Integration of time-series analysis, satellite data and machine learning in water balance assessment for the Kis-Balaton Water Protection System

The Kis-Balaton Water Protection System (KBWPS) is a complex, semi-constructed wetland habitat consisting of three separate units that play a significant role in protecting water quality of Lake Balaton, the largest shallow freshwater lake in Central Europe (Bhomia et al., 2021). Previously, it was noted that the water balance of the KBWPS can only be determined with high uncertainty; specifically, the seasonal variation in the ratio of evaporation to transpiration. Hovewer, estimating evaporation is one of the most crucial factors in water balance calculations. Until now, a “homogeneous method” developed for Fertő/Lake Neusiedl (Bhomia et al., 2021) has been applied for the KBWPS, which is rather an oversimplification for the highly heterogeneous lake and marsh complex of KBWPS. Therefore, the aim of the current study is to develop a system-specific approach tailored to the KBWPS’ spatial heterogeneity.

To quantify and predict the system’s hydrological behaviour, a Long Short-Term Memory (LSTM) model was developed to estimate daily outflow discharge at a single outlet point. The model was trained using meteorological variables and observed daily discharge time series, allowing the network to capture temporal dependencies and delayed system responses. In parallel, monthly Sentinel-2 imagery and daily in-situ measurements were analysed using trend analysis and seasonal decomposition to investigate the temporal variability of key hydro-meteorological parameters. NDVI-based satellite estimates were applied to characterise evapotranspiration dynamics. A comprehensive statistical analysis of time series, including air humidity, air temperature, wind conditions, and water chemistry data, was carried out to identify correlations between the individual parameters. The applied statistical and machine learning methods effectively captured the temporal dynamics of the system.  

In addition, Sentinel-2 satellite data was used to refine the spatial structure of vegetation, which influenced directly the transpiration. The development of a vegetation delineation methodology, based on NDVI classification, contributes to more accurate determination of water balance components by separating water surfaces from vegetation-covered areas.

Another uncertain element of the system is the yield data series from the point-shape civil engineering structure, which connects the Kis-Balaton hydrological system to Lake Balaton. Formerly, the correction of the yield time-series was required human resources. To reduce measurement errors and decrease the need for the manual correction, a deep learning-based model is under development, which determines seasonal correction factors. To address this problem, precipitation and wind speed data are also used as suitable predictors in addition to the daily water flow time series.

The expected outcome of the research is a comprehensive, scientifically sound methodology that will enable more accurate water balance calculations for Kis-Balaton and contribute to more efficient water management support for the system in the long term.

The research was supported by the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1–21-2022–00014 project.

Bhomia, R. K., Clement, A., Látrányi-Lovász, Z., Kaur, R., Rousseau, D., Louage, F., Wang, Q., Hatvani, I. G. (2021). Case studies of (semi) constructed wetlands treating point and non-point pollutant loads to protect downstream natural ecosystems. In Reference module in earth systems and environmental sciences. Elsevier.