TY - JOUR
T1 - Topographic, pedologic and climatic interactions influencing streamflow generation at multiple catchment scales
AU - Ali, Genevieve
AU - Tetzlaff, Doerthe
AU - Soulsby, Christopher
AU - McDonnell, Jeffery
N1 - ACKNOWLEDGEMENTS
Field work in association with this paper was undertaken as part of Grant F/00152/U awarded by the Leverhulme Trust and thanks to additional financial support provided by the Carnegie Trust for the Universities of Scotland. We acknowledge the help of Derek Fraser from the Scottish Environment Agency (SEPA) with regards to providing the precipitation and discharge data. We also thank Mark Speed and Markus Hrachowitz for sample collection in the field and Audrey Innes from the University of Aberdeen for her weekly assistance analysing Gran alkalinity.
PY - 2012/12/15
Y1 - 2012/12/15
N2 - 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.
AB - 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.
KW - end-member mixing
KW - dominant flow pathways (DFPs)
KW - Gran alkalinity
KW - variation partitioning
KW - canonical redundancy analysis
KW - multiple linear regression
U2 - 10.1002/hyp.8416
DO - 10.1002/hyp.8416
M3 - Article
VL - 26
SP - 3858
EP - 3874
JO - Hydrological Processes
JF - Hydrological Processes
SN - 0885-6087
IS - 25
ER -