Evaluation of Global Gridded Climate Datasets in Simulating Agro-Hydrological Variables in Iran

Water resource management and improving its efficiency in Iran, particularly in the agricultural sector, have become critical challenges due to recurring droughts and unique climatic conditions. This study evaluates the accuracy of five global climate datasets—CPC Global, CRU TS, ERA-Interim, ERA5, and MERRA-2—in simulating agro-hydrological variables using the AquaCrop model. Given Iran’s diverse climates and limited data availability, the need for accurate models and reliable climate datasets to enhance water resource management is essential. To this end, climate data from various datasets and synoptic stations were compared for the period from 1989 to 2019. The results indicate that the ERA5 dataset, due to its higher spatial (0.25°×0.25°) and temporal resolution and more precise data, performed best in estimating biomass, evapotranspiration, net irrigation requirement, and crop yield across most climate zones. The CPC Global dataset also showed high accuracy in certain indices, particularly in Mediterranean and arid climates, but was less successful than ERA5 in some cases. The ERA-Interim dataset demonstrated moderate performance. On the other hand, the CRU TS and MERRA-2 datasets, especially MERRA-2, exhibited lower accuracy in humid and very humid climates and are generally not recommended for precise simulations. This research underscores the importance of selecting the appropriate climate dataset to improve crop model simulations and water resource management.