Nowadays, climate change not only leads to riverine floods and flash floods but also to inland excess water (IEW) inundations and drought due to extreme hydrological processes. The Carpathian Basin is extremely affected by fast-changing weather conditions during the year. IEW (sometimes referred to as water logging) is formed when, due to limited runoff, infiltration, and evaporation, surplus water remains on the surface or in places where groundwater flowing to lower areas appears on the surface by leaking through porous soil. In this study, eight different machine learning approaches were applied to derive IEW inundations on three different dates in 2021 (23 February, 7 March, 20 March). Index-based approaches are simple and provide relatively good results, but they need to be adapted to specific circumstances for each area and date. With an overall accuracy of 0.98, a Kappa of 0.65, and a QADI score of 0.020, the deep learning method Convolutional Neural Network (CNN) gave the best results, compared to the more traditional machine learning approaches Maximum Likelihood (ML), Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN) that were evaluated. The CNN-based IEW maps can be used in operational inland excess water control by water management authorities.

In the EU, aquaculture ponds cover an area of 360,000 ha and are a crucial part of the rural landscape. As many ecosystem services (e.g., habitats for protected wildlife, nutrient cycling, etc.) are correlated with the proportion of reed beds relative to open-water areas, it is important in environmental studies to be able to accurately estimate the extent and the temporal dynamics of reed cover. Here, we propose a method for mapping reed cover in fish ponds from freely available Sentinel-2 imagery using the normalized difference vegetation index (NDVI), which we applied to Hungary, the third largest carp producer in the EU. The dynamics of reed cover in Hungarian fish ponds mapped using satellite imagery show a high degree of agreement with the ground-truth points, and when compared with data reported in the annual aquaculture reports for Hungary, it was found that the calculation of reed cover based on the NDVI-based approach was more consistent than the estimates provided in the report. We discuss possible applications of this remote sensing technique in estimating reed-like vegetation cover in fish ponds and the possible use of the results for climate change studies and ecosystem services assessment.

Regularly, large parts of the agricultural areas of the Great Hungarian Plain are inundated due to excessive rainfall and insufficient evaporation and infiltration. Climate change is expected to lead to increasingly extreme weather conditions, which may even increase the frequency and extent of these inundations. Shallow “floods”, also defined as inland excess water, are phenomena that occur due to a complex set of interrelated factors. Our research presents a workflow based on active and passive satellite data from Sentinel-1 and -2, combined with a large auxiliary data set to detect and predict these floods. The workflow uses convolutional neural networks to classify water bodies based on Sentinel-1 and Sentinel-2 satellite data. The inundation data were complimented with meteorological, soil, land use, and GIS data to form 24 features that were used to train an XGBoost model and a deep neural network to predict future inundations, with a daily interval. The best prediction was reached with the XGBoost model, with an overall accuracy of 86%, a Kappa value of 0.71, and an F1 score of 0.86. The SHAP explainable AI method showed that the most important input features were the amount of water detected in the satellite imagery during the week before the forecast and during the period two weeks earlier, the number of water pixels in the surroundings on the day before the forecast, and the potential evapotranspiration on the day of the forecast. The resulting inland excess water inundation time series can be used for operational action, planning, and prevention.

A belvíz-veszélyeztetettség alatt azt a valószínűségi változót értjük, ami megadja, hogy adott területen mekkora valószínűséggel következik be a belvízi elöntés. Jelen cikkünkben a területhasználat váltás, a földhasználat racionalizálásának hatásait jelenítjük meg a belvíz-veszélyeztetettség mértékére. A kedvezőtlen vízgazdálkodású területek művelésének felfüggesztésének, a területhasználat megváltoztatásának (pl. erdősítés, lefolyástalan területek tározóvá alakítása, zöldítés stb.) hatását trendvizsgálatok alapján határozzuk meg, felhasználva a CORINE és a NÖSZTÉP adatbázist. A belvíz-veszélyeztetettségi térképezéshez alkalmazott területhasználati osztályértékeket módosítottuk, mellyel a területhasználatban bekövetkező feltételezett változás hatását jelenítjük meg. Az elemzés során a környezeténél mélyebb fekvésű (~450 ezer ha), erősen belvíz-veszélyeztetett területeket vettük figyelembe. A síkvidéki területeken a változás kismértékűnek tűnik, de ha a mélyfekvésű területekhez mérjük a területhasználat racionalizálását a területek 12,13%-ban csökkentette, illetve 8,42%-ban növelte a belvíz-veszélyeztetettséget. Megállapításaink szerint az egyes mezőgazdasági területek természeti adottságai által keletkező belvíz hajlam kimutathatóan csökkenthető a vízgazdálkodási körülményeket figyelembe vevő területhasználattal. Mindezek alapján kijelenthető, hogy a gazdálkodás során a talajtani és vízgazdálkodási adottságokhoz kell igazítani a művelési ágakat.