Optimum design of level monitoring points for land subsidence

  • Yong Luo
  • Beibei ChenEmail author
  • Kunchao Lei
  • Ruilin Hu
  • Chao Ye
  • Wenjun Cui
Original Paper


Land subsidence caused by long-term over-exploitation of groundwater is the main geological disaster in the plain area of Beijing, China. To obtain accurate and dynamic monitoring information on land subsidence, primary level measurement is carried out every year. However, there are shortcomings in the existing level monitoring network and development of land subsidence, which require optimization of the level monitoring network for land subsidence. Using a GIS analysis method, a zonal map of influencing factors was created by overlaying three thematic maps including hydrogeological units; the decline rate of the groundwater level in the main mining layer; and the thickness of the compressible layer. The data were classified into 448 zones. Based on the zoning map, we created an optimal design for the location of level monitoring points, locating 220 new points. Kriging interpolation error variance was chosen to evaluate map accuracy. Standard deviation contour maps of the existing level network and the optimized level network were created, and the results showed that the error of the optimized level network was smaller than the existing level network. The method proposed by this paper was reasonable for optimizing a level monitoring network for land subsidence.


Land subsidence Optimum design Leveling monitoring GIS 



This work was funded by the Beijing Municipal Natural Science Foundation(8162043); the National Natural Science Foundation of China Youth Fund(41401492/D010702) and the National Natural Science Foundation of China (Nos.41130744/D0107, 41171335/D010702, 41401492/D010702).

We thank Leonie Seabrook, PhD, from Liwen Bianji, Edanz Group China (, for editing the English text of a draft of this manuscript.


  1. Adrian OG, Rudolph LD, Cherry AJ (1999) Analysis of long-term land subsidence near Mexico city: field investigations and predictive modeling. Water Resour Res 35(11):3327–3341CrossRefGoogle Scholar
  2. Abidin HZ, Gumilar I, Andreas H et al (2013) On causes and impacts of land subsidence in Bandung Basin. Environ Earth Sci 68(6):1545–1553CrossRefGoogle Scholar
  3. Buckley SM, Rosen PA, Hensley S, Tapley BD (2003) Land subsidence in Houston, Texas, measured by radar interferometry and constrained by extensometers. J Geophys Res Atmos 108(B11):243–251CrossRefGoogle Scholar
  4. Chen C, Pei S, Jiao J (2003) Land subsidence caused by groundwater exploitation in Suzhou City, China[J]. Hydrogeol J 11(2):275–287CrossRefGoogle Scholar
  5. Chen B, Gong H, Li X, et al (2011) Spatial-temporal characteristics of land subsidence corresponding to dynamic groundwater funnel in Beijing Municipality, China[J]. Chin Geogr Sci 21(6):753-764Google Scholar
  6. Chen B, Gong H, Li X et al (2015) Spatial correlation between land subsidence and urbanization in Beijing, China[J]. Nat Hazards 75(3):2637–2652CrossRefGoogle Scholar
  7. Chen B, Gong H, Li X et al (2017) Characterization and causes of land subsidence in Beijing, China[J]. Int J Remote Sens 38(3):808-826Google Scholar
  8. Chaussard E, Wdowinski S, Cabral-Cano E, Amelung F (2014) Land subsidence in Central Mexico detected by ALOS InSAR time-series. Remote Sens Environ 140:94–106CrossRefGoogle Scholar
  9. Cross PA, Thapa K (1979) The optimal design of levelling networks. Surv Rev 25(192):68–79CrossRefGoogle Scholar
  10. Halicioglu K, Ozener H (2008) Geodetic network design and optimization on the active Tuzla fault (Izmir, Turkey) for disaster management. Sensors 8(8):4742–4757CrossRefGoogle Scholar
  11. Galloway DL, Hudnut KW, Ingebritsen SE et al (1998) Detection of aquifer system compaction and land subsidence using interferometric synthetic aperture radar, Antelope Valley, Mojave Desert, California. Water Resour Res 34(10):2573–2585CrossRefGoogle Scholar
  12. Hu RL, Yue ZQ, Wang LC, Wang SJ (2004) Review on current status and challenging issues of land subsidence in China. Eng Geol 76:65–77CrossRefGoogle Scholar
  13. Li Y, Gong H, Zhu L, Li X (2017) Measuring spatiotemporal features of landSubsidence, groundwater drawdown, and compressible layer thickness in Beijing Plain, China[J]. Water 9(1):64Google Scholar
  14. Liu Y, Ye C, Jia SM (2007) Division of water-bearing zones and compressible layers in Beijing’s land subsidence areas. City Geol 2(1):10–15 (in Chinese)Google Scholar
  15. Lv J, Wang X, Qian K, Tao Z (2011) Optimal design of groundwater level monitoring networks in the area without observation data. Geotech Invest Surv 39(5):32–376Google Scholar
  16. Phien Wej N, Nutalaya P, Giao PH (2006) Land subsidence in Bangkok, Thailand. Eng Geol 82(4):187–201CrossRefGoogle Scholar
  17. Sato C, Haga M, Nishino J (2006) Land subsidence and groundwater management in Tokyo. International Review for Environmental StrategiesGoogle Scholar
  18. Teatini P, Ferronato M, Gambolati G, Bertoni W, Gonella M (2005) A century of land subsidence in Ravenna, Italy. Environ Geol 47(6):831–846CrossRefGoogle Scholar
  19. Xue YQ, Zhang Y, Ye SJ, Wu JC, Li QF (2005) Land subsidence in China. Environ Geol 48(6):713–720CrossRefGoogle Scholar
  20. Xu PL, Grafarend E (1995) A multi-objective second-order optimal design for deforming networks. Geophys J Int 120(3):577–589CrossRefGoogle Scholar
  21. Zhou Y, Dong D, Liu J, Li W (2013) Upgrading a regional groundwater level monitoring network for Beijing plain, China. Geosci Front 4(1):127–138Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yong Luo
    • 1
    • 2
    • 3
  • Beibei Chen
    • 4
    Email author
  • Kunchao Lei
    • 3
  • Ruilin Hu
    • 1
    • 2
  • Chao Ye
    • 3
  • Wenjun Cui
    • 3
  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.Institutions of Earth SciencesChinese Academy of SciencesBeijingChina
  3. 3.Beijing Institute of Hydrogeology and Engineering GeologyBeijingChina
  4. 4.Base of the State Key Laboratory of Urban Environmental Process and Digital ModelingCapital Normal UniversityBeijingChina

Personalised recommendations