Accuracy of daily estimation of grass reference evapotranspiration using ERA-Interim reanalysis products with assessment of alternative bias correction schemes

• Published in: Agricultural water management Vol. 210; pp. 340 - 353
• Main Authors: Paredes, Paula, Martins, Diogo S., Pereira, Luis Santos, Cadima, Jorge, Pires, Carlos
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30-11-2018
• Subjects: Accuracy indicators; Bias correction methods; Cross-validation; FAO Penman-Monteith ETo; Meteorological variables; Raw reanalysis data; FAO Penman-Monteith ETo; Bias correction methods; Accuracy indicators; Meteorological variables; Cross-validation; Raw reanalysis data
• ISSN: 0378-3774; 1873-2283
• DOI: 10.1016/j.agwat.2018.08.003

•ERA-Iterim reanalysis weather variables were used for computing daily PM-ETo.•The accuracy of weather variables estimation was assessed and temperature was corrected for elevation.•Various bias correction approaches were tested for ETo REAN using cross-validation.•Bias correction used data aggregated quarterly to consider seasonality of climate.•Additive bias correction relative to the nearest grid point was selected because accurate and simple.
This study aims at assessing the accuracy of estimating daily grass reference evapotranspiration (PM-ETo) computed with ERA-Interim reanalysis products, as well as to assess the quality of reanalysis products as predictors of daily maximum and minimum temperature, net radiation, dew point temperature and wind speed, which are used to compute PM-ETo. With this propose, ETo computed from local observations of weather variables in 24 weather stations distributed across Continental Portugal were compared with reanalysis-based values of ETo (ETo REAN). Three different versions of these reanalysis-based ETo were computed: (i) an (uncorrected) ETo based on the individual weather variables for the nearest grid point to the weather station; (ii) the previously calculated ETo corrected for bias with a simple bias-correction rule based only on the nearest grid point; and (iii) the ETo corrected for bias with a more complex rule involving all grid points in a 100 km radius of the weather station. Both bias correction approaches were tested aggregating data on a monthly, quarterly and a single overall basis. Cross-validation was used to allow evaluating the uncertainties that are modelled independently of any forcing; with this purpose, data sets were divided into two groups. Results show that ETo REAN without bias correction is strongly correlated with PM-ETo (R2>0.80) but tends to over-estimate PM-ETo, with the slope of the regression forced to the origin b0 ≥ 1.05, a mean RMSE of 0.79 mm day−1, and with EF generally above 0.70. Cross-validation results showed that using both bias correction methods improved the accuracy of estimations, in particular when a monthly aggregation was used. In addition, results showed that using the multiple regression correction method outperforms the additive bias correction leading to lower RMSE, with mean RMSE of 0.57 and 0.64 mm day−1 respectively. The selection of the bias correction approach to be adopted should balance the ease of use, the quality of results and the ability to capture the intra-annual seasonality of ETo. Thus, for irrigation scheduling operational purposes, we propose the use of the additive bias correction with a quarterly aggregation.