Team 10 Presentation Vol. II

18th February 2011Sophia Antipolis, France

Improvement by calibration or with geometry?

IntroductionHydrological AnalysisSpatial rainfall distribution

Relation between rain gaugesHEC-HMS

Model Setup - Methods and ParametersOutput

HEC-RAS Setup

MIKE 11 Setup

MIKE SHESetup and Parameters

CalibrationGeometry

02/18/2011 2

Hydrological AnalysisThiessen PolygonWhy no interpolation?

02/18/2011 3

Carros

Roquesteron Levens

Guillaumes

Puget Théniers

St MartinVésubie

Hydrological AnalysisThiessen Polygon

Table: partial contribution of gages on the subcatchments Strongest influence St. Martin Vesubie Smallest influence Roquesteron

02/18/2011 4

Hydrological Analysis

02/18/2011 5

Hydrological Analysis

Correlation between the stations A strong correlation between the ones that are close to each other

02/18/2011 6

Carros

Roquesteron Levens

Guillaumes

Puget Théniers

St MartinVésubie

Carros Levens RoquesteronPuget

ThéniersGuillaumes

St Martin Vésubie

Carros 1.00          

Levens 0.60 1.00        

Roquesteron 0.73 0.63 1.00      

Puget Théniers 0.65 0.67 0.88 1.00    

Guillaumes 0.70 0.62 0.76 0.84 1.00  

St Martin Vésubie 0.51 0.80 0.58 0.84 0.64 1.00

Hydrological AnalysisCorrelation of Rainfall and Elevation

Weak correlation distance between rain gauges, rainfall caused by frontal depression

02/18/2011 7

Hydrological Analysis – HEC HMS Model

02/18/2011 8

HEC HMS SETUP

Transformation Method: Clark UHSimple, Fast, Risky!

Loss Method: SCS Curve NumberGood Approximations, Simple, Risky too!

Routing: MuskingumEvent, Lumped, Empirical

Baseflowmodel: Constant MonthlyAveraged time series data

02/18/2011 9

Lumped Model Setup – FinishedDistributed Model setup – Not Ready Jet (Difficult Grid Generation)

Lumped (Semi-distributed)

Parameter Setup

02/18/2011 10

Catchment Upper Var Tinee Vesubie Esteron Lower VarArea [m²] 1090215000.00 747483750.00 393536250.00 450860625.00 151509375.00Area [km²] 1090.22 747.48 393.54 450.86 151.51LongestFlowpath 87893.04 71481.75 48448.22 62249.59 37357.69Slope 0.03 0.04 0.06 0.03 0.03CN [land use] 66.00 66.00 66.00 67.00 68.00CN [wet soil] 82.00 82.00 82.00 83.00 83.00S max retention coeff 57.00 55.00 56.00 54.00 51.00Tc [h] KIRPICH 8.02 6.13 3.88 6.15 4.15Tc Ventura 24.21 17.36 10.29 15.57 9.03Tc Ventur 50% 12.11 8.68 5.14 7.78 4.51Storage Coeffi cent 15.00 10.00 3.00 3.00 4.00Impervious 0.30 0.60 0.20 0.50 9.00

Sensitivity

02/18/2011 11

Sensitivity

02/18/2011 12

HEC RAS Goal : comparison with Mike11 data obtained. Realized :

Install network Create cross-sections Integrate Hydrological results

Problems met: To run the unsteady simulation To install the weirs

02/18/2011 13

Total Length : Approx. 24 Km

Branches: 10

Weirs : 9

X-sec: 120

River Network of Lower Var:Q

:WL

Model Inputs Network X-section Weir formula: Weir formula 2 (Honma) Hydrodynamic Parameter

Resistance : roughness coefficient Initial Condition :

Water Depth (1m) and Discharge (10 cumec)

Boundary Condition: Upstream Bnd: Q from hydrological analysis Downstream: WL

Simulation Mode: Unsteady Simulation Period: 05/11/1994 to 6/11/1994

Model Output: Maximum Longitudinal Water Profile

MIKE SHESetup and parameters

Strickler coefficient Extreme values

Net effective rainfall

What is the effect of changing these valueson the hydrograph?

02/18/2011 17

MIKE SHE – Strickler coeffieient

Strickler coeffieient – numerical representation of the catchment and river bed roughness

Extreme values of Strickler coeffieient used

10 – flood plain covered

in trees 60 – tarmac

02/18/2011 18

Strickler coefficient

0

1000

2000

3000

4000

5000

0:00 6:00 12:00 18:00 0:00Time

Disc

harg

e m

3/s

30/20 (default) 10 60

MIKE SHE – Net Effective Rainfall

Proportion of rainfall that forms runoff Losses due to infiltration

Reduction in hydrograph peaks

with decreasing net effective rainfall

Less runoff volume represented

by the area under the hydrograph

0.9 is a suitable value due to

antecedent catchment conditions

02/18/2011 19

MIKE SHE – Parameter Calibration

Parameters make little difference to the simulation.

In this case calibration is not required and can be detrimental to the model results

02/18/2011 20

...and the geometry

02/18/2011 21

Grid resolution

1000m grid – 2 820 data points

600m grid – 7833 data points

300m grid – 31 333 data points

75m grid – 50 133 321 data points

Event of 5 November 1994 modelled using a DEM with a resolution of 300 mfor a river geometry based on 300 m (Model 300a) and 75 m (Model 300d)DEM resolutions. The time is counted from 0000 hours on 5 November 1994.(Guinot, V. And Gourbesville P. 2003)

Resolution is important!!!

Thank You For Your Attention

02/18/2011 25

ReferencesGuinot, V. and Gourbesville, P. (2003). Calibration of physically

based models: back to basics? Journal of Hydroinformatics, 5(4): 233-244