Nineteen percent of the global population may face a high probability of subsidence

Please cite this article as: Martín, P.E., Herrera, G., Sacristán, M.M., Tomás, R., Béjar-Pizarro, M., Marín, R.M., A quasi-elastic aquifer deformational behavior: Madrid aquifer case study, Journal of Hydrology (2014), doi: http:// dx.doi.org/10. 1016/j.jhydrol.2014.08.040 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The use of InSAR (Interferometric Synthetic Aperture Radar) products has greatly increased in the last years because of the technological advances in terms of both acquisition sensors and processing algorithms. The development of multi-interferogram techniques and the availability of free SAR analysis tools has significantly increased the number of worldwide applications of satellite measurements for mapping and monitoring geohazards. InSAR techniques excel in determining ground deformation in urban areas, where the coherence of the radar images is high, and the obtainable results are particularly reliable. Thus, measuring urban subsidence has always been one of the main targets of the InSAR analysis. In this paper, we present a brief review on the applications, in the last decades, of both single and multi-interferogram techniques to monitor ground lowering in urban areas along the Italian Peninsula. Because of its geological context, Italy is prone to slow natural subsidence phenomena sometimes aggravated and accelerated, especially along the coasts and in urbanized areas, by anthropogenic factors (i.e., groundwater overexploitation, consolidation in recent urban expansion, geothermal activities). The review will show how the interferometric data allowed the scientific community to increase the knowledge of the phenomena, map their spatial distribution, and reconstruct their temporal evolution. The final goal of the review is to demonstrate the added value of InSAR data in supporting groundwater management and urban development in Italy.

Landslides are widespread natural hazards that generate considerable damage and economic losses worldwide. Detecting terrain movements caused by these phenomena and characterizing affected urban areas is critical to reduce their impact. Here we present a fast and simple methodology to create maps of vulnerable buildings affected by slow-moving landslides, based on two parameters: (1) the deformation rate associated to each building, measured from Sentinel-1 SAR data, and (2) the building damage generated by the landslide movement and recorded during a field campaign. We apply this method to Arcos de la Frontera, a monumental town in South Spain affected by a slow-moving landslide that has caused severe damage to buildings, forcing the evacuation of some of them. Our results show that maximum deformation rates of 4 cm/year in the line-of-sight (LOS) of the satellite, affects La Verbena, a newly-developed area, and displacements are mostly horizontal, as expected for a planar-landslide. Our building damage assessment reveals that most of the building blocks in La Verbena present moderate to severe damages. According to our vulnerability scale, 93% of the building blocks analysed present high vulnerability and, thus, should be the focus of more in-depth local studies to evaluate the serviceability of buildings, prior to adopting the necessary mitigation measures to reduce or cope with the negative consequences of this landslide. This methodology can be applied to slow-moving landslides worldwide thanks to the global availability of Sentinel-1 SAR data.

Groundwater resources are under stress in many regions of the world and the future water supply for many populations, particularly in the driest places on Earth, is threatened. Future climatic conditions and population growth are expected to intensify the problem. Understanding the factors that control groundwater storage variation is crucial to mitigate its adverse consequences. In this work, we apply satellite-based measurements of ground deformation over the Tertiary detritic aquifer of Madrid (TDAM), Central Spain, to infer the spatiotemporal evolution of water levels and estimate groundwater storage variations. Specifically, we use Persistent Scatterer Interferometry (PSI) data during the period 1992-2010 and piezometric time series on 19 well sites covering the period 1997-2010 to build groundwater level maps and quantify groundwater storage variations. Our results reveal that groundwater storage loss occurred in two different periods, 1992-1999 and 2005-2010 and was mainly concentrated in a region of ~200 km 2. The presence of more compressible materials in that region combined with a long continuous water extraction can explain this volumetric deficit. This study illustrates how the combination of PSI and piezometric data can be used to detect small aquifers affected by groundwater storage loss helping to improve their sustainable management.

In the current context of climate change, improving groundwater monitoring and management is an important issue for human communities in arid environments. The exploitation of groundwater resources can trigger land subsidence producing damage in urban structures and infrastructures. Alto Guadalentín aquifer system in SE Spain has been exploited since 1960 producing an average piezometric level drop of 150 m. This work presents a groundwater model that reproduces groundwater evolution during 52 years with an average error below 10%. The geometry of the model was improved introducing a layer of less permeable and deformable soft soils derived from InSAR deformation and borehole data. The resulting aquifer system history of the piezometric level has been compared with ENVISAT deformation data to calculate a first-order relationship between groundwater changes, soft soil thickness, and surface deformation. This relationship has been validated with the displacement data from ERS and CosmoSkyMed satellites. The resulting regression function is then used as an empirical subsidence model to estimate a first approximation of the deformation of the aquifer system since the beginning of the groundwater extraction, reaching 1 to 5.5 m in 52 years. These rough estimations highlight the limitations of the proposed empirical model, requiring the implementation of a coupled hydrogeomechanical model.

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E–W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources.

Slope failures occur in open-pit mining areas worldwide producing considerable damage and economic losses. Identifying the triggering factors and detecting unstable slopes and precursory displacements, which can be achieved by exploiting remote sensing data, is critical to reduce their impact. Here we present a methodology that