Accuracy evaluation of snow water equivalent global data: The case of the Northern Dvina River basin

Abstract

The article presents a comprehensive multilevel accuracy evaluation of global snow water equivalent (SWE) datasets performed for the territory of the Northern Dvina River basin, where snow plays an important role in the formation of spring runoff and the increase in winter runoff during recent decades due to ongoing climate change. Six global snow water equivalent datasets, available on the National Snow and Ice Data Center website (all currently available global SWE data), were used in this study. Snow survey data of the Roshydromet observation network were used as ground-based observations. The accuracy of dataset values of SWE was evaluated by comparison with ground observations in several successive stages. In the first stage, the global SWE values were compared with the observational data for several selected weather stations, i. e., the dataset values of SWE belonging to a particular grid cell were compared with the snow survey values of a weather station located in this cell. In the second stage, the SWE values averaged over one of the main subbasins of the Northern Dvina, the Small Northern Dvina River basin, were analyzed. The final stage was the SWE comparison for the entire Northern Dvina River basin. The methodology for ranking data depending on their accuracy based on comparison with ground-based observations of several basic parameters of quantitative changes in SWE during periods of formation and before the start of melting (maximum SWE) was specially developed to implement the accuracy evaluation and SWE comparison. A comprehensive comparative analysis performed based on the developed methodology revealed that the accuracy of global SWE data increases with the investigation area (i.e., the volume of information used) for which the comparison is being carried out. The closest to the observed SWE values for all datasets were obtained for the whole Northern Dvina River basin. The ranking methodology and the implemented analysis helped to determine the most promising global SWE data for future use.