The objective of this study was to model a new drought index called the Fusion-based Hydrological Meteorological Drought Index (FHMDI) to simultaneously monitor hydrological and meteorological drought. The required precipitation and runoff monthly datasets were extracted from the Modern-Era Retrospective Analysis for Research and Applications (MERRA2), Global Land Data Assimilation System (GLDAS), ECMWF Reanalysis v5 (ERA5), Terrestrial Climate (TERRA), and Global RUNoff ENSEMBLE (GRUN) for the 1987–2019 period. Aiming to estimate drought more accurately, local measurements were classified into various clusters using the AGNES clustering algorithm. Four Single Artificial Intelligence (SAI) models—namely Gaussian Process Regression (GPR), Ensemble, Feedforward Neural Networks (FNN), and Support Vector Regression (SVR)—were developed for each cluster. To promote the results of single of products and models, four fusion-based approaches, namely Wavelet-Based (WB), Weighted Majority Voting (WMV), Extended Kalman Filter (EKF), and Entropy Weight (EW) methods were used to estimate FHMDI in different time scales, precipitation and runoff .The performance of single and combined products and models was assessed through statistical error metrics, such as Kling Gupta efficiency (KGE), Mean Bias Error (MBE), and Normalized Root Mean Square Error (NRMSE). The performance of the proposed methodology was tested over 24 main river basins in Iran. The validation results of the FHMDI( the compliance of the index with the pre-existing drought index) revealed that it accurately identified drought conditions. The results indicated that individual products performed well in some river basins, while fusion-based models improved dataset accuracy more compared to local measurements.The WMV with the highest accuracy (lowest NRMSE) had a good performance in 60% of the cases compared to all other products and fusion-based models. WMV also showed higher efficiency in 100% of the cases than all other fusion-based and SAI models for simultaneous hydrological and meteorological drought estimation. In light of these findings, we recommend the use of fusion-based approach to improve drought modeling.
