Addressing Weather Data Gaps in Reference Crop Evapotranspiration Estimation: A Case Study in Guinea-Bissau, West Africa

Crop water use (ETc) is typically estimated as the product of crop evapotranspiration (ETo) and a crop coefficient (Kc). However, the estimation of ETo requires various meteorological data, which are often unavailable or of poor quality, particularly in countries such as Guinea-Bissau, where the maintenance of weather stations is frequently inadequate. The present study aimed to assess alternative approaches, as outlined in the revised FAO56 guidelines, for estimating ETo when only temperature data is available. These included the use of various predictors for the missing climatic variables, referred to as the Penman–Monteith temperature (PMT) approach. New approaches were developed, with a particular focus on optimizing the predictors at the cluster level. Furthermore, different gridded weather datasets (AgERA5 and MERRA-2 reanalysis) were evaluated for ETo estimation to overcome the lack of ground-truth data and upscale ETo estimates from point to regional and national levels, thereby supporting water management decision-making. The results demonstrate that the PMT is generally accurate, with RMSE not exceeding 26% of the average daily ETo. With regard to shortwave radiation, using the temperature difference as a predictor in combination with cluster-focused multiple linear regression equations for estimating the radiation adjustment coefficient (kRs) yielded accurate results. ETo estimates derived using raw (uncorrected) reanalysis data exhibit considerable bias and high RMSE (1.07–1.57 mm d−1), indicating the need for bias correction. Various correction methods were tested, with the simple bias correction delivering the best overall performance, reducing RMSE to 0.99 mm d−1 and 1.05 mm d−1 for AgERA5 and MERRA-2, respectively, and achieving a normalized RMSE of about 22%. After implementing bias correction, the AgERA5 was found to be superior to the MERRA-2 for all the studied sites. Furthermore, the PMT outperformed the bias-corrected reanalysis in estimating ETo. It was concluded that PMT-ETo can be recommended for further application in countries with limited access to ground-truth meteorological data, as it requires only basic technical skills. It can also be used alongside reanalysis data, which demands more advanced expertise, particularly for data retrieval and processing.