Optimization of Groundwater Quality Monitoring Network Using Geostatistical Method

Document Type : Research Paper

Author

Department of Geology, Yazd University, Yazd, Iran

10.29252/aridbiom.2021.13640.1790

Abstract

Optimal design of groundwater monitoring network that is capable of providing accurate and informative data is crucial to improve our understanding of complex groundwater systems. In this study, a geostatistical method to optimize a groundwater quality monitoring network in Shamil-Takht plain, Hormozgan province were used. This study uses the spatial variance of collected data from the field to identify areas that are not covered and lack information. The kriging variance and the vulnerability maps were combined and the final map was used to design the optimum network. Selection of the best-fitted model was based on the values of the RMSE. This approach has been used to assess the electrical conductivity (EC)-as the representative of the most important quality parameters- monitoring network. The results show that 23 wells can be removed from the primary sampling network with 44 wells. On the other hand, 15 wells have been added to cover areas without information. As a result, the number of wells in low density areas has increased. Based on contaminant sources available in the plain, six new sites have been proposed to monitor the potential contamination from cemeteries, health centers, food and livestock industries.

Keywords


[1]. Baalousha H. (2010). Assessment of a groundwater quality monitoring network using vulnerability mapping and geostatistics: A case study from Heretaunga Plains, New Zealand, Agricultural Water Management, 97, 240-246.
[2]. Daughney, C.J., Raiber, M., Moreau-Fournier, M., Morgenstern, U., van der Raaij, R. (2012). Use of hierarchical cluster analysis to assess the representativeness of a baseline groundwater quality monitoring network: comparison of New Zealand's national and regional groundwater monitoring programs, Hydrogeology Journal, 20, 185-200.
[3]. Farlin J., Galle T., Pittois D., Bayerle M., Schaul T. (2019). Groundwater quality monitoring network design and optimisation based on measured contaminant concentration and taking solute transit time into account, Journal of Hydrology, 573, 516-523.
[4]. Feng-guang, Y., Shu-you, C., Xing-nian, L., Ke-jun, Y. (2008). Design of groundwater level monitoring network with ordinary kriging, Journal of Hydrodynamic, 20, 339-346.
[5]. Gangopadhyay, S., Das Gupta, A., Nachabe, M.H. (2001). Evaluation of ground water monitoring network by principal component analysis, Ground Water, 39, 181-191.
[6]. Hasani Pak, A. (1998). Geostatistics, First edition, University of Tehran Press. (in Farsi)
[7]. Jabbari, M. (2012). Optimization of groundwater quality monitoring network in Birjand plain using combined geostatistics-Fuzzy methods. MSc. Thesis, Earth Science Faculty, Kharazmi University. (in Farsi)
[8]. Jorgensen, L.F., Stockmarr, J. (2008). Groundwater monitoring in Denmark: characteristics, perspectives and comparison with other countries, Hydrogeology Journal, 17, 827-842.
[9]. Kollat, J.B., Reed, P.M. (2006). Comparing state-of-the-art evolutionary multiobjective algorithms for long-term groundwater monitoring design, Advances in Water Resources, 29(6), 792-807.
[10]. Loaiciga, H.A. (1989). An optimization approach for groundwater quality monitoring network design, Water Resources Research, 25, 1771-1782.
[11]. Loaiciga, H.A., Charbeneau, R., Everett, L., Fogg, G., Hobbs, B., Rouhani, S. (1992). Review of ground-water quality monitoring network design, Journal of Hydraulic Engineering, 118, 11-37.
[12]. Luo, Q.K., Wu, J.F., Yang, Y., Qian, J.Z., Wu, J.C. (2016). Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty, Journal of Hydrology, 534, 352-363.
[13]. Mogheir, Y., Singh, V.P., de Lima, J.L.M.P. (2006). Spatial assessment and redesign of a groundwater quality monitoring network using entropy theory, Gaza strip, Palestine, Hydrogeology Journal, 14, 700-712.
[14]. Mohammadi, J. (2006). Pedometery, Spatial Statistics (2nd Vol.). Pelk publication. (in Farsi)
[15]. Morgenstern, U., Daughney, C.J. (2012). Groundwater age for identification of baseline groundwater quality and impacts of land-use intensification-The National Groundwater Monitoring Programme of New Zealand, Journal of Hydrology, 456-457, 79-93.
[16]. Nielsen D.M. (2006). Practical handbook of environmental site characterization and groundwater monitoring, 2nd ed. USA: Taylor and Francis group; CRC Press.
[17]. Nowak, W., Rubin, Y., de Barros, P.J. (2012). A hypothesis-driven approach to optimize field campaigns, Water Resources Research, 48, 1-16.
[18]. Nunes, L., Paralta, E., Cunha, M., Ribeiro, L. (2007). Comparison of variance-reduction and space filling approaches for the design of environmental monitoring networks, Computer-Aided Civil and Infrastructure Engineering, 22, 489-498.
[19]. Reed, P.M., Kollat, J.B. (2013). Visual analytics clarify the scalability and effectiveness of massively parallel many-objective optimization: a groundwater monitoring design example, Advances in Water Resources, 56, 1-13.
[20]. Song J., Yang Y., Chen G., Sun X., Lin J., Wu J., Wu J. (2019).  Surrogate assisted multi-objective robust optimization for groundwater monitoring network design, Journal of Hydrology, 577, 123994
[21]. Wohling, T., Geiges, A., Nowak, W. (2016). Optimal design of multitype groundwater monitoring networks using easily accessible tools, Groundwater, 54, 861-870.