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Carlos A. Gonzalez-Benecke School of Forest Resources and Conservation University of Florida

Predicting climate change impacts on southern pines productivity in SE United States using physiological process based model 3-PG. Carlos A. Gonzalez-Benecke School of Forest Resources and Conservation University of Florida. Outline. Southern forests in SE United States 3-PG Model

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Carlos A. Gonzalez-Benecke School of Forest Resources and Conservation University of Florida

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  1. Predicting climate change impacts on southern pines productivity in SE United States using physiological process based model 3-PG Carlos A. Gonzalez-Benecke School of Forest Resources and Conservation University of Florida

  2. Outline • Southern forests in SE United States • 3-PG Model • Model Calibration for Pinuselliottii (slash pine) • Model Validation • Case Study Climate Change Impacts on Productivity of Slash Pine Stands

  3. Background • Forests have multiple goods and services: wild-life, water, soil, C seq,… wood. • In SE United States : 60% of landscape if forested including 28 million ha of southern pines. • SE U.S. produces 58% of the total U.S. timber harvest and 18% of the global supply of roundwood(more than any other country). • SE pine forests contain 1/3 of the contiguous U.S. forest C and can sequester 23% of regional GHG emissions. • Most important southern pine species: Pinustaeda(loblolly pine), Pinuselliottii(slash pine) and Pinuspalustris(longleaf pine).

  4. Background Slash Pine (PinuselliottiiEngelm.) • Medium-Long-Lived. • Fast-growing • Important commercial species in SE United States • Objectives: Pulpwood and sawtimber production • Area of timberland: 4.2 million ha http://www.forestryimages.org

  5. 3-PG (Landsberg and Waring, 1997) Forest Production : Tree growth model based on : Physiological Principles that Predict Growth • Light Interception • Carbon Acquisition • Carbon Allocation

  6. 3-PG Model BA Landsberg and Waring 1997

  7. 3-PG Model NPP = Q0* *C * R All modifiers affect canopy production: C = fTfFfN min{fD , fq} fagefCaCx Temperature Frost Nutrition VPD ASW Age CO2 Max Canopy Quantum Efficiency (0  fi  1)

  8. Parameterization for Slash Pine 3-PG Model C = fTfFfN min{fD , fq} fagefCaCx Canopy Quantum Yield = 0.056 mol CO2 / mol PAR where D = current VPD kD = strength of VPD response Gonzalez-Benecke et al. 2014

  9. Parameterization for Slash Pine 3-PG Model C = fTfFfN min{fD , fq} fagefCaCx Teskey et al. 1994 Teskey et al. (in preparation)

  10. Results Validation Sites 14 sites in US 7 sites in Uruguay 118 permanent plots 686 year x plot observations

  11. Results Validation X=observed Y=predicted Height (m) Above Ground Biomass (Mg ha-1) Above Ground Biomass (Mg ha-1) Height (m) Basal Area (m2 ha-1) Trees per hectare Trees per hectare Basal Area (m2 ha-1) Volume (m3 ha-1) Gonzalez-Benecke et al. 2014 Volume (m3 ha-1)

  12. Case Study: Climate Change Effect on Slash Pine Productivity Future Climate Data: CanESM2 model Downscaled using MACA method (Multivariate Adaptive Constructed Analogs) http://maca.northwestknowledge.net/ • Based on Site Quality • (site index) • Productivity Site Index • - Low 19 m • - Medium 23 m • - High 28 m • Scenarios (combination of climate and site quality): • Based on 2 RCPs • (Representative Concentration Pathways) • Scenario Climate Data CO2 • - Historical 1950 – 2010 400 ppm • - RCP 4.5 2070 – 2100 550 ppm • - RCP 8.5 2070 – 2100 850 ppm

  13. Sites location 18.3 +2.1 +3.0 18.8 +2.8 +4.8 18.3 +2.1 +2.9 18.0 +2.1 +3.0 19.1 +2.9 +4.8 19.4 +2.0 +2.7 21.1 +2.0 +2.8 19.6 +2.0 +2.8 19.8 +2.1 +2.8 20.1 +2.0 +2.8 22.9 +1.8 +2.6 11 sites in SE US 4 sites in Northern Limit Historical Mean Annual Temperature (°C) and Mean Increment in Temperature due to Climate Change (RCP 4.5 and 8.5)

  14. Case Study Climate Change Scenarios Summary

  15. Climate Change Effect on Slash Pine Productivity L: 26 - 39 M: 29 - 37 H: 14 - 20 Change in Above Ground Biomass (Mg/ha) at age=25 years RCP's v/s Historical Scenarios L: 24 - 41 M: 17 - 34 H: 16 - 28 L: 25 - 41 M: 10 - 22 H: 6 - 23 L: 32 - 46 M: 32 - 42 H: 16 - 25 L: 18 - 29 M: 8 - 12 H: 5 - 8 L: 26 - 42 M: 16 - 22 H: 12 - 15 L: 18 - 29 M: 8 - 12 H: 5 - 9 L: 18 - 28 M: 8 - 12 H: 4 - 10 L: 18 - 29 M: 8 - 12 H: 4 - 8 L: 22 - 37 M: 12 - 19 H: 7 - 13 L: 10 - 20 M: 8 - 10 H: 3 - 6 L: Low Productivity M: Medium Productivity H: High Productivity

  16. Climate Change Effect on Slash Pine Productivity Change in Above Ground Biomass (Mg/ha) at age=25 years RCP's v/s Historical Scenarios RCP 4.5 RCP 8.5 Medium Medium High High Low Low Site Quality Site Quality

  17. Conclusions: Climate Change Effect on Slash Pine Productivity Under Future Climate Scenarios Used: • For Sites with Mean Annual Temperature > 19 C: • Under RCP4.5 : AGB can be increased between 2% to 27% (Mean=8%). • Under RCP8.5 : AGB can be increased between 2% to 44% (Mean=13%). • For Sites with Mean Annual Temperature < 19 C (North Limit): • Under RCP4.5 : AGB can be increased between 2% to 44% (Mean=17%). • Under RCP8.5 : AGB can be increased between 8% to 63% (Mean=27%).

  18. Conclusions: Climate Change Effect on Slash Pine Productivity Under Future Climate Scenarios Used: • Responses to Climate Change should be larger in colder range of distribution. • Responses to Climate Change should be larger in low productivity sites.

  19. Acknowledgements

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