Transit time distributions and StorAge Selection functions in a sloping soil lysimeter with time‐varying flow paths: Direct observation of internal and external transport variability

Abstract

Transit times through hydrologic systems vary in time, but the nature of that variability is not well understood. Transit times variability was investigated in a 1 m3 sloping lysimeter, representing a simplified model of a hillslope receiving periodic rainfall events for 28 days. Tracer tests were conducted using an experimental protocol that allows time‐variable transit time distributions (TTDs) to be calculated from data. Observed TTDs varied with the storage state of the system, and the history of inflows and outflows. We propose that the observed time variability of the TTDs can be decomposed into two parts: “internal” variability associated with changes in the arrangement of, and partitioning between, flow pathways; and “external” variability driven by fluctuations in the flow rate along all flow pathways. These concepts can be defined quantitatively in terms of rank StorAge Selection (rSAS) functions, which is a theory describing lumped transport dynamics. Internal variability is associated with temporal variability in the rSAS function, while external is not. The rSAS function variability was characterized by an “inverse storage effect,” whereby younger water is released in greater proportion under wetter conditions than drier. We hypothesize that this effect is caused by the rapid mobilization of water in the unsaturated zone by the rising water table. Common approximations used to model transport dynamics that neglect internal variability were unable to reproduce the observed breakthrough curves accurately. This suggests that internal variability can play an important role in hydrologic transport dynamics, with implications for field data interpretation and modeling.

Using StorAge Selection functions to quantify ecohydrological controls on the time-variant age of evapotranspiration, soil water, and recharge

“…These results were similar to lysimeter and hillslope studies, which also showed higher preference of young water movement through the soil under wet conditions relative to drier conditions (Kim et al, 2016;Pangle et al, 2017). The increased preference for young water may be the result of numerous processes including: the limited additional storage in soils while wet and the freely draining nature of the soil structure (Geris 20 et al, 2015;Sprenger et al, 2017b), or rapid lateral transport due to of rising water tables (Kim et al, 2016;Pangle et al, 2017). The amplified inclination for young water fluxes during wet periods has previously been observed in the stream flow within the catchment .…”

“…The framework for using time-variant distributions to temporally differentiate water ages from storages has been defined by the "master equation" , identifying how water preferentially moves through storage. The majority of time-variant approaches to assessing water age have focused on 20 catchment-scales, however, transit and residence times have also been inferred in lysimeter studies through the use of tracer injections and breakthrough curves Harman and Kim, 2014;Benettin et al, 2015;Queloz et al, 2015;Kim et al, 2016). This has led to the identification of time-variant changes of transit time in different soils, directly related to moisture content (Ali et al, 2014;Tetzlaff et al, 2014;Sprenger et al, 2016;Pangle et al, 2017).…”

Transient Groundwater Travel Time Distributions and Age‐Ranked Storage‐Discharge Relationships of Three Lowland Catchments

“…The time variation in groundwater TTs can be represented by dynamic TTDs (Botter et al, 2010;Engdahl et al, 2016;Harman, 2015;Heidbüchel et al, 2012;van der Velde et al, 2012;van der Velde et al, 2010) and occurs due to external variability of the input (i.e., groundwater recharge) as well as internal variability where shallow flow paths become active as the groundwater table rises (Harman et al, 2016;Kim et al, 2016;Rozemeijer & Broers, 2007). Additional information about flow paths combined with the contribution of different ages to streamflow allows direct correlation of, for instance, water chemistry with a specific flow path, which can thus explain variations in water chemistry throughout the year (Benettin et al, 2013;Hrachowitz et al, 2016).…”

How does reach‐scale stream‐hyporheic transport vary with discharge? Insights from rSAS analysis of sequential tracer injections in a headwater mountain stream

“…This confusion may be, in part, a result of limitations in the methods used to interpret solute tracer studies—in particular, their ability to account for transient hydrological dynamics. Recent work has demonstrated that transport variability in response to dynamic hydrologic forcing can be decomposed into two distinct components, termed external and internal variability [ Kim et al ., ]. Both types might contribute to overall transport variability of a given system, though one may dominate over the other.…”

“…A more detailed discussion of these concepts is provided in Kim et al . [], but they can be understood intuitively for the case were a hydrodynamic system can be reduced to a set of streamtubes, or more loosely as a set of flow pathways. External variability refers to changes in the overall flow rate through set of all flow pathways, without implying any change in the proportion of flow through one flow pathway relative to another.…”