hepex - probabilistic forecasts within a time horizon and exact flooding time probability

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HEPEX - Probabilistic Forecasts Within a Time Horizon and Exact Flooding Time Probability

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  • Gabriele Coccia(1)(2) and Ezio Todini(1)(1) University of Bologna, Bologna, Italy

    (2) Idrologia e Ambiente s.r.l, Napoli, Italy

    Probabilistic Forecasts Within a Time Horizon and Exact Flooding

    Time Probability

  • Predictive Uncertainty can be defined as the probability ofoccurrence of a future value of a predictand (such as water level,discharge or water volume) conditional on all the information thatcan be obtained on the future value, which is typically embodied inone or more meteorological, hydrological and hydraulic model forecasts(Krzysztofowicz,1999)

    PREDICTIVE UNCERTAINTY MUST BE QUANTIFIED IN TERMS OF PROBABILITY DISTRIBUTION.

    If the available information is a model forecast, the PredictiveUncertainty can be denoted as:

    PREDICTIVE UNCERTAINTY (PU)

    Krzysztofowicz,R.:Bayesiantheoryofprobabilisticforecastingviadeterministichydrologicmodel,WaterResour.Res.,35,27392750,1999. 2

  • 3) Predictive Uncertainty is obtained by the Bayes Theorem

    4) Reconversion of the obtained distribution from the Normal Space to the Real Space using the Inverse NQT

    1) Conversion from the Real Space to the Normal Space using the NQT

    2) Joint Pdf is assumed to be a Normal Bivariate Distribution or composed by 2 Truncated Normal Distributions

    Todini,E.:Amodelconditionalprocessortoassesspredictiveuncertaintyinfloodforecasting,Intl.J.RiverBasinManagement,6(2),123137,2008.

    G.Coccia andE.Todini:RecentDevelopmentsinPredictiveUncertaintyAssessmentBasedontheModelConditionalProcessorApproach,HESSD, 7, 92199270, 2010

    Imageoftheforecastedvalues

    JointandConditioned Pdf

    Imageoftheobservedvalues a

    aa

    BIVARIATE

    UNIVARIATE

    UNIVARIATE

    MODEL CONDITIONAL PROCESSOR: BASIC CONCEPTS

    3

  • HOWTODEALWITHTHESEFORECASTS?

    WHICHWEIGHTCANBEASSIGNEDTOEACHONE?

    Usually, a real time flood forecasting system is composed by more than onemodel chain, different from each others for structure and results.

    MULTI-MODEL APPROACH

    BAYESIAN COMBINATION

    4

  • THE BASIC PROCEDURE CAN BE EASILY EXTENDED TO MULTI-MODEL CASES

    N+1VARIATE

    NVARIATE

    UNIVARIATE

    N=NUMBEROFMODELS

    5

    MULTI-MODEL APPROACH

  • The knowledge of the Predictive Uncertainty allows to easily extrapolate theprobability to exceed a threshold value, such as the dyke level.

    It can be directly computed from the Predictive Uncertainty, as its integral above the threshold value.

    FLOODING PROBABILITY AT A SPECIFIC TIME HORIZON

    6

  • Which is the probability that the water level will be higher than the dykes at the hour 24th?

    In the decision making process, this methodology allows to answer to the following

    question:

    7

  • Which is the probability that the river dykes will be exceeded within the next 24 hours?

    Probably a more interesting question may be:

    8

    To be able to answer to this question it is necessary to known the correlation between the predicted variable at the different time steps of

    the prediction

  • Following Krzysztofowicz (2008), the procedure can begeneralized including in the bayesian formulation allthe available forecasts within the entire horizon time.

    A MULTI-VARIATE PREDICTIVE DISTRIBUTION isobtained, which accounts for the joint PU of theobserved variable at each time step.

    Krzysztofowicz,R.:Probabilisticfloodforecast:exactandapproximatepredictivedistributions,ResearchPaperRK0802,UniversityofVirginia,September2008. 9

    MULTI-TEMPORAL APPROACH

  • T VARIATE

    ((N+1)T) VARIATE

    (N T) VARIATE

    N=NUMBEROFMODELST=NUMEROFTIMESTEPS

    With respect to the multi-model approach, the dimension of all the distributions is multiplied by the number of the time steps.

    10

    MULTI-TEMPORAL APPROACH

  • FLOODING PROBABILITY WITHIN THE TIME HORIZON OF T TIME STEPS

    ( ) Ta NkTtktTttakttNkTtktTtt

    dydyyyf

    yayPyayP

    ...1

    )|()|(

    1..1;..1;,..1;

    ,..1;..1;,..1;

    ===

    ===

    =>=>L

    Within 24h

    0.00

    0.25

    0.50

    0.75

    1.00

    0 3 6 9 12 15 18 21 24

    P

    (

    y

    i

    >

    s

    )

    i

    =

    1

    ,

    T

    CumulativeFloodingProbability

    11

    Atthe12th h

  • tyayP

    TP ktt > )|()( ,*

    EXACT FLOODING TIME (T*) PROBABILITY

    0.00

    0.05

    0.10

    0.15

    0.20

    0 3 6 9 12 15 18 21 24

    P

    (

    T

    *

    )

    12

    Which is the probability that the river dykes will be exceeded exactly at the hour 24th?

  • Canbeobtainedalsowiththebasicandmultimodelapproachessinceitdoesnotdependonthestateofthe

    variableat12hours

    Which is the probability that the water level will be higher than the dykes one at the hour 24th? RED + GREY

    Which is the probability that the river dykes will be exceeded within the next 24 hours?

    Which is the probability that the river dykes will be exceeded exactly at the hour 24th?

    RED + GREY + YELLOW

    RED

    13

  • Forecastedhourlylevelswithatimehorizonof24and36h

    Observedhourlylevels

    01/05/2000 20/01/200930/06/2004

    MCP: CALIBRATIONVALIDATION

    PORIVERATPONTELAGOSCUROandPONTESPESSA

    Availabledata,providedbytheCivil Protection of EmiliaRomagnaRegion,Italy:

    14

  • ExactFloodingTimeProbability

    CumulativeFloodingProbability

    90%UncertaintyBandwithBASICAPPROACH(TNDs)

    Pontelagoscuro Station(36h)

    P

    (

    T

    *

    )

    DeterministicForecast

    P

    (

    T

    *

    )

    90%UncertaintyBandwithMULTITEMPORALAPPROACH

    15

  • ExactFloodingTimeProbability

    CumulativeFloodingProbability

    PonteSpessa Station(24h)

    P

    (

    T

    *

    )

    P

    (

    T

    *

    )

    90%UncertaintyBandwithBASICAPPROACH(TNDs)

    DeterministicForecast90%UncertaintyBandwithMULTITEMPORALAPPROACH

    16

  • PonteSpessa Station(24h)

    FLOODINGPROBABILITYASSESSMENTVERIFICATIONIf the value provided by the processor is correct, considering all thecases when the computed exceeding probability takes value P, thepercentage of observed exceeding occurrences must be equal to P.

    RedLine=Perfectbeahviour

    Computedwitha5%

    discretization

    VALIDATIONCALIBRATION

    17

  • Most of the existing Uncertainty Processors do notaccount for the evolution in time of the forecastedevents

    The correlation between the predicted variable atdifferent time steps of the prediction should be takeninto account

    The presented multitemporal approach allows toidentify the joint predictive distribution of all theforecasted time steps

    18

    CONCLUSIONS

  • This procedure allows to recognize and reduce thesystematic time errors and it gives importantinformation, such as the probability to have aflooding event within a specific time horizon and theexact flooding time probability

    The comparison of predicted and observed floodingoccurrences verified that, a part small errors due tothe unavoidable approximations, the methodologycomputes the flooding probability with goodaccuracy

    19

    CONCLUSIONS

  • THANK YOU FOR YOUR ATTENTION AND YOUR

    PATIENCE

    20

  • The assumption of the homoscedasticity of errors leads to a lack of accuracy, especially for high flows.

    In the Normal Space the data are divided in two (or more) samplesand each one is supposed to belong to a different Truncated NormalDistribution. Hence, two Joint Truncated Normal distributions (TNDs) areidentified on the basis of the samples mean, variance and covariance.

    HETEROSCEDASTICITY OF THE ERROR: THE TRUNCATED NORMAL DISTRIBUTIONS

    Coccia, G.andTodini, E.:Recentdevelopmentsinpredictiveuncertaintyassessmentbasedonthemodelconditionalprocessorapproach,Hydrol.EarthSyst.Sci.Discuss.,2010. 21

  • HOW TO USE THE TNDs IN THE MULTI-TEMPORAL APPROACH?

    RISINGLIMB

    RECESSIONLIMB

    HIGHFLOWS

    LOWFLOWS

    THRESHOLDFORT=t0

    THRESHOLDFORT=t0+36

    DATAOFTHEMODELTHATBETTERPERFORMSINHIGHFLOWS 22

  • PONTELAGOSCUROPONTESPESSA

    23

  • The choice of the threshold using the Joint Truncated Distributions.

    Is it possible to find an objectiverule, related to the forecast cdfgradient, to identify this threshold?

    How many TNDs must be used?Are 2 enough?

    OPENQUESTIONS

    24

  • A good model fit of the marginal distribution tails is very important:

    For which probabilities should tailsbe used?

    Which is the best curve?

    Would be better the use of tails or to identify a probability modelfor the whole series?

    OPENQUESTIONS

    25

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