Advanced interpretation of subsidence in Murcia (SE Spain) using A-DInSAR data – modelling and validation

Subsidence is a natural hazard that affects wide areas in the world causing important economic costs annually. This phenomenon has occurred in the metropolitan area of Murcia City (SE Spain) as a result of groundwater overexploitation. In this work aquifer system subsidence is investigated using an advanced differential SAR interferometry remote sensing technique (A-DInSAR) called Stable Point Network (SPN). The SPN derived displacement results, mainly the velocity displacement maps and the time series of the displacement, reveal that in the period 2004–2008 the rate of subsidence in Murcia metropolitan area doubled with respect to the previous period from 1995 to 2005. The acceleration of the deformation phenomenon is explained by the drought period started in 2006. The comparison of the temporal evolution of the displacements measured with the extensometers and the SPN technique shows an average absolute error of 3.9±3.8 mm. Finally, results from a finite element model developed to simulate the recorded time history subsidence from known water table height changes compares well with the SPN displacement time series estimations. This result demonstrates the potential of A-DInSAR techniques to validate subsidence prediction models as an alternative to using instrumental ground based techniques for validation.

Subsidence activity maps derived from DInSAR data: Orihuela case study

A new methodology is proposed to produce subsidence activity maps based on the geostatistical analysis of persistent scatterer interferometry (PSI) data. PSI displacement measurements are interpolated based on conditional Sequential Gaussian Simulation (SGS) to calculate multiple equiprobable realizations of subsidence. The result from this process is a series of interpolated subsidence values, with an estimation of the spatial variability and a confidence level on the interpolation. These maps complement the PSI displacement map, improving the identification of wide subsiding areas at a regional scale. At a local scale, they can be used to identify buildings susceptible to suffer subsidence related damages. In order to do so, it is necessary to calculate the maximum differential settlement and the maximum angular distortion for each building of the study area. Based on PSI-derived parameters those buildings in which the serviceability limit state has been exceeded, and where in situ forensic analysis should be made, can be automatically identified. This methodology has been tested in the city of Orihuela (SE Spain) for the study of historical buildings damaged during the last two decades by subsidence due to aquifer overexploitation. The qualitative evaluation of the results from the methodology carried out in buildings where damages have been reported shows a success rate of 100%.

Twenty-year advanced DInSAR analysis of severe land subsidence: The Alto Guadalentín Basin (Spain) case study

A twenty-year period of severe land subsidence evolution in the Alto Guadalentín Basin (southeast Spain) is monitored using multi-sensor SAR images, processed by advanced differential interferometric synthetic aperture radar (DInSAR) techniques. The SAR images used in this study consist of four datasets acquired by ERS-1/2, ENVISAT, ALOS and COSMO-SkyMed satellites between 1992 and 2012. The integration of ground surface displacement maps retrieved for different time periods allows us to quantify up to 2.50 m of cumulated displacements that occurred between 1992 and 2012 in the Alto Guadalentín Basin. DInSAR results were locally compared with global positioning system (GPS) data available for two continuous stations located in the study area, demonstrating the high consistency of local vertical motion measurements between the two different surveying techniques. An average absolute error of 4.6 ± 4 mm for the ALOS data and of 4.8 ± 3.5 mm for the COSMO-SkyMed data confirmed the reliability of the analysis. The spatial analysis of DInSAR ground surface displacement reveals a direct correlation with the thickness of the compressible alluvial deposits. Detected ground subsidence in the past 20 years is most likely a consequence of a 100–200 m groundwater level drop that has persisted since the 1970s due to the overexploitation of the Alto Guadalentín aquifer system. The negative gradient of the pore pressure is responsible for the extremely slow consolidation of a very thick (> 100 m) layer of fine-grained silt and clay layers with low vertical hydraulic permeability (approximately 50 mm/h) wherein the maximum settlement has still not been reached.

Fine-scale estimations of bioclimatic change in the Valencia region, Spain

Recent advances in statistical downscaling have allowed the reconstruction of temperatures for the complete 1948–2011 period in a spatial resolution of 90 m and without gaps for the Valencian Community (Spain) and bordering areas. It presently enables analyses in this region, which allows the determination of recent temperature changes at subregional and local scales. The present work focuses on obtaining the thermicity index according to Rivas-Martínez, a well-known indicator of different thermotypes associated with bioclimatic horizons. The change in this index, which has happened in the region between 1948 and 2011, was calculated by generating fine-scale maps of the potential extension of different thermotypes. The results show a greater regression for the thermotypes in a finicolous position, e.g. Orotemperate, Supratemperate and Supramediterranean horizons, which herein indicate greater potential vulnerability in climate change. In the absence of, and given the need for, such fine-scale information, this work should be useful for specialized researchers to spatially limit the potentially most vulnerable biotopes to climate change.

Model Probability in Self-organising Maps

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.