Recharge signal identification based on groundwater level observations
- 392 Downloads
This study applied a method of the rotated empirical orthogonal functions to directly decompose the space–time groundwater level variations and determine the potential recharge zones by investigating the correlation between the identified groundwater signals and the observed local rainfall records. The approach is used to analyze the spatiotemporal process of piezometric heads estimated by Bayesian maximum entropy method from monthly observations of 45 wells in 1999–2007 located in the Pingtung Plain of Taiwan. From the results, the primary potential recharge area is located at the proximal fan areas where the recharge process accounts for 88% of the spatiotemporal variations of piezometric heads in the study area. The decomposition of groundwater levels associated with rainfall can provide information on the recharge process since rainfall is an important contributor to groundwater recharge in semi-arid regions. Correlation analysis shows that the identified recharge closely associates with the temporal variation of the local precipitation with a delay of 1–2 months in the study area.
KeywordsBayesian maximum entropy Empirical orthogonal functions Potential area recharge Piezometric head Rainfall
This research is supported by a grant from the National Science Council of Taiwan (NSC100-2628-E-002-005-) and National Cheng Kung University. We thank the Water Resources Agency, Taiwan for providing the monitoring data.
- Christakos, G. (2000). Modern spatiotemporal geostatistics (p. 304). New York: Oxford University Press.Google Scholar
- Christakos, G. (2009). Epistematics: An evolutionary framework of real world problem-solving. New York: Springer.Google Scholar
- Christakos, G., & Hristopulos, D. T. (1998). Spatiotemporal environmental health modelling: A tractatus stochasticus (p. xviii). Boston: Kluwer. 400 pp.Google Scholar
- Christakos, G., Bogaert, P., & Serre, M. L. (2002). Temporal GIS: Advanced functions for field-based applications (p. 220). New York: Springer.Google Scholar
- Christakos, G., Olea, R. A., Serre, M. L., Yu, H.-L., & Wang, L. (2005). Interdisciplinary public health reasoning and epidemic modelling: The case of Black Death. New York: Springer.Google Scholar
- Freeze, R. A., & Cherry, J. A. (1979). Groundwater, xvi, 604 pp. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
- Gehrels, H., Peters, N. E., Hoehn, E., Jensen, K., Leibundgut, C., Griffioen, J., Webb, B., & Zaadnoordijk, W. J. (Eds.) (2001). Impact of human activity on groundwater dynamics. IAHS Publication no. 269.Google Scholar
- Mondal, N. C., & Singh, V. S. (2004). A new approach to delineate the groundwater recharge zone in hard rock terrain. Current Science, 87, 658–662.Google Scholar
- Yeh, M. S., Lin, Y. P., & Chang, L. C. (2006). Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms. Environmental Geology, 50, 101–121.Google Scholar
- Yeh, H. F., Lee, C. H., Hsu, K. C., & Chang, P. H. (2009). GIS for the assessment of the groundwater recharge potential zone. Environmental Geology, 58, 185–195.Google Scholar