## 2 Methodology
### InSAR Data and Method  
We use radar imagery acquired by the Copernicus Program Sentinel-1A and 1B (track 14) synthetic aperture radar (SAR) satellites developed and operated by the European Space Agency. We consider InSAR images at both regional and local scales. We start with a regional scale (100 s of km) to detect short wavelength anomalies that correspond to local ongoing deformation and consider local scales (10 s of km) to maximize spatial and temporal resolution for local regions of interest. All our InSAR processing relies on the open-source InSAR Scientific Computing Environment (ISCE; [35]) software. At the regional scale, we use the so-called \"standard product\" produced by the Advanced Rapid Imaging and Analysis (ARIA) system [36]. The ARIA system uses algorithms from ISCE to provide an operational archive of standard Sentinel-1 Geocoded UNWrapped interferogram (GUNW) products, enabling rapid ingestion into post-processing algorithms. These InSAR-derived surface displacement products are reduced from the original resolution down to a 90 m spatial resolution to increase the signal-to-noise ratio. Interferometric pairs are generated between the nearest two acquisitions, leading to temporal baselines of 12 and 24 days. We also produce additional annual interferogram pairs spanning October and May of each year. We use the ARIA-tools open-source package [37] to download and stitch 5102 GUNW products into 252 continuous interferograms from 133 acquisitions spanning the nearly 5-and-a-half-year period between February 2016 and July 2021. The region covered extends from 20N to 28N and 37E to 41E. All our time series are referenced to the date 5 February 2018. We consider the root-mean-square (RMS) residual phase as a measure for our data quality (Figure S1). The original number of acquisitions is 183, where we removed 50 acquisitions using a 24 mm residual threshold. We evaluate the quality of our data by calculating the history of interferometric coherence (Figure S2, left panel). The average coherence is high, with values generally above 0.85. We developed a redundant network for western Arabia (Figure S2, right panel) with each acquisition connected to an average of three other acquisitions. We applied four main corrections to the InSAR data:  
1. Model-based removal of the deformation signal caused by the solid earth tides (SET) [38; 39] (Figure S3). There are both significant spatial and temporal variations in the impacts of SET. Model-based removal of radar propagation effects associated with temporal variability in the quasi-stratified component of the troposphere [40]. * Estimation and removal of error associated with the inaccurate digital elevation models (DEMs), as the sensitivity to this error is proportional to the perpendicular component of the interferometric spatial baseline (so-called \(B_{ppp}\)) and is easily estimated in the time series analysis [41]. * Empirical removal of residual long wavelength (100-km-scale) apparent deformation signals [42]. This removal is recommended for short spatial wavelength deformation signals, which estimate and then remove linear ramps from the displacement time-series at each acquisition on the reliable pixels. In contrast, for regional tectonic deformation signals, this approach is not recommended. We use the ARIA tools package suite to prepare interferograms for time series analysis in the Miami INsar Time series software in Python language (MintPy) [42]. This workflow has been successfully demonstrated for mapping ground deformation in subsidence and tectonic active regions along the Eastern US [37], US west coast [43], and the Afar region of Ethiopia [44]. We also generate a higher-resolution InSAR dataset, with ~16 m spatial resolution, to further investigate the local deformation around the Al-Yutamah Valley identified from the ARIA products. We use Sentinel-1 ascending track 14 data acquired in Interferometric Wide (IW) mode from 6 October 2014 to 19 July 2021 (202 acquisitions in total).