EGU23-7561, updated on 10 Jan 2024
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Deep Learning guided statistical downscaling of climate projections for use in hydrological impact modeling in Danish peatlands

Thea Quistgaard1, Peter L. Langen1, Tanja Denager2, Raphael Schneider2, and Simon Stisen2
Thea Quistgaard et al.
  • 1Aarhus University, Department of Environmental Science, Copenhagen, Denmark (
  • 2GEUS, Department of Hydrology, Copenhagen, Denmark

A course of action to combat the emission of greenhouse gasses (GHG) in a Danish context is to re-wet previously drained peatlands and thereby return them to their natural hydrological state acting as GHG sinks. GHG emissions from peatlands are known to be closely coupled to the hydrological dynamics through the groundwater table depth (WTD). To understand the effect of a changing and variable climate on the spatio-temporal dynamics of hydrological processes and the associated uncertainties, we aim to produce a high-resolution local-scale climate projection ensemble from the global-scale CMIP6 projections.

With focus on hydrological impacts, uncertainties and possible extreme endmembers, this study aims to span the full ensemble of local-scale climate projections in the Danish geographical area corresponding to the CMIP6-ensemble of Global Climate Models (GCMs). Deep learning founded statistical downscaling methods are applied bridge the gap from GCMs to local-scale climate change and variability, which in turn will be used in field-scale hydrological modeling. The approach is developed to specifically accommodate the resolutions, event types and conditions relevant for assessing the impacts on peatland GHG emissions through their relationship with WTD dynamics by applying stacked conditional generative adversarial networks (CGANs) to best downscale precipitation, temperature, and evaporation. In the future, the approach is anticipated to be extended to directly assess the impacts of climate change and ensemble uncertainty on peatland hydrology variability and extremes.

How to cite: Quistgaard, T., Langen, P. L., Denager, T., Schneider, R., and Stisen, S.: Deep Learning guided statistical downscaling of climate projections for use in hydrological impact modeling in Danish peatlands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7561,, 2023.

Supplementary materials

Supplementary material file