1. Developing landscape change scenarios from long-term monitoring databases Christopher E. Soulard U.S. Geological Survey, Western Geographic Science Center 345 Middlefield Rd., MS #531, Menlo Park, CA 94025 csoulard@usgs.gov csoulard@usgs.gov 650-329-4317 U.S. Department of the Interior U.S. Geological Survey

2. Programs and Mission Area Climate and Land Use Change (CLU) Mission Area:  Land Change Science Program  Research & Development Program “The USGS undertakes scientific research, monitoring, remote sensing, modeling, synthesis, and forecasting to address the effects of climate and land use change on the Nation’s resources.”

3. What is a Scenario?  A coherent, internally consistent and plausible description of a possible future state of the world.  It is not a prediction or projection  Projections: Any description of the future and a pathway leading to it (e.g. climate projection).  Prediction/Forecast: When projection is branded “most likely” with level of confidence.  A set of scenarios is often adopted to reflect the range of uncertainty in projections. (adapted from http://www.new.dpi.vic.gov.au/energy/policy/june-2010) Uncertainty around scenarios expands with increasing time

4. Quantitative and Qualitative  Robust scenarios incorporate both quantitative modeling and qualitative context  Quantitative parameterization is the focus of this talk  Qualitative context usually comes in the form of storylines or narratives  Your vision is a narrative/storyline Nakićenović, N. et al. eds. Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2000) “Scenarios draw from the human imagination as well as science to provide an account of the flow of events leading to a vision of the future. In essence scenarios are plausible stories about how the future might unfold from existing patterns, new factors, and alternative human choices.” Paul Raskin, 2005

5. Research Questions  What are the historical rates of land change?  NLCD, LCT, US Census, Long-Term Monitoring Plots  What are the major causes of past/future land change?  How will climate affect land use/land cover (LULC)?  Consequences: How will changes in LULC affect the ability of ecosystems to provide goods and services?  How will the future resemble or differ from the past in terms of LULC rates, causes, and consequences?  Mitigation: How might LULC change be managed to minimize negative impacts and maximize positive outcomes on natural and human systems?

6. Agricultural Scenario Options  Regional/local data and narratives connect “on the ground” users/issues better than a national framework  Mitigation/adaptation scenarios (e.g. CRP) OR  “Business as usual” scenarios based on most recent observed trends  “Worst case” scenarios (e.g. persistent drought)  Historical reconstructions (back-cast and model validation) and empirical projections

7. Existing Projections  FAO  IPCC  World Bank  Etc.

8. State & Transition Modeling Goal: Develop an integrated, easy-to-use, free, and open-shared model emphasizing spatial and temporal variability of LULC change.  State and Transition Simulator (ST-SIM; APEX Resource Management Solutions)  Spatial and non-spatial functionality  Commercially available and maintained, distributed free of charge, robust and stable code, WIKI, extensive community of users, Windows X64 application  State and transition attribute tracking (e.g. forest age, carbon, albedo, water use, population)

9. ST-SIM Model – LULC Change  National carbon assessment incorporates existing strata based on ecoregions used in Land Cover Trends and scenario development.  State classes defined as LULC.  Probability or area target based transitions between states.  Incorporation of uncertainty in historical measurements.  Monte Carlo simulations.  Spatial or non-spatial simulation. ST-SIM box and arrow diagram showing state classes and modeled transitions.

10. Agricultural Change in Australia  National agricultural assessment incorporates annual change mapping from 2001-2014  Trial intended to explore how mapping can be applied in national/global models

11. Australia: Non-Spatial Trial #1  Input -> Annual from/to map conversions used as transition area targets in projections, starting land use from 2014 map 1 sq km cell size <9% of Australia used for agriculture in 2014

12. Australia: Non-Spatial Trial #1  Output -> Annual rates randomized to project change from 2014 to 2064 (50 years) using 100 MC simulations

13. Australia: Non-Spatial Trial #1  Ouput -> Annual rates randomized to project change from 2014 to 2064 (50 years) using 100 MC simulations

14. Australia: Non-Spatial Trial #1

15. Questions Going Forward  How will land use rates change into the future?  Are 14 years of empirical data enough to encapsulate future uncertainty?  Do we use longer-term agricultural census data where available?  How will national/global driving forces differ moving forward?  What are the consequences or benefits of trends moving forward?  More/less reliance on fertilizers  Spatial migration into more vulnerable lands?  As for the spatial component, how do we allocate projections geographically?

16. Future Refinements for National or Global Scenarios