Analysis of extreme hydrological events over the Great Hungarian Plain based on Earth Observation data

In Hungary, especially in the Great Hungarian Plain, hydrological cycle related extreme events – such as floods, inland excess water and droughts – are recurrent problems of increasing economic importance. These extremes often occur in the same area and sometimes within the same growing season, largely affecting agricultural production and raising questions related to water conservation and potential land use adjustments. In addition to climate change, the regulation of large rivers and poor water management are also likely to influence the phenomenon. The last major extreme events occurred in 2022 (drought) and 2023 (inland excess water). Relevant studies are mostly based on meteorological data, with one of the most comprehensive describing the frequency of extremes for the period 1931–2010. However, based on more than two decades of MODIS time series, it is possible to analyze variables such as vegetation conditions and water-covered areas, and hence, to investigate the relationship between the vegetation state and the environmental factors. Our study attempts to provide objective, time-series based statistical evidence specifically on the vulnerability of arable lands of the Great Plain and the relationship between environmental and EO-based variables for the period 2000-2023. In addition to spectral indices and land surface temperatures and their anomalies derived from MODIS measurements, land cover (CORINE), meteorology (FORESEE), soil moisture (ERA5-Land), soil properties (DoSoReMi), optical-based relative inland excess water incidence map (1998–2023), radar-based relative inland excess water incidence maps (2020– 2023), as well as aggregated yield loss compensation claims submitted to the Agricultural Risk Management System are included in the analysis. All the variables are aggregated to a spatial grid of 1-km resolution, and their relationship is analysed with mathematical methods (e.g. BORUTA, linear regression). Project no. 993788 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2020 funding scheme and by the TKP2021-NVA-29 project of the Hungarian National Research, Development and Innovation Fund and by the OTKA FK-146600 and by National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project.