Mrv approaches in the belarus bmu peatland


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Mrv approaches in the belarus bmu peatland

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  • The Republic of Belarus is situated in the geographical centre of Europe covering an area of about 207600 km2. It extends 560 km from north to south and 650 km from west to east. In general the country’s relief is low, with the highest point of 346 meters above the sea level. Physical and climatic conditions of Belarus provide for domination of forest and wetland ecosystems. The northern part of Belarus (Poozerye) is characterised by presence of large coniferous forests and numerous lakes, bogs and rivers. The central part is largely represented by the intensively cultivated open landscapes. The southern part (Polesye) is dominated by fens and transitional bogs, broad-leaved forests, crossed by rivers with vast highly water-logged floodplaines.
  • 11 out of 17 project sites are depleted peatlands
  • 2 sites – used for agriculture
  • large area of degraded peatlands is the reason why EE is one of the world‘s peatland emission hotspots
  • 130 for CH4, 53 for CO2, 84 for N20; Netherlands, Germany, S-Sweden, N-France
  • Mrv approaches in the belarus bmu peatland

    1. 1. <ul><li>MRV approaches </li></ul><ul><li>in the BMU Belarus peatland project </li></ul>Hans Joosten Greifswald University, Germany
    2. 2. Eastern Europe: famous for its vast and largely undisturbed peatlands... Rospuda Valley, Poland
    3. 3. Belarus has high proportion of peatlands... fens (green), bogs (red), transitional peatlands (purple): former extent ~15% of the area
    4. 4. Present area of natural peatlands: 1.5 mio ha
    5. 5. Present area of drained peatlands: 1.5 mio ha (agriculture 72%, forestry 25%, peat extraction 3%)
    6. 6. Drained peatlands are huge emittors of CO 2 + N 2 O
    7. 7. CO 2 emission Central Europe is peatland emission hot spot
    8. 8. Does rewetting reduce greenhouse gas emissions?
    9. 9. How much less emissions after rewetting?
    10. 10. BMU funded rewetting project ( 2008-2011) <ul><li>builds on GEF funded rewetting project (42,000 ha) </li></ul><ul><li>strong support of Belarusian government: </li></ul><ul><li>carbon credits </li></ul><ul><li>reduction of fires (radioactivity!)… </li></ul>
    11. 11. BMU funded rewetting project ( 2008-2011) <ul><li>Deliverables: </li></ul><ul><li>methodology for GHG assessment </li></ul><ul><li>standard for voluntary trade </li></ul><ul><li>15,000 ha rewetted and sustainably managed </li></ul><ul><li>local capacity </li></ul>
    12. 12. Measuring directly is complicated, time consuming, expensive ( € 10,000 /ha/yr)  proxy indicators
    13. 13. Mean water level is best predictor of emissions (meta-analysis of 25 site parameters in W-Europe)
    14. 14. CO 2 emissions clearly correlate with water levels: they become less with higher water levels
    15. 15. CH 4 emissions clearly correlate with water levels: they increase when higher than 20 cm - surface
    16. 16. N 2 O emissions clearly correlate with water levels: they do not occur when higher than 15 cm - surface
    17. 17. N 2 O erratic, but lower with higher water levels Leave N 2 O emissions out  conservative estimate
    18. 18. By rewetting, greenhouse gas emissions decrease, but less between – 20 cm and 0 cm
    19. 19. Emissions strongly related to water level Vegetation strongly related to water level  Use vegetation as indicator for emissions!
    20. 20. 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 1 1 2 2 3 4 5 1 2
    21. 21. Vegetation types calibrated for GHG emissions: GESTs: Greenhouse gas Emission Site Types Some examples: Water level Vegetation CH 4 CO 2 GWP 1 7 11 16.5 16.5 24 0 0 8 13 (8.5 – 16.5) 15 24 1 (0.3 – 1.7) 7 (5.0 – 9.5) 3 3.5 (2.5 – 6) 1.5 (1.3 – 2) 0 FLOODED TALL AND SHORT REEDS WET TALL SEDGE MARSHES VERY MOIST MEADOWS, FORBS & TALL REEDS VERY MOIST MEADOWS MOIST FORBS & MEADOWS MOD. MOIST FORBS & MEADOWS 6+ 5+ 4+ 4+/3+ (3+/2+) 3+ 2-, 2+, 2~
    22. 22. GESTs with indicator species groups Each GEST with typical species Each GEST with typical GHG emissions 2 <0 2 5+ Polytrichum commune Polytrichum-lawn 8 -2 10 5+ Sph. cuspidatum, Scheuchzeria Green Sphagnum hollow 3 -2 5 5+ Sph. magellanicum, Sph. rubellum, Sph. fuscum, Sph. recurvum agg. Red or green Sphagnum lawn (optimal) Glyceria maxima, Berula erecta, Bidens tripartita, B. cernua Bidens-Glyceria-reeds Typha latifolia, Phragmites, Rorippa aquatica, Lemna minor Rorippa-Typha-Phragmites-reeds Scorpidium, Eleocharis quinqueflora - Phragmites + Solanum without Urtica-gr. Solano-Phragmitetum 10 <0 / ±0 10 5+ Phragmites, Solanum dulcamara Sphagnum-Phragmites-reeds Cladium, Scorpidium Scorpidium-Cladium-reeds Equisetum fluviatile Equisetum-reeds Juncus effusus, Sphagnum recurvum agg. Sphagnum-Juncus effusus-marsh Scorpidium, Eleocharis quinqueflora - Carex (shunt) dominated Scorpi dium-Eleocharis-marsh Drepanocladus div. spec., Carex diandra, Carex rostr., Carex limosa - Carex dominated Drepanocladus-Carex-marsh Sph. recurvum agg., Carex nigra, C. curta, Eriophorum angustifolium Sphagnum-Carex-Eriophorum-marsh 12.5 <0 (±0) 12.5 5+ Sphagnum recurvum agg., Carex limosa, Scheuchzeria Sphagnum-Carex limosa-marsh GWP CO 2 CH 4 WL class Typical/differentiating species Vegetation type
    23. 23. Benefits of vegetation as a GHG proxy: <ul><li>reflects long-term water levels </li></ul><ul><li> provides indication on GHG fluxes per yr </li></ul><ul><li>is controlled by factors that control GHG emissions (water, nutrients, acidity, land use…) </li></ul><ul><li>is responsible for GHG emissions via its own organic matter (root exudates!) </li></ul><ul><li>may provide bypasses for increased CH 4 via aerenchyma (“shunt species”) </li></ul><ul><li>allows rapid and fine-scaled mapping </li></ul><ul><li> Vegetation is a more comprehensive proxy than water level! </li></ul>
    24. 24. Disadvantages of vegetation as a proxy: <ul><li>slow reaction on environmental changes: </li></ul><ul><li>~ 3 years before change in water level is reflected in vegetation (negative effect faster) </li></ul><ul><li>needs to be calibrated for different climatic and phytogeographical conditions </li></ul>
    25. 25. Vegetation forms: developed for NE Germany  test of correlations in Belarusian peatlands
    26. 26. BMU Belarus project: <ul><li>Calibration of NE German model for Belarus: </li></ul><ul><ul><li>relation vegetation ↔ water level (CIM position) </li></ul></ul><ul><ul><li>relation water level ↔ GHG emissions (CIM position) </li></ul></ul><ul><li>Completion of model (“gap filling”) </li></ul><ul><li>Consistency test with international literature </li></ul><ul><li>Development of conservative approaches </li></ul><ul><li>Selection of rewetting sites </li></ul><ul><li>Mapping of vegetation before rewetting (assessment of emission baseline ) </li></ul><ul><li>Monitor water level and vegetation development (ex-post emission monitoring) </li></ul>
    27. 27. Major gap: abandoned peat extraction sites
    28. 28. <ul><li>Perspectives of GEST-approach: </li></ul><ul><li>Ex-ante baseline assessment with ex-post evaluation </li></ul><ul><li>Fine-scaled mapping </li></ul><ul><li>Remote sensing monitoring </li></ul><ul><li>Continuous refinement with progressing GHG research </li></ul><ul><li>Addition of new modules (forest, transient dynamics) </li></ul><ul><li>Simple, cheap, reliable… </li></ul>
    29. 29. Developed with <ul><li>Jürgen Augustin (ZALF) </li></ul><ul><li>John Couwenberg (DUENE) </li></ul><ul><li>Dierk Michaelis (Uni Greifswald) </li></ul><ul><li>Merten Minke (APB / CIM) </li></ul><ul><li>Annett Thiele (APB/ CIM) </li></ul><ul><li>And many more… </li></ul>
    30. 30. info: GESTs !