S1
Setting the minimum inter-event time
To set the minimum inter-event time (MIT), we estimated the scale of temporal
autocorrelation of the rainfall time series (Dunkerley, 2008; Morris, 1984). The temporal
autocorrelation (r) between rainfall amount at time ti and rainfall at time h hours later, ti+h,
was calculated for all separation times h using the acf function in R (R Core Team, 2013). The
temporal autocorrelation decreases with larger separation times h. At the separation time
where the autocorrelation drops below a threshold, here set at r = 0.05, rain events were
assumed to be independent. This threshold separation time was used as a measure of the MIT.
Several common and frequently applied theoretical autocorrelation functions in geostatistics
(e.g. Webster & Oliver, 2007) were fitted through the empirical autocorrelation function. A
double exponential model (Eqn S1) fitted the rainfall time series most parsimoniously and
better than a single exponential model or a (double) spherical model, indicating that multiple
scales of temporal autocorrelation need be distinguished. Hence multiple MIT criteria were
employed in further analyses.
𝑟 = 1 − (σ1 [1 − 𝑒
−
ℎ
𝛼1 ]
+ σ2 [1 − 𝑒
−
ℎ
𝛼2 ]
+ 𝑐0 )
(𝐸𝑞𝑛 𝑆1)
In eqn S1, α and σ represent the range and partial sill of the two exponential models. The
intercept c0 (‘nugget’) was not significant and was therefore excluded from the model (Table
S1). The effective ranges, i.e. the MIT values, were set to the range where 95% of the sill was
reached and was calculated as 3·α following Webster & Oliver (2007). As a result, mean MIT
values ± 95% confidence intervals were 1.45 ± 0.06 hours and 10.01 ± 0.34 hours. In order to
match the half hourly measurement frequency, we set the MIT to 1.5 and 10 hours.
Table S1. Fitting parameters for the double exponential model and associated statistics. The root
mean square error (RMSE) and normalized root mean square error (RMSE / data range) of the fit
were respectively 0.008 (-) and 0.8%.
Variable
Estimate
Std. Error
t value
P
α1
3.22
0.18
18.3
< 0.001
α2
0.47
0.03
14.2
< 0.001
c0
0.003
0.009
0.299
0.77
c1
0.40
0.02
17.6
< 0.001
c2
0.58
0.02
23.9
< 0.001
1
References
Dunkerley D (2008) Identifying individual rain events from pluviograph records: a review with
analysis of data from an Australian dryland site. Hydrological Processes, 22, 5024-5036.
Morris CD (1984) A stochastic model for a small-time-interval-intermittent hydrologic process.
Journal of Hydrology, 68, 247-272.
R Core Team (2014) R: A Language and Environment for Statistical Computing, R Foundation for
Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Webster R, Oliver MA (2007) Geostatistics for Environmental Scientists, John Wiley & Sons,
Chichester, England.
2
S2
Frequency distributions of rain characteristics
Figure S2. Frequency distributions of rain amount per event (a), rain event duration (b),
mean rain event intensity (c) and pre-rain drought length (d). The number and vertical black
line represent the median value. Event characteristics are calculated for an MIT of 10 hours
and response timeframe of one day.
3
S3
Estimating physical CO2 displacement by rain
The contribution of physical displacement of air in the peat matrix by an average 6.1 mm rain
event to the observed decrease in ecosystem net CO2 uptake can be estimated using the ideal
gas law (Eq. S3). Here, a mean growing season temperature (T) of 281.5 K, a volume
displacement of an average 5.6 mm rain event (V) of 0.0061m3 m-2 and the gas constant R
(8.3145 J mol-1 K-1) were used to estimate the CO2 displacement CD (gC m-2) for a given area
A of 1 m2. M and n are the molar mass (gram mol-1) and moles of C (mol), respectively.
Henry’s law was applied to estimate the partial CO2 pressure (P) of air in the peat matrix,
assuming the CO2 concentration in the air filled pores is in equilibrium with a measured pore
water CO2 concentration of about 0.002 M (Nilsson & Bohlin, 1993). A van ‘t Hoff
temperature correction following Washington (1996) was applied to obtain a CO2 Henry
coefficient of 18.3 atm M-1 for the given temperature.
𝐶𝐷 =
𝑃𝑉
𝑛𝑀
=
𝐴𝑅𝑇
𝐴
𝐸𝑞. 𝑆3
An average rain event of 5.6 mm could result in an emission of 0.11 gCO2-C m-2, which
corresponds to 22% of the mean rain induced decreased ecosystem net CO2 uptake of
0.54 gCO2-C m-2 per rain event (fulfilling the selection criteria). It thus seems very unlikely
that physical displacement of CO2 is the dominant process responsible for the increased net
atmospheric uptake.
References
Nilsson M, Bohlin E (1993) Methane and carbon dioxide concentrations in bogs and fens – with
special reference to the effects of the botanical composition of the peat, Journal of Ecology, 81,
615–625.
Washington J (1996) Gas partitioning of dissolved volatile organic compounds in the vadose zone:
Principles, temperature effects and literature review, Ground Water, 34, 709-718.
4
S4
Potential reduction in GPP by decreased light availability
It is estimated that over an growing season, daytime rain events may result in a reduction of net
ecosystem C uptake that corresponds with about 10% of gross primary production (GPP) at the mixed
mire complex studied. Reduced light availability associated with rain events is likely the main reason
for reduced net ecosystem carbon uptake due to rain events (Table 3, Figure 4). The aim of this
supplementary material is to determine whether the decreased light availability due to rain is large
enough to reduce GPP with 10%. GPP was estimated from half-hourly NEE measurements by the
procedure described by Barr et al. (2004) and Peichl et al. (2014). Next, the median GPP during
median pre-rain and post-rain light conditions (± 10 μmol m-2 s-1) was estimated. The pre-rain and
post-rain light conditions were extracted from the rain event analysis (Table 3). The reduction in GPP
due to decreased light availability (R; %) was calculated as:
𝑅 = 100
𝐺𝑃𝑃𝑃𝑅𝐸 − 𝐺𝑃𝑃𝑃𝑂𝑆𝑇
𝐺𝑃𝑃𝑃𝑅𝐸
(𝑆4.1)
Another way to estimate of the reduction of GPP due to light availability was obtained by fitting the
frequently employed rectangular hyperbolic model through the light – production relationship to
obtain a light response curve:
𝐺𝑃𝑃 =
𝛼 ∙ 𝑃𝑃𝐹𝐷 ∙ 𝐺𝑃𝑃𝑚𝑎𝑥
𝛼 ∙ 𝑃𝑃𝐹𝐷 + 𝐺𝑃𝑃𝑚𝑎𝑥
(𝑆4.2)
Here, 𝛼 is the initial slope of the curve and 𝐺𝑃𝑃𝑚𝑎𝑥 the maximum (light saturated) GPP. Both
parameters were significant (P < 0.05) and the goodness of fit (R2) is 0.49. The results of the
calculations (Table S4) indicate that, when purely based on reduced light, rain would decrease GPP
with at least 31%. This is well beyond the estimated effect of rain on net C uptake, and supports the
view that positive effects of rewetting may be offset by the more dominant effect of light on GPP.
Table S4: Light availability (PPFD) and calculated gross primary production (GPP) during pre- and post-rain
conditions for two response timeframes (0.5 and 1 days). PPFD and GPP are represented with mean values, n is
the number of GPP and PPFD observations and R the reduction in GPP. Subscripts 1 and 2 indicate whether
GPP is estimated with the median (1) or light response curve (2) based method.
Unit
PPFD
GPP1
GPP2
n
R1
R2
μmol m-2 s-1
μmol C m-2 s-1
μmol C m-2 s-1
count
%
%
Pre-rain
320
2.53
2.85
894
Timeframe = 0.5 day
Post-rain
157
1.70
1.79
1397
33
37
Timeframe = 1 day
Pre-rain
Post-rain
381
205
2.82
1.95
3.14
2.16
900
1214
31
31
References
Barr AG, Black TA, Hogg EH, Kljun N, Morgenstern K, Nesic Z (2004) Inter-annual variability in the leaf area
index of a boreal aspen-hazelnut forest in relation to net ecosystem production. Agricultural and Forest
Meteorology, 126, 237–255
Peichl M, Öquist M, Löfvenius MO et al. (2014) A 12-year record reveals pre-growing season temperature and
water table level threshold effects on the net carbon dioxide exchange in a boreal fen. Environmental
Research Letters, 9, 055006
5