Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision making EGU – 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 decision making EGU – Wien, 7th april 2011 Summary Introduction Scheme Example Actual development Further steps

Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision making EGU – 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 decision making EGU – Wien, 7th april 2011 Scheme State changes Flow for each state Details Intersection reality - information

Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision making EGU – 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 decision making EGU – Wien, 7th april 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 decision making EGU – Wien, 7th april 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 decision making EGU – Wien, 7th april 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 decision making EGU – 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 decision making EGU – Wien, 7th april 2011 oHF based on observed precipitation up to T0(now) + forecasted precipitation from T0 to LAM lead time (e.g. 72 hour) oHF 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 decision making EGU – Wien, 7th april 2011 Details: Deterministic Hydrological forecast analysis

Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision making EGU – Wien, 7th april 2011 Details: Probabilistic Hydrological forecast analysis

Bollettino di previsione, vigilanza e monitoraggio per il bacino del fiume Po Parma, 15 luglio 2010 Servizio IdroMeteoClima 23 November 2002 Observed simulation forecast Deterministic forecast – COSMO I7 Probabilistic forecast – COSMO LEPS ForecastSurveillance State 1 Hydrologic hydraulic forecast analysis - PIACENZA Time (hour)

Bollettino di previsione, vigilanza e monitoraggio per il bacino del fiume Po Parma, 15 luglio 2010 Servizio IdroMeteoClima 24 November 2002 Hydrologic forecast analysis - PIACENZA Observed simulation forecast Deterministic forecast – COSMO I7 Probabilistic forecast – COSMO LEPS State 2 Time (hour) Surveillance

Bollettino di previsione, vigilanza e monitoraggio per il bacino del fiume Po Parma, 15 luglio 2010 Servizio IdroMeteoClima 26 November 2002 Hydrologic forecast analysis - PIACENZA Observed simulation forecast Deterministic forecast – COSMO I7 Probabilistic forecast – COSMO LEPS State 3 Time (hour) Monitoring

Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision making EGU – Wien, 7th april 2011 Example -I

Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision making EGU – Wien, 7th april 2011 Example - II Piacenza: sample analysis of maximum levels

Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision making EGU – Wien, 7th april 2011 Example - III Observed Peak water level: 6.14 m Peak discharge: 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 decision making EGU – 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 decision making EGU – Wien, 7th april 2011 Actual development- II Information import Synoptic - Civil Protection - Regional Centres reports System status checking Updating the system Anomalies 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 decision making EGU – 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 decision making EGU – 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 decision making EGU – Wien, 7th april 2011 Thank you for your attention!

Hydrological forecasting: application, uncertainty, estimation, data assimilation and decision making EGU – Wien, 7th april 2011 Observed levels (gards) Rating curve Hydraulic model Hydrological model Observed precipitation Forecast precipitation