In this study, a new approach for the automated extraction of high-resolution time-variable water surfaces is presented. For that purpose, optical images from Landsat and Sentinel-2 are used between January 1984 and June 2018. The first part of this new approach is the extraction of land-water masks by combining five water indexes and using an automated threshold computation. In the second part of this approach, all data gaps caused by voids, clouds, cloud shadows, or snow are filled by using a long-term water probability mask. This mask is finally used in an iterative approach for filling remaining data gaps in all monthly masks which leads to a gap-less surface area time series for lakes and reservoirs. The results of this new approach are validated by comparing the surface area changes with water level time series from gauging stations. For inland waters in remote areas without in situ data water level time series from satellite altimetry are used. Overall, 32 globally distributed lakes and reservoirs of different extents up to 2482.27 km 2 are investigated. The average correlation coefficients between surface area time series and water levels from in situ and satellite altimetry have increased from 0.611 to 0.862 after filling the data gaps which is an improvement of about 41%. This new approach clearly demonstrates the quality improvement for the estimated land-water masks but also the strong impact of a reliable data gap-filling approach. All presented surface area time series are freely available on the Database of Hydrological Time Series of Inland (DAHITI).
The water surface slope (WSS), hydraulic gradient, or flow gradient of a river is the slope of the hydraulic grade line, that is, the change of the pressure head per distance unit (Gliński et al., 2011;Herrmann & Bucksch, 2014;Julien, 2018b). It is typically defined positive for an decreasing water surface elevation (WSE) in downstream direction (Julien, 2018b). WSS is not stationary but changes over time and space. Especially in natural rivers that are non-uniform and unsteady, the WSS is variable over time because of morphological changes of the river bed and flood waves (Julien, 2018a). Locally, WSS may differ from larger-scale averages and change with every reach because of local characteristics like cascades, pools, or tributary estuaries (Rhoads, 2020;Schumm, 2005). While the WSS of an alluvial river is gradual, bedrock causes natural discontinuities in semi-alluvial rivers (Julien, 2018b). In hydrology, WSS is a critical parameter required to calculate flow velocity and discharge (Manning, 1891;Rhoads, 2020). The flow velocity derived from WSS is also essential for densifying spatial or temporal low-resolution water level measurements from non-repeating satellite altimetry missions such as Cryosat-2 (Tourian et al., 2016). Generally, the WSS can be used to correct any satellite altimetry mission to compensate for the satellites' ground track variability when calculating long-term water level time series at fixed locations, so-called virtual stations (VS). WSE measurements of the "Shuttle Radar Topography Mission" (SRTM) are regularly used to derive WSS (
<div> <p><span data-contrast="auto">The global reach-scale &#8220;ICESat-2 River Surface Slope&#8221; (IRIS, https://doi.org/10.5281/zenodo.7098113) dataset comprises average and extreme water surface slopes (WSS) derived from ICESat-2 observations between October 2018 and August 2022 as a supplement to 121</span><span data-contrast="auto">,</span><span data-contrast="auto">583 reaches from the &#8220;SWOT Mission River Database&#8221; (SWORD</span><span data-contrast="auto">, Altenau et</span><span data-contrast="auto"> al., 2021</span><span data-contrast="auto">).</span><span data-contrast="auto"> WSS is </span><span data-contrast="auto">required to calculate river discharge, which is among the Essential Climate Variables</span><span data-contrast="auto"> as defined by the Global Climate Observing System.</span><span data-ccp-props="{">&#160;</span></p> </div> <div> <p><span data-contrast="auto">To gain full advantage of ICESat-2&#8217;s unique measurement geometry with six parallel lidar beams, the WSS is determined across pairs of beams or along individual beams, depending on the intersection angle of spacecraft orbit and river centerline. The combined results of both approaches are validated against in-situ data in a regional study at 815 reaches in Europe and North America with a median absolute error of 23 mm/km, almost complying with the SWOT science requirements of 17 mm/km</span><span data-contrast="auto"> (Scherer et al., 2022)</span><span data-contrast="auto">.</span><span data-ccp-props="{">&#160;</span></p> </div> <div> <p><span data-contrast="auto">IRIS can be used to research river dynamics, estimate river discharge, and correct water level time series from satellite altimetry for shifting ground tracks. Additionally, by referencing SWORD as a common database, IRIS may be used in combination with observations from the recently launched SWOT mission and could be easily compared against WSS measurements from SWOT&#8217;s new wide-swath sensor.</span><span data-ccp-props="{">&#160;</span></p> </div>
The global reach-scale “ICESat-2 River Surface Slope” (IRIS) dataset comprises average and extreme water surface slopes (WSS) derived from ICESat-2 observations between October 2018 and August 2022 as a supplement to 121,583 reaches from the “SWOT Mission River Database” (SWORD). To gain full advantage of ICESat-2’s unique measurement geometry with six parallel lidar beams, the WSS is determined across pairs of beams or along individual beams, depending on the intersection angle of spacecraft orbit and river centerline. Combining both approaches maximizes spatial and temporal coverage. IRIS can be used to research river dynamics, estimate river discharge, and correct water level time series from satellite altimetry for shifting ground tracks. Additionally, by referencing SWORD as a common database, IRIS may be used in combination with observations from the recently launched SWOT mission.
Despite increasing interest in monitoring the global water cycle, the availability of in situ gauging and discharge time series is decreasing. However, this lack of ground data can partly be compensated for by using remote sensing techniques to observe river stages and discharge. In this paper, a new approach for estimating discharge by combining water levels from multi-mission satellite altimetry and surface area extents from optical imagery with physical flow equations at a single cross-section is presented and tested at the Lower Mississippi River. The datasets are combined by fitting a hypsometric curve, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is derived from the differences between virtual station elevations, which are computed in a least square adjustment from the height differences of all multi-mission satellite altimetry data that are close in time. Using the virtual station elevations, satellite altimetry data from multiple virtual stations and missions are combined to one long-term water level time series. All required parameters are estimated purely based on remote sensing data, without using any ground data or calibration. The validation at three gauging stations of the Lower Mississippi River shows large deviations primarily caused by the below average width of the predefined cross-sections. At 13 additional cross-sections situated in wide, uniform, and straight river sections nearby the gauges the Normalized Root Mean Square Error (NRMSE) varies between 10.95% and 28.43%. The Nash-Sutcliffe Efficiency (NSE) for these targets is in a range from 0.658 to 0.946.
<p lang="en-US" align="justify">Water surface slope (WSS) of rivers is a key parameter in hydrological modelling, which allows for estimation of the transport and erosion capacity of a river, its flow velocity and discharge. On a local scale, WSS can be measured with a GNSS receiver installed on a boat, using remote sensing techniques (e.g. airborne lidar) or from a Digital Elevation Model (DEM). The most accurate method to measure WSS avoiding high-cost field campaigns is based on Water Surface Elevations (WSE) measured at in-situ stations. However, in poorly gauged rivers the neighboring gauges can be up to hundreds of kilometers apart, which inhibits a proper river profile observation. The gap in decreasing number of gauge readings is partially filled with satellite altimetry over rivers. Altimetry based WSE can be used to estimate WSS between neighboring measurements. Here, we present an innovative approach for estimating high-resolution WSS derived from multi-mission satellite altimetry for the largest Polish rivers.</p> <p lang="en-US" align="justify">In this study, we used measurements from 9 altimetry missions: CryoSat-2, Envisat, ICESat-2, Jason-2/-3, SARAL, Sentinel-3A/-B, and Sentinel-6A. These observations cover the years from 2002 to 2022. We extracted the river centerlines from the global &#8220;SWOT Mission River Database&#8221; (SWORD). In order to validate the obtained results, we used WSE from 81 gauges, which are maintained by the Institute of Meteorology and Water Management &#8211; National Research Institute (Instytut Meteorologii i Gospodarki Wodnej &#8211; Pa&#324;stwowy Instytut Badawczy, IMGW-PIB). These measurements are referenced to the Kronsztadt&#8217;86 vertical datum and they range from 01.2016 to 05.2022. Additionally, we used the reach-scale &#8220;ICESat-2 River Surface Slope&#8221; (IRIS) and the DEM-derived WSS values from SWORD.</p> <p lang="en-US" align="justify">To obtain WSS, we first determined WSE at each satellite pass crossing the studied river. Next, we split rivers into sections without dams and reservoirs. The Support Vector Regression (SVR) has been applied to reject outliers. Then, water levels were assigned to a given river kilometer (bin). For each of them a median WSE has been calculated. Finally, WSS were calculated at river sections between bins, excluding those disrupted by hydraulic structures. Finally, we weighted the section-wise WSS inversely proportional to the length of each section and applied a Least Square Adjustment with an additional Laplace condition to obtain bin-wise WSS for each river kilometer.</p> <p lang="en-US" align="justify">To assess the accuracy of the proposed approach, we compared the obtained WSS with the slopes between IMGW-PIB gauges. For large rivers (Vistula, Odra, Warta), the multi-mission approach revealed high accuracy with preliminary Root Mean Squared Error (RMSE) below 30 mm/km. For smaller, mountain rivers (San, Dunajec) the preliminary errors were slightly larger (RMSE ~100 mm/km). We also compared our accuracies with those of the slopes based on DEM models, lidar data, ICESat-2 altimetry, and SWORD database. In general, the multi-mission approach revealed the highest accuracy. The research is supported by the National Science Centre, Poland, through the project no. 2020/38/E/ST10/00295.</p>
For nearly three decades, satellite radar altimetry has provided measurements of the water surface elevation (WSE) of rivers. These observations can be used to calculate the water surface slope (WSS), which is an essential parameter for estimating flow velocity and river discharge. In this study, we calculate a high-resolution WSS of 11 Polish rivers based on multi-mission altimetry observations from 11 satellites in the period from 1994 to 2022. The proposed approach is based on a weighted such gauge stations adjustment with an additional Laplace condition and an a priori gradient condition. The processing is divided into river sections not interrupted by dams and reservoirs. After proper determination of the WSE for each river kilometer (bin), the WSS between adjacent bins is calculated. To assess the accuracy of the estimated WSS, it is compared with slopes between gauge stations, which are referenced to a common vertical datum. Such gauge stations are available for 8 investigated rivers. The root mean squared error (RMSE) ranges from 3 mm/km to 80 mm/km, with an average of 26 mm/km. However, the mean RMSE decreases to 10 mm/km when the 2 mountain rivers are excluded. The WSS accuracies are also compared with those of slope datasets based on digital elevation models, ICESat-2 altimetry, and lidar. For 6 rivers the estimated WSS showed the highest accuracy. The improvement was particularly significant for mountain rivers. The proposed approach allows an accurate, high-resolution WSS even for small and medium-sized rivers and can be applied to almost any river worldwide.
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