This document discusses proxies for greenhouse gas fluxes from peat soils, including subsidence, water table depth, and vegetation. It notes that while subsidence indicates carbon emissions, it does not cover nitrous oxide and methane or drained situations. Water table depth can be difficult to map remotely and monitor directly. Vegetation groups can serve as indicators of mean water levels. The document provides an example of how changes in vegetation from drainage to rewetting of a bog could lead to reduced greenhouse gas emissions.

1. GEST
Greenhouse Emissions Site Types
• The ‘emissions avoided’ model developed by
John Couwenberg et al. at University of
Greifswald.
• Couwenberg et al., 2011, Hydrobiologia.

2. Using a Proxy for GHG fluxes
• Direct methods
– chamber measurements –
– Micro-meteorology measurements–
• Indirect method
– Use a proxy that indicates ghg fluxes

3. Possible Proxies
Three parameters currently emerge as suitable proxies for GHG
fluxes from peat soils:
• Subsidence
• Water level
• Vegetation

4. Subsidence
CONS:
Subisdence (cma-1)
1. hardly covertable into CO2 emission, due
to shrinkage and compaction
2. not an indicator for N2O and CH4
3. only for drained situations
Drainage depth (cm)

5. Water table depth
n = 99flooded site & lysimeter
O - - collated from Augustin
(2003, unpubl.), Augustin &
experiments of Mundel (1976)
Carbon dioxide
Methane Merbach (1998), Augustin et al.
•-
(1996), Bortoluzzi et al. (2006),
based on long term
Drösler (2005), Hendriks et al.
subsidence measurements
(2007), Jacobs et al. (2003), Meyer
(1999),den Akker et al., 2008; et
(van Müller (1999), Sommer
•
al. (2003), Tauchnitz et al. (2008),
Van den Boset al., 2009) den Pol-
Verhagen (2003), Van -
Van Dasselaar et al. (1999), Van
representing NEP
Huissteden et al. (2006), Von
measurements corr. for
Arnold (2004), Wild et al. (2001).
harvest export (Augustin
CONS: unpubl., Bortoluzzi et al. 2006,
Drösler 2005, Flessa et al.
1998, Jacobs et al. 2003, Meyer
1. mapping of water level via remote sensing
1999, Müller et al. 1997,
Mundel 1976, Nieveen et al.
1998, Veenendaal et al. 2007.
is so far impossible
2. direct monitoring is very labour-intensive

6. Vegetation
Emissions strongly related to water level
and
Vegetation can be strongly related to water level –e.g. Ellenberg moisture
values

7. - Species groups  presence/absence as indicator for mean water levels
- Initially NE German metadata, country-specific refinement in Belarus &
Ukraine peatlands
site factor gradient - moisture
species groups
Moisture classes 1 2 3 4 5
subunits 1 2 1 2

8. A simpe GEST example
Re-wetting Bog
-13.5t CO2-eq.·ha-1·a-1
GEST moist forbs & very moist wet peatmoss
meadows peatmoss lawn lawn
Vegetation acidophilous Sphagnum green Sphagnum
Molinia-meadow Eriophorum lawn lawn (optimal)
water level* -15 … -45 +10 … -20 +10 … -10
CH4 1.5 0.5 5
CO2 15 -2 -2
GWP 16.5 -1.5 3
*median of the dry and wet season relative to the surface, cm

9. Rewetting open fen
Future emissions avoided
Year 0 Year 10 Year 20
Baseline Scenario
Moderately dry Moderately dry Moderately dry
cultivated cultivated cultivated
peatland peatland peatland
24 24 24
-8.5/-15.5 8.5/-15,5
Rewetting Scenario
Moderately dry Very moist reeds/ Very moist reeds/
Moderately dry
cultivated peatland Very moist & sedge Very moist
Wet reeds & sedge
Wet reeds
cultivated reeds and Wet fens and Wet
reeds
24 fens
peatland reed/sedge fens reeds/sedge
15.5/8.5 15.5/8.5
24 fens

10. Metadata Initial values for
Indicator avoided emissions
species/communities = Baseline - Project
with water table depth
Final value for
Local refinement avoided emissions
Measuring
Species/communities
Water depth Emissions accounting
mechanisms, auditing
etc