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| Titel |
Influence of spatial interpolation methods for climate variables on the simulation of discharge and nitrate fate with SWAT |
| VerfasserIn |
S. Heijden, U. Haberlandt |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrologic Modelling for the Assessment of Ecosystem Services and Landscape Functions ; Nr. 27 (2010-09-10), S.91-98 |
| Datensatznummer |
250016345
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| Publikation (Nr.) |
 copernicus.org/adgeo-27-91-2010.pdf |
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| Zusammenfassung |
For ecohydrological modeling climate variables are needed on subbasin basis.
Since they usually originate from point measurements spatial interpolation is
required during preprocessing. Different interpolation methods yield data of
varying quality, which can strongly influence modeling results. Four
interpolation methods to be compared were selected: nearest neighbour,
inverse distance, ordinary kriging, and kriging with external drift
(Goovaerts, 1997). This study presents three strategies to evaluate the
influence of the interpolation method on the modeling results of discharge
and nitrate load in the river in a mesoscale river catchment
(~1000 km2) using the Soil and Water Assessment Tool (SWAT,
Neitsch et al., 2005) model:
I. Automated calibration of the model with a mixed climate data set and
consecutive application of the four interpolated data sets.
II. Consecutive automated calibration of the model with each of the four
climate data sets.
III. Random generation of 1000 model parameter sets and
consecutive application of the four interpolated climate data sets on each of
the 1000 realisations, evaluating the number of realisations above a certain
quality criterion threshold.
Results show that strategies I and II are not suitable for evaluation of the
quality of the interpolated data. Strategy III however proves a significant
influence of the interpolation method on nitrate modeling. A rank order from
the simplest to the most sophisticated method is visible, with kriging with
external drift (KED) outperforming all others. Responsible for this behaviour
is the variable temperature, which benefits most from more sophisticated
methods and at the same time is the main driving force for the nitrate cycle.
The missing influence of the interpolation methods on discharge modeling is
explained by a much higher measuring network density for precipitation than
for all other climate variables. |
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