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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.