Dissolved organic matter dynamics in the boreal
landscape mosaic: insights from Canada and
Fennoscandia
M.N. Futter, SLU Uppsala
H.J. Laudon, SLU Umeå
K.H. Bishop, SLU Uppsala
P.J. Dillon, Trent University
K. Rankinen, SYKE
D. Rayner, Göteborg University
D.N. Kothawala, Uppsala University
P.G. Whitehead, University of
Oxford
A.J. Wade, University of Reading
Talk Outline
Surface water DOC from a mosaic of forest
and mire landscape elements
INCA-C: A dynamic model of organic carbon
in a landscape mosaic
Empirical testing of the landscape mosaic
conceptual model
Organic matter quality, colour and the
landscape mosaic
Future Climates
Harp 4A Stream
Dissolved Organic Matter in a Landscape Mosaic
From Dillon and Molot (1997)
www.the-colosseum.net/mages/MosaicNilo.jpg
(c) Dolly Kothawala
Another view of the landscape
There is lots of data showing that
catchments with a larger percent
wetland export more DOM.
Points on a regression of TOC
export versus %wetland can be
interpreted as a mixing model of
TOC from forest and wetland
landscape elements.
From Dillon and Molot 1997
Export from a forest landscape
element is equal to the regression
intercept. Export from a wetland
landscape element is equal to the
regression intercept plus slope *
100% wetland.
Another view of the mosaic
The boreal landscape is comprised of forest, mire
and surface water elements.
Using the data from Dillon and Molot (1997),
DOCExport = 2.39 + 0.261 * % Wetland
Thus, DOCForest = 2.39 (2.39 + 0.261 *0)
and DOCWetland = 28.5 (2.39 + 0.261*100) g/m2/yr
Stream
Mire
Forest
Mire
Forest
Mire
Forest
Stream
Stream
INCA Landscape and biogeochemical model
The INCA modelling framework
simulates a terrestrial
biogeochemical processes in a
landscape mosaic and
subsequent surface water
processing.
Soil
From Wade et al. 2002
Direct
runoff
Soil water
Terrestrial process rates in INCA-C are positively
dependent on soil temperature and moisture.
Organic matter solubility is controlled by sulfate.
Ground
water
Stream
Controls on mass of soil solution DOC in INCA-C
 SMDMax − min (SMD, SMDMax ) 
dDOC
×
= Q (T − 20 ) 
Soil
dt




SMDMax
(k SOC − (b [SO ] + k )DOC )
2 − b1
D
0
4

q
− DOC  86400 ⋅
v r + vd

•
•
•
•
•
•
M



Soil Temperature: Q(T-20)
Soil Moisture: (SMDMax-min(SMD,SMDMax)/SMDMax
Desorption of solid organic carbon: kDSOC:
Sulfate mediated sorption: -b0[SO42-]b1DOC
Mineralization: kMDOC
Hydrologic Flux: DOC(86400 q/(vr + vd))
Modelling the mechanisms that control in-stream dissolved organic carbon
dynamics in upland and forested catchments
M. Futter, D. Butterfield, B.J. Cosby, P.J. Dillon, A.J. Wade and P.G. Whitehead
40
43
Modeled
Measured
Upper 95
Lower 95
38
33
Aug-2000
Jul-1999
Jan-2000
Jun-1998
Dec-1998
Oct-1996
Apr-1997
Nov-1997
Mar-1996
Sep-1995
Jan-1994
Feb-1995
Jun-1993
Aug-1994
Dec-1992
May-1992
Oct-1990
Apr-1991
Nov-1991
Mar-1990
Jul-1988
Feb-1989
-7
Aug-1989
-2
-10
Jan-1988
3
-5
Jun-1987
8
0
Oct-1985
13
5
Dec-1986
18
10
Apr-1985
23
15
May-1986
28
20
Sep-1984
25
Mar-1984
[DOC] mg/l
30
Prediction Range (mg/l)
35
Date
INCA-C, the Integrated Catchments model for Carbon simulates soil and surface water dissolved
organic carbon (DOC) concentrations as a function of climate, hydrology and acid deposition. The
model, which operates on a daily time step, was developed for application to natural and seminatural catchments. It has been applied to catchments in Canada, Finland, Sweden, Norway and the
UK.
The impacts of future climate change and sulphur emission reductions on
acidification recovery at Plastic Lake, Ontario
J. Aherne, M. Futter and P.J. Dillon
Changes in DOC will affect the rate at which ecosystems recover from acidification. We developed a
model chain linking downscaled GCM climate projections to a rainfall-runoff model (HBV) which drove
INCA-C projections. Modelled DOC output from INCA-C was used to drive long term MAGIC (Model of
Acidification of Groundwater in Catchments) simulations. Increasing DOC concentrations and droughtinduced mobilisation of reduced sulfur are projected to delay recovery.
1600
calibration
60.0
acid neutralising capacity
50.0
precipitation (mm)
1400
40.0
1200
30.0
1000
20.0
10.0
800
0.0
600
–10.0
400
–20.0
200
catchment runoff (mm)
0
1960
25.0
1980
2000
2020
2040
2060
2080
–30.0
–40.0
1950
2100
1975
2000
calibration
5.0
dissolved organic carbon (mg L–1)
2025
2050
2075
2100
pH (pondus Hydrogenii)
4.9
20.0
4.8
4.7
15.0
4.6
4.5
10.0
4.4
4.3
5.0
4.2
temperature (°C)
0.0
1960
1980
2000
2020
2040
2060
2080
2100
Base
Redox
4.1
4.0
1950
1975
2000
2025
2050
2075
2100
Modelling deposition and climate effects on
DOC at Valkea Kotinen
Peat #2
Forest
Peat #1
Forest
Lake
Outflow
Catchment map (upper left), lake (upper right), catchment
outflow (lower left) and INCA-C catchment representation
used in modelling
Present-Day [DOC]
20
18
16
[DOC] (mg/l)
14
12
10
8
6
4
2
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
0
120
Date
100
[DOC] (mg/l)
80
60
40
20
Date
Modelled (blue) and observed (grey) DOC in the lake (above) and catchment outflow stream
(below) were simulated using present day (1990-2007) deposition and climate.
Deposition and Climate Drivers of DOC
100
CLE
D23
MFR
90
Simulated annual sulfate deposition (meq/m2/yr )
in southern Finland under currently legislated
emissions (CLE, black), D23 (grey) and maximum
feasible reductions (MFR, black) scenarios.
2
70
60
50
40
30
20
10
0
1860
1880
1900
1920
1940
1960
1980
2000
2020
2040
2060
2080
2100
12
Year
A2
B2
10
Downscaled climate data were obtained
from the PRUDENCE project
8
0
Average T ( C)
4
2
Year
2100
2090
2080
2070
2060
2050
2040
2030
2020
2010
2000
0
1990
Precipitation (not shown) is projected to be
variable and with a small increasing trend.
6
1980
Annual average temperature
at Valkea
Kotinen under A2 (blue) and B2 (grey)
scenarios.
1970
(0C)
1960
Sulphate Deposition (meq/m )
80
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
0
Projected Daily [DOC]
Daily modelled DOC in the lake (above) and stream (below) from 19612099 using parameter set from current-day calibration, MFR deposition
scenario and SRES-A2 climate data
Drivers of Change: Deposition and Climate
100
35
90
30
80
25
Frequency
60
50
20
15
10
40
5
20
0
-3
-2.8
-2.6
-2.4
-2.2
-2
-1.8
-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
3
3.2
3.4
3.6
3.8
4
4.2
4.4
4.6
4.8
5
30
10
Delta T
0
-180
-175
-170
-165
-160
-155
-150
-145
-140
-135
-130
-125
-120
-115
-110
-105
-100
-95
-90
-85
-80
-75
-70
-65
-60
-55
-50
-45
-40
-35
-30
-25
-20
-15
-10
40
Deposition Change (meq/m2)
35
Frequency
30
Frequencies of change in deposition (above),
temperature (top right) and precipitation
(bottom right) resulting in a 1 mg/l increase in
annual modelled [DOC].
25
20
15
10
5
Delta Precip (mm)
400
380
340
360
320
300
280
260
240
220
200
180
160
80
140
60
120
100
0
40
20
-20
-40
-60
-100
-80
-120
-140
-160
0
-180
Less sulfate always leads to lower [DOC].
Modal values for climate suggest that warmer,
wetter conditions will increase [DOC].
-200
Frequency
70
Annual DOC Flux from Lake (blue) and catchment outlet (grey)
9
8
6
2
Flux g DOC/m /yr
7
5
4
3
2
1
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
2055
2060
2065
2070
2075
2080
2085
2090
2095
2100
0
Date
Modelled DOC flux from the lake and catchment outflow using the SRES B2
scenario and maximum feasible reductions of deposition (MFR). Areal exports
from the lake are lower than catchment outlet because of in-lake losses.
(Symbols represent the annual areal export and the lines are a 9-year running
mean)
Modelling seasonal and long-term patterns in stream dissolved organic carbon
concentration in mire and forest dominated landscape elements at Svartberget, Sweden
using INCA-C
M. Futter, S.J. Köhler and K.H. Bishop
An INCA-C model application was able to
capture some TOC dynamics at the mire and
catchment outflow but the overall quality of
the simulation was a concern.
The reasons for lack of fit are being
addressed through the development of
more appropriate models of stream flow
generation and soil temperature and
empirical testing of the “landscape mosaic”
conceptual model.
50
70
Modelled
Modelled
Observed
45
Observed
60
40
[DOC] (mg/)
35
40
30
30
25
20
15
20
10
10
5
Date
Date
01/2006
07/2005
01/2005
07/2004
01/2004
07/2003
01/2003
07/2002
01/2002
07/2001
01/2001
07/2000
01/2000
07/1999
01/1999
07/1998
01/1998
07/1997
01/1997
07/1996
01/1996
07/1995
01/1995
07/1994
01/1994
07/1993
0
01/1993
01/2006
07/2005
01/2005
07/2004
01/2004
07/2003
01/2003
07/2002
01/2002
07/2001
01/2001
07/2000
01/2000
07/1999
01/1999
07/1998
01/1998
07/1997
01/1997
07/1996
01/1996
07/1995
01/1995
07/1994
01/1994
07/1993
0
01/1993
[DOC] (mg/l)
50
Is there empirical support for the INCA ”landscape as a
mosaic” conceptual model ?
SVE
Site M
Kallkällsmyren
Site 4 (Kryckaln)
19 ha
60% Forest/ 40% Mire
SVV
SiteV
Västrabäcken
Site 2 (Kryckaln)
13 ha
100% Forest
SVW
Site S
Site 7 (Kryckaln)
50 ha
85% Forest/15% Mire
25
Jan 19,1994
TOC (mg/l)
20
15
There is a good steady state relationship
between TOC export and % wetland. Is there a
relationship between TOC and % wetland on
individual dates within one catchment?
y = 0.355x + 7.429
R² = 0.816
10
5
0
0
10
20
30
40
50
% Wetland
140
8
120
TOC in a landscape
mosaic at
Svartberget
7
Forest
Mire
NS
6
TOC concentration at
Svartberget can be
conceptualized as time
varying contributions from
forest and wetland landscape
elements having the same
unit runoff.
Nov-03
Oct-02
May-03
Oct-01
May-02
Oct-00
Apr-01
Oct-99
Apr-00
Oct-98
Apr-99
Oct-97
0
Apr-98
-20
Oct-96
1
Apr-97
0
Oct-95
2
Apr-96
20
Oct-94
3
Apr-95
40
Apr-94
4
Oct-93
60
Apr-93
5
Oct-92
80
Nash Sutcliffe Statistic
End Member TOC, mg/l
100
25
Jan 19,1994
Model fit is generally quite good (NS > 0.8) and there
is a clear separation between forest and wetland TOC
production.
TOC (mg/l)
20
15
y = 0.355x + 7.429
R² = 0.816
10
5
0
0
10
20
30
% Wetland
40
50
Svartberget Landscape Mosaic
model summary
70
The landscape mosaic approach is able to
reproduce the observed data from SVV
and SVE. It over-predicts TOC at SVW.
(top), suggesting some in-stream losses.
60
SVV_Pred
Modelled TOC
50
SVW_Pred
SVE_Pred
40
30
20
A clear pattern emerges for monthly
average TOC concentrations from forest
and mire landscape elements (bottom).
10
0
0
10
20
30
40
50
60
70
Observed TOC
This approach shows some value for
understanding the behaviour of other
elements, e.g. mercury
90
AvgOfForest
80
AvgOfMire
TOC (mg/l)
70
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
10
11
12
Harp Streams DOC data
Long term DOC data have been collected by Dillon and
others from a series of headwater catchments in Central
Ontario. Catchments have similar physiography but
differing amounts of wetlands. Data from these catchments
can be used to test the landscape mosiac approach.
5
4
6a
3a
6
3
Outflow
1 000 m
8
DOC concentration, 1983-1994
30
HP3 (Obs)
12
7
25
HP3A (Obs)
HP4 (Obs)
10
6
20
8
5
4
15
6
3
10
4
2
5
0
0
18-Feb-82
2
1
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
35
0
18-Feb-82
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
25
HP5 (Obs)
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
35
HP6 (Obs)
30
18-Feb-82
30
HP6A (Obs)
20
25
25
15
20
15
20
15
10
10
10
5
5
5
0
18-Feb-82
0
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
18-Feb-82
0
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
18-Feb-82
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
Forest and Wetland
End-Member DOC for Harp Streams
5
There is a clear separation between predicted DOC
concentrations exported from forest and wetland
end-members (below)
4
6a
3a
200
6
Wetland
3
180
Wood
NS
160
Outflow
1 000 m
DOC (mg / L)
140
8
DOC
6
120
100
80
60
40
4
2
20
y = 24.72x + 2.894
R² = 0.613
0
0
0
0.05
20-Mar-85
0.1
0.15
Wetland
-20
0
15-Dec-82
28-Apr-84
10-Sep-85
23-Jan-87
06-Jun-88
19-Oct-89
03-Mar-91
15-Jul-92
27-Nov-93
21
Modelled (red) and observed (blue)
DOC at Harp streams
5
4
6a
3a
6
3
Long-term DOC dynamics can be described as production
from forest and wetland landscape elements. Including
losses in surface waters (HP4) would improve model fit.
Outflow
1 000 m
30
12
HP3 (Obs)
25
HP3A (Obs)
HP3
25
10
HP4 (Obs)
HP4
20
HP3A
8
20
15
15
6
10
4
5
2
0
0
DOC (mg /L)
10
18-Feb-82
14-Nov-84
35
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
HP5 (Obs)
5
0
18-Feb-82
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
25
18-Feb-82
35
HP6 (Obs)
HP5
30
14-Nov-84
20
30
HP6
25
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
HP6A (Obs)
HP6A
25
15
20
15
20
15
10
10
10
5
5
5
0
18-Feb-82
0
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
18-Feb-82
0
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
18-Feb-82
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
CDOM is increasing in northern Europe;
is it the colour or the DOM (or both) ?
Increased colour of drinking water
supplies is a major concern in northern
Europe (eg, colour has doubled in Oslo
drinking water reservoirs).
Increased colour may be a result of
increased DOC input, or of the DOM
becoming more coloured over time.
Models able to predict changes in both
DOM quantity and quality (colour) are
needed.
From Haaland et al. 2010
Colour:DOC ratios are not constant in Dorset lakes and
streams
While there are good long-term
relationships between colour and
DOM for lakes and streams (below),
there can be large seasonal and
between site variations (left).
Assuming that colour:DOM ratios
will remain constant in the future
may not be justified.
From Dillon and Molot 1997
Observed Colour
of Harp Streams
5
Long-term colour records have been collected from the
Harp streams. Colour (in Hazen units) is a measure of the
difference in absorbance between 405-450 and 660-740 nm
(from Dillon and Molot 1997).
4
6a
3a
6
3
Outflow
1 000 m
70
400
HP3
350
140
60
300
250
120
HP3A
HP4
50
100
40
80
30
60
20
40
Colour, 1983-1994
200
150
100
10
50
20
0
0
18-Feb-82
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
500
0
18-Feb-82
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
18-Feb-82
350
450
300
400
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
500
HP6A
450
HP6
400
HP5
250
350
300
350
300
200
250
250
200
150
150
100
200
150
100
100
50
50
0
50
0
18-Feb-82
03-Jul-83
14-Nov-84
29-Mar-86
11-Aug-87
23-Dec-88
07-May-90
19-Sep-91
31-Jan-93
15-Jun-94
28-Oct-95
18-Feb-82
0
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
18-Feb-82
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
Colour: DOC Ratios from Harp forest and wetland
landscape elements
18
Colour:DOC ratios were predicted
using observed colour and endmember predicted DOC on each
observation date.
14
12
10
8
6
4
2
Wetland
Forest
0
1982
1984
1986
1988
1990
1992
30
1994
WetlandColourRatio
WoodColourRatio
25
NS
20
Nash Sutcliffe Statistic
Wetland DOC is more coloured than DOC
from forests. There are seasonal and interannual patterns (some of which are
statstically sgnificant, monthly Mann
Kendall, p<0.05) in the colour of DOC
exported from Harp forest and wetlands.
Modelled Colour:DOC Ratio
Colour:DOC ratio
16
15
10
5
0
-5
1983
0
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
Modelled (red) and observed (blue)
colour in Harp Streams
5
4
Seasonal and inter-annual colour patterns can be
simulated as a function of forest or wetland DOC and
colour:DOC ratios.
6a
3a
6
3
Outflow
1 000 m
400
Colour, 1983-1994
160
HP3
350
300
120
250
100
200
80
150
60
100
40
50
20
HP4
250
HP3A_Modelled
14-Nov-84
11-Aug-87
07-May-90
500
HP5
450
HP5_Modelled
31-Jan-93
28-Oct-95
150
100
50
0
18-Feb-82
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
350
450
HP6_Modelled
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
HP6A
HP6A_Modelled
400
250
300
18-Feb-82
500
HP6
300
400
350
HP4_Modelled
200
0
0
18-Feb-82
300
HP3A
140
HP3_Modelled
350
300
200
250
250
200
150
150
100
200
150
100
100
50
50
0
50
0
18-Feb-82
03-Jul-83
14-Nov-84
29-Mar-86
11-Aug-87
23-Dec-88
07-May-90
19-Sep-91
31-Jan-93
15-Jun-94
28-Oct-95
18-Feb-82
0
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
18-Feb-82
14-Nov-84
11-Aug-87
07-May-90
31-Jan-93
28-Oct-95
How might a changing climate affect future DOC
dynamics?
Haei et al. 2010 (in press) have
shown that spring and summer
soil solution [DOC] is a function
of soil frost in the previous
winter.
How will soil frost dynamics
change in the future ? Will soils
be colder as a result of less snow
or will there be less soil frost
due to warmer temperatures ?
1300
Downscaled climate
projections for Svartberget
1200
1100
1000
Precipitation (mm)
900
800
Rayner (in prep) has downscaled
possible future climate at Svartberget.
The precipitation downscaling routine is
considerably more sophisticated than
those used previously.
700
600
500
400
300
200
100
25
0
1960
1980
2000
2020
2040
2060
2080
2100
20
15
Precipitation (above) is projected to
increase. Temperature (right) is also
projected to increase. Modelled summer
temperatures show a small increase, the
greatest warming will occur in the winter
months.
Average Monthly T (C)
10
5
0
-5
-10
-15
-20
-25
1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Implications for Winter Precipitation
700
Winter precipitation
(October – April) is projected
to increase (MK; p<1e-4) and
a greater fraction will fall as
rain (T < 0 during
precipitation event). This will
have profound consequences
for streamflow.
600
Snow
500
400
300
200
100
2098
2094
2090
2086
2082
2078
2074
2070
2066
2062
2058
2054
2050
2046
2042
2038
2034
2030
2026
2022
2018
2014
2010
2006
2002
1998
1994
1990
1986
1982
1978
1974
1970
1966
0
1962
Oct – April Precipitation (mm)
Rain
Soil Temperature
Modelling
2
0
We developed a new model
based on Rankinen et al.
(2004) predicting soil
temperature at discrete
depths from air
temperature and
precipitation .
The model simulated snow
pack aging, heat exchange
to the surface and deep in
the profile and soil freezing
effects.
It was calibrated to winter
(Tsoil < 2 0C, NS=0.51)
conditions at Svartberget.
-1
-2
-3
Modelled
-4
Observed
01/2002
07/2001
01/2001
07/2000
01/2000
07/1999
01/1999
07/1998
01/1998
07/1997
01/1997
07/1996
01/1996
-5
Projected Soil Temperature at Svartberget
1
300
Minimum T
0
250
-1
200
-2
150
-3
100
-4
50
0
-5
1960
1980
2000
2020
2040
2060
2080
2100
Days Soil T < 0
Days < 0
Minimum Soil T (C)
07/1995
Soil Temperature at 11 cm
1
Projected Snow Dynamics at Svartberget
Days with Snow / Snow depth (SWE mm)
250
Days with Snow on Ground
Average Snow Depth (SWE, mm)
200
150
100
50
0
1960
1980
2000
2020
2040
2060
2080
2100
Projected trends in Rain on Snow Events
at Svartberget
60
Rain on Snow Events
50
40
30
20
10
0
1960
1980
2000
2020
2040
2060
2080
2100
Summary
We’re developing better tools to downscale
GCM projections and to model the effects of
climate change on biogeochemically relevant
catchment factors (eg. soil temperature, snow
cover).
This will be helpful in predicting not only
changes in DOM concentration but also
potential changes in quality (i.e. Colour).
Harp 5 Stream
We have demonstrated the value of using the
landscape mosaic (eg. forest/mire) as a
conceptual model of DOM dynamics in the
boreal.
These insights are especially useful for assessing
future threats to drinking water quality in
northern Europe.