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Φ_T (%)

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Sy (-)

α (m²/N)

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Igneous

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Andesite Basalt Dacite Diorite Dolerite Gabbro Granite Granodiorite Ignimbrite Latite Tuff Scoria Peridotite Pumice Monzonite Rhyolite Syenite Amphibolite Gneiss Granulite Marble Migmatite Phyllite Quartzite Schist Serpentine Scarn Slate Anhydrite Breccia Chalk Conglomerate Dolomite Gypsum Halite Limestone Sandstone Shale Siltstone Sand and Gravel Gravel Sand Silt Silt and Clay Glacial till Clay Silty Sand Clay Sand Sand silt and gravel Sand clay and gravel Sand silt and clay

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Φ_T (%)

Φ_e (%)

K (m/d)

S (-)

Sy (-)

α (m²/N)

Log Scale

Select Type

Igneous

Metamorphic

Sedimentary

Unconsolidated

Search by lithology

Select Lithology

Andesite Basalt Dacite Diorite Dolerite Gabbro Granite Granodiorite Ignimbrite Latite Tuff Scoria Peridotite Pumice Monzonite Rhyolite Syenite Amphibolite Gneiss Granulite Marble Migmatite Phyllite Quartzite Schist Serpentine Scarn Slate Anhydrite Breccia Chalk Conglomerate Dolomite Gypsum Halite Limestone Sandstone Shale Siltstone Sand and Gravel Gravel Sand Silt Silt and Clay Glacial till Clay Silty Sand Clay Sand Sand silt and gravel Sand clay and gravel Sand silt and clay

Bins

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Φ_T (%)

Φ_e (%)

K (m/d)

S (-)

Sy (-)

α (m²/N)

Select Parameter

Φ_T (%)

Φ_e (%)

K (m/d)

S (-)

Sy (-)

α (m²/N)

Log Scale y

Log Scale x

Download Database (.csv)

Download References (.pdf)

AquiParameter: Aquifer Parameter Dataset. Compilation and Statistical Analysis using R.

Héctor Báez, Antonio Hernández-Espriú

Hydrogeology Group, Faculty of Engineering, UNAM, 2021.

Description

An accurate and reliable estimation of aquifer hydraulic parameters is essential to assess groundwater pumping conditions, predict solute migration rates, develop remediation strategies, or properly calibrate and validate flow and transport numerical models. One common practice among hydrogeologists is to assign hydraulic parameters as a function of a previously known aquifer lithology. This procedure is often useful as a preliminary evaluation, to further explore a detailed hydraulic characterization, by means of in-situ aquifer testing or lab methods.

Virtually all Groundwater Hydrology textbooks contain very little hydraulic values that generally served as rough guidelines. Here, we introduce "AquiParameter" an extensive hydrogeological dataset that provides ~6,000 records of total porosity, effective porosity, hydraulic conductivity, storage coefficient, specific yield, and rock compressibility, for more than 40 different igneous, sedimentary, metamorphic and unconsolidated lithologies.

The AquiParameter dataset was compiled manually based on a substantial literature review (~900 sources), such as Peer-Review papers, technical reports, textbooks, and academic thesis/dissertations, considering records from several parts of the world. Moreover, some interactive statistical tools were added to allow the user to display, visualize and compare boxplots, histograms, and correlation plots between rock groups and specific lithologies. Finally, in the “Download” Section, the user can freely download the entire dataset in a CSV-based MS Excel compatible format, and to download all our cited references in the PDF file.

Our dataset was developed using R ( R Core Team, 2017 ), a robust and open-source language and cohesive environment for statistical computing. We used Rstudio ( Allaire, 2012 ), an Integrated Development Environment for the R programming language.

Overall, AquiParameter was particularly developed for students, academicians, and teachers. Of course, geoscientists, researchers and groundwater practitioners are more than welcome to use our product. Please feel free to explore, navigate and download AquiParameter. We hope that you can find this interactive dataset useful and fun; and please, don´t forget to cite us! =)

Héctor and Antonio.

Dataset Acronyms

- Φ _T = Total Porosity (%)

- Φ _e = Effective Porosity (%)

- K = Hydraulic Conductivity (Meter/Day; m/d)

- S = Storage Coefficient (Dimensionless)

- Sy = Specific Yield (Dimensionless)

- α = Rock compressibility (Square Meter/Newton; (m²/N) )

- n = Number of observations

- q ₁ = First quartile (the median of the lower half of the dataset)

- q ₃ = Third quartile (the median of the upper half of the dataset)

- σ _logx = Standard deviation of the logarithm of the variable

Author’s Affiliation

Earth Sciences Division, Faculty of Engineering, Universidad Nacional Autonoma de Mexico (UNAM)

Correspondence to ahespriu@unam.mx

Citation

Please cite us as:

Báez, H, Hernández-Espriú, A (2021). AquiParameter: Aquifer Parameter Dataset. Compilation and Statistical Analysis using R.

Báez, H (2021). Hydrogeological Parameters Dataset: Compilation and Statistical Analysis using R [In Spanish: Base de datos de parámetros hidrogeológicos: compilación y análisis estadístico usando R], BS, BE Thesis, Faculty of Engineering, Universidad Nacional Autónoma de México (UNAM), Mexico.

Acknowledgements

We are grateful to the Direccion General de Computo y de Tecnologias de Informacion y Comunicacion, DGTIC, UNAM ( https://www.tic.unam.mx/ ) for granting us a room in their Web Server to host AquiParameter. We truly thank to Mtra. Rebeca Valenzuela Argüelles (Head of the Teaching Technology Department) and to Act. Mario Alberto Hernández Mayorga (Head of the Applications Development Department) for kindly supporting this project.

References

Allaire, J., 2012. RStudio: integrated development environment for R. Boston, MA, 770, p.394.

R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ .