Assessment of land-use Change Impacts on Water Quality Parameters in Sub-basins of Hableh Rood Watershed using Multivariate Statistics and Time Series Models (ARIMA)

Document Type : Research Article

Authors

1 Ph.D. Student of Environmental Sciences- Land use Planning, Gorgan University of Agricultural and Natural Resources, Gorgan, Iran

2 Associate Professor, Faculty of Environment, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

3 Professor, Faculty of Natural Resources and of Environment, Ferdowsi University of Mashhad, Mashhad, Iran

4 Assistant Professor, Faculty of Environment, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

Abstract

The purpose of this study is to assess land use change impacts on quality of surface water resources in sub-basins of Hableh rood watershed. In order to fulfill this task, and by using Principal Component Analysis (PCA) and Factor Analysis (FA), TDS was selected as an indicator of water quality parameter. In the next step, by time series models (ARIMA), TDS was modeled for 30 years and among different ARIMA models, a model with a lowest error and akaike (AIC) criterion was selected as an optimal model for TDS. Desirable models for Benkuh, Delichay, Gursefid, Keylan, Namrud, Marzdaran and Tangeh Rameh were (0,1,2), (0,0,1), (1,1,2), (4,1,0), (0,1,1), (1,1,1) and (1,1,1), respectively. The results of time series models (ARIMA) showed that Namrud, Keylan and Gursefid have maximum amount of TDS. Then, land use was studied that showed maximum area of agricultural lands, residential and roads in these sub-basins. On the other hand, Industrial areas are located in Keylan and Namrud. The results of this study showed efficiency of time series modeling andmultivariate statistics in the analysis of land use change impacts on water quality parameters.

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References

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Iranian journal of Ecohydrology
Volume 6, Issue 1
April 2019
Pages 29-39

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History

  • Receive Date: 21 May 2018
  • Revise Date: 01 November 2018
  • Accept Date: 01 November 2018

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