Getting Started with EEMT¶

Overview¶

This guide will help you set up the EEMT calculation environment and run your first analysis. EEMT calculations require topographic data, climate data, and specialized GIS software.

System Requirements¶

Minimum Hardware¶

• CPU: 4 cores (8+ recommended for parallel processing)
• RAM: 8 GB (16+ GB recommended for large datasets)
• Storage: 50 GB free space (more for large study areas)
• GPU: Optional but recommended for r.sun calculations

Software Dependencies¶

Core GIS Stack¶

# Ubuntu/Debian installation
sudo apt update
sudo apt install grass grass-dev gdal-bin python3-gdal python3-rasterio

# macOS (via Homebrew)
brew install grass gdal python

# Windows: Use OSGeo4W installer or Conda
conda install -c conda-forge grass gdal rasterio

Python Environment¶

# Create virtual environment
python3 -m venv eemt-env
source eemt-env/bin/activate  # Linux/macOS
# eemt-env\Scripts\activate   # Windows

# Install required packages
pip install -r requirements.txt

Required Python Packages¶

numpy>=1.24
pandas>=2.0
xarray>=2024.1
rasterio>=1.3
geopandas>=0.14
matplotlib>=3.7
scipy>=1.11
requests>=2.28

Core Concepts¶

EEMT Components¶

Effective Energy and Mass Transfer quantifies energy flux to the Critical Zone:

EEMT = E_BIO + E_PPT [MJ m⁻² yr⁻¹]

Biological Energy (E_BIO)¶

• Energy from photosynthesis and primary production
• Calculated from Net Primary Production (NPP)
• Formula: E_BIO = NPP × 22 MJ/kg

Precipitation Energy (E_PPT)¶

• Thermal energy from effective precipitation
• Water available for subsurface processes
• Formula: E_PPT = F × c_w × ΔT
• F = effective precipitation flux [kg m⁻² s⁻¹]
• c_w = specific heat of water [4.18 × 10³ J kg⁻¹ K⁻¹]
• ΔT = temperature difference from 273.15K

Calculation Approaches¶

1. Traditional EEMT (EEMT_TRAD)¶

• Uses simple climate averages
• No topographic or vegetation effects
• Good for regional comparisons

2. Topographic EEMT (EEMT_TOPO)¶

• Incorporates slope, aspect, and solar radiation
• Mass-conservative water redistribution
• Accounts for local microclimates

3. Vegetation EEMT (EEMT_TOPO-VEG)¶

• Adds vegetation structure effects
• Uses Leaf Area Index (LAI) and canopy height
• Most accurate for site-specific analyses

Basic Workflow¶

Step 1: Prepare Input Data¶

Digital Elevation Model (DEM)¶

# Download USGS 3DEP data (example for Arizona)
wget "https://cloud.sdsc.edu/v1/AUTH_opentopography/Raster/SRTMGL1/SRTMGL1_srtm.zip"

# Or use OpenTopography API
curl -X GET "https://portal.opentopography.org/API/globaldem" \
  -G -d "demtype=SRTMGL1" \
  -d "south=32.0" -d "north=32.5" \
  -d "west=-111.0" -d "east=-110.5" \
  -d "outputFormat=GTiff"

Climate Data¶

# DAYMET data (automated download in workflow)
# Or manual download from ORNL DAAC
# https://daymet.ornl.gov/

Step 2: Set Up GRASS GIS Environment¶

# Create new GRASS location from DEM
grass -c your_dem.tif ~/grassdata/eemt_project/PERMANENT

# Verify projection
g.proj -p

# Set computational region  
g.region raster=your_dem -p

Step 3: Run Solar Radiation Analysis¶

# Basic solar radiation for single day
r.sun elevation=dem day=180 glob_rad=solar_jun29

# Multi-day parallel processing
python sol/run-workflow --step 15 --num_threads 4 your_dem.tif

Step 4: Calculate EEMT¶

Simple EEMT Calculation¶

import numpy as np
import rasterio

# Load required data
with rasterio.open('solar_annual.tif') as src:
    solar_radiation = src.read(1)

with rasterio.open('precipitation.tif') as src:
    precipitation = src.read(1)

with rasterio.open('temperature.tif') as src:
    temperature = src.read(1)

# Calculate NPP (simplified)
npp = 3000 * (1 - np.exp(1.315 - 0.119 * temperature))  # kg/m²/yr
npp[precipitation <= 0] = 0  # No production without water

# Calculate energy components
e_bio = npp * 22e6 / (365 * 24 * 3600)  # Convert to W/m²
e_ppt = precipitation * 4180 * (temperature - 273.15) / (365 * 24 * 3600)
e_ppt[e_ppt < 0] = 0  # No energy below freezing

# Calculate EEMT
eemt = e_bio + e_ppt  # W/m²
eemt_annual = eemt * 365 * 24 * 3600 / 1e6  # MJ/m²/yr

Using the EEMT Workflow¶

# Full EEMT calculation with topography
cd eemt/
python run-workflow --start-year 2015 --end-year 2020 \
  --step 15 --num_threads 8 \
  --output eemt_results/ \
  your_dem.tif

Validation and Quality Control¶

Check Input Data Quality¶

# Verify DEM characteristics
import rasterio
with rasterio.open('dem.tif') as src:
    print(f"Projection: {src.crs}")
    print(f"Resolution: {src.res}")
    print(f"Bounds: {src.bounds}")
    print(f"Data type: {src.dtypes[0]}")

Validate EEMT Results¶

# Check EEMT value ranges
eemt_stats = {
    'min': np.nanmin(eemt_annual),
    'max': np.nanmax(eemt_annual), 
    'mean': np.nanmean(eemt_annual),
    'std': np.nanstd(eemt_annual)
}

print(f"EEMT range: {eemt_stats['min']:.1f} - {eemt_stats['max']:.1f} MJ/m²/yr")

# Expected ranges by climate zone:
# Arid: 5-15 MJ/m²/yr
# Semiarid: 15-25 MJ/m²/yr  
# Humid: 25-50 MJ/m²/yr

Common Issues and Solutions¶

Memory Issues¶

# Process large DEMs in tiles
gdal_retile.py -ps 1000 1000 -targetDir tiles/ large_dem.tif

# Or use chunked processing with Dask
import dask.array as da
dem_chunked = da.from_array(dem, chunks=(1000, 1000))

Projection Problems¶

# Reproject DEM to match climate data
gdalwarp -t_srs EPSG:4326 input_dem.tif output_dem.tif

# Check coordinate reference systems
gdalinfo dem.tif | grep -i "coordinate system"

Missing Climate Data¶

# Download DAYMET data programmatically
python scripts/download_daymet.py --bbox -111.0,32.0,-110.5,32.5 \
  --years 2015-2020 --variables tmin,tmax,prcp

Next Steps¶

Once you have basic EEMT calculations working:

1. Data Sources Guide - Access higher resolution data
2. GRASS GIS Tutorials - Advanced terrain analysis
3. Workflow Examples - Real-world case studies
4. API Reference - Function documentation

Performance Optimization¶

Parallel Processing¶

• Use --num_threads parameter for CPU cores
• Enable GPU acceleration for r.sun when available
• Process multiple years simultaneously

Memory Management¶

• Tile large datasets with gdal_retile.py
• Use compressed GeoTIFF outputs (-co COMPRESS=LZW)
• Monitor memory usage with htop or Task Manager

Storage Optimization¶

• Use Cloud-Optimized GeoTIFF (COG) format
• Compress intermediate files
• Clean up temporary GRASS locations

For detailed technical information, see the scientific background and API documentation.