This study employed hydrochemical data, traditional hydrogeochemical methods, inverse hydrogeochemical modeling, and unsupervised machine learning techniques to explore the hydrogeochemical traits and origins of groundwater in the Changbai Mountain region. (1) Findings reveal that predominant hydrochemical types include HCO₃⁻Ca·Mg, HCO₃⁻Ca·Na·Mg, HCO₃⁻Mg·Na, and HCO₃⁻Na·Mg. The average metasilicic acid content was found to be at 49.13 mg/L. (2) Rock weathering mechanisms, particularly silicate mineral weathering, primarily shape groundwater chemistry, followed by carbonate dissolution. (3) Water-rock interactions involve volcanic mineral dissolution and cation exchange adsorption. Inverse hydrogeochemical modeling, alongside analysis of the widespread volcanic lithology, underscores the complexity of groundwater reactions, influenced not only by water-rock interactions but also by evaporation and precipitation. (4) Unsupervised machine learning, integrating SOM, PCA, and K-means techniques, elucidates hydrochemical types. SOM component maps reveal a close combination of various hydrochemical components. Principal component analysis (PCA) identifies the first principal component (PC1), explaining 48.15% of the variance. The second (PC2) and third (PC3) principal components, explain 13.2% and 10.8% of the variance, respectively. K clustering categorized samples into three main clusters: one less influenced by basaltic geological processes, another showing strong igneous rock weathering characteristics, and the third affected by other geological processes or anthropogenic factors.

As a numerical indicator, the pollution index of groundwater (PIG) has gained a great deal of popularity in quantifying groundwater quality for drinking purposes. However, its weight-determination procedure is rather subjective due to the absolute dependence on experts’ experience. To make the evaluation results more accurate and convincing, two improved PIG models (CRITIC-PIG and Entropy-PIG) that integrate subjective weights and objective weights were designed, and they were employed to appraise groundwater suitability for drinking purposes in the northern part of Changchun City. A total of 48 water samples (34 unconfined water samples and 14 confined water samples) with abundances of Ca²⁺ and HCO₃⁻ were collected and tested to obtain the data for the analyses. The results showed that 60.4%, 47.9% and 60.4% of the water samples manifested insignificant pollution and were marginally potable based on the values of the PIG, CRITIC-PIG and Entropy-PIG, respectively. Though 48% of the water samples had different evaluation results, their level difference was mostly 1, which is relatively acceptable. The distribution maps of the three sets of PIG values demonstrated that the quality of groundwater was the best in Dehui City and the worst in Nongan County. Groundwater contamination in the study area was mainly caused by the high concentrations of TDS, TH, Fe³⁺, F⁻ and NO₃⁻, which not only came from geogenic sources but also anthropogenic sources.

Characteristics of groundwater level changes may be correlated with subsequent earthquake events. However, the relationship and its determining factors remain unclear. This study examines eight wells situated near the Longmenshan-Anninghe fault zone, which exhibit significant disparities in changes of groundwater level. We quantified these changes by Molchan diagram and investigated factors that may affect it using correlation assessments. The results indicate groundwater levels changes that are more responsive to static stresses and tidal forces also have a high correlation with subsequent earthquake events. Specific leakage, a hydraulic parameter, also effects the correlation between groundwater levels and subsequent earthquakes. Spatial distribution of epicenters may also contribute to differences in this correlation, while aquifer confinement appears to have minimal effect. We used a random forest regression to calculate the comprehensive contribution of these factors to the correlation between groundwater levels and subsequent earthquakes. Notably, epicenter locations showcase the utmost sensitivity to this correlation. These findings can help us understand the complex mechanisms of water level changes before earthquakes and provide insights into the optimal locations for monitoring boreholes.

Both climate change and human activities have had a significant impact on hydrological processes. Quantification of affecting factors on river regime changes is scientifically essential for understanding hydrological processes and sustainable water resources management in the basins. This study investigates the features of variations in meteorological and hydrological variables in the Liuhe River Basin (LRB) from 1956 to 2020 based on various observed records and statistical methods. It then quantitatively identifies the possible impacts of climate variability and human activities on runoff in the LRB using the empirical methods and the Budyko framework. The results show that (1) the runoff demonstrates a significantly decreasing trend over the past 65 years, but the rainfall has no obvious trend with significant interannual fluctuations, and potential evapotranspiration exhibits a weekly decreasing trend, particularly in summer. (2) The runoff series can be divided into two periods, i.e., the baseline (1956–1969) and change (1970–2020) periods, and the change period can also be divided into two stages, i.e., stage I (1970–1999) and stage II (2000–2020). (3) Human activities are the dominant factors in the runoff decline in the LRB, with the contribution rates being greater than 80% in the change period, particularly for stage II. The analysis of this study can provide a reference for the rational utilization of water resources in the LRB.

Based on meteorological data from 106 stations in Northeast China from 1960 to 2020, this study calculated potential evapotranspiration (ET₀) using the Penman-Monteith formula. The research area was divided into four subregions using the K-means clustering method, revealing the sensitivity responses and contribution rates of different meteorological factors to ET₀ changes under climate change. The results are as follows: Daily mean temperature (TEM) significantly increased in all four subregions, while wind speed (WIND) significantly decreased. Relative humidity (RH) and sunshine duration (SSD) showed slight declining trends. ET₀ changes exhibited regional differences: a slight decrease in Subregion I and eastern Subregion II, a significant increase in western Subregion II, no significant trend in Subregion III, and a significant increase in Subregion IV. Sensitivity coefficients for all subregions were higher in the south and lower in the north. The sensitivity coefficient for RH was negative, while those for sunshine duration and WIND were positive. Mean temperature sensitivity coefficients were positive except in Subregion IV, where the anomaly may be regulated by high-latitude factors. The decline in RH was the dominant factor driving ET₀ changes in Subregions III and IV. In Subregions I and II, ET₀ changes may also be influenced by non-climatic factors or other meteorological variables. This study clarifies the dominant driving factors of evapotranspiration changes in each region, providing a theoretical basis for optimizing agro-hydrological models in cold regions and supporting regional agricultural water consumption assessment and water resource optimization.