Growing Season Water and Carbon Dynamics of a Subalpine
Wetland with Complex Terrain
Across the entire 2018 study period (96 days), cumulative
evapotranspiration (ET) reached a total of 157.7 mm. During Snow
Melt (17 days in duration), mean ET was 1.0 mm/day (±0.7) and increased
throughout the seasonal phase (Figure 3), because of evaporation from
snowmelt runoff. During this season ET was most likely dominated by
evaporation, since the majority of the wetland vegetation was still
under a blanket of snow and without leaves. Cummulative ET during
snowmelt was 16.6mm. ET continued to increase into Green Up ,
reaching the seasonal maximum for the entire study period (3.5mm/day)
from 12 – 17 July (DoY 193-198). Overall, Green Up had a daily
average ET of 2.3 mm/day (±1.1), contributing 57.5 mm to the overal
cumulative ET. Green Up represented only 28% of the study period
(27 days), but made up 37% of total study period ET.Throughout Snowmelt and Green Up the site experiencedSteady shade (around 2 hours of shade daily). After Green
Up, ET was influenced by the increasing horizon shadow effect and
followed a decreasing trend, similar to Rg through the remainder ofPeak Growing Season and into Late Growing Season (Figure
2a and Figure 3). Therefore, daily Peak Growing Season ET was
lower than Green Up with a daily average of 2.0mm/day (±0.5).
Since Peak Growing season was longer (35% of study period, 34 days), it
resulted in a greater cumulative total ET (66.6 mm) compared toGreen Up despite the lower daily mean ET. Peak Growing
Season contributed 42% of total study period ET. During Late
Growing Season, ET remained low (mean of 0.94 mm/day (±0.3)) and
contributed only 11% (17.0 mm) to total study period ET. Therefore,Green Up and Peak Growing Season accounted for the largest
(124.1 mm, 79%) chunk of cumulative ET during the course of this study.
In this study we use the term “carbon flux” to represent only carbon
dioxide (CO2) exchange between the wetland and the
atmosphere. The carbon (C) flux was extremely variable during Snow
Melt and represented a strong source, defined as negative Net Ecosystem
Exchange (NEE= GPP- Reco), releasing an average of 1.4
gCm-2 day (±2.2) into the atmosphere for a total
release of 23.8 gC over this period (Figure 4). During Snow Melt ,
there was no photosynthetically active vegetation present so almost all
NEE was defined by Ecosystem Respiration (Reco). The
wetland then fluctuated between a source and sink during Green
Up, when variability in CO2 uptake/release continued
until the 6th of July. During this time the ground
surface became increasingly snow-free and vegetation began leafing-out.
Following the 6th of July, the wetland was a net
C-sink (i.e. CO2 uptake with positive NEE) until theLate Growing Season . During the Green Up season, the
wetland took up an average of 0.58 gCm-2day (±0.7) for
a total carbon sink of 15.6 gC. Maximum CO2 uptake from
photosynthesis (defined as Gross Primary Production: GPP) occurred from
29 July to 2 August, once the entire site became snow-free and
vegetation began to grow, with an average daily GPP of 6.4
gCm-2day and Reco of 4.5
gCm-2day, respectively. This equated to an average NEE
of 1.9 gCm-2day (±0.5), representing a net carbon sink
of 60.5 gC over the Peak Growing Season . Net C sequestration
largely took place between the hours of 08:00 and 19:00 duringPeak Growing Season from the middle of July to the end of August
(Figure 7). At this time, cumulative net CO2 uptake was
high enough to offset CO2 losses during the Snow
Melt period and shift the wetland into a cumulative net sink (Figure
4b). Following the seasonal maximum, there was a decreasing trend in the
net C flux for the remainder of the study. During the Late Growing
Season period, the site remained a consistent C sink with a net
C uptake of 0.6 gCm-2day (±1.0) for a cumulative sink
of 10.6 gC. NEE spiked to 3.3 gC on 7 September because of abnormally
warm air temperature (9.4°C) compared to the Late Growing
Season’s average of 5.5 °C. Over the entire study period, Bonsai was a
net sink of 63gC; however, it is possible that the wetland is an overall
annual source of CO2 if emissions during the
snow-covered period are similar to what we found during Snow
Melt .
Environmental
Factors driving Temporal Variability in Observed Carbon and Water Fluxes
Bonsai carbon flux was influenced by shade and had a statistically
significant negative relationship with GPP (R2=0.75;
p<0.01) and Reco (R2=0.39,
p<0.05), and a statistically significant positive relationship
with NEE (R2=0.73, p<0.01) duringDynamic Shade (Figure 5b). This indicates that each hourly
increase of shade during Dynamic Shade decreased GPP by 0.8
gCm-2day (y=-0.8x+6.7), overall decreasing the C sink
strength (NEE) by an average of 0.5 gCm-2day
(y=-0.5x+3.9). Therefore, horizon shade negatively impacted C uptake at
the site. The period ofStable Shade yielded non-significant results for
Reco (Figure 5a). Thus, other environmental variables
influenced the temporal variability in water and carbon fluxes inStable Shade .
ET also had a statistically significant negative relationship
(R2=0.66, p<0.01) with hours of shade per
day (hrs/day) during Dynamic Shade (Figure 5b), but not duringStable Shade (R2=0.04, p>0.05)
(Figure 5a). During Dynamic Shade , each hour of shade decreased
ET losses by 0.42 mm/day (y=-0.4x+3.3). Therefore, there was a
statistically significant relationship between ET and available energy
over the study period (R2=0.79, p<0.01),
indicating that increased shade (and lower available energy) decreased
evaporative losses and established a greater potential for water
storage.
Figures 6 and 7 displayed similar results to the statistical analysis
above, in which ET and GPP were strongly influenced by the horizon
shadow. In a uniform and non-complex environment, fluxes typically
display a bell-curve pattern, increasing in the morning, peaking in the
afternoon, and gradually decreasing into the evening. However, at Bonsai
there was a noticeable shift from this pattern in the middle of July,
when half hourly ET had a sudden reduction at 16:00 hours (0.13 to
0.05mm, Figure 6). This trend became more intense into August and the
period of Dynamic Shade , when shade decreased energy and water
fluxes in the morning from 09:00 to 11:00 hours and at 15:00 hours in
the afternoon (Figure 7). The response of ET to horizon shade followed a
nearly identical pattern as that of Rg and Q* (Figure 6). ET (and Rg)
should have declined from the afternoon until sunset; however, sharp
reductions were observed at 16:00 hours in the middle of July,
indicating that horizon shade shaped the energy and water fluxes during
the Peak Growing Season . Similar to the results found in the
statistical analysis, GPP did not follow the same pattern as ET, Rg, or
Q* during constant shade, but did have a narrowing bell curve shape in
the portion of increasing shade in Peak Growing Season . High
Reco during the Snow Melt period resulted in a C
source (averaging 1.4 gCm-2day-1)
with the largest C-source contributions occurring from advection
overnight, in the middle of June between 22:00 and 06:00 hours. C uptake
through GPP was greatest midday from the middle of July to the middle of
August between the hours of 08:00 and 18:00. In the middle of August
there was a rapid decline in C uptake, as the wetland transitioned from
0.3 gC/hh to 0.06 - 0.13 gC/hh and closer to net neutral C uptake during
daytime hours.
Using general additive modelling and the list of key environmental
variables that were available to us from measurements at the site, we
were able to explain up to 84% of temporal variability in ET (Table 4),
but only 71% of GPP (Table 5). For both water and carbon fluxes, we
found that the most significant variable in explaining the temporal
variability was incoming solar radiation (Rg). For GPP, Rg was the most
important variable, reducing model performance the most, if excluded.
Interestingly, the model for ET had more variables that were significant
in explaining its temporal variability (Table 4), including surface soil
temperature (at 2 cm depth), vapour pressure deficit, shade – as a
factor variable, and time of day (Hour). In contrast, the model for GPP
had day of the year (DoY) as the only other variable that turned out to
have statistically significant estimated coefficients in the
best-fitting model (Table 5). All other variables tested were not
significant. This finding supported the spectrographs of the data
(Figure 6), where the changes in ET seemed to be more visually
correlated to changes in Rg than GPP. Our analysis suggests that ET was
related to the temporal variability in environmental variables, while
GPP was dependent on plant physiology (which was not measured in this
study) such as leaf area and greenness factor. This is further evidenced
by the fact that GPP continued to increase throughout the Peak
Growing Season , despite decreased Rg, having a higher mean GPP value inDynamic Shade versus Stable Shade (Table 1). Although, the
difference was not found to be statistically significant. Another
interesting finding was that ET was nonlinearly related to Rg and that
the two curves differed between Dynamic Shade versusSteady Shade (Table 4 and Appendix 2). The estimated
parametric coefficient for shade-factor in the ET model was also
negative for the Steady Shade group (Appendix 2:
fShadeSteady=-0.0096116), which meant that the curve for Steady
Shade was offset from Dynamic Shade (lower intercept), such that
for a given Rg value, ET will be higher in Dynamic Shade thanSteady Shade . This was evident if we looked at our observed data
using binned-Rg (Appendix 3). Dynamic Shade had a lower mean ET
because shade reduced overall Rg, resulting in more values at lower Rg
during that period (Appendix 3). Interestingly, this was not the case
for GPP, where shade factor was not found to be significant in the
best-specified gam model (Table 5 and Appendix 1). Here, GPP was higher
with lower Rg-bins (Dynamic Shade ), but not much different
from higher Rg-bins (Steady Shade ) (Appendix 3). These
finding have implications for the local water balance and support the
notion that shaded ecosystems have reduced overall water loss from
shade, because of greater reductions in evaporation than transpiration.