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Linking Land Use Metrics Measured in Aquatic–Terrestrial Interfaces to Water Quality of Reservoir-Based Water Sources in Eastern China

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  • Lilian Ding

    (College of Life Sciences, Zhejiang University, No. 866 Yuhangtang Road, Hangzhou 310058, China)

  • Qiyao Li

    (College of Life Sciences, Zhejiang University, No. 866 Yuhangtang Road, Hangzhou 310058, China)

  • Jianjun Tang

    (College of Life Sciences, Zhejiang University, No. 866 Yuhangtang Road, Hangzhou 310058, China)

  • Jiangfei Wang

    (Zhejiang Environmental Monitoring Center, No. 117 Xueyuan Road, Hangzhou 310012, China)

  • Xin Chen

    (College of Life Sciences, Zhejiang University, No. 866 Yuhangtang Road, Hangzhou 310058, China)

Abstract

The effects of anthropogenic land use on the water quality of reservoir-based water sources are understudied. We trained a self-organizing map (SOM) to measure the spatial pattern of nutrients over the course of a year in 61 reservoirs located in eastern China. In the linear regression model and one-way analyses of variance, we found that water quality was influenced by period of the year (flood, low, and normal periods based on rainfall conditions) and reservoir altitude (plains vs. mountains). Our results indicated that land use metrics measured in aquatic–terrestrial interfaces significantly influenced the water quality of reservoirs. The land use intensity (LUI) and the proportion of construction land had a positive correlation with ammonia nitrogen (NH 3 -N) and chemical oxygen demand (COD Mn ) concentrations, and redundancy analysis indicated that the percent of landscape (PLAND) represented by construction land was positively correlated with COD Mn , NH 3 -N, total phosphorus (TP), and total nitrogen (TN) concentrations. The proportion of cropland was not correlated with any water quality property except for COD Mn concentration. The total explained variance for water quality was highest when the scale was large (the area defined by a 1500 m radius around the reservoir), indicating that management which ensures water safety should be carried out at this scale.

Suggested Citation

  • Lilian Ding & Qiyao Li & Jianjun Tang & Jiangfei Wang & Xin Chen, 2019. "Linking Land Use Metrics Measured in Aquatic–Terrestrial Interfaces to Water Quality of Reservoir-Based Water Sources in Eastern China," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:4860-:d:264550
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    1. Angela Gorgoglione & Alberto Castro & Vito Iacobellis & Andrea Gioia, 2021. "A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    2. Lilian Ding & Yan Liao & Congmou Zhu & Qiwei Zheng & Ke Wang, 2023. "Multiscale Analysis of the Effects of Landscape Pattern on the Trade-Offs and Synergies of Ecosystem Services in Southern Zhejiang Province, China," Land, MDPI, vol. 12(5), pages 1-18, April.

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