Introduction
Transit time distributions (TTD) are a fundamental characteristics of
catchment hydrological function, which can indicate the dynamics of
water movement and solute transport in watersheds (Kirchner et al.,
2000; McDonnell and Beven, 2014). The TTD gives conceptual, integrated
understanding of the nature of flow paths that transform precipitation
inputs (e.g. rainfall, snowmelt) to runoff at the catchment outlet, and
their associated temporal dynamics. Mean transit time (MTT) defines the
average travel time between water entering and leaving a catchment,
which provides indication of water movement under different
meteorological condition and for contrasting watershed characteristics
(Hrachowitz et al., 2009). The MTT can be determined from the TTD;
however, estimates of MTT can often be biased, especially for spatially
heterogeneous catchments with heavy tailing of old water distributions
(Kirchner, 2016a; Seeger and Weiler, 2014). Consequently, the young
water fraction (Fyw), i.e. the average fraction of
stream flow that is younger than a specified threshold age (typically
between two and three months), has recently been proposed as a more
reliable and stable descriptive metric of the TTD for spatially
heterogenous and non-stationary conditions by Kirchner (2016a, b). A
distinct advantage of this approach is that the estimation of
Fyw does not need to assume a particular shape of the
underlying TTD.
Currently, there are two main broad approaches for water transit time
and age estimation: data-driven and model-driven approaches (McCallum et
al., 2014). In the former approach,
TTDs are estimated by fitting conservative tracer concentrations using
inverse lumped parameter models, and then MTTs are determined from
corresponding TTDs (McGuire and McDonnell, 2006; Peralta-Tapia et al.,
2016). In the latter approach, the water transit times and age can be
tracked directly by a tracer-aided hydrological model with calibrated
parameters related to flow and tracer simulations
(Hrachowitz
et al., 2013; Soulsby et al., 2015; Remondi et al., 2018). Although it
is difficult for data-driven approaches to constrain the spatial
variation of flow transit times and water age distributions within a
catchment, the influence of climate and landscape properties can also be
assessed by catchment inter-comparison of the MTT and
Fyw determined by the data-driven method. For example,
comparison of MTT determined by data-driven methods across a range of
environmental gradients catchments, Hrachowitz et al. (2009, 2010) and
Heidbüchel et al. (2013) helped disentangle the relative influence of
controls due to meteorological conditions and watershed characteristics
(e.g. drainage density, topographic wetness index, soil cover and
storage capacity, antecedent moisture conditions and precipitation event
characteristics) on stream water ages.
Similarly, the relationships between
Fyw and terrain, soil, and land-use indices, as well as
the precipitation characteristics have been examined in 22 Swiss
catchments (von Freyberg et al., 2018). In addition, Jasechko et al.
(2016) calculated the Fyw of streamflow of 254
relatively large catchments around the world, and they found there is no
significant correlation between Fyw and annual rainfall,
but there is an inverse correlation with the average topographic
gradients inferring deeper vertical infiltration in steeper catchments.
Alternatively, where transit times and water ages are tracked using
tracer-aided hydrological models (e.g. Benettin et al., 2015a;
Hrachowitz et al., 2013, 2016; McMillan et al., 2012), although such
models are usually conceptual, they have stronger skill in capturing the
spatio-temporal variability of catchment transit times and water age due
to non-stationarity conditions and spatial heterogeneity.
These approaches have contributed to
the enhanced understanding of spatial variation in runoff generation and
solute transport processes and have shown how this influences the
dynamics of transit times and water ages at the catchment scale (Birkel
et al., 2012; Soulsby et al., 2015).
Process-based models, with more
complex structures and parameterisation, can provide more
physically-based descriptions of catchment hydrological processes, and
can give more integrated understanding of tracking flow transit times
and water ages and analysing how hydrometeorological conditions and
spatial heterogeneity may affect the TTD and Fyw (Kuppel
et al., 2018a; Remondi et al., 2018). However, the high parameterisation
of such models can increase uncertainty, unless detailed data on
watershed states (soil moisture storage, groundwater levels etc.) and
hydroclimatic inputs are available for multi-criteria model calibration,
which limit the application of this method to more intensively
instrument catchments (Kuppel et al., 2018b).
As both climate and landscape characteristics interact to determine
transit times and water ages, understanding how catchment morphological
properties and external meteorological forcing control TTDs and water
age remains challenging. Most studies are site specific and focused in
humid temperate catchments, so generalization to different geographical
regions is rarely possible (Burt and McDonnell, 2015; Birkel and
Soulsby, 2015; Maxwell et al., 2016). For example, karst regions cover
12% of the Earth’s surface and are the main source of drinking water to
over 25% of the world’s population (Ford and Williams, 2013). However,
due to the high spatial variability of the hydrodynamic properties and
hydrological connectivity of the karst critical zone, the TTD and water
age of catchment water fluxes have significant spatial and temporal
variability (Zhang et al., 2019). Unfortunately, there is relatively
little research on this issue (see Chen et al., 2018).
Because of the unique nature of karst geology and geomorphology, and
characteristic features such as vertical shafts, caves and sinkholes,
the spatial heterogeneity of drainage systems is high. The complex
underground mixed-flow systems in karst aquifers include low velocity
flows within the matrix and small fractures, and high velocity flow
within large fractures and conduits (White, 2007; Worthington, 2009),
which lead to a highly dynamic spatio-temporal variability of
hydrological processes (Bakalowicz, 2005; Ford and Williams, 2013;
Hartmann et al., 2014a). Hu et al. (2015) estimated the mean residence
time of water at a karst epikarst spring with contributing area less
than 1km2 in South China, based on detailed
observations of hydrogen and oxygen isotopes. In their study, the MTT in
epikarst spring was longer than one year, indicating that the epikarst
had poor connectivity and high water retention, and could thus maintain
continuous contributions to surface water. Hartmann et al., (2014b)
simulated the time-variant transit time distributions of an Austrian
karst system using a semi-distributed model, and showed that the
variation in transit time in the karst area is very large, and can range
from days to several years. However, these studies considered entire
karst basins, and there is a need to understand the TTD and
Fyw of water fluxes in different geomorphological units
(e.g. hillslope and depression) or different mediums (e.g. dual flows in
within the matrix and conduits) within karst landscapes, which are
crucial for the understanding the interactions of hydrological processes
and water quality in drainage waters.
The aim of this study is to address this research gap in the Chenqi
catchment in SW China. The specific objectives are: (1) to use the
output from a tracer-aided model to quantify the young water fraction
(defined according to Kirchner, 2016a, b) of water storages in, and
fluxes between, the main compartments of a complex karst landscape; (2)
to examine the seasonal inter-relationships between storage and the
young water fraction of the dominant water fluxes as hydrological
connectivity changed; and (3) to assess the time variance
of the water age and travel time
distributions between the main seasons using flux tracking.