Challenges to incorporating spatially and temporally explicit phenomena (hotspots and hot moments) in denitrification models
Date of Original Version
Denitrification, the anaerobic reduction of nitrogen oxides to nitrogenous gases, is an extremely challenging process to measure and model. Much of this challenge arises from the fact that small areas (hotspots) and brief periods (hot moments) frequently account for a high percentage of the denitrification activity that occurs in both terrestrial and aquatic ecosystems. In this paper, we describe the prospects for incorporating hotspot and hot moment phenomena into denitrification models in terrestrial soils, the interface between terrestrial and aquatic ecosystems, and in aquatic ecosystems. Our analysis suggests that while our data needs are strongest for hot moments, the greatest modeling challenges are for hotspots. Given the increasing availability of high temporal frequency climate data, models are promising tools for evaluating the importance of hot moments such as freeze-thaw cycles and drying/rewetting events. Spatial hotspots are less tractable due to our inability to get high resolution spatial approximations of denitrification drivers such as carbon substrate. Investigators need to consider the types of hotspots and hot moments that might be occurring at small, medium, and large spatial scales in the particular ecosystem type they are working in before starting a study or developing a new model. New experimental design and heterogeneity quantification tools can then be applied from the outset and will result in better quantification and more robust and widely applicable denitrification models. © 2009 Springer Science+Business Media B.V.
Publication Title, e.g., Journal
Groffman, Peter M., Klaus Butterbach-Bahl, Robinson W. Fulweiler, Arthur J. Gold, Jennifer L. Morse, Emilie K. Stander, Christina Tague, Christina Tonitto, and Philippe Vidon. "Challenges to incorporating spatially and temporally explicit phenomena (hotspots and hot moments) in denitrification models." Biogeochemistry 93, 1-2 (2009). doi: 10.1007/s10533-008-9277-5.