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• This study presents a systematically downscaling framework for GRACE-derived data. • Machine learning and remote sensing tools are used to present drought maps. • The efficiency of using ConvLSTM model is explored over previous methods. • The significant reasons for the water crisis and drought in Iran are investigated. • The results indicate that Iran has experienced water resource overexploitation. The current study proposes a new method to downscale the monthly GRACE-derived Terrestrial Water Storage Anomaly (TWSA) to 10 km spatial resolution over Iran using deep learning methods. First, the growing neural gas (GNG) method is utilized to cluster the TWSA data to find similar patterns. The Silhouette Chou Davies (SCD) validity measure, a combination of three robust cluster validity measures, is used to evaluate the performance of the GNG method. Then, the feasibility of deep learning Convolutional Long Short-Term Memory (ConvLSTM), shallow learning Feed Forward Neural Networks (FFNN), and Random Forest (RF) are examined in downscaling GRACE-derived TWSA using only remote sensing images. The results indicate that the deep learning method outperforms the RF and FFNN models by 7 % and 18 %, respectively. Then, the Ground Water Storage (GWS) data are isolated from TWSA, showing an agreement between the GRACE-derived GWS and ground-based measurements with R2 = 0.76. Additionally, the downscaled TWSA is used to generate annual drought frequency and change detection maps for the GWS from 2002 to 2016. Annual standardized precipitation index (SPI) drought frequency maps are also produced to gain deeper insight into the water resource scarcity of Iran. The results indicate that Iran has experienced water resource overexploitation since 2011 for agricultural activities, while meteorological drought is a trigger and intensifier for the water crisis in Iran. According to the results, Iran experienced exceptional drought in some regions, and the GWS has decreased all over the country. In addition, further analysis of Iran's water resources is provided until 2022. The current study calls for integrated sustainable water resources management in Iran; otherwise, irreversible water and environmental problems with higher frequencies will be expected in the near future. [ABSTRACT FROM AUTHOR]

To evaluate the long-term climate change impacts on groundwater fluctuations of the Ardabil plain, Iran, a groundwater level (GWL) modeling was proposed in this study. Accordingly, the outputs of Global Climate Models (GCMs) under the sixth report of Coupled Model Intercomparison Project (CMIP6) and future scenario of the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5), were used as climate change forcing to the Machine learning (ML) models. The GCM data were first downscaled and projected for the future via Artificial Neural Networks (ANNs). Based on the results, compared to 2014 (the last year of the base period), the mean annual temperature may increase by 0.8 °C per decade until 2100. On the other hand, the mean precipitation may decrease by about 8% compared to the base period. Then, the centroid wells of clusters were modeled by Feedforward Neural Network (FFNN), examining different input combination sets to simulate both autoregressive and non-autoregressive models. Since each of the ML models can extract different kinds of information from a dataset, after finding the dominant input set via FFNN, GWL time series were modeled via various ML methods. The modeling results indicated that the ensemble of shallow ML models could lead to a 6% more accurate outcome than the individual shallow ML models, and 4% than the deep learning models. Also, the simulation results for future GWLs illustrated that temperature can impact groundwater oscillations directly, whereas precipitation may not have uniform impacts on the GWLs. The uncertainty evolving in the modeling process was quantified and observed to be in acceptable range. Modeling results showed that the main reason for the declining GWL in the Ardabil plain could be primarily linked to the excessive exploitation of the water table, while climate change impact could be also notable. • The average temperature will increase at different rates in different seasons. • The average precipitation will change variously in different months. • Ensemble model is more accurate than other ML models. • Temperature has a direct impact on groundwater fluctuations. • Precipitation impacts the groundwater in complex ways. [ABSTRACT FROM AUTHOR]