Premium
Topographic, pedologic and climatic interactions influencing streamflow generation at multiple catchment scales
Author(s) -
Ali Geneviève,
Tetzlaff Doerthe,
Soulsby Chris,
McDonnell Jeffrey J.
Publication year - 2012
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.8416
Subject(s) - streamflow , drainage basin , environmental science , surface runoff , spatial variability , precipitation , baseflow , streams , hydrology (agriculture) , physical geography , geography , geology , ecology , cartography , statistics , mathematics , geotechnical engineering , meteorology , biology , computer network , computer science
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 R 2 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.