Comparison of Impulse response function and Machine learning models for use in groundwater level short to medium term future projections in the Baltic states

Recent years have seen several major groundwater drought instances in Europe, increasing the interest in their research and future predictions. Modeling methods can be used to explore groundwater drought risk in near and medium term future using climate projection data sets as an input. Therefore an optimal modeling approach has to be chosen according to the available data and modeling capacity of the historic series. 

We  explore two approaches for modeling groundwater level time series: Transfer function-noise models with Impulse response functions (TFN-IRF) and machine learning (ML). In both approaches, daily meteorological variables are used as an input and models are calibrated against historical groundwater level observations. 

TFN-IRF input parameters are daily precipitation and potential evapotranspiration. Other time series data, such as groundwater abstraction, can be added to potentially increase the fit. Machine learning models can, in addition to the aforementioned, benefit from a wider variety of site parameters, including derived parameters (e.g., meteorological indices), however at the expense of increased data collection effort. In addition, the obscure input data interpretation in ML methods can erode the trust in these models. In this study, only the most basic meteorological parameters - precipitation, temperature - and derived parameters such as potential evapotranspiration were used. 

We draw from an extensive groundwater level monitoring database of more than one thousand monitoring wells from three Baltic countries. The study provides an insight into differences between the two modelling approaches, keeping in mind the limitations of future projection data.

This research is funded by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165.