Daily groundwater level is an indicator of groundwater resources. Accurate and reliable groundwater level (GWL) prediction is crucial for groundwater resources management and land subsidence risk assessment. In this study, a representative deep learning model, long short-term memory (LSTM), is adopted to predict groundwater level with the selected predictors by partial mutual information (PMI), and bootstrap is employed to generate different samples combination for training many LSTM models, and the predicted values by many LSTM models are used for the uncertainty assessment of groundwater level prediction. Two wells of different climate zones in the USA were used as a case study. Different significant predictors of GWL for two wells were identified by PMI from candidate predictors incorporating teleconnection patterns information. The results show that GWL is significantly affected by antecedent GWL, AO, Niño 3.4, Niño 1 + 2, and precipitation in humid areas, and by antecedent GWL, AO, Niño 3.4, Niño 3, Niño 1 + 2, and PNA in arid areas. Predictor selection can assist in improving the prediction performance of the LSTM model. The relationship between GWL and significant predictors were modeled by the LSTM model, and it achieved higher accuracy in humid areas, while the performance in arid areas was poorer due to limited precipitation information. The performance of LSTM was improved by increasing correlation coefficient (R²) values by 10% and 25% for 2 wells compared to generalized regression neural network (GRNN). Three uncertainty evaluation metrics indicate that LSTM reduced the uncertainty compared to GRNN model. LSTM coupling with PMI and bootstrap can be a promising approach for accurate and reliable groundwater level prediction for different climate zones.

Dissolved Oxygen (DO) concentration is an essential water quality parameter widely used in water environments and pollution assessments, which indirectly reflects the pollution level and the occurrence of blue-green algae. With the advancement of satellite technology, the use of remote sensing techniques to estimate DO concentration has become a crucial means of water quality monitoring. In this study, we propose a novel model for DO concentration estimation in water bodies, termed Dissolved Oxygen Multimodal Deep Neural Network (DO-MDNN), which utilizes synchronous satellite remote sensing data for real-time DO concentration inversion. Using Lake Taihu as a case study, we validate the DO-MDNN model using Himawari-8 (H8) satellite imagery as input data and actual DO concentration in Lake Taihu as output data. The research results demonstrate that the DO-MDNN model exhibits high accuracy and stability in DO concentration inversion. For Lake Taihu, the performance metrics including adj_R², RMSE, Pbias, and SMAPE are 0.77, 0.66 mg/L, −0.44%, and 5.36%, respectively. Compared to the average performance of other machine learning models, the adj_R² shows an improvement of 6.40%, RMSE is reduced by 8.27%, and SMAPE is decreased by 12.1%. These findings highlight the operational feasibility of real-time DO concentration inversion using synchronous satellite data, providing a more efficient, economical, and accurate approach for real-time DO monitoring. This method holds significant practical value in enhancing the efficiency and precision of water environment monitoring.

Dissolved oxygen (DO) concentration is a widely used and effective indicator for assessing water quality and pollution in aquatic environments. Continuous and large-scale inversion of water environments using remote sensing imagery has become a hot topic in water environmental research. Remote sensing technology has been extensively applied in water quality monitoring, but its limited sampling frequency necessitates the development of a high-frequency dynamic water quality monitoring model. In this study, we utilized Lake Chaohu as a case study. Firstly, we constructed a dynamic water quality inversion model for monitoring DO concentrations using machine learning methods, with Himawari-8 (H8) satellite imagery as input data and DO concentrations in Lake Chaohu as output data. Secondly, the developed DO concentration inversion model was employed to estimate the overall grid-based DO concentration in the Lake Chaohu region for the years 2019 to 2021. Lastly, Pearson correlation analysis and significance tests were performed to examine the correlation and significance between the estimated grid-based DO concentration and the ERA5 reanalysis dataset. The results demonstrate that the Random Forest (RF) model performs best in DO concentration inversion, with a high R² score of 0.84, and low RMSE and MAE values of 0.69 and 0.54, respectively. Compared to other models, the RF model improves average performance with a 38% increase in R², 13% decrease in RMSE, and 33% decrease in MAE. The model accurately predicts DO concentrations. Furthermore, the inversion results reveal seasonal differences in DO concentrations in Lake Chaohu from 2019 to 2021, with higher concentrations in spring and winter, and lower concentrations in summer and autumn. The average DO concentrations in the northwest, central-south, and northeast regions of Lake Chaohu are 10.12 mg/L, 9.98 mg/L, and 9.96 mg/L, respectively, with higher concentrations in the northwest region. Pearson correlation analysis indicates a significant correlation (p < 0.01) between DO concentrations and temperature, surface pressure, latent heat flux from the atmosphere to the surface, and latent heat flux from the surface to the atmosphere, with correlation coefficients of −0.615, 0.583, −0.480, and 0.444, respectively. The results verify the feasibility of using synchronous satellites for real-time inversion of DO concentrations, providing a more efficient, economical, and accurate means for real-time monitoring of DO concentrations. This study has practical value in improving the efficiency and accuracy of water environmental monitoring.

Global warming has caused changes in the spatial and temporal distribution of water resources and affected the water environment. Land use change is mainly influenced by human activities, which is also one of the reasons for the water environment problems have become increasingly serious. The Yongding River basin is important water connotation area and ecological barrier in Beijing-Tianjin-Hebei region in China. The basin has been affected by human activities and climate change, there are problems such as significant reduction in runoff and serious water pollution. To study the effects of climate change and land use on runoff and water quality, this paper simulates runoff and water quality by the Hydrological Simulation Program FORTRAN(HSPF) model, then studied the changes of runoff and water quality under different land use and climate scenarios. The results show that: the HSPF model has good applicability in the Yongding River basin, the Relative Error (Re) of runoff and water quality simulation is within 10%, the Nash Coefficient (NSE) and Correlation Coefficient (R) are above 0.6. The simulation results of different land use scenarios show that the expansion of construction land has a facilitating effect on runoff and also leads to the increase of pollutant concentrations in river; forest and grassland inhibit the generation of runoff, while having a purifying effect on water quality. The simulation results of different climate scenarios show that the rainfall rising has a positive effect on runoff generation and water quality improvement and the temperature rising has a negative effect on runoff generation and water quality improvement. At the same time, the “evaporation paradox” also affects the runoff change. In the integrated scenario, simulation results show that the land use and climate change influence the changes in runoff and water quality, but the influence mechanism is not a simple superposition.

With rapid socioeconomic development and intensified human activities, nitrogen (N) loads have continued to rise, exerting significant impacts on the environment. Most existing studies focus on single cities or short time periods, which limits their ability to capture nitrogen dynamics under rapid urbanization. Based on statistical data from multiple cities in Zhejiang Province from 2011 to 2021, this study applied nitrogen balance accounting and statistical analysis to systematically evaluate the spatiotemporal variations in nitrogen inputs, outputs, and surpluses, as well as their driving factors. The results indicate that although nitrogen inputs and outputs fluctuated over the past decade, the overall nitrogen surplus showed an increasing trend, with the nitrogen surplus per unit area rising from 49.89 kg/(ha·a) in 2011 to 62.59 kg/(ha·a) in 2021. Zhejiang’s nitrogen load was higher than the national average but remained below the levels of highly urbanized regions such as the Yangtze River Delta and Pearl River Delta. Accelerated urbanization and increasing anthropogenic pressures were identified as major contributors to the rising nitrogen surplus, with significant inter-city disparities. Cities like Hangzhou, Ningbo, Wenzhou, and Jinhua were found to face higher risks of nitrogen pollution. Redundancy analysis and Pearson correlation analysis revealed that nitrogen surplus was positively correlated with cropland area, livestock population, total population, precipitation, GDP, and industrial output, further highlighting the dominant role of human activities in nitrogen cycling. This study provides the long-term quantitative assessment of nitrogen balance under multi-city coupling at the provincial scale and identifies key influencing factors. These findings provide scientific support for integrated nitrogen management across multiple environmental compartments in Zhejiang Province, including surface water, groundwater, agricultural systems, and urban wastewater, under conditions of rapid urbanization.