1 1. Land cover: carbon is a key attribute L d b i k tt ib t 2. Land use: carbon storage is altered constantly by policies and management actions i 3. Climate change: national/international policies require C assessments and mitigation actions 4. Locally/regionally, carbon assessment provides key indicators y g y y of ecosystem sustainability
Policy example y p “Access to the U.S. Geological Survey’s ‘visualization tool’ to assess the amount of carbon absorbed by landscapes …”
Policy example p y
How is carbon related to land cover and land use? How is carbon related to land cover and land use? CO2 A CO2 Atmosphere concentration h i Rates of contributing components
Wetlands Forests C Carbon cycle in b l i different land cover types t Agricultural lands Grasslands Aquatic systems
Mapping land cover for C assessment: challenges challenges
Two examples: U.S. and Kenya Questions asked: • What are the current land cover and land use? • What are the current carbon stocks and sequestration rates based on the current land cover and land use? • What may be the future potential land cover and land use? • What may be the future potential carbon stocks and sequestration rates under the future scenarios of sequestration rates under the future scenarios of land cover/land use?
Starting from a land cover map 1992 U.S. land cover
Develop past land cover change metrics
Select future land cover change scenarios Select future land cover change scenarios IPCC climate change scenarios are tied directly to factors that control land cover/use change, such as: GDP, population, energy needs technology development environmental policies etc needs, technology development, environmental policies, etc. IPCC AR4 SRES scenarios IPCC AR5 RCP scenarios
Select future land cover change scenarios Select future land cover change scenarios U.S. example Kenya example IPCC AR4 SRES scenarios IPCC AR5 RCP scenarios
IPCC scenarios are global scale, downscaled to continental scale
We further downscaled the scenarios to a regional scale using land cover data • Land cover composition is derived for cells/pixels • Then paired to the underline factors supporting the scenarios. i • For example, amount of agricultural lands needed to support population growth • Using this method, regional land cover transition is developed developed
Used a state and transition model for LULC change modeling • States States – major land use/cover (LULC) classes. – World Resources Institute LULC classification. • Transitions Transitions – changes between state classes. • Attributes Attributes – defined for states and transitions to Att ib t Att ib t d fi d f t t d t iti t track changes in ecosystem carbon by ecoregion S Spatial and non-spatial capability. ti l d ti l bilit • Monte Carlo analysis to assess uncertainty. y y • Spatially explicit and annual time steps.
Results of downscaled scenarios
Using a landscape model to link the past land cover change metrics to the downscaled scenarios to produce change metrics to the downscaled scenarios to produce annual land cover maps from the past to the future A1B 1992 land cover B1 B1 A2
As the result, we have annual maps between 1992 2050 1992 2050 1992-2050
Pine Bluff Little Rock A1B Scenario – Little Rock, Arkansas
Other projections can be similarly produced 2050 Age 250 Forest Age - Years Y t A F 0 2006 Age
Using biogeochemical g g models and the land cover projections, we p j , then estimated both the present and future p carbon stocks and sequestration rates q
Linking C assessment results to other ecosystem services • Example for a test in a county in Mississippi • Using “Ecosystem g y Service Change Indicator” method
Kenya Example (Proof of Concept)
Kenya proof of concept for climate change mitigation by increasing C sequestration • Downscale global environmental change scenarios to local and regional scales useful for decision/policy makers. – Sensitivity analysis for mitigation/adaptation Sensitivity analysis for mitigation/adaptation • Focus on changes in land use as primary driver g p y of ecosystem carbon dynamics.
Select future land cover change scenarios Select future land cover change scenarios U.S. example Kenya example IPCC AR4 SRES scenarios IPCC AR5 RCP scenarios
RCP85 MESSAGE Future Scenario Model, 2005 ‐ 2100 Transition 'cu' (Cropland to Urban) Transition cu (Cropland to Urban) Average Annual Percentage of Half ‐ Degree Grid Cell: 0.000% ‐ 0.008% 0.009% 0.028% 0 009% ‐ 0 028% 0.029% ‐ 0.058% 0.059% ‐ 0.121% 0.122% ‐ 0.231% 0 500 1,000 2,000 Kilometers
RCP Scenario Variability Predicted `sc` (Secondary to Cropland) by Scenario Average Annual Percentage of Half ‐ Degree Grid Cell RCP 8.5 RCP 6.0 0.000% 0.000% 0.001% ‐ 0.020% 0.001% ‐ 0.020% 0.021% ‐ 0.100% 0.021% ‐ 0.100% 0.101% ‐ 1.000% 0.101% ‐ 1.000% 1.001% ‐ 4.234% 1.001% ‐ 3.586% RCP 4.5 RCP 2.6 0.000% 0.000% 0.001% ‐ 0.020% 0.001% ‐ 0.020% 0.021% ‐ 0.100% 0.021% ‐ 0.100% 0.101% ‐ 1.000% 0.101% ‐ 1.000% 1.001% ‐ 3.232% 1.001% ‐ 4.178%
Temporal Distribution of Land Cover Classes & transitions between 2000-2100 under RCP8.5 (km2) Note: RCP8 5 used as reference conditions Note: RCP8.5 used as reference conditions L Land cover classes d l T Transitions iti
Temporal distribution of agricultural and forest carbon stock between 2000-2100 under the RCP8.5 reference scenario (km2) 0-9 yrs. >44 yrs. 10-44 yrs.
Under a Reference RCP8.5 Scenario Forest Biomass Carbon Change Forest Area Forest Area 2000: 43,177 km 2 2100: 34,447 km 2 Carbon Stock Carbon Stock 2000: 365 TgC 2100: 240 TgC Carbon Density Carbon Density 2000: 8.4 Gg/km 2 2100: 6.9 Gg/km 2 Carbon Flux: +125 TgC Carbon Flux: +125 TgC
Mitigation scenarios applied to forest biomass carbon • Strategy 1. Protection of ‘old growth’ forest. – No harvest allowed on stands older than 40 yrs. – Minimum age of 10 yrs. for harvest. Minimum age of 10 yrs for harvest • Strategy 2. Harvest reduction over baseline – Reduction of harvest by 5%. – Reduction of harvest by 20%. R d ti f h t b 20% • Strategy 3. Increase restoration efforts – 5% increase. – 20% increase 20% increase
2000 2100 y-axis units = km 2 5% Combined 20% Combined RCP8.5 Range = min/max over 25 simulations Scenario Scenario Line = mean
Forest Biomass Carbon Change by Mitigation Scenario Carbon Forest Area Carbon Density (Mg y ( g Carbon Flux (km 2 ) Stock (Tg C) C/km 2 ) (Tg C) Avg. Total by Scenario 2000 2100 2000 2100 2000 2100 Annual 2100 Baseline Reference RCP85 43,177 34,447 365.4 240.3 8.5 7.0 1.251 125.1 All 5% Actions 43,143 43,428 365.6 264.5 8.5 6.1 1.011 101.1 All 20% Actions 43,296 65,939 366.0 374.4 8.5 5.7 -0.084 -8.4
Ecosystem Carbon Change by Mitigation Scenario Carbon Stock by Scenario (2100) Carbon Stock by Scenario (2100) Baseline Reference RCP85 Baseline Reference RCP85 400 Old Growth Protection and 5% Reduction in Harvest Rate 350 Old Growth Protection and 20% Reduction in Harvest Rate 300 5% Reduction in Deforestation 5% Reduction in Deforestation Rate 250 20% Reduction in Deforestation Tg C Rate 200 5% Increase in Reforestation Rate 150 20% Increase in Reforestation Rate 100 All 5% Actions 50 All 20% Actions 0 Carbon Stock Change Compared to Reference Carbon Stock Change Compared to Reference Baseline Reference RCP85 B li R f RCP85 60.0% Old Growth Protection and 5% Reduction in Harvest Rate 50.0% Old Growth Protection and 20% Reduction in Harvest Rate 5% Reduction in Deforestation 5% Reduction in Deforestation 40 0% 40.0% Rate 20% Reduction in Deforestation 30.0% Tg C Rate 5% Increase in Reforestation Rate 20.0% 20% Increase in Reforestation 20% Increase in Reforestation 10 0% 10.0% Rate All 5% Actions 0.0% All 20% Actions -10.0%
Analysis: forest biomass carbon example Analysis: forest biomass carbon example • Based on this proof of concept application: – RCP85 results in large scale demand for anthropogenic land use in Kenya and will likely result in a decline in Kenya forest biomass C stocks. t k – Mitigation actions aimed at forest harvest alone have minimal impact on national-scale biomass carbon dynamics impact on national scale biomass carbon dynamics. – Avoiding deforestation (20% decreases) and increasing reforestation (20% increases) have strong potential to increase ( ) g p biomass carbon in forests, especially at high levels of implementation. – An integrated implementation of mitigation strategies at the 20% A i t t d i l t ti f iti ti t t i t th 20% level has the most impact on increasing forest carbon biomass of all evaluated scenarios.
Recommend
More recommend
