Groundwater Level Prediction Research Articles

Spatial and temporal downscaling schemes to reconstruct high-resolution GRACE data: A case study in the Tarim River Basin, Northwest China

Climate change and excessive exploitation of water resources exert pressure on groundwater supply and the ecosystem in drylands. Although The Gravity Recovery and Climate Experiment (GRACE) satellites has demonstrated the feasibility of quantifying global groundwater storage variations, monitoring regional-scale groundwater has been challenging due to the coarse resolution of GRACE. Previous GRACE downscaling studies focused on develop new algorithms based on the perspective of pixel spatial correlation to improve resolution, which cannot better capture the temporal evolution of GRACE data effectively. In this study, we employ the semi-supervised variational autoencoder (SSVAER) algorithm and the multi-scale geographically weighted regression (MGWR) model to establish two different downscaling schemes: pixel temporal continuity downscaling and pixel spatial correlation downscaling. These schemes achieve spatial resolution downscaling of GRACE-derived groundwater storage anomalies (GWSA) from 0.5° to 0.1°. Additionally, the applicability of the PCR-GLOBWB model in drylands is verified. Furtherly, the spatiotemporal distribution patterns of GWSA are analyzed. The results show that (1) Both the temporal and spatial downscaling methods produced consistent results, with data correlations ranged from 0.94 to 0.98 observed in over 80 % of the range before and after downscaling; (2) The groundwater storage change rate in the northern Tarim River Basin (TRB) is 25 times greater than the model results, while in other regions, the average deviation is 2.6 times; (3) The two schemes enhance the correlation (0.27) between GWSA and groundwater level anomaly (GWLA) to 0.59 and 0.52, respectively, with a three-month lag in GWSA relative to GWLA. The temporal downscaling approach exhibited higher CC and lower RMSE, outperforming the spatial downscaling approach. The high-resolution results in this study can well complement groundwater level prediction efforts in arid regions and provide quantitative information for local water resource management.

Groundwater-level prediction in Visakhapatnam district, Andhra Pradesh, India, using Bayesian Neural Networks

Groundwater-level prediction in Visakhapatnam district, Andhra Pradesh, India, using Bayesian Neural Networks

BROACHING UNCOUTH WATER LEVEL SNAG IN UNDERGROUND AGRICULTURE FIELD THROUGH WIRELESS SENSORS

The major problem faced by the agriculture industry is the growth of seeds at optimal fertility. Such uncouth fertility occurs when either no water reaches under soil seed or some obstacles prevent it. This issue degrades agriculture growth at an aggravation level and consequently barren the land. A wireless sensor-based communication model has been developed to notice the accurate status of the water level reaching seeds of various crops such as wheat, maize, barley, and cotton with soil types Loamy, Silty clay, and Black soil. In the proposed model, "Broadening Uncouth Water Level Snag in Underground Agriculture Field with Wireless Sensors" (BUWLN), the water level would be measured in real time and the data would be transmitted to a base station accordingly. The 8 sensor nodes and a base station would be deployed accompanied by an Arduino package in an area of 90 x 30 square feet to monitor water levels efficiently. The study will keep track of water levels, and the farmer will be informed of the water level so they may manage water resources more effectively. In addition, farmers will also be aware of the field if water is distributed uniformly so that the crops yield increases. The performance of the proposed BUWLN model has been compared with Remote water-level monitoring system (RWMS), Low cost wireless sensor network (LCSN), and Ensemble groundwater level prediction (E-GWLP) by conducting simulation using PROTEUS. result showed that the proposed BUWLN model has enough potential for future growth.

Long short‐term memory model for predicting groundwater level in Alabama

AbstractGroundwater serves as a primary source of public‐water and agricultural supply in many areas of Alabama, in particular during drought periods. Long‐term climatic models for the southeastern United States indicate that the region will be subjected to more intense and more frequent precipitation events, with no overall change in the amount of precipitation, resulting in increased runoff and reduced aquifer recharge. Reliable prediction of groundwater levels would be beneficial to water resources decision makers and stakeholders especially for time‐sensitive decisions. This paper uses a compound application of continuous wavelet transform (CWT) analysis and long short‐term memory (LSTM) framework to address the major question with regards to groundwater level: “how long does it take for groundwater to respond to major precipitation events and what is the magnitude of the response?” CWT analysis is used to answer the “how long” part in this question, while the LSTM is used to answer the “what is the magnitude” part of the question. The insights from CWT analysis related to the short‐term and long‐term response in groundwater level were used to set the parameters of the LSTM model. The LSTM model uses daily groundwater levels, precipitation, and maximum/minimum temperatures as input data. The model was able to provide predictions within a 95% confidence interval of actual groundwater levels. The findings of this study suggest a workflow for groundwater level forecasting in the wells of Alabama given a minimum amount of easy‐to‐measure and widely available data.

Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions

Predicting groundwater levels is challenging, especially in regions of water scarcity where data availability is often limited. However, these regions have substantial water needs and require cost-effective groundwater utilization strategies. This study uses artificial intelligence to predict groundwater levels to provide guidance for drilling shallow boreholes for subsistence irrigation. The Bilate watershed, located 80 km north of Arba Minch in southern Ethiopia and covering just over 5250 km2, was selected as the study area. Bilate is typical of areas in Africa with high demand for water and limited availability of well data. Using a non-time series database of 75 boreholes, machine learning models, including multiple linear regression, multivariate adaptive regression splines, artificial neural networks, random forest regression, and gradient boosting regression (GBR), were constructed to predict the depth to the water table. The study considered 20 independent variables, including elevation, soil type, and seasonal data (spanning three seasons) for precipitation, specific humidity, wind speed, land surface temperature during day and night, and Normalized Difference Vegetation Index (NDVI). GBR performed the best of the approaches, with an average 0.77 R-squared value and a 19 m median absolute error on testing data. Finally, a map of predicted water levels in the Bilate watershed was created based on the best model, with water levels ranging from 1.6 to 245.9 m. With the limited set of borehole data, the results show a clear signal that can provide guidance for borehole drilling decisions for sustainable irrigation with additional implications for drinking water.

Improvement technology of water regulation and methods of calculating the parameters of modular drainage systems on the lands of the humid zone

The article examines water regulation on drained lands by innovative modular drainage systems with multi-level connection of regulating drains, diagrams of their design parameters are given. The main goal of the article is to demonstrate the results of analytical studies of water regulation by drainage modules under the condition of laying drains at different depths are given. The equations derived in this article allow you to use them to calculate the distance between regulating drains of different installation depths, perfect in terms of the degree and nature of the opening of the aquifer during infiltration water supply within drainage modules. Variants of the application of methods for calculating the parameters of regulatory drainage modules are proposed, which allow predicting groundwater level regimes, establishing watershed lines between perfect shallow-deep drains.

A hybrid coupling model of groundwater level simulation considering hydrogeological parameter: a case study of Nantong City in Eastern China

Abstract Groundwater level dynamic monitoring data have the characteristics of spatio-temporal non-smoothness and strong spatio-temporal correlation. However, the current groundwater level prediction model is insufficient to consider the spatio-temporal factors of the groundwater level and the autocorrelation of spatio-temporal series, particularly the lack of consideration of hydrogeological conditions in the actual study area. Thus, this study constructed a model based on the hydrogeological conditions and the spatio-temporal characteristics of the dynamic monitoring data of groundwater in the porous confined aquifer III in Nantong, the northern wing of the Yangtze River Delta, China. The spatial autocorrelation coefficient of the hydrogeology important parameter, permeability coefficient K, is used to optimize the distance weighting coefficient of monitoring wells obtained by the K-nearest neighbor (KNN) algorithm and then reconstruct the spatio-temporal dataset and long short-term memory (LSTM) network. A spatio-temporal groundwater level prediction model LSTM-K-KNN that introduces the spatial autocorrelation of hydrogeological parameters was constructed. The reliability and accuracy of LSTM-K-KNN, LSTM, autoregressive integrated moving average (ARIMA) model, and support vector machine (SVM) were evaluated by a cross-validation algorithm. Results showed that the prediction accuracy of LSTM-K-KNN is 19.86, 43.64, and 52.38% higher than that of the other single prediction models (LSTM, ARIMA, and SVM).

Analysis of groundwater-level fluctuation and linear regression modeling for prediction of initial groundwater level during irrigation of rice paddies in the Nasunogahara alluvial fan, central Japan

Analysis of groundwater-level fluctuation and linear regression modeling for prediction of initial groundwater level during irrigation of rice paddies in the Nasunogahara alluvial fan, central Japan

The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco

The planning and management of groundwater in the absence of in situ climate data is a delicate task, particularly in arid regions where this resource is crucial for drinking water supplies and irrigation. Here the motivation is to evaluate the role of remote sensing data and Input feature selection method in the Long Short Term Memory (LSTM) neural network for predicting groundwater levels of five wells located in different hydrogeological contexts across the Oum Er-Rbia Basin (OER) in Morocco: irrigated plain, floodplain and low plateau area. As input descriptive variable, four remote sensing variables were used: the Integrated Multi-satellite Retrievals (IMERGE) Global Precipitation Measurement (GPM) precipitation, Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), MODIS land surface temperature (LST), and MODIS evapotranspiration. Three LSTM models were developed, rigorously analyzed and compared. The LSTM-XGB-GS model, was optimized using the GridsearchCV method, and uses a single remote sensing variable identified by the input feature selection method XGBoost. Another optimized LSTM model was also constructed, but uses the four remote sensing variables as input (LSTM-GS). Additionally, a standalone LSTM model was established and also incorporating the four variables as inputs. Scatter plots, violin plots, Taylor diagram and three evaluation indices were used to verify the performance of the three models. The overall result showed that the LSTM-XGB-GS model was the most successful, consistently outperforming both the LSTM-GS model and the standalone LSTM model. Its remarkable accuracy is reflected in high R2 values (0.95 to 0.99 during training, 0.72 to 0.99 during testing) and the lowest RMSE values (0.03 to 0.68 m during training, 0.02 to 0.58 m during testing) and MAE values (0.02 to 0.66 m during training, 0.02 to 0.58 m during testing). The LSTM-XGB-GS model reveals how hydrodynamics, climate, and land-use influence groundwater predictions, emphasizing correlations like irrigated land-temperature link and floodplain-NDVI-evapotranspiration interaction for improved predictions. Finally, this study demonstrates the great support that remote sensing data can provide for groundwater prediction using ANN models in conditions where in situ data are lacking.

Assessing Artificial Recharge on Groundwater Quantity Using Wells Recharge

In arid and semi-arid countries like Iraq, which suffer from water scarcity due to the effects of climate change and decreased surface water flow, groundwater is considered a vital source of irrigation water. This study is concerned with the influence of artificial recharge on the rehabilitation of the unconfined aquifer called Al-Dibdibba, located between the cities of Najaf and Kerbala in central Iraq around 31°550′ N and 32°450′ N and 43°300′ E and 44°300′ E. Due to excessive groundwater pumping rates for irrigation, this aquifer has suffered from groundwater decline and increased salinization during the previous 20 years. By establishing a conceptual model in the groundwater modeling system software (GMS), a numerical model was made to simulate groundwater flow. Artificial recharge using recycled water (tertiary treatment) from Kerbala's primary WWTP was carried out using 25 injection wells. The model was calibrated against historical and observed water level data for periods from 2016 to 2017. Three scenarios to predict how the aquifer would act with artificial recharge of 5%, 8%, and 10% from the total daily outflow of the WWTP in Kerbala (100000 m3/day) were studied. The calibration model met the observed values of groundwater levels with R2 = 0.989 for steady-state simulations and R2 = 0.987 for transient simulations. In the final analysis of the simulation, the results show that the maximum predicted groundwater level was raised by the injection of treated water through 25 wells by 1.05 m for 5000 m3/day, 2 m for 8000 m3/day, and 3 m for 10,000 m3/day recharge pumping rates. In addition, if water were pumped into the aquifer, it might support the development of agricultural lands covering more than 93 km2. So, artificial recharge can be considered one of the important solutions to adaptation to the effects of climate change and desertification in Iraq. Doi: 10.28991/CEJ-2023-09-09-010 Full Text: PDF

Groundwater Level Prediction with Deep Learning Methods

The development of civilization and the preservation of environmental ecosystems are strongly dependent on water resources. Typically, an insufficient supply of surface water resources for domestic, industrial, and agricultural needs is supplemented with groundwater resources. However, groundwater is a natural resource that must accumulate over many years and cannot be recovered after a short period of recharge. Therefore, the long-term management of groundwater resources is an important issue for sustainable development. The accurate prediction of groundwater levels is the first step in evaluating total water resources and their allocation. However, in the process of data collection, data may be lost due to various factors. Filling in missing data is a main problem that any research field must address. It is well known that to maintain data integrity, one effective approach is missing value imputation (MVI). In addition, it has been demonstrated that machine learning may be a better tool. Therefore, the main purpose of this study was to utilize a generative adversarial network (GAN) that consists of a generative model and a discriminative model for imputation. Although the GAN could not capture the groundwater level endpoints in every section, the overall simulation performance was still excellent to some extent. Our results show that the GAN can improve the accuracy of water resource evaluations. In the current study, two interdisciplinary deep learning methods, univariate and Seq2val (sequence-to-value), were used for groundwater level estimation. In addition to addressing the significance of the parameter conditions, the advantages and disadvantages of these two models in hydrological simulations were also discussed and compared. Regarding parameter selection, the simulation results for univariate analysis were better than those for Seq2val analysis. Finally, univariate was employed to examine the limits of the models in long-term water level simulations. Our results suggest that the accuracy of CNNs is better, while LSTM is better for the simulation of multistep prediction. Therefore, the interdisciplinary deep learning approach may be beneficial for providing a better evaluation of water resources.

Deep convolutional neural network to predict ground water level

Deep convolutional neural network to predict ground water level

Groundwater Level Trend Analysis and Prediction in the Upper Crocodile Sub-Basin, South Africa

Disasters related to climate change regarding water resources are on the rise in terms of scale and severity. Therefore, predicting groundwater levels (GWLs) is a crucial means to aid adaptive capacity towards disasters related to climate change in water resources. In this study, a Gradient Boosting (GB) regression modelling approach for GWL prediction as a function of rainfall and antecedent GWL is used. A correlation analysis carried out from 2011 to 2020 demonstrated that monthly GWLs can be predicted by antecedent GWLs and rainfall. The study also sought to understand the long-term effects of climate events on groundwater levels over the study area through a Mann–Kendall (MK) trend analysis. A total of 50% of the groundwater stations revealed declining trends, while 25% had no trends and the other 25% showed an increasing trend. Again, the correlation analysis results were used in justifying the trends. The GB predictive model performed satisfactorily for all groundwater stations, with the MSE values ranging from 0.03 to 0.304 and the MAE varying from 0.12 to 0.496 in the validation period. The R2 ranged from 0.795 to 0.902 for the overall period. Therefore, based on projected rainfall and antecedent groundwater levels, future GWLs can be predicted using the GB model derived in this study.

Spatial prediction of groundwater levels using machine learning and geostatistical models: a case study of coastal faulted aquifer systems in southeastern Tunisia

Spatial prediction of groundwater levels using machine learning and geostatistical models: a case study of coastal faulted aquifer systems in southeastern Tunisia

Evaluation of shallow groundwater dynamics after water supplement in North China Plain based on attention-GRU model

The continuous ecological river supplement has enhanced the recharge of groundwater in the North China Plain, and alleviated the depletion of groundwater resources. In the study of ecological river supplement, efficient quantitative prediction of groundwater level (GWL) and groundwater storage (GWS) is a crucial issue. To predict GWL and estimate the variation of GWS in the North China Plain, this paper uses the attention-gated recurrent unit (Attention-GRU) model as a primary method and coefficient of determination (R2) and root mean square error (RMSE) as the indices to evaluate model performance. Compared to traditional recursive neural network (RNN) and long short-term memory (LSTM) model, Attention-GRU model demonstrated an outstanding performance, as the RMSE is respectively 0.5 m and 2.28 m in the training and testing periods, and the R2 is 0.87 and 0.68. With the usage of the convolution neural network (CNN) model, the specific yield field was continuously updated from 2018 to 2021. The average annual variation of GWS estimated by this method is respectively −15.19 × 104 m3, −13.29 × 104 m3, 2.55×104 m3 and 100.05×104 m3 from 2018 to 2021 in the North China Plain. Besides, the semiannual GWS variation, calculated using the updated specific yield, is −59.56 × 104 m3 in 2022. Furthermore, the influence of the reference time length and the driving factors were evaluated. Results show that the precipitation, evaporation and human activities are the most important temporal features influencing the changes in GWS, and the changes in different areas have varying local characteristics. In the piedmont plain, river ecological recharge played a crucial role for groundwater storage recovery. When it moved to the coastal plain, the contribution rate of recharge decreased from 23% to 11%. In addition, the optimal reference time length used in Attention-GRU model varies in different hydrogeology divisions, and the specific yield variation would result in substantial differences for the GWS estimation under different climate conditions and human activities. The systematic framework proposed in this paper is effective for evaluating and predicting GWL and GWS, and thereby able to support the management and development of groundwater, and provide a guidance for ecological river supplement.

Multi-collinearity Based Parameter Optimization and Comparison among Multi-criteria Decision Analysis to Map Groundwater Potential Zones

Abstract Zonation of groundwater potential is a vital technique for long-term water governance and urban planning, particularly in agrarian countries such as India. As a result, the present groundwater potential mapping study was carried out in Tiruvannamalai district, Tamil Nadu, India to assess the capability of the aquifer using 21 groundwater conditioning parameters. An overlay analysis was performed to create a database for multicollinearity analysis to optimize the parameters. Normalization, weight allocation, and ranking of locations based on the assessment value were performed using Multi Criteria Decision Analysis (MCDA) techniques such as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Optimization based on Ratio Analysis (MOORA). The results of MCDA techniques were integrated with Remote Sensing (RS) and Geographical Information System (GIS) for the identification of groundwater potential zones. Based on the TOPSIS, 5.17 % of the area was classified as very high potential and 9.38 % of the area was classified as a very low potential zones. Similarly, the MOORA technique classified 5.54% of zones as very high and 13.55% as very low potential. Validation of results were done using groundwater level and groundwater draft data and prediction accuracy of MOORA and TOPSIS was found to be more than 80%.

Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an advanced python code for predicting groundwater level.

Groundwater level prediction is important for effective water management. Accurately predicting groundwater levels allows decision-makers to make informed decisions about water allocation, groundwater abstraction rates, and groundwater recharge strategies. This study presents a novel model, the self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM), for groundwater level prediction. The SATCN-LSTM model combines the advantages of the SATCN and LSTM models to overcome the limitations of the LSTM model. By utilizing skip connections and self-attention mechanisms, the SATCN model addresses the vanishing gradient problem, identifies relevant data, and captures both short- and long-term dependencies in time series data. By demonstrating the improved performance of the SATCN-LSTM model in terms of mean absolute error and root mean square error (RMSE), and by comparing these results with those reported in previous papers, we have highlighted the advancements and contributions of the proposed model. By improving prediction accuracy, the SATCN-LSTM model enables decision-makers to make informed choices regarding water allocation, groundwater abstraction rates, and drought preparedness. The SATCN-LSTM model contributes to the sustainable and efficient use of groundwater resources by providing reliable information for decision-making processes. The SATCN-LSTM model combines the temporal convolutional network (TCN) architecture with LSTM. TCN is known for its ability to capture short-term dependencies in time series data, while LSTM is effective at capturing long-term dependencies. By integrating both architectures, the SATCN-LSTM model can capture the complex temporal relationships at different scales, leading to improved prediction accuracy. Meteorological data were used to predict GWL. The SATCN-LSTM model outperformed the other models. The SATCN-LSTM model had the lowest mean absolute error (MAE) of 0.09, followed by the self-attention (SA) temporal convolutional network (SATCN) model with an MAE of 0.12. The SALSTM model had an MAE of 0.16, while the TCN-LSTM, temporal convolutional network (TCN), and LSTM models had MAEs of 0.17, 0.22, and 0.23, respectively. The SATCN-LSTM model had the lowest root mean square error of 0.14, followed by SATCN with an RMSE of 0.15. The study results indicated that the SATCN-LSTM model was a robust tool for predicting groundwater level.

Soft computing assessment of current and future groundwater resources under CMIP6 scenarios in northwestern Iran

Excessive use of water resources in combination with climate change threaten to significantly reduce groundwater in arid and semiarid regions. We studied the effects of climate change on the groundwater level for the important Dehgolan Aquifer in northwestern Iran. The water level in this aquifer has dropped by about 35 m during the last 30 years. Soft computing techniques were used together with climate projections in three methodological steps to estimate the groundwater level drop by 2045. Firstly, MODFLOW was used to simulate groundwater flow and movement. Secondly, simulation results, support vector regression (SVR), and least-squares SVR (LSSVR) machine learning models were used to predict groundwater levels for the future 20-year period (2026–2045). The whale optimization algorithm (WOA) was used to improve the prediction results by optimizing the SVR parameters. Thirdly, three climate models of CMIP6 (ACCESS-CM2, BCC-CSM2-MR, and CMCC-ESM2) were used to predict the changes in precipitation for the future period (2026–2045) using SSP 2.6 and SSP 8.5 scenarios. The results showed that the MODFLOW-LSSVR model predicted the groundwater level more accurately than MODFLOW-SVR and MODFLOW-SVR-WOA. The calculation scenario containing previous month groundwater level, monthly aquifer withdrawal, and monthly precipitation had the highest performance in predicting groundwater level with root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash Sutcliffe efficiency (NSE) equal to 0.305 m, 0.014 m, and 0.998, respectively. The results indicate that precipitation may decrease in the future period for the SSP 8.5 scenario (about 6% compared to the reference period 1987–2005). This decrease, along with the continuation of the current aquifer withdrawal, will cause a drop of about 36 m (during 28 years) of the groundwater level (1.3 m per year). The results reveal that the drop could be reduced to 12 m by adopting a 25% reduction in the current aquifer withdrawal. The findings show the necessity of providing a suitable management approach to prevent future aquifer exhaustion due to the continuation of the current withdrawal situation in the region.

Characterizing hydraulic heterogeneity of Bayin River Basin using river stage tomography

The drastic spatial and temporal variations of water resources are one of the causes of the fragile ecological environment in the Bayin River Basin. While the temporal variation is less predictable, a detailed spatial distribution of hydrological parameters of the basin is invaluable for developing guidance for allocating and utilizing water resources in the Bayin River Basin. This study estimates the two-dimensional spatial distribution of hydraulic diffusivity (D) of the Bayin River Basin through the relationship between river flow and groundwater using limited available river stages data along the river. It first conducted a wavelet analysis to illustrate strong correlations between river flow and groundwater during the flood season in the basin. Subsequently, it used a simple linear model to estimate temporal and spatial variations of the river stage along the river due to flood. Then, the study applied a recently developed river stage tomography (RST) to the alluvial fan to estimate the spatial distribution of the D values. The RST treats the flood river stage as a signal pressure transmitter, and the groundwater fluctuation recorded in the observation wells as the images of the aquifer heterogeneity between the river and observation wells. The RST then synthesizes these images to characterize the heterogeneous distribution of D in the alluvial fan in the Bayin River Basin. The results show that the northeast area of the alluvial fan has a relatively high D, while the D is relatively low in the tail and the southwest area of the fan. These findings are consistent with the general concept of alluvial fan evolution.Moreover, the predicted groundwater levels during the flood event in 2019 based on the RST-estimated D is more accurate than the preliminary stratigraphic survey, confirming that capturing groundwater responses at various parts of a basin caused by some disturbances could reveal hydraulic connectivity, which the prior geologic information cannot. This result is essential to advancing aquifer characterization technologies and affirming the contribution of RST and the view toward the future subsurface sciences advocated by Yeh et al. (2008).

Groundwater level dynamic simulation and soil salinization prediction of the closed hydrogeological unit in the arid irrigation area

Abstract Aiming at problems such as inaccurate simulation of groundwater level in closed hydrogeological units, difficult quantitative prediction of soil salinization degree, and unclear water and salt migration, a three-dimensional simulation model of groundwater was established, and the development trend of groundwater level and soil salinization was predicted. The groundwater level simulation results are consistent with the changing trend of the observational data and the simulation model can be used to predict groundwater levels in closed hydrogeological units. When climate scenarios and human activity change are set as future scenarios, the average groundwater buried depth will continue to decrease in the next 10 years, the area with a groundwater buried depth of 0–5 m will exceed 50%, and even the groundwater will overflow to the surface. The change of soil salt content is predicted quantitatively and the salinization degree will develop from ‘saline–alkali soil’ and ‘mild saline–alkali soil’ to ‘medium saline–alkali soil’. The process of water and salt migration in three key hydrologic zones, namely ‘irrigation infiltration’, ‘solute migration’, and ‘water and salt accumulation’, is revealed in the closed hydrogeological unit. The research results can provide new ideas for the improvement of soil and water environment problems.