Statistical and Machine Learning Methods for River Water Level Time Series Construction Using Satellite Altimetry

The use of satellite altimetry data for monitoring the water level regime of rivers in Arctic regions is limited due to the negative effect of complex fluvial morphology and ice cover on altimetric radar measurements. The generation of time series of river water level consists of two main stages: (1) accurate geographic selection of satellite measurements over the river channel and (2) calculation of the average level for a given date after filtering outliers. This work is based on measurements from the European altimetry satellites Sentinel-3A and Sentinel-3B. The paper proposes a method for detection of aberrant values in altimetric measurements (outliers) acquired over a wide floodplain section of the Kolyma River. The method improved the accuracy of resulting satellite time series of water level by 0.04–1.59 m (or 4–85%) compared to the widely used standard statistical method of altimetric measurement filtering. The suggested method is based on the combination of three algorithms of different complexity: statistical (Mahalanobis distance), clustering (Density-Based Spatial Clustering of Applications with Noise (DBSCAN)), and machine learning (Isolating Forest) methods. In the combined approach, values classified as outliers by at least two algorithms were considered as outliers. This approach allowed us to reduce the impact of potential individual shortcomings of each of the three methods.