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Terrestrial Ecosystem Model (TEM) ​

The Terrestrial Ecosystem Model (TEM) serves as the core component of SINDBAD's Model-Data Integration (MDI) framework. It provides a comprehensive system for:

  • Representing ecosystem processes

  • Implementing process modeling approaches

  • Managing model execution (spinup, time loops, etc.)

Model Components ​

Ecosystem Processes ​

A Model represents a fundamental ecosystem process that can be modeled using various methods. Each model focuses on a specific, indivisible process. For example, rather than modeling photosynthesis as a single process, it can be decomposed into components like:

  • radiation use

  • transpiration

  • Other sub-processes

Modeling Approaches ​

An Approach defines the specific method used to calculate or emulate a process. For instance, baseflow generation might be modeled using:

  • A linear approach (proportional to groundwater storage)

  • Alternative methods based on different assumptions

Core Methods ​

Every SINDBAD approach implements these fundamental methods:

  1. define

  • Initializes memory allocation

  • Sets up required variables and arrays

  1. precompute

  • Updates variables based on parameters/forcing

  • Prepares for time-dependent calculations

  1. compute

  • Advances model state in time

  • Applies dynamic updates using current data

  1. update (optional)

  • Modifies pools and variables

  • Handles within-time-step adjustments

Parameter Estimation ​

Model parameters control process responses and are often uncertain. SINDBAD supports various parameter estimation methods:

  1. Parameter Calibration

  • Based on modeling principles

  • Incorporates physical constraints

  1. Optimization

  • Mathematical optimization techniques

  • Cost function minimization

  1. Machine Learning

  • Parameter learning approaches

  • Data-driven estimation

Model Structure ​

Ecosystem Model ​

The core of SINDBAD's framework combines:

  • Ecosystem processes

  • Execution methods

  • Initialization procedures

  • Time-stepping algorithms

Model Configuration ​

A Model Structure represents a collection of ecosystem processes designed for specific scientific objectives. It includes:

  • Selected SINDBAD models

  • Defined approaches

  • Process dependencies

Model Dependencies Models may have interdependencies. For example:

  • fAPAR depends on LAI

  • Required models must be included in the structure

Model Selection ​

Experiments can select models from:

  • Standard SINDBAD models

  • Custom model variants

  • Subsets of available models

Viewing Available Models

julia
using Sindbad
standard_sindbad_models
all_available_sindbad_models

Model Implementation Example ​

Here's an example of implementing a custom model structure for vegetation growth with water limitations:

julia
# Define custom model structure
hypothetical_models = (
    :radiation,      # Handles radiation use
    :transpiration,  # Manages water use
    :soilwater,     # Controls soil moisture
    :allocation,    # Distributes resources
    :turnover       # Handles biomass changes
)

# Replace default models in experiment setup
hypothetical_replace_info = (;"model_structure.sindbad_models" => hypothetical_models)
info = getExperimentInfo(experiment_json; replace_info=hypothetical_replace_info)

Model Structure Configuration

  • Models are selected through model structure settings

  • The selected_models field defines which models are active in an experiment

  • Custom model structures must maintain required dependencies

Model Execution Details ​

The runTEM function manages the complete lifecycle of the TEM:

Core Functions ​

  1. Initialization

  • defineTEM: Initializes model variables and arrays

  • precomputeTEM: Updates variables based on new realizations

  1. Time Stepping

  • timeLoopTEM: Manages the temporal evolution

  • computeTEM: Updates land state for each time step

  1. State Management

  • coreTEM: Coordinates overall execution

  • Handles precomputation, spinup, and time-stepping

Execution Flow ​

  1. Setup Phase

  • Initialize model components

  • Configure parameters

  • Set up data structures

  1. Spinup Phase

  • Run equilibrium iterations

  • Stabilize ecosystem states

  • Verify convergence

  1. Main Simulation

  • Execute time steps

  • Update model states

  • Process outputs

Spinup Configuration ​

The spinup process ensures model stability by:

  1. Initialization

  • Set initial conditions

  • Configure climate forcing

  • Define convergence criteria

  1. Equilibrium Search

  • Iterate model states

  • Monitor pool changes

  • Check convergence

  1. Validation

  • Verify state stability

  • Check mass balance

  • Document final states

Spinup Considerations

  • Ensure sufficient spinup duration

  • Monitor convergence carefully

  • Validate equilibrium conditions

  • Document spinup configuration

Best Practices ​

  1. Model Selection

  • Choose appropriate process representations

  • Consider computational requirements

  • Verify model dependencies

  • Test model combinations

  1. Parameter Management

  • Document parameter sources

  • Validate parameter ranges

  • Consider uncertainty

  • Test sensitivity

  1. Execution

  • Monitor convergence

  • Check mass balance

  • Validate results

  • Document configurations

  1. Spinup

  • Verify equilibrium conditions

  • Check convergence criteria

  • Monitor state variables

  • Document spinup duration

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