E-mail alert
Rss
Introducing a new geostatistical approach to classify groundwater samples based on Stiff diagram: Case study of Chahardoly aquifer, west of Iran
-
Abstract: Groundwater quality is pivotal for sustainable resource management, necessitating comprehensive investigation to safeguard this critical resource. This study introduces a novel methodology that integrates stiff diagrams, geostatistical analysis, and geometric computation to delineate the extent of a confined aquifer within the Chahrdoly aquifer, located west of Hamadan, Iran. For the first time, this approach combines these tools to map the boundaries of a confined aquifer based on hydrochemical characteristics. Stiff diagrams were used to calculate geometric parameters from groundwater chemistry data, followed by simulation using a linear model incorporating the semivariogram parameter γ(h). The Root Mean Square Error (RMSE) of the linear model was used to differentiate confined from unconfined aquifers based on hydrochemical signatures. Validation was conducted by generating a cross-sectional hydrogeological layer from well logs, confirming the presence of aquitard layers. The results successufully delineated the confined aquifer's extent, showing strong agreement with hydrogeological log data. By integrating stiff diagrams with semivariogram analysis, this study enhances the understanding of hydrochemical processes, offering a robust framework for groundwater resource identification and management.
-
Key words:
- Geostatistics /
- Stiff diagram /
- Semivariogram /
- Confined aquifer /
- Chahardoly /
- Asadabad
-
Figure 1. Map of the study area showing geological formation and the extent of the confined aquifer area
Figure 2. Location of the Chahardoly confined aquifer (based on Moradi Nazarpoor and Jafari, 2019)
Figure 9. The locations of all aquifer points and the error line that illustrates the boundary of the confined aquifer
Figure 10. a) Results from the proposed method, and (b) the boundary of the confined aquifer determined from hydrogeological well logs
Table 1. The concentration of major ions in groundwater samples collected from the Chahardoly aquifer
Sample Meq/L Mg2+ Ca2+ Na++K+ SO42− HCO3− Cl− W7 1.4 1.8 1.13 0.48 2.6 0.9 W1 0.9 1.8 1.05 0.52 2 1 W12 1.2 3 1.09 1.00 2.8 0.8 W13 1.3 1.8 1.09 0.42 2 1 W14 1 1.9 1.13 0.75 2.3 0.8 W16 1.4 2.6 1.18 0.69 3.9 0.7 W17 1 2.3 0.91 0.42 2.6 0.9 W18 1.8 3.5 1.35 0.98 4.2 1.4 W23 1.5 1.5 0.87 0.52 2 0.8 W24 1.3 1.8 0.87 0.52 2.3 0.7 W3 1.5 2.8 1.18 0.52 3.5 0.9 Ws1 1.4 1.3 1.22 0.65 2.4 1 W32 1.6 2.1 1.26 0.65 2.5 1.1 W31 1.4 2.6 1.18 0.69 2.7 1 W30 1.4 2.9 1.18 0.77 4.1 0.9 W27 1.2 2.3 1.18 0.38 2.7 0.8 W26 1.1 3.5 1.22 1.35 2.8 0.8 W28 1.6 1.7 1.09 0.42 2.5 0.8 
下载: 导出CSV
Table 2. Geometric characteristics of Stiff diagram and RMSE value for each sample compared to W7
Sample X12 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 RMSE W71 1.88 4.40 2.03 1.08 1.20 2.34 1.97 4.12 2.49 1.90 2.88 2.88 3.86 W7 1.88 4.40 2.03 1.08 1.20 2.34 1.97 4.12 2.49 1.90 2.88 2.88 3.86 0.00 W1 1.42 3.80 2.05 1.35 1.25 1.79 1.41 3.07 2.53 1.86 2.97 2.97 3.21 0.47 W12 2.20 5.80 1.89 2.06 2.16 2.06 2.24 4.12 4.12 2.32 3.93 3.93 4.01 0.84 W13 1.72 3.80 2.09 1.12 1.23 1.87 1.41 3.45 2.43 1.81 2.97 2.97 3.25 0.37 W14 1.75 4.20 1.93 1.35 1.26 1.84 1.80 3.45 2.83 2.13 2.88 2.88 3.58 0.30 W16 2.09 6.50 1.88 1.56 1.74 3.36 3.35 5.39 3.44 2.12 3.45 3.45 5.18 1.00 W17 1.42 4.90 1.81 1.64 1.71 2.40 1.97 3.74 2.89 1.66 3.35 3.35 3.65 0.39 W18 2.78 7.70 2.75 1.97 2.37 3.37 2.97 6.08 4.59 2.53 5.00 5.00 5.64 1.69 W23 2.02 3.50 1.67 1.00 1.18 1.79 1.56 3.64 2.25 1.71 2.51 2.51 3.04 0.46 W24 1.82 4.10 1.57 1.12 1.36 2.04 1.89 3.74 2.53 1.71 2.69 2.69 3.33 0.27 W3 2.02 6.30 2.08 1.64 1.91 3.14 2.79 5.10 3.47 1.97 3.83 3.83 4.78 0.89 Ws1 2.05 3.70 2.22 1.00 1.00 2.02 1.72 3.93 2.19 2.11 2.51 2.51 3.75 0.31 W32 2.25 4.60 2.36 1.12 1.30 2.11 1.72 4.22 2.92 2.16 3.35 3.35 3.89 0.29 W31 2.09 5.30 2.18 1.56 1.74 2.25 1.97 4.22 3.44 2.11 3.74 3.74 4.00 0.54 W30 2.17 7.00 2.08 1.80 1.99 3.48 3.35 5.59 3.80 2.19 3.93 3.93 5.37 1.23 W27 1.58 5.00 1.98 1.49 1.51 2.53 2.15 4.03 2.86 1.84 3.26 3.26 4.00 0.31 W26 2.45 6.30 2.02 2.60 2.49 1.76 2.24 4.03 4.96 2.76 4.41 4.41 4.14 1.24 W28 2.02 4.20 1.89 1.00 1.17 2.31 1.97 4.22 2.34 1.81 2.69 2.69 3.72 0.13 aAs a representative of unconfined aquifers, this sample has been chosen.
bInterval between two nodes has been shown by X.
下载: 导出CSV
Table 3. Experimental variogram, Theoretical variogram, and Errors per sample
Sample Distance /m Experimental Value Theoretical Value Error /% W7 0 0.00 0.00 0 W1 1,565 0.11 0.08 32 W12 2,741 0.35 0.14 150 W13 1,001 0.07 0.05 25 W14 972 0.04 0.05 18 W17 2,800 0.07 0.14 48 W23 652 0.10 0.04 183 W24 516 0.04 0.03 24 W3 537 0.40 0.03 1,170 Ws1 1,238 0.05 0.07 26 W32 1,692 0.04 0.09 51 W31 1,739 0.15 0.09 62 W27 870 0.05 0.05 1 W28 1,207 0.01 0.06 87 W16 1,596 0.49 0.08 488 W18 1,917 1.43 0.10 1,329 W30 2,437 0.75 0.12 497
下载: 导出CSV
Table 4. Mean concentration of major ions in confined and unconfined aquifers
Ion Mean-Confined aquifer (Meq/L) Mean-Unconfined aquifer (Meq/L) Mg2+ 1.4 1.5 Ca2+ 1.9 2.4 Na++K+ 1.1 1.2 SO42− 0.5 0.7 HCO3− 2.2 3.0 Cl− 0.8 0.9
下载: 导出CSV
-
Afzal M, Liu T, Bao AM, et al. 2024. Groundwater hydrodynamics and supply sustainability in Punjab, Pakistan: A geological statistical approach using the GAMEAS algorithm based on data from 1105 boreholes. China Geology, 7: 1−16. DOI: 10.31035/cg2024066. Appelo CAJ, Postma D. 2004. Geochemistry, groundwater and pollution. CRC press. DOI: 10.1201/9781439833544 Aweda AK, Jatau BS, Goki NG. 2023. Groundwater geochemistry and hydrochemical processes in the egbako aquifer, northern bida basin, nigeria. RBRH, 28: e31. DOI: 10.1590/2318-0331.282320230010. Chegbeleh LP, Akurugu BA, Yidana SM. 2020. Assessment of groundwater quality in the talensi district, northern ghana. The scientific world Journal, 2020(1): 8450860. DOI: 10.1155/2020/8450860. Delhomme JP. 1978. Kriging in the hydrosciences. Advances in Water Resources, 1(5): 251−266. DOI: 10.1016/0309-1708(78)90039-8. Eyankware M, Omo-Irabor O. 2019. An integrated approach to groundwater quality assessment in deter- mining factors that influence the geochemistry and origin of sandstone aquifers southern niger delta region of nigeria. Malaysian Journal of Geoscience, 23–32. DOI: 10.26480/mjg.02.2019.23.32. Farzaneh G, Khorasani N, Ghodousi J, et al. 2022. Application of geostatistical models to identify spatial distribution of groundwater quality parameters. Environmental Science and Pollution Research, 29(24): 36512−36532. DOI: 10.1007/s11356-022-18639-8. Ji H, Zhu Y, Wu X. 1995. Correspondence cluster analysis and its application in exploration geochemistry. Journal of Geochemical Exploration, 55(1-3): 137−144. DOI: 10.1016/0375-6742(95)00025-9. Johnston K, Ver Hoef JM, Krivoruchko K, et al. 2001. Using ArcGIS geostatistical analyst, volume 380. Esri Redlands. Krasowska M, Banaszuk P. 2017. Geostatistical methods in the assessment of the spatial variability of the quality of river water. In E3S Web of Conferences, 22: 00089. EDP Sciences. DOI: 10.1051/e3sconf/20172200089. Krishan G, Bhagwat A, Sejwal P, et al. 2023. Assessment of groundwater salinity using principal component analysis (PCA): A case study from Mewat (Nuh), Haryana, India. Environmental Monitoring and Assessment, 195(1): 37. DOI: 10.1007/s10661-022-10555-1. Masoud AA. 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. Moonchai S, Chutsagulprom N. 2020. Semiparametric semivariogram modeling with a scaling criterion for node spacing: A case study of solar radiation distribution in thailand. Mathematics, 8(12): 2173. DOI: 10.3390/math8122173. Moradi Nazar Poor S, Jafari H, Safari M, et al. 2022. Hydro-geochemistry of the asadabad plain, west of Iran: Application of statistical methods. Geopersia, 12(2): 369−378. DOI: 10.22059/geope.2022.339940.648653. Moradi Nazarpoor S, Jafari H. 2019. Determining the confined aquifer in Chahardolly (Asadabad). 3rd Iranian National Conference on Hydrology, University of Tabriz. Sa´nchez-Martos F, Jimenez-Espinosa R, Pulido-Bosch A. 2001. Mapping groundwater quality variables using pca and geostatistics: A case study of Bajo Andarax, Southeastern Spain. Hydrological Sciences Journal, 46(2): 227−242. Shuaibu A, Kalin RM, Phoenix V, et al. 2024. Hydrogeochemistry and water quality index for groundwater sustainability in the Komadugu-yobe Basin, sahel region. Water, 16(4): 601. Stiff Jr HA. 1951. The interpretation of chemical water analysis by means of patterns. Journal of Petroleum Technology, 3(10): 15−3. Sun L. 2014. Statistical analysis of hydrochemistry of groundwater and its implications for water source identification: A case study. Arabian Journal of Geosciences, 7: 3417–3425. Wold S, Esbensen K, Geladi P. 1987. Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1-3): 37−52. Yidana SM, Ophori D, Banoeng-Yakubo B, et al. 2012. A factor model to explain the hydrochemistry and causes of fluoride enrichment in groundwater from the middle voltaian sedimentary aquifers in the northern region, ghana. ARPN Journal of Engineering and Applied Sciences, 7(1): 50–68. -
点击查看大图
图(10) / 表ll (4)
计量
- 文章访问数: 85
- HTML全文浏览量: 33
- PDF下载量: 7
- 被引次数: 0
