Network analysis reveals multiscale controls on streamwater chemistry
Network analysis reveals multiscale controls on streamwater chemistry
复制标题
网络分析揭示了河水化学的多尺度控制
DOI：
10.1073/pnas.1404820111
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
S. Bailey
中科院分区：
文献类型：
--
作者：
K. McGuire;C. Torgersen;G. Likens;D. Buso;W. Lowe;S. Bailey
源流是下游生态系统和人类社会的重要水源。这些溪流构成了流域内绝大多数的溪流和河流公里，影响着区域水质。然而，源流水质的实际空间变化往往是未知的。我们的研究使用了来自源头河流网络的高分辨率空间数据集，并采用统计工具客观地描述了河流网络中河流化学的空间模式。这种方法提供了流水如何与周围景观中的植被、土壤和地质物质相互作用的见解。这种方法的应用可能有助于确定影响水质的因素，并为保护水生生态系统的战略提供信息。通过将全流域河流化学评价的天气性数据与基于网络的地统计分析相结合，我们发现空间过程对源头河流网络的生物地球化学条件和模式有不同的影响。我们分析了一个高分辨率的数据集，该数据集由664个水样组成，这些水样来自整个五阶水系的32条支流，每隔100米采集一次。对这些样品进行了详尽的化学成分分析。该研究设计的细粒度和广泛程度使我们能够通过使用明确包含网络拓扑的经验半变异图来量化一系列尺度上的空间模式。在这里，我们表明，由半变分函数的特征形状决定的空间结构在化学成分和空间关系（流连接、流不连接或欧几里得）之间都是不同的。空间结构在单一尺度和多个嵌套尺度上都很明显，表明河流网络和周围陆地景观中存在不同的过程。一些化学成分（如溶解的有机碳、硫酸盐和铝）出现了流量连接关系的预期空间依赖模式（例如，随着下游距离的增加，同质性增加），但其他化学成分（如硝酸盐、钠）则没有。通过比较不同化学成分和空间关系的半变异图，我们能够区分(i)细尺度与大尺度过程和（ii）流内过程与景观控制对河流化学的影响。这些发现提供了对驱动河流网络中生物地球化学模式的本地、纵向和景观过程的层次尺度的见解。
Significance Headwater streams are important sources of water for downstream ecosystems and human communities. These streams comprise the vast majority of stream and river kilometers in watersheds and affect regional water quality. However, the actual spatial variation of water quality in headwater streams is often unknown. Our study uses an unusually high-resolution spatial dataset from a headwater stream network and employs a statistical tool to objectively describe spatial patterns of streamwater chemistry within a stream network. This approach provides insights on how flowing water interacts with vegetation, soil, and geologic materials in the surrounding landscape. Application of this method may help to identify factors impairing water quality and to inform strategies for protecting aquatic ecosystems. By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in an entire fifth-order stream network. These samples were analyzed for an exhaustive suite of chemical constituents. The fine grain and broad extent of this study design allowed us to quantify spatial patterns over a range of scales by using empirical semivariograms that explicitly incorporated network topology. Here, we show that spatial structure, as determined by the characteristic shape of the semivariograms, differed both among chemical constituents and by spatial relationship (flow-connected, flow-unconnected, or Euclidean). Spatial structure was apparent at either a single scale or at multiple nested scales, suggesting separate processes operating simultaneously within the stream network and surrounding terrestrial landscape. Expected patterns of spatial dependence for flow-connected relationships (e.g., increasing homogeneity with downstream distance) occurred for some chemical constituents (e.g., dissolved organic carbon, sulfate, and aluminum) but not for others (e.g., nitrate, sodium). By comparing semivariograms for the different chemical constituents and spatial relationships, we were able to separate effects on streamwater chemistry of (i) fine-scale versus broad-scale processes and (ii) in-stream processes versus landscape controls. These findings provide insight on the hierarchical scaling of local, longitudinal, and landscape processes that drive biogeochemical patterns in stream networks.