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Temporal dynamics of total organic carbon
export rates in Swedish streams
Importance of discharge conditions and
seasonal effects
Nino Amvrosiadi
May, 2012
Copyright © Nino Amvrosiadi and the Department of Earth Sciences, Uppsala University
To my father
i
Abstract
Temporal dynamics of total organic carbon export rates in Swedish streams –
Importance of discharge conditions and seasonal effects
Nino Amvrosiadi
Department of Earth Sciences, Uppsala University
Villavägen 16, SE-752 36 Uppsala, Sweden
The amount of total organic carbon (TOC) in water is a rough indicator of the water
quality. Driven by the question how the TOC concentration would vary across
streams in Sweden under different climate conditions (e.g. more extreme discharge
events), the temporal dynamics of TOC were examined for different stream subgroups
with six orders of magnitude catchment area span. In addition, the relationship
between dissolved inorganic carbon (DIC) export (both downstream and evasion) and
discharge conditions was also studied. Another question addressed was if the amount
of TOC exported can be affected by export conditions dominating the previous
season. TOC export followed closely the discharge, which is in agreement with
previous studies, and all 42 catchments studied across Sweden were described by this
positive relationship regardless their size. A linear TOC export response to discharge
was identified during extreme discharge conditions. Furthermore, the TOC export was
significantly related to the antecedent TOC export conditions for approximately half
of the 18 studied catchments with areas ranging between 2.5·10-3 and 67 km2.
ii
Referat
Mängden organiskt material I vatten (TOC) ger en grov uppskattning av vattnets
kvalitet. Med frågeställningen hur koncentrationerna av TOC kan relateras till
klimatförhållanden (extrema flöden) i Sverige, undersöktes hur TOC varierade med
tiden för olika vattendrag vars avrinningsområden varierade med sex
storleksordningar. Utöver detta undersöktes hur borttransporten av löst oorganiskt kol
(DIC) varierade med flödesförhållanden, både transporten nedströms och avgången
till atmosfären från vattendraget. En ytterligare frågeställning var hurvida mängden
exporterat TOC påverkas av tidigare säsongs flödesförhållanden. TOC visade sig följa
flödet väl, vilket överensstämmer med tidigare studier, och alla 42 avrinningsområden
som studerades uppvisade ett sådant positivt samband oavsett storlek. En linjär TOCexportrespons på vattenföringen identifierades vid extrema flöden. Därtill visade
TOC- exporten ett signifikant samband med tidigare säsongs flödesförhållanden i
ungefär hälften av de 18 undersökta avrinningsområdena, vars ytor varierar mellan
2.5·10-3 och 67 km2.
iii
Acknowledgements
This thesis comprises 30 ECTS credits and is the final part of the Master program in
Earth Sciences, with specialization in Hydrology/ Hydrogeology. The supervisor for
this work was Thomas Grabs, assistant professor at Department of Earth Sciences in
Uppsala University, the subject reviewer was Kevin Bishop, professor at Department
of Earth Sciences in Uppsala University, and the examiner was Allan Rodhe,
professor at Department of Earth Sciences in Uppsala University.
I would like to thank from the bottom of my heart Kevin and Thomas. Probably the
words are not enough to express how grateful I am for your patience, support,
guidance, all the encouragement and of course all the comments. I learned many
things from you, on the subject of organic carbon and life in general :)
I would also like to thank Andes Stenström and the Swedish University of
Agricultural Sciences for kindly providing the data used for this project.
I am very grateful to my examiner, Allan, who very kindly commented on my work.
Last but not least, many thanks to my dear friends for all their help and support!
iv
Abbreviations
DIC
Dissolved inorganic carbon
DOC
Dissolved organic carbon
IMC
Integrated monitoring catchment
KSC
Krycklan study catchment
LC
Large catchment
LC-N
Northern large catchments
LC-S
Southern large catchments
LDIC
Specific flux of DIC
LTOC
Specific flux of TOC
Q
Discharge
q
Specific discharge
q95
Very high specific discharge
qhigh
High specific discharge
qlow
Low specific discharge
qmed
Medium specific discharge
TOC
Total organic carbon
[TOC]
TOC concentration
VWC
Volume weighted concentration
v
Contents
1. Introduction ........................................................................................................................... 1
2. Materials and methods........................................................................................................... 3
2.1 Study sites and data ......................................................................................................... 3
2.2 Discharge conditions, volume weighted concentration and double mass curves ............ 5
2.3 Quantification of errors ................................................................................................... 6
2.4 Influence of antecedent conditions on TOC export ......................................................... 8
3. Results ................................................................................................................................... 9
3.1 Discharge conditions and error analysis .......................................................................... 9
3.2 TOC export .................................................................................................................... 11
3.2.1 TOC export during different discharge conditions – KSC and IMC ...................... 11
3.2.2 TOC export during different seasons...................................................................... 15
3.2.3 TOC export per season and per discharge condition .............................................. 18
3.3 DIC export, KSC ........................................................................................................... 19
3.4 Interseasonal dependence of TOC export...................................................................... 21
4. Discussion ............................................................................................................................ 22
Concluding remarks ................................................................................................................ 25
Cited literature ......................................................................................................................... 26
Further reading ........................................................................................................................ 28
Appendix A ............................................................................................................................. 30
Appendix B.............................................................................................................................. 35
1. Introduction
Total organic carbon (TOC) is the natural product of vegetation and animal decaying,
bacterial growth and metabolic activities. It is abundant in soils and sediments, as well
as in surface and subsurface waters. The most common compounds of TOC are
carbohydrates, proteins, fats, waxes and organic acids (Schumacher, 2002).
TOC in stream-water is allochthonous, as the main input is from wetlands and riparian
peat (e.g. Dosskey and Bertsch 1994). The amount of TOC in the soil water is
determined by the organic matter availability and on the conditions that could be
favorable or not for decomposition (e.g. temperature, acidity, moisture). The amount
of TOC in stream-water depends mainly on the discharge conditions, with large
export during the high discharge events (Seibert et al., 2009) following shallow flow
paths.
Dissolved inorganic carbon (DIC) from streams, both downstream export and evasion,
has been reported as an important part of the carbon cycle (e.g. Wallin et al., 2010),
and according to Kling et al. (1991), the terrestrial CO2 sink can be overestimated by
up to 20% if not taking into account the evasion from small surface water bodies.
The streams thus have been proposed to be active aquatic conduits (Cole et al., 2007)
that receive from the soil and then export towards different directions large amounts
of both organic and inorganic carbon (fig.1). These vertical and lateral fluxes are still
to be carefully quantified.
TOC content in water for human consumption can be a rough estimate of its quality.
For example, too much TOC can trigger bacterial growth, and moreover TOC binding
to other chemical compounds (e.g. heavy metals) increasing their solubility (Visco et
al., 2005) results in water dangerous for human health. Due to bacterial activity CO2
is produced from TOC and released into the atmosphere. These amounts are relatively
small (Öquist et al., 2009), but the importance of their contribution to the global
carbon cycle is still undetermined.
Over the last years an important increase in TOC concentrations in Swedish streams
was observed. The driving force for this process is suggested to be the reduction of
total sulphur depositions (e.g. Oulehle et al., 2011). Due to the less acidic
environment organic material decomposition is more active, and also leaching (due to
remobilization) is enhanced. It seems that Northern Europe soils are returning slowly
but steadily to the pre-industrial acidity levels, which is a positive fact, but at the same
time there are concerns regarding how to quantify and deal with the excess carbon
released during the period until the system returns back to its natural levels of carbon
emissions.
1
Figure 1: Schematic representation of inland aquatic system. Inland waters seen as a passive
pipe (a) and as an active member in the carbon transform system (b). The numbers indicate
the carbon flux (Pg/yr). (Cole et al., 2007)
The amount of TOC in stream waters depends strongly on flow conditions, with
increasing export during high flow. Therefore there is a concern whether an increase
in the number or intensity of the extreme discharge events following a potential
climate change would lead to over-increase of TOC input in the streams. Very high
flow events are important to be examined because even though they covered only
about 5% of the studied period, they were responsible for about one third of the total
discharge and TOC export.
The aim of this study was (1) to quantify the TOC exports from streams across a wide
range of sizes and geographic locations; (2) to quantify the DIC export during
different flow conditions; (3) to identify the role of antecedent TOC export conditions
on present TOC export; and (4) to identify the relationships between TOC - DIC flux
and catchment characteristics.
2
2. Materials and methods
After the preparation of the data-sets, which is described below in detail, the specific
flux of TOC and DIC (LTOC and LDIC respectively) was quantified. In order to
describe qualitatively the dependence of LTOC and LDIC on specific discharge (q)
double mass curves were plotted. Furthermore, the seasonal variations of LTOC and its
dependence on several catchments’ characteristics were described, while in order to
quantify the importance of antecedent conditions statistical analysis was performed.
2.1 Study sites and data
The data analyzed are coming from sites that are categorized in three different groups
here: Krycklan study catchment (KSC), integrated monitoring catchments (IMC) and
large catchments (LC) (fig.2).
KSC
The monitoring of KSC started during the winter 2002/2003 within the frame of
Nyänget study catchment’s expansion. Located at 64o13´N 19ο46´E, and at average
elevation of 132m above sea level, KSC has total area of ~67 , about 56% of
which lies below the highest postglacial coastline. Krycklan consists of 15 nested
subcatchments, of which were included in this study the 14 with available data (table
A.1). The areas of KSCs vary from 2.5·10-2 to 67 km2. On average the catchment
receives 600mm precipitation per year out of which about 35% falls as snow, and
snow covered period is on average five months per year (Ågren et al., 2007).
The dataset, kindly provided by the Krycklan Catchment Study project at the Swedish
University of Agricultural Sciences (SLU), covers the time period from 2006 to 2009
and consists of daily measurements of: q; [DOC]; [DIC]; LDOC; LDIC (downstream and
evasion); temperature; pH; subcatchment area; stream area.
Since more than 95% of TOC in the studied streams consists of DOC, the term TOC
is consistently used below instead of DOC, as it was assumed that the whole amount
of TOC was dissolved.
In order to estimate DIC evasion, DIC concentration of groundwater was measured
and combined with groundwater discharge modeling. Then the evasion was estimated
by the difference of groundwater and stream-water DIC concentration (Öquist et al.,
2009)
3
IMC
The integrated monitoring of about 15 sites in Sweden started in 1981, as a part of the
national Program for Monitoring of Environmental Quality (PMK) run by the
Swedish Environmental Protection Agency (http://info1.ma.slu.se/IM/IMeng.html).
Currently there are four IMCs in Sweden: Gårdsjön (IMC1), Aneboda (IMC2), Kindla
(IMC3) and Gammtratten (IMC4), with catchment areas ranging from 3.7·10-2 to 0.45
km2. These catchments are mostly undisturbed from land use and other anthropogenic
activities. All four catchments are covered by forest, mainly Norway spruce (Picea
abies) and a few small wetlands. The dominant soil type at all catchments is podzol.
The dataset consists of: daily discharge; daily specific discharge; and TOC
concentration. On average there is one measurement of TOC per 15 days. The
summary of IMC characteristics and the data period covered are given in tables A.2
and A.3 respectively.
LC
24 large streams, with catchment areas from 102 to 34441 km2 and outlets located at
latitudes from 55oN to 66oN, had 24 years records (1987-2010) consisting of:
discharge time series at varying resolution (daily, weekly, monthly); LTOC, measured
in tons-exported through a specific sampling point during a month; and catchment
area size. A preliminary analysis of potential trends was performed and only those
sites without a trend were kept (chapt.2.3). The sites and some of their general
characteristics are presented in table A.4.
4
Figure 2 : Locations of study catchments.
catchments KSC (yellow square), IMC (red triangles), LC
(black dots)
2.2 Discharge conditions, volume weighted concentration and double
mass curves
Discharge conditions at each catchment were classified into: (1) low – 33% of the
whole time period with the lowest stream discharge (qlow); (2) medium (qmed); (3) high
– 33% of the whole time period with the highest stream discharge (qhigh); and (4) very
high discharge – not to be exceeded for the 95% of the time (q95). According to this
5
definition no fixed value was set for each limit, as the limits varied depending on the
discharge profile of each site (table 5). Note that the same specific discharge was
assumed for all the subcatchments of Krycklan.
The volume weighted concentration was calculated as:
∑ q ⋅ [TOC ]
VWC =
∑q
i
i
i
, where qi = daily discharge, [TOC ]i = TOC concentration.
i
i
The procedure followed to create double mess curves was: a) Daily and monthly
specific discharge (q) data of the whole studied period were sorted in increasing
order; b) The specific flux (L) data were sorted corresponding to q; c) The cumulative
and normalized L and q were plotted against each-other. The same steps were
followed, but only with daily data this time, in order to produce double mass curves
for each discharge condition separately.
2.3 Quantification of errors
Errors due to interpolation
Since only 6% of [TOC] data was available for the IMCs, linear interpolation was
used to fill the gaps. The LTOC calculated in this way could, however, differ
significantly from the real LTOC. Since KSC dataset was the most complete and the
one with the highest time resolution, it was used to estimate the error.
The error was estimated by leaving only one measurement per 15 days in [TOC]
dataset and then to apply linear interpolation to fill the gaps created, and recalculate
the daily [TOC]. The measured and reconstructed LTOC data were compared, and it
was examined how close to the observed daily values were the estimated ones
(eq.(1)):
PercErr = 100 ⋅
|[TOC ]estimated − [TOC ]observed |
(%)
[TOC ]observed
(1)
where [TOC ]observed is the measured daily concentration, and [TOC ]estimated is the
estimated daily concentration from the linearly interpolated data. Additionally, t-test
was performed to compare the mean and variance of measured and estimated data,
while the K-S statistics were used to test if they were belonging to the same
distribution.
6
Finally, the estimated LTOC was calculated according to eq.(2) and compared to the
measured LTOC.
LTOC ( g / m2 / day ) = q(mm / day) ⋅ [TOC ](mg / L) /1000
(2)
Errors due to different time resolutions
The effect of different resolutions of discharge time series on the calculation of
discharge limits and TOC exports was assessed based on data from the KSC. First the
low and high discharge limits were calculated for the daily data; next were calculated
the weekly and monthly averages of Q and the low/high limits were set again. Finally,
the limits set according to high and low resolution data were compared, with the
percent error calculated according to eq.(3).
PercErr = 100 ⋅
| qdaily (low / high) − qweekly / monthly (low / high) |
qdaily (low / high)
%
(3)
where qx ( y ) is the specific discharge limit; x indicates the time resolution (daily,
weekly or monthly), and y shows which discharge limit we are referring to (low or
high).
Errors due to long term trends
All discharge time series were analyzed for potential long term trends. It is possible
for example that the monthly average discharge of a stream is increasing with time; in
this case although the low/high discharge limits stay fixed over the whole time period,
different values of Q would fall into the low/high discharge limits. This can be better
seen on figure B.1, which is a plot of synthetic time-series with increasing mean and
variance. It would not be a good idea to keep fixed flow limits for this case, as there is
absence of high flow from the first months and of low flow from the last months, and
the flow of each year is not even close to the 33%-33% definition discussed above.
Simple linear regression was used to calculate the slope of annual mean, variance,
maximum and minimum of discharge data. The sites with significant trends in mean
were excluded from the analysis.
7
2.4 Influence of antecedent conditions on TOC export
The influence of antecedent conditions on LTOC was evaluated after removing both the
LTOC variation related to q variations and a (potential) [TOC] long term trend. If for
each site and season q was the only significant variable to determine LTOC, eq.(4)
would satisfactory describe LTOC for any given site (with fixed values for a and b for
each season and subcatchment). Otherwise the data was analyzed for potential
interseasonal dependence.
LTOC= α·qb
(4)
The following steps were taken to investigate the existence of an interseasonal
dependence: a) It was tested if a liner equation could be used instead of eq.(4), in
order to avoid bias error complications when using the exponential form; b) For the
cases that this approach was acceptable, the estimated TOC export was calculated
from LTOC=d·q. The coefficient d was calculated separately for each subcatchment
and season, applying the above equation on the average seasonal TOC export and
runoff; c) The residuals were calculated as: r=LTOC(observed) – LTOC(estimated), and
depending on their distribution it was evaluated whether LTOC is well estimated by
discharge; d) The seasonal TOC export of each year was compared to the average
(over all years) seasonal export.
The results from all the cases (all seasons and catchments) were separated into four
different subgroups: 1) LTOC was well estimated only from q data; 2) Positive
dependence (e.g. high export in summer followed by higher than expected export in
autumn); 3) Negative dependence (e.g. high export in summer followed by lower than
expected export in autumn); 4) The estimated LTOC was significantly different from
the observed, but the preceding season’s export did not differ from the average
seasonal export (e.g. the export of a summer was not either significantly higher or
lower than the average export of all summers).
8
3. Results
3.1 Discharge conditions and error analysis
The estimated relative errors when filling gaps of [TOC] time series indicated that the
method used was accurate (table A.5). The curves of observed and estimated LTOC
almost coincided (fig.3). The discharge limits calculated according to chapt.2.3 are
shown in table 1.
Figure 3: Measured and estimated LTOC . KSC1, 2006-2009.
Table 1: Specific discharge limits for KSCs and IMCs (IMC1 to IMC4 correspond to
Gårdsjön, Aneboda, Kindla and Gammtratten respectively). Also given is the approximate
percentage of the total specific discharge that the q95 accounts for.
KSC
IMC1
IMC2
IMC3
IMC4
0.643
1.13
1.01
1.12
0.99
qhigh limit (mm/day)
0.245
0.25
0.41
0.39
0.34
qlow limit (mm/day)
q95 limit (mm/day) 3.345 (33%) 6.88 (37%) 2.48 (19%) 5.39 (31%) 5.40 (32%)
9
The time resolution effects were important for the cases where only monthly
discharge data were available, with the low and high discharge limits deviating about
43% and 18% from the limits set based on daily data (table 2, fig.4). The interpolated
weekly discharge deviated less from the daily data (~5%), to be more accurate though
only the large streams with daily discharge measurements were included in the
analysis.
Table 2: Difference of discharge limits (percent error) calculated from different time
resolution data.
daily – weekly Perc.Err. (%)
daily – monthly Perc.Err. (%)
low flow limit high flow limit
5.5
5.6
43.3
18.7
Figure 4: Cumulative distribution curves of q(mm/day), KSC 2006-2009. The daily, weekly
and monthly averages are denoted with blue, green and red colors respectively, while the
discharge limits as well as their values for each time resolution are indicated with circles.
Table 3 summarizes the results of trend-test for the annual mean q, as well as for the
annual average of variance, minimum and maximum of q. For the streams that have
no trend it is safe to set one value of low/high discharge limits over the whole study
time period, while the large streams with trend were excluded from the analysis.
The KSCs, IMC2, IMC3 and IMC4 did not show any long term discharge trend
during the observation period, while the annual maximum discharge of IMC1 showed
increasing trend. Furthermore, six LCs average annual discharges had decreasing
trend during the 24 year record.
10
Table 3: Change in time of mean, variance, minimum and maximum of specific discharge
annual averages. The signs +, - , 0 indicate increase, decrease and no significant change
respectively.
KSC
IMC1
IMC2
IMC3
IMC3
LC +
LC LC 0
mean variance minimum maximum
0
0
0
0
0
0
0
+
0
0
0
0
0
0
0
0
0
0
0
0
0
2
4
4
6
2
5
2
24
26
21
24
3.2 TOC export
3.2.1 TOC export during different discharge conditions – KSC and IMC
Average TOC export rates ranged from 1.1 kg/ha/yr (during low discharge conditions)
to 83.1 kg/ha/yr (during high flow conditions). The VWC had a smaller range,
varying from 7.5 to 43.0 mg/l (table A.5).
The average LTOC had negative correlation with the percentage of forest cover and the
catchment area (fig.5). TOC export varied considerably among the discharge
conditions, while the VWC varied less. The slopes of both TOC and VWC vs.
catchment area decreased at qhigh comparing to qlow, indicating that both export and
concentration tended to become independent of the individual characteristics of each
catchment (fig.6). The LTOC difference (%) between the most and least exporting
catchments ranged from ~2.5% during qhigh up to ~10% during qlow (fig.B.2). For all
but one catchment (IMC4) LTOC (%) vs. q (%) closely followed the 1:1 line (table A.6,
fig.B.3).
11
Figure 5: TOC(kg/ha/yr) grouped according to discharge conditions, subcatchment area (a)
and forest cover % (b). The whiskers are set as the closest measured value to 1.5 interquartile
range. The horizontal lines denote the mean export per subcatchment, and their widths are
proportional to subcatchment area and, respectively, forest cover. KSCs, 2006-2009
Figure 6: LTOC (kg/ha/yr) and VWC (mg/L) vs. catchment area (a) and percentage of forest
cover (b). qlow, qmed and qhigh are plotted with solid line/ dots, dashed line/ open circles, and
dotted line/stars respectively. KSCs, 2006-2009
12
In order to examine whether the change in flow patterns influences carbon export, the
numbers of days per year with q95 were plotted against the annual LTOC and VWC
(fig. B.4). In contrast with Krycklan, whose four years long data did not indicate any
clear relationship between the above variables, for IMC there was a linearly
increasing relationship (fig.7).
Figure 7: TOC export (filled circles and solid line) and VWC (open circles and dashed line)
vs. number of days with extreme discharge. IMC1, 1989-2008.
The double mass curve analysis gave similar results after using daily and monthly data, with
the same KSCs lying above or below the 1:1 line, as indicated on fig. 8. The slope changes
were not possible to identify though when using the daily data, and also in this case the curves
appeared to lie closer to the 1:1 line (fig.8b).
Figure 8: Cumulative specific TOC flux (ΣLTOC) vs. cumulative specific discharge (Σq),
using monthly (a) and daily (b) data. The catchments are indicated with the corresponding
figures on the legend; the thick blue line shows the 1:1 position. KSCs, 2006-2009
13
According to the analysis performed using daily data and splitting the whole time
period into discharge conditions, during low flow the TOC export of all KSCs was
less than can be explained by the discharge and also different from sub-catchment to
sub-catchment (fig.9); during medium flow the exports were similar for all KSCs and
in good agreement with the 1:1 line; during high and q95 conditions some sites lay
above (with KSC3 and KSC4 being the most outstanding) and some below the 1:1
line.
Figure 9: Cumulative LTOC vs. cumulative q, based on daily data, split in four different
discharge conditions. The red dashed line shows the 1:1position, while the black lines the
KSCs (a) and IMCs (b). With arrows are indicated the most notable curves (see Discussion).
14
3.2.2 TOC export during different seasons
KSC and IMC
The KSC seasonal exports followed closely the seasonal discharge, while the VWC
followed almost the opposite trend (fig.10, table A.8). In contrast with the KSC, all
but IMC4 have lower TOC export during spring compared to winter (fig.11, table
A.9).
Figure 10: Seasonal average over all KSC sites TOC export and VWC
The stream in the Gårdsjön IMC dries out regularly during February and August.
These drying events did not result in increased VWC during the following months
(fig.11). In accordance with the flow conditions grouping, the TOC export was
different from season to season and mostly discharge dependent, while the VWC
values were less variable. The slopes of linear regression of TOC export and VWC vs.
catchment area were almost the same across the seasons, with only spring having a
smaller slope (fig. B.5), which again indicated a weaker relationship between export
and catchment characteristics during high flow dominant periods.
15
Figure 11: Monthly average TOC export (kg/ha) and volume weighted concentrations for
IMCs.
LCs
The group of LCs consists of many rivers spread over a large area of the country, the
geographic locations of which are affecting their TOC content (fig. 12). The natural
logarithm of export decreased relatively consistently with increasing latitude of
monitoring station, while TOC concentration had a large stepwise drop at 61.21˚N
(with the exception of LC3, since Råne is not a mountain river and the catchment is
forested).
Figure 12: Seasonal TOC export and concentration for different latitudes.
16
LTOC decreased with increasing catchment area as in the previous cases, but here can
also be seen the separation of LC into two subgroups, the northern LC (N-LC) and the
southern LC (S-LC) (fig. 13). The natural logarithm of average seasonal TOC export
from S-LC showed a negative linear relationship with the natural logarithm of
catchment area (R2=0.97 for spring); while N-LC were much more dispersed. Also
the TOC concentration was almost independent of area in both cases but N-LCs, again
with the exception of LC3, were dispersed and much smaller.
Figure 13: Average seasonal LTOC (a) and [TOC] (b) vs. catchment area. With dots and open
circles are denoted the monitoring sites located north and sounth of 61˚N respectively.
All the catchments (except KSC for the subcatchments of which were assumed the
same specific discharge and therefore no difference can be seen on the current plot)
had increasing TOC export with increasing average discharge (fig. 14a). The S-LCs
appeared to be the most sensitive to discharge increase and on average had the
greatest exports per unit area. Concentration dependence on the other hand could most
easily be identified for the N-LCs, while S-LC had little to negligible response (fig.14
b and d) Finally, LTOC from LCs was more strongly related to catchment area than the
smaller IMCs and KSCs (fig.14 c), but generally the increasing catchment area
resulted in decrease of both export and concentration for all size streams.
17
Figure 14: TOC export per unit area and concentration vs. average specific discharge and
catchment area. Winter (w), spring (sp), summer (s) and autumn (a) are plotted with blue,
green, red and light blue respectively.
3.2.3 TOC export per season and per discharge condition
The TOC export across KSCs varied more during low than during high discharge for
all the seasons. During low and medium discharge average exports were similar
during all seasons, except autumn with high export during medium discharge
conditions. The largest difference among the seasonal exports was determined during
high discharge (table A.10, fig.15).
18
Figure 15: TOC export (kg/ha) per season per flow condition for KSCs. The size of the circle
corresponds to the catchment area.
3.3 DIC export, KSC
Both downstream export and evasion of DIC had the same response to flow change
(fig.16). Discharge increase could explain well the amount of DIC export increase
during medium and extreme flow, while during low and high flow all the catchments
deviate from the 1:1 line.
Figure 15: Cumulative LDIC (downstream export) vs. cumulative q, split in four discharge
conditions. All the catchments and the 1:1 line are plotted with black curves and red dashed
line respectively. KSC, 2006-2009
19
The average amount of DIC downstream export per unit area was independent of
catchment area, with linear fitting p-value>0.05 (fig.17a); for evasion though
(fig.17b) the stream area was important, as for small headwater streams the export per
unit area is much larger (also according to Wallin et al., 2010). The DIC evasion was
also dependent on stream length (fig. 18).
Figure 17: Average LDIC vs. catchment area for downstream DIC export (a) and DIC evasion
(b). The p-values are given for the linear fitting of ln(LDIC) vs. ln(area). On (b) are indicated
the numbers of the three subcatchments with the largest evasion.
Figure 18: LDIC (evasion) vs. stream length
20
3.4 Interseasonal dependence of TOC export
LTOC was well estimated from the linear form of eq.(4) during all the seasons except
winter, which was not included in the analysis below.
Over the whole period of observation of KSCs and IMCs, most of the seasonal TOC
exports showed a positive dependence on the preceding season’s export (fig.19a).
Overall, 39% of the seasons with higher than average export were followed by a
season with export higher than could be estimated only using the discharge. For 33%
of the cases the seasonal export was well estimated by the linear form of eq.(4), and
for only 12% there was a negative dependence. The remaining 16% of the cases
belonged to group 4, for which neither the discharge nor the antecedent conditions
could explain the amount of TOC exported.
Splitting the results in seasons (fig.18b), most of the springs and autumns were
categorized into group 2, while most summers, when low discharge conditions were
dominant, belonged to group 1.
Figure 18: All the seasons and sites categorized into subgroups (a), and all the sites
categorized into seasons and subgroups (b). One case is regarded as the TOC export of one
season from one site. The subgroups are: (1) well estimated export, (2) positive dependence,
(3) negative dependence, (4) not a good estimate but not significant deviation of preceding
season’s export from the average.
21
4. Discussion
The first point to be discussed is if the interpolated TOC time series was acceptable.
According to the results, if there is roughly one measurement per 15 days, the
interpolated export is close enough to the actual, with approximately 3.2% error.
Using linear interpolation between the existing points though led to underestimation
of the peak export events, and generally in smoothing the dataset. This could be one
source of error, and a possible reason that the TOC export from KSCs and IMCs
appeared to have fairly different responses to the discharge increase (fig.8). The cutoff of the peaks could account for the IMCs being almost identical with the 1:1 line
during all except low discharge conditions.
It was chosen not to use the catchments with long term discharge trend, therefore only
24 LCs were examined. It would not have been difficult to de-trend the discharge
data, in this case though the identification of a relationship between TOC export and
the de-trended discharge would be misleading. It could appear for instance that the
TOC export had long term variations that were not in accordance with long term
discharge variations. On the other hand, de-trending TOC export data would not be
possible, due to the difficulty to quantify the TOC variations resulting only from
discharge variations.
The TOC export was largely dependent on discharge for all studied catchments,
which is in agreement with previous studies (e.g. Hinton et al., 1998). Especially
during q95 discharge conditions, TOC export was well explained from the discharge,
and a close to linear relationship was seen between these variables (fig.9). The very
high discharge conditions were of particular interest, as they accounted for ~30% of
discharge and TOC export (for KSCs, IMC1, IMC3) while occupying only 5% of the
observation period. Hence, an increase in the intensity or frequency of discharge
events in a potential future climate would be expected to result in linear response of
TOC export from KSCs and IMCs (fig.7).
Here must be noted though that TOC export during q95 from IMC2 and IMC4 was
much smaller as compared to the other catchments (20% and 16% of the total export
respectively), the TOC concentration of which were also reported not to be as much
discharge dependent as the other IMCs (Winterdahl et al. 2011). For the LCs it was
more difficult to identify any effect of very high discharge conditions on LTOC,
because only total monthly TOC export data was available. In contrast with IMCs
though, there was no significant relationship between the number of days with q95 per
year and the total amount of carbon exported per year.
22
The increasing trend in TOC concentration in KSCs during the four years of study
period (43% higher on 2009 than on 2006) could not be explained by long term
changes in discharge, which did not show any trend and was ~7% less on 2009 than
on 2006. This is in agreement with previous studies indicating an increasing trend of
TOC concentration in stream and lakes of Northern Europe, including Sweden, during
the last 20 years (e.g. Löfgren et al., 2010). The most supported explanation for this is
that the declining of sulphur depositions and the gradually achieved less acidic
environment leads to more efficient microbial activity and also to remobilization of
organic carbon (e.g. Lundin et al., 2001; Oulehle et al., 2011).
Regardless of long term trend, both simple and volume weighted concentrations
followed the discharge as suggested by Hinton et al. (1998) and Ågren et al. (2007): at
the two KSCs with the highest percentage of wetlands (approximately 40% and 36%)
the concentration followed a shortly increasing and then decreasing trend with
increasing discharge, while the KSCs with higher percentage of forest cover had
increasing TOC concentration with increasing discharge. This was the case for all the
catchments studied, regardless their size.
The grouping of LTOC and [TOC] according to the seasons showed again that these
two variables were mainly discharge driven. Also, the q – LTOC and q – [TOC]
relationships was similar for all the seasons, as indicated from the fact that both
export and concentration vs. discharge curves were roughly parallel across the
seasons. Other parameters that could possibly influence TOC export were not taken
into account, although the results would have been more accurately explained if
parameters like freezing of soil in the upper layers (Haei et al., 2010), flow paths
(Dosskey and Bertsch,1994) and temperature (Winterdahl et al., 2011).
Increasing catchment area and forest cover resulted in decreasing TOC export per unit
area and concentration for KSCs and IMCs, which is in agreement with previous
studies (e.g. Laudon et al., 2011; Clark et al., 2010). Apart from catchment area, the
LCs’ TOC exports were also dependent on the catchments’ geographical locations.
The southern LCs were more TOC-rich than the northern LCs, with concentrations
not deviating much from site to site and with weaker dependence on catchment area.
Put together all the catchments studied (fig.14), it was possible to identify the same
export/concentration – area trends for all the subgroups, but it was not easy to
quantitatively describe the above relationships. Comparing all the subgroups, it was
also shown that of all the catchments the LTOC of northern LCs was most sensitive to
discharge increase; keeping this in mind, together with the fact that the higher latitude
boreal regions will be more severely affected by the ongoing climate change (Laudon
et al., 2011), it’s essential to study more extensively the northern LCs in order to best
quantify the TOC cycle.
The DIC export per unit area from small head-streams was remarkably larger than
from larger streams; characteristically, during the observation period, KSC5 average
annual CO2 export per square meter was about 500 times larger than that of KSC15.
23
While the downstream LDIC did not vary significantly with the length of the stream,
the evasion was clearly dependent on it (Wallin et al., 2010). This fact points out the
need to monitor small streams in order to construct a more complete picture of the
carbon cycle. Ignoring these small but important streams would lead to
underestimation of CO2 input from surface waters into the atmosphere.
Regarding the interseasonal dependence of TOC exports, most of the cases studied
were categorized into the ‘positive dependence’ group (38% of the cases). The
positive relationship was most common during spring and autumn. Only 12 % of the
cases belonged to the ‘negative dependence’ group, therefore for the discharge and
catchment types examined here the cases of TOC depletion due to higher than average
export were highly unlucky. During summer, when the low discharge was dominant,
the export was well predicted form discharge. The fourth group included the 17% of
the studied cases. For these cases the preceding season did not have too high or too
low export, nevertheless the discharge was not a good estimator of the following
season’s export. This seems to indicate that the perceptual accumulation and flushing
model is too simple to describe the effect of antecedent conditions of the catchment.
All the parameters mentioned previously to be determining TOC concentration in
stream-water (e.g. soil temperature, frost history, organic matter availability) could be
considered as potential causes for interseasonal TOC export dependence.
An example of a catchment that could be described only by the two factors of
discharge and previous season’s TOC export is IMC1. Almost every year (from 19892008) the stream dried out twice, around February and around August, for a duration
of approximately one month (see fig.15a). On the following months there was an
abrupt increase in TOC export. Possible causes for this increase might be related to
the discharge condition change or also to accumulation of organic carbon in the soil
during the drought, which was released afterwards. On the other hand no similarly
marked increase was observed in volume weighted concentration during the following
months (fig. 15b), indicating lack of negative interseasonal dependence. The same
result was obtained by the method proposed here; during the 19 years long record of
IMC1 only the 16% of springs and 0% of autumns following the relatively low export
winters and summers indicated negative correlation.
The result from IMC1 cannot be generalized on other catchments, since there were
too many unpredicted variations from case to case and the method used here was too
general and simplistic. It could indicate though that for the IMC1 the event of around
one month long drought is not enough to accumulate so much TOC to be detected as
excess during the following month.
24
Concluding remarks
The dependence on discharge and catchment area of TOC export from streams with
catchment areas varying across wide range from 2.5·10-2 to 3.4·104 km2 was found to
be the same qualitatively, though it was not possible to quantify this dependence, as it
varied largely among the studied catchments. Even thought TOC concentration was
discharge – dependent, the long term increasing trend of concentration in all studied
catchments could not be explained by any corresponding trend in discharge. The
antecedent conditions effect on current TOC export was identified for 50% of the
studied cases, out of which 38% suggested positive and 12% negative dependence.
The existence of at least one catchment that confirmed the method used here
(estimating the TOC export based only on discharge and previous season’s export)
was encouraging, but this result can by no means be generalized over other
catchments.
25
Cited literature
Clark J.M., Bottrell S.H., Evans C.D., Monteith D.T., Bartlett R., Rose R., Newton R.J.,
Chapman P.J., 2010,The importance of the relationship between scale and process in
understanding long-term DOC dynamics, Science of the Total Environment 408 (2010)
2768–2775
Cole J. J., Prairie Y. T., Caraco N. F., McDowell W. H., Tranvik L. J., Striegl R. G., Duarte
C. M., Kortelainen P., Downing J. A., Middelburg J. J., and Melack J., 2007, Plumbing the
Global Carbon Cycle: Integrating Inland Waters into the Terrestrial Carbon Budget,
Ecosystems, Vol. 10, No. 1 (February, 2007), pp. 171-184
Dosskey, M. G. and Bertsch P. M., 1994. Forest sources and pathways of organic matter
transport to a blackwater stream: a hydrologic approach. Biogeochemistry 24: 1 – 19
Haei M., Öquist M.G., Buffam I., Ågren A., Blomkvist P., Bishop K., Ottosson Löfvenius M.,
and Laudon H., 2010, Cold winter soils enhance dissolved organic carbon concentrations in
soil and stream water, Geophysical Research Letters, vol. 37, L08501,
doi:10.1029/2010GL042821
Kling G.W., Kipphut G.W., Miller M.C., 1991, Arctic Lakes and Streams as Gas Conduits to
the Atmosphere: Implications for Tundra Carbon Budgets, Science, Vol.251 298- 301
Laudon H., Berggren M., Ågren A., Buffam I., Bishop K., Grabs T., Jansson M., and Köhler
S., 2011, Patterns and Dynamics of Dissolved Organic Carbon (DOC) in Boreal, Streams:
The Role of Processes, Connectivity, and Scaling; Ecosystems, doi: 10.1007/s10021-0119452-8
Lundin L., AAstrup M., Bringmark L., Bråkenhielm S., Hultberg H., Johansson K., Kindbom
K., Kvärnas H., Löfgren S., 2001, Impacts from deposition on Swedish forest ecosystems
identified by integrated monitoring; Water, Air and Soil Pollution 130: 1031-1036
Löfgren S., Gustafsson J. P., Bringmark L., 2010, Decreasing DOC trends in soil solution
along the hillslopes at two IM sites in southern Sweden — Geochemical modeling of organic
matter solubility during acidification recovery, Science of the Total Environment 409 201–
210
Hinton M.J., Schiff S.L. and English M.C., 1998, Sources and flowpaths of dissolved organic
carbon during storms in two forested watersheds of the Precambrian Shield, Biogeochemistry
41: 175-197
Oulehle F., Evans C.D., Hofmeister J., Krejci R., Tahovska K., Persson T., Cudlin P. and
Hruska J., 2011, Major changes in forest carbon and nitrogen cycling caused by declining
sulphur deposition, Global Change Biology 17, 3115–3129, doi: 10.1111/j.13652486.2011.02468.x
Seibert J., Grabs T., Köhler S., Laudon H., Winterdahl M., and Bishop K., 2009, Linking soiland stream-water chemistry based on a Riparian Flow-Concentration Integration Model,
Hydrol. Earth Syst. Sci., 13, 2287–2297
Schumacher B.A., April 2002, Methods for determination of total organic carbon (TOC) in
soils and sediments, NCEA-C- 1282 EMASC-001
26
Visco G., Campanella L., Nobili V., 2005, Organic carbons and TOC in waters: an overview
of the I nternational norm for its measurements, Microchemical Journal 79 185– 191
Wallin M., Buffam I., Öquist M., Laudon H., and Bishop K., 2010, Temporal and spatial
variability of dissolved inorganic carbon in a boreal stream network: Concentrations and
downstream fluxes, Journal of Geophysical Research, vol. 115, G02014, doi:
10.1029/2009JG001100
Winterdahl M., Temnerud J., Futter M.N., Löfgren S., Moldan F., Bishop K., 2011, Riparian
Zone Influence on Stream Water Dissolved Organic Carbon Concentrations at the Swedish
Integrated Monitoring Sites ,A Journal of the Human Environment 40(8):920-930, doi:
10.1007/s13280-011-0199-4
Öquist M.G., Wallin M., Seibert J., Bishop K., Laudon H., 2009, Dissolved Inorganic Carbon
Export Across the Soil/Stream Interface and Its Fate in a Boreal Headwater Stream, Environ.
Sci. Technol. 2009, 43, 7364–7369, doi: 10.1021/es900416h
Ågren A., Buffam I., Jansson M., and Laudon H., 2007, Importance of seasonality and small
streams for the landscape regulation of dissolved organic carbon export, Journal of
Geophysical Research, vol. 112, G03003, doi: 10.1029/2006JG000381
Ågren A., Jansson M., Ivarsson H., Bishop K. and Seibert J., 2007, Seasonal and runoffrelated changes in total organic carbon concentrations in the River Öre, Northern Sweden,
Aquat. Sci. 70 , 21 – 29, doi:10.1007/s00027-007-0943-9
27
Further reading
Albert J. M., 2004, Hydraulic analysis and double mass curves of the Middle Rio Grande
from Cochiti to San Marcial, New Mexico. Fort Collins, CO: Colorado State University.
Thesis.
Bishop K., Seibert J., Köhler S.and Laudon H., 2004, Resolving the Double Paradox of
rapidly mobilized old water with highly variable responses in runoff chemistry, Hydrol.
Process. 18, 185–189, DOI: 10.1002/hyp.5209
Bishop K., Buffam I., Erlandsson M., Fölster J., Laudon H., Seibert J. and Temnerud J., 2008,
Aqua Incognita: the unknown headwaters, J., Hydrol. Process. 22, 1239–1242
Brian A. Schumacher, April 2002, Methods for the determination of total organic carbon
(TOC)in soils and sediments, NCEA-C- 1282 EMASC-001
Buffam I., Laudon H., Temnerud J., Mörth C.M., and Bishop K., 2007, Landscape-scale
variability of acidity and dissolved organic carbon during spring flood in a boreal stream
network, Journal of Geophysical Research, vol. 112, G01022, doi:10.1029/2006JG000218
Buffam I., Laudon H., Seibert J., Mörthd C.M., Bishope K., 2008, Spatial heterogeneity of the
spring flood acid pulse in a boreal stream network, Science of the Total Environment
407(2008)708–722
Butman, D. and Raymond P. A., 2011, Significant efflux of carbon dioxide from streams and
rivers in the United States, Nature Geoscience, 4(12), 839-842
Eimers M. C., Buttle J., and Watmough S.A., 2008, Influence of seasonal changes in runoff
and extreme events on dissolved organic carbon trends in wetland- and upland-draining
streams1, Can. J. Fish. Aquat. Sci. 65: 796–808, doi:10.1139/F07-194
Grabs T., 2010, Water quality modeling based on landscape analysis: importance of riparian
hydrology, Dissertations from the Department of Physical Geography and Quaternary
Geology No 24
Hinton M.J., Schiff S.L. and English M.C., 1997, The significance of storms for the
concentration and export of dissolved organic carbon from two Precambrian Shield
catchments, Biogeochemistry 36: 67-88,
Hultberg, H., Hultengren, S., Grennfelt, P., Oskarsson, H., Kalén, C. & Pleijel, H. 2005, Air
pollution, environment and future. 30 years of research on forest, soils and water.
Gårdsjöstiftelsen and Naturcentrum AB.
Kling G.W., Kipphut G.W. & Miller M.C., 1992, The flux of CO2 and CH4 from lakes and
rivers in arctic Alaska, Hydrobiologia 240: 23-36
Löfgren, S. ,2007, Integrated monitoring of the environmental status in the Swedish forest
ecosystems-IM, Annual report of 2007, Department of Environmental Assessment.
Monteith D.T., Stoddard J.L., Evans C.D., de Wit H.A., Forsius M., Høgåsen T., Wilander A.,
Skjelkvåle B.L., Jeffries D.S., Vuorenmaa J., Keller B., Kopácek J. & Vesely J., 2007,
Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry,
Nature, Vol 450, doi:10.1038/nature06316
28
Payment P., M. Waite and A. Dufor, Introducing parameters for the assessment of drinking
water quality (chapter 2), Assessing Microbial Safety in Drinking Water – Improving
approaches and methods
Temnerud J., Seibert J., Jansson M., Bishop K., 2007,Spatial variation in discharge and
concentrations of organic carbon in a catchment network of boreal streams in northern
Sweden, Journal of Hydrology 342, 72– 87
Åkerblom S., Meili M., Bringmark L., Johansson K., D.B. Kleja and Bergkvist B.,
2008,Partitioning of Hg between solid and dissolved organic matter in mor layers , Water Air
and Soil Pollution , Volume: 189, Issue: 1-4, Pages: 239-252, doi: 10.1007/s11270-007-95711
29
Appendix A
Table A.1: Characteristics of KSCs
site
KSC1*
KSC2
KSC 3
KSC 4
KSC 5
KSC 6
KSC 7
KSC 9
KSC 10
KSC 12
KSC 13
KSC 14
KSC 15
KSC 16
subcatchment
area size (ha)
66
13
2.5
19
95
140
50
314
294
540
720
1285
1990
6700
stream area (ha) forest (%)
wetland (%)
0.1
0.02
10
1.5·10
3·10
0.12
0.074
0.63
0.6
0.39
1.16
2.9
10.96
15.35
1.3
0
76
40.4
36.3
24.1
14.9
13.8
25.8
15.5
9.9
5.1
14
8.3
98.7
100
24
59.6
59
72.8
85.1
84.9
74.2
84.1
89.1
90.4
83.2
88
a. Modified from Buffam et al., [2007]
* KSC=Krycklan study catchment
Table A.2: Characteristics of IMCs
Catchment area (ha)
Longitude (˚)
Latitude (˚)
()
− Open mire
Gårdsjön
(IMC1)
3.7
14.90
59.75
114-140
90.7
4.5
0
Aneboda
(IMC2)
19.0
14.50
57.01
210-240
96.3
0
0
Kindla
(IMC3)
19.1
13.18
60.28
312-415
91.4
2.0
1.3
Gammtratten
(IMC4)
45.0
18.13
63.86
410-545
92.1
1.8
4.3
a. Modified after Löfgren (2005)
Table A.3: IMC data available
Study period
Missing data
Gårdsjön
01/01/89–
31/12/08
-
Aneboda
15/01/97–
31/12/08
10/04/03–
17/05/03
30
Kindla
01/01/98–
31/12/08
10/06/9813/07/98
Gammtratten
16/06/99–
31/12/08
21/01/0102/04/01
21/12/0107/02/02
09/11/0225/04/03
Droughts
25/06/8928/09/89
01/10/8915/10/89
08/06/922708/92
13/06/9327/06/93
30/06/9309/07/93
01/07/9402/09/94
05/08/9514/09/95
20/12/9509/01/96
27/01/9612/02/96
17/02/9623/02/96
21/07/9612/09/96
17/09/9628/09/96
19/07/9726/08/97
29/08/0309/10/03
02/06/0424/06/04
07/02/0617/02/06
01/17/0809/07/08
26/07/0803/08/08
03/08/9908/08/99
25/11/9801/12/98
13/06/0128/06/01
28/02/0610/04/06
-
Table A.4: LC general characteristics and monitoring sites locations
Station name
Ref.
number*
Long. (˚)
Lat. (˚)
Catchment
area ( !" )
River length
(km)
Torne älv Mattila
LC1**
24.13
65.88
34441
510
Kalix älv Karlsborg
LC 2
23.17
65.84
23845
450
Råne älv Niemisel
LC 3
21.97
66.02
3781
215
Lule älv Luleå
LC 4
22.01
65.60
25225
460
31
Pite älv Bölebyn
LC 5
21.29
65.38
11285
400
Ume älv Stornorrfors
LC 6
20.05
63.85
26567
465
Öre älv Torrböle
LC 7
19.70
63.70
2860
240
Ångermanälven Sollefteå
LC 8
17.26
63.17
30638
460
Indalsälven Bergeforsen
LC 9
17.39
62.52
25767
430
Ljungan Skallböleforsen
LC 10
16.96
62.36
12085
400
Delångersån Iggesund
LC 11
17.09
61.63
1992
155
Ljusne Strömmar
LC 12
17.08
61.21
19820
440
Gavleån Gävle
LC 13
17.12
60.67
2453
130
Nyköpingsån Spånga
LC 14
16.92
58.83
3589
150
Motala Ström Norrköping LC 15
16.12
58.59
1587
285
Alsterån Getebro
LC 16
16.16
57.01
1333
125
Mörrumsån Mörrum
LC 17
14.75
56.18
3365
185
Skivarpsån Skivarp
LC 18
13.59
55.45
102
25
Råån Helsingborg
LC 19
12.78
56.00
166
30
Rönneån Klippan
LC 20
13.15
56.12
963
115
Nissan Halmstad
LC 21
12.87
56.69
2677
200
Viskan Åsbro
LC 22
12.31
57.24
2160
140
Örekilsälven Munkedal
LC 23
11.69
58.46
1335
90
Enningdalsälv N.Bullaren
LC 24
11.53
58.88
631
90
* This is the reference number in this project and not the real one of the station
** LC= Large Catchment
Table A.5: LTOC (kg/ha/yr) and VWC (mg/l) for different discharge conditions. KSC and
IMC
site
KSC1
KSC2
KSC3
LTOC (kg/ha/yr)
qlow qmed qhigh
2.2 9.6 49.2
2.2 8.7 47.5
6.3 32.7 83.1
q95
22.6
22.0
29.9
VWC (mg/L)
qlow qmed qhigh
13.5 17.5 21.0
13.1 15.8 20.3
28.3 43.0 35.5
32
q95
22.6
22.1
29.9
KSC4
KSC5
KSC6
KSC7
KSC9
KSC10
KSC12
KSC13
KSC14
KSC15
KSC16
IMC1
IMC2
IMC3
IMC4
5.1
3.6
2.3
2.9
1.9
2.3
2.1
2.4
1.5
1.4
3.3
1.1
7.5
1.9
1.5
19.9
13.1
9.9
13.0
8.8
10.7
9.8
10.5
6.9
6.5
3.3
8.8
16.6
6.4
6.3
67.5
51.7
46.4
57.2
43.3
49.3
46.6
49.2
35.3
32.7
17.8
58.7
43.4
29.8
42.6
23.5
22.0
20.6
23.8
19.3
20.2
19.8
21.5
14.9
14.9
15.2
26.1
13.8
12.6
30.7
31.0
21.9
13.7
17.7
12.1
13.6
12.6
14.7
8.3
8.5
6.4
11.1
29.2
8.4
6.7
36.3
23.8
18.1
23.7
16.0
19.5
17.8
19.1
11.8
11.7
10.4
11.6
21.2
7.5
9.2
28.8
22.1
19.8
24.4
18.5
21.1
19.9
21.0
14.2
13.9
13.7
12.6
19.1
7.6
10.6
23.5
22.1
20.6
23.8
19.3
20.2
19.9
21.5
14.9
14.9
15.2
13.1
20.4
7.9
10.6
Table A.6: The fraction of TOC exported during different discharge conditions and the
corresponding discharge fraction (for Krycklan the range is given).
KSC
IMC1
IMC2
IMC3
IMC4
q (% )
TOC (%)
q (% )
TOC (%)
q (% )
TOC (%)
q(% )
TOC (%)
q (% )
TOC (%)
qlow qmed
qhigh
q95
5
18
76.6
32.6
3-6 15-22 73-83 25-39
2
13
85
37
1
13
86
38
8
25
67
19
11
25
64
20
5
17
79
31
5
17
78
33
5
15
80
32
2
53
43
16
Table A.7: Average LTOC (kg/ha/season) and VWC (mg/l), KSC
winter
spring
summer
autumn
KSC:
LTOC
VWC
LTOC
VWC
LTOC
VWC
LTOC
VWC
1
6.2
16.9
26.9
20.2
10.4
21.2
14.8
21.1
2
5.8
14.9
25.6
19.2
10.3
20.9
14.6
20.8
3
12.1
37.8
36.6
27.5
23.4
47.3
33.3
47.4
4
9.7
29.3
29.5
22.1
19.1
38.7
28.5
40.6
5
8.3
25.1
28.8
21.6
10.0
20.3
16.4
23.4
33
6
6.8
18.9
25.2
18.9
9.0
18.3
14.5
20.6
7
7.5
20.7
27.8
20.9
13.9
28.2
20.5
29.2
9
5.9
15.9
22.8
17.1
9.1
18.5
13.7
19.6
10
6.1
16.4
23.4
17.6
12.8
25.9
17.4
24.8
12
5.6
14.9
22.9
17.2
11.8
23.9
15.9
22.7
13
6.6
18.3
24.5
18.4
11.8
24.0
16.1
22.9
14
4.5
10.9
18.1
12.8
8.2
15.7
11.2
15.1
15
4.5
12.1
17.1
12.8
6.7
13.5
10.4
14.8
16
3.8
9.6
17.3
12.9
6.3
12.8
10.2
14.5
Table A.8: Average LTOC (kg/ha/season), IMC
IMC1
IMC2
LTOC
winter
20.8
18.4
(kg/ha/season) spring
13.9
13.6
summer
4.9
16.4
autumn
23.6
19.8
VWC
winter
32.5
45.9
(mg/l)
spring
47.5
83.4
summer
20.5
28.9
autumn
17.7
33.6
IMC3
11.2
9.5
5.9
11.8
20.5
97.3
27.7
35.2
IMC4
3.4
16.3
14.7
11.6
17.7
46.9
19.2
22.9
Table A.9: Average LTOC (kg/ha/season/discharge condition), KSC 2006-2009
season:
discharge:
KSC1
KSC2
KSC3
KSC4
KSC5
KSC6
KSC7
KSC9
KSC10
KSC12
KSC13
KSC14
KSC15
KSC16
winter
low
0.6
0.6
2.1
1.8
1.4
0.8
0.9
0.6
0.6
0.6
0.8
0.4
0.4
0.3
med
1.8
1.5
4.4
3.5
3
2.1
2.3
1.7
1.8
1.6
2
1.2
1.2
1
high
4.9
4.4
10.3
7.8
6.7
5.4
5.9
4.7
4.8
4.4
5.3
3.5
3.7
2.9
spring
low
0.4
0.4
1.2
1.7
0.9
0.5
0.5
0.4
0.4
0.4
0.4
0.3
0.3
0.2
med
1.6
1.3
4.1
3.5
2.7
1.8
2.1
1.5
1.7
1.5
1.8
1.2
1
0.9
34
high
24.8
23.8
31.1
24.6
25.1
22.8
25.2
20.9
21.2
20.9
22.1
16.6
15.7
16.2
summer
low
med
0.7
1.6
0.8
1.7
2.3
4.1
1.3
3.5
1.0
1.8
0.6
1.5
1.0
2.3
0.6
1.5
0.8
1.9
0.8
1.8
0.8
1.8
0.5
1.3
0.5
1.1
0.4
1
high
8
7.8
16.9
14.2
7.2
6.9
10.6
7
10
9.1
9.1
6.3
5.1
5
autumn
low med
0.5
4.1
0.5
3.7
1.3
9.9
1.1
8.6
0.6
4.8
0.5
4
0.7
5.8
0.5
3.7
0.5
4.8
0.5
4.4
0.5
4.3
0.3
2.9
0.3
2.8
0.3
2.6
high
10.3
10.4
22.2
18.8
11
10
14
9.5
12
11.1
11.3
8
7.3
7.3
Appendix B
Figure B.1: Synthetic dataset of monthly average specific discharge, with increasing mean
and variance. This example illustrates that if there is a trend it is not reliable to use fixed
discharge limits over the whole dataset.
Figure B.2: Double mass curve ΣDOC-ΣQ for five different sites. The decreasing TOC
export variation among the sites with increasing discharge is notable.
35
Figure B.3: Discharge (%) during different discharge conditions vs. percentage of TOC
export. Filled circles correspond to IMC4 (Gammtratten).
Figure B.4: Dependence of TOC export and VWC on the number of days with very high
discharge. KSC 2006-2009
36
Figure B.5: Volume weighted concentration and average TOC export vs. catchment area size
for the four seasons, KSC 2006-2009.
37
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