Document Type : REVIEW PAPER
1 School of Environment, Harbin Institute of Technology, Harbin, China
2 Department of Civil Engineering, University of Engineering and Technology Peshawar, Peshawar, Pakistan
3 School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Surface waters are the most important economic resource for humans which provide water for agricultural, industrial and anthropogenic activities. Surface water quality plays vital role in protecting aquatic ecosystems. Unplanned urbanization, intense agricultural activities and deforestation are positively associated with carbon, nitrogen and phosphorous related water quality parameters. Multiple buffers give robust land use land cover and water quality model and highlight the impacts of land use land cover characteristics on water quality parameters at various scales which will guide watershed managers for particular application of best management practices to enhance stream health. Traditionally, water quality data collections are based on discrete sampling and were analyzed through statistical techniques which were designed for spatially isolated measurements. Traditional multivariate statistical approaches uncover hidden information in water quality data but they are unable to expose spatial relationship. The complexity of information in water quality data needs new statistical approaches which uncover spatiotemporal variability. This review briefly discusses influences of land use land cover characteristics on surface water quality, effects of spatial scale on land use land cover- water quality relationship, and water quality modeling using various statistical approaches. Every statistical method has unique purpose, application and solves different problems. This review article pinpoints that how statistical approaches in combination with spatial scale can be applied to develop statistically significant land use land cover- water quality relationship for better water quality evaluation.
- Statistical techniques were easy and needed comparatively less data record as compared to water quality models
- BHLR and GWR had the ability to analyze spatial variation between water quality and land use
- Storm runoff drained more pollutants from land as compared to dry season
- All land use did not contribute equal amount of pollutant to the nearby water body.
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