TY - JOUR
T1 - Statistical analysis relating variations in groundwater level to droughts on Jeju Island, Korea
AU - Jung, Hyejung
AU - Ha, Kyoochul
AU - Koh, Dong Chan
AU - Kim, Yongcheol
AU - Lee, Jeonghoon
N1 - Funding Information:
We are grateful to Bong-Rae Kang at the Jeju Research Institute for providing the groundwater usage data. We also express our gratitude to the editor and the two anonymous reviewers, whose inputs significantly improved the quality of the paper. This work was supported by the Basic Research Project ( GP2020-012 ) of the Korea Institute of Geoscience and Mineral Resources (KIGAM), funded by the Ministry of Science and ICT of Korea . This work was partially supported by two research grants, the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( 2021R1A6A3A13043968 ), and the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20201510100020 ).
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/8
Y1 - 2021/8
N2 - Study region: A volcanic island (Jeju Island, South Korea). Study focus: To predict and prepare for a drought, the variables that affect groundwater-level variations are better understood through groundwater monitoring networks. In this study, we analyzed various observational data (including precipitation, evapotranspiration, groundwater usage, tidal levels, and groundwater levels) to investigate the effects of historic droughts on groundwater level in a volcanic island in 2017 by using principal component analysis (PCA) and correlation analysis. New hydrological insights for the region: Based on the results of these analyses, we can select index wells that are vulnerable to droughts. This is demonstrated by the decrease in total water input caused by the low amount of precipitation and the increased groundwater usage to compensate for this water shortage. This study indicated that PCA could be a powerful tool for summarizing large datasets to select index wells that are vulnerable to droughts, which would significantly reduce the expense of monitoring programs. In addition, we investigated whether the observed variables changed with the temporal resolution of the monitoring. When the temporal resolution changed from monthly to daily and hourly groundwater-level data, the main variables that affected the groundwater-level variations at each temporal resolution were different. Therefore, it is helpful to comprehensively develop a groundwater management plan using statistical approaches with proper temporal monitoring resolution during drought.
AB - Study region: A volcanic island (Jeju Island, South Korea). Study focus: To predict and prepare for a drought, the variables that affect groundwater-level variations are better understood through groundwater monitoring networks. In this study, we analyzed various observational data (including precipitation, evapotranspiration, groundwater usage, tidal levels, and groundwater levels) to investigate the effects of historic droughts on groundwater level in a volcanic island in 2017 by using principal component analysis (PCA) and correlation analysis. New hydrological insights for the region: Based on the results of these analyses, we can select index wells that are vulnerable to droughts. This is demonstrated by the decrease in total water input caused by the low amount of precipitation and the increased groundwater usage to compensate for this water shortage. This study indicated that PCA could be a powerful tool for summarizing large datasets to select index wells that are vulnerable to droughts, which would significantly reduce the expense of monitoring programs. In addition, we investigated whether the observed variables changed with the temporal resolution of the monitoring. When the temporal resolution changed from monthly to daily and hourly groundwater-level data, the main variables that affected the groundwater-level variations at each temporal resolution were different. Therefore, it is helpful to comprehensively develop a groundwater management plan using statistical approaches with proper temporal monitoring resolution during drought.
KW - Drought
KW - Groundwater level
KW - Multivariate statistical method
KW - Principal component analysis
KW - Temporal groundwater monitoring
UR - http://www.scopus.com/inward/record.url?scp=85111313977&partnerID=8YFLogxK
U2 - 10.1016/j.ejrh.2021.100879
DO - 10.1016/j.ejrh.2021.100879
M3 - Article
AN - SCOPUS:85111313977
SN - 2214-5818
VL - 36
JO - Journal of Hydrology: Regional Studies
JF - Journal of Hydrology: Regional Studies
M1 - 100879
ER -