Linguistic Terms of Greek Origin in English and Bulgarian.

The Greek language has been the source of linguistic terms for centuries up to the present. Greek word-forming patterns, words and word elements were adopted and adapted into Latin (Neo-Latin) 1,500 years ago, and passed through Latin into many European and other languages, being used in the main for scholarly and technical purposes. The analytical study of language began in the second half of the first millennium BC in both Greece and India. The present day study of grammar descends from the Greek tradition and thus many Greek technical terms were converted into English (via Latin) and into Bulgarian (under more direct Greek influence — for historical and geographical reasons). The corpus of linguistic terms dealt with in this paper contains 696 English words and 248 Bulgarian words. They have been classified according to three criteria: the time they entered the language, the extent of their adaptation and the branches of linguistics they belong to

Designing sustainable soils in Earth’s critical zone

The demographic drivers of increasing human population and wealth are creating tremendous environmental pressures from growing intensity of land use, resulting in soil and land degradation worldwide. Environmental services are provided through multiple soil functions that include biomass production, water storage and transmission, nutrient transformations, contaminant attenuation, carbon and nitrogen storage, providing habitat and maintaining the genetic diversity of the land environment. One of the greatest challenges of the 21st century is to identify key risks to soil, and to design mitigation strategies to manage these risks and to enhance soil functions that can last into the future. The scientific study of Earth’s Critical Zone (CZ), the thin surface layer that extends vertically from the top of the tree canopy to the bottom of aquifers, provides an essential integrating scientific framework to study, protect and enhance soil functions. The research hypothesis is that soil structure, the geometric architecture of solids, pores and biomass, is a critical indicator and essential factor of productive soil functions. The experimental design selects a network of Critical Zone Observatories (CZOs) as advanced field research sites along a gradient of land use intensity in order to quantify soil structure and soil processes that dictate the flows and transformations of material and energy as soil functions. The CZOs focus multidisciplinary expertise on soil processes, field observation and data interpretation, management science and ecological economics. Computational simulation of biophysical processes provides a quantitative method of integration for the range of theory and observations that are required to quantify the linkages between changes in soil structure and soil functions. Key results demonstrate that changes in soil structure can be quantified through the inputs of organic carbon and nitrogen from plant productivity and microbial activity, coupled with particle aggregation dynamics and organic matter mineralization. Simulation results show that soil structure is highly dynamic and is sensitive to organic matter production and mineralisation rates as influenced by vegetation, tillage and organic carbon amendments. These results point to a step-change in the capability to design soil management and land use through computational simulation. This approach of “sustainability by design” describes the mechanistic process linkages that exist between the above-ground inputs to the CZ and the internal processes that produce soil functions. This approach provides a rational, scientific approach to selecting points of intervention with the CZ in order to design methods to mitigate soil threats and to enhance and sustain vital soil functions. Furthermore, this approach provides a successful pilot study to the use of international networks of CZOs as a planetary-scale laboratory to test the response of CZ process rates along gradients of global environmental change – and to test adaptation strategies to manage the risks arising from the CZ impacts.JRC.H.8-Sustainability Assessmen

Mapping monthly rainfall erosivity in Europe

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year

Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall erosivity and presents the spatial patterns of monthly rainfall erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall erosivity.JRC.H.5-Land Resources Managemen

Measuring, modelling and managing gully erosion at large scales: A state of the art

Soil erosion is generally recognized as the dominant process of land degradation. The formation and expansion of gullies is often a highly significant process of soil erosion. However, our ability to assess and simulate gully erosion and its impacts remains very limited. This is especially so at regional to continental scales. As a result, gullying is often overlooked in policies and land and catchment management strategies. Nevertheless, significant progress has been made over the past decades. Based on a review of >590 scientific articles and policy documents, we provide a state-of-the-art on our ability to monitor, model and manage gully erosion at regional to continental scales. In this review we discuss the relevance and need of assessing gully erosion at regional to continental scales (Section 1); current methods to monitor gully erosion as well as pitfalls and opportunities to apply them at larger scales (section 2); field-based gully erosion research conducted in Europe and European Russia (section 3); model approaches to simulate gully erosion and its contribution to catchment sediment yields at large scales (section 4); data products that can be used for such simulations (section 5); and currently existing policy tools and needs to address the problem of gully erosion (section 6). Section 7 formulates a series of recommendations for further research and policy development, based on this review. While several of these sections have a strong focus on Europe, most of our findings and recommendations are of global significance.info:eu-repo/semantics/publishedVersio

Rainfall Erosivity in Europe

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 min. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 min using linear regression functions. Precipitation time series ranged from a minimum of 5 years to a maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression (GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wet-test months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha(-1) h(-1) yr(-1), with the highest values (>1000 MJ mm ha(-1) h(-1) yr(-1)) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha(-1) h(-1) yr(-1)) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also the highest in Mediterranean regions which implies high risk for erosive events and floods

Reply to the comment on “Rainfall erosivity in Europe” by Auerswald et al.

5 Pags.- 1 Fig. Under a Creative Commons license: Attribution 4.0 International (CC BY 4.0)Recently, in the Auerswald et al. (2015) comment on “Rainfall erosivity in Europe”, 5 criticisms were addressed: i) the neglect of seasonal erosion indices, ii) the neglect of published studies and data, iii) the low temporal resolution of the data, especially of the maximum rain intensity, iv) the use of precipitation data instead of rain data and the subsequent deviation of the R-factor in Germany and Austria compared with previous studies, and v) the differences in considered time periods between countries. We reply as follows: (i) An evaluation of the seasonal erosion index at the European scale is, to our knowledge, not achievable at present with the available data but would be a future goal. Synchronous publication of the seasonal erosion index is not mandatory, specifically because seasonal soil loss ratios are not available at this scale to date. We are looking forward to the appropriate study by the authors of the comment, who assert that they have access to the required data. (ii) We discuss and evaluate relevant studies in our original work and in this reply; however, we cannot consider what is not available to the scientific community. (iii) The third point of critique was based on a misunderstanding by Auerswald et al. (2015), as we did indeed calculate the maximum intensity with the highest resolution of data available.(iv) The low R-factor values in Germany and the higher values in Austria compared with previous studies are not due to the involvement of snow but are rather due to a Pan-European interpolation. We argue that an interpolation across the borders of Austria creates a more reliable data set. (v) We agree that the use of a short time series or time series from different periods is generally a problem in all large-scale studies and requires improvement in the future. However, because this affects countries with a rather low variability of the R-factor in our study, we are confident that the overall results of the map are not biased. In conclusion, the Pan-European rainfall data compilation (REDES) was a great success and yielded data from 1541 stations with an average length of 17.1 years and a temporal resolution of < 60 min. However, a Pan-European data collection will never be complete without the help and supply of data from its users. Thus, we invite the authors of the comment to share their data in the open REDES to help build even better rainfall-erosivity maps at regional or European scales.Peer reviewe

Rainfall Erosivity in Europe

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1,541 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha-1 h-1 yr-1, with the highest values (>1,000 MJ mm ha-1 h-1 yr-1) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods.JRC.H.5-Land Resources Managemen

Reply to the comment on "Rainfall Erosivity in Europe" by Auerswald et al.

Recently, In the Auerswald et al. (2015) comment on “Rainfall Erosivity in Europe” 5 points of critic are addressed: i) the neglect of seasonal erosion indices, ii) the neglect of published studies and data, iii) the low temporal resolution of the data, especially of the maximum rain intensity , iv) the use of precipitation data instead of rain data and thus the deviation of the R-factor in Germany and Austria compared to previous studies, and finally iv) the differences in considered time periods between countries, . We reply that: (i) An evaluation of the seasonal erosion index at European scale is to our knowledge not achievable at the moment with the available data but would be one of the next goals. Synchronous publication of the seasonal erosion index is not mandatory; particularly since seasonal soil loss ratios are not available at this scale yet. We are looking forward to the appropriate study by the authors of the comment as they assert they have access to the required data. (ii) We discuss and evaluate relevant studies in our original work and also in this reply but we cannot consider what is not available to the scientific community. (iii) The third point of critic was based on a misunderstanding by Auerswald et al. (2015) as we did calculate the maximum intensity with the highest resolution of data available. (iv) The low R-factor values in Germany and the higher values in Austria compared to previous studies are not due to the involvement of snow but due to a Pan-European interpolation. We argue that interpolation across the borders of Austria creates a more reliable data set. (iv) We agree that the use of short time series or time series from differing periods is generally a problem in all large scale studies and needs improvement in the future. However, because this affects countries with rather low variability of R-factor in our study, we are confident that overall results of the map are not biased. Concluding, the Pan-European rainfall data compilation (REDES) was a great success and yielded data from 1541 stations with an average length of 17.1 years and a temporal resolution of <60-min. However, a Pan-European data collection will never be complete without the help and supply of data by users. Thus, we invite the authors of the comment to share their data in the open available REDES in order to gain even better Rainfall-erosivity maps at regional or European scale.JRC.H.5-Land Resources Managemen

Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall erosivity and presents the spatial patterns of monthly rainfall erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall erosivity