Landscape impacts of bioenergy production using state-and-transition modeling
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The document evaluates the landscape impacts of potential bioenergy production scenarios in North Carolina through modeling land-use changes and vegetation dynamics over time, specifically from 2009 to 2050. It highlights the necessity of understanding how biomass production may affect forest management practices and wildlife habitats, noting both positive and uncertain implications for species. Using a state-and-transition simulation model (STSM), the study aims to assess and summarize these environmental changes and their potential impacts.
Landscape impacts of bioenergy production using state-and-transition modeling
- 1. Assessing landscape impacts of potential bioenergy production scenarios using spatially explicit STSMs Jennifer Costanza Robert C. Abt, Alexa McKerrow, Jaime Collazo September 17, 2014
- 2. Biomass for energy Best alternative for society and the environment? Landscape and species impacts have not been fully addressed Recent studies Assume impacts will be negative (Evans et al. 2013 NWF Report) 2nd generation biofuel feedstocks Wood pellets
- 3. How will biofuels affect the NC landscape? 1. Increased “conventional” forest management • Sweet sorghum • Switchgrass • Short-rotation loblolly pine based on supply chain (Gonzalez et al. 2011, 2012) 2. “Purpose-grown crops” on marginal ag and forest lands
- 4. Overview How will potential biomass production scenarios for biofuels affect landscapes in North Carolina? 1. Create scenarios of land use change 2. Map initial conditions in NC: 2009 3. Model vegetation dynamics: conventional forest management and associated land use changes 4. Model conversion of marginal ag and forest land to purpose-grown crops 5. Incorporate urban growth 6. Simulate landscape change with ST-Sim: 2010 - 2050 7. Summarize results, especially in forests 8. Translate to species impacts
- 5. Why STSMs (ST-Sim)? 1. Our past experience and model infrastructure 2. Existing LANDFIRE VDDT models 3. Ability to simulate major types of change expected from biofuels production 4. Ability to incorporate output from timber supply model 5. Spatial simulation capability so we can model resulting species impacts
- 6. Biomass production scenarios for NC Scenario Conv. forests Other forest products Marg. ag to purpose grown Marg. forest to purpose grown Portion of NC Fuel Baseline None BAU No No 0% Ag No BAU Yes No 10% Ag-Forest No BAU Some Some 10% Conventional Yes Reduced No No 1.5% Conventional- Ag Yes Reduced Yes No 10% Conventional- Ag-Forest Yes Reduced Some Some 10%
- 7. NC vegetation and land use types Current Forests: 55% Agriculture: 23% Urban: 10%
- 8. Initial conditions: NC vegetation and land use types Circa 2009 Spatial conditions based on GAP 2001 Land cover updated with 2009 urban 60 m resolution 73 vegetation and land use types 59 have state and transition models
- 9. Modeling vegetation dynamics Example: Longleaf pine woodlands
- 10. Modeling vegetation dynamics Example: Longleaf pine woodlands Conventional forest management added To other veg and land uses
- 11. Initial conditions: state classes, ages States: Based on 2008 LANDFIRE S-class, NLCD canopy cover Ages: Based on FIA data
- 12. Modeling conventional forest management: Thinning, harvest, land use change SRTS timber supply model (Abt et al. 2009 For. Prod. J.) Not all demand is met by increased harvest Estimates timber supply based on • Inventory: how much timber exists? (FIA data; economic land use change model) • Demand: 1. Empirical harvests (FIA data) 2. Annual demand in NC increases to 4 million green tons biomass by 2018 • Forest residues harvested • Other products displaced
- 13. Modeling conventional forest management: Thinning, harvest, land use change Result: Annual areas thinned, harvested, converted to and from broad forest types and age classes Harvest Thinning 200,000 100,000 0 2010 2020 2030 2040 20502010 2020 2030 2040 2050 Year Hectares Scenario Baseline Conventional
- 14. Modeling purpose grown crops on marginal agricultural and forest land Based on soil and land use Excludes protected areas Based on life cycle analysis
- 15. Urban growth through time: 2010 - 2050 Series of spatial multipliers Terando et al. 2014 PLOS ONE
- 16. Overview How will potential biomass production scenarios for bioenergy affect landscapes in North Carolina? 1. Create scenarios of land use change 2. Map initial conditions in NC: 2009 3. Model vegetation dynamics: conventional forest management and associated land use changes 4. Model conversion of marginal ag and forest land to purpose-grown crops 5. Incorporate urban growth 6. Simulate landscape change with ST-Sim: 2010 - 2050 7. Summarize results, especially in forests 8. Translate to species impacts
- 17. Results: Simulated land use and vegetation Baseline scenario 2009
- 18. Results: Simulated land use and vegetation 2050 Baseline scenario
- 19. 2009 Spatial results: Conventional biomass scenario Fayetteville, NC
- 20. 2020 Spatial results: Conventional biomass scenario Fayetteville, NC
- 21. 2030 Spatial results: Conventional biomass scenario Fayetteville, NC
- 22. 2040 Spatial results: Conventional biomass scenario Fayetteville, NC
- 23. 2050 Spatial results: Conventional biomass scenario Fayetteville, NC
- 24. 8,000,000 6,000,000 4,000,000 2,000,000 0 2010 2020 2030 2040 2050 Year Area (ha) Results: General statewide trends Forests Agriculture Urban areas
- 25. Agriculture Forest Urban 0% −5% −10% −15% Scenario Proportion Difference Scenario Ag Ag−Forest Conventional Conventional−Conventional−2050 Proportion difference from Baseline Scenario Mixed pine hardwood Natural pine Upland hardwood Scenario Ag Ag−Forest Conventional Conventional−Ag Conventional−Ag−Forest
- 26. 2050 Proportion difference from Baseline Scenario Lowland hardwood Mixed pine hardwood Natural pine Planted pine Upland hardwood 10% 5% 0% −5% −10% 10% 5% 0% −5% −10% Scenario Proportion Difference Scenario Ag Ag−Forest Conventional Conventional−Conventional−Mixed pine hardwood Natural pine Upland hardwood Scenario Ag Ag−Forest Conventional Conventional−Ag Conventional−Ag−Forest
- 27. Early Mid Late 10% 5% 0% Scenario Proportion Difference Scenario Ag Ag−Forest Conventional Conventional−Conventional−2050 Proportion difference from Baseline Scenario: Seral Stage, All Forests Upland hardwood Scenario Scenario Ag Ag−Forest Conventional Conventional−Ag Conventional−Ag−Forest
- 28. 2050 Proportion difference from Baseline Scenario: Seral Stage, Lowland Hardwoods Upland hardwood Scenario Scenario Ag Ag−Forest Conventional Conventional−Ag Conventional−Ag−Forest
- 29. 2050 Proportion difference from Baseline Scenario: Seral Stage, Pine plantations Scenario Ag Ag−Forest Conventional Conventional−Ag Conventional−Ag−Forest
- 30. Translating landscape results to potential species habitat State classes Suitable/unsuitable habitat • Deductive modeling based on literature • Pixels in the current species range classified as suitable or unsuitable: potential habitat • Not considering species interactions, etc.
- 31. Example results: Brown-headed Nuthatch - Affinity: open, mature pine forests - Recent sharp declines due to pine plantations Photo: David Bell % Change in potential habitat from 2009 Difference in area compared to baseline
- 32. Wildlife species we modeled Birds (12) Brown-headed Nuthatch Cerulean Warbler Grasshopper Sparrow Hairy Woodpecker Kentucky Warbler Loggerhead Shrike Prairie Warbler Prothonotary Warbler Red-headed Woodpecker Swainson’s Warbler Wood Thrush Yellow-breasted Chat Amphibians (4) Eastern tiger salamander Gopher frog Oak toad Mole salamander
- 33. Summary • The STSM framework is useful for assessing change through time • Biomass demand, especially in conventional forests, helps keep some forests on the landscape • Remaining forest tends to have more area in early succession state • In some cases, species could be positively impacted by biomass production • There remains much uncertainty regarding landscape and species impacts • This famework can be applied to assess potential positive and negative impacts of other bioenergy and forest management scenarios
- 34. Acknowledgements People: Todd Earnhardt Matt Rubino Nathan Tarr Ashton Drew Steve Williams Ronalds Gonzalez Dennis Hazel Funding: