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author:

Huang, L. (Huang, L..) [1] | Mao, Z. (Mao, Z..) [2] (Scholars:毛政元) | Fu, S. (Fu, S..) [3] | Xu, P. (Xu, P..) [4]

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Scopus PKU CSCD

Abstract:

Rainfall is the main driving force resulting in soil and water loss. Commonly, it is quantitatively assessed by rainfall erosivity index (REI) which is denoted as R. The classical method for calculating the value of R is precise, but is very complicated, and the required data is difficult to obtain. So how to ensure the model' s precision while reduce its complexity is the key to establishing a simple and easy method. Based on several rainfall observed stations' data of recent years in Changting County, this paper creatively used the method of multiple regression analysis and proposed a new simple and easy algorithm of REI for Changting County with reference to the results of classical method. With the proposed method, REI of the mentioned area was calculated. Then the spatiotemporal distribution of R in the county and its relationship with soil erosion were analyzed. It turns out that, the accuracy of the method presented herein is much better than that of similar research for the same region, and that the analysis conclusions agree with the actual situation.

Keyword:

Chagnting county; Rainfall erosivity; Simple algorithm; Soil and water loss; Spatiotemporal distribution

Community:

  • [ 1 ] [Huang, L.]National Engineering Research Centre of Ceospatial Information Technology, Spatial Information Engineering Research Center of Fujian Province, Key Lab. of Spatial Data Mining and Information Sharing of MOE, Fuzhou University, Fuzhou, 350002, China
  • [ 2 ] [Mao, Z.]National Engineering Research Centre of Ceospatial Information Technology, Spatial Information Engineering Research Center of Fujian Province, Key Lab. of Spatial Data Mining and Information Sharing of MOE, Fuzhou University, Fuzhou, 350002, China
  • [ 3 ] [Fu, S.]National Engineering Research Centre of Ceospatial Information Technology, Spatial Information Engineering Research Center of Fujian Province, Key Lab. of Spatial Data Mining and Information Sharing of MOE, Fuzhou University, Fuzhou, 350002, China
  • [ 4 ] [Xu, P.]National Engineering Research Centre of Ceospatial Information Technology, Spatial Information Engineering Research Center of Fujian Province, Key Lab. of Spatial Data Mining and Information Sharing of MOE, Fuzhou University, Fuzhou, 350002, China

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Source :

Journal of Natural Disasters

ISSN: 1004-4574

CN: 23-1324/X

Year: 2015

Issue: 5

Volume: 24

Page: 103-111

Cited Count:

WoS CC Cited Count:

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ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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