Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R² value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R² value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin.

The water footprint (WF) of national wheat production has been previously estimated for the whole world in global-scale studies. These studies used assumptions which must be assessed and evaluated by estimates from national or regional studies. Here, previous estimates of different components (green, blue, gray and white) of WF of national wheat production in Iran were compared to the national-scale estimates. A new component (white WF) was proposed to account for the irrigation losses. Different components of the wheat WF were estimated for 236 plains over fifteen major wheat producing provinces. Then, the average values of each province were estimated. Finally, the weighted average values of each WF component were estimated by using the shares of irrigated and rainfed productions as weighting factors. The average total WF for irrigated areas and between all selected provinces is about 3,188 m³/ton with comparable shares of blue and green water, while the average total WF for rainfed areas is about 3,071 m³/ton with the share of the green WF nine times that of the gray WF. The results show that the total national WF of wheat production for the period 2006–2012 is about 42,143 million cubic meters (MCM) per year (41 % green, 18 % blue, 16 % gray and 25 % white) with the share of the green WF about 2.3 times the blue WF. Comparison of the obtained estimates with the results of the previous studies at a global scale revealed that estimating the WFs of crops at a global scale, ignores the variations of climatic conditions, water resources availability and crop yields at the national and regional levels and some of the assumptions made in global-scale studies must be reassessed.

Although the Urmia Lake has undergone remarkable drought conditions within the past decades mainly due to climate change, drought studies covering the entire Urmia Lake Basin and all drought aspects are lacking. The present study investigated the spatial and temporal drought conditions in the Urmia Lake Basin for the past (1988–2017) and future (2021–2050 and 2051–2080) periods using five general circulation models (GCMs) under the IPCC (Intergovernmental Panel on Climate Change) scenarios RCP 2.6, RCP 4.5, and RCP 8.5. The standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI) were compared. The SPEI predicted more drought events than the SPI, and it seemed to be a more suitable drought index than the SPI for the basin. In general, the future periods would encounter less drought conditions in terms of significant drought trends and duration than the observed period under all scenarios, but the frequency of quarterly severe drought events in the future periods would be higher than in the observed period. Furthermore, the stations Urmia (western bank) and Tabriz and Maragheh (eastern banks) would face the highest frequency of different types of quarterly drought events in the future periods compared with the observed period. The predicted high frequencies of drought events for the future periods can intensify the current low water level situation of the Urmia Lake, which seriously threatens all types of ecosystems in the basin. Therefore, serious actions need to be taken into account for efficient ecosystem and water resources management in the basin.