Natural Hazards

, Volume 92, Issue 3, pp 1733–1762 | Cite as

A method predicting pumping-induced ground settlement using back-analysis and its application in the Karla region of Greece

  • Constantine Stamatopoulos
  • Petros Petridis
  • Issaak Parcharidis
  • Michael Foumelis
Original Paper


In many arid planar regions of the world, ground subsidence induced by the lowering of the water table line due to pumping has recently caused damage to houses and other overlying structures. The depth of the water table lowering is usually tens of meters, the depth of the underlying soil layers may be hundreds of meters, and the region where the lowering is applied may extend tens of square kilometers. In this aspect, the problem under consideration differs drastically from other geotechnical engineering problems and the application of the physical models may have the serious deficiency that required geotechnical information may be incomplete and very costly to obtain: The change in water table variation and the depth of rock are usually known from results of pumping borings and geophysical investigations, but the location, width, compressibility and consolidation characteristics of the clay layers, are usually not known. New space technologies, such as the phase shifting interferometry radar method, provide cost-effective measurements of past displacement data. Based on past displacement measurements, an alternative approach is proposed to predict ground subsidence induced by the lowering of the water table. In particular, the work derives a simplified equation and corresponding methodology which predicts ground subsidence in terms of water table history, based primarily on data of past ground subsidence. This equation was derived and validated based on a state-of-the-art proposed model predicting one-dimensional ground subsidence induced by water level lowering in planar regions. Based on the derived simplified expression, a method predicting the risk at the built environment due to future ground subsidence induced by water level lowering was proposed and applied successfully in a well-documented case study of ground subsidence: the Niki village at Thessaly, Greece.


Consolidation Ground subsidence Change in water table elevation Pumping Clays Differential interferometry radar Back-analysis Thessaly plain Karla region 

List of symbols

ac, acw, ah

Factors defined by Eq. (3)

A1, B1, Ao

Factors given by Eqs. (12), (28b)

a, b

Parameters of Eq. (10)

Cr, αr

Compression index under unloading, factor of Eq. (16)

Cc, Cci

Compression index under loading, Compression index under loading of layer i

cv, cvi

Coefficient of consolidation, coefficient of consolidation of layer i


Constant given by Eq. (17b)

D, Dci, Dc, Dca, Dcb

Depth of rock below the initial depth of the water table line, thickness of the clay layer i, total thickness of the clay layers, total thickness of the clay layers at parts a and b (defined in Sect. 2.2), respectively

Dcci, Dcc

Drainage height of layer i, the average drainage height

e, eo, eoi, eav, eL

Void ratio, initial value of void ratio, initial value of void ratio of layer i, average void ratio, the void ratio at the liquid limit

errn2, n2

Error in the prediction of n2 data points, number of data points



hw, Δhwji, Δhwi, hwm, dhw

Water table depth, water level depth change at step j above layer i, total water level depth change at layer i, maximum change in the water table level depth, the peak-to-peak change in h w in the case of yearly cyclic variation of h w

hwm (t), hwm (to), hwm (t1), hwm (t2)

Maximum change in the water table line depth at time t, to, t1, t2

n, m

Total number of clay sub-layers and water level change times considered, respectively

M, μ

Dimensionless parameters of Eq. (1c)


Plasticity index, plasticity index of layer i

qpred−i, qmeas−i

Predicted and measured value of data point i


In Eq. (5), C c , c v , e o , γ t

R1, R2

Two regions defined in Fig. 13a

S (t), Si (t), Sf, Si, Sf(ij), Sf (t)

Settlement at time t, Settlement at time t for layer i, total (final) settlement, settlement of layer i, total settlement due to water table lowering j for layer i, total settlement due to current (at time t) water table change

t, tjT, Tij, Ti, Tav

Time, time of water fall j, dimensionless time, dimensionless time of layer i at water fall j, average dimensionless time of layer i, average value of dimensionless time

t1, t2, to

Times of measurements 1 and 2 and time where measurements start


Final time in case of linear variation of water table with time


In the case where h w returns at its initial position the time that this occurs


Time where 95% of the total settlement accumulates

thwm (t), tcr (t), tcr (t1), tcr (t2)

Time of application of the change in hwm (t), thwm (t)/2, t cr at time t1 and at time t2

U (t), Uij (t), Ui (t)

Degree of consolidation in terms of time, degree of consolidation for layer i at time of water change j, degree of consolidation for layer i

Ucom, tcom (0.95), Sf−com

Computed values of U, t (0.95), S f


Liquid limit, liquid limit of layer i


Water table line

zi, zav, zwo

Clay sub-layer i average depth, average depth of all the clay layers, water table initial depth

Δtοs (t1), Δtοs (t2)

Settlement change relative to settlement at time to, at times t1 and t2

\(\sigma_{v}^{\prime } ,\sigma_{v - i}^{\prime } ,\sigma_{v - av}^{\prime } , \, \Delta \sigma^{\prime } v,\Delta \sigma_{v - i - j}^{\prime } ,\Delta \sigma_{v\left( a \right)}^{\prime } ,\Delta \sigma_{v(b)}^{\prime }\)

Effective initial vertical stress, effective initial vertical stress at layer i, average effective vertical stress, additional vertical effective stress, additional vertical effective stress of layer i due to Δhwij, additional vertical effective stress of regions (a) and (b) at parts a and b (defined in Sect. 2.2)

γt−ave−i, γtave, γw

Average total unit weight of the soil above the midpoint of layer i, average total unit weight of the soil, unit weight of water



The work was funded by the project “Novel methodologies for the assessment of risk of ground displacement” under ESPA 2007–2013 of Greece, under action: Bilateral S&T Cooperation between China and Greece (Grant No. 12CHN245). The scientists Miranda Dandoulaki, Eleni Stavroyanopoulou, Lydia Balla assisted in parts of this work.


  1. Al-Khafaji AWN, Andersland OB (1992) Equations for compression index approximation. J Geotech Eng (ASCE) 118(1):148–153CrossRefGoogle Scholar
  2. Azzouz AS, Krizek RJ, Corotis RB (1976) Regression analysis of soil compressibility. Soils Found 16(2):19–29CrossRefGoogle Scholar
  3. Bangladesh National Building Code (2012) Soils and foundations. Housing and Building Research Institute,  Dhaka, Bangladesh, pp 6–225Google Scholar
  4. Bowles JE (1997) Foundation analysis and design. McGraw-Hill, New York City, pp 88–89Google Scholar
  5. Chai JC, Shen SL, Zhu HH, Zhang XL (2005) 1D analysis of land subsidence in Shanghai. Lowland Technol Int 7(1):33–41Google Scholar
  6. Danos G (2013) Water Department of Thessaly, Water borings in Larissa Prefecture 2005–2010, Larissa, Greece (private communication)Google Scholar
  7. European Standard (2003) Eurocode 8: design of structures for earthquake resistance. Final Draft, prEN 1998-5, DecemberGoogle Scholar
  8. Foumelis M, Papageorgiou E, Stamatopoulos C (2016) Episodic ground deformation signals in Thessaly Plain (Greece) revealed by data mining of SAR interferometry time series. Int J Remote Sensing 37(16):3696–3711CrossRefGoogle Scholar
  9. Hong Z et al (2012) Compression behaviour of reconstituted clays”. Geotechnique 62(4):291–301CrossRefGoogle Scholar
  10. Institute of Geology, Mineral Exploitation of Greece (IGME) (2007a) Technical–geological investigation of subsurface soil cracks in areas of ThessalyGoogle Scholar
  11. Institute of Geology, Mineral Exploitation of Greece (IGME) (2007b) Investigation of hydrogeolical conditions in areas of Larisa and Magnisia, with soil cracks phenomenaGoogle Scholar
  12. Institute of Geology, Mineral Exploitation of Greece (IGME) (2014) Manakos A. Hydrogeological study, Groundwater Resources of Thessaly, recording and evaluation of hydrogeological character of groundwater and aquifers country systemsGoogle Scholar
  13. KEDE, Ministry of Public Works (1963) 1964, 1966, 1973, 1996. Boring reports No. 137, 145, 484, 653, of Larisa Platikampos Dam and of Omorfochori of Larisa PrefectureGoogle Scholar
  14. Koppula SD (1981) Statistical estimation of compression index. Geotech Test J 4(2):68–73CrossRefGoogle Scholar
  15. Lambe T, Whitman R (1969) Soils mechanics. Wiley, Hoboken, p 156Google Scholar
  16. Lopez-Caballero F, Modaressi-Farahmand-Razavi A, Modaressi H (2007) Non linear numerical method for earthquake site response analysis I—elastoplastic cyclic model and parameter identification strategy. Bull Earthq Eng 5(3):303–323CrossRefGoogle Scholar
  17. Marcial D, Delage P, Cui Y (2002) On the high stress compression of bentonites. Can Geotech J 39:816CrossRefGoogle Scholar
  18. Marsters JC, Manghnani MH (1993) Consolidation tests results and porosity rebound of Ontong Java Plateau sediments. In: Proceedings of the ocean drilling program, scientific results, vol 130Google Scholar
  19. Morris P, Lockington D (2002) Geotechnical compressibility and consolidation parameters and correlations for remoulded fine grained marine and riverine sediments. Research Report, CRC, Appendix 2Google Scholar
  20. Nishida Y (1956) A brief note on compression index of soils. J Soil Mech Found Div ASCE 82(SM3):1027-1–1027-14Google Scholar
  21. Ortiz-Zamora D, Ortega-Guerrero A (2010) Evolution of long-term land subsidence near Mexico City: review, field investigations, and predictive simulations. Water Resour Res 46(1).
  22. Parcharidis E, Lagios V, Sakkas D, Raucoules D, Feurer S Le, Mouelic C, King C, Carnec F, Novali A, Ferretti R Capes, Cooksley G (2006) Subsidence monitoring within the Athens Basin (Greece) using space radar interferometric techniques. Earth Planets Space 58:505–513CrossRefGoogle Scholar
  23. Parcharidis I, Foumelis M, Benekos G, Kourkouli P, Stamatopoulos C, Stramondo S, (2015) Time series synthetic aperture radar interferometry over the multispan cable-stayed Rio-Antirio Bridge (central Greece): achievements and constraints. J Appl Remote Sensing 9(1):096082CrossRefGoogle Scholar
  24. Parhizkar S, Ajdary K, Kazemi GA, Emamgholizadeh S (2015) Predicting water level drawdown and assessment of land subsidence in Damghan aquifer by combining GMS and GEP models. Geopersia 5(1):63–80Google Scholar
  25. Poulos HG, Davis EH (1974) Elastic solutions for soil and rock mechanics. Wiley, New YorkGoogle Scholar
  26. Rahmanian D (1986) Land subsidence and earth fissures due to groundwater withdrawal in Kerman. J Water 6:21–27Google Scholar
  27. Raju PSRN, Pandian NS, Nagaraj TS (1995) Analysis and estimation of coefficient of consolidation. Geotech Test J 18(2):252–258CrossRefGoogle Scholar
  28. Raucoules D, Le Mouélic S, Carnec C, Maisons C, King C (2003) Urban subsidence in the city of Prato (Italy) monitored by satellite radar interferometry. Int J Remote Sens 24(4):891–897CrossRefGoogle Scholar
  29. Shen SL, Xu YS (2011) Numerical evaluation of land subsidence induced by groundwater pumping in Shanghai. Can Geotech J 48:1378–1392CrossRefGoogle Scholar
  30. Stamatopoulos A, Kotzias P (1983) Settlement-time predictions in preloading. J Geotech Eng 109(6):807–820CrossRefGoogle Scholar
  31. Stamatopoulos C, Petridis P, Bassanou M, Stamatopoulos A (2005) Increase in horizontal stress induced by preloading. Proc Inst Civil Eng Ground Improvement 9(2):47–57CrossRefGoogle Scholar
  32. Stamatopoulos C, Petridis P, Bassanou M, Allkja S, Loukatos N, Small A (2013a) Improvement of dynamic soil properties induced by preloading verified by a field test. Eng Geol 163:101–112CrossRefGoogle Scholar
  33. Stamatopoulos C, Petridis P, Balla L, Parharidis I, Foumelis M, Fountoulis D, Lalehos S, Metaxas Ch (2013b) Predicting ground subsidence induced by pumping combining space measurements and geotechnical modeling: application in the Thessaly region, Greece. In: Seventh international conference on case histories in geotechnical engineering, Wheeling, IL (Chicago Area)—April 29 to May 4Google Scholar
  34. Stamatopoulos CA et al (2014) Project “Novel methodologies for the assessment of risk of ground displacement” under ESPA 2007–2013 of Greece, under action: Bilateral S&T Cooperation between China and Greece. Deliverables D2 and D4Google Scholar
  35. Stramondo S, Bozzano F, Marra F, Wegmuller U, Cinti FR, Moro M, Saroli M (2008) Subsidence induced by urbanisation in the city of Rome detected by advanced InSAR technique and geotechnical investigations. Remote Sens Environ 112:3160–3172CrossRefGoogle Scholar
  36. Teartisup P, Kerdsueb P (2013) Land subsidence prediction in central plain of Thailand. Int J Environ Sci Dev 4(1):59–61CrossRefGoogle Scholar
  37. Teatini P, Strozzi T, Tosi L, Wegmuller U, Werner C, Carbognin L, Rosselli R, Cecconi G, Giada M (2007) ERS and envisat SAR interferometry to measure land subsidence in the venice lagoon on natural and artificial point targets. In: European Space Agency, (Special Publication) ESA SP, Issue SP-636Google Scholar
  38. Tomas R, Marquez Y, Lopez-Sanchez JM, Delgado J, Blanco P, Mallorqui JJ, Martinez M, Herrera G, Mulas J (2005) Mapping ground subsidence induced by aquifer overexploitation using advanced Differential SAR Interferometry: vega Media of the Segura River (SE Spain) case study. Remote Sens Environ 98:269–283CrossRefGoogle Scholar
  39. Tripathy S, Schanz T (2007) Compressibility behavior of clays at large pressures. Can Geotech J 44:355–362CrossRefGoogle Scholar
  40. Ziaie A, Rahnama MB (2007) Prediction of single well land subsidence due to ground water drainage. Int J Agric Res 2:349–358.

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Constantine Stamatopoulos
    • 1
  • Petros Petridis
    • 2
  • Issaak Parcharidis
    • 3
  • Michael Foumelis
    • 4
  1. 1.Stamatopoulos and Associates Co, Hellenic Open UniversityAthensGreece
  2. 2.Stamatopoulos and Associates CoAthensGreece
  3. 3.Department of GeographyHarokopio UniversityAthensGreece
  4. 4.Risk and Prevention Unit at the French Geological Survey (BRGM)OrléansFrance

Personalised recommendations