MRV approaches in the BMU Belarus peatland project Hans Joosten Greifswald University, Germany

Eastern Europe: famous for its vast and largely undisturbed peatlands... Rospuda Valley, Poland

Belarus has high proportion of peatlands... fens (green), bogs (red), transitional peatlands (purple): former extent ~15% of the area

Present area of natural peatlands: 1.5 mio ha

Present area of drained peatlands: 1.5 mio ha (agriculture 72%, forestry 25%, peat extraction 3%)

Drained peatlands are huge emittors of CO 2 + N 2 O

CO 2 emission Central Europe is peatland emission hot spot

Does rewetting reduce greenhouse gas emissions?

How much less emissions after rewetting?

BMU funded rewetting project ( )  builds on GEF funded rewetting project (42,000 ha)  strong support of Belarusian government:  carbon credits  reduction of fires (radioactivity!)…

BMU funded rewetting project ( ) Deliverables:  methodology for GHG assessment  standard for voluntary trade  15,000 ha rewetted and sustainably managed  local capacity

Measuring directly is complicated, time consuming, expensive ( € 10,000 /ha/yr)  proxy indicators

Mean water level is best predictor of emissions (meta-analysis of 25 site parameters in W-Europe)

CO 2 emissions clearly correlate with water levels: they become less with higher water levels

CH 4 emissions clearly correlate with water levels: they increase when higher than 20 cm - surface

N 2 O emissions clearly correlate with water levels: they do not occur when higher than 15 cm - surface

N 2 O erratic, but lower with higher water levels Leave N 2 O emissions out  conservative estimate

By rewetting, greenhouse gas emissions decrease, but less between – 20 cm and 0 cm

Emissions strongly related to water level Vegetation strongly related to water level  Use vegetation as indicator for emissions!

In an environmental gradient some plant species occur together; others exclude each other. Species groups (and their absence!) indicate site conditions much sharper than individual plant species: “vegetation forms”. site factor gradient species groups site factor classes subunits

Vegetation types calibrated for GHG emissions: GESTs: Greenhouse gas Emission Site Types Some examples: 2-, 2+, 2~(3+/2+) 3+4+/ MOD. MOIST FORBS & MEADOWS MOIST FORBS & MEADOWS VERY MOIST MEADOWS VERY MOIST MEADOWS, FORBS & TALL REEDS WET TALL SEDGE MARSHES FLOODED TALL AND SHORT REEDS 01.5 (1.3 – 2) 3.5 (2.5 – 6) 37 (5.0 – 9.5) 1 (0.3 – 1.7) (8.5 – 16.5) Water level Vegetation CH 4 CO 2 GWP

Vegetation typeTypical/differentiating species WL class CH 4 CO 2 GWP Sphagnum-Carex limosa- marsh Sphagnum recurvum agg., Carex limosa, Scheuchzeria <0 (±0) 12.5 Sphagnum-Carex- Eriophorum-marsh Sph. recurvum agg., Carex nigra, C. curta, Eriophorum angustifolium Drepanocladus-Carex-marshDrepanocladus div. spec., Carex diandra, Carex rostr., Carex limosa - Carex dominated Scorpidium-Eleocharis-marshScorpidium, Eleocharis quinqueflora - Carex (shunt) dominated Sphagnum-Juncus effusus- marsh Juncus effusus, Sphagnum recurvum agg. Equisetum-reedsEquisetum fluviatile Scorpidium-Cladium-reedsCladium, Scorpidium Sphagnum-Phragmites-reedsPhragmites, Solanum dulcamara 5+10 <0 / ±0 10 Solano-PhragmitetumScorpidium, Eleocharis quinqueflora - Phragmites + Solanum without Urtica-gr. Rorippa-Typha-Phragmites- reeds Typha latifolia, Phragmites, Rorippa aquatica, Lemna minor Bidens-Glyceria-reedsGlyceria maxima, Berula erecta, Bidens tripartita, B. cernua Red or green Sphagnum lawn (optimal) Sph. magellanicum, Sph. rubellum, Sph. fuscum, Sph. recurvum agg Green Sphagnum hollowSph. cuspidatum, Scheuchzeria Polytrichum-lawnPolytrichum commune 5+2<02 GESTs with indicator species groups Each GEST with typical species Each GEST with typical GHG emissions

Benefits of vegetation as a GHG proxy: reflects long-term water levels  provides indication on GHG fluxes per yr is controlled by factors that control GHG emissions (water, nutrients, acidity, land use…) is responsible for GHG emissions via its own organic matter (root exudates!) may provide bypasses for increased CH 4 via aerenchyma (“shunt species”) allows rapid and fine-scaled mapping  Vegetation is a more comprehensive proxy than water level!

Disadvantages of vegetation as a proxy: slow reaction on environmental changes: ~3 years before change in water level is reflected in vegetation (negative effect faster) needs to be calibrated for different climatic and phytogeographical conditions

Vegetation forms: developed for NE Germany  test of correlations in Belarusian peatlands

BMU Belarus project: Calibration of NE German model for Belarus: –relation vegetation ↔ water level (CIM position) –relation water level ↔ GHG emissions (CIM position) Completion of model (“gap filling”) Consistency test with international literature Development of conservative approaches Selection of rewetting sites Mapping of vegetation before rewetting (assessment of emission baseline ) Monitor water level and vegetation development (ex-post emission monitoring)

Major gap: abandoned peat extraction sites

Perspectives of GEST-approach: Ex-ante baseline assessment with ex-post evaluation Fine-scaled mapping Remote sensing monitoring Continuous refinement with progressing GHG research Addition of new modules (forest, transient dynamics) Simple, cheap, reliable…

Developed with Jürgen Augustin (ZALF) John Couwenberg (DUENE) Dierk Michaelis (Uni Greifswald) Merten Minke (APB / CIM) Annett Thiele (APB/ CIM) And many more…

info: GESTs!