Biogeochemical transformation processes along lateral fronts · Improve understanding on biogenic...
Transcript of Biogeochemical transformation processes along lateral fronts · Improve understanding on biogenic...
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?