Spatial-temporal pattern recognition of groundwater head variations for recharge zone identification

Jui Pin Tsai, Liang-Jeng Chang, Ping Yu Chang, Yuan Chien Lin, You Cheng Chen, Meng Ting Wu, Hwa Lung Yu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The delineation of groundwater recharge zones is crucial for the conservation of groundwater quality and quantity. To objectively estimate groundwater recharge zones, many field surveys are required that are costly in both time and money. To facilitate the assessment of recharge zones with high efficiency and low expense, this study proposes a ‘fast-filter’ approach based on empirical orthogonal function analysis and applies it in a synthetic case study and to Taiwan's Yilan Plain. In the synthetic case study, we demonstrate that the proposed method can effectively identify the recharge area by considering the head variations driven by rainfall recharge. For the case of Yilan Plain application, the field investigations (i.e., collected wellbore logs and electrical resistivity tomography [ERT] surveys) and a groundwater simulation model support the recharge zones estimated by the proposed method. The study results show that within the estimated recharge zone, all of the collected wellbore logs consist of coarse grains, and thick and continuous high resistivity zones were shown in the ERT profile images. Moreover, the groundwater model indicates that the recharge within the estimated recharge zone is 57.6% of the total recharge despite that the area of the estimated zone is only 26.8% of the study area. Therefore, the proposed method is shown to delineate recharge zones at low cost.

Original languageEnglish
Pages (from-to)351-362
Number of pages12
JournalJournal of Hydrology
StatePublished - 1 Jun 2017


  • Aquifer type
  • Electrical resistivity tomography
  • Empirical orthogonal function analysis
  • High recharge areas
  • Spatiotemporal analysis


Dive into the research topics of 'Spatial-temporal pattern recognition of groundwater head variations for recharge zone identification'. Together they form a unique fingerprint.

Cite this