Identification of the control factors affecting water quality variation at multi-spatial scales in a headwater watershed

被引:24
作者
Wu, Jianhong [1 ,2 ]
Jin, Yanan [1 ]
Hao, Yun [1 ]
Lu, Jun [1 ,2 ,3 ]
机构
[1] Zhejiang Univ, Coll Environm & Nat Resources, Hangzhou 310058, Peoples R China
[2] Zhejiang Univ, Zhejiang Prov Key Lab Subtrop Soil & Plant Nutr, Hangzhou 310058, Peoples R China
[3] Zhejiang Univ, Key Lab Environm Remediat & Ecol Hlth, China Minist Educ, Hangzhou 310058, Peoples R China
基金
中国国家自然科学基金;
关键词
Water quality; Land use composition; Land use configuration; Topographical metrics; Spatial scales; Partial least squares regression; LEAST-SQUARES REGRESSION; RIPARIAN BUFFER ZONE; LOW-ORDER STREAMS; LAND-USE; RIVER-BASIN; SEDIMENT YIELD; POPULATION-DENSITY; LANDSCAPE; PATTERNS; COVER;
D O I
10.1007/s11356-020-11352-4
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Understanding the effect of landscape characteristics on water quality can provide insight into mitigating water quality impairment. However, there is no consensus about the key controlling factors influencing water quality. This paper examined the combined effects of land use and topography on water quality across multi-scale, and identified the key controlling factors determining water quality variation in the headwater watershed of the Hengxi reservoir in Eastern China. Water quality impairment (WQI), expressed as a composite variable, was established to measure the overall water quality. We used the partial least squares (PLSR) method to explore the combination of landscape metrics and identify the key controlling factors. Results showed that the optimal PLSR model at 50-m, 100-m, and 150-m buffer scales and catchment scale explained 77%, 63%, 60%, and 56% of variability in WQI, respectively. At catchment scale, patch density, the percentage of paddy field, and hypsometric integral were the key controlling factors impacting water quality. At buffer scales, the slope gradient, the percentage of forest land, and topographic wetness index were more effectively determined WQI variation. Thus, the key controlling factors depend on spatial scales. Both spatial scales and corresponding key controlling factors should be considered in the adjustment of land use composition and planning of landscape configuration to better protect water quality.
引用
收藏
页码:11129 / 11141
页数:13
相关论文
共 36 条
  • [31] Evaluation of Seasonal and Spatial Variations in Water Quality and Identification of Potential Sources of Pollution Using Multivariate Statistical Techniques for Lake Hawassa Watershed, Ethiopia
    Lencha, Semaria Moga
    Ulsido, Mihret Dananto
    Muluneh, Alemayehu
    APPLIED SCIENCES-BASEL, 2021, 11 (19):
  • [32] Landscape and anthropogenic factors affecting spatial patterns of water quality trends in a large river basin, South Korea
    Mainali, Janardan
    Chang, Heejun
    JOURNAL OF HYDROLOGY, 2018, 564 : 26 - 40
  • [33] Spatial variation, source identification, and quality assessment of surface water geochemical composition in the Indus River Basin, Pakistan
    Qaisar, Faizan Ur Rehman
    Zhang, Fan
    Pant, Ramesh Raj
    Wang, Guanxing
    Khan, Sardar
    Zeng, Chen
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2018, 25 (13) : 12749 - 12763
  • [34] Spatial variation, source identification, and quality assessment of surface water geochemical composition in the Indus River Basin, Pakistan
    Faizan Ur Rehman Qaisar
    Fan Zhang
    Ramesh Raj Pant
    Guanxing Wang
    Sardar Khan
    Chen Zeng
    Environmental Science and Pollution Research, 2018, 25 : 12749 - 12763
  • [35] Temporal and Spatial Variation Characteristics of Water Quality in the Middle and Lower Reaches of the Lijiang River, China and Their Responses to Environmental Factors
    Zhu, Dantong
    Cheng, Xiangju
    Li, Wuhua
    Niu, Fujun
    Wen, Jianhui
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (13)
  • [36] Identifying key environmental factors influencing spatial variation of water quality in upper Shitoukoumen Reservoir Basin in Jilin Province, China
    Tang Yanling
    Zhang Guangxin
    Yang Yuesuo
    Gao Yingzhi
    CHINESE GEOGRAPHICAL SCIENCE, 2009, 19 (04) : 365 - 374