discharge splinter - swot.odyseallc.net · • evaluation of algorithms across entire river...
TRANSCRIPT
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DISCHARGE SPLINTERM. Durand & E. Martin
www.swotdawg.wordpress.com
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Top Priority • Intercomparison of three algorithms • Evaluation of algorithms using real data • Integration of instrument simulator for testing discharge algorithms • Evaluation of algorithms across entire river networks with varying river reach lengths • Global map of rivers that SWOT will observe • Create or compile benchmark SWOT-like and in situ datasets • Determine how to define reaches Middle Priority • Identify systematic discharge algorithm testing procedures • Reducing uncertainty in cross-section area estimates, width, and height • Adapt algorithms to use wetted perimeter rather than top width • Demonstrate use of single discharge gage within a network to constrain discharge • Continue exploring new algorithms • Look at geoid slope errors globally • Demonstrate how to use and hydrologic/geomorphologic information to constrain
discharge Lower Priority • Adapt algorithm to handle coupled floodplain-channel systems • Global map of which rivers are affected by floodplains • Global map of rivers for which diffusive approximation will be adequate
Developed with WG input swotdawg.wordpress.com
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Top Priority
• Intercomparison of three algorithms*
• Evaluation of algorithms across entire river networks*
• Evaluation of algorithms using real data*
• Integration of instrument simulator for testing discharge algorithms
• Evaluation of algorithms across entire river networks with varying river reach lengths
• Global map of rivers that SWOT will observe
• Create or compile benchmark SWOT-like and in situ datasets
• Determine how to define reaches
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Manning’s equation
Hydraulic geometry
n and A0 unknown; all others observed
a and b are unknown. W observed
Manning validation for full St Venant 1d model of Garonne (Credit: Roux+Garambois)
Hydraulic geometry validation for full St Venant 1d model of Sacramento.
METHODS FOR DISCHARGE ESTIMATION CRUX IS ESTIMATION OF UNKNOWN PARAMETERS
Q =1
n(A0 + �A)5/3 W�2/3S1/2
Q = aW b
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Manning’s equation
Hydraulic geometry
n and A0 unknown; all others observed
a and b are unknown. W observed
Manning validation for full St Venant 1d model of Garonne (Credit: Roux+Garambois)
Hydraulic geometry validation for full St Venant 1d model of Sacramento.
METHODS FOR DISCHARGE ESTIMATION CRUX IS ESTIMATION OF UNKNOWN PARAMETERS
Q =1
n(A0 + �A)5/3 W�2/3S1/2
Q = aW b
Paper accepted JoH!Paper submitted
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COMPLETED & ONGOING CASE STUDIES
• Upper Garonne (Pierre Andre Garambois)
• Lower Garonne (Lucie Berthon)
• Sacramento (Durand, Smith, Bjerklie)
• Severn with real data (Durand et al., JOH, in press)
• Mississippi with real data (Smith, Gleason submitted)
• Athabasca with real data (Smith, Gleason submitted)
• Yangtze with real data (Smith, Gleason submitted)
• Severn (Durand)
• Platte (Durand)
• Ohio (Durand et al., JSTARS 2010)Typically, 10-30% RMSE
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Top Priority
• Intercomparison of three algorithms*
• Evaluation of algorithms across entire river networks*
• Evaluation of algorithms using real data*
• Integration of instrument simulator for testing discharge algorithms
• Evaluation of algorithms across entire river networks with varying river reach lengths
• Global map of rivers that SWOT will observe
• Create or compile benchmark SWOT-like and in situ datasets
• Determine how to define reaches
*Progress since last SDT
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Top Priority
• Intercomparison of three algorithms*
• Evaluation of algorithms across entire river networks*
• Evaluation of algorithms using real data*
• Integration of instrument simulator for testing discharge algorithms
• Evaluation of algorithms across entire river networks with varying river reach lengths
• Global map of rivers that SWOT will observe
• Create or compile benchmark SWOT-like and in situ datasets
• Determine how to define reaches
*Progress since last SDT
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Algorithm intercomparison study, withholding experiments, side-by-side tests, possibilities for inter-combination for Sacramento River simulation
Perfect, daily observations, Smith method, discharge RMSE 17%
SWOT observations, Durand method, discharge RMSE 19%
Perfect, daily observations, Bjerklie method, discharge RMSE 4%
0"
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1" 11" 21" 31" 41" 51" 61" 71" 81" 91" 101"111"121"131"141"151"
Q,#$
3/s#
Time#Data on the blog, described at: swotdawg.wordpress.com
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Top Priority
• Intercomparison of three algorithms*
• Evaluation of algorithms across entire river networks*
• Evaluation of algorithms using real data*
• Integration of instrument simulator for testing discharge algorithms
• Evaluation of algorithms across entire river networks with varying river reach lengths
• Global map of rivers that SWOT will observe
• Create or compile benchmark SWOT-like and in situ datasets
• Determine how to define reaches
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LARGE-SCALE BRAHMAPUTRA SIMULATION
From Faisal Hossain
Work in progress on using SRTM + Landsat to estimate Q via Manning: see blogswotdawg.wordpress.com
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Interpolation of discharge: From SWOT space-time sampling to continuous Q over networks
Courtesy Rodrigo Paiva swotdawg.wordpress.com
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Top Priority
• Intercomparison of three algorithms*
• Evaluation of algorithms across entire river networks*
• Evaluation of algorithms using real data*
• Integration of instrument simulator for testing discharge algorithms
• Evaluation of algorithms across entire river networks with varying river reach lengths
• Global map of rivers that SWOT will observe
• Create or compile benchmark SWOT-like and in situ datasets
• Determine how to define reaches
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Evaluation with real data — AirSWOT
AirSWOT(Discharge(–(Sacramento(River(Premilinary(results(
+"Pre&defined:"• Bathymetry(• Manning(Coef.((0.035)(• Water(Mask(
swotdawg.wordpress.comCourtesy Rodrigo Paiva
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AirSWOT(Discharge(–(Sacramento(River(Premilinary(results(
Slope( Width(
Depth(
Discharge(–(3(reaches( Discharge(–(6(reaches(
WSE(profile(
• Discharge*es+mates*agree*with*USGS*gages*• Q*decreases*downstream*(water*withdrawals?)*
Evaluation with real data — AirSWOT
swotdawg.wordpress.com Courtesy Rodrigo Paiva
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Evaluation with real data — AirSWOT
There are a total of six gages — each likely has a similar flow
However, we can calculate Q at many reaches, and show Q(x) measured and observed, extending what Rodrigo has already done
This will be a validation of Manning’s equation for one particular river, at one flow condition, but for six gages, with potential for some decrease in flow downstream
AirSWOT Sacramento 2014
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Evaluation with real data —OlentangyTwenty level loggers measure continuous water elevation, mapping temporal variations in slope
Width measured via undergrad+range finder
Data this week!
swotdawg.wordpress.com
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!1.5%
!1%
!0.5%
0%
0.5%
1%
1.5%
2%
!0.00001%
!0.000005%
0%
0.000005%
0.00001%
0.000015%
0.00002%
0.000025%
0.00003%
1% 62%
123%
184%
245%
306%
367%
428%
489%
550%
611%
672%
733%
794%
855%
916%
977%
1038%
1099%
1160%
1221%
1282%
1343%
1404%
1465%
1526%
1587%
1648%
1709%
1770%
1831%
1892%
1953%
2014%
2075%
2136%
2197%
2258%
2319%
2380%
2441%
2502%
2563%
2624%
2685%
2746%
2807%
2868%
2929%
2990%
3051%
3112%
3173%
3234%
3295%
3356%
3417%
Slope%
Vel%
Slope and river velocity on the Connecticut River Courtesy Dave Bjerklie
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Evaluation with real data — Other sites
Suggestion by Al Pietroniro (EC/WSC)
• Deploy many (20?) water loggers and obtain slope, height, and width data along several major rivers
• Perhaps two in Canada, two in the US, two in France?
• What characteristics do we want these rivers to have?
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DISCUSSION: SLOPE IN THE SRD
• Should there be a goal for slope on small (50 m) rivers?
• Should there be a science product listed in the SRD for slope?
• Note that it is a requirement to produce discharge for these small rivers
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DISCUSSION: PROPOSED NEXT STEPS• Compare the three algorithms: Identify strengths & limitations of each,
discuss ways to combine
• How to define reach lengths? Use AirSWOT data combined with Manning for the 2013 data as an example to start conversation
• How well does algorithm work for real data? Discuss the Olentangy River case
• What rivers to select for in situ SWOT-like studies?
• Others?Recommend setting up a phone call within the next two weeks
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Discussion
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Top Priority?
• Intercomparison of three algorithms*
• Evaluation of algorithms across entire river networks*
• Evaluation of algorithms using real data*
• Integration of instrument simulator for testing discharge algorithms
• Evaluation of algorithms across entire river networks with varying river reach lengths
• Global map of rivers that SWOT will observe
• Create or compile benchmark SWOT-like and in situ datasets
• Determine how to define reaches
• Develop schedule for when algorithms to be delivered etc.
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Extra Slides
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Reach length
What experiments do we need to determine the optimal reach length?
• AirSWOT and Sacramento data (2013, 2014)
• Model results
• Height profile from data
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CAVEATS, OBSERVATIONS
0"
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1" 11" 21" 31" 41" 51" 61" 71" 81" 91" 101"111"121"131"141"151"
Q,#$
3/s#
Time#
•Not run on exactly the same observations: Smith & Bjerklie run on true measurements, for now
•In this and other tests, Durand approach better at getting dynamics, Smith & Gleason approach better at baseflow
•Bjerklie method accuracy limited to that of a priori discharge estimates.
•For Durand & Bjerklie approaches if we had a single flow measurement, SWOT would give true discharge
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ALGORITHM COMPARISON
SWOT data used Parameters estimated Comment
Durand W,H,S A0,nGood at baseflow, sometimes misses
dynamics
Smith & Gleason W a,b
Good at baseflow, sometimes misses
dynamics
Bjerklie W,H,S H0
Accuracy limited by a priori flow (e.g. minimum,
mean annual)
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Spa$otemporal+discharge+es$ma$on+in+a+river+network!+SWOT+measurements+are+not+con$nuous+or+simultaneous+
+How+to+es$mate+con$nuous+spa$otemporal+fields+of+discharge?????+
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Spa$otemporal+dependence+of+discharge+
Confluence(Weights(((((((((((((((((((((((((((((((((((((((((((((((((((((((((((
Variance(((( (((((((((((((((((((Correla3on(Matrix(
River+discharge+correla$on:+Physically9based+model+
Q
t#
Δt#
Advec3on(and(dispersion(of(floodwaves(
Observa3on(Errors(Covariance(matrix((((((((((((((((((((((((((((((
Q
t#
Theore3cal(correla3on(model(
Diffusive(wave(model(
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Examples of daily estimates of discharge (red line) based on SWOT measurements (blue points) compared with true vales (blue line). Ganges R. (site 4) using combined SWOT and in situ data and using a linear interpolation.
Linear SWOT SWOT+3 gages
R2 0.72 0.83 0.91
Interpolation of discharge
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MANNING ON BRAIDED RIVER
4
For reaches of 1,600 m in length, Manning provided good approximation of discharge.
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River Platte 2009 - Flow Velocity
00:00 07/30
04:00 07/30
08:00 07/30
12:00 07/30
20:00 07/30
00:00 07/31
16:00 07/30
Coordinate System: NAD 1983 UTM Zone 14NProjection: Transverse MercatorDatum: North American 1983Units: Meter
0 500 1,000 1,500250Meters
flow velocity (m/s)
0.0 - 0.1
0.1 - 0.2
0.2 - 0.3
0.3 - 0.4
0.4 - 0.5
0.5 - 0.6
0.6 - 0.7
0.7 - 0.8
0.8 - 1.0
1.0 - 2.0
PLATTE RIVER SIMULATION
0 1000 2000 3000 4000 5000 6000 7000 8000 90000
20
40
Distance (m)
Dis
char
ge (m
3 /s) 7/30 12:00
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Distance (m)
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ge (m
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ge (m
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Flow velocity
0 1000 2000 3000 4000 5000 6000 7000 8000 90000
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3 /s) 7/30 12:00
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0 1000 2000 3000 4000 5000 6000 7000 8000 90000
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char
ge (m
3 /s) 7/31 18:00
200 M REACH AVERAGE 1600 M REACH AVERAGE
(Credit: Sanders + Schubert)
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METHOD FOR ESTIMATING UNKNOWN PARAMETERS: MANNING
Manning’s equation
n and A0 unknown; all others observed
KEY: VARIABLE OBSERVATION TIMESERIES
• A Bayesian approach used to solve inverse problem for reach-averaged unknown parameters. Manning Q estimates then filtered
• Example: HEC-RAS simulation on Sacramento River (100 m width)
• AirSWOT estimation scenario: Ten daily observations, with height (5 cm), width (7 m) and slope (0.55 cm/km) errors added
• n and A0 estimates not unique: n underestimated; Q unbiased
Q =1
n(A0 + �A)5/3 W�2/3S1/2
(Credit: Durand+Yoon)
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METHOD FOR ESTIMATING UNKNOWN PARAMETERS: HG
Hydraulic geometry Q = aW b a and b are unknown. W observed
KEY: VARIABLE OBSERVATION TIMESERIES
• A new discovery links the a and b parameters for cross-sections within a reach (shown)
• A genetic algorithm approach used to solve inverse problem for unknown parameters for a number of sub-reach cross-sections
• The a and b parameters, once identified, can be used to estimate Q, averaged across all sub-reach cross-sections
(Credit: Smith+Gleason)
R² = 0.9537
0
0.15
0.3
0.45
0.6
0.75
0.9
-1 0 1 2 3
b
log a
Snake River from Washington/Idaho Border to Jackson Lake, WY. (~700 mi)
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CASE STUDY 1: GARONNE RIVER WITH MANNING
• Truth (blue) from 1d St Venant simulation on Garonne River with daily sampling, 15 reaches, 11 days
• AirSWOT errors added (5 cm height, 7 m width, .75 cm/km slope)
• A0 and n estimation enables discharge estimate (blue). Final discharge RMSE: 19%
• Error budget sensitive to width errors. Sensitive to height errors greater than 15 cm (Credit: Roux+Garambois)
USING TIMESERIES OF SYNTHETIC AIRSWOT OBS.