High resolution (15 min) continuous environmental tracer data (conductivity, pH and derived Gran alkalinity) were used to investigate the hydrological functioning of the 233 km² Feugh catchment in NE Scotland and two of its nested sub-catchments (42 km² and 1 km²). Over the 2003-2004 hydrological year, a fine resolution Gran alkalinity time series was derived and indicated detailed and subtle changes in stream chemistry. Diurnal variation in alkalinity and flow were observed under low flow conditions, attributed to instream-respiration and riparian-evapotranspiration respectively. At high flows, abrupt threshold-like behaviour was evident during storm events as hydrological sources in the acidic surface horizons of the catchment soils replace groundwater as the dominant source of runoff. Using Gran alkalinity to define end-member compositions, chemically-based hydrograph separations revealed that as catchment scale increased, groundwater contributions to annual runoff increased from 52 ± 10%, to 67 ± 6%, to 70 ± 11%. This is consistent with previous mean residence times (MRT) estimated from weekly δ¹⁸O data which respectively increased from 1.3-4.7 months^-1 to 2.4-10.6 months^-1 to 2.5-11.1 months^-1. Linking continuous tracer data with GIS interpretation of landscape characteristics increased the sophistication of our conceptual model of catchment processes. Increasing dominance of responsive peaty soils leads to more saturation overland flow, increased flashiness of runoff, reduced groundwater recharge, reduced MRTs and more marked diurnal variations in flow, which drive concomitant difference in hydrochemistry. Conversely, increased cover of free-draining soils and aquifers in drift, reduced flashiness, increased groundwater contributions and increased MRTs. It is proposed that high resolution tracer data, in conjunction with other measurements defining catchment characteristics, represent a resource and challenge to modellers if models can be produced which use tracer data, as well as physical parameters as objective functions in model evaluation.