year 13, Issue 4 (Winter 2023)
E.E.R. 2023, 13(4): 256-272 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
YAZDANI M R, DARMANI M, NOHTANI M, ARA H, Ebrahimiy S. Investigating the Qualitative Parameters of Using Geostatistical Methods (Case Study: Mashhad Plain). E.E.R. 2023; 13 (4) :256-272
URL: http://magazine.hormozgan.ac.ir/article-1-775-en.html
URL: http://magazine.hormozgan.ac.ir/article-1-775-en.html
Investigating the Qualitative Parameters of Using Geostatistical Methods (Case Study: Mashhad Plain)
Faculty of Desert Studies, Semnan University , m_darmani@semnan.ac.ir
Abstract: (1189 Views)
Introduction
Selection and accuracy of appropriate zoning methods and preparation of maps corresponding to variation of groundwater qualitative characteristics depend upon the region's condition and the available statistics and data. The goal of this research is to determine the most appropriate method of interpolation and spatial analysis of qualitative components such as Cl, EC, SO4, SAR, TDS and TH of groundwater in Mashhad Plain. In this research, at first, the data of 177 wells were selected with respect to their dispersion and accuracy for two consecutive years (1392-1393); then controlling and reconstruction of data were performed. Kolmogorov-Smirnov test showed that the data were not normal and to normalize them their logarithm was obtained. Then using GS+software, the best variogram model was fitted to the spatial structure of data. The results show that for qualitative parameters, the exponential, Semi-variogram and linear models are the best semi variogram models. The precision of groundwater qualitative parameters in the three methods of Kriging, Cokriging and Inverse Distance Weighting (IDW) was assessed and the precision rate results showed that Cokriging method has a higher precision and lower error. The spatial structure of studied characteristics follows the exponential, spHerical and linear model with an influence range of 9656 to 71100001m and the threshold range of 0.965 to 5.65, and the spatial dependency class was in the range of 0-0.87. Finally, the zoning maps of water qualitative parameters were prepared using GIS software. Due to population concentration in the southern parts and overuse of wells and recent droughts, the highest values of qualitative components belong to the south of the study area. According to the geological formations and the type of rocks in the northwest and southeast, the concentration of calcium, potassium, sodium, chlorine, as well as the percentage of evaporation and transpiration and temperature are more evident.
2- Methodology
In this research, three methods of interpolation, namely, Weighted distance image, Kriging and Co-kriging are used to predict some quality indicators such as Na, TH, EC, SAR, CL, CA, Mg, and SO4 in 2013-2014. Data have been collected from 177 piezometric wells in the Mashhad Plain. In this study, after data normalization, parameters were interpolated using three geostatistical methods (ordinary kriging, co-kriging, and IDW). In order to evaluate the methods of interpolation, the mutual evaluation test was used. Then, based on the best method of mediation and the use of geographic information system, a map of some parameters and plain zoning maps were prepared.
3- Results
The purpose of this research was to determine the most appropriate interpolation method in order to investigate and spatially analyze the changes in some quality characteristics of underground water in Mashhad plain. After the appropriate model was fitted, according to CO/(CO+C) observations, the strength of the spatial structure in the quality components of the studied water is not very strong. For the zoning of the quality components, the Co-kriging method was introduced as a suitable method, and considering that correlation between different variables is used in this method. It can be said that there is a significant relationship between the components of groundwater (Zahtabian et al., 2019). The results of zoning the quality of the parameters showed two inappropriate ranges in the northwest and east of the region, which about 70% of the exploitation wells are located in these areas. Also, due to the recent droughts, the process of desertification will be accelerated in this region; therefore, more attention should be paid to the proper management of underground water in this region.
4- Discussion & Conclusions
According to the obtained results, the best fitted model for CA and MG parameters is linear model; EC parameters are exponential model and NA parameters are Semi-variogram model. Regarding the spatial structure, parameters MG and TH have strong location continuity (less than 0.25) and parameters MG, NA, EC have weak continuity (more than 0.75). According to the obtained results, was used to estimate TH from anion, CL from TDS, anion from cation, CL from anion and vice versa and estimate CL from SAR.
The results show that the concentration of calcium, potassium, sodium, chlorine, SAR, TH, PH is higher in the northwest and southeast, and as a result, the quality of water for various uses is lower, which is one of the possible reasons, in addition to the different formations of the earth. The geology and type of rocks (that is, the presence of gypsum materials and stones in the northwest and southeast parts or smaller amounts of these materials in the north, central and south parts (Owaisi, 2000)) can lead to higher temperature and drier environment which is the result of the higher rate of evaporation and transpiration as well as the type and density of land use (Laleh-Zari, 2019)
Selection and accuracy of appropriate zoning methods and preparation of maps corresponding to variation of groundwater qualitative characteristics depend upon the region's condition and the available statistics and data. The goal of this research is to determine the most appropriate method of interpolation and spatial analysis of qualitative components such as Cl, EC, SO4, SAR, TDS and TH of groundwater in Mashhad Plain. In this research, at first, the data of 177 wells were selected with respect to their dispersion and accuracy for two consecutive years (1392-1393); then controlling and reconstruction of data were performed. Kolmogorov-Smirnov test showed that the data were not normal and to normalize them their logarithm was obtained. Then using GS+software, the best variogram model was fitted to the spatial structure of data. The results show that for qualitative parameters, the exponential, Semi-variogram and linear models are the best semi variogram models. The precision of groundwater qualitative parameters in the three methods of Kriging, Cokriging and Inverse Distance Weighting (IDW) was assessed and the precision rate results showed that Cokriging method has a higher precision and lower error. The spatial structure of studied characteristics follows the exponential, spHerical and linear model with an influence range of 9656 to 71100001m and the threshold range of 0.965 to 5.65, and the spatial dependency class was in the range of 0-0.87. Finally, the zoning maps of water qualitative parameters were prepared using GIS software. Due to population concentration in the southern parts and overuse of wells and recent droughts, the highest values of qualitative components belong to the south of the study area. According to the geological formations and the type of rocks in the northwest and southeast, the concentration of calcium, potassium, sodium, chlorine, as well as the percentage of evaporation and transpiration and temperature are more evident.
2- Methodology
In this research, three methods of interpolation, namely, Weighted distance image, Kriging and Co-kriging are used to predict some quality indicators such as Na, TH, EC, SAR, CL, CA, Mg, and SO4 in 2013-2014. Data have been collected from 177 piezometric wells in the Mashhad Plain. In this study, after data normalization, parameters were interpolated using three geostatistical methods (ordinary kriging, co-kriging, and IDW). In order to evaluate the methods of interpolation, the mutual evaluation test was used. Then, based on the best method of mediation and the use of geographic information system, a map of some parameters and plain zoning maps were prepared.
3- Results
The purpose of this research was to determine the most appropriate interpolation method in order to investigate and spatially analyze the changes in some quality characteristics of underground water in Mashhad plain. After the appropriate model was fitted, according to CO/(CO+C) observations, the strength of the spatial structure in the quality components of the studied water is not very strong. For the zoning of the quality components, the Co-kriging method was introduced as a suitable method, and considering that correlation between different variables is used in this method. It can be said that there is a significant relationship between the components of groundwater (Zahtabian et al., 2019). The results of zoning the quality of the parameters showed two inappropriate ranges in the northwest and east of the region, which about 70% of the exploitation wells are located in these areas. Also, due to the recent droughts, the process of desertification will be accelerated in this region; therefore, more attention should be paid to the proper management of underground water in this region.
4- Discussion & Conclusions
According to the obtained results, the best fitted model for CA and MG parameters is linear model; EC parameters are exponential model and NA parameters are Semi-variogram model. Regarding the spatial structure, parameters MG and TH have strong location continuity (less than 0.25) and parameters MG, NA, EC have weak continuity (more than 0.75). According to the obtained results, was used to estimate TH from anion, CL from TDS, anion from cation, CL from anion and vice versa and estimate CL from SAR.
The results show that the concentration of calcium, potassium, sodium, chlorine, SAR, TH, PH is higher in the northwest and southeast, and as a result, the quality of water for various uses is lower, which is one of the possible reasons, in addition to the different formations of the earth. The geology and type of rocks (that is, the presence of gypsum materials and stones in the northwest and southeast parts or smaller amounts of these materials in the north, central and south parts (Owaisi, 2000)) can lead to higher temperature and drier environment which is the result of the higher rate of evaporation and transpiration as well as the type and density of land use (Laleh-Zari, 2019)
Keywords: Water quality parameters, Mashhad Plain, Geostatistics, Distance photo, Kriging, Cokriging
Type of Study: Research |
Received: 2023/01/28 | Published: 2023/12/31
Received: 2023/01/28 | Published: 2023/12/31
References
1. Akhavan, R.; Khoramabadi Karami, M.; & J, Soosani, 2012. Application of Kriging and IDW methods in mapping of crown cover and density of coppice oak forests Hay (Case Study: the amount suspended Khorramabad), Journal of the forest, the third year. ShfhHay 305 to 316.
2. Alaa, Ahmed, Masoud, 2014. Groundwater quality assessment of the shallow aquifers west of the Nile Delta (Egypt) using multivariate statistical and geostatistical techniques, Journal of African Earth Sciences, 95, 123-137. [DOI:10.1016/j.jafrearsci.2014.03.006]
3. Al Saleh, H. A.; Saify, S.; & N. Y. Othman, 2022. Spatial Distribution of Groundwater Quality Parameters in Al-Najaf City Using GIS and Geostatistics Techniques, In IOP Conference Series: Earth and Environmental Science (Vol. 952, 1, p. 012003), IOP Publishing. [DOI:10.1088/1755-1315/952/1/012003]
4. Arsalan, H., 2012. Spatial and temporal mapping of groundwater salinity using ordinary kriging and indicator kriging: The case of Bafra Plain, Turkey, Agricultural Water Management, 113, 57-63. [DOI:10.1016/j.agwat.2012.06.015]
5. Asakereh, H., 2008. Kriging Application in Climatic Element Interpolation a Case Study: Iran Precipitation in 1996.12.16, GeograpHy and Development, 6(12), 25-42. doi: 10. 22111/g d ij.2008.1241. (in Persian)
6. Barcae, E., & G. Passarella., (2008). Spatial evaluation of the risk of groundwater quality degradation: A comparison between disjunctive kriging and geostatistical simulation. Environmental Monitoring and Assessment. 133, 261-273. [DOI:10.1007/s10661-007-9758-3]
7. Bay, Mahbube.; Haghizadeh, A.; & N. Tahmasebipour, 2018. Spatial Variations of quality Groundwater use Geostatistical Method, Journal Geographic Spase, 18(63), 147-164. (in Persian).
8. Geological map of Fasa with a scale of 100,000 along, 2000. (in Persian)
9. Gharechelou, S.; Akbari Ghoochani, H.; Golian, S.; & K. Ganji, 2021. Evaluation of land subsidence relationship with groundwater depletion using Sentinel-1 and ALOS-1 radar data (Case study: Mashhad plain), Journal of RS and GIS for Natural Resources, 12(3), 40-61. doi: 10.30495/girs.2021.680336. (in Persian).
10. Habibi, V.; Ahmadi, A.; & M. M. Fattahi, 2008. Modeling Spatial Variability of Ground Water Chemical Properties Using Geostatistical Methods , 3(7), 23-34. (in Persian)
11. Haji Hashemi Jezi, M. R.; Ateshgahi, M.; & A. H. Hamidian, 2012. Spatial estimation of groundwater quality components using geostatistical methods (case study: Golpayegan plain), Journal of Natural Environment, 63(4), 347-35. (in Persian)
12. Hedayati Aghmashhadi, A.; Jafari, H.; Mehrdadi, N.; Fahmi, H.; & P. Farshchi, 2016. Environmental policy and management of freshwater resources in the Haraz-Ghareh Su Basin in comparison to other Caspian sub basins, 1(4), 387-402.
13. Hosni Pak, A. A., 2019. geostatistics University of Tehran Printing and Publishing Institute Language: Farsi Dewey ranking: 551.072Year of publication: Printing turn: 3Circulation: 1000 copies Number of pages: 330 Cutting and type of cover: Waziri (paper)ISBN: 978964 0340219 Book code in Gisum: 1686692. (in Persian)
14. Jahanshahi, A.; RouhiMoghadam, E.; & A. H. Dehvari, 2014. Assessment of groundwater quality parameters using GIS and Geostatistical (Case Study: Shahr Babak Plain), Journal of Soil and Water Science, 24(2), 183-197.
15. Jiang, Y.; Ma Wei, L.; & W. Huihuang, Huang, 2013. Analysis of spatial distribution of goundwater quality in Huaihe river basin. 21st International Conference on Geoinformatics. [DOI:10.1109/Geoinformatics.2013.6626133]
16. Khazaei, S. H.; Abbasi Tabar, H.; & R. Taghizadeh Mehrjardi, 2012. Nitrate pollution zoning in the groundwater of Fars province using geostatistical method (case study: Siakh Darangun region), Journal of Natural Environment, 64 (3), 267-279. (in Persian)
17. Keith S. J.; Wilson, L. G.; Fitch, H. R.; & D. M. Esposito, 1983. Sources of spatial-temporal variability in groundwater quality data and methods of control. Groundwater Monitoring Program Report, Spring, Arizona, USA. [DOI:10.1111/j.1745-6592.1983.tb01196.x]
18. Kresic, N., 1997. Quantitative Solutions in Hydrogeology and Groundwater Modeling. Lewis Publishers, Boca Raton, FL, 464pp, Chicago. 464p.
19. Kulinkina, A. V.; Plummer, J. D.; Chui, K. K. H.; Kosinski, K. C.; Adomako-Adjei, T.; & A. I. Egorov, 2017. PHysicochemical Parameters Affecting the Perception of Boreholewater Quality in Ghana, Int J Hyg Environ Health, 220(6), 990-7. [DOI:10.1016/j.ijheh.2017.05.008]
20. Laleh Zari, R., & SH. Tabatabai., (2011). Chemical Characteristics of Shahrekord Plain Underground Water, https://civilica.com/doc/157807. (in Persian)
21. Lashkaripour, G. R.; Ghafouri, M.; Swayzi, Z.; & Z. Takhni, 2004. Symposium of Geological Society of Iran، Tehran،https://civilica.com/doc/31903. (in Persian).
22. Maria PaulaMendesLuísRibeiro, 2010. Nitrate probability mapping in the northern aquifer alluvial system of the river Tagus (Portugal) using Disjunctive Kriging, Science of The Total Environment, 408(5), 1021-1034. [DOI:10.1016/j.scitotenv.2009.10.069]
23. Maroufi, S.; Taranjian, A.; & H. Abyaneh, 2012. Evaluation of geostatistical methods for estimating electrical conductivity and pH of stream drained water in Hamedan-Bahar Plain, Journal of Water and Soil Conservation, 16(2), 169-187. (in Persian)
24. Maroufpoor, S.; fakheri fard, A.; & J. Shiri, 2017. Study of the spatial distribution of groundwater quality using soft computing and geostatistical models, ISH Journal of Hydraulic Engineering, 1-7. [DOI:10.1080/09715010.2017.1408036]
25. Mirabdolahi, S. E., 2022. Investigating Spatio-Temporal Variations of Groundwater Quality Using Geostatistical Method (Case Study of Guilan Province), Environment and Water Engineering.
26. Mohammadi Ghaleni, M.; Ebrahimi, K.; & S. H. Araghinejad, 2011. Groundwater Quantity and Quality Evaluation, Journal of water and soil protection research, 19(4), 81-93. (in persion)
27. Regional Water Authority of Khorasan Razavi, 2015. Kashfarud Rescue Plan with the Participation of the People, the Deputy for Planning and Management Improvement, 2, 72 p. (In Persian).
28. Rooki, R.; Aryafar, A.; & J. Adelinasab, 2017. Investigating the groundwater quality in aquifer of Gonabad basin, Khorasan Razavi, using multivariate statistical methods and artificial intelligence, Journal of Mineral Resources Engineering, (2)1, 49-61.
29. Seyedmohammadi, J.; Esmaeelnejad, L.; & M. Shabanpour, 2016. Spatial variation modeling of groundwater electrical conductivity using geostatistics and GIS Model. Earth Syst, Environ, 2, 169. [DOI:10.1007/s40808-016-0226-3]
30. Sheikh Gudarzi, M.; Mousavi, H; & N. Khorasani, 2011. Simulation of spatial changes in the qualitative characteristics of groundwater with geostatistical methods (case study: Tehran-Karaj Plain), Journal of Natural Environment, 65 (1), 83-93. doi: 10.22059/ jne. 2012. 29465. (in Persian)
31. Yazdani, M. R.; Koh Banani, H. R.; Dashti Amirabad, J.; & M. Aziminejad, 2016. Evaluation of Mashhad plain underground water quality indicators using geostatistics and GIS techniques, https://civilica.com/doc/115761. (in Persian)
32. Yousefi, H.; Al Sheikh, A. A.; & M. Minaei, 2016. Zoning the quality of underground water resources in Saveh using geostatistical analysis, Promotional Scientific Journal of Mapping and Spatial Information Engineering, 9(1), 42-33. (in Persian)
33. Zabihi, A.; Solaimani, K.; Shabani, M.; & S. Abravsh, 2012. An Investigation of Annual Rainfall Spatial Distribution Using Geostatistical Methods (A Case Study: Qom Province), PHysical GeograpHy Research Quarterly, 43(78), 102-112. (in Persian)
34. Zehtabian, G. R.; Janfaza, A.; Mohammad asgari, H.; & M. J. Nematollahi, 2010. Modeling of ground water spatial distribution for some chemical properties (Case study in Garmsar watershed), 17(1), 61-73. (in Persian)
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
