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Dynamic critical groundwater depth as a predictor of irrigation-intensified salinization in lowland Hungary

Dynamic critical groundwater depth as a predictor of irrigation-intensified salinization in lowland Hungary

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Authors

Fehér Zsolt Zoltán

Abstract

Shallow groundwater in continental lowland environments sustains upward capillary fluxes that transport dissolved salts to the land surface. However, the depth below which this capillary-driven contribution becomes negligible, often parameterized as the extinction depth in groundwater model ET packages, has been treated as a static, soil-dependent parameter. We argue that salinization risk is governed by the dynamic overlap between the seasonal cycles of groundwater level (GWL) and evapotranspiration (ET), and develop a time-varying risk index based on the proximity of the water table to a dynamic, GWL-dependent critical depth, and capillary fringe. The framework is applied to a 4,041 km² sub-region of the TIKEVIR system in southeastern Hungary using 74 years (1951–2025) of gridded GWL observations and FORESEE-HUN climate forcing. The domain-averaged seasonal cycle (shallowest in April at 2.50 m, deepest in October at 3.04 m, amplitude 0.54 m) defines a critical salinization window in March–June when the water table resides within the critical zone (zGWL < dcrit) under rising evaporative demand. A ConvLSTM network with eight input channels, trained on 1971–2019 data and evaluated on 2020–2024, achieves NSE = 0.839 and RMSE = 0.416 m for 1-month-ahead GWL forecasting at 1 km resolution, with 27–40 % RMSE reduction over persistence at 1–12-month lead times. Monte Carlo dropout analysis has verified a narrow prediction uncertainty, with a mean risk credible interval width of 0.014 (dimensionless). By integrating the forecast with the risk index, it was determined that 19.2% of the domain, equivalent to 776 km², is persistently at high or critical risk, predominantly located in discharge zones. A notable secular deepening of the water table, measured at 0.051 m per decade (p = 0.032), has slightly diminished the domain-averaged risk; however, the feedback loop between irrigation and salinization may counterbalance this trend. This methodology holds potential for application to similar lowland aquifer systems where the groundwater level (GWL) and evapotranspiration (ET) cycles are phase-offset.

DOI

https://doi.org/10.31223/X5GJ1N

Subjects

Agriculture, Hydrology, Soil Science

Keywords

critical groundwater depth; extinction depth; salinization; deep learning; ConvLSTM; groundwater level forecasting; Hungarian Great Plain; TIKEVIR; dynamic risk index; irrigation–salinization feedback

Dates

Published: 2026-02-27 09:57

Last Updated: 2026-02-27 09:57

License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability:
The CriticalGWL framework's source code, including ConvLSTM model, training scripts, risk index modules, and Streamlit dashboard, is archived at https://doi.org/10.5281/zenodo.18770854 (Fehér 2026).The trained ConvLSTM model weights and normalization parameters, gridded GWL product (`gwl_interpolated.nc`, 894 monthly time steps, 1951–2025, 100 × 65 cells at 1 km resolution), and aligned training data cube (including FORESEE RCP 4.5 climate projections to 2050) were deposited alongside the code. The FORESEE-HUN meteorological database is publicly available from the Department of Meteorology at the Eötvös Loránd University (ELTE), Budapest (https://nimfea.geo.elte.hu/FORESEE/). Groundwater level observations from the national monitoring network were obtained from the General Directorate of Water Management (OVF) upon request from the authors. The ESDAC topsoil physical properties (Ballabio et al., 2016) and EU-SoilHydroGrids (Tóth et al., 2017) are available from the European Soil Data Center (ESDAC; https://esdac.jrc.ec.europa.eu). The AGROTOPO soil database is maintained by the Institute for Soil Sciences at the HUN-REN Center for Agricultural Research.

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