TY - CONF AU - Molnár, Bence AU - Galsa, Attila TI - Importance of thermal dispersion in porous medium based on synthetic numerical simulations T2 - 54. Ifjú Szakemberek Ankétja Absztraktkötet - LIV. Meeting of Young Geoscientists Book of Abstracts PY - 2024 SP - 16 EP - 17 PG - 2 UR - https://m2.mtmt.hu/api/publication/34846531 ID - 34846531 LA - English DB - MTMT ER - TY - CONF AU - Rapai, Tibor AU - Baják, Petra AU - Lukács, András AU - Székely, Balázs AU - Erőss, Anita TI - Understanding near-surface hydrogeological processes around Lake Velence (Hungary) – using mesh graph neural networks on multidimensional remote sensing data T2 - EGU General Assembly 2024 : abstracts PB - European Geosciences Union (EGU) C1 - Wien PY - 2024 DO - 10.5194/egusphere-egu24-5561 UR - https://m2.mtmt.hu/api/publication/34802132 ID - 34802132 AB - Lake Velence is a shallow soda lake in Hungary whose water budget is mainly driven by precipitation and evaporation. The lake has shown a deteriorating tendency recently, including extremely low lake levels and poor water quality, which indicates its vulnerability against changing climatic conditions. At the same time several water usage conflicts appeared in the catchment area. Until recently, the groundwater component in the lake's water budget and the hydrogeological processes in the catchment area have not been taken into consideration. Recent hydrogeological studies, however, show groundwater discharge into the lake. Thus, further investigating this question is of high importance, hence groundwater could reduce climatic vulnerability. Our ongoing work aims at developing a model-based evaluation technique, utilizing all map-based geophysical information and time series of different satellite data products, having sufficient spatial resolution and providing information about parameters strongly connected to subsurface processes, showing up on the surface. The basic DEM raster layer is imported from Copernicus GLO-30 dataset, having vertical precision <4 m. The Region Of Interest is a rectangular part of the catchment area: 47.1–47.4N, 18.4–18.8E. The first segmentation of the ROI is done using elevation data combined with lithographic and soil type information, resulting in almost uniform Voronoi-like polygon tessellation, with cells classified by geostructure. Further refinement by land cover type is done using Sentinel-1 SAR data. Other fixed data of point and polygon layers are important terrain features, points of surface inflows, (known) water takeouts and monitoring wells. The machine learning regression model has time series of measured data at all its layers, daily input from Agárd meteorological station, like precipitation, average temperature, wind speed and relative humidity. Another important input data comes from Sentinel-2 (GREEN-NIR)/(GREEN+NIR)=NDWI spectral index, available in about weekly time steps, varying between 2 days-2 weeks. A crucial feature of all remote sensing data used here is the spatial resolution being better (10 m) or similar to the resolution of the basic DEM model. During training a graph neural network is generated dynamically from the Voronoi tessellation, where cells are nodes and physical processes between neighbouring cells give edge attributes for the graph. We use rectilinear approximations for water runoff/subsurface water exchange between cells, vertical infiltration/discharge under cells and estimated evapotranspiration from them. Learnable parameters governing the intensity of these flows are connected to geostructure and land cover classes. Parameters are optimized with time interval cross validation, with one part of the time series data being left out from optimization in each epoch and used for evaluation against target water level data. Automatic detection of spatio-temporal patterns, connected to near-surface hydrogeological processes helped visualizing and quantifying estimated physical flows. Comparison with field measurements confirmed theoretic results from MODFLOW basin modelling, proving topography as a driving factor for subsurface flows. Our model is also suitable to handle isotope tracers, and extension to deep learning model promises predictive functionality for water table level. The research is part of a project which was funded by the National Multidisciplinary Laboratory for Climate Change, (Hungary) RRF-2.3.1-21-2022-00014. LA - English DB - MTMT ER - TY - CONF AU - Kahuthu, Dennis Wambugu AU - Amimo, Meshack O. AU - Oiro, S. AU - Székely, Balázs TI - Analysis of Hydrogeological Parameters of the Nairobi Aquifer Suite Using GIS-Based Spatial Interpolation Methods T2 - EGU General Assembly 2024 : abstracts PB - European Geosciences Union (EGU) C1 - Wien PY - 2024 DO - 10.5194/egusphere-egu24-899 UR - https://m2.mtmt.hu/api/publication/34802125 ID - 34802125 AB - Groundwater resources in the Nairobi Aquifer Suite (NAS), Kenya, face significant problems largely due to rapid urbanization and the rising water demand. The depletion of groundwater resources at the local level could potentially extend to regional extents, and hence affect natural water flows. This therefore calls for the prediction of aquifer hydrogeological parameters for sustainable groundwater management. This study aims to utilize GIS-based spatial interpolation methods for the in-depth analysis of NAS hydrogeological parameters. Classical geostatistical tools are employed to develop models that can be used to accurately predict hydrogeological parameters of the NAS. Field-measurable predictors, that is, geographic position, elevation, depths and first water struck level, are used to demonstrate the efficacy of the predictive models. Data from hydrogeological measurements, geological surveys and satellite imagery are integrated during the development of the predictive models for key hydrogeological parameters, including, groundwater level, discharge, drawdown, electrical conductivity, and transmissivity. Classical geostatistical tools such as kriging and natural neighbour interpolation are applied to develop spatially explicit maps of the NAS hydrogeological parameters. The distribution of borehole data is analyzed using geostatistical tools such as trend analysis and semi variogram. Cross-validation has been performed to identify the most suitable spatial interpolation model. While, in general, the prediction worked well based on model evaluation metrics such as mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R2), during the testing we observed characteristic deviations from the measured values at some locations. These differences could be due to the geological setting; however, a few outliers may appear due to yet unknown reasons. Further studies utilizing machine learning techniques are expected to develop accurate predictive models that can help in sustainable groundwater management in the NAS. The generated spatial maps provided insightful information on the spatial distribution of hydrogeological parameters in the NAS, facilitating the accurate identification of prospective locations for ideal groundwater extraction. LA - English DB - MTMT ER - TY - CONF AU - Musyimi, Peter Kinyae AU - Székely, Balázs AU - Hellen, W. Kamiri AU - Tom, Ouna AU - Weidinger, Tamás TI - Meteorological and Soil Moisture Measurements in Mount Kenya Region at Various Scales T2 - EGU General Assembly 2024 : abstracts PB - European Geosciences Union (EGU) C1 - Wien PY - 2024 DO - 10.5194/egusphere-egu24-579 UR - https://m2.mtmt.hu/api/publication/34762086 ID - 34762086 LA - English DB - MTMT ER - TY - JOUR AU - Rubóczki, Tibor AU - Galsa, Attila AU - Novák, Attila AU - Prácser, Ernő TI - 3D magnetotellurikus numerikus modell fejlesztése JF - MAGYAR GEOFIZIKA J2 - MAGYAR GEOFIZIKA VL - 64 PY - 2024 IS - 4 SP - 170 EP - 181 PG - 12 SN - 0025-0120 UR - https://m2.mtmt.hu/api/publication/34750065 ID - 34750065 AB - A felszíni elektromágneses (EM) tér összetevőit már közel hetven éve használják a felszín alatti térrész fajlagos elektromos ellenállás-eloszlásának vizsgálatára. A magnetotellurika (MT) az egyik legszélesebb körben alkalmazott EM geofizikai kutatómódszer, működésének átfogó megértése azonban az EM tér és a közeg tulajdonságai okán, 3D-ben történő gondolkodást igényel. A munka során egy olyan általános 3D MT numerikus modell felállítását valósítottuk meg, amelyben mind tetszőleges alakú ellenállás-eloszlás, mind ellenállás-anizotrópia számítására lehetőség nyílik. A leírásban áttekintjük a 3D MT-modell felállításához szükséges elméleti ismereteket az EM fizikát leíró egyenletektől a modell verifikálásáig egy általunk készített numerikus szimuláció felhasználásával. Továbbá egy egyszerűsített geológiai szerkezet tesztelését is bemutatjuk, melyben egy anizotróp ellenállással rendelkező nyírózóna EM térkomponensekre kifejtett torzító hatását és annak kapcsolatát szemléltetjük a felszínen szimulált MT-mérésekkel. Az általunk fejlesztett 3D MT-modell koncepciója a továbbiakban hasznos eszközként szolgálhat az MT-kutatások során felmerülő összetett elméleti és gyakorlati kérdések megválaszolásában. LA - Hungarian DB - MTMT ER - TY - JOUR AU - Rubóczki, Tibor AU - Novák, Attila AU - Liptai, Nóra AU - Porkoláb, Kristóf AU - Molnár, Csaba AU - Galsa, Attila AU - Molnár, Gábor AU - Wesztergom, Viktor AU - Kovács, István János TI - The Pannon LitH2Oscope magnetotelluric array in the Pannonian Basin JF - ACTA GEODAETICA ET GEOPHYSICA J2 - ACTA GEOD GEOPHYS PY - 2024 PG - 26 SN - 2213-5812 DO - 10.1007/s40328-024-00434-1 UR - https://m2.mtmt.hu/api/publication/34608098 ID - 34608098 N1 - HUN-REN Institute of Earth Physics and Space Science, Sopron, Hungary MTA FI Lendület Pannon LitH2Oscope Research Group, Sopron, Hungary Doctoral School of Earth Sciences, Eötvös Loránd University, Budapest, Hungary Department of Geophysics and Space Science, Institute of Geography and Earth Sciences, ELTE Eötvös Loránd University, Budapest, Hungary Alba Regia Technical Faculty, Institute of Geoinformatics, Óbuda University, Székesfehérvár, Hungary Export Date: 25 March 2024 Correspondence Address: Rubóczki, T.; HUN-REN Institute of Earth Physics and Space ScienceHungary; email: ruboczki.tibor@epss.hun-ren.hu AB - The Pannonian Basin is one of the best natural laboratories in the world to study the lithospheric response to continental extension and subsequent tectonic inversion. Here we address the topic of lithospheric structure by a combined geochemical and magnetotelluric analysis, which has been carried out in the framework of the Pannon LitH2Oscope project. The main objective was to detect the resistivity distribution over the entire lithosphere by magnetotelluric measurements, considering the lithological resistivity properties and relate the results to the structure and evolution of the Pannonian Basin. The Pannon LitH 2 Oscope MT array was used to estimate the depth of the Lithosphere-Asthenosphere Boundary (LAB), considering the legacy MT data and compared to previous estimates for the region. Using the MT and geomagnetic response functions, major structural zones of the Pannonian basin, such as the Mid-Hungarian Shear Zone or fault systems like the Makó Trough and the Békés Basin, were also imaged. In addition, we used the apparent resistivity soundings to compare 1D resistivity models computed from geochemistry and obtained from field MT measurements. This comparison provided new constrains for the composition, fluid and melt content variations at the local lithosphere-asthenosphere boundary. The Pannon LitH 2 Oscope MT dataset and the results presented in this paper provide input for more complex 3D inversions and further investigations of the lithospheric structure in the Carpathian-Pannonian region. LA - English DB - MTMT ER - TY - JOUR AU - Rotich, Ibrahim Kipngeno AU - Musyimi, Peter Kinyae TI - Indoor Air Pollution in Kenya JF - AEROSOL SCIENCE AND ENGINEERING J2 - AEROSOL SCI ENG PY - 2024 SN - 2510-375X DO - 10.1007/s41810-023-00205-5 UR - https://m2.mtmt.hu/api/publication/34589617 ID - 34589617 AB - Indoor air pollution is an environmental health challenge in Kenya, particularly in rural households, and low-income urban areas. This review aims to provide an overview of the sources, health effects and mitigation strategies for indoor air pollutants in Kenya. The main goal of our study was to review existing literature on indoor air pollution in Kenya with the aim of identifying research gaps for future research. Our methodology involved a critical examination of the existing literature review. This is because traditional fuel burning for cooking and heating, and kerosene lamps are major sources of indoor air pollution. Exposure to air pollutants can lead to respiratory and cardiovascular disease among women and children who are more vulnerable. Despite efforts to improve indoor air quality, significant challenges remain including access to clean fuels and technologies, inadequate infrastructure, and low awareness of health impact of indoor air pollution. Mitigation strategies include the transition to cleaner cooking sources, solar lamps for lighting and education campaigns on health impacts. The review concludes that a multifaceted approach involving various stakeholders is necessary to effectively address indoor air pollution in Kenya and improve public health. LA - English DB - MTMT ER - TY - JOUR AU - Timár, Gábor AU - Jakab, Gusztáv AU - Székely, Balázs TI - A Step from Vulnerability to Resilience: Restoring the Landscape Water-Storage Capacity of the Great Hungarian Plain—An Assessment and a Proposal JF - LAND (BASEL) J2 - LAND-BASEL VL - 13 PY - 2024 IS - 2 SN - 2073-445X DO - 10.3390/land13020146 UR - https://m2.mtmt.hu/api/publication/34536076 ID - 34536076 N1 - Department of Geophysics and Space Science, Institute of Geography and Earth Science, ELTE Eötvös Loránd University, Budapest, H-1117, Hungary Department of Environmental and Landscape Geography, Institute of Geography and Earth Sciences, ELTE Eötvös Loránd University, Budapest, H-1117, Hungary Export Date: 28 March 2024 Correspondence Address: Timár, G.; Department of Geophysics and Space Science, Hungary; email: timar.gabor@ttk.elte.hu AB - The extreme drought in Europe in 2022 also hit hard the Great Hungarian Plain. In this short overview article, we summarize the natural environmental conditions of the region and the impact of river control works on the water-retention capacity of the landscape. In this respect, we also review the impact of intensive agricultural cultivation on soil structure and on soil moisture in light of the meteorological elements of the 2022 drought. The most important change is that the soil stores much less moisture than in the natural state; therefore, under the meteorological conditions of summer 2022, the evapotranspiration capacity was reduced. As a result, the low humidity in the air layers above the ground is not sufficient to trigger summer showers and thunderstorms associated with weather fronts and local heat convection anymore. Our proposed solution is to restore about one-fifth of the area to the original land types and usage before large-field agriculture. Low-lying areas should be transformed into a mosaic-like landscape with good water supply and evapotranspiration capacity to humidify the lower air layers. Furthermore, the unfavorable soil structure that has resulted from intensive agriculture should also be converted into more permeable soil to enhance infiltration. LA - English DB - MTMT ER - TY - JOUR AU - Kern, Anikó AU - Dobor, Laura AU - Hollós, Roland AU - Marjanović, Hrvoje AU - Torma, Csaba AU - Kis, Anna AU - Fodor, Nándor AU - Barcza, Zoltán TI - Seamlessly combined historical and projected daily meteorological datasets for impact studies in Central Europe: The FORESEE v4.0 and the FORESEE-HUN v1.0 JF - CLIMATE SERVICES J2 - CLIM SERV VL - 33 PY - 2024 SN - 2405-8807 DO - 10.1016/j.cliser.2023.100443 UR - https://m2.mtmt.hu/api/publication/34472868 ID - 34472868 N1 - ELTE Eötvös Loránd University, Institute of Geography and Earth Sciences, Department of Geophysics and Space Science, Space Research Group, Budapest H-1117, Pázmány P. st. 1/A, Hungary Czech University of Life Sciences Prague, Faculty of Forestry and Wood Sciences, 165 21 Prague 6, Kamýcká 129, Czech Republic ELTE Eötvös Loránd University, Institute of Geography and Earth Sciences, Department of Meteorology, Budapest H-1117, Pázmány P. st. 1/A, Hungary ELTE Eötvös Loránd University, Excellence Center, Faculty of Science, H-2462 Martonvásár, Brunszvik u. 2., Hungary Agricultural Institute, Centre for Agricultural Research, H-2462 Martonvásár, Brunszvik u. 2, Hungary Croatian Forest Research Institute, Department of Forest Management and Forestry Economics, Jastrebarsko HR-10450, Cvjetno naselje 41, Croatia Cited By :1 Export Date: 28 March 2024 Correspondence Address: Kern, A.; ELTE Eötvös Loránd University, Budapest H-1117, Pázmány P. st. 1/A, Hungary; email: aniko.kern@ttk.elte.hu LA - English DB - MTMT ER - TY - JOUR AU - Czecze, Barbara AU - Kalmár, Dániel AU - Marótiné Kiszely, Márta AU - Süle, Bálint AU - Fodor, László TI - Earthquake swarms near the Mór Graben, Pannonian Basin (Hungary): implication for neotectonics JF - JOURNAL OF SEISMOLOGY J2 - J SEISMOL VL - 28 PY - 2024 SP - 19 EP - 38 PG - 20 SN - 1383-4649 DO - 10.1007/s10950-023-10181-5 UR - https://m2.mtmt.hu/api/publication/34470927 ID - 34470927 N1 - Kövesligethy Radó Seismological Observatory, Institute of Earth Physics and Space Science, Budapest, Hungary Department of Geophysics and Space Sciences, Institute of Geography and Earth Sciences, Eötvös Loránd University, Budapest, Hungary Institute of Earth Physics and Space Science, Sopron, Hungary Department of Geology, Institute of Geography and Earth Sciences, Eövös Loránd University, Budapest, Hungary Export Date: 2 January 2024 CODEN: DXUEF Correspondence Address: Czecze, B.; Kövesligethy Radó Seismological Observatory, Hungary; email: czecze.barbara@hun-ren.epss.hu LA - English DB - MTMT ER -