Modelling Spatiotemporal Dynamics of Floods, Population Movement and Road Travel for Enhanced Risk Assessment Kate Rawlings Email:
[email protected] Twitter: @geogkate 1. Project Background
2. Research Objectives
Risk is often described as a function of hazard, exposure and vulnerability. In reality, risk varies over space (S) and time (T) and this affects the severity of impacts on surrounding populations.
For example, although most studies use census data when assessing exposure to hazards, this does not capture daily and seasonal population movement, which substantially changes the number of people exposed to a hazard at any given time.
Event 2
Event 3
Event 4
1 in 100-year flood extent
1 in 100-year flood extent 1 in 100-year flood extent
x
x
x
Night-time population, Road network
Road access
x
Summer Daytime x population, Road network Winter Daytime x population, Road network
Night-time population e.g children, elderly,
(Research Objective 3)
Evaluation of Case Study Results (Research Objective 3)
Hours
Road access Summer population 1500 people = e.g. tourists, 300 Vulnerable Road access 600 people Winter population, = 100 Vulnerable Road access
Daily Travel
Infrastructure Updates
Daily Activity (Children and workers)
Months
Years
Decades
6. Case Study Locations
c) Vulnerability c) Vulnerability
Seasonal Tourists
Hours
10km 10m
(immobile population) Minutes
Days
Months
Emergency service callout
Years
Decades
Temporal Change
Critical Road Network
Demographic changes (e.g. increase in elderly population)
Children’s travel
100m
Topography
Demographic Shifts and Migration
1km
(Covered in average daily travel)
Daily Activity
Days
Examine how the case study findings apply to other local authorities and flood risk areas
Macro-economic Policy
Spatial Change
10km
Spatial Change
Expansion of Urban Area
Road Network
1km
Large Flood Protection Schemes
Temporal Change
500 people = 70 Vulnerable
100km
100km
100km 10km 1km
Minutes
Expansion of Urban Area
Flood Defences
Secondary Hazards
(Research Objective 3)
Travel Model and Transport Analysis
Macro-economic Policy
100m
Daytime population e.g children, elderly, x = 750 people students, 100 Vulnerable
100m
Risk
b) Exposure b) Exposure
Climate Change
Flood Event Characteristics (depth, velocity, extent)
Flood Modelling
(Research Objective 4)
10m
x
Daytime population, Road network
=
10m
Event 1
1 in 100-year flood extent
Vulnerability
(Research Objective 2)
4.To assess the implications of scenario findings for all local authorities who have to respond to flood events.
a) Hazard
Spatial Change
Table 1: Examples of changing risk for a 1 in 100 year flood. Risk here refers to the number of people at risk from the flood event, if it happened at that point in time. x
(Research Objective 1)
5. Defining the Spatial and Temporal Scales of the Project
Table 1 illustrates how the time of flood onset interacts with daily and seasonal changes in the population to produce different risk outcomes, as described in the project background.
Exposure
Road Network Vulnerability Analyses
3. To evaluate how the consequences of a flood event differ between groups of people and at different times of day/year.
Therefore the aim of this project is to analyse the interactions between the time of flood onset and daily/seasonal population movements, to see how the impact on road travel and service accessibility varies, particularly for vulnerable groups.
x
Spatiotemporal Population Modelling
2. To explore how critical road links can be defined through network vulnerability and accessibility methods.
A major issue during UK floods is road network disruption. Whilst the spatial dimension is well studied, the temporal aspects of risk have not been applied as frequently to flood risk assessments for roads, despite the time of flood onset having a large effect on the level of disruption.
4. Hypothetical Example
3. Proposed Methodology
1. To model how the location of the general population, and those who are most vulnerable in flood events, changes at a daily and seasonal scale.
Risk = f(HazardST,ExposureST,VulnerabilityST)
Hazard
Supervisors: Dr Jim Wright, Dr Alan Smith, Dr Sally Brown
Seasonal Tourists
Somerset Levels, UK. Susceptible to long duration floods which cause wide-ranging travel issues, a good case study of rural isolation. Photo: Langport, Somerset (2014). Harvey Hook/Hotspot Media/Daily Mail)
Perception of Risk
Immobile Population Minutes
Hours
Days
Months
Years
Decades
Temporal Change
Figure 1: Plots showing the spatial and temporal characteristics of a) Hazard, b) Exposure and C) Vulnerability in this research project. The dotted box demonstrate the factors considered within the temporal and spatial scales of this project. Connected circles indicate a link between factors. Blue: Environmental Factors and Processes Green: Man Made Structures Yellow/Orange: People Pink: Policy
York, UK. Frequent flooding disrupts the transport network. It is a good case study of a disrupted urban network and reduction in access to services. Photo: York (2015) AFP/Getty Images/Daily Mail)
