Observational and modeling studies of the impacts of agriculture-related land use change on planetary boundary layer processes in the central U.S.

Abstract

The impact of agricultural land use change on atmospheric boundary layer processes, the associated feedbacks and their regional scale impacts, are examined with particular emphasis on the central United States. Specifically, the role of contrasting forested and agricultural land covers in the initiation and subsequent evolution of summertime cloud patterns in the U.S. Midwest; and the impact of agricultural practices, including irrigation, on the surface climate of the U.S. High Plains are discussed in detail. Satellite-based observational results of previous work summarized in this paper indicate that the timing and intensity of cloud development appears to be influenced by both synoptic flow regimes and agricultural land use type. For example, under conditions characterized by high pressure with surface winds generally less than 5 m s −1, peak cloud development occurred almost two hours earlier over cropland than over the forest or boundary locations in Michigan. Cloud masses were also considerably taller over cropland in the mid-afternoon than over forest and land cover transition zones. The modeling results discussed here for a model domain centered over Nebraska indicate significant differences in the surface energy fluxes between the irrigated (control) and non-irrigated (dry) simulations. Surface latent heat flux was higher by 36% and dewpoint temperature higher by 2.3 °C in the control simulation. Also, surface sensible heat flux of the control simulation was 15% less and the near-ground (2 m) temperature was 1.2 °C less compared to dry run, indicating irrigation-induced surface cooling effect. Recent investigations on crop–climate interactions in which crop and ecological models were coupled to regional climate models show that incorporating important perturbations such as prolonged droughts and the resulting changes in soil and plant nutrient conditions remains one of the biggest challenges in developing effective and realistic ecological-climate integrated modeling systems.