Abstract
Accurate and precise values of hydrodynamic parameters are needed for groundwater modeling and management. Pumping test in the aquifer is the standard method to estimate the transmissivity, hydraulic conductivity, and storage coefficient as the key hydrodynamic parameters. Analytical solutions with curve matching and numerical modeling are two methods to estimate these parameters in the aquifer. Graphical analyses are commonly applied to time-drawdown/water table data which are time-consuming and approximate. Graphical type-curve methods as promising tools are used extensively in water resources studies, while applying these methods is still new in pumping test analysis. In the current study, the first effort based on our knowledge, we have reviewed the literature type-curve graphical methods in pumping test analysis. To achieve this goal, we reviewed and compared the journal articles regarding the characteristics and capabilities of the modeling process from 2000 to 2022. We have clustered the reviewed papers into graphical, modeling, and hybrid categories. Then, a comprehensive review of the selected papers was presented to delineate the highlight of every paper. This review could guide researchers in pumping test analysis. Also, we have presented various recommendations for future research to improve the quality of hydrodynamic parameter estimation.
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Abbreviations
- AI:
-
Artificial intelligence
- DE:
-
Differential evolution
- K :
-
Hydraulic conductivity
- K r :
-
Horizontal hydraulic conductivity
- K R :
-
Radial hydraulic conductivity
- K s :
-
Skin hydraulic conductivity
- K V :
-
Vertical hydraulic conductivity
- l :
-
Leakage
- PSO:
-
Particle swarm optimization
- PUSA:
-
Storage per unit surface area
- S :
-
Storage coefficient
- S s :
-
Specific storage
- S y :
-
Specific yield
- T :
-
Transmissivity
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Z. Dashti reviewed the literature, gathered the papers, performed data visualization and interpretations, and drafted the manuscript. M. Nakhaei and G.H Karami contributed to the interpretation and manuscript revision. M. Vadiati reviewed the literature, gathered the papers, performed data visualization, revised the manuscript, and contributed to the review and edit. O. Kisi revised and drafted the manuscript and contributed to the review.
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Highlights
• The paper reviews the pumping test analysis.
• Graphical type-curve methods were compared with other methods.
• Survey shows machine learning’s ability to enhance pumping test analysis performance.
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dashti, Z., Nakhaei, M., Vadiati, M. et al. A literature review on pumping test analysis (2000–2022). Environ Sci Pollut Res 30, 9184–9206 (2023). https://doi.org/10.1007/s11356-022-24440-4
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DOI: https://doi.org/10.1007/s11356-022-24440-4