Understanding Space-time Patterns of Groundwater System by Empirical Orthogonal Functions: a Case Study in the Choshui River Alluvial Fan, Taiwan
Resource
Journal of Hydrology, In Press
Journal
Journal of Hydrology
Journal Volume
381
Journal Issue
3��4��
Pages
239-247
Date Issued
2009
Date
2009
Author(s)
Chu, Hone-Jay
Abstract
Natural or anthropogenic activities contribute to changes of groundwater levels in space and time. Understanding the major and significant driving forces to changes in space-time patterns of groundwater levels is essential to groundwater management. This study analyzes monthly observations of piezometric heads from 66 wells during 1997-2002 located in the Choshui River alluvial fan of Taiwan, where groundwater has been the important local water resource for myriads of agricultural or industrial demands. Following spatiotemporal estimations of piezometric heads by Bayesian Maximum Entropy method (BME), this work performs rotated empirical orthogonal function (REOF) analysis to decompose the obtained space-time heads into a set of spatially distributed empirical orthogonal functions (EOFs) and their associated uncorrelated time series. Results show that the leading EOFs represent the most significant driving forces to spatiotemporal changes of groundwater levels in the Choshui River aquifer. These include rainfall recharges from upstream Choshui and Pei-Kang River, pumping activities from aquaculture usages in the coastal areas, as well as water exchanges between surface and subsurface flow of Choshui River. In summary, this study shows the strength of the REOF analysis which can effectively provide integrative views of spatiotemporal changes of groundwater, gaining insights of interactions between the groundwater system and other natural and human activities. © 2009 Elsevier B.V. All rights reserved.
Subjects
Bayesian maximum entropy; Choshui River alluvial fan; Empirical orthogonal function; Groundwater; Space-time data analysis
SDGs
Other Subjects
Alluvial fans; Bayesian maximum entropies; Choshui River; Data analysis; Empirical Orthogonal Function; Agriculture; Aquaculture; Aquifers; Bayesian networks; Coastal zones; Data handling; Entropy; Fourier analysis; Hydrogeology; Landforms; Nanofluidics; Orthogonal functions; Rivers; Time series; Time series analysis; Water management; Groundwater resources; alluvial fan; Bayesian analysis; coastal zone; entropy; groundwater flow; human activity; resource management; spatiotemporal analysis; subsurface flow; time series; water exchange; Choshui River; Peikang River; Taiwan
Type
journal article
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