2010-2011 TEAM

2010-2011TEAM

1st Topic Presentation, Friday 18th February 2011Polytech’Nice - Sophia

INTEREST OF DISTRIBUTED

HYDROLOGICAL MODELS(Mike SHE & HEC-HMS)

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Definition

Rainfall distribution

methods

Mike-Shecomparison

Conclusion

HEC-HMScomparison

1) lumped = complete basin as single homogeneouselement with average rainfall input

2) quasi-distributed = discretization of watershed into homogeneous sub-basins with single rainfall parameters based on topography 3) distributed = physically based, division into elementary unit areas as grid cells, solving equations in each cell

Different types of models

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• much simpler than deterministic models

• obtained by aggregating or averaging the spatially variable processes

• usually not done in a formal mathematical manner

• rather, some simple assumptions are made which invariably start with continuity equation

• can’t be used for longer simulation periods considering groundwater infiltration and flow

Model type - lumped

3


Definition


methods

Mike-Shecomparison

Conclusion

HEC-HMScomparison

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•hydrological phenomena vary spatially and temporally

• in accordance with the conservation laws of mass, energy and momentum

• models based on conservation laws are physically-based

• laws expressed as partial differential equations (PDEs)

• solutions of PDEs on finite differences or finite element grid computationally demanding

Model type - distributed

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Definition


methods

Mike-Shecomparison

Conclusion

HEC-HMScomparison

5

Loss Methods Categorization

Initial and constant rate

Event, lumped, empirical, fitted parameter

SCS Curve Number (CN)

Event, lumped, empirical, fitted parameter

Gridded SCS CN Event, distributed, empirical, fitted parameter

Green and Ampt Event, distributed, empirical, fitted parameter

Deficit and Constant Event, lumped, empirical, fitted parameter

Transform Methods Categorization

User-specified unit Event, lumped, empirical, fitted parameter

Hydrograph (UH) Event, lumped, empirical, fitted parameter

Clark’s UH Event, lumped, empirical, fitted parameter

ModClark Event, distributed, empirical, fitted parameter

Kinematic Wave Event, lumped, empirical, fitted parameter

Methods Categorization

Baseflow Lumped

Channel Flow Routing

Lumped

HEC-HMSmainly lumped

modelsExample of method’s

categorization:

5


Definition


methods

Mike-Shecomparison

Conclusion

HEC-HMScomparison

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Fully Lumped Model based on mean value

Quasi Distributed ExampleUsing new Rainfall distribution based on Kriging

method for each sub-catchments

Distributed Model Inconvenient: Take time to build a model based

on gridded file (.dss)Advantage: Good for long time computation

window

HEC-HMS


Definition


methods

Mike-Shecomparison

Conclusion

HEC-HMScomparison

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HEC-HMSFully Lumped Model Vs Lumped Model

Required parameters:

SCS Unit Hydrograph

SCS CNPercent

Impervious (%)

Initial Abstraction

(mm)Tc (hr)

Storage Coeff. (hr)

Lag Time (min)

77 5,3 15,17 28,934 0,500 578,678 - -75 5,22 16,93 21,459 0,400 429,174 - -70 5,46 21,77 13,323 0,400 266,465 - -75 5,31 16,93 19,364 0,400 387,276 - -80 14,6 12,70 19,885 0,800 397,704 - -

75,40 7,18 16,70 20,59 0,50 411,86- - - - - 0,9 0,4- - - - - 0,7 0,2- - - - - 1,2 0,1- - - - - 0,9 0,3

Hydrologic Elements

Runoff volume Parameters Direct Runoff Parameters Channel Flow SCS Curve Number Clark Unit Hydrograph

Muskingum K (hr)

Muskingum X

Upper Var

Esteron

Fully Lumped Model

Vesubie

Lower Var

Fully Lumped Model

Reach1Reach2Reach3

Tinee

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Definition


methods

Mike-Shecomparison

Conclusion

HEC-HMScomparison

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0

500

1000

1500

2000

2500

3000

3500

4000

Observed hydrograph (cm/s)

SCS Unit Hydrograph (cm/s)

SCS fully Lumped (cm/s)

SCS Unit Hydrograph

(cm/s)

SCS fully Lumped (cm/s)

Observed hydrograph (cm/s)

Total Residual SCS U H

Total Residual SCS fully Lumped

Peak Outflow (cm/s) 3585,8 3411,4 3680 94,20 268,60Time of peak flow (h) 5/11/94 21:00 5/11/94 21:00 5/11/94 18:00 3h 3hVolume (mm) 94,43 67,61 91,56 -2,87 23,95

Fully Lumped Vs Lumped Model

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Definition


methods

Mike-Shecomparison

Conclusion

HEC-HMScomparison

Rainfall Distribution: Thiessen Polygons

Lumped : assign 1 rainfall value for 1 polygon.

Quasi distributed :Assign 2 rainfall values in 1 sub-basin.

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Definition


methods

Mike-Shecomparison

Conclusion

HEC-HMScomparison

Interpolation method :Best Linear Estimator Unbiased

Could be interesting to apply with :

•More point of measurements,•Taking into account the altitude,•Taking into account the distance to the sea.

Lack of data to built a variogram.

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Definition


methods

Mike-Shecomparison

Conclusion

HEC-HMScomparison

Comparison

3/11/94 0:00

4/11/94 0:00

5/11/94 0:00

6/11/94 0:00

7/11/94 0:00

8/11/94 0:00

0

500

1000

1500

2000

2500

3000

3500

4000

Comparison of the Hydrograph produced by Thiessen and Krigging distribution method

Observed Hydrograph

SCS Unit Hydrograph with Thiessen Poly-gons

SCS Unit Hydrograph with Kriging Method

date / hours

Discharge (m3/s)

In our case of study, the rainfall distribution method= Thiessen polygons

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Definition


methods

Mike-Shecomparison

Conclusion

HEC-HMScomparison

Fully distributed.

The spatial and temporal variation of meteorological, hydrological, geological and hydrogeological data across the model area is described in gridded form for the input as well as the output from the model.

Mike SHE model

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Definition


methods

Mike-SHEcomparison

Conclusion

HEC-HMScomparison

Modelling Process

Network :

Manning MTopographyDomain

Thiessen Polygons and precipitation rates

very complex and time

consuming

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Definition


methods

Mike-SHEcomparison

Conclusion

HEC-HMScomparison

Results

0

400

800

1200

1600

2000

2400

2800

3200

3600

Disc

harg

e Q

[m³/

s]

Time

Hydrographs at Napoleon III

Mike SHE

estimated

Mike SHE Tutorial

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Definition


methods

Mike-SHEcomparison

Conclusion

HEC-HMScomparison

Results• Mike SHE hydrograph doesn’t match observed/estimated discharge rates and peak time at Napoleon III - bridge

• although model is set up more precisely is doesn’t give more accurate results than lumped models

• Manning coefficients M can be adjusted, however no big influence is expected

• most likely destroyed sills/structures during the flood event responsible for “time-shifting”

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Definition


methods

Mike-SHEcomparison

Conclusion

HEC-HMScomparison

CONCLUSION

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400

800

1200

1600

2000

2400

2800

3200

3600

Disc

harg

e Q

[m³/

s]

Time

Hydrographs at Napoleon III

Mike SHE

estimated

Mike SHE Tutorial

HEC-HMS

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Definition


methods

Mike-SHEcomparison

Conclusion

HEC-HMScomparison

CONCLUSION

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Are 5 rain gauges sufficient enough to distribute the rainfall well across the catchment?

The observed/estimated discharge provided at Napoleon III Bridge is not accurate due to extrapolation of the rating curve.

There is no justification in employing a complex, spatially distributed model when a simple one will suffice. Simpler models invariably have fewer parameters and are easy to calibrate.

Definition


methods

Mike-SHEcomparison

Conclusion

HEC-HMScomparison

Which model should we use?• purpose (resolution of prediction)

• available data (spatially and temporal varying data – soil, landuse, precipitation, temp. and other forcing variables)

• time window (the more complex the more time needed)

• scale of the project (small – homogeneous / large– inhomogeneous catchment)

• budget (computational and engineering time are expensive

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Definition


methods

Mike-SHEcomparison

Conclusion

HEC-HMScomparison

Thank you for your attention

2010-2011 TEAM

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