[A-HW25] Near Surface Investigation and Modeling for Groundwater Resources Assessment and Conservation

[AHW25-P04] Estimation of basin-scale heterogeneous fields based on signal analysis and river stage tomography

*Bo-Tsen Wang¹、Chia-Hao Chang¹、Jui-Pin Tsai¹ (1.Department of Bioenvironmental Systems Engineering, National Taiwan University)

キーワード：River stage tomography, Parameters estimation, Hydraulic diffusivity (D), Signal decomposition

River stage tomography (RST) is a potential method for delineating spatial heterogeneity in regional aquifers by utilizing groundwater head variations and their corresponding river stage fluctuations. However, groundwater head data often reflect mixed signals from external stimuli, such as rainfall and river stage, leading to potentially unreasonable parameter field estimates. To address this issue, we propose a novel systematic method to integrate empirical mode decomposition method (EMD), dynamically dimensional search algorithm (DDS), and RST. This novel method is evaluated through synthetic and real case studies. Synthetic case results indicate that the proposed method accurately reconstructs river-induced head variations from groundwater data containing mixed rainfall and river features. The estimated hydraulic diffusivity (D) field based on the reconstructed river-induced head variations closely matches the sample diffusivity (D) field. In the real case, this study used the groundwater level data, rainfall data, and river stage data in the Zhuoshui River alluvial fan in 2006. The hydraulic diffusivity (D) values of five observation wells were used as the reference for parameter estimation. The results show that the Difference of the D value is 0.036 (m2/s). The other three observation wells were selected for validation purposes, and the derived difference is 0.11 (m2/s). The estimated diffusivity values align well with reference values at sample points in the calibration and validation process. These findings demonstrate that the proposed method successfully extracts and reconstructs river-induced head variations from original head observations and accurately delineates regional aquifer features. This method shows the significant potential for enhancing RST studies by offering a robust approach for mixed-feature signal decomposition and reconstruction.