A multi-tracer approach to delineate groundwater dynamics in the Rio Actopan Basin,Veracruz State,Mexico

Geochemistry and environmental tracers were used to understand groundwater resources, recharge processes, and potential sources of contamination in the Rio Actopan Basin, Veracruz State, Mexico. Total dissolved solids are lower in wells and springs located in the basin uplands compared with those closer to the coast, likely associated with rock/water interaction. Geochemical results also indicate some saltwater intrusion near the coast and increased nitrate near urban centers. Stable isotopes show that precipitation is the source of recharge to the groundwater system. Interestingly, some high-elevation springs are more isotopically enriched than average annual precipitation at higher elevations, indicating preferential recharge during the drier but cooler winter months when evapotranspiration is reduced. In contrast, groundwater below 1,200 m elevation is more isotopically depleted than average precipitation, indicating recharge occurring at much higher elevation than the sampling site. Relatively cool recharge temperatures, derived from noble gas measurements at four sites (11–20 °C), also suggest higher elevation recharge. Environmental tracers indicate that groundwater residence time in the basin ranges from 12,000 years to modern. While this large range shows varying groundwater flowpaths and travel times, ages using different tracer methods (¹⁴C, ³H/³He, CFCs) were generally consistent. Comparing multiple tracers such as CFC-12 with CFC-113 indicates piston-flow to some discharge points, yet binary mixing of young and older groundwater at other points. In summary, groundwater within the Rio Actopan Basin watershed is relatively young (Holocene) and the majority of recharge occurs in the basin uplands and moves towards the coast.     

Identifying influence areas with connectivity analysis – application to the local perturbation of heterogeneity distribution for history matching

Numerical representations of a target reservoir can help to assess the potential of different development plans. To be as predictive as possible, these representations or models must reproduce the data (static, dynamic) collected on the field. However, constraining reservoir models to dynamic data – the history-matching process – can be very time consuming. Many uncertain parameters need to be taken into account, such as the spatial distribution of petrophysical properties. This distribution is mostly unknown and usually represented by millions of values populating the reservoir grid. Dedicated parameterization techniques make it possible to investigate many spatial distributions from a small number of parameters. The efficiency of the matching process can be improved from the perturbation of specific regions of the reservoir. Distinct approaches can be considered to define such regions. For instance, one can refer to streamlines. The leading idea is to identify areas that influence the production behavior where the data are poorly reproduced. Here, we propose alternative methods based on connectivity analysis to easily provide approximate influence areas for any fluid-flow simulation. The reservoir is viewed as a set of nodes connected by weighted links that characterize the distance between two nodes. The path between nodes (or grid blocks) with the lowest cumulative weight yields an approximate flow path used to define influence areas. The potential of the approach is demonstrated on the basis of 2D synthetic cases for the joint integration of production and 4D saturation data, considering several formulations for the weights attributed to the links.     

Crustal melting and recycling: geochronology and sources of Variscan syn-kinematic anatectic granitoids of the Tormes Dome (Central Iberian Zone). A U–Pb LA-ICP-MS study

International Journal of Earth Sciences - In this study, we report U–Pb Laser Ablation ICP-MS zircon and ID-TIMS monazite ages for peraluminous granitoid plutons...     

Rainfall thresholds for the triggering of landslides considering previous soil moisture conditions (Asturias,NW Spain)

Given its geological and climatic conditions and its rugged orography, Asturias is one of the most landslide prone areas in the North of Spain. Most of the landslides occur during intense rainfall episodes. Thus, precipitation is considered the main triggering factor in the study area, reaching average annual values of 960 mm. Two main precipitation patterns are frequent: (i) long-lasting periods of moderate rainfall during autumn and winter and (ii) heavy short rainfall episodes during spring and early summer. In the present work, soil moisture conditions in the locations of 84 landslides are analysed during two rainfall episodes, which represent the most common precipitation patterns: October–November 2008 and June 2010. Empirical data allowed the definition of available water capacity percentages of 99–100% as critical soil moisture conditions for the landslide triggering. Intensity-duration rainfall thresholds were calculated for each episode, considering the periods with sustained high soil moisture levels before the occurrence of each analysed landslide event. For this purpose, data from daily water balance models and weather stations were used. An inverse relationship between the duration of the precipitation and its intensity, consistent with published intensity-duration thresholds, was observed, showing relevant seasonal differences.     

Exploratory Factor Analysis of Wireline Logs Using a Float-Encoded Genetic Algorithm

In the paper, a novel inversion approach is used for the solution of the problem of factor analysis. The float-encoded genetic algorithm as a global optimization method is implemented to extract factor variables using open-hole logging data. The suggested statistical workflow is used to give a reliable estimate for not only the factors but also the related petrophysical properties in hydrocarbon formations. In the first step, the factor loadings and scores are estimated by Jöreskog’s fast approximate method, which are gradually improved by the genetic algorithm. The forward problem is solved to calculate wireline logs directly from the factor scores. In each generation, the observed and calculated well logs are compared to update the factor population. During the genetic algorithm run, the average fitness of factor populations is maximized to give the best fit between the observed and theoretical data. By using the empirical relation between the first factor and formation shaliness, the shale volume is estimated along the borehole. Permeability as a derived quantity also correlates with the first factor, which allows its determination from an independent source. The estimation results agree well with those of independent deterministic modeling and core measurements. Case studies from Hungary and the USA demonstrate the feasibility of the global optimization based factor analysis, which provides a useful tool for improved reservoir characterization.     

Petrogenesis of low-δ<Superscript>18</Superscript>O quartz porphyry dykes,Koegel Fontein complex,South Africa

This paper investigates the origin of low-δ¹⁸O quartz porphyry dykes associated with the 144–133 Ma Koegel Fontein Igneous Complex, which was intruded during the initial phase of breakup of Africa and South America. The 25-km diameter Rietpoort Granite is the largest and youngest phase of activity, and is roofed by a 10-km diameter pendant of gneiss. Quartz porphyry (QP) dykes, up to 15 m in width, strike NW–SE across the complex. The QP dykes that intruded outside the granite have similar quartz phenocryst δ¹⁸O values (average 8.0‰, ± 0.7, n?=?33) to the granite (average 8.3?±?1.0, n?=?7). The QP dykes that intruded the roof pendant have quartz phenocrysts with more variable δ¹⁸O values (average 1.6‰, ± 2.1, n?=?55). In some cases quartz phenocrysts have δ¹⁸O values as low as ? 2.5‰. The variation in δ¹⁸O value within the quartz crystal population of individual dykes is small relative to the overall range, and core and rim material from individual quartz phenocrysts in three samples are identical within error. There is no evidence that quartz phenocryst δ¹⁸O values have been affected by fluid–rock interaction. Based on a ?_quartz?magma value of 0.6‰, magma δ¹⁸O values must have been as low as ? 3.1‰. Samples collected along the length of the two main QP dykes that traverse the roof pendant have quartz phenocryst δ¹⁸O values that range from +?1.1 to +?4.6‰, and ? 2.3 to +?5.6‰, respectively. These δ¹⁸O values correlate negatively (r = ? 0.96) with initial ⁸⁷Sr/⁸⁶Sr, which can be explained by the event that lowered δ¹⁸O values of the source being older than the dykes. We suggest that the QP dykes were fed by magma produced by partial melting of gneiss, which had been variably altered at high temperature by ¹⁸O-depleted meteoric water during global glaciation at ~?550 Ma. The early melts had variable δ¹⁸O value but as melt pockets interconnected during melting, the δ¹⁸O values approached that of average gneiss. Variable quartz phenocryst δ¹⁸O values in the same dyke can be explained by vertical emplacement, at variable rates of ascent along the dyke. The lateral variation in quartz, and hence magma δ¹⁸O value at a particular point along a single dyke would depend on the rate of ascent of magma at that point along the dyke, and the ‘age’ of the particular magma batch.     

Composition of barian mica in multiphase solid inclusions from orogenic garnet peridotites as evidence of mantle metasomatism in a subduction zone setting

Multiphase solid inclusions in minerals formed at ultra-high-pressure (UHP) provide evidence for the presence of fluids during deep subduction. This study focuses on barian mica, which is a common phase in multiphase solid inclusions enclosed in garnet from mantle-derived UHP garnet peridotites in the Saxothuringian basement of the northern Bohemian Massif. The documented compositional variability and substitution trends provide constraints on crystallization medium of the barian mica and allow making inferences on its source. Barian mica in the multiphase solid inclusions belongs to trioctahedral micas and represents a solid solution of phlogopite KMg₃(Si₃Al)O₁₀(OH)₂, kinoshitalite BaMg₃(Al₂Si₂)O₁₀(OH)₂ and ferrokinoshitalite BaFe₃(Al₂Si₂)O₁₀(OH)₂. In addition to Ba (0.24–0.67 apfu), mica is significantly enriched in Mg (X_Mg ~ 0.85 to 0.95), Cr (0.03–0.43 apfu) and Cl (0.04–0.34 apfu). The substitution vector involving Ba in the I-site which describes the observed chemical variability can be expressed as BaFe^IVAlClK_?1Mg_?1Si_?1(OH)_?1. A minor amount of Cr and ^VIAl enters octahedral sites following a substitution vector ^VI(Cr,Al)₂□^VI(Mg,Fe)_?3 towards chromphyllite and muscovite. As demonstrated by variable Ba and Cl contents positively correlating with Fe, barian mica composition is partly controlled by its crystal structure. Textural evidence shows that barian mica, together with other minerals in multiphase solid inclusions, crystallized from fluids trapped during garnet growth. The unusual chemical composition of mica reflects the mixing of two distinct sources: (1) an internal source, i.e. the host peridotite and its garnet, providing Mg, Fe, Al, Cr, and (2) an external source, represented by crustal-derived subduction-zone fluids supplying Ba, K and Cl. At UHP–UHT conditions recorded by the associated diamond-bearing metasediments (c. 1100 °C and 4.5 GPa) located above the second critical point in the pelitic system, the produced subduction-zone fluids transporting the elements into the overlying mantle wedge had a solute-rich composition with properties of a hydrous melt. The occurrence of barian mica with a specific chemistry in barium-poor mantle rocks demonstrates the importance of its thorough chemical characterization.     

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.     

Carbon costs and benefits of France’s biomass energy production targets

Background

Concern about climate change has motivated France to reduce its reliance on fossil fuel by setting targets for increased biomass-based renewable energy production. This study quantifies the carbon costs and benefits for the French forestry sector in meeting these targets. A forest growth and harvest simulator was developed for French forests using recent forest inventory data, and the wood-use chain was reconstructed from national wood product statistics. We then projected wood production, bioenergy production, and carbon balance for three realistic intensification scenarios and a business-as-usual scenario. These intensification scenarios targeted either overstocked, harvest-delayed or currently actively managed stands.

Results

All three intensification strategies produced 11.6–12.4 million tonnes of oil equivalent per year of wood-based energy by 2026, which corresponds to the target assigned to French wood-energy to meet the EU 2020 renewable energy target. Sustaining this level past 2026 will be challenging, let alone further increasing it. Although energy production targets can be reached, the management intensification required will degrade the near-term carbon balance of the forestry sector, compared to continuing present-day management. Even for the best-performing intensification strategy, i.e., reducing the harvest diameter of actively managed stands, the carbon benefits would only become apparent after 2040. The carbon balance of a strategy putting abandoned forests back into production would only break even by 2055; the carbon balance from increasing thinning in managed but untended stands would not break even within the studied time periods, i.e. 2015–2045 and 2046–2100. Owing to the temporal dynamics in the components of the carbon balance, i.e., the biomass stock in the forest, the carbon stock in wood products, and substitution benefits, the merit order of the examined strategies varies over time.

Conclusions

No single solution was found to improve the carbon balance of the forestry sector by 2040 in a way that also met energy targets. We therefore searched for the intensification scenario that produces energy at the lowest carbon cost. Reducing rotation time of actively managed stands is slightly more efficient than targeting harvest-delayed stands, but in both cases, each unit of energy produced has a carbon cost that only turns into a benefit between 2060 and 2080.