Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling
Other literature type
(issn: 1607-7938, eissn: 1607-7938)
Fluxes of the three main greenhouse gases (GHG) CO<sub>2</sub>, CH<sub>4</sub> and
N<sub>2</sub>O 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 (WL<sub>t</sub>).
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 WL<sub>t</sub> 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
WL<sub>t</sub> 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
WL<sub>t</sub> 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 WL<sub>t</sub> variance cannot be explained by nationally available
predictor variables and that predictors with stronger WL<sub>t</sub> indication,
relying, for example, on detailed water management maps and remote sensing products,
are needed to substantially improve model predictive performance.