Abstract. Uncertainty in hydrological modeling is of significant concern due to its
effects on prediction and subsequent application in watershed management.
Similar to other distributed hydrological models, model uncertainty is an
issue in applying the Soil and Water Assessment Tool (SWAT). Previous
research has shown how SWAT predictions are affected by uncertainty in
parameter estimation and input data resolution. Nevertheless, little
information is available on how parameter uncertainty and output uncertainty
are affected by input data of varying complexity. In this study,
SWAT-Hillslope (SWAT-HS), a modified version of SWAT capable of predicting
saturation-excess runoff, was applied to assess the effects of input data
with varying degrees of complexity on parameter uncertainty and output
uncertainty. Four digital elevation model (DEM) resolutions (1, 3, 10 and
30 m) were tested for their ability to predict streamflow and saturated
areas. In a second analysis, three soil maps and three land use maps were
used to build nine SWAT-HS setups from simple to complex (fewer to more soil
types/land use classes), which were then compared to study the effect of
input data complexity on model prediction/output uncertainty. The case study
was the Town Brook watershed in the upper reaches of the West Branch Delaware
River in the Catskill region, New York, USA. Results show that DEM resolution
did not impact parameter uncertainty or affect the simulation of streamflow
at the watershed outlet but significantly affected the spatial pattern of
saturated areas, with 10m being the most appropriate grid size to use for our
application. The comparison of nine model setups revealed that input data
complexity did not affect parameter uncertainty. Model setups using
intermediate soil/land use specifications were slightly better than the ones
using simple information, while the most complex setup did not show any
improvement from the intermediate ones. We conclude that improving input
resolution and complexity may not necessarily improve model performance or
reduce parameter and output uncertainty, but using multiple temporal and
spatial observations can aid in finding the appropriate parameter sets and in
reducing prediction/output uncertainty.
A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction