A. Randrianasolo (1), M.H. Ramos (1), G. Thirel (2), V. Andréassian (1), E. Martin (2)
(1) Hydrology Research Group, Cemagref HBAN, Antony, France (contact: [email protected])(2) CNRM-GAME, Météo-France, CNRS, GMME/MOSAYC, Toulouse, France (contact: [email protected] )
Objective:1
Data and methods:2
Fig. 2b: GR3P model
2) the lumped soil-moisture-accounting type rainfall-runoff model (GR3P) developed at Cemagref (Fig.2b)
Fig. 2a: SIM model
Skill scores3
alarmsfalsehits
alarmsfalseFAR
Contingency table
misseshits
hitsPOD
• Two critical thresholds for observed events:
Qref1 = 50th percentile (Q50)
Qref2 = 90th percentile (Q90)
• Threshold for forecasted events:
if p = 50% of the members are greater than Qref, the event is considered as a « forecasted event »
Two hydrological models:
211 catchments in France (170 to 9390 km2) (Fig. 1)
Weather forecasts from the PEARP ensemble prediction system of Météo-France (March 2005-July 2006):
- 11 perturbed members for a forecast range of 60 h (skill scores computed for the first two days of forecast range)
Time series of observed data: daily discharge, precipitation, temperature
www.cemagref.fr/webgr
Updating
E P PEARP
En Pn
Es Ps Pn-Ps
PR
PRS = X1*PR
R2
HU(X3)
Q'2p
Q'1 X2
650 mm
Perc
HEPEX09 Workshop – Toulouse, 15-18 June 2009
Fig. 1: Location of the catchments
Impact of the use of two different hydrological models on scores of hydrological ensemble forecasts
Standard deviation (or spread)
Ratio-RMSE: RMSE / Mean of observed streamflows
Ratio-σ: Standard Deviation / Mean of forecasted streamflow
BSS: the reference used is the climatology
)()(
1
1
2 mmomN
RMSEN
iii
oi observed data for the day i
mi mean of the ensemble forecasts for the day i
N number of days used to compute the score
n number of forecast members
N number of days used to compute the score
xi mean of the ensemble forecasts for the day i
xk,i value of the member k for the day i
)()(
11
1 1
2, mmxx
nN
N
i
n
kiik
Root Mean Square Error Brier Skill Score
misseshits
alarmsfalsehitsBIAS
Results4
PEARP-based ensemble streamflow forecasts predicted well discharges over the studied catchments
Better scores are obtained from the GR3P model with updating, while SIM results are closer to the results from GR3P model without updating (for data assimilation in SIM model, see Thirel et al., 2009)
Conclusions5
Fig. 3: POD, FAR, BIAS (Leadtime = Day 1 and Qref2 = Q90)
catchment area (km2)
GR without updatingSIMGR with updating
Fig. 4: Ratio-RMSE values (Leadtime = Day 2)
to assess the quality of ensemble streamflow forecasts issued by two different modelling conceptualizations of catchment response, both driven by the same weather ensemble prediction system
1) the coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU developed at Météo-France, based on a distributed catchment model (Fig. 2a)
Fig. 5: Spread
References:1. Thirel, G., Rousset-Regimbeau, F., Martin, E., Habets, F. (2008) On the impact of short-range meteorological forecasts for ensemble streamflow predictions. J. Hydrometeorology (9), 1301-1317. 2. Tangara, M. (2005) Nouvelle méthode de prévision de crue utilisant un modèle pluie-débit global. PhD Thesis EPHE-Cemagref, Paris, 374 p.3. Thirel, G., E. Martin, J. F. Mahfouf, S. Massart, S. Ricci, and F. Habets (2009) A streamflow assimilation system for ensemble streamflow forecast over France. Abstract EGU2009-6890.4. Randrianasolo, A. (2009) Evaluation de la qualité des prévisions pour l'alerte aux crues. MSc Thesis ENGREF, Cemagref (ongoing).
Fig. 6: BSS
n
jjj xp
NBS
1
2)(1
N number of days used to compute the score
xj = 0 (the event occurs)
xj = 1 (the event does not occur)
pj probability of the event to occur
refBS
BSBSS 1
Q50 Q90
Day 1
Day 2
GR with updating SIM GR without updating
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