Iran's groundwater hydrochemistry has not been well understood. In this study, Iran's groundwater hydrochemistry is evaluated using a rich, ground‐trusted data sampled from 9,468 wells distributed across the country in 2011. Twelve groundwater quality parameters were analyzed in each sample, resulting in 113,616 parameters over the study period. Examination of anions‐cations shows that concentrations of sodium, calcium, chloride, and sulphate are higher than the acceptable threshold for drinking‐use suggested by the World Health Organization in about 40%, 21%, 25%, and 20% of the samples, respectively. The results of the water quality index reveal that most of the groundwater resources in the central, southern and eastern regions of Iran, which supply the majority of the domestic water for populated cities, do not meet the requirements for drinking‐use. Although the groundwater in northern parts fulfills the requirements for irrigation‐use, it is only suitable for irrigation of salinity‐friendly crops in central, eastern and southern regions. Ionic types and hydrochemistry facies indicate the dominance of mix water type in 13 out of 30 of Iran's sub‐basins, followed by sodium‐chloride water type in nine sub‐basins. Local geology and lithology are mainly attributed to the distribution of groundwater facies in Iran. In general, our findings reveal a distinctive relationship between Iran's geological‐geomorphological features and hydrochemical facies/groundwater quality. The findings can be used in the formulation of new strategies and policies for Iran's groundwater quality management in the future.

Soil temperature is a very important variable in agricultural meteorology and strongly influences agricultural activities and planning (e.g. the date and depth of sowing crops, frost protection). There are many physically based studies in the literature which model soil temperature, but few are easily applicable for use in the field. Simple and precise short-term forecasting of soil temperature with minimum data requirements is the main goal of this study. The soil temperature at 0300, 0900 and 1500 GMT was forecast based only on surface air temperatures using artificial neural network (ANN) and wavelet transform artificial neural network (WANN) models. The hourly data were collected from the Mashhad synoptic station in Khorasan Razavi province in Iran between 2010 and 2013. The results of this study showed that using a wavelet transform for preprocessing improved the accuracy of soil temperature forecasting. It was also found that changing the temporal increment in forecasting time did not have a noticeable effect on errors in the WANN models. WANN models can be used as accurate tools to forecast soil temperature 1–7 days ahead at depths of 5–30 cm.