OOS 24-10
Representing belowground processes in land surface models: Applications at the regional scale and remaining challenges

Thursday, August 8, 2013: 11:10 AM
101B, Minneapolis Convention Center
Tracy E. Twine, Soil, Water, and Climate, University of Minnesota, St Paul, MN
Jian Sun, Soil, Water, and Climate, University of Minnesota, St Paul, MN
Andy VanLoocke, Global Change and Photosynthesis Research Unit, USDA-ARS, University of Illinois, Urbana, IL
Carl J. Bernacchi, Department of Plant Biology/ Global Change and Photosynthesis Research Unit, University of Illinois/USDA-ARS, Urbana, IL
Christopher J. Kucharik, Agronomy/Center for Sustainability and the Global Environment, University of Wisconsin-Madison, Madison, WI

Land surface models were originally developed to act as a boundary for global climate models. Without representation of the exchange of heat and moisture between the atmosphere and land surface, atmospheric processes could not be accurately simulated. Over time, representation of vegetation processes and the carbon cycle further improved simulations of the climate.  Today, these models are used to analyze a number of global environmental issues including the carbon sequestration potential of ecosystems, ecosystem response to climate change, and land use and land cover change effects on ecosystem goods and services. Several decades of satellite observations of the land surface along with global networks of measurements including FLUXNET and LTER provide a means to evaluate how aboveground processes are simulated in these models; however, less attention has been given to below ground processes. One reason the simulation of belowground processes is challenging results from the dearth of observations—satellites cannot directly measure below the surface and field observations are sparse. Furthermore, when observations are available, it is difficult to know how well they might scale regionally. 


It is especially challenging to represent the physics of belowground processes across a large domain in land surface models. For example, soil temperature is an important variable for many plant-soil processes, but it is difficult to simulate. Results from the simulation of agroecosystems using one model (Agro-IBIS) show that soil temperature is biased after harvest because the model simulates a completely bare soil rather than a more realistic surface that includes plant residue. This bias in soil temperature affects the simulated surface energy budget and carbon budget through its effects on microbial respiration. This is less of a challenge in perennial grass systems used for bioenergy as the soil surface is covered by vegetation for most of the year; however, simulation results show that low vegetation cover during establishment of miscanthus results in low biases in simulated soil temperature, which hinders establishment across large areas of the Midwest U.S. Soil temperature can be increased through the addition of a simulated residue layer. Water and nitrogen use are also important below ground components of land surface models. Results will be presented that highlight the model’s ability to simulate differences in water use efficiency and nitrate loss from the soil among different food and bioenergy crops.