English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  DetailsSummary

Released
Journal Article

Uncertainties in up-scaling N2O flux from field to 1 degrees X 1 degrees scale: A case study for Inner Mongolian grasslands in China

MPS-Authors
/persons/resource/persons62609

Xu-Ri,  Xu-Ri
Department Biogeochemical Synthesis, Prof. C. Prentice, Max Planck Institute for Biogeochemistry, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Xu-Ri, X.-R., Niu, H.-S., Li, C.-S., Wang, Y.-S., & Wang, M.-X. (2006). Uncertainties in up-scaling N2O flux from field to 1 degrees X 1 degrees scale: A case study for Inner Mongolian grasslands in China. Soil Biology and Biochemistry, 38(4), 633-643.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000E-D4BF-0
Abstract
Since N2O emissions cannot be measured easily at large scales, global emission estimates inevitably involve problems with scaling. To date, up-scaling processes depend highly on the models and database. Because of the limitation in resolution of the databases, which provide input parameters to drive the model's regional simulations, the uncertainties generated from the up-scaling processes must be quantified. In this paper, the uncertainties in up-scaling N2O emissions from the field scale (similar to 1 km(2)) to 1 degrees X 1 degrees scale (similar to 10,000 km(2)) were quantified in a case study from the Xilin River basin of Inner Mongolia, China. A revised process-based DNDC model was applied in the study for quantifying N2O fluxes with a high-resolution (1 km(2)) soil database constructed with remote sensing data and GIS technique. The results showed that the uncertainties coming from spatial scaling effect is 63.6%, and from the partitioning of sensitive model parameter (SOC) is 86.4%. We found that inclusion of spatial heterogeneity of soil factors resulted in lower regional N2O emission estimates. Utilization of the spatial structural information based on soil type was more effective for reducing the spatial scaling effect in comparison with the variability information calculated from Monte Carlo method. (c) 2005 Elsevier Ltd. All rights reserved. [References: 36]