A Prototype Tool for Dynamic Pluvial-Flood Emergency Planning

Nuno Melo (corresponding author)

Research Unit for Inland Development, Scientific Area of Civil Engineering,

Polytechnic of Guarda, Guarda, Portugal.

Av. Dr. Francisco Sá Carneiro, 50 – 6300-559 Guarda – Portugal; Telephone: +351

271220120; Fax: +351 271220150; email: nuno_melo@ipg.pt

Bruno Filipe Santos

Air Transport and Operations Section, The Technical University of Delft (TUD), Delft,

The Netherlands

CIEC, Department of Civil Engineering, University of Coimbra, Portugal

Faculty Aerospace Engineering, Kluverweg 1, 2629 HS Delft – The Netherlands; email:

b.f.santos@tudelft.nl

Jorge Leandro

Institute of Hydrology, Water Management and Environmental Techniques, Ruhr-

University Bochum, Germany

Universitätsstraße 150, 44801 Bochum – Germany; email: Jorge.leandro@ruhr-uni-

bochum.de

Subject classification codes: Special Issue on "Towards more Flood Resilience Cities" - 3rd

Pillar “Flood Emergency Logistics”

Please cite this article in press as:

Nuno Melo, Bruno Filipe Santos & Jorge Leandro (2014): A prototype tool for dynamic pluvial-flood emergency

planning, Urban Water Journal, DOI: 10.1080/1573062X.2014.975725

A Prototype Tool for Dynamic Pluvial Flood Emergency Planning

Due to the increased frequency of extreme rainfall events caused by climate

change, flooding in urban areas are becoming increasingly frequent.

Nevertheless, mitigation and response actions to flood events are still defined

according to the best judgments of civil protection authorities, based on their

experience and on simple flood modelling tools. In this paper we present the

methodological structure of an innovative prototype tool for dynamic pluvial

flood emergency planning. The tool is aim at helping civil protection authorities

(and population) in the preparation, mitigation and response to flood events. The

2009 flood event at the village of Agualva (Terceira Island, Azores), Portugal, is

used to exemplify the calibration of the model and to illustrate the capabilities of

the prototype. The results evidence the importance of considering a dynamic

approach when doing pluvial flood emergency planning.

Keywords: pluvial floods modelling; emergency planning; calibration models;

accessibility maps; evacuation routes.

Subject classification codes: Special Issue on "Towards more Flood Resilience

Cities" - 3rd Pillar “Flood Emergency Logistics”

1. Introduction

Flooding in urban areas is one of the most common environmental hazards, due to the

settling of human communities along the watercourses flood plains and the increase of

impervious surfaces in urban areas. According with OECD (2012) the number of severe

floods has increased worldwide. In addition, from the study of Changnon (2008), it is

clear that during 1972-2006 there was a clear upward trend of flood over time (in

particular, in convective-storm floods) and a significant increase on annual flood losses

over time.

Floods can be generally arranged in two categories: flash floods, which are the product

of heavy localized rainfall during a short period over a given location; and general

floods, which are caused by precipitation over a longer period over a given river basin.

Although flash flood occurs often in steep mountain streams, it is also common in urban

areas with high impervious surfaces (Ravazzani et al. 2009).

Flood damage in urban areas may be split into direct and indirect damage (e. g.

traffic disruptions, crop losses, etc.) (Mark 2004). In most countries in the world, civil

protection authorities are in charge of developing flood emergency plans. In general,

these plans comprise the definition of disaster management structures, lines of

command, authorities to be involved, flood risk maps, and (in some cases) evacuation

maps that divide the territory in zones and assign an evacuation to each zone. The

definition of exact response actions are left for real time judgments, mitigation measures

are out of scope, and the uncertainty of these events in time and space is rarely dealt

with in these plans. Regardless of the importance that emergency plans have on floods

management, they are usually developed with base on simple flood modelling tools and

on the experience of civil protection authorities, resulting in generally effective but

probably few efficient and highly costly disaster responses. Existent flood management

context will certainly evolve in the future, in order to cope with the complexity of

disaster management, and improve mathematical modelling tools will be necessary to

support this evolution (Simonovic 2011).

This work presents a Prototype Tool for Dynamic Pluvial-Flood Emergency

Planning aimed at supporting civil authorities in their decisions with regard to rain

driven flooding mitigation. The Prototype Tool can help civil protection authorities (and

population) to identify flood-prone areas, and verify the preferred routes for evacuation

and estimate the time needed for evacuation.

To the authors’ knowledge, this is the first approach in the literature to fully

integrate a flood simulation model with an emergency logistic model. When compared

to hurricane or earthquake literature, planning for flood emergency logistics preparation

has received little attention. One of the rare, but interesting, papers dealing with this

issue is presented by Chang et al. (2007). However, the authors considered flood

scenarios by taking into account potential existing flood-maps. They did not consider

the dynamics of the flood event in the evacuation demand and, consequently, in the

emergency plan.

To illustrate the capabilities of the current Prototype Tool version, we will

reproduce the flood of December 15, 2009 that occurred in the village of Agualva

(Terceira Island, Azores), Portugal.

The paper is structured as follows: the second section presents the Prototype

Tool for Dynamic Pluvial Flood Emergency Planning and its modelling framework; the

third section describes the characteristics of the case study, details the setup and

calibration of the modelling framework, presents a summary of the results and discusses

the application of the Prototype Tool. Conclusions are provided next, in the final

section.

2. Prototype Tool for Dynamic Pluvial Flood Emergency Planning

Pluvial floods can be very devastating events, causing huge property damage and

human losses. Given the complexity of the flood-prevent infrastructures, the

densification of some flood-prone urban areas, and the diverse of construction

environment, flood events are changing in nature, becoming more complex and hard to

manage (Simonovic 2011). However, in practice, most disaster plans are still being

developed with base on simple flood modelling tools and on the experience of civil

protection authorities. The existing disaster management framework needs to evolve

and, in fact, there is an increasing recognition of the need of advanced mathematical and

decision support tools (Altay and Green 2006). Moreover, due to flood disasters

uncertainty, the quick response required, and the amount of resources that need to be

allocated, disaster management is a very suitable problem for operational research and

simulation techniques.

The presented Prototype Tool combines a land-use model, a pluvial flood model,

and an emergency routing model (Figure 1) (Santos et al. 2012).

(Locate Figure 1 approximately here)

The data needed to run the modelling tool consists on basic information

regarding historic rain-flood data (e.g., precipitation, water depths at different points of

the river) landscape information (e.g., slope, soil typology), land-use data (e.g,

occupancy, percentage of impervious surfaces), demographic data (e.g., population per

zone), infrastructure data (e.g., location and characteristics of weirs and channels,

location and characteristics of roads, building data).

With the previous data, the tool runs the following three models:

• Land-use model: it consists on a schematic representation of the landscape,

drainage infrastructure, roads and buildings. This model is used to

parameterize the pluvial flood model, to identify the potential evacuation

request points (residential buildings) and to define the road network

structure.

• Pluvial flood model: a state-of-the art uni-dimensional discretization

(1D/1D) Storm Water Management Model (SWMM) for urban flooding

(Leandro et al. 2009). The model is used to predict the magnitude and time

evolution of different rainfall scenarios. Together with the drainage

network, the transportation network, the surrounding orography and land-

use, all merged in a GIS (Global Information System) software, it predicts

the flooded areas, the water depths and the water velocity at different points

of the flooded areas, including the road links.

• Routing model: a multi-period vehicle routing problem (see, e.g., Özdamar

et al. 2004) used to define evacuation routes. To compute the traveling

speeds in the road network, the water depth and velocity at the roads are

considered. The traveling speeds are computed assuming different vehicles

types (e.g., jeeps and fire trucks) and their capability to run with different

road conditions. The resulting distances, measured in time, are used to

compute the fastest evacuation routes and to measure the instantaneous

accessibility between the evacuation request points and the closest shelter

(i.e., the travel distance at a specific time-period).

The three main outcomes from this tool are:

• Flood maps illustrating the areas potentially affected by the flood at

different periods and according to different scenarios, represented in the

form of water depth and velocity.

• Multi-time period accessibility-maps based on the evolution of the flood and

the consequent change in road flow conditions. Given that some zones of

the territory will experience different accessibility levels throughout the

disaster, the accessibility maps will allow the identification of rescue

demand points over time.

• Evacuation route maps, defining the shortest and safe routes between the

evacuation request points and the closest shelter. These evacuation route

maps will have information on the travel time, forecasted traveling speed,

and type of vehicle to be used for evacuation by periods of the flood.

All the outcomes are displayed in the GIS software, for a better understanding and

usage of the tool by civil protection authorities and other stakeholders involved in the

planning process.

3. Prototype Tool Application and Results

A real case study is used in this section in order to test the modelling framework and to

illustrate the applicability of the Prototype Tool. The case study is described next,

followed by the modelling setup and calibration to the specific case study, a summary of

the results and the discussion of the Prototype application.

3.1 The case study

The case study is located in the village of Agualva, in the north coast of Terceira Island,

Portugal. This village has 1 432 inhabitants, according to 2011 census. On the early

morning of 15th of December 2009 a severe flooding caused by a short duration and

extreme rainfall event was reported. According to the measurements in the nearby

meteorological station of the American Air-base 4, a total of 70 millimetres of rain fell

in less than four hours, flooding several streets and causing the main water course to

overflow in some points, and causing severe damages in several bridges and houses

(Figure 2).

(Locate Figure 2 approximately here)

The village of Agualva (meaning "clear water") because of its historical and

economic importance in Terceira Island as a centre of water-mill houses between the

16th to the 19th century led to an urban development that closely follows the main water

stream. The Agualva River is the longest watercourse in Terceira with approximately 8

kilometres long. It starts at Pico Alto, which is the highest point at 797m, and ends at

the ocean. The slope varies between 30% and 6% whit an average of 10% and its basin

has approximately 9 square kilometres (Figure. 3).

(Locate Figure 3 approximately here)

The flood event of December 2009 was associated with the overflow of the main

water course. As a result, several roads and buildings were flooded, two road bridges

were destroyed with a large amount of mud, rocks and debris transported, and some

other roads were disrupted due to landslides. These effects constrained the mobility and,

consequently, reduced the accessibility of the population to safer places or to civil

protection facilities (e.g., hospitals and temporary shelters).

3.2 Land-use model setup

The land-use model is a GIS-based model capturing the relative layout and the

characteristics of the systems that compose the territory of the river hydrologic basin.

This comprises the topography of the basin (including the soil features), the river and

the drainage infrastructure, the road network and the existing buildings.

For our case study, the necessary geographic features of each system were

obtained with the help of local civil protection authorities and manipulated into a single

GIS platform. These geographic features were then complemented with additional

information, such as the maximum water depths observed in several locations (river and

roads) during the flood, the road speeds, the inhabitants per residential block and the

location of the emergency shelters. Two emergency shelters were considered in this

study. The location of these shelters was chosen according to the civil protection

authorities guidelines: they were located at high-elevated areas, where emergency

shelters can be installed and from where major regional health facilities can be easily

reached.

3.3 Pluvial-flood model Setup

To model the flood event we used the Storm Water management Model (SWMM). This

is a dynamic rainfall-runoff model, which operates on a collection of sub-catchment

areas that receive precipitation and generate runoff. The routing portion of the SWMM

transports this runoff through the system of channels, pipes and devices. The flow

routing in this case is calculated, using the complete one-dimensional Saint Venant flow

equations (Dynamic Wave Routing) (Rossman 2010). These equations consist of the

continuity (1) and momentum (2) equations:

!"!"+ !"

!"= 0 (1)

!"!"+ ! !! !

!"+ 𝑔𝐴 !"

!"+ 𝑔𝐴𝑆! + 𝑔𝐴ℎ! = 0 (2)

where 𝑥 is distance along the conduit, 𝑡 is time, 𝐴 is cross-sectional area, 𝑄 is

flow rate, 𝐻 is the hydraulic head of water in the conduit (elevation head plus any

possible pressure head), 𝑆! is the friction slope (head loss per unit length), ℎ! is the

local energy loss per unit length of conduit, and 𝑔 is the acceleration of gravity.

The hydrologic basin, according to the different soil occupation and its

topographic features, was divided into 10 sub-catchments (Figure 4). A drainage system

was implemented based on three main elements (Figure 5): (1st) the river, (2nd) the

channels along the main streets and (3rd) the weirs that allow the bi-directional

transference of flow between the river and the streets (details 1 to 4 in Figure 5).

Transverse weirs were considered to allow the transference of flow between the water

course and the street, and the passage over the bridge (detail 3 in Figure 5). The three

other weirs are of side-flow type (details 1, 2 and 4 in Figure 5).

(Locate Figure 4 approximately here)

(Locate Figure 5 approximately here)

3.4 Pluvial-flood model Calibration

In order to model and calibrate the SWMM model of the case study a methodology was

developed to take into account a hydrograph obtained with the SCS method and

maximum water depths observed in situ. Figure 6 presents this methodology in a

flowchart.

(Locate Figure 6 approximately here)

The calibration methodology was split into two steps. In a first step (the inner

cycle defined by the solid line in Figure 6), based on the rainfall measured and the

physiographic characteristics of the different sub-catchments and channels of Agualva

River, a first simulation was carried and a SWMM runoff hydrograph generated by the

overall basin. The SWMM hydrograph was then compared with the SCS hydrograph

(Chow et al. 1988) obtained by (Leandro and Leitão 2010, Leandro et al. 2012). The

SCS hydrograph was obtained (Leandro and Leitão 2010) based on CN parameters

estimated on a local survey, and propagation velocities calibrated in agreement with

local reports in terms of relative magnitude of peaks and times of occurrence. Based on

that hydrograph, the more comprehensive model SWMM was calibrated (in terms of

impervious areas, depression storage and roughness of the channels) in order to the two

hydrographs closely matches one to another (see Figure 7).

(Locate Figure 7 approximately here)

In a second step (the outer dashed cycle in Figure 6), the water depths obtained

by the model calibrated in the previous step were compared with those observed. We

verified that the values obtained by the SWMM were smaller than the water depths

measured. In order to obtain similar values, the rainfall recorded in the American Air-

base-4 weather station was gradually incremented up to 20%, in accordance with rain-

gauge discrepancies reported by Curtis and Burnash (1996). Water depths obtained by

SWMM along the streets were closely matched with the ones observed (Figure 8).

However, the water depths measured along the water course were larger than those

obtained with the SWMM model (Figure 8). Two plausible explanations are the change

due to erosion in the shape of the cross section during the flood event, and the difficulty

in measuring those water-depths (Figure 2); unlike, along the roads had a visible mark

of the maximum water-depth. Looking at the water depths verified in the bridges

locations and in the streets, we verify that the agreement is much better because these

sections did not suffer shape changes during the flooding event (Melo et al. 2011).

(Locate Figure 8 approximately here)

Figure 7 presents the three hydrographs, the SCS hydrograph and the SWMM

hydrograph with and without the 20% rainfall increment.

3.5. Routing model

The travel times between the potential rescue demand points and the emergency shelters

are computed in the routing model. The model takes into consideration the land-use

model – in particular the road network, the potential rescue demand points (residential

buildings) and the location of the two shelters – and the pluvial-flood model – in

particular the water depths and velocity estimated for different flood measurement

points in the road network for different time periods during the flood event. The results

from the pluvial-flood model are uploaded to the routing model after being manipulated

and associated with the land-use model GIS features.

The driving speeds at the roads are assumed to be a function of the water depths

and water velocity in the road. For this case study, only the water depths were

considered. Based on Nayak and Zlatanova (2008), we assumed five water depths

intervals and considered that for more than 0.5 meters cars can hardly be driven (Table

1).

(Locate Table 1 approximately here)

For each road link in the road network we associated a flood measurement point

with the information of water depths for the different time periods. With this

information, we estimated the instantaneous driving speed in each road link. This means

that we obtained the speeds for the static driving conditions considering the water

depths for that specific time period. Given that water depths vary on time, as more time

periods we consider (higher time resolution) more accurate will be our routing model

results.

To compute the shortest path (in time) between a potential rescue demand point

d and an emergency shelter s we used the following binary (0-1) integer programming

(Ahuja et al. 1993):

𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒  𝑍!" = 𝐶!! . 𝑥!"!∈𝑴!∈𝑴 (3)

subject to:

𝑥!" = 1!∈𝑴 ,          𝑗 ≠ 𝑑 (4)

𝑥!"!∈𝑴 − 𝑥!"!∈𝑴 = 0,          ∀  𝑖 ≠ 𝑑  𝑜𝑟  𝑠 (5)

𝑥!" = 1!∈𝑴 ,          𝑗 ≠ 𝑠 (6)

wher, Zds is the travel time between the potential rescue demand point d and the

emergency shelter s; M is the set of road links in the network; Cij is the travel time in

road link (i, j); xij is the binary decision variable that is equal to 1 if the road link (i, j) is

part of the shortest path, and is equal to 0 otherwise. In this simple optimization

problem, well known as the shortest-path problem, the objective is to minimize the

travel time between the origin (demand point) and destination (shelter) (3). The travel

time between the demand point and the shelter is the sum of travel times in the road

links that are part of the shortest-path. The first constraint (4) ensures that the potential

evacuation demand is transported from the evacuation point d. The set of constraints (5)

impose that all the other nodes in the road network (except the evacuation points and the

shelters) are transhipment nodes, while the last constraint (6) ensures that the evacuation

demand is received at the shelter node s.

This optimization problem is solved for each pair of demand points and shelters,

with the help of a GIS script. The travel time values are then used to develop the

accessibility maps and the emergency routing maps. The accessibility maps show a

“snap-shot” of the rescue travel times for all demand points. These are potential travel

times, meaning that they represent the travel times assuming that the driving conditions

remain static for that specific period. Accessibility maps from different periods allow

civil protection authorities to understand how the flood is expected to evolve and how

the driving conditions for rescuing activities will be affected by this. Thus, it facilitates

the identification of areas that could potentially become isolated (or with a very poor

accessibility) at some period and that need to be evacuated before that, helping the civil

protection authorities to prioritize their actions. The emergency routing maps show the

shortest paths between demand points and the closest emergency shelter, given the

driving conditions for that time period. They indicate the path that should be used for

evacuation activities during that specific time period and highlight potential disrupted

roads.

3.6. Results from the case study

For the sake of simplicity, in this work we will just present the results for five different

time periods of the flood event: (1) before the flood; (2) a flow peak at 3.15 am; (3) a

down period at 4.50 am; (4) a second peak at 5.50 am; and (5) the last flow peak at

11.30 am (Figure 7). For each time period a time interval of 10 minutes – the 5 minutes

before and after the indicated time – was considered. In order to better reproduce the

real flood event, for the Time Period 4 it was assumed that the two bridges collapsed

(points R6m and R3m in Figure 5).

The results showed that floodwater rising had a large impact on population

accessibility during the flood event (Table 2). For instance, some residential units that

were less than three minutes away from an emergency shelter, during the first flood

peak (Time Period 2) become more than 15 minutes away from the same shelter. For

the Time Period 4, the scenario is even worst; some residential units experienced a

potential rescue time of more than one hour, given the driving conditions in some roads

during this time period. This does not necessarily means that people would have to

travel for more than one hour to get to a shelter. In fact, the lowering of the water depth

in the following minutes will certainly allow a faster evacuation. But it means that, due

to the fact that travel speeds lower than 1km/h and the fall of the two bridges, these

residential units became isolated during this specific time period and no rescue activity

could be operated. Moreover, this example illustrates the importance of considering

flood dynamics in emergency planning. Given that during Time Period 3 the

accessibility levels almost returned to normal, civil protection authorities could have

programed and scheduled the evacuation of this isolated areas before they become

isolated.

(Locate Table 2 approximately here)

An example of an accessibility map is provided in Figure 9. The figure

represents the 1307 potential rescue demand points (residential units) of the village

divided by colours. The colours are associated with the accessibility level for the Time

Period 4 (the second flood peak, at 5:50 am). Darker red buildings are those with lower

accessibility (higher travel times) and lighter red buildings are residential units with

fairly good accessibility (low travel times). The yellow and red crosses represent the

two emergency shelters. The dark blue line delimits the Agualva basin, while the light

blue lines are the watercourses.

(Locate Figure 9 approximately here)

The evacuation routes also changed during the flood event, due to the water

level rising and the collapse of the bridges. Figure 10 compares the evacuation routes

for Time Periods 3 and 4. The routes are defined according to the colours of the

respective emergency shelters (crosses).

(Locate Figure 10 approximately here)

3.7. Application discussion

This simple application illustrates the practical potentiality of the proposed Prototype

Tool. The inclusion of the flood dynamics in an emergency evacuation-planning

framework can help civil protection to better plan their evacuation activities, defining

priorities and scheduling these activities according to the evolution of the flood and to

the limitation of their resources.

In a real world application, the Prototype Tool can be used to forecast pluvial

flood events and evacuation plans. To do this, different pluvial scenarios based on

historic data should be considered and modelled. In addition, a higher temporal

resolution will be needed to give more detailed information about the possible flood

evolution and the driving conditions on the road network. This, in fact, can already be

done with the current Prototype Tool. A resolution of time intervals of 10 minutes (or

less) can already be obtained. For a case study of Agualva size, after having the land-

use model developed, the pluvial flood model takes about 10 seconds to compute the

flood scenario and the routing model takes less than 5 seconds to compute the total

travel times for each time period.

6. Conclusions

This work presented a Prototype Tool for Dynamic Pluvial Flood Emergency Planning.

The objective of this Tool is to support civil authorities in their decisions regarding

pluvial flood disasters mitigation, preparation and response. The modelling framework

integrates a land-use model, a rain-flood model, and an evacuation routing model in a

GIS platform. To illustrate the present capabilities of the Prototype Tool we used the

pluvial flood case study of the 2010 flood in the village of Agualva (Terceira Island,

Azores), Portugal.

As part of the present paper, we also present the methodology to setup and

calibrate a flood model based on a known hydrograph and observed water depths in situ.

This procedure allowed the determination of water depths throughout the study area.

Based on this information it was possible to produce the flood water depths throughout

time and develop multi-period evacuation routes and accessibility maps between

potential rescue demand points and the emergency shelters. The results illustrate the

capabilities of the prototype and evidence the need for adopting dynamic modelling

frameworks for flood emergency planning.

As illustrated by the Agualva case study, we believe that the current Prototype

Tool is already useful in practical applications. Nevertheless, we recognize that it can be

improved with regard to a number of features. In particular, we identify four important

lines of improvement. The first line relates to the simulation of multiple rain scenarios

and the integration of the results into a single results window. We can run several

scenarios with the current tool but the user has do it sequentially and compare the

results separately in order to derive some conclusions from the multiple scenarios. It

would be helpful for the civil protection authorities to have the results from the different

scenarios combined in a single results window and to identify areas that are affected in

all the scenarios tested and areas that are flooded depending on the rain scenario. The

second line relates to the addition of a shelter location model. In the present tool, it is

assumed that the locations of the emergency shelters are pre-defined. We plan to extend

the current tool by endogenously define the optimal location of the shelters, in order to

minimize the total evacuation travel times. The third line is related with the use of

multi-type vehicles. Different water depths and velocity influence differently the

different types of vehicles used in emergency evacuation. The differentiation between

light vehicles, trucks or even boats, can enhance the potentiality of the routing model.

Finally, the fourth line of improvement could be to introduce a scheduling model to help

the civil protection authorities to define the sequence of their evacuation activities.

Acknowledgements

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TABLES

Table 1 – Maximum speeds (km/h) for different free-flow speed roads according to

water depth on the roads (based on Nayak and Zlatanova, 2008)

Table 2 - Evacuation time values for the five time intervals

Table 1 – Maximum speeds (km/h) for different free-flow speed roads according to

water depth on the roads (based on Nayak and Zlatanova, 2008)

Water-depth

0 m ]0, 0.1] m ]0.1, 0.2] m ]0.2, 0.5] m ≥ 0.5 m

50 20 10 2 0.5

40 16 8 2 0.5

30 12 6 2 0.5

20 8 4 2 0.5

Table 2 - Evacuation time values (min) for the five time intervals

Time Periods

(1) (2) (3) (4) (5)

Maximum 3.59 16.38 3.64 337.60 7.53

Average 1.62 4.59 1.81 10.36 2.47

St. Deviation 0.595 3.635 0.707 29.751 1.451

FIGURES

Figure 1 – A Prototype Tool for Dynamic Pluvial-Flood Emergency Planning

Figure 2 – Road closed and a bridge collapsed due to the storm event

Figure 3 – Agualva basin stream

Figure 4 – Sub-catchments of the Agualva watercourse

Figure 5 – Scheme of the watercourse and roads modelled in SWMM

Figure 6 – Flowchart of the calibration method

Figure 7 – Rainfalls and corresponding hydrographs

Figure 8 – Relation between water depths measured and the obtained in SWMM model

Figure 9 – Accessibility maps for Time Period 3

Figure 10 – Evacuation routes for the two shelter locations (red and yellow) for Time

Period 3 (left) and for Time Period 4 (right)

Figure 1 – A Prototype Tool for Dynamic Pluvial-Flood Emergency Planning (based on

Santos et al. 2012)

Figure 2 – Road closed and a bridge collapsed due to the storm event

Figure 3 – Agualva basin stream

Figure 4 – Sub-catchments of the Agualva watercourse

Figure 5 – Scheme of the watercourse and roads modelled in SWMM

Figure 6 – Flowchart of the calibration method

Figure 7 – Rainfalls and corresponding hydrographs

Figure 8 – Relation between water depths measured and the obtained in SWMM model

Figure 9 – Accessibility maps for Time Period 3

Figure 10 – Evacuation routes for the two shelter locations (red and yellow) for Time

Period 3 (left) and for Time Period 4 (right)