Ecosystems and Biogeochemical Models
Ecosystems & Biogeochemical Models
The Terrestrial Ecosystem Model
The Terrestrial Ecosystem Model (TEM) is a process-based ecosystem model that describes carbon and nitrogen dynamics of plants and soils for terrestrial ecosystems. The TEM uses spatially referenced information on climate, elevation, soils, vegetation and water availability as well as soil- and vegetation-specific parameters to make monthly estimates of important carbon and nitrogen fluxes and pool sizes of terrestrial ecosystems. The TEM now operates on global scale, at a monthly time step and a 0.5 degrees latitude/longitude spatial resolution.
Following is a structure diagram of TEM
Soil thermal model (STM)
The STM was developed based on the Goodrich model, the vertical profile is divided into snow cover, moss, upper organic soil, lower organic soil, and mineral soil layers (Figure 1a). Appli-cation of the model for a site requires specification of the thickness of each layer and simulation depth steps within each layer. The thermal properties of each layer also need to be prescribed. In addition, the dynamics of phase changes in the soils depend on the phase temperature, which we set to 0oC for applications of the model in this study. Specification of the upper boundary condition includes the temperature at the top of the moss layer during the summer and at the surface of snow during the winter. In this study we prescribed the depth, density, and thermal properties of snow cover. For the lower boundary condition we assumed a constant heat flux. Alter-natively, the lower boundary condition can be specified as a temporally varying function of temperature or heat flux. Ap-plication of the model requires the prescription of initial con-ditions, which include specifying the initial soil temperatures of the system and the presence or absence of permafrost (Zhuang et al., 2001).
Following is a diagram of the STM:
Water Balance Model 1.0
Fig.1 Sturcture diagram for Water Blance Model 1.0
Fig. 1. (a) Overview of the model used in this study, which required coupling a hydrological model (HM) with a soil thermal model (STM) and a terrestrial ecosystem model (TEM). The HM receives information on active layer depth from the STM and information on leaf area index from TEM. The STM receives information on moss thickness from TEM and information on soil moisture and snow pack from the HM. The TEM receives information on soil temperature from STM and information on soil moisture and evapotranspiration from HM. (b) The HM considers the dynamics of eight state variables for water including (1) rain intercepted by the canopy (RI), (2) snow intercepted by the canopy (SI), (3) snow layer on the ground (GS), (4) moisture content of the moss plus fibric organic layer (MMO), (5) rainfall detention storage (RDS), (6) snowfall detention storage (SDS), (7) moisture content of the humic organic layer (MHU), and (8) moisture content of the mineral soil layer (MMI). The HM simulates changes in these state variables at monthly temporal resolution from the fluxes of water identified in Fig. 2b, which include (1) Rainfall (RF), (2) Snowfall (SF), (3) canopy transpiration (TC = TC1 + TC2), (4) canopy evaporation (EC), (5) through fall of rain (RTH), (6) canopy snow sublimation (SS), (7) through fall of snow (STH), (8) ground snow sublimation (GSS), (9) soil surface evaporation (EM), (10) snow melt (SM), (11) percolation from moss plus fibric organic layer to humic organic layer (P1), (12) percolation from humic organic layer to mineral soil layer (P2), (13) runoff from the moss plus fibric layer to the rainfall detention storage pool (RORMO), (14) runoff from the moss plus fibric layer to the snow melt detention storage pool (ROSMO), (15) runoff from the rainfall detention storage pool to surface water networks (RORDS), (16) runoff from the snow melt detention storage pool to surface water networks (ROSDS), and (17) drainage from mineral soil layer to ground water (DR) (Zhuang et al., 2002)
Fire version of TEM
In this version of TEM, we integrated more effective algorithms of biogeochemistry after fire with the soil thermal dynamics simulated by STM and the hydrology simulated by HM. After fire, the STM and HM require information from TEM on how the thickness of moss and leaf area index change. Therefore, we modified TEM by including formulations to simulate changes in the thickness of moss, canopy biomass, and leaf area index as the stand recovers from disturbance. The TEM requires information from STM on soil temperature and from HM on soil moisture of the humic organic layer and on estimated actual evapotranspiration (EET). We modified TEM so that soil temperature and soil moisture of the humic organic soil layer influences the simulation of heterotrophic respiration, nitrogen mineralization, and nitrogen uptake by the vegetation. Similar to previous versions of TEM, EET influences the simulation of gross primary production (GPP) (Zhuang et al, 2002).
Following is a structure diagram of the FireTEM
Updated Water Balance Model (WBM)
The hydrological module [HM, Zhuang et al., 2002] was further developed including: 1) the consideration of surface runoff when determining infiltration rates from rain throughfall and snow melt; 2) the inclusion of the effects of temperature and vapor pressure deficit on canopy water conductance when estimating evapotranspiration based on Waring and Running  and Thornton ; 3) a more detailed representation of water storage and fluxes within the soil profile of upland soils based on the use of the Richards equation in the unsaturated zone [Hillel, 1980]; and 4) the development of daily estimates of soil moistures and water fluxes within the soil profile instead of monthly estimates. As the original version of the HM is designed to simulate water dynamics only in upland soils, algorithms have also been added to simulate water dynamics in wetland soils. For wetlands, the soil profile is divided into two zones based on the water table depth: 1) an oxygenated, unsaturated zone; and 2) an anoxic, saturated zone. The soil water content and the water table depth in these wetland soils are determined using a water-balance approach that considers precipitation, runoff, drainage, snow melt, snow sublimation, and evapotranspiration. We assume that wetland soils are always saturated below 30 cm, which represents the maximum water table depth [Granberg et al., 1999]. Daily soil moisture at each 1 cm depth above the water table is modeled with a quadratic function and increases from the soil surface to the position of the water table [Granberg et al., 1999]. Infiltration, runoff, snowmelt, snow sublimation and evapotranspiration are simulated in wetlands using the same algorithms as for uplands. Drainage from wetlands is assumed to vary with soil texture, but does not exceed 20 mm day-1 (Zhuang et al., 2004GBC).
Following is a structure digram of the Updated WBM
Land-Use and Land-Change version of TEM
Fig.2 Structure diagram for TEM 5.0
Fig. 2. (a) The structure of TEM 5.0 includes (1) hydrological dynamics based on a Water Balance Model (WBM, Vorosmarty et al., 1989), biogeochemistry dynamics based on the Terrestrial Ecosystem Model (TEM 4.2, McGuire et al., 2001), and soil thermal dynamics based on the Soil Thermal Module (STM, Zhuang et al., 2001). Among the three components, the STM receives vegetation characteristics from TEM, receives snow depth and snow properties from the WBM, and provides simulated soil temperatures and freeze-thaw dynamics to TEM to drive ecosystem processes. (b) Overview of the simulation protocol implemented by TEM 5.0 in this study to assess the concurrent effects of increasing atmospheric CO2, climate variability, and cropland establishment and abandonment during the 20th Century.
Methane Modeling Framework
Fig. 1. Conceptual framework of coupled models to estimate net fluxes of methane and carbon dioxide. Shown are external spatial inputs (blue) for driving or calibrating models, internal information exchange (yellow) between models, and final model outputs (green). Arrows indicate the direction of information exchange among all components. See text and supplementary materials for details (available at stacks.iop.org/ERL/8/045003/mmedia) (Zhu et al., 2013).
Fig. 1. N cycling among the atmosphere, biosphere, and pedosphere. Major processes were modeled in AgTEM. SOM, soil organic matter; N2, nitrogen; NH3, ammonia; NOX, nitrogen oxides; N2O, nitrous oxide; NO, nitric oxide (Qin et al., 2013a).