Welcome to dvmdostem’s documentation!

Note

This project is under active development.

DVM-DOS-TEM is a process-based biosphere model, designed to simulate biophysical and biogeochemical processes between the soil, the vegetation and the atmosphere. The model is spatially explicit and focuses on representing carbon and nitrogen cycles in high latitude ecosystems and how they are affected at seasonal (i.e. monthly) to centennial scales by climate, disturbances and biophysical processes such as permafrost, soil thermal and hydrological regimes, snow cover or canopy development.

The code is available on Github: https://github.com/uaf-arctic-eco-modeling/dvm-dos-tem

Note

What’s with the name? DVM-DOS-TEM is the acronym for the Terrestrial Ecosystem Model (TEM) with Dynamic Vegetation Model (DVM) and Dynamic Organic Soil Model (DOS). Sometimes we write dvmdostem, sometimes we write dvm-dos-tem and sometimes we write DVM-DOS-TEM. These are all the same thing. The order is always the same, but sometimes we use capitals and sometimes lower case, sometimes with hyphens and sometimes without. This is a fluke of history. In some cases it looks better capitalized, sometimes it looks better lower case. The repository ended up with hyphens in the name, but the compiled binary executable does not have hyphens.

Contents

• 1. Model Overview
  • 1.1. Structure
    • 1.1.1. Spatial
    • 1.1.2. Temporal
      • pre-run (pr)
      • equilibrium (eq)
      • spinup (sp)
      • transient (tr)
      • scenario (sc)
    • 1.1.3. Community Types (CMTs)
    • 1.1.4. Vegetation Types (PFTs)
    • 1.1.5. Soil (Layers)
  • 1.2. Processes
    • 1.2.1. Carbon
    • 1.2.2. Water
    • 1.2.3. Nitrogen
    • 1.2.4. Energy Balance
    • 1.2.5. Permafrost
    • 1.2.6. Disturbance
    • 1.2.7. Methane
  • 1.3. Inputs/Outputs (IO)
    • 1.3.1. Inputs
    • 1.3.2. Outputs
      • Output Selection
      • Process
      • Variable Output Combinations
• 2. Running
  • 2.1. Practical
    • 2.1.1. Getting Started
      • Download
      • Dependencies
      • Compile
      • Install
    • 2.1.2. Control Options
      • Command line
      • Configuration
      • Parameters
    • 2.1.3. Running the Model
      • Setting up a run
      • Spatial Size
      • Single Site or Small Regions
      • Large Regions (HPC)
        • Requirements
        • Splitting
        • Starting a run
        • Merging
    • 2.1.4. Running from Restart Files
      • Set up
      • Restart
    • 2.1.5. Running a Sensitivity Analysis
    • 2.1.6. Parallel Options
    • 2.1.7. Processing Outputs
    • 2.1.8. Processing Inputs
      • From IEM/SNAP data
      • From ERA5
  • 2.2. Conceptural Modeling Method and Workflow
    • 2.2.1. Develop goals
    • 2.2.2. Conceptualization
    • 2.2.3. Formulation
    • 2.2.4. Implementation
    • 2.2.5. Parameterization
    • 2.2.6. Model Testing
    • 2.2.7. Model Analysis
    • 2.2.8. Pre- and Post- Processing
• 3. Dev Info
  • 3.1. Languages, Software Structure
  • 3.2. Coding Conventions
  • 3.3. Documentation
    • 3.3.1. Preview -> Editing -> Contributing
      • Previewing
      • Editing
      • Contributing
    • 3.3.2. Publishing
    • 3.3.3. Note about images
  • 3.4. Version Management
    • 3.4.1. Version Control and Hosting
    • 3.4.2. Branching Model
    • 3.4.3. Workflow
    • 3.4.4. Releases and Version Numbering
    • 3.4.5. Keeping your repo up to date with upstream
  • 3.5. Testing and Deployment
  • 3.6. Setting up a dev environment
    • 3.6.1. Setting up with Vagrant
    • 3.6.2. Setting up with Docker
    • 3.6.3. Setting up with Ubuntu
• 4. SA and UQ
• 5. Command Cheat Sheet
  • 5.1. Git
    • 5.1.1. Status
    • 5.1.2. Branch Management
    • 5.1.3. Keeping master up to date
    • 5.1.4. Keeping master up to date while retaining local changes
    • 5.1.5. Changing branches
    • 5.1.6. Making a commit
    • 5.1.7. Code Management
  • 5.2. Docker
  • 5.3. dvmdostem
• 6. Common Project Acronyms
• 7. Calibration
  • 7.1. What is calibration for dvmdostem?
  • 7.2. When to conduct a calibration?
  • 7.3. How to conduct a calibration?
    • 7.3.1. Calibration targets
      • Vegetation Targets
      • Soil Targets
    • 7.3.2. Calibration parameters
      • Vegetation parameters
      • Soil parameters
  • 7.4. Calibration Process - Conceptual
    • 7.4.1. Calibrate vegetation parameters without N limitation
    • 7.4.2. Calibrate vegetation parameters with N limitation
    • 7.4.3. Calibrate soil parameters
  • 7.5. Reference
• 8. References

Examples and Tutorials

• 1. Prelude
  • 1.1. General Programming
    • 1.1.1. General Workflow Loops
      • Core Development Process Loop
      • Loop with individual version control
      • Incorporating more people
    • 1.1.2. Software Development Patterns
  • 1.2. Version Control
    • 1.2.1. Why use version control?
    • 1.2.2. Getting Help
    • 1.2.3. Tools
    • 1.2.4. Setup
  • 1.3. Documentation Types
  • 1.4. Docker
  • 1.5. Plotting and Graphical Outputs
    • 1.5.1. Interactive Window
    • 1.5.2. Static Plot
    • 1.5.3. Interactive Browser Plots
• 2. Basic Model Setup and Run
  • 2.1. Install tools
    • 2.1.1. Version control
    • 2.1.2. Run time environment and virtualization with Docker
    • 2.1.3. Text editors and terminal emulator
    • 2.1.4. Summary
  • 2.2. Get the code
  • 2.3. Build Docker images
  • 2.4. Start and run Docker containers
  • 2.5. Setting up a dvmdostem run
    • 2.5.1. Setting up the working directory
    • 2.5.2. Adjusting the config file
    • 2.5.3. Adjusting the run mask
    • 2.5.4. Choosing the outputs
  • 2.6. Launch the dvmdostem run
• 3. EE 0 - Single Site Run
  • 3.1. Experiment Design
  • 3.2. Example Python setup
  • 3.3. Explore Input Datasets
  • 3.4. Computing Mean Vegetation C and Soil C
  • 3.5. Computing Monthly NEE
  • 3.6. Computing Mean GPP, RA, RH, NEE
  • 3.7. Plot Active Layer Depth
  • 3.8. Plot Seasonal Dynamic
  • 3.9. Plot Soil Temperatures
• 4. Data Summary and Processing
  • 4.1. Common Tools
    • 4.1.1. NCOs
    • 4.1.2. Unidata
    • 4.1.3. Python netCDF4 package
    • 4.1.4. Python xarray package
    • 4.1.5. R
  • 4.2. Common Operations
    • 4.2.1. Explore structure of NetCDF file
    • 4.2.2. Compress NetCDF file
    • 4.2.3. Subset netcdf files by dimensions
    • 4.2.4. Display variable values to terminal
    • 4.2.5. Compute sum, average and standard deviation across dimensions
    • 4.2.6. Append files of same dimensions
    • 4.2.7. Operations with multiple variables
    • 4.2.8. Concatenate files along the record dimension
• 5. Plotting
  • 5.1. Existing Tools and Patterns
    • 5.1.1. Details
    • 5.1.2. Off the Shelf Tools
  • 5.2. General Process
  • 5.3. Example Plots
    • 5.3.1. Basic 1 pixel timeseries
    • 5.3.2. Interactive map of inputs using Bokeh
      • Starting the server
      • View in your browser
    • 5.3.3. Plot Driving Inputs
• 6. Manual Calibration
• 7. MADS Assisted Calibration
  • 7.1. Sensitivity Analysis (SA)
  • 7.2. MADS Assisted Calibration (CA)
• 8. Contributing to the Codebase
  • 8.1. Single person topic branch and pull request process
  • 8.2. Multi-person topic branch process
• 9. Using Github’s Features
  • 9.1. Repository Hosting, Organizations
  • 9.2. Code Browsing
  • 9.3. Releases
  • 9.4. Documentation
  • 9.5. Pull requests
  • 9.6. Issue tracker
  • 9.7. Tools we have not used (yet)

Command Line Interfaces

• SA_setup_and_run.py
  • Positional Arguments
  • Named Arguments
• param.py
  • Named Arguments
• runmask.py
  • Positional Arguments
  • Named Arguments
• input.py
  • Positional Arguments
  • Sub-commands
    • query
      • Named Arguments
    • crop
      • Positional Arguments
      • Named Arguments
    • climate-ts-plot
      • Positional Arguments
      • Named Arguments
    • climate-gap-plot
      • Positional Arguments
• qcal.py
  • Positional Arguments
  • Named Arguments
• outspec.py
  • Positional Arguments
  • Named Arguments

Python Scripts API

• drivers.BaseDriver
• drivers.Sensitivity
• drivers.MadsTEMDriver
• mads_calibration.SA_post_hoc_analysis
• util.param
• util.output
• util.input
• util.metrics