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We have used the following models in our research. Please feel free to contact us if you have questions about any of these models.

1. Process-based ecosystem models

(1) DeNitrification-DeComposition (DNDC)

The process-based biogeochemical model, DeNitrification-DeComposition (DNDC), has incorporated a relatively complete suite of biophysical and biogeochemical processes, which enables it to simulate vegetation growth, complex transport and transformations of carbon (C) and nitrogen (N), and GHG fluxes in terrestrial ecosystems (Li et al., 1992a; Li, 2000; Li et al., 2012). Over the last three decades, DNDC has been widely used to simulate C and N cycling of different ecosystems and over various spatial and temporal scales (Gilhespy et al., 2014; Giltrap et al., 2010). However, the model has not incorporated processes of surface energy exchange. This limitation in simulating energy exchange hinders applications of DNDC to assess the radiative forcing of different land types and the impacts of land use change on climate.

To overcome this limitation and reduce uncertainties in assessing the radiative forcing of different land use/land cover types, we improved DNDC by incorporating processes simulating energy fluxes in the model. The improved DNDC model can explicitly simulate both GHG (CO2, CH4, and N2O) and energy fluxes for different land cover types. We tested the model against field observations of energy and CO2 fluxes from three different land use types (i.e., forest, hayfield, and cornfield) in the northeastern United States (i.e., the New England region). We further applied the model to predict long-term (100-years) radiative forcing of the forest, hayfield, and cornfield by considering their differences in both albedo and GHG fluxes, and analyzed the resulting differences in cumulative radiative forcing among these three dominant land use types in the northeastern United States.

Deng, J., Xiao, J., Ouimette, A., Zhang, Y., Sanders-DeMott, R., Frolking, S., Li, C. (2020) Improving a biogeochemical model to simulate surface energy, greenhouse gas fluxes, and radiative forcing for different land use types in Northeastern United States. Global Biogeochemical Cycles, 34, e2019GB006520. [PDF]

(2) PnET-CN

PnET-CN is a process-based model that simulates carbon, water, and nitrogen dynamics of forest ecosystems at monthly time steps (Aber et al. 1997; Ollinger et al. 2002). Though primarily a temperate forest model, the model has been adapted to grassland systems (e.g., Reich et al. 1999) and work is currently underway to generalize the model and produce a simple, alternative model applicable to all terrestrial ecosystem types.

We have translated the code of PnET-CN from Matlab to C, and also modified the model to run at the daily time step. We have successfully integrated PnET-CN and a Bayesian parameter estimation approach, Metropolis Markov chain Monte Carlo (MCMC). We have tested PnET-MCMC by optimizing 14 selected parameters. Our preliminary results show that the optimization of parameters using carbon fluxes from flux towers can constrain model parameters and significantly improve the model performance for simulating carbon fluxes. Our next step is to optimize the key parameters of PnET-CN using multiple constraints including carbon and water fluxes as well as streamwater chemistry and examine the coupling of carbon, water and nitrogen cycles

We recently generalized PnET-CN and made it applicable to grasslands, shrublands, and savannas. This version was implemented in R. The R code of the generalized PnET-CN can be downloaded at the Global Ecology Repository: Generalized PnET-CN. The following paper should be cited for this version:

Thorn, A., Xiao, J., Ollinger, S.V. (2015) Generalization and evaluation of the process-based forest ecosystem model PnET-CN for other biomes. Ecosphere 6(3):43. [PDF]

(3) Terrestrial Ecosystem Model (TEM)

TEM is a global biogeochemistry model that simulates the cycling of carbon, nitrogen, and water among vegetation, soils, and the atmosphere at monthly time steps. TEM has been used to examine the time-dependent responses of terrestrial carbon storage and the net carbon exchange with the atmosphere as influenced by historical climate, atmospheric CO2, land use, and soil thermal regime. The model structure and parameterization are well documented elsewhere (e.g., Raich et al., 1991; Tian et al., 1999; Zhuang et al., 2003).

We have used TEM to examine the impacts of severe extended drought on terrestrial carbon cycling in mid-latitude regions in past work (Xiao et al. 2009). Our results show that severe extended droughts substantially affected the terrestrial carbon budget in China during the 20th century.

2. Data-driven modeling approach (EC-MOD)

Our EC-MOD system upscales fluxes from tower footprint to regional, continental or global scales to produce gridded flux fields over these broad regions. The core of our system is a data-driven approach based on an ensemble of regression models (Xiao et al. 2008). This approach relies on rule-based models, each of which is a set of conditions associated with a multivariate linear submodel. These rule-based, piecewise regression models allow both numerical (e.g., carbon fluxes, temperature, vegetation index) and categorical variables (e.g., land cover type) as input variables, and account for possible nonlinear relationships between predictive and target variables. We have used EC-MOD to produce gridded flux fields (GPP and NEE) for temperate North America over the period 2000-2006 (Xiao et al. 2008, 2010, 2011).