@CONFERENCE{MTMT:34804501,
	title = {Application of natural radionuclides in the hydrogeological characterization of karst system supplying the Lake Hévíz},
	url = {https://m2.mtmt.hu/api/publication/34804501},
	author = {Saeed, Bidar Kahnamuei and Hegedűs-Csondor, Katalin and Baják, Petra and Horváth, Ákos and Szieberth, Dénes and Czuppon, György and Vargha, Márta and Izsák, Bálint and Németh, György and Tóth, György and Erőss, Anita},
	booktitle = {54. Ifjú Szakemberek Ankétja Absztraktkötet - LIV. Meeting of Young Geoscientists Book of Abstracts},
	unique-id = {34804501},
	year = {2024},
	pages = {50-51},
	orcid-numbers = {Hegedűs-Csondor, Katalin/0000-0002-3368-9620; Horváth, Ákos/0000-0003-2611-4287; Czuppon, György/0000-0002-7231-6042; Erőss, Anita/0000-0002-2395-3934}
}

@CONFERENCE{MTMT:34804470,
	title = {Spatial and temporal variabiliy in drinking water quality in a riverbank filtered drinking water supply system},
	url = {https://m2.mtmt.hu/api/publication/34804470},
	author = {Erőss, Anita and Baják, Petra and Mezei, Máté Márk and Csiszár, E and Hegedűs-Csondor, Katalin and Izsák, Bálint and Vargha, Márta and Czuppon, György and Horváth, Ákos},
	booktitle = {EGU General Assembly 2024 : abstracts},
	doi = {10.5194/egusphere-egu24-13213},
	unique-id = {34804470},
	year = {2024},
	orcid-numbers = {Erőss, Anita/0000-0002-2395-3934; Hegedűs-Csondor, Katalin/0000-0002-3368-9620; Czuppon, György/0000-0002-7231-6042; Horváth, Ákos/0000-0003-2611-4287}
}

@CONFERENCE{MTMT:34804459,
	title = {Identifying Mixing Components by Natural Tracers in the Lake Hévíz System},
	url = {https://m2.mtmt.hu/api/publication/34804459},
	author = {Saeed, BK and Hegedűs-Csondor, Katalin and Baják, Petra and Horváth, Ákos and Szieberth, Dénes and Czuppon, György and Vargha, Márta and Izsák, Bálint and Erőss, Anita},
	booktitle = {EGU General Assembly 2024 : abstracts},
	doi = {10.5194/egusphere-egu24-888},
	unique-id = {34804459},
	year = {2024},
	orcid-numbers = {Hegedűs-Csondor, Katalin/0000-0002-3368-9620; Horváth, Ákos/0000-0003-2611-4287; Czuppon, György/0000-0002-7231-6042; Erőss, Anita/0000-0002-2395-3934}
}

@CONFERENCE{MTMT:34804442,
	title = {Regional groundwater flow mapping in NE Hungary – a tool to understand drinking water quality and quantity problems for sustainable resource management},
	url = {https://m2.mtmt.hu/api/publication/34804442},
	author = {Hegedűs-Csondor, Katalin and Jávorcsik, Réka and Reyana, Dawn Garcia and Baják, Petra and Kohuth-Ötvös, Viktória and Erőss, Anita},
	booktitle = {EGU General Assembly 2024 : abstracts},
	unique-id = {34804442},
	year = {2024},
	pages = {EGU24-12831},
	orcid-numbers = {Hegedűs-Csondor, Katalin/0000-0002-3368-9620; Erőss, Anita/0000-0002-2395-3934}
}

@CONFERENCE{MTMT:34804431,
	title = {Preliminary results of two-dimensional multicomponent reactive transport modelling to understand the controlling factors on uranium mobility in a siliciclastic aquifer in Hungary},
	url = {https://m2.mtmt.hu/api/publication/34804431},
	author = {Baják, Petra and Daniele, Pedretti and Csepregi, András and Muhammad, Muniruzzaman and Hegedűs-Csondor, Katalin and Erőss, Anita},
	booktitle = {EGU General Assembly 2024 : abstracts},
	unique-id = {34804431},
	year = {2024},
	pages = {EGU24-12663},
	orcid-numbers = {Hegedűs-Csondor, Katalin/0000-0002-3368-9620; Erőss, Anita/0000-0002-2395-3934}
}

@CONFERENCE{MTMT:34804353,
	title = {Investigating the groundwater contribution to the lakes and streams by environmental tracers in the catchment area of Lake Velence (Hungary)},
	url = {https://m2.mtmt.hu/api/publication/34804353},
	author = {Pénzes, V and Erőss, Anita and Hegedűs-Csondor, Katalin and Baják, Petra and Horváth, Ákos and Czuppon, György},
	booktitle = {EGU General Assembly 2024 : abstracts},
	doi = {10.5194/egusphere-egu24-941},
	unique-id = {34804353},
	year = {2024},
	orcid-numbers = {Erőss, Anita/0000-0002-2395-3934; Hegedűs-Csondor, Katalin/0000-0002-3368-9620; Horváth, Ákos/0000-0003-2611-4287; Czuppon, György/0000-0002-7231-6042}
}

@CONFERENCE{MTMT:34802132,
	title = {Understanding near-surface hydrogeological processes around Lake Velence (Hungary) – using mesh graph neural networks on multidimensional remote sensing data},
	url = {https://m2.mtmt.hu/api/publication/34802132},
	author = {Rapai, Tibor and Baják, Petra and Lukács, András and Székely, Balázs and Erőss, Anita},
	booktitle = {EGU General Assembly 2024 : abstracts},
	doi = {10.5194/egusphere-egu24-5561},
	unique-id = {34802132},
	abstract = {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.},
	year = {2024},
	orcid-numbers = {Lukács, András/0000-0003-3955-9824; Székely, Balázs/0000-0002-6552-4329; Erőss, Anita/0000-0002-2395-3934}
}

@CONFERENCE{MTMT:34289554,
	title = {Investigation of groundwater discharge and projection of future lake level changes of Lake Velence by using numerical simulation},
	url = {https://m2.mtmt.hu/api/publication/34289554},
	author = {Erőss, Anita and Baják, Petra and András, Csepregi and Szabó, Péter},
	booktitle = {19th World Lake Conference - Book of Abstracts},
	unique-id = {34289554},
	year = {2023},
	pages = {35-35},
	orcid-numbers = {Erőss, Anita/0000-0002-2395-3934}
}

@article{MTMT:34189386,
	title = {Natural Radioactivity in Drinking Water in the Surroundings of a Metamorphic Outcrop in Hungary: The Hydrogeological Answer to Practical Problems},
	url = {https://m2.mtmt.hu/api/publication/34189386},
	author = {Baják, Petra and Molnár, Bence and Hegedűs-Csondor, Katalin and Tiljander, M. and Jobbágy, V. and Kohuth-Ötvös, V. and Izsák, B. and Vargha, Márta and Horváth, Ákos and Csipa, E. and Óvári, Mihály and Tóbi, Csaba and Völgyesi, Péter and Pelczar, K. and Hult, M. and Erőss, Anita},
	doi = {10.3390/w15091637},
	journal-iso = {WATER-SUI},
	journal = {WATER},
	volume = {15},
	unique-id = {34189386},
	year = {2023},
	eissn = {2073-4441},
	orcid-numbers = {Hegedűs-Csondor, Katalin/0000-0002-3368-9620; Horváth, Ákos/0000-0003-2611-4287; Erőss, Anita/0000-0002-2395-3934}
}

@article{MTMT:34019535,
	title = {Az esőcsepptől a forrásvízig – Szűcs Péter akadémikus székfoglaló előadása a Magyar Tudományos Akadémián},
	url = {https://m2.mtmt.hu/api/publication/34019535},
	author = {Erőss, Anita},
	journal-iso = {HIDROL KOZL},
	journal = {HIDROLÓGIAI KÖZLÖNY},
	volume = {103},
	unique-id = {34019535},
	issn = {0018-1323},
	year = {2023},
	eissn = {2939-8495},
	pages = {73-74},
	orcid-numbers = {Erőss, Anita/0000-0002-2395-3934}
}