景观格局对袁河水质参数影响的季节性差异及机理研究
- Seasonal Differences and Mechanisms of Landscape Patterns on Yuan River Water Quality ParametersYANG Wei1,2,WANG Peng1,2,*, CHEN Qinwei1,2, LIU Ru1,2, DING Mingjun1,2, ZHANG Hua1,2,NIE Minghua1,2,HUANG Gaoxiang1,2
- 基金项目：国家自然科学(42167013);江西省教育厅研究生创新(YJS2024-012)* 责任作者, 王鹏教授, wangpengjlu@jxnu.edu.cn ,2*,陈钦威1,2,刘茹1,2,丁明军1,2,张 华1,2,聂明华1,2,黄高翔1,2
- 摘要：为探究景观格局对河流水质影响的季节性变化及机理研究。本研究以袁河流域为研究对象,于2023年3月至2024年1月期间,在干支流17个采样点开展了6次采样。采用不同宽度的河岸缓冲区和子流域作为景观分析单元,利用冗余分析研究子流域和河岸缓冲区空间尺度下景观格局对水质的影响程度,并分析该影响程度与水文因子的相关性关系。研究结果表明：(1)基于水质参数的时空分布特征,运用聚类分析将水质参数分为pH组(pH、DO)、EC组(EC、SO?2?、Cl?)和营养盐组(TP、TN、NO??-N)；(2)景观格局对水质参数具有显著影响,其平均解释率EC组(48.6%)＞营养盐组(37.7%)＞pH组(17.5%),影响的最佳空间尺度存在季节性变化。pH组与营养盐组在1月、3月、7月和11月的最佳空间尺度是子流域,5月和9月为河岸100m缓冲区；EC组的最佳空间尺度在各个季节均为子流域尺度。(3)影响不同类型水质参数的关键景观指标存在差异。EC组主要受农田和建设用地占比及斑块密度(PD)影响；营养盐组主要受农田和建设用地占比影响,而pH组未显示出统一的关键景观指标；(4)关键景观指标对水质的影响程度与流量和温度显著相关。在EC组和营养盐组中,河岸缓冲区(100m,200m)内的农田占比对水质的解释率分别与流量和温度呈显著正相关,而EC组在所有空间尺度内建设用地占比对水质的解释率与流量呈显著负相关。综上所述,研究发现不同类型水质参数在时空尺度上对景观格局均表现出差异化的响应特征与机理,为制定针对性的流域景观规划和水质改善策略提供科学支撑。
- Abstract：We investigated the seasonal variations and mechanisms of landscape pattern impacts on river water quality in the Yuan River basin. Six sampling campaigns were conducted at 17 sampling points across the main and tributary streams from March 2023 to January 2024. Riverine buffer zones of varying widths and sub-catchments were used as landscape analysis units. We employed redundancy analysis (RDA) to examine the influence of landscape patterns on water quality at both sub-catchment and riparian buffer scales, and analyzed the correlation between this influence and hydrological factors. Our results showed that: (1) Based on the spatiotemporal distribution characteristics of water quality parameters, cluster analysis grouped the parameters into a pH group (pH, DO), an EC group (EC, SO?2?, Cl?), and a nutrient group (TP, TN, NO??-N). (2) The optimal spatial scale for the landscape pattern"s influence on water quality parameters exhibited seasonal variation. The optimal scale for the pH and nutrient groups was the sub-catchment scale in January, March, July, and November, while it shifted to the 100m riparian buffer in May and September. The optimal scale for the EC group remained at the sub-catchment scale throughout the year. (3) Key landscape indicators influencing different water quality parameter groups varied. The EC group was primarily affected by the proportion of farmland and built-up and patch density (PD). The nutrient group was mainly influenced by the proportion of farmland and built-up land, while the pH group did not show consistent key landscape indicators. (4) The explanatory power of key landscape indicators on water quality was significantly correlated with water temperature and discharge. In the EC and nutrient groups, the proportion of farmland within the 100m and 200m riparian buffers showed a significant positive correlation with the explanatory power of water quality for water temperature and discharge. Conversely, the proportion of built-up land in the EC group showed a significant negative correlation with the explanatory power of water quality for discharge at all spatial scales. In conclusion, we revealed the seasonal impacts of landscape patterns on different grouped water quality parameters, highlighting the optimal spatial scale differences and the crucial role of hydrological factors. These findings provide scientific support for developing seasonally differentiated watershed landscape planning and water quality improvement strategies.
