Population dynamics of cryptogenic calcarean sponges (Porifera,Calcarea) in Southeastern Brazil

The calcarean sponge Paraleucilla magna is classified as being an invasive species on the Mediterranean Sea, where it causes economic damages to mollusc farms. On the Brazilian coast, this species is considered to be cryptogenic, and information on its ecology is scarce. The same is true for Sycettusa hastifera, another calcarean sponge with a worldwide distribution. Data on the ecology of these species could help in elucidating their potential to become a threat if they are found to be exotic species in Brazil. In the present work, we studied habitat selection, growth and mortality of early juveniles of P. magna and habitat selection of S. hastifera in a Marine Reserve from Southeastern Brazil, where these species are abundant in the benthic community. Granite plates were used for habitat selection analysis, varying in substrate inclination (vertical and horizontal) and exposure to light and hydrodynamism (exposed and sheltered). To analyse the growth and mortality rates, sponges were mapped and then measured once a week for 10 weeks. If a monitored sponge was not found in the following week, it was considered to be dead. Our results showed that, although P. magna and S. hastifera are capable of inhabiting substrates exposed to different environmental conditions, they showed habitat preferences. Growth of the juveniles of P. magna seemed not to have damaged any neighbouring invertebrates. The mortality of juveniles of this species was higher during the first 2 weeks of life but its causes could not be elucidated.     

Development,urban planning and political decisions. A triad that built territories at risk

Natural Hazards - Chilean geography exposes the country to high-level risks such as earthquakes and tsunamis. The disasters of 1930, 1960, 2010, and 2014 testify to the continuous link between...     

Anisotropic visco-hypoplasticity

Apart from time-driven creep or relaxation, most viscoplastic models (without plastic and viscous strain separation) generate no or a very limited accumulation of strain or stress due to cyclic loading. Such pseudo-relaxation (or pseudo-creep) is either absent or dwindles too fast with increasing OCR. For example, the accumulation of the pore water pressure and eventual liquefaction due to cyclic loading cannot be adequately reproduced. The proposed combination of a viscous model and a hypoplastic model can circumvent this problem. The novel visco-hypoplasticity model presented in the paper is based on an anisotropic preconsolidation surface. It can distinguish between the undrained strength upon triaxial vertical loading and horizontal loading. The strain-induced anisotropy is described using a second-order structure tensor. The implicit time integration with the consistent Jacobian matrix is presented. For the tensorial manipulation including numerous Fréchet derivatives, a special package has been developed within the algebra program MATHEMATICA (registered trade mark of Wolfram Research Inc.). The results can be conveniently coded using a special FORTRAN 90 module for tensorial operations. Simulations of element tests from biaxial apparatus and FE calculations are also shown.     

Multiple Random Walk Simulation: A Fast Method to Map Grade Uncertainty with Large Datasets

Multiple Random Walk Simulation consists of a methodology adapted to run fast simulations if close-spaced data are abundant (e.g., short-term mining models). Combining kriging with the simulation of random walks attempts to approximate traditional simulation algorithm results but at a computationally faster way when there is a large amount of conditioning samples. This paper presents this new algorithm illustrating the situations where the method can be used properly. A synthetic study case is presented in order to illustrate the Multiple Random Walk Simulation and to analyze the speed and goodness of its results against the ones from using Turning Bands Simulation and Sequential Gaussian Simulation.     

High-performance three-horizon composition algorithm for large-scale terrains

This work presents a high-performance algorithm to compute the horizon in very large high-resolution DEMs. We used Stewart's algorithm as the core of our implementation and considered that the horizon has three components: the ground, near, and far horizons. To eliminate the edge-effect, we introduced a multi-resolution halo method. Moreover, we used a new data partition approach, to substantially increase the parallelism in the algorithm. In addition, several optimizations have been applied to considerably reduce the number of arithmetical operations in the core of the algorithm. The experimental results have demonstrated that by applying the above-described contributions, the proposed algorithm is more than twice faster than Stewart's algorithm while maintaining the same accuracy.     

A new multiscale scheme for computing statistical moments in single phase flow in heterogeneous porous media

In this work we develop a new multiscale procedure to compute numerically the statistical moments of the stochastic variables which govern single phase flow in heterogeneous porous media. The technique explores the properties of the log-normally distributed hydraulic conductivity, characterized by power-law or exponential covariances, which shows invariance in its statistical structure upon a simultaneous change of the scale of observation and strength of heterogeneity. We construct a family of equivalent stochastic hydrodynamic variables satisfying the same flow equations at different scales and strengths of heterogeneity or correlation lengths. Within the new procedure the governing equations are solved in a scaled geology and the numerical results are mapped onto the original medium at coarser scales by a straightforward rescaling. The new procedure is implemented numerically within the Monte Carlo algorithm and also in conjunction with the discretization of the low-order effective equations derived from perturbation analysis. Numerical results obtained by the finite element method show the accuracy of the new procedure to approximated the two first moments of the pressure and velocity along with its potential in reducing drastically the computational cost involved in the numerical modeling of both power-law and exponential covariance functions.     

Analysis of aquifer vulnerability and water quality using SINTACS and geographic weighted regression

Aquifer vulnerability and water quality were assessed in the Central Valleys of Oaxaca (Mexico) using the SINTACS method, based on a geographic information system. SINTACS layers were prepared using data such as climate (rainfall and temperature), water table, hydraulic conductivity, geology, soil type and topographic model. Maps for water quality index (WQI), contamination index and pollution sources index (PSI) were also obtained by this work. Groundwater quality in the Central Valleys may be affected by two factors, those with an anthropogenic origin and those with natural origin. High vulnerability values are located in the valleys of the basin, where granular sediments are exposed. Low vulnerability values are distributed in the basin??s ranges, where metamorphic rocks are found. Given that many of the zones with the highest groundwater vulnerability values correspond to zones with the greatest PSI values, there is great risk of groundwater contamination for the area of study because external (indicated by PSI) and internal (indicated by SINTACS) factors that cause pollution can be frequently observed in the same place. Geographic weighted regression (GWR) is used to test the dependency between WQI as dependent variable and SINTACS, PSI, Urban localities, Agriculture, Pastures and Rivers as predictors. The results indicate the non-stationary behavior of the dependent variable with respect to the predictors. While the obtained GWR models used to model WQI cannot be used in practical situations to predict the behavior of said variable, they can be used to estimate the degree to which the predictors influence the variable of interest.     

A robust debris-flow and GLOF risk management strategy for a data-scarce catchment in Santa Teresa,Peru

The town of Santa Teresa (Cusco Region, Peru) has been affected by several large debris-flow events in the recent past, which destroyed parts of the town and resulted in a resettlement of the municipality. Here, we present a risk analysis and a risk management strategy for debris-flows and glacier lake outbursts in the Sacsara catchment. Data scarcity and limited understanding of both physical and social processes impede a full quantitative risk assessment. Therefore, a bottom-up approach is chosen in order to establish an integrated risk management strategy that is robust against uncertainties in the risk analysis. With the Rapid Mass Movement Simulation (RAMMS) model, a reconstruction of a major event from 1998 in the Sacsara catchment is calculated, including a sensitivity analysis for various model parameters. Based on the simulation results, potential future debris-flows scenarios of different magnitudes, including outbursts of two glacier lakes, are modeled for assessing the hazard. For the local communities in the catchment, the hazard assessment is complemented by the analysis of high-resolution satellite imagery and fieldwork. Physical, social, economic, and institutional vulnerability are considered for the vulnerability assessment, and risk is eventually evaluated by crossing the local hazard maps with the vulnerability. Based on this risk analysis, a risk management strategy is developed, consisting of three complementing elements: (i) standardized risk sheets for the communities; (ii) activities with the local population and authorities to increase social and institutional preparedness; and (iii) a simple Early Warning System. By combining scientific, technical, and social aspects, this work is an example of a framework for an integrated risk management strategy in a data scarce, remote mountain catchment in a developing country.