High Resolution Mapping of EvapoTranspiration (HRMET)

Author: Sam Zipper (samzipper@ku.edu)
Python Port and Optimizations: Alexis Suero (alexis.esmb@gmail.com)

HRMET is a model designed for high-resolution mapping of evapotranspiration (ET), using surface temperature and weather data for precision-agriculture and drought sensitivity assessments.

Table of Contents

• Installation
• Repository Contents
• Key Assumptions of HRMET
• Known Issues
• Citation
• License

Installation

1. Clone the repository:

   git clone https://github.com/alex-suero/HRMET
   cd HRMET

2. Install the required Python libraries:

   • Install rasterio package:

    pip install rasterio

   • Install scikit-image package:

    pip install scikit-image

Repository Contents

• HRMET.py: this is the vectorized HRMET code.

• HRMET_single.py: this is the original HRMET code translated to Python.

• HRMET Example.ipynb: a Python Notebook that provides an example of HRMET use. (Note: data used in this notebook is not included in this repository.)

Key Assumptions of HRMET

• Spatial Homogeneity: HRMET calculates a 1D surface energy balance and can be applied over fields to create raster maps of ET. When generating ET maps, assumptions of spatial homogeneity should be considered carefully. For example, Zipper et al. (2014) assumes uniform meteorological conditions across a relatively small field (~600 x 600 m). This assumption may become less valid as the spatial scale increases.

• Precision-Agriculture Scale: HRMET is designed for small, precision-agriculture applications. While the physical principles may extend to larger scales, sufficiently high-resolution input data is essential for accurate results.

Known Issues

• Short Canopies: HRMET does not perform well in areas with extremely short canopies or desert-like environments (canopy height approaching 0 meters).

• Canopy Height vs. Measurement Height: The model may produce inaccurate results if the canopy height exceeds the height of temperature and wind speed measurements.

• G_Tw Coefficient: The G_Tw coefficient (used in cloudiness estimation) defaults to a summer value. Future versions should include automatic selection based on the day of the year (DOY).

Citation

HRMET is introduced and described in the following publication:

Zipper, S.C. & S.P. Loheide II (2014). Using evapotranspiration to assess drought sensitivity on a subfield scale with HRMET, a high resolution surface energy balance model. Agricultural & Forest Meteorology 197: 91-102. DOI: 10.1016/j.agrformet.2014.06.009

Link: http://dx.doi.org/10.1016/j.agrformet.2014.06.009

License

This project is licensed under the GNU General Public License v3.0.