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  94025csoulard@usgs.gov

650-329-4317

U.S. Department of the InteriorU.S. Geological Survey

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.”

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

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

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?

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

Existing Projections

FAO IPCC World Bank Etc.

State & Transition ModelingGoal: 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)

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.

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

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

Australia: Non-Spatial Trial #1 Output -> Annual rates randomized to project change from 2014

to 2064 (50 years) using 100 MC simulations

Australia: Non-Spatial Trial #1

Ouput -> Annual rates randomized to project change from 2014 to 2064 (50 years) using 100 MC simulations

Australia: Non-Spatial Trial #1

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?

Future Refinements for National or Global Scenarios