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Identification of the Dominant Factors in Groundwater Recharge Process, Using Multivariate Statistical Approaches in a Semi-Arid Region

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  • José Luis Uc Castillo

    (Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, Camino a la Presa San José 2055, Lomas 4a Sección, San Luis Potosí 78216, Mexico)

  • José Alfredo Ramos Leal

    (Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, Camino a la Presa San José 2055, Lomas 4a Sección, San Luis Potosí 78216, Mexico)

  • Diego Armando Martínez Cruz

    (CONACYT-Centro de Investigación en Materiales Avanzados, S.C. Calle CIMAV 110, Ejido Arroyo Seco, Col. 15 de mayo (Tapias), Durango 34147, Mexico)

  • Adrián Cervantes Martínez

    (Unidad Académica Cozumel, Universidad de Quintana Roo, Av. Andrés Quintana Roo, Calle 11 con calle 110 sur s/n, Cozumel 77600, Mexico)

  • Ana Elizabeth Marín Celestino

    (CONACYT-Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, Camino a la Presa San José 2055, Col. Lomas 4ta Sección, San Luis Potosí 78216, Mexico)

Abstract

Identifying contributing factors of potential recharge zones is essential for sustainable groundwater resources management in arid regions. In this study, a data matrix with 66 observations of climatic, hydrogeological, morphological, and land use variables was analyzed. The dominant factors in groundwater recharge process and potential recharge zones were evaluated using K-means clustering, principal component analysis (PCA), and geostatistical analysis. The study highlights the importance of multivariate methods coupled with geospatial analysis to identify the main factors contributing to recharge processes and delineate potential groundwater recharge areas. Potential recharge zones were defined into cluster 1 and cluster 3; these were classified as low potential for recharge. Cluster 2 was classified with high potential for groundwater recharge. Cluster 1 is located on a flat land surface with nearby faults and it is mostly composed of ignimbrites and volcanic rocks of low hydraulic conductivity (K). Cluster 2 is located on a flat lowland agricultural area, and it is mainly composed of alluvium that contributes to a higher hydraulic conductivity. Cluster 3 is located on steep slopes with nearby faults and is formed of rhyolite and ignimbrite with interbedded layers of volcanic rocks of low hydraulic conductivity. PCA disclosed that groundwater recharge processes are controlled by geology, K, temperature, precipitation, potential evapotranspiration (PET), humidity, and land use. Infiltration processes are restricted by low hydraulic conductivity, as well as ignimbrites and volcanic rocks of low porosity. This study demonstrates that given the climatic and geological conditions found in the Sierra de San Miguelito Volcanic Complex (SSMVC), this region is not working optimally as a water recharge zone towards the deep aquifer of the San Luis Potosí Valley (SLPV). This methodology will be useful for water resource managers to develop strategies to identify and define priority recharge areas with greater certainty.

Suggested Citation

  • José Luis Uc Castillo & José Alfredo Ramos Leal & Diego Armando Martínez Cruz & Adrián Cervantes Martínez & Ana Elizabeth Marín Celestino, 2021. "Identification of the Dominant Factors in Groundwater Recharge Process, Using Multivariate Statistical Approaches in a Semi-Arid Region," Sustainability, MDPI, vol. 13(20), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11543-:d:659769
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    References listed on IDEAS

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    1. Guigui Xu & Xiaosi Su & Yiwu Zhang & Bing You, 2021. "Identifying Potential Sites for Artificial Recharge in the Plain Area of the Daqing River Catchment Using GIS-Based Multi-Criteria Analysis," Sustainability, MDPI, vol. 13(7), pages 1-15, April.
    2. J. P. Royston, 1983. "Some Techniques for Assessing Multivarate Normality Based on the Shapiro‐Wilk W," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(2), pages 121-133, June.
    3. Hydar Ebrahimi & Reza Ghazavi & Haji Karimi, 2016. "Estimation of Groundwater Recharge from the Rainfall and Irrigation in an Arid Environment Using Inverse Modeling Approach and RS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 1939-1951, April.
    4. Laura E. Condon & Adam L. Atchley & Reed M. Maxwell, 2020. "Evapotranspiration depletes groundwater under warming over the contiguous United States," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
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    Cited by:

    1. Babak Mohammadi, 2022. "Application of Machine Learning and Remote Sensing in Hydrology," Sustainability, MDPI, vol. 14(13), pages 1-2, June.
    2. Chen Li & Baohui Men & Shiyang Yin, 2022. "Spatiotemporal Variation of Groundwater Extraction Intensity Based on Geostatistics—Set Pair Analysis in Daxing District of Beijing, China," Sustainability, MDPI, vol. 14(7), pages 1-17, April.

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