REDD-PAC in the Congo Basin

Transcription

1 REDD-PAC in the Congo Basin Aline Mosnier, Martin Tadoum, Peguy Tonga, Johannes Pirker, Adeline Makoudjou, Roland Gyscard Ngonga, Didier Bokelo REDD-PAC steering committee meeting, 14 th October 2014, Laxenburg, Austria

2 1. Timeline

3 Timeline Kick-off meeting in the Congo Basin 3

4 Timeline Regional workshop with climate and biodiversity focal points Recruitment of experts 1 st REDD-PAC school in Douala 4

5 Timeline 2d REDD-PAC school in Douala 3rd REDD- PAC school at IIASA Apr May June July Aug Sept Oct Project midterm meeting in Douala 5 Death of Eustache Awono, expert for Cameroon Recruitment of Adeline Makoudjou, new expert for Cameroon

6 2. Project setup in the Congo Basin 6

7 Geographical scope 10 COMIFAC countries 4 pilot countries initially: DRC, Congo, Cameroon and CAR Due to the conflict, low modeling capacity, and absence of statistics, CAR is not a pilot country anymore 7

8 Biodiversity focal points Institutional setting UNEP- WCMC IIASA COMIFAC Expert in Cameroon 1 REDD-PAC position Expert in DRC Expert in Congo Expert in CAR Climate focal points REDD+ National Coordination team REDD+ National Coordination team REDD+ National Coordination team 8

9 3. Improvements of GLOBIOM-Congo Basin 9

10 Land cover map From GLC, no cropland in Bandundu and Katanga, 2 important agricultural regions! Need to find another land cover map 10 Source: GLC2000

11 Land cover map 4 land cover maps available for Congo Basin: MODIS, GlobCover, GLC2000 and UCL Reclassification of initial land cover classes into the same 7 land cover classes to facilitate comparison REDD-PAC Geo-Wiki and simplified interface of Quantum GIS with land cover maps distributed to experts In April 2014, final choice of land cover classes to be used in the Congo Basin in GLOBIOM and the land cover maps to be used Different land cover maps can be used for each land cover class Different land cover maps can be used in different regions Hybrid approach to compute a new land cover map using the best information available 11

12 Land cover map The sum of land cover classes in a pixel should be equal to the total land area in the pixel 12

13 Hybrid land cover map Cropland Humid forests Dry forests 13

14 National statistics Crops Available harvested area and production at the second administrative level for Cameroon (departments) and DRC (districts) Available production at the second administrative level for DRC (districts) and some limited information for yields at the province level Some limited information for production at the first administration unit level in Congo Number of livestock heads Volumes of harvested timber 14

15 CROPLAND Harmonization of land cover map with national statistics = sum of all crops PASTURE =sum of all ruminants needs Cross-entropy approach to allocate harvested area by crop to simulation unit level on available cropland Takes into account need for fallow Calculation of prior depends on transportation costs, population density and productivity potential/suitability 15 Name - Title 14/10/2014

16 Harvested area by crop in DRC Cassava Corn Rice Groundnut 16 Potatoes Sweet potatoes

17 Harvested area by crop in Cameroon Beans Cassava Corn Groundnuts Millet- Sorghum Oil Palm Potatoes Sweet Potatoes 17

18 Oil palm suitability map Green: very suitable Red: marginally suitable 18

19 Model improvements Representation of subsistence agriculture: introduction of fallow time which depends on the evolution of population density (dynamic) Fuel wood can come from 3 sources leading to different impacts on forest cover (no impact/degradation/deforestation) Fuel wood demand Shifting agriculture Natural forests Managed Forests 19

20 Model improvements Better representation of current agricultural systems 20

21 4. Preliminary results NB: These results have been obtained before last improvements in land cover map and harvested crop area maps 21

22 BAU- Deforestation Results: Simulated deforestation over (in million ha/year) using different GDP growth projections Historical deforestation : 0.36 Mha/year 22

23 BAU- Emissions from deforestation and forest degradation using different GDP growth and different carbon biomass maps Uncertainty in future emissions due to carbon biomass is the highest 23

24 Deforestation risk in REDD+ projects area BAU deforestation rate is almost 2 times higher in REDD+ project areas than the national average in the next two decades In Mai Ndombe 24

25 25 5. Next steps

26 Scenarios and policy options From the work in the Atlas on the synergies between REDD+ and biodiversity targets, the key issues in the Congo Basin are: Reforestation and forest restauration Sustainable management of forest concessions Forest conservation including through protected areas Test REDD+ policies which could provide incentives to encourage these practices or compute the opportunity costs of implementing targets on these objectives 26

27 Scenarios and policy options Most of the Congo Basin countries want to become emerging countries by 2020: Development of agro-industrial plantations e.g. oil palm Expand infrastructure network Achieve food security Development of mining activities Investment in hydro-electricity Increase competitiveness of forest sector Investigate how some of these objectives would impact future forest cover, conservation opportunity costs and if they could be compatible with REDD+ policies 27

28 Spatial scale of the results Reference level computed at: National level Province level Biome level REDD+ pilot project area Flexible: the model is solved at the 50x50km grid resolution level (nb of hectares in each grid) and can then be aggregated to any polygon area 28

29 Merci! Pour plus d informations : Contact: mosnier@iiasa.ac.at 29