Characterization of Spatio-temporal Trends in Long-term Rainfall of Three Weather Stations in Benin: Implications for Agricultural Decision Making.

A better understanding of rainfall variability and trends is vital for agricultural production systems. This study evaluates the spatio-temporal variability and trends in annual, seasonal and daily rainfall in Benin. Daily rainfall data for the 1970-2016 period measured at three weather stations (Savè, Malanville, and Tanguiéta) were obtained from the Benin National Weather Agency. Descriptive statistics, standardized anomaly of rainfall (SAR) and rainfall intensity were used to analyze rainfall variability. For rainfall trends analysis, we tested for auto-correlation and used the Mann-Kendall and Modified Mann-Kendall tests for non-auto-correlated and auto-correlated data, respectively. Trend magnitude was estimated using Sen’s slope. Globally a moderate-to-high seasonal rainfall and low variability of yearly rainfall were observed. The SAR indicated more than 50% of the years in the studies period experienced dry years. Between 1970 and 2016, a significant 20 % increase was observed in the yearly rainfall in Tanguiéta whereas no significant trends were observed in Malanville (10% increase) and Savè (0.6% decrease). The general rainfall increase observed during the post-monsoon season (October to November) in the three weather stations potentially increases flood frequencies during the harvest period of some crops, which can reduce crop yields. The changes in the pre-monsoon season (March to May) and monsoon season (June to September) were not globally uniform and can have positive/negative impact on agriculture, certainly when no adaptation strategies are applied. These findings are essential to the resilience building and climate risk management in agriculture which is largely dependent on weather conditions.

Why Differential Data Work

ABSTRACT This article explains the features of differential data that make them attractive, their shortcomings, and the situations for which they are best suited. The use of differential data is ubiquitous in the seismological community, in which they are used to determine earthquake locations via the double-difference method and the Earth’s velocity structure via geotomography; furthermore, they have important applications in other areas of geophysics, as well. A common assumption is that differential data are uncorrelated and have uniform variance. We show that this assumption is well justified when the original, undifferenced data covary with each other according to a two-sided exponential function. It is not well justified when they covary according to a Gaussian function. Differences of exponentially correlated data are approximately uncorrelated with uniform variance when they are regularly spaced in distance. However, when they are irregularly spaced, they are uncorrelated with a nonuniform variance that scales with the spacing of the data. When differential data are computed by taking differences of the original, undifferenced data, model parameters estimated using ordinary least squares applied to the differential data are almost exactly equal to those estimated using weighed least squares applied to the original, undifferenced data (with the weights given by the inverse covariance matrix). A better solution only results when the differential data are directly estimated and their variance is smaller than is implied by differencing the original data. Differential data may be appropriate for global seismic travel-time data because the covariance of errors in predicted travel times may have a covariance close to a two-sided exponential, on account of the upper mantle being close to a Von Karman medium with exponent κ≪12.

HANDLING OF CORRELATIONS OF SPECTRAL QUANTITIES IN TRACEABILITY CHAIN – BASICS FOR A PYTHON-BASED ANALYSIS FRAMEWORKE

In this contribution a framework is presented that aims to help for handling correlations within measurement uncertainty calculations for spectral quantities. Taking correlations for spectral quantities into account is necessary as they directly influence the measurement uncertainties especially for integral quantities. Therefore, determination of correlations within traceability chains at national metrology institutes (NMIs) and disseminations of correlated data to test laboratory level is encouraged and a major goal of the EMPIR project 19NRM02 “Revision and extension of standards for test methods for LED lamps, luminaires and modules” (RevStdLED). The presented python-based analysis framework is used in photometry and spectroradiometry at PTB to calculate the results and associated measurement uncertainty for spectral irradiance, spectral irradiance responsivity and luminous responsivity based on spectral calibrations.

Machine learning concepts for correlated Big Data privacy

With data becoming a salient asset worldwide, dependence amongst data kept on growing. Hence the real-world datasets that one works upon in today’s time are highly correlated. Since the past few years, researchers have given attention to this aspect of data privacy and found a correlation among data. The existing data privacy guarantees cannot assure the expected data privacy algorithms. The privacy guarantees provided by existing algorithms were enough when there existed no relation between data in the datasets. Hence, by keeping the existence of data correlation into account, there is a dire need to reconsider the privacy algorithms. Some of the research has considered utilizing a well-known machine learning concept, i.e., Data Correlation Analysis, to understand the relationship between data in a better way. This concept has given some promising results as well. Though it is still concise, the researchers did a considerable amount of research on correlated data privacy. Researchers have provided solutions using probabilistic models, behavioral analysis, sensitivity analysis, information theory models, statistical correlation analysis, exhaustive combination analysis, temporal privacy leakages, and weighted hierarchical graphs. Nevertheless, researchers are doing work upon the real-world datasets that are often large (technologically termed big data) and house a high amount of data correlation. Firstly, the data correlation in big data must be studied. Researchers are exploring different analysis techniques to find the best suitable. Then, they might suggest a measure to guarantee privacy for correlated big data. This survey paper presents a detailed survey of the methods proposed by different researchers to deal with the problem of correlated data privacy and correlated big data privacy and highlights the future scope in this area. The quantitative analysis of the reviewed articles suggests that data correlation is a significant threat to data privacy. This threat further gets magnified with big data. While considering and analyzing data correlation, then parameters such as Maximum queries executed, Mean average error values show better results when compared with other methods. Hence, there is a grave need to understand and propose solutions for correlated big data privacy.

Breeding birds of ‘Nomentum’ nature reserve (central Italy): a forest remnant landscape surrounded by an agro-urbanized matrix

We carried out a standardized breeding bird atlas of “Nomentum” nature reserve (central Italy), located in a fragmented hilly forest near a large urbanized area (Rome). In order to obtain data about local composition, occurrence, distribution and richness, we correlated data with environmental heterogeneity and vegetation structure variables. We recorded 58 species in 48 500x500 m-wide atlas units, with Parus major, Corvus cornix, Turdus merula, Sylvia atricapilla, Sylvia melanocephala, as the most occurring in frequency (> 80%). Although synanthropic species represent only slightly more than 20% in number and urban environments are relatively reduced in size, these species show a higher mean occurrence when compared to mosaic species, despite the fact that these last are higher in species number and mosaic habitats are widely diffused. Local urbanization may disrupt communities, facilitating opportunistic species linked to these environments (i.e. synantropic) and inducing a decline in mosaic species. Moreover, the homogenization induced by anthropization could, at least partially, explain the lack of correlation between habitat diversity and species richness, at local scale. Finally, tree density and diameter do not affect total bird richness at this spatial grain/scale. In this regard, further analyses could test for possible correlations between habitat variables and single ecological guilds.

Methodological issues to analyse real-world longitudinal occupational health data (Preprint)

UNSTRUCTURED Ever greater technological advances and democratization of digital tools such as computers and smartphones offer researchers new possibilities to collect large amounts of health data in order to conduct clinical research. Such data, called real-world data (RWD), appears to be a perfect complement to traditional randomized clinical trials (RCTs) and has become more important in health decisions. Due to its longitudinal nature, RWD is subject to well-known methodological issues that can occur when collecting this type of data. In this article, we present the three main methodological problems encountered by researchers, these include, the longitudinal data itself, missing data (not available - NA) and cluster-correlated data. These concepts have been widely discussed in the literature and many methods and solutions have been proposed to cope with these issues. As examples, mixed and trajectory models have been developed to explore longitudinal data sets, imputation methods can resolve NA issues, and multilevel models facilitate treating cluster-correlated data. This article reviews the various solutions proposed and attempts to analyze all three in detail. Although solutions exist to meet these data collection challenges, solutions are not always correctly exploited, especially in cases where data collection issues overlap. In an attempt to solve this problem, we have conceived a process that considers all three issues simultaneously. This process can be divided into two parts: the first part of data management comprises of several phases such as definition of data structure, identification of suspect data and application of imputation methods. The second part of the analysis relates to the application of different models for repeated data using the modified data set. As a result, it should be possible to facilitate work with data sets and provide results with higher confidence levels. To support our proposal, we have used results from the “Wittyfit” database, which is an epidemiological database of occupational health data.