A hybrid data-driven approach to analyze the drivers of lake level dynamics
Abstract. Lakes are directly exposed to climate variations, as their recharge processes are driven by precipitation and evapotranspiration, and indirectly via groundwater trends, changing ecosystems and changing water use.
In this study, we present a downward model development approach that uses models of increasing complexity to identify and quantify the dependence of lake level variations on climatic and other factors. The presented methodology uses high-resolution gridded weather data inputs that were obtained from dynamically downscaled ERA5 reanalysis data. Previously missing fluxes and previously unknown turning points in the system behavior are identified via a water balance model. The detailed lake level response to weather events is analyzed by calibrating data-driven models over different segments of the data timeseries. Changes in lake level dynamics are then inferred from the parameters and simulations of these models.
The methodology is developed and presented on the example of the Groß Glienicker Lake, a groundwater-fed lake in eastern Germany, that has been experiencing increasing water loss in the last half century. We show that lake dynamics were mainly controlled by climatic variations in this period, and we identified two different phases with systematic differences in behavior. The increasing water loss during the last decade, however, cannot be accounted for by climate change. Our analysis suggests that this alteration is caused by the combination of regional groundwater decline and vegetation growth in the catchment area, with some additional impact from changes in the local rainwater infrastructure.
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