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Multiple time scales analysis of spatial differentiation characteristics of non-point source nitrogen loss within watershed.

LIU Mei-bing1,2,3, CHEN Xing-wei2,3, CHEN Ying1,3   

  1. (1Institute of Geography, Fujian Normal University, Fuzhou 350007, China; 2College of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China; 3State Key Laboratory Cultivation Base of Humid Subtropical Mountain Ecology (Funded by Ministry of Science and Technology and Fujian Province), Fuzhou 350007, China)
  • Online:2015-07-18 Published:2015-07-18

Abstract: Identification of the critical source areas of non-point source pollution is an important means to control the non-point source pollution within the watershed. In order to further reveal the impact of multiple time scales on the spatial differentiation characteristics of non-point source nitrogen loss, a SWAT model of Shanmei Reservoir watershed was developed. Based on the simulation of total nitrogen (TN) loss intensity of all 38 subbasins, spatial distribution characteristics of nitrogen loss and critical source areas were analyzed at three time scales of yearly average, monthly average and rainstorms flood process, respectively. Furthermore, multiple linear correlation analysis was conducted to analyze the contribution of natural environment and anthropogenic disturbance on nitrogen loss. The results showed that there were significant spatial differences of TN loss in Shanmei Reservoir watershed at different time scales, and the spatial differentiation degree of nitrogen loss was in the order of monthly average > yearly average > rainstorms flood process. TN loss load mainly came from upland Taoxi subbasin, which was identified as the critical source area. At different time scales, land use types (such as farmland and forest) were always the dominant factor affecting the spatial distribution of nitrogen loss, while the effect of precipitation and runoff on the nitrogen loss was only taken in no fertilization month and several processes of storm flood at no fertilization date. This was mainly due to the significant spatial variation of land use and fertilization, as well as the low spatial variability of precipitation and runoff.