Welcome to GLASS (From Tom Hertel, the director of GLASS)
GLASS on the GeoHub is designed to facilitate analysis of local stresses emerging from global change drivers such as population, income, bioenergy, climate and technological change. It also facilitates analysis of local responses to these stresses. Such responses might include regulations, as well as fiscal interventions, including subsidies or taxes. GLASS facilitates analysis of the national and international spillovers from such interventions. As such, it allows users to complete the global-to local-to global (GLG) nexus related to food, land, water, energy and climate.
While we envision GLASS accommodating a wide range of modeling tools in the future. Initial efforts have focused on SIMPLE-G - a tool developed for the express purpose of linking global drivers with local sustainability challenges. SIMPLE-G builds on SIMPLE: A Simplified International Model of Prices Land use and the Environment (Baldos and Hertel 2013). It is a global model, designed to run at a variety of grid resolutions. The current version runs on a global grid (0.5 degrees resolution) and has been used to examine the interplay between water scarcity, land use, terrestrial carbon and food security (Liu et al. 2017). Users can access the model on the GeoHub where results from that publication can be replicated, and a wide range of other scenarios can be explored.
Individuals who wish to become users of SIMPLE-G are encouraged to begin by studying the non-gridded version of the SIMPLE model and its many applications to sustainability issues as developed in the textbook developed by Uris Baldos and Tom Hertel (2016) which is freely available as a download from many university libraries. This text accompanies an interdisciplinary course, offered at Purdue University in the spring semester. The associated material (lectures and lab assignments) are freely available on the GeoHub. Once you have worked through these assignments you will be in a position to design your own project with SIMPLE and/or SIMPLE-G, and even to modify the model itself.
By its very nature, GLG analysis is data intensive and uses sophisticated, open-source, Geographical Information Systems (GIS) programs developed with NSF funding. For this reason, GLASS also aims to support the posting and sharing of important data resources which can be linked to different versions of SIMPLE-G and similar modeling frameworks. An important illustration of this kind of shared data set is the historical irrigation data set published on the GeoHub by Siebert et al. As more GLG-style analysis is undertaken, greater emphasis will be placed on global gridded data sets, and such historical data sets will be critical for model estimation and validation. For those wishing to contribute new data sets, we offer an econometric tool for allocating variables collected at the level of administrative units to individual grid cells. It is nicknamed FLAT: Fine-scaled Land Allocation Tool, and this confronts head-on the challenge faced by those seeking to distribute data on land use to a uniform grid.
GLASS also supports the development and dissemination of spatially explicit economic and biophysical parameters for use in gridded analysis of sustainability issues. One such example is LANDPARAM. This is a tool which builds on an econometrically estimated model of cropland allocation to provide users with spatially disaggregated land supply elasticities.
In addition to offering a platform for the sharing of data and parameters for global gridded analysis of sustainability, GLASS supports tools and data for analysis of alternative scenarios, including climate change scenarios. The Climate Scenario Aggregator facilitates access to, and automated aggregation of, global gridded data on bias-corrected, monthly mean historical and future temperature and precipitation from five General Circulation Models. In addition, GLASS offers a facility for readily accessing estimated climate impacts on crop yields drawn from the Agricultural Modeling Intercomparison Project (AgMIP). These can be used to construct alternative climate impact scenarios for agricultural crops.