In order to assess the rainfall erosivity in the Pannonian basin, several parameters which describe distribution, concentration and variability of precipitation were used, as well as 9 extreme precipitation indices. The precipitation data is obtained from the European Climate Assessment and Dataset project for the period 1961-2014, for 8 meteorological stations in northern Serbia, 5 in Hungary and 1 in eastern Croatia. The extreme values of precipitation were calculated following the indices developed by the ETCCDI. RclimDex software package was used for indices calculation. Based on statistical analysis and the calculated values, the results have been presented with Geographic Information System (GIS) to point out the most vulnerable parts of the Pannonian basin, with regard to pluvial erosion. This study presents the first result of combined rainfall erosivity and extreme precipitation indices for the investigated area. Results of PCI indicate presence of moderate precipitation concentration (mean value 11.6). Trend analysis of FI (mean value 22.7) and MFI (mean value 70.2) implies a shift from being largely in the low erosivity class, to being completely in the moderate erosivity class in the future, thus indicating an increase in rainfall erosivity for most of the investigated area (except in the northwestern parts). Furthermore, the observed precipitation extremes suggest that both the amount and the intensity of precipitation are increasing. The knowledge about the areas affected by strong soil erosion could lead to introducing effective measures in order to reduce it. Long term analysis of rainfall erosivity is a significant step concerning flood prevention, hazard mitigation, ecosystem services, land use change and agricultural production.
Several indices, with focus on extreme climate and bioclimate events have been calculated and their trends (over 1961 to 2014) analyzed in order to identify possible changes in temperature-related climate extremes over Vojvodina region, Northern Serbia. Physiologically equivalent temperature (PET) has been used for the assessment of bioclimatological extremes in Vojvodina. A number of indices for temperature extremes have been defined by CLIVAR (Climate Variability and Predictability) and they are modified in order to detect extremes in bioclimatological variables (PET). In this study, we used 11 PET indices. The results indicate that the whole area is dominated by significant increase in the frequency of warm conditions and significant decrease in the cold conditions. These results could indicate that the warming in Vojvodina could be due to the more pronounced and higher frequency of warm bioclimatological extreme conditions. The frequency and the duration of heat waves are increasing for the whole area, while the decrease in number and duration of cold waves is not so pronounced.
Land cover is one of the key terrestrial variables used for monitoring and as input for modelling in support of achieving the United Nations Strategical Development Goals. Global and Continental Land Cover Products (GCLCs) aim to provide the required harmonized information background across areas; thus, they are not being limited by national or other administrative nomenclature boundaries and their production approaches. Moreover, their increased spatial resolution, and consequently their local relevance, is of high importance for users at a local scale. During the last decade, several GCLCs were developed, including the Global Historical Land-Cover Change Land-Use Conversions (GLC), the Globeland-30 (GLOB), Corine-2012 (CLC) and GMES/ Copernicus Initial Operation High Resolution Layers (GIOS). Accuracy assessment is of high importance for product credibility towards incorporation into decision chains and implementation procedures, especially at local scales. The present study builds on the collaboration of scientists participating in the Global Observations of Forest Cover—Global Observations of Land Cover Dynamics (GOFC-GOLD), South Central and Eastern European Regional Information Network (SCERIN). The main objective is to quantitatively evaluate the accuracy of commonly used GCLCs at selected representative study areas in the SCERIN geographic area, which is characterized by extreme diversity of landscapes and environmental conditions, heavily affected by anthropogenic impacts with similar major socio-economic drivers. The employed validation strategy for evaluating and comparing the different products is detailed, representative results for the selected areas from nine SCERIN countries are presented, the specific regional differences are identified and their underlying causes are discussed. In general, the four GCLCs products achieved relatively high overall accuracy rates: 74–98% for GLC (mean: 93.8%), 79–92% for GLOB (mean: 90.6%), 74–91% for CLC (mean: 89%) and 72–98% for GIOS (mean: 91.6%), for all selected areas. In most cases, the CLC product has the lower scores, while the GLC has the highest, closely followed by GIOS and GLOB. The study revealed overall high credibility and validity of the GCLCs products at local scale, a result, which shows expected benefit even for local/regional applications. Identified class dependent specificities in different landscape types can guide the local users for their reasonable usage in local studies. Valuable information is generated for advancing the goals of the international GOFC-GOLD program and aligns well with the agenda of the NASA Land-Cover/Land-Use Change Program to improve the quality and consistency of space-derived higher-level products.
CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform an analysis of CDR data for the city of Milan that originate from Telecom Italia Big Data Challenge. A set of graphs is generated from aggregated CDR data, where each node represents a centroid of an RBS (Radio Base Station) polygon, and each edge represents aggregated telecom traffic between two RBSs. To explore the community structure, we apply a modularity-based algorithm. Community structure between days is highly dynamic, with variations in number, size and spatial distribution. One general rule observed is that communities formed over the urban core of the city are small in size and prone to dynamic change in spatial distribution, while communities formed in the suburban areas are larger in size and more consistent with respect to their spatial distribution. To evaluate the dynamics of change in community structure between days, we introduced different graph based and spatial community properties which contain latent footprint of human dynamics. We created land use profiles for each RBS polygon based on the Copernicus Land Monitoring Service Urban Atlas data set to quantify the correlation and predictivennes of human dynamics properties based on land use. The results reveal a strong correlation between some properties and land use which motivated us to further explore this topic. The proposed methodology has been implemented in the programming language Scala inside the Apache Spark engine to support the most computationally intensive tasks and in Python using the rich portfolio of data analytics and machine learning libraries for the less demanding tasks.
The paper aims to provide an overview of the most important parameters (the occurrence, frequency and magnitude) in Vojvodina Region (North Serbia). Monthly and annual mean precipitation values in the period 1946–2014, for the 12 selected meteorological stations were used. Relevant parameters (precipitation amounts, Angot precipitation index) were used as indicators of rainfall erosivity. Rainfall erosivity index was calculated and classified throughout precipitation susceptibility classes liable of triggering soil erosion. Precipitation trends were obtained and analysed by three different statistical approaches. Results indicate that various susceptibility classes are identified within the observed period, with a higher presence of very severe rainfall erosion in June and July. This study could have implications for mitigation strategies oriented towards reduction of soil erosion by water.
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