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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
19
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
37
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)
38
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…