Stream water age distributions controlled by storage dynamics and nonlinear hydrologic connectivity: Modeling with high-resolution isotope data

Abstract

To assess the influence of storage dynamics and nonlinearities in hydrological connectivity on time‐variant stream water ages, we used a new long‐term record of daily isotope measurements in precipitation and streamflow to calibrate and test a parsimonious tracer‐aided runoff model. This can track tracers and the ages of water fluxes through and between conceptual stores in steeper hillslopes, dynamically saturated riparian peatlands, and deeper groundwater; these represent the main landscape units involved in runoff generation. Storage volumes are largest in groundwater and on the hillslopes, though most dynamic mixing occurs in the smaller stores in riparian peat. Both streamflow and isotope variations are generally well captured by the model, and the simulated storage and tracer dynamics in the main landscape units are consistent with independent measurements. The model predicts that the average age of stream water is ∼1.8 years. On a daily basis, this varies between ∼1 month in storm events, when younger waters draining the hillslope and riparian peatland dominates, to around 4 years in dry periods when groundwater sustains flow. This variability reflects the integration of differently aged water fluxes from the main landscape units and their mixing in riparian wetlands. The connectivity between these spatial units varies in a nonlinear way with storage that depends upon precipitation characteristics and antecedent conditions. This, in turn, determines the spatial distribution of flow paths and the integration of their contrasting nonstationary ages. This approach is well suited for constraining process‐based modeling in a range of northern temperate and boreal environments.

Water ages in the critical zone of long-term experimental sites in northern latitudes

“…Our simulations emphasize the time-variant character of water ages and travel times in hydrological systems, as observed at the catchment scale in various recent studies (van der Velde et al, 2012;Benettin et al, 2013;Heidbüchel et al, 2013;Soulsby et al, 2015;Benettin et al, 2017). Further, the age dynamics in the soil waters were driven by the variability in water stored in the soils, supporting an "inverse storage effect" as discussed by Harman (2015).…”

“…In contrast, the rooting zone covered the entire soil profile for the forested sites and down to 15 cm soil depth for the heather site, which affected the resulting travel times and water ages of T accordingly. Investigation of water ages in the ET flux is relatively new (Botter et al, 2011) and age dynamics have usually been assessed for the bulk ET flux (Harman, 2015;Soulsby et al, 2015;van der Velde et al, 2015;van Huijgevoort et al, 2016;Soulsby et al, 2016). While it was shown that tracer-aided modelling using stable isotopes of water benefits from partitioning ET into a fractionating E flux and a non-fractionating T flux (Knighton et al, 2017), separate water age analyses for E and T have been considered only recently (Smith et al, 2018).…”

“…At the catchment scale, several modelling studies have shown younger water ages during wet periods with high storage volumes (van der Velde et al, 2012;Benettin et al, 2013;Heidbüchel et al, 2013;Soulsby et al, 2015;Benettin et al, 2017). Detailed experimental work on a sloping lysimeter with pulse irrigation provided insights into the flow path changes that can explain the inverse relationship between storage volume and runoff age (Pangle et al, 2017).…”

Age-ranked hydrological budgets and a travel time description of catchment hydrology

“…As an illustrative example we take a simple model from Birkel et al (2010) and Soulsby et al (2015).…”

“…2 in Soulsby et al, 2015). The lower reservoir is responsible for groundwater description and represents a large storage which also has the function of dumping the solute concentration.…”