Abstract
With the aggravation of the ocean–atmosphere cycle anomaly, understanding the potential teleconnections between climate indices and drought/flood conditions can help us know natural hazards more comprehensively to better cope with them. This study aims at exploring the spatiotemporal patterns of drought and its multi-scale relations with typical climate indices in the Huaihe River Basin. First, the spatial patterns were identified based on the seasonal Standardized Precipitation Index (SPI)-3 during 1956–2020 by means of the Empirical Orthogonal Function (EOF). The two leading sub-regions of spring and winter droughts were determined. Then, we extracted the periodicity of spring and winter SPI-3 series and the corresponding seasonal climate indices (Arctic Oscillation (AO), Bivariate El Niño–Southern Oscillation (ENSO)Timeseries (BEST), North Atlantic Oscillation (NAO), Niño3, and Southern Oscillation Index (SOI)) and the sunspot number by using the Continuous Wavelet Transform (CWT). We further explored the teleconnections between spring drought, winter drought, and climate indices and the sunspot number by using Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analyses. The results show that there are in-phase multi-scale relations between spring/winter PC1 and AO, BEST, and Niño3, of which the climate indices lead spring PC1 by 1.5–2 years and the climate indices lag winter PC1 by 1.5–3 years. Anti-phase relations between spring PCs and SOI and the sunspot number were observed. NAO mainly affects the interdecadal variation in spring drought, while AO and Niño3 focus on the interannual variation. In addition, Niño3 and SOI are more related to the winter drought on interdecadal scales. Moreover, there is a positive correlation between the monthly average precipitation/temperature and Niño3 with a lag of 3 months. The results are beneficial for improving the accuracy of drought prediction, considering taking NAO, AO, and Niño3 as predictors for spring drought and Niño3 and SOI for winter drought. Hence, valuable information can be provided for the management of water resources as well as early drought warnings in the basin.
Highlights
Empirical Orthogonal Function (EOF) analysis, analysis,the thecomplexity complexityofofthe the spatial pattern of drought conditions over the study area can be explained by a small number of spatial structures (PCs) effecover the study area can be explained by a small number of spatial structures (PCs) effectively and reliably, i.e., by a dimensionality reduction
The spatiotemporal variability in drought and its multi-scale linkages with climate indices in the Huaihe River Basin (Central China and East China) during
The possible sub-regions of the drought condition were identified for all seasons of Standardized Precipitation Index (SPI)-3 and SPI-1 by conducting an EOF analysis
Summary
The Intergovernmental Panel on Climate Change’s Sixth Assessment Report (IPCCAR6) shows that global climate change will be further aggravated in the coming decades, intensifying the water cycle and indicating that more extreme droughts will occur in many regions all over the world [1]. Drought is a kind of complex and dynamically accumulating meteorological disaster, with negative impacts on socioeconomic development and ecosystem sustainability. Researchers pay more attention to the teleconnection of drought and atmospheric circulation [2,3,4,5,6]. It is of great significance to understand the potential linkages between climate indices and drought variability in order to make reasonable predictions or assumptions about future regional droughts
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