Future Scenarios of Land Use and Land Cover Change for Integrated Resources Assessment

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This research project aims to develop a portfolio approach to development of land change scenarios for the United States based on empirical data and global integrated assessment modeling.This research will continue the development and capabilities of the Land Use and Carbon Scenario Simulator (LUCAS), which has been developed by USGS scientists for the purposes of projecting land change and its impact on ecosystem carbon dynamics. 

Statement of Problem: Climate change and the potential adoption and implementation of mitigation and/or adaptation strategies are important aspects to projecting future land change. Climate change impacts land-use through alteration of biophysical characteristics that control suitability for a given land use, while land change impacts climate through alteration of surface and atmospheric boundaries and resultant energy and water fluxes. This research examines important interactions between land use and climate under a range of future conditions in order to identify regions which are most at risk to negative impacts and where climate-land interactions provide new potential opportunities. Additionally, the biosphere offers opportunities for climate mitigation through the offset of greenhouse-gas emissions. Research should further our understanding of how climate mitigation and adaptation strategies might evolve and impact the composition and trajectories of important land sectors.

Research is needed to develop geostatistical approaches to projecting land change based on empirical data. Additionally, future projections should incorporate causal mechanisms where possible, such as abrupt policy interventions (e.g. Conservation Reserve Program, Endangered Species Act), economic drivers (e.g. Great Recession), and environmental drivers (e.g. episodic and prolonged drought, climate change, wildfire). This research should aim to develop a portfolio approach to land change projection and work to robustly characterize, and reduce where possible, the large uncertainties associated with land-change prediction, forecasting, and projection.

Why this Research is Important: The recognition of the need for improving the understanding and management of climate and land change is longstanding. The National Research Council identified this issue as one of the 21st century grand challenges, it is a formal element of the US Global Change Research Program and relates to the central goals of the Climate Action Plan. The USGS Climate and Land Use Change science strategy also identifies the need to improve the understanding of the combined effects of climate and land use change. Because resource managers are keenly aware of the threats that climate and land use change have on their management goals, and are asking for information on land use, cover, and condition for their activities, research on climate and land change questions should be an important element of the USGS Land Change Science Program.

Objective(s):

  • Develop geostatistical methods for developing a portfolio of empirical-based projections of land-use and land-cover change for the United States over short (10-20 years), medium (20-50 years), and long-time horizons (50-100 years). Scenarios will explore changes in major land use conversions, including urbanization, expansion and contraction of agriculture, and forestry, as well as changes in land cover due to natural disturbances (e.g. wildfire, insect/disease) and climate change.
  • Develop a coupled modeling capability which can be used to explore the impacts of potential climate-based mitigation and adaptation strategies on changes in land use and land cover from local to global scales.
  • Work with local, regional, and national stakeholders to develop alternative scenarios of land change (e.g. “what-if” scenarios) based on local, regional, and national driving forces analysis and policy-based assumptions.

Methods: This project will develop an entirely new set of statistical tools, methods, and models to project land change over short, medium, and long-time horizons across a range of spatial scales. Additionally, this research will develop new modeling approaches to link coarse-scale global integrated assessment models (IAM’s) with empirically-based land change forecasting methods. Lastly, this research will need to develop cutting-edge computational capabilities, including parallelization of models for deployment on high performance computing facilities. Currently, work has been done utilizing USGS compute facilities in Denver, CO as well as on the NASA Earth Exchange.