Effects of land-use patterns on seasonal water quality at multiple spatial scales in the Jialing River, Chongqing, China

CATENA(2024)

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Abstract
Land-use patterns usually impact river water quality as non-point pollution sources at both spatial and seasonal scales, which can provide important guidance for water pollution control and land-use planning after quantitative analysis. Here, surface water samples were collected from ten sampling points in the Chongqing Section of the Jialing River to monitor the physicochemical parameters of river water from March 2021 to February 2022. Our results showed significant seasonal changes in river water quality based on higher concentrations of dissolved oxygen (DO), ammonium nitrogen (NH (+)(4)-N), and nitrate nitrogen (NO3--N) in the wet season than those in the dry season, while the concentrations of chemical oxygen demand (CODcr) and total nitrogen (TN) in the dry season are higher than those in the wet season. Redundancy analysis (RDA) for elucidating the relationship between land-use patterns and river water quality parameters at multiple spatial (reach and riparian scale) and seasonal scales (dry and wet season) shows that water quality indices exhibited a strong positive correlation with construction land and farmland, inducing a negative impact on river water quality; while forest and grassland were inversely correlated to water quality indices, causing a positive effect on river water quality. On a seasonal scale, the land-use pattern showed more association with river water quality parameters in the wet season, compared to that in the dry season. The impact of the different buffer zones associated with land-use types on the river water quality was in the following order: 2000 > 500 approximate to 1000 m buffer. These findings contribute to sustainable land-use development and multi-scale landscape optimization to improve water quality in the Chongqing Section of the Jialing River.
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Key words
Land -use,Scale effects,River water quality,Redundancy analysis,Jialing River
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