Recall SWOT Hydro Data Assimilation Meeting recommendations

1. Recall SWOT Hydro Data Assimilation Meeting recommendations All participants to the workshop

2. 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.

3. 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.

4. 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.

5. On going SWOT assimilation studies

6. Large/global scale hydrology modeling and SWOT DA Vanessa Pedinotti PhD (2009-2013) Charlotte Emery PhD thesis (2013-2016)

7. ISBA-TRIP ISBA-TRIP ‘truth’ Instrument and orbit errors Simulateur SWOT (Biancamaria et al., 2011) EKF Assimilation New model state Corrected Manning coeficient ISBA-TRIP 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

8. 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 NO ASSI REF The relative difference of WL is 30% improved over the river. ASSI • Frequency of flooding events classified by intensity in the delta over the assimilation period. • Relative difference of water level averaged over the river

9. 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).

10. Régional hydrology modeling and SWOT DA Vincent Häfliger PhD thesis (2012-2015)

11. 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.

12. 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]

13. Hydraulic modeling and SWOT DA SWOT SDT – Toulouse, 17th of June 2014 Outline - 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)

14. 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

15. 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 Couderc-Larnier-Madec-Monnier-Vila-Dartus] 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

16. 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)

17. 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, K=30 Euler 1st orer Qin~2000 m3/s, K=30 Flow [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.

18. 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).)

19. 2D hydrodynamic modeling Garonne river and SWOT DA Nabil El Mocayd PhD thesis (2013-2016)

20. 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)

21. 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.

22. UQ with Manning's equation

23. UQ with Polynomial Chaos

24. 1D hydraulic modeling and SWOT DA

25. 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.

26. Irstea-CLS PhD (2014-2017) • 4D-Var • Example of Data Assimilation for unknowntributaryinflows, usingspline interpolation withlimited points • Possible withBathymetry, Boundary conditions, Friction, Cross Devicecharacteristics

27. Reservoir operation and SWOT DA Funded by NASA/JPL

28. DA-SWOT data for operational water resources management Upper Niger River Basin target discharge Selingue dam used (namely) to maintain environmental minimum streamflows in the Niger Inner Delta meteorological forcings Hydrology dam releases Reservoir SWOT water levels assimilation Hydrodynamics Automatic controller downstream discharge target discharge Munier et al. (2014) JPL, U. Washington

29. DA-SWOT data for operational water resources management target discharge Munier et al. (2014) JPL, U. Washington

30. Large-scale data assimilation Funded by NASA/JPL

31. 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

32. Impact of assimilation on forecasting Funded by NASA/JPL

33. 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