A Review on Process-Based Groundwater Vulnerability Assessment Methods
Abstract
:1. Introduction
2. The Methodology of the Process-Based Groundwater Vulnerability Assessment
2.1. The Principles of the Process-Based Groundwater Vulnerability Assessment
2.2. The Physical and Chemical Processes Involved in Process-Based Groundwater Vulnerability Assessment
2.2.1. Convection
2.2.2. Hydrodynamic Dispersion
- (1)
- Molecular diffusion
- (2)
- Mechanical diffusion
2.2.3. Adsorption
2.3. The Influence Factors of Solute Transport in Vadose Zone
2.3.1. Initial Concentration
2.3.2. Boundary Conditions
- (1)
- The constant concentration boundary condition
- (2)
- The recharge concentration
- (3)
- The evapotranspiration
- (4)
- The point source
2.3.3. The Component Parameters
2.3.4. The Dispersion Coefficient
3. The Commonly Used Method for Numerical Simulation
3.1. The Vadose Zone Models
3.1.1. The Geometric Model
- (1)
- The leaching model
- I.
- The soil pores are cylindrical tubes with a diameter of D;
- II.
- The solute and water flow at the same speed v, without considering flow velocity distribution and reactions between soil and solutes;
- III.
- Molecular diffusion is not considered;
- IV.
- Changes in soil structure are not taken into account.
- (2)
- The capillary bundle model
- I.
- Soil is composed of a series of capillaries of varying thickness, with the diameter distribution reflecting the soil moisture characteristics;
- II.
- Solutes primarily move through convection in the soil, with molecular diffusion being negligible and therefore ignored;
- III.
- Soil moisture is divided into two parts, mobile water and immobile water, with mass exchange between them in a transient equilibrium state;
- IV.
- The structure of the soil remains unchanged
3.1.2. The Convection–Diffusion Equation Model (CDE)
- (1)
- Unicomponent solute transport
- (2)
- Multiple solute transport
3.1.3. Random Model
3.2. The Model for Saturated Zone
- (1)
- The Euler method is the earliest method used for solute transport simulation. It is still widely used today, especially in hydraulic simulations, due to its effectiveness [69,70]. Its advantage lies in the use of a fixed grid, which satisfies the law of mass conservation, allowing for precise and efficient handling of migration problems where dispersion is dominant. Additionally, it is easy to implement programmatically. However, in dealing with the prevalent convective dominant problem in field conditions, the Euler method often brings about excessive numerical diffusion and oscillations. Within the Euler method, finite difference and finite element methods each have their own advantages and disadvantages. Finite element methods are more flexible in terms of spatial discretization, whereas finite difference methods have lower numerical complexity and often require less computation.
- (2)
- The Lagrangian method, which abstracts fluid and solutes as a large number of moving particles, is employed to represent convection and diffusion. It can precisely solve migration processes dominated by convection and effectively eliminate numerical diffusion [71]. However, due to the lack of a fixed simulation network, the Lagrangian method can cause numerical instability and computational difficulties. These shortcomings are especially difficult to control when dealing with non-uniform media with multiple sources/sinks and complex boundary conditions, resulting in local conservation errors and anomalies. The representative method in the Lagrangian method is the stochastic walking method, which approximately handles migration caused by convection using particle tracking, and characterizes diffusion processes by adding a random displacement term to the particle position during convection. By adjusting the flow velocity and particle mass, adsorption and decay can be treated.
- (3)
- The mixed Euler–Lagrange method seeks to combine the advantages of the Euler method and the Lagrangian method [72,73]. The Euler method, which employs finite differences and finite element methods, is used to handle dispersion and other terms, while the Lagrangian method, which employs particle tracking, is used to solve convection terms. Currently, the trend in the development of the mixed Euler–Lagrange method is to seek solutions that satisfy mass conservation and are more computationally efficient. The primary approaches for the mixed Euler–Lagrange method are the Method of Characteristics (MOC), Modified Method of Characteristics (MMOC), and Hybrid Method of Characteristics (HMOC). The Method of Characteristics employs conventional particles to solve convection terms, but the particles do not carry mass; they represent the concentration field. Its advantage is that it has very low numerical dispersion, and the concentration obtained is less discrete than that obtained with the Random Walk Method. The Modified Method of Characteristics is similar to the Method of Characteristics, but it differs in how it handles convection terms. The Method of Characteristics requires a large number of particles that move with time and records the concentration and position of each particle during tracking, while the Modified Method of Characteristics only requires one particle at each differential or finite element location. Therefore, the Modified Method of Characteristics, when combined with simple low-order concentration interpolation, is more computationally efficient than the Method of Characteristics. The Hybrid Method of Characteristics attempts to combine the advantages of the Method of Characteristics and the Modified Method of Characteristics, and adjusts the solution method automatically according to the nature of the concentration field. By using appropriate control criteria to switch between the Method of Characteristics and the Modified Method of Characteristics, the solution method can achieve almost no numerical dispersion, while using far fewer particles than the Method of Characteristics.
4. Application and Progress in the Assessment of Process-Based Groundwater Vulnerability
5. Challenges and Prospects
- (1)
- The operation of the unsaturated zone model requires a large number of parameters and boundary conditions. Existing technologies still have deficiencies in dealing with the heterogeneity of soil parameters at the field scale, which increases the uncertainty of simulation results;
- (2)
- It requires sufficient long-term groundwater level and water quality data;
- (3)
- There is uncertainty in the process-based method in groundwater vulnerability assessment; the reasons include objective uncertainty factors and subjective uncertainty factors. The randomness and complexity of the groundwater system determine the objective uncertainty, while the researcher’s one-sided understanding of the research object causes subjective uncertainty;
- (4)
- There is uncertainty in the transport and transformation rules of characteristic pollutants in the vadose zone and saturated zone and in obtaining quantitative characterization parameters;
- (5)
- This method focuses on simulating the process of surface pollutants entering the vadose zone and saturated zone. The model can quantitatively describe the vulnerability changes caused by the lowering of groundwater levels and the increase in vadose zone thickness due to excessive groundwater exploitation, leading to an extended time for pollutants to enter the saturated zone. However, there are some shortcomings in describing the mechanisms of groundwater quality changes caused by changes in groundwater quantity.
- (1)
- In evaluating groundwater resource vulnerability, focus on theoretical research on solute migration and transformation in the vadose zone. In evaluating groundwater source vulnerability, how to comprehensively reflect the impact of vadose zone and saturated zone on groundwater vulnerability and coupling of water flow and pollutant migration models in vadose zone and saturated zone deserves further study;
- (2)
- Explore methods to combine the process-based method with stochastic models and various intelligent methods (gray system, BP neural network, projection pursuit, extension theory, etc.) to study groundwater vulnerability;
- (3)
- From a technical perspective of groundwater vulnerability assessment, the combination of GIS technology with various mathematical models will be a major development direction for groundwater vulnerability assessment;
- (4)
- The process-based method has clear physical meaning, high reliability of evaluation results, low subjectivity, and strong practicality in evaluating groundwater vulnerability. However, due to the complex random uncertainty of the groundwater system itself, further research is needed on both the evaluation method and uncertainty analysis of results.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model Name | Function |
---|---|
Linear Model | |
Freundlich Model | |
Redlich–Peterson Model | |
Langmuir Model | |
Langmuir-–Freundlich Model | |
Multipoint Langmuir Model | |
Multipoint Freundlich Model |
Model Name | Applicable Area | Model Type | Suitable for Field Application | Performance | Limitations |
---|---|---|---|---|---|
Leaching model | Unsaturated zone | Equilibrium model | Suitable | Simple calculation; Few parameters. | Simply generalized without considering processes such as pollutant transformation and absorption. |
Capillary bundle model | Unsaturated zone | Equilibrium model | Suitable | Simple calculation; Few parameters. | Simply generalized without considering processes such as pollutant transformation and absorption. |
CDE | Unsaturated zone | Equilibrium model | Suitable | Simple and reliable calculations; Requires few data; Strong expandability. | Does not consider complex processes such as chemical reactions and adsorption of pollutants; The simplified model may have differences from the reality. |
MIM | Unsaturated zone | Nonequilibrium model | Moderate | Can accurately simulate the nonequilibrium transport of solutes; Suitable for various pollutants. | Numerous parameters are disadvantageous for simulating field scale. |
Two-site model | Unsaturated zone | Nonequilibrium model | Moderate | Capable of estimating the distribution of sorbed and dissolved phases; Can estimate the pollutions transport rate; Can estimate amount of pollutions retained in soil. | Numerous parameters are disadvantageous for simulating field scale; Some assumptions do not match the actual situation; The impact of nonequilibrium adsorption and slow release of pollutants from soil particles cannot be considered. |
Two region model | Unsaturated zone | Nonequilibrium model | Moderate | Can better describe the advective transport of solutes. | Numerous parameters are disadvantageous for simulating field scale; Not applicable to non-uniform flow fields. |
Random model | Unsaturated zone | Equilibrium and Nonequilibrium | Suitable | The ability to consider the heterogeneity of the medium; A high level of prediction accuracy. | Requires a large amount of data; Unable to depict the transport mechanism of pollutants. |
ADE | Saturated zone | Equilibrium model | Suitable | Applicable to a variety of pollutants; Computationally stable and efficient; Widely used. | Existence of homogeneous and linear assumptions; Inability to describe complex processes such as adsorption and chemical reactions. |
OTIS | Saturated zone | Equilibrium model | Suitable | Takes into account physical, chemical, and biological processes of pollutant transport; Computation is reliable and stable. | Existence of homogeneous and linear assumptions; 1D model; Cannot describe dynamic physical and chemical processes. |
MTAK | Saturated zone | Equilibrium model | Suitable | Applicable to multiple pollutants; Applicable to multiple processes; Wide range of applications. | Can only describe short-term solute transport processes; Adsorption and chemical reaction models have limitations; Many parameters involved. |
STOMP | Saturated zone | Equilibrium model | Suitable | Applicable to a variety of pollutants; Applicable to multiple processes; | Large data requirements; Too many parameters; complex calculations. |
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Geng, C.; Lu, D.; Qian, J.; Xu, C.; Li, D.; Ou, J.; Ye, Z. A Review on Process-Based Groundwater Vulnerability Assessment Methods. Processes 2023, 11, 1610. https://doi.org/10.3390/pr11061610
Geng C, Lu D, Qian J, Xu C, Li D, Ou J, Ye Z. A Review on Process-Based Groundwater Vulnerability Assessment Methods. Processes. 2023; 11(6):1610. https://doi.org/10.3390/pr11061610
Chicago/Turabian StyleGeng, Cheng, Debao Lu, Jinglin Qian, Cundong Xu, Dongfeng Li, Jian Ou, and Zhou Ye. 2023. "A Review on Process-Based Groundwater Vulnerability Assessment Methods" Processes 11, no. 6: 1610. https://doi.org/10.3390/pr11061610
APA StyleGeng, C., Lu, D., Qian, J., Xu, C., Li, D., Ou, J., & Ye, Z. (2023). A Review on Process-Based Groundwater Vulnerability Assessment Methods. Processes, 11(6), 1610. https://doi.org/10.3390/pr11061610