资源环境科技发展态势分析平台

Understanding the spatio-temporal variability of rainfall over mountainous regions such as the Andes is crucial for the maintenance of water resources and ecosystems. This study provided a comprehensive analysis of the signal, statistical significance and spatial pattern of rainfall trends in central and southern Peruvian Andes (CSPA) from 1965 to 2010. Rainfall from 47 quality-controlled rain gauge stations was examined using the hydrologic calendar year. Total and extreme rainfall indices proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated and trends were examined with Mann-Kendall test and Sen's slope estimator. Significant regional patterns of changes in rainfall extremes were investigated and compared with previous studies. Four major regions of stations were identified based on principal component analysis and clustering techniques: Amazon, central Pacific, southern Pacific and Titicaca basins. Statistically significant trend patterns showed that the annual total rainfall has decreased in the Amazon basin, despite the increase in the number of rainy days and some extreme rainfall indices. Decrease in 1-day and 5-day yearly maximum rainfall was observed in central Pacific, along with an increase in the number of wet days. Positive trends in indices related to the intensity of very strong daily rainfall were detected in southern Pacific. Titicaca basin showed an increase in the intensity of rainfall extremes. Rainfall variability and trends were evaluated during contrasting El Nino-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Most stations in the CSPA exhibited positive (negative) anomaly of total and extreme rainfall indices (consecutive dry days) during La Nina (El Nino) years. Positive (negative) anomalies of annual total rainfall and consecutive dry days during positive (negative) PDO occurred in the Amazon, southern Pacific and Titicaca basins, respectively. The ENSO-PDO conditions may have influenced the complex and mostly non-statistically significant long-term trends in CSPA.