Groundwater level prediction from meteorological indices

Due to climate change, an assessment of future changes in hydrological systems is necessary for appropriate planning, particularly for water management. So far, especially flood and low flow events have been studied. But groundwater levels are also influenced by extended dry periods and seasonal shifts in precipitation, as groundwater recharge is directly related to precipitation and evaporation. In the study area of Lower Saxony (Germany), groundwater is an important resource for drinking water and supplies about 86 % of the demand (MU 2021). Therefore, knowledge about possible changes is of highest importance for a possible need for action.

In this study, climate characterising indices are used. Based on the assumed relationship between groundwater levels and meteorological indices, a simplified statistical approach should be used. Therefore, multiple linear regression models were set up for groundwater level estimation. Local models were set up for 734 groundwater monitoring wells in Lower Saxony, Germany. In order to take the persistence of the meteorological indices into account, moving averages and time lags were also included. Using a split validation procedure, which could be carried out for 114 stations with sufficient time series lengths, shows a good performance of the models. In order to make statements about future changes, the models were applied using climate model data based on the RCP8.5 scenario. Analyses for the reference period show that the groundwater levels can be sufficiently estimated. Slight changes were detected for the near future (2021-2050) and for the far future (2071-2100). For the majority of the measuring stations, decreases in mean annual low groundwater levels and slightly decreases in mean annual high groundwater levels are observed for both future periods. The number of low-level months does not change, while the number of high-level months increases slightly. In addition, a delayed timing of the annual extremes can be detected.