workshop flood impacts observation - so-ii

Workshop Flood Impacts Observation 5-7 November 2019, Montpellier

Katerina Papagiannaki

National Observatory of Athens

Institute for Environmental Research & Sustainable Development

Spatial analysis of high-impact flood events based on the number of fire-service operations.

Identifying flood triggering rainfall thresholds.

NOA is the first research Institution created in Greece in 1842

1. Institute for Environmental

Research and Sustainable

Development (IERSD):

Meteorology & numerical weather

prediction, climatology, physics of the

atmospheric environment and solar

and wind energy, hydrology & natural

resources management, air quality

and energy saving, climate and

climate change impacts. 2. Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing (IAASARS) 3. Institute of Geodynamics (GI)

METEO group Monitoring and forecasting of weather related natural disasters

Vassiliki Kotroni

Kostas Lagouvardos

Antonis Bezes….

Operate the largest network of surface

meteorological stations over Greece: 405 To address the needs of the:

•research community.

•sectors of the economy (agriculture, fishery,

construction, tourism, etc)

•contribute to citizens quality of life through

improved access to information

Data available οn-line updated every 10-min

Operational Weather Monitoring Networks

ZEUS VLF Lightning detection network since 2005. 6 sensors : • Spain • UK • Denmark • Romania • Cyprus • Egypt

Contribute in international campaigns.

Operational Weather Monitoring Networks

Satellite products: NWC-SAF

products, precipitation estimates, storm tracking, etc

Observatory of Crete 50 weather stations

2 PM10 stations

2 level meters and

2 snow stations

Operational Weather Monitoring Networks

Global Cryosphere Watch initiative of WMO

Observatory of Athens – Smart City 60 weather stations

Building a network of microsensors

Monitoring

of heat waves.

Monitoring precipitation events

and flood risk.

Operational Weather Monitoring Networks

Numerical weather prediction and operational activities

Provide tailor-made forecasts focusing

on severe weather events

Provide alerts Road network weather conditions

Numerical weather prediction and operational activities

Provide tailor-made forecasts focusing

on severe weather events

Provide weather alerts

Lightning forecasting

Numerical weather prediction and operational activities

Provide tailor-made forecasts focusing

on severe weather events

Provide weather alerts

Lightning forecasting

Dust forecasting

Numerical weather prediction and operational activities

Provide tailor-made forecasts focusing

on severe weather events

Provide weather alerts

Lightning forecasting

Dust forecasting

Wave forecasting

UV forecasting

www.meteo.gr 350.000 daily visitors 1st in terms of visits

among public sector sites

Among the 10 most visited sites in Greece

Numerical weather prediction and operational activities

meteonow app

Numerical weather prediction and operational activities

Since September 2016

4400 iphone downloads

7350 android downloads

6600 reports in 1 year

2015 Emergency response n = 800

Urban Area Response to Flash Flood–Triggering Rainfall, Featuring Human Behavioral

Factors: The Case of 22 October 2015 in Attica, Greece

Papagiannaki, K., Kotroni, V., Lagouvardos, K., Ruin, I., & Bezes, A.

Weather, Climate, and Society, 10.1175/wcas-d-16-0068.1.

2016 Drivers of precautionary behavior n = 1855

How awareness and confidence affect flood-risk precautionary behavior of Greek citizens:

the role of perceptual and emotional mechanisms

Papagiannaki, K., Kotroni, V., Lagouvardos, K., & Papagiannakis, G.

Nat. Hazards Earth Syst. Sci., doi:10.5194/nhess-19-1329-2019.

2019 Hydrogeological and Climatological Risks Perception in a Multi-

Hazard Environment n = 2330

Hydrogeological and Climatological Risks Perception in a Multi-Hazard Environment: The

Case of Greece

Papagiannaki, K, Diakakis, M., Kotroni, V., Lagouvardos, K., and Andreadakis, E.

Water, doi.org/10.3390/w11091770. ‘Special Issue Damaging Hydrogeological Events’.

meteo.gr: On-line survey questionnaires

Forest Fires: A rapid-response fire spread system IRIS

IRIS was developed in the frame of

DISARM project with the aim to

support operational fire suppression activities of the Greek Fire Service: • It is based on WRF-SFRIRE fire-

atmosphere modeling system • Prototype fuel models have been

developed and adapted to correctly represent the Greek territory

• The system is fully automatized.

Societal impacts of weather related natural disasters

1. Database of high impact weather

events and related natural disasters

since 2000: including

>450 severe weather events

>300 flash floods

Especially for flash floods the

database goes back to 1980-

today.

Systematically updated

Analysis of risk and vulnerability of

areas

Contribution to FLOOD-HyMeX db Floods & Flash-floods,

wind storms, hail,

tornados, snow & frosts, lightning, heat waves

https://www.meteo.gr/weatherEvents.cfm

Societal impacts of weather related natural disasters

1. Database of high impact weather

events and related natural disasters

since 2000: including

>450 severe weather events

>300 flash floods

2. Database of Flood Fatalities

1980-today:

Contribution to MEFF & EUFF

databases

https://www.meteo.gr/weatherEvents.cfm

Floods & Flash-floods,

wind storms, hail,

tornados, snow & frosts, lightning, heat waves

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Hydro-meteorological events & societal impact analysis

Underlying goals

To monitor and keep consistent records of events

To understand risks and highlight the reasons behind the ineffective response of communities to weather-related hazards

To contribute to more accurately informing the public and the authorities about potential impact due to weather-related hazards

To address the issues of warning & access to risk information and knowledge

The Fire-service operations as a flash-flood impact indicator

Positives Constraints

Consistent indicator of flood impact: all calls/operations are consistently

recorded

Impact types, not always known: flooded/damaged buildings/infrastructure, rescues, citizens trapped in vehicles/roads

Positives Constraints

Consistent indicator of flood impact: all calls/operations are consistently

recorded

Impact types, not always known: flooded/damaged buildings/infrastructure, rescues, citizens trapped in vehicles/roads

Data is accessible Long-term data at municipality level

The Fire-service operations as a flash-flood impact indicator

Positives Constraints

Consistent indicator of flood impact: all calls/operations are consistently

recorded

Impact types, not always known: flooded/damaged buildings/infrastructure, rescues, citizens trapped in vehicles/roads

Data is accessible Long-term data at municipality level

Dynamic indicator Exact spatio-temporal data is available after strictly formal and time-consuming procedure (personal data protection)

The Fire-service operations as a flash-flood impact indicator

Positives Constraints

Consistent indicator of flood impact: all calls/operations are consistently

recorded

Impact types, not always known: flooded/damaged buildings/infrastructure, rescues, citizens trapped in vehicles/roads

Data is accessible Long-term data at municipality level

Dynamic indicator Exact spatio-temporal data is available after strictly formal and time-consuming procedure (personal data protection)

Performs well as an alternative measure of material damage: • significant correlations with rainfall • rainfall thresholds can be defined

High geographical resolution is required to capture local vulnerabilities

The Fire-service operations as a flash-flood impact indicator

Fire Service

operations

2012-2018

Operations /

population

Operations /

population

density

Damaging flash-floods

2012-2018

Attica: most frequently affected

High-impact weather events database

Papagiannaki et al., 2013. 10.5194/nhess-13-727-2013

Target Area:

Greater Athens area

analysis at municipality level

0-10

10-20

20-30

30-40

40-50

0-400

400-800

800-1200

1200-1600

1600-2000

Complementary studies about flash-flood triggering rainfall hazard vs impact

Case study 1: Rainfall thresholds of damaging flash flood events in urban areas of Greater Athens

Papagiannaki, K.; Lagouvardos, K.; Kotroni, V.; Bezes, A. Flash flood occurrence and relation to the rainfall hazard in a highly urbanized area. Nat. Hazards Earth Syst. Sci. 2015. 10.5194/nhess-15-1859-2015

Case study 2: Greater Athens urban areas’ response to an emergency flash-flood event

Papagiannaki, K.; Kotroni, V.; Lagouvardos, K.; Ruin, Bezes, A. Urban Area Response to Flash Flood–Triggering Rainfall, Featuring Human Behavioral Factors: The Case of 22 October 2015 in Attica, Greece. Weather, Climate, and Society 2017. 10.1175/wcas-d-16-0068.1

26

Population density

10-year (2005–2014) flash flood events with > 10 fire-service operations 50 events, 3500 operations

Most affected sub-area: Athens city (17,000 inh/km2)

Case study 1 Target area: Greater Athens area

Objectives

1. Define and assess flash flood hazard & impact indicators

what is the relationship between hazard (rainfall) and impact (fire service operations)?

2. Identify triggering rainfall intensity thresholds at a local level

how reliable these thresholds are?

27

28

Division of Greater Athens into 15 sub-areas, each one surrounded by representative meteo-stations

Max precipitation

Peak rainfall intensity

10-min, 30-min, 60-min

2-h, 3-h,12-h, 24-h

Meteo-group datasets

Analysis: 15 sub-areas

Stations & division of target area in 15 sub-areas

Frequency of flash-flood events

29

35% of events: 10 mm ≤ R10 < 15 mm Impact intensity: high for R10 > 10 mm

(>120 oper./event on average)

0

5

10

15

20

25

25 50 75 100

Max

10

min

rai

n -

R1

0 (

mm

)

Fraction of data (% of total events)

max R 10min

0

200

400

600

800

1000

1200

1400

0

4

8

12

16

20

R10 ranges (mm)

Number of events Operations Operations/event (av.)

50% of events: 30 mm ≤ R24 < 60 mm Impact intensity: high for R24 > 60 mm

(>160 oper./event on average)

0

50

100

150

200

25 50 75 100

Max

24

h r

ain

- R

24

(m

m)

Fraction of data (% of total events)

max R 24h

0

200

400

600

800

1000

1200

0

5

10

15

20

25

30

R24 ranges (mm)

Number of events Operations Operations/event (av.)

13 mm

187 mm

2.5 mm

21 mm

30

Differences in local vulnerability tend to become smoother as the accumulation period increases and the various small-scale intensities are normalized.

1. Entire target area Spearman correlation * p < .05, ** p < .01, *** p < .001 2

34

56

7

oopera

tions (

ln)

0 50 100 150 200

R24

23

45

67

opera

tions (

ln)

0 20 40 60 80

R60

23

45

67

opera

tio

ns (

ln)

0 5 10 15 20

R10

ρ=0.61*** ρ=0.40** ρ=0.31*

R 24h R 60min R 10min

2. Athens city (events that affected the south, east and centre together)

23

45

67

opera

tions (

ln)

20 40 60 80 100

R24

ρ=0.85**

23

45

67

opera

tions (

ln)

10 20 30 40 50

R60

ρ=0.85**

23

45

67

opera

tions (

ln)

8 10 12 14 16

R10

ρ=0.69**

31

23

45

67

opera

tions (

ln)

20 40 60 80 100

R24

ρ=0.85**

23

45

67

opera

tions (

ln)

10 20 30 40 50

R60

ρ=0.85**

23

45

67

opera

tions (

ln)

8 10 12 14 16

R10

ρ=0.69**

Analysis on a more local scale better captured the rainfall effect. Short-duration rain a good indicator – sufficient network of rain gauges is required

2. Athens city (events that affected the south, east and centre together)

32

Data: entire time series of precipitation records (not only of the flood events) for different accumulation durations Flash flood occurrence is highlighted in red

no flooding

always flooding

both

Peak rainfall intensity of various time intervals against their respective durations thresholds triggering flood (>10 operations)

The case of October 22, 2015:

Urban areas response to flash flood-triggering rainfall

featuring human behavioural factors

picture: Attica, October 22, 2015

Case study 2

The rainfall episode that affected Attica on 22 October 2015.

Among the most catastrophic weather

events that affected Attica or Greece in the

past 15 years

1300 emergency calls to the Fire

Service:

i. cause: water extraction, fallen tree, car accident, human or animal trapped)

ii. exact time iii. location of the reported problem

4 human fatalities

Methods: 2 approaches to understand the response of 4 urban subareas to the rainfall episode that affected Attica on 22 October 2015.

1. Fire-service operations as a dynamic

impact indicator

Time and rain accumulation needed for

damaging flood occurrence

2. Individuals’ awareness

on-line behavioral survey questionnaire launched at www.meteo.gr: 800 responses in 5 days ! Questionnaire: in collaboration with Centre National de la Recherche Scientifique (CNRS), Grenoble

BLUE: Calls

RED: Questionnaire respondents’ location at the time they witnessed damage/problem

NW

W

NE

E

36

Low-impact threshold: 37 mm (NE) – 61 mm (NW), in the expected range (Papagiannaki et al., 2015)

High-impact threshold: 70 mm (NE) – 100 mm (NW)

10-min rain intensity when sudden increase of 10-min calls occurred: 53 mm/h (E) - 112 mm/h (NE) (high prob. for damage) – more indicative of the overall impact magnitude.

Significant correlation 10-min calls – accum.rain: rho from 0.5 (E) to 0.7 (NW)

10-min Rainfall – Calls Rain records from station with highest daily accumulated rainfall in the sub-area

NW 131 mm 399 calls

NE 102 mm 443 calls

W 105 mm 201 calls

E 70 mm 102 calls

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Citizens awareness

How do they perceive/feel the peak of rainfall?

05

01

00

acc

umu

late

d ra

in

0 1 2 3 4 5

level of worry

050

100

accu

mul

ated

rai

n

0 1 2 3 4 5risk perception (0-5: rate of rain severity)

Level of severity perception vs rain

Spearman’s p = 0.26*** Spearman’s p = 0.25***

Level of worry vs rain

Significant worry (levels 4-5) > 60 mm (high-impact threshold

for the Greater Athens area)

More worried more adjustment to scheduled activities

(rho=0.35, p<0.001)

More affected sub-area more worry (rho=0.20, p<0.001)

More alerted less fear / worry (rho=0.16, p<0.001)

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Significant correlation between flash-flood triggering rainfall hazard and impact (fire service operations).

Reliability of flood triggering rainfall thresholds depends a lot on the representativity of the existing rain gauge network in terms of density, location and record length.

Rainfall in short time intervals is proven a good indicator of the induced impact when the analysis is performed on a more local scale.

Citizens’ awareness & coping responses during crisis are affected by the level of rainfall severity & their alert status.

Conclusions

1. Flash flood triggering rainfall thresholds, with supervised machine

learning technics

impact indicators: fire service operations

vulnerability/exposure population, DEM, slope, land-use

2. Project about weather-related risk assessment

impact measured by insurance losses

National funding, in cooperation with Insurance Company

INTERAMERICAN, to ‘estimate and map weather risk and vulnerability in

high spatial analysis…’

Currently…