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Drought exerts a significant impact on the sustainable development of social economy. For sustainable water use and rapid drought response, it is very meaningful to explore the correlation between meteorological drought and hydrological drought. This paper investigates the features of drought evolution based on cross wavelet transform (XWT) and mutual entropy. The meteorological drought was measured by the standardized precipitation index (SPI), while the hydrological drought was evaluated by the standardized runoff index (SRI). The research data were collected from hydrological stations and meteorological stations in the Yellow River basin of Henan Province, China. The main conclusions are as follows: (1) Both meteorological drought and hydrological drought had obvious periodicity, especially on the scale of 2–4a. (2) The evolution of meteorological drought varied greatly from place to place; through the study period, Henan province did not suffer any extreme drought, though the drought situation in the western region has an aggravating trend. (3) Hydrological drought has a strong positive correlation with meteorological drought, and generally lagged behind meteorological drought by 1–2 months in the study area. The research results lay a good basis for drought response in the Yellow River basin. [ABSTRACT FROM AUTHOR]

Rainfall is a complexity dynamics process. In this paper, our objective is to find the evidence of self-organized criticality (SOC) for rain datasets in China by employing the theory and method of SOC. For this reason, we analyzed the long-term rain records of five meteorological stations in Henan, a central province of China. Three concepts, that is, rain duration, drought duration, accumulated rain amount, are proposed to characterize these rain events processes. We investigate their dynamics property by using scale invariant and found that the long-term rain processes in central China indeed exhibit the feature of self-organized criticality. The proposed theory and method may be suitable to analyze other datasets from different climate zones in China. [ABSTRACT FROM AUTHOR]