research, CAS, Beijing 100101, China
    2. Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
  • Received:2018-05-12 Accepted:2018-11-23 Online:2019-06-25 Published:2019-07-25
  • About author:

    Author: Zhang Jie, PhD, specialized in climate change and hydrological process. E-mail: zhangjie@igsnrr.ac.cn

  • Supported by:
    National Key Research and Development Program of China, No.2016YFC0401401, No.2016YFA0602402;Key Program of the Chinese Academy of Sciences, No.ZDRW-ZS-2017-3-1;The Chinese Academy of Sciences (CAS) Pioneer Hundred Talents Program;National Natural Science Foundation of China, No.41601035

Abstract:

Spatio-temporal patterns of drought from 1961 to 2013 over the Beijing-Tianjin-Hebei (BTH) region of China were analyzed using the Palmer Drought Severity index (PDSI) based on 21 meteorological stations. Overall, changes in the mean-state of drought detected in recent decades were due to decreases in precipitation and potential evapotranspiration. The Empirical Orthogonal Functions (EOF) method was used to decompose drought into spatio-temporal patterns, and the first two EOF modes were analyzed. According to the first leading EOF mode (48.5%), the temporal variability (Principal Components, PC1) was highly positively correlated with annual series of PDSI (r=+0.99). The variance decomposition method was further applied to explain the inter-decadal temporal and spatial variations of drought relative to the total variation. We find that 90% of total variance was explained by time variance, and both total and time variance dramatically decreased from 1982 to 2013. The total variance was consistent with extreme climate events at the inter-decadal scale (r=0.71, p<0.01). Comparing the influence of climate change on the annual drought in two different long-term periods characterized by dramatic global warming (P1: 1961-1989 and P2: 1990-2013), we find that temperature sensitivity in the P2 was three times more than that in the P1.

Key words: PDSI, spatial and temporal patterns, sensitivity analysis, global warming