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United States Department of Agriculture

Agricultural Research Service

Title: Modeling Denitrification in Terrestrial and Aquatic Ecosystems at Regional Scalles. Ecological Applications.

Authors
item Boyer, E - U OF C, BERKELEY, CA
item Alexander, R - USGS
item Parton, W - CSU, FORT COLLINS, CO
item Butterbach-Bahl, K - INS. METEOR. CL RE, DRG
item Donner, S - PRINCETON UNIV.
item Skaggs, R - NO. CAROLINA ST. UNIV.
item Del Grosso, Stephen

Submitted to: Ecological Applications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 2, 2006
Publication Date: December 12, 2006
Citation: Boyer, E.W., Alexander, R.B., Parton, W.J., Butterbach-Bahl, K., Donner, S.D., Skaggs, R.W., Del Grosso, S.J. 2006. Modeling Denitrification in Terrestrial and Aquatic Ecosystems at Regional Scalles. Ecological Applications 16, 2123-2142.

Interpretive Summary: Denitrification is the biogeochemical process that results in the reduction of nitrate (NO3) to nitrous oxide (N2O) and molecular nitrogen (N2). This process occurs in soils and in water ways under anoxic conditions. Understanding and quantifying denitrification rates are important because N2O is a strong greenhouse gas and production of N2 contributes to closing the global nitrogen cycle. That is, fertilizer production and legume cropping convert atmospheric N2 to biologically active ammonia (NH3) which results in a large increase in biologically available nitrogen as a result of human activities. Denitrification returns some of this biologically reactive nitrogen back to the inert form (N2). Because it is difficult, if not impossible, to measure denitrification rates at scales beyond the plot level, models must be used to estimate denitrification rates at regional and larger scales for global nitrogen budgets. Denitrification models range from simple empirical relationships to detailed mechanistic models that simulate the processes that contribute to denitrification. How well terrestrial and aquatic models simulated denitrification is highly uncertain and model results need to be compared with data collected from various systems.

Technical Abstract: Quantifying where, when, and how much denitrification occurs on the basis of measurements alone remains particularly vexing at virtually all spatial scales. As a result, models have become essential tools for integrating current understanding of the processes that control denitrification with measurements of rate-controlling properties so that the permanent losses of N within landscapes can be quantified at watershed and regional scales. In this paper, we describe commonly used approaches for modeling denitrification and N cycling processes in terrestrial and aquatic ecosystems based on selected examples from the literature. We highlight future needs for developing complementary measurements and models of denitrification. Most of the approaches described here do not explicitly simulate microbial dynamics, but make predictions by representing the environmental conditions where denitrification is expected to occur, based on conceptualizations of the N cycle and empirical data from field and laboratory investigations of the dominant process controls. Models of denitrification in terrestrial ecosystems include generally similar rate-controlling variables, but vary in their complexity of the descriptions of natural and human-related properties of the landscape, reflecting a range of scientific and management perspectives. Models of denitrification in aquatic ecosystems range in complexity from highly detailed mechanistic simulations of the N cycle to simpler source–transport models of aggregate N removal processes estimated with empirical functions, though all estimate aquatic N removal using first-order reaction rate or mass-transfer rate expressions. Both the terrestrial and aquatic modeling approaches considered here generally indicate that denitrification is an important and highly substantial component of the N cycle over large spatial scales. However, the uncertainties of model predictions are large. Future progress will be linked to advances in field measurements, spatial databases, and model structures.

Last Modified: 11/24/2014