A study on the impacts of landscape structures on water quality under different spatial scales in the Xiangjiang River Basin

被引:11
作者
Lu, Jiangang [1 ,2 ]
Cai, Haisheng [1 ]
Fu, Yanmei [3 ]
Zhang, Xueling [1 ]
Zhang, Wei [2 ]
机构
[1] Jiangxi Agr Univ, Res Ctr Selenium Rich Agr Ind, Key Lab Po Yang Lake Watershed Agr Resources & Ec, Nanchang 330045, Jiangxi, Peoples R China
[2] Jiangxi Water Resources Inst, Nanchang 330013, Jiangxi, Peoples R China
[3] Yuzhang Normal Univ, Nanchang 330103, Jiangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Xiangjiang River Basin; Landscape structure; Water quality; Interpretation rate; Spatial scales; NONPOINT-SOURCE POLLUTION; LAND-USE; PATTERNS; INDEX;
D O I
10.1007/s11270-022-05646-1
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The impact of landscape structures on the water environment has attracted great attention recently. Studying the effects of landscape structure on nutrient and heavy metals at different spatial division scales positively affect ecological protection. The Xiangjiang River Basin, with few external disturbances and light pollution, is the research object, and 16 sampling points were set up on the major stream and tributaries of the Xiangjiang River. The water-quality indicators were divided into conventional water quality indicators and heavy metal indicators. Eleven research scales were set up, including circular buffer zones (100 m, 200 m, 300 m, 500 m, and 1000 m) upstream monitoring points, riparian buffer zones (100 m, 200 m, 300 m, 500 m, and 1000 m), and sub-basin scales. The impacts of landscape structures on conventional indicators and heavy metals in water were quantified at different spatial division scales based on methods, such as principal component analysis and redundancy analysis. The results show the following: (1) the land structure of the Xiangjiang River Basin is dominated by forests, grasslands, and croplands, accounting for more than 97%. The difference in land-use structure is the largest in the circular buffer zone. (2) Settlement, croplands, PRD, grasslands, and COHESION were the main landscape structure indexes affecting the nutrient content in the water. Alternatively, ENN_MN, grassland, PD, LPI, and SHEI are the main landscape structure indicators affecting the heavy metal content in water. (3) For the riparian buffer zone and the circular buffer zone, the landscape structure at 300-m scale has the highest interpretation rate for conventional water quality indicators (93.6% and 85.45%, respectively). The landscape structure at 200-m riparian buffer zones has the highest interpretation rate for heavy metal indicators (93.8%), the landscape structure at 1000-m circular buffer zones has the highest interpretation rate for heavy metal indicators (88.65%). (4) The division of riparian buffer zones helps enhance the interpretation ability of landscape structures on the changes in conventional indicators and heavy metals in water. The 300-m and 200-m riparian buffer zones is the key scales affecting conventional indicators (93.6%), and the 200-m riparian buffer zones is the key scales affecting heavy metals indicators (93.8%). (5) The interpretation rate of landscape structures on conventional water quality indicators was higher in the rainy season than in the dry season; furthermore, the interpretation rate of landscape structures on heavy metals in the rainy and dry seasons is not too different. According to different pollution types, using different division methods and buffer scales helps enhance the accuracy of quantitative analysis. The conclusions can provide a scientific basis for water environment protection, landscape optimization, and Xiangjiang River Basin management.
引用
收藏
页数:18
相关论文
共 54 条
[1]   Modelling the spatial and seasonal variability of water quality for entire river networks: Relationships with natural and anthropogenic factors [J].
Alvarez-Cabria, Mario ;
Barquin, Jose ;
Penas, Francisco J. .
SCIENCE OF THE TOTAL ENVIRONMENT, 2016, 545 :152-162
[2]   Effects of Land Transformation on Water Quality of Dal Lake, Srinagar, India [J].
Amin, Arshad ;
Fazal, Shahab ;
Mujtaba, Ahmad ;
Singh, Sudhir Kumar .
JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2014, 42 (01) :119-128
[3]   Modeling the Linkage Between River Water Quality and Landscape Metrics in the Chugoku District of Japan [J].
Amiri, Bahman Jabbarian ;
Nakane, Kaneyuki .
WATER RESOURCES MANAGEMENT, 2009, 23 (05) :931-956
[4]   Landuse-based nonpoint source pollution: a threat to water quality in Murchison Bay, Uganda [J].
Banadda, E. N. ;
Kansiime, F. ;
Kigobe, M. ;
Kizza, M. ;
Nhapi, I. .
WATER POLICY, 2009, 11 :94-105
[5]   Assessment of land cover changes & water quality changes in the Zayandehroud River Basin between 1997-2008 [J].
Bateni, Fatemeh ;
Fakheran, Sima ;
Soffianian, Alireza .
ENVIRONMENTAL MONITORING AND ASSESSMENT, 2013, 185 (12) :10511-10519
[6]   Beyond Impervious: Urban Land-Cover Pattern Variation and Implications for Watershed Management [J].
Beck, Scott M. ;
McHale, Melissa R. ;
Hess, George R. .
ENVIRONMENTAL MANAGEMENT, 2016, 58 (01) :15-30
[7]  
Carter LD, 2012, FRESHW SCI, V31, P1214, DOI 10.1899/11-177.1
[8]   A Study on the Relationship between Land Use Change and Water Quality of the Mitidja Watershed in Algeria Based on GIS and RS [J].
Chen, Dechao ;
Elhadj, Acef ;
Xu, Hualian ;
Xu, Xinliang ;
Qiao, Zhi .
SUSTAINABILITY, 2020, 12 (09)
[9]   Development of a new index for integrating landscape patterns with ecological processes at watershed scale [J].
Chen Liding ;
Tian Huiying ;
Fu Bojie ;
Zhao Xinfeng .
CHINESE GEOGRAPHICAL SCIENCE, 2009, 19 (01) :37-45
[10]  
China MoEaEtPsRo, 2002, GB38382002