Hydrogeology and sustainable future groundwater abstraction from the Agua Verde aquifer in the Atacama Desert,northern Chile

The hyper-arid conditions prevailing in Agua Verde aquifer in northern Chile make this system the most important water source for nearby towns and mining industries. Due to the growing demand for water in this region, recharge is investigated along with the impact of intense pumping activity in this aquifer. A conceptual model of the hydrogeological system is developed and implemented into a two-dimensional groundwater-flow numerical model. To assess the impact of climate change and groundwater extraction, several scenarios are simulated considering variations in both aquifer recharge and withdrawals. The estimated average groundwater lateral recharge from Precordillera (pre-mountain range) is about 4,482 m³/day. The scenarios that consider an increase of water withdrawal show a non-sustainable groundwater consumption leading to an over-exploitation of the resource, because the outflows surpasses inflows, causing storage depletion. The greater the depletion, the larger the impact of recharge reduction caused by the considered future climate change. This result indicates that the combined effects of such factors may have a severe impact on groundwater availability as found in other groundwater-dependent regions located in arid environments. Furthermore, the scenarios that consider a reduction of the extraction flow rate show that it may be possible to partially alleviate the damage already caused to the aquifer by the continuous extractions since 1974, and it can partially counteract climate change impacts on future groundwater availability caused by a decrease in precipitation (and so in recharge), if the desalination plant in Taltal increases its capacity.     

From 2D TO 3D Through Modelling Based On A Single Image

Three-dimensional modelling from single images remains an interesting topic of investigation in the research community, even though range sensors are becoming a common alternative for the generation of 3D information. The interest in single-image-based modelling is motivated by a wide spectrum of applications such as cultural heritage, civil engineering, urban planning and even criminology. In this paper a complete new production flowline is presented for modelling based on a single image. The modelling process consists of a series of familiar steps in photogrammetry and computer vision: feature extraction, vanishing point computation, camera self-calibration, 3D reconstruction and dimensional analysis. In particular, the methodology developed for single-image-based modelling takes a scientific approach combining several proven techniques with robust estimators. Finally, in order to demonstrate its capabilities, the reported examples include several real situations applied in different contexts.     

Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery

The airborne laser scanning LIDAR (LIght Detection And Ranging) provides high-resolution Digital Terrain Models (DTM) that have been applied recently to the characterization, quantification and monitoring of coastal environments. This study assesses the contribution of LIDAR altimetry and intensity data, topographically-derived features (slope and aspect), and multi-spectral imagery (three visible and a near-infrared band), to map coastal habitats in the Bidasoa estuary and its adjacent coastal area (Basque Country, northern Spain). The performance of high-resolution data sources was individually and jointly tested, with the maximum likelihood algorithm classifier in a rocky shore and a wetland zone; thus, including some of the most extended Cantabrian Sea littoral habitats, within the Bay of Biscay. The results show that reliability of coastal habitat classification was more enhanced with LIDAR-based DTM, compared with the other data sources: slope, aspect, intensity or near-infrared band. The addition of the DTM, to the three visible bands, produced gains of between 10% and 27% in the agreement measures, between the mapped and validation data (i.e. mean producer's and user's accuracy) for the two test sites. Raw LIDAR intensity images are only of limited value here, since they appeared heterogeneous and speckled. However, the enhanced Lee smoothing filter, applied to the LIDAR intensity, improved the overall accuracy measurements of the habitat classification, especially in the wetland zone; here, there were gains up to 7.9% in mean producer's and 11.6% in mean user's accuracy. This suggests that LIDAR can be useful for habitat mapping, when few data sources are available. The synergy between the LIDAR data, with multi-spectral bands, produced high accurate classifications (mean producer's accuracy: 92% for the 16 rocky habitats and 88% for the 11 wetland habitats). Fusion of the data enabled discrimination of intertidal communities, such as Corallina elongata, barnacles (Chthamalus spp.), and stands of Spartina alterniflora and Phragmites australis, which presented misclassification when conventional visible bands were used alone. All of these results were corroborated by the kappa coefficient of agreement. The high classification accuracy found here, selecting data sources, highlights the value of integrating LIDAR data with multi-spectral imagery for habitat mapping in the intertidal complex fringe.     

Daily precipitation concentration in Central Coast Vietnam

Theoretical and Applied Climatology - Empirical frequency distribution of daily precipitation amounts can be fitted by a negative exponential distribution, because anywhere there are many small...     

Building a 3D geomodel for water resources management: case study in the Regional Park of the lower courses of Manzanares and Jarama Rivers (Madrid, Spain)

Water resources management of protected sites requires a powerful tool to analyze the process and changes that are occurring in the environment. This paper describes a 3D geomodel design of the Jarama River Detrital Aquifer located in Madrid (Spain). That hydrogeological unit is included in the “Parque Regional de los Cursos Bajos de los Ríos Manzanares y Jarama” (Regional Park of the Lower Courses of Manzanares and Jarama Rivers). The goal of this work is to define a method by which a three-dimensional (3D) model can be created with hydrogeologic geometry real of main aquifer, to accomplish an adequate management of the groundwater resources. All data used in this study were integrated in a geographic database: geological and hydrogeological information, geological map (1:25,000), eleven cross-sections, piezometric maps and a digital elevation model. The constructed 3D model of the Jarama Aquifer shows geometric features and spatial distribution and variations of geologic units. Thus, the 3D model allows the assessment of volumes of each unit, the depth and thickness variations of the main aquifer, and the spatial and temporal variations of water tables. From the 3D model, the most suitable areas (in terms of groundwater protection) for managed recharge and mining works have been identified.     

Sediment Organic Carbon in Todos Santos Bay, Baja California, Mexico

Sediment grain size and organic carbon (OC) data collected over the past 50 years, together with δ¹³C values of OC in recently collected samples, were analyzed to improve understanding of sediment OC distribution and abundance in Todos Santos Bay. Sediments in the submarine canyon at the mouth of the bay and in quiet-water locations along the shore are fine grained, high in OC, and have generally low δ¹³C values; sediments in high-energy environments are low in OC and have high δ¹³C values. A bivariate isotopic mixing model indicates that none of the sediments contain >50% terrigenous OC (average ~30%), and that the terrigenous OC content of the sediments is a small proportion of the OC content of local soils. Sediment OC composition is apparently controlled by energy-related sorting and deposition, oxidation of much of the original terrigenous OC, and replacement of some terrigenous OC by marine OC.     

Probabilistic leak detectability assessment via state estimation in water transport networks

Leak detectability or leakage awareness refers to the capability of sensing losses from a water supply system. Several methods exist in the technical literature to tackle this problem, but only few address it with a state estimation approach. The aim of this paper is to present a new methodology that enables probabilistic assessment of the extent to which water loss could be detected using state estimation by only analysing a single hydraulic state, i.e. one time period. Significant leaks are sensed by identifying unusually high normalised state estimation residuals, which can be identified based on the largest normalised residual test. More specifically, the probability of detecting leaks is computed here by working with the multivariate distribution among measurements and estimates to take into account the noisy nature of measurements with an analytical approach rather than with sampling experiments, which are time-consuming. The methodology set out herein also provides a procedure to systematically assess the minimum leak that could be detected in different parts of the network for a specific measurement setting and operating condition. The method has been applied to a water transport network case study to show its potential and to highlight the usefulness of such a tool for practitioners. The limitations of such a methodology are also discussed, including its possible use for on-line leak detection strategies.     

Changes in heat wave characteristics over Extremadura (SW Spain)

Theoretical and Applied Climatology - Heat wave (HW) events are becoming more frequent, and they have important consequences because of the negative effects they can have not only on the human...     

Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics

This paper puts together some techniques that have been previously developed by the authors, but separately, relative to fuzzy classification within a remote sensing setting. Considering that each image can be represented as a graph that defines proximity between pixels, certain distances between the characteristic of contiguous pixels are defined on such a graph, so a segmentation of the image into homogeneous regions can be produced by means of a particular algorithm. Such a segmentation can be then introduced as information, previously to any classification procedure, with an expected significative improvement. In particular, we consider specific measures in order to quantify such an improvement. This approach is being illustrated with its application into a particular land surface problem.