Linking metrics of hydrological function and transit times to landscape controls in a heterogeneous mesoscale catchment

Abstract

Abstract:Long-term river flow data and one year of isotopic tracer data in a nested 749 km 2 catchment were analysed conjunctively to evaluate the relationships between hydrometric statistics, transit times, and catchment characteristics. The catchment comprised two distinct geomorphic provinces; upland headwaters draining glaciated landscapes underlain by crystalline geology and lowland headwaters draining a major regional sandstone aquifer. In the uplands, flow regimes were 'flashy' with high runoff coefficients for storm hydrographs, steep recession curves and strong nonlinearity in event responses. In the lowlands, runoff coefficients were low, recessions less steep, and event responses more linear. Flow data from the catchment outfall showing damping of these extremes, but was most strongly influenced by the upland headwaters where precipitation was highest. The damping of variability in stable water isotopes between precipitation inputs and streamflow outputs reflected this; with upland tributaries least damped and lowland tributaries most damped. Attempts to quantify the mean transit times of the sampling points met with mixed success; partly reflecting the short run (1 year) of data, but mainly as a result of the marked damping in lowland sites. As a consequence, MTT estimates can only be said to be in the order of a few years in upland sites, but are probably decadal or greater in lowland tributaries. Again, the catchment outfall averages these extremes, but is more similar to the upland headwaters. Despite the difficulties in quantifying MTTs, it is clear that they, like the hydrological response, primarily reflect the dominant control of catchment soil cover, which in turn is determined by geology and glacial history.

Reevaluation of transit time distributions, mean transit times and their relation to catchment topography

“…They identified the catchments' proportions of responsive soils and their drainage densities as best predictors of MTTs. Soulsby and Tetzlaff (2008) and Capell et al (2012) also found good correlations between MTTs and the proportions of responsive soils. Probably due to the small sample size of four catchments, Mueller et al (2013) found no significant correlation between MTTs and any topographic index, but the highest correlation coefficient of 0.62 was obtained for the drainage density; however, they did not test for a correlation to L/G.…”

“…Transit time estimations based on lumped convolution modelling approaches have been carried out in various studies, reviewed by McGuire and McDonnell (2006), and subsequent studies such as Soulsby and Tetzlaff (2008), Tetzlaff et al (2009b), Hrachowitz et al (2010), Roa-García and Weiler (2010), Lyon et al (2010), Soulsby et al (2011), Heidbüchel et al (2012), and Capell et al (2012). Lumped convolution modelling approaches are based on the convolution of an input signal with a transfer function (TF) to obtain an appropriate output signal.…”

What can flux tracking teach us about water age distribution patterns and their temporal dynamics?

“…While transport process studies provided crucial insights in small-scale dynamics, black-box model inter-comparison studies have shed light on the physical controls of the long-term average TTDs on the catchment scale (e.g. Laudon et al, 2007;Broxton et al, 2009;Hrachowitz et al, 2009a;Tetzlaff et al, 2009a;Katsuyama et al, 2009Katsuyama et al, , 2010Lyon et al, 2010a;Speed et al, 2010;Ali et al, 2012;Asano and Uchida, 2012;Capell et al, 2012a;McGrane et al, 2012).…”

Sampling frequency trade-offs in the assessment of mean transit times of tropical montane catchment waters under semi-steady-state conditions

“…Along with the increase of their applicability, the handling and processing of tracer data, and even the estimation of uncertainties of the inferred results, is becoming a routine process in hydrologic research (e.g., McGuire and McDonnell, 2006). Solutions, formerly based only on the best fit to a particular model, now frequently include a range of behavioral or possible solutions (Weiler et al, 2003;Vaché and McDonnell, 2006;McGuire et al, 2007;Hrachowitz et al, 2009Hrachowitz et al, , 2010Hrachowitz et al, , 2013Birkel et al, 2011;Capell et al, 2012;Muñoz-Villers and McDonnell, 2012;Timbe et al, 2014). However, an appropriate sensitivity analysis of the model parameters to factors such as the degree of temporal resolution of the input data used to calibrate tracer-based lumped models is still uncommon as it is in traditional rainfall-runoff modeling (McGuire and McDonnell, 2006).…”