Preview

Dokuchaev Soil Bulletin

Advanced search

Soil surface subsidence time series modeling of an area with Aridisols and Vertisols complex using surveying and drone imagery in Central Iran

https://doi.org/10.19047/0136-1694-2025-122-62-88

Abstract

Soil surface subsidence is a natural hazard that has been reported in arid and semi-arid lands of the world. From the last few decades to the present, soil surface subsidence has been a major phenomenon of most plains in Iran. The core reason of this phenomena is water extraction from ground water by pumping wells. The study area located on the clayey plain covered by the complex of Aridisols and Vertisols in the east of Yazd city in central Iran with cracks of longitudinal and polygonal shapes. This experiment had been planned to find micro-relief dynamics in a time series after rainfall and drought periods followed by soil surface subsidence and soil cracking. For modeling of soil vertical dynamics and cracking processes, a sampling area was selected with 100 points for surveying with Box Jenkins model. The topography measurements of surveying data showed soil surface height variations from a few millimeters to some centimeters (-14 to +14 mm in a year) with sinusoidal rhythms. Auto Regressive (AR) model could predict the land height variations up to 5 years ahead with high accuracy (3 mm). Based on field surveying, drone imagery data confirmed the temporal forecasting model. In the study area land depressing resulted from minerals degradation into amorphous silicas after soil alkalization. Thereupon the monthly changes of soil surface wetting and drying were major factors for land altitude dynamics, whereas the very deep level of groundwater had no effect on soil surface subsidence. It is suggested that for monitoring of soil surface subsidence and soil cracks over time, the surveying with complementary and drone imagery could be much more appropriate method, which allows predicting temporal soil surface subsidence in local scale.

About the Authors

P. Amin
Yazd University
Islamic Republic of Iran

Desert Control and Management, Faculty of Natural Resources and Desert studies

Yazd



M. Akhavan Ghalibaf
Yazd University
Islamic Republic of Iran

Soil Science, Faculty of Natural Resources and Desert studies

Yazd



M. Hosseini
Yazd University
Islamic Republic of Iran

Geodesy, Department of Civil Engineering, Faculty of Technology and Engineering

Yazd



References

1. Aghelpour P., Amiri A., Saraf A.P., Prediction of suspended river sediment using time series model, Proc. 3 rd International conference on research in science and technology, Berlin, Germany, 2016.

2. Agung I.G.N., Time series data analysis using EViews, John Wiley & Sons, 2011, 250 p.

3. Akhavan Ghalibaf M., Clay forming by smectite groups in old alluvial soils in Yazd, 17 th congress of crystallography and mineralogy, Iran-Hamedan, 2008. (In Persian).

4. Amin P., Akhavan Ghalibaf M., Hosseini M., Land subsidence and soil cracks monitoring by surveying on the clayey plain soils in central Iran (case study: Yazd city), Arabian Journal of Geosciences, 2019, Vol. 12, No. 84, DOI: https://doi.org/10.1007/s12517-019-4241-3.

5. Aminihosseini K., Land subsidence caused by presence of canals and underground spaces, Journal of Sharif civil, 1993, Sharif University, Tehran. (In Persian).

6. Anurogo W., Lubis M.Z., Khoirunnisa H., Hanafi D.S.P.A., Rizki F., Surya G., Dewanti N.A., A simple aerial photogrammetric mapping system overview and image acquisition using unmanned aerial vehicles (UAVs), Geospatial Information, 2017, Vol. 1, No. 1., pp. 11–18.

7. Azarakhsh Z., Azadbakht M., Matkan A., Estimation, modeling, and prediction of land subsidence using Sentinel-1 time series in Tehran-Shahriar plain: A machine learning-based investigation, Remote sensing applications: Society and Environment, 2022, Vol. 25, 100691, DOI: https://doi.org/10.1016/j.rsase.2021.100691.

8. Bazargan J., Esmaeil S.D., Evaluation and modification of chemical criteria to detect divergence potential of clay soils, Journal of Engineering Geology, 2010, Vol. 4, No. 2., pp. 917–942. (In Persian).

9. Bhattarai R., Alifu H., Maitiniyazi A., Kondoh A., Detection of land subsidence in Kathmandu Valley, Nepal, using DInSAR technique, Land, 2017, Vol. 6, No. 39, DOI: https://doi.org/10.3390/land6020039.

10. Bjerrum L., Geotechnical properties of Norwegian marine clays, Geotechnique, 1954, Vol. 4, pp. 49–69.

11. Box G.E.P., Jenkins G.M., Time series analysis: Forecasting and control, Holden-Day, San Francisco, C.A., 1976, 553 p.

12. Chen B., Gong H., Li X., Lei K., Gao M., Zhou C., Yinghai K., Spatial-temporal evolution patterns of land subsidence with different situation of space utilization, Natural hazards, 2015, Vol. 77, pp. 1765–1783, DOI: https://doi.org/10.1007/s11069-015-1674-1.

13. Chu H.J., Ali M.Z., Burbey T.J., Spatio-temporal data fusion for fine-resolution subsidence estimation, Environ modeling software, 2021, Vol. 137, 104975, DOI: https://doi.org/10.1016/j.envsoft.2021.104975.

14. Dellaert F., Seitz S.M., Thorpe C.E., Thrun S., Structure from motion without correspondence, In: Proc. IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), IEEE, 2000, pp. 557–564.

15. Dixon J.B., Weed S.B., Minerals in soil environments, Soil science society of America Book series publication, 1992, 1244 p.

16. Ekeleme A., Agunwamba J., Experimental Determination of Dispersion Coefficient in Soil, Emerging Science, 2018, Vol. 2, No. 4., pp. 213–218.

17. Fulton A., California Department of Water Resources, Northern District, 2014, 4 p.

18. Gee G.W., Bauder J.W., Particle size analysis. In: Klute (Ed.), Methods of soil Analysis. Part 1, Agron. Monogr. 9, ASA and SSSA, Madison, WI. 1986, pp. 404–407.

19. Golden M.L., Keys to soil Taxonomy, By Soil Survey Staff, United States Department of Agriculture Natural Resources Conservation Service, 2014, 372 p.

20. Hajmollaali A., Majidifard M.R., Geological survey of Iran, 2000, geology map of Yazd (1 :100 000).

21. Ilia I., Loupasakis C., Tsangaratos P., Land subsidence phenomena investigated by spatiotemporal analysis of groundwater resources, remote sensing techniques, and random forest method: the case of Western Thessaly, Greece, Environmental monitoring and assessment, 2018, Vol. 190, pp. 623, DOI: https://doi.org/10.1007/s10661-018-6992-9.

22. Jebur A., Abed F., Mohammed M., Assessing the performance of commercial Agisoft PhotoScan software to deliver reliable data for accurate3D modelling, In: MATEC Web of Conferences, 2018, Vol. 162, pp. 03022, EDP Sciences.

23. Li H., Zhu L., Dai Z., Gong H., Guo T., Wang J., Teatini P., Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR, Science of total environment, 2021, Vol. 799, 149244, DOI: https://doi.org/10.1016/j.scitotenv.2021.149244.

24. Mohebbi Tafreshi Gh., Nakhaei M., Lak R., A GIS-based comparative study of hybrid fuzzy-gene expression programming and hybrid fuzzy-artificial neural network for land subsidence susceptibility modeling, Stochastic environmental research and risk assessment, 2020, Vol. 34, pp. 1059–1087, DOI: https://doi.org/10.1007/s00477-020-01810-3.

25. Orhan O., Monitoring of land subsidence due to excessive groundwater extraction using small baseline subset technique in Konya, Turkey, Environmental monitoring and assessment, 2021, Vol. 193, pp. 174, DOI: https://doi.org/10.1007/s10661-021-08962-x.

26. Pacheco-Martínez et al., Land subsidence and ground failure associated to groundwater exploitation in the Aguascalientes Valley, México, Engineering Geology, 2013, Vol. 164, pp. 172–186.

27. Rahmati et al., Land subsidence modelling using tree-based machine learning algorithms, Science of total environment, 2019, Vol. 672, pp. 239–252.

28. Rankka K., Andersson-Skold Y., Hulten C., Larsson R., Eroux V., Dahlin T., Quick clay in Sweden, 1.3. Geotechnical properties of quick clays, Swedish Geotechnical Institute, 2004, Report, 65, 148.

29. Skempton A.W., Soil mechanics in relation to geology, Proc. of the Yorkshire geological society, Hull, 1953, 30 p.

30. Shahriari S., Sisson S.A., Rashidi T., Copula ARMA-GARCH modelling of spatially and temporally correlated time series data for transportation planning use, Transportation Research Part C, 2022, Vol. 146, 103969, DOI: https://doi.org/10.1016/j.trc.2022.103969.

31. Shalabh, Sampling theory, Systematic Sampling. IIT Kanpur, 2018, Chapter 11, 1–17 p.

32. Su G., Wu Y., Zhan W., Zheng Z., Chang L., Wang J., Spatiotemporal evolution characteristics of land subsidence caused by groundwater depletion in the North China plain during the past six decades, Journal of Hydrology, 2021, Vol. 600, pp. 126678.

33. Ty T.V., Minh H.V.T., Avtar R., Kumar P., Hiep H.V., Kurasaki M., Spatiotemporal variations in groundwater levels and the impact on land subsidence in CanTho, Vietnam, Groundwater for sustainable development, 2021, Vol. 15, pp. 100680.

34. UNESCO, 2017, online resource, URL: https://whc.unesco.org/en/list/1544/.

35. Werner A.P.H., A calendar of the history of surveying, Australian Surveyor, 1968, Vol. 22, No. 1., pp. 55–81.

36. Yang J., Cao G., Han D., Yuan H., Hu Y., Shi P., Chen Y., Deformation of the aquifer system under groundwater level fluctuations and its implication for land subsidence control in the Tianjin coastal region, Environmental monitoring and assessment, 2019, Vol. 191, pp. 162, DOI: https://doi.org/10.1007/s10661-019-7296-4.

37. Yule G., On a Method of Investigating Periodicities in Disturbed Series, with Special Reference to Wolfer's Sunspot Numbers, Philosophical Transactions of the Royal Society of London, 1927, Ser. A, No. 226, pp. 267–298.

38. Zamanirad M., Sarraf A., Sedghi H., Saremi A., Rezaee P., Modeling the influence of groundwater exploitation on land subsidence susceptibility using machine learning algorithms, Natural resources research, 2020, Vol. 29, No. 2, pp. 1127–1141, DOI: https://doi.org/10.1007/s11053-019-09490-9.


Supplementary files

Review

For citations:


Amin P., Akhavan Ghalibaf M., Hosseini M. Soil surface subsidence time series modeling of an area with Aridisols and Vertisols complex using surveying and drone imagery in Central Iran. Dokuchaev Soil Bulletin. 2025;(122):62-88. https://doi.org/10.19047/0136-1694-2025-122-62-88

Views: 163


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 0136-1694 (Print)
ISSN 2312-4202 (Online)