u.r hydrologie-hydraulique, lyon m. lang
TRANSCRIPT
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 20081
Do monsters resist to rating curves scrutiny ?
U.R Hydrologie-Hydraulique, Lyon M. Lang
Example of flowing conditions during floods (Ardèche & Volane, 20 Oct. 2001)
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es b
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M.
Lan
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ema
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f L
yon
)
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 20082
Degree of rating curve extrapolation in France
Class
0
1
2
3
4
1831753Size (%)
T > 100
yrs
10 < T <
100 yrs
2< T < 10
yrs
1 < T < 2
years
T < 1 yrCriteria
......Class
• 60% of the stations have not been gauged beyond the 2 year flood peak
• Less than 10% of the stations have not been gauged beyond the 10 year flood
• Large basins with smooth floods have been better gauged
(measurements of discharge are easier)
Return period of the maximum gauged flow
�few rating curves on extreme discharges
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 20083
• Active policy of gauging measurement
main source of information
• Data collection on flood marks
several marks to assess the water surface profile, for model calibration
• Hydraulic extrapolation of the rating curves
no empirical calculation, specific behaviour for overflowing discharge
• Feedback on gauging equipment and gauging methodologies
knowledge of the various limitations
• Exchange groups on new technologies
ADCP, video information
How to improve the discharge assessment on large floods ?
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 20084
Review of rating curves by hydraulic modelling
• Morphological data (main channel, flood plain, bridges, thresholds)
• Hydrometrical data (discharge data series, rating curves, gauging values, flood marks)
Data collection
• Sensitive analysis on the downstream condition
• Determination of the roughness coefficients
� main channel : calibration with gauged values
� flood plain : a priori values from field visit & Ven Te Chow tables
Calibration of the hydraulic model
0
5
10
15
20
25
30
35
0 10 20 30 40 50 60 70
Discharge(m3/s)
K=
1/n q
q-10%
q+10%
Overflow of the main channel
Domains : 1 2 3 4
�Representative Manning
coefficient from domain 2
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 20085
Review of rating curves by hydraulic modelling
• Sensitive analysis on the gauging values (H, Q)
(H-5 cm, Q+10%) and (H+5 cm, Q-10%)
Extrapolation of the hydraulic model
0
20
40
60
80
0 0.5 1 1.5 2 2.5
Water depth (m)
Dis
cha
rge
(m3/s
)
Maximal curveMedian curveMinimal curve
Q+10%
QQ-10%
H
H-5 cm H+5 cm
135
140
145
150
155
160
67000 67500 68000 68500 69000 69500 70000 70500
Pk (m)
Lev
el (
m N
GF
)
Q=3420 m3/s (Kmin)
Q=2470 m3/s (Kmed)Q=1730m3/s (Kmax)marks of the 1992 flood
Bed level (m NGF)
New
bridge
Railroad
bridge
Old
bridge
• Validation with past flood levels
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 20086
Review of rating curves by hydraulic modelling
• Small to medium error on gauge discharge : 3-5% to 10%
• Large error on the extrapolated domain of the rating curve
� with good hydraulic conditions : about 30% for the 10 year flood
� without gauged values or with complex hydraulic conditions : 60-100 % !
Main results on 20 stations
• Specific difficulties
� downstream influence, hydraulic jump, submerged bridge
� flowing on large flood plain
� debris flow
• Flood marks
� useful for extreme discharge assessment
� not only at the flood scale
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 20087
Data set of 195 long series
1. Choice of hydrological stations
Criteria : long series (> 40 years), good quality
(no influence, no evolution of the bed river, good
rating curves)
Preliminary visit to the data managers
2. Local analysis of changes
Use of various samping variables (from the daily discharge data series)
Checking of the results for each station: discussion with the data managers
Example of erroneous diagnosis for trend detection
National study on flood and droughts (Renard, 2006)
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 20088
First results of a set of 195 long data series(test of deviance, GEV distribution)
�Many significant changes (10% error level) : 1/4 for floods, 1/3 for low flows
� About the same number of increasings and decreasings
� Poor spatial coherency
Impact of climate change ?
NFlood (Maxan) Low Flow (7 day Discharge)
Example of erroneous diagnosis for trend detection
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 20089
Review of the results
On the whole set
of 195 stations….
Flood Low Flow
New data set124 stations for rainfall-runoff floods
25 stations for snow melting floods
90 stations for low flows
Example of erroneous diagnosis for trend detection
Suspected explanation
of change
Metrological issue No explanation
Influence No information
Detected change
No change
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 200810
New results on rainfall-runoff floods (reduced data set of 124 stations)
0 2 3N=
A few detected changes (10% error level)
About the same number of increasings and decreasings
Poor spatial coherency
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 200811
The number of detected changes is not significant
227Mean discharge3686Mean
discharge
722Date of MINAN
1222MINAN
3658Low flows
2833Date of QCX10
833Threshold
discharge QCX104115
Snow melting
floods
318Date of MAXAN
1219MAXAN
3683Floods
Observed
Percentage
Critical
percentage N0.05
VariableNb of
common
years
Nb of
stations
Regional significance of local tests Local and national risk: 5%
The Court of Miracles of Hydrology, 18-20 June, Engref Paris 200812
How to deal with discharge uncertainty ?
1. Reduce uncertainty� Promote investment on data measurement (gaugings, flood marks, good rating curves)
� Exchange information for the setup of reviewed data sets
2. Consider uncertainty� Include discharge uncertainty within statistical analysis, model calibration …
3. Use rainfall information� Extreme flood is mainly due to extreme rainfall …