Constraining water age dynamics in a south‐eastern Australian catchment using an age‐ranked storage and stable isotope approach

Abstract

Improving our knowledge of the travel times of water through catchments is critical for the management and protection of water resources and to improve our understanding of fundamental catchment behaviour. In this study we use the age-ranked storage framework StorAge Selection (SAS) to investigate travel times in the Corin catchment, a headwater catchment in the southeast of Australia covered by native Eucalyptus species. Few studies have applied the SAS framework globally and in energy-intensive areas where catchment losses are heavily in favour of evapotranspiration relative to streamflow. A combination of observed and modelled values of oxygen-18 (δ 18 O), the stable isotope in water, are used to constrain storage selection preferences of streamflow and evapotranspiration and the size of the catchment active storage. The highest performing parameter combinations that could reproduce δ 18 O in streamflow were dependent on a strong preference for young water in evapotranspiration, and a mixture of weak young and old water preference in streamflow. The mean travel time of streamflow over the study period 2007-2019, weighted by the flow rate, is limited to within a probable range of 2.81-9.77 years. The size of the active storage, a key parameter in the SAS framework, was poorly identified, and in combination with the isotopic inputs into the model, contributed to the uncertainty of the results. We discuss the implications of the results with respect to the study area, as well as within the context of SAS research globally and identify ways to improve the modelling process.

A comparison of catchment travel times and storage deduced from deuterium and tritium tracers using StorAge Selection functions

“…Our storage value is more consistent with the ∼ 1600 mm derived from depth to bedrock and porosity data used for the Colpach catchment (containing the Weierbach) that was modeled with CATFLOW (Loritz et al, 2017). Large differences between hydrometrically derived and tracer-derived storage estimates are not uncommon (Soulsby et al, 2009;Fenicia et al, 2010;Birkel et al, 2011) and, in fact, highlight the ability of tracers to reveal the existence of stored water that is not directly involved in streamflow generation (Dralle et al, 2018;Carrer et al, 2019). This hydraulically disconnected storage is nevertheless important for explaining the long residence times in catchments (Zuber, 1986).…”

“…The storage value derived from unsteady travel times constrained by tracer data (Table 4; ∼ 1200-1700 mm) is noticeably larger than the maximum storage ( 250 mm) estimated from point measurements of porosity and water content (Martínez-Carreras et al, 2016), from water balance analyses (Pfister et al, 2017), from water balance analyses combined with recession techniques (Carrer et al, 2019), and from a distributed hydrological model (≤ 700 mm, Glaser et al, 2016Glaser et al, , 2020. Our storage value is more consistent with the ∼ 1600 mm derived from depth to bedrock and porosity data used for the Colpach catchment (containing the Weierbach) that was modeled with CATFLOW (Loritz et al, 2017).…”

“…This study is carried out in the Weierbach catchment, which has been the focus of an increasing number of investigations in the last few years about streamflow generation (Glaser et al, 2016(Glaser et al, , 2019Scaini et al, 2017Scaini et al, , 2018Carrer et al, 2019;Rodriguez and Klaus, 2019), biogeochemistry (Moragues-Quiroga et al, 2017;Schwab et al, 2018), and pedology and geology (Juilleret et al, 2011). The Weierbach catchment is a forested headwater catchment of 42 ha located in northwestern Luxembourg (Fig.…”

Disparate Seasonal Nitrate Export From Nested Heterogeneous Subcatchments Revealed With StorAge Selection Functions

“…Furthermore, we modified the parameterization of SAS functions in the mHM‐SAS model. In this study, we focus on the two‐parameter beta function (Equation ) because of its flexibility in representing different types of selection preferences for outflows and its practical use (Buzacott et al., 2020; J. Yang, Heidbüchel, et al., 2018; Nguyen et al., 2021; van der Velde et al., 2015). In previous studies, the temporal variability of the beta function parameters was restricted to certain limited types of selection preferences.…”

Using water age to explore hydrological processes in contrasting environments

“…controls are transferable. Buzacott et al (2020) also found that high evapotranspiration and deep groundwater sources (this time in fractured bedrock aquifers) had a strong influence on the Corin catchment in the Australian Alps. They used time series of stable isotope data in precipitation and stream flow to identify storage selection (SAS) functions to estimate the ages of stream flow, catchment storage, and evapotranspiration.…”