Tracking hidden crisis in India's capital from space: implications of unsustainable groundwater use.

Funder: Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum - GFZNational Capital Region (NCR, Delhi) in India is one of the fastest-growing metropolitan cities which is facing a severe water crisis due to increasing water demand. The over-extraction of groundwater, particularly from its unconsolidated alluvial deposits makes the region prone to subsidence. In this study, we investigated the effects of plummeting groundwater levels on land surface elevations in Delhi NCR using Sentinel-1 datasets acquired during the years 2014-2020. Our analysis reveals two distinct subsidence features in the study area with rates exceeding 11 cm/year in Kapashera-an urban village near IGI airport Delhi, and 3 cm/year in Faridabad throughout the study period. The subsidence in these two areas are accelerating and follows the depleting groundwater trend. The third region, Dwarka shows a shift from subsidence to uplift during the years which can be attributed to the strict government policies to regulate groundwater use and incentivizing rainwater harvesting. Further analysis using a classified risk map based on hazard risk and vulnerability approach highlights an approximate area of 100 square kilometers to be subjected to the highest risk level of ground movement, demanding urgent attention. The findings of this study are highly relevant for government agencies to formulate new policies against the over-exploitation of groundwater and to facilitate a sustainable and resilient groundwater management system in Delhi NCR

Railways’ Stability Observation by Satellite Radar Images

Remote sensing has many vital civilian applications. Space-borne Interferometric Synthetic Aperture Radar has been used to measure the Earth’s surface deformation widely. In particular, Persistent Scatterer Interferometry (PSI) is designed to estimate the temporal characteristics of the Earth’s deformation rates from multiple InSAR images acquired over time. This chapter reviews the space-borne Differential Interferometric Synthetic Aperture Radar techniques that have shown their capabilities in monitoring of railways displacements. After description of the current state of the art and potentials of the available radar remote sensing techniques, one case study is examined, pertaining to a railway bridge in the Campania region, Italy

Imaging multi-age construction settlement behaviour by advanced SAR interferometry

This paper focuses on the application of Advanced Satellite Synthetic Aperture Radar Interferometry (A-DInSAR) to subsidence-related issues, with particular reference to ground settlements due to external loads. Beyond the stratigraphic setting and the geotechnical properties of the subsoil, other relevant boundary conditions strongly influence the reliability of remotely sensed data for quantitative analyses and risk mitigation purposes. Because most of the Persistent Scatterer Interferometry (PSI) measurement points (Persistent Scatterers, PSs) lie on structures and infrastructures, the foundation type and the age of a construction are key factors for a proper interpretation of the time series of ground displacements. To exemplify a methodological approach to evaluate these issues, this paper refers to an analysis carried out in the coastal/deltaic plain west of Rome (Rome and Fiumicino municipalities) affected by subsidence and related damages to structures. This region is characterized by a complex geological setting (alternation of recent deposits with low and high compressibilities) and has been subjected to different urbanisation phases starting in the late 1800s, with a strong acceleration in the last few decades. The results of A-DInSAR analyses conducted from 1992 to 2015 have been interpreted in light of high-resolution geological/geotechnical models, the age of the construction, and the types of foundations of the buildings on which the PSs are located. Collection, interpretation, and processing of geo-thematic data were fundamental to obtain high-resolution models; change detection analyses of the land cover allowed us to classify structures/infrastructures in terms of the construction period. Additional information was collected to define the types of foundations, i.e., shallow versus deep foundations. As a result, we found that only by filtering and partitioning the A-DInSAR datasets on the basis of the above-mentioned boundary conditions can the related time series be considered a proxy of the consolidation process governing the subsidence related to external loads as confirmed by a comparison with results from a physically based back analysis based on Terzaghi's theory. Therefore, if properly managed, the A-DInSAR data represents a powerful tool for capturing the evolutionary stage of the process for a single building and has potential for forecasting the behaviour of the terrain-foundation-structure combination

A Hybrid Clustering-Fusion Methodology for Land Subsidence Estimation

A hybrid clustering-fusion methodology is developed in this study that employs Genetic Algorithm (GA) optimization method, k-means method, and several soft computing (SC) models to better estimate land subsidence. Estimation of land subsidence is important in planning and management of groundwater resources to prevent associated catastrophic damages. Methods such as the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) can be used to estimate the subsidence rate, but PS-InSAR does not offer the required efficiency and accuracy in noisy pixels (obtained from remote sensing). Alternatively, a fusion-based methodology can be used to estimate subsidence rate, which offers a superior accuracy as opposed to the traditionally used methods. In the proposed methodology, five SC methods are employed with hydrogeological forcing of frequency and thickness of fine-grained sediments, groundwater depth, water level decline, transmissivity and storage coefficient, and output of land subsidence rate. Results of individual SC models are then fused to render more accurate land subsidence rate in noisy pixels, for which PS-InSAR cannot be effective. We first extract 14,392 different input-output patterns from PS-InSAR technique for our study area in Tehran province, Iran. Then, k-means method is used to divide the study area to homogenous zones with similar features. The five SC models include Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP) neural network and two optimized models, namely, Radial Basis Function (RBF) and Generalized Regression Neural Network (GRNN). To fuse individual SC models, three methods including Genetic Algorithm (GA), K-Nearest Neighbors (KNN) and Ordered Weighted Average (OWA) based on ORNESS method and ORLIKE method, are developed and evaluated. Results show that the fusion-based method is significantly superior to each of the employed individual methods in predicting land subsidence rate

Deformational behaviours of alluvial units detected by advanced radar interferometry in the Vega Media of the Segura River, southeast Spain

It is widely known that differential land subsidence in a valley significantly controls its fluvial dynamics. Nevertheless, major uncertainty exists about the way in which alluvial forms respond to this process. In this study, morphological and lithostratigraphic data have been combined with advanced differential interferometry (A-DInSAR) to detect changes in alluvial landform elevations and to verify the existence of a differential subsidence pattern influenced by active sedimentary dynamics. For this purpose, the middle reach of the Segura River valley (Vega Media of the Segura River), in southeast Spain, was chosen as the study area. The Vega Media of the Segura River is an alluvial area affected by subsidence processes in close conjunction with depositional conditions, ground-water withdrawals and faults. A high scale mapping of the main younger sedimentary units was carried out by combining multi-temporal aerial photographs, high-resolution digital elevation models derived from LIDAR data, global navigation satellite system data and fieldwork. In addition, lithostratigraphic descriptions were obtained from geotechnical drilling and trenching. Finally, ground surface displacements, measured using A-DInSAR for the periods 1995–2005 and 2004–2008, allowed the determination of elevation rates and ground deformation associated with the different alluvial units. The results from this analysis revealed four typical deformational behaviours: non-deformational units (cemented alluvial fans and upper fluvial terraces); slightly deformable units (lower terraces and old abandoned meanders); moderately deformable units (lateral accretion zones and abandoned meanders before channelisation in 1981); and highly deformable areas (recently active meanders associated with artificial cutoffs by channelisation, non-active floodplains and spilling zones).This work has been supported by project 15224/PI/10 (Dynamics and recent morphological adjustments in the Lower Segura River, Middle Valley) from the Fundación SENECA of the Regional Agency of Science, Murcia, Spain, and the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER, under Projects TEC2011-28201-C02-02, TIN2014-55413-C2-2-P, ESP2013-47780-C2-2-R and PRX14/00100. The European Space Agency’s (ESA) Terrafirma project has provided all the SAR data processed with the SPN technique and the processing itself was funded by this project