continuous urban hydrological modelling of discharge peaks

Continuous urban hydrological modelling ofdischarge peaks with SWMM

Ina Storteig

Thesis submitted for the degree ofMaster in Physical Geography, Hydrology and Geomatics

(Hydrology)60 Credits

Department of GeosciencesFaculty of mathematics and natural sciences

UNIVERSITY OF OSLO

Spring 2019

c© 2019 Ina Storteig

Continuous urban hydrological modelling of discharge peaks with SWMM

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AbstractUrbanization increase the area of impermeable surfaces, and thus the pressure on the drainage

system. Installation of Low Impact Development (LID) can increase the infiltration and storage

of stormwater from the impermeable surfaces. Evaluation of the hydrological module in Storm

Water Management Model (SWMM) and the reduction in the discharge after implementation

of LID measures are studied by applying SWMM to observed data sets for an urban catchment

at Grefsen in Oslo. The model is calibrated and validated on the magnitude of the observed

discharge peaks with the coefficient of determination (R2) criteria. The study found two com-

parably accurate model setups, where the difference between the model setups are the active

aquifer depth and the Directly Connected Impervious Area (DCIA). The results reveal that the

hydrological module in combination with the aquifer module inadequately described the stor-

age capacity of the pervious areas. In the part of the catchment area with Combined Sewer

Systems (CSS) LIDs are implemented in both model setups. For a continuous simulation on

observed rainfall the maximum average reduction in the discharge peaks for the main outlet

(AK52) was 22-24% for the two models, while for a 5-year and a 20-year model rainfall events

the maximum reduction in the discharge peak was respectively 32-33% and 27-32%. For an

none-calibrated outlet (Jupiterjordet) where the entire area consists of CSS, the reduction in the

stormwater discharge peaks between the two model setups is significantly different. The max-

imum average reduction for the continuous rainfall is 76-80% for model setup A and 57-60%

for model setup B. For a 5-year rainfall the reduction in the stormwater discharge is 81-82%

for model setup A and 56% for model setup B, while for a 20-year rainfall 80-81% for model

setup A and 53-54% for model setup B. The findings from this study indicate that there is vari-

ation in the reduction of the discharge peaks by use of LIDs with different model setups. This

demonstrate the sensitivity of the effect of planned LID installations to the parameterization of

the hydrological part of the model.

i

AcknowledgementsI would like to thank my supervisors, Chong-Yu Xu, Nils Roar Sælthun, Hong Li and Bent C.

Braskerud, for their guidance through this thesis. A special gratefulness to Nils Roar for all

the discussion related to urban hydrology and for your all-round competence and experience

in hydrology. Thank you Hong, for our great conversations and your optimism and positive

mindsets. Thank you Bent for your encouragement for the Grefsen plateau and LIDs. Last but

not least, thank you Chong-Yu for your corrections and for introducing me to the exciting and

interesting country China.

The help and support from Water and Sewage Agency in Oslo Municipality (Oslo VAV) have

been crucial in order to conduct this study. Thank you, Samatar Mahammud Abdi for insight

into the urban hydrological models at Oslo VAV and how they are made. Thank you, Bjørn

Christoffersen for an introduction to the modelling at Oslo VAV. Thank you, Alexander Pham

for your expertise on the data collection and sensors. It have been extremely helpful and from

this I learned a lot.

Thank you, Anne Fleig from the The Norwegian Water Resources and Energy Directorate for

letting me conduct infiltration tests and use a whole summer on getting more knowledge on the

urban hydrology. Thank you, Thomas Skaugen for discussions about catchment boundaries and

general knowledge about hydrological modelling. Both of your expertise has provided valuable

input for my work.

I also want to thank the project New Water Ways, the Department of Hydrology and Water Re-

source Engineering at Wuhan University and the Yellow River Institute for Hydraulic Research

for allowing me present my thesis work.

Finally, I would like to thank family and friends for the support over the past year. Also, thank

you to all students in study room 210 for all the shared moments and frustrations.

iii

ContentsAbstract i

Acknowledgements iii

List of Figures ix

List of Tables xix

Acronyms and abbreviations xxv

1 Introduction 1

2 Theoretical framework and models 3

2.1 Water Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1 Precipitation and evapotranspiration . . . . . . . . . . . . . . . . . . . 4

2.1.2 Infiltration, runoff and groundwater . . . . . . . . . . . . . . . . . . . 4

2.2 Urban hydrological systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1 Sewer networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.2 Stormwater management . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.3 Low Impact Development (LID) . . . . . . . . . . . . . . . . . . . . . 8

2.3 (Urban) hydrological modelling . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.1 Model background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.2 Calibration and validation . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4 The urban hydrological model in SWMM . . . . . . . . . . . . . . . . . . . . 13

2.4.1 Subcatchments and runoff . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4.2 Hydraulic routing model . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.4.3 Model parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.4.4 Infiltration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.4.5 Groundwater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.4.6 Precipitation and evapotranspiration . . . . . . . . . . . . . . . . . . . 19

2.4.7 Network components . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

v

CONTENTS

2.4.8 LIDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.4.9 Input data and parameters . . . . . . . . . . . . . . . . . . . . . . . . 23

3 Study area and data 25

3.1 Geography and climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.2 Hydrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.3 Available data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3.1 Discharge data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3.2 Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.3.3 Calibrated model in MOUSE from Oslo VAV . . . . . . . . . . . . . . 31

3.3.4 Infiltration measurements . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.3.5 Sewage systems and DEM . . . . . . . . . . . . . . . . . . . . . . . . 34

4 Methods 35

4.1 Pre-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.2 Model setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.2.1 Drainage system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.2.2 Catchment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.2.3 Climate data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.2.4 Dry weather flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.2.5 Simulation periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.3 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4.3.1 Subcatchments and infiltration parameters . . . . . . . . . . . . . . . . 44

4.3.2 Groundwater parameters . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.4 Model performance metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.5 LID implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5 Results 57

5.1 Model setups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.1.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.1.2 Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5.1.3 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.1.4 Runoff coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

5.2 LIDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

vi

CONTENTS

5.2.1 Continuous rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5.2.2 Design rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

6 Discussion 83

6.1 Model selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

6.2 Available data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

6.2.1 Input data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

6.2.2 Uncertainties in the input data . . . . . . . . . . . . . . . . . . . . . . 86

6.3 Model criteria and equifinality . . . . . . . . . . . . . . . . . . . . . . . . . . 87

6.4 LID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

6.4.1 Continuous rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

6.4.2 Design rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

6.5 Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

6.5.1 Model limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

6.5.2 Model setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

7 Conclusions 97

7.1 Further work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Bibliography 101

APPENDICES 111

A Discharge data analysis 111

B Infiltration measurements 115

C Model parameters 117

D LID setup 121

D.1 Parameter description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

D.2 Parameter values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

D.3 Rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

D.3.1 Calculated discharge after design rainfall . . . . . . . . . . . . . . . . 127

D.4 Continuous simulation with LIDs . . . . . . . . . . . . . . . . . . . . . . . . . 129

D.5 LID implementation with a different approach . . . . . . . . . . . . . . . . . . 130

vii

CONTENTS

D.5.1 Continuous rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

D.5.2 Design rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

D.5.3 Parameter values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

E Model simulations 149

F GIS analysis 151

G Spatial storage capacity 153

H Pictures 155

I AK52 157

viii

List of Figures2.1 Illustration of a CSO event. The figure is from the City of West Lafayette CSO

Relief Interceptor Project

(URL: http://www.riverroadwl.com/index.php/project-information/, 14.05.2019). . . 6

2.2 Illustration of the management of stormwater with the S3SA. Taken from Lind-

holm et al. (2008, pp. 8, 37) modified by the author. . . . . . . . . . . . . . . . 8

2.3 The subcatchment is divided into subareas; pervious subarea (S1), impervious

subarea with depression storage (S2) and impervious subarea without depres-

sion storage (S3). The sketch is not scaled. Inspired by Rossmann and Huber

(Rossmann and Huber, 2016a, pp. 52, 55) . . . . . . . . . . . . . . . . . . . . 14

2.4 Sketch of the groundwater aquifer. The heights h∗, hSW and dL are used to

calculate the lateral groundwater flow, fG. Inspired by Rossmann and Huber,

2016a, pp. 129,138. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.5 Sketch of the bio-retention cell in SWMM. The bio-retention cell is made up of

three layers: a surface layer, a soil layer and a storage layer. Here displayed in

combination with their fluxes. . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.6 Sketch of the rain barrel in SWMM together with the fluxes in and out of the

barrel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.7 Sketch of the conceptualization of disconnection of rooftops. Inspired by Ross-

mann and Huber (2016a). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.1 Catchment boundaries at Grefsen shown in orange, north east in Oslo, Norway.

The background map is from the Norwegian Mapping Authority c©Kartverket . 26

3.2 Map of the different sewer conduits; wastewater (green - SP), stormwater (black

- OV) and combined conduit (red - AF). The location of the measurements is

also included, where the blue triangles represent discharge measurements and

orange dot represent rain gauge. The background map is from the Norwegian

Mapping Authority c©Kartverket. Both the Norwegian Mapping Authority and

Water and Sewage Agency in Oslo Municipality (Oslo VAV) have given their

permission to use this map. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

ix

LIST OF FIGURES

3.3 Spatial distribution of sediment types mapped by the Geological Survey of Nor-

way Geological Survey of Norway (NGU). The background map is from the

Norwegian Mapping Authority c©Kartverket. . . . . . . . . . . . . . . . . . . 28

3.4 Map showing infiltration capacity provided by Geological Survey of Norway

(NGU). The background map is from the Norwegian Mapping Authority c©Kartverket. 29

3.5 Available discharge data for the two different locations (AK52 left and 297077

right). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.6 Map of the location of the infiltration tests conducted. The catchment to AK52

is marked in orange and the location of the measurements is in green-blue

squares. The background map is from the Norwegian Mapping Authority c©Kartverket. 33

4.1 Rating curves of the available data in the period 2017. Raw data of discharge

and depth (left). Removed the observation where the discharge is zero (right).

The red dotted line shows the overflow threshold. . . . . . . . . . . . . . . . . 35

4.2 Shows two CSO events from 2017 occurring in AK52. Blue stippled line reveals

unlikely discharge values of magnitude zero. . . . . . . . . . . . . . . . . . . . 36

4.3 Pattern of the mean dry weather flow for both weekend and weekdays in AK52

in 2017. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.4 Map of the catchment Grefsen generated by GRASS GIS. The two surface

runoff stream lines of each side of AK52 each gives two different catchments;

Grefsen plateau (0.31 km2) and Grefsenkollen (1.13 km2). The background map

is from the Norwegian Mapping Authority c©Kartverket. . . . . . . . . . . . . 39

4.5 More conduits are located in the catchment, than what is included in the model.

These are highlighted in the red square. A manhole which is located outside

the catchment boundaries is included in the catchment’s sewer system. The

background map is from the Norwegian Mapping Authority c©Kartverket. . . 40

4.6 Exploration of different aquifer coefficients, A1, A2 and exponents, B1 ,B2, . . 49

4.7 Illustration of DCIA of roof and road area in model setup A and B. The road

and roof area in both model setups are equal, but distributed differently on each

side of the routing line (red stippled line). The area above the red stippled line

is routed to the permeable areas, while the shaded area under the line is routed

to the outlet (DCIA) in each subcatchment. . . . . . . . . . . . . . . . . . . . 51

x

LIST OF FIGURES

4.8 Map showing the outlets, AK52 and Jupiterjordet, where the reduction in dis-

charge peaks by LIDs is tested. The orange dotted line is the catchment to AK52

(1.44 km2). The LIDs is placed inside the area to the Grefsen plateau of 0.31

km2 (green). Jupiterjordet is the outlet of one of the subcatchments within the

plateau where there is only combined sewers. The background map is from the

Norwegian Mapping Authority c©Kartverket. . . . . . . . . . . . . . . . . . . 55

5.1 Time series of the observed (green) and simulated (red) discharge [l/s] for

AK52 during the calibration period. The precipitation [mm/h] for the corre-

sponding time of discharge is included on the right axis (blue). . . . . . . . . . 58

5.2 The observed discharge and the associated discharge peaks for 2017 (left). Scat-

ter plot of the simulated and observed discharge peaks for model setup A (right).

The black stippled line is the 1:1-line. This shows the perfect fit between the

simulated and observed peaks. The shaded area around the 1:1-line is the range

of the variability in the observations. The dispersion measures used are the

standard deviation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5.3 The observed discharge and the associated discharge peaks for 2017 (left). Scat-

ter plot of the simulated and observed discharge peaks for model setup B (right).

The black stippled line is the 1:1-line. This shows the perfect fit between the

simulated and observed peaks. The shaded area around the 1:1-line is the range

of the variability in the observations. The dispersion measures used are the

standard deviation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5.4 Plots of decreasing ratio of DCIA/TIA with model setup A, from upper left to

lower right. All other parameters are kept constant. The performance, R2, in

the bottom right corner of each figure represents how well simulated discharge

peaks resemble the observed peaks. The black dotted line represents the 1:1-

line. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

5.5 Plots of the observed discharge peaks against the simulated discharge peaks for

AK52 for year 2017. The simulations are for different values of DCIA/TIA

with model setup B. The fraction DCIA/TIA increases from upper left to lower

right. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

xi

LIST OF FIGURES

5.6 Plots of increasing hydraulic saturated conductivity, Ksat , from upper left to

lower right. All other parameters are kept constant. The performance, R2, in the

bottom right corner of each figure represents how well the simulated discharge

peaks fit the observed peaks. The black dotted line represents the 1:1-line. . . . 62

5.7 Plots of the observed discharge peaks against the simulated discharge peaks

for AK52 for year 2017. The simulations are for different values of hydraulic

saturated conductivity, Ksat . The Ksat increases from upper left to lower right. . 63

5.8 Time series of the observed (green) and simulated (red) discharge [l/s] for

AK52 in 2018 (validation period) with models A (upper) and B (lower). The

precipitation [mm/h] for the corresponding time of discharge is included on the

right axis (blue). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5.9 The observed discharges in 2018 together with the discharge peaks studied

(left). The observed discharge plotted against the simulated discharge peaks

for model setup A (right). The black dotted line is the 1:1-line. The shaded

area around the 1:1-line is the range of the variability in the observations. The

dispersion measures used is the standard deviation of the observed discharge

peaks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

5.10 The observed discharges in 2018 together with the discharge peaks studied

(left). The observed discharge plotted against the simulated discharge peaks

for model setup B (right). The black dotted line is the 1:1-line. The shaded

area around the 1:1-line is the range of the variability in the observations. The

dispersion measures used is the standard deviation of the observed discharge

peaks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

5.11 Simulations of the discharge peaks with BRC of 80% and 100% for two types,

BRC II and BRC III, against simulations with no LIDs (scenario 0). The re-

gression line represents the mean discharge magnitude at AK52 after the LIDs

are implemented in model setup A. . . . . . . . . . . . . . . . . . . . . . . . . 68




are implemented in model setup B. . . . . . . . . . . . . . . . . . . . . . . . . 69

xii

LIST OF FIGURES

5.13 The LID measures RD for 80% (upper left) and 100% (lower left) roof discon-

nection at the Grefsen plateau. Simulations with RB where 80% (upper right)

and 100% (lower right) of the roof area area disconnected with rain barrels. The

regression line shows the average reduction in the discharge peaks at AK52 with

model setup A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

5.14 The LID measures RD for 80% (upper) and 100% (lower) roof disconnection at

the Grefsen plateau. Simulations with RB where 80% (upper) and 100% (lower)

of the disconnected roof area with rain barrels. The regression line shows the

average reduction in the discharge peaks at AK52 with model setup B. . . . . . 71



gression line represents the mean discharge magnitude at Jupiterjordet after the

LIDs are implemented in model setup A. . . . . . . . . . . . . . . . . . . . . . 72




LIDs are implemented in model setup B. . . . . . . . . . . . . . . . . . . . . . 73



and 100% (lower right) of the connected roof areas are disconnected with rain

barrels. The regression line shows the average reduction in the discharge peaks

for Jupiterjordet with model setup A. . . . . . . . . . . . . . . . . . . . . . . . 74



and 100% (lower right) of the connected roof areas are disconnected with rain

barrels. The regression line shows the average reduction in the discharge peaks

for Jupiterjordet with model setup B. . . . . . . . . . . . . . . . . . . . . . . . 75

5.19 Discharge in AK52 (upper) and Jupiterjordet (lower) during a 5-year rainfall

with model setup A. The dotted red line in the upper plot, at AK52, represents

discharge where CSOs occurs. . . . . . . . . . . . . . . . . . . . . . . . . . . 76

xiii

LIST OF FIGURES

5.20 Discharge in AK52 (upper) and Jupiterjordet (lower) during a 5-year rainfall

with model setup B. The dotted red line in the upper plot, at AK52, represents

discharge where CSOs occurs. . . . . . . . . . . . . . . . . . . . . . . . . . . 77

5.21 Discharge at AK52 (upper) and Jupiterjordet (lower) during a 20-year rainfall.

The dotted red line in the upper plot, AK52, represents discharge where CSOs

occurs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

5.22 Discharge at AK52 (upper) and Jupiterjordet (lower) during a 20-year rainfall.


occurs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

A.1 Rating curve for the discharge and depth data available for 2018 . . . . . . . . 111

A.2 Pattern of the dry weather flow during weekends in AK52 in 2017. . . . . . . . 111

A.3 Patternof dry weather flow during weekdays in AK52 in 2017. . . . . . . . . . 112

D.1 5-year IDF-curves and hyetographs with 5 minutes timestep. The duration of

intensity is of 10 minutes. Left: IDF-curves for 5-years precipitation from Blin-

dern 18701 and Disen 18420 generated from NCCS. Manually interpolated val-

ues are dots not filled. Right: 5-years hyetographs from Oslo VAV together

with hyetographs for Blindern 18701 and Disen 18420 from the IDF-curves

presented in the left figure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

D.2 20-year IDF-curves and hyetographs with 5 minutes timestep. Duration of in-

tensity is 10 minutes. Left: IDF-curves for 20-years precipitation from Blindern

18701 and Disen 18420 generated from NCCS. Manually interpolated values

are dots not filled. Right: 20-years hyetographs for Blindern 18701 and Disen

18420 from the IDF-curves presented in the left figure. . . . . . . . . . . . . . 126

D.3 The discharge peaks used for the continuous simulations with LIDs (left) and

the discharge peaks for 100% RD at AK52. The peaks not filled with orange in

the left figure is the same peaks seen in the right figure. . . . . . . . . . . . . . 129

D.4 Distribution of road and rooftop area on each side of the routing line. 100%

LID as illustrated in the lower figures covers more than the roof area in model

B, while in model A not all the rooftop area is covered by the LID, even though

the same amount of the LID-modules have been used . . . . . . . . . . . . . . 130

xiv

LIST OF FIGURES

D.5 Simulations of the discharge peaks with BRC of 25% and 100% for two types,



are implemented in model A. . . . . . . . . . . . . . . . . . . . . . . . . . . . 131




are implemented in model B. . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

D.7 Left: The LID measures RD for 80% (upper) and 100% (lower) roof discon-

nection at the Grefsen plateau. Right: Simulations with RB where 25% (upper)

and 100% (lower) of the connected roof areas are disconnected with rain bar-

rels. The reduction in the discharge peaks at AK52 with model A. . . . . . . . 133



and 100% (lower) of the connected roof areas is disconnected with rain barrels.

The reduction in the discharge peaks at AK52 with model B. . . . . . . . . . . 134




LIDs are implemented in model A. . . . . . . . . . . . . . . . . . . . . . . . . 135




LIDs are implemented in model B. . . . . . . . . . . . . . . . . . . . . . . . . 136




rels. The reduction in the discharge peaks is for Jupiterjordet with model A. . . 137




rels. The reduction in the discharge peaks is for Jupiterjordet with model B. . . 138

xv

LIST OF FIGURES

D.13 Discharge in AK52 (upper) and Jupiterjordet (lower) during a 5-year rainfall

with model A. The dotted red line in the upper plot, at AK52, represents dis-

charge where CSOs occurs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

D.14 Discharge in AK52 (upper) and Jupiterjordet (lower) during a 5-year rainfall

with model B. The dotted red line in the upper plot, at AK52, represents dis-

charge where CSOs occurs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

D.15 Discharge at AK52 (upper) and Jupiterjordet (lower) during a 20-year rainfall.


occurs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

D.16 Discharge at AK52 (upper) and Jupiterjordet (lower) during a 20-year rainfall.


occurs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

E.1 The contribution from the CSS at the Grefsen plateau (161146) and the wastew-

ater system from Grefsenkollen (297077). The simulation is from a 5-year rain-

fall with model setup A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

E.2 The contribution from the CSS at the Grefsen plateau (161146) and the wastew-

ater system from Grefsenkollen (297077). The simulation is for a 5-year rainfall

with model setup B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

E.3 Calibration process at AK52. Illustrates the pulsing of water into AK52 when

the groundwater exponents, B1 and B2, are 0.75 for A1 and A2 of 1. . . . . . . 150

F.1 The spatial distribution of the slopes in the catchment in degrees. The green

color represent flat areas and red represent steep areas. . . . . . . . . . . . . . 151

F.2 Map of the different catchment used in present and previous study of Grefsen

and the investigation of CSOs at AK52 . . . . . . . . . . . . . . . . . . . . . . 152

G.1 Four pictures with the storage capacity of the subsurface calculated with HBV-

model from NVE. The pictures can be seen at SeNorge.no. The pictures are

generated at the same time of the date, 07:32, based on interpolated weather

observations. Grefsen is labeled northwest for the label Oslo. Data owner is

The Norwegian Water Resources and Energy Directorate (NVE) . . . . . . . . 153

xvi

LIST OF FIGURES

H.1 Photo of a stormdrain in Waldemar Thranes gate in Oslo. It illustrates the fact

that some stormdrains can be exposed to clogging. Photo: Ina Storteig . . . . . 155

H.2 Photo of Grefsenveien 21.09.2018 (left) and 21.05.2019 (right). The photos are

taken against north and is approximately at the boarder of the catchment. Photo:

Ina Storteig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

H.3 Photo of where the stormwater sewer from the catchment comes out. During

CSO events, the pipe transports diluted wastewater. Photo: Ina Storteig . . . . 156

I.1 A drawing of the conduits at AK52. The picture is from Oslo VAV and is

published with permission from Oslo VAV. . . . . . . . . . . . . . . . . . . . 157

xvii

List of Tables4.1 Values for the parameters; outlet, area, width and percentage impervious (%im-

perv) used in the model setup for the subcatchments at Grefsen. For full de-

scription see table C.1. Area, width and %imperv are calculated in ArcMap. . . 41

4.2 The parameter %slope for the different subcatchments used during the calibra-

tion process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.3 Parameters used in the calibration process for the subcatchments. The de-

scription of the parameters can be seen in table C.1. Width, percentage slope

(%slope) and percentage impervious (%imperv) are different for individual sub-

catchments. The symbol "*" means that the optimal value depend on the model

selected. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.4 Parameters used in the calibration process for the aquifers. . . . . . . . . . . . 47

4.5 Each subcatchment has its own aquifer name and bottom elevation. . . . . . . . 48

4.6 Each aquifer have a surface elevation connected to the elevation of the recev-

ing (here outlet) node (see figure 2.4). Depending on the model setups used,

different surface elevations is implemented. . . . . . . . . . . . . . . . . . . . 49

4.7 Table of values used for 80% and 100% connection of the rooftops to BRC and

RB in model A. Number of units is number of BRC or RB in the subcatchment.

% of impervious area treated is the fraction of impervious area (buildings and

roads) contributing with runoff to the BRC or RB. When the BRC and RB is

full, the overflows are distributed on the pervious area. New % impervious area

is the new fraction of impervious area (only used for BRC) since parts of the

total area decreases due to occupation of the bio-retention cells units. The area

of the bio-retention cell is 13.1 m2 and the rain barrel is 0.4 m2. . . . . . . . . . 52

4.8 Table of values used for 80% and 100% connection of the rooftops to BRC and

RB in model B. The description of this is equivalent to the one found in table

4.7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

xix

LIST OF TABLES

4.9 Fraction of impervious area disconnected to the outlet in each subcatchment for

100% and 80% disconnection of rooftops to downspouts, when it is assumed

that 55% of the rooftops are already disconnected. Disconnection of 55% of the

roof area implies that 82% of the road area is disconnected at all time. . . . . . 54

5.1 Time for the CSO events recorded from 2017 to 2018. Maximum peak dis-

charge, P, for observations is Pobs in l/s. PA and PB are the maximum discharge

peaks for respectively model setup A and model setup B, in l/s. The event-based

runoff coefficients, ci, for the observations, model setup A and model setup B

are calculated as described in subsection 2.3.2. ∆T is the time difference in

minute between the observed peak and the simulated peak for the two model

setups, A and B. * indicate missing data points of the event discharge . . . . . 66

5.2 Relative comparison of the discharge (% discharge peak) and volume of water

above the overflow weir (% Volume) at AK52 for a 5-year rainfall with model

setup A and model setup B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

5.3 Relative comparison of the discharge (% discharge peak) at Jupiterjordet for a

5-year rainfall for the different scenarios presented in the study. . . . . . . . . . 78

5.4 Relative comparison of the discharge (% discharge peak) and volume of water


A and B for the different scenarios presented in the study. . . . . . . . . . . . . 81

5.5 Relative comparison of the discharge (% discharge peak) at Jupiterjordet for a

20-year rainfall with model A and B for the different scenarios presented in the

study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

A.1 Table of the relative daily variation in dry weather flow for AK52 . . . . . . . . 112

A.2 Table of the relative hourly variation in dry weather flow for AK52 from the

mean value in the dry weather period. . . . . . . . . . . . . . . . . . . . . . . 113

B.1 Measured saturated hydraulic conductivity from gardens at different proper-

ties measured with MPD infiltrometers at Grefsen. The measured saturated

hydraulic conductivity, Ksat , is found where the conductivity is constant, i.e.

not changing more than 20% from the last three measurements (Solheim et al.,

2018, p. 4). NA means that the test is not conducted for that location. . . . . . 115

xx

LIST OF TABLES

B.2 The measured and the corrected saturated hydraulic conductivity from gardens

at different properties measured with MPD infiltrometers at Grefsen. The satu-

rated hydraulic conductivity, Ksat , is found where the conductivity is constant,

i.e. not changing more than 20% from the last two-three measurements (Sol-

heim et al., 2018, p. 4). The measured Ksat values are corrected to remove the

effects of lateral movements of water during the test. . . . . . . . . . . . . . . 116

B.3 The measured and corrected saturated hydraulic conductivity from different

bio-retention cells in Deichmans gate in Oslo measured with MPD infiltrome-

ters. The hydraulic saturated conductivity, Ksat , is found where the conductivity

is constant, i.e. not changing more than 20% from the last two-three measure-

ments (Solheim et al., 2018, p. 4). The measured Ksat values are corrected to

remove the effects of lateral movements of water during the test. The mean of

the measured values and the mean of the corrected values are both visible in the

table. DRX represent bio-retention cell number X. . . . . . . . . . . . . . . . . 116

C.1 Description of the parameters used for each subcatchment (Rossmann, 2015,

p. 196) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

C.2 Description of the parameters for the infiltration method Green-Ampt (Ross-

mann, 2015, p. 232) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

C.3 Description of the data requirement for the aquifers (Rossmann, 2015, p. 210). . 118

C.4 Description of the data requirement for the Groundwater Flow editor (Ross-

mann, 2015, p. 229). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

D.1 Table of description of required parameters for setup of the module Rain Barrel

in SWMM (Rossmann and Huber, 2016b, Rossmann, 2015). . . . . . . . . . . 121

D.2 Table of description of parameters used in the bio-retention cell (BRC) module

in SWMM (Rossmann and Huber, 2016b, Rossmann, 2015) . . . . . . . . . . 122

D.3 Table of required parameters for setup of the module rain Barrel in SWMM.

Sources for the individual values are also included. . . . . . . . . . . . . . . . 123

D.4 Table of required parameters for bio-retention cell module in SWMM. Sources

of values used are also included. . . . . . . . . . . . . . . . . . . . . . . . . . 124

D.5 Maximum discharge peaks (Pmax) [l/s] at AK52 for a 5-year rainfall with differ-

ent LID implementations presented in the study. . . . . . . . . . . . . . . . . . 127

xxi

LIST OF TABLES

D.6 Maximum discharge peaks (Pmax) [l/s] at AK52 for a 20-year rainfall with dif-

ferent LID implementations presented in the study. . . . . . . . . . . . . . . . 128

D.7 Relative comparison of the discharge (% Discharge peak ) and volume of water


A and model B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

D.8 Relative comparison of the discharge (% Discharge peak) in manhole 172350

for a 5-year rainfall and a 20-year rainfall for the different scenarios presented

in the study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

D.9 Relative comparison of the discharge (% Discharge peak) and volume of water


A and B for the different scenarios presented in the study. . . . . . . . . . . . . 144

D.10 Relative comparison of the discharge (% Discharge peak) at Jupiterjordet for a

20-year rainfall with model A and B for the different scenarios presented in the

study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

D.11 Table of values used for 25% and 100% connection of the rooftops to bio-

retention cells. Number of units is number of bio-retention cells in the sub-

catchment. % of impervious area treated is the fraction of impervious area

(buildings and roads) contributing with runoff to the bio-retention cells. New

% impervious area is the new fraction of imperviousness since parts of the to-

tal area is decreased due to occupation of bio-retention cells. The area of the

bio-retention cell is 13.1 m2. . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

D.12 Table of values used for 25% and 100% disconnection of the rooftops to rain

barrel. Number of units represents number of rain barrels in each subcatchment.

%impervious area treated is the fraction of impervious area where the runoff

is caught by the rain barrel, until it is full. When the rain barrel is full, the

overflows are distributed on the pervious area. Each rain barrel occupies 0.4 m2. 146

D.13 Fraction of impervious area disconnected to the outlet in each subcatchment for

100% and 80% disconnection of rooftops, when it is assumed that already 55%

of the rooftops are disconnected. 55% of rooftops already disconnected implies

that 82% of the road area is disconnected at all time. . . . . . . . . . . . . . . 147

xxii

LIST OF TABLES

D.14 Fraction of impervious area disconnected to the outlet in each subcatchment for

100% and 80% disconnection of rooftops, when it is assumed that already 55%

of the rooftops are disconnected. 55% of rooftops already disconnected implies

that 82% of the road area is disconnected at all time. . . . . . . . . . . . . . . 147

xxiii

Acronyms and abbreviationsAK52 A weir with overflow in manhole 161143

BRC Bio-retention cell

BRC II Bio-retention cell with seepage rate of 1 mm/h and a drain

BRC III Bio-retention cell with seepage rate of 10 mm/h and none drain

CSO Combined Sewer Overflow

CSS Combined Sewer Systems

DCIA Directly Connected Impervious Area

DEM Digital Elevation Model

DHI Danish Hydraulic Institute

GRASS Geographic Resources Analysis Support System

IDF Intensity-Duration-Frequency

LID Low Impact Development

METNO Norwegian Meteorological Institute

MOUSE Model for Urban Sewers

MPD Modified Phillip-Dunne

NCCS The Norwegian Centre for Climate Services

NGU Geological Survey of Norway

NSE Nash-Sutcliffe Efficiency

NVE The Norwegian Water Resources and Energy Directorate

xxv

Acronyms and abbreviations

Oslo PBE Planning and Building Agency in Oslo Municipality

Oslo VAV Water and Sewage Agency in Oslo Municipality

R2 Coefficient of determination

RB Rain barrel

RD Downspouts disconnection

S3SA Stormwater three step approach

SUDS Sustainable Urban Drainage Systems

SWMM Storm Water Management Model

TA Time-Area

TIA Total Impervious Area

US EPA US Environmental Protection Agency

xxvi

1 IntroductionUrbanization increases the density of impermeable surfaces in cities (NOU 2015:16, p. 31).

Increase in impermeable surfaces results in less infiltration and storage, which causes a rapid

response in the surface runoff (Fletcher et al., 2013, p. 261, Healy et al., 2007). An increase in

the surface runoff results in a sharply peaked hydrograph compared to a hydrograph from rural

areas. The surface runoff is an important process in the water cycle and an important component

in the water balance equation. Any changes in the components in the water balance may affect

the others (Healy et al., 2007).

The driving forces that contribute to the increase in surface runoff due to stormwater are the in-

crease in building density and intense precipitation events (NOU 2015:16, p. 30). Surface water

in cities tends to seep into buildings. Therefore, most of the water is drained from the surface

into a storm drain. When the water exceed the the sewer network’s capacity, it is not drain and

intrudes lower elevated areas. Between 2008 and 2014, Norwegian insurance companies have

covered losses for 4189 million NOK due to water intrusion for private actors. The cost reflects

the degree of damages caused by stormwater (NOU 2015:16, pp. 33-35).

The sewer systems are designed and constructed for specified load of wastewater and stormwa-

ter. In a combined sewer system Combined Sewer Systems (CSS), a Combined Sewer Overflow

(CSO) is necessary to prevent backwater effects. An increase in both wastewater and stormwa-

ter amounts can increase the frequency of CSO events. With increase in building density the

amount of wastewater increases (Oslo kommune, 2018, p. 11) and with larger amount of pre-

cipitation, stormwater amount increases. This results in an increase of CSOs events. A conse-

quence of CSO events is unwanted wastewater in the recipient water bodies. Such events can

pollute the receiving waters for recreation, swimming or other water activities.

Urban hydrological models like Storm Water Management Model (SWMM) from the US Envi-

ronmental Protection Agency (US EPA) and the Model for Urban Sewers (MOUSE) from Dan-

ish Hydraulic Institute (DHI) simulate sewer network responses to urbanization and increased

stormwater for current and changed climates. To model the present, measurement or estimation

of input data and model parameters are necessary. With a model describing the present situa-

tion, measures to reduce CSO events can be implemented. Design structures such as LIDs aim

1

CHAPTER 1. INTRODUCTION

to infiltrate and delay the stormwater to reduce the amount of CSO events.

The aims of the study were:

• To evaluate the performance of the hydrology module described in the SWMM model for

conditions in Oslo, Norway

• To model the effect of LID measures and to study the effect of different parametrizations

of the hydrological module on their apparent performance

The rest of the thesis is structured as follows; the theoretical framework and SWMM are pre-

sented in chapter two, the study area and data are described in chapter three. Model setup and

parameter estimation are discussed in chapter four, and the results, discussion, and conclusion

are presented in chapters five, six and seven, respectively.

2

2 Theoretical framework and models

This chapter describes the background for the thesis work. It starts with the water balance and

how the components are altered by urbanization. The water balance concept is necessary to un-

derstand the movement of water in the hydrological cycle within a watershed. Next, the chapter

introduces the urban hydrological system including sewer networks, sewer systems interactions

with surface water, and challenges especially Combined Sewer Systems (CSS) withstands due

to urbanization. Afterwards, an introduction to hydrological modelling is presented, focusing

on differences in urban and ordinary hydrological modelling. Finally, the urban hydrological

model SWMM is introduced.

2.1 Water Balance

The hydrologic water balance describes the incoming and outgoing water in a catchment. The

incoming water comes from precipitation that further evaporates, infiltrates into the soil or goes

into the rivers and lakes as surface runoff. The infiltrated water can be stored in the soil as

groundwater. Groundwater feeds rivers and lakes when they are lower than the groundwater

table. The groundwater flux thus fluctuates with the seasons, depending on the input of wa-

ter (Dingman, 2015, p. 389, Healy et al., 2007, p. 29). Evapotranspiration is all water vapor

lost from the catchment. It includes evaporation from land surface and bodies of water, and

transpiration from vegetation (Dingman, 2015, pp. 18, 71, 253)

Urbanization is a disturbance of natural landscapes. The replacement of vegetated surfaces

with impermeable surfaces, such as buildings, roads and parking lots, is the main component

responsible for the hydrological impacts of urbanization (Shuster et al., 2005, p. 263).

In Butler and Davies (2010, p. 106), the surface runoff generation is at least as important as the

process in the sewer network. Therefore, understanding the runoff process and the hydraulic

processes in the sewers provides a better understanding of the responses in the catchment. Each

of the components in the water balance is affected by urbanization, as described below.

3

CHAPTER 2. THEORETICAL FRAMEWORK AND MODELS

2.1.1 Precipitation and evapotranspiration

Precipitation can be rainfall or snowfall, depending on the air temperature. Before the rainfall

reaches the ground surface, part of it is retained or caught by vegetation; this is called inter-

ception. For urban areas, the magnitude of interception loss is less than 1 mm and is either

neglected or included as a part of the depression storage (Butler and Davies, 2010, p. 107).

The rainfall removed from the surface by evaporation infiltrates or contributes to the surface

runoff. In Scandinavia, Canada and northern USA, it is observed that flooding can occur from

snowmelt in urban areas (Bengtsson and Westerström, 1992, p. 263).

The effect of urbanization has on precipitation is uncertain (Chen et al., 2015, Wai et al., 2017,

p. 650). Higher temperatures and changes in wind pattern might influence the occurrence,

intensity and rain pattern. Changes in intensity and pattern are most likely (Salvadore et al.,

2015, p. 66).

Evapotranspiration is water loss from soil surface by evaporation and transpiration of water

from vegetation. Measurable factors influencing evapotranspiration include radiation, air tem-

perature, humidity and wind (Craul, 1999, p. 69, Dingman, 2015, pp. 253,265).

Evaporation decreases in urban areas mainly due to lack of vegetation (Salvadore et al., 2015,

p. 66). Vegetation can counteract the higher temperature in the cities by shadowing, but also by

having higher albedo than pavements and buildings (Craul, 1999, Tyrväinen et al., 2005, p. 96).

Higher temperatures increase the direct evaporation of water stored in depression storages and

are of hydrolgical relevance (Salvadore et al., 2015, p. 66).

2.1.2 Infiltration, runoff and groundwater

Infiltration is the process by which the water from rainfall or snowmelt enters the ground surface

and progresses into the soil. The infiltrated water moves into the upper unsaturated zone, is

removed by evapotranspiration or percolates to the groundwater. Infiltration rate describes how

fast water infiltrates into the soil. There are several factors influencing the infiltration rate at

the surface: the intensity of the rainfall or snowmelt, the hydraulic conductivity, frost in the soil

which makes the surface impermeable, inwashing of fine sediments and human modifications

(Dingman, 2015, pp. 345-355;358). The consequence of the replacement of pervious areas with

impervious areas reduces infiltration with an associated increase in stormwater runoff (Fletcher

et al., 2013, p. 264). Stormwater is defined as water from snowmelt and rainfall causing surface

4

2.2. URBAN HYDROLOGICAL SYSTEMS

runoff (Butler and Davies, 2010, pp. 77-106).

Surface runoff occurs when the surface gets saturated, either from above or below. Saturation

from above is likely if the rate of rainfall and/or snowmelt exceeds the infiltration rate and

usually refers to as Hortonian overland flow. Saturation from below is possible if the water

table rises to the surface and refers to Saturation Overland Flow. Hortonian overland flow is

an important response in areas where the surface conductivity is low. Surface conductivity can

be reduced by frost, human activity and the presence of impermeable areas (Dingman, 2015,

pp. 480-481). Saturation Overland Flow occurs where the water table already is shallow, a

shallow impermeable layer or where the hydraulic conductivity decreases with depth (Dingman,

2015, p. 483). The water not contributing to the runoff infiltrates into the ground surface.

After the water has infiltrated into the unsaturated zone, the water can progress to the saturated

zone. This process is called percolation (Dingman, 2015, p. 345). The groundwater flow occurs

in the saturated zone following the principles from Darcy’s law. Groundwater is an important

part of the water balance. In till areas, the main soil deposits in Norway, the groundwater tables

are shallow and hence follow the topography due to decreased hydraulic conductivity with depth

(Beldring, 2002, p. 350). The groundwater can contribute to 60-80% of the runoff in areas with

till terrain (Lundin, 1982, p. 202). Increase of impervious surfaces changes the distribution of

water falling on the surface, from partly subsurface flow to almost all surface runoff (Shuster et

al., 2005, p. 264). The results should give lower groundwater levels. However, the groundwater

recharge could increase because of leakage from drinking water network. Leakages of 20-30%

of water from drinking water in developed countries and around 30-60% in developing countries

are typical (Salvadore et al., 2015, p. 68, Ødegård et al., 2014). A reduction of the groundwater

level in cities can cause subsidence and again damages on buildings (Ødegård et al., 2014,

p. 299). Construction of the sewer system under the surface and the challenge of urbanization

are described below.

2.2 Urban hydrological systems

During a precipitation event, the water on most of the impervious surfaces drains to the nearest

storm drain. If the amount of water draining to a storm drain exceeds the inflow capacity, the

water flows further downstream in the catchment to another storm drain. The stormwater enters

the sewer system by inflow to the storm drains.

5


2.2.1 Sewer networks

There are two types of sewer networks; combined sewer systems and separate sewer systems.

Separate sewer network consists of two conduits; wastewater and stormwater conduits. The

wastewater conduits transport sanitary wastewater to the wastewater plant and the stormwater

conduits transport the stormwater to the recipient. The amount of wastewater oscillates hourly,

daily and seasonally. The amount of stormwater varies much more from day to day, depending

on the rate of precipitation and snowmelt. In developing areas, separate sewer network is usually

constructed (Ødegård et al., 2014).

The combined sewer network consists of one conduit which transports both sanitary wastewater

and stormwater to the wastewater plant. In a combined sewer network an overflow is needed,

to unburden the sewers downstream to avoid backwater effects. During heavy rainfalls or in

combination with snow melting, the capacity in the combined sewer can be exceeded, causing

a combined sewer overflow (CSO) event (see figure 2.1). The water above the crest height in a

combined sewer is transported out to a recipient water body during CSO events. These events

release diluted wastewater into the waterways, causing pollution (Ødegård et al., 2014).

Figure 2.1: Illustration of a CSO event. The figure is from the City of West Lafayette CSORelief Interceptor Project(URL: http://www.riverroadwl.com/index.php/project-information/, 14.05.2019).

Not all surface water generated on the impervious areas runs into the storm drain. Some is

lost by infiltration into nearby pervious areas. Some terms help distinguish between different

types of impervious areas. The total impervious areas are usually referred to as TIA. TIA

consist of disconnected impervious areas and connected impervious areas. TIA directly con-

nected to the drainage system is called directly connected impervious areas (DCIA). The dis-

6

2.2. URBAN HYDROLOGICAL SYSTEMS

connected impervious areas route the water onto pervious areas, while DCIA route the water

to the sewer drainage system (Jacobson, 2011, p. 1440, Shuster et al., 2005, p. 265). Jacobson

(2011, p. 1442) points out that the relation between DCIA and TIA may be elusive, due to the

variability in the estimates. Quantification of the catchment’s distribution of DCIA is difficult

and may change with the intensity of the rainfall. During heavy rainfall, the capacity of the

gutter can be exceeded, and additional rainfall is routed to the pervious areas (Jacobson, 2011,

p. 1442).

The time of concentration is the time it takes for the water to travel from the most distant part

in the catchment to the outlet (Dingman, 2015, pp. 471, 476). In a rural catchment it can be

of several hours or days, while in urban catchment it is usually in a scale of minutes. The

time of concentration in an urban catchment is defined by the time it takes for the water to

flow to a storm drain in addition to the time in the sewers. In Norway, time of concentration

from catchment boundary to the nearest storm drain is approximate 3-7 minutes. The time from

the storm drain to the outlet is the length of the pipe divided by the speed of the water in the

pipe, where the speed is estimated at 1.5-2 m/s (Ødegård et al., 2014, p. 346). The length of

pipes and area of the catchment determine the time of concentration. The difference in time of

concentration, as one of several reasons, implies that the same rainfall event would generate a

higher discharge peak in an urban catchment than in a rural catchment.

2.2.2 Stormwater management

Reported damages from water intrusion related to the weather have increased since 2008, when

the reporting started (Finans Norge, 2018). One reason is that the sewer system is not designed

for the increase in building density and impermeable areas. Additionally, the precipitation in-

tensity increased (Ødegård et al., 2014). Increased stormwater discharge increases the time

and volume of CSOs events more than proportionally (Ødegård et al., 2014). Increase in CSO

events is a problem for the environment and can damage buildings due to backwater effects.

The stormwater three step approach (S3SA) is a strategy to take care of the stormwater in an

efficient and sustainable way (see figure 2.2). The first step delays the stormwater by infiltration.

The second step delays the stormwater by storage. Step two should take care of a rainfall event

of return level of 20 year. Rainfall of larger than 20-year return periods but less than 200-year

is treated as step three. Step three secures safe floodways where the water is transported from

the surface out to the recipient water body (Paus, 2018, p. 68, Lindholm et al., 2008, p. 8, NOU

7


2015:16, p. 67, Ødegård et al., 2014, p. 353). The damages from CSO events can be reduced

by controlling the water on the surface instead of replacing the combined sewer system with a

separate system. Replacement is not optimal from a financial perspective if the sewer network

does not need upgrades (Ødegård et al., 2014, p. 372). Local measures to reduce stormwater

generation, so called Low Impact Developments (LIDs), have been become a viable alternative.

Figure 2.2: Illustration of the management of stormwater with the S3SA. Taken from Lindholmet al. (2008, pp. 8, 37) modified by the author.

2.2.3 Low Impact Development (LID)

The terminology used in stormwater management and urban drainage system for the same con-

cept varies. It is determined by where it was first used. Low Impact Development (LID) was

first applied in the US in 1977. LIDs are original site designs retaining natural areas and mini-

mizing impervious areas. Sustainable Urban Drainage Systems (SUDS) were first used in the

UK in 1997. SUDS are the technique of replicate the natural, pre-development drainage at a

site. SUDS are consistent with the principles behind LIDs (Fletcher et al., 2015, pp. 526-529).

The objective of such stormwater reduction measures, hereafter referred to as LID, is to restore

the pre-developed hydrology (US Enviromnetal Protection Agency, 2009, p. 7). In this way, the

CSO events can be reduced. The pre-developed hydrology is restored by using natural covers to

infiltrate and detain the water on the surface. Examples of LIDs that increase the infiltration rate

in a catchment are bio-retention cells and pervious pavement. Rain barrels and disconnection of

downspouts from the combined system, are examples that contribute to the delay and reduction

of the surface runoff.

The LIDs of interest in this thesis are disconnection of downspouts, bio-retention cells and rain

8

2.3. (URBAN) HYDROLOGICAL MODELLING

barrels. They are mainly relevant to steps one and two of S3SA. Disconnections of downspouts

lead the water away from the sewer system and out on the plot to the building’s owner. If the

plot consists of permeable media, the water infiltrates from the surface. The DCIA is reduced.

Bio-retention cells are vegetated depressions filled with high conductive medium. The saturated

hydraulic conductivity should be larger than 100 mm/h (Paus and Braskerud, 2013, p. 55). The

purpose is to infiltrate and store stormwater. Rain barrels are containers collecting runoff from

roofs and can reuse the water during periods without rainfall.

The effect of LIDs depends on the proportion of the total catch area of the LIDs, like bio-

retention cell catchments, or how many downspouts are disconnected. It is not only about the

number and area, but also the combination of measures and devices (Butler and Davies, 2010,

p. 531). The climate and local variations may also affect how well the LIDs work. Saksæther

and Kihlgren (2012) tested the infiltration capacity in a bio-retention cell during winter. If the

bio-retention cell has an ice layer there was no infiltration, while if the soil was frozen the bio-

retention cell had limiting effect. Local variations and climate can affect the effectiveness of

the LIDs. Sjöman and Gill (2014, pp. 311-313) observed that the native soil of a catchment

can be an important factor controlling the effects of different LIDs. In a residential area with

clay, replacing the area with permeable pavements better reduced the surface runoff than in a

residential area with sandy soil. Hydrological models are used to simulate how different LIDs

would affect the surface runoff, but with variations in the effectiveness of LIDs, the results from

modelling can vary from reality.

2.3 (Urban) hydrological modelling

2.3.1 Model background

Hydrological systems are too complex to be understood in all detail. A hydrological model

represents the complex system by a similar but simpler structure. The purpose with the model

is then to simulate and predict the complex system (Singh, 1988, pp. 26, 27). Hydrological

models are usually lumped, distributed or a combination. A lumped model does not consider

the spatial variability of the input, and the complexity is reduced. It is most useful when data

are limited. In a distributed model, the catchment is divided into a finite number of elements

which can be zones, cells or regions, where runoff is calculated separately for each element.

A semi-distributed model uses subcatchments to better describe the spatial variability, but the

9


subcatchments internal description is lumped. The size of the elements or subcatchments de-

cides to what degree the spatial variability is accounted for in a model (Jacobson, 2011, p. 1443,

Salvadore et al., 2015, pp. 69-70).

Hydrological models describe the runoff generation. They calculate the peak discharge by

solving the water balance. The water balance is derived from the continuity equation for a

closed system. A hydraulic model describes the runoff routing. The depth of flow and the

discharge downstream in a channel can be found by solving both the continuity equation and

the momentum equation, also called the St. Venant equations.

In hydrological models a catchment needs to be defined to delimit area for input and output of

water. For surface water modelling, the catchment is defined based on the topography. For sur-

face water and groundwater modelling, a catchment based on topography can be used, but the

groundwater may not have the same boundaries as the surface water (Dingman, 2015, p. 15).

The groundwater in Scandinavia, mostly follows the topography (Beldring, 2002), thus a catch-

ment based on the topography for hydrological modelling can be used. In an urban catchment,

the sewer network influences the distribution of the water. Water on the surface drains into

the nearest storm drain, and does not necessarily follow the topography as water in more nat-

ural catchments. Also, the difference in Manning’s roughness coefficient for different surfaces

affects the flow velocity in an urban catchment. The sewer transports the water faster to an

outlet than the natural flow direction on the surface. The flow direction of the sewer system is

important information and can affect the catchment boundaries.

There are several urban hydrological models, of which MOUSE and SWMM are the most pop-

ular ones in Norway. Lindholm (1998) compared these two models, together with NIVANETT,

for different types of simulations and found that MOUSE performs best. MOUSE is developed

by DHI and is available through the software MIKE Urban. SWMM is an open source model

developed by US EPA.

Urban hydrological models typically combine a hydrological module with hydraulic simula-

tions of the sewage and stormwater networks.

Urban hydrological models’ main use is to design and dimension the stormwater network. Ad-

ditonally, they can predict or evaluate the effects of urbanization (Salvadore et al., 2015, p. 68).

A model of the present requires calibration and validation before simulations with input of

design rainfall can be modelled.

10

2.3. (URBAN) HYDROLOGICAL MODELLING

2.3.2 Calibration and validation

Calibration is a process of comparing model outputs with observed discharge using different

sets of parameters together with precipitation as input. The model parameter that gives the best

model performance is selected (Dingman, 2015, p. 510). The process can be done manually

by adjusting parameters by the trial and error method. The trial and error method is subjective

but enables the modeler to use all the information about the catchment and the model, not just

the results from the objective function. Automatic optimization is another calibration process

where the smallest difference between observations and simulations is obtained systematically

through mathematical algorithms. An objective function is used as a measure of how good

the performance of the model is. The objective function calculates the difference between the

observed and simulated values. The motivation is to get the difference as small as possible by

tuning the parameters.

Validation is application of the calibrated model without changing the parameter values found

in the calibration phase on an independent period (Dingman, 2015, p. 510, Refsgaard, 1997,

p. 73). The purpose with the validation is to test the model’s applicability on a set of input data

not used to tune the model. Validation is an important verification of the model’s performance.

The model performance is again evaluated with an objective function. If the value from the

objective function falls within acceptable range set by the modeler, the model is validated. If

not, the process of calibration is repeated with new information or another model is selected.

The challenge with calibration and validation is the problem of equifinality. Several parameter

combinations give similar results (Dingman, 2015, p. 510). In addition, there is an optimum

model complexity beyond which the predictive performance decreases because lack of data to

verify the model and the large number of parameters. With a complex model, detailed spatial

data are required. If the data are not available, it may be challenging to calibrate the model

accurately and the predictive performance is reduced. In many models, the subcatchment is

considered as a homogeneous unit. Detailed spatial data implemented into the homogeneous

subcatchment unit reduce the complexity (Jacobson, 2011, p. 1442).

An objective function needs to be specified for the model evaluation. Objective functions are a

mathematical measure of how well the model simulation fits the observation for each time step.

In hydrology, Nash-Sutcliffe Efficiency (NSE) is a popular objective function for comparison

11


of models’ performance. NSE is defined as (Nash and Sutcliffe, 1970, p. 287)

NSE = 1− ∑nt (Qsim,t −Qobs,t)

2

∑nt (Qobs,t − Qobs)2 [−] (2.1)

where Qsim [l/s] is the predicted discharge, Qobs [l/s is the observed discharge, Qobs [l/s] is

the mean of the observed discharge and n is the number of time steps. NSE describes how the

variance in the observations is explained by the variance in the residuals. The range is between

−∞ and 1, where 1 is perfect fit. NSE is sensitive to extremes (Krause et al., 2005, p. 97).

Earlier simulations with SWMM have considered NSE larger than 0.5 acceptable (Dongquan

et al., 2009).

The coefficient of determination, R2, an established measure in statistics quantifying the model’s

fit is described as

R2 = 1−∑

mj=1(Pj − Pj)

2

∑mj=1(Pj − P)2 [−] (2.2)

where P is the estimated values, P is the observed values, P is the observed mean and m is

the number of discharge peaks. R2 describes how much of the observed variation can be ex-

plained by the simulation. The denominator is the total variance in the observed data and the

numerator is the variance between the simulated and observed data. The closer R2 is to 1, the

larger proportions of the observed variability can be explained with the simulations of estimated

values (Devore and Berk, 2007, p. 686). As predicting flood peaks is a main purpose of urban

hydrological models, R2 of observed and predicted flood peaks measure of performance.

NSE is defined to investigate the difference between the dynamics of model simulation and

observations, but for corresponding time steps (Nash and Sutcliffe, 1970, p. 287). R2 on the

other hand is a statistical measure of how well the predictions approximate the real data points.

In investigation of design of infrastructure or floods in hydrology, the peaks are of importance.

Therefore, matching peak magnitudes has been considered as the most important criteria in this

study.

Event-based runoff coefficients are a way to quantify the relation between event rainfall and

surface runoff. In urban areas the runoff coefficient is usually around 0.6-0.95 and in rural areas

around 0.05-0.4, but it depends on the return period of the storm (Dingman, 2015, pp. 515-518,

Guo and Urbonas, 2013, p. 7). Dingman (2015, p. 468) holds the view that there is high degree

of temporal variations in the ratio of the event-flow and rainfall inside one urban watershed.

12

2.4. THE URBAN HYDROLOGICAL MODEL IN SWMM

The antecedent conditions have a major influence, and thus the runoff coefficient may vary for

different storms. In Guo and Urbonas (2013, p. 10) different runoff coefficients are calculated

for different storms and they vary for the type of storm and DCIA. Calculation of the runoff

coefficients largely depends on the techniques used to separate the components: baseflow and

event flow. The volume-based runoff coefficient is described as the volume of the event runoff

hydrograph divided by the volume of the hyetograph (Blume et al., 2007, p. 849, Guo and

Urbonas, 2013, p. 7)

c =

TB∑

t=1∆t(Qt −Qt+1)/2

ATd∑

t=1Pt

[−] (2.3)

where Qt [m3/s] is the discharge at time t, TB is the time when the hydrograph reaches the initial

level, Td is the time of duration of the rainfall, δPt m is the rainfall segment at time t and A [m2]

is the area of the catchment.

2.4 The urban hydrological model in SWMM

SWMM5 is a dynamic semi-distributed rainfall-runoff model. It simulates the relation between

hydrology and hydraulic, and runoff quality. The water quality will not be considered in this

research. An introduction to the different parts of the model is given in this section.

2.4.1 Subcatchments and runoff

A catchment is divided into several subcatchments. This allows for a representation of the spa-

tial variability in the catchment; difference in topography, land covers and/or soil properties.

The subcatchments are ideally thought of as a rectangular surface that drains to one channel

outlet (Rossmann and Huber, 2016a, p. 51). Each of the subcatchments can again be divided

into different subareas: pervious (S1), impervious with depression storage (S2) and impervious

without depression storage (S3) (see figure 2.3). Runoff generates differently and independently

on each subarea. This allows water from one subarea to reach the outlet before water from an-

other subarea. The differences in time can be explained by two factors. The first factor is a

difference in properties such as Manning’s roughness coefficient, n, and depression storage, ds.

The second is that water infiltrates in the pervious subarea, and not in the impervious subareas

(S2 and S3). However, all subareas have equal width, slope, precipitation and evaporation rates

(Rossmann and Huber, 2016a, pp. 54-55). Almost immediately after precipitation reaches the

13


Figure 2.3: The subcatchment is divided into subareas; pervious subarea (S1), impervioussubarea with depression storage (S2) and impervious subarea without depression storage (S3).The sketch is not scaled. Inspired by Rossmann and Huber (Rossmann and Huber, 2016a,pp. 52, 55)

surface, runoff, q, generates from the subarea S3, which is impervious area without depression

storage. The precipitation rate, i, that enters the subareas with depression storage, ds, is ex-

posed to evaporation, e, for S1 and S2, but also infiltration, f , for S1. If the depth, d, in the

depressions after evaporation and possibly infiltration exceeds the depression storage, ds, runoff

is generated. The water balance to the surface of the catchment is solved with an expression of

the runoff found by Manning’s equation. The Manning’s equation uses the depth difference be-

tween the actual depth of the water in the depressions and the depth of the depression, (d−ds),

as a radius. The total equation is then a differential equation integrated with a Runge Kutta of

5th order (Rossmann and Huber, 2016a, p. 56).

The runoff generated from impervious subarea can be routed to the subcatchments outlet di-

rectly or re-routed from the impervious subarea to the pervious subarea. Alternatively, the

impervious subarea can gain runoff generated from pervious subarea. The proportion of routing

between subareas can also be decided in the model. Ratio of DCIA and TIA is a measure of the

fraction of impervious area directly connected to the drainage system (in this context the out-

let). The ratio of DCIA to TIA for residential areas is, according to Bjørn Christofferesen (Oslo

VAV, personal communication, 31.02.2019), 0.25-0.35, while Wibben (1976, pp. 11, 13) finds

0.05-0.10 for 11.7-37% TIA. In Alley and Veenhuis (1983, p. 314), the mean ratio is 0.085-0.23

for 15-36% TIA and for an average TIA of 60%, the average ratio is 0.42 .The runoff from a

subcatchment does not necessarily need to be routed to an outlet or to a subarea, it can also be

routed to another subcatchment. The runoff from a subcatchment upstream cannot be routed

14


to the specified pervious or impervious subarea of the subcatchment, but will be uniformly dis-

tributed over the subcatchment downstream, in the same way as precipitation is distributed on a

subcatchment (Rossmann and Huber, 2016a, p. 60).

2.4.2 Hydraulic routing model

Routing is a computation process that derives the downstream hydrograph given the upstream

hydrograph. The computation process solves the St. Venant equations. Different routing models

are available for calculating the downstream hydrograph (Dingman, 2015, p. 505)

There are three different routing options in SWMM, i.e. steady state, kinematic wave routing

and dynamic wave routing. They all solve the St. Venant equations, but with different ap-

proaches. Steady state flow routing is the simplest routing and it assumes a uniform and steady

flow. This routing model does not change the shape or delay of the upstream hydrograph, but

only translates it into the downstream end (Rossmann and Huber, 2016a, p. 36). Kinematic

wave routing solves a simplified form of the St. Venant equations for uniform, unsteady free

surface flow. The disadvantage is that it cannot model pressurized flow, reverse flow or backwa-

ter effects. The advantage is that the method allows large times steps, which make it possible to

compute more efficiently (Rossmann, 2017, p. 63). Dynamic wave routing solves the complete

St. Venant equations for gradually varied, unsteady free surface flow. This routing gives the best

theoretical approaches of the three methods. The downside of this approach is the requirement

of small time steps to maintain numerical stability (Rossmann and Huber, 2016a, p. 40).

2.4.3 Model parameters

The width, W, of the subcatchment is the width of the overland flow in the subcatchment, as

shown in figure 2.3. The parameter is used in calibration to optimize the shape of the hydro-

graph. Large width gives a rapid rise in the hydrograph, while a narrow width gives longer

rising limb and recession (Rossmann and Huber, 2016a, p. 68). A large width allows the water

in the subcatchment to enter the outlet much faster than a small width, hence the rapid rise in

the hydrograph.

The slope of the subcatchment is the difference in elevation divided by the distance between the

points. The slope should reflect the dominant slope to the overland flow pathway to the inlet

(Rossmann and Huber, 2016a, p. 73).

15


The manual suggests setting the Manning’s roughness coefficient and the slope for a catchment

and then calibrates the width of the subcatchments to obtain reasonable results, where the slope

is estimated, and Manning’s roughness coefficient is chosen from a table (Rossmann and Huber,

2016a, p. 74). For simulations with SWMM in Finland values of 0.011-0.015 have been used

for Manning’s roughness coefficient for impervious areas (asphalt/concrete) and 0.15-0.8 used

for pervious areas (grass/trees) (Guan et al., 2015). In the manual, values for three different

studies are listed. The range for asphalt in the manual is suggested as 0.010-0.015, and for other

types of surfaces there is no consensus between the values in the studies.

Rossmann and Huber (2016a) suggest being careful with the estimations of the amount of im-

perviousness in the subcatchment, since it is a very sensitive parameter. The amount of im-

perviousness for the residential catchment is based on area of buildings and roads inside the

catchment.

The depression storage represents loss of water, i.e. interception from vegetation or storage

on the surface. The depression storage for pervious surfaces is influenced by evaporation and

infiltration, while the depression storage on impervious areas is only influenced by evapora-

tion. The water stored in depression storages is therefore faster emptied for a pervious subarea

than an impervious subarea (Rossmann and Huber, 2016a, pp. 74-75). Values for depression

storages used in SWMM from a case study in Finland (Guan et al., 2015) are of 0.0-2.5 mm

for impervious areas and 2.5-7.6 mm for pervious areas. A study from Poland (Skotnicki and

Sowinski, 2015) uses 1.5 mm for impervious areas and 5.0 mm for pervious areas. In a general

review for urban hydrological modelling by Salvadore et al. (2015) the range of 0.2-3.2 mm for

impervious areas and 0.5-15 mm for pervious areas is used, while Ødegård et al. (2014, p. 44)

suggests approximately 2.5 mm for concrete and asphalt and 5.0 mm for permeable surfaces.

2.4.4 Infiltration

Infiltration can be simulated in SWMM with five different methods; Horton, Modified Horton,

Green-Ampt, Modified Green-Ampt and Curve Number. The Green-Ampt method is used in

this project. All the infiltration methods are dependent on parameters that describe the type and

condition of the soil (Rossmann and Huber, 2016a, p. 86).

Green-Ampt infiltration method depends on the amount of water infiltrated and not on the time

as Horton. In Green-Ampt method the infiltrated water creates a saturated layer at the top of

16


the soil column which moves downward according to Darcy’s law. The infiltration rate depends

on the former infiltrated volume of water, together with the parameters, i.e. saturated hydraulic

conductivity, suction head and the initial moisture deficit. The initial moisture deficit is the

difference between the saturated soil moisture and the soil moisture at the start of the simulation.

The infiltration method is not sensitive to estimates of the suction head at the wetting front


The values for the parameters in the infiltration methods used depend on the soil properties in

the area. The soils in urban areas are usually more compact and do not necessarily have the

same properties as the same soil type in a rural area. Compaction of the soil is seen as a larger

influencer of the infiltration than type of soil. The soils are also heterogeneous, leading to spatial

variability of infiltration rates (see table B.1). In Rossmann and Huber (2016, p. 114), different

values for saturated hydraulic conductivity are listed. For clay soil the hydraulic conductivity is

0.25 mm/h, while for sand the saturated hydraulic conductivity is 120.40 mm/h. Solheim (2017)

measured the hydraulic conductivity with various methods at different locations in Oslo in the

summer of 2016. The saturated hydraulic conductivity ranged between 100.6 to 540.2 mm/h for

sandy soil and 5.0 to 200.2 mm/h for silty clay with use of both Modified Phillip-Dunne (MPD)

infiltrometers and Double Ring.

2.4.5 Groundwater

The infiltrated water in SWMM is assumed lost from the system. The runoff calculation is

based on the rate of evaporation and does not include transpiration. To include the infiltrated

water in the system and transpiration from pervious areas, a groundwater aquifer is included.

The groundwater aquifer is linked to the subcatchment through a node called the receiving

node (see figure 2.4). The receiving node is the same as the outlet node for all aquifers in

this study. The aquifer is divided into two zones; unsaturated and saturated. The lower zone

from the bedrock and up to the water table is the saturated lower zone, while the zone from

the water table up to the ground surface is the upper unsaturated zone. The groundwater table

changes with time thus the height of the zones varies with time. The upper zone receives

water only by excess rainfall by infiltration as calculated from Green-Ampt in subsection 2.4.4.

The infiltration is only simulated for the pervious subarea, while the groundwater extends over

the entire area of the subcatchment. The depth of the groundwater table is the same for the

entire subcatchment. The water is lost from the upper zone in two different ways: one by

17


evapotranspiration and the second one is due to percolation from the upper zone to the lower

zone. The rate of percolation is based on Darcy’s law for unsaturated flow. The lower zone

receives water from percolation from the upper zone and from lateral groundwater flow. It

loses water due to deep percolation, evapotranspiration and lateral groundwater flow. The deep

percolation depends on a coefficient, DP, and the pressure head above the bedrock (Rossmann

and Huber, 2016a, pp. 136-137). The saturated zone can both gain and lose water from the

Figure 2.4: Sketch of the groundwater aquifer. The heights h∗, hSW and dL are used to calculatethe lateral groundwater flow, fG. Inspired by Rossmann and Huber, 2016a, pp. 129,138.

lateral groundwater flow depending on the height of the groundwater table, dL, relative to the

height of water in the receiving node, hSW . The groundwater flow can be described as

fG = A1(dL −h∗)B1 −A2(hSW −h∗)B2 +A3dLhSW (2.4)

where A1, A2 and A3 are groundwater coefficients and B1 and B2 are groundwater exponents.

In this thesis, A1 = A2, A3 = 0 and B1 = B2.

18


2.4.6 Precipitation and evapotranspiration

Precipitation is measured at a certain point and can only give information about precipitation at

that location. SWMM5 has the option to use several rain gauges with time series of precipita-

tion at different locations to capture the spatial variability. Precipitation is divided into rainfall

or snowfall depending on a user-supplied dividing temperature. The precipitation can have a

maximum resolution of 24 hours and a minimum of 1 minute. The unit can be in intensity,

volume or accumulated. Each subcatchment needs to define which rain gauge it receives the

water from. One subcatchment cannot receive water from more than one rain gauge.

SWMM5 estimates evaporation by Hargreaves method (Rossmann and Huber, 2016a, p. 49).

The method is based on the air temperature and the latitude. In order to include temperature

in the model a climate file is required. The climate file contains columns of maximum and

minimum temperatures for each day. SWMM converts the maximum and minimum to hourly

mean temperatures. From the temperatures a running average of 7 days is calculated and used

in the formula together with the daily mean temperature from each day. The last parameter

needed to estimate the evaporation is the incoming extraterrestrial radiation based on latitude

and Julian day.

SWMM calculates the evaporation losses from the surface of the subcatchments. To account

for transpiration, the groundwater part needs to be included. Evapotranspiration from the upper

unsaturated zone is the soil moisture lost from vegetation cover and by direct evaporation of

the pervious subarea. The lower zone evapotranspiration is transpiration from the lower satu-

rated zone. If the water table depth is below a user-specified depth, no transpiration can occur


2.4.7 Network components

In SWMM the system network is composed mainly of nodes and links. Links are the trans-

porting element between nodes. The nodes are the end and/or start point of a link and are the

connection points between links. SWMM offers several types of nodes and links according to

the objective of the simulation.

The nodes can behave as a manhole in a sewer system, a junction between two channels, a

flow divider which separates the flow based on, for example, a weir or a certain inflow value,

a storage unit that stores volume of a lake or a catch basin, or it can be the end-point of the

19


network system working as a boundary conditions for the simulations (Rossmann, 2015, p. 31)

The links can behave as conduits or channels. They transport liquids from one node to another

in the network of links and nodes. The link can have the geometry that is preferred by the

user, either by using the suggestions in SWMM or by using a user-defined shape. The shape

determines if the links are open or not. No liquid can be routed to the links. Links only transport

water between the nodes, but the links can lose a constant value of liquids into the soil if the

user defines a value of loss (Rossmann, 2015, p. 33).

2.4.8 LIDs

SWMM5 can model the following LIDs; bio-retention cells, rain gardens, green roofs, infiltra-

tion trenches, continuous permeable pavements, block paver, rain barrels, rooftop disconnection

and vegetative swales. The performance of the LIDs modeling depends on the surface area the

LIDs cover, the hydraulic conductivity, the depth and the media of the unit (Rossmann and

Huber, 2016b, p. 99).

Based on a survey sent to 462 householders at the Grefsen plateau connected to the combined

sewer network, 78% out of the 192 respondents could imaging having LID measures on their

own property (Furuseth et al., 2019, p. 397). The householders where given the following

options to choose from; bio-retention cell, permeable surfaces, rain barrels and green roofs.

The bio-retention cell was the LID measures the householders were most interested in. This

thesis therefore mainly has focuses on testing the performance on bio-retention cells. Rain

barrels and disconnection of downspouts are also included because of their low cost.

Bio-retention cells in SWMM5 are thought of as a box of three layers (see figure 2.5). The first

layer is the surface layer. Water from rainfall and runoff on a user-specified impervious area is

captured in it. The water at the surface layer is removed from the layer by evapotranspiration,

infiltration into the soil layer (layer 2) or by runoff if the surface layer is overfilled. In the soil

layer, water is lost by evapotranspiration or percolation from soil layer to storage layer. Storage

layer consists mostly of coarser sediments and loses water by percolation into the natural soil

beneath, so called seepage from the bio-retention cell, or by evapotranspiration. Evapotranspi-

ration occurs for each individual layer (see figure 2.5). If water infiltrates into the soil layer,

no evapotranspiration from the soil and storage layer occurs. Also, if the soil layer is saturated,

no evapotranspiration can occur from the storage layer. It is optional to add a drain into the

20


storage layer. Water above the drain height is lost from the bio-retention cell. The drain can

transport the water to the outlet within respectively subcatchment, pervious subareas within its

subcatchment, another subcatchment or another outlet, depending on the modeler’s choice. The

bio-retention cell module requires 15 user-supplied parameters (Rossmann and Huber, 2016b,

pp. 99-107). The rain barrel in SWMM5 is thought of as a storage layer with a drain (see figure

Figure 2.5: Sketch of the bio-retention cell in SWMM. The bio-retention cell is made up ofthree layers: a surface layer, a soil layer and a storage layer. Here displayed in combinationwith their fluxes.

2.6). The inflow to the rain barrel is water from a specified part of the impervious area. Rainfall

and evaporation are not accounted for because the barrel is closed at the top. The filling rate

of the barrel depends on the available storage in the barrel and how much of the water that is

draining out of the barrel. If the barrel is full, the inflow turns into overflow. The overflow

is either routed to the outlet of the subcatchment or to the pervious part of the subcatchment

(Rossmann and Huber, 2016b, pp. 113-114). Disconnection of rooftops in SWMM5 can either

be done with the LID module Disconnection of Rooftop or by SWMM’s re-routing option of

21


Figure 2.6: Sketch of the rain barrel in SWMM together with the fluxes in and out of the barrel.

the runoff within a subcatchment (see figure 2.7). The module requires parameters about the

slope of the rooftops, Manning’s roughness coefficient of the roof and width, so the re-routing

option is used. Some percentage of the runoff on the impervious area is routed to the pervious

to illustrate the disconnection of downspouts.

Figure 2.7: Sketch of the conceptualization of disconnection of rooftops. Inspired by Rossmannand Huber (2016a).

22


2.4.9 Input data and parameters

The parameters requested for the properties of the subcatchments are found in table C.1. The

Green-Ampt infiltration method requires three parameters (see table C.2).

The aquifers within each subcatchment require data for the fields in table C.3 and additionally,

some parameters and values connecting the aquifer to the outlet node specified in table C.4.

The input of meteorological data is precipitation and potential evaporation. Precipitation is

added as a list of values with a minimum resolution of one minute. Potential evaporation is cal-

culated by Hargreaves method in SWMM. To account for transpiration, a groundwater aquifer

is added to each subcatchment.

Design rainfall is rainfall of a certain duration with a given exceedance probability. It is widely

used in hydrological models to estimate design discharge used in planning and design of in-

frastructure projects and plans (Mamoon and Rahman, 2014, p. 65, Dingman, 2015, p. 512).

Recorded rainfall data, minimum 10 years, can be used to develop intensity-duration-frequency

(IDF) curves by statistical techniques (Mamoon and Rahman, 2014, p. 65, Dyrrdal and Før-

land, 2018, p. 1). Construction of IDF-curves requires stationary rainfall data series. Climate

changes can affect the rainfall data statistics such as the mean and hence the statistical distribu-

tion (Mamoon and Rahman, 2014, pp. 67-69). Therefore, long rainfall series of many years is

thus not necessarily more reliable.

Ødegård et al. (2014, p. 349) suggest convert information in an IDF-curve to construct a sym-

metric hyetograph. Rainfall of high intensities is rarely constant but varies with time throughout

the storm with a maximum rainfall peak after the rainfall has started (Butler and Davies, 2010,

p. 90). An advantage with the symmetric hyetograph is that the hyetograph can be used for all

size of urban catchments, as long as the meteorological conditions are the same. The highest

intensities are designed for the contribution from areas with short time of concentration, while

in areas where the time of concentration is larger, the whole hyetograph gives design discharge

from these areas (Ødegård et al., 2014, p. 349).

23

3 Study area and dataThis chapter gives an introduction to the study area and data used. A short description with

figures of location is included with a description of the hydrological and climate conditions in

the area. Finally, the available data of the area are described.

The study area (see figure 3.1) is an urban catchment located in the residential area Grefsen

in Oslo, the capital city of Norway. Oslo won the European Green Capital Award for 2019.

However, the jury ranked the city inferior to several other cities in the categories of Water

Management and Waste Water Management (RPS, 2017). The present situation of wastewater

management is not sufficient to meet the increasing population, increase in sealed surfaces and

more intense rainfall (Oslo kommune, 2018, p. 11, Oslo VAV, 2014, p. 3). In 2013 Oslo Mu-

nicipality made a strategy for stormwater management, where the goals are to avoid damages

due to urban flooding and stormwater, reduce the pollution from stormwater into the recipients

and use the stormwater as a resource (Oslo kommune, 2013). To achieve these goals the mu-

nicipality examines the possibility of using LID measures in areas that are under development

(Oslo VAV, 2014, p. 9).

Grefsen is a residential area in the northern part of Oslo (see figure 3.1). The sewer network

is partly combined and partly separated (see figure 3.2). A CSO is located in manhole 161143

(see blue triangle in figure 3.2) and is called AK52. The recipient to the CSO is the stream

Myrerbekken draining to the river Akerselva. The stream and the river are located west of

AK52.

Earlier studies (Hernes, 2018, Ingebrigtsen, 2017) have used MOUSE in MIKE Urban to simu-

late the present situation and different LIDs scenarios at Grefsen. They have both used a model

parameterization from 2014 established by Water and Sewage Agency in Oslo Municipality

(Oslo VAV), but for different catchments (see figure F.2). In Ingebrigtsen (2017) different LIDs

are implemented at the Grefsen plateau (western part of the catchment) in the model to find the

best option to reduce the CSO events. The study finds a reduction in volume of CSO events

of 81% if 78% of the downspouts are disconnected from the CSS in the western part. Hernes

(2018) uses Grefsen as a test catchment to test the performance of the new modules for green

roof and bio-retention cell in MIKE Urban.

25

CHAPTER 3. STUDY AREA AND DATA

Figure 3.1: Catchment boundaries at Grefsen shown in orange, north east in Oslo, Norway.The background map is from the Norwegian Mapping Authority c©Kartverket

.

3.1 Geography and climate

The area of the catchment is 1.44 km2 (144 ha) and has been divided into 15 subcatchments.

The catchment consists of 22% impervious areas, roads and buildings. The area is in general

quite flat (see figure F.1) at the Grefsen plateau, except for the subcatchments close to Gref-

senkollen in the eastern part of the catchment. The area under the marine limit has sediments

with medium infiltration capacity (see figure 3.4). The area in the east is mostly bedrock without

any storage capacity (see figure 3.3). The precipitation station within the catchment, Kjelsås,

only has measurements from the period 2013 to 2019 (Regnbyge, 2018c). The closest station

with the long record, from 1931 to 2019, is the rain gauge 18700 Oslo - Blindern. The station is

monitored by Norwegian Meteorological Institute (METNO) and Kjelsås is monitored by Oslo

Municipality. The average amount of precipitation from the normal period (1961-1990) is 763

mm per year at 18700 Blindern. The wettest month is September with an average precipitation

of 90mm. On average, there are 113 days a year where the station has measured more than

26

3.2. HYDROLOGY

Figure 3.2: Map of the different sewer conduits; wastewater (green - SP), stormwater (black- OV) and combined conduit (red - AF). The location of the measurements is also included,where the blue triangles represent discharge measurements and orange dot represent raingauge. The background map is from the Norwegian Mapping Authority c©Kartverket. Boththe Norwegian Mapping Authority and Water and Sewage Agency in Oslo Municipality (OsloVAV) have given their permission to use this map.

1 mm. The annual average air temperature is 5.7 ◦C, where the lowest average monthly air

temperature is -6.8 ◦C in January and February. The highest average monthly air temperature is

21.5 ◦C in July from the period 1961-1990 (Norwegian Meteorological Institute, 2019).

3.2 Hydrology

The wastewater conduits from the separated system are connected to the combined sewer net-

work close to the manhole 161143 also called AK52 (see figure 3.2). AK52 is one of the

overflow weirs with largest activity along river Akerselva, with 5 hours and 23 minutes of CSO

events into Akerselva in 2017. The CSO events are caused due to shrinking of the diameter

of the combined sewer downstream of AK52, from 0.8 m to 0.5 m. At the weir, a stormwa-

ter sewer is placed by the side of the combined sewer. When the water level in the combined

sewer exceeds 40 cm, the weir crest height, the water from the combined sewers goes into the

27


Figure 3.3: Spatial distribution of sediment types mapped by the Geological Survey of NorwayGeological Survey of Norway (NGU). The background map is from the Norwegian MappingAuthority c©Kartverket.

stormwater sewer, then the stream Myrerbekken, and finally the river Akerselva.

The velocity and depth in the sewer pipes at Grefsen are measured at several locations in the

catchments for different time period. The velocity and depth measured at the weir AK52 are

from the combined sewer, so the measurements are not only affected by the stormwater, but

also the rate of water consumption in the area. The measurements in manhole 297077 are from

a sensor placed in the wastewater sewer in the separate system. Parts of the combined sewers

are connected into the wastewater sewer. In this way, the wastewater sewer acts as a combined

sewer. The measurements are done by Oslo VAV and the data are available for registered users

at the website www.Regnbyge.no (Regnbyge, 2018a, Regnbyge, 2018b). Oslo VAV has done

measurements in AK52 since 2010. The discharge data in AK52 before 2016 are according

to Alexander Pham (Oslo VAV, personal communication, 19.03.2018) of poor condition with

several gaps in the batches of continuous time series for each year (see figure 3.5).

28

http://www.regnbyge.no/

3.2. HYDROLOGY

Figure 3.4: Map showing infiltration capacity provided by Geological Survey of Norway(NGU). The background map is from the Norwegian Mapping Authority c©Kartverket.

Figure 3.5: Available discharge data for the two different locations (AK52 left and 297077right).

29


3.3 Available data

3.3.1 Discharge data

The velocity and depth in AK52 are measured by the ADS FlowShark Triton flow monitor.

Two sensors, Peak Combo Sensor and Surface Combo Surface, are mounted to a stainless-steel

expandable ring installed in the pipe. The ring is installed upstream of the manhole to AK52

to minimize the hydraulic effects. The Peak Combo Sensor is mounted at the bottom of the

pipe, and the Surface Combo Sensor is mounted at the crown of the pipe. The Peak Combo

Sensor consists of three independent sensors that measure ultrasonic depth, pressure depth and

peak velocity. To measure depth, a sound wave of high frequency is sent upward from the

Ultrasonic Depth Sensor to the flow surface. The wave is reflected to the sensor when it reaches

the interface between the air and water surface. The travel time for the ultrasonic signal from

the sensor and to the water surface and back to the sensor again is converted to distance, based

on the speed of the sound. The distance is known as the depth of the flow. The pressure depth is

mostly used to register the depth during surcharge conditions, when the pressure level is above

a full pipe. The Pressure Depth Sensor measures water pressure, which is converted to depth

using the weight of the water. The peak velocity is measured by transmitting an ultrasonic

signal from the Peak Velocity Sensor at an angle up through the water. The particles in the

water reflect the sound signal with a shift in the frequency to the sensor. The change in the

frequency between the transmitted and reflected signal is used to determine the peak velocity

on the flow. The Surface Combo Sensor mounted at the top of the pipe also measures ultrasonic

depth. The surface sensor finds the range (or distance between the sensor and the water surface)

then calculates the depth of flow by subtracting the range measured from the pipe diameter, a

user specified value. The setup of each of the sensors can be found in ADS LLC (2015).

The discharge is calculated based on the measured velocities and depth values. The depth is

measured with three independent sensors. The final depth used to calculate the discharge is

selected based on accuracy of measured versus manual depth measurements, measured precipi-

tation, and resolution. Problematically, matter sticks to the sensors in contact with the combined

sewer water. Covered sensors disturb measurements and are often observed in small sewers (di-

ameter 200mm to 300 mm) with low water velocity and depth.

30

3.3. AVAILABLE DATA

3.3.2 Climate

The precipitation station providing data to the model is the rain gauge Kjelsås monitored by

Oslo Municipality (Regnbyge, 2018c). The measurements started in 2013. The rain gauge is

located within the catchment outer boundaries. The rain gauge is a pluviometer of the type

NIVUS RM 202 with a resolution of 0.1 mm with an accuracy of ±0.1mm (Nivus, 2015). The

rain gauge records the time when 0.1 mm of precipitation is detected. Such rain gauges are

efficient to measure intensities in precipitation. The temperature series used in the modelling

for catchment Grefsen is from 16420 Oslo – Disen monitored by METNO (Norwegian Meteo-

rological Institute, 2018). The temperature station is approximately 1.5 km south from the rain

gauge station Kjelsås. The temperature series is found on eKlima and the precipitation series

from Kjelsås is found on Regnbyge.

3.3.3 Calibrated model in MOUSE from Oslo VAV

The rainfall-runoff model used in earlier projects is made by the Oslo VAV. The details of

the setup for the model is not documented, but are described in this subsection based on a

meeting with Samatar Mahammud Abdi (Oslo VAV, personal communication 31.09.2019). The

calibration was conducted between 2012 and 2013. This model was combined with others from

elsewhere in Oslo to make one larger model in 2014. The model was calibrated on data from

2010 from both manholes 161143 (AK52) and 161151. Manhole 161151 is on the combined

system from the Grefsen plateau. For the calibration the whole time series from 2010 was used,

including both the events and dry weather discharge.

The selection of the best model is based on how well the model fits the largest peak observed,

12.07.2010, independent of the correspondence for the rest of the time series. No model evalua-

tion criteria are used, and only subjective evaluation is made. The model has not been validated

or re-calibrated after the calibration in 2014.

The model has not included any groundwater aquifers, the discharge is multiplied by a factor

accounting for some additional inflow from the groundwater. The percentage of imperviousness

is based on the Planning and Building Agency in Oslo Municipality (Oslo PBE) layers of build-

ings and roads. The discharge generated from each surface type; roads, roofs, pervious, in the

model is multiplied by a fraction to get the total discharge. The fraction varies from catchment

to catchment. The runoff is calculated by the Time-Area (TA) method supported by MOUSE.

31

http://eklima.met.no

http://www.regnbyge.no/


The parameters needed are the fraction of impervious area, a factor of initial loss that needs to

be filled before calculations of runoff take place (depression storage), a factor for hydrological

reduction (evapotranspiration), time area coefficient that determines the shape of the Time-Area

Curve and the concentration time. More details about the runoff in MOUSE can be found in

DHI (2017). The depression storage is set to 12 mm and the DEM giving information of the

slope and direction of water movement is from 2010 (Samatar Mahammud Abdi, Oslo VAV,

personal communication 31.09.2019).

The limitations in use of the TA method are the absence of storage effects. The consequence

is overestimation of peak discharge because the storage effect creating the attenuation is not

accounted for. In small watersheds where storage effects are small, TA method can give reason-

able results (Singh, 1988, p. 134). Water infiltrated is lost from the system (Ingebrigtsen, 2017,

p. 45).

3.3.4 Infiltration measurements

During the summer 2018 infiltration measurements were done at different locations in Oslo with

a Modified Phillip-Dunne (MPD) infiltrometer. The measurements were performed as part of a

project between Oslo VAV and The Norwegian Water Resources and Energy Directorate (NVE),

conducted by Ingrid Kristiansen (UiO) and the author. The tests were conducted according to

a guide published by Oslo Municipality (Solheim et al., 2018) on how to preform infiltration

measurements. The MPD infiltrometer does not consider the lateral flow of water like the Dou-

ble ring infiltrometer and a correction factor is needed to find the estimated saturated hydraulic

conductivity from the measurements with the MPD infiltrometer. The correction factor depends

on the clay content in the soil. The factor is 0.6 for high content of clay and 0.8 for more sandy

soil samples. The saturated hydraulic conductivity, Ksat is found when the infiltration rate gets

constant, defined as where the measured infiltration does not change more than ± 20% from

the last three measurements. Depending on the soil, is it after 20 - 110 minutes (Solheim et al.,

2018, pp. 2-4, Solheim, 2017, p. 56).

In summer, 2018, some of the infiltration tests were also conducted on properties of potential

bio-retention cells at the Grefsen plateau (see figure 3.6). The corrected saturated hydraulic con-

ductivity values from the measurements at Grefsen vary from 80 mm/h to 4160 mm/h. Within

one property, only two meters apart, the measured saturated hydraulic conductivity could vary

from 100 mm/h to 5200 mm/h. The variability in the measurements makes it difficult to se-

32

3.3. AVAILABLE DATA

Figure 3.6: Map of the location of the infiltration tests conducted. The catchment to AK52is marked in orange and the location of the measurements is in green-blue squares. Thebackground map is from the Norwegian Mapping Authority c©Kartverket.

lect one saturated hydraulic conductivity value from one property. After correction is done, the

lowest Ksat value is used in the model setup.

During the infiltration tests, some residents could inform about pounding of water on their prop-

erties during rainfall. From a survey at the Grefsen plateau, 27% of the respondents have expe-

rienced pounding of water on their property or water draining through their property (Furuseth

et al., 2019, p. 395). Out of 462 households 192 answered the survey.

The infiltration tests were also conducted in bio-retention cells during the summer 2018 (see

figure 3.6). In Deichmansgate, nine bio-retention cells were finished in 2017. After one year

of use, the estimated infiltration is the lowest at the inlets, with 32 mm/hr at the lowest in bio-

retention cell 3 (DR3). The highest was estimated to be 960 mm/hr in bio-retention cell 7 (DR7)

and the mean is 358 mm/hr.

The infiltration values correspond well with other studies (Saksæther and Kihlgren, 2012, Sol-

33


heim, 2017, Uglum, 2019). In the summer, 2017, Solheim (Solheim, 2017, p. 57) measured

the saturated hydraulic conductivity at Jupiterjordet (at Grefsen) to range between 63 and 167

mm/h at the surface.

3.3.5 Sewage systems and DEM

The data of the sewage system, such as manhole elevation, manhole depth, length of links,

elevation of links, roughness of links and so on are given by Oslo VAV. The data is not freely

available in the same manner as the discharge data. The data of the sewers system from Oslo

VAV have been converted (by Hong Li, UiO) from MIKE Urban to SWMM.

A Digital Elevation Model (DEM) of 0.5 meter resolution is available for the study area. The

DEM is from Oslo PBE and is from a laser scanning in 2014. Maps over the area of buildings

and roads are also provided by Oslo PBE.

34

4 MethodsSWMM has been set up for the study area at Grefsen. Because the total water budget is un-

known, the total water balance cannot be examined. The correspondence between the simulated

and observed discharge peaks is considered as a measure of how well the hydrology is described.

The hydrology is changed when LIDs are implemented. How LIDs are implemented and the

effects the LIDs have on the discharge peaks are investigated. This chapter describes the pre-

analysis of the available data and a description of the model setup including implementation of

LIDs. The model setup is described for the model version SWMM5.1.012.

4.1 Pre-analysis

To create a model presenting the present situation, the newest available data are used. The

period of data for 2017, 5th August 2017 08:00:00 to 26th November 2017 23:45:00, is used

for calibration and the period of data from 2018, 29th May 2018 08:35:00 to 15th August 2018

10:25:00 for validation.

The discharge data for 2017 at Grefsen have several values with 0 l/s which is unlikely since the

wastewater is always transported in the sewers at all time. Thus, the values where the discharge

is 0 l/s were removed from the raw data series to get a new time series (hereafter referred to as

observed) used in the calibration process (see figure 4.1).

Figure 4.1: Rating curves of the available data in the period 2017. Raw data of discharge anddepth (left). Removed the observation where the discharge is zero (right). The red dotted lineshows the overflow threshold.

35

CHAPTER 4. METHODS

The same procedure is done for the discharge data available for validation and can be seen in

figure A.1.

The problem with 0 l/s values in the discharge data is common because the sensors have been

blocked, as shown in figure 4.2. Event 4 is missing parts of the discharge peak, while event

3 has complete data. Event 3 has a total rainfall of 16.3 mm and event 4 has 7.9 mm. The

maximum intensity according to Regnbyge is 2.6 mm/5min for both the events. The missing

data for event 4 and other events in the time series make it challenging to calibrate a model,

when the magnitude of the discharge peak is uncertain.

Figure 4.2: Shows two CSO events from 2017 occurring in AK52. Blue stippled line revealsunlikely discharge values of magnitude zero.

To study the volume-based runoff coefficients for all the CSO events, the event runoff hydro-

graph is separated from the baseflow by the straight line method. The straight line method

connects the point from where the hydrograph starts to increase to the point with the same dis-

charge on the recession limb of the hydrograph. The separation is a straight line from the rise

to the recession. Compared with other methods, the straight line method usually gives a higher

runoff coefficient due to larger volume of water in the event hydrograph (Blume et al., 2007,

p. 849). The runoff coefficients calculated are for all the CSO events, during the period from

2017 to 2018. The event runoff coefficients are calculated for both the observed and simulated

discharge.

After the rainfall event has stopped for 24 hours, the discharge in the pipes is considered dry

weather flow. The mean variation of dry weather flow for 2017 can be seen in figures 4.3.

Details are shown in figures A.3 and A.2. The variations reflect the water consumption pattern.

36

4.2. MODEL SETUP

Figure 4.3: Pattern of the mean dry weather flow for both weekend and weekdays in AK52 in2017.

4.2 Model setup

The model includes contributions to sewer discharge from surface runoff, groundwater leakage

and dry weather flow. The area draining to AK52 is defined together with the sewer network

contributing to AK52. Delineation of subcatchments and parameters defined for each subcatch-

ments are described in this section.

4.2.1 Drainage system

The drainage system converted from MIKE Urban required some adjustments. In particular,

the inlet and outlet depths of the conduits have been manually corrected for 176 conduits. The

area of sewers contributing to AK52 has also been manually determined. In manholes where

the water is divided in two directions the boundary of the sewage network to catchment is set

manually.

4.2.2 Catchment

Delineation of the catchment to AK52 has been done with the software Geographic Resources

Analysis Support System (GRASS) GIS, version 7.4.1. GRASS GIS is a free and open source

software developed by the U.S. Army and used for data management and image processing.

The software needs a DEM as an input file to generate stream lines. Therefore, the catchment is

37

CHAPTER 4. METHODS

mainly based on surface runoff stream lines as shown in figure 4.4. Defining a watershed, one

point along one of the surface runoff stream lines is selected. All the area draining to the point

selected is calculated by GRASS GIS. The green area in figure 4.4 is a catchment generated by

GRASS GIS after selecting a point on the surface runoff stream line left of AK52.

First, this study attempted to make a model for the Grefsen plateau (0.31 km2). In this way,

it is possible to control the discharge and volume of water from the Grefsen plateau to AK52.

Unfortunately, there are no measurements upstream from AK52 with series longer than one

year. Downstream from manhole 297077 (see figure 3.2 for location), the sewage from the east

is connected to the sewage from the west. In AK52 the combined sewer discharge is measured,

and in manhole 297077 the wastewater and some stormwater from the east is measured. A

difference between the two measured series could give a good indication on the volume and the

discharge of the combined sewer discharge at the Grefsen plateau. The time series resulted to

be very noisy. Therefore, the whole catchment draining to AK52 is included.

AK52 lies between two surface runoff stream lines (see figure 4.4). The contribution from the

Grefsen plateau was too small, hence two points are selected, one from each surface runoff

stream line. The final catchment consists of both the Grefsen plateau and the Grefsenkollen in

figure 4.4.

The 15 subcatchments used in this thesis are determined in the same way as the catchment to

AK52. One point along a surface runoff stream line is selected to define a subcatchment. Only

where the surface runoff stream line matches the location of a manhole, the subcatchments

are selected. To export the area of the subcatchments to SWMM, the area is transformed into

points where the vertexes are located. This is done in ArcMap (version 10.4.1). A Python script

is made to convert the coordinates from the subcatchments vertex into a readable format for

SWMM.

The conduits and manholes included in the catchment all bring water down to AK52 or the

stormwater conduits close to AK52. One manhole is located outside the catchment boundary

(see the purple frame in figure 4.5). The manhole gains water that contributes to AK52 even

though it is not a part of the catchment. The contribution is minimal, the storm drain, not

the manhole, directly receives the water. The location of the storm drain is unknown. The

catchment boundary is not changed, even though one of the manholes that transport water into

the system is located outside the catchments boundaries.

38

4.2. MODEL SETUP

Figure 4.4: Map of the catchment Grefsen generated by GRASS GIS. The two surface runoffstream lines of each side of AK52 each gives two different catchments; Grefsen plateau (0.31km2) and Grefsenkollen (1.13 km2). The background map is from the Norwegian MappingAuthority c©Kartverket.

More manholes and links are originally located in a subcatchment as shown in the red frame

in figure 4.5. The blue links are not transporting wastewater or stormwater to AK52. In the

red frame in figure 4.5, the area is only composed of separate system. Surface runoff generated

in this area does not contribute to AK52, since the outlets to the subcatchments are stormwa-

ter manholes and the receiving water is transported to Myrerbekken (outlet of the stormwater

conduits close to AK52).

If the whole subcatchment consists of separate system, the water generated in the subcatchment

is routed to the stormwater manhole for the separate system and to the stream Myrerbekken.

If the catchment consists of both separate and combined systems, the manhole for combined

system is used, since the water from the separated system is connected to the combined sys-

tem further down, and thus influences the combined flow and hence the discharge in AK52.

The water generated in catchments T1_613, T242796, T242790 and T237320 is routed to the

stormwater manholes. The remaining 11 catchments are connected to the combined system.

39

CHAPTER 4. METHODS

The subcatchments are shown in figures 3.4 and 3.3.

Figure 4.5: More conduits are located in the catchment, than what is included in the model.These are highlighted in the red square. A manhole which is located outside the catchmentboundaries is included in the catchment’s sewer system. The background map is from theNorwegian Mapping Authority c©Kartverket.

Area and width of the subcatchments are calculated in ArcMap after the delineation of the

subcatchments. The values for each of the properties are found in table 4.1. The impervious

area was found with the maps from Oslo PBE. They have maps of all roads and rooftop. By

merging the two layers, the total impervious area was found inside each subcatchment. Table

4.1 list the impervious fraction of the subcatchment area.

40

4.2. MODEL SETUP

Table 4.1: Values for the parameters; outlet, area, width and percentage impervious (%im-perv) used in the model setup for the subcatchments at Grefsen. For full description see tableC.1. Area, width and %imperv are calculated in ArcMap.

Properties of Subcatchment Part 1

Subcatchments Outlet Area [ha] Width [m] % Imperv

T161143 (AK52) 161143 4.57 104 29.01

T161147 161147 9.37 130 32.59

T161185 161185 1.25 58 29.82

T161193 161193 5.15 160 31.94

T172350 (Jupiterjordet) 172350 8.69 300 29.78

T310533 310533 2.33 193 31.51

T237308 237308 9.96 146 29.21

T237293 237293 6.72 116 19.97

T237320 237320_02 10.20 149 11.76

T1_585 1_585_Ak52+81 33.31 372 20.47

T159351 159351 2.54 83 28.59

T242790 242789 25.52 400 13.80

T242796 1_658_Ak52+81 2.92 110 19.95

T1_613 1_613_Ak52+81 18.31 92 19.39

T161143e 161143 3.27 77 33.97

The slope of the area is found in ArcMap from the DEM given by Oslo PBE. The slope is given

in degrees for each raster cell as shown in figure F.1. Within each subcatchments, the mean and

median slope are calculated. In table 4.2, both the median slope and mean slope are included.

The median slope is determined by converting the DEM to an integer raster instead of a float

raster.

4.2.3 Climate data

The precipitation data used are from station Kjelsås. The pluviometer records the precipitation

when the volume exceeds 0.1 mm. During a minute, the pluviometer can record 0.1 mm value

several times. SWMM cannot handle a format of finer resolution than one minute. Therefore,

all precipitation measurements within one minute are summed up. Afterwards, the data are

converted into a format readable for SWMM. The conversion of resolution and format is done

41

CHAPTER 4. METHODS

Table 4.2: The parameter %slope for the different subcatchments used during the calibrationprocess

Property Slope

Subcatchments Mean % Slope Median %Slope

T161143 9.42 5.24

T161147 9.59 5.24

T161185 10.46 5.24

T161193 8.20 3.49

T172350 7.26 3.49

T310533 7.19 3.49

T237308 11.42 5.24

T237293 23.68 14.05

T237320 32.58 26.79

T1_585 22.25 22.25

T159351 16.68 8.75

T242790 29.84 8.75

T242796 25.65 23.09

T1_613 29.30 14.05

T161143e 10.28 5.24

with a script made in Python.

Evaporation is calculated by the Hargreaves method in SWMM. The method requires the lati-

tude, set to be 59.9. Also, the daily maximum and minimum air temperatures are required in a

climate file. The climate file is on a specified format (Rossmann and Huber, 2016a, p. 41) and

is converted with a Python script. The air temperatures are from 16420 Oslo – Disen.

4.2.4 Dry weather flow

The mean dry weather flow is calculated to be 28.8 l/s. The relative daily and hourly variations

are found in tables A.2 and A.1.

The variation in the dry weather flow is set to be the same for all simulations in SWMM. It is

included in the model as inflow of dry weather flow into AK52 to construct a more comparable

time series with the observed time series. It is possible to remove the daily, hourly and weekly

42

4.3. CALIBRATION

variations in the observed discharge in AK52 and only compare the rainfall dependent flow, but

there is a chance of removing some of the rainfall dependent flow and care should be taken. The

focus in this thesis has not been on the separation of sanitary flow, groundwater and rainfall-

dependent flow in the observed data.

4.2.5 Simulation periods

The simulations in this study are done for a continuous period and not for single events. In

the calibration phase, the simulation period is set from 5th September 2017 to 27th November

2017. A warm-up period of one month between 5th August 2017 and 5th September 2017 is

used to remove effects of initial input variables.

The time step is set to three seconds, but the model reports the discharge every 5th minute. The

time step of three second is found to give a numerical stable discharge after the trial and error

procedure. The simulations are done with the dynamic wave routing option.

The time step for wet period is set to be five minutes and for dry is one hour. The unit on the

rain gauge in the model is set to volume with time step one minute during the calibration and

validation and five minutes during the simulations with LIDs.

The climate file includes maximum and minimum air temperatures for each day. The simulated

and observed CSO events are all for periods during the year where the minimum (and maxi-

mum) temperatures are positive. In this way, the simulated and observed CSO events are not a

consequence of limited infiltration capacity because of frozen ground.

The model starts to read the precipitation time series and the climate file from 5th August 2017.

After all the required data for the model are included, the model is manually calibrated using

the trial and error method.

4.3 Calibration

The calibration process done is for parameters controlling the runoff generation in the subcatch-

ments and for parameters controlling the behavior of the aquifer. During the calibration process,

two different model setups gave almost equally performance of R2. Both model setups, A and

B, are presented in the results.

43

CHAPTER 4. METHODS

4.3.1 Subcatchments and infiltration parameters

The size of the area and the outlet selected in table 4.1 were never changed during the calibra-

tion. The initial width and percentage impervious (%imperv) in table 4.1 were changed during

calibration with ± 30% of the initial value as shown in table 4.3. Rossmann and Huber(2016a,

p. 74) suggest use the width as a calibration parameter.

Table 4.3: Parameters used in the calibration process for the subcatchments. The descriptionof the parameters can be seen in table C.1. Width, percentage slope (%slope) and percentageimpervious (%imperv) are different for individual subcatchments. The symbol "*" means thatthe optimal value depend on the model selected.

Values for properties to the subcatchments

Name Initial value Calibrationinterval

Optimal value

Width [m] Width in table 4.1 ±30% Width in table 4.1

%Slope Mean %slope intable 4.2

±30% Median %slopein table 4.2

%Imperv %imperv in table4.1

±30% %imperv in table4.1

N-Imperv 0.010 0.010-0.015 0.010

N-Perv 0.2 0.2 - 0.3 0.2

Dstore-Imperv [mm] 1.5 3.0 1.5

Dstore-Perv [mm] 5.0 10 5.0

Subarea Routing OUTLET OUTLET, PERV PERV

Percentage Routed (1 - DCI-A/TIA)

100 30-100 *

Properties of Infiltration Method: Green-Ampt

Name Inital value Calibrationinterval

Optimal value

Suction Head [mm] 60 10-200 100

Conductivity [mm/h] 30 1-200 80

* Model dependent

The percentage slope (%slope) was changed with ± 30% of original value of both the median

and mean %slope found in table 4.1 to obtain a sufficient model. The %slope selected is the

44

4.3. CALIBRATION

median slope for each subcatchment. The mean slope had a tendency to be very steep, especially

for the eastern subcatchments. When using median, few extremes are weighted less implying

less steep slopes for the individual subcatchments.

The optimal value for Manning’s roughness coefficient found to be 0.010 for impervious sur-

faces and 0.2 for pervious surfaces. The parameters selected are inside the suggested range

for both the manual (Rossmann and Huber, 2016a, p. 75) and other studies (Guan et al., 2015,

Kourtis et al., 2017). The Manning’s roughness coefficient selected is the same for all the sub-

catchments.

The optimal depression storage found during calibration is 1.5 mm for an impervious surface

and 5.0 mm for a permeable surface. The depths of the depression storages are within the range

suggested by Guan et al. (2015) and Salvadore et al. (2015), and exactly the same values found

by Skotnicki and Sowinski (2015).

The percentage of the subcatchment without depression storage is set equal to the default value

of 25%.

The subarea routing was at the beginning OUTLET, but the simulated discharge at AK52

was very high. Bjørn Christofferesen (Oslo VAV, personal communication 26.11.2018 and

31.02.2019) had experienced that only 25-35% of the impervious area in residential areas was

DCIA. The subarea routing was changed to PERV with a Percentage Routed of 65%. Other

literature (Wibben, 1976, pp. 11, 13, Alley and Veenhuis, 1983, p. 314) suggested 8.5-60% of

DCIA to TIA in residential areas. The Percentage Routed was of that reason used as a calibra-

tion parameter when the saturated hydraulic conductivity, Ksat , was fixed. The optimal value of

Percentage Routed is 65% for model setup A and 50% for model setup B.

In the infiltration module, values for suction head and initial deficit are also required for the

Green-Ampt method. The Green-Ampt method is not highly sensitive to values for the suction

head (Rossmann and Huber, 2016a, p. 114). The suction head is chosen from the manual (Ross-

mann and Huber, 2016a, p. 114) for sandy loam. It is further adjusted during the calibration,

but did not improve the performance of the discharge peaks. The initial deficit is chosen to be

0.33 from the manual (Rossmann and Huber, 2016a, p. 116) and is not changed during the cal-

ibration since a warm up period is used. The saturated hydraulic conductivity is at first tried to

be calibrated, before the lowest estimated value from the infiltration measurements conducted

at Grefsen, Ksat = 80mm/h is set as a fixed parameter. Both models uses the same parameters

45

CHAPTER 4. METHODS

for the infiltration module.

4.3.2 Groundwater parameters

The groundwater module needs to be implemented into each subcatchments as described in

subsection 2.4.5. Each groundwater aquifer has its own properties and elevation connected to

the subcatchment. The properties to the aquifers are in this study the same for all the aquifers,

except for the bottom and surface elevation (see table 4.4). For an explanation of the parameters,

see tables C.3 and C.4.

The parameters porosity, wilting point, field capacity, conductivity and conductivity slope in

table 4.4 were changed after suggested values in Rossmann and Huber (Rossmann and Huber,

2016a, p. 114) and Rossmann (Rossmann, 2015, p. 178), where the default values were used as

the initial values.

The lower evaporation depth had a default value of 14.0, but was changed to a smaller value

mainly due to the difference in feet and meters. The smaller values were obtained from Ross-

mann and Huber (2016a, p. 156).

The lower groundwater loss rate is changed according to table 4.4.

The bottom elevation cannot be higher than the receiving node also called the outlet node to each

subcatchment. The initial value of the bottom elevation was at the same elevation as the outlet

node (see figure 4.5). The bottom elevation of the aquifers was changed during the calibration

according to table 4.4. To obtain a fast response in the aquifer, the optimal bottom elevation is

at the elevation of the receiving (or outlet) nodes used as the initial value (see figure 4.5). The

bottom elevation is the same in both model setups.

The surface elevation at the location of the outlets found from the DEM is considered as the true

surface elevation. The true surface elevation is the surface elevation used for model setup B in

table 4.6. The surface elevation in the aquifers differs from each subcatchment due to the spatial

variation in surface elevation. The thickness of the aquifers, the elevation difference between

the surface and bottom elevation, called the active depth, are different for each subcatchments

as shown in column "B: Surface Elevation" in table 4.6. The initial surface elevation is the true

elevation. The active depth of the aquifers was adjusted by mainly changing the elevation of the

surface ground of the aquifers according to table 4.4. The optimal elevations for each model

setups are found in table 4.6.

46

4.3. CALIBRATION

Table 4.4: Parameters used in the calibration process for the aquifers.

Data Fields for Aquifer

Property Initial value Range Optimal value

Porosity [−] 0.5 0.43-0.501 0.5

Wilting Point [−] 0.15 0.047-0.135 0.15

Field Capacity [−] 0.30 0.105-0.33 0.30

Conductivity [mm/h] 5.0 1-30 10.0

Conductivity Slope [−] 10.0 3-44 10.0

Lower Evaporation Depth [m] 14.0 2.0 - 14.0 4.5

Lower Groundwater Loss 0.002 0.001-0.005 0.002

Rate [mm/h]

Bottom Elevation [m] ** 0-1 m below inletnode

**

Data Fields for Groundwater Flow

Property Initial value Range Optimal value

Surface Elevation [m] * 15cm-3m above *

inlet node

A1 [−] 1 0.001-0.03 0.02

B1 [−] 1 0.75-1.25 1.25

A2 [−] 0 0.00-0.03 0.02

B2 [−] 0 0-1.25 1.25

Lower Groundwater Loss 0.002 0.001-0.005 0.002

Rate [mm/h]

** Distributed parameter

* Model dependent

The groundwater coefficients and exponents are selected by trial and error and inspiration from

the open learning resource Open SWMM. The aquifer parameters are found to be very sensitive

to small changes in the groundwater coefficients, A1 ,A2 and exponents, B1, B2 as illustrated in

figure 4.6. The shape of the hydrograph changed slightly for different values used. In the case

where A2=B2= 0, the outlet only gains water (see eq. 2.4). In that case, the discharge is higher

47

CHAPTER 4. METHODS

Table 4.5: Each subcatchment has its own aquifer name and bottom elevation.

Properties of Aquifer

Subcatchments AquiferName

Bottom Elevation

T161143 A161143 164.92

T161147 A161147 167.78

T161185 A161185 172.46

T161193 A161193 174.36

T172350 A172350 175.82

T310533 A310533 178.55

T237308 A237308 172.74

T237293 A237293 185.29

T237320 A237320 189.79

T1_585 A1_585 170.52

T159351 A159351 172.58

T2A790 A2A790 225.97

T2A796 A2A796 224.0

T1_613 A1_613 173.12

compared to the other curves where values for A2 and B2 are selected. The exponents affect

how fast the recession limb of the discharge curve decreases, while the coefficients influence

the amount of water interacting between the aquifer and the node. With an exponent of 0.75

the recession limb of the discharge curve decreases slower. It is the curve that most resembles

the observed discharge curve. However, during periods with no rainfall, the discharge curve

tends to oscillate widely (see figure E.3). The choice of 0.75 works well for events, like in

figure 4.6, but makes the total discharge becomes unstable for continuous simulations (see figure

E.3). The value used for the exponents largely depends on the value of the coefficients. In the

calibration phase, it is experienced that a value of exponent larger than 1 if the coefficient is

0.02 is necessary to obtain numerical stable results.

48

4.3. CALIBRATION

Table 4.6: Each aquifer have a surface elevation connected to the elevation of the receving(here outlet) node (see figure 2.4). Depending on the model setups used, different surfaceelevations is implemented.

Properties of Groundwater Flow

Subcatchments Receiving Node A: Surface Elevation B: Surface Elevation

T161143 161143 165.92 169.57

T161147 161147 167.98 170.94

T161185 161185 172.66 175.04

T161193 161193 174.56 177.76

T172350 172350 176.02 178.96

T310533 310533 178.75 180.99

T237308 237308 172.94 175.34

T237293 237293 185.49 188.A

T237320 237320_02 189.99 195.36

T1_585 1_585_Ak52+81 170.72 173.98

T159351 159351 172.78 174.52

T2A790 2A790 226.17 229.32

T2A796 2A796_Ak52+81 224.20 230.60

T1_613 1_613_Ak52+81 173.32 177.50

T161143e 161143 165.12 169.57

Figure 4.6: Exploration of different aquifer coefficients, A1, A2 and exponents, B1 ,B2,

49

CHAPTER 4. METHODS

4.4 Model performance metrics

During calibration, the NSE is very low for simulations where there is some delay in the sim-

ulations compared to the observations, even though the shape of the curves matches well. To

represent the hydrology in this thesis, the discharge peaks have been the major focus. The

discharge peaks above a threshold, 10% of maximum discharge reported, are considered as dis-

charge peaks. This gives a total sample of 36 discharge peaks for the calibration period and 12

discharge peaks for the validation period. During calibration, the performance of the simulated

discharge peaks is tested against the observed discharge peaks. The coefficient of determination,

R2, has been used as the performance measure for the discharge peaks.

4.5 LID implementation

As mentioned in subsection 2.4.8, bio-retention cell, rain barrels and disconnection of down-

spouts are tested in this study. The LID measures applied in the study are only located at the

Grefsen plateau (see figure 4.8). It is the area where most of the combined sewer system is situ-

ated, hence of interest for testing the effectiveness of LIDs. According to Furuseth et al. (2019,

p. 395), 465 householders are living at the Grefsen plateau. The total area of rooftop area at the

plateau is 60627 m2. An average rooftop area per householder then is 131 m2. At the Grefsen

plateau already 55% of the rooftop area is disconnected from the sewer system according to

Ingrid Russwurn (Oslo VAV, personal communication 12.02.2019), leaves 45% of the rooftop

area connected to the combined systems.

The bio-retention cells (BRC) and rain barrels (RB) are implemented in the model through

the LID-modules. Disconnection of downspouts (RD) is done with the re-routing option. The

intention with the LID implementation is to increase the disconnected rooftop area from 55%

to 80% and 100% in each of the subcatchments at the Grefsen plateau.

Following is a description of how the implementation was done with the models and which

consequences that have for the results.

The total rooftop area at the plateau is 64% of the total impervious area. The remaining 36%

of the impervious area is roads. The connected road area is unknown, implying a unknown

fraction of DCIA/TIA. During calibration, an optimum ratio of DCIA/TIA is achieved. This

gives a fraction of the connected road area when it is assumed that the rooftop area disconnected

50

4.5. LID IMPLEMENTATION

is 55%.

In model setup A, the initial DCIA/TIA is 35%. Of the 35% connected area, 45% of the rooftop

area is connected and 18% of the roads is connected to the outlet (see figure 4.7). With a discon-

nection of 100% of the rooftops, the total DCIA/TIA is 6% (disconnected 94%). Disconnection

of 80% of the roof area gives a total DCIA/TIA of 19 %.

For model setup B, the initial DCIA/TIA is 50%, where the connected roof area is 45% of the

total roof area and the connected road area is 59% of the road area (see figure 4.7). After imple-

mentation of LIDs disconnection 100% of the roof area, the DCIA/TIA is 21% (disconnected

79%). With an increase in the disconnection from 55% to 80% the DCIA/TIA decreased from

50% to 34 %.

Figure 4.7: Illustration of DCIA of roof and road area in model setup A and B. The road androof area in both model setups are equal, but distributed differently on each side of the routingline (red stippled line). The area above the red stippled line is routed to the permeable areas,while the shaded area under the line is routed to the outlet (DCIA) in each subcatchment.

Because the model cannot distinguish between rooftop and road area, the number of LID-

modules (BRC and RB) for 100% roof disconnection in model setup A is 529 and in model

setup B is 416. See tables 4.7 and 4.8 for number of BRCs and RBs in model setups A and

B. At first, the same number of LID-modules as there is householders, 465, were implemented

into both model setup. That resulted in a larger increase in the total disconnected area in model

setup B compared to model setup A as described and illustrated in section D.5.

The RDs is done through the re-routing options described in subsection 2.4.8. The ratio of

DCIA/TIA is increased, to obtain an rooftop disconnection of 80% and 100% when the initial

51

CHAPTER 4. METHODS

Table 4.7: Table of values used for 80% and 100% connection of the rooftops to BRC and RBin model A. Number of units is number of BRC or RB in the subcatchment. % of imperviousarea treated is the fraction of impervious area (buildings and roads) contributing with runoffto the BRC or RB. When the BRC and RB is full, the overflows are distributed on the perviousarea. New % impervious area is the new fraction of impervious area (only used for BRC) sinceparts of the total area decreases due to occupation of the bio-retention cells units. The area ofthe bio-retention cell is 13.1 m2 and the rain barrel is 0.4 m2.

LID setup with modules in model setup A

100% disconnection

Subcatchment Number ofunits

% of impervious areatreated

New % imperviousarea

T161143 93 94.0 29.2

T161147 175 76.7 32.8

T161185 25 90.3 29.9

T161193 100 81.4 32.1

T172350 161 83.2 29.9

T310533 42 76.3 31.7

80% disconnection




T161143 51 51.5 28.4

T161147 94 41.2 32.0

T161185 16 57.8 29.1

T161193 55 44.8 31.3

T172350 89 46.0 29.2

T310533 23 41.8 30.9

connection was 55% of rooftops. The new ratio of DCIA/TIA for the two model setups can be

seen in table 4.9.

The effect the LIDs has on AK52 is applicable only for the catchment Grefsen. To test the

performance of the LIDs in a subcatchment with combined sewers not influenced by other areas

or an area with a certain combination of separated and combined sewer system, the outlet to

subcatchment Jupiterjordet (T172350) is also studied. The subcatchment is the largest upstream

subcatchment in the combined sewer area (see figure 4.8). By investigating the effects of LIDs

at Jupiterjordet, the potential effects of LIDs can be found.

52


Table 4.8: Table of values used for 80% and 100% connection of the rooftops to BRC and RBin model B. The description of this is equivalent to the one found in table 4.7.

LID setup with modules in model setup B

100% disconnection




T161143 65 65.7 29.0

T161147 120 52.6 32.5

T161185 20 72.3 29.8

T161193 70 57.0 31.9

T172350 112 57.9 29.7

T310533 29 52.7 31.5

80% disconnection




T161143 36 36.4 28.7

T161147 66 28.9 32.3

T161185 11 39.7 29.5

T161193 39 31.8 31.6

T172350 62 32.0 29.5

T310533 16 29.1 31.2

53

CHAPTER 4. METHODS

Table 4.9: Fraction of impervious area disconnected to the outlet in each subcatchment for100% and 80% disconnection of rooftops to downspouts, when it is assumed that 55% of therooftops are already disconnected. Disconnection of 55% of the roof area implies that 82% ofthe road area is disconnected at all time.

MODEL A

Roof Disconnection (RD)

Subcatchment PctRouting for 100 % RD PctRouting for 80% RD

T161143 95 81

T161147 92 81

T161185 96 80

T161193 93 81

T172350 93 81

T310533 93 81

MODEL B



T161143 84 69

T161147 75 64

T161185 88 72

T161193 78 66

T172350 79 66

T310533 76 64

54


Figure 4.8: Map showing the outlets, AK52 and Jupiterjordet, where the reduction in dischargepeaks by LIDs is tested. The orange dotted line is the catchment to AK52 (1.44 km2). The LIDsis placed inside the area to the Grefsen plateau of 0.31 km2 (green). Jupiterjordet is the outletof one of the subcatchments within the plateau where there is only combined sewers. Thebackground map is from the Norwegian Mapping Authority c©Kartverket.

55

5 ResultsIn this chapter the results from the calibration and validation are presented. Two different model

setups gave almost equally performance of R2. Both model setups, A and B, are presented in this

chapter. Both of the model setups were validated before LID implementation was conducted.

The LID results are given for continuous rainfall, a 5-year and a 20-year model rainfall.

5.1 Model setups

In this section, model setups A and B are presented. As previously mentioned, the difference is

in the ratio DCIA/TIA and the surface elevation. Model setup A has a thin active aquifer and

the ratio of DCIA/TIA is 0.35, while model setup B has a thicker active aquifer and the ratio of

DCIA/TIA is 0.50.

5.1.1 Calibration

The calibration is conducted on the time series of available data from 2017. The observed and

simulated discharges are plotted in figure 5.1. The obtained NSE in model setup A is 0.604 and

in model setup B is 0.357. The R2 is 0.677 for model setup A and 0.658 for model setup B.

57

CHAPTER 5. RESULTS

Figure 5.1: Time series of the observed (green) and simulated (red) discharge [l/s] for AK52during the calibration period. The precipitation [mm/h] for the corresponding time of dis-charge is included on the right axis (blue).

The discharge peaks simulated with model setup A and B differ from the ones measured (see

figures 5.2 and 5.3). In figure 5.2, model setup A underestimate three of the four observed peaks

above 600 l/s, while for the observed discharge peaks in the range from 200 to 400 l/s model

setup A is more prone to overestimation.

In figure 5.3, model setup B underestimates all the largest discharge peaks, but the smaller

58

5.1. MODEL SETUPS

discharge peaks, in the range 200 to 400 l/s, are better represented in this model compared to

model setup A.

Figure 5.2: The observed discharge and the associated discharge peaks for 2017 (left). Scatterplot of the simulated and observed discharge peaks for model setup A (right). The blackstippled line is the 1:1-line. This shows the perfect fit between the simulated and observedpeaks. The shaded area around the 1:1-line is the range of the variability in the observations.The dispersion measures used are the standard deviation.

Figure 5.3: The observed discharge and the associated discharge peaks for 2017 (left). Scatterplot of the simulated and observed discharge peaks for model setup B (right). The blackstippled line is the 1:1-line. This shows the perfect fit between the simulated and observedpeaks. The shaded area around the 1:1-line is the range of the variability in the observations.The dispersion measures used are the standard deviation.

5.1.2 Sensitivity

The parameters hydraulic saturated conductivity, Ksat , and the ratio of DCIA to TIA are found to

be sensitive parameters in the study. The values of these parameters are crucial for the behavior

of the peak discharge in the model setups.

59

CHAPTER 5. RESULTS

The best performance for the peak discharge is where the ratio of DCIA to TIA is 0.35 in model

setup A and 0.50 in model setup B. That means that 35% or 50% of the impervious area is

connected to the outlet (DCIA), while 65% or 50% of the impervious area is disconnected. It

is already assumed that 55% of the roof area on the Grefsen plateau is disconnected, indicating

a disconnection of the road area at the plateau to be 82% in model setup A and 41% in model

setup B.

Figure 5.4: Plots of decreasing ratio of DCIA/TIA with model setup A, from upper left to lowerright. All other parameters are kept constant. The performance, R2, in the bottom right cornerof each figure represents how well simulated discharge peaks resemble the observed peaks.The black dotted line represents the 1:1-line.

The performance, measured by R2, decreases in model setup A when the ratio of DCIA and

TIA is above 0.4 and less than 0.30 as seen in figure 5.4. In model setup B, the performance

decreases when DCIA/TIA is above or below 0.5 as shown in figure 5.5.

Increase of DCIA/TIA increases the magnitude of the peaks, resulting in overestimation of the

smaller observed discharge peaks (200-400 l/s) for both model setups. The CSO events are not

60

5.1. MODEL SETUPS

affected especially by the increase except the discharge peaks from 9th August 2017.

Figure 5.5: Plots of the observed discharge peaks against the simulated discharge peaks forAK52 for year 2017. The simulations are for different values of DCIA/TIA with model setupB. The fraction DCIA/TIA increases from upper left to lower right.

The discharge peaks are highly sensitive to low values of the saturated hydraulic conductivity.

Several of the discharge peaks are overestimated with a saturated hydraulic conductivity of 1

mm/h. Both the small observed discharge peaks, in the range 200 to 400 l/s, and the larger

discharge peaks, in the range 600 to 800 l/s, are increased when the saturated hydraulic conduc-

tivity is less than 10 mm/h. A saturated hydraulic conductivity, Ksat larger than 10 mm/h, does

not change the performance significantly in any of the two model setups, see figures 5.6 and 5.7

61

CHAPTER 5. RESULTS

Figure 5.6: Plots of increasing hydraulic saturated conductivity, Ksat , from upper left to lowerright. All other parameters are kept constant. The performance, R2, in the bottom right cornerof each figure represents how well the simulated discharge peaks fit the observed peaks. Theblack dotted line represents the 1:1-line.

62

5.1. MODEL SETUPS

Figure 5.7: Plots of the observed discharge peaks against the simulated discharge peaks forAK52 for year 2017. The simulations are for different values of hydraulic saturated conduc-tivity, Ksat . The Ksat increases from upper left to lower right.

5.1.3 Validation

The validation period starts in May 2018, whereas the calibration period starting in September

2017. The observed times series has three peaks where the discharge is high enough to give

CSO events. Two of them are on the same date, see table 5.1 and figure 5.8

It is clear from figure 5.8 that the dry weather period in mid-June, around the 17th is simulated

to be higher than what is observed for both model setup A and model setup B. The NSE for the

time series with model A is 0.397 and 0.148 for model B.

63

CHAPTER 5. RESULTS

Figure 5.8: Time series of the observed (green) and simulated (red) discharge [l/s] for AK52in 2018 (validation period) with models A (upper) and B (lower). The precipitation [mm/h]for the corresponding time of discharge is included on the right axis (blue).

The simulations of the discharge peaks are shown in figures 5.9 and 5.10. The simulated dis-

charge peaks correspond well with the observations for both model setups. R2 for the discharge

peaks in model setup A is 0.875 and 0.837 in model setup B.

The observed discharge peaks at around 200 l/s is slightly overestimated with both model setups,

a similar behavior as observed for the calibration period (see figures 5.2 and 5.3), where the

64

5.1. MODEL SETUPS

smaller discharge peaks were overestimated.

Figure 5.9: The observed discharges in 2018 together with the discharge peaks studied (left).The observed discharge plotted against the simulated discharge peaks for model setup A(right). The black dotted line is the 1:1-line. The shaded area around the 1:1-line is therange of the variability in the observations. The dispersion measures used is the standarddeviation of the observed discharge peaks.

Figure 5.10: The observed discharges in 2018 together with the discharge peaks studied(left). The observed discharge plotted against the simulated discharge peaks for model setup B(right). The black dotted line is the 1:1-line. The shaded area around the 1:1-line is the rangeof the variability in the observations. The dispersion measures used is the standard deviationof the observed discharge peaks.

5.1.4 Runoff coefficients

The simulated discharge peaks for both models and the calculated discharge peaks for the mod-

els and the observed discharge are calculated in table 5.1.

During some of the CSO events the measured discharge is 0 l/s for some of the recorded time

steps as mentioned in subsection 4.1. The CSO events where there are some adjustments are for

65

CHAPTER 5. RESULTS

Table 5.1: Time for the CSO events recorded from 2017 to 2018. Maximum peak discharge,P, for observations is Pobs in l/s. PA and PB are the maximum discharge peaks for respectivelymodel setup A and model setup B, in l/s. The event-based runoff coefficients, ci, for the obser-vations, model setup A and model setup B are calculated as described in subsection 2.3.2. ∆Tis the time difference in minute between the observed peak and the simulated peak for the twomodel setups, A and B. * indicate missing data points of the event discharge

Time Pobs PA PB cobs cA cB ∆TA ∆TB

2018

17.06 07:00 -17.06 11:00

521.2 646.0 631.7 0.080 0.099 0.078 10 10

17.06 11:00 -18.06 03:00

607.5 504.0 500.2 0.142 0.095 0.067 5 5

10.08 00:00 -17.06 18:00

672.8 696.2 631.2 0.105 0.106 0.085 15 15

2017

09.08. 08:00 -10.08. 06:00*

876.7 951.6 746.3 0.232 0.212 0.144 20 20

09.09. 15:30 -10.09. 00:00*

856.6 514.0 498.2 0.322 0.184 0.138 0 0

12.09. 01:00 -12.09. 21:00

894.6 490.6 474.7 0.245 0.166 0.130 10 15

13.09. 15:30 -13.09. 22:00*

737.9 421.2 502.0 0.284 0.195 0.177 10 30

the CSO events from 9th August 2017, 9th September 2017 and 13th Septmeber 2017. The dis-

charge curve for the CSO event 13th September 2017 is shown in figure 4.2. The observed and

simulated discharge peaks correspond well in terms of magnitude for 2018, but there is a differ-

ence in the time of occurrence of 5-15 minutes. The runoff coefficients between model setup A

and the observations match best out of the two models. In 2017, the discharge peaks correspond

best for the discharge peaks 9th August 2017. The runoff coefficients for the observations are

higher than what is calculated for both model setup A and model setup B.

5.2 LIDs

The LIDs used in this study are bio-retention cell (BRC), rain barrel (RB) and disconnection

of downspout (RD). The simulations with the continuous rainfall are first presented, followed

by a 5- and a 20-year rainfall. All the LIDs for AK52 are first presented, before the results for

Jupiterjordet. Afterwards, the results from the design rainfall are presented for both outlets. The

model simulation without any LIDs is referred to as scenario 0.

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5.2. LIDS

The disconnected roof area at the Grefsen plateau (see figure 4.8) is 55%. The simulations with

LIDs are for 80% and 100% disconnection of the roof area.

The bio-retention cell is tested for two different types: BRC II and BRC III. BRC II has a drain

and a low seepage rate. For more details on the differences between the BRCs, see table D.4.

5.2.1 Continuous rainfall

The regression line in the following figures shows an average reduction in the discharge peaks

caused by the LIDs.

The difference between the BRC II and BRC III at AK52 is not noticeable for the continuous

rainfall for model setup A or model setup B, even for 80% disconnection or 100% disconnection

as shown in figures 5.11 and 5.12.

The average discharge peaks can be reduced with 12% (y = 0.88x) and 22% at AK52 for re-

spectively 80% BRC and 100% BRC with model setup A.

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CHAPTER 5. RESULTS

Figure 5.11: Simulations of the discharge peaks with BRC of 80% and 100% for two types,BRC II and BRC III, against simulations with no LIDs (scenario 0). The regression line repre-sents the mean discharge magnitude at AK52 after the LIDs are implemented in model setupA.

68

5.2. LIDS

The average discharge peaks can be reduced with 14% (y = 0.86x) and 23 % at AK52 for

respectively 80% BRC and 100% BRC with model setup B.

The slopes of the regression lines in model setup A and B are not significant different from each

other for either 80% or 100% disconnection after using a t-test with significance level of 5%

and a sample size of 17.

Figure 5.12: Simulations of the discharge peaks with BRC of 80% and 100% for two types,BRC II and BRC III, against simulations with no LIDs (scenario 0). The regression line repre-sents the mean discharge magnitude at AK52 after the LIDs are implemented in model setupB.

The rain barrels (RB) are implemented in the same way as the BRCs. In model setup A, the

average reduction in the discharge peaks are 22% for 100% and 11% for 80% RB for AK52 as

shown in figure 5.13. Model setup B (see figure 5.14) reduce the average discharge peaks with

22% for 100% RB and 13% for 80% RB at AK52. There are no significant difference between

the average reduction between model setups A and B.

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CHAPTER 5. RESULTS

The disconnection of downspouts is implemented with the re-routing option in SWMM (sub-

section 2.4.8). In the setup of disconnection of roof area, the connected road area before im-

plementation of LIDs is assumed the same after the implementation. The total disconnection

percentage is of that reason larger for model setup A than B, since model setup A initially has a

higher disconnection rate than model setup B (see subsection 4.5)

In model setup A, the average discharge peaks are reduced with 23 % for 100% RD and 13%

for 80% RD at AK52.

Figure 5.13: The LID measures RD for 80% (upper left) and 100% (lower left) roof disconnec-tion at the Grefsen plateau. Simulations with RB where 80% (upper right) and 100% (lowerright) of the roof area area disconnected with rain barrels. The regression line shows theaverage reduction in the discharge peaks at AK52 with model setup A.

In model setup B, 100% RD gives an average reduction of 24 % on the discharge peaks at

AK52. And 80% RD gives a mean reduction in the discharge peaks of 13%.

70

5.2. LIDS

Figure 5.14: The LID measures RD for 80% (upper) and 100% (lower) roof disconnectionat the Grefsen plateau. Simulations with RB where 80% (upper) and 100% (lower) of thedisconnected roof area with rain barrels. The regression line shows the average reduction inthe discharge peaks at AK52 with model setup B.

The reduction in the discharge peaks simulated with 100% roof disconnection at AK52 are not

significantly different from each other. Also, 80% roof disconnection reveal that the average

discharge reduction is not significant different for the different LIDs.

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CHAPTER 5. RESULTS

At Jupiterjordet, where all the sewer system is CSS, the average relative reduction in discharge

peaks for the LIDs is higher compared to AK52. The wastewater from the householders is not

included in the discharge. Therefore, the percentage average reduction is only for stormwater.

In figure 5.15, the average reduction in the discharge peaks are 43% with 80% BRC and 76%

with 100% BRC with model setup A. There is no difference between the BRCs in model setup

A.

Figure 5.15: Simulations of the discharge peaks with BRC of 80% and 100% for two types,BRC II and BRC III, against simulations with no LIDs (scenario 0). The regression line repre-sents the mean discharge magnitude at Jupiterjordet after the LIDs are implemented in modelsetup A.

In model setup B, there is no significant difference for BRC II and BRC III (see figure 5.16).

For 100% BRC the average reduction is 57% and for 80% BRC the average reduction is 33%.

The average reduction with 100%BRC with model setup A is higher than what is simulated for

model setup B. The same is observed with 80% BRC.

72

5.2. LIDS

Figure 5.16: Simulations of the discharge peaks with BRC of 80% and 100% for two types,BRC II and BRC III, against simulations with no LIDs (scenario 0). The regression line repre-sents the mean discharge magnitude at Jupiterjordet after the LIDs are implemented in modelsetup B.

In figure 5.17, disconnection of 80% of the roof area with RB gives an average reduction in

discharge peaks of 44% in model setup A. When all the roof is disconnected, 100% RB, the

average discharge peaks is reduced with 78%. In model setup B an average reduction in the

discharge peaks of 34% is observed for 80% RB and 67% for 100% RB (see figure 5.18).

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CHAPTER 5. RESULTS

The 80% RD gives a reduction in the average discharge peaks with 46% for model setup A

(see figure 5.17). For 100% RD the regression line has a slope of 20, indicating a reduction of

80%. However, the coefficient of determination, R2, to the regression line is very low. Similar

is evident for 100% BRC and 100% RB with model setup A. These results must be interpreted

with caution. R2 describes the linear fit of the regression line. In model setup B, 33% reduction

in the discharge peaks is observed for 80% RD and a 60% reduction for 100% RD as shown in

figure 5.18.

Figure 5.17: The LID measures RD for 80% (upper left) and 100% (lower left) roof disconnec-tion at the Grefsen plateau. Simulations with RB where 80% (upper right) and 100% (lowerright) of the connected roof areas are disconnected with rain barrels. The regression lineshows the average reduction in the discharge peaks for Jupiterjordet with model setup A.

The average reduction in the discharge after implementation of LIDs in model setup A is com-

parably larger than for model setup B. There is no significant difference between the 100% RD

or 100%RB with model setup A. The same is also observed for model setup B.

74

5.2. LIDS

Figure 5.18: The LID measures RD for 80% (upper left) and 100% (lower left) roof disconnec-tion at the Grefsen plateau. Simulations with RB where 80% (upper right) and 100% (lowerright) of the connected roof areas are disconnected with rain barrels. The regression lineshows the average reduction in the discharge peaks for Jupiterjordet with model setup B.

5.2.2 Design rainfall

The simulations of a 5-year rainfall for model setup A are plotted in figure 5.19 and for model

setup B in figure 5.20.

In AK52 and Jupiterjordet, model setup A and B has the lowest discharge peak with 100% LID.

The calculated discharges are almost equal for 100% disconnection (see table D.5) and it is

difficult to distinguish the lines from each other in the figures 5.19 and 5.20. The other LIDs

with 100% disconnection is almost hidden by the solid green line to 100% BRC. The discharge

peak at AK52 is higher in model setup A compared to model setup B. On the other hand, the

discharge peak at Jupiterjordet is larger in model setup B than in model setup A.

75

CHAPTER 5. RESULTS

Figure 5.19: Discharge in AK52 (upper) and Jupiterjordet (lower) during a 5-year rainfallwith model setup A. The dotted red line in the upper plot, at AK52, represents discharge whereCSOs occurs.

76

5.2. LIDS

Figure 5.20: Discharge in AK52 (upper) and Jupiterjordet (lower) during a 5-year rainfallwith model setup B. The dotted red line in the upper plot, at AK52, represents discharge whereCSOs occurs.

At AK52 the volume reduced by the implementation of LIDs is almost identical for the two

model setups (see table 5.2) with different disconnection percentage of the roof area. The

number in the table can be read as, with 100% RD with model setup A the discharge peak from

5-year rainfall is reduced to 68% of the initial peak (the peak from scenario 0).

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CHAPTER 5. RESULTS

Table 5.2: Relative comparison of the discharge (% discharge peak) and volume of waterabove the overflow weir (% Volume) at AK52 for a 5-year rainfall with model setup A andmodel setup B.

5-year rainfall at AK52

Model A Model A Model B Model B

Scenario % Dischargepeak

% Volume % Dischargepeak

% Volume

Scenario 0 100 100 100 100

100% RD 68 68 68 69

100% RB 67 67 68 69

100% BRC III 67 65 68 69

80% RD 82 82 84 83

80% RB 82 82 83 83

80% BRC III 82 81 82 82

To more easily distinguish between the relative reductions in the discharge peak at Jupiterjordet,

the relative comparison is made in table 5.3. The largest reduction in the discharge peak for a

5-year rainfall is found with model setup A for 100% disconnection.

Table 5.3: Relative comparison of the discharge (% discharge peak) at Jupiterjordet for a5-year rainfall for the different scenarios presented in the study.

5-year rainfall at Jupiterjordet

Model A Model B

Scenario % Discharge peak % Discharge peak

Scenario 0 100 100

100% RD 18 44

100% RB 18 44

100% BRC III 19 44

80% RD 55 71

80% RB 55 72

80% BRC III 56 71

78

5.2. LIDS

The relative reduction in discharge peak at AK52 decreases for a 20-year rainfall compared to

the 5-year rainfall with model setup B. The relative volume reduction at AK52 is not changed

significantly from the 5-year rainfall to the 20-year rainfall for none of the model setups (com-

pare table 5.2 and 5.4). The relative reduction in the discharge peaks at Jupiterjordet is also not

changed from the 5-year rainfall to the 20-year rainfall simulation with the LID implementation

described in subsection 4.5.

Figure 5.21: Discharge at AK52 (upper) and Jupiterjordet (lower) during a 20-year rainfall.The dotted red line in the upper plot, AK52, represents discharge where CSOs occurs.

79

CHAPTER 5. RESULTS

The 20-year rainfall creates some unstable discharge. This comes into sight at Jupiterjordet

between the time 15:15 and 15:45 (see figure 5.22). The continuity errors to the simulation are

less than 0.7% for both the runoff and flow routing.

As for the 5-year rainfall, 100% LIDs (RB, RD and BRC) for the 20-year rainfall give the largest

reduction in the discharge in model setup A and B for both AK52 and Jupiterjordet (see figures

5.21 and 5.22). To better distinguish the difference in the performance of the LIDs see tables

5.4 and 5.5 for a relative comparison of the discharge at AK52 and Jupiterjordet.

Figure 5.22: Discharge at AK52 (upper) and Jupiterjordet (lower) during a 20-year rainfall.The dotted red line in the upper plot, AK52, represents discharge where CSOs occurs.

The volume reduction in the discharge after the 20-year rainfall is not significantly changed

from the volume reduction after the 5-year rainfall (compare 5.2 with 5.4).

80

5.2. LIDS

Table 5.4: Relative comparison of the discharge (% discharge peak) and volume of waterabove the overflow weir (% Volume) at AK52 for a 20-year rainfall with model A and B for thedifferent scenarios presented in the study.





% Volume

Scenario 0 100 100 100 100

100% RD 71 72 73 65

100% RB 70 71 73 65

100% BRC III 68 67 73 65

80% RD 85 84 90 81

80% RB 85 84 90 82

80% BRC III 84 82 90 81

Table 5.5: Relative comparison of the discharge (% discharge peak) at Jupiterjordet for a20-year rainfall with model A and B for the different scenarios presented in the study.


Model A Model B


Scenario 0 100 100

100% RD 19 46

100% RB 19 47

100% BRC III 20 47

80% RD 56 74

80% RB 56 75

80% BRC III 57 74

81

6 DiscussionThis chapter summarizes the results presented in previous chapter and discusses their impli-

cations. It starts with a discussion about the model setup and the performance of the model

for different parameters and setup. Afterwards, the input data are discussed before model per-

formance is considered. Moreover, the effect of the LIDs on the discharge peaks is presented

together with a discussion about the implementation of the LIDs in the catchment. At last, the

uncertainties in the model structure and parameters are considered.

6.1 Model selection

Two different parameter sets gave similar model performance. The results of the two model

setups are presented. The two setups describe the dynamics in the catchment in different ways,

but they have similar performance of the model criteria, R2. By including both model setups, the

different approaches to represent the hydrology are preserved. All the parameters and constants

used are the same for both the model setups except the active depth of the aquifer and the

percentage of the directly connected impervious area (DCIA).

Model A has a relatively thin active aquifer and DCIA as 35% of TIA. Model B considers the

full height of soils above the bottom of the manholes and pipes as an active aquifer, and DCIA

is 50% of TIA. In both cases the aquifer is homogeneous.

From the calibration, model A simulates the largest discharge peaks best. The thin active aquifer

forces the infiltrated water to create a fast response in the aquifer, as a result of the limited stor-

age capacity. The thin active aquifer in the model can be thought of as a perched aquifer. Ac-

cording to NGU, the deposits in the area are mainly coastal, marine or so thin that the bedrock is

visible at the surface (figure 3.3). The marine deposits, according to description from NGU, are

of smaller grain sizes. The grain size samples from the infiltration tests, table B.2, indicate low

content of clay at the surface imply a high infiltration capacity at the surface (table B.1). Uglum

(2019) and Kristiansen (2019) found evidence for a perched aquifer in the park Torshovdalen in

Oslo. The surface consisted of soil with high infiltration capacity (30-300 mm/h) followed by a

layer with clay rich soil above the bedrock. It is possible that the same layering exists at Gref-

sen. Further investigation of the soil composition at Grefsen can give more insight to whether

83

CHAPTER 6. DISCUSSION

the model setup with the thin active aquifer is reasonable or not. Moreover, the investigations

can give more unique parameters of the soil in the model setups. The calibration indicates a sat-

urated hydraulic conductivity, Ksat , between 10 and 200 mm/h. The infiltration measurements

(see table B.2) indicates very high saturated conductivity. The lowest corrected, 80 mm/h, was

selected for both models.

The hydraulic conductivity usually decreases with depth in urban areas (Edmondson et al., 2011,

p. 772, Sari, 2017, p. 98, Lundin, Lundin, 1982, p. 171). The map of the sediment distribution

mainly suggests marine deposits, coastal deposits together with no deposits (bedrock) for the

Grefsen area (see figure 3.3). In Dingman (2015, p. 325), saturated hydraulic conductivity for

respectively clay and silt is 10−11 to 10−7 mm/h and 10−6 to 10−3 mm/h. The models are of that

reason not realistic with a saturated hydraulic conductivity of 10 mm/h for the aquifer. However,

in general the ditches for the sewers are filled with gravel of size 11 to 16 mm (VA Norm Oslo

kommune, 2018a, VA Norm Oslo kommune, 2018b). The saturated hydraulic conductivity

of such materials is according to Dingman (2015, p. 325) of about 0.1-10 mm/h. During the

calibration process, the response of the groundwater was observed to be too slow when the

saturated hydraulic conductivity, Ksat , is less than 5 mm/h. The subcatchments are thought of

as a homogeneous volume (Rossmann and Huber, 2016a), while the reality is not. To replicate

the interaction between the groundwater and sewers in the ditches, a hydraulic conductivity of

10 mm/h in the aquifer is used.

The ratio of DCIA to TIA in figures 5.4 and 5.5 is observed to be a sensitive parameter. As men-

tioned in chapter 2, the ratio of DCIA/TIA ranges from 0.085 to 0.6 (Bjørn Christoffersen, Oslo

VAV, personal communication, 31.02.2019, Wibben, 1976, pp. 11, 13, Alley and Veenhuis,

1983, p. 314). The ratio of DCIA/TIA has been the major calibration parameter. It is adjusted

to get optimized fit after the other parameters are selected, since the value of DCIA/TIA is

unknown. This is because it is difficult to quantify the DCIA. Studies and manuals suggest

the impervious area (also called TIA) being the most sensitive parameter in urban hydrological

modelling (Dvergnes, 2016, p. 44, Rossmann and Huber, 2016a, p. 65). In this study, per-

centage impervious only consist of the road and building area. Pavements and driveways are

not included. Thus, %imperv in this study is probably underestimated. However, the ratio of

DCIA/TIA with an underestimated TIA can be balanced by increasing the DCIA.

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6.2. AVAILABLE DATA

6.2 Available data

6.2.1 Input data

The available discharge data after 2016 did not have any rainfall events with larger return periods

than two to five year return level (www.Regnbyge.no). Some of the available discharge data did

have some missing sequences of values, described in subsection 4.1.

The period of the calibration is from August-November, while the validation period is from

June-August. In 2017, both the model setups underestimated the discharge peaks in September

(see table 5.1). In August (9th August 2017), model A overestimated the discharge peak. In Au-

gust 2018 (10th August 2018), model A again overestimated (slightly) the discharge peak. The

observed and simulated runoff coefficients are also similar for the CSO events. In September

2017, all the CSO events are underestimated in both the models setups (see figures 5.2 and 5.3)

and for different values of saturated hydraulic conductivity (see figures 5.6 and 5.7). This can

indicate that the models have challenges with describing the hydrological conditions in Septem-

ber. For the date of the CSO events in September 2017, the soil had less storage capacity as

a result of higher degree of saturation (see figure G.1). When the soil is almost saturated, less

amount of rainfall is needed before the soil is saturated and all excess rainfall contributes to

runoff. It is possible that the model underrepresent the importance of increased saturation in

September and probably October.

Where the observed discharge peaks match well with the simulated, the runoff coefficients are

almost equal. This indicates that the volume of water simulated is similar with the observations,

even though the discharge peaks occur earlier than what is observed. The reason for the differ-

ence in the timing can be because of a too large Manning’s roughness coefficient, width, slope

or too small depression storage. Oslo VAV uses a depression storage of 12 mm, while in the

model setup a depression storage of 1.5 mm for the impervious areas are used.

The simulated discharge during mid-June in 2018 is overestimated in both model setup A and

model setup B. This is an indication of that the sewage in the combined system consists of a

larger part of groundwater than assumed in the calibration phase. The inflow in AK52 with a

daily and hourly variation is from the time series from 2017. The calibration period does not

have data from June, where the discharge in 2018 is at its minimum. Therefore, the groundwater

inflow into the outlet AK52 can possibly be larger and the user-specified inflow should be

85


lowered.

The saturated hydraulic conductivity used at the surface is 80 mm/h. This is the lowest saturated

hydraulic conductivity (corrected) from the measurements summer 2018. During calibration, it

was observed that a low saturated hydraulic conductivity of 1 mm/h could give a fast response

in the runoff generation in similar manner as tuning the ratio of DCIA/TIA. The use of the

saturated hydraulic conductivity of 80 mm/h instead of 1-5 mm/h can probably influence the

recovery of the soil in September and October. Solheim (2017, p. 57) measured a saturated

hydraulic conductivity at Jupiterjordet from 63 and 167 mm/h. Use of a saturated hydraulic

conductivity of 63 mm/h would not have had any difference in the modelled results, as shown

in figures 5.6 and 5.7. The saturated hydraulic conductivity need to be lower than 10 mm/h to

influence the average discharge peaks for a continuous rainfall.

6.2.2 Uncertainties in the input data

Factors that can influence the input data are 1) measuring sensors, 2) point measurements and

3) areal changes.

The measuring sensors at AK52 had problems with measuring the entire hyetograph during

CSO events. Parts of the discharge peaks are missing for some of the CSO events. The to-

tal volume of water, may of that reason be incorrect imply insufficient peak magnitudes and

volumes. Another aspect of the sensors that might influence the observed data is the way the

data from the sensors are chosen. There are three sensors measuring the depth of the water.

For each time the locally stored sensor data are collected from AK52, the depth of water in

the pipe is manually measured. The correspondence between the individual depth sensors and

the manual measurements mainly define which one of the sensors being used for the discharge

calculations. The sensor chosen can be different for each time the measurements are collected.

This can influence the time series used for the calibration and validation.

Secondly, precipitation is measured from a single point in the entire catchment area. The volume

of water from precipitation is assumed constant over the entire area of the catchment. It is

well known that the intensity of precipitation is variable over a certain area (Ødemark et al.,

2012, p. 38, Healy et al., 2007, p. 36). The variability can be large enough to create differences

between the observations and simulations. It is not just the precipitation measurements that vary

spatially. The infiltration measurement gave large variability within one of the householder’s

86

6.3. MODEL CRITERIA AND EQUIFINALITY

gardens. One meter apart, the measured saturated hydraulic conductivity was 100 mm/h and

5200 mm/h. In this study, the lowest corrected saturated hydraulic conductivity (80 mm/h)

is used for all the subcatchments despite the spatial variability in soil cover in the catchment

shown in figure 3.3 and 3.4. Measurements of a parameter used in modelling, does not apply

for the whole subcatchment. Therefore, measurements of parameter values do not necessarily

give satisfactory values.

Lastly, areal changes can affect the distribution of water. Changes in gardens, like flattening or

draining of the pervious areas out to the roads, re-distribute the water. Such changes, if there are

many enough, can influence the total discharge. From late 2018, there are large areal changes

in the area. The main road, Grefsenveien, is renovated (see figure H.2). Also, new separate

conduits will replace the combined system in the eastern part of the catchment. The changes

can be large enough to influence further characteristics of the catchment. New DEM to retrieve

slope, surface runoff stream lines and new subcatchments can be necessary.

6.3 Model criteria and equifinality

Table 5.1 shows that there is a time lag between the observed and simulated CSO events. It

has a large impact on the calculated NSE, even though the peak magnitude had been perfectly

predicted (Beven, 2012, p. 240).

The NSE obtained in calibration for the continuous series is 0.604 for model setup A and 0.357

for model setup B. Despite a NSE of 0.357, the coefficient of determination, R2, for model

setup B is 0.629. The model setups are regarded as acceptable in this study, since the predictive

performance of the discharge peaks is the major focus.

For the validation, both models have NSE less than 0.4 but a R2 larger than 0.8. The major

reason for the low NSE is the delay of the time of magnitudes and probably less variation in the

observed data for the validation period compared to the calibration period (compare figure 5.1

with 5.8).

Models with few (four or five) parameter values require at least 15 to 20 flood events to achieve

a robust calibration, when the only data used for calibration is observed discharge data (Beven,

2012, p. 19). AK52 only have three to four CSO events each year and the record of data extends

over three years with records of acceptable quality. If the aim is to predict CSO events, a model

with less parameters or measurements of CSO events over a longer period can help to improve

87


the predictability of the model.

The two model setups have similar performance in simulating discharge peaks according to R2

even though the parameters are different. This is an example of the problem of equifinality. The

problem of equifinality is more frequently observed in semi-distributed and distributed models.

Especially, when only the observations from the outlet of the catchment area are used for model

calibrated (Beven, 2012, p. 236). At AK52 the only information of the combined sewer flow for

calibration is the discharge at AK52. During calibration, an increase in the runoff from the Gref-

sen plateau can be offset by decrease in the discharge from the Grefsenkollen. It is impossible to

distinguish between which one of the representations that is correct, since there is only one lo-

cation where the combined discharge is measured. Discharge measurements from 297077 could

be used in the calibration to get correct discharge in the wastewater pipe. However, there is no

measurements from the stormwater pipe that could control the total water contribution from

the Grefsenkollen. A second measurement from the wastewater pipe is therefore not enough

to select the correct representation. Also, the time used during the manual calibration would

increase with a second calibration point. Several model structures or parameter sets can give

acceptable discharge simulations, as illustrated by model setup A and model setup B.

An automatic optimization method would probably give another parameter set with better per-

formance than in this study, but the automatically optimized parameters may not be realistic

given the insufficiency of quality and quantity of the observed data as mentioned above. With a

short period to train the model and limited registered measurements of CSO events, the model’s

ability to simulate CSO events is low, and the problem of equifinality arises, i.e. different model

parameter sets give similar model performance. The problem of equifinality can be dealt with

GLUE method, giving a maximum likelihood probability to sample of reasonable parameters

and models (Beven and Binley, 1992, p. 285). To limit the parameter range, in-situ measure-

ments of the parameters with widest possible range can be useful.

The consequences of equifinality is increased uncertainty outside the range of calibration and

validation. Two model setups with the same performance can give widely different predictions

as observed in this study. This is of importance in decision making. In this case it applies to

predicting CSO events for larger return levels than it is calibrated after, and to the effectiveness

of LID measures.

88

6.4. LID

6.4 LID

To test the maximum potential effects of the LIDs, all the measures are conducted with 100%

disconnection of the rooftop area. It is not necessarily a realistic approach, but gives further in-

sight to the expected maximum effects of the measures for each LID. A more realistic approach

also tested is disconnection of 80% of the rooftop area. Before the implementation of LIDs,

55% of the rooftop area was disconnected.

A combination of several types of LIDs has given higher reduction in discharge peaks and

volume during CSO events compared to implementation of only one type (Ingebrigtsen, 2017,

Sjöman and Gill, 2014). However, investigation of individual LIDs can give further insight

into the physical effects of LIDs in catchment hydrology. This knowledge can together with an

economic investigation be important for decision makers.

In the presentation of the result, the discharge from Jupiterjordet is also included. Only the sur-

face runoff generated from the subacatchment is the output discharge. The results from AK52

is only valid for a catchment with similar combination of separate and combined system. The

reduction in the discharge peaks at Jupiterjordet can give a more comparable representation of

the performance of the LIDs on the surface runoff discharge where there is a CSS. However,

none of the model setups are calibrated or validated for discharge at Jupiterjordet. The magni-

tude of the discharge is not necessarily correct, but have been used to compare the individual

measures and can indicate the expected effect of LIDs on the stormwater in an area with the

same hydrological conditions as Grefsen.

The implementation of 100%LIDs with the LID-module (BRC or RB) requires 529 LIDs in

model setup A and 416 LIDs in model setup B, even though only 465 rooftops exists at the

Grefsen plateau. This is done to get a correct roof disconnection percentage of 100%, since

the model cannot distinguish between roof and road area. If the same number of LIDs as

rooftops, 465, is implemented in the two model setups, a larger fraction of impervious areas

is disconnected in model setup B than in model setup A due to the difference in initial DCIA.

Simulations with implementation of the same number of LIDs, 465, in both model setups was

first conducted and can be seen in section D.5. Therefore, the effects of LIDs presented in the

results reflect how the LIDs are implemented.

89


6.4.1 Continuous rainfall

The two model setups gave almost equal reduction in the discharge peaks of 11-14% for 80%

disconnection and 22-24% for 100% disconnection at AK52. At Jupiterjordet, the average

reduction in discharge peaks for both 80% and 100% was larger for model setup A than B. The

reason for the difference in the reduction of the discharge at Jupiterjordet and not AK52 with

the two model setups can be the way the LIDs are implemented. The contribution of runoff

for 100% disconnection in model setup A is from 18% of the roads, while in model B the

connected area is 59% of the rods. Therefore, almost the entire impervious area at the Grefsen

plateau is disconnected with 100% LIDs in model setup A compared to model setup B. Another

explanation can be differences in the contribution from the Grefsen plateau. If the contribution

from the Grefsen plateau is comparably small to the contribution from the Grefsenkollen in

model setup A, different effects at Jupiterjordet and AK52 can be expected. The difference

in the contributions from the wastewater pipe into AK52 and a point upstream for AK52 in

the CSS indicates that the contribution from the Grefsen plateau in model setup B is larger

compared to model setup A (see figures E.1 and E.2).

The coefficient of determination, R2, calculated for the regression line for 100% disconnection

at Jupiterjordet was very low (0.284-0.368) in model setup A (see figures 5.15 and 5.17). There-

fore, the effect of 78-80% reduction in the discharge is more uncertain and not as reliable as the

obtained results from the other simulations. The low R2 indicate that the reduction with 100%

RD, 100% BRC and 100% RB is not linearly. However, the average reduction in discharge

peaks for 80% disconnection is also smaller for model setup A than B. Therefore, it is likely

that the reduction with model setup A for 100% disconnection has larger effect than for model

setup B.

The different types of BRC, BRC II and BRC III, did not show any noticeable difference in the

average reduction in the discharge peaks at AK52 or Jupiterjordet. This is probably because the

events of rainfall in the simulation had a low design level (less than five year). Hence, BRC

II was not filled up to the elevation of the drain or the outflow from the drain was too small.

Another reason can be the construction of the BRCs. They were probably not different enough

to cause remarkable differences.

The reduction in the discharge peaks at Jupiterjordet was 60-80% with the continuous rainfall

for 100% roof disconnection. This study indicates that the reduction in the discharge peaks from

90

6.4. LID

LIDs locally (Jupiterjordet) is large. The discharge at Jupiterjordet is only from the surface

runoff while at AK52 the inflow of wastewater is also included. Therefore, it is likely that

the reduction in the discharge peaks is larger for Jupiterjordet than for AK52. Additionally,

a larger area of the rooftop areas in the catchments are disconnected at Jupiterjodet compared

to AK52. Unfortunately, none discharge measurements from the Grefsen plateau are available

and a verification of the modelled result is of that reason not possible. However, the results can

indicate potential reduction in the discharge after implementation of LIDs.

The three different LIDs implemented gave almost equal reduction in the discharge peaks for

the same percentage of disconnection at both AK52 and Jupiterjordet. The RB and BRC were

implemented with the modules, while the RD was implemented with the re-routing option. The

implementation with RB and BRC had similar effects as the RD. This partly because the discon-

nected area is the same in both model setups compared to section D.5 where the same amount

of LID-modules are implemented in each model setup. Another reason is the use of the satu-

rated hydraulic conductivity of 80 mm/h. If the saturated hydraulic conductivity of the surface

is reduced to 1-5 mm/h, simulations have shown that the effects of the RD is reduced. Also

the effects of RB would be reduced by decreasing the surface saturated hydraulic conductivity

to 1-5 mm/h since the overflows from the barrel flows onto the permeable areas. The effect

of the BRC is the same as for the RB and RD, although the saturated conductivity of the soil

layer is 360 mm/h. During the simulation period the infiltration rate was never exceeded, and

no measurable difference between the measures was found.

6.4.2 Design rainfall

For a 5-year rainfall and a 20-year rainfall, the reduction in the discharge at AK52 is respec-

tively 32-33% (see table 5.2) and 27-32% (see table 5.4). The reduction in the discharge peaks

decreases (slightly) from the simulation with 5-year rainfall to the 20-year rainfall, but is not

significant. For the continuous simulations the reduction was described as a linear relation (see

figures D.5, D.6, D.7 and D.8). Since the percentage reduction do not decreases significantly,

it indicates that the LIDs are not saturated. However, the rain barrel is quickly filled up during

a 20-year rainfall and all the excess water flows onto the permeable areas. Because of the high

infiltration capacity, as mentioned above, the stormwater infiltrates into the permeable areas and

do not flow to the outlet. The groundwater table in model setup A almost reaches the surface

in the subcatchment at Jupiterjordet. Residents living within the subcatchment Jupiterjordet

91


frequently observes pounding during heavy rainfalls. Probably, the model cannot fully describe

the saturation of the subsurface.

The reduction in the discharge peaks is larger for event simulations than continuous simula-

tions. For 100% LIDs in both of the models, the average reduction in the discharge peaks for

continuous rainfall is 22-24%. Mainly, because of the antecedent conditions. In most cases, the

discharge peaks closest to the 1:1-line are from events after a period with several rainfall events

(see figure D.3). For discharge peaks caused by a series of rainfall events, the average reduction

with LIDs are not as large as for discharge events caused by a single event. This shows the

importance of evaluating LIDs with a observed rainfall series and not just event rainfalls.

With the implementation methods and model setup used, 100% of roof disconnection to LIDs

at the Grefsen plateau is not enough to prevent CSO events for a 5-year rainfall or a 20-year

rainfall at AK52. According to these model result, several measures is needed to reduce the

amount of CSOs events for step one and two in the S3SA.

In model setup B, the discharge for a 20-year rainfall simulations show a unstable discharge

for Jupiterjordet. The runoff generated from the subcatchment might exceed the capacity of the

sewer network at the outlet of the Jupiterjordet.

The relative volume of CSO events at AK52 does not decrease significantly from a 5-year rain-

fall to a 20-year rainfall simulations. This means that the capacity of the LIDs is not exceeded.

This is probably because of the saturated hydraulic conductivity of 80 mm/h as discussed ear-

lier. However, the models are calibrated to predict discharge peaks, hence the models may not

give correct estimates on the discharge volume (Bokulich and Oreskes, 2017, p. 900).

Earlier studies (Ingebrigtsen, 2017, Hernes, 2018) examine the reduction in the discharge of the

water that is already in the stormwater conduit by AK52. With a 5-year rainfall in the MOUSE

model, the maximum discharge in the CSO AK52 is 377 l/s, while in the manhole it is 840 l/s

(Ingebrigtsen, 2017, pp. 55, 65). In Ingebrigtsen (2017, p. xvii) the discharge in the CSO is

reduced from 377 l/s to 102 l/s with 78% roof downspouts disconnection at the Grefsen plateau

with a 5-year rainfall. That is a reduction in the discharge magnitude of 73 %. In Hernes (2018,

pp. 33, 34) a reduction in DCIA of 18% for the whole catchment with BRC and green roofs

reduces the peak magnitude with respectively 87% and 86%. The effects of the LIDs studied by

Ingebrigtsen (2017) or reduction in DCIA studied by Hernes (2018) will give larger percentage

effect since they investigate the reduction in the discharge in the CSO AK52. In this study,

92

6.5. UNCERTAINTIES

the total discharge (stormwater and wastewater) in the CSS is used for the calibration. The

reduction in the discharge peaks in the combined sewer, AK52, for a 5-year rainfall is in this

study found to range between 22-24% for 100% roof disconnection. At Jupiterjordet where the

entire subcatchment is implemented with LIDs for 100% of the roof area, a reduction in the

stormwater discharge of 81-82% is obtained for model setup A and 56% in model setup B. The

results from Jupiterjordet reveals that the model setup used determines the modelled reduction

on the discharge peaks.

6.5 Uncertainties

There are several sources adding uncertainties to the obtained results. Some of them can be

because of the limitations in the model approach to simulate the reality. Others can be in the

data used to calibrate and validate the model or in data used to find certain parameters. This

section focuses on the model limitations and model construction.

6.5.1 Model limitations

The catchment is divided into several subcatchments to describe the spatial variability. Within

one subcatchment physical characteristics are assumed to be homogeneous; same slope, width

and water table elevation. All the impervious subareas are considered as one connected square

and the pervious subarea is one large connected part. In the models used in this study the re-

routing option from impervious to pervious subarea is used. With the large distinct impervious

and pervious subareas, the re-routing process in the model works differently than in reality. In

reality, there are patches with impervious areas, surrounded by pervious area. Furthermore,

the water in the model is re-routed from all impervious areas in the same way. In reality, the

water is re-routed differently for road area and roof area. The partitioning of the impervious

and pervious subareas is not a description of the reality, but is a way to simplify the complexity

of areas with impervious and pervious surface. How much the uncertainties are caused by the

simplification in the simulated discharge is not known.

The runoff generated in a subcatchment is routed to an outlet. In this model setup the outlets

are specified manholes along the sewage network. It is a known problem that the capacity of

the storm drains along the roads sometimes is exceeded because there is too much water, the

storm drain is blocked by obstacles or dust (see figure H.1). The water along the road would

then continue downstream to the next storm drain, to pervious areas or out of the subcatchment

93


boundaries and perhaps into a storm drain in the other subcatchment. The speed of the stormwa-

ter at the surface is slower than the water in the sewers, implying a delay of the stormwater on

the surface before it reaches the sewage network. The delay described is not accounted for in

the model setup. Therefore, a delay of minutes in the observations compared to the simulations

is reasonable and can be the reason for parts of the observed delay in this study.

A groundwater aquifer is included in the model simulations. The groundwater aquifer in

SWMM has a uniform groundwater table in the whole subcatchment and the groundwater can-

not move from one subcatchment to another. The groundwater is only connected to the outlet of

the subcatchment, so the height of the water table is only in connection to the outlet specified.

The groundwater tables in Norway largely follow the topography, especially in rural areas. This

is not possible to include in the model structure made. However, the groundwater around build-

ings in urban areas is usually lowered with a weeping tile to prevent damages due to moisture

(Ødegård et al., 2014, p. 305). How the reduction of the water table around buildings affects the

groundwater table in the outlet of the subcatchment is not known. In the model setup, there is

an interaction between the groundwater and the outlet. The sediments surrounding the conduits

in the ditches are very coarse (grain size 11-16 mm) (VA Norm Oslo kommune, 2018a, VA

Norm Oslo kommune, 2018b). Coarser sediments separated by finer sediments can result in

lateral movement of groundwater into the ditches. Water from the finer sediments seeps into

the coarser sediments. Storage of water in the ditches can create a pressure of water above the

conduits, increasing the groundwater seepage into the conduits. This is not accounted for in the

modelling.

6.5.2 Model setup

The LIDs in SWMM require several parameters. The values of the parameters can affect the

obtained effect. Correct understanding of what the parameters in the model describe and how

they are found is important for the result. Also, the rainfall used as input can give different

effect of the LIDs. In this study, the reduction in the discharge peaks is larger for the event

rainfall than for the continuous rainfall. However, there was not found any difference in the

discharge reduction between the LIDs implemented. The LIDs were implemented with both

the re-routing (RD) and the LID-modules (BRC and RB). This is to large extent caused by the

use of the saturated hydraulic conductivity of 80 mm/h at the surface. Decision makers should

be informed about the uncertainty related to the obtained reduction in discharge magnitudes and

94

6.5. UNCERTAINTIES

the assumptions done.

The size of the subcatchments decides the complexity of the study area to be modelled. How-

ever, an increase in subcatchments is not necessarily associated with an increase in complexity

and spatial variability. An increase in subcatchments requires higher resolution of the dis-

tributed data. If finer resolution of, for example, soil moisture is unavailable, a degree of com-

plexity is lost (Jacobson, 2011, p. 1443). Nevertheless, several subcatchments might give a

more distributed contribution of stormwater to the sewage system. Few subcatchments give a

larger volume of stormwater into one manhole, implies a more semi-distributed model than a

distributed model. Meaning that the discharge in the outlets from the subcatchments can be

seen as correct, but the discharge in the other manholes, that are not a outlet, is not necessarily

correct.

Urban models require more parameters than classical hydrological models because of the finer

scale and the attempt to simulate many more processes (Salvadore et al., 2015). Jacobson

(2011, p. 1442) discussed that there is an optimum model complexity where the predictive

performance decreases because there are too many model parameters and limited data to test

the model properly. The performance of the models selected is good for R2 and not good for

NSE. This is to a large extent caused by peak timing issues. It affects NSE but not the peak

value correlation (R2). The hydrographs however also reveal that the model has problems in

simulating the recession part. It has not been possible to solve this in the calibration efforts.

This can therefore be indicative of weakness in the model structure when applied to catchments

like the present.

95

7 ConclusionsThis study has calibrated and validated two continuous model setups in SWMM with the criteria

R2 on the discharge peaks. The two models have almost equally good performance indicating

a problem of equifinality. The problem arises because of limited information of the subsurface

soil structure and the unknown DCIA.

The first objective was evaluation of the hydrological module. The CSO events in September

2017 are probably caused by saturation of the subsurface. During the calibration, the discharge

peaks of these CSO events were always underestimated. This is probably caused by a structural

limitation of the aquifer module in SWMM; the aquifer is considered as homogeneous. Com-

bined with the high infiltration rates measured at Grefsen the model rarely reaches saturation

conditions, although this is observed in the field.

The second objective of this study was to model the effects of LIDs measure for different param-

eterizations. The disconnection of roof area was with LIDs; bio-retention cell, disconnection

of downspouts and rain barrel, at a part of the catchment (Grefsen plateau). The model setups

selected gave different effects of the LIDs examined in a subcatchment located at the Grefsen

plateau, Jupiterjordet, but similar performance at the main outlet AK52. Disconnection of 100%

of roof area with LIDs at AK52 gave an average reduction in the discharge peaks of 22-24%

for continuous simulations. The reduction in the discharge peaks increases from a continuous

simulations to event simulations, with 32-33% for a 5-year rainfall and 27-32% for a 20-year

rainfall.

This is probably because of saturation of the subsurface of the permeable surfaces. At Jupiter-

jordet, a none-calibrated outlet located at the Grefsen plateau, the stormwater discharge was

studied. Disconnection of 100% roof gave a reduction in the average stormwater discharge of

57-60% with model setup B and 76-80% with model setup A for a continuous rainfall. This

indicates that different results of implementation of LIDs can be obtained by use of different

model setups.

The different ways the LIDs are implemented have been found to be of major importance on

the effect the LIDs has on the discharge peaks. Implementation of the same number of LID-

97

CHAPTER 7. CONCLUSIONS

modules in each of the model setups gives different total disconnection and hence different

results of the LIDs performance. Modelers should be aware of that the resulting effects of LIDs

on discharge peaks vary between models model parametrizations. It is important that the range

in obtained effects of LIDs is analyzed and communicated so it can be used in planning and in

cost-benefit analysis to find the best solution to reduce CSO events.

7.1 Further work

The selected study area has been used as a case study for thesis and projects. There are still sev-

eral aspects that can be investigated. At Grefsen several setups can be used and new measuring

locations can give extensive information.

• Water balance

To better understand the dynamics in the catchment, controlled the movement of water in

the catchment can be useful. Install velocity and depth sensors downstream for AK52 in

the stormwater sewer. In this way the total outflow from the catchment is measured.

• Floodways

Roads can be implemented as open links in SWMM to evaluate the use of roads as flood-

ways.

• Continuous simulations during winter

In Norway, several CSOs can occur in the winter period. A model taking the frost and

snow accumulation into account can be useful to better understand the driving dynamics

in a catchment through the year.

• Subcatchment distribution and size

To better control the water from roof areas routed to pervious areas, roof areas can be

defined as one subcatchment. Assessment of the LIDs effectiveness and implementation

might be more easily accomplished.

• Geophysical measurements

To get more information about the near surface structural geology, measurements such

as Electrical resistivity tomography (ERT), Ground Penetrating Radar (GPR) and other

geophysical methods can be used. Information about the groundwater table is of interest.

• Flow Separation

98

7.1. FURTHER WORK

Separation of groundwater contribution to the combined conduit can be useful to control

the amount of groundwater interacting in the catchment. This can be done with time series

analysis, where separation of daily and hourly seasonality of the discharge measurements

can be done during dry weather.

• Description of the hydrology

The description of the hydrology in the model can be further explored. During this thesis a

model of a rural small catchment (631 ha), Sæternbekken located at the boundary of Oslo,

is made. Using models of a rural and urban catchments the description of the hydrology

between rural and urban areas can be investigated. However, the calibration process was

time consuming, and the parameters of the groundwater aquifer were difficult to represent

correctly. Following up with the comparison can further give valuable knowledge of the

model representation of the hydrology in an urban hydrological model as SWMM.

99

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A Discharge data analysis

Figure A.1: Rating curve for the discharge and depth data available for 2018

Figure A.2: Pattern of the dry weather flow during weekends in AK52 in 2017.

111

APPENDIX A. DISCHARGE DATA ANALYSIS

Figure A.3: Patternof dry weather flow during weekdays in AK52 in 2017.

Table A.1: Table of the relative daily variation in dry weather flow for AK52

Day Relative daily deviation from mean discharge 28.8 l/s

Monday 1.12

Tuesday 1.09

Wednesday 1.00

Thursday 0.90

Friday 0.90

Saturday 1.00

Sunday 1.05

112

Table A.2: Table of the relative hourly variation in dry weather flow for AK52 from the meanvalue in the dry weather period.

Hour Relative hourly deviation from mean value 28.8 l/s

03:00 0.68

04:00 0.73

05:00 0.84

06:00 1.11

07:00 0.90

08:00 0.90

09:00 1.00

10:00 1.05

11:00 1.13

12:00 1.08

13:00 1.02

14:00 1.00

15:00 1.03

16:00 1.09

17:00 1.14

18:00 1.16

19:00 1.15

20:00 1.11

21:00 1.07

22:00 1.05

23:00 0.93

00:00 0.80

01:00 0.71

02:00 0.69

113

B Infiltration measurementsTable B.1: Measured saturated hydraulic conductivity from gardens at different propertiesmeasured with MPD infiltrometers at Grefsen. The measured saturated hydraulic conductivity,Ksat , is found where the conductivity is constant, i.e. not changing more than 20% from thelast three measurements (Solheim et al., 2018, p. 4). NA means that the test is not conductedfor that location.

Location MPD 1: Ksat[mm/h]

MPD 2: Ksat[mm/h]

MPD 3: Ksat[mm/h]

MPD 4: Ksat[mm/h]

NV5 350± 100 2000 ± 400 200 ± 40 400 ± 40

NV13 2000±200 3500±200 800±40 950±100

NV39 1500±100 1000±300 4800±200 NA

GKV25 220±40 100±40 5200±200 200±100

GKV34 300±200 1100±300 3500±200 NA

When reading the values in the infiltrometer, the water moved downward at the same time as

the height of the water was determined. The reading error was set to 2 mm and is included in

the table with ± error. The error is usually larger for infiltration capacity of larger magnitudes.

The measured infiltration capacity is a calculation of measured height of water divided by the

time it has used from the start to the measured height. The measured infiltration is corrected

depending on the clay content suggested by Solheim et al. (2018, p. 3). The corrections and the

mean for the different locations are presented in table B.2.

115

APPENDIX B. INFILTRATION MEASUREMENTS

Table B.2: The measured and the corrected saturated hydraulic conductivity from gardens atdifferent properties measured with MPD infiltrometers at Grefsen. The saturated hydraulicconductivity, Ksat , is found where the conductivity is constant, i.e. not changing more than20% from the last two-three measurements (Solheim et al., 2018, p. 4). The measured Ksat

values are corrected to remove the effects of lateral movements of water during the test.

Location [Clay:Silt:Sand:Gravel] in%

Ksat [mm/h]measuredmean

Ksat [mm/h]correctedmean

Number ofinfiltrationtests, N

NV5 [2.7:16.4:76.2:3.6] 736 590 4

NV13 [1.5:13.3:78.1:8.9] 1812 1450 4

NV39 [1.3:5.6:85.0:1.4] 2433 1947 3

GKV25 [3.3:15.1:61.5:16.8] 1430 1947 4

GKV34 [1.4:11.6:65.5:20.7] 1633 1307 3

MEAN [2.0:12.0:73.2:10.3] 1458 1250 4

Table B.3: The measured and corrected saturated hydraulic conductivity from different bio-retention cells in Deichmans gate in Oslo measured with MPD infiltrometers. The hydraulicsaturated conductivity, Ksat , is found where the conductivity is constant, i.e. not changing morethan 20% from the last two-three measurements (Solheim et al., 2018, p. 4). The measured Ksat

values are corrected to remove the effects of lateral movements of water during the test. Themean of the measured values and the mean of the corrected values are both visible in the table.DRX represent bio-retention cell number X.

Location [Clay:Silt:Sand:Gravel] in%

Ksat [mm/h]measuredmean

Ksat [mm/h]correctedmean

Number ofinifltrationtests, N

DR3 mean [0.3:3.2:78.8:13.5] 241 193 7

DR5 mean [0.7:7.2:80.5:10.7] 323 258 4

DR6 mean [0.3:3.3:82.5:13.9] 475 380 4

DR7 mean [0.1:0.6:84.8:8.0] 750 600 3

MEAN [0.3:3.6:81.6:11.5] 447 358 5

116

C Model parametersTable C.1: Description of the parameters used for each subcatchment (Rossmann, 2015,p. 196)

Properties of Subcatchment

Name Description

Rain Gauge Name of the rain gauge used for the subcatchment

Outlet Name of the node or subcatchment receiving the runofffrom existing subcatchment

Area [ha] Area of the subcatchment included both permeable and im-permeable area

Width [m] Width of runoff flow path in existing subcatchment

%Slope Average percentage slope in the subcatchment

%Imperv Percent of surface area which is impervious

N-Imperv Manning’s n for runoff over impervious areas of the sub-cathment

N-Perv Manning’s n for runoff over permeable areas of the sub-catchment

Dstore-Imperv [mm] Depth of depression storage on impervious areas of the sub-catchment

Dstore-Perv [mm] Depth of depression storage on impervious areas of the sub-catchment

%Zero-Imperv Percentage of the impervious areas without depression stor-age

Subarea Routing- IMPERV: runoff from pervious areas flows to imperviousareas

- PERV: runoff from impervious areas flows to pervious ar-eas

- OUTLET: runoff from both impervious and pervious flowsdirectly to the outlet

Percentage Routed Percent of runoff routed between subareas

117

APPENDIX C. MODEL PARAMETERS

Table C.2: Description of the parameters for the infiltration method Green-Ampt (Rossmann,2015, p. 232)

Properties of Infiltration Method: Green-Ampt

Name Description

Suction Head [mm] Average value of soil capillarity suction along the wettingfront

Conductivity [mm/h] Soil saturated hydraulic conductivity

Initial Deficit Fraction of soil volume initial dry

Table C.3: Description of the data requirement for the aquifers (Rossmann, 2015, p. 210).

Data Fields for Aquifer

Property Description

Aquifer Name User-defined aquifer name

Porosity [-] Volume of voids/Total soil volume

Wilting Point [-] Soil moisture content where plants cannot extract waterfrom the soil

Field Capacity [-] Soil moisture content after gravitational drainage

Conductivity [mm/h] The saturated hydraulic conductivity of the soil in theaquifer

Conductivity Slope [-] Average Slope of conductivity vs. soil moisture deficitcurve

Tension Slope [mm] Average Slope of soil tension vs. soil moisture contentcurve

Upper EvaporationFraction [-]

Fraction of total evaporation available for evapotranspira-tion in the upper unsaturated zone

Lower EvaporationDepth [m]

Maximum depth below the surface at which evapotranspi-ration from the lower saturated zone can still occur

Lower GroundwaterLoss Rate [mm/h]

Rate of percolation to deep groundwater when the water ta-ble reaches the ground surface

Bottom Elevation [m] Elevation of the bottom of the aquifer

Water Table Elevation[m]

Initial elevation of the water table in the aquifer when thesimulation starts

Unsaturated ZoneMoisture [-]

Initial soil moisture of the unsaturated upper zone when thesimulation starts

118

Table C.4: Description of the data requirement for the Groundwater Flow editor (Rossmann,2015, p. 229).

Data Fields for Groundwater Flow

Property Description

Receiving Node Name of node that receives groundwater from the aquifer

Surface Elevation [m] Elevation of the ground surface that lies above the bottomof the aquifer

A1 [-] Value of A1

B1 [-] Value of B1

A2 Value of A2

B2 Value of B2

Surface-GW Interac-tion Coefficient

Value of A3

Surface Water Depth[m]

Fixed depth of surface water above the receiving node’s in-vert elevation. If the surface water depth varies, it is set tozero.

Threshold Water TableElevation [m]

Minimum water table elevation that must be reached beforeany flow occur. Blank field symbolize receiving node’s in-vert elevation

Lower GroundwaterLoss Rate [mm/h]

Rate of percolation to deep groundwater when the water ta-ble reaches the ground surface

Unsaturated ZoneMoisture [-]

Initial soil moisture of the unsaturated upper zone when thesimulation starts

119

D LID setup

D.1 Parameter description

Table D.1: Table of description of required parameters for setup of the module Rain Barrel inSWMM (Rossmann and Huber, 2016b, Rossmann, 2015).

Parameters Rain Barrel

Parameter Source

Stor

age

Lay

er Barrel Height [mm] Height of barrel

Dra

in

Flow Coefficient [mm/h] Determines the rate of flow through a drain

Flow Exponent [−] Determines the rate of flow through a drain (Ross-mann and Huber, 2016b, p. 133)

Offset Height [mm] Height of the drain above the bottom of the storagelayer

Drain Delay [h] Period of time after a rainfall event until the rain bar-rel is allowed to drain

121

APPENDIX D. LID SETUP

Table D.2: Table of description of parameters used in the bio-retention cell (BRC) module inSWMM (Rossmann and Huber, 2016b, Rossmann, 2015)

Parameters Bio-retention cell (BRC)

Parameter Description

Surf

ace

Lay

er

Berm Height [mm] Maximum depth to which water can pond above thesurface of the unit before overflows occur

Vegetation volume frac-tion [−]

The fraction of volume with in the storage depth filledwith vegetation Normally this volume can be ignored.

Surface Roughness [−] Manning’s roughness coefficient for overland flowover surface soil cover

Surface Slope [%] Use 0 for LIDs like rain barrel and bio-retention cells

Soil

Lay

er

Thickness [mm] The thickness of the soil layer. Range from 450-900mm for bio retention based units

Porosity [−] The volume of pore space relative to total volume ofsoil

Field Capacity [−] Volume of pore water relative to total volume after thesoil has been drained

Wilting Point [−] Volume of pore water relative to total volume for adried soil

Conductivity [mm/h] Saturated hydraulic conductivity

Conductivity Slope [−] Slope of the curve for log(conductivity) vs. soil mois-ture. Typical 30 to 60.

Suction Head [mm] The average value of soil capillary suction along thewetting front

Stor

age

Lay

er

Thickness [mm] Thickness of gravel layer. Typical 150-450 mm.

Void Ratio [−] Volume of void space relative to the volume of solidsin the layer

Seepage rate [mm/h] The rate at which water seeps into the native soil be-low the layer

Clogging Factor [−] Total volume of treated runoff it takes to completelyclog the bottom of the layer divided by the void vol-ume of the layer. Use value 0 to ignore clogging

Dra

in

Flow Coefficient [mm/h] Determines the rate of flow through a drain

Flow Exponent [−] Determines the rate of flow through a drain

Offset Height [mm] Height of the drain above the bottom of the storagelayer

122

D.2. PARAMETER VALUES

D.2 Parameter values

The parameters used for the bio-retention cell and rain barrel can be seen in the respectively

tables D.4 and D.3.

Table D.3: Table of required parameters for setup of the module rain Barrel in SWMM. Sourcesfor the individual values are also included.

Parameters Rain Barrel

Parameter Value Source

Stor

age

Lay

er

Barrel Height [mm] 990.6 (The Great American Rain BarrelCompany, 2019)

Dra

in

Flow Coefficient [mm/h] 298 (The Great American Rain BarrelCompany, 2019)

Flow Exponent [−] 0.5 (Rossmann and Huber, 2016b,p. 133)

Offset Height [mm] 101.6 (The Great American Rain BarrelCompany, 2019)

Drain Delay [h] 6

123


Table D.4: Table of required parameters for bio-retention cell module in SWMM. Sources ofvalues used are also included.

Parameters Bio-retention cell (BRC)

Parameter BRC II BRC III Source

Surf

ace

Lay

er

Berm Height [mm] 200 - (Paus and Braskerud, 2013, p. 55)

Vegetation volumefraction [−]

0 - (Rossmann, 2015, p. 246)

Surface Roughness [−] N-Perv N-Perv (Table 4.3)

Surface Slope [%] 0 - (Rossmann, 2015, p. 246)

Soil

Lay

er

Thickness [mm] 800 - (Braskerud et al., 2012, p. 493, Pausand Braskerud, 2013, p. 55)

Porosity [−] 0.4 - (Dingman, 2015, pp. 319,338, Sak-sæther and Kihlgren, 2012, p. 47)

Field Capacity [−] 0.135 - (Dingman, 2015, p. 319, Saksætherand Kihlgren, 2012, p. 47)

Wilting Point [−] 0.05 - (Dingman, 2015, p. 319, Saksætherand Kihlgren, 2012, p. 47)

Conductivity [mm/h] 358 - (section 3.3.4)

Conductivity Slope [−] 42.1 - (Paus and Braskerud, 2013, p. 56,Rossmann, 2015, p. 248)

Suction Head [mm] 71.7 - (Dingman, 2015, p. 371, Saksætherand Kihlgren, 2012, p. 47)

Stor

age

Lay

er Thickness [mm] 300 - (Paus and Braskerud, 2013, p. 55)

Void Ratio [−] 0.653 - (Dingman, 2015, p. 338, Saksætherand Kihlgren, 2012, p. 47)

Seepage rate [mm/h] 1 10

Clogging Factor [−] 0 - (Rossmann, 2015, p. 248)

Dra

in

Flow Coefficient[mm/h]

2.1 0 (Paus and Braskerud, 2013, p. 64,Rossmann and Huber, 2016b,p. 133)

Flow Exponent [−] 0.5 0 (Rossmann and Huber, 2016b,p. 133)

Offset Height [mm] 200 0 (Paus and Braskerud, 2013, p. 55)

124

D.3. RAINFALL

D.3 Rainfall

To test the performance on the different LIDs, a continuous rainfall series from the rain gauge

Kjelsås from the period 25.05.2018 to 30.09.2018 is used in addition to design rainfalls. The

continuous series account for antecedent conditions, which have a major influence on the runoff

response (Dingman, 2015, p. 468), while the event based rainfall can test whether the infras-

tructure is designed for the return period of interest. According to the report from Norsk Vann

(Lindholm et al., 2008, p. 50) a design rainfall of 10 year return period should not give flooding

in basements in 1/20 years in residential areas and a 20 year design rainfall should not give

flooding in basements in 1/30 years for areas in city center and for industrial areas.

Earlier studies (Hernes, 2018, Ingebrigtsen, 2017) in the same area, Grefsen, have used a sym-

metric hyetograph for a frequency of 5 years. Both the studies use a symmetric hydrograph

generated by Oslo VAV for station Blindern 18701 in their model simulation. It is unknown for

how many seasons the hydrograph is based on and whether Ingebrigtsen (2017) used a warm-up

period such as Hernes (2018). The IDF-curves from The Norwegian Centre for Climate Ser-

vices (hereby referred to as NCCS) together with the hyetograph used by Hernes (2018) and

Ingebrigtsen (2017) for 5-years rainfall are found in figure D.1. The station Blindern 18701 has

an IDF-curve based on data of 49 seasons (1968-2018), while Disen 18420, the closest station

to Grefsen with IDF-curves, is based on 20 seasons (1998-2018).

Figure D.1: 5-year IDF-curves and hyetographs with 5 minutes timestep. The duration ofintensity is of 10 minutes. Left: IDF-curves for 5-years precipitation from Blindern 18701 andDisen 18420 generated from NCCS. Manually interpolated values are dots not filled. Right:5-years hyetographs from Oslo VAV together with hyetographs for Blindern 18701 and Disen18420 from the IDF-curves presented in the left figure.

Oslo VAV has chosen precipitation for the 10th most intensive minutes as the middle point, and

125


then used a 5 minutes timestep, where 10 minutes/2 is the precipitation amount on each side

of the mid axis. To create such a hyetograph for Blindern and Disen, points on the IDF-curve

for each 10th minute are found by manual interpolation, see figure D.1. The value of the point

is used to create the graph on right hand side in figure D.1. The same procedure is used on

the 20-year rainfall (Figure D.2). A general description of construction of hyetographs from

IDF-curves can be found in Ødegård et al. (2014, pp. 349-350).

Figure D.2: 20-year IDF-curves and hyetographs with 5 minutes timestep. Duration of in-tensity is 10 minutes. Left: IDF-curves for 20-years precipitation from Blindern 18701 andDisen 18420 generated from NCCS. Manually interpolated values are dots not filled. Right:20-years hyetographs for Blindern 18701 and Disen 18420 from the IDF-curves presented inthe left figure.

To test the performance of LIDs on the discharge volume and magnitude, simulations with 5-

and 20-year design rainfalls are conducted. The choice of rainfall is due to earlier studies,

Hernes (2018) and Ingebrigtsen (2017), which both used 5-year rainfall with the model from

Oslo VAV. They both used the same model, but for slightly different catchments (see figure

F.2). The 20-year rainfall is chosen to test if the relative magnitude of the discharge with LIDs

is affected by the intensity of the rainfall. The 20-year rainfall is also where step two in S3SA

starts (see section 2.2.2).

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D.3. RAINFALL

D.3.1 Calculated discharge after design rainfall

Table D.5: Maximum discharge peaks (Pmax) [l/s] at AK52 for a 5-year rainfall with differentLID implementations presented in the study.


Scenario Pmax,A Pmax,B

Scenario 0 1279.3 1168.5

100% RD 871.8 796.6

100% RB 860.7 792.0

100% BRC III 851.7 789.1

80% RD 1044.3 979.9

80% RB 1042.6 966.1

80% BRC III 1045.5 957.0



Scenario 0 141.1 192.6

100% RD 25.0 84.0

100% RB 24.9 85.3

100% BRC III 26.1 84.4

80% RD 77.0 136.2

80% RB 76.9 138.8

80% BRC III 79.2 135.8

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Table D.6: Maximum discharge peaks (Pmax) [l/s] at AK52 for a 20-year rainfall with differentLID implementations presented in the study.



Scenario 0 1638.8 1288.4

100% RD 1154.7 945.5

100% RB 1140.1 937.9

100% BRC III 1119.9 942.6

80% RD 1384.0 1165.1

80% RB 1384.7 1152.8

80% BRC III 1373.0 1163.7



Scenario 0 184.6 238.9

100% RD 35.2 110.8

100% RB 36.4 111.4

100% BRC III 34.9 112.7

80% RD 103.9 177.3

80% RB 105.1 176.9

80% BRC III 103.8 180.1

128

D.4. CONTINUOUS SIMULATION WITH LIDS

D.4 Continuous simulation with LIDs

Figure D.3: The discharge peaks used for the continuous simulations with LIDs (left) and thedischarge peaks for 100% RD at AK52. The peaks not filled with orange in the left figure isthe same peaks seen in the right figure.

129


D.5 LID implementation with a different approach

In SWMM the model cannot distinguish between roof area and road area. 100% roof area

corresponds to 64% of the impervious area at the Grefsen plateau and 465 LID-modules. Dis-

connection of 64% of the impervious area equals a fraction of total disconnection of 22% in

model A and 32% in model B. Before implementation of LIDs the total disconnected area was

65% and 50% for respectively models A and B. After implementation of LIDs the total dis-

connected area has increased to 87% and 82% for models A and B. With this approach, the

disconnected area is comparable larger with model B than A as illustrated in figure D.4

Figure D.4: Distribution of road and rooftop area on each side of the routing line. 100%LID as illustrated in the lower figures covers more than the roof area in model B, while inmodel A not all the rooftop area is covered by the LID, even though the same amount of theLID-modules have been used

In the results below, 25% LID indicate that the LID-modules (BRC and RB) receives water

from an area equivalent to 25% of the roof area in the catchment. Because of the initial routing,

the implementation of 25% LIDs in model setup A results in a total roof disconnection of 64%

and 66% in model setup B. The ratio DCIA/TIA in model A have decreased from 0.35 to 0.29,

while in model B from 0.5 to 0.42. So 25% in the plots below is the same as 64% in for model

130

D.5. LID IMPLEMENTATION WITH A DIFFERENT APPROACH

setup A and 66% in model setup B.

D.5.1 Continuous rainfall

The regression line in the following plots shows an average reduction in the discharge peaks

caused by the LIDs.

The difference between the BRC II and BRC III is not noticeable for the continuous rainfall for

model A or model B, even for 25% disconnection or 100% disconnection as shown in figures

D.5 and D.6.

The average discharge peaks can be reduced with respectively 5% (y= 0.95x) and 19% at AK52

for 25% BRC and 100% BRC with model A.

Figure D.5: Simulations of the discharge peaks with BRC of 25% and 100% for two types, BRCII and BRC III, against simulations with no LIDs (scenario 0). The regression line representsthe mean discharge magnitude at AK52 after the LIDs are implemented in model A.

The average discharge peaks can be reduced with respectively 11% (y = 0.89x) and 28 % at

131


AK52 for 25% BRC and 100% BRC with model B.

Figure D.6: Simulations of the discharge peaks with BRC of 25% and 100% for two types, BRCII and BRC III, against simulations with no LIDs (scenario 0). The regression line representsthe mean discharge magnitude at AK52 after the LIDs are implemented in model B.

The rain barrels (RB) are implemented in the same way as the BRCs. In model A, the average

reduction in the discharge peaks are 18% for 100% and 7% for 25% RB for AK52 as shown in

figure D.7. Model B, see figure D.8, reduce the average discharge peaks with 26% for 100%

RB and 8% for 25% RB for AK52.

132


The disconnection of downspouts is implemented with the re-routing option in SWMM (sub-

section 4.5). In the setup of disconnection of roof area, the connected road area before imple-

mentation of LIDs is assumed the same after the implementation. The total disconnection is of

that reason larger for model A than B, since model A initially has a higher disconnection rate

than model B.

In model A, the average discharge peaks are reduced with 23 % for 100% RD and 13% for 80%

RD at AK52.

Figure D.7: Left: The LID measures RD for 80% (upper) and 100% (lower) roof disconnectionat the Grefsen plateau. Right: Simulations with RB where 25% (upper) and 100% (lower) ofthe connected roof areas are disconnected with rain barrels. The reduction in the dischargepeaks at AK52 with model A.

In model B, 100% RD gives an average reduction of 24 % on the discharge peaks at AK52. And

80% RD gives a mean reduction in the discharge peaks of 13%.

133


Figure D.8: Left: The LID measures RD for 80% (upper) and 100% (lower) roof disconnectionat the Grefsen plateau. Right: Simulations with RB where 25% (upper) and 100% (lower) ofthe connected roof areas is disconnected with rain barrels. The reduction in the dischargepeaks at AK52 with model B.

The largest reduction in one model is not the same as for the other one. In model A, 100%

RD gives the largest reduction of 23% followed by 100% BRC with 19%. In model B, 100%

BRC gives largest reduction of 28% followed by 100% RB of 26%. The range of reduction

in discharge peaks for 100% disconnection at AK52 is 18-23% for model A and 24-28% for

model B.

134


At Jupiterjordet, where all the sewer system is CSS, the percent reduction in discharge peaks

for the LIDs is higher. The wastewater from the householders is not included in the discharge.

Therefore, the percentage average reduction is only for stormwater. In figure D.9, the average

reduction in the discharge peaks are 15% with 25% BRC and 63% with 100% BRC with model

A. No difference between the BRCs is found in model A. In model B, there is a slightly small

difference for BRC II and BRC III, where BRC III gives the largest reduction (see figure D.10).

Figure D.9: Simulations of the discharge peaks with BRC of 25% and 100% for two types, BRCII and BRC III, against simulations with no LIDs (scenario 0). The regression line representsthe mean discharge magnitude at Jupiterjordet after the LIDs are implemented in model A.

135


Figure D.10: Simulations of the discharge peaks with BRC of 25% and 100% for two types,BRC II and BRC III, against simulations with no LIDs (scenario 0). The regression line repre-sents the mean discharge magnitude at Jupiterjordet after the LIDs are implemented in modelB.

In figure D.11, disconnection of 25% of the roof area with RB gives an average reduction in

discharge peaks of 16% in model A. When all of the roof area is disconnected, 100% RB, the

average discharge peaks is reduced with 63 %. In model B an average reduction in the discharge

peaks of 18% is observed for 25% RB and 67% for 100% RB (see figure D.12). The measures

with 100% RB, 100 % BRC II and 100% BRC III have almost the same average reduction in

the discharge peaks for model A and B.

136


The 80% RD gives a reduction in the average discharge peaks with 46 % for model A (see figure

D.11). For 100% RD the regression line has a slope of 20, indicating a reduction of 80%. The

coefficient of determination, R2, is however very low. Therefore, the effects of the 100% RD is

more uncertain. In model B, a 33% reduction in the discharge peaks is observed for 25% RD

and a 60% reduction for 100% RD as shown in figure D.12.

Figure D.11: Left: The LID measures RD for 80% (upper) and 100% (lower) roof disconnec-tion at the Grefsen plateau. Right: Simulations with RB where 25% (upper) and 100% (lower)of the connected roof areas are disconnected with rain barrels. The reduction in the dischargepeaks is for Jupiterjordet with model A.

137


Figure D.12: Left: The LID measures RD for 80% (upper) and 100% (lower) roof disconnec-tion at the Grefsen plateau. Right: Simulations with RB where 25% (upper) and 100% (lower)of the connected roof areas are disconnected with rain barrels. The reduction in the dischargepeaks is for Jupiterjordet with model B.

At Jupiterjordet, model A has the largest reduction for 100% RD. In model B, 100% BRC II,

100% BRC III and 100% RB give almost equal reduction of 64-68% in the discharge peaks.

D.5.2 Design rainfall

The simulations of a 5-year rainfall for model A are plotted in figure D.13 and for model B in

figure D.14.

In AK52 and Jupiterjordet, model A has the lowest discharge peak with 100% RD. On the other

hand, model B has the lowest discharge peak for 100% BRC and 100% RB.

The effects from BRC and RB are almost equal in both models. This is the reason for the pink

solid line (100% BRC) is almost hidden by the green solid line (100% RB).

138


Figure D.13: Discharge in AK52 (upper) and Jupiterjordet (lower) during a 5-year rainfallwith model A. The dotted red line in the upper plot, at AK52, represents discharge where CSOsoccurs.

139


Figure D.14: Discharge in AK52 (upper) and Jupiterjordet (lower) during a 5-year rainfallwith model B. The dotted red line in the upper plot, at AK52, represents discharge where CSOsoccurs.

At AK52, LID implementation has largest effect in terms of reducing the peak magnitude and

volume in model B compared to model A with BRC and RB. This is shown in table D.7. The

number in the table can be read as, with 100% RD in model A the discharge peak from 5-year

rainfall is reduced to 68% of the initial peak (the peak from scenario 0).

140


Table D.7: Relative comparison of the discharge (% Discharge peak ) and volume of waterabove the overflow weir (% Volume) at AK52 for a 5-year rainfall with model A and model B.





% Volume

Scenario 0 100 100 100 100

100% RD 68 68 68 69

100% RB 75 75 65 67

100% BRC III 74 73 64 66

80% RD 82 82 84 83

25% RB 94 94 90 92

25% BRC III 94 94 89 92

To more easily distinguish between the relative reductions in the discharge peak at Jupiterjordet,

the relative comparison is made in table D.8.

Table D.8: Relative comparison of the discharge (% Discharge peak) in manhole 172350 fora 5-year rainfall and a 20-year rainfall for the different scenarios presented in the study.


Model A Model B


Scenario 0 100 100

100% RD 17 44

100% RB 36 37

100% BRC III 37 37

80% RD 55 71

25% RB 85 88

25% BRC III 86 87

The reduction in discharge peak and volume at AK52 and at Jupiterjordet is decreasing for a

20-year rainfall compared to the 5-year rainfall. And 100% RD gives the largest reduction in the

discharge for model A in outlet AK52 and Jupiterjordet, see figure D.15. The lowest reduction

141


in the discharge is found for 25% BRC and 25% RB. The LID giving the highest reduction

is associated with the measure giving the largest reduction in the volume (see tables D.9 and

D.10).

Figure D.15: Discharge at AK52 (upper) and Jupiterjordet (lower) during a 20-year rainfall.The dotted red line in the upper plot, AK52, represents discharge where CSOs occurs.

The 20-year rainfall creates some numerical unstable results, as a consequence of the large

runoff created inside each subcatchment. This comes into sight at Jupiterjordet between the

time 15:15 and 15:45. As for the 5-year rainfall, 100% BRC and 100% RB give the largest

reduction in the discharge in model B and 25% BRC and 25% RB give least reduction. To

distinguish the difference in the performance of BRC and RB, see tables D.9 and D.10.

142


Figure D.16: Discharge at AK52 (upper) and Jupiterjordet (lower) during a 20-year rainfall.The dotted red line in the upper plot, AK52, represents discharge where CSOs occurs.

143


Table D.9: Relative comparison of the discharge (% Discharge peak) and volume of waterabove the overflow weir (% Volume) at AK52 for a 20-year rainfall with model A and B for thedifferent scenarios presented in the study.





% Volume

Scenario 0 100 100 100 100

100% RD 71 72 73 65

100% RB 78 78 70 62

100% BRC III 76 75 69 60

80% RD 85 84 90 81

25% RB 97 95 97 89

25% BRC III 96 95 96 88

Table D.10: Relative comparison of the discharge (% Discharge peak) at Jupiterjordet for a20-year rainfall with model A and B for the different scenarios presented in the study.


Model A Model B


Scenario 0 100 100

100% RD 19 46

100% RB 37 40

100% BRC III 38 39

80% RD 56 74

25% RB 86 87

25% BRC III 87 86

D.5.3 Parameter values

144


Table D.11: Table of values used for 25% and 100% connection of the rooftops to bio-retentioncells. Number of units is number of bio-retention cells in the subcatchment. % of imperviousarea treated is the fraction of impervious area (buildings and roads) contributing with runoffto the bio-retention cells. New % impervious area is the new fraction of imperviousness sinceparts of the total area is decreased due to occupation of bio-retention cells. The area of thebio-retention cell is 13.1 m2.

LID setup bio-retention cells

100% Bio-retention cell (BRC)




T161143 72 72.5 29.0

T161147 133 58.2 32.6

T161185 22 79.7 29.8

T161193 78 63.4 31.9

T172350 125 64.6 29.8

T310533 32 58.9 31.5

25% Bio-retention cell (BRC)




T161143 18 18.1 28.6

T161147 33 14.6 32.1

T161185 6 19.9 29.3

T161193 198 15.9 31.5

T172350 31 16.1 29.4

T310533 8 14.7 31.1

145


Table D.12: Table of values used for 25% and 100% disconnection of the rooftops to rainbarrel. Number of units represents number of rain barrels in each subcatchment. %imperviousarea treated is the fraction of impervious area where the runoff is caught by the rain barrel,until it is full. When the rain barrel is full, the overflows are distributed on the pervious area.Each rain barrel occupies 0.4 m2.

LID setup for rain barrel

100% Rain Barrel (RB)



T161143 72 72.5

T161147 133 58.2

T161185 22 79.7

T161193 78 63.4

T172350 125 64.6

310533 32 58.9

25% Rain Barrel (RB)



T161143 18 18.1

T161147 33 14.6

T161185 6 19.9

T161193 198 15.9

T172350 31 16.1

310533 8 14.7

146


The RD does not have any parameters, because the re-routing option is used. In this way, the

exact fraction of DCIA/TIA depends on the initial DCIA/TIA and cannot be set before the

model is validated. RD is done for 80% roof disconnection and 100% roof disconnection.

Table D.13: Fraction of impervious area disconnected to the outlet in each subcatchment for100% and 80% disconnection of rooftops, when it is assumed that already 55% of the rooftopsare disconnected. 55% of rooftops already disconnected implies that 82% of the road area isdisconnected at all time.

MODEL A



T161143 95 81

T161147 92 81

T161185 96 80

T161193 93 81

T172350 93 81

T310533 93 81

Table D.14: Fraction of impervious area disconnected to the outlet in each subcatchment for100% and 80% disconnection of rooftops, when it is assumed that already 55% of the rooftopsare disconnected. 55% of rooftops already disconnected implies that 82% of the road area isdisconnected at all time.

MODEL B



T161143 84 69

T161147 75 64

T161185 88 72

T161193 78 66

T172350 79 66

T310533 76 64

147

E Model simulations

Figure E.1: The contribution from the CSS at the Grefsen plateau (161146) and the wastewatersystem from Grefsenkollen (297077). The simulation is from a 5-year rainfall with model setupA.

Figure E.2: The contribution from the CSS at the Grefsen plateau (161146) and the wastewatersystem from Grefsenkollen (297077). The simulation is for a 5-year rainfall with model setupB.

149

APPENDIX E. MODEL SIMULATIONS

Figure E.3: Calibration process at AK52. Illustrates the pulsing of water into AK52 when thegroundwater exponents, B1 and B2, are 0.75 for A1 and A2 of 1.

150

F GIS analysis

Figure F.1: The spatial distribution of the slopes in the catchment in degrees. The green colorrepresent flat areas and red represent steep areas.

151

APPENDIX F. GIS ANALYSIS

Figure F.2: Map of the different catchment used in present and previous study of Grefsen andthe investigation of CSOs at AK52

152

G Spatial storage capacityThe spatial storage capacity in September 2017 at Grefsen can be seen in figure G.1. During the

CSOs event at Grefsen in September 2017, the storage capacity was low. The subsurface storage

capacity is compared to the maximum simulated value in the reference period 1981-2010 using

HBV-model.

Figure G.1: Four pictures with the storage capacity of the subsurface calculated with HBV-model from NVE. The pictures can be seen at SeNorge.no. The pictures are generated at thesame time of the date, 07:32, based on interpolated weather observations. Grefsen is labelednorthwest for the label Oslo. Data owner is The Norwegian Water Resources and EnergyDirectorate (NVE)

153

H Pictures

Figure H.1: Photo of a stormdrain in Waldemar Thranes gate in Oslo. It illustrates the factthat some stormdrains can be exposed to clogging. Photo: Ina Storteig

Figure H.2: Photo of Grefsenveien 21.09.2018 (left) and 21.05.2019 (right). The photos aretaken against north and is approximately at the boarder of the catchment. Photo: Ina Storteig

155

APPENDIX H. PICTURES

Figure H.3: Photo of where the stormwater sewer from the catchment comes out. During CSOevents, the pipe transports diluted wastewater. Photo: Ina Storteig

156

I AK52

Figure I.1: A drawing of the conduits at AK52. The picture is from Oslo VAV and is publishedwith permission from Oslo VAV.

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continuous urban hydrological modelling of discharge peaks

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