Daily Water Surface Elevations on Rivers from Multi-Mission Satellite Altimetry

1. Summary This repository contains the daily Water Surface Elevation WSE time series derived from multi-mission satellite altimetry (SWOT, Sentinel-3A/B, Sentinel-6) using the Reach-Reg method, as detailed in Halicki et al., (2026). This methods uses WSE from the Dahiti webportal (Schwatke et al., (2015), dahiti.dgfi.tum.de/en), as well as river vectors from the SWORD database (Altenau et al., 2021). In the version v1, the dataset includes validated WSE estimates and associated accuracy metrics for 8 major global rivers, covering 95 Reference Stations (RS) across four continents. Future versions will include also other river data. The Python implementation of Reach-Reg is also freely available on GitHub (https://github.com/MichalHalicki4/Reach-Reg). When using WSE data from this repository, please cite Halicki et al. (2026). 1. Data formats For each river there is a separate .rar file, containing (1) an accuracy metadata sheet, as well as (2) a separate .csv sheet for a daily WSE time series on each RS. 3. Attribute description Daily WSE time series: Column Name Description Units Index Date of the observation (YYYY-MM-DD) Date wse Daily Water Surface Elevation WSE meters (m) wse_u WSE Uncertainty meters (m) N Number of individual altimetry measurements aggregated within the day count Accuracy metadata: Column Name Description Units id Reference Station (RS) ID integer x, y Coordinates of the RS (longitude, latitude) decimal degrees chain Chainage of the RS (from the downstream boundary of the river section) km river River name text g_chain Chainage of the gauge (from the downstream boundary of the river section) km velocity Estimated mean river flow velocity used for time synchronization m/s c Estimated parameter 'c' from the at-a-station hydraulic geometry simplification variable v_uncrt_range Uncertainty range of the estimated velocity m/s num_of_all_meas Total number of all raw altimetry measurements used for RS densification during the study period count num_of_vs Total number of Virtual Stations (VS) aggregated to densify WSE at RS count mean_bias Mean error of all VS and RS WSE measurements m prct_in_unct Percentage of in situ data falling within the WSE +/- uncertainty range % mean_rmsd_agg Mean aggregated RMSD (from the chained regression) m wl_amp Water Level Amplitude (based on the densified WSE time series at RS) m rmse_rr Root Mean Squared Error of the Reach-Reg daily WSE time series (validated against in situ) m rmse_raw RMSE of the raw RS time series (not densified, just interpolated) m rmsd_cval RMSD derived from the cross-validation m nse_rr Nash-Sutcliffe Efficiency of the Reach-Reg daily WSE time series - nse_raw NSE of the raw RS time series (not densified, just interpolated) - nse_cval NSE derived from the cross-validation - nrmse_rr Normalized Root Mean Squared Error of the Reach-Reg daily WSE time series % nrmse_raw NRMSE of theraw RS time series (not densified, just interpolated) % nrmse_cval NRMSE derived from the cross-validation % 4. References Altenau, E. H., Pavelsky, T. M., Durand, M. T., Yang, X., Frasson, R. P. de M., & Bendezu, L. (2021). The surface water and ocean topography (SWOT) mission river database (SWORD): A global river network for satellite data products. Water Resources Research, 57(7). https://doi.org/10.1029/2021WR030054 Halicki, M., Niedzielski, T., Schwatke, C., Scherer, D., & Dettmering, D. (2026). Daily river water levels from multi-mission altimetry: A reach-based regression method using the unique SWOT data geometry. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2026.135367. Schwatke, C., Dettmering, D., Bosch, W., & Seitz, F. (2015). Dahiti – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry, Hydrology and Earth System Sciences, 19, 4345–4364. https://doi.org/10.5194/hess-19-4345-2015