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Volume 18, issue 9 | Copyright
Hydrol. Earth Syst. Sci., 18, 3319-3339, 2014
https://doi.org/10.5194/hess-18-3319-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Sep 2014

Research article | 01 Sep 2014

Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling

M. Bechtold1, B. Tiemeyer1, A. Laggner1, T. Leppelt1, E. Frahm1,*, and S. Belting1,2 M. Bechtold et al.
  • 1Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany
  • 2Belting Umweltplanung, Quernheim, Germany
  • *now at: Physikalisch-Technische Bundesanstalt, Braunschweig, Germany

Abstract. Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other soils with high organic carbon contents are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new data set comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip-well-specific long-term annual mean water level (WL) as well as a transformed form (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insight into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and that predictors with stronger WLt indication, relying, for example, on detailed water management maps and remote sensing products, are needed to substantially improve model predictive performance.

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