The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models

SummaryThe objective of this study is to test the potential of snow cover data from the MODIS satellite sensor for calibrating and validating a conceptual semi-distributed hydrological model. The methodology is based on an indirect comparison of snow water equivalent simulated by the hydrologic model and the MODIS snow cover data. The analysis is performed for 148 catchments in Austria using the original Terra and Aqua MODIS images as well as MODIS snow cover products based on the combination of Terra and Aqua and on different spatial and temporal filters that reduce cloud coverage by using information from neighbouring non-cloud covered pixels in space or time. The results indicate that the use of the MODIS snow cover data improves the snow model performance as measured against independent ground snow depth data. In a verification mode, the median snow cover overestimation error of 7.1% of mismatch decreases to 5.6% and the corresponding underestimation error decreases from 4.7 to 4.1% if the combined MODIS data are used for calibration as compared to the case where no MODIS data are used. MODIS snow cover data also slightly improve the runoff model performance. In a verification mode, the median runoff model efficiency increases from 0.67 to 0.70 if MODIS data are used for calibration as compared to the case where no MODIS data are used. Sensitivity analyses indicate that the magnitude of the model efficiency is sensitive to the choice of the threshold of snow covered area used in estimating the snow underestimation errors, and the cloud cover threshold used in deciding whether a MODIS image can be used for model analysis. Evaluation of the model performance against merged MODIS snow products shows that the combination and filtering of the Aqua and Terra images does not significantly affect the runoff and snow model efficiency.