Biogeochemical transformation processes along lateral gradients

Christoph Humborg

● Improve understanding on biogenic element inputs from the catchment

● Disentangle dynamics of terrestrial and marine derived DOM

● The role of DOC, DON and DOP on PP, pCO2, denitrification etc patterns

Urban areas

Bare areas

Cultivated land

Pastures and natural grassland

Open herbaceous vegetation with shrubs

Lichens and mosses

Cropland-woodland mosaic

Wetlands

Snow and ice

Sparse vegetation

Broadleaved deciduous closed forest

Broadleaved deciduous open forest

Mixed closed forest

Mixed open forest

Needleleaved closed forest

Needleleaved open forest

Water

Land cover of the Baltic Sea Basin

Ledwith (2003), GLC2000 project

Urban areas

Bare areas

Cultivated land

Pastures and natural grassland

Open herbaceous vegetation with shrubs

Lichens and mosses

Cropland-woodland mosaic

Wetlands

Snow and ice

Sparse vegetation

Broadleaved deciduous closed forest

Broadleaved deciduous open forest

Mixed closed forest

Mixed open forest

Needleleaved closed forest

Needleleaved open forest

Water

Land cover of the Baltic Sea Basin

Urban areas

Bare areas

Cultivated land

Pastures and natural grassland

Open herbaceous vegetation with shrubs

Lichens and mosses

Cropland-woodland mosaic

Wetlands

Snow and ice

Sparse vegetation

Broadleaved deciduous closed forest

Broadleaved deciduous open forest

Mixed closed forest

Mixed open forest

Needleleaved closed forest

Needleleaved open forest

Water

Land cover of the Baltic Sea Basin

Urban areas

Bare areas

Cultivated land

Pastures and natural grassland

Open herbaceous vegetation with shrubs

Lichens and mosses

Cropland-woodland mosaic

Wetlands

Snow and ice

Sparse vegetation

Broadleaved deciduous closed forest

Broadleaved deciduous open forest

Mixed closed forest

Mixed open forest

Needleleaved closed forest

Needleleaved open forest

Water

Land cover of the Baltic Sea Basin

Improve DOC production: Dissolved organic carbon (DOC) submodel*

litter solid organic C (M)

decom- position f(T,θ)

vegetation

DOC production

PρQ10(T–20)/10

dissolved organic C (θc)

sorbed soluble organic C (ρs)

sorption –τρKc

desorption –τρs

mineralisation µksρs

mineralisation µθc

export

DOC runoff

CO2 emission

T temperature θ volumetric water content P microbial decomposition at 20°C ρ peat bulk density s SSOC concentration Q10 modifier to account for effect of T c DOC concentration in water ks constant reducing mineralisation of SSOC K partitioning coefficient describing sorption equilibrium τ desorption kinetic constant

water flux

−0.1 0.0 0.1 0.2 0.3

Råne älv Niemisel Torne älv Mattila

Töre älv Infl.Bölträsket Kalix älv Karlsborg

Lule älv Luleå Skellefte älv Slagnäs

Alterälven Norrfjärden Pite älv Bölebyn

Rickleån Utl Ume älv Stornorrfors

Öre älv Torrböle Lögde älv Lögdeå

Gide älv Gideåbacka Ångermanälven Sollefteå

Ljusnan Funäsdalen Indalsälven Bergeforsen Ljungan Skallböleforsen

Delångersån Iggesund Gavleån Gävle

Dalälven Älvkarleby Forsmarksån Johannisfors

Norrström Stockholm Nyköpingsån Spånga

Motala Ström Norrköping Göta Älv Trollhättan

Botorpström Brunnsö Viskan Åsbro

Emån Emsfors Ätran Falkenberg Nissan Halmstad

Ljungbyån Ljungbyholm Lagan Laholm

Lyckebyån Lyckeby Mörrumsån Mörrum

Rönneån Klippan Helgeån Hammarsjön

TOC concentration trend (mgC l−1 yr−1)

Ljungbyån

0

5

10

15

20

25

30

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

TOC

(mgC

l−1)

∆ DOC production (1996-2005)−(1976-1988)

LPJ-GUESS

gC m−2 yr−1

∆ runoff DOC concentration (1996-2005)−(1976-1988)

LPJ-GUESS

mgC l−1

∆ precipitation (1996-2005)−(1976-1988)

mm yr−1

Simulated versus observed DOC concentration trends*

*Measurements: T. Wällstedt Modelling: Guy Schurgers (wetlands only)

Total catchment N retention for 117 catchments draining to the Baltic Sea calculated by combining the results from the MESAW and DAISY models.

~ +15%

~ +25%

~ +25%

Future climate scenarios

1975 2000 2025 2050 2075 2100

mean scenario (A1B) business-as-usual (A2) best case (B1)

DOC

Alka

linity

DI

C

mol yr−1

CSIM

LPJ-GUESS

RCA3

ECHAM5

emissions scenario

Runoff to Bothnian Sea

Annual fluxes (Tg) of TC, DIC and DOC. 1996-2000 compared to 2090-2095

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

BB BS BP GF GR DS KT

TC 1996-2000

TC 2090-2095

DIC 1996-2000

DIC 2090-2095

DOC 1996-2000

DOC 2090-2095

Terrestrial source (end member) δ13C = -28 ‰

2) Challenge: Separate the terrestrial DOM from marine produced DOM

δ13C: (‰)

-21 -28

δ13C = -25 ‰ 57 % terrestrial DOC 43 % marine

Marine source (end member) δ13C = -21 ‰

Share of terrestrial DOC

Bothnian Bay: 70-83% Bothnian Sea: 52-55% Baltic Proper: 43-50% Oder Bight: 60-72%

5 10 15 20 25 30Long

52

54

56

58

60

62

64

66

68

Lat

43%

50%

52%

60-72%

55%

70%66%

83%

Hypotheses based on studies on DOCter inferred from isotope signatures

● At salinities >2 the DOCter

distribution along the

Baltic seems to be

determined mainly by

mixing.

● Most of the DOCter removal

(~50%) seems to occur in

the estuaries.

WEGAS on board the German research vessel METEOR 87 (May/June 2012)

Conceptual model

Expected isotope patterns for δ13C in CO2 and δ18O-O2 dissolved in water as a result of the different processes that drive the fractionation of the isotopes in the different pools.

River input DOC/DIC CO2≈-12.5‰

Respiration ≈-27‰

Salinity=1.5 PSU Alk=0.62 mmol/L DIC=0.65 mmol/L DOC=0.4 mmol/L PCO2=630 ppm

Salinity=0 PSU Alk=0.21 mmol/L DIC=0.25 mmol/L DOC=0.5 mmol/L PCO2=800 ppm

Production, isotope fractionation -13‰

Needed respiration to maintain isotope value is ≈32 mmol m-2 per day

CO2sys gives air sea flux of ≈ 20 mmol m-2 degassing per day.

00,10,20,30,40,50,60,70,80,9

1 2 3 4 5 6 7

DOC t

er (m

oles

/m3 )

Station

REM

Deutsch et al. 2012

1 2

3 4

5

6

7

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

1 2 3 4 5 6 7

DO

C ter

(mol

es/m

3 )

Station

9.1

Deutsch et al. 2012

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

1 2 3 4 5 6 7

DO

C ter

(mol

es/m

3 )

Station

2.1

Deutsch et al. 2012

100% refractory

20% refractory, 80% labile

100% labile

Filllipa Franser, MISU

100% labile 20% refractory, 80% labile

Mean (water column) removal rate in tons/year

Meridional gradients of seasonal dynamics (statistics for 1970-2006)

Simulation Observations Bothnian Bay

Gotland Sea

Central Kattegat

Phosphate, µM

Phosphate, µM

Phosphate, µM

Simulation Observations Bothnian Bay

Gotland Sea

Central Kattegat

Nitrate, µM

Nitrate, µM

Nitrate, µM

Meridional gradients of seasonal dynamics (statistics for 1970-2006)

Simulated vs. observed primary production (the simulated are systematically lower)

BB BS BPa

Annual integrals (g C m-2 yr-1) simulated over two periods (mean ± s.d.) vs. literature data

1970/82 4.7 ± 0.8 18 ± 3 52 ± 10

Data 12-20i 50-70i 40-140d

1994/06 4.6 ± 0.6 23 ± 3 98 ± 16

Data 16j – 17i 32j – 52i 65j – 200i

Table 2 Primary production in the major Baltic Sea basins, simulated with BALTSEM and compiled from published estimates for different time intervals a – aggregation weighted with basin areas; b – month of blooming in the model; c – Dahlgren et al., 2010; d – Renk and Ochocki, 1999; e –Lignell, 1990; f – Silina, 1967; g – Savchuk, 2002 and references therein; h – Rydberg et al., 2006; i- Wasmund et al., 2001a and references therein; j – median value from Larsson et al., 2010; k – Raateoja et al., 2004; l - Carstensen et al., 2003 Generally, both ERGOM and SCOBI give even lower simulated rates of PP!

Hypoxia effects on nitrogen pool in the Baltic Proper. Relationships between annual means of simulated and reconstructed from observations hypoxic volume and DIN pool (A) or reconstructed DIN pool with a 2 year delay and simulated bioavailable nitrogen (B) Dalsgaard et al. GCA 2013: “When extrapolated to the entire Baltic Proper (BP) denitrification in the water column was in the range of 132–547 kton N yr1 and was thus at least as important as sediment denitrification which has recently been estimated to 191 kton N yr1.“

Aim: Indicator development based on the generation of high-resolution functional databases throughout the Baltic Sea based on metagenomics.

BONUS call 2012: Viable ecosystem Project: Biological lenses using gene prints

(BLUEPRINT)

Modified from

: Matthew

Vaughn

Outcome: A publicly available resource with the capacity to deduce environmental status and dominant biogeochemical pathways from the biodiversity and genetic functional profiles of microbes, the blueprint, in a seawater sample.

Indicative blueprints

Chial, H. (2008)

Bioinformatic evaluation

WP5: Incorporation of BLUEPRINT in biogeochemical modeling

Outlook Develop more realistic catchment (hydrological and land surface) models -> DOM and nutrient retention are the big unknowns! Coastal degradation processes appear significant-> how much reaches the open Baltic? To develop a general model to quantitatively link CO2 and CH4 fluxes from aquatic ecosystems to primary productivity, degradation and outgassing using new isotope techniques –>how much of the terrestrial respiration actually occurs in the sea? Shed light on DOM dynamics in the Baltic Sea-> how it contributes to nutrient dynamics and PP? DOM dynamics along the estuarine gradient in the Baltic play a similar important role as dissolved nutrient dymanamics but is poorly parameterized in the models -> new microbiological tools needed?