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应用生态学报 ›› 2024, Vol. 35 ›› Issue (4): 1112-1122.doi: 10.13287/j.1001-9332.202404.022

• 研究论文 • 上一篇    下一篇

非点源污染对景观格局响应的空间尺度效应——以东北太子河流域为例

吕乐婷1, 郑晓宇1, 刘琦2, 孙才志3,4*, 毕丝淇1   

  1. 1辽宁师范大学地理科学学院, 辽宁大连 116029;
    2岭南师范学院地理科学学院, 广东湛江 524048;
    3辽宁师范大学海洋可持续发展研究院, 辽宁大连 116029;
    4辽宁省“海洋经济高质量发展”高校协同创新中心, 辽宁大连 116029
  • 收稿日期:2023-10-24 接受日期:2024-02-18 出版日期:2024-04-18 发布日期:2024-10-18
  • 通讯作者: * E-mail: suncaizhi@lnnu.edu.cn
  • 作者简介:吕乐婷, 女, 1984年生, 博士, 副教授。主要从事水资源、水生态评估与管理研究。E-mail: lvleting@lnnu.edu.cn
  • 基金资助:
    辽宁省社科基金重点项目(L22AJY011)

Spatial scale effects of landscape patterns on non-point source pollution: A case study of Taizi River Basin in Northeast China

LYU Leting1, ZHENG Xiaoyu1, LIU Qi2, SUN Caizhi3,4*, BI Siqi1   

  1. 1School of Geography, Liaoning Normal University, Dalian 116029, Liaoning, China;
    2School of Geography, Lingnan Normal University, Zhanjiang 524048, Guangdong, China;
    3Research Center for Marine Economy and Sustainable Development, Liao-ning Normal University, Dalian 116029, Liaoning, China;
    4Liaoning Provincial University Collaborative Innovation Center of High-Quality Development of Marine Economy, Dalian 116029, Liaoning, China
  • Received:2023-10-24 Accepted:2024-02-18 Online:2024-04-18 Published:2024-10-18

摘要: 河流水质是自然与人类活动综合影响的结果,多尺度景观格局通过改变不同空间尺度污染物的产生和运移过程对河流水质产生不同程度的影响。本研究以中国北方太子河流域为例,基于水质监测数据和土地利用数据,运用相关性分析、冗余分析方法,分析景观格局与河流非点源污染的关系,确定非点源污染对景观格局响应的最佳空间尺度,识别影响河流非点源污染的关键景观指标。结果表明: 太子河流域水质在时间上具有季节差异,汛期水质优于非汛期;在空间上,污染物全氮(TN)和全磷(TP)的高值出现在支流汇入点和下游地区;景观格局对非点源污染的影响在非汛期强于汛期,且TN强于TP。在汛期500 m以内河岸带缓冲区、非汛期集水区尺度下,景观格局对太子河TN和TP的解释率最高;类型水平上,建设用地、耕地和裸地与TN和TP呈正相关,林地与TN和TP呈负相关,是影响非点源污染的关键类型;景观水平上,斑块密度、相似邻近比例和蔓延度指数是影响流域水质的关键指标。斑块密度越低,“汇”型景观连通性越好,对TN的净化效果越好,但对TP的截留效应更明显。反之,景观多样性增加并构成多种类型的密集格局,越易导致水质恶化。合理配置流域内及河岸带缓冲区景观类型、适当丰富景观多样性、优化景观聚集性和连通性,是流域水质改善及可持续生态管理的有效措施。

关键词: 景观格局, 非点源污染, 尺度效应, 太子河流域

Abstract: River water quality is influenced by natural processes and human activities. Multi-scale landscape patterns can affect river water quality by altering the generation and transport processes of pollutants at different spatial scales. Taking Taizi River Basin in Northeast China as an example, we analyzed the relationship between landscape patterns and non-point source pollution in rivers based on water quality monitoring data and land use data by using correlation analysis and redundancy analysis methods. We aimed to determine the key spatial scales for the responses of landscape patterns to non-point source pollution and identify the key landscape indices influencing river non-point source pollution. The results showed that water quality of Taizi River Basin had seasonal differences, with better water quality during the flood season than non-flood season. Spatially, total nitrogen (TN) and total phosphorus (TP) were higher at the confluence points of tributaries and downstream areas. The impact of landscape patterns on non-point source pollution was stronger during the non-flood season than the flood season, while the influence on TN was stronger than on TP. At the spatial scale of within 500 m buffer zone during the flood season and at the sub-watershed scale during the non-flood season, landscape patterns showed the highest explanatory power for the variations of TN and TP. At the type level, built-up land, cropland, and bare land were positively correlated with TN and TP, while forest was negatively correlated with TN and TP, which were the key types influencing non-point source pollution. At the landscape level, patch density, percentage of like adjacencies, and contagion index were key indicators affecting watershed water quality. Lower patch density was associated with better connectivity and aggregation of “sink” landscapes, leading to better purification effects on TN, but more pronounced retention effects on TP. Conversely, higher landscape diversity and denser pattern of multiple types would cause the deterioration of water quality. Our results suggested that rational allocation of landscape types within the watershed and riparian buffer zones, appropriately enriching landscape diversity, and optimizing landscape aggregation and connectivity would be effective measures for improving water quality and achieving sustainable ecological management.

Key words: landscape pattern, non-point source pollution, spatial scale effect, Taizi River Basin