A Framework for Estimating Time-Independent Elastic Skeletal Storage by Combining InSAR and Hydraulic Head Data

In this study, we propose a framework that combines InSAR and hydraulic head data to estimate the aquifer parameter, elastic skeletal storage (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S_ke</i>). This framework introduces an indicator to quantify the time-independence of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S_ke</i> and comprehensively incorporates three major seasonal signal extraction methods: multichannel singular spectrum analysis (M-SSA), continuous wavelet transform (CWT), and independent component analysis (ICA). We reveal that no single method is universally applicable for seasonal signal extraction at observation wells with varying hydrological properties and aliasing signals to ensure the time-independence of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S_ke</i>. The proposed framework is capable of estimating most time-independent and least bias-prone <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S_ke</i>, addressing the limitation of previous studies that relied on single seasonal signal extraction method while neglecting the time-independence of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S_ke</i> We apply this framework to a study area in the North China Plain (NCP), using time-series Interferometric Synthetic Aperture Radar (TS-InSAR) to survey land deformation from June 2015 to December 2017. The study area is dominated by four major subsidence bowls, with a maximum cumulative subsidence of approximately 400 mm. We then estimate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S_ke</i> with the joint constrains of seasonal InSAR deformation and hydraulic head. The estimated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S_ke</i> range from 3.10×10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> to 16.94×10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and exhibit spatial heterogeneity. Additionally, we quantify the total groundwater storage (TGWS), recoverable groundwater storage (RGWS), and irreversible groundwater storage (IGWS). The TGWS depletions in the major subsidence bowls reach to 1.82×10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>, and the IGWS depletions accounting for 1.25×10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>, and these bowls exhibit varying degrees of unsustainable groundwater exploitation. We argue that the proposed framework can enhance the accuracy of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S_ke</i> estimation and transferable to other regions facing groundwater crises to support efforts toward sustainable groundwater management.