Identification of Outliers in Grace Data for Indo-Gangetic Plain Using Various Methods (Z-Score, Modified Z-score and Adjusted Boxplot) and Its Removal

Abstract:

Gravity Recovery and Climate Experiment (GRACE) data are widely used for the hydrological studies for large scale basins (≥100,000 sq km). GRACE data (Stokes Coefficients or Equivalent Water Height) used for hydrological studies are not direct observations but result from high level processing of raw data from the GRACE mission. Different partner agencies like CSR, GFZ and JPL implement their own methodology and their processing methods are independent from each other. The primary source of errors in GRACE data are due to measurement and modeling errors and the processing strategy of these agencies. Because of different processing methods, the final data from all the partner agencies are inconsistent with each other at some epoch. GRACE data provide spatio-temporal variations in Earth’s gravity which is mainly attributed to the seasonal fluctuations in water level on Earth surfaces and subsurface. During the quantification of error/uncertainties, several high positive and negative peaks were observed which do not correspond to any hydrological processes but may emanate from a combination of primary error sources, or some other geophysical processes (e.g. Earthquakes, landslide, etc.) resulting in redistribution of earth’s mass. Such peaks can be considered as outliers for hydrological studies. In this work, an algorithm has been designed to extract outliers from the GRACE data for Indo-Gangetic plain, which considers the seasonal variations and the trend in data. Different outlier detection methods have been used such as Z-score, modified Z-score and adjusted boxplot. For verification, assimilated hydrological (GLDAS) and hydro-meteorological data are used as the reference. The results have shown that the consistency amongst all data sets improved significantly after the removal of outliers.