université de grenoble space-time characterization of ... · université de grenoble phd...

Université de Grenoble

PhD candidate: Davide Ceresetti

Director: Jean-Dominique CREUTINCo-director: Gilles MOLINIÉ

Université de GrenobleUniversité de Grenoble

Space-time characterization of heavy rainfall events:Space-time characterization of heavy rainfall events:Application to the Cévennes-Vivarais regionApplication to the Cévennes-Vivarais region

Université de Grenoble

!IntroductionIntroduction

!Methodological developmentMethodological development

!Application: Severity DiagramsApplication: Severity Diagrams

!ConclusionsConclusions

OUTLINE OF THE PRESENTATIONOUTLINE OF THE PRESENTATION

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Université de GrenoblePART IPART I

INTRODUCTIONINTRODUCTION

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Context: Extreme rainfall in a Mediterranean Mountainous Region

1958-1994:

Daily amount > 190 mmTotal: 144 events

Jacq (1994)Warm humid air from Mediterranean Sea + Orography = Storms

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General overviewGeneral overview

General overviewGeneral overviewCévennes-Vivarais: region prone to catastrophic fl ash-fl oods

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Social and economic impact (human lives, damages,...)Social and economic impact (human lives, damages,...)

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Specifi c discharge: 5-10 m3 s 1 km 2

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General overviewGeneral overview

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How can we measure the magnitude of extremes?How can we measure the magnitude of extremes?

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Is it a « hydrological monster »or a regular event?Is it a « hydrological monster »or a regular event?

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Spatial and temporal scales are related

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Impact of storms at various durationsImpact of storms at various durations


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Aim of the studyAim of the studyHOW TO ESTIMATE THE MAGNITUDE OF RAINFALL EVENTS?

(c)19 September 2000

(a)22–23 September 1993

(b)7 September 1998

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HOW TO ESTIMATE THE MAGNITUDE OF RAINFALL EVENTS?(c)

19 September 2000(a)

22–23 September 1993(b)

7 September 1998

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NO DAMAGES NO DAMAGES 60 M€

Classic statistics are unable to detect the more dangerous event

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Aim of the studyAim of the study

Need of a multi-scale descriptor of stormsNeed of a multi-scale descriptor of stormsMaximum rainfall intensity

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Proposition: transform max intensity into FREQUENCYProposition: transform max intensity into FREQUENCY

SEVERITY DIAGRAMS: Event magnitude at all scalesSEVERITY DIAGRAMS: Event magnitude at all scales

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SSeverity diagrams: a storm comparison tooleverity diagrams: a storm comparison tool

(c)19 September 2000

(a)22–23 September 1993

(b)7 September 1998

Weak event Local event Heavy and extended event

DS4;ODTBU V%.%EB;4;ODTBU DANGERDANGER

Ramos et al., 2005

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Improvements proposed in the thesisImprovements proposed in the thesis

BEFORE AFTER

Size of the region 250 km2 32000 km2

Involved events Urban fl oods Flash-fl oods

Regional model

Point rainfall extremes

Spatial rainfall extremes

EMPIRICAL SCALE-INVARIANTMODEL

SPACE-TIMEMODELEMPIRICAL

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A larger regionA larger region

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Take into accountTake into accountspatial heterogeneityspatial heterogeneity

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Improvements proposed in the thesisImprovements proposed in the thesis

Mediterranean Sea

Rhône River

Cévennes Massif

Geographical contextGeographical contextCévennes-Vivarais région

Size 160 x 200 km2

Elevation 0 – 1950 m

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Geographical contextGeographical contextCévennes-Vivarais région

The region gathers fl at lands , a SE oriented foothill , a mountain ridge and a plateau .

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Climatic features: average annual rainfall (mm)Climatic features: average annual rainfall (mm)

Mountain ridge:Over 2000 mm / year

Mediterranean sea shore:less than 1000 mm / year

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Measurement networkMeasurement networkOHM-CV:

OHM-CV: one of the Europe densest rain gauge networks (1/50 km2)

Cévennes- Vivarais Hydro-Meteorological ObservatoryRadar ARAMIS network Rain gauge network

Hourly (150 gauges, 1993-2008)Daily (225 gages, 1958-2000)

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Université de GrenoblePART IIPART II

METHODOLOGICAL METHODOLOGICAL DEVELOPMENTDEVELOPMENT

ACCURATE MODELING OF EXTREMESACCURATE MODELING OF EXTREMES

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SERIES RECONSTRUCTION THROUGHSERIES RECONSTRUCTION THROUGH SCALE-INVARIANCE METHODSSCALE-INVARIANCE METHODS

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ACCURATE MODELINGACCURATE MODELING OF EXTREMESOF EXTREMES

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Dealing with ungauged scales: SCALINGDealing with ungauged scales: SCALINGSCALING OF A PROCESS

relation between probability distributions of a process at different scales

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Prerequisite: Evaluation of rain gauge uncertaintiesPrerequisite: Evaluation of rain gauge uncertainties

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Wind effect Neglectable for high intensities

Bottom hole lamination underestimation in case ofvery high intensities

Tipping-bucket device

Rain collector

Heavy rainfall Underestimation: 5-10% 5-min rainfall 2-5 % hourly rainfall

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Reversal time: ~ 0.2 s in which no water is stored

!Experimental calibration!Numerical Simulation

Evaluation of rain gauge uncertaintiesEvaluation of rain gauge uncertainties

Tails behaviorTails behaviorIdentifi cation of the behavior of point-rainfall extremes

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Open question: how are the distribution tails of rainfall?

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At various duration --> tail behavior of point rainfall series

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Rigorous method

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Straight line in log-log Power-law Fréchet distribution

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Flat landsFlat lands

Mountainous regionMountainous region

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Need to model extremes in space


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44444444444!!Z;b4G=KK4P7?H

44444444444!!Z;b4G=K4P7?H

44444444444!!Z;b4G!4P7?H

RADAR IMAGERYspatial scale-invariance

detected in the range 1-400 km2

RADAR Few events, not enough data

Solution 1: Statistics on radar data

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Solution 2: Interpolation of point data

Signifi cant underestimation of maxima in coarse networks

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$A"#$


Solution 2: Interpolation of point data

Spatial undersampling Underestimation maxima 20-50%

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%6)O/3(&1!.6&/1 F5(&+'0/3(605!0.!'065(!)&37O+&)&5(7

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ARF computed from historical series 1993-2008

ARF: Areal Reduction Factor

8

OUb

O4GP7?H

Example: rainfall fi eld

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2)3%" 2

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Solution 3: Semi-empirical model based on gages

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Solution 3: Semi-empirical model based on gages

We can build AREAL REDUCTION FACTOR

Dynamic scaling model for ARF

(

24%#5+16

Dynamic scaling ratio

De Michele et al., 2001

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ARF in Cévennes-ARF in Cévennes-Vivarais regionVivarais region

Duration has lower infl uence in mountainC+&3!HW),J

CNY

Flat Lands

?0 =0 @0 !?0 ?=0


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C+&3!HW),J

CNY

Mountainous region

?0 =0 @0 !?0 ?=0

Point rainfall modelPoint rainfall model Spatial rainfall modelSpatial rainfall modelh

9!U Y/3(!/3517

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Point rainfall modelPoint rainfall model Spatial rainfall modelSpatial rainfall model

Regional model for assessing the magnitude of extremesRegional model for assessing the magnitude of extremes

h

9!U Y/3(!/3517

Severity DiagramsSeverity Diagrams

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Université de GrenoblePART IIIPART III

APPLICATION: APPLICATION: SEVERITY DIAGRAMSSEVERITY DIAGRAMS

Storm comparisonUse of Severity DiagramsUse of Severity Diagrams

Observed stormVirtual storm

(numerical simulation)

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=$"#$

From rainfall fi elds to severity diagramsFrom rainfall fi elds to severity diagrams

O(/34GP7?H

O,,+7+13'-)&4*+(3'-)&4G0H

Multi-step process involving historical series

9/5/(-':4;-32(374G:(8H

Z6)&!HUJ

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U3*3(4*3'34)(48-7+13'/*47)*/1

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=="#$

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Severity Diagrams computationSeverity Diagrams computation

AREAL REDUCTION FACTOR INTENSITY- DURATION- FREQUENCY

9ZOE%OV4UO%DbOVV

9BCBU%E_

Assign to each spatial rainfall observationAssign to each spatial rainfall observation a frequency value (severity)a frequency value (severity)

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=#"#$

Application of severity diagramsApplication of severity diagrams

U/86/,'4')4U37)84QQe413(2/(4*)73-&4c-'0

!4X/'/()2/&/-':4)M4(3-&M3114/L'(/7/4F/035-)(4GM13'413&*8a47)+&'3-&IIH!4<)3(8/(4(3-&M3114&/'c)(P

\/4,3&4+8/4)+(4863,/Q'-7/47)*/14')4,)76+'/49BCBU%E_4;%OTUO.9M)(43&:48')(74),,+((-&24-&4'0/4<W5/&&/8

Applications: 1. Evaluation of meso-scale deterministic simulations (MesoNH)2. Evaluation of the variability of Ensemble simulations (AROME)

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=A"#$

Evaluation of deterministic simulations performance: 2005, Sep 06 Evaluation of deterministic simulations performance: 2005, Sep 06

Wrong Maximum Location - Rainfall Underestimation – Different space-time scalesWrong Maximum Location - Rainfall Underestimation – Different space-time scales4&+&7&((6!&(!3/[!,-88[!7O*)6((&1!(0!\CY

]*7&+M&1!+365.3//

:&702^!+365.3//

SF8/(5/*48/5/(-':

9-7+13'/*48/5/(-':

Small scale max ~ 500 yrs3-4 hours / 0-100 km2

Large scale max ~ 300 yrs7-10 hours / 0-30 km2

Small scale max ~ 50 yrs3-6 hours / 0-50 km2

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Deterministic simulation performance: 2003, Dec 03 Deterministic simulation performance: 2003, Dec 03

4&+&7&((6!&(!3/[!,-88[!7O*)6((&1!(0!\3Y

]*7&+M&1!+365.3//

:&702^!+365.3//

SF8/(5/*48/5/(-':

9-7+13'/*48/5/(-':

Maximum Severity: ~500 yrs

Time scale: 9-14 hSpatial scale: 0-200 km2

Maximum Severity: ~500 yrs

Time scale: 14-18 hSpatial scale: 200-500 km2

Severity: an effective multiscale diagnostic

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=R"#$

Evaluation of ensemble simulations variabilityEvaluation of ensemble simulations variability

Determine the variability of the members

.)*/1N4OUS.B4S6/(3'-)&314.)*/14G.W'W)Qb(3&,/H

Z()F3F-1-8'-,4/&8/7F1/N42()+64)M48-7+13'-)&84)F'3-&/*46/('+(F-&2

F56(63/!40516(6057

_0O513+`!40516(6057

?KK@a4D)5/7F/(4K!49')(7

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=@"#$

Application: effect of initial conditions Application: effect of initial conditions

Space-time scales OK, LOW magnitude

S^9BUCB;9BCBU%E_

ZBUES^9QOUS.Bi!

ZBUES^9QOUS.Bi?

ZBUES^9QOUS.Bi$

ZBUES^9QOUS.Bi=

ZBUES^9QOUS.Bi#

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=J"#$

Université de GrenoblePART IVPART IV

CONCLUSION AND CONCLUSION AND PERSPECTIVESPERSPECTIVES

ConclusionConclusion

.O%D4UB9YVE9N

!4j+3&'-M-,3'-)&4)M4'0/4/(()(4,)77-''/*4F:4(3-&423+2/4*/5-,/8

!4j+3&'-M-,3'-)&4)M4'0/4+&*/(/8'-73'-)&4*+/4')4863'-314+&*/(83761-&24

!4O88/887/&'4)M4'0/4'3-14F/035-)(4)M4(3-&M3114/L'(/7/84

!4U/2-)&31-k3'-)&4)M46)-&'"863'-314(3-&M3114/L'(/7/84G%;bhOUb4,+(5/8H

!4;/'/,'-)&4)M4'0/4863'-3143&*4'/76)(3148,31/Q-&53(-3&,/4(3&2/8

!4B8'-73'/4'0/4732&-'+*/4)M40/35:4(3-&M3114/5/&'84-&4'0/4<W5/&&/84(/2-)&

!4B531+3'-)&4)M46/(M)(73&,/4)M47/'/)()1)2-,3147)*/18

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#!"#$

PerspectivesPerspectives

!4U3-&423+2/4+&,/('3-&'-/8N4*/8-2&4)M434&/c4(3-&423+2/4&/'c)(P4GX:./g46()l/,'H

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#?"#$

;-88/7-&3'-)&4)M434(-2)()+84M(37/c)(P4-&4c0-,04

6(/,-6-'3'-)&4-848//&438434,)&'-&++74)M48,31/8

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#$"#$

<3&4c/43661:4'0-84M(37/c)(P4')4)'0/(453(-3F1/8]

Université de GrenobleUniversité de GrenobleUniversité de Grenoble

EXODm4_SYn

;-88/7-&3'-)&4)M434(-2)()+84M(37/c)(P4-&4c0-,04

(3-&M3114-848//&438434,)&'-&++74)M48,31/8

%&'()*+,'-)&%&'()*+,'-)& ./'0)*)1)2-,314*/5/1)67/&'8./'0)*)1)2-,314*/5/1)67/&'8 9/5/(-':4;-32(3789/5/(-':4;-32(378 <)&,1+8-)&<)&,1+8-)&

%&'()*+,'-)&%&'()*+,'-)& ./'0)*)1)2-,314*/5/1)67/&'8./'0)*)1)2-,314*/5/1)67/&'8 9/5/(-':4;-32(3789/5/(-':4;-32(378 <)&,1+8-)&<)&,1+8-)&

;-88/7-&3'-)&4)M434(-2)()+84M(37/c)(P4-&4c0-,04

(3-&M3114-848//&438434,)&'-&++74)M48,31/8

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