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| Titel |
A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape |
| VerfasserIn |
G. S. H. Pau, G. Bisht, W. J. Riley |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 5 ; Nr. 7, no. 5 (2014-09-17), S.2091-2105 |
| Datensatznummer |
250115723
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| Publikation (Nr.) |
 copernicus.org/gmd-7-2091-2014.pdf |
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| Zusammenfassung |
| Existing land surface models (LSMs) describe physical and biological
processes that occur over a wide range of spatial and temporal scales. For
example, biogeochemical and hydrological processes responsible for carbon
(CO2, CH4) exchanges with the atmosphere range from the molecular scale
(pore-scale O2 consumption) to tens of kilometers (vegetation
distribution, river networks). Additionally, many processes within LSMs are
nonlinearly coupled (e.g., methane production and soil moisture dynamics),
and therefore simple linear upscaling techniques can result in large
prediction error. In this paper we applied a reduced-order modeling (ROM)
technique known as "proper orthogonal decomposition mapping method" that
reconstructs temporally resolved fine-resolution solutions based on
coarse-resolution solutions. We developed four different methods and applied
them to four study sites in a polygonal tundra landscape near Barrow, Alaska.
Coupled surface–subsurface isothermal simulations were performed for summer
months (June–September) at fine (0.25 m) and coarse (8 m) horizontal
resolutions. We used simulation results from three summer seasons
(1998–2000) to build ROMs of the 4-D soil moisture field for the study sites
individually (single-site) and aggregated (multi-site). The results indicate
that the ROM produced a significant computational speedup
(> 103) with very small relative approximation error
(< 0.1%) for 2 validation years not used in training the
ROM. We also demonstrate that our approach: (1) efficiently corrects for
coarse-resolution model bias and (2) can be used for polygonal tundra sites
not included in the training data set with relatively good accuracy
(< 1.7% relative error), thereby allowing for the possibility
of applying these ROMs across a much larger landscape. By coupling the ROMs
constructed at different scales together hierarchically, this method has the
potential to efficiently increase the resolution of land models for coupled
climate simulations to spatial scales consistent with mechanistic physical
process representation. |
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