Using residual analysis, auto- and cross-correlations to identify key processes for the calibration of the SWAT model in a data scarce region

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.