Extreme subsidence in a populated city (Mashhad) detected by PSInSAR considering groundwater withdrawal and geotechnical properties.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
09 Jul 2020
09 Jul 2020
Historique:
received:
18
06
2019
accepted:
16
06
2020
entrez:
11
7
2020
pubmed:
11
7
2020
medline:
11
7
2020
Statut:
epublish
Résumé
Ground deformation can cause serious environmental issues such as infrastructure damage, ground compaction, and reducing the ground capacity to store water. Mashhad, as one of the largest and most populated cities in the Middle East, has been suffering from extreme subsidence. In the last decade, some researchers have been interested in measuring land subsidence rates in the Mashhad valley by InSAR techniques. However, most of those studies were based on inaccurate measurements introducing uncertainties in the resulting subsidence rates. These researches used a small number of EnviSat data with long perpendicular and inhomogeneous temporal baseline. This paper seeks to determine the subsidence rate in urban areas of Mashhad in recent years, the threat that was neglected by the city managers and decision-makers. For this purpose, the Persistent Scatterer InSAR technique was applied in the study area using two time-series of descending and ascending Sentinel-1A acquisitions between 2014 and 2017. The results demonstrated the maximum line-of-sight deformation rate of 14.6 cm/year and maximum vertical deformation (subsidence) rate about 19.1 cm/year which could have irreversible consequences. The results were assessed and validated using piezometric data, GPS stations, and geotechnical properties. This assessment confirms that the main reason for subsidence in the interested area is groundwater over-extraction. Also, investigation of geotechnical properties shows that thick fine-grained layers in the northwest of the city could strongly affect the results. At the end of this paper, a new simplified method was proposed to estimate specific storage in special cases to predict the subsidence rate.
Identifiants
pubmed: 32647281
doi: 10.1038/s41598-020-67989-1
pii: 10.1038/s41598-020-67989-1
pmc: PMC7347893
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
11357Références
Hung, W. C. et al. Surface deformation from persistent scatterers SAR interferometry and fusion with leveling data: A case study over the Choushui River Alluvial Fan, Taiwan. Remote Sens. Environ. 115, 957–967. https://doi.org/10.1016/j.rse.2010.11.007 (2011).
doi: 10.1016/j.rse.2010.11.007
Galloway, D. L. et al. Detection of aquifer system compaction and land subsidence using interferometric synthetic aperture radar, Antelope Valley, Mojave Desert, California. Water Resour. Res. 34, 2573–2585. https://doi.org/10.1029/98WR01285 (1998).
doi: 10.1029/98WR01285
Kim, J.-W., Lu, Z., Jia, Y. & Shum, C. K. Ground subsidence in Tucson, Arizona, monitored by time-series analysis using multi-sensor InSAR datasets from 1993 to 2011. ISPRS J. Photogramm. Remote Sens. 107, 126–141. https://doi.org/10.1016/j.isprsjprs.2015.03.013 (2015).
doi: 10.1016/j.isprsjprs.2015.03.013
Alizadeh, B., Limon, R. A., Seo, D.-J., Lee, H. & Brown, J. Multiscale post-processor for ensemble streamflow prediction for short-to-long ranges. J. Hydrometeorol. https://doi.org/10.1175/JHM-D-19-0164.1 (2019).
doi: 10.1175/JHM-D-19-0164.1
Hatami, M., Flood, I., Franz, B. & Zhang, X. In Computing in Civil Engineering 2019: Data, Sensing, and Analytics, 368–375 (American Society of Civil Engineers Reston, 2019), https://doi.org/10.1061/9780784482438.047 .
Du, Z. et al. Correlating the subsidence pattern and land use in Bandung, Indonesia with both Sentinel-1/2 and ALOS-2 satellite images. Int. J. Appl. Earth Obs. Geoinf. 67, 54–68. https://doi.org/10.1016/j.jag.2018.01.001 (2018).
doi: 10.1016/j.jag.2018.01.001
Rosi, A. et al. Subsidence mapping at regional scale using persistent scatters interferometry (PSI): The case of Tuscany region (Italy). Int. J. Appl. Earth Obs. Geoinf. 52, 328–337. https://doi.org/10.1016/j.jag.2016.07.003 (2016).
doi: 10.1016/j.jag.2016.07.003
Peduto, D. et al. A general framework and related procedures for multiscale analyses of DInSAR data in subsiding urban areas. ISPRS J. Photogramm. Remote Sens. 105, 186–210. https://doi.org/10.1016/j.isprsjprs.2015.04.001 (2015).
doi: 10.1016/j.isprsjprs.2015.04.001
Castellazzi, P., Garfias, J., Martel, R., Brouard, C. & Rivera, A. InSAR to support sustainable urbanization over compacting aquifers: The case of Toluca Valley, Mexico. Int. J. Appl. Earth Obs. Geoinf. 63, 33–44. https://doi.org/10.1016/j.jag.2017.06.011 (2017).
doi: 10.1016/j.jag.2017.06.011
Figueroa-Miranda, S., Tuxpan-Vargas, J., Ramos-Leal, J. A., Hernández-Madrigal, V. M. & Villaseñor-Reyes, C. I. Land subsidence by groundwater over-exploitation from aquifers in tectonic valleys of Central Mexico: A review. Eng. Geol. 246, 91–106. https://doi.org/10.1016/j.enggeo.2018.09.023 (2018).
doi: 10.1016/j.enggeo.2018.09.023
Riel, B., Simons, M., Ponti, D., Agram, P. & Jolivet, R. Quantifying ground deformation in the Los Angeles and Santa Ana coastal basins due to groundwater withdrawal. Water Resour. Res. 54, 3557–3582. https://doi.org/10.1029/2017WR021978 (2018).
doi: 10.1029/2017WR021978
Voyiadjis, G. Z. & Zhou, Y. Time-dependent modeling of subsidence due to drainage in bounding shales: Application to a depleted gas field in Louisiana. J. Petrol. Sci. Eng. 166, 175–187. https://doi.org/10.1016/j.petrol.2018.03.032 (2018).
doi: 10.1016/j.petrol.2018.03.032
Khorrami, M. et al. How groundwater level variation and geotechnical properties lead to asymmetric subsidence: A PSInSAR analysis of land deformation over a transit corridor in the Los Angeles metropolitan area. Remote Sens. 11, 377. https://doi.org/10.3390/rs11040377 (2019).
doi: 10.3390/rs11040377
Bekaert, D., Hamlington, B., Buzzanga, B. & Jones, C. Spaceborne synthetic aperture radar survey of subsidence in Hampton Roads, Virginia (USA). Sci. Rep. 7, 14752. https://doi.org/10.1038/s41598-017-15309-5 (2017).
doi: 10.1038/s41598-017-15309-5
pubmed: 29116168
pmcid: 5677032
Khorrami, M. et al. Impact of ground subsidence on groundwater quality: A case study in Los Angeles, California. In Proceedings of the 2019 ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19, https://doi.org/10.1061/9780784482445.021 (2019).
Nikos, S. et al. Land subsidence rebound detected via multi-temporal InSAR and ground truth data in Kalochori and Sindos regions, Northern Greece. Eng. Geol. 209, 175–186. https://doi.org/10.1016/j.enggeo.2016.05.017 (2016).
doi: 10.1016/j.enggeo.2016.05.017
Declercq, P. Y. et al. Subsidence related to groundwater pumping for breweries in Merchtem area (Belgium), highlighted by Persistent Scaterrer Interferometry. Int. J. Appl. Earth Obs. Geoinf. 63, 178–185. https://doi.org/10.1016/j.jag.2017.07.012 (2017).
doi: 10.1016/j.jag.2017.07.012
de Luna, R. M., dos Anjos Garnés, S. J., Cabral, J. J. & dos Santos, S. M. Groundwater overexploitation and soil subsidence monitoring on Recife plain (Brazil). Nat. Hazards 86, 1363–1376. https://doi.org/10.1007/s11069-017-2749-y (2017).
doi: 10.1007/s11069-017-2749-y
Fernandez, J. et al. Modeling the two-and three-dimensional displacement field in Lorca, Spain, subsidence and the global implications. Sci. Rep. 8, 1–4. https://doi.org/10.1038/s41598-018-33128-0 (2018).
doi: 10.1038/s41598-018-33128-0
Hsieh, C. S. et al. Using differential SAR interferometry to map land subsidence: a case study in the Pingtung Plain of SW Taiwan. Nat. Hazards 58, 1311–1332. https://doi.org/10.1007/s11069-011-9734-7 (2011).
doi: 10.1007/s11069-011-9734-7
Motagh, M. et al. Land subsidence in Mashhad Valley, northeast Iran: results from InSAR, levelling and GPS. Geophys. J. Int. 168, 518–526. https://doi.org/10.1111/j.1365-246X.2006.03246.x (2007).
doi: 10.1111/j.1365-246X.2006.03246.x
Akbari, V. & Motagh, M. Improved ground subsidence monitoring using small baseline SAR interferograms and a weighted least squares inversion algorithm. IEEE Geosci. Remote Sens. Lett. 9, 437–441. https://doi.org/10.1109/LGRS.2011.2170952 (2012).
doi: 10.1109/LGRS.2011.2170952
Dehghani, M., Valadan Zoej, M. J., Entezam, I., Mansourian, A. & Saatchi, S. InSAR monitoring of progressive land subsidence in Neyshabour, northeast Iran. Geophys. J. Int. 178, 47–56. https://doi.org/10.1111/j.1365-246X.2009.04135.x (2009).
doi: 10.1111/j.1365-246X.2009.04135.x
Motagh, M. et al. Quantifying groundwater exploitation induced subsidence in the Rafsanjan plain, southeastern Iran, using InSAR time-series and in situ measurements. Eng. Geol. 218, 134–151. https://doi.org/10.1016/j.enggeo.2017.01.011 (2017).
doi: 10.1016/j.enggeo.2017.01.011
Sadeghi, Z., Zoej, M. J. V., Dehghani, M. & Chang, N.-B. Enhanced algorithm based on persistent scatterer interferometry for the estimation of high-rate land subsidence. J. Appl. Remote Sens. 6, 063573–063571–063573–063515, https://doi.org/10.1117/1.JRS.6.063573 (2012).
Dehghani, M. et al. Hybrid conventional and persistent scatterer SAR interferometry for land subsidence monitoring in the Tehran Basin, Iran. ISPRS J. Photogramm. Remote Sens. 79, 157–170. https://doi.org/10.1016/j.isprsjprs.2013.02.012 (2013).
doi: 10.1016/j.isprsjprs.2013.02.012
Foroughnia, F., Nemati, S., Maghsoudi, Y. & Perissin, D. An iterative PS-InSAR method for the analysis of large spatio-temporal baseline data stacks for land subsidence estimation. Int. J. Appl. Earth Obs. Geoinf. 74, 248–258. https://doi.org/10.1016/j.jag.2018.09.018 (2019).
doi: 10.1016/j.jag.2018.09.018
Mahmoudpour, M., Khamehchiyan, M., Nikudel, M. R. & Ghassemi, M. R. Numerical simulation and prediction of regional land subsidence caused by groundwater exploitation in the southwest plain of Tehran, Iran. Eng. Geol. 201, 6–28. https://doi.org/10.1016/j.enggeo.2015.12.004 (2016).
doi: 10.1016/j.enggeo.2015.12.004
Rajabi, A. M. A numerical study on land subsidence due to extensive overexploitation of groundwater in Aliabad plain, Qom-Iran. Nat. Hazards 93, 1085–1103. https://doi.org/10.1007/s11069-018-3448-z (2018).
doi: 10.1007/s11069-018-3448-z
Taravatrooy, N., Nikoo, M. R., Sadegh, M. & Parvinnia, M. A hybrid clustering-fusion methodology for land subsidence estimation. Nat. Hazards 94, 905–926. https://doi.org/10.1007/s11069-018-3431-8 (2018).
doi: 10.1007/s11069-018-3431-8
Khorrami, M., Abrishami, S. & Maghsoudi, Y. Mashhad subsidence monitoring by interferometric synthetic aperture radar technique. Amirkabir J. Civil Eng. https://doi.org/10.22060/ceej.2018.14300.5617 (2018).
doi: 10.22060/ceej.2018.14300.5617
Dehghani, M. et al. Radar interferometry time series analysis of Mashhad subsidence. J. Indian Soc. Remote Sens. 37, 147–156. https://doi.org/10.1007/s12524-009-0006-x (2009).
doi: 10.1007/s12524-009-0006-x
Lashkaripour, G. R., Ghafoori, M. & Mosavi Maddah, S. M. An investigation on the mechanism of land subsidence in the northwest of Mashhad city, NE Iran. J. Biodivers. Environ. Sci. 5, 321–327 (2014).
Amelung, F., Galloway, D. L., Bell, J. W., Zebker, H. A. & Laczniak, R. J. Sensing the ups and downs of Las Vegas: InSAR reveals structural control of land subsidence and aquifer-system deformation. Geology 27, 483–486. https://doi.org/10.1130/0091-7613(1999)027 (1999).
doi: 10.1130/0091-7613(1999)027
Tesauro, M. et al. Urban subsidence inside the city of Napoli (Italy) observed by satellite radar interferometry. Geophys. Res. Lett. 27, 1961–1964. https://doi.org/10.1029/2000GL008481 (2000).
doi: 10.1029/2000GL008481
Ferretti, A., Prati, C. & Rocca, F. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 39, 8–20. https://doi.org/10.1109/36.898661 (2001).
doi: 10.1109/36.898661
Lazecký, M., Jirankova, E. & Kadlečík, P. Multitemporal monitoring of Karvina subsidence troughs using Sentinel-1 and TerraSar-X interferometry. 14, https://doi.org/10.13168/AGG.2016.0027 (2017).
Ge, L., Chang, H.-C., Rizos, C. & Omura, M., Mine subsidence monitoring: a comparison among Envisat, ERS, and JERS-1, In 2004 ENVISAT Symposium. 4–9 (2004).
Wegmuller, U., Werner, C., Strozzi, T. & Wiesmann, A. Monitoring mining induced surface deformation, In IGARSS, IEEE International Geoscience and Remote Sensing Symposium. 1933–1935 (2004).
Chang, H.-C., Ge, L. & Rizos, C. In Proceedings IEEE International Geoscience and Remote Sensing Symposium, IGARSS'05. 1742–1745 (2005).
Canaslan, F. & Ustun, A. Impact of perpendicular and temporal baseline characteristics on InSAR coherence maps, In Proc. FIG Working Week, (2012).
Wang, J. et al. Demonstration of time-series InSAR processing in Beijing using a small stack of Gaofen-3 differential interferograms. J. Sens. https://doi.org/10.1155/2019/4204580 (2019).
doi: 10.1155/2019/4204580
Pepe, A. & Calò, F. A review of interferometric synthetic aperture RADAR (InSAR) multi-track approaches for the retrieval of Earth’s surface displacements. Appl. Sci. 7, 1264. https://doi.org/10.3390/app7121264 (2017).
doi: 10.3390/app7121264
Marbouti, M., Praks, J., Antropov, O., Rinne, E. & Leppäranta, M. A study of landfast ice with Sentinel-1 Repeat-Pass Interferometry over the Baltic Sea. Remote Sens. 9, 833. https://doi.org/10.3390/rs9080833 (2017).
doi: 10.3390/rs9080833
Gaber, A., Darwish, N. & Koch, M. Minimizing the residual topography effect on interferograms to improve DInSAR results: Estimating land subsidence in Port-Said City, Egypt. Remote Sens. 9, 752. https://doi.org/10.3390/rs9070752 (2017).
doi: 10.3390/rs9070752
Haji Aghajany, S. & Voosoghi, B. Effects of perpendicular and temporal baseline characteristics in accuracy of InSAR displacement velocity fields. Int. J. Geo Sci. Environ. Plan. (2016).
Dai, K. et al. Monitoring highway stability in permafrost regions with X-band temporary scatterers stacking InSAR. Sensors 18, 1876. https://doi.org/10.3390/s18061876 (2018).
doi: 10.3390/s18061876
Riley, F. S. Analysis of borehole extensometer data from central California. Land Subsidence 2, 423–431 (1969).
Hanson, R. T. Aquifer-system compaction (Tucson Basin and Avra Valley, Arizona, 1989).
Pavelko, M. T. Estimates of hydraulic properties from a one-dimensional numerical model of vertical aquifer-system deformation, Lorenzi Site, Las Vegas, Nevada. (US Department of the Interior, US Geological Survey, 2004).
Galloway, D. L. & Hoffmann, J. The application of satellite differential SAR interferometry-derived ground displacements in hydrogeology. Hydrogeol. J. 15, 133–154. https://doi.org/10.1007/s10040-006-0121-5 (2007).
doi: 10.1007/s10040-006-0121-5
Tomás, R. et al. A ground subsidence study based on DInSAR data: Calibration of soil parameters and subsidence prediction in Murcia City (Spain). Eng. Geol. 111, 19–30. https://doi.org/10.1016/j.enggeo.2009.11.004 (2010).
doi: 10.1016/j.enggeo.2009.11.004
Nasseh, S., Hafez Moghaddas, N., Ghafoori, M., Asghari, O. & Bolouri Bazaz, J. Spatial variability analysis of subsurface soil in Mashhad city NE Iran. Int. J. Mining Geo-Eng. 50, 219–229. https://doi.org/10.22059/ijmge.2016.59832 (2016).
doi: 10.22059/ijmge.2016.59832
Felfelani, F. & Kerachian, R. Municipal water demand forecasting under peculiar fluctuations in population: A case study of Mashhad, a tourist city. Hydrol. Sci. J. 61, 1524–1534. https://doi.org/10.1080/02626667.2015.1027208 (2016).
doi: 10.1080/02626667.2015.1027208
55Canaslan Comut, F., Ustun, A., Lazecky, M. & Perissin, D., Capability of detecting rapid subsidence with COSMO SKYMED and sentinel-1 dataset over Konya city. In Living Planet Symposium, (2016).
Perissin, D., Wang, Z. & Lin, H. Shanghai subway tunnels and highways monitoring through Cosmo-SkyMed Persistent Scatterers. ISPRS J. Photogramm. Remote Sens. 73, 58–67. https://doi.org/10.1016/j.isprsjprs.2012.07.002 (2012).
doi: 10.1016/j.isprsjprs.2012.07.002
Perissin, D., Interferometric SAR multitemporal processing: Techniques and applications, In Multitemporal Remote Sensing 145–176, https://doi.org/10.1007/978-3-319-47037-5_8 (Springer, Berlin, 2016).
Perrone, G. et al. Current tectonic activity and differential uplift along the Cottian Alps/Po Plain boundary (NW Italy) as derived by PS-InSAR data. J. Geodyn. 66, 65–78. https://doi.org/10.1016/j.jog.2013.02.004 (2013).
doi: 10.1016/j.jog.2013.02.004
Hanssen, R. F. Radar Interferometry: Data Interpretation and Error Analysis. Vol. 2, (Springer Science & Business Media, Berlin, 2001) https://doi.org/10.1007/0-306-47633-9 .
Ferretti, A., Prati, C. & Rocca, F. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans. Geosci. Remote Sens. 38, 2202–2212. https://doi.org/10.1109/36.868878 (2000).
doi: 10.1109/36.868878
Ferretti, A. et al. Submillimeter accuracy of InSAR time series: Experimental validation. IEEE Trans. Geosci. Remote Sens. 45, 1142–1153. https://doi.org/10.1109/TGRS.2007.894440 (2007).
doi: 10.1109/TGRS.2007.894440
Zebker, H. A. & Villasenor, J. Decorrelation in interferometric radar echoes. IEEE Trans. Geosci. Remote Sens. 30, 950–959. https://doi.org/10.1109/36.175330 (1992).
doi: 10.1109/36.175330
Perissin, D., Wang, Z. & Wang, T. The SARPROZ InSAR tool for urban subsidence/manmade structure stability monitoring in China, In Proceedings of 34th International Symposium on Remote Sensing of Environment. (Sydney, Australia, 2011).
Perissin, D. & Wang, T. Repeat-pass SAR interferometry with partially coherent targets. IEEE Trans. Geosci. Remote Sens. 50, 271–280. https://doi.org/10.1109/TGRS.2011.2160644 (2012).
doi: 10.1109/TGRS.2011.2160644
Wright, T. J., Parsons, B. E. & Lu, Z. Toward mapping surface deformation in three dimensions using InSAR. Geophys. Res. Lett. 31 (2004).
Colesanti, C., Ferretti, A., Novali, F., Prati, C. & Rocca, F. SAR monitoring of progressive and seasonal ground deformation using the permanent scatterers technique. IEEE Trans. Geosci. Remote Sens. 41, 1685–1701. https://doi.org/10.1109/TGRS.2003.813278 (2003).
doi: 10.1109/TGRS.2003.813278
Safdari Seh Gonbad, M., Nakhaei, P. & Kalantary, F. In 4th International Conference on Long-Term Behaviour and Environmentally Friendly Rehabilitation Technologies of Dams (Tehran, Iran, 2017).
Moghadam, Z., Jahanshahi, R., Asadi, N. & Behzadifar, V.-A. In 38th National Geoscience Congress, Geological Survey & Mineral Explorations of Iran (Iran, 2019).
Murray, J. R. & Svarc, J. Global Positioning System data collection, processing, and analysis conducted by the US Geological Survey Earthquake Hazards Program. Seismol. Res. Lett. 88, 916–925. https://doi.org/10.1785/0220160204 (2017).
doi: 10.1785/0220160204
Atkinson, J. The Mechanics of Soils and Foundations (CRC Press, Boca Raton, 2017).
Das, B. Principles of Geotechnical Engineering (Taylor & Francis, Routledge, 2018).
Sundell, J., Rosén, L., Norberg, T. & Haaf, E. A probabilistic approach to soil layer and bedrock-level modeling for risk assessment of groundwater drawdown induced land subsidence. Eng. Geol. 203, 126–139. https://doi.org/10.1016/j.enggeo.2015.11.006 (2016).
doi: 10.1016/j.enggeo.2015.11.006
Shadmehri Toosi, A., Ghasemi Tousi, E., Ghassemi, S. A., Cheshomi, A. & Alaghmand, S. A multi-criteria decision analysis approach towards efficient rainwater harvesting. J. Hydrol. 582, 124501. https://doi.org/10.1016/j.jhydrol.2019.124501 (2020).
doi: 10.1016/j.jhydrol.2019.124501
Alipour, M. H., Kibler, K. & Alizadeh, B. Flow alteration by diversion hydropower in tributaries to the Salween River: a comparative analysis of two streamflow prediction methodologies. Int. J. River Basin Manag. https://doi.org/10.1080/15715124.2020.1760289 (2020).
doi: 10.1080/15715124.2020.1760289
Ghazi, A., Moghadas, N. H., Sadeghi, H., Ghafoori, M. & Lashkaripour, G. R. Spatial variability of shear wave velocity using geostatistical analysis in Mashhad City, NE Iran. Open J. Geol. 4, 354. https://doi.org/10.4236/ojg.2014.48027 (2014).
doi: 10.4236/ojg.2014.48027
Alavi, M. Sedimentary and structural characteristics of the Paleo-Tethys remnants in northeastern Iran. Geol. Soc. Am. Bull. 103, 983–992. https://doi.org/10.1130/0016-7606(1991)103 (1991).
doi: 10.1130/0016-7606(1991)103
Jozaghi, A. et al. A comparative study of the AHP and TOPSIS techniques for dam site selection using GIS: A case study of Sistan and Baluchestan Province, Iran. Geosciences 8, 494. https://doi.org/10.3390/geosciences8120494 (2018).
doi: 10.3390/geosciences8120494