1. DIFFERENTIATION OF THE MUNICIPALITIES OF INTEREST
Analysis of social vulnerability to flash floods in urban areas of Castilla y León (Spain)
Estefanía Aroca-Jiménez (1), José María Bodoque (1), Juan Antonio García (2) and Andrés Díez-Herrero (3)
(1) Mining and Geological Engineering Department University of Castilla-La Mancha, Toledo, Spain
(2) Business Management Department, University of Castilla-La Mancha, Talavera de la Reina, Spain
(3) Geological Survey of Spain, Madrid, Spain
Fotos: 1. Merindad de Sotoscueva
2. Navaluenga
3. Ponferrada
4. Tubilla del Agua
Introduction
Methodology
A flash-flood is a flood caused by a sudden increase of the river stream flow usually produced by heavy rainfall concentrated in a short period of time. The short duration together with the lack of knowledge in this area cause that flash-floods are considered as one of the more destructive hazards and with the greatest capacity to generate risk.
High number of casualties, growing economic losses and the increasing frequency of extreme events do that a change in the assessment and management of flood risks be necessary.
DIFFERENTIATION OF THE MUNICIPALITIES OF INTEREST
Longitudinal slope > 0.01
Areas where potential significant flash flood risk exists
Areas with low or exceptional probability of flooding
DATABASE GENERATION
Initial characterisation of 81 variables
DATA ANALYSIS
Hierarchical Segmentation
Factor Analysis
Latent Class Analysis
Results
Aim
Factor
Description
Factor Name 1
Total of inhabitants
Health facilities
Hospital beds
Health staff
Nurseries
Elementary schools
Secondary schools
Homes for elderly people
Tourist accommodations
Total social exposure 2
Population per settlement area
Vacant houses
Total above ground built-up area
Total underground built-up area
Main houses
Exposure in the urban built environment 3
Households in good conditions
Households in bad conditions
Constructive resilience 4
Households with 1 story a.g.l.(*) and/or some story b.g.l.(**)
Households with 2 or more stories a.g.l.
Constructive exposure 5
Population below age 5
Population from age 5 to 14
Population projection age 0-4 for 2025
Population projection age 5-14 for 2025
Youth social vulnerability 6
Population from age 15 to 65
Population projection age for 2025
Mature social resilience 7
Unemployment rate
Long-term unemployed
Dwellings with no unemployed or inactive people
Labour social vulnerability 8
Population age 65 and older
Population projection age 65 and older for 2025
Men dependency ratio
Women dependency ratio
Households where a person age 65 or older live
Illiterate people
Social vulnerability due to dependency 9
Total spending per inhabitant
Budget destined for municipality per capita
Economic resilience due to investments 10
Distance to the nearest hospital
Time to the nearest hospital
Hospital social vulnerability 11
Distance to the nearest health centre
Time to the nearest health centre
Health social vulnerability 12
Areas suited for evacuation
Social resilience due to evacuation
The gathering of different variables able to reflect the social vulnerability to flash floods and the identification of vulnerability factors within this information in order to improve the current flood risks management.
Study area
This research has been performed in the region of Castilla y León, in the northwest of Spain. This region has an extension of 94,226 km2, being the widest of the whole territory of Spain.
Table 1. List of the twelve factors obtained by Factor Analysis of variables, which make up the subsequent Social Vulnerability Index.
(*) a.g.l.: above ground level
(**) b.g.l.: below ground level
Rivers
Municipalities of interest
Areas with low or exceptional probability of flooding
Conclusions
The Social Vulnerability Index obtained shows the large differences and the high spatial heterogeneity in the vulnerability component through the study area.
The Latent Class Analysis has enabled to group the municipalities of interest, which can be translate into a customization of flood risk reduction strategies carried out by the competent authorities.
Figure 2. Social Vulnerability Index for the municipalities of interest. Vulnerability categories coincide with the 25th, 50th and 75th percentiles of the table of frequencies values. The number of clusters was defined by Latent Class Analysis of factors scores of the municipalities of interest.
Figure 1. Dendrogram obtained by Hierchical Segmentation of the initial variables considered, where five clusters are differentiated.
ACKNOWLEDGEMENTS: This research has been funded by the project MARCoNI (MINECO, CGL R)