Analysis of time-space varying relationship between land use and water quality in a tropical watershed
Over the years, studies have been conducted to examine not only the relationship between land use and water pollution using ordinary least square (OLS) regression but also their spatially varying relationships using geographical weighted regression (GWR). However, the relationships between land use...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Springer
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/95800/ https://link.springer.com/article/10.1007/s12517-021-06596-4 |
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| Summary: | Over the years, studies have been conducted to examine not only the relationship between land use and water pollution using ordinary least square (OLS) regression but also their spatially varying relationships using geographical weighted regression (GWR). However, the relationships between land use and water quality indicators may vary not only spatially but also over time. Therefore, this study was conducted to analyse the spatiotemporal variations in land use-water quality relations over time within a tropical watershed. To achieve this goal, land use data for 2006, 2010, and 2015 and the corresponding years of frequently sampled water quality data were utilised. The kriging interpolation technique was applied to estimate the unknown water quality values at 12 unmonitored sampling points using the measured values from the 9 monitored sites that were used for the regression analyses. Both OLS and GWR were applied using four groups of land use and seven water quality variables. The study found better performances of GWR models over OLS models throughout these investigation periods. The GWR results indicated that the ability of land use indicators to explain water quality change is not the same with time and space and neither the same among the different water quality parameters. For examples, in 2015, agricultural land most predicted the change in most water quality variables compared with its prediction proportion in 2010 and 2006, while urban land most predicted the change in most water quality variables in 2010 compared to other years. However, other groups of lands were more positively associated with most of the water pollutants compared to forest, agricultural, and urban areas. Therefore, this study suggests that control and management policies be adjusted to different periods and areas according to the time-space varying sources of pollution and good predictors of water quality. Although the approach used in this study tends to reveal the complex spatiotemporal relationships between land use and water quality over different time periods, it should be noted that this approach is more appropriate for comparative analysis to understand the implication of the land use chance pattern into surface water pollution. |
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