Development of a groundwater-drawdown function to estimate spatially
varying land subsidence: a case study of the Choshui River basin, Taiwan
Abstract
Land subsidence caused by groundwater overexploitation is a critical
global problem. Spatial distribution of land subsidence is crucial for
environmental management and land planning due to the sensitive nature
of land-surface gradients. Given the nonlinear relationship of
subsidence and long-term drawdown from groundwater exploitation, and the
heterogeneity of the aquifer in the Choshui River alluvial fan, a
developed spatial regression model can effectively estimate nonlinear
and spatially varying subsidence. That is, the root-mean-square-errors
(RMSEs) of annual subsidences are less or equal to 0.8 cm. Considering
various data inputs in the Choshui River alluvial fan, the spatial
regression model offers a robust method for estimating the spatial
patterns of subsidence using drawdown as observations. Results show that
the largest water-level cone of depression occurs in the distal fan
area. Nonetheless, the calculated subsidence bowl closely approximates
the observed one located much farther inland. Without requiring
extensive calibration or an elaborate numerical groundwater flow and
subsidence model, the model provides reasonable and detailed patterns
using a spatially varying relationship between drawdown and resulting
land subsidence. Results indicate that the spatial regression model
reasonably estimates the spatial distribution of the skeletal storage
coefficient in the aquifer system. The large coefficient that represents
inelastic compaction occurs in the inland areas, whereas the small
coefficient that represents elastic compaction occurs along the coastal
area. Furthermore, this model is relevant for water policy or land
subsidence regulation.