Deriving Global Discharge Records from SWOT Observations
Abstract
River flows are poorly monitored in many regions of the world, hindering our ability to accurately estimate water global water usage, and thus estimate global water and energy budgets or the variability in the global water cycle. Recent developments in satellite remote sensing, such as water surface elevations from radar altimetry or surface water extents from visible/infrared imagery, aim to fill this void; however, the streamflow estimates derived from these are inherently intermittent in both space and time. There is then a need for new methods that are able to derive spatially and temporally continuous records of discharge from the many available data sources. One particular application of this will be the Surface Water and Ocean Topography (SWOT) mission, which is designed to provide global observations of water surface elevation and slope from which river discharge can be estimated. Within the 21-day repeat cycle, a river reach will be observed 2-4 times on average. Due to the relationship between the basin orientation and the orbit, these observations are not evenly distributed in time or space. In this study, we investigate how SWOT will observe global river basins and how the temporal and spatial sampling impacts our ability to reconstruct discharge records.River flows can be estimated throughout a basin by assimilating SWOT observations using the Inverse Streamflow Routing (ISR) model of Pan and Wood [2013]. This method is applied to 32 global basins with different geometries and crossing patterns for the future orbit, assimilating theoretical SWOT-retrieved "gauges". Results show that the model is able to reconstruct basin-wide discharge from SWOT observations alone; however, the performance varies significantly across basins and is driven by the orientation, flow distance, and travel time in each, as well as the sensitivity of the reconstruction method to errors in the satellite retrieval. These properties are combined to estimate the "observability" of each basin. We then apply this metric globally and relate it to the discharge reconstruction performance to gain a better understanding of the impact that spatially and temporally sparse observations, such as those from SWOT, may have in basins with limited in-situ observations. Pan, M; Wood, E F 2013 Inverse streamflow routing, HESS 17(11):4577-4588
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.H44H..08P
- Keywords:
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- 1855 Remote sensing;
- HYDROLOGY;
- 1856 River channels;
- HYDROLOGY;
- 1857 Reservoirs (surface);
- HYDROLOGY;
- 1860 Streamflow;
- HYDROLOGY