Application of geostatistical methods in the spatio-temporal modeling of groundwater levels in the Sébaco Valley aquifer, Nicaragua.

Authors

DOI:

https://doi.org/10.15359/rca.56-2.10

Keywords:

Empirical Bayesian Kriging; geostatistics; water table acceleration; water table velocity.

Abstract

[Introduction]: Georeferenced environmental information from environmental institutions is the basis for improving environmental management and sustainability in the Latin American and Caribbean region. [Objective]: To evaluate the spatio-temporal evolution of the water table using groundwater level data measured in the network of monitoring wells of the Sébaco Valley aquifer. [Methodology]: The fluctuation of groundwater levels was evaluated during the dry seasons from 2010 to 2018 (9 years) based on water level measurements collected from the network of monitoring wells of the Sébaco Valley aquifer. The geostatistical approach applied to this dataset to reveal the predictive models was the Empirical Bayesian Kriging (EBK) method. This method obtained a spatial representation of the phreatic surface of the aquifer and was subsequently used to calculate the velocity and acceleration of the water table. [Results]: During the period analyzed, water table acceleration maps showed mean values of -0.52 m/year. These values suggest that part of the aquifer is experiencing a decline in water table levels that could be rapidly aggravated by climatic events and increased demand of water resources. [Conclusions]: The approach used for quantitative assessment of groundwater levels is suitable for countries lacking a database of aquifer-specific hydraulic parameters.


Author Biography

  • Bianka Guadalupe Castillo Treminio, Universidad Nacional Autónoma de Nicaragua.

    Docente. 

References

Adhikary, P. P., & Dash, C. J. (2017). Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth. Applied Water Science, 7(1), 339-348. https://doi.org/10.1007/s13201-014-0249-8

Ahmadi, S. H., & Sedghamiz, A. (2007). Geostatistical analysis of spatial and temporal variations of groundwater level. Environmental Monitoring and Assessment, 129(1-3), 277-294. https://doi.org/10.1007/s10661-006-9361-z

Bodrud-Doza, M., Bhuiyan, M. A. H., Islam, S. M. D. U., Quraishi, S. B., Muhib, M. I., Rakib, M. A., & Rahman, M. S. (2019). Delineation of trace metals contamination in groundwater using geostatistical techniques: A study on Dhaka City of Bangladesh. Groundwater for Sustainable Development, 9(August 2018). https://doi.org/10.1016/j.gsd.2019.03.006

Boudibi, S., Sakaa, B., & Benguega, Z. (2021). Spatial variability and risk assessment of groundwater pollution in El-Outaya region, Algeria. Journal of African Earth Sciences, 176(January), 104135. https://doi.org/10.1016/j.jafrearsci.2021.104135

Comisión Económica para América Latina y el Caribe (CEPAL). (2005). Estadísticas del medio ambiente en América Latina y el Caribe: Avances y perspectivas. https://repositorio.cepal.org/bitstream/handle/11362/5609/1/S05629_es.pdf

Flores Meza, Y. (2004). Criterios hidrogeológicos para la formulación del plan de gestión en el acuífero del valle de Sébaco. CIRA-UNAN Managua. https://repositorio.unan.edu.ni/2357/

INETER. (2005). Clasificación climática según Koppen. Período 1971-2000. [Imagen] (2200×1700). https://webserver2.ineter.gob.ni//mapas/Nicaragua/clima/atlas/Clasificacion Climatica/Clasificacion_Climatica_Koppen.jpg

Gribov, A., & Krivoruchko, K. (2020). Empirical Bayesian kriging implementation and usage. Science of the Total Environment, 722, 137290. https://doi.org/10.1016/j.scitotenv.2020.137290

Kresic, N., & Mikszewski, A. (2013). Hydrogeological Conceptual Site Models. https://doi.org/10.1201/b12151

Krivoruchko, K., & Gribov, A. (2019). Evaluation of empirical Bayesian kriging. Spatial Statistics, 32, 100368. https://doi.org/10.1016/j.spasta.2019.100368

Kurtulus, B., & Flipo, N. (2012). Hydraulic head interpolation using anfis-model selection and sensitivity analysis. Computers and Geosciences, 38(1), 43-51. https://doi.org/10.1016/j.cageo.2011.04.019

Li, Y., Hernández, J. H., Aviles, M., Knappett, P. S. K., Giardino, J. R., Miranda, R., Morales, J. (2020). Empirical Bayesian Kriging method to evaluate inter-annual water-table evolution in the Cuenca Alta del Río Laja aquifer, Guanajuato, México. Journal of Hydrology, 582(August 2019), 124517. https://doi.org/10.1016/j.jhydrol.2019.124517

Malekzadeh, M., Kardar, S., & Shabanlou, S. (2019). Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models. Groundwater for Sustainable Development, 9(July), 100279. https://doi.org/10.1016/j.gsd.2019.100279

Moghaddam, H. K., Moghaddam, H. K., Kivi, Z. R., Bahreinimotlagh, M., & Alizadeh, M. J. (2019). Developing comparative mathematic models, BN and ANN for forecasting of No groundwater levels. Groundwater for Sustainable Development, 9(June), 100237. https://doi.org/10.1016/j.gsd.2019.100237

Sahebjalal, E. (2012). Application of geostatistical analysis for evaluating variation in groundwater characteristics. World Applied Sciences Journal, 18(1), 135-141. https://doi.org/10.5829/idosi.wasj.2012.18.01.664

Tahal Consulting. (1997). Estudio hidrológico del Valle de Sébaco. Managua, Nicaragua.

Vessia, G., Di Curzio, D., Chiaudani, A., & Rusi, S. (2020). Regional rainfall threshold maps drawn through multivariate geostatistical techniques for shallow landslide hazard zonation. Science of the Total Environment, 705, 135815. https://doi.org/10.1016/j.scitotenv.2019.135815

Published

2022-06-01

How to Cite

Castillo Treminio, B. G. (2022). Application of geostatistical methods in the spatio-temporal modeling of groundwater levels in the Sébaco Valley aquifer, Nicaragua. Tropical Journal of Environmental Sciences, 56(2), 196-212. https://doi.org/10.15359/rca.56-2.10