Spatial and temporal distributions of trends in climate extremes of the Feilaixia catchment in the upstream area of the Beijiang River Basin, South China

Abstract

To evaluate the changes in extreme climatic events in the Feilaixia catchment in South China, the spatial and temporal distributions of extreme climate indices trends during 1969-2011 were investigated. With quality control and homogeneity assessment, daily maximum and minimum surface air temperature from 11 meteorological stations and daily precipitation from 24 rainfall stations were used. Eight indices of extreme temperature and six indices of extreme precipitation were chosen. Trends were calculated using Sen's slope estimator. Statistical significance of trends was checked with the Mann-Kendall method. High correlations were found between the mean annual temperature and temperature extremes, as well as between the annual total precipitation and precipitation extremes in most cases. The analyses of extreme temperature indices detected significant and stable trends in the majority of the stations. The strongly stable downward trends in cold extremes and the strongly stable upward trends in hot extremes were recorded in the whole region, except for in some small areas primarily located in the central part of the region. In contrast, significant and stable positive trends were sporadically recorded for precipitation extremes in the study area during 1969-2011, which predominantly occurred in the northern part of the region. Furthermore, the positive trends were much more frequent than the negative trends in most extreme precipitation indices. There were significant and stable trends recorded in most of the average temperature extremes, whereas insignificant and unstable trends were found for most of the average precipitation extremes.

Trend analysis of temperature and precipitation extremes in major grain producing area of China

“…In this study, the quality control procedure in RClimdex (software and documentation available for download from http://etccdi.pacificclimate.org/software.shtml) is adopted (Alexander et al, ). RClimdex is an efficient tool approved by World Meteorological Organisation (WMO), which has been widely acknowledged and used in recent years for meteorological data processing and extreme climate event analysis (You et al, ; Wang et al , , ; Wu et al, ). Outliers in daily precipitation, daily maximum and minimum temperature are identified and the outside ranges are defined as lying beyond four standard deviations (std) of the mean value (mean ± 4 × std).…”

“…The stability of the trend for each extreme index is expressed by the percentage of the 30‐year moving periods during which the tested trends are statistically significant (Lupikasza, ; Wu et al, ). where S ij is trend stability at meteorological station i for index j , K is the total number of the 30‐year moving periods, and K 1 ij , and K 2 ij are the numbers of the 30‐year moving periods with respectively very strong and strong trends at station i for index j .…”

Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China

“…The climate of the region is warm, wet tropical to subtropical, and precipitation during the flood season (April to September) accounts for 70-80 % of the annual precipitation. Due to climate warming, extreme rainfall events are recently occurring more frequently in the Feilaixia catchment (Wu et al, 2014a), which leads to more intense and frequent flooding (e.g. the large floods in June and August 1994, June 1998, June 2005, and July 2006, causing extensive inundations and severe flood damage.…”

Spatiotemporal Analysis of Extreme Hourly Precipitation Patterns in Hainan Island, South China

“…The stability coefficients of FQ 20 mm in the northeastern and southwestern regions are between 0.25 and 0.6. As described by Wu [28], stability coefficients ranging from 0.25 to 0.6 indicate a stable trend. Most regions of Hainan Island show an unstable trend for Max 3 h. In the southern region, BT, LS and SY stations, show stable trends.…”