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Exact knowledge of wind energy potential is a fundamental issue in wind energy utilization. The vertical changes in wind speeds, that is, the wind profile, have a predominant impact on the wind energy available at a location because the kinetic energy of moving air is proportional to the square of the wind speed. Roughness describes the resistance of a 3D surface to moving air. The exponent α of the power law of Hellmann and the roughness length (z0) are two parameters that describe the effects of the roughness of the surface on the wind profile. They can be used for the vertical extrapolation of wind speeds. The exponent α can be determined using multiple height level wind speed measurement data, whereas a reliable technique for the calculation of the roughness length requires detailed knowledge of the 3D geometry of the measurement site. In the present study, the exponent α was calculated based on SODAR wind speed measurements, while (z0) was determined using a combination of GIS and UAS-based aerial survey methods. Wind speeds measured at 50 m were extrapolated for height levels of 80, 90, 100, 110, and 120 m using dynamic power law exponent values. Wind power was determined using the power law (method V1), roughness length (method V2), frequency distribution (method W-RF), and gamma distribution (method W-G), and Windographer software was compared to the values calculated from the empirical (measured) wind speeds. A comparative statistical analysis of the datasets of the power law and roughness length methods on monthly/diurnal, annual/diurnal, and month/direction contexts showed no significant differences at all height levels. Differences can be detected in the distribution of the signs of the differences at heights of 80 and 120 m for the entire dataset. Underestimation was dominant with a significant frequency (over 70 %) in the case of both methods and heights. There were no significant differences between the wind power estimations provided by the different methods, and all the methods involved in the study underestimated the wind speeds and wind energy potential for each height level. Methods V1 and V2 can be used alternatively, depending on the input data available for analysis. The major advantage of method V2 is that it provides the same accuracy as V1, which requires a UAS-based aerial survey at the beginning, but continuous wind measurements must be performed at a lower height only. This means that there is no need for a high measurement tower, which makes the measurements simpler, more cost-effective, and causes much less disturbance to the environment. Another important advantage of the methods presented here is that they use a dynamic approach of power law exponent values that provide a more realistic estimation of wind speed and energy on a diurnal scale.

Wind erosion is one of the most important land degradation processes in Hungary in the areas with low yearly precipitation values. The total land area suffering from wind erosion is approximately 10,000 km2, 10 per cent of the country area. Observations and discussions on wind erosion and its negative impacts in Hungary started in the last century. Since the 1950s, scientists investigated wind erosion processes and its role in the evolution of alluvial fans in an integrative way, including laboratory measurements and field observations with respect to the stabilization and utilization of soils in agricultural areas. Since the late 2000s, there is an increasing demand worldwide to characterize the role of climate change and human activities in triggering land degradation processes. Studies have been conducted to investigate the mechanics, causes and control techniques related to wind erosion applying laboratory and field wind tunnel simulation tests and field observations. Some encouraging achievements have been made. In this paper we summarize the main research results of wind erosion research, and put forward some perspectives and suggestions on the problems of wind erosion research and control practices in Hungary.

Climate change is one of the most important issues of anthropogenic activities. The increasing drought conditions can cause water shortage and heat waves and can influence the agricultural production or the water supply of cities. The Carpathian region is also affected by this phenomenon; thus, we aimed at identifying the tendencies between 1960 and 2010 applying the CarpatClim (CC) database. We calculated the trends for each grid point of CC, plotted the results on maps, and applied statistical analysis on annual and seasonal level. We revealed that monthly average temperature, maximum temperature and evapotranspiration had similar patterns and had positive trends in all seasons except autumn. Precipitation also had a positive trend, but it had negative values in winter. The geospatial analysis disclosed an increasing trend from West to East and from north to west. A simple binary approach (value of 1 above the upper quartile in case of temperature and evapotranspiration, value of 1 below the lower quartile; 0 for the rest of the data) helped to identify the most sensitive areas where all the involved climatic variables exceeded the threshold: Western Hungary and Eastern Croatia. Results can help to prepare possible mitigation strategies to climate change and both landowners and planners can draw the conclusions.

Landscape aesthetic research that emerged from the second half of the 20th century has become increasingly appreciated and popular in the last few decades. There are two main reasons for this. On the one hand, it was recognized the role of landscape aesthetics in land use and environmental planning, management and conservation. On the other hand, its definition among Cultural Ecosystem Services has made it clear that landscape aesthetics has significant impact on human well-being and there is a need to examine it in the concept of Ecosystem Services and, in particular, Cultural Ecosystem Services (CES). The mapping of landscape aesthetics is mostly based on the exclusive evaluation of objective, biophysical landscape factors. The aim of the research was to create the landscape aesthetic map of Hungary with a novel method based on human perception. For this, a questionnaire survey and a GIS approach were used. In order to better understand the role of factors influencing the aesthetic value of the landscape, value maps separately for land cover and elevation that are decisive for the landscape experience were prepared. To validate the results of the maps, and contribute a better understanding of the interrelationship between CES, a certain tourism product was chosen, and the connection between landscape aesthetics and the offer of rural tourism was examined in Hungary and in the Danube Bend priority tourism development area. Our findings show that there is a difference in the results of the objective (GIS-based) and subjective (questionnaire-based) assessment of landscape aesthetic value with the more important role of elevation in the latter. According to our tourism product-based analysis, which represents a niche approach in its kind, landscape values are higher in the areas with rural accommodation. At the same time, based on the results of the Danube Bend region, it can also be concluded that elevation and land cover together are crucial factors in landscapes considered to be the most valuable in aesthetic terms. The most direct practical application of our research is to orientate further tourism development of the new Danube Bend area designated in 2017.

Revealing the driving forces of changes in landscape pattern is a key question of landscape ecology and landscape analysis. Temperature and precipitation as climatic variables have a dominant role in triggering vegetation changes; thus, a database, which contain their interaction, can support the understanding of spatio-temporal changes in vegetation patterns even on a large scale. The dataset provided in this article contain the R-squared values of bivariate linear regression analysis between the Normalized Difference Vegetation Index (target variable; as a general quantitative descriptor of surface greenness) of the TERRA satellite's MODIS sensor and the climatic variables of the CarpatClim database (predictor variables; maximum monthly temperature, aridification index, evapotranspiration and precipitation). Environmental variables are also included to support further analysis: terrain height, macro regions, land cover classes. The dataset has a spatial projection (i.e. maps) and covers the area of Hungary. Tabular version provides the possibility of traditional statistical analysis, while maps allow the investigation to involve the spatial characteristics of absolute and relative position of the data points. This data article is related to the paper “NDVI dynamics as reflected in climatic variables: spatial and temporal trends – a case study of Hungary” (Szabo et al., 2019). Keywords: NDVI, Trend, Climatic factors, R-squared, Pattern