Spatiotemporal evolution and the driving factors of meteorological drought in the Hun-Taizi River basin, NE China

Yue, Shupeng; Yang, Fengtian; Sheng, Xiaodan

doi:10.2166/wcc.2022.388

Cited by 11 publications

(1 citation statement)

References 33 publications

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“…Constructing a drought index is an effective method for analysing drought, and choosing an objective and reasonable drought index can effectively assess the drought situation in different regions [7][8][9]. Drought indices include the standardized precipitation index (SPI) [10], the Palmer Drought Severity Index (PDSI) [11], the Precipitation anomaly (Pa) [12], and the Standardized Precipitation Evapotranspiration Index (SPEI) [13].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatiotemporal Variation Characteristics and Driving Factors of Drought in Yinshanbeilu Inner Mongolia Based on a Cloud Model

Zhang,

Fu,

Wang

et al. 2024

Water

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Drought refers to a meteorological disaster that results in insufficient soil moisture due to a long-term lack of rainfall and disrupts the moisture balance of crops. Yinshanbeilu in Inner Mongolia is an arid and semi-arid region, and the onset of drought and its subsequent transmission is a key challenge in water resource management. This study takes Yinshanbeilu as the study area, analyses the changing characteristics of multi-timescale drought from 1971 to 2020 based on the Standardised Precipitation Index (SPI), and analyses the stochasticity and stability of the drought by using a cloud model. Finally, the cross-wavelet transform method and Pearson’s test are used to explore the correlation between atmospheric circulation factors, PRE and PET, and drought. The results indicate that (1) on the annual scale, the frequency of drought in Yinshanbeilu mainly ranges from 22% to 28%, with a high frequency of light droughts, a low frequency of severe droughts, a high frequency of droughts in the east and west, and a low frequency of droughts in the north and south; on the seasonal scale, the frequency of droughts in winter is the highest, with a rate of 34.6%, and the lowest frequency of droughts is in autumn, with a rate of 28.3%. (2) There is a decreasing trend in Entropy (En) and Hyper-Entropy (He), and an increasing trend in Expectation (Ex) for the inter-annual SPI-12 cloud model. Spatially, Ex and He are negatively correlated, while En and He are positively correlated. The inter-annual variation in cloud eigenvalues is greater than the inter-site variation, so the cloud model better reflects the spatial stochasticity and stability of regional inter-annual SPI. For the seasonal-scale SPI-3 cloud model, Ex is smaller in all seasons, En is also smaller, and He is larger. (3) Sunspot, PRE (precipitation), and PET (Potential Evapotranspiration) are all positively correlated with SPI and have the highest correlation. This study reveals the characteristics and causes of variations of drought in Yinshanbeilu, which can be applied to future research areas related to regional drought risk management.

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