by Chieh-Hsin Chen
To solve the problem of energy security, there are many policies to increase the production of biofuel feedstock. In the central United States, there have been studies and simulations of conversion of land use for agriculture; the models are used to assess the environmental and economic impacts from the conversions. Anderson et al. (2013) looked into two simulations performed using the same atmospheric forcing data for the period 1979−2004: one as control group with present day land use and phenology, and other with land use change from food crops to switchgrass. Kansas and Oklahoma are set for the simulation sites; the agro-economic model predicts about 15−30% conversion to switchgrass. The result shows a slight climate change on the temperature, humidity, and the soil moisture; the regional climate model simulates lower temperature and higher humidity in spring while lower humidity and a depletion of soil moisture in summer. The authors also conclude that using agro-economic and climate models interactively would reduce the possibility of unforeseen consequences from rapid changes in the agriculture production system.
The first step for biofuel expansion is to increase the production of biofuel feedstock; many assessments and simulation have been done on the expansion of biofuel, to examine the changes in the atmospheric levels of carbon dioxide and of surface temperature. The authors focus on the geophysical aspects of a biofuel policy projection. The land surface of the central United States is a core of its climate through the terrestrial-atmospheric hydrological cycle. Land use land cover (LULC) change from biofuel policies may attenuate the region’s surface-warming trend. The biofuel policy is predicted to support 30% of the present U.S. petroleum consumption; it is also predicted that the Kansas will have 20−30% conversion to switchgrass and Oklahoma has 30−45% conversion.
The simulations were produced using the Weather Research Forecasting model (WFR) with domains covering the central U.S. at a grid size of 24 km. The model is also coupled with the Noah land surface model, which uses satellite imagery to estimate bulk vegetation parameters at each grid cell, representing the interaction of soil moisture and vegetation with the atmosphere. The model includes computation of transpiration, albedo, and soil interaction with the atmosphere; with a specific equation, which includes factors such as volumetric soil moisture of soil layers and canopy resistance.
The two simulation models are designed to show differences between implementation of biofuel policies. The first model is the control group with 24 vegetation classes and satellite-based (1991−1995) monthly vegetation fraction from which the simulations are derived. The switchgrass simulation contains changes in land use, vegetation parameters, and vegetation phenology. The land use conversion for switchgrass production was emulated in WRF by creating four new vegetation classes based upon Noah dryland categories. The lands converted are mostly non-irrigated, because irrigated crops are high economic value and would not be displaced by a lower value biofuel feedstock crop. Overall leaf index area (LAI) is expected to increase in this model.
The results show that the control group has very little variation. The temperature was comparatively lower in February – June with slightly higher humidity; the temperature didn’t show significant changes from July – October, but the humidity decreased. With the increase of LAI, the latent heat flux should also increase due to the inverse relation of canopy resistance to LAI, thus an increase in temperature was expected from July – October, but didn’t show in the simulation. The albedo is unchanged as well as the net radiation. Evapotranspiration is positive in March – June and negative in August – November, there is also an insufficient in precipitation, which offset the rate of evapotranspiration. The switchgrass simulations show an depletion of the soil moisture.
There are some errors and setbacks in the simulations; first, the climate feedback from agriculture did not considered the socio-economic and policy scenarios, which may create bias toward expectation of yield that cannot be realized. Second, the simulations should consider whether LULC change is exogenous or an adaptation to climate change.
Anderson, CJ, Anex, RP, Arritt, RW, Gelder, BK, Khanal, S, DE, Herzmann, PW, Gassman., 2013. Regional Climate Impacts of a Biofuel Policy Projection. Geophysical Research Letter 40, 1217−1222 http://bit.ly/1ufaGYn