Trends of precipitation in Beijiang River Basin, Guangdong Province, China

Abstract

Abstract:An analysis of spatial and temporal trends of precipitation in Beijiang River basin, Guangdong Province, China during 1959-2003 was performed using 17 time series (including monthly, annual, wet season, dry season, early flood period and late flood period totals) both on station based and sub-basin based data sets. Two nonparametric methods (Mann-Kendall and Sen's T) were used for data analysis. The results showed that (1) downward trends of temporal distribution were mostly detected during the early flood period, especially in May, while upward trends were observed in July and the dry season; (2) downward trends of spatial distribution were mostly detected in the southern Beijiang River basin, while upward trends were observed north of this area. Our results indicated a delayed rainy season and a northward trend of the precipitation belt compared to recent years.

Extreme Wave Storms and Atmospheric Variability at the Spanish Coast of the Bay of Biscay

“…Autocorrelations were computed for all the time series at varying time-lags, checking for randomness. As all lag-1 serial correlation coefficients were statistically not significant, there was no need to pre-white the data; thus, the statistical tests described above were applied to the original time series [34].…”

Trends in total rainfall, heavy rain events, and number of dry days in San Juan, Puerto Rico, 1955-2009

“…This is a nonparametric test to detect trends in data (McCuen 2003). This test ( 1) and (2) has been used by different researchers for trend studies with hydroclimatic data (Yue et al 2001, Hamed 2007, Luo et al 2008, McBean and Mootie 2008. The MK test involves computing the S statistic, which is the difference between the numbers of pairwise differences that are positive minus the numbers that are negative.…”

“…MK statistical analysis is very robust and highly recommended by different researchers, as well as various environmental administrations, for studies of trends with hydro-climatic data (Yue et al 2001, Luo et al 2008. The MK test can indicate whether there is any negative or positive trend in rainfall (Allende and Mendoza 2007).…”

Assessment of Temperature Anomalies Trend at Local Scale for Delhi, India

“…The autocorrelation test was performed to check the randomness and periodicity in the time series of temperature data at all considered temporal resolutions Modarres and Silva [59]. If lag-1 serial coefficients are not statistically significant then, MK test was applied to the original time series Luo et al [60] Karpouzos et al [61]. The MMK test was applied to statistically significant time series after removing the effect of serial correlation.…”