the po flood management in italy: guidelines and methodologies
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Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
The Po flood management in Italy:guidelines and methodologies
G.Ricciardi[3], L.Casicci [1], L.Fortunato[1], S.Pecora[3], N.Rebora[2], M.Vergnani [1]
[1] AIPO Interregional Agency for the Po river
[2] CIMA Research Foundation, Italy
[3] ARPA EMR Environmental Agency of Emilia Romagna Region
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Summary
Introduction
Scheme
Example
Actual development
Further steps
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Introduction
An operative forecasting and modeling system for hydrological cycle and extreme events is adopted on the Po river
It is composed of three modelling chains simulating hy/hy behaviour feeded by observations and forecasting meteorological data
A methodology for utilization of system output is needed expecially to compare system capabilities with resources and needs and finally choose the best operational procedures
This methodologic scheme is here presented
It is based on a State approach.
State changes are defined according to observed and predicted values of hydrological parameters and traveling times of river sections
Analysis of input/output data and of forecast performances are taken into account
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Scheme
State changes
Flow for each state
Details
Intersection reality - information
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Forecasted event on secondary reaches
Guided by observation
Guided by run on observation
Forecasted event on the main reach
State 1 (forecast)
State 2 (surveillance )
State 3 (monitoring)
Imminent event on the main reach
End of event
Not confirmed event
State changes (information layer)
Secondary reaches:Response time less than 12 h
Guided by deterministic forecast
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
1. Meteorological Forecast analysis
2. Deterministic Hydrological Forecast (on a reach list)
3. Probabilistic HF analysis (only if an event is forecasted)
Go on with State 1: no Forecasted Events
Go to State 2: FE on the main reach
Go to State 3: FE on secondary reaches
Step flow in State 1 (Forecast)
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
1. Deterministic Hydrological forecast
2. Observed simulation HF ( if an event is forecasted)
Go back to State 1: no event is forecasted in Step 1
Go back to Step 1: no event is forecasted in Step 2
Go to State 3: an event is forecasted in Step 2
Step flow in State 2 (Surveillance)
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
1. Observed level analysis
2. Unbiased observed simulation Hydrologic Forecast (no Threshold Exceedings in Step 1)
3. Deterministic HF (no TE in Step 2)
Continuous monitoring: TE in Step 1 Real Time notifications
High frequency monitoring:
TE in Step 2 and low frequency monitoring is on
Low frequency monitoring:
TE in Step 3 and monitoring on shortest response time reaches (e.g. 12h) is off
Go back to State 1: no more TE in Step 3 (end of event)
Step flow in State 3 (Monitoring)
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Saturation degree of sub basins (AMC)
Forecasted and observed total rainfall on sub basins (LAM runs)
(aggregation on RT)
Localization of critical reaches (scenarios)
Details: Meteorological forecast analysis
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
o HF based on observed precipitation up to T0(now) + forecasted precipitation from T0 to LAM lead time
(e.g. 72 hour)
o HF based on observed precipitation up to T0 + null precipitation from T0 to hydrological lead time
(Observed simulation HF)
Details: Deterministic Hydrological forecast analysis
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Details: Deterministic Hydrological forecast analysis
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Details: Probabilistic Hydrological forecast analysis
Bollettino di previsione, vigilanza e monitoraggio per il bacino del fiume PoBollettino di previsione, vigilanza e monitoraggio per il bacino del fiume PoParma, 15 luglio 2010Parma, 15 luglio 2010
Servizio Servizio IdroMeteoClimaIdroMeteoClima
23 November 2002
Observed simulation forecast
Deterministic forecast – COSMO I7
Probabilistic forecast – COSMO LEPS
Forecast Surveillance
State 1 Hydrologic hydraulic forecast analysis - PIACENZA
Time (hour)
Bollettino di previsione, vigilanza e monitoraggio per il bacino del fiume PoBollettino di previsione, vigilanza e monitoraggio per il bacino del fiume PoParma, 15 luglio 2010Parma, 15 luglio 2010
Servizio Servizio IdroMeteoClimaIdroMeteoClima
24 November 2002
Hydrologic forecast analysis - PIACENZA
Observed simulation forecast
Deterministic forecast – COSMO I7
Probabilistic forecast – COSMO LEPS
State 2
Time (hour)Surveillance Surveillance
Bollettino di previsione, vigilanza e monitoraggio per il bacino del fiume PoBollettino di previsione, vigilanza e monitoraggio per il bacino del fiume PoParma, 15 luglio 2010Parma, 15 luglio 2010
Servizio Servizio IdroMeteoClimaIdroMeteoClima
26 November 2002
Hydrologic forecast analysis - PIACENZA
Observed simulation forecast
Deterministic forecast – COSMO I7
Probabilistic forecast – COSMO LEPS
State 3
Time (hour)
Monitoring Monitoring
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Example -I
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Example - II Piacenza: sample analysis of maximum levels
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Example - III
Observed
Peak water level: 6.14 m
Peak discharge: 5.400 mc/s
Date of peak: 18/03/2011 5:00 a.m.
Time at L1 exceeding: 17/03/2011 4:00 a.m. (+100 hour)
Time at L2 exceeding: 17/03/2011 9:00 p.m. (+117 hour)
No L3 exceeding
Duration of L1 exceeding: 40 h
Duration of L2 exceeding: 12 h
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Actual development -I Methodology
• Layout of information scheme
• Analysis and computations
• Terminology/simbology
• End users
• Frequency
• Equipments, human resources, activities - User manual
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Actual development- II Information import Synoptic - Civil Protection - Regional Centres reports
System status checking Updating the systemAnomalies
Analysis P/Q_WL(hydrologic state), system performances, post processing Briefing
Condivision National Civil Protecion, Agency for Po river, Regional Centres
Information diffusion
Information storing
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Actual development- III Procedure, scheme, information and analysis, terminology and
symbology, time of emission and the other elements are different
for each state
They are intended to supply both useful information, with agreed
uncertainty, and the best perception of what is occurring,
balancing execution times and lead times required by decision
makers, reducing missed alarms and false alarms
Knowledge and awareness can be increased across the different
states giving more and more information and analysis, raising
diffusion frequency and giving diffusion advance
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Further steps
Applications and further developments of the proposed methodology are related to:
• additions of modeling components to the operative system
• next operational steps (operational procedures, prototype definition and testing,
errors recording and corrective actions)
In the second phase coordination and information exchange actions will be
furthermore focused.
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
Thank you for your attention!
Hydrological forecasting: application, uncertainty, estimation, data assimilation and Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision makingdecision making
EGU – Wien, 7th april 2011EGU – Wien, 7th april 2011
• Observed levels (gards)
• Rating curve
• Hydraulic model
• Hydrological model
• Observed precipitation
• Forecast precipitation
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