Change patterns of precipitation anomalies and possible teleconnections with large-scale climate oscillations over the Yangtze River Delta, China
In view of the key factor in regional hydrological processes and water resource management, the temporal patterns of precipitation anomalies and oscillations were detected by the Quantile Perturbation Method (QPM) and the Singular Spectrum Analysis (SSA) Method, and the spatial patterns were identif...
| Published in: | Journal of Water and Climate Change |
|---|---|
| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
IWA Publishing
2022
|
| Subjects: | |
| Online Access: | https://doi.org/10.2166/wcc.2022.097 https://doaj.org/article/a2c506f47a564d0184026fdea0a61713 |
| Summary: | In view of the key factor in regional hydrological processes and water resource management, the temporal patterns of precipitation anomalies and oscillations were detected by the Quantile Perturbation Method (QPM) and the Singular Spectrum Analysis (SSA) Method, and the spatial patterns were identified using the Principal Component Analysis (PCA) Method. In addition, the teleconnections and lagged influence with large-scale climate oscillations in the Yangtze River Delta (YRD) of China from 1957 to 2016 were also analyzed. Results showed that, temporally, the main oscillations of precipitation were all found to be 2, 7–11 and 3–4 years in the annual and seasonal scales. Precipitation quantiles are subject to strong temporal oscillations at (multi-)decadal time scales, with high and low anomalies at specific periods. Spatially, the whole region could be divided into two main sub-regions in annual and seasonal scales, respectively. Among the selected large-scale climate oscillations in this study, the Pacific Decadal Oscillation (PDO) has a stronger influence on precipitation in March, July and September, but significant correlations were detected in more than 18% of the total stations. These stations were mainly in the southeast regions. The North Pacific index (NP) controlled the precipitation in February (13.95% of the total stations) and October (37.21% of the total stations) in the north region. Generally, the indicators of the Southern Oscillation Index (SOI) and Oceanic Niño 4 SST Index (ONI) had the strongest influence in regional precipitation variations, but significant correlations were detected in more than 20% of the total stations in March, September, October and November. Also, large-scale climate oscillations have a delayed way on precipitation. Among the oscillations, the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) showed that significant cross-correlations on precipitation were 0 and 3–5 months, respectively. NP showed significant cross-correlations with precipitation in many ... |
|---|