2nd World BOSAI Forum/International Disaster Risk Conference 2019spin disaster knowledge to wave BOSAI wisdom
Sendai, 09-12 November 2019

A social-ecological approach to disaster risk management
applied to the case study of the Marche Region, Italy
A.Colocci, E. Gioia, F. Marincioni

speaker: Alessandra Colocci
D3, CIVIL AND ENVIRONMENTAL PROTECTION
advisor: Prof. Fausto Marincioni
Department of Life and Environmental Sciences

visiting researcher
SOCIAL SYSTEMS FOR DISASTER RISK GOVERNANCE
Disaster Prevention Research InstituteCRITICAL ISSUES
 THEORETICAL ISSUES:•need to consider complex human-natural systems•

need to merge disaster resilience and environmental sustainability agendas

 ASSESSMENT ISSUES:•

no commonly accepted tool•disaster metrics not included

 HOW CAN THIS INFORM LOCAL POLICIES?

THEORETICALBACKGROUNDPANARCHY MODEL – prof. C. S. Holling

INTERACTIONSADAPTIVE CYCLE

conservation
reorganisation

revoltremember

exploitation

releaseMODELLING SOCIAL-ECOLOGICAL SYSTEMSNATURAL ECOSYSTEMS CYCLE→ river drainage area

ANTHROPIC CYCLE→ urban area

NATURAL PROCESSES CYCLE→ river floodplain

Source: http://www.journey2050.com/what-is-a-watershed/INTERACTIONS IMPACTING
ON THE ANTHROPIC CYCLE
adaptation strategies

natural ecosystems cycle
land-use plans

rememberreverse-revolt
reverse-remember

anthropic cycle

revoltwater-cycle alterations

natural processes cycle

RESILIENCE
floodsINTERACTIONS IMPACTING
ON THE NATURAL CYCLES
renaturalisation

local maintenancenatural ecosystems cycle
reverse-remember

revoltrememberanthropic cycle

reverse-revolt

ecosystem alteration

natural processes cycle

SUSTAINABILITY
hydrological alterations

ASSESSMENT
FRAMEWORKMETHODOLOGY

1st PHASEindicators related to flood disaster 2nd PHASE

quantitative analysis

general significant indicators (from literature)cluster analysis

discriminant analysis

 3rd PHASE

MR-HS

questionnaires delivered to local communities
MR-MS

qualitative analysis
 4th PHASE

Promethee method,correlation analysis

comparison between quantitative and qualitative data
HR-MS

LR-LS

CASE STUDY – Marche Region, Italy

 Marche Region.

.



1 522 608 residents

9 401 km² area

229 municipalities.

 Time interval:
2008-2018

 Flood events:
2011 → 2015

1 st p h a s eclassification

1st PHASE – cluster analysis

attribute
LEARN

indicator
variation of population exposed to flood hazard
POP_FLOODRESILIENCEABSORB

grants for extraordinary and emergency interventions
EMERG_GRANTRECOVERratio of tax revenue after 2 years and on last flood's year
IRPEF_VAR

attribute
FUNCTIONSSUSTAINABILITY

indicator
variation of land take per total municipality area
LAND_TAKESERVICES

variation of clean water input
CLEAN_WATERINTEGRITY

number of species in inadequate or bad state
SPECIES_INADBAD

1st PHASE – cluster analysis mixed clustering procedure: hierarchical (Ward’s) method + non-hierarchical (k-means) method
 3 clusters:--

high resilience (HR)medium resilience (MR)low resilience (LR)high sustainability (HS)medium sustainability (MS)low sustainability (LS)centroids of final clusters

POP_FLOOD(learn)LAND_TAKE(functions)

EMERG_GRANT(absorb)

CLEAN_WATER(services)valuesvaluescentroids of final clusters

IRPEF_VAR(recover)Cluster

ValidMissing

12

127
86

316

SPECIES_INADBAD(integrity)

Cluster

ValidMissing

2290clusters

clusters
ANOVA

ANOVA
Z-score: POP_FLOOD

F
Sig.
46,567 ,000

Z-score: EMERG_GRANT

106,185,000

Z-score: IRPEF_VAR

122,460,000

Z-score: LAND_TAKE
Z-score: CLEAN_WATER
Z-score: SPECIES_INADBAD

F
Sig.
216,286 ,000
23,691 ,000
135,584 ,000

12315248292290

1st PHASE – cluster analysis

 RESILIENCE

Number of cases per cluster
Cluster
1127
286
316
Valid229
Missing0

 SUSTAINABILITY

Number of cases per cluster
Cluster
1152
248

329

Valid229

Missing01st PHASE – output
RESILIENCE & SUSTAINABILITY#881853159
258501121cluster
HR-HS
HR-MS
MR-HS
foreloop
MR-MS
MR-LS
LR-MR
LR-LS
LR-HS
HR-LS%
38,43%
7,86%
23,14%
69,43%
10,92%
3,49%
2,18%
0,00%
4,80%
9,17%

HOW CAN THIS INFORM LOCAL POLICIES?
 Identify criticalities and excellences within the territory (e.g. river basins)
#88
1853
159
25
8501121cluster
HR-HS
HR-MS
MR-HS
foreloop
MR-MS
MR-LS
LR-MR
LR-LS
LR-HS
HR-LS

%
38,43%
7,86%
23,14%
69,43%
10,92%
3,49%
2,18%
0,00%
4,80%
9,17%

2 nd p h a s echaracterisation

2nd PHASE – RESILIENCE
discriminant analysis
dimension

variablesourceimmigrants
DEMOGRAPHIC

SOCIALRESILIENCEECONOMIC

population over 65 years oldfemale population
population density
population with higher education or moreterritory with UWB internet accesspopulation involved in civic organisations
public revenues of civil protection organisations
volunteers expenditure of civil protection organisations
employment rate
per-capita incomesocial expenditure for poverty, social assistance, …
(present – resident population) / resident population

HEALTHmental health dischargesresidence and non-residence structures for elderly people
hospital staff
hospital beds
average time of arrival on place over the past 5 years
local expenditure for flood mitigation

INFRASTRUCTURAL

extension of municipal roads
wasted drinking water / produced drinking watermean building construction year

Italian National Institute of Statistics,
Ministry of Interior,
Italian National Firefighters Corps,
Regional Agency for Health2nd PHASE – RESILIENCE
discriminant analysis

EIGENVALUES
functioneigenvalue%variance%cumulative

WILKS LAMBDA
canon.
correlation

10,28173,573,50,469

20,102

26,5100,00,304

discriminant function 𝑫 = σ𝒏𝒊=𝟏 𝒄𝒊 ∙ 𝒗𝒊

function

Wilks Lambda

Chi-square

dfsig.

10,70873,266440,004

20,90820,569210,486

where:
𝑛 = number of considered variables
𝑐𝑖 = standardised coefficient of the 𝑖𝑡ℎ variable
𝑣𝑖 = value of the 𝑖𝑡ℎ variable

In this case:𝑫 = 0,551128*IMMIGR - 6,17363*POP_over65 + 7,57278*POP_FEM - 0,119824*DENSPOP - 0,597299*ISTR_SUP 0,25919*UWB_ACCESS - 1,31271*VOLONT + 0,44112*ENTRATE_ENTIPUBB_ASSPC 0,266433*USCITE_RIMB_VOLONT_ASSPC + 0,285924*TASSO_OCCUP_2011 + 0,680172*REDDITO_PROCAP +0,0611737*SPESA_SERVSOC_POV + 0,277892*POP_PRE_RES_PERC - 0,595948*DIST_PSIC_DIM +0,221489*STRUTT_NON_RES_NUM_COM + 0,942819*PL_TOT - 0,715504*PERS_COMP - 0,0548045*VVFF_TARR_MEDIO- 0,290362*INVEST_RIDRA - 0,324971*STRADE_COM + 0,061268*DISP_IDRO - 0,00811112*AVG_RESBUILD_AGE

HOW CAN THIS INFORM LOCAL POLICIES?
 Evaluate the effects of policies
 Monitor the state of the municipalities

WILKS LAMBDAEIGENVALUES
functioneigenvalue%variance%cumulative

canon.
correlation

10,28173,573,50,469

20,10226,5100,00,304

discriminant function 𝑫 = σ𝒏𝒊=𝟏 𝒄𝒊 ∙ 𝒗𝒊

function

Wilks Lambda

Chi-square

dfsig.

10,70873,266440,004

20,90820,569210,486

where:
𝑛 = number of considered variables
𝑐𝑖 = standardised coefficient of the 𝑖𝑡ℎ variable
𝑣𝑖 = value of the 𝑖𝑡ℎ variable

In this case:𝑫 = 0,551128*IMMIGR - 6,17363*POP_over65 + 7,57278*POP_FEM - 0,119824*DENSPOP - 0,597299*ISTR_SUP 0,25919*UWB_ACCESS - 1,31271*VOLONT + 0,44112*ENTRATE_ENTIPUBB_ASSPC 0,266433*USCITE_RIMB_VOLONT_ASSPC + 0,285924*TASSO_OCCUP_2011 + 0,680172*REDDITO_PROCAP +0,0611737*SPESA_SERVSOC_POV + 0,277892*POP_PRE_RES_PERC - 0,595948*DIST_PSIC_DIM +0,221489*STRUTT_NON_RES_NUM_COM + 0,942819*PL_TOT - 0,715504*PERS_COMP - 0,0548045*VVFF_TARR_MEDIO- 0,290362*INVEST_RIDRA - 0,324971*STRADE_COM + 0,061268*DISP_IDRO - 0,00811112*AVG_RESBUILD_AGE

3 rd p h a s elocalisation

3rd PHASE – questionnaires

Topic

ThemeRESILIENCE perception

Sub-theme Purpose

learn#Question

Answers

cluster

Effect of last flood on development path  reverse-remember interaction

1 Do you think that the last flood has
influenced the economic, social andinfrastructural development of yourmunicipality?

a. yes, it prompted initiatives tomake it safer

HRb. yes, it compromised itLR

c. noMRHOW CAN THIS INFORM LOCAL POLICIES ?
 Identify mismatches, raise awareness#88185325
8501121cluster
HR-HS
HR-MS
MR-HS
MR-MS
MR-LS
LR-MR
LR-LS
LR-HS
HR-LS

#?

cluster
HR-HS?

HR-MS?
?
?
?
?
?
?

MR-HS
MR-MS
MR-LS
LR-MR
LR-LS
LR-HS
HR-LS

thank you