Analysis of Aquifer‐System Deformation in the Doñana Natural Space (Spain) Using Unsupervised Cloud‐Computed InSAR Data and Wavelet Analysis

Abstract

This work is focused on the Almonte‐Marismas aquifer located within Doñana Natural Space (SW Spain); this aquifer is threatened by droughts, irrigation‐driven groundwater overexploitation, urban use, and the potential reactivation of gas extraction and storage projects. We present ground deformation measurements from Sentinel‐1 Interferometric Synthetic Aperture Radar (InSAR) data ranging from 1‐2.5 cm, covering ∼2,500 km2 from 2014‐2020. Detecting spatially distributed ground deformation over this agricultural area is challenging due to the low‐coherence radar signals; consequently, the ground movement results are on the same order of magnitude as the errors associated with InSAR data. We have approached this issue by considering auxiliary information such as groundwater levels, climatic time series, and pumping rates and analyzing their spatio‐temporal connections to ground displacements. We correlate InSAR and hydrogeological information through wavelet analysis, developing a Python package that allows applying the same methodology in other similar study areas. For the first time in the Doñana area, a significant relationship between distances to pumping wells and the displacement extent is detected. Moreover, other subsidence‐related triggering factors are identified, such as the soil moisture balance, clay shrinking‐swelling processes, and creep of geological formations. These results are highly valuable to support aquifer management decisions in the Doñana Natural Space; in this border region, three groundwater bodies were officially declared overexploited in 2020. Our findings provide a ground motion baseline assessment to help differentiate historical variations from any future anthropogenic effect in this complex marsh land ecosystem.

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

“…However, when multiple error blocks are clustered in an interferogram due to medium-wavelength errors, the corrected results obtained after surface fitting are prone to spatial error aggregation, which leads to low precision in the deformation time series. In addition, with the development of cloud computing and cloud platforms for InSAR data processing (Lazeckỳ et al 2020;González-Jiménez et al 2023), additional publicly released interferogram products have become available, greatly reducing the computational volume and difficulty of InSAR data processing.…”