The Impact of outlier detection to estimate groundwater fluctuations using GRACE satellite data; Case Study: Khuzestan Province, Iran

Document Type: Research Paper


1 Department of Civil Engineering, Faculty of Civil Engineering and Architecture,Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Department of Civil Engineering, Engineering Faculty, Khatam Al-Anbia University of Technology, Behbahan, Iran

3 Department of Geodesy, Faculty of Surveying and Spatial Information, College of Engineering, University of Tehran, Tehran, Iran



Groundwater aquifers are one of the most significant freshwater resources in the world. Hence, the monitoring of these resources is particularly important for available water resources planning. Piezometric wells have traditionally been used to monitor groundwater. This approach is costly and pointwise, which is not feasible for places with steep topography and mountainous areas. Nowadays, remote sensing techniques are widely used in various fields of engineering as appropriate alternatives to traditional methods. In water resources management, the Gravity Recovery and Climate Experiment (GRACE) satellites can monitor groundwater changes with acceptable accuracies. This paper applied the GRACE satellite data for a 40-month period to assess the variation of the groundwater level in Khuzestan province. The Global Land Data Assimilation System (GLDAS) model was used to counteract the soil moisture effect in final results. The observed data from piezometric wells were pre-processed to detect outliers using the Mahalanobis algorithm in Khuzestan province. At last, the outputs of GRACE were compared with these processed observed data. Despite the relatively small size of the area in question, the results indicated the efficiency of GRACE data (RMSE = 0.8, NRMSE = 0.2) for monitoring the groundwater level changes.


  1. Esmaeili, Mand Motagh, M (2016) Improved persistent scatterer analysis using amplitude dispersion index optimization of dual polarimetry dataISPRS Journal of Photogrammetry and Remote Sensing, 117, pp.108-114.
  2. Rodell, M., Houser, P., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., & Bosilovich, M (2004) The global land data assimilation systemBulletin of the American Meteorological Society, 85, pp.381-394
  3. Abart, C (2005) Assessment of solution strategies for GRACE gravity field processing.
  4. Wahr, J., Swenson, S., Zlotnicki, V., & Velicogna, I (2004) Time‐variable gravity from GRACE: First resultsGeophysical Research Letters, 31.
  5. Ramillien, G., Famiglietti, J.S., & Wahr, J (2008) Detection of continental hydrology and glaciology signals from GRACE: a reviewSurveys in geophysics, 29, pp.361-374
  6. Awange, J., Sharifi, M., Keller, W., & Kuhn, M (2009) GRACE application to the receding Lake Victoria water level and Australian droughtObserving our changing earth: Springer, pp387-396
  7. Longuevergne, L., Scanlon, B.R., & Wilson, C.R (2010) GRACE Hydrological estimates for small basins: Evaluating processing approaches on the High Plains Aquifer, USAWater Resources Research, 46.
  8. Voss, K.A., Famiglietti, J.S., Lo, M., De Linage, C., Rodell, M., & Swenson, S.C (2013) Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the Tigris‐Euphrates‐Western Iran regionWater Resources Research, 49, pp.904-914
  9. Ferreira, V.G., Gong, Z., & Andam-Akorful, S.A (2012) Monitoring mass changes in the Volta River basin using GRACE satellite gravity and TRMM precipitationBoletim de Ciências Geodésicas, 18, pp549-563
  10. Ahmed, M., Sultan, M., Wahr, J., Yan, E., Milewski, A., Sauck, W., Becker, R., & Welton, B (2011)Integration of GRACE (Gravity Recovery and Climate Experiment) data with traditional data sets for a better understanding of the time-dependent water partitioning in African watershedsGeology, 39 , pp479-482
  11. Ahmed, M., Sultan, M., Wahr, J., & Yan, E (2014) The use of GRACE data to monitor natural and anthropogenic induced variations in water availability across AfricaEarth-Science Reviews, 136, pp.289-300
  12. Rodell, M., Famiglietti, J., Wiese, D., Reager, J., Beaudoing, H., Landerer, F., & Lo, M (2019) Emerging trends in global freshwater availability Nature, 565, E7-E7, vol 557, pg 651.
  13. Tapley, B.D., Bettadpur, S., Watkins, M., & Reigber, C (2004) The gravity recovery and climate experiment: Mission overview and early resultsGeophysical Research Letters, 31.
  14. Buis, A (2012) At 10, GRACE Continues Defying, and Defining, GravityNASA.
  15. Rummel, R., Balmino, G., Johannessen, J., & Visser, P.a.W., P (2002) Dedicated gravity field missions-principles and aimsJGeodyn, 33, pp3-20
  16. Wahr, J., Molenaar, M., & Bryan, F (1998) Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACEJournal of Geophysical Research: Solid Earth, 103, pp30205-30229
  17. Joodaki, G (2014) Earth mass change tracking using GRACE satellite gravity data.
  18. Houser, P., & Rodell, M (2002) GLDAS: an important contribution to CEOPGEWEX Newsletter, May, 2, 9.
  19. Fang, H., Hrubiak, P., Kato, H., Rodell, M., Teng, W.L., & Vollmer, B.E (2008) Global land data assimilation system (GLDAS) products from NASA hydrology data and information services center (HDISC).
  20. Fang, H., Beaudoing, H.K., Teng, W.L., & Vollmer, B.E (2009) Global Land data assimilation system (GLDAS) products, services and application from NASA hydrology data and information services center (HDISC).
  21. Rui, H., & Beaudoing, H (2015) Global Land Data Assimilation System Version 2 (GLDAS-2) Products, Last revisedNational Aeronautices and space administration.
  22. Grubbs, F.E (1969) Procedures for detecting outlying observations in samplesTechnometrics, 11, pp.1-21
  23. Barnett, V.L., T (1994) Outliers in statistical dataSchool of Mathematical & Physical Sciences, National Technical University of Athens, Greece.
  24. Das, P., & Haimes, Y.Y (1979) Multiobjective optimization in water quality and land managementWater Resources Research, 15, pp1313-1322
  25. Hair, J.F., & R. E (1998) Andersen, R.LMultivariate data analysisRentice Hall, Upper Saddle River, New Jersey,.
  26. Filzmoser, P., Garrett, R.G., & Reimann, C (2005) Multivariate outlier detection in exploration geochemistryComputers & geosciences, 31, pp.579-587