Synthetic river datasets built for testing and development of the Surface Water and Ocean Topography mission discharge algorithms
Creators
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Frasson, Renato Prata de Moraes1
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Michael T. Durand2
- Kevin Larnier3
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Colin Gleason4
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Konstantinos M. Andreadis4
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Mark Hagemann5
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Robert Dudley6
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David Bjerklie6
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Hind Oubanas7
- Pierre-André Garambois8
- Pierre-Olivier Malaterre7
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Peirong Lin9
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Tamlin M. Pavelsky10
- Jérôme Monnier3
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Craig B. Brinkerhoff4
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Cédric H. David1
- 1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
- 2. Byrd Polar and Climate Research Center, the Ohio State University
- 3. INSA Toulouse - Math. Institute of Toulouse (IMT), Toulouse, France
- 4. Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, Massachusetts, USA.
- 5. EAB: Education Technology, Services, and Research, Richmond, Virginia, USA.
- 6. United States Geological Survey, New England Water Science Center, Augusta, Maine, USA
- 7. Irstea, Montpellier, France.
- 8. Irstea, Aix Marseille Université, RECOVER, Aix-en-Provence, France
- 9. Princeton University
- 10. Department of Geological Sciences, University of North Carolina, Chapel Hill, North Carolina, USA
Description
1.Summary
Datasets used for testing the performance of discharge estimation algorithms built in support of the Surface Water and Ocean Topography satellite mission. The benchmarking manuscript entitled “Exploring the factors controlling the performance of the Surface Water and Ocean Topography mission discharge algorithms” is currently under review at Water Resources Research. Once the manuscript is accepted, its DOI will be included here.
2.File description
The dataset is divided into four groups: 1-Ideal data, 2-Varying Temporal Sampling, 3-Measurement Uncertainty, and 4-SWOT Sampling and Uncertainty. Ideal data contains daily measurements with no observational uncertainty. Varying Temporal Sampling downsamples the ideal measurements considering different temporal frequencies with complete sets assuming: 1 measurement every 2 days, 3 days, 4 days, 5 days, 7 days, 10 days, and 21 days. The measurement uncertainty set adds errors to cross-sectional heights and widths, which are used to compute reach average height, width, and slope considering error corruption. The final set SWOT Sampling and Uncertainty accounts for SWOT temporal sampling and measurement uncertainty. Sets containing uncertainty have extra height, width, and slope attributes with the word true appended to the attribute name. Such attributes represent the uncorrupted measurements at the cross-section and reach scales. Height, width, and slopes for the SWOT sampling and Uncertainty dataset containing the value of negative 9999 denote points that are not observed at a particular location and time step.
Data will be contained in one NetCDF file per river. The file contains the following groups and variables:
/River_Info/
Name: River name, data type: char
QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)
rch_bnd: Reach boundaries measured in meters from the upstream end of the model
gdrch: Reaches used in the study. Used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.
/XS_Timeseries/
t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.
Z: Bed elevation in meters. Dimension: Cross-section, time step.
xs_rch: Reach number for each cross-section. Dimension: Cross-section,1.
X: Flow distance measured from the most upstream end of the model to the cross-section (meters). Dimension: Cross-section, 1.
longitude: Cross-section longitude in decimal degrees. Dimension: Cross-section,1.
latitude: Cross-section latitude in decimal degrees. Dimension: Cross-section,1.
W: River width in meters. Dimension: Cross-section, time step.
Wtrue: River width in meters. Dimension: Cross-section, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.
Q: Discharge (m3/s). Dimension: Cross-section, time step.
H: Water surface elevation in meters. Dimension: Cross-section, time step.
Htrue: Water surface elevation in meters. Dimension: Cross-section, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.
A: Cross-sectional area of flow in m2. Dimension: Cross-section, time step.
P: Wetted perimeter in meters. Dimension: Cross-section, time step.
n: Manning’s roughness. Dimension: Cross-section, time step.
/Reach_Timeseries/
t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1,time step.
W: Reach averaged river width in meters. Dimension: Reach, time step.
Wtrue: Reach averaged river width in meters. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the width value with no uncertainty.
Q: Reach averaged discharge (m3/s). Dimension: Reach, time step.
H: Reach averaged water surface elevation in meters. Dimension: Reach, time step.
Htrue: Reach averaged water surface elevation in meters. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the water surface elevation value with no uncertainty.
S: Reach averaged water surface slope in meters per meter. Reach, time step.
Strue: Reach averaged water surface slope in meters per meter. Dimension: Reach, time step. Only present in datasets containing measurement uncertainty, in which case, this variable holds the slope value with no uncertainty.
A: Reach averaged area of flow in m2. Dimension: Reach, time step.
References
Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, Glob. Planet. Change, 115, 44-58, doi: https://doi.org/10.1016/j.gloplacha.2014.01.011.
Files
1-Ideal-Data.zip
Files
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