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.

     

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.     

Climatology and trend analysis of extreme precipitation in subregions of Northeast Brazil

The present paper aims of computing climatology and trend analysis of occurrence and intensity of extreme events of precipitation in subregions of Northeast Brazil (NEB). We used daily rainfall data of 148 rain gauges collected from the hydrometeorological network managed by the National Water Agency during 1972 to 2002 and used quantiles technique in order to select rainfall events. Defining heavy rainfall events as those when at least one rain gauge recorded rainfall above the 95th percentile, normal rainfall was between the 45th and 55th percentiles, and weak rainfall events were under the 5th percentile. The Mann-Kendall nonparametric test was used to calculate the linear trend of the quantity and intensity of rainfall events. The NEB was divided in five subregions using the cluster analysis based on Euclidean distance and Ward’s method: Northern coast, Northern semiarid, Northwest, Southern semiarid, and Southern coast. The results suggest that the subregions are less influenced by El Niño and La Niña, and dry areas have higher variability, with the greatest number of intense events.     

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.     

Amphipoda assemblages in a disturbed area (Alicante,Spain, Western Mediterranean)

The distribution and composition of Amphipoda assemblages were analysed off the coasts of Alicante (Spain, Western Mediterranean), a disturbed area affected by several co‐occurring anthropogenic impacts. Although differences among sampled stations were mainly related to natural parameters, anthropogenic activities were linked with changes in amphipod assemblages. Expansion of the Port of Alicante, a sewage outfall and a high salinity brine discharge could be causing the disappearance of amphipods at stations closer to these disturbances. However, the completion of port enlargement works and mitigatory dilution of the brine discharge has led to the recovery of the amphipod assemblage. Among the natural parameters, depth determines the distribution of some of the species. While Siphonoecetes sabatieri was abundant at shallow stations, Ampelisca spp., Photis longipes, Pseudolirius kroyeri, Apherusa chiereghinii and Phtisica marina were more abundant at deeper stations. Grain size and percentage of organic matter also influenced amphipod distribution, resulting in changes in species composition and in the relative percentages of different trophic groups. Species such as Ampelisca brevicornis, Perioculodes longimanus, Urothoe hesperiae and Urothoe elegans were more abundant at stations with a high content of fine sand. Carnivorous species, mainly of the Oedicerotidae family, were more abundant at those stations with a low organic matter content, while detritivorous species were more abundant at stations with a higher mud content. Among 62 identified species, three were reported for the first time from the Spanish Mediterranean coast, two species were recorded for the second time and a new species of Siphonoecetes was found, Siphonoecetes (Centraloecetes) bulborostrum. These results confirm the need for further data on amphipods from the Mediterranean Spanish coast.