Spatiotemporal Characteristics of Drought in the Heihe River Basin Based on the Extreme-Point Symmetric Mode Decomposition Method

Assessment of spatiotemporal characteristics of drought under climate change is significant for drought mitigation. In this study, the standardized precipitation evapotranspiration index (SPEI) calculated at different timescales was adopted to describe the drought conditions in the Heihe River Basin (HRB) from 1961 to 2014. The period characteristics and spatiotemporal distribution of drought were analyzed by using the extreme-point symmetric mode decomposition (ESMD) and inverse distance weight interpolation methods. Four main results were obtained. (1) The SPEI series of the upper reaches of the HRB at different timescales showed an upward trend (not significant) during 1961-2014. In the middle and lower reaches, the SPEI series exhibited significant downward trends. (2) The annual SPEI series of the lower reaches was decomposed through the ESMD method and exhibited a fluctuating downward trend as a whole. The oscillation showed quasi-3.4-year and quasi-4.5-year periods in the interannual variation, while a quasi-13.5-year period occurred in the interdecadal variation. The interannual period plays a leading role in drought variation across the HRB. (3) The entire research period was divided into three subperiods by the Bernaola-Galvan segmentation algorithm: 1961-1966, 1967-1996, and 1997-2014. The spring drought frequency and autumn drought intensity arrived at their maxima in the lower reaches during 1997-2014, with values of 72.22% and 1.56, respectively. The high frequency and intensity areas of spring, summer, and autumn drought moved from the middle-upper reaches to the middle-lower reaches of the HRB during 1961-2014. (4) Compared to the wavelet transform, the ESMD method has self-adaptability for signal decomposition and is more accurate for drought period analysis. Extreme-point symmetric mode decomposition is a more efficient decomposition method for nonlinear and nonstationary time series and has important significance for revealing the complicated change features of climate systems.