Introduction
Drought is one of the most complex and costly natural disasters, and its accurate monitoring is essential for water resources management and vulnerability reduction. The Standardized Precipitation-Evapotranspiration Index (SPEI), as a bivariate index, utilizes precipitation and temperature data (to estimate evapotranspiration) and has gained widespread application due to its multiscalar nature and sensitivity to climate change. However, limited access to long-term and uniform station-based data in a country with high climatic diversity and complex topography like Iran poses a significant challenge for accurate drought monitoring. The global gridded database SPEIbase, with a spatial resolution of 0.5 arc degrees, has been developed by integrating satellite data, climate models, and in-situ measurements. This study aims to evaluate the reliability of SPEIbase compared to station-based data across Iran's catchments to assess the feasibility of substituting this database due to its extensive spatial coverage and easier accessibility.
 
Materials and Methods
Two datasets were used in this research: 1) Station data including monthly precipitation time series and monthly mean temperature from 43 meteorological stations in Iran for the statistical period 1986-2023, obtained from the Iran Meteorological Organization (IRIMO). 2) Gridded SPEIbase data, version 2.11, based on the CRU TS 4.09 dataset, extracted for the same period from the Climatic Research Unit (CRU) at the University of East Anglia. The station-based SPEI was calculated using potential evapotranspiration estimated via the Thornthwaite method at time scales of 1, 3, 6, 9, and 12 months. The SPEIbase index, based on potential evapotranspiration estimated via the FAO-56 Penman-Monteith method, was extracted for the corresponding period. The concordance and agreement between the two indices were evaluated using three statistical methods: 1) Pearson's correlation coefficient to assess linear relationship, 2) Weighted Cohen's Kappa statistic to measure agreement in drought classes (8 classes ranging from extreme drought to extremely wet), and 3) Bland-Altman plots to examine limits of agreement and systematic bias.
 
Results and Discussion
Findings indicated that the Pearson correlation coefficient between SPEI and SPEIbase fell within the strong correlation range (0.70 to 0.90) for most stations and catchments in Iran. The highest correlations were observed at stations in mountainous areas, such as Khorramabad (0.92), Hamedan (0.87), and Urmia (0.86) at the 12-month scale. Conversely, coastal and lowland stations like Bandar Abbas, Ramsar, and Babolsar showed relatively weaker correlations (0.63 to 0.67) at shorter time scales. Results from the Weighted Cohen's Kappa statistic also demonstrated substantial agreement (0.60 to 0.80) in drought classification between the two indices in mountainous regions and moderate agreement (0.40 to 0.60) in lowland and coastal areas. Bland-Altman plots revealed narrow limits of agreement and random scatter of points around the mean line, indicating no significant systematic bias. A key finding was the increase in correlation and agreement with increasing time scale; the highest agreement was observed at 9 and 12-month scales (hydrological drought) and the lowest at the 1-month scale (meteorological drought). Bam station, with a mean annual precipitation of approximately 53.7 mm, exhibited the lowest correlation (0.64) and agreement (0.39) at the 12-month scale, possibly due to hyper-arid conditions and limitations of evapotranspiration estimation methods in such climates. Constructed violin plots confirmed the concentration of correlation and kappa coefficients at higher ranges with increasing time scale.
 
Conclusion
The SPEIbase database demonstrates suitable accuracy for drought monitoring across Iran's catchments and shows high concordance and agreement with station-based data in many regions, particularly mountainous areas. As the time scale increases from 1 to 12 months, correlation and agreement improve, indicating higher reliability of this database for monitoring agricultural and hydrological droughts. In coastal and lowland regions, especially at shorter time scales, caution is recommended when using this database. Given its extensive spatial coverage, appropriate temporal resolution, and free accessibility, SPEIbase can serve as a reliable alternative to station-based data in data-sparse regions or areas with incomplete records. It is suggested that future research calculate station-based SPEI using the FAO-56 Penman-Monteith method to evaluate the impact of the evapotranspiration estimation method on the level of agreement with SPEIbase
 
Acknowledgement 
The authors would like to thank the Iran Meteorological Organization (IRIMO) for providing the meteorological data used in this research free of charge.