Denitrification Modeling Workshop, November 2006
MODEL SUMMARY
DNDC summary
Christina Tonitto and Changsheng Li
1. What is the basic structure of the model? Is this a process-based model, statistical
model (e.g., regression-based), other?
DNDC was developed as a field-scale, process-based, mechanistic model of N and C
dynamics in agroecosystems. DNDC has been developed to study dynamics in forest and
grassland ecosystems.
In DNDC the primary drivers (e.g., climate, soil, vegetation and anthropogenic activity)
determine the soil environmental factors (e.g., temperature, moisture, pH, Eh and
substrate concentration gradients). Biogeochemical processes of nitrification,
denitrification, and fermentation are represented as microbially-mediated processes
regulated by the soil environmental factors. Model calculations are preformed at a daily
or hourly time step, though it is often not possible to validate this level of temporal
resolution for application in agricultural management.
2. How is denitrification estimated? What are the controlling factors?
In DNDC, denitrification rate is driven by two equations: 1) the Nernst equation and 2)
the Michaelis-Menten equation. The Nernst equation is a basic thermodynamic formula
defining soil Eh based on concentrations of the oxidants and reductants existing in a soil
liquid phase [Stumm & Morgan, 1981]. The Michaelis-Menten equation is a widely
applied formula describing the kinetics of microbial growth with dual nutrients [Paul &
Clark, 1989], which are DOC and electron acceptor (i.e., nitrogen oxidants) in the
denitrification reactions. The Nernst and the Michaelis-Menten equations can be coupled
since they by share a common factor, the oxidant concentration. This coupling has been
realized in DNDC through a simple kinetic scheme called the “anaerobic balloon”. The
anaerobic balloon in DNDC defines the effective anaerobic volumetric fraction of a soil
based on the soil bulk Eh. As soon as the Eh value for a soil layer is estimated based on
the dominant oxidant species with the Nernst equation, the size of the anaerobic balloon
will be determined, and hence the soil substrates will be allocated inside and outside of
the balloon proportionally. It is defined that only the substrates allocated within the
balloon will be involved in the anaerobic reactions (e.g., denitrification etc.), and the
substrates allocated outside of the balloon will be involved in the aerobic reactions (e.g.,
nitrification etc.). When the anaerobic balloon swells, several processes will take place,
including (1) more substrates (e.g., DOC, NO3-, NO2-, NO, or N2O) will be allocated
within the balloon, (2) rate of the reductive reactions (e.g., sequential denitrification
reactions) will increase within the constraints imposed by Michaelis-Menten mediatedmicrobial growth, and (3) the intermediate product gases (e.g., N2O, NO etc.) will take
longer to diffuse from the anaerobic to the aerobic fraction, increasing the rate at which N
gases are further reduced to N2. Equipped with the Nernst equation and the MichaelisMeneten equation, DNDC is able to simultaneously model nitrification and
denitrification in a soil.
1
The factors directly controlling denitrification rate are soil Eh, denitrifier activity, and
concentration of substrates (e.g., DOC, NO3-, NO2-, NO, or N2O). The indirect factors
include soil temperature, moisture, pH and any C or N-related processes. The production
of N2 and N2O is regulated by microbial population dynamics. The flux of N gas from
the soil to the atmosphere is regulated by soil clay, soil moisture (WFPS), and soil
temperature.
3. Where/what ecosystems has it been applied, and over what space/time scales, to
answer what questions?
DNDC was originally developed to study annual cropping systems, particularly grain
systems. Currently the agricultural version of DNDC includes more than 50 grain,
vegetable, and fruit crops. DNDC has been extensively applied in China and India to
study rice and wheat systems. Annual grain systems in IL, IA, and PA have been studied
with DNDC. DNDC has been developed to study grazing lands in CO and arid grain and
pasture lands in Australia and New Zealand. DNDC has also been developed to study
forest ecosystems, and has been applied to European forest systems which have N trace
gas validation data. DNDC was recently selected as the central model serving a EU-scope
project, NitroEurope. Currently, DNDC is being used mainly for trace gas inventory and
Best Management Practice studies at site, regional or continental scale.
Most applications of DNDC have been conducted at annual to decadal time scales.
4. How (if at all) are denitrification measurement data used to specify these model
structures or parameters, or to evaluate model output?
Denitrification functional relationships were developed from experimental data in
controlled environments.
In systems where denitrification data are available these data have been used for model
calibration and validation. In many systems, denitrification data is not available and N
dynamics have been bounded by other measurements, such as nitrate leaching, soil
inorganic N, and plant N. Additionally, soil environment data such as temperature,
moisture, freezing, thawing have been used to assess DNDC performance.
To optimize DNDC-simulated denitrification, a checklist should be followed:
(1)
(2)
(3)
(4)
Crop growth and yield must be correctly simulated;
Soil climate must be correctly simulated;
Soil C dynamics must be correctly simulated; and
The dominant N fluxes (i.e., nitrate leaching, N uptake by plants and ammonia
volatilization) need to be estimated.
5. What is your view of the strengths and weaknesses of the denitrification
estimates?
2
DNDC is a process-based model tracking each step of the sequential reactions of
denitrification. Trace gas flux in DNDC depends on the interaction between microbial
populations and the soil environment. This directly regulates gas flux rates based on
biotic controls, rather than relying strictly on N availability and the soil environment.
DNDC calculates denitrification hourly and aggregates denitrification trends at a daily
time scale. This temporal resolution exceeds available measurements and therefore the
model cannot be validated at this temporal scale. At the same time, model outcomes at
this fine temporal resolution can be used to think about the potential for high variation in
N trace gas flux depending on the soil environment and climate conditions.
To validate the detailed processes simulated by the model, well designed laboratory
experiments are required, which require a significant technology and time investment.
6. What are the opportunities for adaptations and couplings of this model with other
approaches?
DNDC is currently being modified to incorporate the hydrologic dynamics of the SWAT
model.
DNDC has been compared to other models in terms of modeled C dynamics. Currently
we are comparing DNDC outcomes to other estimates of aggregated N dynamics in the
Corn Belt including mass balance and simulations using EPIC.
Several watershed DNDC models (e.g., Elkhorn Slough-DNDC, Old Woman CreekDNDC etc.) have been developed by integrating DNDC with detailed GIS databases to
serve optimizing Best Management Practice at watershed scale.
7. How accessible is the model? Is the code available to the public? Is it userfriendly or is there a very steep learning curve?
DNDC can be downloaded as a .exe file for use in the Microsoft Windows operating
system environment. The model code, written in C++, is available upon request from
Changsheng Li. Basic application of the DNDC model using the downloadable .exe is
user-friendly. The model can be run after defining soil conditions, crop management, and
climate. A thorough understanding of the DNDC model requires a significant amount of
reading model code.
8. How do you see this model being useful in the scope of modeling denitrification
along the soils to coastal ecosystem continuum? What specific progress or potential
applications to our case study basins (i.e., what you responded to the CASE STUDY
MODEL email with)...?
DNDC has been developed as a tool for assessing dentrification from upland and wetland
ecosystems. DNDC can be applied to model C and N dynamics in agroecosystems and
forest ecosystems, providing detailed predictions of upstream landscape C and N
3
dynamics. DNDC has been applied in IL and will be presented as part of the workshop
case study analysis.
DNDC has not been widely applied to riparian or coastal environments although the
potential exists. By linking to spatially distributed hydrological models such as SWAT,
MIKE SHE etc., DNDC has been successfully utilized to predict C sequestration, trace
gas emissions and biomass production for forested wetlands in the coastal zone of South
Carolina (Li et al. 2004; Cui et al., 2005).
9. What would be needed to scale up the model to the whole watershed scale?
Spatial data of soil texture, soil C, cropping system management, and climate would be
needed to model terrestrial C and N dynamics for an entire watershed using DNDC.
10. Please list a few key references that describe the model and/or your work with
the above.
Cui, J., C. Li, and C. Trettin. 2005. Analyzing the ecosystem carbon and hydrologic
characteristics of forested wetland using a biogeochemical procees model, Global
Change Biology, 11:278-289, doi: 10.1111/j.1365-2486.2005.00900.x.
Li, C., J. Cui, G. Sun, and C. Trettin. 2004. Modeling impacts of management on carbon
sequestration and trace gas emissions in forested wetland ecosystems. Environmental
Management DOI: 10.1007/s00267-003-9128-z.
Li, C., A. Mosier, R. Wassmann, Z. Cai, X. Zheng, Y. Huang, H. Tsuruta, J. Boonjawat,
and R. Lantin. 2004. Modeling Greenhouse Gas Emissions from Rice-Based
Production Systems: Sensitivity and Upscaling. Global Biogeochemical Cycles 18:
GB1043, doi:10.1019/2003GB002045.
Li C., Frolking S.E. and Frolking T.A. 1992. A model of nitrous oxide evolution from
soil driven by rainfall events: 1. Model structure and sensitivity. J. Geophys. Res.
97:9759-9776.
Li C., Aber J., Stange F., Butterbach-Bahl K., and Papen H.. 2000. A process-oriented
model of N2O and NO emissions from forest soils: 1. Model development. J.
Geophys. Res. 105:4369-4384.
Li C., Frolking S., Xiao X., Moore B. III, Boles S., Qiu J., Huang Y., Salas W. and Sass
R.. 2005. Modeling impacts of farming management alternatives on CO2, CH4, and
N2O emissions: a case study for water management of rice agriculture of China.
Global Biogeochemical Cycles 19:GB3010.
Tonitto C., David M.B., Drinkwater L.E. and Li C. In Review. Application of the DNDC
Model to Tile-drained Illinois Agroecosystems: Model Calibration, Validation, and
Sensitivity Analysis. Nutr. Cycl. Agroecosys.
4