Assessment of correlation amongst physico-chemical, topographical, geological, lithological and soil type parameters for measuring water quality of Rawal watershed using remote sensing

Abstract

Abstract The quality of water is traditionally assessed by the collection of physico-chemical parameters, i.e., pH, turbidity, dissolved oxygen of the water bodies. However, the variations in environmental factors may have an impact on the quality of water, as changes in these attributes may affect the water bodies. These factors include the topographical, geological, lithological and soil type parameters of the watershed. In this study, the relationship amongst the physico-chemical, topographical, geological, lithological and soil type parameters of the Rawal watershed was evaluated. The parameters included in the present study could be classified as follows: (a) water quality parameters (b) topographical parameters, (c) geological parameters, (d) lithological parameters, and (e) soil type parameters. Water quality parameters consisted of dissolved oxygen, pH, turbidity and temperature. The topographical parameters include the slope and aspect of the watershed while the lithological, geological and soil type parameters include the lithology, geology and soil type of the watershed. Pearson's correlation was used to determine the relationship amongst these different parameters. The results have revealed that the correlations of the topographical, lithological, geological parameters with the water quality parameters in the Rawal watershed for the monsoon seasons of June to August mostly have the same trend. Throughout the four-year time period, turbidity and temperature parameters had positive correlations with soil type (ranging 0.03–0.24), but however had weak correlation with geological and lithological parameters. Dissolved oxygen did not show any relationship with topographical and lithological parameters. The results for pH show that it has weak to fair positive correlations with topographical parameters. However, this analysis is based on the Landsat 8 images extracted for the monsoon seasons of the years 2017–2020, and to examine a more prominent impact of geographical or environmental factors on the physico-chemical features, a large dataset should be considered.