Fine-scale dynamic modelling of water table depths (WTD) in Danish peatlands

In Denmark, re-wetting of drained peatland is considered an effective measure for reduction of agricultural greenhouse gas (GHG) emissions, due to the well-established relationship between water table depth and GHG emissions. Returning peatlands to their natural hydrological state, has additional benefits for nutrient loads and biodiversity and has becomes central in environmental policies.

Prevailing WTD-dependent GHG upscaling methods for peatlands are based on long term average WTD estimates, while there is limited understanding of the impact of WTD variability, extremes and how those effect rewetting strategies. This project aims to increase our knowledge on peatland WTD variability in space and time in high resolution to enable better estimation of the emission reduction potential and to support the rewetting strategies. Process-based hydrological models are important tools to support that effort.

We base our detailed simulation of peatland hydrology on an optimization of the national-scale Danish groundwater flow model with focus on the spatio-temporal patterns in peatlands. We identify the processes that govern peatland dynamics, including estimation of model parameters corresponding to those processes.

Besides local-scale insights on WTD dynamics from a highly instrumented peatland, we combine the physically based 3D groundwater flow model with remote sensing-based estimates of WTD in a spatial oriented optimization of the hydrological model.

Through scenario simulations we analyze the effects of climate variability and change, and especially how extreme events (e.g. droughts) impact GHG emissions controlled by WTD.

Those achievements enhance simulation of peatland processes, and the understanding of the climate response to the changes in WTD and will thereby support the Danish rewetting strategies and enables better upscaling of GHG emissions for national inventories.