Using LSTM and Metrological Time Series to forecast Lysimeter leachate

Quantifying groundwater recharge remains a central challenge in hydrology, particularly in the context of climate variability and water resource management. Lysimeter measurements provide direct estimates of recharge but are spatially sparse and costly to maintain. In this study we train and evaluate long short-term memory (LSTM) networks on high-resolution Lysimeter data of multiple decades to predict seepage fluxes based on precipitation, temperature, and related meteorological  features. LSTM architectures are well-suited to capture the delayed and nonlinear nature of recharge processes, where precipitation may influence measurable seepage weeks or months later. The selection of meteorological features is guided by well-established empirical relations. We use feature importance to investigate the relevance of meterological input parameters on the model prediction and to guide the design of a compact neural network using the fewest possible input features to simplify future data acquisition. We envision the replacement of Lysimeters by trained neural networks as soft-sensors.

Our results highlight key limitations, particularly the need for sufficiently large datasets and the degradation of model performance in the presence of data gaps. Nevertheless, machine learning shows promise for extrapolating recharge dynamics in data-sparse regions if trained appropriately. This work contributes to the growing discourse on integrating physical understanding with data-driven methods to support groundwater assessments.