Climate-Land Interaction Project (CLIP) in East Africa
David J. Campbell firstname.lastname@example.org (Principal Investigator)
Jennifer M. Olson (Co-Principal Investigator)
Bryan C. Pijanowski (Co-Principal Investigator)
Jeffrey A. Andresen (Co-Principal Investigator)
Jiagou Qi (Co-Principal Investigator)
The intensity and spatial reach of contemporary human alterations of the Earth's land surface are unprecedented. Land use and land cover change (LULCC) are among the most significant of these human influences. Many studies demonstrate the influence of LULCC on local and regional climate which, when aggregated, may significantly alter the global climate. Meanwhile, climate change related to increased atmospheric concentrations of greenhouse gases is expected to considerably affect people and ecosystems due to altered temperature and precipitation patterns. While significant research has focused on global climate modeling and socioeconomic drivers of land use change, an integrated assessment at the regional scale of coupled human-climatic systems is required to address the question: “What is the magnitude and nature of the interaction between land use and climate change at regional and local scales?” An international multi-disciplinary team, including social, ecological, atmospheric and statistical scientists, will address this question by exploring the linkages between two foci of global change research, LULCC and climate change, which until now have had largely independent scientific paths. A major goal of global change science is to obtain a more reliable estimation of future climatic conditions. This goal increasingly requires high resolution regional scale climate modeling that includes feedbacks between the land and atmosphere. This project is among the first to complete the loop of land use/climate/land use impacts assessment. Its contribution is in analysis of the complex linkages among components “how does land use change affect climate, and how will climate change affect land use?”. These linkages will be examined through analysis and modeling of agricultural systems, land use change, the physical properties of land cover, and regional climate dynamics. East Africa, with its variety of ecosystems, wide range of tropical climatic conditions, areas of rapid land use change, and a population vulnerable to climatic variability, will be the location of the research.
The project (see Figure below) will integrate: (1) future local level climate scenarios derived from downscaling of global climate models; 2) information derived from detailed long-term case studies of LULCC; (3) models to project LULCC from local to regional scales; (4) analyses of time-series satellite imagery to translate the effects of LULCC on land surface characteristics; (5) net primary productivity simulations; and (6) a regional climate model parameterized using local land surface parameters.
The culmination of our work will be a series of climate-land system feedback experiments identifying the magnitude and nature of interactions between land and climate dynamics at regional scales. We will characterize these interactions in terms of the determining form of feedbacks, the strength of linkages, issues of spatial and temporal scales and the affect of “tipping points” on climate-land interactions. In addition to addressing many fundamental climatic, ecological and socioeconomic questions, the research will be tightly integrated with the education of students from elementary school to graduate school; it involves capacity building for young and mid-career American and African scientists, as well as outreach to various stakeholders from local to international communities.
Forecasts of land use are being made using the Land Transformation Model. Rainfed agricultural expansion is being estimated using population, local towns, location of roads, soils, crop yield potential and climate. An example simulation is shown here:
Here is a zoom of an LTM probability map showing the likelihood of each cell (1 km) being rainfed agriculture based on climate, soils, elevation, and proximity to towns, roads and national parks. The color red indicates high likelihood of being rainfed agriculture, yellow medium and blue low. Areas in grey are non-candidate cells for being rainfed because they are national parks, mountain tops or urban. Boxes represent the 36km RAMS climate grid (you can click on the image to get a large, more detailed graphic).
Propagation of errors from the Land Transformation Model to the Regional Atmospheric Modeling Systems (RAMS) is being assessed using a variety of techniques and metrics. For example, we have developed an uncertainty metric that quantifies the number of cells that are predicted to be rainfed ag or not rainfed ag discounted by cells that are contained within the mid-range of values. These are assessed for each 36km climate grid and mapped using the GIS as such:
We are also using MatLab visualization tools to examine model goodness-of-fit across different model configurations defined by variables, the window size of error analysis and the number of neural network training cycles. We used the correctly classified percentage of presence of agriculture cells as the goodness-of-fit measure:
Coupling of land use to surface parameters required by RAMS is being accomplished using MODIS products, in particular, leaf area index (LAI). Here is a mosaic of several 8-day LAI scenes for East Africa. Notice the pronounced variation in vegetation biomass across the region over the coarse of the year.
A potential "tipping point" is show below in the following analysis. The amount of rainfed agriculture was matched with the yield estimates from the CERES-Maize crop simulation model. Along the x-axis is annual yield, the y-axis contains the percentage of cells within the region that are located in rainfed agriculture in our land use/cover map. Note that high yields contain large percentage of cells that fall within the rainfed ag land use/cover class. There are two break points shown here as well, possibly areas that in "low", "medium" and "high" potential areas for growing crops.
The project will make a significant contribution both to science and to policy. Its scientific importance lies in addressing the complex interactions and feedback between climate change and land use change integrating state-of-the-art methodologies and multidisciplinary approaches, and in explicitly addressing issues important to biocomplexity theory and complex system modeling including feedback, thresholds, uncertainty and non-linearity.
These are inherent issues in linking information derived from a variety of models and case studies in order to understand the multi-scale dynamics associated with linking LULCC and climate change over time. The resultant scientific findings and new methodologies will advance future climate change research at regional and global scales. With respect to application, the project will provide plausible scenarios of future climate change and its impacts upon the dominant livelihood systems of East Africa. The team will work with policy-makers and other stakeholders, including farmers and herders, to assess the implications of these scenarios for future agricultural research and policy, conservation, and land use planning in the region. The findings will be relevant to scientists and policy-makers in many other parts of the world who are concerned with the implications for society and environment of complex interactions between land use and climate. This project is supported by an award resulting from the FY 2003 special competition in Biocomplexity in the Environment focusing on the Dynamics of Coupled Natural and Human Systems.
Last updated March 7, 2011