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Redesigning and monitoring groundwater quality and quantity networks by using the entropy theory

  • Mohammad Nazeri Tahroudi
  • Abbas Khashei SiukiEmail author
  • Yousef Ramezani
Article
  • 47 Downloads

Abstract

This study aimed at redesigning and monitoring the groundwater network of Naqadeh plain in the southwest of Lake Urmia to examine the number and position of optimal wells for the salinity information transfer (EC) and survey of groundwater level at aquifer. In this regard, groundwater level data (35 wells) and electrical conductivity values (24 wells) were used during a 10-year period (2002–2012). In the first stage, simulation was conducted using the multivariate regression method and quantitative and qualitative values and the interaction of wells was observed. In the next stage, number of different classes was considered for clustering quantitative and quantitative values. The results of studying different classes of data clustering showed that the 12-class cluster had more accurate results based on the root mean square error and coefficient of determination. The root mean square error was improved by about 40, 21, and 15%, respectively, compared to the 3, 5, and 9-classe clusters. Finally, by choosing proper cluster of data, entropy indicators were investigated for quantitative and qualitative values at the aquifer level. The results of entropy indices at the aquifer showed that there was a severe shortage of information in terms of salinity in the Northwest of the aquifer, which necessitates drilling a new well in this area to accurately monitor the EC values. However, since more than 90% of the basin area is in surplus and approximately surplus conditions in terms of transferring information, the studied area has a good dispersion for qualitative monitoring. Information transfer index for the quantitative groundwater network monitoring showed that piezometers near Lake Urmia were faced with a lack of information, which according to piezometers ranking, is ranked last in terms of value of maintaining or keeping the network. Eastern areas of aquifer are also faced with shortage of piezometers accounting for about 3% of the total area. The results of survey of surplus wells in the aquifer showed that nine and six surplus wells are in the aquifer for the qualitative and quantitative network, respectively. There were also wells in which information transfer was not well done and their information could not be assured. Finally, based on the conditions, a new arrangement of wells and a new optimal network were proposed.

Keywords

Information transfer Quality monitoring Entropy theory Groundwater network Lake Urmia 

Notes

Acknowledgements

The authors would like to thank West Azerbaijan Regional Water Authority for providing the data.

References

  1. Chadalavada, S., Datta, B., & Naidu, R. (2011). Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site. Environmental Monitoring and Assessment, 173(1), 929–940.  https://doi.org/10.1007/s10661-010-1435-2.CrossRefGoogle Scholar
  2. Chapman, T. G. (1986). Entropy as a measure of hydrologic data uncertainty and model performance. Journal of Hydrology, 85(1–2), 111–126.  https://doi.org/10.1016/0022-1694(86)90079-X.CrossRefGoogle Scholar
  3. Chen, Y. C., Wei, C., & Yeh, H. C. (2008). Rainfall network design using kriging and entropy. Hydrological Processes, 22(3), 340–346.  https://doi.org/10.1002/hyp.6292.CrossRefGoogle Scholar
  4. Harmancioglu, N. B., & Alpaslan, N. (1992). Water quality monitoring network design: A problem of multiobjective decision making. Journal of the American Water Resources Association, 28(1), 179–192.  https://doi.org/10.1111/j.1752-1688.1992.tb03163.x.CrossRefGoogle Scholar
  5. Harmancioglu, N. B., Ozkul, S. D., & Alpaslan, M. N. (1998). Water quality monitoring and network design. Environmental Data Management, 27, 61–106.  https://doi.org/10.1007/978-94-015-9056-34.
  6. Jaynes E. T. 1957. Information theory and statistical mechanics, I. Phys, Rev, 106, 620–630.CrossRefGoogle Scholar
  7. Jessop, A. (1995). Informed assessments, an introduction to information, entropy and statistics. New York: Ellis Horwoo.Google Scholar
  8. Keum, J., Kornelsen, K., Leach, J., & Coulibaly, P. (2017). Entropy applications to water monitoring network design: a review. Entropy, 19(11), 1–21.  https://doi.org/10.3390/e19110613.CrossRefGoogle Scholar
  9. Khalili, K., Tahoudi, M. N., Mirabbasi, R., & Ahmadi, F. (2016). Investigation of spatial and temporal variability of precipitation in Iran over the last half century. Stochastic Environmental Research and Risk Assessment, 30(4): 1205–1221.Google Scholar
  10. Krstanovic, P. F., & Singh, V. P. (1992). Evaluation of rainfall networks using entropy: I. Theoretical development. Water Resources Management, 6(4), 279–293.  https://doi.org/10.1007/BF00872281.CrossRefGoogle Scholar
  11. Lubbe, C. (1996). Information theory. Cambridge: Cambridge University Press.Google Scholar
  12. Markus, M., Knapp, H. V., & Tasker, G. D. (2003). Entropy and generalized least square methods in assessment of the regional value of stream gages. Journal of Hydrology, 283(1), 107–121.  https://doi.org/10.1016/S0022-1694(03)00244-0.CrossRefGoogle Scholar
  13. Mogheir, Y., & Singh, V. P. (2003). Specification of information needs for groundwater management planning in developing country. Groundwater Hydrology, Balema Publisher, Tokyo, 2, 3–20.Google Scholar
  14. Mogheir, Y., De Lima, J. L. M. P., & Singh, V. P. (2004). Characterizing the spatial variability of groundwater quality using the entropy theory: II. Case study from Gaza strip. Hydrological Processes, 18(13), 2579–2590.CrossRefGoogle Scholar
  15. 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(5), 700–712.  https://doi.org/10.1007/s10040-005-0464-3.CrossRefGoogle Scholar
  16. Ozkul, S., Harmancioglu, N. B., & Singh, V. P. (2000). Entropy-based assessment of water quality monitoring networks. Journal of hydrologic engineering., 5(1), 90–100.  https://doi.org/10.1061/(ASCE)1084-0699(2000)5:1(90)#sthash.eBtsb7ac.dpuf.CrossRefGoogle Scholar
  17. Quevauviller, P. (2009). Groundwater monitoring. Hoboken: John Wiley & Sons.CrossRefGoogle Scholar
  18. Şarlak, N., & Şorman, A. (2006). Evaluation and selection of streamflow network stations using entropy methods. Turkish Journal of Engineering and Environmental Sciences, 30(2), 91–100.Google Scholar
  19. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423.  https://doi.org/10.1145/584091.584093.CrossRefGoogle Scholar
  20. Van Luin, A. B., & Ottens, J. J. (1997). Conclusions and recommendations of the international workshop monitoring tailor-made II--information strategies in water management. European Water Pollution Control, 4(7), 53–55.Google Scholar
  21. Wu, S., & Zidek, J. V. (1992). An entropy-based analysis of data from selected NADP/NTN network sites for 1983–1986. Atmospheric Environment, Part A: General Topics, 26(11), 2089–2103.  https://doi.org/10.1016/0960-1686(92)90093-Z.CrossRefGoogle Scholar
  22. Zhou, Y. (1996). Spatial data generation program (COVRAN). The Netherlands: Delft.Google Scholar
  23. Zhu, Q., Shen, L., Liu, P., Zhao, Y., Yang, Y., Huang, D., & Yang, J. (2015). Evolution of the water resources system based on synergetic and entropy theory. Polish Journal of Environmental Studies, 24(6), 2727–2738.  https://doi.org/10.15244/pjoes/59236.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohammad Nazeri Tahroudi
    • 1
  • Abbas Khashei Siuki
    • 1
    Email author
  • Yousef Ramezani
    • 1
  1. 1.Department of Water Engineering, Faculty of AgricultureUniversity of BirjandBirjandIran

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