Planning Support for Reducing Risks Related to Flooding a Case Study of Flood Response in Kista Residential Area and Igelbäcken Stream, Sweden

EXAMENSARBETE INOM ENERGI OCH MILJÖ, AVANCERAD NIVÅ, 30 HP STOCKHOLM, SVERIGE 2017

Planning support for reducing risks related to flooding A case study of flood response in Kista residential area and Igelbäcken stream, Sweden

KAJSA LUNDGREN

KTH SKOLAN FÖR ARKITEKTUR OCH SAMHÄLLSBYGGNAD

Planning support for reducing risks related to flooding: A case study of flood response in Kista residential area and Igelbäcken stream, Sweden KAJSA LUNDGREN

TRITA-LWR Degree Project ISSN 1651-064X LWR-EX 2017:13

Abstract

Flooding has been identified as the most widespread and most frequently occurring natural disaster by the United Nation. Sweden is no exception when it comes to being affected by flooding, and several major flood events have been seen in recent years. The Swedish National Board of Building, Housing and Planning published a report on climate adaption in 2010 where they stated that Sweden is missing over all strategies and goals meet the demands of more frequent and intense rain events.

Present thesis aimed to develop planning support for integration of hydrological perspectives in urban planning to manage water related risks such as flooding and high water levels. This was done through hydrodynamic modelling in MIKE FLOOD, developed by the DHI, where a 1D stream model was coupled with a 2D free-surface flow model. The model was run for three different scenarios reflecting current conditions (Scenario 1), climate change (Scenario 2) and land use change (Scenario 3).

The study area chosen for present study was Kista residential area, located northwest of Stockholm, and part of Igelbäcken stream that runs by Kista. Igelbäcken stream was represented by a 1D stream model in the software MIKE 11 provided by DHI and Järfälla municipality, whereas a 2D model in MIKE 21 for Kista and the stream surroundings was setup throughout the project. Data was provided by Stockholm Vatten och Avfall and processed in ArcMap before it could be used in the modelling. The MIKE 21 model required data regarding topography, land use, and precipitation. A 100-year flood, based on a series of designed rain events with various duration and intensity, was used as precipitation input to replicated a hypothetical major rain event.

Flooding in Scenario 2 was more extensive than flooding in Scenario 1, which was expected since Scenario 2 was based on a 100-year flood with a climate change factor of 1.25 and projection for year 2100. Scenario 3, which represented a “worst case” scenario with all planned exploitation of Kista identified as impermeable surface, forced the water to move further down in the topography compared with Scenario 1. Several buildings were more or less surrounded by at least 0.3 meter of water in Scenario 3. Water levels in Igelbäcken stream were strongly affected by the rain events and showed an increase of 0.4, 0.9, and 0.4 meter for the three scenarios at the end of the simulations which lasted for six hours.

In conclusion, findings of present study show larger flooding extent that previously performed studies in the area and they reflect fast response in Igelbäcken stream with respect to increased water level. Indicating that effects from major rain events should not be underestimated. Furthermore, the findings could prove useful for identification of major runoff pathways and identification of suitable locations for multifunctional with respect to infiltration and retardation, if available at an early stage in the planning process. Thus, this type of study could prove useful for integration of hydrology in the urban planning process.

Keywords: Planning support, blue green solutions, hydrodynamic modelling, MIKE FLOOD, climate change adaption, land use change

Sammanfattning

Översvämningar har identifierats som världens mest utspridda och vanligast förekommande naturkatastroftyp av FN. Sverige är inget undantag när det kommer till påverkas av översvämningar, under de senaste åren har flera stora översvämningar förekommit i landet. Boverket publicerade 2010 en rapport gällande Sveriges hantering av klimatanpassning. Slutsatsen av denna rapport var att övergripande strategier och mål för klimatanpassning saknas när det gäller hantering av kraftiga skyfall och att bättring krävs för en hållbar samhällsutveckling.

Denna studie syftade till att utveckla planeringsstöd för integrering av ett hydrologiskt perspektiv i urban planering för att hantera vattenrelaterade risker så som översvämning och höga vattennivåer. Detta gjordes genom hydrodynamiks modellering i mjukvaran MIKE FLOOD, utvecklad av DHI, där en 1D vattendragsmodell kopplades till en 2D ytavrinningsmodell. Modellen kördes för tre scenarion: nuläget (Scenario 1), klimatförändring (Scenario 2) och förändrad markanvändning (Scenario 3).

Förorten Kista, belägen nordväst om Stockholm, och den del av Igelbäcken som passerar Kista valdes som studieområde. Igelbäcken representerades av en 1D vattendragsmodell, MIKE 11, som tillhandahölls av DHI, medan en 2D ytavrinningsmodell i MIKE 21 sattes upp för Kista och Igelbäckens omgivning under projektets gång. Data tillhandahölls av Stockholm Vatten och Avfall och bearbetades i ArcMap innan den kunde användas i modelleringen. MIKE 21 modellen baserades på data rörande topografi, markanvändning och nederbörd. Ett 100-års regn, baserat på en serie möjliga 100-års regn med varierande intensitet och varaktighet, användes som nederbördsdata för att efterlikna ett hypotetiskt kraftigt skyfall.

Översvämning i Scenario 2 hade en större utbredning än Scenario 1, vilket var väntat då Scenario 2 baserades på ett 100-års regn med en klimatförändringfaktor på 1.25 och en klimatprojektion för år 2100. Scenario 3, vilket representerade ett ”värsta möjliga” scenario med all planerad exploatering i Kista definierad som icke genomsläpplig yta, tvingade vatten som ansamlats på ytan att röra sig längre ner i topografin eller fångade det på nya ställen i studieområdet jämfört med Scenario 1. Ett flertal byggnader var till stor del omringade av ett vattendjup på åtminstone 0.3 meter i Scenario 3. Vattennivåer i Igelbäcken var inledningsvis väldigt låga, men påvisade sedan en ökning av 0.4, 0.9 samt 0.4 meter i respektive scenario vid simuleringens slut (vilken varade i sex timmar).

Sammanfattningsvis påvisade studien större översvämningsspridning än tidigare genomförd översvämningsmodellering i området. Vidare visade resultaten en snabb respons i Igelbäcken med avseende på vattennivåförändringar vid simuleringens slut. Detta indikerar att påverkan från kraftiga skyfall inte bör underskattas. Resultaten ses som användbara i ett tidigt stadie av planeringsprocessen för identifiering av viktiga ytavrinningsvägar i landskapet samt för lokalisering av lämpliga ytor för etablering av multifunktionella ytor, till exempel parker, med avseende på infiltration och fördröjning av dagvatten. Denna typ av studie kan därmed ses som användbar för integration av ett hydrologiskt perspektiv i den urbana planeringsprocessen.

Nyckelord: Planeringsstöd, blå-gröna lösningar, hydrodynamiskmodellering, MIKE FLOOD, klimatanpassning, markanvändning

Acknowledgements

First, I want to acknowledge my supervisor Zahra Kalantari, senior research scientist at Stockholm University, department of physical geography, and examiner Ulla Mörtberg, associate professor at KTH Royal Institute of technology, division of land and water resources engineering, for initiating the project and giving me an introduction to the academic world. They, together with funding from the Bolin Centre for Climate Research, made it possible for me to gain new experiences through participation in the European Geosciences Union General Assembly (EGU) in Vienna, Austria, where I got to present my work. It has been an honour to work with such experienced researchers. Secondly, I would like to express my gratitude to Henny Samuelsson and Stockholm Vatten och Avfall, Sweden’s largest water and waste company, for providing me with data and guidance. Without their support, the performance and outcome of present project would not have been possible to achieve. Furthermore, I want to thank the DHI for providing free software license and Steve Berggren-Clauser, DHI, for helping me with the MIKE 11 model used for representation of Igelbäcken stream. This recognition also extends to Järfälla municipality whom allowed me to use the existing model of Igelbäcken stream in my work. At last, a big thank you to the ISSUE-project from which the idea of this thesis was sprung, and which has contributed with important meetings for networking and project input.

TABLE OF CONTENTS

1 Introduction ...... 1 1.1 Problem formulation ...... 2 1.2 Aim and objectives ...... 3 1.3 Focus and limitations ...... 4 2 Background ...... 4 2.1 Hydrodynamic modelling ...... 4 2.1.1 MIKE 11 ...... 5 2.1.2 MIKE 21 ...... 6 2.2 Hydrological model of Igelbäcken ...... 6 2.2.1 The NAM rainfall-runoff model...... 6 2.3 Ecosystem services and blue green solutions ...... 7 2.4 Planning support ...... 8 2.4.1 Flood mapping ...... 9 2.4.2 Previous flood mapping in Stockholm ...... 9 3 Materials and methods ...... 10 3.1 Igelbäcken catchment and the study area ...... 10 3.1.1 Climate and CDS-rain ...... 11 3.1.2 Land cover and land use ...... 11 3.1.3 Igelbäcken stream ...... 13 3.1.4 Kista ...... 13 3.2 Data and initial preparations ...... 13 3.2.1 Input data ...... 13 3.2.2 ArcMap ...... 15 3.2.3 MIKE 21 ...... 17 3.2.3.1 Basic parameters ...... 17 3.2.3.2 Hydrodynamic parameters ...... 17 3.2.4 MIKE 11 ...... 18 3.2.5 Scenarios ...... 18 3.2.5.1 Scenario 1: Current conditions ...... 19 3.2.5.2 Scenario 2: Climate change ...... 19 3.2.5.3 Scenario 3: Land use change ...... 19 3.3 Calibration and Validation ...... 19 4 Results ...... 20 4.1 Model set-up ...... 20 4.1.1 Mike 11 ...... 21 4.1.2 Mike 21 ...... 22 4.2 Scenarios ...... 23 4.2.1 Scenario 1: Current conditions ...... 24 4.2.2 Scenario 2: Climate change ...... 26 4.2.3 Scenario 3: Land use change ...... 28 4.3 Combined result ...... 30 5 Discussion ...... 32 6 Conclusion and recommendations ...... 33 7 Referenser ...... 34 Appendix A – Infiltration parameters ...... i Appendix B – Precipitation input ...... ii Appendix C – Calibrated NAM-parameters from DHI ...... iii

1 INTRODUCTION

Flooding, identified as one of the most widespread and frequently occurring natural disasters, continues to cause damage all over the world. The extent of the damage caused by flooding is increasing, causing casualties, homelessness and large economic loss. Flooding is not a new phenomenon, according to the Centre for Research on the Epidemiology of Disasters (CRED) it is the type of event that affected most people between 1900 and 2009. Although flood events are natural phenomenon, the likelihood of them to occur and cause damage to human property and life is increased by land use and climate change (Ramesh, 2013, pp. 1-5).

A phenomenon contributing to land use change is urbanization, which can be describes as a proportional growth of people living in urban areas compared to people living in rural areas (Population Reference Bureau, 2017). Urbanization has been a strong trend in many areas of the world for the past two centuries, and today more than 50 percent of the world’s population live in urban areas (Dye, 2008; Kalantari, et al., 2017). Social, economic and environmental pressures change as more people move toward bigger cities and these changes tend to cause the urban suburbs to expand and become more heavily exploited (Couch, et al., 2007; Deal, et al., 2017).

As human demands for food, transport, energy and housing increase, the landscape transforms from natural environments to areas strongly affected by human presence. Only in the EU, approximately 1500 hectares are converted from agricultural use to infrastructure and urban areas each day. This landscape transformation is characterised by changed soil cover, increased noise levels and a decrease in habitat area for many species. Hence, pre-existing natural conditions are drastically changed and only species which are able to adapt to these changes can continue to thrive. Historically, land use change from urbanization has had a negative effect on the ecosystem services that humanity benefits from. For example, through reduction of green areas that can infiltrate and retard heavy rainfalls, which are ecosystem services closely connected to flooding of urban areas (Biodiversity Information System for Europe, 2017; Goldenberg, et al., 2017).

In September 2015, the United nations adopted 17 sustainable development goals, known as Agenda 2030, to transform our world to the better in the sense of ending poverty, protecting the planet and ensuring prosperity for all (United Nations, 2017). The goals cover economic-, social-, and ecological sustainability, which also have a strong focus in Sweden’s legislation and future development.

Stockholm, capital of Sweden, is growing fast as people move to the city to work and study. In 2010, the City of Stockholm’s City Planning (SBN) brought forth a new comprehensive plan with a vision for year 2030. Economic-, social- and ecological sustainability were important perspectives of this vision and SBN acknowledged that long term sustainable development is an ongoing process that needs to be maintained rather than a fixed goal. SBN emphasised the importance of corporation and common visions amongst stakeholders such as the city council, municipal committees and boards (Stadsbyggnadsnämnden, 2010, pp. 5-8).

Flooding directly effects all three aspects of sustainable development mentioned above. Hydrological impacts, for example flood response, of climate and land use change can be studied using hydrological and hydrodynamic modelling. Where hydrodynamic models describe and represents the motion of water, whereas hydrological models use empirical methods to convert rainfall volumes into runoff volumes. Furthermore, phenomenon such as runoff changes as well as waterborne sediment and nutrient transport can be modelled through use of hydrological and hydrodynamic models (Destouni, et al., 2013;Kalantari, et al., 2014a; Kalantari, et al., 2014b; Kalantari, et al., 2015).

In recent years, flooding due to intense rain fall events has become more frequent in Sweden and the country is now following the example of Denmark, where extensive work regarding flood mapping and prevention has been performed, to handle and prepare for these events. In their storm

1 water strategy, the City of Stockholm (2015) defined four objectives for sustainable storm water management:

1. Improved water quality in the city’s waters 2. Robust and climate adapted storm water management 3. Resource and value creation for the city 4. Environmental and cost-effective implementation

One way to address the storm water strategy is through construction of general planning support. The ISSUE-project, Integrating Sustainability Strategies in Urban Environments, started in 2015, aims to “…develop innovative strategies to support sustainable development in urban and peri- urban areas, advance knowledge regarding sustainability through multi-stakeholder collaboration and analyse future developments.” (Balfors, 2016). Present study was initiated by the ISSUE-project for development of general planning support with hydrological aspects.

As mentioned above, flood prevention and management have become important aspects when talking about sustainable development in Sweden. The National Board of Housing, Building and Planning published a report in 2010 stating that physical planning and building in urban areas requires broad perspectives on climate adaption to minimize the effects of natural hazards such as floods (Boverket, 2010b).

1.1 PROBLEM FORMULATION Stockholm municipality, referred to as the City of Stockholm, has a strong focus on sustainable development as well as new implementation strategies and ideas for innovative solutions. The City of Stockholm’s City Planning has pointed out Kista, a district located 15 km northwest of the inner core of Stockholm, as an important development area for the City of Stockholm. Kista is an example of an area in which many companies have been eager to invest. The area differs from other areas outside Stockholm as it is starting to offer the same services as the inner city and has one of the city’s highest plot ratios. Ecosystem services in and around Kista are discussed in the strategic plan, with a strong focus on Järva cultural reserve and the stream Igelbäcken (City of Stockholm, 2010, pp. 14, 32, 37, 63),Figure 1.An attempt to identify areas prone to flooding in Stockholm was made by the Stockholm County Administrative Board. This was done through topographical mapping of low points in the landscape. Comparison of the generated flood map was made with hydrological models developed for three municipalities within the study area, and this comparison showed that the topographical mapping could serve as a suitable basis for larger areas without any greater extent of urban development, but the result was less suited as basis for densely built areas with regulating storm water systems (Länsstyrelsen Stockholm, 2016).

Another project, performed by the City of Stockholm, performed 2D surface modelling of the Stockholm region with the same purpose as the study mentioned above. This study can be regarded as more advanced than mapping performed by the Stockholm County Administrative Board since it does not only take topography into account, but also precipitation, and a simple infiltration module. This allows modelling of flooding extent, and not only identification of sensitive areas. The result of the 2D surface flood mapping pointed out sensitive areas prone to flooding and potential extent, but no detailed information regarding specific real estates. Furthermore, it was highlighted that flood risks along streams was not modelled in a correct way (Pramsten, 2015).

To deepen understanding of the hydrological situation in Igelbäcken, a hydrological model was made for the stream catchment in the years 2005-2008. The model described the contemporary situation for the summer of 2006 in the area and was built in the MIKE Powered by DHI software (DHI, 2008).

However, the model had not been updated since the report was published and the City of Stockholm together with Stockholm Vatten och Avfall, Sweden’s largest water and waste company, were interested in updating the model and extend it with a flood mapping module. It was suggested that this would be done through use of the dynamically coupled modelling system MIKE FLOOD. A modelling system which allows one-dimensional models such as MIKE 11, in this case replicating Igelbäcken stream, to be integrated with the two-dimensional model MIKE 21, replicating land use distribution (DHI, 2016). The MIKE FLOOD results were expected to deepen knowledge about potential flooding in the Kista area, and flow interactions between Igelbäcken stream and its catchment in case of intense rainfall events. This knowledge could then be used by planners in the City of Stockholm to improve flood management and prevention.

0 0,5 1 2 Kilometers

Figure 1. Map of Stockholm, showing location of the study area, and a close up on the study area which is located northwest of Stockholm. Igelbäcken stream is located southwest of Kista, Husby, and Akalla (Hitta.se, 2017).

1.2 AIM AND OBJECTIVES The aim of present thesis was to develop planning support for integration of hydrology, through efficient use of ecosystem services, in urban planning to manage water related risks such as flooding and high water levels. In this specific case, the main focus was to study the response to a 100-year flood in the part of Igelbäcken stream catchment that belongs to the City of Stockholm. Part of the software MIKE powered by DHI was used for this purpose with the aim to result in an integrated 1D/2D hydrodynamic MIKE FLOOD model. Consisting of two components: a 1D hydrodynamic

3 model for Igelbäcken stream network (MIKE 11) and a 2D-hydrodynamic model (MIKE 21) reflecting land use and infiltration capacity in the study area.

Specific objectives:

• To set up a working modelling system for 2D free-surface flow in MIKE 21 • To couple the MIKE 21 model with the already existing MIKE 11 model for a combined 1D/2D hydrodynamic model of Igelbäcken catchment in MIKE FLOOD. • To investigate climate and land use change through three scenarios

1.3 FOCUS AND LIMITATIONS Present thesis was focused on the part of Igelbäcken stream catchment that belongs to the City of Stockholm. Parts of the catchment located in Järfälla or Solna municipality were not included. Due to time restrictions and extensive simulation times, only a limited number of model runs could be performed.

2 BACKGROUND

Hydrodynamic modelling in general and used software are presented in section 2.1. Already existing hydrodynamic model of Igelbäcken stream catchment is then presented in section 2.2. This is followed by a deeper explanation of ecosystem services, legislation, and planning support in section 0-2.4.

2.1 HYDRODYNAMIC MODELLING The motion of liquids, particularly water, is studied through hydrodynamic modelling. Hydrodynamic modelling has been developed alongside numerical models of other advanced computational systems and has thus become part of computational fluid dynamics, a larger field in which meteorology, aerospace design and ventilation systems are part. The main difference between hydrodynamic modelling and these other fields is that hydrodynamic modelling focuses on water movement. Base for this type of modelling is the Navier-Stokes equations which describe fluid motions and are derived from Newton’s laws of motion (Office of Coast Survey, 2017).

Models are in general used to understand different phenomenon, complex situations and relations between various parameters. That is, models are used to represent a chosen target (Grüne-Yanoff, 2016). Hydrodynamic models have many application areas: land use analysis, climate change analysis, flood prediction and rainfall-runoff modelling (Song, et al., 2015, p. 740) and the output can, for example, show modelled time series of water surface elevation, current velocity and direction, temperature, and particle transport (Office of Coast Survey, 2017).

Models tend to seem credible and accurate, but there are many factors that can give rise to uncertainty in a model. According to DHI (2008) the risk of producing deceptive results can be reduced by choice of suitable modelling tools that can answer the research questions, adequate accuracy in physical and geological in-data, and available time series for verification of important variables. Furthermore, uncertainties can be reduced by uncertainty and sensitivity analysis. Where uncertainty analysis refers to uncertainty in the model output caused by uncertainty in model parameters, and sensitivity analysis refers to identification of specific parameters that cause uncertainty in the model output (Song, et al., 2015).

Koivumäki et. al. (2010), list three steps of flood risk mapping within which potential sources of uncertainty might be found: Flood hazard modelling, flood exposure modelling, and flood damage modelling. Furthermore, they state that there should be a balance between producing as reliable flood mapping as possible and finding an acceptable level of accuracy within the results even though there is some uncertainty within them. Koivumäki et.al. specifically looked at flood mapping

4 approaches with respect to damage to buildings and they found that accurate topography data is a key feature when it comes to producing accurate flood risk mapping.

Mankind has tried to understand and deal with mechanisms that drive flooding for thousands of years and hydrodynamic modelling provides an engineering approach to this understanding. Despite all the uncertainties that lay within a model, if used properly, the results can be used for estimation of flood extent. It is a useful tool for flood mapping and visualization which is important when trying to communicate scientific knowledge to stakeholders such as municipalities and city planners (Ullah, et al., 2016).

There are several different software that can be used for hydrodynamic modelling. In present study, MIKE FLOOD was chosen since the stream network had already been modelled in the MIKE powered by DHI software and the scope of the project, flood mapping, falls within the application area of the software. A simplified scheme is shown in Figure 2, showing how the stream network is replicated in MIKE 11, land use in MIKE 21, and their interaction in MIKE FLOOD. Processes included in the different models are explained in section 2.1.1 and 2.1.2.

1D : MIKE 2D : MIKE 11 21

1D/2D : MIKE FLOOD

Figure 2. Scheme of what processes that are included in MIKE 11, MIKE 21 and how these are coupled in MIKE FLOOD.

2.1.1 MIKE 11 This 1D-hydrodynamic model is used for simulation of flows, water quality, and sediment transport in a variation of water bodies, for example rivers and streams. A hydrodynamic module is the base of this model and additional modules such as Rainfall-Runoff, Advection-Dispersion, and Water Quality can be added to be used for different applications.

Set-up of the hydrodynamic module requires input data regarding the network such as cross- sections, boundary data and hydrodynamic parameters. The hydrodynamic parameters are used for setting supplementary data for the model simulation, most of these parameters are set as default values that normally do not require any adjustments. Cross-sections and boundary conditions are normally set-up according to data given by field measurements or literature studies. In present study MIKE 11 was set-up by DHI, used modules and parameters are described further in section2.2.

2.1.2 MIKE 21 MIKE 21 can be used for modelling in coastal or marine areas, of physical, chemical or biological processes. It can be applied in cases where stratification can be neglected, that is, if one is not studying the groundwater (DHI, 2017a).

There are several modules to choose from when working with MIKE 21 Flow Model. The basic hydrodynamic (HD) module simulates the response to forcing functions as variations in water level and flows (DHI, 2015b, p. 13). In present study, a HD module was used for the MIKE 21 flow model set up and the most vital functions for the flood mapping were precipitation and infiltration.

Precipitation is normally described and discussed together with evapotranspiration. Precipitation is defined as the part of the hydrological cycle that includes rain and snow. Evapotranspiration is defined as the total amount of water that leaves open land and water surfaces, and vegetation, due to high temperatures, to returns to the atmosphere as water vapour. Thus, due to a run time of four hours and intense precipitation, the evapotranspiration process was neglected in the HD flow model of present study.

Infiltration reflects the water that can seep through the surface and thereby be removed from the amount of water that gathers on the surface to generate runoff. The infiltration capacity of a surface depends on several factors (Jutebring Sterte, 2016, pp. 3-4). Factors considered in the input data for the HD module were: infiltration velocity [mm/h], porosity coefficient [fraction], soil thickness [meter], leakage [mm/hour] and initial fraction of water in the soil [fraction].

2.2 HYDROLOGICAL MODEL OF IGELBÄCKEN A hydrological model had previously been created by the DHI on behalf of Stockholm Vatten och Avfall for the catchment of Igelbäcken. The work was performed to deepen understanding of current situation and suitable measures to improve hydrological and qualitative conditions. Moreover, the model was supposed to serve as a baseline scenario for future investigation. Parts of the software MIKE powered by DHI were chosen for the modelling due to their suitability for modelling of surface and ground water, rivers and reservoirs, and water quality and ecology (DHI, 2017c).

To study the water balance and hydrogeology a combination of MIKE SHE, MIKE11 HD and MOUSE was used. MIKE 11 HD and MOUSE built the hydrodynamic model, where MIKE11 HD is a one-dimensional hydrodynamic model that calculates flow, flow rate and water levels throughout Igelbäcken stream. Calculations in the module were based on St Venant’s differential equations. The MOUSE module was used for calculation of tunnel flows in the study area.

The hydrological modelling was performed in a model system MIKE SHE where processes such as ground water transport, groundwater pressure, infiltration and percolation, evapotranspiration and runoff are included. When coupling the modules of MIKE SHE and MIKE11 HD, the interaction between streams, runoff and groundwater is mirrored (DHI, 2008).

A different hydrological model called MIKE11 NAM was used for the study of flows and water quality. It was used in combination with LOAD Calculator and MIKE11 AD + ECOLab for pollution loads and transport.

2.2.1 The NAM rainfall-runoff model When setting up a rainfall-runoff model one can choose between various model types. The NAM model type was chosen for parts of the modelling performed by DHI in Igelbäcken stream catchment. In present study, MIKE 11 modelling was also performed with a NAM rainfall-runoff model. NAM is a deterministic, lumped and conceptual model type. Groundwater storages, root zone and surface zones are represented in the NAM model by 9 parameters:

• Umax – maximum water content in surface storage • Lmax – maximum water constant in root zone storage • CQOF – overland flow runoff coefficient • CKIF – time constant for interflow surface-rootzone • CK1, 2 – time constants for routing overland flow • TOF – root zone threshold value for overland flow • TIF – root zone threshold value for inter flow • CKBF – time constant for routing baseflow ground water • TG – root zone threshold value for ground water recharge overall parameters

Calibrated parameters for different sub-catchments are found in Appendix C. The NAM model simulates the following catchment runoff components: overland flow, interflow and baseflow (DHI, 2015a, pp. 252-254).

Together, these three components make up a river discharge hydrograph, Figure 3. Where overland flow and interflow come from fast runoff to the river. They show in the hydrograph after rain events with higher rainfall intensities. The baseflow reflects groundwater inflow and contributes to a majority of the river discharge during dryer seasons (DHI, 2017b) .

Figure 3. Example of a river discharge hydrograph. Showing the three components overland flow, interflow and baseflow. Originally, the figure is found in Handbook of Hydrology, Maidment 1992 (DHI, 2017b)

2.3 ECOSYSTEM SERVICES AND BLUE GREEN SOLUTIONS Ecosystem services are divided into four major groups: provisioning services, regulating services, habitat services and cultural services. By definition, set by the Biodiversity Information System for Europe, provisioning services are products produced by nature. Such as: food, fresh water, fibre, genetic resources and medicine. Regulating services include climate regulation, natural hazard regulation, water purification and waste management, pollination and pest control. Habitat services includes provision of spaces large enough, and in appropriate areas, to allow mitigating species and good gene-pools. Cultural services cover benefits such as spiritual enrichment, intellectual development, recreation and aesthetic values. That is, non-material benefits (Biodiversity Information System for Europe, 2017).

Historically, urbanization has been linked with decoupling of ecosystem services from our societies and technology has been used to create cities independent of ecosystem services (Gómez-Baggethun & Barton, 2013). This attitude is changing as many cities around the world are faced with large economic losses as ecosystem services are decoupled from urban areas, an example of this is economic loss due to flood events (Di Baldassarre, et al., 2017). Advanced technology is not enough to protect human assets and does not necessarily reduce the risk of flooding (Kalantari, et al., 2014 & 2017). Thus, it is important that all stakeholders recognize that existing flood management approaches, and urban planning systems, need improvement through implementation of new ideas (van Herk, et al., 2011).

The term blue-green solutions refer to multi-functional solutions where the positive effects of both green- and blue structures are used to handle increased precipitation rates (Kalantari & Karlsson, 2017). It is hard to distinguish green- and blue structures from one another when discussing ecosystem services since many areas, for example parks, provide both regulation of water through infiltration and habitat for various species. Furthermore, a park with rich vegetation provides air purification and recreation for people visiting the park. Other examples of blue-green solutions are rain gardens, green roofs and infiltration beds (Wihlborg, 2016, pp. 2, 8-9).

It must be clarified that blue-green solutions, such as rain gardens, green roofs and infiltration beds, often serve as good solutions and supplements to the storm water system with respect to volume and quality loads. On the other hand, when it comes to intense rain events, such as a 100-year flood, the need to handle large volumes is vital. This requires solutions such as areas that can be allowed to be flooded, for example grass covered parks. It is important to emphasise the difference between traditional blue-green solutions and solutions suitable to handle intense rain events (Samuelsson, 2017).

2.4 PLANNING SUPPORT The Swedish National Board of Housing, Building and Planning performed an investigation of “…how the system for planning and building in municipalities can promote climate change adaption.” (Boverket, 2010a, p. 3) and one of their conclusions states that municipalities play a vital role in the climate change adaption since they own large pieces of land and have responsibility for local planning. The absence of a national strategy for climate change adaption is identified as problematic and storm water management is one of the main focuses of the analysis. An expected increase in amount and intensity of precipitation, combined with changed land use, raises this concern. According to the investigation, detailed development plans should include recommendations of areas suitable for local treatment of storm water, such as areas with good infiltration capacity.

Furthermore, existing water management systems are often combined, that is, sewage and storm water are mixed as they move toward the treatment plant. It is hard to dimension these systems according to predicted climate change and blue-green solutions are therefore recommended to investigate and implement early in the planning process (Boverket, 2010a).

Wihlborg (2016) identifies barriers and drivers for implementation of blue-green measures in a multi-level perspective. Three levels are included in the multi-level perspective: landscape (e.g. EU directives), regime (e.g. municipalities) and niche (e.g. pilot projects). The main barriers identified for implementation of green-blue solutions are economy, roles and responsibilities, legislation and uncertainty (Goldenberg, et al., 2017). In total, eight barriers were identified, but only three drivers: ecosystem services, climate change and economy. Most of the actors participating in the study highlighted that climate change and increased precipitation are the key drivers for implementation of green-blue measures, and that the biggest barrier is uncertainties regarding roles and responsibility (Wihlborg, 2016).

2.4.1 Flood mapping The definition of an extreme flood is, according to the Swedish Meteorological and Hydrological Institute (SMHI), at least 50 mm of rain in an hour or at least 1 mm in a minute. An additional term used by SMHI is “heavy rain within 24 hours” which corresponds to at least 10 mm per 24 hours (City of Stockholm, 2017).

A report with the purpose to supply a methodology for mapping of extreme rain events’ effect on essential societal services on municipal level was published in 2014 by the Swedish Civil Contingencies Agency. The described methodology identifies areas prone to flooding and is recommended to be used in municipalities risk and vulnerability analysis (Mårtensson & Gustafsson, 2014).

Flood mapping performed by the Stockholm County Administrative Board and by the City of Stockholm were briefly described in the introduction, a more detailed description of the flood mapping performed by the City of Stockholm is found in section 2.4.2.

2.4.2 Previous flood mapping in Stockholm The City of Stockholm performed flood mapping of the Stockholm region in 2015. SMHI’s climate prediction for year 2100 was used to produce results that were more likely to mirror future climate. In the model, it was assumed that sewer systems were dimensioned to handle a 10-year flood for prevailing climate in 2100. Flooding of basements and other spaces below ground surface were not included in the model, only flooding on the ground surface was modelled. Examples of characteristics of a 100-year flood were described and can be seen in Table 1.

Table 1. Examples of 100-year floods. Variation of the characteristics duration and rain depth.

Duration Rain depth [min] [mm] 15 44 30 56 60 68 120 82 240 96 480 113

It was pointed out in the report that several simplifications and generalisations that need to be considered when studying the result were made in the model. The most important ones were:

• The percentage of surfaces that is assumed to be impermeable is based on generalisation and assumptions. • The ground’s infiltration capacity is not described with respect to local conditions. • The sewage system was not described with the networks actual configuration and capacity. • The model only takes the effects of infiltration and pipe system into account in the point where the rain is deposited. • Underground constructions are not included in the model. • The modelled terrain is represented by one coherent surface. • The model’s spatial resolution is limited.

Water depth at the end of the simulation, maximum water depth throughout the simulation, time for occurrence of maximum depth and maximum water velocity and flow throughout the simulation were generated for each of the scenarios (Pramsten, 2015).

3 MATERIALS AND METHODS

Definition and characteristics of the study area are described in section 3.1. To set up a hydrodynamic model several tasks had to be performed and data had to be processed. These different tasks are described in section 3.2. Furthermore, model calibration and validation, data analysis, and model performance are described in section 3.3.

3.1 IGELBÄCKEN CATCHMENT AND THE STUDY AREA The Royal National City Park, Sweden´s only national city park, is found in and around the capitol of Sweden, Stockholm. The park spans over three municipalities: Solna, Stockholm and Lidingö. It was created as an attempt to stop further exploitation and scattering of the green grounds with royal history as well as to protect the rich flora and fauna found in the park (County Administrative Board of Stockholm, 2017b).

Two national reserves are found within the Royal National City Park, Ulriksdal and Igelbäcken (County Administrative Board of Stockholm, 2017a). Igelbäcken stream is located northwest of Stockholm and southwest of the residential area Kista that was of special interest throughout the study. Any effects on Igelbäcken from exploitation of Kista is of major importance to the City of Stockholm due to laws and regulations regarding national and cultural reserves in Sweden. Furthermore, there are strong regulations regarding allowed impact from anthropogenic activities on water bodies.

Igelbäcken stream catchment has an area of 20.78 km2 (SMHI, 2017). Thus, for further studies, the study area was reduced to only cover catchment parts that belong to the City of Stockholm and its immediate surroundings. The identified study area covers 10.3 km2. Figure 4 shows the study area location within the catchment, existing roads, buildings and the path of Igelbäcken stream.

Figure 4. Location of the study area within Igelbäcken stream catchment.

3.1.1 Climate and CDS-rain Climate can be described as average weather conditions in a region. That is, the average conditions over a longer period of time, hundreds or thousands of years, regarding factors such as precipitation, temperature, wind and humidity (NASA, 2015).

Precipitation was identified as the most important climate factor for present study. Stockholm receives precipitation of approximately 550 mm/year of which 450-500 mm/year become storm water and the remaining 50 mm/year leaves the system through evapotranspiration. All future projections point toward a wetter and warmer climate in the Stockholm region, with more intense precipitation, heat strikes, longer vegetation period and rising sea levels (City of Stockholm, 2015).

An increase in short time precipitation intensity of 25 per cent for year 2100 has been predicted by the Swedish Meteorological and Hydrological Institute, SMHI (Pramsten, 2015). Climate predictions made by SMHI are based on the Rossby Centre regional Atmospheric climate model, RCA. The model resolution is 50 by 50 kilometres and covers Europe (SMHI, 2017).

Modelling of storm water runoff requires precipitation data describing rain intensity and duration. When working with climate change and extreme rain events, this type of data is often scarce and an alternative is therefore to design representative precipitation series. Designed precipitation series are traditionally used for dimensioning and analysis of sewer systems, but can also be used for flood modelling. This type of precipitation is often dependent on and coupled to intensity-duration plots with specific return periods. Important factors for designed precipitation series are total volume, time distribution, as well as magnitude and position of maximum rain intensity.

One example of designed precipitation series is the CDS-rain (Chicago Design Storm). A CDS-rain is most easily constructed through the assumption that the maximum intensity takes place half way through the rain duration and that the rain is symmetrically distributed around this maximum. One of the most important properties of a CDS-rain is that the maximum rain intensity for various durations follows an intensity-duration curve. The precipitation series used by the City of Stockholm in their flop mapping is seen in section 2.4.2, Table 1.

3.1.2 Land cover and land use Igelbäcken stream with surroundings was declared a cultural reserve by the City of Stockholm in 2006. Findings and agricultural land in the area dates to the bronze age, and culture and nature values have grown through interaction between humans and nature (DHI, 2008).

Land cover refers to the physical and biological characteristics found on the ground surface, whereas land use refers to various ways in which humans use and manage land and natural resources found in an area. Examples of different land use areas are: cropland, grassland for livestock, and urban land. Urban land refers to cities, towns and other residential areas (The Environmental Literacy Council, 2017).

Land use change as a parameter affecting the extent of potential flooding was one of the main focuses of present study and this was reflected in the use of the terms land cover and land use. The land use classes used were roads, buildings, and other open surfaces, 20 %, 13 % and 67 % of the study area, respectively. No further emphasis was put on what specific purposes other open surfaces were used for since infiltration capacity and topography, that is, run of paths, were of major importance for the result. Thus, not if the open surface was used as a playground, recreational activities or agriculture.

Land cover distribution in the study area can be seen in Figure 5. In present study, both land cover and land use were referred to with a strong focus on infiltration capacity. Soil types and impermeable areas, with negligible infiltration capacity, such as roads and buildings, were therefore the land cover types described. Figure 5 shows that clay and till were dominating soils, covering 35% and 18% of the study area, respectively. Impermeable surfaces such as roads and buildings covered approximately a third of the area. Spatial distribution of the land cover is displayed in Figure 6.

Land cover distribution

13% 6% Rock outcrop

18% Till Organic material

20% Clay Strongly alternating layers 5% Roads Buildings 3%

35%

Figure 5. Chart of land use distribution in the study area. Showing the different soil types that make up the land use class other open surface and impermeable surfaces such as roads and buildings.

Figure 6. Land cover map showing spatial distribution of existing roads, houses and other hard surfaces as well as distribution of geological cover.

3.1.3 Igelbäcken stream The stream stretches from the lake Säbysjön to Edsviken bay and is approximately ten kilometres long (City of Stockholm, 2017). After leaving Säbysjön lake in the north, the stream travels through the landscape toward Barkaby airport. When passing the airport, the stream flows through a 250- meter-long tunnel and then it passes a culvert under the road Akallavägen. Another stream, Djupanbäcken, connects to Igelbäcken before it crosses Akallavägen and then the stream continues through the valley between Akalla-Hjulsta and Tensta-Rinkeby for four kilometres.

Igelbäcken then flows under the road Kymlingelänken, a part of E18 road that passes Kista, and finally travels another four kilometres before reaching Edsviken bay. On the last stretch, Igelbäcken stream is culverted under railroad and E4 highway and the stream outlet is dammed (DHI, 2008, p. 1).

A third of the surface water that naturally reaches Igelbäcken was redirected in the 70´s when the Kista-Akalla neighbourhoods were densified. The redirected water is now lead through Järva storm water tunnel, a tunnel which also reduces groundwater flow to Igelbäcken due to in leakage. Drinking water, approximately 35 000 m3 per year, is added to Igelbäcken outside Akalla. Another 40 m3 per 24 hours of draining water is pumped to Igelbäcken from a tunnel. Furthermore, there is an infiltration tunnel in the area between Kymlinelänken and the stream outlet in which 50 000 m3 of drinking water is added to maintain groundwater levels in the area.

Although, it should be noted that no municipal water was added to the stream during the time that flow measurements in the stream were made. Hence, modelling performed by DHI was calibrated for natural flows in the stream (DHI, 2008, pp. 1, 8).

3.1.4 Kista Kista has been identified as a core area, with great development potential, when it comes to creating modern suburbs tending to all major needs of their inhabitants, such as: work opportunity, schools, recreation, shopping and sports.

The area can already be compared with the inner city of Stockholm when it comes to tending to the needs of its inhabitants. Kista Science City is an example of a big project where the City of Stockholm are investing big resources in the area and other initiatives such as tram connections are also planned (City of Stockholm, 2010).

By the end of 2016, 50 000 people were approximated to live in the Kista area and by 2025 approximately 65 000 people are expected to live there (City of Stockholm, 2016). Besides this, some 30 000 people commute to Kista for work (City of Stockholm, 2014). Figure 7 shows a satellite photo of the residential area Kista. Pointing out important infrastructure such as the metro station, tunnels and by passes.

3.2 DATA AND INITIAL PREPARATIONS The MIKE FLOOD model was made up by two parts. A MIKE 21 HD module that was built throughout the process of present project, and a MIKE 11 NAM module for Igelbäcken stream catchment that was provided by DHI and Järfälla municipality.

3.2.1 Input data A combination of data available from the Swedish University of Agricultural Sciences (SLU) and data supplied by Stockholm Vatten och Avfall and the City of Stockholm was used. The data is listed in Table 2.

Tunne Metro l station

Bypas s

Figure 7. Satellite photo of Kista. Pointing out location of the metro station and a tunnel that goes under Kista shopping centre.

Table 2. Data available from SLU and provided by Stockholm Vatten och Avfall and the City of Stockholm.

Data Data Resolution Description Source type Digital elevation Raster 2 x 2 m Covering part of the study City of model (DEM) area that belongs to the City of Stockholm Stockholm. Soil map Vector Polygon Soil types and cover in parts of City of the study area that belong to Stockholm the City of Stockholm. Land use Raster 0.5 x 0.5 m Data displaying impermeable City of surfaces such as roads, Stockholm buildings and other hard surfaces. Digital elevation Raster 2 x 2 m Covers the entire stream SLU model (DEM) catchment. Soil map Vector Polygon Plygon shape file displaying SLU soil types and cover outside the City of Stockholm.

3.2.2 ArcMap The geographic information system software ArcGIS by ESRI was used for initial preparation of the data before it was used as input to the MIKE 21 HD module. ArcGIS can for example be used for geovisualization and geoprocessing, that is, to display desired features and relationships in and to derive new datasets from geographical maps (ESRI, 2004, p. 2)

The demographical elevation model from SLU was used to create Igelbäcken stream catchment together with natural water runways through the landscape. This was performed with the ArcMap extension ArcHydro tools. In 2015, the county administrative board of Jönköping, Sweden, published a report of recommendations and methods of how to work with flood mapping in ArcMap (County administrative board of Jönköping, 2015). The methodology described in this report was used as guideline when creating the outline of Igelbäcken stream catchment. The sequence of tools used to arrive at the stream catchment and natural water runways can be seen in Figure 8. Output catchment and natural water runways are displayed in Figure 9.

Figure 8. Systematic scheme displaying the sequence of tools used to arrive at Igelbäcken stream catchment. Tools are marked with blue and input/output files with green.

Figure 9. Output from using the AcrHydro tools to display Igenbäcken stream catchment and natural water runways through the landscape as well as the chosen study area.

As previously mentioned, only a part of the catchment was used as study area. This part was now processed further for preparation of input data to MIKE 21. All raster files were projected into the coordinate system SWEREF99_18_00 and a grid size of 4 x 4 meters. The grid size 4 x 4 meters was chosen to correlate with the grid size of previously performed flood mapping and to reduce computational time when running the MIKE 21 HD module and later the MIKE FLOOD model.

In the original DEM provided by City of Stockholm the elevation measured reflected the height of bridges and bypasses. Thus, grid cells covering these objects formed wall-like obstacles in the terrain, preventing water from flowing with gravity through the landscape. Locations of bridges and bypasses therefore had to be identified and the elevation of these grid cells lowered to fit into surrounding elevation and create more realistic flow paths.

A number of raster files were extracted from the original data to enable easy selection of cells with certain attributes in MIKE 21. Separate files where generated for the land cover types seen in Table 3. Once desired raster files were generated, these were converted into ASCII file format for input to MIKE 21.

Table 3. Description of different land cover and soil types.

Surface type Geological characteristics Rock outcrop Bedrock exposed at ground surface Till Post glacial, rounded, sediment deposits of various size. Ranging from clay to boulders. Organic material Combination of marshy peat and post glacial clay. Clay Postglacial clay Alternating layers Thin, alternating layers. A mixture of post glacial sand and manmade filling. Roads - Buildings -

3.2.3 MIKE 21 A MIKE 21 Flow Model was created to model water movement through the landscape and eventually arrive at identification of areas prone to flooding. The model required input data regarding topography, precipitation, infiltration and surface resistance together with specification of a number of parameters.

Before using the ArcGIS processed DEM as input to the model, grid cells covering buildings had to be identified and adjusted to reflect their topographical difference from surrounding topography. Clear boundaries between buildings and surroundings was not mirrored in the DEM since the elevation in each grid cell is an average of topography in the area that the cell covers. The elevation in grid cells identified as buildings was raised by two meters after discussion with Stockholm Vatten och Avfall. If raising elevation of the buildings more than this, there is a risk that the software will start to generate an excess of water as it drops from roof tops down on surrounding surfaces (Samuelsson, 2017).

3.2.3.1 Basic parameters The module selection was set to hydrodynamic only, permitting inland flooding. Hydrodynamic only was chosen since the aim was to model runoff on the ground surface and no sediment or particle transport was to be taken into account at this stage. Originally, MIKE 21 was used for modelling of costal floods, now that it is being used for flood mapping in urban areas one needs to allow inland flooding to model surface runoff patterns and areas that are prone to flooding (DHI, 2015b). Bathymetry, which defines the model area and its topography, was set as cold start and the processed DEM was used as input file. Coriolis forcing was implemented on the bathymetry.

A time span of four hours was set for the simulation period, with a time step interval of 0.2 seconds, ranging from 2015-06-29 14:45:00 to 2015-06-29 20:45:00. Boundaries were to be program detected, no source and sink was identified and the mass budged was left blank.

Flooding and drying were enabled, where drying depth and flooding depth were specified as 0.001 and 0.002, respectively.

3.2.3.2 Hydrodynamic parameters The processed DEM was used as input for the initial surface elevation and no boundaries were specified.

Two different types of precipitation were used for three scenarios. Precipitation design and scenarios are described in section 3.2.5.

Infiltration type and infiltration format were set as constant infiltration with capacity and constant in time, varying in space, respectively. Infiltration zone extent was given as depth and initial water volume in infiltration zone as percentage of the capacity.

The infiltration input file contained five parameters, shown in Table 4. Selection files for buildings, roads and different soil types were used to assign appropriate values to all grid cells for each parameter. Assigned values are found in Appendix A.

Table 4. List of parameters included in the infiltration file.

Variable Type Unit Infiltration velocity Infiltration mm/hour Porosity Porosity coefficient - Thickness Depth Below Ground meter Leakage velocity Leakage mm/hour Degree of saturation Fraction -

Furthermore, the eddy viscosity was given as constant value with velocity based type of formulation. The viscosity was set to 2.0. Resistance type was specified as manning number and in the input file roads, buildings and other surfaces were assigned values of 50, 20 and 2, respectively. The wind conditions were specified as no wind. These parameters were set after discussion with Stockholm Vatten och Avfall (Samuelsson, 2017).

Output items specified for the results were: water depth, P flux, Q flux, current speed and current direction. The text file belonging to the MIKE 21 model was modified according to instructions from Stockholm Vatten och Avfall to generate a total water correction file that allows the user to see if an excess of water is generated in any grid cells.

3.2.4 MIKE 11 MIKE 11 was run with hydrodynamic and a rainfall-runoff model. There are several different model versions of the rainfall-runoff model. In this case the NAM model type, described in section 0, was chosen.

Furthermore, the MIKE11 model was run with NAM input regarding boundary data and rainfall- runoff parameters. Where the rainfall-runoff parameters where set up with precipitation and air temperature for 2010-2015 gathered from SMHI. Evapotranspiration, which also was needed for the rainfall-runoff module, was calculated according to Equation 1 and 2. Where T, P, Ep, and Ea represent air temperature [C], precipitation [mm/day], potential evapotranspiration [mm/day], and actual evapotranspiration [mm/day]. The aim of this simulation was to generate initial flow conditions in Igelbäcken stream.

2 Equation 1 퐸푝 = 325 + (21 ∗ 푇) + (0.9 ∗ 푇 )

푃 Equation 2 퐸푎 = 푃2 √0.9+ 2 퐸푝 (Greffe, 2013)

A second MIKE 11 model was then created where the result of the 2010-2015 run was used as HD hotstart. In this case, hotstart refers to that initial conditions were loaded from an existing result file. Initial HD conditions were taken from the result file at user specified time and date (DHI, 2015a).

3.2.5 Scenarios Previously mentioned models, MIKE 21 and MIKE 11, were coupled in MIKE FLOOD for a combined 1D/2D hydrological model. Three different scenarios were run in MIKE FLOOD. One scenario describing current land use conditions together with a 100-year flood, one reflecting

18 climate change with a climate change factor of 1.25 applied to the 100-year flood and current land use conditions, and one reflecting impact of both a 100-year flood and impacts of land use change in the sense of planned exploitation in Kista.

3.2.5.1 Scenario 1: Current conditions The first scenario run was intended to reflect what flooding that might occur due to a 100-year flood when applied on current land use conditions in the study area.

Precipitation intensity and duration was set according to recommendations from Stockholm Vatten och Avfall. With a four hour duration and a time step of 15 minutes. Division of the CDS-rain described in section 3.1.1 is shown in Figure 10. The pre-rain was divided between the initial soil water content and water handled by the sewer system, depending on if the precipitation fell on impermeable surfaces such as roads and buildings or if it fell on surfaces identified as permeable. That is, the pre-rain was not included as part of the 100-year flood. Furthermore, the sewer system is assumed to handle a 10-year flood and this volume is thus removed from precipitation that falls on exploited areas. Intensities used for the 100-year flood can be seen in Appendix B. Current land use conditions, described in section 3.1.2, were used for Scenario 1.

Peak, contributes to flooding

Rain handled by the Pre-rain, included in sewer system initial soil water

content. Intensity Intensity [mm/h]

Duration [min]

Figure 10. Partition of the CDS-rain and explanation of the different volumes.

3.2.5.2 Scenario 2: Climate change The second scenario was based on the 100-year flood described in section 3.2.5.1 with a climate change factor of 1.25. The City of Stockholm used this precipitation design in their flood mapping and it was therefore of interest to present study to use this set up to be able to compare generated results with results from the flood mapping that was performed in 2015. Scenario 2 has the same land use setup as Scenario 1.

3.2.5.3 Scenario 3: Land use change Scenario 3 was based on the precipitation design used in Scenario 1, hence, a 100-year flood with respect to current climate conditions. The land use was changed according to an early pilot study of exploitation potential in Kista and set up as a “worst case” scenario where planned exploitation was assumed to increase the fraction of impermeable land in the study area. Thus, no green roofs or other surfaces with infiltration and retardation capacity were considered.

3.3 CALIBRATION AND VALIDATION Calibration is often complicated when modelling floods with large return periods since events like this seldom occur and data is scarce. In the flood mapping performed by the City of Stockholm several scenarios with varying input parameters were used to handle model uncertainty. The

19 simulations covered a spectra of possible land use scenarios going from favourable to adverse, with respect to infiltration and retardation (Pramsten, 2015).

In the MIKE 21 model set up throughout present project, no calibration could be performed due to lack of data. However, a quality control was performed through generation and study of the total water correction file described in section 3.2.3.2. This file displayed grid cells in which an excess of water was generated during the simulation. Cells with a total water correction value higher than one meter were then studied and surrounding topography was modified in the bathymetry to remove obstacles created by misleading information in the elevation data. This procedure was carried out to ensure that no continuous calculation errors were made in the model.

MIKE 11 had previously been calibrated by DHI and no further calibration of the model was performed in present study. However, to validate the rainfall-runoff model for 2010-2015, the same MIKE 11 runs that were made for this simulation period was made for 2001-2006 with precipitation, evapotranspiration and air temperature data prepared by DHI. Their precipitation and air temperature data was gathered from SMHI’s precipitation station in Oservatiorielunden, central Stockholm. Whereas the evapotranspiration data was calculated by SMHI and given as monthly average values of potential evapotranspiration.

Validation of the MIKE FLOOD model was performed by comparison of the results to results from previously performed flood mapping in the area.

4 RESULTS

The result section is divided into three sections. Section 4.1 presents individual results from MIKE 11 and MIKE 21. Section 0 presents MIKE FLOOD results for the three scenarios., and section 4.3 combine these results.

Figure 11. Layout of the river network modelled in MIKE 11, black line, and the study area in MIKE 21 represented by topography. Where dark blue represents the highest points and light green the lowest points in the topography.

4.1 MODEL SET-UP Before coupling MIKE 11 and MIKE 21, several runs of both models were performed to make sure that they were performing well one by one. Figure 11shows layout of the stream network in MIKE 11 and location of the study area in MIKE 21. Interaction between the two models was modelled for the full MIKE 21 model and the part of MIKE 11 that lay within this area.

4.1.1 Mike 11 Figure 12 and Figure 13 show water level (black line) and precipitation (blue line) for 2002-2006 and 2010-2014, both time series starting by the end of June and running to April. The original MIKE 11 runs covered somewhat longer time periods, but the graphs were adjusted to show as similar simulation periods as possible for better comparison. Water level is shown at chainage 10584 m in Igelbäcken stream which is approximately where the stream network leaves the study area.

The water level is given as elevation, in meters, and the precipitation is given in millimetres per day. Maximum precipitation in 2002-2006 was 38 mm/day and 44 mm/day in 2010-2014. Maximum water level in 2002-2006 was 5.50 m and 5.80 m in 2010-2014.

The results from running MIKE 11 with a NAM rainfall-runoff model were then used as hotstart, initial conditions, for hydrodynamic parameters in a new simulation that was run for June 29th 14.45.00-18:00:00, 2005 and 2015. The 2015 model was later used as input to MIKE FLOOD. Initial water level in Igelbäcken stream at the start of the MIKE FLOOD simulations is displayed in Figure 14. The stream outlet is in Edsviken bay, part of Lake Mälaren.

Figure 12. Water level and precipitation from June 2002 to April 2006. Maximum precipitation and water level was 38 mm/day and 5.50 m, respectively.

Figure 13. Water level and precipitation from June 2010 to April 2014. Maximum precipitation and water level was 44 mm/day and 5.80 m, respectively.

Figure 14.Initial water level along Igelbäcken stream at the start of the MIKE FLOOD simulations. Water levels along the stream are generally low. The stream outlet is in Edsviken bay, part of Mälaren lake.

4.1.2 Mike 21 The first runs of MIKE 21 were used to find misleading topography data. For some grid cells the water correction file showed as high water levels as 86 meters. All cells with values higher than one meter were identified and topography around these cells was adjusted to let the water run smooth.

4.2 SCENARIOS Results from the three scenario runs are presented in section 0-4.2.3. The land cover distribution found in Table 5 show that till and clay were dominating soil types in the study area and that impermeable surfaces such as roads and buildings made up approximately 33 per cent in Scenario 1 and 2 and 35 per cent in Scenario 3. Figure 15 show flooding extent according to the flood mapping performed by the City of Stockholm.

Figures in this section that show flooding extent show the maximum flood depth in each grid cell throughout the simulation. Thus, the maximum flood depth in each grid cell is displayed, but these maximum food depths to not necessarily occur at the same time since water will move through the landscape throughout the simulation.

Table 5. Land cover distribution for the three scenarios.

Surface type Scenario 1 & 2: Scenario 3: Fraction of Fraction of landcover [%] landcover [%] Rock outcrop 6.14 6.01 Till 18.55 18.05 Organic material 4.87 4.87 Clay 35.02 33.48 Alternating layers 3.19 2.25 Roads 19.59 16.02 Buildings 12.64 19.26

Figure 15. Extent of flooding in the study area according to the results of flood mapping performed by the City of Stockholm. All flooding shown has a depth of at least 0.3 meters.

4.2.1 Scenario 1: Current conditions Figure 16 shows spatial distribution of flood mapping in MIKE FLOOD for Scenario 1 at the end of the MIKE FLOOD simulation, June 29th 20:00:00, 2015. A majority of the flooding occurred along natural waterways. All flooding smaller than 0.3 meters was excluded from the map in order to point out areas with a larger sensitivity to flooding. Some flooding occurred along Igelbäcken stream in Scenario 1. Figure 17 shows a close up on Kista residential area and flooding extent in Scenario 1.

Figure 16. Flooding extent for Scenario 1: Current conditions displayed together with natural waterways in the landscape, existing roads and buildings.

Figure 17. Close up on Kista. Displaying existing exploitation, flooding from Scenario 1 and natural waterways generated through processing of topographical data from the City of Stockholm.

4.2.2 Scenario 2: Climate change Figure 18 shows spatial distribution of flood mapping in MIKE FLOOD for Scenario 2. All flooding smaller than 0.3 meter was excluded from the map to show the most sensitive areas. Flooding in the nature reserve surrounding Igelbäcken stream and in Kista residential area showed a larger flooding extent and depth than Scenario 1. When studying Figure 19 it can be seen that several streets were blocked by water in this scenario and that there were two areas with major floods. One to the north and one to the southwest in the study area.

Figure 18. Flooding extent for Scenario 2: Climate change displayed together with natural waterways in the landscape, existing roads and buildings.

Figure 19. Close up on Kista. Displaying existing exploitation, flooding from Scenario 2: Climate change and natural waterways generated through processing of topographical data from the City of Stockholm.

4.2.3 Scenario 3: Land use change Scenario 3 was, as mentioned in section 3.2.5.3 set up as a “worst case” scenario where a pilot study of exploitation potential in Kista was used to represent land use change. All flooding with a depth smaller than 0.3 meter was excluded to point out areas extra sensitive to being flooded. Figure 20 show flooding extent for the full study area. This scenario showed flooding both in the nature reserve along Igelbäcken stream and in the residential area. Figure 21 show a close up on Kista, several buildings were more or less surrounded by water in this scenario, marked by red circles in the map. Most flooding occurred along the natural runoff paths that are represented as blue lines in Figure 21.

New exploitation showed in this scenario is missing details such as courtyards and potential elevation changes in roads etc. This added uncertainty regarding runoff paths and locations of where water might be trapped. Furthermore, all grid cells identified as new exploitation was lifted up 2 meters from surrounding topography, which is another factor that might lead to misleading results.

Figure 20. Flooding extent for Scenario 3: Land use change displayed together with natural waterways in the landscape, existing roads and buildings.

Figure 21. Close up on Kista. Displaying existing exploitation, flooding from Scenario 3: Land use change and natural waterways generated through processing of topographical data from the City of Stockholm. Red circles mark areas with buildings that are surrounded by water to a large extent.

4.3 COMBINED RESULT Figure 22shows a comparison of flooding extent in Scenario 1 and Scenario 2, Figure 23 shows comparison of flooding extent in Scenario 1 and Scenario 3. It can be seen that Scenario 1 and Scenario 2 produced flooding in the same areas, but that the flooding was more extensive in Scenario 2. Whereas the flooding is moved to new locations, further down in the topography, in Scenario 3 compared to Scenario 1.

Water level at the outlet of the study area are shown in Figure 24 for the start of the simulation and for each of the three scenarios at the end of the simulation time, that is, at 20:45:00 June 29th, 2015. The water levels were at this point are 5.9 meters, 6.3 meters, 6.8 meters, and 6.3 meters for Scenario 1, Scenario 2, and Scenario 3, respectively.

Figure 22. Comparison of flooding extent in Scenario 1: Current conditions and Scenario 2: Climate change.

Figure 23. Comparison of flooding extent in Scenario 1: Current conditions and Scenario 3: Land use change.

Figure 24. Water level at the study area outlet at the start of the simulation as well as the end of the MIKE FLOOD simulation for Scenario 1, 2, and 3. The water levels are located at an elevation of 5.9, 6.3, 6.8 and 6.3 meter, respectively.

5 DISCUSSION

The setup of a 1D/2D hydrodynamic model in MIKE FLOOD, through use of the 1D river network model MIKE 11 and the 2D surface model MIKE 21, was supposed to contribute to the understanding of hydrological response to intense rainfall. Thereby, the project outcome was to result in development of planning support, through efficient use of ecosystem services, in urban planning to manage water related risks such as flooding and high water levels.

Results produced by Scenario 1-3 showed an example of how flooding extent and location in an urban area can change due to more intense rainfall events and changed land use. Furthermore, the response to these different flooding events in Igelbäcken stream was shown through the result of MIKE 11. The cross-section showing these water levels was chosen since it represents the outlet from the study area and thus the most downstream point in the river network that was coupled to the surface runoff.

The report regarding climate change adaption published by the National Board of Housing, Building and Planning in 2010 stated that overall strategies and goals regarding climate adaption are missing in Sweden, section 2.4. Results presented in section 4.3 provide a good representation of where water will accumulate in Kista residential area, to what extent flooding can be expected, and how the flooding will be forced to move due to changes in land use.

Large areas identified as permeable surface is covered by clay, which has a low permeability, this indicates that topographical changes due to changed land use have a larger impact on changes in flooding extent and depth than the loss of permeable surface.

In Scenario 3 it could be seen that some major constructions were planned in areas of extensive flooding. These areas were identified as being prone to be flooded already in the flood mapping performed by the City of Stockholm and the reason for why this have been over looked can be discussed. One reason might be that the information simply never reached the city planners. Another reason might be that it is desirable to build in areas that require as little blasting as possible due to the costs that come with blasting. This indirectly means that areas located at higher elevation, which are likely to require more blasting, will be saved for parks and recreational activities. If assuming that this is the case, many residential areas will have parks which require irrigation during the summers, since no storm water moves there naturally, and buildings that need to handle large amounts of storm water due to their location at low points in the topography.

Compared to the flood mapping previously performed by the City of Stockholm, results of this study show larger flooding extent. This was the case even in Scenario 1 which does not include the climate change factor of 1.25 that the City of Stockholm included in their mapping. There may be several reasons for this, but one major difference between the two models is that they use different rain types. A block-rain was used in the City of Stockholm’s model whereas a CDS-rain was used in present study. As described in section 3.1.1, a CDS-rain is based on a series of rains with various intensity and duration, which gives a dynamically changing precipitation. A block-rain on the other hand, has a set intensity throughout the entire duration of the rain and the water volumes in these two models may therefore not correlate.

Nevertheless, major areas within Kista residential area sensitive to flooding in case of more intense rain events correspond in the two models. Even if there are uncertainties within both models, this correlation increases the credibility of the results as a general guideline for pointing out sensitive areas in the landscape.

An unexpected finding in the results was the extent of flooding seen in the nature reserve around Igelbäcken. The expectation was that flooding extent in the reserve would decrease after addition of a more dynamic infiltration module. An explanation to this might be that the reserve surrounds Igelbäcken, which is located in a valley, and the soil here consists of clay to a large extent which

32 limits the infiltration capacity. Thus, the addition of a more representative infiltration module decreases infiltration capacity in the reserve.

As far as uncertainties goes, there are several aspects to consider. First, infiltration capacity for different soil types was set according to recommendation from Stockholm Vatten och Avfall. The accuracy of these infiltration capacities has not been evaluated. There is a lot of research going on in the field of infiltration capacity, which means that this might be a parameter to update if the model is to be improved. Second, as mentioned in section 3.2.3, the topographical data was manually adjusted to remove fictive obstacles such as bridges and overpasses from the topography. There may still be locations within the study area where the water is not allowed to flow properly due to this type of errors in the data. Furthermore, the processing of resolution from 0.5 meter to 4 meters grid size affects exact locations of buildings and roads. The effects of this uncertainty can be closely studied if a simulation with the original grid size of 0.5 meter was to be run. This has not been done due to time restrictions.

Another point to discuss is boundary conditions set for the 2D surface model, these boundaries were closed, which results in water only being allowed to enter and exit the study area through Igelbäcken stream. Resulting in gathering of water along the southeast boundary, see Figure 16 and Figure 18. If allowed to flow naturally across this boundary, there would most likely be no flooding here.

At last, the limited calibration and validation possibilities that come from modelling fictive scenarios must be mentioned. Since the topic of present study is flood adaption and mitigation, the model uncertainty should not be underestimated. In the sense that actual flooding extent might be even more extensive than what the model shows in case of an extreme rain event.

6 CONCLUSION AND RECOMMENDATIONS

The final model is able to show flooding extent and depth in the study area with an accuracy good enough to serve as general guideline when working with mitigation measures of current land use and when planning future developments. As the most flooded areas correspond in the result from present study and the results from modelling performed by the City of Stockholm, these areas should be treated carefully when planning further developments.

Major water level changes in Igelbäcken stream showed at the end of the simulation, six hours after the start of the intense rain events. This indicates that close monitoring and mitigation strategies are needed to ensure that the quality of water being fed to the stream is good enough.

If results from present study would have been available at an earlier stage in the planning process they could have been used as guideline for what natural storm water runoff paths to keep open and placement of parks with respect to multifunctionality and mitigation measures such as infiltration and retardation.

Considering the large investments required for city planning and development, as well as potential costs due to flood damage, it is recommended to look into the process of systemizing the kind of modelling that has been performed in present study. For many municipalities, it could be interesting to see the effects of Scenario 3 and the quick response in Igelbäcken stream. This type of modelling provides visual result maps that are easily understood for people with other expertise areas than hydrology, for example city planners or consultants. If more resources would have been available, it would have been interesting to model the effects of implementation of blue-green solutions, such as multifunctional parks. However, this might not be possible due to difficulties in performing this type of modelling with a higher resolution and more details.

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APPENDIX A – INFILTRATION PARAMETERS

The table below shows infiltration parameters used as input to MIKE 21 in the infiltration module. The parameters were set by Stockholm Vatten och Avlopp, Sweden’s largest water and waste company.

Infiltration Porosity Thickness Leakage Degree of velocity [-] [m] velocity saturation [mm/h] [mm/h] [%] Rock outcrop 0 0.01 0.01 0 0 Till 36 0.4 0.3 3.6 18 Organic material 18 0.4 0.3 1.8 32 Clay 3.6 0.4 0.3 0.36 40.5 Alternating 36 0.4 0.3 3.6 8 layers Roads 0 0.01 0.3 0 0 Buildings 0 0.01 0.3 0 0

i APPENDIX B – PRECIPITATION INPUT

The table seen below shows precipitation intensity for each timestep of the two different precipitation series. Precipitation series were provided by Stockholm Vatten och Avlopp, Sweden’s largest water and waste company.

Time step 100-year flood 100-year flood *1.2 (climate change factor) Surface without Surface with Surface without Surface with sewer system. sewer system. sewer system. sewer system. Green areas. Impermeable Green areas. Impermeable [mm/h] area. [mm/h] [mm/h] area. [mm/h] 2015-06-29 14:45:00 0 0 0 0 2015-06-29 15:00:00 164,7497517 94,52696984 205,9371896 138,9894078 2015-06-29 15:15:00 26,2 0 32,75 0 2015-06-29 15:30:00 14,2 0 17,75 0 2015-06-29 15:45:00 10,5 0 13,125 0 2015-06-29 16:00:00 7,4 0 9,25 0 2015-06-29 16:15:00 6,7 0 8,375 0 2015-06-29 16:30:00 6 0 7,5 0 2015-06-29 16:45:00 4,9 0 6,125 0 2015-06-29 17:00:00 4,6 0 5,75 0 2015-06-29 17:15:00 4 0 5 0 2015-06-29 17:30:00 3,7 0 4,625 0 2015-06-29 17:45:00 3,5 0 4,375 0 2015-06-29 18:00:00 3,3 0 4,125 0

ii APPENDIX C – CALIBRATED NAM-PARAMETERS FROM DHI

The table seen below shows NAM-parameters calibrated by DHI. These parameters were used as input in the MIKE 11 NAM rainfall-runoff model. Details regarding the calibration is found in the report Igelbäcken. Uppbyggnad av hydrologisk modell samt beräkningar av vattenbalas, geohydrologi och föroreningar (in Swedish), published by Stockholm Vatten and DHI.

iii TRITA-LWR Degree Project ISSN 1651-064X LWR-EX 2017:13