Storm Water Management Model and the REO model

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Storm Water Management Model and the REO model

An Urban Hydrology Study, Vestli Hydrometric Station, Oslo

Ellen Birgitte Folgerø

Master’s Thesis, Spring 2020

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This master’s thesis is submitted under the master’s programHydrology and Glaciologyat the Department of Geoscience, Faculty of Mathematics and Natural

Sciences, University of Oslo.

The scope of the thesis is 60 credits.

Storm Water Management Model and the REO model. An Urban Hydrology Study, Vestli Hydrometric Station, Oslo

This work is published digitally through DUO - Digiale Utgivelser ved UiO:

http://www.duo.uio.no/

Printed: Reprosentralen, University of Oslo

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Abstract

Stormwater management is important for social security and the economy since stormwater can cause large damages to building and infrastructure. Implementation of stormwater measures would reduce the danger associated with stormwater. NOU 2015: 16, 2015 discuss the municipalities possibilities of introducing stormwater fee. The REO model is developed as a tool to estimate impacts of urban development on stormwater production at the property level, and thereby guide calculation of stormwater fees. The model needs to be validated, with and without low impact development measures (LIDs).

The Storm Water Management Model (SWMM) was used as a benchmark, and calibrated and validated on historical data for the study area Vestli with the focus on discharge peaks. Two different methods for constructing design rainfall was used for comparison of SWMM and REO simulations.

LID was implemented to find a reduction of peak and volume, by decreasing the percentage of routing from impermeable to permeable areas.

The findings from this study show that implementing LIDs will reduce the runoff and the results indicate that the profile of the design rainfall has a large influence on the discharge peak and is important estimation when evaluating LID implementation. The second main finding was that REO model overestimates both peak flow and volume and a reduction in runoff coefficient from the permeable area will give better results. The runoff coefficient from lawn should be reduced from 0.3 to 0.1 and the runoff coefficient for forest should be reduced from 0.2 to 0.1.

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Acknowledgements

First and foremost, I wish to thank my supervisors, Chong-Yu Xu, Nils Roar Sælthun and David Barton, for all their expert guidance and encouragements through this thesis. Especially, Nils Roar for all the inputs and discussion relating to urban hydrology and your expert competence in the field of hydrology.

You have truly been an inspiration of me. I also want to thank the project New Water Ways, for making this project possible.

I would also thank Hong Li, for helping me with data, contacts to the Water and Sewage Agency in Oslo Municipality (Oslo VAV).

Also, I wish to thank Samatar Mahammud Abdi and Are Skytterholm from the Water and Sewage Agency in Oslo Municipality (Oslo VAV) and Fred Wenger from The Norwegian Water Resources and Energy Directorate for providing me data for my thesis and your overall knowledge about this field.

I especially want to show my gratitude to my dear friend Ina Storteig for giving me comments and ideas throughout this thesis. The discussion we had has helped me to understand more about this field and I appreciate your will to share your knowledge with me.

Finally, thank you to all students in study room 210 - it was the best of times and the worst of times. Mina Tangen, thank you for always making me laugh.

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List of Figures

2.1 The three step approach. The figure is from the thesis of Storteig, 2019 . . . 4 2.2 The calculated BGF in Oslo . . . 6 2.3 The percentage increase when implemented green roofs in Oslo for

the roofs that had a slope between 0-30^◦. Case 1 indicates a soil depth larger than 80 cm, case 2 indicates a soil depth between 2-39 cm and case 3 indicate a soil depth between 2-39 cm. . . 7 3.1 The upper figure shows Stovner district in black outline, Vestli in

light green and the catchment generated for the thesis are shown in red. The lower figure shows the location of the catchment in the northeast part of Oslo, Norway. . . 14 3.2 Distribution of sediment types at Vestli. Mapped by Geological

Survey of Norway (NGU) . . . 15 3.3 Monthly precipitation and temperature . . . 15 3.4 Stormwater sewer network at Vestli. The outer black line is the

catchment area, the green lines are the stormwater network used in the thesis, the black dots are the manholes, while the red triangle is the discharge and weather station. . . 16 5.1 Baseflow through a week using different method for baseflow

separation. . . 27 5.2 Baseflow through a day, showing mean hourly values and the mean

hourly data for weekdays and weekends. . . 28 6.1 The time series for the observed and simulated discharge [l/s],

the green representing the observed discharge and the red is the simulated discharge. The precipitation [mm/h] for the corresponding times is shown in blue. . . 34 6.2 The cumulative observed and simulated discharge, from 12th of

September to 15th of November. . . 34 6.3 The time series for the observed and simulated discharge [l/s] for the

validation period. The green representing the observed discharge and the red is the simulated discharge. The precipitation [mm/h]

for the corresponding times are shown in blue. . . 35

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6.4 The time series for the observed and simulated discharge [l/s] for the validation period when the precipitation data is correct. The green representing the observed discharge and the red is the simulated discharge. The precipitation [mm/h] for the corresponding times

are shown in blue. . . 36

6.5 IDF- curves and hyetograph for Vestli. Interpolated between values from NCCS was done with a python script, using interpolation method cubic spline. Upper right: IDF-curve for 2, 5 and 20 years generated from NCCS. Upper left: Hyetograph for 2 year design rainfall with 1 minute time step for both methods DR1 and DR2. Lower right: Hyetograph for 5 year design rainfall with 1 minute time step for both methods DR1 and DR2. Lower left: Hyetograph for 20 year design rainfall with 1 minute time step for both methods DR1 and DR2. . . 37

6.6 Hydrograph for design rainfall at study area, Vestli. Upper left: showing catchment response for 2 year design rainfall, method DR1 in blue and DR2 in orange. Upper left: showing catchment response for 5 year design rainfall, method DR1 in blue and DR2 in orange. Lower left: showing catchment response for 20 year design rainfall, method DR1 in blue and DR2 in orange. . . 38

6.7 Subcatchment runoff at subcatchment 23343, from a design rainfall for 2, 5 and 20 year. With 50% routing being the optimal parameter, i.e. scenario present today, where 75% and 90% routing will correspond to a LID implementation. Both methods for finding design rainfall are included, named DR1 and DR2. . . 40

6.8 Subcatchment runoff at subcatchment 55268, from a design rainfall for 2, 5 and 20 year. With 50% routing being the optimal parameter, i.e. scenario present today, where 75% and 90% routing will correspond to a LID implementation. Both methods for finding design rainfall are included, named DR1 and DR2. . . 41

A.1 Discharge station at Vestli . . . 57

A.2 Rain gutter are directed onto impervious area . . . 58

A.3 Stormwater passing alongside the stormwater drain . . . 58

A.4 Vestli consists of residential housing surrounded by lawns . . . 59

C.1 Spatial distribution of the slope in catchments. The black area represents the 15 different subcatchments, while the red triangle is the discharge and weather station. The green area represents smaller slopes, while red areas represent the steepest part of the catchment. . . 65

D.1 Stormwater system in catchement and around. The light green lines are the stormwater pipes used in the thesis, while the red lines are the stormwater pipes nearby the catchment. The green points are the stormwater drain and the red triangle is the discharge and weather station. . . 67

E.1 Daily dry weather flow for different method . . . 69

E.2 Hourly and weekly water consumption at Rødtvet - Stovner . . . . 71

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List of Figures

F.1 Difference in generated catchment (light green) and generated catchment from NVE (black outline) . . . 73 G.1 Subcatcment used for LID implementation and comparison between

SWMM and REO model . . . 75

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List of Tables

2.1 Number of damages connected to water instruction from the outside of buildings. Source: Finans Norge (VASK - Vannskadestatistikk) 3 5.1 Values for the input parameters in SWMM: name of subcatchments,

area, percentage of impervious, and the mean and median percentage slope. . . 21 5.2 The results from different approaches for finding the width of each

subcatchment. Column named "Average flow" are the chosen width 24 5.3 Calibration of parameters for the subcatchment. Values for width,

% slope and % imperv vary for the subcatchment and can be found in 5.1. . . 29 5.4 Calibration of parameters for infiltration in the Green-Ampt

Infiltration Method. . . 30 5.5 Calibration parameters for aquifer and groundwater components in

model . . . 31 6.1 Table showing the simulated discharge peak, volume and runoff

coefficient for subcatchment 23343. PCT indicating the percentage of routing, at 50% which illustrate the scenario represented today, and for LID implementation with percentage routing increase at 75% and 90%. . . 43 6.2 Table showing the simulated discharge peak, volume and runoff

coefficient for subcatchment 55268. PCT indicating the percentage of routing, at 50% which illustrate the scenario represented today, and for LID implementation with percentage routing increase at 75% and 90%. . . 44 E.1 Daily variation in dry weather flow for the different methods . . . 69 E.2 Hourly variation in dry weather for the manual method . . . 70 H.1 First 30 minutes of the design rainfall . . . 78 H.2 Last 30 minutes of the design rainfall . . . 79 I.1 Datasets that was used in the analysis for finding BGF per

subdistrict in Oslo. . . 81 I.2 Weights for the landcover and different green roof depths . . . 82

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Acronyms and Abbreviation

d Index of agreement DEM Digital Elevation Model DWF Dry Weather Flow

GIS Geographical Information System

GRASS GIS Geographic Resources Analysis Support System IDF Intensity-Duration-Frequency

LID Low Impact Development

MET Norwegian Meteorological Institute NCCS Norwegian Centre for Climate Services NGU Geological Survey of Norway

NSE Nash-Sutcliffe Efficiency

NVE The Norwegian Water Resources and Energy Directorate Oslo VAV Water and Sewage Agency in Oslo Municipality

Oslo PBE Planning and Building Agency in Oslo Municipality R² Coefficient of determination

SWMM Storm Water Management Model

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CHAPTER 1 Introduction

With urban growth the impermeable surfaces in cities are increasing and the runoff will be larger and more rapid due to the decrease in infiltration and depression storage. Additionally, the volume will increase (NOU 2015: 16, 2015, Healy et al., 2007). Climate change is predicted to give an increase in precipitation, both in intensity and volume (Norsk Klimaservicesenter, 2017).

Stormwater cause damages on buildings and infrastructure, and is a problem for social security and economy. There is there a need for for enhanced stormwater management.

The Blue Green Factor is a norm for building projects and was implemented to enhance the effort with challenges in the stormwater (Oslo Kommune, Plan- og bygningsetaten, 2018).

The REO model is a simple model for estimating surface runoff from urban areas using a minimum of parameters. The simplicity of the model is main appeal since data is often scarce. The REO model is intended to be a tool for setting a stormwater fee at property level (Sælthun and Barton, 2020). A stormwater fee could intensity the effort in reducing stormwater problems, if the implementation of LID on a property would result in a reduction in fee.

The aim of the study was to validate the REO model by comparing results from REO with subcatchment runoff generated from SWMM. The study area was Vestli, Oslo.

1.1 Outline

The rest of the thesis is organised as follows:

Chapter 2 Theory related to urban hydrology. SWMM and REO model are introduced, giving a short description of the models and how they work.

Chapter 3 A general description of the study area, including catchment properties, geology, measuring station and stormwater network.

Chapter 4 The data used for creating a SWMM model over Vestli are described.

Chapter 5 The model-set up for Vestli is described, covering the physical catchment, stormwater network and input data. The calibration process

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and parameter estimation are discussed. Constructing design rainfall and LID implementation is also included.

Chapter 6 Results from the calibration and validation of the SWMM model are presented, along with the design rainfall. LID implementation are described. The results from comparing the REO model with SWMM are also presented.

Chapter 7 The results are discussed and model set-up and limitation are represented.

Chapter 8 The conclusion is presented and further work is suggested.

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CHAPTER 2 Theory and Models

2.1 Urban Hydrology and Stormwater Management

It is predicted that in the year 2040 the population in Oslo will increase with 27% compared with the year 2018. This will lead to increasing urbanization of the city and the urban growth puts pressure on green areas and challenge outdoor life, recreation opportunity, biodiversity and climate adaptation(Oslo Kommune, 2018). Densification of areas will lead to more impervious area, such as buildings, roads and parking lots, and cause less infiltration and storage opportunities for water, hence the cause a more rapid response in the runoff and increase the volume of runoff. This can cause damages to buildings and infrastructure NOU 2015: 16, 2015, Healy et al., 2007. The damages caused by water intrusion on buildings can be seen in table 2.1 and it illustrates the effect of damages caused by water. The data is collected from VASK - Finans Norge and show the damage insurance for Norway, and not Oslo. NOU 2015:

16, 2015, states that the largest urban areas, generally, do not appear to be more prone to stormwater damage than a national average.

Table 2.1: Number of damages connected to water instruction from the outside of buildings. Source: Finans Norge (VASK - Vannskadestatistikk)

Year Companies Private Sum

2008 1548 8989 10537

2009 1340 7317 8657

2010 2236 13881 16117

2011 2856 17999 20855

2012 1476 10803 12279

2013 2380 15855 18235

2014 2397 14705 17102

2015 2096 15130 17225

2016 2313 14995 17308

2017 2519 16480 18999

2018 1985 17378 19363

2019 2768 20049 22817

Sum 25913 173582 199495

Additionally, Norsk Klimaservicesenter, 2017, predicts that precipitation will

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increase in both intensity and frequency in the future. The rainfall amount is predicted to increase with 20%. The challenges with handling stormwater will thereby increase. There is, therefore, a need for climate adaptation measures.

Oslo Kommune, 2013 has developed a strategy to deal with stormwater and stating that water should be dealt with locally instead of using the sewer system.

The focus is on the three-step approach, which is also recommended by Norsk VANN to deal with stormwater. The first step is prevention measures ensuring natural water balance through infiltration, evaporation and absorption of water in vegetation, purifying contaminated water, reducing the amount of unwanted water added to the wastewater treatment plant. The second step is the focus on delaying water from major rainfall events, before emission to sewer system or rivers. The last step is to ensure safe drainage from the surface through floodways (Paus, 2018, Lindholm et al., 2008, Ødegaard et al., 2014, p. 352-356).

And illustration of the three step approach is shown in fig. 2.1 and is from the thesis of Storteig, 2019.

Figure 2.1: The three step approach. The figure is from the thesis of Storteig, 2019

Step 1 in the three-step approach is focusing on infiltration. To increase the infiltration the pervious area must increase and it can be done by implementing Low Impact Developments (LID). LID is designed for water to infiltrate to the ground or retained and prevent water from draining directly to the sewer system or rivers. LID could be green roofs, disconnecting of rain gutters, swals or rainbed. When LID is implemented and integrated into green measures such as lawns, the notation is Blue-Green measures (Magnussen et al., 2015.) The Blue-Green Factor (BGF) is used as a municipal norm for building projects in Oslo and was implemented to intensify the effort to reduce stormwater problems, hence ensuring social security and economy. The main appeal with the BGF is to reduces the vulnerability for stormwater, but also increases the biodiversity, enhance the recreational side of a city, cleaner air and water. BGF applies to property level (Oslo Kommune, Plan- og bygningsetaten, 2018).

A GIS analysis was performed by the author in Oslo to find the BGF for subdistricts. Figure 2.2 shows the BGF calculated in the current situation in Oslo. This illustrates that the BGF is the highest outside the city centre due to the fact of more pervious area. Fig 2.3 shows the percentage increase in BGF when implementing green roof on all roofs in Oslo that had a slope

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2.2. Sewer System

between 0-30^◦, compared with the calculated BGF in the current siutation.

The soil depth varies from 2-39 cm (case 3), 40-79 cm (case 2) and larger than 80 cm (case 1), according to the BGF for green roofs. It was assumed that all buildings could carry the weight of a green roof and it was not taking into account that some buildings already have green roofs. Nonetheless, it shows that the blue-green factor increases the most in the city centre, stating that this area has the most to earn from implementing BGF, and the deeper the soil in the green roof the better the increase in BGF due to the fact that it has the highest weights. Information about the data used and weights, see appendix I.

2.2 Sewer System

The sewer networks is build to redirect wastewater and stormwater. The network could either be a separate system or a combined system. For the separate system wastewater and stormwater are divided into separate pipelines, where the wastewater is transported to a wastewater treatment plant and the stormwater is redirected to a recipient. In a combined system wastewater and stormwater are conducted in the same pipe to the wastewater treatment plant.

(Ødegaard et al., 2014, p. 296-297)

At the study area, Vestli, the sewer network is a separate system, and the stormwater is transported to Tokerudbekken.

2.3 The Rational Method

The rational method equation is given in eq. 2.1. This is a well known method for drainage design in small urban areas (Dingman, 2015, p. 514-515, Ødegaard et al., 2014, p.346), and the main appeal lies in the simplicity of the equation.

Qmax=c·i(Tc)·A (2.1)

where Qmax is the discharge peak [l/s], c is the runoff coefficient [-],i(Tc) is the intensity of design rainfall [l/s ha], were (Tc) is the time of concentration and A is the area of the catchment [ha]. The design rainfall is found from intensity-duration-frequency curves. The time of concentration is the time the water use from the hydraulically most distant part in the catchment to the outlet (Dingman, 2015, p.471, Ødegaard et al., 2014, p. 346-347). The runoff coefficient is ratio of peak flow per unit area to rainfall intensity and is a highly sensitive parameter (Dingman, 2015, p. 515). The runoff coefficient range lies between 0-1, where 0 indicates that none of the rainfall produces runoff.

Sælthun and Barton, 2020, list multiple runoff coefficient based on literature review for different landuse types. The runoff coefficient increases with the increase of impervious area. Assumption made by the rational equation is that precipitation is uniform over the catchment and does not vary in time and space, the peak flow occurs when the rainfall intensity lasts as long as the time of concentration, the runoff coefficient is constant during the storm and does not vary with antecedent soil moisture and the basin storage effects are neglected (Dingman, 2015, p 515, Cleveland et al., 2011, Hayes and Young, 2006).

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Figure 2.2: The calculated BGF in Oslo

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2.3. The Rational Method

Figure 2.3: The percentage increase when implemented green roofs in Oslo for the roofs that had a slope between 0-30^◦. Case 1 indicates a soil depth larger than 80 cm, case 2 indicates a soil depth between 2-39 cm and case 3 indicate a soil depth between 2-39 cm.

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2.4 EPA’s Storm Water Management Model (SWMM)

The EPA’s Storm Water Management Model (SWMM) is a dynamic rainfall- runoff simulation model which simulates runoff quality and quantify, mainly from urban areas. It is used worldwide for design of storm water runoff and combined sewers among others. The model uses three components that are interconnected, the first one being the precipitation event. The second component being the runoff where the subcatchment generates runoff from the precipitation events. The last component is the routing where water is transported through a conventional sewer system with pipes, channels, pumps etc. (L. A. Rossman, 2015, p.12). SWMM represents spatial variability by diving a catchment into subcatchments and is therefore a dynamic semi- distributed model. Each subcatchment has a representative area, width, slope and percentage of impervious area. For the subcatchment, Manning’s n for overland flow for both impervious and pervious area, and depression depth for both impervious and pervious area, need to be defined. An outlet that receives the subcatchment runoff and a rain gauge that’s linked to the subcatchment are also needed (L. A. Rossman, 2015, p. 47-48 and 196-197).

The runoff is generated based on the properties set for each subcatchment, allowing the generated runoff to vary between the subcatchments. For impervious area without depression storage the runoff is generated immediately after a rainfall event, while the impervious and pervious areas with depression storage the runoff generated are depended upon the rate of evapotranspiration and infiltration capacity. (L. Rossman and Huber, 2016, p. 54-56, L. A. Rossman, 2015, p.74) Runoff can be generated straight to subcatcments outlets or routed between impervious and pervious areas (L. A. Rossman, 2015, p.196). In this study the routing was set to route runoff from impervious areas to pervious areas. The hydraulic routing in SWMM solves the St. Venant equation and the chosen method was the dynamic wave routing which solves the St.Venant equation completely for unsteady free surface flow. This method gives the best theoretically results. A drawback with this method is that is requires smaller time steps in the simulated and for long continuous simulation this could be a restriction due to the simulation time. (L. Rossman and Huber, 2017, p.36, 40, L. A. Rossman, 2015, p.77).

The Green-Ampt method was used for infiltration. This method depends on the water infiltrated and water moves downwards in the soil, according to Darcy’s law (Dingman, 2015, p. 366, L. Rossman and Huber, 2016, p. 104).

The infiltration is depended on the parameters describing the soil, which are capillary suction, saturated conductivity and initial soil moisture deficit (L. A.

Rossman, 2015, p.290) and the former infiltrated volume of water (L. Rossman and Huber, 2016, p.106).

Groundwater is implemented into each subcatchment through an aquifer and the connected to each subcatchment is through a node (L. A. Rossman, 2015, p. 298). The aquifer consists of a saturated and unsaturated zone. The saturated zone is defined from bedrock and up to the water table, whereas the unsaturated zone is defined from the water table up to the surface elevation.

The unsaturated zone receives water from the infiltration and loses water due to evapotranspiration and percolation. The saturated zone will receive water from

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2.5. REO Model

percolation from the unsaturated zone and lateral groundwater flow, and will lose water from deep percolation, evapotranspiration and lateral groundwater flow (L. Rossman and Huber, 2016, p. 129-138).

The precipitation is assumed to fall uniformly over the catchment area and the user-defined data could be implemented as intensity, volume or cumulative volume with different time steps (L. Rossman and Huber, 2016, p.31-32). The chosen method was to use the format of volume at a minimum time interval at 1 minute. The temperature is included as a climate file with daily minimum and maximum values for temperatures, in tenths of degree Celsius (L. Rossman and Huber, 2016, p.40). SWMM uses the Hargreaves method for obtaining the evaporation rate. The temperature data is used to compute evaporation, along with the area’s latitude. Since the evaporation rate is depended on the water amount that is available, SWMM computes the potential evaporation rates (L. Rossman and Huber, 2016, p.49, L. A. Rossman, 2015, p.58).

The sewer system in SWMM is represented by nodes and links. Nodes represent the connection between links and are the point were water enter the sewer network, and for this study these were considered to the manholes. Links are the elements that transport water between nodes, i.e the stormwater pipes in this study (L. Rossman and Huber, 2017, p.31).

2.5 REO Model

REO model was developed to find runoff and peak flow at property level. The reason the REO model is focusing on property level is to find fees for property that corresponds to the peak flow and runoff the property produces and use the fees to finance stormwater management (Sælthun and Barton, 2020, p.7). If a household implemented LID on the property the peak flow and runoff would decrease and hence reduce the fees. It can then be assumed that more people will themselves implement LID on their property and in return receive a reduced fee.

The REO model uses the rational equation, as describes in2.3, to calculate the urban runoff and the only parameters needed for the model are the areal, slope, length and routing between landuse. Other parameters are set by the model.

The landuses types are divided into roof, impervious area, impervious area with trees, partly open areas, permeable areas (lawn), permeable area (forest). The LID are green roofs, with varying depth, rainbed, swales and terrain depression.

The model operates with two runoff coefficients, the volumetric surface runoff and the runoff coefficient for peak flow (Sælthun and Barton, 2020, p. 7-8).

The landuse in the REO model is compatible with the measures for BGF.

The model calculate which of the different landuse types that contribute the most to surface runoff and then use this area to find the peak flow using the time of concentration. Other landuses type are included in the peak flow by weighting the relationship between the dominating subarea and the areas time of concentration if the time of concentration for the area is larger than the dominating area. Hence, only part of the different landuse contributed to the peak flow (Sælthun and Barton, 2020, p. 16). The water is by default routed directly to the sewer system. It is possible to route water to impervious area,

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partly open areas, lawn, forest, rainbed, terrain depression, swales or directly out of the system to rivers and creaks (Sælthun and Barton, 2020, p.7, 16-17).

2.6 Design Rainfall

Intensity-duration-frequency (IDF) curve represents the rainfall intensity with different duration’s and frequencies of occurrences (Ødegaard et al., 2014, p.

47). Norwegian Centre for Climate Services (NCCS) has developed IDF-curves, using Gumbel distribution (Dyrrdal et al., 2015), for mainland Norway and can be downloaded directly from their webpage (https://klimaservicesenter.no/

faces/desktop/index.xhtml).

A hyetograph represents the rainfall intensity over time. The total volume of rainfall for design hyetograph should be consistent with the rainfall volume from IDF-curves with equal duration. An advantage of using a hyetograph is that heavy rainfall often has its highest peak a time after precipitation occurs, so rainfall intensity is seldom constant in these events. The rainfall event is thereby represented in a hyetograph. (Ødegaard et al., 2014, p.349-350 and Bøyum et al., 1997). Hyetographs are constructed based on the IDF-curve and it is assumed that the hyetograph is symmetric around the middle axis.

Ødegaard et al., 2014, p. 250, states that a reasonable time step calculation is 5 minutes since higher calculation steps will give a high peak intensity in the middle axis. Based on this a 10 minutes time interval for the rainfall intensity from the IDF-curve can be chosen. The duration at 10 minutes will give the highest rainfall intensities and thereby being the top and middle axis on the hyetograph. The intensity for different time steps is calculated with equation, eq. 2.2.

It2−It₁= It2·t2−It1·t1

t (2.2)

whereIis the rainfall intensity [mm/min], tis the time step [min],t₁ andt₂ are the time corresponding time step toI.

2.7 Calibration and Validation

An urban hydrological model describes catchment characteristics, such as slope, percentage of impervious area, the porosity and hydraulic saturation of the soil and groundwater properties and so on. There will be uncertainties related to the catchment characteristics and it is, therefore, a need for calibration and validation of the parameters in the model. The process of calibration is the comparison of model output to the observed values, with different parameters describing the catchment. From the calibration, the optimal parameter is chosen to represent the catchment. Calibration can be done by trial and error, as done in this thesis, or by automatic calibration. When the optimal parameters are obtained the model is validated on an independent time period and again comparing the observed and simulated data (Dingman, 2015, p.510).

There are many ways to evaluate the goodness of a model. This could be subjective assessment, such as the overall visual performance between observed

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2.7. Calibration and Validation

and simulated data in terms of over prediction or under prediction, or it can be the timing of events such as rising limb and falling limb. To assess a model objective there is a need for a mathematical estimate of the errors between observed and simulated data. These are referred to as efficiency criteria (Krause et al., 2005 and Chen and Guo, 2019, p.242).

One efficiency criterion is the coefficient of determination, R². This criterion is used to analyze how differences in observed variance can be explained by a difference in simulated data, i.e. how well predictions are to the observed data (Krause et al., 2005). R² is defined as:

R²=

n

P

k=1

(O_i−O)(P¯ _i−P)¯ s n

P

k=1

(Oi−O)¯ ² s n

P

k=1

(Pi−P)¯ ²

!2

(2.3)

where O is the observed value, P is the simulated value, ¯O is the observed mean value and ¯P is the simulated mean,kis the step and n is the number of discharge peaks. Equation 2.3 express the ratio between explained variance between simulated and observed data to total variance being the variance in observed data, respectively in the numerator and denominator. R² values ranges between 0 and 1, where 1 is a perfect linear relation between observed and simulated values. A drawback of this criterion is that even if the model overestimates or underestimates at all time steps might still result in a good R²2 value (Krause et al., 2005).

Another popular efficiency criterion is Nash-Sutcliffe efficacy (NSE).

N SE= 1−

n

P

k=1

(Oi−Pi)²

n

P

k=1

(O_i−O)¯ ²

(2.4)

where O is the observed value, P is the simulated value and ¯O is the observed mean. NSE describe the difference between simulation and observed data at corresponding time steps. The range lies between−∞and 1, where 1 indicates a perfect match between simulated and observed data. Storteig, 2019 p. 12, states that a NSE larger than 0.5 is acceptable for SWMM simulations. NSE has emphasis on higher errors while smaller errors has less influence. This can cause higher streamflow to be more emphasized than lower streamflows, since higher streamflows often result in higher errors Krause et al., 2005.

A third efficiency criterion is the Index of agreement, d. This criterion measures the degree of model prediction error and is used since NSE and R²are insensitive to differences in the observed and simulated means and variances (Krause et al., 2005).

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d= 1−

n

P

k=1

(Oi−Pi)²

n

P

k=1

(|Pi−O|¯ +|Oi−O|)¯ ²

(2.5)

where O is the observed value, P is the simulated value, ¯Ois the observed mean value and ¯P is the simulated mean. The range is 0 - 1, where 1 indicates a perfect fit while 0 indicates that there is no correlation between observed and simulated values.

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CHAPTER 3 Study Area

This chapter describes the study area, including population at Vestli, catchment, geology, measuring station and stormwater network.

The study area is an urban catchment that lies at Vestli, in the northeast part of Oslo, Norway. Vestli is located at the district of Stovner, which consists of the subdistricts Vestli, Høybråten, Stovner, Fossum, Haugenstua and Rommen (fig 3.1). In the year 2010, a total of 6198 people lived at Vestli whereas in 2020 the population was 7065 (Bydelsfakta, n.d.). The study area consists of a residential area, covering houses, subway station, school and kindergarten, and includes lawn and forest. Fig. A.4 shows a typical example of Vestli, with a residential house surrounded by lawns.

The catchment area is generated from GRASS GIS and found to be 0.37 km² (37.42 ha) and was divided into 15 subcatchments. Analysis in ArcGIS Pro found that 28.03% of the catchment consists of impervious area, where 13.24%

of this is buildings (i.e roofs) and 14.79% is roads, sidewalks and parking lots etc. Of the 71.97% of permeable area, 24.32% is forest area and 47.65% is other permeable areas, mainly lawn. The forest lies in the eastern part of the catchment.

The catchment area different from the information in about the catchment found in the metadata in Hydra II. The metadata states that there are uncertainties attached to the catchment area. The area varies from 0.33 - 0.41 km². The differences are assumed to be in chosen method for finding catchment and impervious area.

NGU’s has map of infiltration, but for the study area the infiltration capacity is not classified. Figure 3.2 shows that the area mainly consists of fill material, but the western part of the catchment is mainly bar rock. Close to the discharge station marine deposits occur. The study area has a varying slope, fig C.1. The steepest parts are in the eastern part of the catchment, both in the northern and southern directions.

From 2010 to 2019 the average annual temperate is 5.5^◦C at the weather station.

July is the warmest month while January is the coldest, receptively 16.3^◦C and -5^◦C. Taking the precipitation data from year 2012 to 2019, excluding 2014 since no there were no data in that year, the wettest month is June with 75 mm and the driest month being March with 14 mm. A representation of the

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Figure 3.1: The upper figure shows Stovner district in black outline, Vestli in light green and the catchment generated for the thesis are shown in red. The lower figure shows the location of the catchment in the northeast part of Oslo, Norway.

monthly variation at Vestli can be seen in fig. 3.3. This information should be considered with care, due to lack of data poor quality. For further information about data quality, see chapter 4.

The sewers network at Vestli is a separate system and in this thesis the stormwater pipes were used, fig 3.4. Water drains to stormwater pipes and will be directed to the discharge station before it will be released to Tokerudbekken

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Figure 3.2: Distribution of sediment types at Vestli. Mapped by Geological Survey of Norway (NGU)

Figure 3.3: Monthly precipitation and temperature

and further into Alnaelven. The stormwater network has a dividing stormwater drain, seen in fig. D.1 marked as a red circle. Hence, the network has a pipe leading out of the catchment. According to Brennhovd, 2014, p.35, this stormwater pipe has a 35 cm threshold. The stormwater network outside this point has a lower elevation, and it is assumed that minimal water was lost from this nor gained from this point. These pipes connect to a combined system downstream at Fossum.

The discharge station and weather station are at the same location, see fig. 3.4.

The discharge station (6.12.0), fig. A.1, has been operating since 1974 and is a V-notch weir with an angle of 136.4. The weather station (SN18270) has since been operating as long as the discharge station and measure precipitation and temperature. It is an automatic station. The rain gauge tilts each time 0.1 mm of rainfall is accumulated and the frequency of tilt become a measures of rainfall intensity. The rain gauge has therefore a good resolution.

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Figure 3.4: Stormwater sewer network at Vestli. The outer black line is the catchment area, the green lines are the stormwater network used in the thesis, the black dots are the manholes, while the red triangle is the discharge and weather station.

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CHAPTER 4 Data and Data Quality

The discharge data was extracted from HYDRA II. HYDRA II is a database with hydrological and meteorological data and is owned by The Norwegian Water Resources and Energy Directorate (NVE), which do the acquisition and control of the data. Discharge data with 5 minutes time steps was extracted for year 2018 and 2019. The discharge data was extracted from HYDRA II, using HYKVAL, which is the primary controlled data. The quality of the discharge data at Vestli (6.12.1001) is variable and much data is missing.

The temperature and precipitation data was extracted from Frost API (henceforth: frost) and is developed and owned by The Norwegian Meteorological Institute (MET). Frost is database for meteorological and climate data. The metadata about weather stations are also included in frost. The data is controlled for daily, monthly and yearly temperature, precipitation, and wind data. Other information, like metadata about weather stations, is also available through the API. Since the time resolution of 1 minute for precipitation was used in SWMM, frost is a good database since it handles large volumes of data. The precipitation data from Vestli weather station (SN18270) is of variable quality with periods of missing data. The temperature data was downloaded with a hourly resolution and the maximum and minimum observed temperature was found using a python script to obtain the right format for the climate file. Data for relative humidity, vapor pressure and dew point can also be extracted from Vestli weather station (SN18270) after 2017.

Due to the fact that both discharge data and precipitation data had missing values, the calibration process was demanding.

The data for the sewage system was provided from Oslo Municipality, Water and Sewage Agency (Oslo VAV). The data was given in the form of ESRI-files and ArcGIS PRO was used to obtain information about the sewage system such as the manholes elevation, manhole depth, length and elevation of links, roughness of links and the coordinates. Oslo VAV also contributes with a data set for the stormwater drains.

From the Oslo Municipality, Planning and Building Agency (Oslo PBE), a digital Elevation model (DEM) was used extract the subcatchment in GRASS GIS.

Oslo PBE also provided information about roads and roofs in the study area, and was used to find the percentage of impervious area. It should be mentioned that the dataset of road and roof, from Oslo PBE, was modified since several

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impermeable areas were missing. This mainly applied to pathways, as well as private driveways. It was done using a recent satellite image as a background map of the ArcGIS PRO and manually finding the area of impermeable areas that were missing.

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CHAPTER 5 Method

The model for Vestli was used to calculate surface runoff, and stormwater sewer discharge, groundwater leakage and dry weather flow contributing to the simulated runoff. The main parameters categories for urban runoff modelling at Vestli, are the physical catchment characteristics, stormwater network, data input of DWF, air temperature and precipitation.

This chapter gives a description of how the model was setup at Vestli, including the calibration and validation of the model, design rainfall and LID implementation.

5.1 Physical Catchment Characteristics

The input file to obtain a model in SWMM require the catchment to be divided into subcatchment and defaults properties that must be determined for each subcatchments. The properties are as following; subcatchments area [ha],width [m], slope [%], impervious area [%] (L. A. Rossman, 2015, p.196-197) Delineation of the catchment was done with Geographic Resources Analysis Support System (GRASS) GIS, version 7.4.1. and subcatchment properties, such as area, width,

slope and impermeable area, was obtained with ArcGIS Pro.

5.1.1 Subcatchment and Area

Both GRASS GIS and the extension tool ArcHydro in ArcGIS Pro can delineate catchment from DEM. The two methods were tried to find the best approach and processing time. GRASS GIS uses directory and does not load the files such as ArcGIS Pro, therefore this software can be better for large data sets.

ArcHydro uses many processes to delineate subcatchments and the method was found by using the method of Khan et al., 2014, whereas GRASS GIS works with raw DEM files and demand fewer steps (Neteler and Mitasova, 2013, p.143-145). Another advantage of GRASS GIS is that is produces more accurate flow accumulation from the DEM than ArcGIS (Olsman, 2019, p.15-16). The two methods gave some small differences in results and the processing time was considerably faster in GRASS GIS. Since GRASS GIS seems to produce more accurate flow accumulation, is faster to work with and is a well tried method, this approach was chosen for delineation of Vestli catchment.

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Grass GIS is an open source and was developed by the U.S Army Construction Engineering Research Laboratories. The required data for this software are a digital terrain model, point of the urban discharge station and the storm drain which is linked to the discharge station at Vestli. The software generates hydrological parameters such as streams, drainage, basin and accumulation from the DEM using the tool r.watershed. The program operates with two tools to generate flow direction. This first being single flow direction, which calculates the direction of flow according to the elevation from neighbour cells, where water is forced to flow to the cell with steepest downward slope. The second approach being the multiple flow direction (MFD) where water is distributed to neighbour cells with lower elevation based on the slope between cells (Wainwright and Mulligan, 2004, p.109). The multiple flow direction approach was chosen. The subcatchment was created by the tool r.water.outlet using the drainage direction map created from the r.watershed function and corresponding streams and storm drains. The subcatchment were exported to tiff files and converted to polygons using ArcGIS Pro, and automatically calculating the area of subcatchment.

There were in total 15 subcatchments that was generated and can be seen in Table 5.1. The first seven subcatchments was found from the generated streamlines and storm drains (Table 5.1). Subcatchments S_274206, S_ 274228 and S_ 274074 were found from storm drains discovered from field investigations, but were not in the dataset provided from Oslo VAV. Subcatchments, S_274064, S_274050 and S_274206, were found from field investigation and no storm drains is located nearby, but due to elevation the overland water will run in the direction of other storm drains. The subcatchment called "sub_NW_drain" and

"sub_SW_drain" are connected to the storm water piped by private outlets and are therefore included as subcatchments in the model (personal communication with field hydrologist Fred Wegner 06.12.2019 at NVE).

The catchment area does not cover the lower pipes draining to the discharge station due to the elevation in terrain, this was not possible to conduct in GRASS GIS. Catchment area vary from other studies, Brennhovd, 2014, p.34 (also see fig F.1). This could have been manually corrected, but was not done, since there are no stormwater drains connected to pipes in this area and it is assumed that the infiltration in this area is minimal to the stormwater system.

5.1.2 Slope

The slope represents the average percentage slope within each subcatchment (L. A. Rossman, 2015, p.196). This was obtained from a DEM to determine the surface slope of each grid square by identifying the steepness at each cell in the raster. The mean and median slope were found, and in order to find these properties the zonal statistic tool in ArcGIS Pro was used. This tool calculates the statistic of the slope raster such as mean based on overlay analysis where the shapefil of the subcatchment and slope raster are used (Shen and Zhang, 2014 p.6099-6100 and ESRI, n.d.(a)). The mean slope uses floating point raster, while the median slope can only be found from an integer raster. To acquire the median slope the int tool in ArcGIS Pro which converts pixel from raster to integer by truncation, limiting number of decimal (ESRI, n.d.(b)). In SWMM the slope is given in percentage [%] so the mean and median are found by converting degree to percentage by eq. 5.1.

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5.1. Physical Catchment Characteristics

slope percent =tan(Θ)·100 (5.1) The resulting calculations of slopes for each subcatchment are listed in Table 5.1.

5.1.3 Impervious Area

Another default property to the SWMM model is the percentage of impervious area within each subcatchment (L. A. Rossman, 2015, p.96 and 196). The impermeable areas are mainly road and buildings. This data was obtained from Oslo VAV. In addition to this, field observations were made which showed that some smaller roads, parking spaces and so forth were missing from the data provided by the Oslo VAV. Thus, these areas were recorded in ArcGIS PRO from field observations and basemap in images provided by ESRI. The shapefiles containing information about impervious area were merged in ArcGIS Pro, clipped to each subcatchment and the tool Summary Statistics found the total area [m²] of imperviousness per subcatchment using the ModelBuilder for a faster workflow (ESRI, n.d.(c)). Knowing the area [m²] and impervious area [m²], of each subcatchment the percentage of impervious area was calculated by eq.5.2.

% Imperv = Impervious area·100

area (5.2)

The percentage of impervious area within each subcatchments are found in Table 5.1.

Table 5.1: Values for the input parameters in SWMM: name of subcatchments, area, percentage of impervious, and the mean and median percentage slope.

Subcatchment Area [ha] % imp % mean % Median

S_23335 4.91 16.67 22.27 17.63

S_23343 5.59 29.33 19.23 14.05

S_55268 4.46 19.71 24.05 17.63

S_55277 7.87 20.60 21.75 15.84

S_55295 1.71 41.17 12.34 6.99

S_55311 3.33 39.27 17.22 10.51

S_55342 0.45 16.82 9.92 5.24

S_274206 1.52 41.38 16.28 12.28

S_274228 0.14 36.38 6.80 3.49

S_274074 0.05 54.19 6.79 3.49

S_274064 0.78 24.49 13.87 6.99

S_274050 0.62 50.10 10.74 6.99

S_274206 0.27 22.64 25.25 17.63

S_NW 1.78 49.37 26.87 26.79

S_SW 3.94 33.11 13.90 8.75

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5.1.4 Width of Subcatchment

The width is an important physiographic factor for overland flow affecting time of concentration in the subcatchment and hence the hydrograph. A catchment with a broad shape, large width, the peak flow occur faster than for fan shaped catchment due to the time of concentration. This gives catchment with large width a narrow hydrograph with high peak, in other words fast rising and recession limbs in the hydrograph (L. Rossman and Huber, 2016, p.68). The width for the polygons on Vestli was difficult to determine since the many of the subcatchments has an odd shape and different method for finding the optimal width was investigated. The result for each method is found in table 5.2.

Two mathematical method was investigated to find the width of polygons. First method is assuming that the polygons are rectangular and finding the width from perimeter and area using eq. 5.3. The resulting width can be found in the column named "Mathematical rectangular" in Table 5.2. The second method assuming that the polygons is circular and finding the width from area and perimeter named "Mathematical circle" in Table 5.2 . The perimeter of polygons was found in ArcGIS Pro using the tool "calculate field" and choosing perimeter.

For more detailed information about the equation see Appendix B.

Rectangular :w= P−√

P²−16A

4 (5.3)

Circular :w= 4A

P (5.4)

(5.5) The second method was to use the tool Xtools Pro 19 which is an extension tool to ArcGIS Pro. This tool calculates the width by creating points where each point is the node of the polygon center line. Each point gets a length to the boundary of the polygon and is written to the attribute table. This tool calculates the shortest distances from the center line nodes to the boundary of the polygon. The output is nodes and the mean width of the polygon was found by finding the mean of all nodes for each polygon in the output attributes table. This can be found in Table 5.2 in the column named "Xtools Pro" and

"original". The column "Changed" is the mean of the nodes when the outliers of nodes are removed to get a more average width and not the shortest distances from the center lines.

The third method was to draw a rectangular around the polygons and finding the width of each rectangular named "Draw rectangle" in table 5.2. The fourth method was to measure the polygons in ArcGIS Pro and find a subjective width of the polygons, named "Measure" in table 5.2.

In the end two method was tried by using the length of streamlines. This method was used to find a way to represent the width of the field in relation to the length of the streamlines and area, hence also the concentration time of the subcatchments. The first approach were to find the length of the overland flow lines that were generated from GRASS GIS. The last and chosen approach was to find the half of the length of streamflow since the stormwater pipes mainly

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5.1. Physical Catchment Characteristics

laid on one side of the catchment and SWMM assumes inflow from runoff from two sides.

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Table 5.2: The results from different approaches for finding the width of each subcatchment. Column named "Average flow" are the chosen width

Width of subcatchments Xtools Pro Mathematical

rectangular

Mathematical

circle Measure Draw

rectangle

Longest overland flow

Average flow Subcatchment Original Changed

S_23335 46.07 61.35 64.28 118.58 112.00 288.16 640.12 320.06

S_23343 68.41 85.76 74.75 135.92 139.00 183.17 427.66 213.83

S_55268 47.59 87.20 78.07 137.38 140.00 198.76 402.16 201.08

S_55277 72.51 116.33 96.69 172.83 170.00 270.70 788.28 394.14

S_55295 36.55 87.69 64.61 103.82 105.00 160.00 141.99 71.00

S_55311 30.06 54.03 50.40 93.65 130.00 243.00 313.05 156.53

S_55342 22.53 28.59 24.35 43.07 42.00 83.00 156.76 78.38

S_274206 39.67 68.30 59.83 96.90 145.00 174.00 171.27 85.64

S_ 274228 12.85 21.21 17.38 28.56 28.00 44.00 99.32 49.66

S_ 274074 8.59 8.59 9.29 15.73 13.00 16.70 44.57 22.29

S_ 274064 31.26 41.64 46.97 73.33 63.00 98.00 113.41 56.71

S_ 274050 26.55 36.36 33.17 56.35 54.00 77.00 116.34 58.17

S_ 274206 15.95 28.40 25.67 41.18 43.00 67.00 113.32 56.66

S_NW 29.95 55.49 58.41 98.02 93.00 117.00 150.53 75.27

S_SW 40.05 81.79 64.35 116.45 115.00 216.00 247.08 123.54

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5.2. Stormwater network

5.2 Stormwater network

The stormwater pipes define the conduits, for nodes manholes are used and not stormwater drains used to generate subcatchments. The reason for this is that the map of stormwater drains that was provided from Oslo VAV did not contained the information to generate a model in SWMM, such as depth of drains, height and connected pipelines. Hence, the manholes was selected.

Manholes are generally close to the stormwater drains and will not affect the results of the model. Additionally there is few stormwater drain and some do not lead to the stormwater network according to their location. The maximum depth and elevation for the manhole bottom of the nodes are needed, and the max depth was set to be the offset of the corresponding pipe since no other information was given. The elevation of manhole bottom was then the surface elevation subtracting the maximum depth of the node. The effect of constructing the elevation in such ways are assumed to be negligible. The conduits need information about connecting nodes, length, the offset of upstream and downstream of conduits. Both information about node and conduits was provided from Oslo VAV in shapefil for ESRI software, and ArcGIS Pro was used to extract the information needed.

5.3 Data

5.3.1 Meteorological data

Air temperatures were provided to SWMM through an external climate file, showing the daily maximum and minimum air temperature in tenths of a degree Celsius. Air temperature is necessary to compute potential evapotranspiration through the Hagreaves method (L. Rossman and Huber, 2016, p.39-40) and it must have a specific format and was thereby provided through a Python script The temperature data was provided from the weather station at Vestli. For calculating evapotranspiration the latitude is required and is sat to be at the same latitude as the weather station.

Precipitation data were provided from frost with finest resolution at 1 minute was used. The time series need a specific time format (L. A. Rossman, 2015, p.335) and a Python script was used to obtain this format.

5.3.2 Dry Weather Flow

Dry weather flow can be given in different formats, such as monthly, daily, hourly (L. A. Rossman, 2015 p.337). It was first assumed that since the system is a separate network no flow would occur when there was no precipitation.

Taking a closer look at the observed discharge data it was clear that there was always some water in the stormwater network.

Different methods of obtaining the dry weather flow are discussed here. BFI+

is a tool delevoped by HydroOffice (Gregor, 2010) that separates the baseflow from discharge using different methods. Three methods were used, these were fixed interval method, sliding interval method and local minimum method. The removal of high frequency quick flow is possible but was not done in this case.

The fixed interval method creates an interval which is given the lowest discharge.

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The first day being the first day of the input file. The interval will move with 2N days until it intersects the hydrograph. All days within this interval will get the same value for baseflow. Then the next 2N days will undergo the same process and so forth (Sloto and Crouse, 1996, p.5). The sliding interval method looks at the lowest discharge at one half the interval minus one day, [0.5(2N-1) day], for the day before and the day after the actual day and this baseflow is selected for the actual day (Sloto and Crouse, 1996, p.6). The local minimum method joins the lowest point on the discharge with straight lines using linear interpolation. Each day checks to find out if it is the lowest discharge in one half the interval minus one day, [0.5(2N-1)days], before and after the day that is being considered (Sloto and Crouse, 1996, p.6). N would be the mouth of days where surface runoff ends and was chosen to be 5 for all methods. The input file for these methods is a textfile with daily observed discharge and was prepared with a Python Script.

Hydrorecession is an open source MatLab graphical user interface and was developed for streamflow recession analysis. The development was done by Arciniega-Esparza et al., 2017 and can be downloaded from MathWorks File Exchange. This program separates baseflow amongst others. For baseflow the GUI uses automatic baseflow separation by Wittenberg (Arciniega-Esparza et al., 2017) and uses a Recursive Digital Filter to find days where the streamflow increases. The input file required for this method is an Excelsheet for daily discharge and was prepared with a Python Script.

Hydrograph-py is a package installation for Python and was developed by Terink, 2019. Among the functions in this package is the separation of baseflow and peakflow from observed discharge. Missing data in the observed discharge will be filled by interpolation. To run the baseflow separation function information about the total runoff in m³/s , chosen time step interval in minutes, which was chosen to be 5 minutes, catchment area, slope of line which determines when peakflow starts and baseflow separation will occur. This was chosen to the packaged default value.

As a last method a manual method was used. This was to extract the discharge for days where it had not been raining for 24 hours, considering the stormwater system to be “dry”. This was done with a Python script, finding days without rain and the corresponding discharge.

Every method consists of the same input file, only with different format, but the discharge data are in the same period. The period used is discharge from year 2018 to 2019, excluding the data from the winter (31. October - 15.April) since calibration and validation of the model do not consider the month during winter and cold periods. All the methods were grouped by day of the week in a Python script. Figure 5.1 displays the different methods grouped by day of a week. The methods differ in calculating baseflow, but the all methods keep relative constant at a level except the manual methods. This method has an increase from from Monday to Thursday, with a peak on Thursday and a secondary peak on Sunday.

Hourly baseflow was calculated from the manual method and is seen in figure 5.2. Since the method from different tools (BFI+, MatLab GUI and Python Package) is mainly developed for natural catchment and not urban, the manual

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5.4. Simulation Periods and Options

Figure 5.1: Baseflow through a week using different method for baseflow separation.

approach was the one that was used as dry weather flow input to SWMM. Both the hourly and weekly baseflow were used.

5.4 Simulation Periods and Options

The flow routing option that produces the most accurate results is the Dynamic Wave Routing and is able to simulate non-uniform, unsteady flow condition (L. A. Rossman, 2015, p. 76-77 and Hossain et al., 2019). Consequently, this method was used. The Green-Ampt method was used as infiltration method.

Flow units are set to liter per second. The model reports simulated discharge for every 5 minutes and the time step for. 5 minutes was used since the discharge data from Hydra II was downloaded with 5 minutes time step. Additionally, urban catchment tends to have a fast response to rainfall input which also justifies the choice of short report time, since large report step may miss the extremes computed by the SWMM. Dry flow time steps are calculated for 1 hour, since it is also the time step of which the rain gauge reports precipitation when no rainfall occur. Wet flow time step are calculated for a time step of 5 minutes, chosen from inspiration from open learning source OPEN SWMM.

The model was run for a continuous period. This calibration period starts at 20th of July 2018 to 15th of November 2018. The warm-up period is set for approximately one month, from 15th of June 2018 to 20th of July 2018. The Climate file, consisting for daily maximum and minimum values, starts reading at the warm-up period.

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Figure 5.2: Baseflow through a day, showing mean hourly values and the mean hourly data for weekdays and weekends.

5.5 Calibration

The calibration process was done by trial and error of different parameters settings in SWMM. Four major parameters categories for calibration of the runoff modelling were the physical catchment, infiltration, aquifer, and groundwater.

The main efficiency criterion for this part was the performance of R2, but NSE, and d were also calculated and taken into account when determining optimal parameters.

5.5.1 Physical Catchment

The parameters for the physical catchment that were calibrated calibration can be seen in 5.3. The area for each subcatchment was kept constant during the calibration and is found in table 5.1. Since the width is an important factor for the runoff rate of a catchment (L. Rossman and Huber, 2016 p. 53 and p.68) and affect the shape of the hydrograph (L. Rossman and Huber, 2016, p.78) this was changed during calibration. The optimal width was found to be the initial width. The slope also affect the shape of the hydrograph, but is less sensitive than the width and the calibration was tested for the mean slope found in table 5.1, but the median slope was chosen since the mean slope is in some areas very steep affecting the hydrograph negatively. The imperviousness is tested for ±20% of the initial value, and the optimal value was the initial condition.

The roughness parameters, Manning’s n, for impervious and pervious surfaces were selected from the manual, L. A. Rossman, 2015, p.182 and L. Rossman and

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5.5. Calibration

Table 5.3: Calibration of parameters for the subcatchment. Values for width,

% slope and % imperv vary for the subcatchment and can be found in 5.1.

Subcatchment

Parameter Initial value Calibration Chosen value Width [m] Average flow,

table 5.2

All the calculated widths in table 5.2 and

±20% of initial value

Initial value

%slope Median % slope,

table 5.1 % Mean Inital value

% imperv % imp,

table 5.1 ±% 20 av initial value Inital value

N-imperv 0.01 0.01-0.02 0.015

N-Perv 0.2 0.15-0.4 0.2

S-Imp [mm] 1.5 1.5-3.4 1.5

S-Perv [mm] 5 4-8 5

Subarea Routing OUTLET OUTLET,

PERVIOUS PERVIOUS

Precentaged routed 65 20-100 50

Huber, 2016 p.75, and the optimal values were found to be 0.015 for impervious surfaces and 0.2 for pervious surfaces.

The depression storage will only have a significant effect on low depth storms (L. Rossman and Huber, 2016, p.78) and the initial values were chosen to be 1.5 mm for impervious surfaces and 5 mm for pervious surfaces based on the article from Skotnicki and Sowiński, 2015 with similar imperviousness for an urban catchment. The optimal values were chosen to be the initial values and correspond well with suggested value in L. A. Rossman, 2015, p.181.

Percentage with no depression in storage were for all subcatchment chosen to the default value, at 25%.

The subarea routing determines the routing of flow and the initial condition was OUTLET as it is the default value in SWMM, but this gave very high simulated flow. The optimal value was therefore PERVIOUS. PERVIOUS determines that runoff can run from impervious area to pervious surfaces, e.g. from rooftops to lawn. It was observed in field that some of the rain gutters were directly connected to the lawn. The percentage of runoff routed are the parameter that determines how much flow is transferred from impervious surfaces to pervious surfaces. The optimal value was found by calibration to fit the observed and simulated discharge and this was 50%. As mentioned, some rain gutters were disconnected and water is routed to the lawn and some were not. There were in field observed multiple stormwater drains in the roads and they will drain water straight to the stormwater system. Storteig, 2019 have made SWMM modelling at Grefsen-Kjelsås in Oslo, which is a similar area to Vestli. The SWMM models in this thesis had a percentage routed at 65% and 50%.

Storm Water Management Model and the REO model