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| Titel |
A comparison of the effect of land use, soil composition and topography on the energy fluxes in the Rur catchment |
| VerfasserIn |
Alessandra Trevisan, Stefan Kollet, Victor Venema, Clemens Simmer |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250086876
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| Publikation (Nr.) |
 EGU/EGU2014-818.pdf |
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| Zusammenfassung |
| As part of the MiKlip program on seamless decadal climate prediction, we study the
effects of subsurface hydrodynamics and land cover on the land surface energy
fluxes by analyzing the results of a fully coupled land surface-subsurface model, in a
simulation performed over a computational domain that includes the Rur catchment,
Germany.
The modeling system consists of the hydrologic model ParFlow, a 3D parallel watershed
model that simulates surface and subsurface water flow, and of the Community Land Model
CLM, which simulates physical, chemical and biological processes at the land surface. The
atmospheric forcing is obtained from the weather prediction and climate model of the
German weather service, COSMO.
A Model Complexity Reduction Approach is proposed to reduce the computational burden of
fully coupled simulations, by running a simplified model over the entire domain over large
spatial scales (on the order of 106 km2) and applying corrections based on the results of the
full complex system over smaller subdomains (on the order of 104 km2) and similarity rules
between the cells.
In order to develop the corrections, we analyzed individual and correlated impacts of
vegetation cover, soil composition and topography on the energy fluxes at the land surface.
We focused on the topographic wetness index (TI), as an indicator of the topographic
structure and as a source of static information about the groundwater dynamics. With
stepwise multivariate regression several correction functions for the evapotranspiration
(ET) were found, for example, TI with water table depth, and for the temporally
varying spatial correlations TI with water table depth and ET. This is based on
characteristic locations in the watershed where moisture limitation reduces ET or
moisture abundance along river corridors enhances e.g., transpiration. The temporal
variability in the correlations stems mainly from the diurnal and seasonal cycles.
Additional variables in the analysis may be land and soil cover, which are also
useful in explaining a smaller part of the spatial variance. However, TI appears
to be the primary predictor for the spatial variability of the exchange of energy
between the subsurface, land surface and atmosphere in simulations and may play
an important role in the complexity reduction approach developed in this study. |
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