Alfonso Rivera

Assessing Groundwater Depletion and Dynamics Using GRACE and InSAR: Potential and Limitations

In the last decade, remote sensing of the temporal variation of ground level and gravity has improved our understanding of groundwater dynamics and storage. Mass changes are measured by GRACE (Gravity Recovery and Climate Experiment) satellites, whereas ground deformation is measured by processing synthetic aperture radar satellites data using the InSAR (Interferometry of Synthetic Aperture Radar) techniques. Both methods are complementary and offer different sensitivities to aquifer system processes. GRACE is sensitive to mass changes over large spatial scales (more than 100,000 km2 ). As such, it fails in providing groundwater storage change estimates at local or regional scales relevant to most aquifer systems, and at which most groundwater management schemes are applied. However, InSAR measures ground displacement due to aquifer response to fluid-pressure changes. InSAR applications to groundwater depletion assessments are limited to aquifer systems susceptible to measurable deformation. Furthermore, the inversion of InSAR-derived displacement maps into volume of depleted groundwater storage (both reversible and largely irreversible) is confounded by vertical and horizontal variability of sediment compressibility. During the last decade, both techniques have shown increasing interest in the scientific community to complement available in situ observations where they are insufficient. In this review, we present the theoretical and conceptual bases of each method, and present idealized scenarios to highlight the potential benefits and challenges of combining these techniques to remotely assess groundwater storage changes and other aspects of the dynamics of aquifer systems.

Groundwater depletion in Central Mexico: Use of GRACE and InSAR to support water resources management

Groundwater deficits occur in several areas of Central Mexico, where water resource assessment is limited by the availability and reliability of field data. In this context, GRACE and InSAR are used to remotely assess groundwater storage loss in one of Mexicos most important watersheds in terms of size and economic activity: the Lerma-Santiago-Pacifico (LSP). In situ data and Land Surface Models are used to subtract soil moisture and surface water storage changes from the total water storage change measured by GRACE satellites. As a result, groundwater mass change time-series are obtained for a 12 years period. ALOS-PALSAR images acquired from 2007 to 2011 were processed using the SBAS-InSAR algorithm to reveal areas subject to ground motion related to groundwater over-exploitation. In the perspective of providing guidance for groundwater management, GRACE and InSAR observations are compared with official water budgets and field observations. InSAR-derived subsidence mapping generally agrees well with official water budgets, and shows that deficits occur mainly in cities and irrigated agricultural areas. GRACE does not entirely detect the significant groundwater losses largely reported by official water budgets, literature and InSAR observations. The difference is interpreted as returns of wastewater to the groundwater flow systems, which limits the watershed scale groundwater depletion but suggests major impacts on groundwater quality. This phenomenon is enhanced by ground fracturing as noticed in the field. Studying the fate of the extracted groundwater is essential when comparing GRACE data with higher resolution observations, and particularly in the perspective of further InSAR/GRACE combination in hydrogeology.

Field characterization and data integration to define the hydraulic heterogeneity of a shallow granular aquifer at a sub-watershed scale

Providing a sound basis for aquifer management or remediation requires that hydrogeological investigations carried out to understand groundwater flow and contaminant transport be based on representative data that capture the heterogeneous spatial distribution of aquifer hydraulic properties. This paper describes a general workflow allowing the characterization of the heterogeneity of the hydraulic properties of granular aquifers at an intermediate scale of a few km2. The workflow involves characterization and data integration steps that were applied on a 12-km2 study area encompassing a decommissioned landfill emitting a leachate plume and its main surface water receptors. The sediments composing the aquifer were deposited in a littoral–sublittoral environment and show evidence of small-scale transitional heterogeneities. Cone penetrometer tests (CPT) combined with soil moisture and electrical resistivity (SMR) measurements were thus used to identify and characterize spatial heterogeneities in hydraulic properties over the study area. Site-specific statistical relationships were needed to infer hydrofacies units and to estimate hydraulic properties from high-resolution CPT/SMR soundings distributed all over the study area. A learning machine approach was used due to the complex statistical relationships between colocated hydraulic and CPT/SMR data covering the full range of aquifer materials. Application of this workflow allowed the identification of hydrofacies units and the estimation of horizontal hydraulic conductivity, vertical hydraulic conductivity and porosity over the study area. The paper describes and discusses data acquisition and integration methodologies that can be adapted to different field situations, while making the aquifer characterization process more time-efficient and less labor-intensive.

Predicting hydrofacies and hydraulic conductivity from direct-push data using a data-driven relevance vector machine approach: Motivations, algorithms, and application

The spatial heterogeneity of hydraulic conductivity (K) exerts a major control on groundwater flow and solute transport. The heterogeneous spatial distribution of K can be imaged using indirect geophysical data as long as reliable relations exist to link geophysical data to K. This paper presents a nonparametric learning machine approach to predict aquifer K from cone penetrometer tests (CPT) coupled with a soil moisture and resistivity probe (SMR) using relevance vector machines (RVMs). The learning machine approach is demonstrated with an application to a heterogeneous unconsolidated littoral aquifer in a 12 km2 subwatershed, where relations between K and multiparameters CPT/SMR soundings appear complex. Our approach involved fuzzy clustering to define hydrofacies (HF) on the basis of CPT/SMR and K data prior to the training of RVMs for HFs recognition and K prediction on the basis of CPT/SMR data alone. The learning machine was built from a colocated training data set representative of the study area that includes K data from slug tests and CPT/SMR data up-scaled at a common vertical resolution of 15 cm with K data. After training, the predictive capabilities of the learning machine were assessed through cross validation with data withheld from the training data set and with K data from flowmeter tests not used during the training process. Results show that HF and K predictions from the learning machine are consistent with hydraulic tests. The combined use of CPT/SMR data and RVM-based learning machine proved to be powerful and efficient for the characterization of high-resolution K heterogeneity for unconsolidated aquifers.

An Experimentation of Expert Systems Applied to 3D Geological Models Construction

To address the challenge of sustainable exploration and exploitation of oil, gas, mineral or groundwater resources, engineers and scientists often exploit 3D geological models to visualize, assess and understand the complexity of underground systems. But the construction of 3D geomodels is time–consuming and it generally requires the use of specialized software and specific expertise, in particular in geology where the system is not fully visible and necessitates complex efforts of human conceptualization. In order to simplify this process we propose to assess the ability of an expert system to analyze a particular source of geological information – geological cross-sections - and to enable several technical steps to build 3D geological models. This paper presents the procedure used to enable the development of this new expert system applied to the context of 3D geological modeling. We will try to answer questions such as the kind of information that is required, the way to build a knowledge base, the limits of using such a system, the quality of the 3D model output, etc. The discussion is based on an experimentation done in collaboration with the Geological Survey of Canada by comparing the current 3D geomodel and the one produced by the prototype 3D GeoExpert.

C-band D-InSAR and field data for calibrating a groundwater flow and land subsidence model

Differential Synthetic Aperture Radar Interferometry (D-InSAR) is a powerful technique used for detecting and measuring surface deformation with sub-centimetre accuracy. Using C-band data from three different satellites, the D-InSAR technique is used to calibrate a coupled groundwater flow and land subsidence numerical model. Additionally, D-InSAR results from different sensors are compared and contrasted. When comparing D-InSAR results with extensometers and water levels, a direct correlation is nociced. For all D-InSAR image pairs, large baselines, atmospheric effects, temporal decorrelation, and vegetative cover were limiting factors in obtaining a maximum number of usable interferograms. The total maximum subsidence for a point location in the valley between November 2003 and May 2008 is approximately 40 cm reaching a maximum total subsidence of over 2.0 metres since 1962. When contrasting the ENVISAT ASAR and RADARSAT-1 data, subsidence rates were similar yet the distribution had significant differences. Additionally, ENVISATs shorter baselines led to more accurate results.