Various characteristics of precipitation concentration index and its cause analysis in China between 1960 and 2016

Abstract

The precipitation concentration index (PCI) is a powerful indicator for temporal precipitation distribution and is also very useful for the assessment of seasonal precipitation changes. The primary objectives of this study are to investigate and analyse the temporal–spatial variability patterns of annual and seasonal PCI values based on monthly precipitation data. These data were collected from 597 meteorological stations located throughout China, for the time period of 1960–2016, and were used to assess the impacts of geographical parameters (latitude, longitude, and altitude) on the PCI. Additionally, the possible teleconnection with the large‐scale circulation pattern was investigated. Our results reveal that the variation trend of annual PCI values has decreased significantly at a rate of −.234/10 year (α = .01) in China over the past 57 years. For all studied station records, 434 (72.7%) stations showed decreasing trends of PCI values, and these stations are distributed over large areas in China. On an annual scale, the average PCI value ranged from 11 in Hunan province to 44 in Qinghai province. The precipitation concentration in China can be described as strongly irregular in the western and northern parts of the northwest and in the northern region of the Tibetan Plateau, while it is irregular in the southwest and the north of China, and moderately irregular in some parts of the middle‐lower regions of the Yangtze River and southern China. The regularity of the annual precipitation pattern significantly decreased in spring, autumn, and winter from southeastern to northwestern China, and was the most in winter. However, the summer precipitation dispersion and the pattern in the considered period were more regular than those of the other seasons. Furthermore, changes in the PCI appear to be rather complex and possibly related to global atmospheric characteristics as well as geographical factors (latitude, longitude, and altitude). The results presented in this study indicate that the PCI is an essential feature for water resource planning, prediction of risk due to droughts or floods, and the management of natural resources.

Nonuniform variations of precipitation and temperature across China over the period 1960–2015

“…Some nonuniform variations in PCD and PCP were examined. The PCD variations in this study, increase in the southeast and the decrease in the northeast, are consistent with results of Zhang et al (2019) and Deng et al (2019), who used Precipitation Concentration Index and Drought Severity Index to represent annual distribution feature, respectively. PCD in Regions V and VI showed increasing trends, closely related to precipitation variation in southern China, which significantly decreases in spring and autumn and significantly increases in summer (Wang et al, 2006).…”

Investigating changes in rainfall seasonality across South Africa: 1987–2016

“…Although few (~12%) statistically significant trends exist (Table 2), this aligns with investigations into rainfall seasonality changes using observed records (Cortesi et al ., 2012; Zhang et al ., 2019). It is also expected as trends for rainfall parameters are sensitive to the temporal coverage of observations (Kruger, 2006; MacKellar et al ., 2014; Kruger and Nxumalo, 2017b; Otto et al ., 2018).…”

Analysis of Local Rainfall Characteristics as a Mitigation Strategy for Hydrometeorology Disaster in Rain-fed Reservoirs Area

“…Rainfall volatility analysis is based on calculating the coefficient of variation (CV), the standardized precipitation anomaly index (SPAI), and the precipitation concentration index (PCI) [17], while the trend is analyzed by using Mann-Kendall (MK) test [2,[18][19][20][21]. SPAI was used to display quantitative analysis related to changes in rainfall and identify potential floods or droughts [8,22], where rainfall is seasonal and periodic in a monsoon-dominated climate context [23], like the climate in Indonesia.…”

“…Based on the calculation of rainfall anomalies standardized as Z in Table 2, the severity class of meteorological drought is presented in Table 3. It can be seen that severe meteorological drought is occurred only in August A strong indicator for the distribution of temporal rainfall is shown by PCI [17,20]. Table 4 present the annual PCI values based on monthly rainfall data each year.…”