Washington Luiz Félix Correia Filho

Bio: Washington Luiz Félix Correia Filho is an academic researcher from Federal University of Alagoas. The author has contributed to research in topics: Geography & Normalized Difference Vegetation Index. The author has an hindex of 9, co-authored 29 publications receiving 162 citations.

Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product

Abstract: Brazilian biomes are home to a significant portion of the world’s biodiversity, with a total of 14% of existing species and still concentrate 20% of the world’s water resources. However, ch...

Climate-induced variations in global wildfire danger from 1979-2013

Abstract: Climate strongly influences global wildfire activity, and recent wildfire surges may signal fire weather-induced pyrogeographic shifts. Here we use three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013. We show that fire weather seasons have lengthened across 29.6 million km2 (25.3%) of the Earth's vegetated surface, resulting in an 18.7% increase in global mean fire weather season length. We also show a doubling (108.1% increase) of global burnable area affected by long fire weather seasons (>1.0 σ above the historical mean) and an increased global frequency of long fire weather seasons across 62.4 million km2 (53.4%) during the second half of the study period. If these fire weather changes are coupled with ignition sources and available fuel, they could markedly impact global ecosystems, societies, economies and climate.

Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts

Abstract: This paper reviews recent progress in the study and understanding of extreme seasonal events in the Amazon region, focusing on drought and floods. The review includes a history of droughts and floods in the past, in the present and some discussions on future extremes in the context of climate change and its impacts on the Amazon region. Several extreme hydrological events, some of them characterized as ‘once in a century’, have been reported in the Amazon region during the last decade. While abundant rainfall in various sectors of the basin has determined extreme floods along the river's main stem in 1953, 1989, 1999, 2009, 2012–2015, deficient rainfall in 1912, 1926, 1963, 1980, 1983, 1995, 1997, 1998, 2005 and 2010 has caused anomalously low river levels, and an increase in the risk and number of fires in the region, with consequences for humans. This is consistent with changes in the variability of the hydrometeorology of the basin and suggests that extreme hydrological events have been more frequent in the last two decades. Some of these intense/reduced rainfalls and subsequent floods/droughts were associated (but not exclusively) with La Nina/El Nino events. In addition, moisture transport anomalies from the tropical Atlantic into Amazonia, and from northern to southern Amazonia alter the water cycle in the region year-to-year. We also assess the impacts of such extremes on natural and human systems in the region, considering ecological, economic and societal impacts in urban and rural areas, particularly during the recent decades. In the context of the future climate change, studies show a large range of uncertainty, but suggest that drought might intensify through the 21st century.

Global synthesis of drought effects on cereal, legume, tuber and root crops production: A review

Abstract: As a result of climate change, drought is predicted to pose greater pressure on food production system than in the past. At the same time, crop yield co-varies with both environmental (e.g., water, temperature, aridity) and agronomic variables (i.e., crop species, soil texture, phenological phase). To improve our quantitative understanding on the effects of these co-varying factors on agricultural productivity, we synthesized previous meta-analysis studies summarizing the results of numerous independent field experiments on drought and its effect on the production of cereal, legume, root and/or tuber (root/tuber) crops. We also included new crops species that were not covered in previous meta-analyses and the effects of heat stress. Our results indicated that cereals tended to be more drought resistant than legumes and root/tubers. Most crops were more sensitive to drought during their reproductive (i.e., grains filling, tuber initiation) than during their vegetative phase, except for wheat, which was also sensitive during vegetative phase. Recovery from drought impact at reproductive phase was either: (i) unfeasible for crops experiencing damage to their reproductive organs (e.g., maize, rice) or (ii) limited for root/tuber crops, provided that water was abundant during the subsequent root/tuber bulking period. Across soil texture, the variability of yield reduction for cereals was also lower in comparison to legume or root/tuber crops, probably due to the extensive and deep rooting system of cereal crops. As crop species, plant phenology, and soil texture were important co-varying factors in determining drought-induced crop yield reduction, no single approach would be sufficient to improve crop performance during drought. Consequently, a combination of approaches, particularly site-specific management practices that consider soil conditions (i.e., intercropping, mulching, and crop rotation) and selection of crop varieties adjusted to the local climate should be adopted in order to improve the sustainability of agricultural production in a changing climate.

Analysis of climate extremes indices over northeast Brazil from 1961 to 2014

Abstract: An analysis of climate extremes indices was conducted for maximum temperatures (TMax), minimum temperatures (TMin), and daily rainfall data (PRCP) over the northeast region of Brazil for the period of 1961–2014. The indices were calculated for 96 weather stations using RClimDex software in a sub-regional study based on cluster analysis, as well as for each individual weather station, after a rigorous process of quality control, gap filling of missing values, and homogenization. The Mann-Kendall non-parametric trend test was employed to assess the statistical significance of the series. The results indicate unequivocal signs of heating. In the past decades, there were predominant trends of decrease in the percentage of cold nights (−8.4% days/decade), increase in the percentage of the number of warm nights (10.6% days/decade), increase in the number of days per decade in which the minimum temperature exceeded the threshold of 20 °C, and a trend showing an increase in heat waves. This was corroborated by the declining trend in the number of consecutive days in which TMin did not exceed the 10th percentile, and the increase in the number of consecutive days in which TMax exceeded the 90th percentile in the data distribution. With regard to PRCP, in most weather stations where there is a significant increase in consecutive dry days, there is also a trend of significant increase in consecutive wet days, thereby intensifying the seasonality, with the dry seasons becoming drier and the rainy seasons wetter. In most weather stations, a reduction was found in the total annual precipitation and in the frequency of rainy days. Moreover, in many stations, an increase in rainfall events that surpasses the threshold of 95% and 99% in the distribution was discovered. These factors highlight concerns of a region of Brazil marked by the scarcity of perennial water resources and the threat of desertification.

UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture

Abstract: Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite's output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d'Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers.