Topographic, pedologic and climatic interactions influencing streamflow generation at multiple catchment scales

被引:23
作者
Ali, Genevieve [1 ]
Tetzlaff, Doerthe [1 ]
Soulsby, Chris [1 ]
McDonnell, Jeffrey J. [1 ,2 ]
机构
[1] Univ Aberdeen, Sch Geosci, Aberdeen AB24 3UF, Scotland
[2] Oregon State Univ, Dept Forest Engn Resources & Management, Corvallis, OR 97331 USA
关键词
end-member mixing; dominant flow pathways (DFPs); Gran alkalinity; variation partitioning; canonical redundancy analysis; multiple linear regression; MODELING STREAMWATER CHEMISTRY; MEMBER MIXING ANALYSIS; SOILWATER END-MEMBERS; RUNOFF PROCESSES; TRANSIT TIMES; CAIRNGORM MOUNTAINS; RESIDENCE TIMES; HYDROGRAPH SEPARATION; HYDROLOGICAL PATHWAYS; SPATIAL INTERPOLATION;
D O I
10.1002/hyp.8416
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
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?km(2) (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 (c) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码:3858 / 3874
页数:17
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