Late Quaternary vegetation and climate dynamics in central-eastern Brazil: insights from a ~35k cal a bp peat record in the Cerrado biome

The late Quaternary evolution of central-eastern Brazil has been under-researched. Questions remain as to the origin of the Cerrado, a highly endangered biome, and other types of vegetation, such as the Capões – small vegetation islands of semi-deciduous and mountain forests. We investigated the factors that influenced the expansion and contraction of the Cerrado and Capões during the late Quaternary (last ~35 ka), using a multi-proxy approach: stable isotopes (δ¹³C, δ¹⁵N), geochemistry, pollen and multivariate statistics derived from a peat core (Pinheiro mire, Serra do Espinhaço Meridional). Five major shifts in precipitation, temperature, vegetation and landscape stability occurred at different timescales. Our study revealed that changes in the South Atlantic Convergence Zone (SACZ) seem to have been coeval with these shifts: from the Late Glacial Maximum to mid-Holocene the SACZ remained near (~29.6 to ~16.5k cal a bp ) and over (~16.5 to ~6.1 k cal a bp ) the study area, providing humidity to the region. This challenges previous research which suggested that climate was drier for this time period. At present, the Capões are likely to be a remnant of a more humid climate; meanwhile, the Cerrado biome seems to have stablished in the late Holocene, after ~3.1 k cal a bp .     

The information system of the French Peatland Observation Service: Service National d'Observation Tourbières – A valuable tool to assess the impact of global changes on the hydrology and biogeochemistry of temperate peatlands through long term monitoring

Mitigating and adapting to global changes requires a better understanding of the response of the Biosphere to these environmental variations. Human disturbances and their effects act in the long term (decades to centuries) and consequently, a similar time frame is needed to fully understand the hydrological and biogeochemical functioning of a natural system. To this end, the ‘Centre National de la Recherche Scientifique’ (CNRS) promotes and certifies long-term monitoring tools called national observation services or ‘Service National d'Observation’ (SNO) in a large range of hydrological and biogeochemical systems (e.g., cryosphere, catchments, aquifers). The SNO investigating peatlands, the SNO ‘Tourbières’, was certified in 2011 ( https://www.sno-tourbieres.cnrs.fr/ ). Peatlands are mostly found in the high latitudes of the northern hemisphere and French peatlands are located in the southern part of this area. Thus, they are located in environmental conditions that will occur in northern peatlands in coming decades or centuries and can be considered as sentinels. The SNO Tourbières is composed of four peatlands: La Guette (lowland central France), Landemarais (lowland oceanic western France), Frasne (upland continental eastern France) and Bernadouze (upland southern France). Thirty target variables are monitored to study the hydrological and biogeochemical functioning of the sites. They are grouped into four datasets: hydrology, fluvial export of organic matter, greenhouse gas fluxes and meteorology/soil physics. The data from all sites follow a common processing chain from the sensors to the public repository. The raw data are stored on an FTP server. After operator or automatic processing, data are stored in a database, from which a web application extracts the data to make them available ( https://data-snot.cnrs.fr/data-access/ ). Each year at least, an archive of each dataset is stored in Zenodo, with a digital object identifier (DOI) attribution ( https://zenodo.org/communities/sno_tourbieres_data/ ).     

MARC: a robust method for multiple-aspect trajectory classification via space,time, and semantic embeddings

ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.     

Correction to: Spatial distribution and estimation of rainfall trends and erosivity in the Epitácio Pessoa reservoir catchment,Paraíba,Brazil

Natural Hazards - The article “Spatial distribution and estimation of rainfall trends and erosivity in the Epitácio Pessoa reservoir catchment, Paraíba, Brazil”, written by da...     

Studies of Plasma Dynamics in a Small Plasma Focus Operating in Hydrogen-Argon Mixtures

We report on experimental observations in PFP-I, a small 3.8 kJ plasma focus, which is operated in Hydrogen-Argon mixtures to investigate the effect of parameter modifications on the overall performance of the device. An extensive array of diagnostics is been used, which includes voltage and current probes in the external circuit, a novel small magnetic probes array located along the cathode rods, filtered PIN diodes located side and end on, and filtered multi-pinhole and slit-wire X-ray camera. Extended operating range from below 0.2 Torr upwards has been achieved. Hot spot formation has been investigated as a function of H₂-Ar mixing ratio. Hot spot sizes around 150 m in the soft X-ray region, have been inferred from the slit-wire measurements. Pin-hole time integrated X-ray pictures and time resolved PIN diode measurements have been used to determine characteristic hot-spot temperatures in the 350 to 450 eV range.     

Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

Background

Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them.

Results

Our results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m^?2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R² ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha^?1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha^?1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R² ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha^?1 [between 0.69–0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha^?1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R² was between 0.58–0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha^?1 for the echo-based model, whereas for the CHM R² was between 0.37–0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha^?1.

Conclusions

Metrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m^?2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m^?2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m^?2.

     

Cenozoic tectonics of Amazon Mouth Basin

 The Cenozoic shelf margin of the Amazon Mouth Basin is characterized by a thick prograding prism of siliciclastic sediments. This prism, composed mainly of Upper Miocene and younger sediments, overlies a Lower Tertiary carbonate shelf. Two tectonic–sedimentary models for the area were developed with the aid of new deep-reflection seismic data. Gravitational tectonics dominate the regional geological framework. Tensional stresses are created near the shelf margin, and compressional features dominate at the base of the slope. The morphology of this compressional zone is closely associated with the St. Paul Fracture Zone and the boundary between continental and oceanic crusts. Received: 20 August 1996 / Revision received: 11 June 1998     

Geological Facies Recovery Based on Weighted $$\ell _1$$ ℓ 1 -Regularization

Mathematical Geosciences - A weighted compressed sensing (WCS) algorithm is proposed for the problem of channelized facies reconstruction from pixel-based measurements. This strategy integrates...     

Application of open tools and datasets to probabilistic modeling of road traffic disruptions due to earthquake damage

The disruption of a transportation network can have a high social and economic impact on the welfare of a society, as it can significantly affect the daily routines of a community. Although many studies have focused on the estimation of physical risk in the components that compose these networks, only a limited number have analyzed their interconnections and impact in the traffic flow. The present study analyzes how earthquake damage can disrupt the road network in an urban environment, and how this will influence the ability of the population to travel. Traffic due to daily commutes is modeled for different layouts of the network, corresponding to possible disruptions caused by earthquake damage. The duration and length of each trip were calculated both for the undamaged network conditions and for the disrupted network. The increase in the median duration and length of each trip allows estimating the economic loss for each event due to drivers' delay. By combining the probability of a specific road being blocked with its number of users, the average number of affected vehicles was estimated, and the most critical segments identified. The methodology was applied to a case study concerning the road network of the area around the Italian city of Messina in Sicily. The results were calculated for both a repetition of the well-known historical event of 1908 and a set of simulated earthquakes consistent with the national probabilistic seismic hazard model of Italy.     

Separation of PP- and PS-wave reflected seismic data using two-dimensional finite offset common-reflection-surface traveltime approximation

Recently, the interest in PS-converted waves has increased for several applications, such as sub-basalt layer imaging, impedance estimates and amplitude-versus-offset analysis. In this study, we consider the problem of separation of PP- and PS-waves from pre-stacked multicomponent seismic data in two-dimensional isotropic medium. We aim to demonstrate that the finite-offset common-reflection-surface traveltime approximation is a good alternative for separating PP- and PS-converted waves in common-offset and common shot configurations by considering a two-dimensional isotropic medium. The five parameters of the finite-offset common-reflection-surface are firstly estimated through the inversion methodology called very fast simulated annealing, which estimates all parameters simultaneously. Next, the emergence angle, one of the inverted parameters, is used to build an analytical separation function of PP and PS reflection separation based on the wave polarization equations. Once the PP- and PS-converted waves were separated, the sections are stacked to increase the signal-to-noise ratio using the special curves derived from finite-offset common-reflection-surface approximation. We applied this methodology to a synthetic dataset from simple-layered to complex-structured media. The numerical results showed that the inverted parameters of the finite offset common-reflection-surface and the separation function yield good results for separating PP- and PS-converted waves in noisy common-offset and common shot gathers.