Numerical modeling and time series analysis to quantify the neglected groundwater component in Lake Velence’s water budget – a case study from Hungary

Lake Velence is a shallow soda lake in Hungary, which has a diverse ecosystem and is a popular tourist destination. Because of that, the lake is the focus of continuous interest and is constantly examined in terms of water quality and quantity. In recent years, it has been observed that the lake's water and nutrient budget is negatively affected by climate change. Since the very existence of the lake is threatened, it has become important to assess the quantity of water flowing into and out of the lake. In our research, the emphasis is on the investigation of the groundwater component, since groundwater can represent a significant buffer in the lake's water balance against climate change, and since in water management practice, neither inflow nor outflow of groundwater is currently considered in the lake’s water budget. Therefore, we wanted to understand the nature of the relationship between the lake and the groundwater and quantify the amount of inflowing and outflowing groundwater.

To achieve our aim, we created a regional-scale transient 3D numerical groundwater flow model for the lake's catchment area using Visual MODFLOW. The time series of weather parameters (i. e. amount of precipitation, evaporation, temperature), the discharge rate of surface water courses, and groundwater extraction data from 1990-2020 have been incorporated into the model. To calibrate the model, we used the time series of monitoring wells of unconfined and confined aquifers. The mentioned time series were also analyzed using statistical methods such as the relationship between rainfall, the groundwater level measured in wells, and the lake level.

Our results complemented the previous studies on the lake's catchment area: there is a not insignificant connection between the lake and groundwater, and the lake is fed by local flow systems with shallow penetration depth and relatively short residence time, which are known to be more sensitive to climate change. Finally, we used the calibrated model to test different scenarios, e. g. we have reduced rainfall or increased water withdrawals to highlight the lake's vulnerability to future changes.

The research was supported by the ÚNKP-22-3 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. Part of the research was funded by the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project.