The ENSO phenomenon is considered one of the most important interannual oscillations in the Earth–atmosphere system and plays a significant role in precipitation variability across different regions of the world. In this study, to identify the multiscale relationship between different ENSO phases and monthly precipitation variability in Iraq, the monthly Niño3.4 index, the Maximum Overlap Discrete Wavelet Transform (MODWT), and the Continuous Morlet Wavelet Transform were employed. First, using multiresolution decomposition of the monthly precipitation signal from 16 stations across Iraq (1990-2020) into six (6) frequency levels (from monthly to multi-year scales), it was revealed that the precipitation signals at all stations follow a relatively similar pattern, although with different oscillation amplitudes. The amplitude of precipitation fluctuations at monthly and seasonal scales was found to be stronger at northern and foothill stations (Kirkuk, Mosul, and Khanaqin) compared to other regions of Iraq, indicating a shorter transition between wet and dry months in northern Iraq. Furthermore, the overall trend of the A6 component at all stations exhibited a decreasing pattern during 1995–2010, with this downward trend being more pronounced in the central and southern regions than in the north. The results of correlation analysis and multiscale wavelet coherence demonstrated a positive and multiscale relationship between ENSO and monthly precipitation in Iraq. Surrogate significance testing indicated that this relationship is not significant at wavelet levels 1 to 3 but becomes significant at 1.5–3-year and 2.5–5.5-year scales (wavelet levels 4 and 5). Overall, precipitation in Iraq tends to increase during El Niño (warm ENSO phase) events and decrease during La Niña (cold ENSO phase) events.

Modeling provides the studying of groundwater managers as an efficient method with the lowest cost. The purpose of this study was comparison of the numerical model, neural intelligent and geostatistical in groundwater table changes modeling. The information of Hamedan – Bahar aquifer was studied as one of the most important water sources in Hamedan province. In this study, MODFLOW numerical code in GMS software, artificial neural network (ANN) and neural – fuzzy (CANFIS) method in NeuroSolution software, wavelet-neural method in MATLAB software and geostatistical method in ArcGIS software were used. The results showed that the accuracy of methods in estimation of the groundwater table with the lowest Normal Root Mean Square Error (NRMSE) include Wavelet-ANN, CANFIS, geostatistical, ANN and numerical model, respectively. The NRMSE value in Wavelet-ANN method as optimization method was 0.11 % and in numerical model was 2.2 %. Also the correlation coefficients were 0.998 and 0.904, respectively. So application of neural combination models, specially, wavelet theory in estimated the groundwater table is most suitable than geostatistical and numerical model. Moreover, in the neural intelligent models were applied latitude, longitude and altitude as available variables in input models. The zoning results of groundwater table indicated that the decreased trend of groundwater table was from the west to the east of aquifer which was in line with the hydraulic gradient.