Surface drainage nitrate loading estimate from agriculture fields and its relationship with landscape metrics in Tajan watershed Academic Article uri icon


  • © 2016, The International Society of Paddy and Water Environment Engineering and Springer Japan. Pasture, forest, and farmland are the dominant land covers in the Tajan River watershed and this landscape status has a direct connection with nitrate pollution. Understanding the correlations between landscape variables and nitrate pollutant is a priority in order to assess pollutants loading and predicting the impact on surface water quality. The soil and water assessment tool was used to simulate nitrate loads in different land cover types in different years. The landscape pattern was calculated by FRAGSTATS. The contributing share of each land use/land cover shows nitrate pollutant produced by grassland (5.7%) and forest (29%) are less than those produced by agricultural land (64.2%). Agricultural land was identified as the main source of nitrate pollution. Paddy fields and orchards had the most intensive soluble nitrate loss especially in spring and summer. Statistical analysis indicated that nitrate was positively associated with patch density, edge density, patch number, total edge, effective mesh size, largest patch index, and landscape shape index (p ≤ 0.01). We then analyzed how nitrate was related to landscape attributes in six different sites. Also the regression analysis results suggested that landscape metrics could account for more than 94% of the variance of nitrate in the whole catchment. The regression models confirmed the great importance of the agriculture metrics and forest metric in predicting nitrate in watershed. Defining the generation and extent of pollution in this particular watershed which discharges into the Caspian Sea can constitute an important step toward protecting this ecosystem.

altmetric score

  • 0.25

author list (cited authors)

  • Rajaei, F., Sari, A. E., Salmanmahiny, A., Delavar, M., Bavani, A., & Srinivasan, R.

citation count

  • 14

publication date

  • December 2016