Interactive impact of landscape composition and configuration on river water quality under different spatial and seasonal scales

文献类型： 外文期刊

第一作者： Pei, Wei

作者： Pei, Wei;Xu, Qiyu;Lei, Qiuliang;Du, Xinzhong;An, Miaoying;Zhang, Tianpeng;Liu, Hongbin;Luo, Jiafa;Qiu, Weiwen

作者机构：

关键词： River water quality; Landscape composition; Landscape configuration; Interactive impact; Spatio-temporal variation

期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT （ 影响因子：8.2； 五年影响因子：8.6 ）

ISSN： 0048-9697

年卷期： 2024 年 950 卷

页码：

收录情况： SCI

摘要： Currently, the comprehensive effect of the landscape pattern on river water quality has been widely studied. However, the interactive influences of landscape type, namely composition (COM) and configuration (CON) on water quality variations, as well as the specific landscape driving types affecting water quality variations under different spatial and seasonal scales remain unclear. To further improve the effectiveness of landscape planning and water quality protection, this study collected monthly water samples from the Fengyu River Watershed in southwestern China from 2018 to 2021, the Biota-Environment Matching Analysis (Bioenv) was used to identify key metrics representing landscape COM and CON, respectively. Then, the multiple regression (MLR) and redundancy analysis (RDA) were used to explore the relationship between these landscape metrics and water quality. In addition, this study used a variation partitioning analysis (VPA) to quantify the interactive and independent influence of landscape COM and CON on water quality. Results revealed that construction land and the Shannon's diversity index (SHDI) were the key metrics of landscape COM and CON, respectively, for predicting water pollution concentrations. The interactive contribution was particularly sensitive to seasonal changes in riparian buffer areas (27.66 % to 48.73 %), while it remained relatively stable at the sub-watershed scale (38.22 % to 40.51 %). Moreover, landscape CON had a higher independent contribution to variations on water quality across most spatio-temporal scales. Overall, identifying and managing key landscape type and consequential metrics, matching with the spatio-temporal scale, holds promise for enhancing water quality conservation. Furthermore, this study provides valuable insights into the identification and selection of core landscape metrics.

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