|Using residual analysis, auto- and cross-correlations to indentify key processes for the calibration of the SWAT model in a data scarce region
|Bieger, K., G. Hormann and N. Fohrer
|Advances in Geosciences
|URL (non-DOI journals):
|Broad Application Category:
|Primary Application Category:
|calibration, sensitivity, and/or uncertainty analysis
|Secondary Application Category:
|3,099 km^2 Xiangxi in northwest Hubei Province, in central China
|Hydrological modeling poses a particular challenge
in data scarce regions, which are often subject to dynamic
change and thus of specific interest to hydrological
modeling studies. When a small amount of data available for
a catchment is opposed by extensive data requirements by the
chosen hydrologic model, ways have to be found to extract
as much information from the available data as possible.
In a study conducted in the Xiangxi Catchment in the
Three Gorges Region in China, the use of residual analysis
as well as auto- and cross-correlations for enhanced
model evaluation and for the identification of key processes
governing the hydrological behavior of the catchment prior
to model calibration was tested. The residuals were plotted
versus various variables such as time, discharge and precipitation.
Also, auto-correlations were calculated for measured
and simulated discharge and cross-correlations of measured
and simulated discharge with precipitation were analyzed.
Results show that the analysis of residuals as well as
auto- and cross-correlations can provide valuable information
about the catchment response to rainfall events, which
can be very helpful for calibration of hydrologic models in
data scarce regions.