Elucidation of the relationship between geographic and time sources of stream water using a tracer approach in a headwater catchment
Elucidation of the relationship between geographic and time sources of stream water using a tracer approach in a headwater catchment
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DOI：
10.1029/2008wr007458
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发表时间：
2009-06
影响因子：
5.4
通讯作者：
M. Katsuyama;N. Kabeya;N. Ohte
中科院分区：
文献类型：
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作者：
M. Katsuyama;N. Kabeya;N. Ohte
示踪剂方法已在世界范围内用于澄清集水区溪流水的时间和地理来源，尽管这两个来源之间的关系很少被讨论。我们考虑了平均停留时间（MRT）及其空间分布，以确定地理源成分和时间源成分之间的关系。沿河岸地下水体垂直剖面浅层、中层和底层溶质浓度和MRT存在明显差异。溪水中的水体与浅层和中层水体相比处于中等水平；因此，一致的地理来源是这些层中的地下水。然而，在端元混合分析（EMMA）中，端元是降雨、山坡地下水和底层河岸地下水。其他河岸地下水是端元的混合物。通过考虑 MRT（在流域内移动最终成员或地理源所需的时间）来解决地理源和最终成员之间的差异。我们的方法阐明了地理源、时间源和水文路径之间的关系，它们是径流生成过程和水化学过程的基本因素。因此，为了超越以往基于观测数据系统学习的EMMA应用，结合常用方法来分析景观异质性和过程复杂性，并重新考虑不同区域和多个空间尺度的水生生物地球化学模型框架是有见地的。
Tracer approaches have been used worldwide to clarify time and geographic sources of stream water in catchments, although the relationship between these two sources is poorly discussed. We considered the mean residence time (MRT) and its spatial distribution to determine the relationship between geographic source components and time source components. There were clear differences in solute concentrations and MRT among shallow, middle, and bottom layers along the vertical profile of the riparian groundwater body. Those in the stream water were intermediate compared to those in the shallow and middle layers; thus, the consistent geographic sources were groundwater in these layers. In the context of end‐members mixing analysis (EMMA), however, the end‐members were rainfall, hillslope groundwater, and riparian groundwater in the bottom layer. The other riparian groundwaters were a mixture of end‐members. The discrepancy between the geographic sources and the end‐members was resolved by considering the MRT, the time required to move the end‐member or geographic source within the catchment. Our approach clarified the relationships among the geographic sources, time sources, and hydrological pathways, which are the essential factors of runoff generation processes and hydrochemical processes. Therefore, to go beyond previous applications of EMMA on the basis of systematic learning from observed data, it is insightful to combine the common approaches to analyze landscape heterogeneity and process complexity and to reconsider the framework of the hydrobiogeochemical models in various regions and at multiple spatial scales.