Abstract's details
Assessment of SWOT estimated hydraulic parameters through hydrological experiments over Ganga River
Event: 2025 SWOT Science Team Meeting
Session: Hydrology: River Science Working Group
Presentation type: Poster
River discharge is one of the important hydrological essential climate variables (ECV) that is a key for understanding the water cycle and prevention strategies for efficient water resources. In this study, we investigate the Ka band Radar Interferometer (KaRIn) sensor data of Surface Water Ocean Topography (SWOT) mission to estimate river discharge over Ganga River exclusively from remotely sensed hydraulic observations. A series of field campaigns were carried out in-synchronous with the SWOT Cal-Val orbit (track 10, daily repetivity) and Science orbit (pass 36 & 273, 21 day’s cycle) during April-2023 to June 2024. More than 15000 kinematic observations were collected using DGPS to monitor spatial variability of the water stage within the 120 km of swath altimetry during Cal-Val and science phase. The observation from Acoustic Doppler Current Profiler (ADCP) was also collected simultaneously at various locations to estimate river discharge for calibration/validation purposes. SWOT Level 2 Water Mask Pixel Cloud Data Product, Version C and River Single-Pass Vector Data Product, Version 2 was downloaded from PODAAC. For analyzing the long time series, we utilized the Hydrocron-API. For our selected ROI, in total 16 reaches and 1142 nodes were found on the main stream of Ganga River. The PIX-C product contains multiple attributes such as: Geolocated elevations, geophysical range corrections, tidal corrections, Classification mask and Surface areas that was used estimating WSE. Prior to analysis, WSE were preprocessed for estimating the land contaminated pixels, we use the following flags: geolocation quality flag (geolocation_qual), Classification mask (water/land flags, and water fraction), interferogram flag and Sigma0 >15 dB. This step has significantly reduced the number of valid observations. SWOT data release is still in the commissioning phase and recently distributed version D data may reduce these erroneous observations. To remove the erroneous pixels, we also make use of maximum extent of wetland boundaries. The National Wetland Inventory and Assessment (NWIA) database at 1:12500 scale is a pan India database, developed by the Space Applications Centre (ISRO) that integrates data from the Resourcesat-2/2A LISS-IV of spatial resolution 5.6 meter. SWOT WSE observations were interpolated at 60 meter intervals to prepare the surface elevation maps.
WSE along with river width and water slope are the three hydraulic variables essential for river discharge computation. SWOT retrieved WSE during Cal-Val phase is validated using in-situ hydrological observations. During the experiment the water stage was found to be varying between 53.37 m - 73. 94 m with respect to mean sea surface and the river discharge value were sound to be varying between 430 m3/s to 1658 m3/s. The in-situ water stage during experiment was observed to be between 63.28 - 69.53 meter with corresponding river discharge of 510-1409 m3/s. The water surface profile fitted linearly and the slope was estimated (8.09 cm/Km). Similar order of variation in the water levels was observed by SWOT observations, with root mean square error (RMSE) of the order of 25 cm. The SWOT derived river slope (8.89 cm/ Km) as shown in figure 1e was also in good agreement with the GPS observations. To evaluate the capability of SWOT observations in estimating river discharge, the extreme downstream location of the ROI was selected. GloFAS model estimated river discharge was used to compare the SWOT estimated discharge. The integration of SWOT measured high-resolution WSE along with river width with a modified Manning’s equation has provided a scalable approach for estimating discharge without relying on traditional in situ measurements. SWOT estimated river widths were highly erroneous, varying between 400-3000 meters without any seasonal pattern. In this work, river width was estimated using Landsat-8/9 in Google Earth Engine (GEE). River depth and slope is inferred from WSE by subtracting a reference bed elevation (minimum WSE as an approximation) and from the gradient along the river's reach. With these parameters in hand, Manning's equation is applied to estimate discharge with n (0.018 for natural rivers).
The results demonstrate that SWOT-derived discharge successfully captures seasonal variations and large-scale hydrological trends with R2 0.8368, RMSE 2418.43 m3/s and NSE of 0.82. The overall correlation between altimetry measured river discharge with GloFAS data, reinforcing the reliability of SWOT observations under natural flow conditions. This study generated a reliable dataset to validate the SWOT observations along with the estimated river discharge data set of the Ganga River near Varanasi with the aim of enhancing the accuracy of swath altimeter products. Initial results from swath altimetry measurements have added a new dimension in the field of land hydrology.
Back to the list of abstractWSE along with river width and water slope are the three hydraulic variables essential for river discharge computation. SWOT retrieved WSE during Cal-Val phase is validated using in-situ hydrological observations. During the experiment the water stage was found to be varying between 53.37 m - 73. 94 m with respect to mean sea surface and the river discharge value were sound to be varying between 430 m3/s to 1658 m3/s. The in-situ water stage during experiment was observed to be between 63.28 - 69.53 meter with corresponding river discharge of 510-1409 m3/s. The water surface profile fitted linearly and the slope was estimated (8.09 cm/Km). Similar order of variation in the water levels was observed by SWOT observations, with root mean square error (RMSE) of the order of 25 cm. The SWOT derived river slope (8.89 cm/ Km) as shown in figure 1e was also in good agreement with the GPS observations. To evaluate the capability of SWOT observations in estimating river discharge, the extreme downstream location of the ROI was selected. GloFAS model estimated river discharge was used to compare the SWOT estimated discharge. The integration of SWOT measured high-resolution WSE along with river width with a modified Manning’s equation has provided a scalable approach for estimating discharge without relying on traditional in situ measurements. SWOT estimated river widths were highly erroneous, varying between 400-3000 meters without any seasonal pattern. In this work, river width was estimated using Landsat-8/9 in Google Earth Engine (GEE). River depth and slope is inferred from WSE by subtracting a reference bed elevation (minimum WSE as an approximation) and from the gradient along the river's reach. With these parameters in hand, Manning's equation is applied to estimate discharge with n (0.018 for natural rivers).
The results demonstrate that SWOT-derived discharge successfully captures seasonal variations and large-scale hydrological trends with R2 0.8368, RMSE 2418.43 m3/s and NSE of 0.82. The overall correlation between altimetry measured river discharge with GloFAS data, reinforcing the reliability of SWOT observations under natural flow conditions. This study generated a reliable dataset to validate the SWOT observations along with the estimated river discharge data set of the Ganga River near Varanasi with the aim of enhancing the accuracy of swath altimeter products. Initial results from swath altimetry measurements have added a new dimension in the field of land hydrology.