TSEB modeling and the comparison between the model results and the eddy-covariance monitored data within the footprint area

Rui Gao;Torres-Rua, Alfonso Faustino;Nassar, Ayman;Lawrence Hipps;Hector Nieto;Mahyar Aboutalebi;William A. White;Martha Anderson;William P. Kustas;Maria Mar Alsina;Joseph Alfieri;Nick Dokoozlian;Feng Gao;Lynn McKee;John H. Prueger;Luis Sanchez;Andrew J. Mcelrone;Nicolas Bambach Ortiz;Ian Gowing;Calvin Coopmans

Description

The widely used two-source energy balance (TSEB) model coupled with AggieAir (https:// uwrl.usu.edu/aggieair/, a type of small Unmanned Aerial System) data can provide high-resolution modeled energy components, evapotranspiration partitioning, etc., at a subfield scale. When the research area is equipped with eddy-covariance (EC) towers, researchers often want to compare between the modeling results and the EC monitoring data within the footprint area once they run the TSEB model. However, we found the process to be time-consuming due to the large number of AggieAir flight images in the archive, as well as the uncertainty of some parameters (e.g., the G ratio). In order to increase the efficiency of modeling and verifying modeling results, this project adds some python scripts after the published TSEB model runs that consider the connection among the modeling results, the AggieAir platform, and the EC tower. This project is a part of our pending paper. Other researchers can also consider using this project if the available data are similar to ours.

Citations (0)

Mentions (0)

Metrics

Dataset Index

2.2

FAIR Score

92%

Citations

4

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Consortium of Universities for the Advancement of Hydrologic Science, Inc

License

This resource is shared under the Creative Commons Attribution CC BY.

Assigned Domain

Subfield

Environmental Engineering

Field

Environmental Science

Domain

Physical Sciences

Confidence Score

71%

Source

Open Alex

Keywords

High resolution imageEC towerTSEB modelsUASFootprint areaPython

Normalization Factors

FT

65.38

CTw

1.00

MTw

1.00