Assessment of Multi-Mission Satellite Altimetry GDR L2 Products for River Water Surface Elevation in the Ganga Basin

Precise monitoring of water surface elevation (WSE) in data-deficient areas such as the Ganga River stretch is essential for hydrological modelling, flood prediction, and comprehensive water resource management. This study introduces a comprehensive evaluation framework for Level-2 Geophysical Data Records (GDR L2) derived from various satellite altimetry missions, including Sentinel-3A/B, Sentinel-6A, Jason-3, and SWOT Nadir, validated against in-situ gauge stations from the Central Water Commission (CWC) across a range of hydrological conditions. The process includes advanced geographical analysis. Gaussian-process Kriging interpolation generates continuous longitudinal WSE profiles across strategically placed virtual stations; rigorous outlier detection employs interquartile range (IQR) and Hampel filters; bias correction employs dry-season median alignment to a common orthometric datum; and Kalman filter smoothing effectively reduces measurement noise while preserving critical hydrological signal dynamics.

Comprehensive performance evaluations employ co-located time series analysis, scatter plots, and flow duration curves (FDCs), with seasonal stratification distinguishing monsoon high-flow variability from stable non-monsoon baseflow conditions. The evaluation stresses physically significant parameters based on Kling-Gupta Efficiency (KGE) and RMSE. Sentinel-6A is the strongest performer in all situations with high non-monsoon accuracy (KGE 0.894, RMSE 0.089 m) and monsoon performance (KGE 0.57, RMSE 3.08 m) despite turbulent flow issues, but SWOT Nadir's processing potential is limited by specific hooking artifacts. During non-monsoon periods, measurement reliability is consistently 2-4 times higher. This proven multi-mission system demonstrates satellite altimetry as an operationally viable method for WSE retrieval in major braided rivers, allowing for accurate rating curve generation and discharge computation. In future machine learning data fusion and hydrodynamic modelling can be incorporated to increase basin-scale forecast capabilities.