|Title:||Effects of input data content on the uncertainty of simulating water resources |
|Authors:||Camargos, C., S. Julich, T. Houska, M. Bach and L. Breuer |
|Article ID:||621 |
|URL (non-DOI journals):|| |
|Broad Application Category:||hydrologic only |
|Primary Application Category:||uncertainty analysis |
|Secondary Application Category:||hydrologic assessment |
|Watershed Description:||104 km^2 drainage area, located in southwest Belgium and northwest Luxembourg. |
|Calibration Summary:|| |
|Validation Summary:|| |
|General Comments:|| |
|Abstract:||The widely used, partly-deterministic Soil and Water Assessment Tool (SWAT) requires a
large amount of spatial input data, such as a digital elevation model (DEM), land use, and soil maps.
Modelers make an effort to apply the most specific data possible for the study area to reflect the
heterogeneous characteristics of landscapes. Regional data, especially with fine resolution, is often
preferred. However, such data is not always available and can be computationally demanding.
Despite being coarser, global data are usually free and available to the public. Previous studies
revealed the importance for single investigations of different input maps. However, it remains
unknown whether higher-resolution data can lead to reliable results. This study investigates how
global and regional input datasets affect parameter uncertainty when estimating river discharges.
We analyze eight different setups for the SWAT model for a catchment in Luxembourg, combining
different land-use, elevation, and soil input data. The Metropolis-Hasting Markov Chain Monte
Carlo (MCMC) algorithm is used to infer posterior model parameter uncertainty. We conclude that
our higher resolved DEM improves the general model performance in reproducing low flows by 10%.
The less detailed soil-map improved the fit of low flows by 25%. In addition, more detailed land-use
maps reduce the bias of the model discharge simulations by 50%. Also, despite presenting similar
parameter uncertainty (P-factor ranging from 0.34 to 0.41 and R-factor from 0.41 to 0.45) for all setups,
the results show a disparate parameter posterior distribution. This indicates that no assessment of all
sources of uncertainty simultaneously is compensated by the fitted parameter values. We conclude
that our result can give some guidance for future SWAT applications in the selection of the degree of
detail for input data. |
|Keywords:||discharge; uncertainty analysis; input data; SPOTPY; Soil and Water Assessment Tool (SWAT) |