Effects of the ENSO on rainfall erosivity in the Fujian Province of southeast China

被引:30
|
作者
Chen, Shifa [1 ,2 ]
Zha, Xuan [2 ,3 ]
机构
[1] Shaoguan Univ, Coll Tourism & Geog, Shaoguan 512005, Guangdong, Peoples R China
[2] Fujian Normal Univ, Coll Geog Sci, Fuzhou 350007, Fujian, Peoples R China
[3] Fujian Normal Univ, Inst Geog, Fuzhou 350007, Fujian, Peoples R China
关键词
SOI; Erosive rainfall; Correlation analyses; El Nino3.4 SST anomalies; MEI; RUSLE EI30 INDEX; PRECIPITATION; TRENDS; PATTERNS; EXTREMES; MONSOON; REGION; TESTS; MODEL;
D O I
10.1016/j.scitotenv.2017.10.090
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Rainfall erosivity is one important factor that controls soil erosion. The interannual variability of rainfall erosivity in southeast China connected to the ENSO (El Nino-Southern Oscillation). Rainfall erosivity across southeast China was assessed using daily rainfall data from 60 meteorological stations during the period from 1980 to 2013. We determined that models of erosivity based on daily rainfall can accurately predict annual rainfall erosivity. This paper presents a study of the effects of Nino3.4 SST (Sea Surface Temperatures) anomalies, the SOI (Southern Oscillation Index) and the MEI (Multivariate El Nino-Southern Oscillation Index) on rainfall erosivity in the southeast of China. Results indicated that average rainfall erosivity is stronger during El Nino events and weaker during La Nina events. Correlation analyses were applied to rainfall erosivity and Nino3.4 SST anomalies, SOI, and MEI. The effects of Nino3.4 SST and SOI on rainfall erosivity are evident, as demonstrated by a statistically significant correlation (>95% confidence level). MEI was the best indicator (P < 0.01) for representing the state of the ENSO. Results further indicated that 45, 31, and 40 of the 60 meteorological stations showed a significant correlation (P < 0.05) between rainfall erosivity and SST anomalies, SOI, and MEI, respectively. Most of these stations were located in western Fujian Province. The ENSO was determined to exert the strongest influence on rainfall erosivity. This information would be useful in the implementation of new soil conservation strategies. (c) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:1378 / 1388
页数:11
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