Title: | Influence of spatial interpolation methods for climate variables on the simulation of discharge and nitrate fate with SWAT |
Authors: | van der Heijden, S. and U. Haberlandt |
Year: | 2010 |
Journal: | Advances in Geosciences |
Volume (Issue): | 27 |
Pages: | 91-98 |
Article ID: | |
DOI: | 10.5194/adgeo-27-91-2010 |
URL (non-DOI journals): | |
Model: | SWAT |
Broad Application Category: | hydrologic and pollutant |
Primary Application Category: | calibration, sensitivity, and/or uncertainty analysis |
Secondary Application Category: | nitrogen cycling/loss and transport |
Watershed Description: | 1000 km^2 Leine River, a tributary of the Aller River located in the southeast part of the State of Lower Saxony in northern Germany. |
Calibration Summary: | |
Validation Summary: | |
General Comments: | |
Abstract: | 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 km^2) 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. |
Language: | English |
Keywords: | |