Yones Khaledian - M.Sc Student, Dept of soil science, Gorgan University of Agricultural Sciences and Natural Resources
Sohaila Ebrahimi - Assistant Professor, Dept of soil science, Gorgan University of Agricultural Sciences and Natural Resources
Nabee Basatnia - M.Sc Student, Dept of soil science, Tehran University
Mojtaba Zeratpisheh - PhD student, Dept. of Soil Science, College of Agriculture, Isfahan University of Technology

The population growth and new housing is a ubiquitous feature of modern life in the developing and developed world alike land use changes in response to rising social, demographic and economic pressures. Inevitably, these new changes will have an impact on the environment around them. The aim of the study is the analysis of runoff and water quality variation due to land use changes in the Gharaso watershed in northern Iran. For this study five stations were chosen including: Poleordogah (S1), Ghazmahaleh (S2), Siahab (S3), Shastkalateh (S4), Iasaghi (S5). Water parameters were including: TDS, EC, runoff and sediment. Water samples collected every month over a four-year period from five sampling stations that are under the influence of anthropogenic and natural changes were analyzed. Multivariate statistics including principal component/factor analysis (PCA/FA), analysis of variance (ANOVA), and cluster analysis (CA) were employed to assess water quality, and the receptor model of factor analysis-multiple linear regression (FA-MLR) was used for source identification/apportionment of pollutants from natural processes and anthropogenic activities to river waters. PCA has allowed identification of a reduced number of mean 2 varifactors, pointing out 99%, in TDS and EC, 77%, in runoff and sediment. CA classified similar water quality stations and indicated Siahab as the lowest quality rather than the rest of stations, Poleordogah (S1), Ghaznahaleh (S2), Shastkalateh (S4), Iasaghi (S5) were identified as affected by land use changes. CA and PCA led to similar results, though Siahab was identified as the lowest quality station in both methods.

Land Use Change; Multivariate Statistical Analysis; Soil Erosion; Water Quality