Dissolved organic matter dynamics in the boreal landscape … · 2018. 1. 19. · P.J. Dillon, Trent University K. Rankinen, SYKE D. Rayner, Göteborg University D.N. Kothawala, Uppsala

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

Harp 4A Stream

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

Dissolved Organic Matter in a Landscape Mosaic

(c) Dolly Kothawala

www.the-colosseum.net/mages/MosaicNilo.jpg

From Dillon and Molot (1997)

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.

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 landscape

From Dillon and Molot 1997

Stream

Fo

rest

Mire

Stream

Fo

rest

Mire

Stream

Fo

rest

Mire

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

INCA Landscape and biogeochemical model

From Wade et al. 2002

Soil Stream

Direct

runoff

Soil water

Ground

water

The INCA modelling framework

simulates a terrestrial

biogeochemical processes in a

landscape mosaic and

subsequent surface water

processing.

Terrestrial process rates in INCA-C are positively

dependent on soil temperature and moisture.

Organic matter solubility is controlled by sulfate.

Controls on mass of soil solution DOC in INCA-C

• 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))

( ) ( )

[ ]( )( )

+⋅−

+−

×

−=

−

−

dr

M

b

D

Max

MaxMaxT

vv

qDOC

DOCkSObSOCk

SMD

SMDSMDSMDQ

dt

dDOCSoil

86400

,min

12

40

20

Modelling the mechanisms that control in-stream dissolved organic carbon

dynamics in upland and forested catchmentsM. Futter, D. Butterfield, B.J. Cosby, P.J. Dillon, A.J. Wade and P.G. Whitehead

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 semi-

natural catchments. It has been applied to catchments in Canada, Finland, Sweden, Norway and the

UK.

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Date

[DOC] mg/l

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Prediction Range (mg/l)

Modeled

Measured

Upper 95

Lower 95

The impacts of future climate change and sulphur emission reductions on

acidification recovery at Plastic Lake, OntarioJ. 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 drought-

induced mobilisation of reduced sulfur are projected to delay recovery.

calibration

calibration pH ( )pondus Hydrogenii

acid neutralising capacity

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BaseRedox

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precipitation (mm)

catchment runoff (mm)

temperature (°C)

dissolved organic carbon (mg L )–1

Modelling deposition and climate effects on

DOC at Valkea Kotinen

Lake

Forest

Peat #1

Outflow

Forest

Peat #2

Lake

Forest

Peat #1

Lake

Forest

Peat #1

Outflow

Forest

Peat #2

Outflow

Forest

Peat #2

Catchment map (upper left), lake (upper right), catchment

outflow (lower left) and INCA-C catchment representation

used in modelling

Present-Day [DOC]

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[DOC] (mg/l)

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[DOC] (mg/l)

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

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Year

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CLE

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MFR

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Average T (0C)

A2

B2

Downscaled climate data were obtained

from the PRUDENCE project

Annual average temperature (0C) at Valkea

Kotinen under A2 (blue) and B2 (grey)

scenarios.

Precipitation (not shown) is projected to be

variable and with a small increasing trend.

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.

Projected Daily [DOC]

Daily modelled DOC in the lake (above) and stream (below) from 1961-

2099 using parameter set from current-day calibration, MFR deposition

scenario and SRES-A2 climate data

Drivers of Change: Deposition and Climate

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Delta T

Frequency

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Deposition Change (meq/m2)

Frequency

Frequencies of change in deposition (above),

temperature (top right) and precipitation

(bottom right) resulting in a 1 mg/l increase in

annual modelled [DOC].

Less sulfate always leads to lower [DOC].

Modal values for climate suggest that warmer,

wetter conditions will increase [DOC].

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Date

Flux g DOC/m

2/yr

Annual DOC Flux from Lake (blue) and catchment outlet (grey)

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-CM. Futter, S.J. Köhler and K.H. Bishop

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[DOC] (mg/l)

Modelled

Observed

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Date

[DOC] (mg/)

Modelled

Observed

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.

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) SVW

13 ha Site S

100% Forest Site 7 (Kryckaln)

50 ha

85% Forest/15% Mire

y = 0.355x + 7.429

R² = 0.816

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10

15

20

25

0 10 20 30 40 50

TOC

(m

g/l

)

% Wetland

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?

Jan 19,1994

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r-0

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r-0

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t-0

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y-0

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t-0

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y-0

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v-0

3

Na

sh S

utc

liff

e S

tati

stic

En

d M

em

be

r TO

C,

mg

/l

Forest

Mire

NS

Model fit is generally quite good (NS > 0.8) and there

is a clear separation between forest and wetland TOC

production.

TOC in a landscape

mosaic at

Svartberget

y = 0.355x + 7.429

R² = 0.816

0

5

10

15

20

25

0 10 20 30 40 50

TOC

(m

g/l

)

% Wetland

Jan 19,1994

TOC concentration at

Svartberget can be

conceptualized as time

varying contributions from

forest and wetland landscape

elements having the same

unit runoff.

Svartberget Landscape Mosaic

model summary

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10

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30

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Mo

de

lle

d T

OC

Observed TOC

SVV_Pred

SVW_Pred

SVE_Pred

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30

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50

60

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90

1 2 3 4 5 6 7 8 9 10 11 12

TO

C (

mg

/l)

AvgOfForest

AvgOfMire

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.

A clear pattern emerges for monthly

average TOC concentrations from forest

and mire landscape elements (bottom).

This approach shows some value for

understanding the behaviour of other

elements, e.g. mercury

Harp Streams DOC data

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18-Feb-82 14-Nov-84 11-Aug-87 07-May-90 31-Jan-93 28-Oct-95

HP3 (Obs)

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HP5 (Obs)

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HP6 (Obs)

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HP6A (Obs)

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3a

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6a

5

Outflow1 000 m

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.

DO

C c

on

cen

tra

tio

n,

19

83

-19

94

Forest and Wetland

End-Member DOC for Harp Streams

21

4

3a

36

6a

5

Outflow1 000 m

0-20

0

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40

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100

120

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160

180

200

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

Wetland

Wood

NS

There is a clear separation between predicted DOC

concentrations exported from forest and wetland

end-members (below)

DO

C (

mg

/ L

)

y = 24.72x + 2.894

R² = 0.6130

2

4

6

8

0 0.05 0.1 0.15

DO

C

Wetland20-Mar-85

Modelled (red) and observed (blue)

DOC at Harp streams

0

5

10

15

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25

30


HP3 (Obs)

HP3

0

2

4

6

8

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HP3A (Obs)

HP3A

0

5

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25


HP4 (Obs)

HP4

0

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HP5 (Obs)

HP5

0

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25


HP6 (Obs)

HP6

0

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30

35


HP6A (Obs)

HP6A

4

3a

36

6a

5

Outflow1 000 m

DO

C (

mg

/L)

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.

CDOM is increasing in northern Europe;

is it the colour or the DOM (or both) ?

From Haaland et al. 2010

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 Dillon and Molot 1997

Colour:DOC ratios are not constant in Dorset lakes and

streamsWhile 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.

Observed Colour

of Harp Streams

0

50

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150

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350

400


HP3

0

10

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30

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HP3A

0

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40

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120

140


HP4

0

50

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500

18-Feb-8203-Jul-8314-Nov-8429-Mar-8611-Aug-8723-Dec-8807-May-9019-Sep-9131-Jan-9315-Jun-9428-Oct-95

HP5

0

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350


HP6

0

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HP6A

4

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36

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5

Outflow1 000 m

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).

Co

lou

r, 1

98

3-1

99

4

Colour: DOC Ratios from Harp forest and wetland

landscape elements

0-5

0

5

10

15

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25

30

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994

Na

sh S

utc

liff

e S

tati

stic

Mo

de

lle

d C

olo

ur:

DO

C R

ati

o

WetlandColourRatio

WoodColourRatio

NS

0

2

4

6

8

10

12

14

16

18

1982 1984 1986 1988 1990 1992 1994

Wetland

Forest

Wetland DOC is more coloured than DOC

from forests. There are seasonal and inter-

annual patterns (some of which are

statstically sgnificant, monthly Mann

Kendall, p

Modelled (red) and observed (blue)

colour in Harp Streams

0

50

100

150

200

250

300

350

400


HP3

HP3_Modelled

0

20

40

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120

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160


HP3A

HP3A_Modelled

0

50

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150

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250

300


HP4

HP4_Modelled

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450

500

18-Feb-8203-Jul-8314-Nov-8429-Mar-8611-Aug-8723-Dec-8807-May-9019-Sep-9131-Jan-9315-Jun-9428-Oct-95

HP5

HP5_Modelled

0

50

100

150

200

250

300

350


HP6

HP6_Modelled

0

50

100

150

200

250

300

350

400

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500


HP6A

HP6A_Modelled

4

3a

36

6a

5

Outflow1 000 m

Co

lou

r, 1

98

3-1

99

4

Seasonal and inter-annual colour patterns can be

simulated as a function of forest or wetland DOC and

colour:DOC ratios.

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 ?

How might a changing climate affect future DOC

dynamics?

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1960 1980 2000 2020 2040 2060 2080 2100

Pre

cip

ita

tio

n (

mm

)

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-5

0

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10

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20

25

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Av

era

ge

Mo

nth

ly T

(C

)

Downscaled climate

projections for Svartberget

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.

Rayner (in prep) has downscaled

possible future climate at Svartberget.

The precipitation downscaling routine is

considerably more sophisticated than

those used previously.

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19

62

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66

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98

Rain

Snow

Implications for Winter Precipitation

Winter precipitation

(October – April) is projected

to increase (MK; p

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-4

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7/1

99

5

01

/19

96

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/19

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02

So

il T

em

pe

ratu

re a

t 1

1 c

m

Modelled

Observed

Soil Temperature

Modelling

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.

0

50

100

150

200

250

300

-5

-4

-3

-2

-1

0

1

1960 1980 2000 2020 2040 2060 2080 2100

Da

ys

So

il T

< 0

Min

imu

m S

oil

T (

C)

Minimum T

Days < 0

Projected Soil Temperature at Svartberget

0

50

100

150

200

250

1960 1980 2000 2020 2040 2060 2080 2100

Days with Snow on Ground

Average Snow Depth (SWE, mm)

Projected Snow Dynamics at SvartbergetD

ays

wit

h S

no

w /

Sn

ow

de

pth

(S

WE

mm

)

Projected trends in Rain on Snow Events

at Svartberget

0

10

20

30

40

50

60

1960 1980 2000 2020 2040 2060 2080 2100

Ra

in o

n S

no

w E

ven

ts

Summary

Harp 5 Stream

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).

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.

Documents

Dissolved organic matter dynamics in the boreal landscape … · 2018. 1. 19. · P.J. Dillon, Trent University K. Rankinen, SYKE D. Rayner, Göteborg University D.N. Kothawala, Uppsala