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Recall SWOT Hydro Data Assimilation Meeting recommendations. All participants to the workshop. Meeting recommendations. SWOT data and errors: Have access to realistic SWOT error to assess impact of random and systematic errors.

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Recall SWOT Hydro Data Assimilation Meeting recommendations

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Recall SWOT Hydro Data Assimilation Meeting recommendations

All participants to the workshop

Meeting recommendations

  • SWOT data and errors:

    • Have access to realistic SWOT error to assess impact of random and systematic errors.

    • For some hydraulic model, assimilation of water elevations in geoprojected point cloud format at intrinsic KaRIn resolution.

  • Need to prepare now long term perspectives for hydrology (after SWOT).

  • Scale issues: need to develop SWOT down/upscalling methods for hydrodynamic and hydrology models.

Meeting recommendations

  • Open question: could DA be used to decrease error on official floodplain and cross-section topography SWOT product?

  • Data exchange issues:

    • Develop over the long term means to exchange important amount of datasets (model codes, input/output datasets).

    • Access and exchange SWOT simulator outputs among SDT members but also other research teams involved in SWOT studies.

Meeting recommendations

  • 2 working groups have been created:

    • Hydraulic modeling (leads: J. Monnier, I. Gejadze)

    • Large scale hydrology (leads: A. Getirana, S. Munier)

  • Purposes:

    • Promote scientific discussions among members;

    • Discuss model benchmarkings, sensitivity studies and assimilation schemes;

    • Sharing tools of common interest;

    • Propose roadmap of suggested studies to develop SWOT-specific DA methods.

On going SWOT assimilation studies

Large/global scale hydrology modeling and SWOT DA

Vanessa Pedinotti PhD (2009-2013)

Charlotte Emery PhD thesis (2013-2016)




Instrument and orbit errors

Simulateur SWOT

(Biancamaria et al., 2011)

EKF Assimilation

New model state

Corrected Manning



Large/global scale: Niger River, OSSE with ISBA-TRIP (V. Pedinotti’s PhD)

‘True’ parameter

Perturbed parameter

Perturbed simulation

SWOT virtual water levels

V. Pedinotti’s CNES/Région Midi-Pyrénées PhD grant + CNES/TOSCA

Large/global scale: Niger River, OSSE with ISBA-TRIP (V. Pedinotti’s PhD)

  • Relative difference of Manning coefficient averaged over the river vs assimilation cycles



The relative difference of WL is 30%

improved over the river.


  • Frequency of flooding events classified by intensity in the delta over the assimilation period.

  • Relative difference of water level averaged over the river

DA-SWOT at the global scale

  • Investigate SWOT potential for large/global scale hydrology modeling, using SURFEX platform (ISBA-TRIP).

  • How can distributed water elevations help to better constrain the water budget estimates at regional to global scales?

  • Assimilate (test methods: EKF, EnKF?) virtual SWOT data to correct parameters (Manning, bankfull depth) and model outputs (water elevation).

  • data downscaling issues (ISBA-TRIP spatial resolution ~1/4°)?

  • Assimilation of water elevation or global discharge?

  • Work done by C. EMERY’s (CNES/Region Midi-Pyrénées PhD grant + TOSCA/CNES).

Régional hydrology modeling and SWOT DA

Vincent Häfliger PhD thesis (2012-2015)

DA-SWOT with MODCOU at the medium range scale

I- Assimilation of data to modifyprognosticvariables

  • Adaptation and comparisonwith the in-situ dischargeassimilation system developed by Thirel et al. (2010) withExtended KalmanFilter.

    II- Assimilation of data to correct MODCOU parameters

  • Mainly friction coefficients, to becoordinatedwithotherworks

    Framework: V. Häfliger’sPhD (CNRM) + CNES/TOSCA propal on the Garonne catchment.

DA-SWOT with MODCOU atthe medium range scale

  • Issues to betreated:

  • Adapt the system to treatspatialized data (SWOT/AirSWOT) instead of local data (river gauges). Choice of the DA method ?

  • Considermodifyingother variables than the soilwetness (river water, aquiferexchanges, snowcover, upstream dam releases, vegetation)

  • Scalingissues (average of SWOT data, consider anomalies vs absolute data ?)

  • Impact of the routingmodel (Muskingum, MCT, Manning) comparisonwithhydrodynamicmodels. Consequencesfor ISBA/TRIP [Manning]

Hydraulic modeling and SWOT DA

SWOT SDT – Toulouse, 17th of June 2014


- 4D-var for 2D SWE : DassFlow software – Garonne test case

- 1D effective river models

- Combination of multi-dimensional and hierarchical models and their inversions

Studies in progress by P.-A. Garambois (LEGOS, IMT, IMFT),

J. Monnier (IMT),

H. Roux, D. Dartus (IMFT)

S. Biancamaria, S. Calmant (LEGOS)

DassFlow software : sensitivity maps, identification – calibration, data assimilation (adjoint, 4d-var) for 2D SWE

  • Forward models: 2D SWE (streambeds, flood plains with wet/dry fronts).

    Finite volume schemes 1st order / 2nd order, accurate-stable for flood plain dynamics.

  • Adjoint code automatically generated (source-to-source, Tapenade INRIA + home-made scripts).

  • MPI codes (direct + adjoint). Scotch library (U. Bordeaux).

  • Interfaced with standard pre & post-processors + Telemac 2D input data

    In progress : multi-dimensional observation operator SWOT like

Sensitivities Maps : a flood plain example (Lèze river)

Water elavation measured

Observations: elevation time series at the 2 stations (with ~ % noise)

Sensitivity wrt topography

Sensitivity wrt

friction coefficient

Lèze River, Toulouse


In progress

These sensitivity maps have not really been exploited in applications yet.

They may be useful to the modeler – expert to understand better the topography-friction uncertainties and correlation ; also the representation scales required.

Rich information before performing « blind » assimilation / fitting process.

Present example : Identification of friction coefficient and/or inflow discharge can be performed accurately(given the 2 times series of h): the numerical data assimilation process does its job well

Effective representation of river sections/reaches. Braided river case : Xingu river with altimetric data

[Garambois-Monnier] submitted

[Garambois-Calmant-Monnier-Biancamaria] under progress

Case of single multi-thread sections, backwater curve

  • Effective representation of sections (here: braided and fitted on ENVISAT data)

    → flow line curvature change between low and high flows (SWOT has to detect it !)

  • Towards improved control sections detection (cf. O'Loughlin et al 2014, Congo river)

From 1D to floodplain dynamics, Garonne River.Direct modeling, sensitivities and variational data assimilation

Garonne (80km) downstream of Toulouse, (proposed study zone for AirSWOT)

160 m3/s,


Euler 1st orer

Qin~2000 m3/s, K=30


[Garambois-Monnier-Roux-Chorda-Dartus] study in progress

  • Spatially distributed sensitivities to (bathymetry, roughness) help to define finely the reaches

  • Toy tests: Assimilation (adjoint method) of synthetic data by combining 1D effective river model and 2D SW model in progress

  • Assimilation of simulated SWOT data : next step with S. Biancamaria et al.

Summary – On-going studies

  • Reconstruction of effective river models for :

  • - 1D flows – SWOT like data (< 15% discharge error, Garambois-Monnier submitted)

  • - Braided rivers: same approach in progress (Garambois-Calmant-Monnier-Biancamaria)

  • - Flood plains – SWOT like data, Garonne river in progress

    • (Garambois-Monnier-Roux-Biacamaria-Dartus-Chorda)

  • Ingredients:

  • - Hierarchical 1D models, adequate with the observation scale; least-square inversions.

  • - 2D SWE with 4d-var sensitivity maps / optimization

    • (DassFlow, low-water, flood plain dynamics)

  • - Combination of these hierarchical models / inversion methods

  • Gange

    Garonne (DassFlow 2D)

    Inland Niger Delta

    (images Landsat (NASA/USGS).)

    2D hydrodynamic modeling Garonne river and SWOT DA

    Nabil El Mocayd PhD thesis (2013-2016)

    Using DA algorithmwih SWOT products to correct Bathymetry and roughness' parameter.

    Uncertainty Quantification on rougnessparameter and bathymetrywith MASCARET (TELEMAC). (which DA algorithm ?)

    EstimateDischarge and Water level by correctingHydraulic'sparameters.

    Development of the TL and ADJ with TAPENADE.

    Studies over Garonde and Gironde Catchments.

    Using SWOT Data Assimilation to correct bathymetry and roughness parameters (Thesis – CNES -EDF- CNRS)

    Uncertainty Quantification (UQ) using MASCARET

    • Random values of Ksrespectinggaussianhypothesis.

    • The behavior of roughnessparameterisstrongly non-linear.

    • Asymetric PDF for WLE for MASCARET.

    • UsinganalyticalManning'sequation.

    • Using PC for UQ over Manning'sequation.

    UQ with Manning's equation

    UQ with Polynomial Chaos

    1D hydraulic modeling and SWOT DA

    Irstea-CLS PhD (2014-2017)

    • DA of SWOT data for reconstruction of discharges, friction and bathymetry

    • Full Saint Venant 1D hydrodyn model + 4D-var.

    • Adjoint generated with Tapenade.

    Irstea-CLS PhD (2014-2017)

    • 4D-Var

    • Example of Data Assimilation for unknowntributaryinflows, usingspline interpolation withlimited points

    • Possible withBathymetry, Boundary conditions, Friction, Cross Devicecharacteristics

    Reservoir operation and SWOT DA

    Funded by NASA/JPL

    DA-SWOT data for operational water resources management

    Upper Niger River Basin



    Selingue dam used (namely) to maintain environmental minimum streamflows in the Niger Inner Delta

    meteorological forcings





    SWOT water levels assimilation


    Automatic controller





    Munier et al. (2014)

    JPL, U. Washington

    DA-SWOT data for operational water resources management



    Munier et al. (2014)

    JPL, U. Washington

    Large-scale data assimilation

    Funded by NASA/JPL

    • Data assimilation of SWOT observations to primarily estimate river discharge

    • Very large area ~700,000 km2

    • Test-bed for assimilation algorithms

    • Use hydraulic geometry in constrained EnKF

    • Uses simulator output

    • Assess sensitivity to model and observation errors

    • Examine scaling behavior of estimates

    Impact of assimilation on forecasting

    Funded by NASA/JPL

    • Evaluate ability of SWOT observables to reduce forecast errors

    • Study area of Ohio River basin

    Obs: WSE

    Longest forecast lead time when impact Is still positive

    Obs: Width

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