Quantifying precipitation extremes and their relationships with large-scale climate oscillations in a tropical country,Singapore: 1980–2018 |
| |
| Authors: | Rengui Jiang Ruijuan Cao Xi Xi Lu Jiancang Xie Yong Zhao Fawen Li |
| |
| Institution: | 1. State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi’an, China;2. Department of Geography, National University of Singapore, Singapore;3. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China;4. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China |
| |
| Abstract: | Extreme precipitation indices (EPIs) were defined to quantify the precipitation extremes in Singapore, a typical tropical country situated near the equator. The paper investigated the spatial and temporal variability of precipitation extremes based on seventeen EPIs using non-parametric Mann-Kendall test and Sen’s slope, and further explored the linear and nonlinear relationships between precipitation extremes and four large-scale global climate oscillations using correlation and wavelet analysis, during the period of 1980–2018 in Singapore. The results indicated that the trends of precipitation extremes varied for different EPIs, regions and stations. Increasing trends dominated thirteen out of seventeen EPIs. The trends of EPIs were scattered and irregularly distributed. The cross-correlation analysis between different EPIs demonstrated that annual total precipitation on wet days (PRCPTOT) was strongly correlated with other EPIs. The result of composite analysis indicated that El Niño Southern Oscillation (ENSO) exerted stronger impacts on southwest monsoon season (SMS) precipitation than PRCPTOT and northeast monsoon season (NMS) precipitation. The SMS precipitation composite suggested that ENSO created more influence on dry spells than wet spells. The linear and nonlinear relationships revealed that all climate oscillations were negatively correlated with precipitation. The wavelet coherence and phase differences were consistent with the results of correlation analysis, indicating possible prediction of precipitation extremes using climate oscillations as potential predictors. |
| |
| Keywords: | Precipitation extremes spatiotemporal variability large-scale global climate oscillations linear and nonlinear relationships Singapore |
|
|
