The feasibility of low CO2 concentration targets and the role of bio-energy with carbon capture and storage (BECCS)

The United Nations Framework Convention on Climate Change (UNFCCC 1992) calls for stabilization of atmospheric greenhouse gas (GHG) concentrations at a level that would prevent dangerous anthropogenic interference with the climate system. We use three global energy system models to investigate the technological and economic attainability of meeting CO₂ concentration targets below current levels. Our scenario studies reveal that while energy portfolios from a broad range of energy technologies are needed to attain low concentrations, negative emission technologies—e.g., biomass energy with carbon capture and storage (BECCS)—significantly enhances the possibility to meet low concentration targets (at around 350 ppm CO₂).     

A new scenario framework for Climate Change Research: scenario matrix architecture

This paper describes the scenario matrix architecture that underlies a framework for developing new scenarios for climate change research. The matrix architecture facilitates addressing key questions related to current climate research and policy-making: identifying the effectiveness of different adaptation and mitigation strategies (in terms of their costs, risks and other consequences) and the possible trade-offs and synergies. The two main axes of the matrix are: 1) the level of radiative forcing of the climate system (as characterised by the representative concentration pathways) and 2) a set of alternative plausible trajectories of future global development (described as shared socio-economic pathways). The matrix can be used to guide scenario development at different scales. It can also be used as a heuristic tool for classifying new and existing scenarios for assessment. Key elements of the architecture, in particular the shared socio-economic pathways and shared policy assumptions (devices for incorporating explicit mitigation and adaptation policies), are elaborated in other papers in this special issue.     

Extending F‐Perceptory to model fuzzy objects with composite geometries for GIS

When analyzing spatial issues, geographers are often confronted with many problems with regard to the imprecision of the available information. It is necessary to develop representation and design methods which are suited to imprecise spatiotemporal data. This led to the recent proposal of the F‐Perceptory approach. F‐Perceptory models fuzzy primitive geometries that are appropriate in representing homogeneous regions. However, the real world often contains cases that are much more complex, describing geographic features with composite structures such as a geometry aggregation or combination. From a conceptual point of view, these cases have not yet been managed with F‐Perceptory. This article proposes modeling fuzzy geographic objects with composite geometries, by extending the pictographic language of F‐Perceptory and its mapping to the Unified Modeling Language (UML) necessary to manage them in object/relational databases. Until now, the most commonly used object modeling tools have not considered imprecise data. The extended F‐Perceptory is implemented under a UML‐based modeling tool in order to support users in fuzzy conceptual data modeling. In addition, in order to properly define the related database design, an automatic derivation process is implemented to generate the fuzzy database model.     

Evaluating the impact of classification algorithms and spatial resolution on the accuracy of land cover mapping in a mountain environment in Pakistan

Satellite images of various spatial resolutions and different image classification techniques have been utilized for land cover (LC) mapping at local and regional scale studies. Mapping capabilities and achievable accuracies of LC classification in a mountain environment are, however, influenced by the spatial resolution of the utilized images and applied classification techniques. Hence, developing and characterizing regionally optimized methods are essential for the planning and monitoring of natural resources. In this study, the potential of four non-parametric image classification techniques, i.e., k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and neural network (NN) on the accuracy of LC classification was evaluated in the Hindu Kush mountains ranges of northern Pakistan. Moreover, we have assessed the impact of the spatial resolution of the utilized satellite imagery, i.e., SPOT-5 with 2.5 m and Landsat-8 with 30 m on the accuracy of the derived LC classification. For the classification of LC based on SPOT-5, we have achieved the highest overall classification accuracy (OCA) = 89% with kappa coefficient (KC = 0.86) using SVM followed by k-NN, RF, and NN. However, for LC classification derived from Landsat-8 imagery, we achieved the highest OCA = 71% with KC = 0.59 using RF and SVM followed by k-NN and NN. The higher accuracy derived from SPOT-5 versus Landsat-8 indicated that the results of LC classification based on SPOT-5 are more accurate and reliable than Landsat-8. The findings of the present study will be useful for the classification and mapping task of LC in a mountain environment using SPOT-5 and Landsat-8 at local and regional scale studies.     

Applicability of artificial neural networks for obtaining velocity models from synthetic seismic data

Seismic velocity analysis is a crucial part of seismic data processing and interpretation which has been practiced using different methods. In contrast to time consuming and complicated numerical methods, artificial neural networks (ANNs) are found to be of potential applicability. ANN ability to establish a relationship between an input and output space is considered to be appropriate for mapping seismic velocity corresponding to travel times picked from seismograms. Accordingly a preliminary attempt is made to evaluate the applicability of ANNs to determine velocity and dips of dipping layered earth models corresponding to travel time data. The study is based on synthetic data generated using inverse modeling approach for three earth models. The models include a three-layer structure with same dips and same directions, a three-layer model with different dips and same directions, as well as a two-layer model with different dips and directions. An ANN structure is designed in three layers, namely, input, output, and hidden ones. The training and testing process of the ANN is successfully accomplished using the synthetic data. The evaluation of the applicability of the trained ANN to unknown data sets indicates that the ANN can satisfactorily compute velocity and dips corresponding to travel times. The error intervals between the desired and calculated velocity and dips are shown to be acceptably small in all cases. The applicability of the trained ANN in extrapolating is also evaluated using a number of data outside of the range already known to ANN. The results indicate that the trained ANN acceptably approximates the velocity and dips. Furthermore, the trained ANN is also evaluated in terms of capability of handling deficiency in input data where acceptable results were also achieved in velocity and dip calculations. Generally, this study shows that velocity analysis using ANNs can promisingly tackle the challenge of retrieving an initial velocity model from the travel time hyperbolas of seismic data.     

Curie point depth beneath the Barramiya–Red Sea coast area estimated from aeromagnetic spectral analysis

The geothermal structure beneath of the Barramiya?CRed Sea coast area of the Central Eastern Desert of Egypt has been determined using Curie point depth (CPD), which is temperature-dependent. The CPD and the surface heat flow (q) maps of such area are estimated by analyzing aeromagnetic data. Such data are low-pass-filtered and analyzed to estimate the magnetic bottom using the centroid method. The heat flow map reflects the geothermic nature of the region. However, it is suggested that the shallow Curie point temperature depth pattern depends on the tectonic regime and morphology, which continues eastwards through the Red Sea. Particularly, the coastal regions are characterized by high heat flow (83.6?mW/m²) and shallow Curie depth (22.5?km), whereas the western portion of the studied area has a lower heat flow (<50?mW/m²) and deeper Curie depth (~40?km). In addition to its bordering to the Red Sea margin, such high heat flow anomaly is associated with the increased earthquake swarms activity in the Abu Dabbab area. El-Hady (1993) attributed the swarm activity to the geothermal evolution. Also, the heat flow pattern is correlatable by the numerous results of shallow borehole temperature measurements as reported by Morgan and Swanberg (1979). A significant low heat flow extending in the northeast?Csouthwest direction, which is associated with NE?CSW large areal extent negative Bouguer gravity anomaly and NE/SW-trending belt of the deep CPD region, seems to be directly related to the surface outcrops of Precambrian older granitoids of the mountainous range of that trend.     

Seismic tectono-stratigraphy of fluvio-deltaic to deep marine Miocene silicoclastic hydrocarbon reservoir systems in the Gulf of Hammamet,northeastern Tunisia

Most of hydrocarbon accumulations within the Gulf of Hammamet foreland basins in eastern Tunisia are reservoired within the Upper Miocene Birsa and Saouaf sandstones. It is the case of Birsa, Tazarka, Oudna, Baraka, Maamoura, Cosmos and Yasmine fields. These sandstones constitute oil and gas fields located on folded and faulted horst anticline highs and described as varying from shoreface to shallow marine and typically exhibit excellent reservoir quality of 30 to 35% porosity and good permeability from 500 to 1100 md. In addition, the fracturing of faults enhanced their reservoir quality potential. However, due to the lack of seismic stratigraphic studies to highlight depositional environment reservoir characterization and distribution, petroleum exploration faces structural and stratigraphic trap types and remains on targeting only high fold closures with limited reserve volumes of hydrocarbons. As an example of the Birsa concession case, syn-sedimentary tectonic structuring and geodynamic evolution during Middle to Upper Miocene Birsa reservoir sequences have guided the distribution of depositional environment of sandstone channel systems around horst and grabens by E-W, NE-SW and N-S strike slip flower faults controlling the subsidence distribution combined with the eustatic sea level variations. Seismic sequence stratigraphy study of Miocene Birsa reservoir horizons, based on the analysis and interpretations of E-W and N-S 3D selected regional lines that were compared and correlated to outcrops and calibrated by well data, permitted to highlight the basin configuration and sequence deposit nature and distribution. Sedimentary infilling of the basin from Langhian Ain Ghrab carbonate to Serravallian Tortonian Birsa and Saouaf sandstone and shale formations is organized in four third-order seismic sequences, limited by regional erosional toplap, onlap and downlap unconformity surfaces and by remarkable chronostratigraphic horizons of forced and normal erosive lowstand and highstand system tracts separated by transgressive and maximum flooding surfaces. Reconstructed sedimentary paleo-environment distribution vary from deltaic fluvial proximal deposits in the northern part of the high central Birsa horst to a delta front and prodelta coastal and shelf shore face and shore line channelized deposits in the surrounding borders of grabens. Distal deposits seem to be distributed from upper to lower slope fans and probably to the basin floor on the flanks of the subsiding grabens. Synthetic predictive paleogeographic depositional reservoir fairway map distribution of Lower, Middle and Upper Birsa sandstone reservoirs highlights four main domains of channelized superposed and shifted reservoirs to explore.     

Topical Collection: Groundwater-based agriculture in the Mediterranean

This essay introduces a collection of articles that explore the future of groundwater-based agriculture in the Mediterranean from an interdisciplinary perspective, in a context of declining water tables due to intensive groundwater use. The imminent crisis that many groundwater economies face due to very rapid and intense global change may have severe irreversible social, economic and environmental consequences, but could also be the opportunity to make a clear break with current agricultural development models and move towards more sustainable agricultural practices. The Mediterranean region is, therefore, an interesting case for the future of intensive groundwater use, as innovative ideas and practices may emerge and inspire similar groundwater-based agricultural systems around the world.     

3-D magnetic inversion and satellite imagery for the Um Salatit gold occurrence,Central Eastern Desert,Egypt

The Um Salatit is a gold occurrence situated in the Central Eastern Desert of Egypt. The studied area is characterized by an intensive imbrication zone of serpentinites and metavolcanic rocks. The integrated use of aeromagnetic and spectral reflectance remote sensing data has proved effective for mapping geology related to ore deposits. Spectral reflectance maps have been produced to discriminate various rocks, such as serpentinites, gabbros, and metavolcanic rocks. 3-D inversion of aeromagnetic data acquired over the Um Salatit study area produced a 3-D magnetic susceptibility model showing magnetic bodies surrounded by less magnetic host rock. The magnetic features are elongated ENE-WSW parallel to the major thrust fault and mapped the Um Salatit serpentinized rocks. Remote sensing data allow investigating surficial geological features and mapping the mineralized areas. Remote results, in conjunction with 3-D inversion of aeromagnetic data, demonstrate that gold occurrence in the study area was effectively restricted to the highly magnetic zone interpreted as host rocks.