4.1 Complexities in Transient Storage Flow Paths
We assume, based on our results, that the flume is more sensitive to
surface flow paths and the numerical model to subsurface flow paths. For
example, a mean arrival time of only minutes in the flume is likely
consistent with a combination of slow flow in surface transient storage
zones plus some of the fastest flow through subsurface zones. However,
it is too short for the slowest flow through the subsurface (see
groundwater ages in Figure 3). We therefore use the numerical model to
quantify the behavior of the longest solute travel times in the
subsurface. Both physical experiments and numerical models represent
simplifications of real conditions, but the lack of fine sediment
accumulation in the logjam backwaters, the rigid lateral boundaries, and
the impermeable boundary underlying the sediment fill in the flume may
all have influenced subsurface flow paths. In the models, it is unclear
whether more skew (which we interpret as more transient storage) equates
to a greater hyporheic exchange flux or longer residence times
(Supplemental Table 5). As has been shown in past studies (Cardenas et
al., 2004; Cardenas et al., 2008; Sawyer and Cardenas; 2009; Pryshlak et
al., 2015), the two tend to work against each other (greater exchange
rates tend to occur with shorter residence times).
We observed complex dynamics in the relationship between logjam
distribution density, permeability, discharge, and transient storage. As
logjams accumulate in a stream channel and accumulate more fine
material, upstream pooling increases, driving surface and hyporheic flow
paths and heightened complexity of the stream system (Abbe and
Montgomery, 1996; Sear et al., 2010). Evolution in jam complexity (both
in terms of permeability and longitudinal distribution density) can
result in the activation of the floodplain and other portions of the
hyporheic zone that might otherwise be dormant (Gooseff et al., 2006;
Wondzell et al., 2009; Doughty et al., 2020). Changes in inundation area
have been shown to have a profound influence on hyporheic connectivity
at field scales on the order of tens of square kilometers (Helton
et al., 2014). We see this in our results where increasing both
longitudinal distribution density of logjams and the amount of coarse
particulate organic material in a jam leads to an increase in wetted
area and exchange fluxes between the surface water and groundwater
(Supplemental Table 5).
Existing work provides evidence for both increased transient storage
during low discharge conditions, when the water table near the stream is
low (Harvey and Bencala, 1993; Harvey et al., 1996; Wroblicky et al.,
1998) and during high discharge conditions, when the stream experiences
greater channel wetted area and floodplain inundation (Nyssen
et al., 2011; Doughty et al., 2020; Wilhelmsen et al., 2021).
Comparisons of mean arrival times and skew in the surface and subsurface
show consistencies across the flume and model. Our results show less
skew and shorter mean arrival times when discharge increases (Figures 2,
5, 7). In the flume, flow paths are better imaged at low discharge when
we can load more of the flow paths with a short salt pulse. In the
models, all the mean arrival times are already limited to the subsurface
(a transient storage zone), so skew is only telling us about the longest
transient storage times in that case, and a larger skew indicates a
wider, heavier-tailed distribution of residence times. We suspect
understanding the role of discharge in these environments is dependent
on sensitivity to detecting the smallest fraction of flows that stick
around the longest, which becomes increasingly challenging to do at
higher discharge. Both the flume and model results reflect
simplification of the river corridor in which flow paths cannot access a
floodplain. We recognize that longer and deeper flow paths might be
constrained by the base of the sediment box in the flume and impermeable
walls of the model. The extent of these flows in natural streams could
depend strongly on the depth of alluvial cover (Tonina and
Buffington, 2009), which is in turn influenced by the presence of large
wood, because logjams store sediments (Massong and Montgomery, 2000;
Montgomery et al., 2003). The interacting effect of jams, sediment
storage, and longer, deeper hyporheic exchange flows cannot be tested in
these experiments but is an area for future research.