River discharge estimation at daily resolution from satellite altimetry over an entire river basin

Tourian, Mohammad J.; Schwatke, C; Sneeuw, Nico

doi:10.1016/j.jhydrol.2017.01.009

Cited by 100 publications

(68 citation statements)

References 31 publications

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“…Responding to the need for better understanding of rivers at continental and global scales, recent studies have explored existing remote sensing data to trace river networks (e.g., Allen & Pavelsky, 2015;Lehner et al, 2008;Lehner & Grill, 2013;Yamazaki et al, 2014), extract basin and floodplain parameters and features (Nardi et al, 2019;Shen et al, 2017), map the extent of flooding and flood risk (Andreadis et al, 2017;Brakenridge, 2018;Van Dijk et al, 2016), and estimate discharge (e.g., Brakenridge et al, 2007;Gleason & Wang, 2015;Tarpanelli et al, 2013;Tourian et al, 2013;Tourian et al, 2017). However, additional information about rivers can be extracted from currently available satellite imagery, particularly descriptors of shapes of rivers and meanders.…”

Section: Introductionmentioning

confidence: 99%

Global Relationships Between River Width, Slope, Catchment Area, Meander Wavelength, Sinuosity, and Discharge

Frasson

Pavelsky

Fonstad

et al. 2019

Geophysical Research Letters

115

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Using river centerlines created with Landsat images and the Shuttle Radar Topography Mission digital elevation model, we created spatially continuous maps of mean annual flow river width, slope, meander wavelength, sinuosity, and catchment area for all rivers wider than 90 m located between 60°N and 56°S. We analyzed the distributions of these properties, identified their typical ranges, and explored relationships between river planform and slope. We found width to be directly associated with the magnitude of meander wavelength and catchment area. Moreover, we found that narrower rivers show a larger range of slope and sinuosity values than wider rivers. Finally, by comparing simulated discharge from a water balance model with measured widths, we show that power laws between mean annual discharge and width can predict width typically to −35% to +81%, even when a single relationship is applied across all rivers with discharge ranging from 100 to 50,000 m3/s.

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Section: Introductionmentioning

confidence: 99%

Global Relationships Between River Width, Slope, Catchment Area, Meander Wavelength, Sinuosity, and Discharge

Frasson

Pavelsky

Fonstad

et al. 2019

Geophysical Research Letters

115

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“…Remote sensing estimates of discharge typically utilize radar altimetry to measure elevations (Birkinshaw et al, 2010;Hall et al, 2012;Kouraev et al, 2004;Tourian et al, 2017;Zakharova et al, 2006), imagery data to measure width or inundation extent Smith et al, 1996;Van Dijk et al, 2016), or a combination of the two (Birkinshaw et al, 2014;Bjerklie et al, 2018). Remote sensing estimates of discharge typically utilize radar altimetry to measure elevations (Birkinshaw et al, 2010;Hall et al, 2012;Kouraev et al, 2004;Tourian et al, 2017;Zakharova et al, 2006), imagery data to measure width or inundation extent Smith et al, 1996;Van Dijk et al, 2016), or a combination of the two (Birkinshaw et al, 2014;Bjerklie et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Estimating River Discharge With Swath Altimetry: A Proof of Concept Using AirSWOT Observations

Tuozzolo

Lind²,

Overstreet³

et al. 2019

Geophysical Research Letters

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The forthcoming Surface Water and Ocean Topography (SWOT) satellite mission will provide global measurements of the free surface of large rivers, providing new opportunities for remote sensing‐derived estimates of river discharge in gaged and ungaged basins. SWOT discharge algorithms have been developed and benchmarked using synthetic data but remain untested on real‐world swath altimetry observations. We present the first discharge estimates from AirSWOT, a SWOT‐like airborne Ka‐band radar, using 6 days of measurements over a 40‐km segment of the Willamette River in Oregon, USA. The three evaluated discharge algorithms estimated discharge with normalized root‐mean‐square errors of 10–31% when compared with in situ gage data but were sensitive to an initial estimate of mean annual discharge. Our results show that these discharge algorithms provide reliable discharge estimates on remotely sensed data at SWOT‐like spatial scales while highlighting the need for further algorithm sensitivity tests.

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“…Numerous studies have already shown the potential of estimating water level time series over inland waters using different altimeter missions such as Topex/Poseidon (Birkett, 1995), Envisat (Frappart et al, 2006a), Saral (Schwatke et al, 2015b), Cryosat-2 (Villadsen et al, 25 2015) or ICESat (Zhang et al, 2011). Water levels from satellite altimetry have also been used for hydrological applications such as the estimation of river discharge (Kouraev et al, 2004;Tourian et al, 2017;Zakharova et al, 2006) and lake volumes (Duan and Bastiaanssen, 2013;Tong et al, 2016;Zhou et al, 2016).…”

Section: Satellite Altimetrymentioning

confidence: 99%

A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

Busker¹,

Roo²,

Gelati³

et al. 2018

Preprint

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Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use and irrigation. Although their monitoring is crucial in times of increased 10 pressure on water resources by both climate change and human interventions, publically available datasets of lakes and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 135 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a 15 very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh/dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with an R 2 > 0.8 and an average R 2 of 0.91. For these lakes and for lakes with a nearly constant lake area 20 (coefficient of variation < 0.008), volume variations were calculated. Lakes with a poor linear relationship were not considered. Reasons for low R 2 values were found to be (1) a nearly constant lake area, (2) winter ice coverage, (3) small lake sizes and (4) a predominance of no data within the GSW dataset for those lakes. Lake volume estimates were validated for 18 lakes in the U.S., Spain, Australia and Africa using in situ volume time series, and gave an excellent Pearson correlation coefficient of on average 0.97, and a normalized RMSE of 7.42 %. These results show a high potential for 25 measuring lake volume dynamics using a pre-classified GSW dataset, which easily allows the method to be scaled up to an extensive global volumetric dataset. This dataset will help to validate large scale hydrological models, to improve regional and global flood and drought forecasting systems and to update hydropower estimations.Hydrol. Earth Syst. Sci. Discuss., https://doi

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River discharge estimation at daily resolution from satellite altimetry over an entire river basin

Cited by 100 publications

References 31 publications

Global Relationships Between River Width, Slope, Catchment Area, Meander Wavelength, Sinuosity, and Discharge

Global Relationships Between River Width, Slope, Catchment Area, Meander Wavelength, Sinuosity, and Discharge

Estimating River Discharge With Swath Altimetry: A Proof of Concept Using AirSWOT Observations

A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

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