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Assessment of spatiotemporal variations of drought is an efficient method for implementing drought mitigation strategies and reducing its negative impacts. This study aimed to assess the spatiotemporal pattern of short- to long-term droughts for an area with different climates. Therefore, 31 stations located in Iran were selected and the Standardized Precipitation Index (SPI) series with timescales of 3, 6, and 12 months were computed during the 1951-2016 period. A hybrid methodology including Maximal Overlap Discrete Wavelet Transform (MODWT) and K-means methods was used for obtaining the SPIs time-frequency properties and multiscale zoning of the area. The energy amounts of the decomposed subseries via the MODWT were applied as inputs for K-means. Also, the statistics in drought features (i.e. drought duration, severity, and peak) were assessed and the results showed that shorter term droughts (i.e. SPI-3 and -6) were more frequent and severe in the northern parts where the lowest values were obtained for drought duration. It was observed that the regions with more droughts frequency had the highest energy values. For shorter term droughts a direct relationship was obtained between the energy values and the mean SPIs, drought severity, and drought peak, whereas an inverse relationship was obtained for longer term drought. It was found that increasing the degree of SPI led to more similarity between the stations of each cluster. Also, the homogeneity of stations for the SPI-12 was slightly higher than the SPI-3 and -6. [ABSTRACT FROM AUTHOR]

Accurate prediction of drought indices is a useful method to reduce its undesirable consequences. In this study, the workability of newly integrated hybrid forecasting approach based on Meta model and data processing methods was assessed for forecasting the Standardized Precipitation Evapotranspiration Index (SPEI) in districts with different climates. The short-, mid-, and long-term SPEIs series (i.e. timescale of 3, 9, and 24 month) were computed during the period of 1951–2019 for five sites located in Iran. In this regard, first temporal features of the SPEIs were broken down using Wavelet Transform (WT). Then, for obtaining features with higher stationary properties, Ensemble Empirical Mode Decomposition (EEMD) was applied to further decompose the obtained subseries. Finally, the most efficient subseries were selected and inserted to Meta model approaches [i.e. Feed Forward Neural Network (FFNN), Kernel Extreme Learning Machine (KELM), and Gaussian Process Regression (GPR)] as inputs. Results showed that the proposed methods enhanced the models' capability between 35 to 45%. The capability of the proposed model was verified via Overlap Discrete Wavelet Transform (MODWT) method. Results showed that the distribution range of the Root Mean Square Errors (RMSE) criteria for integrated methods decreased from 0.036–0.172 (in raw data) to 0.025–0.109 (in decomposed data). The Monte Carlo uncertainty analysis was used to assess the applied models dependability. Results showed that the integrated model with having values of 72.8% to 89.2% for the 95PPU indicator had an allowable degree of uncertainty in short- to long-tern drought modeling. [ABSTRACT FROM AUTHOR]