1. Mediterranean Meeting on ″Monitoring, modelling and early warning of extreme events triggered by heavy rainfalls″.
MED-FRIEND project. University of Calabria, Cosenza (Italy), June 26th-28th, 2014
The 6 November 2011 flood event in Catalonia: Analysis using the DRIHM infrastructure
Llasat, M.C. (1), O. Caumont (2), I. Flores(3), L. Garrote(3), J. Gilabert(1), M.Llasat-Botija(1), R. Marcos(1), O. Nuissier (2), E. Richard(4), T. Rigo(5)
(1) Department of Astronomy and Meteorology, University of Barcelona, Spain
(2)CNRM-GAME (Météo-France and CNRS), France
(3)Department of Hydraulic and Energy Engineering, Technical University of Madrid, Spain
(4) Centre National de la Recherche Scientifique, France
(5)Meteorological Service of Catalonia, Spain

2. Outline Floods in the Mediterranean The DRIHM project
The November 2011 “critical cases”: the Muga river
Hydro-Meteorological chains
Conclusions

3. 1981-2010: 385 flood events (10. 3%, catastrophic; 53
: 385 flood events (10.3%, catastrophic; 53.5%, extraordinary). 19% have produced casualties (61.1% of them attributed to catastrophic floods).
Calabria (107): 33.6% cat., 37% extr.
Balearic I. (36): 22.2% cat., 77% extr.
Catalonia (213): 10% cat., 53% ext
SE France (29): 100% cat.
(Llasat, Llasat-Botija, Petrucci, Pascua, Boissier, Vinet, NHESS, 2013)
FLOOD HYMEX database

4. The DRIHM project November 2011, 3 critical cases, to test DRIHM
Increasing incidence of
floods in the Mediterranean region
Need of an innovative
strategy for ICT in
Hydro-Meteorology
(Llasat et al, AdGeo, 2010)
The DRIHM
project
November 2011, 3 critical cases, to test DRIHM
e-Infrastructure through different multimodel workflows
(Llasat et al, NHESS, 2013)

5. The DRIHM project Objectives Reducing uncertainty in forecasts
To support the development and deployment
of a HMR e-Science environment
To provide integrated HMR services
To design and deploy user-friendly interfaces
(researcher, public organizations, citizen scientists)
To support hydro-meteorological
forecasting chains (workflows)
Reducing uncertainty in forecasts
reducing working time
Learning on hydrometeorological modeling

6. Already developed stages
The DRIHM project
Already developed stages
Forecasting chain
Meteorological layer
Hydrological layer
Hydraulic layer

7. METEOROLOGICAL MODEL BRIDGE
The DRIHM project
Change of paradigm in HMR chains with DRIHM project
Before DRIHM
No direct ingestion
High computational time
With DRIHM
Supercomputing capability
Interoperability
Standard interface
METEOROLOGICAL MODEL BRIDGE 4
DRIHM project

8. FLOODS IN CATALONIA: 3 and 6 NOVEMBER 2011
Odena
Onyar in Girona (La Vanguardia, A. Ensesa, 7/11/2011)
: >€
: 110 casualties (70% in roads, crossing water streams).
Radio Nova Vilanova del Camí

9. Ligthning 3/11/11 Accumulated rainfall, 1-8/11/2011.
Meteorological stations, radar and lightning networks
Radar image 3/11/11
Ligthning 3/11/11

11. 6 Raingauges
November 2011
16 Raingauges
Data
RIBS Calibration ( )

12. Catalonia “critical case”
HM chains performed
WRF
MesoNH
Arome
Raingauge
observations
Meteorological layer
DETERMINISTIC
ENSEMBLE
ENSEMBLE 10 8 4
CALIBRATION
DRiFt
Streamflow
observations
Hydrological layer
RIBS 4 10 8
WRF-DRiFt
Workflow
AROME-RIBS
Workflow
Observed discharge
MesoNH-DRiFt
Workflow
Arome-DRiFt
Workflow

13. 1° HMR chain: AROME Arome 8 Ensemble runs Domain: 2,5 km
Domain selection through the DRIHM portal
Arome
8 Ensemble runs
Domain: 2,5 km
Initialization
Parametrization
6 Nov. 18UTC
AROME forecast for 24-h acumulated rainfall on 6 Nov.
The intensity and extension of the Muga maximum varies among the 8 members from less than 100 mm to more than 150 mm

14. 2° HMR chain: RIBS Real-time Interactive Basin Simulator
RIBS model (Garrote and Brass, 1995a, b)works over the grid of a digital terrain model and the data are stored in layers of raster-type information. It consists of two modules: The runoff-generation module is based on the Brooks-Corey soil properties parameterization; the runoff propagation module simulates the runoff travel through the hillslope path, distinguishing the velocity along the hillslope and through the river-bed path, are considered they uniform throughout the basin at any time.
The probability distribution functions were obtained through the probabilistic calibration methodology proposed by Mediero et al. (2011) based on a simultaneous minimisation of multiple objective functions applied to the results of a 1000 Monte Carlo simulation, using uniform probability density functions on all parameters. In probabilistic mode, 50 ensemble members were computed with the hydrological model for every ensemble member of the meteorological model, totaling 300 realizations.
A total of 14 flooding events were considered to calibrate the model

15. Deterministic simulations with RIBS using AROME input
Deterministic simulations with RIBS using AROME input. Observed discharge is the dotted magenta line. Simulated discharge with observed rainfall is the blue line and simulated discharge with simulated rainfall is the red line. The simulations with observed rainfall show a good agreement with observed flow in terms of peak flow (400 m3/s) and timing (11/06),

16. Probabilistic simulations with RIBS using AROME input
Probabilistic simulations with RIBS using AROME input. Observed discharge is the dotted magenta line. Simulated discharge with observed rainfall are the blue lines and simulated discharge with simulated rainfall are the red lines. The spread of uncertainty linked to model parameters is very large, with peak flows ranging from 800 m3/s to 200 m3/s. The large uncertainty associated to model parameters prevents an accurate forecast.

17. Conclusions
The RIBS application over the Muga flood case 2011 show a good agreement with observed flow in terms of peak flow (400 m3/s) and timing (11/06), when punctual 5-min observed rainfall is used.
The AROME simulation ran at 18 UTC the day before shows a good agreement in terms of cumulated rainfall and time
Overall the scheme based on AROME-RIBS proved to be a useful early warning tool that can identify the risk of flash flooding before the rainfall actually occurs: a decision maker would have issued a warning based on AROME predictions for several days in the episode analysed.
Other applications (Piemontese, 2014) have show that other good rainfall simulation has been provided by MesoNH and Drift
The DRIHM hydrometeorological channel is a good tool to simulate flood events with different meteorological anf hydrological models

19. THANKS A LOT FOR YOUR ATTENTION