Global climate change and consequential assessment of groundwater levels in Lower Saxony (KliBiW, Phase 7)

The findings of current climate research and the status report of the Intergovernmental Panel on Climate Change (IPCC, 2014) show clear indications that global climate change is occuring. Temperature rises, changes in the spatial and temporal distribution of precipitation and the frequency of extreme weather events are all expected outcomes of climate change and will vary regionally.

The KliBiW project therefore aims to investigate global climate change and water management impact assessments for inland areas in Lower Saxony. Previous studies have extensively evaluated projected developments in temperature and precipitation in Lower Saxony. In addition, the water management consequences of climate change regarding floods and low flows have been quantified.

Extended droughts and seasonal shifts in precipitation as a result of climate change also affect groundwater levels, as groundwater recharge is directly related to precipitation and evaporation. To date, only a few and regionally limited studies on the development of groundwater levels have been carried out in Lower Saxony (e.g. Wriedt (2017) and the projects NoRegret (LWK, 2008) and AQUARIUS (LWK, 2012)). Therefore, in this project phase, the effects of climate change on groundwater levels in Lower Saxony are to be investigated and evaluated in more detail. In doing so (analogous to the previous investigations within the framework of this project), investigations will first be carried out on observations and then projections will be made for the future based on various climate scenarios. From this, adaptation measures and recommendations for action are to be developed and evaluated.

The effects of climate change on groundwater levels are to be assessed with the help of various methods developed by the project partners involved:

• Time series modelling methods (NLWKN, Hannover-Hildesheim)
• Statistical approaches (Leibniz-University Hannover, Institute for Hydrology and Water Resources Management)
• Artificial neural networks (BGR)
• Groundwater flow model (LBEG)

With the Institute's focus on statistical approaches, the aim is to investigate whether, similar as for the previously performed low-flow investigations, developments in groundwater levels can be successfully predicted using multiple linear regression.

Further information can be found at https://www.nlwkn.niedersachsen.de/startseite/wasserwirtschaft/klimawandel/projekt_klibiw/das-projekt-klibiw-104191.html