This repository provides jupyter notebooks with analyses for the paper:
Tran, B. N., Dehati, S., Seyoum, S., van der Kwast, J., Jewitt, G., Uijlenhoet, R., & Mul, M. (2026). Evaluating reanalysis datasets as meteorological input for estimating reference evapotranspiration in Africa and Southwest Asia. Hydrological Sciences Journal, 1–19. https://doi.org/10.1080/02626667.2025.2600682
In this study, we evaluated the meteorological input for RET from reanalysis data.
Our assessment entails three components: uncertainty between products, nominal accuracy, and quantitative impact of uncertainty in inputs on RET.
The uncertainty between products was assessed by spatial and temporal pair-wise comparison. The nominal accuracy was assessed by comparison with time-series data from in-situ measurements. Finally, the impact of uncertainty in inputs on RET was assessed by two error propagation methods (Monte Carlo simulations and Taylor expansion)
- ERA5 data are accessible at https://doi.org/10.24381/cds.adbb2d47.
- AgERA5 data are accessible at https://doi.org/10.24381/cds.6c68c9bb.
- GEOS5 data are accessible at https://opendap.nccs.nasa.gov/dods/GEOS-5/fp/0.25_deg/assim.
- TAHMO dataset is available on request to the data providers at https://tahmo.org/climate-data/
conda env create --file environment.yml
Tran, B. N., Dehati, S., Seyoum, S., van der Kwast, J., Jewitt, G., Uijlenhoet, R., and Mul, M. (2024). Evaluating reanalysis datasets as meteorological input for estimating reference evapotranspiration over Africa and Southwest Asia (Version 2.0) [Code]. https://doi.org/10.5281/zenodo.13970799