Hydrological modelling of the Ob river

1
Hydrological modelling of the Ob river

• Sylvain Biancamaria(1), Nelly Mognard(1), Paul
  Bates(2), Aaron Boone(3) Jean-François
  Cretaux(1)
• (1) LEGOS, FR
• (2) University of Bristol, UK
• (3) CNRM/CNRS, FR

February 1st, 2008
2
Models used

• ISBA (Interaction Soil Biosphere Atmosphere) from
  CNRM (spatial resolution 1x1) for lateral
  inflows LISFLOOD-FP, flood inundation model
  from University of Bristol, for the river

Atmospheric input
Incoming flow to the study domain
Lateral inflow to the river from study domain
ISBA LSM
Discharge from Belogorje gauging station
Runoff from each pixel
TRIP
Lateral inflow from the whole river basin
Flood plain topography
LISFLOOD
Manning coefficients (friction) for river and
flood plain
River centre, width and depth
Discharge, water depth and elevation (river
flood plain)
River modelling
3
Study domain

• Lower Ob (between Belogorje and Salkhard)

8
7
Legend Incoming flow (Belogorje)
Lateral inflow (ISBA) River (CIA WDB)
Gauging station
6
5
4
3
2
1
Time period year 1993 (daily discharge at
Belogorje only available from 01/01/1993 to
10/31/1994)
4
Modelled discharge

• Comparison between modelled and measured
  discharge at Salekhard as a function of the river
  depth (Manning coefficient0.015)

Discharge underestimation (7000 m3/s) -gt too
weak lateral inflow (linked with precipitation)
5
Comparison Topex/modelled height

• Comparison between April and October 1993

TopexVirtual station shown
River depth10m Manning coefficient0.015
River depth15m Manning coefficient0.015
6
SWOT simulator

• The synthetic SWOT measurement simulator
  developped by Noveltis for oceans has been
  adapted for hydrology

Instrument ground location
Orbit Parameters
Instrumental Error (Gaussian noise)
Synthetic SWOT measurement
Nadir/wide swath selection
Water elevation at the measurement points
Water elevation
7
Assimilation

• Use of an Ensemble Kalman Filter to assimilate
  the synthetic SWOT observations into LISFLOOD.
• Model uncertainties represented through an
  ensemble of model states, generated by
  perturbing
• 1. Precipitation (rainsnow),
• 2. Air temperature,
• 3. River depthflood plain topography,
• 4. Manning coefficient.
• Different Orbits can be tested. It will also be
  possible to assess how SWOT will improve the
  modelling compared to the use of only nadir data.