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Stable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerous climatic and geographic factors. To address this, forty-two precipitation sampling stations were selected across Iran to assess the fractional importance of these climatic and geographic parameters influencing stable isotopes. Additionally, deep learning models were employed to simulate the stable isotope content, with missing data initially addressed using the predictive mean matching (PMM) method. Subsequently, the recursive feature elimination (RFE) technique was applied to identify influential parameters impacting Iran's precipitation stable isotope content. Following this, long short-term memory (LSTM) and deep neural network (DNN) models were utilized to predict stable isotope values in precipitation. Interpolated maps of these values across Iran were developed using inverse distance weighting (IDW), while an interpolated reconstruction error (RE) map was generated to quantify deviations between observed and predicted values at study stations, offering insights into model precision. Validation using evaluation metrics demonstrated that the model based on DNN exhibited higher accuracy. Furthermore, RE maps confirmed acceptable accuracy in simulating the stable isotope content, albeit with minor weaknesses observed in simulation maps. The methodology outlined in this study holds promise for application in regions worldwide characterized by diverse climatic conditions.

This study determined the main local and regional parameters affecting the stable isotopes ( 18 O, 2 H) in the Bangkok precipitation and developed the Bangkok meteoric water line (BMWL) ( δ 2 H = (7.68 ± 0.07) δ 18 O + (7.25 ± 0.48)). First, Pearson correlation coefficients were used to determine the correlation between local and regional parameters. Six different regression methods were used based on Pearson correlation coefficients. The stepwise regression had the most accurate performance among them according to the R 2 values. Second, three different methods were used to develop the BMWL, and their performances were also studied. Third, the stepwise regression method was used to study the effects of local and regional parameters on the stable isotope content of precipitation. The results showed that the local parameters had a greater effect on the stable isotope content than the regional ones. The stepwise models developed based on the northeast and southwest monsoons showed that moisture sources also affected the stable isotope content of precipitation. Finally, the developed stepwise models were validated by calculating the root mean square error (RMSE) and R 2 . This study demonstrated that the local parameters mainly controlled the stable isotopes in the Bangkok precipitation, while the regional parameters had a slight effect on them.

The Chao Phraya River Deltaic Plain is the largest basin in Thailand and the second largest one in Southeast Asia after the Mekong River Delta. In recent decades, the groundwater quality in the Lower Chao Phraya River Basin in Thailand has deteriorated due to salinization caused by seawater intrusion. In the present study, hydrogeochemical and statistical methods were employed to determine the hydrochemical characteristics of the groundwater and to investigate the possible sources of salinity in the study region for the years 2008 and 2020. In addition, samples were taken from precipitation, sea water, and river water to analyze their hydrochemical properties. Then, they were used as input in the "Simmr" code in the R programming language to model the hydrochemical conditions of the study area and their evolution over time. The results indicated that in the non-coastal regions, water-rock interaction (mineral weathering and ion exchange), and brine/connate water infiltration affected the quality of the groundwater. However, the seawater intrusion was limited only to the coastal regions. Furthermore, the groundwater quality deteriorated from 2008 to 2020. Finally, using stepwise regression in the R language, the salinity of the groundwater was simulated and compared with the measured salinity data. The results obtained by the stepwise model were in close agreement with those obtained from the hydrochemical studies. This study confirmed seawater intrusion in the coastal aquifer as well as the deterioration of groundwater quality over time. To slow down this process and to achieve sustainable conditions, groundwater extraction should be reduced in the study region., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Iran is a semi-arid and arid country which always faces a water shortage crisis. Thus, the water resources in Iran should be studied by accurate methods such as stable isotope techniques. In precipitation sampling stations across Iran, the δ 18 O (ranges from -16.3 to -0.3 ‰, -4.9 ‰ average), δ 2 H (-114 to -13 ‰, -24.2 ‰ average) and d -excess (-2.1 to -22.7, 16.5 ‰ average) values are higher compared to δ 18 O (ranges from -10.9 to -3.1 ‰, -6.7 ‰ average), δ 2 H (-71 to -6 ‰, -37.4 ‰ average) and d -excess (1.0 to -21.6 ‰, 14.9 ‰ average) values in groundwater stations. Stable isotope distribution maps in precipitation and groundwater were also developed for Iran. The stepwise technique was used to study the role of parameters influencing stable isotopes in Iran precipitation. Results show the dominant role of temperature, elevation and latitude as well as 'cP and MedT' air masses mixture on stable isotope values in precipitation. Furthermore, the contribution percentage of each air mass which influences Iran in groundwater resources recharge was studied using 'Simmr' package in R programming language. Finally, the accuracy of the developed stable isotope distribution maps was validated.

The north-east of Iran is a semi-arid region and faces a water shortage crisis. Therefore, monitoring water resources using accurate methods such as stable isotopes technique is vitally important. In this study, precipitation events were sampled in 10 stations in the Mashhad basin and the Bojnourd region in 2008, 2009, 2011, and 2015, additional surface and groundwater. These samples were analysed at the Ottawa University for both oxygen and hydrogen isotopes. In addition, the moisture sources were determined using the backward trajectories of the HYSPLIT model. The backward trajectories showed that both high- and low-latitude water bodies provide moisture for the north-east of Iran. However, the role of high-latitude water bodies including the Caspian, the Black, and the Mediterranean Seas is stronger. On the other hand, the stable isotopes showed large variations and the developed meteoric water lines deviated in both slope and intercept from the global meteoric water line. This showed that the precipitation events of the north-east of Iran were provided by various air masses and moisture sources. Finally, plotting the isotope values of the surface water resources on high- and low-latitude meteoric water lines demonstrated that these water resources were dominantly recharged by precipitation events originating from high-latitude water bodies.