Skip to main content

Advertisement

SpringerLink
Log in

Modeling Short-Term Spatial and Temporal Variability of Groundwater Level Using Geostatistics and GIS

  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

Continuous depletion of groundwater levels from deliberate and uncontrolled exploitation of groundwater resources lead to the severe problems in arid and semi-arid hard-rock regions of the world. Geostatistics and geographic information system (GIS) have been proved as successful tools for efficient planning and management of the groundwater resources. The present study demonstrated applicability of geostatistics and GIS to understand spatial and temporal behavior of groundwater levels in a semi-arid hard-rock aquifer of Western India. Monthly groundwater levels of 50 sites in the study area for 36-month period (May 2006 to June 2009; excluding 3 months) were analyzed to find spatial autocorrelation and variances in the groundwater levels. Experimental variogram of the observed groundwater levels was computed at 750-m lag distance interval and the four most-widely used geostatistical models were fitted to the experimental variogram. The best-fit geostatistical model was selected by using two goodness-of-fit criteria, i.e., root mean square error (RMSE) and correlation coefficient (r). Then spatial maps of the groundwater levels were prepared through kriging technique by means of the best-fit geostatistical model. Results of two spatial statistics (Geary’s C and Moran’s I) indicated a strong positive autocorrelation in the groundwater levels within 3-km lag distance. It is emphasized that the spatial statistics are promising tools for geostatistical modeling, which help choose appropriate values of model parameters. Nugget-sill ratio (<0.25) revealed that the groundwater levels have strong spatial dependence in the area. The statistical indicators (RMSE and r) suggested that any of the three geostatistical models, i.e., spherical, circular, and exponential, can be selected as the best-fit model for reliable and accurate spatial interpolation. However, exponential model is used as the best-fit model in the present study. Selection of the exponential model as the best-fit was further supported by very high values of coefficient of determination (r 2 ranging from 0.927 to 0.994). Spatial distribution maps of groundwater levels indicated that the groundwater levels are strongly affected by surface topography and the presence of surface water bodies in the study area. Temporal pattern of the groundwater levels is mainly controlled by the rainy-season recharge and amount of groundwater extraction. Furthermore, it was found that the kriging technique is helpful in identifying critical locations over the study area where water saving and groundwater augmentation techniques need to be implemented to protect depleting groundwater resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

References

  • Ahmadi, S. H., & Sedghamiz, A. (2007). Geostatistical analysis of spatial and temporal variations of groundwater level. Environmental Monitoring and Assessment, 129, 277–294.

    Article  Google Scholar 

  • Ahmadi, S. H., & Sedghamiz, A. (2008). Application and evaluation of kriging and cokriging methods on groundwater depth mapping. Environmental Monitoring and Assessment, 138, 357–368.

    Article  Google Scholar 

  • Amarasinghe, U. A., Shah, T., Turral, H., & Anand, B. K. (2007). India’s water future to 2025-2050: Business-as-usual scenario and deviations. IWMI Research Report 123. Colombo, Sri Lanka: International Water Management Institute.

  • ASCE Task Committee. (1990). Review of geostatistics in geohydrology. I: Basic concepts. Journal of Hydraulic Engineering, ASCE, 116(5), 612–632.

    Article  Google Scholar 

  • Barnett, T. P., Adam, J. C., & Lettenmaier, D. P. (2005). Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438, 303–309. doi:10.1038/nature04141.

    Article  Google Scholar 

  • Bhuiyan, C., Singh, R. P., & Kogan, F. N. (2006). Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 8(4), 289–302.

    Article  Google Scholar 

  • Bruland, G. L., & Richardson, C. J. (2005). Spatial variability of soil properties in created, restored, and paired natural wetlands. Soil Science Society of America Journal, 69, 273–284.

    Google Scholar 

  • Cameron, K., & Hunter, P. (2002). Using spatial models and kriging techniques to optimize long-term ground-water monitoring networks: A case study. Environmetrics, 13, 629–656.

    Article  Google Scholar 

  • CGWB. (2006). Dynamic groundwater resources of India (as on March, 2004). New Delhi, India: Central Ground Water Board. Available at http://cgwb.gov.in/.

  • Chen, Y., Takara, K., Cluckie, I. D., & Smedt, F. H. D. (Eds.). (2004). GIS and remote sensing in hydrology, water resources and environment. IAHS Publication No. 289. Wallingford: IAHS Press.

  • Delhomme, J. P. (1978). Kriging in the hydrosciences. Advances in Water Resources, 1(5), 251–266.

    Article  Google Scholar 

  • Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115–145.

    Article  Google Scholar 

  • Goodchild, M. F., Parks, B. O., & Steyaert, L. T. (Eds.). (1993). Environmental modeling with GIS. New York: Oxford University Press.

    Google Scholar 

  • Goovaerts, P. (1997). Geostatistics for natural resources evaluation. New York: Oxford University Press.

    Google Scholar 

  • Hoque, M. A., Hoque, M. M., & Matin Ahmed, K. (2007). Declining groundwater level and aquifer dewatering in Dhaka metropolitan area, Bangladesh: Causes and quantification. Hydrogeology Journal, 15, 1523–1534. doi:10.1007/s10040-007-0226-5.

    Article  Google Scholar 

  • ILWS. (2001). Integrated Land and Water Information System, 3.2 academic, user’s guide (pp. 428–456). Enschede, The Netherlands: International Institute for Aerospace Survey and Earth Sciences (ITC).

    Google Scholar 

  • Isaaks, E., & Srivastava, R. M. (1989). An Introduction to applied geostatistics. New York: Oxford University Press.

    Google Scholar 

  • Journal, A. G., & Huijbregts, C. J. (1978). Mining geostatistics. London: Academic.

    Google Scholar 

  • Kitanidis, P. K. (1997). Introduction to geostatistics: Applications in hydrogeology. New York: Cambridge University Press.

    Book  Google Scholar 

  • Kitanidis, P. K. (1999). Geostatistics: Interpolation and inverse problems. In: J. W. Delleur (Editor-in-Chief), The handbook of groundwater engineering (pp. 12-1–12-20). Florida: CRC Press.

  • Kumar, R., Singh, R. D., & Sharma, K. D. (2005). Water resources of India. Current Science, 89, 794–811.

    Google Scholar 

  • Legendre, P., & Fortin, M.-J. (1989). Spatial pattern and ecological analysis. Vegetatio, 80, 107–138.

    Article  Google Scholar 

  • Liu, D., Wang, Z., Zhang, B., Song, K., Li, X., & Li, J. (2006). Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, northeast China. Agricultural Ecosystems and Environment, 113, 73–81.

    Article  Google Scholar 

  • Ma, T.-S., Sophocleous, M., & Yu, Y. S. (1999). Geostatistical applications in ground-water modeling in south-central Kansas. Journal of Hydrologic Engineering, ASCE, 4(1), 57–64.

    Article  Google Scholar 

  • Machiwal, D., & Jha, M. K. (2012). Hydrologic time series analysis: Theory and practice. New Delhi, India: Springer/Germany and Capital Publishing Company.

    Google Scholar 

  • Machiwal, D., Nimawat, J. V., & Samar, K. K. (2011). Evaluation of efficacy of groundwater level monitoring network by graphical and multivariate statistical techniques. Journal of Agricultural Engineering, ISAE, 48(3), 36–43.

    Google Scholar 

  • Mall, R. K., Gupta, A., Singh, R., Singh, R. S., & Rathore, L. S. (2006). Water resources and climatic change: An Indian perspective. Current Science, 90(12), 1610–1626.

    Google Scholar 

  • Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58, 1246–1266.

    Article  Google Scholar 

  • Moran, P. A. P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society: Series B, 37, 243–251.

    Google Scholar 

  • Prakash, M. R., & Singh, V. S. (2000). Network design for groundwater monitoring—A case study. Environmental Geology, 39, 628–632.

    Article  Google Scholar 

  • Pucci, A. A., & Murashige, J. A. E. (1987). Application of universal kriging to an aquifer study in New Jersey. Ground Water, 25, 672–678.

    Article  Google Scholar 

  • Samra, J. S. (2004). Review and analysis of drought monitoring, declaration and management in India. Working Paper 84. Colombo, Sri Lanka: International Water Management Institute (IWMI).

  • Shah, T., Molden, D., Sakthivadivel, R., & Seckler, D. (2000). The global groundwater situation: Overview of opportunities and challenges. Colombo, Sri Lanka: IWMI.

    Book  Google Scholar 

  • Singh, S. (2002). Water management in rural and urban areas. Udaipur, India: Agrotech Publishing Academy.

    Google Scholar 

  • Sophocleous, M. (2005). Groundwater recharge and sustainability in the high plains aquifer in Kansas, USA. Hydrogeology Journal, 13, 351–365.

    Article  Google Scholar 

  • Sophocleous, M., Paschetto, J. E., & Olea, A. (1982). Ground water network design for northwest Kansas, using the theory of regionalized variables. Ground Water, 20, 48–58.

    Article  Google Scholar 

  • Stafford, D. B. (Ed.). (1991). Civil engineering applications of remote sensing and geographic information systems. New York: ASCE.

    Google Scholar 

  • Theodossiou, N., & Latinopoulos, P. (2006). Evaluation and optimization of groundwater observation networks using the kriging methodology. Environmental Modelling and Software, 21, 991–1000.

    Article  Google Scholar 

  • Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.

    Google Scholar 

  • UNDP. (2002). Mission report on drought damage assessment and agricultural rehabilitation for drought affected districts of Rajasthan draft 2 (pp. 1–20). United Nations Development Programme (UNDP). http://www.undp.org.in/dmweb/RAJASTHAN%20DROUGHT.pdf. Accessed 30 March 2009.

  • Voudouris, K. S. (2006). Groundwater balance and safe yield of the coastal aquifer system in N, Eastern Korinthia, Greece. Applied Geography, 26(3–4), 31–291.

    Google Scholar 

  • Waller, L. A., & Gotway, C. A. (2004). Applied spatial statistics for public health data. New York: Wiley.

    Book  Google Scholar 

  • Wu, J., Norvell, W. A., Hopkins, D. G., Smith, D. B., Ulmer, M. G., & Welch, R. M. (2003). Improved prediction and mapping of soil copper by kriging with auxiliary data for cation-exchange capacity. Soil Science Society of America Journal, 67, 919–927.

    Article  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge All India Coordinated Research Project on Groundwater Utilization, College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur, India for providing necessary groundwater-level data for the present study. They are also very thankful to two anonymous reviewers for providing constructive comments and suggestions, which greatly helped in improving the quality of earlier version of this article.

Author information

Authors and Affiliations

  1. Central Arid Zone Research Institute, Regional Research Station, Bhuj, 370105, India

    Deepesh Machiwal

  2. SWE Department, College of Technology and Engineering, MPUAT, Udaipur, 313001, India

    Amit Mishra, Arun Sharma & S. S. Sisodia

  3. AgFE Department, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Madan K. Jha

Authors
  1. Deepesh Machiwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amit Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Madan K. Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arun Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. S. Sisodia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deepesh Machiwal.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Machiwal, D., Mishra, A., Jha, M.K. et al. Modeling Short-Term Spatial and Temporal Variability of Groundwater Level Using Geostatistics and GIS. Nat Resour Res 21, 117–136 (2012). https://doi.org/10.1007/s11053-011-9167-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-011-9167-8

Keywords

Search

Navigation