With the increasing threat of climate change, projections of its impact on the availability of natural resources, such as groundwater, are becoming significantly more important. While extrapolation or prediction is a valid application for environmental system models, their assumptions and limitations are often not clearly communicated or investigated. This is especially true for data-driven models, which have been applied more frequently, and often with great success, to groundwater problems in the last decade. But are these techniques applicable to long-term future predictions of the impact of climate scenarios on groundwater resources, and if so, under which conditions and limitations?
Within the context of KlimaKonform (Technische Universität Dresden, 2023), a research project studying the impact of climate change on the Central German Uplands, ensembles of multi-layer perceptron (MLP) models have been derived to estimate groundwater levels for a variety of wells in a region in Saxony-Anhalt in Germany. Once trained to replicate historical time series with climatological input data such as precipitation and temperature, these model ensembles were then tasked to predict the groundwater levels up until the end of the current century under different climate scenarios.
We analyse the plausibility of the model ensemble predictions to shed light on the above-proposed question: what are factors (and metrics) of success, as well as limitations and possible failures, of data-driven methods (MLPs in this case) for long-term prediction? First, we propose that using ensembles of models, rather than “the single-best” trained model, is a necessity for such applications. We identify different methods of pre-processing of input and target data, structural model setup as well as target-oriented post-processing of ensemble simulations as vital factors for the successful application of MLPs to long-term prediction of groundwater levels under climate change scenarios. We further highlight remaining limitations and pose the question of how they could potentially be overcome.