Topographic, pedologic and climatic interactions influencing streamflow generation at multiple catchment scales
Topographic, pedologic and climatic interactions influencing streamflow generation at multiple catchment scales
复制标题
地形、土壤和气候相互作用影响多个流域尺度的水流生成
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
J. McDonnell
中科院分区：
文献类型：
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作者：
G. Ali;D. Tetzlaff;C. Soulsby;J. McDonnell
中尺度流域中的主要水流通道(DFP)的特征和了解程度较低。在这里，我们利用一种保守的示踪剂(GRAN碱度)和有关气候条件和物理性质的详细信息来研究时间和空间可变因素如何相互作用来确定12个流域的DFP，这些区域的面积从3.4km2到1829.5 km2(苏格兰凯恩戈姆斯)。在应用端元混合来区分近地表和深层地下水径流路径后，采用变量划分、典型冗余分析和回归模型来解决：(I)每个流域的DFPS的时间变异性是什么？(Ii)DFPS是如何在空间尺度上变化的，以及哪些因素控制了水文响应的差异？(Iii)DFPS的时空变异性能否作为气候、地形和土壤特征的函数得到解释？总体而言，流域特征仅有助于解释DFPS的时间变化，而不能解释其在不同尺度上的空间变化。DFPS的时间变异性受普遍的水文气候条件影响最大，其次是土壤排水能力。基于前7天的累积降雨量、日平均气温和RANKERS覆盖面积等因素，在土壤支持快速产流的集水区，主动DFPS的预测性较好。最好的回归模型R2为0.54，这表明分析中包含的因素没有完全反映流域的内部复杂性。然而，这项研究强调了将示踪研究与数字景观分析和多元统计技术相结合以深入了解DFPS的时间(气候)和空间(地形和土壤)控制的有效性。版权所有©2011 John Wiley&Sons，Ltd.
Dominant flow pathways (DFPs) in mesoscale watersheds are poorly characterized and understood. Here, we make use of a conservative tracer (Gran alkalinity) and detailed information about climatic conditions and physical properties to examine how temporally and spatially variable factors interact to determine DFPs in 12 catchments draining areas from 3.4 to 1829.5 km² (Cairngorms, Scotland). After end‐member mixing was applied to discriminate between near surface and deep groundwater flow pathways, variation partitioning, canonical redundancy analyses and regression models were used to resolve: (i) What is the temporal variability of DFPs in each catchment?; (ii) How do DFPs change across spatial scales and what factors control the differences in hydrological responses?; and (iii) Can a conceptual model be developed to explain the spatiotemporal variability of DFPs as a function of climatic, topographic and soil characteristics? Overall, catchment characteristics were only useful to explain the temporal variability of DFPs but not their spatial variation across scale. The temporal variability of DFPs was influenced most by prevailing hydroclimatic conditions and secondarily soil drainability. The predictability of active DFPs was better in catchments with soils supporting fast runoff generation on the basis of factors such as the cumulative precipitation from the seven previous days, mean daily air temperature and the fractional area covered by Rankers. The best regression model R2 was 0.54, thus suggesting that the catchments’ internal complexity was not fully captured by the factors included in the analysis. Nevertheless, this study highlights the utility of combining tracer studies with digital landscape analysis and multivariate statistical techniques to gain insights into the temporal (climatic) and spatial (topographic and pedologic) controls on DFPs. Copyright © 2011 John Wiley & Sons, Ltd.
影响因子：
3.2
作者：
F. Barthold;Jin-kui Wu;K. Vaché;K. Schneider;H. Frede;L. Breuer
通讯作者：
F. Barthold;Jin-kui Wu;K. Vaché;K. Schneider;H. Frede;L. Breuer