Paper presented at the 33rd IAHR 2009 Congress – Water Engineering for a sustainable
environment. 9 – 14 August 2009. Vancouver, British Columbia.
Social Flood Impacts in Urban Areas: Integration of Detailed Flow Modelling
and Social Analysis
J. Ernst1, I. Coninx2, B.J. Dewals1,3, S. Detrembleur1, S. Erpicum1,
K. Bachus2 & M. Pirotton1
1
Unit of Hydrology, Applied Hydrodynamics and Hydraulic Constructions (HACH),
ArGEnCo, University of Liege, Chemin des Chevreuils 1 B52/3+1, 4000 Liege,
Belgium; email: j.ernst@ulg.ac.be
2
Research group Environmental Policy and Sustainable Development, HIVA,
Katholieke Universiteit Leuven, Parkstraat 47 3000 Leuven, Belgium; email:
ingrid.coninx@hiva.kuleuven.be
3
Postdoctoral Researcher of the Belgian Fund for Scientific Research (F.R.S.-FNRS)
ABSTRACT
Social flood risk management is currently shifting from the full protection against
flooding towards the management of the consequences of flooding. This new
approach requests an interdisciplinary collaboration of scientists from hydraulic and
social sciences, as presented in this paper. The research teams have developed a
methodology to evaluate socio-economic consequences of flooding based on the
analysis at a micro-scale of the exposure and the vulnerability of elements-at-risk and
the adaptive capacity of society. The hydraulic simulations have been conducted by
means of 2D flow modelling run on a highly accurate Digital Surface Model (DSM).
The methodology have been applied to evaluate flood protection measures along the
main tributary of river Meuse in Belgium.
INTRODUCTION
Floods cause social impacts since the event changes the way people live and relate to
each other (Burdge 1998). Although several studies have demonstrated that social
impacts may not be ignored (Grinwis and Duyck 2000; Werritty 2007), they are
rarely considered in the evaluation of policy measures. In contrast, the goal of the
present research is to account for these social impacts. Moreover, while most of
social risk analyses are undertaken at a macro- or meso-scale, the present paper
describes an original micro-scale analysis.
Three types of flood measures can be used while addressing social flood risks:
measures directly limiting the flood characteristics (e.g. dikes), measures addressing
the exposure of houses (e.g. ban on living in flood risk areas) and measures
addressing the vulnerability of people and the adaptive capacity of the society (e.g.
flood warning, emergency plan, risk communication). Therefore, the proposed
methodology enables to assess the influence of various structural and non-structural
flood protection measures on each of the three components of social flood risk,
namely the exposure, the vulnerability of people and the societal adaptive capacity.
OVERVIEW OF THE METHODOLOGY
The methodology of social flood risk evaluation described in the present paper is
summarized in Figure 1Error! Reference source not found.. It combines
contributions from hydraulic, geomatic and social sciences approaches. The main
components of such social flood risk analysis are the flood characteristics (including
water depth, flow velocity, water rising rate and flood duration), the exposure of the
people-at-risk and the vulnerability of the people as well as the adaptive capacity of
the society facing the flood. Once outputs from hydraulic, geomatic and social
analyses are available, they are combined in the social impact analysis module,
which eventually leads to a social risk index.
Firstly, the inundation modelling starts from the river statistical discharges to enable
the intermediate step of mapping inundation characteristics obtained from twodimensional hydraulic modelling. The presently used flow model WOLF, which is
developed by HACH-ULg, enables to conduct flow simulations in complex
topographies such highly urbanized areas. The second step, namely the geomatic
analysis, consists in the identification of the elements-at-risk and the determination
of the value of the flow parameters affecting each of them. Finally, the third step is
the psycho-social analysis developed by the sociologists from HIVA-KUL, which
leads to the quantification of vulnerability and adaptive capacity of people at risk.
This study is based on data from the most recent Belgian national socio-economic
population survey, which was conducted in 2001, as well as on data collected by
means of interviews and documents analysis.
Since the social risk evaluation procedure is undertaken here at a micro-scale, each
house endangered by flooding is studied individually. Consistently with the flow
modelling performed on a 2m by 2m grid, the overall methodology relies on several
sources of highly accurate geographic information datasets.
In the following paragraphs, the method developed for quantifying and combining
the parameters influencing the social risk is described in detail. Then, the overall
methodology is applied for a case study, involving the assessment of the social
avoided risk for two specific flood protection measures, namely the activation of
passive floodplains and the increase in adaptive capacity.
Treatments
Hydrological statistics
Socio economic data (INS)
2D HYDRAULIC MODELLING
Inundation characteristics
(i) extent, (ii) water depth and (iii) flow velocity
PSYCHO SOCIAL ANALYSIS
3 driving
forces
GEOMATIC ANALYSIS
Flood-Exposure Index
Elements-at-risk as a function of the probability
SOCIAL IMPACT ANALYSIS
Adaptive capacity
index
Social flood
vulnerability index
SOCIAL RISK
Figure 1. Flow of data within the social flood risk analysis methodology.
INUNDATION MODELLING
Prior to running flow simulations, boundary conditions have to be set by means of a
hydrologic analysis of the catchment behaviour. This study also leads to the
exceedance probability associated to a given discharge, based on the statistical
analysis of the river flow time series (expressed in terms of a return period in years).
Since interactions between the main channel and the floodplain of rivers during
flooding are important and may not be neglected (McMillan and Brasington 2008),
the assumption of one dimensional modelling would not be acceptable in many cases
involving complex floodplain geometries, in particular in urbanized areas. Therefore,
the present study is based on the detailed two-dimensional flow model WOLF 2D,
solving the depth-averaged equations of volume and momentum conservation, i.e.
the “shallow-water” equations. Very accurate Light Detection And Ranging (LiDAR)
topographic data are used, obtained from an airborne laser remote sensing. They are
characterized by a horizontal resolution of 1 by 1 meter and an elevation accuracy of
15 centimetres.
The bottom friction is conventionally modelled thanks to an empirical law, such as
the Manning formula. The model enables the definition of a spatially distributed
roughness coefficient. The internal friction may be reproduced by different
turbulence closures included in the modelling system, from simple algebraic ones to
a complete depth-averaged k- model.
The space discretization is performed by means of a finite volume scheme. Variable
reconstruction at cells interfaces is performed linearly, in combination with slope
limiting, leading to a second-order spatial accuracy. The advective fluxes are
computed by a Flux Vector Splitting (FVS) technique developed by HACH-ULg.
The diffusive fluxes are legitimately evaluated by means of a centred scheme. Since
the model is applied to compute steady-state solutions, the time integration is
performed by means of a 3-step first order accurate Runge-Kutta algorithm,
providing adequate dissipation in time. The time step is constrained by the CourantFriedrichs-Levy (CFL) condition based on gravity waves. A semi-implicit treatment
of the bottom friction term is used, without requiring additional computational cost.
The model has been extensively validated and has shown its efficiency for numerous
practical applications (Dewals et al. 2006; Dewals et al. 2008b; Dewals et al. 2008c;
Erpicum et al. 2009a; Erpicum et al. 2009b; Roger et al. 2009), including flood
modelling (Dewals et al. 2008a; Erpicum et al. 2008).
EXPOSURE ANALYSIS
In flood risk analysis, the exposure is the identification of the elements-at-risk and
the determination of corresponding flood characteristics (Ernst et al. 2008b). In the
case of a micro-scale flood risk analysis, each element-at-risk threatened by flooding
is taken into account individually. In this context, the procedure has been developed
following an “object oriented” approach, as detailed below.
In social risk analysis, the elements-at-risk are the local community, the people
facing the flood. In order to compute the exposure of these elements-at-risk, four
very accurate GIS datasets are available (Ernst et al. 2008a):
 LiDAR data, DSM including obstacles relevant to the flow (raster files);
 flood maps, resulting from two-dimensional flow modelling (raster files);
 land use and building type database, called PICC, from the “SPW” (Shapefiles);
 statistical sectors containing mainly socio-economic information at meso-scale
with a mean area of about 0.25 to 1km² (ESRI Shapefiles).
Among these datasets, none of them contains information about people location in
the area at risk. But, the combination of these datasets and the assumption that the
number of people living in each residential building in a given statistical sector
remains the same leads to an estimation of the location of people at risk.
The first operation of the procedure consists in the conversion of datasets into raster
format with the same resolution and geographical references as the flood maps and
the LiDAR model. A new raster file, called building-ID, is created based on the PICC
information. The file locates and assigns a unique identifier to each residential
building. Jointly, an attribute value file is also created. The link between the attribute
value file and the raster file is made by the building-ID. All information about these
buildings are subsequently stored in the attribute text file, such as street name and
postal number obtained from the PICC database. To evaluate the number of people
living in each house, the procedure relies on the following sub-steps: first, the
number of residential buildings in each statistical sector is evaluated; then, since the
number of people living in the sector is known, the average number of people living
in each residential building can be estimated and recorded in the attribute value file.
Since the two-dimensional flow model is run on a LiDAR DSM including obstacles
relevant to the flow, the flow characteristics are not known inside “over grounded”
structures and have thus to be interpolated. Two methods are used in this process in
order to work out respectively the water level and the velocity inside each building.
For the water level, the free surface (from the flood maps) and the ground level (from
the LiDAR model) are linearly interpolated inside the building and the average value
of the difference between these two plans is assigned to the object as the
representative water depth in the attribute table. If the number of wet cells around a
building is lower than three, the average water depth in these cells is allocated to the
corresponding house attribute. As regards the velocity, the most representative value
is the maximum value computed around the house.
Once the elements-at-risk are identified and the flood parameters computed, the
exposure index F can be evaluated (Coninx and Bachus 2008):
F  0.45 WD  0.22 WR  0.22 V  0.11 D ,
(1)
where WD = water depth score, WR = water rise score, V = velocity score and
D = duration score. Each of these scores is binary valued (0 or 1) based on a
threshold value. As regards the water depth, the threshold value is 0.3m. For the
velocity, the threshold value is 2 ms-1, 12 hours for the duration (Penning-Rowsell et
al. 2003), whereas the water rising rate has to be appreciated as slow or sudden in
comparison with warning and evacuation time. For the case study on river Ourthe
(see hereafter), the typical floods may be considered as slow and long. Hence, WR
and D scores were set respectively to 0 and 1.
SOCIAL RISK EVALUATION
Vulnerability of individuals and adaptive capacity of the society are related to each
other in the way that increased adaptive capacity will reduce the actual vulnerability
of the people. According to Blaikie and Cannon (1994), vulnerable people are
individuals that are unable to cope with the effects of flooding due to social,
economic, political and cultural attributes. Those people will experience more severe
social flood impacts. However, the society may have organized mechanisms to
alleviate the flood impacts for those people. To be effective, these mechanisms have
to be organized at society level such as social networks, economic mechanisms (e.g.
savings or available governmental budget), institutional mechanisms (e.g. collective
action to reduce vulnerability), technological and non-technological mechanisms that
prevent or reduce disaster impact (Bouwer and Vellinga 2007).
Vulnerability of people is measured by the social flood vulnerability index. Since
privacy arguments restrict the availability of data at the individual level, data at the
level of the districts have been used. The selected indicators in the social flood
vulnerability index are proxies of the vulnerable social groups (Cutter et al. 2003;
Tapsell et al. 2002). The present analysis takes into account the ratio of (i) elderly,
(ii) ill people, (iii) lone parents, (iv) foreigners, (v) financially deprived people and
(vi) people living in one-storey houses, as vulnerability indicators:
n
I S  1   1  X i  i ,
W
with
Wi > 0
for
i = 1, … n
(2)
i 1
where Xi = social vulnerability indicator, Wi = weights and IS = social flood
vulnerability index. This index is a number between 0 and 1 and enables relative
comparisons. The higher the figure, the larger the proportion of vulnerable people.
The adaptive capacity is analyzed by an analytical framework, developed and
applied based on literature reviewing, policy documents and in collaboration with
public officers. Eight variables are classified: non-technological measures,
availability and distribution of resources, institutional structure and capacity, social
capital, risk spreading instruments, information, public perception. By scoring the
indicators, a total adaptive capacity score is received.
To estimate the social flood impacts for those people who are exposed to floods, the
three indices representing flood characteristics, vulnerability and adaptive capacity
are combined as follows (Coninx and Bachus 2008):
SI  0.5 F  0.25 V  0.25 AC
(3)
with SI = social impact, F = flood characteristics index, V = vulnerability index and
AC = adaptive capacity index. The social impacts is considered as low, medium or
high for calculated values of SI respectively in the ranges [0,0.33], [0.33,0.66] and
[0.66,1]. These indices are computed for each residential building and the values are
recorded in the attribute file. The result is a figure between 0 and 1 for each
residential building. In this way, it is possible to identify social hotspot areas, which
are areas where people are expected to be affected by medium and high social
impacts. The social flood impacts are expressed as the number of people that are
living in low, medium or high social flood impact areas.
CASE STUDY
The case study is located in the lower part of river Ourthe (Belgium), the basin of
which has a catchment area of about 2900 km2 at the downstream limit of the case
study. Two towns are located along the studied part of the river: the town of Esneux
and the town of Tilff. Concerning the hydraulic modelling, the case study is located
in the Ardennes massif, where the valleys are quite narrow and the water storage in
the inundated floodplain is very low. Thus, the steady state assumption is valid.
The risk analysis was applied on an area that covers three reaches of the river Ourthe
located respectively 18.5 km (reach n°1), 12.5 km (reach n°2) and 10 km (reach n°3)
upstream of the mouth of river Ourthe into river Meuse. The total length of the
simulated reaches is about 16 km with a computational Cartesian grid of 2 by 2
meters, leading to 8.2 × 105 computational cells.
For this case study, the most significant factors influencing vulnerability are the high
proportion of single parents and deprived people.
Two flood protection measures have been considered: 1) the activation of passive
floodplain and 2) the implementation of adaptive capacity increasing measures (e.g.
emergency plan, flood warning, floodproof buildings). The first herein described
example of flood protection measure consists in transforming non-urbanized passive
floodplains into active ones. Passive floodplain refers to an inundation area in which
flow velocity remains extremely small. As shown in Figure 2, the activation of a
passive floodplain consists in modifying the topography to enable the development
of higher flow velocities in the floodplain. As a result, the effective width (and thus
section) of the river is increased and, consequently, the water level upstream is
reduced (by 50 cm in the example shown in Figure 2 b.).
The results of the social risk evaluation in terms of number of people threatened by
flooding in each class of social impacts are presented in Figure 3. Graph a. illustrates
the results of the activation of a passive floodplain. Graph b. shows the influence on
social impact of the combination of this structural measure with a non-structural one,
i.e. an increase of the adaptive capacity of people threatened by flood. This measure
might be put in place by perfecting an emergency planning or introducing flood
proof buildings for example.
In Figure 3, the lower class of social risk is plotted in grey and the middle class in
black (there are no affected people in the higher social impacts class). Considering
both cases, the dotted curves plot the current situation, i.e. without adaptation
measure, and the situation with adaptation measure is plotted in continuous lines.
The comparison of the current situation with the activated floodplains (Figure 3 a.)
shows a significant effectiveness for all the modelled discharges mainly for the total
number of people (reduction of inundation extent). Furthermore, the efficiency of the
measure increases with the discharge.
Contrary to the conclusions drawn for the first protection measure, the combination
of structural and non-structural measures leads, in addition, to significant changes in
the distribution between social impact classes (reduction of impact severity). But
since this non structural measure does not influence the flow, the total number of
people affected remains the same as for the first adaptation measure.
(a)
(b)
Figure 2. Numerical elevation model and velocity field near the activated
floodplain: (a) present situation with passive floodplain, (b) activated floodplain.
Figure 3. Impact of protection measures on social risk: (left) activated
floodplain, (right) floodplain activation and increase in adaptive capacity.
CONLCUSION
The procedure presented in this paper constitutes a genuine risk modelling chain
handling the whole flow process from statistical analysis of river time series to the
social risk evaluation including the flow modelling. The risk modelling system also
provides practical support for selecting and designing most appropriate protection
measures.
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