944 resultados para climatic extremes
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Before using the basic precipitation data in any agroclimatic study to assess the productivity it is important to check the data series for homogeneity. For this purpose data of 105 locations for the period 1912-1981 over northeast Brazil were used. The preliminary study indicate nonhomogeneity in the time series during 1940's at few locations. The amplitude of variation of time series when taken as 10-year moving average show quite different for different regions. It appears that this amplitude is related to time of onset of effective rains in some extent. There is also great diversity in the fluctuations. They present a great regional diversity. Some diversity. Some of the data in the low latitudes indicate presence of four cycles namely 52, 26, 13 & 6.5. years. The 52-year cycle is also evident in the case of onset of southwest Monsoon over a low latitude zone (Kerala Coast) in India. In the case of south Africa the prominent cycles are 60, 30, 15 & 10 similar situation appears to be present in the higher latitudes of northeast Brazil.
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This paper analyses the advantages and limitations in using the Troll, Hargreaves and modified Thornthwaite approaches for the demarcation of the semi-arid tropics. Data from India, Africa, Brazil, Australia and Thailand, were used for the comparison of these three methods. The modified Thornthwaite approach provided the most relevant agriculturally oriented demarcation of the semi-arid tropics. This method in not only simple, tut uses input data that are avaliable for a global network of stations. Using this method the semi-arid tropics include major dryland or rainfed agricultural zones with annual rainfall varying from about 400 to 1,250 mm. Major dryland crops are pearl millet, sorghum, pigeonpea and groundnut. This paper also presents the brief description of climate, soils and farming systems of the semi-arid tropics.
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Doutoramento em Engenharia Florestal - Instituto Superior de Agronomia - UL
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2016
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The elevational distributions of tropical treelines are thought to be determined by temperature, and are predicted to shift upslope in response to global warming. In contrast to this hypothesis, global-scale studies have shown that only half of all studied treelines are shifting upslope. Understanding how treelines will respond to climate change has important implications for global biodiversity, especially in the tropics, because tropical treelines generally represent the upper-elevation distribution limit of the hyper-diverse cloudforest ecosystem. In Chapter 1, I introduce the idea that grasslands found above tropical treelines may represent a potential grass ceiling which forest species cannot cross or invade. I use an extensive literature review to outline potential mechanisms which may be acting to stabilize treeline and prevent forest expansion into high-elevation grasslands. In Chapters 2-4, I begin to explore these potential mechanisms through the use of observational and experimental methods. In Chapter 2, I show that there are significant numbers of seedlings occurring just outside of the treeline in the open grasslands and that seed rain is unlikely to limit seedling recruitment above treeline. I also show that microclimates outside of the closed-canopy cloudforest are highly variable and that mean temperatures are likely a poor explanation of tropical treeline elevations. In Chapter 3, I show that juvenile trees maintain freezing resistances similar to adults, but nighttime radiative cooling near the ground in the open grassland results in lower cold temperatures relative to the free atmosphere, exposing seedlings of some species growing above treeline to lethal frost events. In Chapter 4, I use a large-scale seedling transplant experiment to test the effects of mean temperature, absolute low temperature and shade on transplanted seedling survival. I find that increasing mean temperature negatively affects seedling survival of two treeline species while benefiting another. In addition, low temperature extremes and the presence of shade also appear to be important factors affecting seedling survival above tropical treelines. This work demonstrates that mean temperature is a poor predictor of tropical treelines and that temperature extremes, especially low temperatures, and non-climatic variables should be included in predictions of current and future tropical treeline dynamics.^
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The main purpose of this study is to assess the relationship between four bioclimatic indices for cattle (environmental stress, heat load, modified heat load, and respiratory rate predictor indices) and three main milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when the cows use the natural pasture. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty information in the confidence intervals. The main results identify an interesting relationship between the milk compounds and climate indices under all climate conditions. During spring, there are reasonably high correlations between the fat and protein concentrations vs. the climate indices, whereas there are insignificant dependencies between the milk yield and climate indices. During summer, the correlation between the fat and protein concentrations with the climate indices decreased in comparison with the spring results, whereas the correlation for the milk yield increased. This methodology is suggested for studies investigating the impacts of climate variability/change on food and agriculture using short term data considering uncertainty.
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The Ibero-American Network of Viticulture, a component of the program of agricultural technology of the CYTED (Ibero-American Program of Science and Technology for Development), is developing the project ?Zoning Methodology and Application in Viticultural Regions of Ibero-America?. An objective of the project is the climatic characterization of this large viticultural region with the participation of ten countries: Argentine, Bolivia, Brazil, Chile, Cuba, Spain, Mexico, Peru, Portugal, and Uruguay.
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2005
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2011
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2012
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The wine production is an important activity in many Ibero-American countries. The wine producer regions of these countries configure a large use of different climate types and viticultural climates. In a vitivinicultural zoning project of CYTED (Ibero-American Program for Science, Technology and Development), a viticultural climatic characterization was done in this macro viticultural region. The project have assembled a climatic database that characterizes the viticultural regions, including relevant variables for viticulture: air temperature (mean, maximum, and minimum), precipitation, relative humidity, solar radiation, number of sunshine hours, wind speed, and evapotranspiration. Using indices of the Geoviticulture MCC System (HI, CI and DI), more than 70 viticultural regions in different countries (Argentina, Bolivia, Brazil, Chile, Cuba, Spain, Mexico, Peru, Portugal and Uruguay) were characterized according to its viticultural climatic. The results, which will be integrated to the worldwide database of the MCC System, showed that the Ibero-American viticulture is placed in a wide range of climatic groups of the wine producing regions around the world. This article presents the climatic groups found in Ibero-America, identifying also some new climatic groups not yet found in other regions of the world. This work also identifies some climatic groups not found in Ibero-America viticulture. The research has also highlighted viticultural areas characterized by climates with ?intra-annual climatic variability?, with the potential to produce more than one growing cycle per year. The results allow to conclude that the wide variability and climatic diversity present in Ibero-America may be one of the reasons to explain the diversity in terms of wine types, sensorial characteristics, typicity and uniqueness of wines produced on this macro-region.
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2012
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The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.
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Reliable budget/cost estimates for road maintenance and rehabilitation are subjected to uncertainties and variability in road asset condition and characteristics of road users. The CRC CI research project 2003-029-C ‘Maintenance Cost Prediction for Road’ developed a method for assessing variation and reliability in budget/cost estimates for road maintenance and rehabilitation. The method is based on probability-based reliable theory and statistical method. The next stage of the current project is to apply the developed method to predict maintenance/rehabilitation budgets/costs of large networks for strategic investment. The first task is to assess the variability of road data. This report presents initial results of the analysis in assessing the variability of road data. A case study of the analysis for dry non reactive soil is presented to demonstrate the concept in analysing the variability of road data for large road networks. In assessing the variability of road data, large road networks were categorised into categories with common characteristics according to soil and climatic conditions, pavement conditions, pavement types, surface types and annual average daily traffic. The probability distributions, statistical means, and standard deviation values of asset conditions and annual average daily traffic for each type were quantified. The probability distributions and the statistical information obtained in this analysis will be used to asset the variation and reliability in budget/cost estimates in later stage. Generally, we usually used mean values of asset data of each category as input values for investment analysis. The variability of asset data in each category is not taken into account. This analysis method demonstrated that it can be used for practical application taking into account the variability of road data in analysing large road networks for maintenance/rehabilitation investment analysis.
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Monitoring urban growth and land-use change is an important issue for sustainable infrastructure planning. Rapid urban development, sprawl and increasing population pressure, particularly in developing nations, are resulting in deterioration of infrastructure facilities, loss of productive agricultural lands and open spaces, pollution, health hazards and micro-climatic changes. In addressing these issues effectively, it is crucial to collect up-to-date and accurate data and monitor the changing environment at regular intervals. This chapter discusses the role of geospatial technologies for mapping and monitoring the changing environment and urban structure, where such technologies are highly useful for sustainable infrastructure planning and provision.