Influence of climatic variables on crown condition in pine forests of Northern Spain

Producción CientíficaThe aim of this study was to find relationships between crown condition and some climatic parameters to identify which are those having a main influence on crown condition, and how this influence is shown in the tree (crown transparency), and to contribute to the understanding of how these parameters will affect under future climate change scenarios

Nature Relation Between Climatic Variables and Cotton Production

This study investigated the effect of climatic variables on flower and boll production and retention in cotton (Gossypium barbadense). Also, this study investigated the relationship between climatic factors and production of flowers and bolls obtained during the development periods of the flowering and boll stage, and to determine the most representative period corresponding to the overall crop pattern. Evaporation, sunshine duration, relative humidity, surface soil temperature at 1800 h, and maximum air temperature, are the important climatic factors that significantly affect flower and boll production. The least important variables were found to be surface soil temperature at 0600 h and minimum temperature. There was a negative correlation between flower and boll production and either evaporation or sunshine duration, while that correlation with minimum relative humidity was positive. Higher minimum relative humidity, short period of sunshine duration, and low temperatures enhanced flower and boll formation

How perilous are broad-scale correlations with environmental variables?

Many studies correlate geographic variation of biotic variables (e.g., species ranges, species richness, etc.) with variation in environmental variables (climate, topography, history). Often, the resulting correlations are interpreted as evidence of causal links. However, both the dependent and independent variables in these analyses are strongly spatially structured. Several studies have suggested that spatially structured variables may be significantly correlated with one another despite the absence of a causal link between them. In this study we ask: if two variables are spatially structured, but causally unrelated, how strong is the expected correlation between them? As a specific example, we consider the correlations between broad-scale variation in gamma species richness and climatic variables. Are these correlations likely to be statistical artefacts? To answer these questions, we randomly generated pseudo-climatic variables that have the same range and spatial autocorrelation as temperature and precipitation in the Americas. We related mammal and bird species richness both to the real and the pseudo-climatic variables. We also observed the correlations among pseudo-climate simulations. Correlations among randomly generated, spatially unstructured, variables are very small. In contrast, the median correlations between spatially structured variables are near r2=0.1 – 0.3, and the 95% confidence limits extend to r2=0.6 – 0.7. Viewing this as a null expectation, given spatially structured variables, it is worth nothing that published richness–climate correlations are typically marginally stronger than these values. However, many other published richness–environment correlations would fail this test. Tests of the “predictive ability” of a correlation cannot reliably distinguish correlations due to spatial structure from causal relationships. Our results suggest a three-part update of Tobler’s “First Law of Geography”: #1) Everything in geography that is spatially structured will be collinear. #2) Near things are more related than distant things. #3) The more strongly spatially structured two variables are, the stronger the collinearity between them will be

Assessing the influence of climatic variables on electricity demand

The electricity demand is significantly dependent on the weather information. Such weather information is comprised of different climatic variables such as temperature, humidity, wind speed, evaporation, rain fall and solar exposure which constantly change. Therefore, analysing the impacts of these variables on demand is necessary for predicting the future change in demand. In this paper, the cooling and heating degree days are utilised to capture the relationship between the per capita demand to temperature, which is one of the key climatic variables. In addition, Pearson correlation analysis has been employed to investigate the interdependency between different climatic variables and electricity demand. Finally, back-ward elimination based multiple regression is used to exclude non-significant climatic variables and evaluate the sensitivity of significant variables to the electricity demand. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The results reveal that the climatic variables such as heating degree days, humidity, evaporation, and wind speed predominantly affect the electricity demand of the state of New South Wales

Climatic differences and Economic Growth across Italian Provinces: First Empirical Evidence

The purpose of this study consists in verifying if climatic differences can help to explain the different economic growths path across Italian provinces. Focusing on literature on economic convergence on one hand, and that on economics of climate on the other, the work depicts how climatic variables can enter into the traditional Solows neoclassical growth model developing two alternative models. Afterwards, it tests whether climatic characteristics actually exert an influence on economic convergence using an original climate dataset composed by average yearly min and max temperatures (C), humidity grade (%), number of frost-days and annual precipitations (mm) for 58 Italian Provinces uniformly distributed over the Peninsula. The results, obtained through the Arellano-Bond GMM estimator, show how some of climatic variables employed in this study actually affect the level of Provincial income.Climate; Economic growth; Convergence; Italian Provinces

Activity schedule and foraging in Protopolybia sedula (Hymenoptera, Vespidae)

Protopolybia sedula is a social swarming wasp, widely spread throughout many countries in the Americas, including most of Brazil. Despite its distribution, studies of its behavioral ecology are scarce. This study aimed to describe its foraging activity and relation to climatic variables in the city of Juiz de Fora in southeastern Brazil. Three colonies were under observation between 07:00 and 18:00 during April 2012, January 2013, and March 2013. Every 30 minutes, the number of foragers leaving and returning to the colony was registered along with air temperature and relative humidity. Activity began around 07:30¸ increased between 10:30 and 14:30, and ended around 18:30. A mean of 52.7 exits and 54 returns were measured every 30 minutes. The daily mean values were 1,107 ± 510.6 exits and 1,135 ± 854.8 returns. Only one colony showed a significant correlation between forager exits and temperature (rs = 0.8055; P \u3c 0.0001) and between exits and relative humidity (rs = -0.7441; P = 0.0001). This paper shows that climatic variables are likely to have little control on the foraging rhythm of P. sedula when compared to other species, suggesting the interaction of other external and internal factors as stimuli of species foraging behavio

Conservation priorities for Prunus africana defined with the aid of spatial analysis of genetic data and climatic variables

Conservation priorities for Prunus africana, a tree species found across Afromontane regions, which is of great commercial interest internationally and of local value for rural communities, were defined with the aid of spatial analyses applied to a set of georeferenced molecular marker data (chloroplast and nuclear microsatellites) from 32 populations in 9 African countries. Two approaches for the selection of priority populations for conservation were used differing in the way they optimize representation of intra-specific diversity of P. africana across a minimum number of populations. The first method (Si) was aimed at maximizing genetic diversity of the conservation units and their distinctiveness with regard to climatic conditions, the second method (S2) at optimizing representativeness of the genetic diversity found throughout the species' range. Populations in East African countries (especially Kenya and Tanzania) were found to be of great conservation value, as suggested by previous findings. These populations are complemented by those in Madagascar and Cameroon. The combination of the two methods for prioritization led to the identification of a set of 6 priority populations. The potential distribution of P. africana was then modeled based on a dataset of 1,500 georeferenced observations. This enabled an assessment of whether the priority populations identified are exposed to threats from agricultural expansion and climate change, and whether they are located within the boundaries of protected areas. The range of the species has been affected by past climate change and the modeled distribution of P. africana indicates that the species is likely to be negatively affected in future, with an expected decrease in distribution by 2050. Based on these insights, further research at the regional and national scale is recommended, in order to strengthen P. africana conservation efforts

Fourier Representation of Climatic Data of Kothara-Kutch

Fourier series representations of some climatic variables were developed using data from the site of an experimental greenhouse at Kothara (Kutch). Hourly data was averaged over a month to yield a profile of an average day of that month. That was put through harmonic analysis to determine Fourier coefficients . Analytical expressions would be useful to those working on modelling.

Future potential evapotranspiration changes and contribution analysis in Zhejiang Province, East China

PublishedJournal ArticleThis is the final version of the article. Available from Wiley via the DOI in this record.Potential evapotranspiration is an important component of hydrological modeling. In this study, the objective is to project potential evapotranspiration in the future period 2011-2040 and understand their changes in Zhejiang Province, East China. The sensitivity of potential evapotranspiration to five climatic variables (solar radiation, daily minimum and maximum air temperature, relative humidity, and wind speed) is analyzed based on observation data from 1955-2008 using a global sensitivity analysis method, Sobol's method. The changes in potential evapotranspiration during the future period are generated using one regional climate model, Providing Regional Climates for Impacts Studies, with two global climate models, ECHAM5 and Hadley Centre Coupled Model version 3, and their causes are analyzed based on sensitivity analysis results. Global sensitivity analysis results reveal substantial spatial-temporal variations in the sensitivity of potential evapotranspiration to climatic variables and unignorable interactions among climatic variables. Rather similar spatial change patterns of annual mean potential evapotranspiration (PET) are generated for both general circulation models; however, seasonal or monthly changes are very different due to different spatial-temporal changes in climatic variables. Different contributory sources to potential evapotranspiration changes are identified at different months and stations; the PET changes in 2011-2040 are mainly due to three climatic variables including solar radiation, relative humidity, and daily minimum temperature. © 2014. American Geophysical Union. All Rights Reserved

Model Selection, Forecasting and Monthly Seasonality of Hotel Nights in Denmark

Foreigners’ demand for hotel nights in Denmark by nationality are examined using monthly time series covering 30years, and divided into 11 nationalities. Special attention is given to the role of seasonality. Three univariate seasonal presentations of non-stationary data with different characteristics are considered, a stochastic, deterministic, and an error correction mechanism (ECM) approach taking into account economic as well as climatic variables. Based on a presentation of different measures to evaluate the forecasting performance a model selection is under taken. It is found that the single variable presentations in most cases are superior to the ECM. On the other hand the ECM presentation provides a more detailed description of the evolution of inbound tourism. In many cases it is found that the climatic indicators have significant influence on tourism. With regard to the single variable models it is found that seasonality in general is of stochastic nature, but the deterministic presentation is in many cases superior in forecasting performance. Theme: Tourism, regional econometric modelling Key words: Tourism, seasonality, fore casting, climatic variables. JEL Classification: C32.