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Abstract. In Hungary, wind erosion is one of the most serious natural hazards. Spatial and temporal variation in the factors that determine the location and intensity of wind erosion damage are not well known, nor are the regional and local sensitivities to erosion. Because of methodological challenges, no multi-factor, regional wind erosion sensitivity map is available for Hungary. The aim of this study was to develop a method to estimate the regional differences in wind erosion sensitivity and exposure in Hungary.Wind erosion sensitivity was modelled using the key factors of soil sensitivity, vegetation cover and wind erodibility as proxies. These factors were first estimated separately by factor sensitivity maps and later combined by fuzzy logic into a regional-scale wind erosion sensitivity map. Large areas were evaluated by using publicly available data sets of remotely sensed vegetation information, soil maps and meteorological data on wind speed. The resulting estimates were verified by field studies and examining the economic losses from wind erosion as compensated by the state insurance company. The spatial resolution of the resulting sensitivity map is suitable for regional applications, as identifying sensitive areas is the foundation for diverse land development control measures and implementing management activities.

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The model framework MULBO (Multicriteria Landscape Assessment and Optimisation) is a spatially explicit decision support method on the basis of risk evaluations for landscape functions. Its principal purpose is the establishment of optimal land use patterns as scenarios, which are balanced compromises between conflicting goals for the reduction of assessed risks. A user manual for MULBO has been developed which contains the individual assessment tools, the landscape optimisation method LNOPT 2.0, a multiplicity of applications, as well as information about data and techniques. After an introductory discussion of fundamentals for spatial decision-making, the methods and contents of MULBO are presented and discussed on basis of applications in a rural area in the southern part of Saxony-Anhalt (Germany). An applied project converts the scenario results recently into possible practices.

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The potential impacts of climate change on the Great Hungarian Plain based on two regional climate models, REMO and ALADIN, were analyzed using indicators for environmental hazards. As the climate parameters (temperature, precipitation, and wind) will change in the two investigated periods (2021-2050 and 2071-2100), their influences on drought, wind erosion, and inland excess water hazards are modeled by simple predictive models. Drought hazards on arable lands will increasingly affect the productivity of agriculture compared to the reference period . The models predict an increase between 12.3 % (REMO) and 20 % (ALADIN) in the first period, and between 35.6 % (REMO) and 45.2 % (ALA-DIN) in the second period. The increase of wind erosion hazards is not as obvious (?15 % for the first period in the REMO model). Inland excess water hazards are expected to be slightly reduced (-4 to 0 %) by both model predictions in the two periods without showing a clear tendency on reduction. All three indicators together give a first regional picture of potential hazards of climate change. The predictive model and data combinations of the regional climate change models and the hazard assessment models provide insights into regional and subregional impacts of climate change and will be useful in planning and land management activities.

This study visualizes and quantifies extant publications of rural landscape research (RLR) in Web of Science using CiteSpace for a wide range of research topics, from a multi-angle analysis of the overall research profile, while providing a method and approach for quantitative analysis of massive literature data. First, it presents the number of papers published, subject distribution, author network, the fundamental condition of countries, and research organizations involved in RLR through network analysis. Second, it identifies the high-frequency and high betweenness-centrality values of the basic research content of RLR through keyword co-occurrence analysis and keyword time zones. Finally, it identifies research fronts and trending topics of RLR in the decade from 2009 to 2018 by using co-citation clustering, and noun-term burst detection. The results show that basic research content involves protection, management, biodiversity, and land use. Five clearer research frontier pathways and top 20 research trending topics are extracted to show diversified research branch development. All this provides the reader with a general preliminary grasp of RLR, showing that cooperation and analysis involving multiple disciplines, specialties, and angles will become a dominant trend in the field.