roadapt roads for today, adapted for tomorrow case study ... · 2 detailed vulnerability assessment...

CEDR Transnational Road Research Programme Call 2012: Road owners adapting to climate change Funded by: • Germany • Denmark • Norway • The Netherlands

ROADAPT Roads for today, adapted for tomorrow

Case study Öresund

May 2015

ROADAPT consortium:

Deltares (coordinator)

SGI

Egis

KNMI

CEDR Call 2012: Road owners adapting to climate change

CEDR Call2012: Road owners adapting to climate change

ROADAPT Roads for today, adapted for tomorrow

Case study report, Öresund region

Draft version: 11.2014 Final version: 05.2015

Start date of project: 01.2013 End date of project: 10.2015 Author(s) this deliverable: Stefan Falemo, ÅF / SGI, Sweden Linda Blied, SGI, Sweden Per Danielsson, SGI, Sweden Martial Chevreuil, EGIS, France

Version: final


Table of contents Executive summary ................................................................................................................. i 1 Introduction .................................................................................................................... 1

1.1 ROADAPT ............................................................................................................... 1

1.2 Target Audience ...................................................................................................... 1

1.3 Objective ................................................................................................................. 1

1.4 The case study area ................................................................................................ 1

2 Detailed vulnerability assessment using the Blue spot model ......................................... 3

2.1 Introduction ............................................................................................................. 3

2.2 About the blue spot model ....................................................................................... 3

2.2.1 Level 1 - Find the blue spots ............................................................................ 3

2.2.2 Level 2 - Calculate the rain sensitivity .............................................................. 4

2.2.3 Level 3 – Create a hydrodynamic model .......................................................... 5

2.3 Demonstration examples ......................................................................................... 5

2.3.1 Geographical area ............................................................................................ 5

2.3.2 Data ................................................................................................................. 6

2.3.3 Results ............................................................................................................. 7

2.3.4 Conclusions ................................................................................................... 11

3 Quickscan in the Öresund region ................................................................................. 12

3.1 Quickscan exercise in a small case study area in southwest Sweden ................... 12

3.1.1 Organization of the Quickscan ....................................................................... 12

3.1.2 Quickscan results step by step ....................................................................... 12

3.1.3 Conclusions ................................................................................................... 20

3.2 Quickscan application in Denmark ........................................................................ 20

3.2.1 Organization of the Quickscan ....................................................................... 20

3.2.2 Quickscan results step by step ....................................................................... 21

3.2.3 Conclusions ................................................................................................... 32

4 Socio-economic assessment ........................................................................................ 33

4.1 Network level......................................................................................................... 33

4.2 Territory level ........................................................................................................ 34

4.3 Economic system as a whole ................................................................................ 36

4.4 Conclusions .......................................................................................................... 37

5 General conclusions ..................................................................................................... 38

6 Acknowledgement ........................................................................................................ 39

7 References ................................................................................................................... 40

Annex A: ROADAPT Blue spots - Adapting to climate change, methods for assessing the risk of road flooding


(i)

Executive summary

Within the ROADAPT project (a part of the CEDR Call 2012 Road owners adapting to climate change) a case study has been performed in the Öresund region in Denmark and Sweden. The case study includes a blue spot analysis carried out in Sweden and then compared with the blue spot analysis results of the Danish Road Directorate (DRD) for the Danish part of the case study area. The Quickscan-method is then tested on TEN-T roads in both countries. The Blue spot analyses performed in Denmark and Sweden were both based on a concept evaluated by the Danish Road Institute (DRI) in 2010. The concept involves three levels to identify and analyze depressions and low-lying areas where there is a danger of flooding due to heavy precipitation or sea level rise. The studies are based on national laser scanned DEM:s (Digital Elevation Models) with a spatial resolution of 1,6 m and 2,0 m respectively. The models have also been handling current climate scenarios for precipitation in year 2100. The Blue spot concept seems to be a useful tool in identifying sections at risk. In the Swedish study, modelled results have been compared with actual sites of occurred flooding and there is a connection. The Quickscan-method is intended to consist of three workshops, and can be divided into four steps;

1. Preparation for workshop. This step provides the frames for the coming workshop with definition of scope, identification of risk sources and determination of the importance of different road sections.

2. Workshop I. Establishing of consequence criteria (where the participants use a system to individually rank different criteria such as ‘costs’ and ‘safety’) and estimating the consequences of the threats.

3. Workshop II. Evaluating the probability of each threat and prioritizing the risk. Finally the locations of the threats are identified.

4. Workshop III. A final discussion on acceptability of risk and determination of an action plan to decide which threat that require action.

The conducted workshops didn’t completely follow guidelines above but where put together: in total one workshop was organized in Malmö, Sweden and two in Copenhagen, Denmark, involving participants with different competence from the national road authority of each country. Both road authorities are at present working with methods similar to the Quickscan-method but conclusions were that this method might be beneficial for informing maintenance contractors about the risk in their operating area and to transfer knowledge between old and new contractors.


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1 Introduction

1.1 ROADAPT

The ROADAPT project is part of the CEDR Call 2012 Road owners adapting to climate change. ROADAPT has an integral approach following the RIMAROCC (Risk Management for Roads in a Changing Climate) framework that was developed for ERA NET ROAD. ROADAPT aims at providing:

1. Methodologies and tools enabling tailored and consistent climate data information for road owners. This will also ensure a good communication between climate researchers and road authorities.

2. A fast, preliminary quickscan method for estimating the climate change related risks for roads. Results of the quickscan can be translated in an action plan for adaptation.

3. A method for a socio economical impact analysis of climate change induced events. 4. A method for detailed vulnerability assessments. 5. Adaptation pathways plus strategy charts with specific input from adaptation

techniques related to geotechnics and drainage, pavements and traffic management.

The ROADAPT consortium consists of the following partners:

• Deltares (coordinator) • SGI • Egis • KNMI

1.2 Target Audience

This report is targeted at road-owner staff and consultants who wish to get insight into the Quickscan-method and the detailed vulnerability assessment method.

1.3 Objective

WP4 is tested in Chapter 2 through an application of the detailed vulnerability assessment approach using existing methods. A blue spot analysis is carried out in the Swedish part of the case study area with guidance from DRD. The Swedish results are then compared with the blue spot analysis results of DRD for the Danish part of the case study area. The Quickscan-method is tested and evaluated in Chapter 3, through a feasibility study and small-scale test in Sweden and a Quickscan study (2*1/2 day) in Denmark.

1.4 The case study area

The Quickscan-method has been performed and evaluated in the Öresund region, on both the Danish and the Swedish side of the strait. Studied roads include road E55 between Helsingør and Copenhagen and road E20 from Amager to Køge in Denmark and road E20/E6 in Sweden between the Öresund bridge and Landskrona (see Figure 1-1).


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Blue spot analysis has also been conducted in an extended stretch of National roads in Jutland, Denmark and in Sweden, roughly between Ängelholm and Trelleborg.

Figure 1-1. Area of conducted blue spot analysis on Jutland, Denmark (left) and roads studied with

blue spot analysis and quickscan method respectively in the Öresund region (right).


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2 Detailed vulnerability assessment using the Blue spot model

2.1 Introduction

Within work package 5 in the ROADAPT-project two demonstration projects on the Blue spot model has been studied to evaluate the WP4 strategy to identify and use existing methods for vulnerability assessment. The two demonstration projects are both based on a concept evaluated by the Danish road Institute (DRI) in 2010 and were performed in Denmark and Sweden respectively by DRI and the Swedish Transport Administration (STA). The Swedish blue spot study was funded by STA and was performed as a part of the ROADAPT Öresund case study. It was performed by Metria, with technical advice and knowledge transfer from the Danish Road Directorate (DRD) in association with ROADAPT. This report will involve comparing and evaluating the results of the two studies.

2.2 About the blue spot model

The Blue spot model is a method to identify depressions and low-lying areas where there is a danger of flooding due to heavy precipitation or sea level rise. Through the model it is also possible to estimate the potential water depth and the volume of water needed to fill the depression. The Blue spot model is conducted with GIS-tools and demands a detailed digital terrain model. The model can be applied on large areas and which enables a relatively quick finding of objects and stretches that might be at risk for flooding. By using this method on a road network, road owners can get a first input of where to take measures to protect their roads from flooding. In 2010 DRI published a series of four reports concerning the blue spot-analysis:

• Report 181-2010: The blue spot concept – Methods to predict and handle flooding of highways

• Report 182-2010: Background report – Literature, questionnaire and data collection for blue spot identification

• Report 183-2010: The blue spot model – Development of a screening method to assess flood risk on highways

• Report 184-2010: Inspection and maintenance – guide to reduce vulnerability due to flooding of roads.

The reports were a result of the SWAMP project which was a part of an ERA-NET ROAD initiated transnational research program called “Road Owners Getting to Grips with Climate Change”. The following description of the Danish Blue Spot-analysis is derived from the reports. DRI work with a chain of procedures called “the Blue spot concept”, intended for use at large and important roads in a non-urban setting. The concept consists of the following three levels and DRI recommend analysis on at least level 1 and level 2 in all blue spot analyses.

2.2.1 Level 1 - Find the blue spots Screening is performed to identify all depressions in the elevation model, see Figure 2-1. This is done by letting rain fall on the surface and then flow until it reaches a depression


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where it gets collected. No infiltration or evaporation is assumed. The depressions that end up with a water volume that exceeds 10 m3 and are close to a road are considered as threats and are included in the following analysis.

Figure 2-1. Blue spots identified. In addition the risk of flooding due to sea level rise and water level rise in rivers is mapped by incrementing the water level and tracking how far inland the water reaches.

2.2.2 Level 2 - Calculate the rain sensitivity Each depression that was identified as a threat in Level 1 undergoes a calculation of rain sensitivity. It is done by continuously assuming no drainage or evaporation from the depression but letting the impermeability of its catchment area vary between 20, 40, 50, 60, 80 and 100%. This gives a risk map showing the amount of precipitation needed to fill each depression, see Figure 2-2. After level 2 it is often necessary to minimize the number of blue spots for further elevation. This can be done with a simple risk analysis and prioritization.

Figure 2-2. The amount of rain needed to fill identified depressions.


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2.2.3 Level 3 – Create a hydrodynamic model A 1D-1D hydrodynamic model is created to find pathways, catchments and ponds in the risk area, see Figure 2-3. The model takes both water flows on the surface and in the drainage systems into account and therefor gives a better calculation of the flood risk as well as dealing with a time variable.

Figure 2-3. Pathways, catchments and ponds calculated by hydro dynamical modeling.

2.3 Demonstration examples

To test the blue spot concept as described above DRI has performed a case study in Denmark. The study is presented in Report 183-2010. In 2014 Metria, on behalf of the Swedish Transport Administration (STA) performed a similar study in Sweden, based on the concept of DRI (ROADAPT Blue spots, 2014). The following chapter compares the input and results of the two studies who both focuses on level 1 and 2 as mentioned in the former chapter.

2.3.1 Geographical area The area studied in the Danish project was placed in Mid-Jutland and the area studied in the Swedish project was placed near the coast of Skåne and Halland, separated from Denmark by the strait of Öresund, see Figure 2-4. DRI:s area is about 7500 km2 and is by that about 1,5 times the area studied by STA.


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2.3.2 Data The studies are based on national laser scanned DEM:s (Digital Elevation Models) which have a spatial resolution of 1,6 m in Denmark and 2,0 m in Sweden. In both cases the DEM had to be manually adjusted to allow flow under bridges, viaducts and through piped rivers and streams. This was performed by using national databases regarding the location of bridges on the national roads, aerial photos and maps showing the location of streams and rivers. Furthermore, STA performed the function “Fill” in ArcGIS to remove sinks in the dataset. In its analysis DRI had to divide the DEM into three parts to increase the calculation speed. This gives some uncertainties to roads crossing the split boundaries. STA used precipitation data from SMHI (Swedish Meteorological and Hydrological Institute) collected from 2012. Daily maximum precipitation estimates used for current and future climate (year 2100) are presented in Table 2-1.

Table 2-1. Daily maximal precipitation estimates (mm) in the study area of Sweden (current climate / future climate)

Return period

1 year 10 years 100 years

Min 25 / 30 45 / 54 70 / 84 Max 35 / 42 65 / 78 90 / 108

DRI analyses effects of sea level rise and flooding from rivers, which STA do not take into account. The scenarios of sea level rise that were used were 1, 2 and 3 meters respectively. DRI also performs the analysis of level 3. Specifications of used data, comparison of the Danish (performed by DRI) and the Swedish (performed by Metria for STA) are presented in Table 2-2.

Figure 2-4. The study area of DRI (to the left) in Denmark and STA/ Metria (to the right) in Sweden


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Table 2-2. Specifications of used data and analyses for the Danish and the Swedish analyses respectively.

Parameter Denmark Sweden Year of analysis 2010 2014 DEM resolution 1,6 m 2,0 m DEM height resolution 0,1 – 0,25 m < 0,5 m Studied roads National roads (875 km) TEN-T network (E6 and E4) Database for bridges and crossings

Bridge database (DRI) – 98 bridges

BatMan (STA) – 559 bridges

Software ArcGIS with extensions ArcGIS with extensions Precipitation data (current climate)

Not specified Daily maximal precipitation with 1, 10 and 100 years return period (SMHI)

Precipitation data (future climate, year 2100)

Ca 20 % increase of maximal daily precipitation (based on three scenarios from IPCC and EU)

Ca 20 % increase of 1, 10 and 100 years return period in current climate (numbers developed from SMHI)

Consideration of sea level rise

Yes No

Consideration of flooding in rivers

No1 No

Criteria for blue spots to be considered as threats

Volume: >10 m3

Distance from road: < 10 m Volume: >10 m3

Distance from road: < 20 m

Criteria for blue spot to have catchment calculated

Volume: >100 m3

Upstream area: > 1 hectare Volume: >10 m3

Distance from road: < 20 m

2.3.3 Results Following is a brief presentation of results from the two different studies. Performed analyses and the results given are listed inTable 2-3.

Table 2-3. Results presented by the two different studies. Result Denmark Sweden Level 1: Blue spot area/ precipitation Yes Yes Level 1: Blue spot area/ sea level rise Yes No Level 1: Blue spot area/ flooding of rivers No No Level 1: Blue spot depths and volumes Yes Yes Level 2: Flow paths and watersheds Yes Yes Level 2: Rain sensitivity Yes Yes Level 3: Hydrodynamic modelling Yes No Comparison to occurred flooding No Yes Comparison to occurred traffic accidents No Yes

1 An explicit analysis of expected flooding of rivers has not been performed but is partly given implicit as rivers and their valleys might act as depressions.


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An example of the Level1: blue spot areas due to precipitation from both the Danish and the Swedish study is presented in Figure 2-5. The Danish analysis is performed within an area of 1 km from the road whereas the example from Sweden is already filtered by 20 m from the road.

Figure 2-5. Identified blue spots due to precipitation in Denmark (left) and Sweden (right). Examples of the Level 1: blue spot areas due to sea level rise in Denmark are shown in Figure 2-6. This analysis has not been performed in Sweden.

Figure 2-6. Example of identified blue spots due to sea level rise 1 m (top) and 3 m (bottom) in

Denmark.


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Examples of Level 1: blue spot depths and volumes in Denmark and Sweden are presented in Figure 2-7 and Figure 2-8.

Figure 2-7. Example of identified blue spots in Denmark with area, volume and depths.

Figure 2-8. Example of identified blue spots in Sweden with depths. The star symbol shows location of

traffic accidents due to rain and flooding.


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Examples of Level 2: flow paths and watersheds are shown in Figure 2-9.

Figure 2-9. Example of identified waterways and watersheds in Denmark (left) and Sweden (right). Analysis of Level 2: rain sensitivity was in both Denmark and Sweden performed for runoff of 20, 40, 50, 60, 80 and 100 %. Examples are shown in Figure 2-10.

Figure 2-10. Examples of rain sensitivity of depressions for 100 % runoff in Denmark (left) and 80 %

runoff in Sweden (right). Analysis of Level 3: hydrodynamic modelling has only been performed for an area at risk of flooding in Denmark. Mike Urban was used to calculate the flooding and data such as runoff coefficient, roughness coefficient etc. were estimated. The risk area identified is shown in Figure 2-3. The Swedish analysis is verifying its results by comparing them to recorded flooding events and traffic accidents due to rain and flooding (see Figure 2-8 and Figure 2-11). Of 13 recorded flooded areas at the TEN-T roads, 11 intersect spots identified as blue spots. The remaining two are thought to be due to lack of precision in the flood event database.


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Figure 2-11. Flooding events and blue spots (in Sweden).

2.3.4 Conclusions Both studies generally use the same input and give the same type of result. The Swedish study uses an elevation model that is gridded with about 50 % bigger pixels than the Danish elevation model. To be able to compare the effects of this the study would have to be performed on the same objects. The Swedish study states that the performance of the model relies on the elevation model and that it is hydrological correct (i.e. bridges and roads crossing culverts are removed to allow a water flow on the surface). This is, together with the estimated precipitation, the most profound and also uncertain input in the model. However, comparison with occurred flooding indicates that this is a useful method of identifying objects at risk.


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3 Quickscan in the Öresund region

3.1 Quickscan exercise in a small case study area in southwest Sweden

This chapter presents the Quickscan case study that has been performed for the Sweden case, on the TEN-T road E6 Malmö - Landskrona. The Quickscan followed the Quickscan method description draft dated March 2014.

3.1.1 Organization of the Quickscan The workshop was organised as a 1 hour exercise as part of a workshop on risk assessment methods for roads in Sweden. The main workshop was organised by STA’s national maintenance organisation and the invited participants have key roles in road maintenance activities and risk management in the Malmö region. Focus of the QS exercise was steps 2.3, 4.2, 4.4 and 4.5. Step 2.2 was carried out before the workshop as part of the preparations.

3.1.2 Quickscan results step by step Step 1.1 Scope definition The study area covered approximately 50 km of the TEN-T road E6 Malmö - Landskrona, as shown in Figure 3-1. The areas surrounding the roads consist of farmland and built-up areas. Small forests are present in the northern part of the study area. The landscape is flat and close to sea level, and in parts the road is situated close to the sea. Clayey till is the dominating soil, followed by glaciofluvial deposits and sand/gravel.


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Figure 3-1. The case study area in Skåne is marked in orange. The roads generally consist of 2 lanes in each direction. ADT is 20 – 40 000 vehicles per day. The road is an important freight route for transports across the border to Denmark on the Öresund bridge. Expected climate change in south-western Sweden by year 2100 is summarized in Table 3-1 (SMHI, 2011). The climate change is calculated using an ensemble of 16 climate scenarios. The figures are changes for the period 2071-2100 in relation to the normal period 1961-1990.


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Table 3-1. Expected climate change in Skåne by year 2100. Summarized from SMHI 2011.

Climate parameter Change by year 2100

Mean temperature summer + 3 degrees (+18 degrees )

Mean temperature winter + 5 degrees (+4 degrees )

Nr of days with temperature zero crossings -10-20 days

Mean precipitation, spring/autumn/winter +20 -30%

Mean precipitation, summer No change

Heavy showers (30-minutes precipitation with 10-year

return period) +ca 30 % precipitation

Nr of days with heavy showers +5 days

100 year flow in the river Höje å -10 %

Sea level, mean level + 90 cm

Sea level, high water with 100 year return period + 90 cm (215 - 260 cm, RH2000)

Heat waves; nr of periods with 5 or more consecutive

days with temperatures exceeding 20°C. +5-7

Drought, nr of days with no precipitation + 50-80 days

Step 1.2 Identify risk sources and to-be-examined-threats A preliminary list of risks relevant to the study area was compiled based on the climate change data, the current climate, the conditions of the study area and its surroundings, and the list of climate change-induced threats proposed in the Quickscan procedure. The proposed risk list was limited to six risks in order to make possible a 1-hour test of the method. The final risk list after step 2.1 is shown in Table 3-4. Step 1.3 Determine importances of road sections in road network It was decided to skip this step in order to save valuable workshop time for steps further on in the Quickscan process. Step 1.4 Preparation of workshop I STA organised a workshop with the purpose to demonstrate and discuss different risk assessment models for climate change related risks to roads. As a part of this workshop a QS exercise was planned. STA sent out invitations to workshop participants that well covered the specification in the Quickscan method description. A presentation was prepared that included information on ROADAPT, the Quickscan method, the scope of the study, the study area and characteristics of the surroundings, and expected climate change effects. A short list of relevant risks was prepared. Step 2.2 Establish consequence criteria Step 2.2 was done in the preparation stage of the workshop with three of the STA workshop participants. It was agreed that the six consequence criteria that are proposed in the guideline were adequate. The participants were asked to weight the criteria, reflecting STA’s priorities, by


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individually dividing 21 points over the criteria. Then the scoring was normalised. Results are shown in Table 3-2.

Table 3-2. Consequence criteria weights.

Criterion Criterion weight

Ranking

sum

Mean

weight

Normalised

value

Participant 1 Participant 2 Participant 3

Availability 4 5 4 13 4,3 0,21

Safety 8 6 5 19 6,3 0,30

Surroundings 2 4 4 10 3,3 0,16

Direct costs 2 2 3 7 2,3 0,11

Reputation 1 1 2 4 1,3 0,06

Environment 4 3 3 10 3,3 0,16

Sum 21 21 21 1

Definitions of the consequence criteria were discussed and it was agreed to use the definitions given as example in the Quickscan guideline draft, apart from the reputation definition which was redefined. Costs were transformed from EUR to SEK, but the same consequence class divisions were used. The used definitions are shown below: Availability

1. A negligible impact on the availability 2. A minimal negative impact on the availability 3. A serious impact on the availability 4. A catastrophic impact on the availability

Safety

1. A negligible impact on the user safety (light material damage), but within acceptable limits

2. An influence that reaches the boundaries of acceptable user safety, with as a consequence a number of extra accidents with temporary loss of health or injuries without absence (material damage, slight injuries)

3. An influence to such extent that the boundaries of user safety are exceeded, with as a consequence a serious increase of the number of accidents with permanent loss of health (serious material damage, heavy injuries)

4. A catastrophic influence on user safety, with as a consequence extra deadly danger during normal use (serious material damage, heavy injuries, casualties)

Surroundings (effects on the surrounding road network)

1. A negligible impact on the use of the local network, a road segment is at stake 2. A minimal negative impact on the use of the regional network, a road section is at

stake 3. A serious impact on the use of the regional network, a road stretch is at stake 4. A catastrophic impact on the use of the nationwide network, the road network is at

stake Direct technical costs (costs for management during incident and repair)

1. Less than k€ 25 (225 000 SEK) 2. Between k€ 25 and k€ 100 (255 000 – 900 000 SEK) 3. Between k€ 100 and k€ 500 (900 000 – 4 500 000 SEK)


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4. More than k€ 500(4 500 000 SEK) Reputation

1. No to slight loss of reputation; no complaints 2. Slight to moderate loss of reputation; notices in media with attention to (fictive) loss

for road users 3. Substantial loss of reputation; reputation has a set-back, notices in media with

attention to physical damage / hardships of road users, gets attention in nationwide politics

4. Extreme loss of reputation; position of minister at stake Environment

1. No to slight impact to the natural environment directly surrounding the road 2. Slight to moderate impact on the natural environment in the near vicinity of the road 3. Major impact on the natural environment in the near vicinity of the road 4. Extreme impact on the natural environment in the wide vicinity of the road

Step 2 Workshop I This workshop was held on April 3rd as a 1-hour morning session at STA’s regional office in Malmö. Participants and their backgrounds are listed in Table 3-3. The workshop was led by Stefan Falemo, ÅF, on behalf of SGI.

Table 3-3. Attending workshop participants. Name Organization Competence

Stefan Falemo ÅF Infrastructure Workshop leader, risk management engineer

Bo Kristofersson Swedish Transport Administration Risk expert

Stephanie Hellstrand Swedish Transport Administration Investigator risk and climate

Eva Liljegren Swedish Transport Administration Expert climate change adaptation

Johan Höglund Swedish Transport Administration Project manager maintenance for the studied road

Monika Mårtensson Swedish Transport Administration Safety manager

Catarina Johansson Swedish Transport Administration Maintenance planner

Agne Gunnarsson Swedish Transport Administration Expert geotechnics

Bengt Randau Svevia Maintenance manager for the studied road

Step 2.1 Agree with participants on Quickscan approach The participants agreed to the background information on the road, surroundings and expected climate change that was presented (step 1.1). It was agreed to assess the six proposed threats and to add one new threat: snow drift. This risk was not found in the ROADAPT list of threats. Snowdrift was added since it is seen as a typical problem in this region, is expensive to have preparedness for, and it occurs in the same locations over and over. Consequence criteria as suggested in step 2.2 were accepted.


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Step 2.3 Estimating the consequences of the threats The participants were divided into two groups, each with at least one participant with local hands-on knowledge about the studied road. The threats were divided equally between the groups and were scored in parallel sessions as group discussions with common decisions. The final risk list is presented in Table 3-4.

Table 3-4. List of relevant threats and results of consequence scoring. Threats 1, 2 and 5 were scored by group A, and the remaining threats were scored by group B.

Step 2.4 Evaluation of the scoring A discussion on the scoring results would have been beneficial, but could not be held due to time limitations. Step 4.2 Scoring of the probabilities of the threats Probability assessment criteria were discussed and it was agreed to use the following criteria:

1. Very seldom less than once every 50 years 2. Seldom once every 10 to 50 years 3. Sometimes once every 2 to 10 years 4. Often more than once every 2 years

Probabilities were scored in two parallel sessions as group discussions with common decisions. The same groups as in step 2.3 were used, and the groups were responsible for the same risks. Probabilities of the threats were assessed for the present situation and for year 2100. Where future probability could not be assessed, a trend was indicated; ‘increase’, ‘decrease’ or ‘unknown’. Probability and consequence scores are shown in Table 3-5.

Threat sub Threat nr Availibility Safety

Effect on

surrounding

network

Direct

costsReputation Environment

Summed weighted

consequence score

Pluvial flooding (overland flow after precipitation,

increase of groundwater levels, increase of aquifer

hydraulic heads)

1 3 2 2 2 1 1 2,0

Inundation of roads in coastal areas, combining the

effects of sea level rise and storm surges 2 3 2 3 2 1 2 2,3

Cracking, rutting, embrittlement 5 2 2 1 4 3 1 2,0

Reduced visibility during snowfall, heavy rain

including splash and spray10 2 2 1 1 1 1 1,5

Flooding of road surface due to low capacity of

storm water runoff11 3 1 3 1 2 2 2,0

Aquaplaning in ruts due to precipitation on the

road, splash and spray12 2 2 1 1 3 1 1,6

Snow drift 19 3 2 1 2 3 2 2,1


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Table 3-5. Probability scores for the present climate and for year 2100 are presented along with the consequence scores.

Step 4.3 Evaluation of the scoring A discussion on the scoring results would have been beneficial, but could not be held due to time limitations. Step 4.4 Evaluation and prioritization of the risks The threats in Table 3-5 were plotted in a risk matrix for visualisation and evaluation purposes. This step was carried out by the workshop leader in parallel to step 4.5.

Figure 3-2. Risk matrix. The threats are numbered according to Table 3-5. Step 4.5 Identify locations of threats STA’s map showing the Malmö maintenance area in A1 format was used as a background map. Known or likely threat locations were marked with highlighting pens with colours corresponding to each risk. Only the two project leaders with local experience of the studied road felt that they had enough knowledge to pinpoint threat locations, although the other participants also took part in the discussions (Figure 3-3). Identified threats are presented in Figure 3-4.

Threat sub Threat nrSummed weighted

consequence score

Probability

year 2014

Probability

year 2100

Probability trend

(increase/decrease)



hydraulic heads)

1 2,0 2 3 increase


effects of sea level rise and storm surges 2 2,3 1 1

Cracking, rutting, embrittlement 5 2,0 1 2

Reduced visibility during snowfall, heavy rain

including splash and spray10 1,5 4 4 increase

Flooding of road surface due to low capacity of

storm water runoff11 2,0 3 3 increase

Aquaplaning in ruts due to precipitation on the

road, splash and spray12 1,6 3 3 increase

Snow drift 19 2,1 4 4 unknown


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Figure 3-3. Identifying risk locations and drawing a risk matrix.

Figure 3-4. Threats in locations identified in the Quickscan.


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3.1.3 Conclusions Workshop participants with knowledge of RIMAROCC recognized Quickscan as a quick version of RIMAROCC. Quickscan and STA’s present method “Risk analysis chosen road stretch” are considered equal. Steps 2.4 and 4.3, evaluation of consequence and probability scoring, was skipped in this case study. It seems the evaluation of the scoring is not absolutely necessary, but still preferred in order to gain mutual understanding of the results. Also, the scoring might change after discussion. More about this can be found in the description of the steps in the Quickscan guidelines. Some detailed comments on the method:

• Socio-economic costs should be covered in the consequence assessment • Snowdrift should be added as a risk related to wind, with snow as a prerequisite. An

example of an effective measure is tree-planting in vulnerable road sections. • It was pointed out that for the Quickscan method to be of help to a maintenance

contractor, it is important that the method is designed as cyclic, and that the results are a “live” document where measures are described and registered. A history of earlier developments in risk assessment and reduction measures is important.

Only the participants with local experience of the road in question were comfortable with identifying the locations of the threats. This shows the importance of knowledge transfer when one contractor’s maintenance contract is terminated and handed over to a new contractor. Presently no demands on knowledge transfer are stated in the maintenance contracts, and it takes many years for a new contractor to identify these “known” risks. Some participants saw a Quickscan workshop involving the previous and the new contractor as an attractive alternative for such knowledge transfer. It was discussed if a short (less than ½ day) Quickscan workshop could be held in the beginning and end of each maintenance contract with the purpose of knowledge transfer between contractors. A challenge when implementing Quickscan in STA is that responsibilities for the steps are divided among the different administrative departments within STA’s organisation. Roughly, the Regional maintenance department is responsible for regular maintenance but does not have a risk reduction budget. Risk identification is partly done on national level, but can also be done locally by the maintenance contractor. When a risk is identified that needs a risk reduction measure, this is investigated in the Society department, which investigates alternative risk remediation measures and finalises the measure. The department Investments then realises the selected measure. Therefore it is difficult to say who should have the responsibility for carrying out a full Quickscan.

3.2 Quickscan application in Denmark

This chapter presents the Quickscan case study that has been performed for the Denmark case, on the TEN-T roads E55 Copenhagen - Helsingör and E20 Köge - Copenhagen. The Quickscan followed the Quickscan method description draft dated March 2014.

3.2.1 Organization of the Quickscan In consultation with DRD it was decided to compress the proposed three workshops into two half days to limit the workload for DRD employees. The Quickscan was arranged as two consecutive ½ day workshops.


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3.2.2 Quickscan results step by step Step 1.1 Scope definition The study area covered the TEN-T roads E55 Copenhagen - Helsingör and E20 Köge – Copenhagen, as shown in Figure 3-5. The study area was slightly changed in step 2.1 to exclude the eastern-most part of E20 on the island Amager that is owned by a private company. Figure 3-5 shows the final case study area.

Figure 3-5. The Quick scan study area is marked in orange. The areas surrounding the roads consist of farmland and built-up areas. Small forests are present in the north of the network, and single trees exist in parts of the area. The landscape is flat and close to sea level, and in parts the road is situated close to the sea. One road stretch is situated below sea level, and is protected with dikes. The geology in the area is dominated by clayey till and glaciofluvial deposits. The roads generally consist of 3 lanes in each direction and a fourth lane is planned for large parts of the E20 in the study area. ADT is 30 – 100 000 vehicles per day. The roads are important freight routes for transports across the border to Sweden with ferry in Helsingör and on the Öresund bridge east from Copenhagen. Expected climate change in Denmark by year 2050 is summarized in Table 3-6. The climate change is calculated using DMI’s regional climate model HIRHAM5. The figures are changes for the period 2021-2050 in relation to the normal period 1961-1990. The data was compiled


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by the Danish cross-disciplinary governmental climate change platform Klimatilpasning (Klimatilpasning, 2014).

Table 3-6. Climate change in Denmark by year 2050, based on the IPCC SRES A1B emission scenario.

Year mean temperature +1.2°C (±0.2°C)

Winter +1.5°C (±0.2°C)

Summer +0.9°C (±0.1°C)

Year mean precipitation +7% (±3%)

Winter +11% (±3%)

Summer +4% (±4%)

Mean wind

Sea +1%

Sea + Land +3%

Extreme events

Frost days -24 days

Heat waves +1.3 days

Tropical nights +5 nights

Nr of days with more than 10 mm precipitation +3 days

5-day precipitation +6 mm

Extreme events, precipitation +1 day

Extreme sea level at storm flood, including isostatic

uplift and wind effects +0.0-0,60 m

Step 1.2 Identify risk sources and to-be-examined-threats The climate change projection data used in the study was compiled by the cross-disciplinary Danish governmental organisation Klimatilpasning.dk and originates from the A1B emission scenario. A preliminary list of risks relevant to the study area was compiled based on the climate change data, the current climate, the conditions of the study area and its surroundings, and the list of climate change-induced threats proposed in the Quickscan procedure. The risk list was constructed in a conservative way so that no relevant risks would be left out. The final risk list after step 2.1 and 4.1 is shown in Table 3-9. The list of climate change-induced threats proposed in the Quickscan procedure was checked thoroughly Step 1.3 Determine importances of road sections in road network Since only two workshop sessions were available in this case study, it was decided to skip this step in order to save valuable workshop time for steps further on in the Quickscan process. Step 1.4 Preparation of workshop I Michael Larsen, DRD, identified possible workshop participants within DRD that covered the specification in the Quickscan method description. Invitations were sent out by SGI to the proposed participants.


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A presentation was prepared that included information on ROADAPT, the Quickscan method, the scope of the study, the study area and characteristics of the surroundings, and expected climate change effects. Step 2 Workshop I This workshop was held on April 1st as an afternoon session at DRD’s Copenhagen office. Workshop I and II participants and their backgrounds are listed in Table 3-7. The workshop was led by Stefan Falemo, ÅF, on behalf of SGI. Due to a notification of illness only two people participated in workshop I. It was planned to follow the Quickscan procedure to step 4.3 on day 1. However, knowledge gaps on pavement related risks and probabilities for risks within the day 1 group led to re-planning. Day 1 finished after step 2.4, which is in line with the suggested Quickscan procedure.

Table 3-7. Invited and attending workshop participants. All participants except the workshop leader were DRD employees.

Name Competence Present day 1

Present day 2

Stefan Falemo, ÅF Workshop leader, risk management engineer

Yes Yes

Marianne Grauert Climate change expert Yes -

Michael Larsen Climate change adaptation researcher - Yes

Erik Nielsen Road engineer, pavement bitumen - Yes

Finn Thøgersen Road engineer, road stability - Yes

Christian Axelsen Blue spot expert - Yes

Niels Peter Albrechtsen Maintenance expert especially water and drainage systems

Yes Yes

Michael Quist Coordinator of environmental issues - -

Ulrik Mørch Jensen Hydrology Engineer, especially drainage system

- -

Bjarne Schmidt Road engineer, pavement - -

Step 2.1 Agree with participants on Quickscan approach The material from step 1 was presented to the participants and then discussed. A slight change was made to the suggested case study area: on the E20 towards the Öresund bridge, the study area was limited to the part of the road that is owned and managed by DRD. This means that part of the road on the island Amager was excluded. The final case study area is shown in Figure 3-5. The participants agreed to the description of the case study area (details are given in step 1.1). The area does not include any tunnels or larger bridges. The climate change projection data that was presented was based on the SRES A1B emission scenario, which is the same scenario that DRD uses for other climate change studies. It was concluded that 2050 is a relevant time horizon to study considering the life time of the infrastructure components.


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The proposed risk list from step 1.2 was checked thoroughly. Risks from the list of climate change-induced threats proposed in the Quickscan procedure were removed from and added to the risk list according to decisions in the group. It was agreed to keep all pavement and road structure related risks in the list until workshop II, for experts within these fields to decide which risks are not relevant. Step 2.2 Establish consequence criteria It was agreed that the six consequence criteria that are proposed in the guideline were adequate. The participants were asked to weight the criteria, reflecting DRD’s priorities, by individually dividing 21 points over the criteria. Then the scoring was normalised. Results are shown in Table 3-8.

Table 3-8. Consequence criteria weights.

Criterion Criterion weight Ranking sum Mean weight Normalised value

Participant 1 Participant 2

Availability 5 5 10 5,0 0,24

Safety 7 7 14 7,0 0,33

Surroundings 4 2 6 3,0 0,14

Direct costs 2 3 5 2,5 0,12

Reputation 1 2 3 1,5 0,07

Environment 2 2 4 2,0 0,10

Sum 21 21 1

Definitions of the consequence criteria were discussed and it was agreed to use the definitions given as example in the Quickscan guideline draft, apart from the reputation definition which was redefined. Costs were transformed from EUR to DKK, but the same consequence class divisions were used. The used definitions are shown below: Availability

5. A negligible impact on the availability 6. A minimal negative impact on the availability 7. A serious impact on the availability 8. A catastrophic impact on the availability

Safety

5. A negligible impact on the user safety (light material damage), but within acceptable limits

6. An influence that reaches the boundaries of acceptable user safety, with as a consequence a number of extra accidents with temporary loss of health or injuries without absence (material damage, slight injuries)

7. An influence to such extent that the boundaries of user safety are exceeded, with as a consequence a serious increase of the number of accidents with permanent loss of health (serious material damage, heavy injuries)

8. A catastrophic influence on user safety, with as a consequence extra deadly danger during normal use (serious material damage, heavy injuries, casualties)

Surroundings (effects on the surrounding road network)

5. A negligible impact on the use of the local network, a road segment is at stake 6. A minimal negative impact on the use of the regional network, a road section is at

stake


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7. A serious impact on the use of the regional network, a road stretch is at stake 8. A catastrophic impact on the use of the nationwide network, the road network is at

stake Direct technical costs (costs for management during incident and repair)

5. Less than k€ 25 (188 000 DKK) 6. Between k€ 25 and k€ 100 (188 000 – 750 000 DKK) 7. Between k€ 100 and k€ 500 (750 000 – 3 750 000 DKK) 8. More than k€ 500(3 750 000 DKK)

Reputation

5. No to slight loss of reputation; no complaints 6. Slight to moderate loss of reputation; notices in media with attention to (fictive) loss

for road users 7. Substantial loss of reputation; reputation has a set-back, notices in media with

attention to physical damage / hardships of road users, gets attention in nationwide politics

8. Extreme loss of reputation; position of minister at stake Environment

5. No to slight impact to the natural environment directly surrounding the road 6. Slight to moderate impact on the natural environment in the near vicinity of the road 7. Major impact on the natural environment in the near vicinity of the road 8. Extreme impact on the natural environment in the wide vicinity of the road

Step 2.3 Estimating the consequences of the threats This step was carried out as a group discussion with common decisions. All threats in the risk list from step 2.1 were scored on one consequence criterion before moving on to the next criterion. Threats regarding pavement and road structure where left blank to be completed in workshop II. The final risk list including consequence scoring from workshop II is presented in Table 3-9.


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Table 3-9. List of relevant threats and results of consequence scoring.

Step 2.4 Evaluation of the scoring Scores on different threats regarding each criterion were checked and compared as a part of step 2.3. No further evaluation was needed. Step 3 Preparation of workshop II Step 4 Workshop II This workshop was held in the morning of April 2nd in DRD’s Copenhagen office. The workshop participants are listed in Table 3-7. The workshop was led by Stefan Falemo, ÅF, on behalf of SGI. Steps 4, 6.2 and parts of 6.3 were completed in the workshop. Step 4.1 Agree on study method and share status of research The material from step 1 with the agreed changes from step 2.1 was presented to the participants and then discussed. The participants agreed to the scope and information on the case study area, the climate change information as well as the changes made in step 2.1. Step 2.3 was revisited to fill in the gaps on pavement and road structure related risks. As a result, the following threats to pavement could be dismissed: ‘Frost heave’, ‘Aggregate loss and detachment of pavement layers’, ‘Cracking due to weakening of the road base by thaw’, ‘Thermal expansion of pavements’. ‘Cracking, rutting and embrittlement’ were considered relatively different threats. Rotting was considered the dominating threat of these three, while embrittlement was considered a minor threat. The lower parts of the base course of the studied roads consist of till to a large extent.

Threat description Threat nr

Threat sub Availibility Safety

Effect on

surrounding

network

Direct

costsReputation Environment

Summed

weighted

consequence

score

1 3 2 2 1 2 1 2,0

2 3 2 2 1 2 1 2,0

3 3 1 1 4 1 1 1,8

Weakening of the road embankment and road

foundation by standing water 4 2 1 1 3 2 1 1,5

Cracking, rutting, embrittlement 5 2 2 1 2 2 1 1,8

Reduced visibility during snowfall, heavy rain including

splash and spray10 1 2 1 1 2 1 1,4

Flooding of road surface due to low capacity of storm

water runoff11 3 2 2 2 2 1 2,1

Aquaplaning in ruts due to precipitation on the road,

splash and spray12 1 2 1 1 2 1 1,4

Damage to signs, lighting fixtures, pylones, canopies,

noise barriers and supports16 1 1 1 3 2 1 1,3

Damage to energy supply, communication networks

(eg. pylones) and/or matrix boards by wind, snow,

heavy rainfall and/or lightning

17 1 1 1 2 2 1 1,2

Trees, wind mills, noise barriers, trucks falling on the

road18 2 2 2 1 1 1 1,7

Consequence score

Impact on soil moisture levels (increase of watertable),

affecting the structural integrity of roads, bridges and

tunnels



hydraulic heads)


effects of sea level rise and storm surges


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The threat ‘Decrease in skid resistance on pavements from migration of liquid bitumen’ is considered a necessary trade-off to be able to cope with freeze-thaw cycles. A high bitumen content is needed to reduce the negative effects of freeze-thaw cycles which are considered a more important issue. Therefore this threat is governed by pavement management practice. In built-up areas noise-reducing pavement is becoming more and more popular. These pavements have smaller grain size which increases the risks for aquaplaning and for the forming of ruts/tracks. Therefore ‘Aquaplaning in ruts due to precipitation on the road, splash and spray’ is a risk that is expected to increase as a trade-off for reduced noise. Step 2.4 was revisited to fill in the consequence score gaps related to pavement and road structure. Step 4.2 Scoring of the probabilities of the threats Probability assessment criteria were discussed and it was agreed to use the following criteria:

5. Very seldom less than once every 50 years 6. Seldom once every 10 to 50 years 7. Sometimes once every 2 to 10 years 8. Often more than once every 2 years

Probabilities were assessed in a group discussion with common decisions. Probabilities of the threats were assessed for the present situation and for year 2050. Where future probability could not be assessed, a trend was indicated; ‘small increase’, ‘increase’ or ‘considerable increase’. Probability and consequence scores are shown in Table 3-10.


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Table 3-10. Probability scores for the present climate and for year 2050 are presented along with the consequence scores.

Step 4.3 Evaluation of the scoring Since step 4.2 was handled in a group exercise, the evaluation of the scoring was a natural part of the discussions and was not handled in a separate step. Step 4.4 Evaluation and prioritization of the risks The threats in Table 3-10 were plotted in a risk matrix for visualisation and evaluation purposes. Based on the risk matrix, it was agreed to try and localize the threats numbered 1, 5, 10, 11, 12 and 18.

Threat description Threat nrSummed weighted

consequence score

Probability year

2014

Probability year

2050

Probabaility trend

(increse/decrease)

Pluvial flooding (overland flow after precipitation, increase

of groundwater levels, increase of aquifer hydraulic heads)1 2,0 3,0 ? increase

Inundation of roads in coastal areas, combining the effects

of sea level rise and storm surges 2 2,0 1,0 ? small increase

Impact on soil moisture levels (increase of watertable),

affecting the structural integrity of roads, bridges and

tunnels

3 1,8 1,0 2,0considerable

increase

Weakening of the road embankment and road foundation

by standing water 4 1,5 1,0 2,0

considerable

increase

Cracking, rutting, embrittlement (rotting dominerande,

embrittlement litet problem)5 1,8 3,0 3,0 increase

Reduced visibility during snowfall, heavy rain including

splash and spray10 1,4 4,0 4,0 increase

Flooding of road surface due to low capacity of storm water

runoff11 2,1 2,0 3,0 increase

Aquaplaning in ruts due to precipitation on the road, splash

and spray12 1,4 3,0 4,0 increase

Damage to signs, lighting fixtures, pylones, canopies, noise

barriers and supports16 1,3 3,0 3,0 small increase

Damage to energy supply, communication networks (eg.

pylones) and/or matrix boards by wind, snow, heavy rainfall

and/or lightning

17 1,2 3,0 3,0 small increase

Trees, wind mills, noise barriers, trucks falling on the road 18 1,7 2,0 2,0 small increase


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Figure 3-6. Risk matrix. Risks are numbered according to Table 3-10. Step 4.5 Identify locations of threats A Map printout from Google maps in A1 format was used as a background map to localize the threats. Possible locations were marked with highlighting pens with colours corresponding to each risk. Some risks were considered difficult to mark on maps based only on experience and the maps. The participants agreed that a more accurate and detailed identification of risk locations can be done with the aid of DRD’s internal databases and management systems. Identified threats are shown in Figure 3-7.


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Figure 3-7. Threats in locations identified in the Quickscan. Step 5 Desktop III This step was skipped since workshop II and III were put together. Step 6 Workshop III Step 6.1 Wrap up previous results This step was skipped since workshop II and III were put together.


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Step 6.2 Finalize discussion on acceptability of risk; which threats require action? It was successful to use the risk matrix and the map as a risk overview, and the risks were discussed. The major identified risks were related to flooding and to pavement and road structure integrity. It was agreed to work on action plans for risks 1, 5 and 11. Step 6.3 Determine action plan First, the concerned components and operational procedures of the infrastructure were identified (Table 3-11). Then the maintenance frequency and the timing of critical climate change were assessed for the different infrastructure components (Table 3-12).

Table 3-11. Concerned infrastructure components and operational procedures.

Table 3-12. Maintenance frequencies and critical climate change time horizons.

This quick analysis shows that wearing course and binder soil have a shorter maintenance interval than the critical time horizon for climate change-related damages. Actions involving these components, e.g. changing asphalt mixture to reduce the risk for rutting, can be scheduled as a part of the regular maintenance activities. Such risk reduction measures are preferred since the additional cost to regular maintenance can be rather small. Risk reduction measures that involve the base course, the road base as a whole, or the drainage system means that actions need to be taken before the end of the design life of the components. Such actions are more costly.

Threat description Threat nrConcerned components of

the infrastructure

Concerned

operational

procedures

Pluvial flooding (overland flow after precipitation, increase

of groundwater levels, increase of aquifer hydraulic heads)1 Wearing course, road base -

Cracking, rutting, embrittlement (rotting dominerande,

embrittlement litet problem)5

Wearing course, base

course

Max 10mm track

depth before repair

measure is

performed.

Flooding of road surface due to low capacity of storm water

runoff11

Road surface, drainage

system

Maintenance

inverval of drainage

pipes. Return

period of design

flood event (25

yrs).

Concerned components of the infrastructure

Maintenace frequency / life span Critical time horizon regarding damages related to climate changes (years less than the asset life span)

wearing course 13-15 years (10-12 years for noise-reducing pavement) >maintenance frequency

binder soil 20-25 years >maintenance frequencybase course >60 years 20 yearsroad base >60 years 20 yearsdrainage system >60 years 20 years


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There was no time to conclude action plans for the risks in this workshop, but some measures were mentioned and discussed. Permeable pavement is considered one way of reducing the risk of flooding of the road surface. Pavement related measures is although always a compromise of many properties: reflective capacity for enhanced visibility, grain size for friction and noise reduction and bitumen content for resisting frost-thaw related damages. Ensuring drainage system capacity by more frequent maintenance is considered a relevant measure. However, the design flood event with a return period of 25 years might force an upscaling of the drainage system when this return period changes and flooding becomes more frequent. It was also mentioned that in a long time perspective, a whole new road corridor might be planned, replacing the existing road. If the traffic on the studied road decreases in the future, some risks might be more acceptable.

3.2.3 Conclusions DRD has worked with road models and documentation systems for many years and the participants agreed that much of the knowledge that a Quickscan analysis gives is already available in their organisation. However it was concluded that maintenance contractors can benefit from the method in order to learn more about the risks in the areas where they have maintenance contracts. Many of the threats are dependent on each other. Reducing one risk may increase other risks and it is essential to identify the positive and negative impacts of all studied risk reduction measures and to a balance between them. Steps 2.4 and 4.3, evaluation of consequence and probability scoring, was not carried out in this case study. It seems the evaluation of the scoring is not absolutely necessary if scoring is done in a group session. However it is still preferred in order to gain mutual understanding of the results. Also, the scoring might change after discussion. More about this can be found in the description of the steps in the Quickscan guidelines. The suggested criteria for reputation consequences in the guideline should be changed; they should be related to consequences of the unwanted event only.


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4 Socio-economic assessment

The Öresund case study has been used in order to perform some of the methodologies that are presented in part D of the guidelines. For detailed explanation on the methodology, the reader can refer to this part D. In the following sections we present the main results that have been obtained at different levels concerning the impact assessment of a traffic event: the closure of the motorway E20 South of Copenhagen. This section of road was selected for its importance in terms of traffic flows and the presence of high different risks (in particular flooding risk). See illustration 3.7. Three levels of analysis have been undertaken:

- Network level - Local territory level - Economic system as a whole.

4.1 Network level

The objective is to estimate the time lost by the road network users during the event duration and translate it in monetary values. Other losses such as toll revenue, are not taken into account as they are of second order in general. The following table gives a rough estimation of the economic losses considering a closure of the E20 South of Copenhagen for 3 days.

Table 4-1. example of socio-economic impact results

Value for

Car

Value for

HGV2

Total

(Car+HGV)

Duration of incident (days) 3

Number of vehicles per day (Car+Truck) 50 000 10 000 60 000

Individual additional distance travelled (km) 10 10

Unit cost per km (€/km) 0,1 0,5

Total cost for user (€) 150 000 150 000 300 000

Individual time loss (h) 0,5 0,5

Value of time (€/h) 15 45

Total loss time in € 1 125 000 675 000 1 800 000

Total loss (€) 1 275 000 825 000 2 100 000

These results can be obtained simply by:

- An estimation of the daily traffic which is rerouted (based on historical data) - The length of the alternative route which is used and the additional distance - The additional travel time using the alternative route

Of course, using a traffic model which will assign the traffic on various alternative routes, and which can take into consideration the traffic intensity per hour, allows having a more accurate

2 Heavy Goods Vehicles


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assessment. However, if it requires building the model from scratch the benefits are low in comparison with the simple method.

4.2 Territory level

The simple network model is focused on the network users and doesn’t take into consideration the likely impact on the existing traffic which doesn’t use the main network. For a better assessment, and particularly in case of events affecting several stretches of the network, it is necessary to use a traffic model. Traffic models require knowing the traffic patterns, typically the Origin-Destination matrices. O/D matrices are generally available from the transport planning authorities thanks to surveys, regular calibration with the observed traffic and nowadays using tracking tools based on cellular phones. For the Öresund case study they were not available, but they have been estimated using a gravity model based on the population and employment. The following maps present such data.

Figure 4-1 Population of Öresund region in 2013


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Figure 4-2 Empoyment of Öresund region in 2013 Using this data it has been possible to simulate the travel time from each origin and destination and the impact due to the traffic event (Closure of the E20 highway). The following maps illustrate this travel time by zone (colours corresponding to isochrones) for accessing to the south of Copenhagen (red dot).

Figure 4-3. Travel time to Copenhagen (reference situation)


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Figure 4-4. Travel times with E20 closed. These travel times can be translated in monetary figures weighting them by the traffic on each O/D pair (considering different categories of users if the data are available).

4.3 Economic system as a whole

The third level of impact analysis is to consider not only the impact on travel time, but also the accessibility to the various economic poles (employment zones, commerce, factories). This analysis introduces for each O/D pair an economic value. They are different theories concerning this as explained in Part D of the guidelines. The following maps give the results of using one method based on a utility value for travelling from each O to D. The utility value integrates the “product supply” at destination. The objective is to better reflect the economic impact as a whole. The difficulty to use this method is the collection of data according to the various economic poles and the calibration with the observed situation.

Figure 4-5. Measure of global accessibility (reference case)


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Figure 4-6. Difference in accessibility (E20 closed)

4.4 Conclusions

In conclusion, different methodologies exist for assessing the impact of events due to climate changes and impacting the mobility service. Though not detailed concerning the Öresund case study, the data available have allowed illustrating what kind of results can be obtained at the 3 levels of analysis and demonstrating the feasibility.


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5 General conclusions

The two Blue spot studies performed in Sweden and Denmark generally use the same input and give comparable results. It’s noted that the performance of the model relies on a hydrological correct model that allows water to flow on the surface. STA have also compared the modelled results to sites with occurred flooding-induced accidents and the comparison indicates that this is a useful method. Both STA and DRD are at present working with methods that in many ways are considered equal to the Quickscan method. However, both workshops concluded that the Quickscan method might be beneficial for informing maintenance contractors about the risk in their operating area and to transfer knowledge between old and new contractors. Quickscan workshops could then be held in the beginning and end of each maintenance contract with the purpose of knowledge transfer between contractors. Comments about the Quickscan method:

• Socio-economic costs should be covered in the consequence assessment. The first level of analysis (network level) seems appropriate for the Quickscan approach. It is of course a first step and if issues at stake needs to estimate more precisely the consequence of the events a territory level approach using a traffic model is recommended.

• Snowdrift should be added as a risk related to wind, with snow as a prerequisite. • It was pointed out that for the Quickscan method to be of help to a maintenance

contractor, it is important that the method is designed as cyclic, and that the results are a “live” document where measures are described and registered. A history of earlier developments in risk assessment and reduction measures is important.

• Many of the threats are dependent of each other and reducing one risk may increase other risks. Socio-economic assessment may help to choose the best strategy in that case.

• The suggested criteria for reputation consequences in the guideline should be changed; they should be related to consequences of the unwanted event only.

Comments about conducting a Quickscan workshop:

• Only the participants with local experience of the road in question were comfortable with identifying the locations of the threats.

• Within STA, implementing Quickscan might be a challenge as the responsibility for the different steps are divided among different administrative departments.

Comments about the socio-economic assessment:

• The case study has demonstrated that applying the methodologies and the respective tools for socio-economic assessment is feasible. However, the availability of sound data is a key issue.

• But methodology and tools needs to associate a socio-economic expert for the assessment, or at least somebody familiar with transport planning tools.


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6 Acknowledgement

The research within the ROADAPT project has been carried out as part of the CEDR Transnational Road research Programme Call 2012. The funding for the research is provided by the national road administrations of the Netherlands, Denmark, Germany and Norway. Additional funding to the ROADAPT project has been provided by all participating partners. The Project Executive Board from CEDR is composed of Kees van Muiswinkel (project manager, Rijkswaterstaat, the Netherlands), Gordana Petkovic (Norwegian Public Roads Administration), Henrik Fred Juelsby Larsen (Danish Road Directorate) and Markus Auerbach (BASt, Germany). They have in a constructive way contributed to the project for which we gratefully acknowledge them. Our sincere thanks go also to all the other people who have made contributions to the project. We mention in particular: Christian Axelsen – Danish Road Directorate Philippe Crist – International Transport Forum Jakob Haardt – BASt Elja Huijbregtse –TNO Michael Ruben Anker Larsen – Danish Road Directorate Eva Liljegren – Swedish Transport Administration Herbert ter Maat – Alterra Christoph Matulla – Zentralanstalt für Meteorologie und Geodynamik Joachim Namyslo – Deutscher Wetterdienst Franziska Schmidt – IFSTTAR Alexander Bakker – KNMI Pierre Charcellay – Egis Ad Jeuken – Deltares Dirk Pereboom – Deltares Anna Maria Varga – Egis Hessel Winsemius – Deltares The project was undertaken by Deltares, SGI, Egis and KNMI. It was organized in several work packages and cases. The following persons all have made large contributions to the results. • Deltares: Thomas Bles (coordinator and work package leader), Arjan Venmans (work

package leader), Mike Woning (case leader) and Niels Eernink • SGI: Per Danielsson (work package leader), Stefan Falemo (case leader, hired from ÅF),

Hjördis Löfroth and Linda Blied • Egis: Martial Chevreuil (work package leader), Yves Ennesser (case leader), Eric

Jeannière, Olivier Franchomme and Lise Foucher • KNMI: Janette Bessembinder (work package leader) and Alexander Bakker


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7 References

Klimatilpasning (2014). Climate change in Denmark to year 2050 following the A1B scenario. http://www.klimatilpasning.dk/viden-om/klima/klimaaendringeridanmark.aspx SMHI (2011). Klimatanalys för Skåne län. (Climate analysis for Skåne, in Swedish). Report nr 2011-52. http://www.lansstyrelsen.se/skane/SiteCollectionDocuments/Sv/miljo-och-klimat/klimat-och-energi/klimatanpassning/kunskapsunderlag/SMHI_klimatanalys_2012.pdf

ROADAPT Blue spots - Adapting to climate change, methods for assessing the risk

of road flooding

Report for the Swedish Transport Administration

February 2014

ROADAPT Blue spots

M135219

Authors: Greger Lindeberg, Oscar Törnqvist, Metria AB

Front cover: Flooded municipal road east of Helsingborg, Skåne, August 2013. Photo: Sven-Erik Svensson.

For more information, contact:

Greger Lindeberg ([email protected])

Metria Box 30016 104 25 Stockholm

Sweden

Visiting address: Warfvinges väg 35

Phone: 010 - 121 80 00

www.metria.se

Preface

The research assignment is part of a ROADAPT project, included in CEDRs research area Road owners Adapting to climate change. In ROADAPT, several European road authorities are involved, including Sweden by the Swedish Transport Administration. Within the framework of ROADAPT project, several methods to reduce climate related risks to the road system are implemented.

Three cross-border case studies will be made in Europe, of which one of these is carried out by the Swedish Transport Administration and the Danish Road Institute. These authorities finance their respective part of the case study, of which this report concerns the Swedish section. The ROADAPT project also finance part of the expert assistance required in connection with the case study. The results of the case study will be included as part of ROADAPTs final report.

The research project aims to develop a so-called blue-spot analysis (i.e. using mapping tools to find points on the road network which is low-lying and in danger of being inundated during severe rainfall). Blue spot analysis is one of six different analytical methods to be tested in the Swedish case study.

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Contents

Preface ................................................................................................................................................ 1

Summary ............................................................................................................................................. 3

1 Introduction ................................................................................................................................ 4

1.1 Background ......................................................................................................................... 4

1.2 Goals and deliverables ........................................................................................................ 4

2 Study area and data .................................................................................................................... 5

3 Method ....................................................................................................................................... 7

3.1 Step 1: Preparations ........................................................................................................... 7

3.2 Step 2: Potential blue spots, level 1 ................................................................................. 10

3.3 Step 3: Flow calculus, level 2 ............................................................................................ 10

3.4 Step 4: Identifying potentially filled blue spots, level 2.................................................... 11

3.5 Step 5: Data package and delivery ................................................................................... 12

4 Results and metadata ............................................................................................................... 13

4.1 Validation .......................................................................................................................... 14

5 Conclusions and recommendations ......................................................................................... 16

5.1 Methodological improvements ........................................................................................ 16

5.1.1 A complete hydrological DTM .................................................................................. 16

5.1.2 Local precipitation .................................................................................................... 16

5.1.3 Adjusting for local water traps ................................................................................. 17

5.1.4 Scalability .................................................................................................................. 17

6 References ................................................................................................................................ 18

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Summary

The Swedish Transport Administration has, within the research programme ROADAPT (Road owners adapting to climate change), commissioned Metria to conduct a pilot study to investigate climate-induced risks in the road network. Within this programme lies the task of identifying “blue spots”, representing locations particularly vulnerable to extreme precipitation.

The goal is to create an assessment of TEN-T road sections vulnerable to extreme daily precipitation in the pilot area, taking future projections on precipitation by the year 2100 into account. The study concerns the area roughly between Trelleborg, Skåne and Mellbystrand, Halland, southern Sweden.

Data used consist of bridges, roads and the LIDAR – based digital terrain model (GSD-Höjddata, grid 2+) provided by the National land survey. Precipitation estimates originate from SMHI, the Swedish Meteorological and Hydrological Institute. The model used relies on extreme daily maximums (in mm) with a return period of 1, 10 and 100 years. Estimates of future precipitation levels are calculated by compiling various sources on climate projections and precipitation models.

The assignment included the following main topics:

A literature review of current and future precipitation in the area.

Mapping potential "blue spots" in the study area based on terrain (level 1).

Modelling potential "blue spots" in the study area based on runoff models and ground

permeability (level 2).

Production of a short report describing the input, processing and results (this report).

Delivery of GIS data sets and maps.

Deliverables comprise 42 PDF maps in A0 format, one shape file and six .lyr files.

In conclusion, the goals of the project have been met. The limitation of the results owe to large extent to lack of a hydrologically adjusted DTM. However, as an indication of the presence and location of vulnerable locations that ought to be investigated further, the model seems to be satisfactory according to a rough validation by comparing with historically inundated road sections.

Conclusions for a future implementation of a blue spot model for a national coverage are made, and consist of the following points:

A complete hydrologically adjusted DTM should be produced, eliminating false barriers in

the terrain and compensating for subsurface culverts and pipes.

Local water traps within the blue spot watershed could be considered as these are

buffering the response to the blue spots.

To be able to scale this analysis to a national scale, the process should be divided into

smaller areas. Suggested here are the watersheds established by the Swedish

Meteorological and Hydrological Institute.

The results were validated against recorded flooding events and have to be compared to the future projected daily extremes in precipitation. An estimation of future extreme precipitation has been made from a literature survey and is also part of the results of the project.

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1 Introduction

1.1 Background

Effects of climate change have become one of the focus areas for the National Road Authorities. Focus is on minimizing the impacts and effects of the already experienced and anticipated climate changes to ensure safety and passability. This means that road constructions, equipment and buildings are protected against failure and that there is an emergency plan in case of extreme weather conditions. (Grauert et al. 2010)

The uncertainties inherent in predictions of future climate are significant. The climate research community is convinced that there will be a change, but admits it is difficult to precisely quantify the changes in e.g. terms of magnitude and frequency of rainfall. However, floods have always occurred through history and always will. Hence, identifying and improving road sections vulnerable to flooding are of great value irrespective of the severity of climate change.

Areas close to roads that are prone to flooding are referred to as blue spots by the Danish Road Institute, corresponding to e.g. black spots denoting serious accidents on the road network. Given the vast distances covered by roads, an effective tool to assist in finding the weak sections would be very useful. (Hansson et al. 2010).

The SWAMP project is part of an ERA-NET ROAD initiated transnational research programme called "Road Owners Getting to Grips with Climate Change". The four projects commissioned under this programme are funded jointly by the road administrations of Austria, Denmark, Finland, Germany, Ireland, Netherlands, Norway, Poland, Spain, Sweden and United Kingdom. (Larsen et al. 2010). Knowledge gained in SWAMP is used by The Swedish Transport Administration within the research programme ROADAPT (Road owners adapting to climate change), to conduct a pilot study to investigate climate-induced risks in the road network. Within this programme lies the task of identifying blue spots, representing locations particularly vulnerable to extreme precipitation. On commission of the transport administration, Metria has conducted this pilot study, detailed in this report.

The project background is described by Grauert et al. (2010) and the method used is described by Hansson et al. (2010) and Larsen (2010). For the Swedish area, the methods proposed for the blue spot concept have previously been tested for the purpose of creating a method framework for land/road slide risk (MSB 2012).

1.2 Goals and deliverables

The goal is to create an assessment of TEN-T road sections vulnerable to extreme daily precipitation in the pilot area, taking future projections on precipitation for the year 2100 into account. The assessment should point out the saturation, flooding, precipitation for the vulnerable spots.

Results are to be delivered as spatial datasets readable in a GIS environment and as maps with layout.

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2 Study area and data The study concerns the area roughly between Trelleborg, Skåne and Mellbystrand, Halland, southern Sweden. The delineation of the study area is given by Figure 1.

Figure 1. The study area.

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Road Network

Relevant roads in the area are part of the TEN-T network, with national designations E6 and E4. Data was delivered by Swedish National Transport Administration.

Bridges

An excerpt from the database “BatMan“, comprising 559 bridges, was delivered from the Swedish National Transport Administration.

Digital terrain model (DTM)

Underlying the hydrological modelling is the LIDAR – based, bare-earth DTM provided by the National land survey of Sweden. The DTM has a spatial resolution of 2 meters. The DTM was delivered by the Swedish Geotechnical Institute, SGI.

Precipitation data

Precipitation estimates originate from the Swedish Meteorological and Hydrological Institute (SMHI). Sources are detailed in chapter 3.

The model used relies on extreme daily maximums (in mm) with a return period of 1, 10 and 100 years (cf. fig. 2). For the scope of this project, the maximum precipitation in the study is used for the whole area.

Figure 2.Daily extremes of precipitation with a one year return period. Source: Wern 2012.

Climate projections

Estimates of future were compiled from various sources on climate and precipitation models, detailed in chapter 3.

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3 Method The assignment included the following main tasks:

A literature review of current and future precipitation in the area.

Mapping potential "blue spots" in the study area based on terrain (level 1).

Modelling potential "blue spots" in the study area based on runoff models and ground

permeability (level 2).

Production of a short technical report describing the input, processing and results (this

report).

Production of GIS data sets and pdf-maps.

The analysis is performed according to the model developed by the Danish Road Institute (Hansson et al. 2010). The analysis includes the analysis of blue spots level 1 and 2. Level two involves assumptions about ground permeability factored into the model. The work also included a trip to the Danish Road Institute t in Roskilde to ensure that assessments are conducted in a comparable manner.

3.1 Step 1: Preparations

Precipitation data

Precipitation data covering the study area for daily extremes (1, 10, 100 years return period) was collected from SMHI (Wern 2012:47f, 113f). The result consist of values between 25 and 90 mm, which is roughly within the range observed by Bengtsson (2008, one value of 101 mm at Barkåkra in the interval 1961-1990).

Table 1. Present time, daily maximal precipitation estimates (mm) in the study area with 1, 10 and 100 years return period, based on data from Wern (2012).

Return period 1 year 10 years 100 years

Min (SW) 25 45 70

Max (NE) 35 65 90

Projections of future precipitation

Climate change is expected to generate higher short-term precipitation intensities which may have negative consequences on for example urban hydrology. There are numerous reports and scientific papers dealing with predictions of the future climate and also more specifically future precipitation scenarios on national and regional levels (e.g. Raisanen and Petersson 2001, Räisänen et al. 2004, Nikulin et al. 2011, Andréasson et al. 2011, Olsson & Kean 2013, Olsson & Foster 2013).

A recent paper by Olsson and Foster (2013) concentrates on projections of short-term precipitation in Sweden. With the use of the RCA3 regional climate model simulations of precipitation were made projected towards periods 2011 – 2040 (F1), 2041-2070 (F2) and 2071-2100 (F3). The model simulations suggest that 10-year short-term precipitation will increase with an average of 8% until 2070 and 13% until 2100. The authors conclude that daily extremes with a 10 year return time can be expected to increase 0-15% until year 2050, and by 10-30% until 2100

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(Figure 3). Shorter rain intervals are predicted at 5 percentage points higher. The results also indicate that it is reasonable approximate the increase of precipitation with 100 years return time by the same figures. A simple and conservative approach would in this case be to assume future increase of daily precipitation extremes to be around 20% (Table 2).

Table 2 Extreme precipitation (24 hours) situation in Skåne, adjusted for a 20% increase of future daily maximum precipitation with a return period of 1, 10 and 100 years.

Return period 1 year 10 years 100 years

Min (SW) 30 54 84

Max (NE) 42 78 108

A more elaborated review of current research on climate projections and precipitation patterns can be found in Appendix 1.

Figure 3.Increase in daily maximum precipitation by 2070 and 2100, respectively, with a return period of 10 years. Prediction based on several models detailed in Olsson & Foster 2013.

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Producing a layer of bridges and viaducts

The DTM had to be adjusted so that water is allowed to flow under bridges, viaducts and through water passages under roads etc. These objects were identified by using the bridge register, the hydrological network and aerial photos from the national land survey (GSD).

Bridges (points) from the BatMan database were plotted on the map and corrected for geometrical errors. The precise extent of the passages under then bridges was digitized, and their z level was calculated by using the ArcGIS function zonal statistics, MIN of the underlying DTM.

To aid visual identification, delineation and adjustment, aerial photographs and streams from the Swedish cadastral map were used.

A total of 415 bridges and viaducts where thus identified and digitized.

Creating a hydrologically relevant DTM and identifying sinks

A new DTM was created by taking the MIN of the values of the original DTM and the passages, effectively removing bridges and viaducts from the DTM.

Sinks were identified and removed by using the function “Fill” in the ArcGIS hydrology toolset. The absolute depths of the sinks were calculated by comparing the adjusted DTM and the filled DTM on a pixel by pixel basis, according to:

Figure 4. An example of bridge data from the BatMan database (green), corrected for spatial misplacement and with digitized extent of bridges (in black).

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3.2 Step 2: Potential blue spots, level 1

Potential blue spots were delineated by vectorizing the sinks and selecting areas adjacent to the roads (E4/E6). By calculating zonal statistics for these potential blue spots (bs), their volume was obtained by a sum of depth and cell size (Sc) squared, according to:

∑

{( ) }

According to the prerequisites, only blue spots of a volume greater than 10 m3 and closer than 20m to the road were to be considered. Objects of lesser volume were thus discarded.

Figure 5. Potential blue spots based on sinks in the terrain.

3.3 Step 3: Flow calculus, level 2

With the adapted DTM, flow direction and accumulation rasters were created with the respective functions provided in the ArcGIS environment, hydrological tool set. Due to processing constraints, computation had to be divided into subsections comprising the watersheds established by the Swedish national hydrological institute, SMHI.

Pour points were placed at the location of the highest runoff flux at each potential blue spot.

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The watershed of each pour point was calculated with the watershed function in ArcGIS.

The precipitation scenarios were run against the watersheds to compute the total amount of water for each watershed. The volume was calculated according to:

Vb = R24h Ci Yt

where Vb = volume of blue spot, R24h = mm rain during 24 hours, Ci = Infiltration coefficient, and Yt = watershed area.

Figure 6. Five blue spots, their watersheds (yellow) and water flow directions.

3.4 Step 4: Identifying potentially filled blue spots, level 2

According to the different rain scenarios, the saturation point (in mm rain) for each blue spot has to be compared to the amount of rain predicted to fall within 24 hours by the year 2100.

For each potential blue spot, R24h was calculated, yielding a figure for the amount of rain that is required to fill each blue spot. From the formula:

Vb = R24h Ci Yt

R24h was obtained according to: R24h = Vb/( Ci Yt)

One calculation per Ci of 20, 40, 50, 60, 80 and 100% was performed

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For illustrative purposes and comparisons with the projected precipitation, a visualization of the saturation precipitation for each blue spot vas made using an infiltration coefficient of 20, 40, 50,

60, 80 and 100%.

3.5 Step 5: Data package and delivery

The saturation precipitation for each blue spot and impermeability was added to the polygons of the shape file. Layer definitions (.lyr files) were created for each Ci. PDF maps were rendered in ArcView and all data made available through digital delivery.

Figure 7. A closeup of blue spots near and on the road network, with the depth of the depression given i meters.The star symbol

shows locations of traffic acidents due to rain and flooding.

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4 Results and metadata The topographic identification of blue spots near the TEN-T roads resulted in a total of 1254 spots, varying in size between 100 (minimum) and 1 929 292 m2 (median 1216 m2). The volume of the spots varies between 10 (minimum) and 2 800 870 m3 (median 687 m3).

The majority of the spots (897) were smaller than 5000 m2 and 870 had a volume less than 2500 m3.

Area (square metres)

Fre

qu

en

cy

17500001500000125000010000007500005000002500000

200

0

Histogram of area

Figure 8. Distribution of areal coverage.Note the truncated Y and X axis.

Volume (cubic metres)

Fre

qu

en

cy

80000400000

250

200

150

1009080706050403020100

Histogram of volume

Figure 9. Distribtion of volume. Note the truncated Y and X axis.

These areas were vectorized into a shape file with the following attributes:

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Table 3. Fields in the delivered shape file with data on blue spot properties.

AREA Area in square metres

VOL_M3 Volume in cubic metres

AREA_WS Area of the watershed of the blue spot, in square metres

R24H_IMP20 Saturation point of precipitation, in mm, at a ground impermeability of 20%






The deliverables also include 42 PDF maps in A0 format, and six layer (.lyr) files illustrating the saturation precipitation.

The saturation precipitation for the blue spots has to be compared to the projected future precipitation in the area. The review on climate projections resulted in an estimation for daily extremes by the year 2100, which amounts to 39 mm (1 year returns), 75 mm (10 years) and 110 mm (100 years).

4.1 Validation

Blue spots level 1 were compared to the recorded flooding events by using data from the National Transport Administration.

Of the 13 flooded areas historically recorded at the TEN-T roads, 11 intersect spots identified as blue spots in the analysis. The remaining 2 do probably also intersect blue spots and the discrepancy could be due to lack of precision in the flood event database (se fig. 12 below).

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Figure 10. Flooding events and blue spots.

Figure 11. A suspected spatial error in the flood event database. Recorded coordinates do not represent a topographical depression and the actual flooding must have occurred either east or west of the records.

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5 Conclusions and recommendations In conclusion, the goals of the project have been met. The limitation of the results owe to large extent to the generalized precipitation model, uncertainties as to future precipitation and also lack of data mainly on smaller streams and culverts not visible in the DTM.

However, as an indication of the presence and location of vulnerable locations that ought to be investigated further, the model seems to be satisfactory.

5.1 Methodological improvements

5.1.1 A complete hydrological DTM

The method used in this project relies on the availability of a hydrologically correct DTM. While missing masks for bridges, missing culverts etc. will produce false blue spots (e.g. water “trapped” where it in reality continues to flow under the road/bridge), a DTM with such artefacts upstream the blue spot will in the same manner create artificial and false barriers trapping water that in reality continues to flow and fill then blue spot and create erroneous water sheds.

The difference between a correct and uncorrected DTM is often profound, see fig. 12.

Figure 12. A false indication pf a blue spot due to a missing channel in the DTM (A), and a false barrier upstream (B), trapping water otherwise flowing to point A.

5.1.2 Local precipitation

To properly reflect reality, the model should take local variations in precipitation into account. A compilation of precipitation statistics (Wern 2012) shows that daily extremes in certain areas are above twice or less than half of national averages. Especially in pronounced topographical basins,

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this will lead to local deviations in runoff patterns. This concern is especially relevant when scaling an analysis up to a national level, with dramatic local and regional variations.

5.1.3 Adjusting for local water traps

The basin or watershed model will give a misleading estimation of water accumulation if the accumulation model does not consider local topography and the effect local depressions have in accumulating water, in effect reducing the amount of water reaching each blue spot.

Thus, the volume of the local depressions in the same watershed as a blue spot should be subtracted.

Figure 13. Upstream water traps (black arrow) will act as buffers for any excessive precipitation before water reaches the blue spot (red arrow).

In this process, bridges, viaducts and culverts must med edited within or adjacent to the watersheds of each blue spot. This process would require a major effort with several hundred thousand edits, in effect creating a hydrologically correct DTM.

5.1.4 Scalability

As a mask and rough delimiter for the watershed analysis, existing watersheds from SMHI were used, to reduce computational requirements. It is also advised to use a buffer zone around the SMHI watershed to compensate for small imperfections of the watershed boundaries.

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6 References Andréasson, J., Bergström, S., Gardelin, M., German, J., Gustavsson, H., Hallberg, K. & J. Rosberg (2011). Dimensionerande flöden för dammanläggningar för ett klimat i förändring, Elforsk Rapport 11:25, Elforsk AB, Stockholm.

Bengtsson, L. (2008). Extrema dygnsregn och trender i Skåne och på Västkusten. Vatten 2008:1, 31-39.

Bergström, S., Hellström, S-S. Lindström, G. & Wern, L. (2008). Follow-Up of the Swedish Guidelines for Design Flood Determination for Dams. Report No. 1:2008, BE90. Svenska Kraftnät.

Emanuelsson, H. (2008). Klimatanpassning av Kungshults avloppsnät. Graduate thesis. Lunds Universitet, Institutionen för Kemiteknik.

Grauert, M., Hansson, K. & Hellman, F. (2010). Background Report. Literature, questionnaire and data collection for blue spot identification. Report 182. DRI, Denmark.

Hansson, K., Hellman, F., Grauert, M. & Larsen, M. (2010). Report 1 – The Blue Spot Concept. Methods to predict and handle flooding on highway systems in lowland areas. Project Nr. TR80A 2008-72545, DRI, Denmark.

Hernebring, C. (2006). 10-års regnets återkomst förr och nu, VA-forsk publ. 2006-04, Svenskt Vatten AB, Stockholm.

Larsen, M. (2010). Report 3 – The Blue Spot Model. Project Nr. TR80A 2008-72545, DRI, Denmark.

MSB (2012). Riskinventering vid väg med hjälp av nationell höjdmodell och andra databaser. Publ. nr MSB624. Myndigheten för samhällsskydd och beredskap.

Nikulin, G., Kjellström, E., Hansson, U., Strandberg, G. & Ullerstig, A. (2011). Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations, Tellus, 63A:41-55.

Olsson, J. & Foster, K. (2013). Extrem korttidsnederbörd i klimatprojektioner för Sverige. Norrköping: Sveriges meteorologiska och hydrologiska institut (SMHI).

Persson, G., Sjökvist, E., Åström, S., Eklund, D., Andréasson, J., Johnell, A., Asp, M., Olsson, J. & Nerheim, S. (2011). Klimatanalys för Skåne län. Rapport Nr 2011-52. Norrköping: Sveriges meteorologiska och hydrologiska institut (SMHI).

Räisänen, J. & Joelsson, R. (2001). Changes in average and extreme precipitation in two regional climate model experiments, Tellus, 53A:547-566.

Räisänen, J., Hansson, U., Ullerstig, A., Döscher, R., Graham, L.P., Jones, C., Meier, H.E.M., Samuelsson, P. & Willén, U. (2004). European climate in the late 21st century: regional simulations with two driving global models and two forcing scenarios, Clim. Dyn., 22: 13-31.

Wern, L. (2012). Extrem nederbörd i Sverige under 1 till 30 dygn, 1900-2011. Norrköping: Sveriges meteorologiska och hydrologiska institut (SMHI).

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Appendix 1

Future short- term precipitation

Extreme precipitation on a daily scale or shorter in climate projections for Sweden have been investigated for more than 10 years. As for precipitation, Raisanen and Petersson (2001) estimated in an early study predicted future changes in annual maximum daily precipitation in two projections with different temporal horizons. In Scandinavia, the results showed an increase of 15-20%. In an analysis of four projections, Räisänen et al. (2004) saw an increase in the maximum annual daily rainfall in Sweden by 5-15% from the period 1961-1990 to the period 2071-2100.

A literature review of Emanuelsson (2008:18f) shows that the northern part of Skåne probably will have an increase in rainfall intensity of extreme rainfall up to 20-40%. DRI anticipates an increase in peak daily precipitation for northern Europe to about 20-22% during the period 2071-2100 (Larsen 2010, table 1), a number acquired specifically in a blue spots context.

Nikulin et al. (2011) analyzed the future change in precipitation by 20 year return in six projections for Europe and found an increase (in summer) from 1961-1990 to 2071-2100 by 10-30%, the highest overall being in western Sweden. Andréasson et al. (2011) analyzed daily precipitation with a 100 year return from 16 projections for Sweden and found from 1961-1990 to 2071-2100 a nationwide rather similar average increase of ~ 20%.

Olsson & Kean (2013) believes that daily rainfall will increase by perhaps 8% (2070) and 12% (2100) as a national average with a return period of 10 years, using the RCA3 regional climate model. In their literature review of previous projects they consider the one-day rainfall to be 5% (2050) or 20% (2100) higher than today. For a return period of 10 years they suggest that in the NE in the present area, precipitation will decrease by more than 5% and increase by up to 5 % in SW, by 2040. In 2070 there is a uniform increase of 10-15 % (ibid., fig. 6), as well as in 2100 (fig. 8). For a return period of 100 years the prediction shows a slightly higher increase than for the 10 year return.

In a recent study Hernebring et al. (2012) has estimated the change factors corroborating the changes estimated in the present study. For five Swedish cities (Gothenburg, Malmö, Östersund, Växjö and Västerås), factors have been estimated by analysis of daily precipitation over the 90th percentile in nine climate projections (it may be noted that this definition denotes strong rather than extreme precipitation). The average increase from the period 1961-1990 was found to be 5 % to the period 2011-2040, 11% for 2041-2070 and 13% for 2071-2100.

For Skåne, Persson et al. (2011) suggests that the change in the 30 minute precipitation (10 year return period) from the period 1961-1990 (which is used as reference) to the period 2011-2040 will fall in between -10% and +25% with a mean of +11 %. Towards 2070, the average (out of 6 models) is predicted close to 20 % and for 2100 at 30%.

In western Sweden, the greatest increase is expected in short-term extremes. A figure mentioned is 13% for 1 day duration (Olsson & Foster 2013:8) Expressed in figures, the future growth of extremes of 100 year returns average at almost one percentage point higher than the mean values of the 10 year returns (ibid., p. 15). Olsson & Foster indicates 0-15% (by 2050) to 10-30% more precipitation (by 2100, see ibid., p. 17). Shorter rain interval is predicted at 5 percentage points higher. The text states that for Sweden as a whole, expected daily extremes will increase by 5% (2050) to 20 % (2100 , ibid. p. 18). Generally, short bursts of precipitation seem to increase the most, as well as the standard deviation. Yearly rainfall may even decrease according to several models.

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As a bare minimum, any increase in precipitation the last few preceding decades must be considered as a minimum value of the increase to 2100, if one takes the precipitation of the 1900s as the norm as base for models. The historic change in precipitation has already proved to be 4 % (at most 10%) when comparing past decade with the first part of the 1900s (Olsson & Foster 2013:5). The increase is more pronounced for brief showers, with around 6% (Olsson & Foster 2013:7). Persson et al. (2011) argue that the total rainfall has increased by 8 % (ibid. p. 21). Bergström et al. (2008) noted an increase in extreme precipitation by about 10% during the late 1900s (ibid. table 5, page 20). At any rate, then, we must assume a future increase compared to the figures in table 1 to at least 10%.

Unlike most other scientists, Bengtsson (2008) do not see any trend of increased extreme precipitation, a view which in the light of the rest of the studies must be refuted for the purpose of this project.