DOI: 10.5194/hess-18-3319-2014
Scopus记录号: 2-s2.0-84906837264
论文题名: Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling
作者: Bechtold M ; , Tiemeyer B ; , Laggner A ; , Leppelt T ; , Frahm E ; , Belting S
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2014
卷: 18, 期: 9 起始页码: 3319
结束页码: 3339
语种: 英语
Scopus关键词: Boundary conditions
; Carbon dioxide
; Conservation
; Groundwater
; Soils
; Water levels
; Water management
; Wetlands
; Boosted regression trees
; Distribution of water
; Greenhouse gases (GHG)
; Model calibration
; Parameter interactions
; Predictor variables
; Statistical modeling
; Water table depths
; Greenhouse gases
英文摘要: Fluxes of the three main greenhouse gases (GHG) CO2, CH 4 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 tht 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. © 2014 Author(s). CC Attribution 3.0 License. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78152
Appears in Collections: 气候变化事实与影响
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作者单位: Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany; Belting Umweltplanung, Quernheim, Germany; Physikalisch-Technische Bundesanstalt, Braunschweig, Germany
Recommended Citation:
Bechtold M,, Tiemeyer B,, Laggner A,et al. Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling[J]. Hydrology and Earth System Sciences,2014-01-01,18(9)