The spatial distribution of the annual mean urban heat island (UHI) intensity was simulated applying empirical models based on datasets from urban areas of Szeged and Debrecen, using simple and easily determinable urban surface cover variables. These two cities are situated on the Alföld (Great Hungarian Plain) and have similar topographic and climatic conditions. Temperature field measurements were carried out, Landsat satellite images were evaluated, and then one‐ and multiple variable models were constructed using linear regression techniques. The selected multiple‐parameter models were verified using independent datasets from three urban settlements. In order to obtain some impression of the mean UHI patterns in other cities with no temperature measurements available, the better model was extended to urban areas of four other cities situated in geographical environments similar to Szeged and Debrecen. The main shortcoming of typical empirical models, namely that they are often restricted to a specific location, is overcome by the obtained model since it is not entirely site but more region specific, and valid in a large and densely populated area with several settlements. Copyright © 2009 Royal Meteorological Society

Abandoned channels are essential in the Quaternary floodplains, and their infill contains different paleoenvironment recorders. Grain-size distribution (GSD) is one proxy that helps characterize the alluviation and associated sedimentological processes of the abandoned channels. The classic statistical methods of the grain-size analysis provide insufficient information on the whole distribution; this necessitates a more comprehensive approach. Grain-size endmember modeling (EMM) is one approach beyond the traditional procedures that helps unmix the GSDs. This study describes the changes in the depositional process by unmixing the GSDs of a Holocene abandoned channel through parameterized EMM integrated with lithofacies, age–depth model, loss-on-ignition (LOI), and magnetic susceptibility (MS). This approach effectively enabled the quantification and characterization of up to four endmembers (EM1-4); the characteristics of grain-size endmembers imply changes in sedimentary environments since 8000 BP. EM1 is mainly clay and very fine silt, representing the fine component of the distribution corresponding to the background of quiet water sedimentation of the lacustrine phase. EM2 and EM3 are the intermediate components representing the distal overbank deposits of the flood. EM4 is dominated by coarse silt and very fine sand, representing deposition of overbank flow during the flood periods. This paper demonstrates that the parametrized grain-size EMM is reasonable in characterizing abandoned channel infill sedimentary depositional and sedimentation history.

Over the years many studies have been conducted to understand the delta system of the Algyő field, many of them dealing with the Ap-13 reservoir. In the present study, therefore, several papers have been reviewed and analyzed to provide the basis for a more detailed description of this reservoir. A macro-scale sedimentological model was developed using Markov analysis. Golden Software's Surfer 8.1 and Strater were used to construct the maps and to define the vertical sedimentological facies of the A-993 borehole. The mega-scale sedimentological 3-D model and the petrophysical parameters of 144 boreholes were analyzed using the Rockware RockWorks 15 and SPSS software. It is concluded, when comparing the vertical section of the A-993 borehole (from the core description) with the sand content from the 3-D model at similar depth, considering the results of the embedded Markov model and the 3-D sedimentological model, that the reservoir is a deep-marine sand body, with a sand content less than 55% and siltstone content of around 30%. It is characterized by the features of proximal middle fan systems

This study introduces a robust methodology utilizing Multi-Criteria Decision Analysis (MCDA) combined with an Analytic Hierarchy Process (AHP) to repurpose abandoned hydrocarbon fields for energy storage, supporting the transition to renewable energy sources. We use a geostatistical approach integrated with Python scripting to analyze reservoir parameters—including porosity, permeability, thickness, lithology, temperature, heat capacity, and thermal conductivity—from a decommissioned hydrocarbon field in Southeast Hungary. Our workflow leverages stochastic simulation data to identify potential zones for energy storage, categorizing them into high-, moderate-, and low-suitability scenarios. This innovative approach provides rapid and precise analysis, enabling effective decision-making for energy storage implementation in depleted fields. The key finding is the development of a methodology that can quickly and accurately assess the feasibility of repurposing abandoned hydrocarbon reservoirs for underground thermal energy storage, offering a practical solution for sustainable energy transition.