Potassic and low- and high-Ti mildly alkaline volcanism in the Neoproterozoic Ramada Plateau, southernmost Brazil

Ramada Plateau Neoproterozoic volcanism represents a portion of the shoshonitic and mildly alkaline magmatism related to postcollisional events of the Brasiliano/Pan African cycle of southernmost Brazil. It is constituted by shoshonitic basic-intermediate lavas, followed by a bimodal sequence characterized by pyroclastic deposits, lava flows, and hypabyssal rocks with ages of 549±5 Ma. The shoshonitic magmatism presents greater K₂O than Na₂O₂, K₂O/Na₂O ratios close to 1, and moderate large ion lithophile and high-field strength element contents. The bimodal basic-acid volcanism presents a transitional chemical affinity with features of sodic, silica-saturated alkaline to continental tholeiitic series. Observed basic and acid rocks with contrasting Ti contents are referred to as high- and low-Ti basalt-rhyolites. Another group of acid rocks with higher Nb, Ta, and Rb values was identified as high-Nb rhyolites. The Ramada Plateau magmatism is comparable to associations related to the final stages of orogenic cycles, in which shoshonitic and high- and low-Ti alkaline magmatism reflects the melting of subduction-modified sources, whereas the high-Nb magmas show less influence of subduction-related metasomatism and are closer to magmas produced from anorogenic sources. A model of magma generation in collisional settings involving slab break-off and asthenospheric upwelling is applied to the evolution of magmatism from subduction-related to anorogenic in the Ramada Plateau.     

Long-period seismic source inversions using global tomographic models

We investigate the effect of laterally varying earth structure on centroid moment tensor inversions using fundamental mode mantle waves. Theoretical seismograms are calculated using a full formulation of surface wave ray theory. Calculations are made using a variety of global tomographic earth models. Results are compared with those obtained using the so-called great-circle approximation, which assumes that phase corrections are given in terms of mean phase slowness along the great circle, and which neglects amplitude effects of heterogeneity. Synthetic tests suggest that even source parameters which fit the data very well may have large errors due to incomplete knowledge of lateral heterogeneity. The method is applied to 31 shallow, large earthquakes. For a given earthquake, the focal mechanisms calculated using different earth models and different forward modelling techniques can significantly vary. We provide a range of selected solutions based on the fit to the data, rather than one single solution. Difficulties in constraining the dip-slip components of the seismic moment tensor often produce overestimates of seismic moment, leading to near vertical dip-slip mechanisms. This happens more commonly for earth models not fitting the data well, confirming that more accurate modelling of lateral heterogeneity can help to constrain the dip-slip components of the seismic moment tensor.     

Seasonal dynamics of a large, shallow lake, laguna Chascomús: The role of light limitation and other physical variables

The most conspicuous waterbodies in the Pampa region of Argentina are the so-called “lagunas”. A typical Pampean laguna may be described as a relatively large (100+ha), permanent, shallow lake. Here, we report the dynamics of laguna Chascomús, sampled weekly, from April 2001 to June 2003. During the period, the lakes experienced three consecutive floods waves that affected the concentration of major ions and the optical signature of the dissolved organic matter. Despite these hydrological alterations, laguna Chascomús was permanently limited by light. Transparency was to a great extent controlled by the incident photosynthetic available radiation irradiance. We hypothesize that wind contributes to the permanent mixing of the lake, as well as to lessen the sedimentation losses of photoautotrophs.     

Machine learning for accelerating 2D flood models: Potential and challenges

Two-dimensional hydrodynamic models numerically solve full Shallow Water Equations (SWEs). Despite their high accuracy, these models have long simulation run times and therefore are of limited use for exploratory or real-time flood predictions. We investigated the possibility of improving flood modelling speed using Machine Learning (ML). We propose a new method that replaces the computationally expensive parts of the hydrodynamic models with simple and efficient data-driven approximations. Our hypothesis is that by integrating ML with physics-based numerical methods, we can achieve improved generalization performance: that is, the trained model for one case study can be used in other studies without the need for new training. We tested two ML approaches: for the first, we integrated curve fitting, and, for the second, artificial neural networks (ANN) with a finite volume scheme to solve the local inertial approximation of the SWEs. The data-driven models approximated the Momentum Equation, which explicitly solved the time derivative of flow rates. Water depths were then updated by applying a water balance equation. We also tested two different training datasets: the simulated dataset, generated from the results of hydrodynamic model, and the random dataset, generated by directly solving the momentum equation on randomly sampled input data. Various combinations of input features, for example, water slope and depth, were explored. The proposed models were trained in a small hypothetical case and tested in a different hypothetical and in two real case studies. Results showed that the curve-fitting method can be implemented successfully, given sufficient training and input data. The ANN model trained with a random dataset was substantially more accurate than that of the model trained with the simulated dataset. However, it was not successful in the real case studies. The curve-fitting method resulted in better generalization performance and increased the simulation speed of the local inertial model by 23%. Future research should test the performance of ML in terms of an increase in stable time step size and approximation of the full SWEs.     

An integrated approach for investigating geomorphic response to extreme events: methodological framework and application to the October 2011 flood in the Magra River catchment,Italy

A high‐magnitude flash flood, which took place on 25 October 2011 in the Magra River catchment (1717 km²), central‐northern Italy, is used to illustrate some aspects of the geomorphic response to the flood. An overall methodological framework is described for using interlinked observations and analyses of the geomorphic impacts of an extreme event. The following methods and analyses were carried out: (i) hydrological and hydraulic analysis of the event; (ii) sediment delivery by event landslide mapping; (iii) identification and estimation of wood recruitment, deposition, and budgeting; (iv) interpretation of morphological processes by analysing fluvial deposits; (v) remote sensing and geographic information system (GIS) analysis of channel width changes. In response to the high‐magnitude hydrological event, a large number of landslides occurred, consisting of earth flows, soil slips, and translational slides, and a large quantity of wood was recruited, in most part deriving from floodplain erosion caused by bank retreat and channel widening. The most important impact of the flood event within the valley floor was an impressive widening of the overall channel bed and the reactivation of wide portions of the pre‐event floodplain. Along the investigated (unconfined or partly confined) streams (total investigated length of 93.5 km), the channel width after the flood was up to about 20 times the channel width before the event. The study has shown that a synergic use of different methods and types of evidence provides fundamental information for characterizing and understanding the geomorphic effects of intense flood events. The prediction of geomorphic response to a flood event is still challenging and many limitations exist; however a robust geomorphological analysis can contribute to the identification of the most critical reaches. Copyright © 2016 John Wiley & Sons, Ltd.     

Exploring the policy and social factors fueling the expansion and shift of sugarcane production in the Brazilian Cerrado

Within the past decade, the sugarcane ethanol industry in Brazil has increased its production capacity to meet rising domestic demand for ethanol. However, to achieve this growth the industry has had to expand into new frontiers in the Brazilian Cerrado, specifically in the states of Goiás and Mato Grosso do Sul, which is now the second largest producing area of ethanol in Brazil. We argue that the expansion into the Brazilian Cerrado is a result of the interactions between the increase in demand, governmental policies, infrastructure, and factors that have prevented traditional sugarcane producing regions from meeting the increasing demand for ethanol. This paper analyzes the policies that have shaped the sugarcane industry in Brazil and the expansion of the industry across the country. The policies and issues examined include those that led to the concentration and consolidation of the sugarcane industry in the state of São Paulo and the consequences of this consolidation; the influence of governmental policies on the decision-making process of producers and industry; the state-level policies designed to expand the industry into Goiás and Mato Grosso do Sul states; infrastructure development; and the Sugarcane Agroecological Zoning policies. Through this study we conclude that the ethanol industry identified the Cerrado states as an opportunity for investment, given the good agricultural conditions for producing sugarcane, affordable land prices and favorable state-level fiscal incentive policies. In addition, there is a need for further regional development policies as the interaction between the sugarcane sector and regional government is likely to continue into the future.     

The Hegemony of Global Politics: News Coverage of Climate Change in a Small Country

Researching media coverage of climate change may shed light on the different configurations of global and domestic factors affecting journalism and politics. This article analyzes climate change coverage in Portugal from 2007 to 2014 in comparison with 14 other countries. It shows that the Portuguese press tends to reproduce the global political agenda on climate change, mainly focusing on international events associated with global political decision-making processes, instead of providing a domesticated coverage, as observed in other countries. National and local levels of action are thus obscured. The interplay between global and domestic factors—including characteristics of Portugal’s press and politics, such as national political leaders’ lack of mobilization and communication on climate change, media’s deference to powerful sources, and reliance on international news feeds—creates the conditions for global politics to play an hegemonic role in media representations, which is likely to influence public engagement with climate change.     

Pattern and rate of erosion inferred from Inca agricultural terraces in arid southern Peru

Earthworks of assumed age and their initial and current morphologies provide an ideal basis for developing and testing models for long-term landform erosion. Inca agricultural terraces abandoned at  1532 A.D in the drylands of southern Peru may be used to document morphological changes since the abandonment. The objective of this research is to determine the erosion pattern and process to estimate the erosion rate.The development of rills and channels on the Inca agricultural terraces is evidence for erosion by wash processes on slopes where the anchoring effect of vegetation is absent and loose material is available for removal. The pattern and amount of erosion from 1532–2005 A.D. is estimated by comparing elevation models of the observed morphology and reconstructed models of the original morphology of the Inca terraces. The results show that in areas of sediment accumulation surface elevation increased up to 0.5 m. Elevation lowering on the terrace treads was 0.7 m at maximum, and a temporally and spatially averaged lowering rate was 0.094 mm yr^− 1. This gives insights about how the rate of erosion occurs on currently disturbed lands in arid environments where soil resources are scarce and lands are prone to desertification.     

The impact of spatial elements on the forecasting of Spanish labour series

In this paper, we analyse the ability of a dynamic spatial panel data model without explanatory variables to explain a variable of interest, in this case employment in the fifty Spanish provinces. The best model is a dynamic fixed effect with a spatial lag structure in an equation estimated through the unconditional ML procedure. Predictions derived from this selected model are compared with those derived from fifty seasonal ARIMA models that also treat outlier observations. The results indicate that forecasts derived from a single estimated spatial panel data model are as accurate as those derived from the estimation of fifty seasonal ARIMA models. This shows that spatial panel data models play an important role in forecasting.