土地利用及景观格局对东西大河水质的多尺度影响

Multi-scale impacts of land use and landscape pattern on Dongxi River water quality

  • 摘要: 为探究不同时空尺度土地利用及景观格局对河流水质的影响,以星云湖东西大河流域为研究对象,基于1000m河段缓冲区、200m河岸带缓冲带的土地利用类型及景观格局指标以及旱雨季的河道水质数据,采用Spearman、RDA分析等方法,探讨土地利用及景观格局变量对水质的时空尺度效应。结果表明:(1)各时空尺度下COD、NH3-N、TN、TP浓度与耕园地、建设用地呈正相关,与林地、PD、SHDI呈负相关。(2)建设用地、耕园地是对河流水质产生负面影响的最关键因子,景观格局指标PD、SHDI对河道水质解释水平低于土地利用类型。(3)时间尺度上,雨季的土地利用及景观格局对河道水质的影响更显著。空间尺度上,雨季以200 m河岸缓冲带为更显著影响范围,水域为主要解释变量;旱季则以1000 m河段缓冲区为显著范围,建设用地为主要解释变量。研究影响河流水质关键驱动因子及时空尺度效应,对优化星云湖流域水环境管理策略,落实精准管控具有参考价值。

     

    Abstract: To investigate the impacts of land use and landscape patterns at different temporal and spatial scales on water quality in Dongxi River, 1000 m river buffer zone and 200 m riparian buffer zone were selected. Based on the land use and landscape pattern of the buffer zone, combined with‌ the water quality of the river in the dry-rainy season, Redundancy Analysis (RDA) and Spearman Correlation Analysis was used to assess the spatiotemporal scale effects of land use and landscape pattern variables on water quality. The results showed that: (1) COD、NH3-N、TN and TP exhibited the ‌positive correlation‌ with construction land and farmland, while demonstrating the ‌negative correlation‌ with forest land, patch density (PD), and Shannon diversity index (SHDI). (2) Construction land and farmland represent the most significant negative determinants of water quality , while land use exhibits a higher level of contribution for water quality than landscape pattern indicators (PD and SHDI). (3) The rainy season amplifies the impact of land use and landscape configuration on water quality, with temporal variations demonstrating heightened significance. Spatial scale analysis reveals that 200 m riparian buffer zone significantly influences water quality during the rainy season, with water as the primary explanatory variable, whereas a 1000 m river buffer zone along the river section becomes critical in the dry season, where construction land emerges as the dominant explanatory variable. Analyzing the key drivers and spatiotemporal scale effects of water quality provides critical insights for optimizing management strategies in the Xingyun Lake Basin and enabling precise control measures.

     

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