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Geological factors controlling daily- to multi-year-discharge variability in 48 sub-catchments spanning 10 to 1000 km2 in the Thames Basin were investigated using cross-spectral analysis. The analyses represent a ‘transfer function approach’ applied to daily observed streamflow (output) versus catchment-wide precipitation (input) for data spanning 1990-2014. Catchments dominated by high-permeability bedrock have significant attenuation of high-frequency precipitation variability and large delays at all frequencies with streamflow dominated by baseflow (high lag1 autocorrelation and high Base Flow Index, BFI). Catchments dominated by low-permeability rocks have little high-frequency attenuation and small delays and consequently ‘flashy’ behaviour. For all sub-catchments >300 km2 in the Thames Basin, attenuation of the highest frequency precipitation variability caused by mixing of flow from upstream plus groundwater flow (representing ‘older’ variability) with direct surface flow (‘younger’ variability) constitutes real-world moving averaging as indicated by a roll-off in power at the highest frequencies. The success of the JULES land surface model in simulating discharge (i.e. surface and sub-surface runoff routed between grid boxes) is also linked to the underlying geology. Larger catchments (> 300 km2) are modelled well because routing between numerous grid boxes leads to moving averaging that is a good analogue for the observations. Modelling was least successful (e.g. lowest Kling-Gupta Efficiency) for small catchments (< 300 km2) dominated by high-permeability bedrock - with far too little attenuation of high-frequency precipitation variability and insufficient delays at all frequencies. Experimentally switching the soil saturated hydraulic conductivity to that of the underlying bedrock for grid boxes dominated by aquifers significantly improves modelled discharge variability in small sub-catchments - confirming the importance of bedrock permeability in modelling. For small catchments in data-sparse regions, knowledge of the relative proportions of different hydrogeological units (aquifers, aquitards) potentially could be used to predict and model discharge variability as characterised by BFI and lag1 autocorrelation.
Weedon et al. (Wed,) studied this question.