Abstract: Climate and management factors in isolation or in combination influence ecosystem properties and energy, water, and elements. Considerable uncertainity is associated with model projections due to the complex interactions and diverse nature of ecosystem variables under variable climate and management conditions.
This research focuses on the use of various tools and methods, such as field observations (eddy covariance and automated chamber), remote sensing data, process-based light use efficiency, and statistical models, to quantify the biophysical and biogeochemical response of ecosystems to climate and management variability. A new drought algorithm was developed and tested to understand the response of tallgrass prairie to drought in the Southern Great Plains. Eddy covariance data showed that grassland converted to winter wheat was a net source of carbon on an annual scale.
Results from both statistical regression and biogenic models tested with the field measured data showed that human management coupled with weather events altered the biophysical properties and biogeochemistry of ecosystems, resulting in higher greeenhouse gas emissions. The results highlight the importance of assessing and quantifying ecosystem responses to climate and management factors. This information can be used to reduce the uncertainity in model projections.