Abstract
In groundwater studies, highly skewed data that are not conducive to kriging interpolation are very common. In this work, the total dissolved solids (TDS) results of 1047 phreatic groundwater samples in the Junggar Basin were used to obtain high-precision spatial continuous data through the multi-scale cokriging (CoK) model. The groundwater system in the Junggar Basin was divided into four secondary groundwater systems. Box-Cox (BC) and normal score transformation (NST) methods were used to reduce the data sets kurtosis and skewness. The ordinary kriging, simple kriging, universal kriging, and disjunctive kriging (DK) were considered for interpolation. CoK was used to combine the interpolation results of basin and secondary groundwater systems to improve the interpolation quality. The results indicated that NST was suitable for all data sets, but BC performed better for moderately skewed data. DK performed better than other linear kriging models, especially for highly skewed data. DK-NST was the best basic interpolation scheme for basin groundwater system, but the test R2 was only 0.47. The R2 of the best basic interpolation schemes for the secondary groundwater systems could reach 0.73 ~ 0.99, but the interpolation results at the boundaries were greatly affected by the distribution of sampling points and system scales. Multi-scale CoK model improved the interpolation quality of TDS in basin and secondary groundwater systems. The kriging model performed well when the human impact is weak and the spatial autocorrelation is strong. In addition, the effect of samples uniformity on interpolation is stronger than that of density.
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Amin H, Xavier E, Mehrnoosh AS (2019) Comparing linear and non-linear kriging for grade prediction and ore/waste classification in mineral deposits. Int J Min Reclam Environ 33:247–264. https://doi.org/10.1080/17480930.2017.1386430
Asa E, Saafi M, Membah J, Billa A (2012) Comparison of linear and nonlinear kriging methods for characterization and interpolation of soil data. J Comput Civ Eng 26:11–18. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000118
Berke O (2001) Modified median polish kriging and its application to the Wolfcamp-Aquifer data. Environmetrics 12:731–748. https://doi.org/10.1002/env.495.abs
Bostan PA, Heuvelink GBM, Akyurek SZ (2012) Comparison of regression and kriging techniques for mapping the average annual precipitation of Turkey. Int J Appl Earth Obs 19:115–126. https://doi.org/10.1016/j.jag.2012.04.010
Box GEP, Cox DR (1964) An analysis of transformations. J R Stat Soc Ser B (Methodol) 26:211–252. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
Bui DT, Khosravi K, Karimi M, Busico G, Khozani ZS, Nguyen H et al (2020) Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm. Sci Total Environ 715:136836. https://doi.org/10.1016/j.scitotenv.2020.136836
Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF et al (1994) Field-scale variability of soil properties in central Iowa soils. Soil Sci Soc Am J 58:1501–1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x
Chen J, Zhang HT, Qian H, Wu JH, Zhang XD (2013) Selecting proper method for groundwater interpolation based on spatial correlation. 4th International Conference on Digital Manufacturing and Automation, Qindao, China. https://doi.org/10.1109/icdma.2013.282
Chung J-W, Rogers JD (2012) Interpolations of groundwater table elevation in dissected uplands. Ground Water 50:598–607. https://doi.org/10.1111/j.1745-6584.2011.00889.x
D’Agostino V, Greene EA, Passarella G, Vurro M (1998) Spatial and temporal study of nitrate concentration in groundwater by means of coregionalization. Environ Geol 36:285–295. https://doi.org/10.1007/s002540050344
Fischer A, Lee M-K, Ojeda AS, Rogers SR (2021) GIS interpolation is key in assessing spatial and temporal bioremediation of groundwater arsenic contamination. J Environ Manage 280:111683. https://doi.org/10.1016/j.jenvman.2020.111683
Fu CC, Zhang HB, Tu C, Li LZ, Luo YM (2018) Geostatistical interpolation of available copper in orchard soil as influenced by planting duration. Environ Sci Pollut Res 25. https://doi.org/10.1007/s11356-016-7882-8
Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York
He L, Chen SZ, Liang Y, Hou MQ, Chen JY (2020) Infilling the missing values of groundwater level using time and space series: case of Nantong City, east coast of China. Earth Sci Inform 13:1445–1459. https://doi.org/10.1007/s12145-020-00489-y
Huang H (2020) Spatial interpolation methods and pollution assessment of heavy metals in soil at the small-scale site. Environ Ecol 2:33–40 (In Chinese)
Islam ARMT, Shen S, Bodrud-Doza M, Atiqur RM, Das S (2017) Assessment of trace elements of groundwater and their spatial distribution in Rangpur district. Bangladesh Arab J Geosci 10:95. https://doi.org/10.1007/s12517-017-2886-3
Jia ZL (2016) Study on spatial variability and relationship of soil arsenic and soil nitrogen. South China Agricultural University, Guangzhou. https://doi.org/10.7666/d.D01036674 (In Chinese)
Juang KW, Lee DY, Ellsworth TR (2001) Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site. J Environ Qual 30:894–903. https://doi.org/10.2134/jeq2001.303894x
Karami S, Jalali M, Katibeh H, Marj AF (2020) Groundwater hydrogeochemical assessment using advanced spatial statistics methods: a case study of Tehran-Karaj plain aquifer. Iran Arab J Geosci 13:84. https://doi.org/10.1007/s12517-019-5047-z
Lark RM, Webster R (2006) Geostatistical mapping of geomorphic variables in the presence of trend. Earth Surf Proc Land 31:862–874. https://doi.org/10.1002/esp.1296
Li J, Heap AD (2010) A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecol Inform 6:228–241. https://doi.org/10.1016/j.ecoinf.2010.12.003
Li J, Heap AD, Potter A, Daniell JJ (2011) Application of machine learning methods to spatial interpolation of environmental variables. Environ Modell Softw 26:1647–1659. https://doi.org/10.1016/j.envsoft.2011.07.004
Li J, Pang ZH, Froehlich K, Huang TM, Kong YL, Song WH et al (2015) Paleo-environment from isotopes and hydrochemistry of groundwater in East Junggar Basin, Northwest China. J Hydrol 529:650–661. https://doi.org/10.1016/j.jhydrol.2015.02.019
Liao YL, Li DY, Zhang NX (2018) Comparison of interpolation models for estimating heavy metals in soils under various spatial characteristics and sampling methods. T GIS 22:409–434. https://doi.org/10.1111/tgis.12319
Ling W, Wang XJ, Wu WH (2020) Comparison on spatial interpolation methods of average annual precipitation in Xinjiang. J Liaoning Forest Sci Technol 5–9+58. https://doi.org/10.3969/j.issn.1001-1714.2020.04.002 (In Chinese)
Litaor MI, Allen L (1996) A comprehensive appraisal of Am-241 in soils around Rocky Flats, Colorado. Health Phys 71:347–357. https://doi.org/10.1097/00004032-199609000-00012
Liu G, Niu JJ, Zhang C, Guo GL (2015) Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies. Environ Sci Pollut Res 22:20121–20130. https://doi.org/10.1007/s11356-015-5122-2
Manzione RL, Ferreira Silva CdO, Castrignano A (2021) A combined geostatistical approach of data fusion and stochastic simulation for probabilistic assessment of shallow water table depth risk. Sci. Total Environ. 765:142743. https://doi.org/10.1016/j.scitotenv.2020.142743
Nico DL, Paolo F, Leonardo M, Leonardo P, Marco P (2017) Geostatistics as a tool to improve the natural background level definition: An application in groundwater. Sci Total Environ 598:330–340. https://doi.org/10.1016/j.scitotenv.2017.04.018
Nielsen DR (1985) Soil spatial variability. Pudoc, Wageningen
Ohmer M, Liesch T, Goeppert N, Goldscheider N (2017) On the optimal selection of interpolation methods for groundwater contouring: An example of propagation of uncertainty regarding inter-aquifer exchange. Adv Water Resour 109:121–132. https://doi.org/10.1016/j.advwatres.2017.08.016
Qiao T, Yao CY, Yu DS, Shi XZ, Xing SH, Zhang LM (2020) Optimal interpolation method for spatial-temporal evolution of soil organic carbon in paddy fields. J Fujian Agric Forest Univ (Nat Sci Ed) 49:683–694. https://doi.org/10.13323/j.cnki.j.fafu(nat.sci.).2020.05.017(InChinese)
Ren Y, Zhang F, Wang J, Yue Z, Yang ST, Abduwasit G et al (2017) Spatio-temporal characteristics and source identification of surface water pollutants in Lake Ebinur Watershed, Xinjiang. J Lake Sci 29:1143–1157. https://doi.org/10.18307/2017.0512(InChinese)
Ricardo AO (2007) Declustering of clustered preferential sampling for histogram and semivariogram inference. Math Geol 39:453–467. https://doi.org/10.1007/s11004-007-9108-6
Rostami AA, Karimi V, Khatibi R, Pradhan B (2020) An investigation into seasonal variations of groundwater nitrate by spatial modelling strategies at two levels by kriging and co-kriging models. J Environ Manage 270:110843. https://doi.org/10.1016/j.jenvman.2020.110843
Saito H, Goovaerts P (2000) Geostatistical interpolation of positively skewed and censored data in a dioxin-contaminated site. Environ Sci Technol 34:4228–4235. https://doi.org/10.1021/es991450y
Sakizadeh M, Ahmadpour E, Sharafabadi FM (2019) Spatial analysis of chromium in southwestern part of Iran: probabilistic health risk and multivariate global sensitivity analysis. Environ Geochem Hlth 41:2023–2038. https://doi.org/10.1007/s10653-019-00260-3
Shawgar K, Hassan M, Homayoon K, Ahmad FM (2018) Assessment and modeling of the groundwater hydrogeochemical quality parameters via geostatistical approaches. Appl Water Sci 8:23. https://doi.org/10.1007/s13201-018-0641-x
Tan QL, Xu X (2014) Comparative analysis of spatial interpolation methods: an experimental study. Sensors & Transducers 165:155–163
Wagh VM, Panaskar DB, Muley AA, Mukate SV (2017) Groundwater suitability evaluation by CCME WQI model for Kadava River Basin, Nashik, Maharashtra. India Modeling Earth Systems and Environment 3:557–565. https://doi.org/10.1007/s40808-017-0316-x
Wang XP, Zhang F, Yu HY, Te KH (2017) Comparison of prediction accuracies of TDS in the surface water in Ebinur Lake based on multivariate linear model, SVM model, and geostatistics method. Environ Chem 36:666–676. https://doi.org/10.7524/j.issn.0254-6108.2017.03.2016070104
Wang WK, Wang Z, Hou RZ, Guan LY, Dang Y, Zhang ZY et al (2018) Modes, hydrodynamic processes and ecological impacts exerted by river-groundwater transformation in Junggar Basin. China Hydrogeol J 26:1547–1557. https://doi.org/10.1007/s10040-018-1784-4
Webster R, Oliver MA (2007) Geostatistics for Environmental Scientists, 2nd edn. John Wiley & Sons Inc., New York
Wu CF, Wu JP, Luo YM, Zhang HB, Teng Y, DeGloria DS (2011) Spatial interpolation of severely skewed data with several peak values by the approach integrating kriging and triangular irregular network interpolation. Environ Earth Sci 63:1093–1103. https://doi.org/10.1007/s12665-010-0784-z
Xiao Y, Gu XM, Yin SY, Shao JL, Cui YL, Zhang QL et al (2016) Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China. Springerplus 5:425. https://doi.org/10.1186/s40064-016-2073-0
Xie BN, Jia XX, Qin ZF, Zhao CL, Shao MA (2020) Comparison of interpolation methods for soil moisture prediction on China’s Loess Plateau. Vadose Zone J 19:e20025. https://doi.org/10.1002/vzj2.20025
Yang YT, Song CC, He S (2015) Jurassic tectonostratigraphic evolution of the Junggar basin, NW China: A record of Mesozoic intraplate deformation in Central Asia. Tectonics 34:86–115. https://doi.org/10.1002/2014tc003640
Yao CY (2020) Strategy for optimal interpolation method and efficient sampling of upland soil carbon and nitrogen based on spatiotemporal variation of the soil. Fujian Agriculture and Forestry University, Fuzhou. https://doi.org/10.27018/d.cnki.gfjnu.2020.000087 (In Chinese)
Yin SY, Xiao Y, Gu XM, Hao QC, Liu HL, Hao ZY et al (2019) Geostatistical analysis of hydrochemical variations and nitrate pollution causes of groundwater in an alluvial fan plain. Acta Geophys 67:1191–1203. https://doi.org/10.1007/s11600-019-00302-5
Zeng HW, Li LJ, Zhang YX, Liu YM (2011) Study on spatial interpolation of precipitation with large scale samples: a case study on 2009s precipitation of China. Prog Geogr 30:811–818. https://doi.org/10.11820/dlkxjz.2011.07.005
Zhang Z, Yan DT, Zhuang XG, Yang SG, Wang G, Li GQ et al (2019) Hydrogeochemistry signatures of produced waters associated with coalbed methane production in the Southern Junggar Basin, NW China. Environ Sci Pollut Res 26:31956–31980. https://doi.org/10.1007/s11356-019-06350-0
Zhen JC, Pei T, Xie SY (2019) Kriging methods with auxiliary nighttime lights data to detect potentially toxic metals concentrations in soil. Sci Total Environ 659:363–371. https://doi.org/10.1016/j.scitotenv.2018.12.330
Zhou XY (2015) Boundary and division of groundwater aquifer system in Junggar basin, Xinjiang. Ground Water 37:37–38. https://doi.org/10.3969/j.issn.1004-1184.2015.01.014 (In Chinese)
Zhou JL, Dong XG, Li GM, Wang YP, Guo XJ (2010) Evaluation of groundwater quality in the Xinjiang plain area. Front Environ Sci Eng 4:183–186. https://doi.org/10.1007/s11783-010-0021-8
Acknowledgements
This study was supported by China Geological Survey (No. DD20190352). The authors would like to thank the anonymous reviewers for their very valuable comments.
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Zhao, Z., Zhang, L. & Bi, E. Spatial interpolation of highly skewed data of the Junggar Basin phreatic groundwater through the multi-scale cokriging model. Earth Sci Inform 15, 1737–1748 (2022). https://doi.org/10.1007/s12145-022-00835-2
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DOI: https://doi.org/10.1007/s12145-022-00835-2