Modeling olive-crop forecasting in Tunisia

Tunisia is the world’s second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia’s five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993–2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.     

Drought in Northeast Brazil—past,present, and future

This study provides an overview of the drought situation in Northeast Brazil for the past, present, and future. Droughts affect more people than any other natural hazard owing to their large scale and long-lasting nature. They are recurrent in the region and while some measures have been taken by the governments to mitigate their impacts, there is still a perception that residents, mainly in rural areas, are not yet adapted to these hazards. The drought affecting the Northeast from 2012 to 2015, however, has had an intensity and impact not seen in several decades and has already destroyed large swaths of cropland, affecting hundreds of cities and towns across the region, and leaving ranchers struggling to feed and water cattle. Future climate projections for the area show large temperature increases and rainfall reductions, which, together with a tendency for longer periods with consecutive dry days, suggest the occurrence of more frequent/intense dry spells and droughts and a tendency toward aridification in the region. All these conditions lead to an increase in evaporation from reservoirs and lakes, affecting irrigation and agriculture as well as key water uses including hydropower and industry, and thus, the welfare of the residents. Integrating drought monitoring and seasonal forecasting provides efficient means of assessing impacts of climate variability and change, identifying vulnerabilities, and allowing for better adaptation measures not only for medium- and long-term climate change but also for extremes of the interannual climate variability, particularly droughts.

     

An urban geomonumental route focusing on the petrological and decay features of traditional building stones used in Madrid, Spain

The stone traditionally used to build cities contributes to their personality and attests to the geological substrate on which they stand. While stone decay in the built heritage can be attributed to a number of causes, anthropic activity has a particularly significant impact. The geomonumental routes project is one of the initiatives proposed in recent years for urban routes that convey geological fundamentals by observing the rocks present in heritage structures. Its innovative approach addresses traditional stone properties, original quarrying sites and mechanisms of decay. Madrid’s Royal Palace is a fine example of the use of traditional building stone in the centre of the Iberian Peninsula. In the geomonumental route proposed, the building doubles as an in situ laboratory that affords an overview of the main petrological properties of the two traditional stones most commonly used in the city’s built heritage, the forms of decay they are subject and the factors underlying such alterations. This route constitutes a tool for showing the main petrological features and decay forms in traditional building stones found in urban heritage façades, with a special focus on anthropic impact, primarily air pollution and the use of conservation treatments that time has proven to be unsuitable.     

Copper Biosorption by Spent Coffee Ground: Equilibrium,Kinetics, and Mechanism

The potential use of spent coffee ground (SCG) for the removal of copper has been investigated as a low‐cost adsorbent for the biosorption of heavy metals. Adsorption batch experiments were conducted to determine isotherms and kinetics. The biosorption equilibrium data were found to fit well the Freundlich model and an experimental maximum biosorption capacity of copper ions 0.214 mmol/g was achieved. The biosorption kinetics of SCG was studied at different adsorbate concentrations (0.1–1.0 mM) and stirring speeds (100–400/min). The results showed an increase in the copper ion uptake with raising the initial metal concentration and the kinetic data followed the pseudo‐second order rate expression. The effect of stirring speed was a significant factor for the external mass transfer resistance at 100/min and coefficients were estimated by the Mathews and Weber model. Biosorption of copper ions onto SCG was observed to be related mainly with the release of calcium and hydrogen ions suggesting that biosorption performance by SCG can be attributed to ion‐exchange mechanism with calcium and hydrogen ions neutralizing the carboxyl and hydroxyl groups of the biomass.     

Aquifer interactions with a polluted mountain river of Nicaragua

The interactions between a stream and nearby shallow aquifers were investigated in a mountain basin being polluted by mercury released during mining in central Nicaragua. Hourly data series of water levels and temperatures were analysed using cross‐correlation. Resistivity imaging was used to map the subsurface and to complement the hydrological data interpretation. The results show the complex hydrogeological conditions that characterize the region, with weathering and fractured rock as main contributors to groundwater transport. The resistivity images suggest the presence of two vertical dykes perpendicular to the stream, and zones rich in clay. The data series indicate a rapid response from the aquifers to recharge events, followed by immediate discharge on a yearly basis. Furthermore, alternating periods of stream infiltration and aquifer discharge were identified. This work demonstrates that surface water pollution is a threat to groundwater quality in the area. Copyright © 2007 John Wiley & Sons, Ltd.     

Uncertainty quantification in numerical simulation of particle-laden flows

Numerical models can help to push forward the knowledge about complex dynamic physical systems. Modern approaches employ detailed mathematical models, taking into consideration inherent uncertainties on input parameters (phenomenological parameters or boundary and initial conditions, among others). Particle-laden flows are complex physical systems found in nature, generated due to the (possible small) spatial variation on the fluid density promoted by the carried particles. They are one of the main mechanisms responsible for the deposition of sediments on the seabed. A detailed understanding of particle-laden flows, often referred to as turbidity currents, helps geologists to understand the mechanisms that give rise to reservoirs, strategic in oil exploration. Uncertainty quantification (UQ) provides a rational framework to assist in this task, by combining sophisticated computational models with a probabilistic perspective in order to deepen the knowledge about the physics of the problem and to access the reliability of the results obtained with numerical simulations. This work presents a stochastic analysis of sediment deposition resulting from a turbidity current considering uncertainties on the initial sediment concentrations and particles settling velocities. The statistical moments of the deposition mapping, like other important features of the currents, are approximated by a Sparse Grid Stochastic Collocation method that employ a parallel flow solver for the solution of the deterministic problems associated to the grid points. The whole procedure is supported and steered by a scientific workflow management engine designed for high performance computer applications.     

Fusion of airborne LiDAR and multispectral sensors reveals synergic capabilities in forest structure characterization

Forest stand structure is an important concept for ecology and planning in sustainable forest management. In this article, we consider that the incorporation of complementary multispectral information from optical sensors to Light Detection and Ranging (LiDAR) may be advantageous, especially through data fusion by back-projecting the LiDAR points onto the multispectral image. A multivariate data set of both LiDAR and multispectral metrics was related with a multivariate data set of stand structural variables measured in a Scots pine forest through canonical correlation analysis (CCA). Four statistically significant pairs of canonical variables were found, which explained 83.0% accumulated variance. The first pair of canonical variables related indicators of stand development, i.e. height and volume, with LiDAR height metrics. CCA also found attributes describing stand density to be related to LiDAR and spectral variables determining canopy coverage. Other canonical variables pertained to Lorenz curve-derived attributes, which are measures of within-stand tree size variability and heterogeneity, able to discriminate even-sized from uneven-sized stands. The most relevant result was to find that metrics derived from the multispectral sensor showed significant explanatory potential for the prediction of these structural attributes. Therefore, we concluded that metrics derived from the optical sensor have potential for complementing the information from the LiDAR sensor in describing structural properties of forest stands. We recommend the use of back-projecting for jointly exploiting the synergies of both sensors using similar types of metrics as they are customary in forestry applications of LiDAR.