Climate Change and Geographic Information Systems
The problem of climate change has an implicit geographic component; climate change scenarios, analysis and evaluation are done on a global scale and predictions often down-scaled to make them more relevant to local conditions. All of these processes can be facilitated by GIS by virtue of its versatality in providing necessary storage, retrieval, analysis and visualization capabilities.
The Geographic Information Systems Spatial Analysis Laboratory (GISSAL) at the Department of Geography provides the necessary infrastructure support for studying climate change/mitigation phenomena for different bio-physical systems.
Applied Geoprocessing Models for Climate Change/Mitigation Analysis in Kansas
Daymet daily weather surface extraction Model:
A series of GIS models and python scripts were built to extract and mosaic spatially and temporally isolated Daymet surfaces into to seamless Kansas weather surfaces on a daily time-step. The weather parameters include max, min temperatures, precipitation, shortwave radiation, day length, vapor pressure deficit, and snow-water equivalent. The nested geoprocessing models give researchers the ability to compare past climatic conditions with future scenarios on a local scale, among other applications.
The picture above represents a seamless T_max layer for Kansas on March 1st 2005, and daily surfaces such as this can be automatically derived using these geoprocessing models for other Daymet weather parameters.
Daily Evapotranspiration (ET) surface model:
The above geoprocessing model was built at K-State using ArcGIS 9.3.1 to take numerical ET prediction values on a daily-step for all 23 weather stations in Kansas and output daily interpolated ET surfaces for the entire state using simple Kriging method. The output surfaces of the model could then be used to study climate change effects on water budget requirements for different crops in Kansas.
Geoprocessing Kansas Soil Survey Geographic Database (SSURGO)
Soil attribute data has vital information pertaining to crop productivity. By analyzing soil data using econometric models under different climate change scenarios in a GIS environment it becomes possible to simulate future cropping patterns in Kansas and farmers' potential decisions. A series of SQL and python geoprocessing scripts were developed at K-State that enables the retrieval of county level SSURGO data into seamless statewide layer with map units representing over 200 soil attributes. This database is utilized by researchers involved in different NSF EPSCoR projects.