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Exploring data flows and system links in real-time urban drainage modelling in thewake of digitalisation

Lund, Nadia Schou Vorndran

Publication date:2019

Document VersionPublisher's PDF, also known as Version of record

Link back to DTU Orbit

Citation (APA):Lund, N. S. V. (2019). Exploring data flows and system links in real-time urban drainage modelling in the wake ofdigitalisation. Technical University of Denmark.

Nadia Schou Vorndran Lund PhD Thesis November 2019

Exploring data flows and system links in real-time urban drainage modelling in the wake of digitalisation

i

Exploring data flows and system links in

real-time urban drainage modelling in the

wake of digitalisation

Nadia S. V. Lund

PhD Thesis

November 2019

DTU Environment

Department of Environmental Engineering

Technical University of Denmark

ii

Exploring data flows and system links in real-time urban drainage mod-

elling in the wake of digitalisation

Nadia Schou Vorndran Lund

PhD Thesis, November 2019

The synopsis part of this thesis is available as a pdf-file for download from

the DTU research database ORBIT: http://www.orbit.dtu.dk.

Address: DTU Environment

Department of Environmental Engineering

Technical University of Denmark

Bygningstorvet

Building 115

2800 Kgs. Lyngby

Denmark

Phone reception: +45 4525 1600

Fax: +45 4593 2850

Homepage: http://www.env.dtu.dk

E-mail: reception@env.dtu.dk

Cover: STEP

iii

Preface

This PhD thesis was conducted at the Department of Environmental Engi-

neering at the Technical University of Denmark (DTU Environment) from

June 2016 to August 2019, including a stay at IHE Delft from September to

December 2017. The thesis was supervised by Professor Peter Steen Mikkel-

sen and Associate Professor Morten Borup from DTU Environment as well as

Dr. Techn. Ole Mark and Technical Director Henrik Madsen from DHI. The

PhD project was partly supported by the Smart Cities Water Solutions pro-

ject, which was funded by Realdania through the Klimaspring programme.

The thesis is based on five scientific papers. Two of these have been pub-

lished in international journals, and one has been accepted for publication.

These will be referred to in the text by their paper number written with Ro-

man numerals (I–V). The numbering follows their appearance in the thesis.

I Lund, N.S.V., Falk, A.K.V., Borup, M., Madsen, H., and Mikkelsen,

P.S., 2018. Model predictive control of urban drainage systems: A

review and perspective towards smart real-time water management.

Crit. Rev. Env. Sci. Tech., 48(3), 279–339. doi:10.1080/10643389.

2018.1455484.

II Lund, N.S.V., Kirsten, J.K., Madsen, H., Mark, O., Mikkelsen, P.S.,

and Borup, M., manuscript in preparation. Using an urban drainage

model to link smart meter consumption data with in-sewer observa-

tions to estimate wastewater flows and identify system anomalies.

III Lund, N.S.V., Madsen, H., Mazzoleni, M., Solomatine, D., and Bo-

rup, M., 2019. Assimilating flow and level data into an urban drain-

age surrogate model for forecasting flows and overflows. J. Environ.

Manage., 243(109052), 1–13. doi:10.1016/j.jenvman.2019.05.110.

IV Lund, N.S.V., Borup, M., Madsen, H., Mark, O. Arnbjerg-Nielsen,

K., and Mikkelsen, P.S., 2019. Integrated stormwater inflow control

for sewers and green structures in urban landscapes. Nat. Sustain.,

accepted.

V Lund, N.S.V., Borup, M., Madsen, H., Mark, O., and Mikkelsen,

P.S., submitted. Model predictive control of stormwater inflows us-

ing a linear surrogate model to integrate the operation of above- and

belowground infrastructure.

iv

In this online version of the thesis, Papers I–V are not included but can be

obtained from electronic article databases e.g. via www.orbit.dtu.dk or on

request from DTU Environment, Technical University of Denmark,

Miljoevej, Building 113, 2800 Kgs. Lyngby, Denmark, info@env.dtu.dk.

In addition, the following publications were also concluded during the PhD

study but are not included in the thesis:

Lund, N., Borup, M., Halvgaard, R., Falk, A.K., Mark, O., Madsen, H., and

Mikkelsen, P.S., 2017. Advancing from underground to above-ground model

predictive control in urban drainage. In Proceedings of the 14th International

Conference on Urban Drainage, Prague, Czech Republic. September 10–15,

2017.

Pedersen, L.B., Mikkelsen, P.S., Christiansen, L.E., Falk, A.K., Borup, M.,

Lund, N.S.V., Halvgaard, R., Sørensen, H.R., Bassøe, L., and Madsen, H.,

2017. From vision to operation – smart real-time control of water systems in

Aarhus, Denmark. In Proceedings of the 14 th International Conference on Ur-

ban Drainage, Prague, Czech Republic. September 10–15, 2017.

Halvgaard, R., Falk, A.K., Lund, N.S.V., Borup, M., Mikkelsen, P.S., and

Madsen, H., 2017. Model predictive control for urban drainage: testing with a

nonlinear hydrodynamic model. In Proceedings of the 14 th International Con-

ference on Urban Drainage, Prague, Czech Republic. September 10–15, 2017.

Lund, N., Mazzoleni, M., Madsen, H., Mark, O., Mikkelsen, P.S,

Solomatine, D., and Borup, M., 2018. Using the Ensemble Kalman filter to

update a fast surrogate model for flow forecasting. In Proceedings of the 11 th

International Conference on Urban Drainage Modelling, Palermo, Italy.

September 23–26, 2018.

Lund, N., Borup, M., Kirstein, J.K., Mark, O., Madsen, H., and Mikkelsen,

P.S., 2019. Lessons learned from comparing smart meter water consumption

data with measured wastewater flow in the drainage system. In Proceedings

of the 17th International Computing and Control for the Water Industry

Conference, Exeter, United Kingdom, September 1–4, 2019.

Lund, N.S.V., Pedersen, J.W., Hamouz, V., Jensen, D.M.R., Marques

T.C.F.S., Palmitessa, R., Pedersen, A.N., Skrydstrup, J., Stentoft, P., Arn-

bjerg-Nielsen, K., and Mikkelsen, P.S., manuscript in preparation. Fit-for-

indicator urban drainage modelling – the missing link between the ‘modelling

whys’ and ‘modelling hows’.

v

Acknowledgements

I would like to thank my supervisors Professor Peter Steen Mikkelsen and

Associate Professor Morten Borup from DTU Environment as well as Dr.

Techn. Ole Mark and Technical Director Henrik Madsen from DHI for their

support and help throughout the PhD study.

The PhD project was during the first year part of the Smart Cities Water

Solutions project, which was funded by Realdania through the Klimaspring

programme. The PhD study has furthermore been peripherally connected to

the Water Smart Cities project. I would like to thank the partners within these

two programmes for professional discussions, and Anders Breinholt and Lone

Bo from HOFOR for providing data and the MIKE URBAN model for the

Damhusåen catchment. I would also like to place a special thanks to all my

colleagues at DHI, especially Anne Katrine Vinther Falk and Rasmus

Halvgaard for assistance with MIKE OPERATIONS and the M1D API as

well as numerous discussions on MPC of urban drainage systems throughout

the PhD project.

I would also like to thank Professor Dimitri Solomatine and Postdoc Maurizio

Mazzoleni who supervised me during my external research stay at IHE Delft.

A special thanks goes to all of my colleagues there who ensured that my stay

in the fall of 2017 was not only prosperous, but also a lot of fun.

Part of the PhD study was conducted with help from Andreas Bech Mikkel-

sen, Lotte Krogh and other staff members from Utility Elsinore as well as

their partners from Kamstrup, BlueNorth and LNH water. Thank you for

sharing your expertise, data and models.

Finally, I would like to thank all my colleagues at DTU Environment, espe-

cially in the Urban Water Systems section. You have ensured that these three

years have been filled with laughter. Thanks to Francesco Righetti for setting

up the surrogate model for the Damhusåen catchment. Also, a special thanks

to Jonas Kirstein for helping me to keep my spirits up at work as well as at

home.

vi

vii

Summary

An increased number of combined sewer overflows (CSOs), as well as flood-

ing of streets and basements, drives an on-going transformation of the urban

drainage sector. Digitalisation is seen as a means of mitigating these events

and requires the collection of system attribute data, input data and in-sewer

observations. This data needs to be handled and analysed before it can be ap-

plied to decision-making, and this process is often dependent on models. Re-

al-time models can be divided into monitoring, forecast and control models

for system replication, forecasting of the system state and decision-making,

respectively. The adequateness of these models may be evaluated by as-

sessing how much information is embedded in each modelling aspect (the

‘granularity’) and comparing this with the granularity of the quantity used to

evaluate the performance of the modelled solution (the ‘indicator’). Some of

the underlying methods for the models have been around for decades, but

technical, human and linguistic barriers have impeded their uptake in prac-

tice. Furthermore, the urban drainage system is physically linked to other

natural and man-made sub-systems through the flow of water; however, this

has traditionally been either ignored or not fully exploited in real-time urban

drainage models. The current challenges may require that we explore these

system links further.

The hypothesis of this thesis is that digitalisation can facilitate the exploita-

tion of existing and emerging data flows as well as system links in real-time

models to obtain better solutions. This hypothesis is investigated through

three digitalisation examples including a monitoring, a forecast and a control

model.

Digitalisation of the water distribution system has led to an increased number

of smart meters measuring the water consumption at each house with a high

temporal resolution. Linking water supply and sewer systems by expanding

the observational space to include smart meter data may be used to obtain

wastewater inputs distributed in both space and time. In this thesis, smart me-

ter consumption data was therefore coupled to an urban drainage monitoring

model and the results compared with flow measurements from the sewer sys-

tem. This was not straightforward since the required data was hidden in dif-

ferent data silos and thus difficult to access. Moreover, the simulated

wastewater flow did not always match the measurements, most likely due to

erroneous in-sewer observations. Smart meter data may thus also be used as

an independent data source to validate and question in-sewer observations.

viii

Digitalisation of urban drainage systems is expected to lead to increased in-

sewer sensing. This makes it possible to expand the application area of tradi-

tional in-sewer observations to include real-time model updating, hereby

keeping the models in touch with reality. In this thesis, the updating of a sur-

rogate forecast model with level and flow observations improved the flow

and overflow forecast performance two hours into the future. Overall, level

observations improved the model the most. The forecasts were also improved

by using a distributed representation of the dry weather flow compared to

adding the dry weather flow profile to the downstream model output. Obtain-

ing such a distributed dry weather flow through in-sewer sensing requires a

large number of sensors. The use of smart meter data may thus also be rele-

vant for forecast models.

Digitalisation is expected to promote the development of new types of actua-

tors to be regulated by more advanced control schemes, hereunder model

predictive control (MPC). This allows for the activation of otherwise passive

links between the sewer system, stormwater control measures and the urban

landscape. This control can be used to minimise CSO, bypass, energy costs,

etc. with lower environmental impacts than by constructing new grey infra-

structure. Having water visible on the surface in a controlled manner is ex-

pected also to increase amenity values and the public’s resilience. Nuisances

caused by the control may include an increased risk of unintended flooding

and disturbance of people’s daily lives. In this thesis, the control was imple-

mented by expanding the model boundaries of an internal MPC model (a

coarsely grained model that simulates the sewage flow) to cover both the un-

derground and above-ground systems. The control concept was close to opti-

mal in respect to CSO minimisation when the prediction horizon was near the

transport time through the system. Input forecast models such as the updated

surrogate model may facilitate such a horizon.

Silo thinking in respect to data, systems and institutions, low data quality, a

conservative and fragmented sector and linguistic uncertainty pose limiting

factors for reaching the full potential of using digitalisation to exploit tradi-

tional and emerging data flows, as well as the interlinked nature of the urban

drainage system in real time. The thesis provides several structures to dimin-

ish linguistic uncertainty and shows that it is possible to expand observational

spaces, application areas and model boundaries, not only to obtain a more

efficient urban drainage management, but also to tap into new areas such as

sustainability, resilience and liveability.

ix

Sammenfatning

Afløbssektoren forandres i disse år som følge af et øget antal overløb fra fæl-

leskloakker (’combined sewer overflow’, CSO) samt oversvømmelser af veje

og kældre. Digitalisering opfattes som et værktøj til at mindske disse hændel-

ser, og dette kræver indsamling af systemattributdata, inputdata og observati-

oner fra afløbssystemet. Dataene skal håndteres og analyseres, før de kan

bruges som beslutningsgrundlag, og denne proces er ofte afhængig af model-

ler. Realtidsmodeller kan opdeles i monitorerings-, prædiktions- og styrings-

modeller til henholdsvis at replikere systemet, at forudse systemets tilstand

og at træffe beslutninger. Hvor passende disse modeller er, kan vurderes ved

at bedømme hvor meget information, der er indeholdt i hvert modellerings-

aspekt (’granulariteten’) og sammenligne dette med granulariteten af den

kvantitet, der bruges til at evaluere den modellerede løsning (’indikatoren’).

Nogle af de metoder, der ligger til grund for modellerne, har eksisteret i årti-

er, men tekniske, menneskelige og lingvistiske barrierer har forhindret deres

implementering i praksis. Afløbssystemet er desuden fysisk forbundet med

andre naturlige og menneskeskabte systemer gennem flowet af vand, men

dette bliver traditionelt set ofte enten ignoreret eller ikke fuldt udnyttet til

fulde i realtidsmodeller af afløbssystemet. De nuværende udfordringer kan

dog betyde, at vi bør undersøge disse systemforbindelser yderligere.

Hypotesen bag denne ph.d.-afhandling er, at digitalisering kan fremme udnyt-

telsen af eksisterende og nye datatyper samt systemforbindelser i realtidsmo-

deller for at opnå bedre løsninger. Denne hypotese undersøges gennem tre

digitaliseringseksempler, der inkluderer en monitorerings-, prædiktions- og

styringsmodel.

Digitalisering af vandforsyningen har betydet et forøget antal af smart meters,

der måler vandforbruget på husstandsniveau med høj tidslig opløsning. Ved

at udvide observationsområdet til at inkludere data fra smart meters kan for-

bindelsen mellem vandforsyningen og afløbssystemet bruges til at opnå spil-

devandsinputs, der er fordelt i både tid og sted. I denne ph.d.-afhandling blev

data fra smart meters derfor koblet med en monitoreringsmodel af afløbssy-

stemet, og resultaterne blev sammenlignet med flowmålinger fra kloakken.

Dette var ikke ligetil, da de nødvendige data var gemt i forskellige datasiloer

og derfor svære at tilgå. Derudover passede det modellerede spildevandsflow

ikke altid med målingerne, sandsynligvis fordi målingerne var fejlbehæftede.

Data fra smart meters kan derfor også bruges som uafhængig datakilde til at

validere og stille spørgsmålstegn ved målingerne af spildevandsflow.

x

Digitalisering af afløbssystemet forventes at forøge mængden af data fra af-

løbssystemet. Dette gør det muligt at udvide anvendelsesområdet for traditio-

nelle observationer til at inkludere realtidsopdatering af modeller, hvilket sik-

rer, at modellerne stemmer overens med virkeligheden. I denne ph.d.-

afhandling forbedrede opdateringen af en surrogat-prædiktionsmodel med

niveau- og flowmålinger modellens evne til at forudsige flows og CSO to ti-

mer ud i fremtiden. Overordnet set forbedrede niveaumålinger modellen

mest. Forudsigelserne blev også forbedrede ved at fordele det simulerede tør-

vejrsflow i oplandet i forhold til at tilføje tørvejrsflowprofilet til det ned-

strøms modeloutput. Det kræver mange målinger fra afløbssystemet at få så-

dan et fordelt tørvejrsflow, og brugen af smart meter data kan derfor også

være relevant for prædiktionsmodeller.

Digitalisering forventes at fremme udviklingen af nye typer af aktuatorer, der

vil blive reguleret af mere avancerede styringssystemer, herunder modelpræ-

diktiv styring (’model predictive control’, MPC). Dette gør det muligt at ak-

tivere ellers passive forbindelser mellem afløbssystemet, elementer til lokal

afledning af regnvand og det urbane landskab. Styringen kan bruges til at mi-

nimere CSO, bypass, udgifter til energi, etc. med lavere miljømæssige om-

kostninger end ved at konstruere ny grå infrastruktur. Ved at have synligt

vand på terræn under kontrollerede forhold kan forskønnelsesværdien og ind-

byggernes resiliens også forøges. Gener ved styringen kan medføre en øget

risiko for utilsigtede oversvømmelser og forstyrrelse af borgernes daglige liv.

I denne ph.d.-afhandling blev styringen implementeret ved at udvide model-

grænserne af en intern MPC-model (en grovkornet model, der simulerer flo-

wet af regnvand) til at inkludere både systemet under og over jorden. Styrin-

gen var tæt på optimal i forhold til minimering af CSO, når prædiktionshori-

sonten var tæt på transporttiden gennem systemet. En prædiktionsmodel som

den opdaterede surrogatmodel kan bruges til at opnå en sådan horisont.

Muligheden for at opnå det fulde potentiale ved at bruge digitalisering til at

udnytte traditionelle og nye datastrømme samt afløbssystemets forbundne

natur i realtid begrænses af siloopfattelser af data, systemer og institutioner,

lav datakvalitet, en konservativ og fragmenteret sektor samt lingvistisk usik-

kerhed. Denne ph.d.-afhandling opstiller flere strukturer for at mindske den

lingvistiske usikkerhed og viser, at det er muligt at udvide observationsområ-

der, anvendelsesområder og modelgrænser ikke kun for at opnå en mere ef-

fektiv drift af afløbssystemet, men også for at spille ind i nye felter såsom

bæredygtighed, resiliens og ’liveability’.

xi

Table of contents

Preface .......................................................................................................... iii

Acknowledgements ....................................................................................... v

Summary ..................................................................................................... vii

Sammenfatning ............................................................................................ ix

Table of contents ......................................................................................... xi

Abbreviations............................................................................................. xiii

1 Introduction ............................................................................................. 1

1.1 Background and motivation ......................................................................... 1

1.2 Research questions ....................................................................................... 3

1.3 Methodology ................................................................................................ 4

1.4 Thesis structure ............................................................................................ 5

2 The interlinked urban drainage system ................................................. 7

2.1 Management objectives ................................................................................ 7

2.2 Solution types .............................................................................................. 9

2.2.1 Structural solutions ........................................................................................9

2.2.2 Real-time solutions ........................................................................................9

2.3 The traditional meaning of integrated urban drainage management............ 10

2.4 The interlinked system – a subset of integration ........................................ 12

3 Digitalisation: real-time models and data ............................................ 15

3.1 A new boy in class or an old friend? .......................................................... 15

3.2 Concept and terminology ........................................................................... 17

3.3 Real-time modelling ................................................................................... 18

3.3.1 Data ............................................................................................................. 20

3.3.2 Monitoring models ...................................................................................... 21

3.3.3 Forecast models ........................................................................................... 22

3.3.4 Control models ............................................................................................ 23

3.4 Choosing data and models for the task at hand ........................................... 25

4 Real-time exploitation of data and links .............................................. 29

4.1 Smart meter data-based wastewater flow in monitoring models ................. 29

4.2 Updating forecast models with in-sewer levels and flows .......................... 33

4.3 Model-based integrated stormwater inflow control .................................... 38

5 Discussion .............................................................................................. 43

5.1 Data-driven or HiFi model-based online models ........................................ 43

5.2 Silo-thinking and data quality .................................................................... 44

5.3 Management objectives .............................................................................. 45

xii

6 Conclusions ............................................................................................ 47

7 Perspectives ........................................................................................... 49

8 References .............................................................................................. 51

9 Papers .................................................................................................... 59

xiii

Abbreviations

CSO Combined sewer overflow

DWF Dry weather flow

EnKF Ensemble Kalman filter

HiFi model High-fidelity model

MPC Model predictive control

NSE Nash-Sutcliffe efficiency

PID controller Proportional-integral-derivate controller

RQ Research question

SCM Stormwater control measure

WWTP Wastewater treatment plant

xiv

1

1 Introduction

1.1 Background and motivation In Europe, the systematic and widespread construction of sewer systems be-

gan during the industrial revolution in the 19 th century, mainly to mitigate the

spread of harmful diseases such as cholera. This had an immense impact on

public hygiene, which is why sanitation has been deemed one of the most im-

portant medical advances since 1840 (Ferriman, 2007). Most of the sewer

systems built at that time are ‘combined systems’, meaning that wastewater

and stormwater are carried in the same pipes. This is in contrast to ‘separate

systems’, whereby the two types of water flow in distinct pipes. From the

1960s onwards, the focus shifted from public health towards environmental

protection. Wastewater treatment plants (WWTPs) were constructed at the

end of the sewer systems to reduce the discharge of pollutants to natural wa-

ter bodies. Since then, densification of cities has, together with changes in

precipitation patterns, increased the amount of runoff from urban areas. The

World Economic Forum publishes each year the Global Risks Report stating

the biggest threats that our world is facing. In 2019, ‘extreme weather events

(e.g. floods, storms, etc.)’, ‘failure of climate-change mitigation and adap-

tion’ and ‘water crisis’ were all ranked in the top 10 (Myers and Whiting,

2019). This increased threat also relates to urban drainage; sewer systems are

increasingly being pushed beyond their capacity limits, leading to an in-

creased amount and number of combined sewer overflows (CSOs) as well as

flooding of basements and streets. These failures have negative consequences

for both the environment and human health, and drive an on-going transfor-

mation of urban drainage management. Both structural and real-time solu-

tions can be used to respond to these failures. Structural solutions include, for

example, sewer separation, construction of basins and storage tunnels and

implementation of stormwater control measures (SCMs, known under a wide

range of names (Fletcher et al., 2015)), such as bioretention units, rain gar-

dens and soakaways. Non-structural solutions, by contrast, seek to better use

the existing system capacity through real-time control or to prepare the public

through real-time warnings.

The ‘smart cities’ concept is increasingly being promoted in many different

parts of our society (Albino et al., 2015). The International Water Association

recently published a report on ‘Digital Water’ (Sarni et al., 2019), and the

term ‘digitalisation’ is increasingly being used within the field of urban water

2

management. In digitalisation, decisions on structural and real-time solutions

are based on digitised data, and often also on the use of models. This is, for

example, the case when using model predictive control (MPC) to optimise

pump and gate settings in the sewer system. In real-time applications, these

online models must be able to run at or faster than real time. Urban drainage

models are fundamentally conceptualisations of reality and therefore also

subject to uncertainty. This uncertainty stems from, for example, the model

structure, parameter values, initial conditions and input. The uncertainty can

be diminished by continual and automatic updating of the models with data

from the system (denoted ‘data assimilation’); by this means, the model is

kept in touch with reality. Literature on data assimilation of urban drainage

models is scarce, however, and only few studies update real-time models. An

increased use of data assimilation is expected to be facilitated by increased

in-sewer sensing as part of the digitalisation trend.

The urban water cycle contains a range of natural and man-made water sub-

systems, and the urban drainage system receives and emits water to many of

these, making it inherently physically ‘interlinked’. This thesis will simply

use the terms ‘interlinkages’, ‘links’ or the ‘interlinked urban drainage sys-

tem’ to describe the fact that different water sub-systems are connected with

the sewer system through the flow of water, implying that these links are

physical. Optimal solutions to urban drainage issues require an understanding

of these links in the urban water cycle (Rauch and Kleidorfer, 2014). This is

not a new insight, and some links have already been included in offline mod-

els for evaluating or designing structural solutions or control rules. This in-

cludes the link between the sewer system, WWTP and recipients, the link

with the terrain through rainfall-runoff processes and flooding (Chang et al.,

2015; Domingo et al., 2010), the link with SCMs (Quigley and Brown, 2014;

Xu et al., 2018) and with groundwater (Karpf and Krebs, 2013; Roldin, 2012)

and the link between upstream rural catchments, receiving waters and the ur-

ban landscape (Vandet fra landet, 2015). However, these and other links are

less frequently included or fully exploited in online models, which may be

because earlier online solutions that included these interlinkages were either

too complex to implement in practice, did not provide good enough results,

were not communicated to a wide enough audience, or were simply unneces-

sary due to a satisfactory system operation. Current challenges may require

that we keep exploring this line of thinking by using improved methodologies

provided by digital developments. There is thus an untapped potential for in-

3

cluding urban drainage links in real-time monitoring, forecast and control

models.

One such unexploited link exists between the water supply and urban drain-

age systems. These systems are connected in several ways. As early as the

1990s, Krejci et al. (1994) showed that drawdown of the groundwater table

due to increased drinking water abstraction led to a lower dilution of CSO

discharges in the recipient and thus a larger environmental impact. Further-

more, some literature looks into water recycling and how stormwater and/or

wastewater can re-enter the water supply system (Bach et al., 2014). If the

consumed water is not recycled, however, most of it ends up in the urban

drainage system. Digitalisation of the water distribution system has led to the

installation of smart meters measuring water consumption at each house with

a high temporal resolution. Most of this water is discharged to the sewer sys-

tem with no or little delay, and the performance of real-time urban drainage

models can thus potentially be improved by using real-time smart meter data

to estimate the magnitude, timing and distribution of the wastewater compo-

nent of the dry weather flow (DWF); this provides an alternative to more

crude traditional estimations based on, for example, annual water consump-

tion data or reference values of sewage discharges per inhabitant (De Bene-

dittis and Bertrand-Krajewski, 2005).

Another example of an unexploited link is the connection between the terrain

and the urban drainage system. During minor to medium-sized rain events,

this link is a one-way effect of rainfall runoff on the sewer system. During

heavy rain, the urban drainage system surcharges and a two-way interaction

is established. Although this link between the terrain and the sewer system is

normally included in real-time models, it is only considered a passive bound-

ary where the inflow to the sewer system is constrained solely by the simulat-

ed capacity of the physical infrastructure. Digitalisation is expected to pro-

mote the invention of new types of actuators to be used in more advanced

control schemes, including MPC. This allows for the real-time activation of

the otherwise passive links between the two sub-systems, which provides the

option of purposely keeping stormwater on the surface. This may pose an al-

ternative to traditional control of the underground sewer system (Garcia et

al., 2015) and of single SCMs (Quigley and Brown, 2014).

1.2 Research questions The overall hypothesis of this thesis is that digitalisation will facilitate more

informed, and potentially new types of, real-time decisions by exploring new

4

applications of traditional and emerging data sources, as well as new ways of

exploiting the links between the combined urban drainage system and other

sub-systems. The hypothesis is examined through the investigation of the fol-

lowing five research questions.

1) How does the interlinked urban drainage system relate to integrated urban

water management, and which water sub-systems are linked to the urban

drainage system?

2) How can real-time models and data be categorised and adequately chosen

for the task at hand?

3) What are the challenges and opportunities for using emerging water sup-

ply data to represent the wastewater flow in monitoring models?

4) How can forecast models be updated with traditional in-sewer observa-

tions to improve predictions of flows and overflows?

5) How can the link between the urban drainage system and the terrain be

activated using control models, and what are the related societal benefits

and nuisances?

1.3 Methodology Research questions 1 and 2 are investigated by studying scientific literature

and through discussions with professional peers from academia, utilities and

consulting companies. The three remaining research questions are also ex-

plored through literature and discussions, but are additionally investigated

through the modelling of three digitalisation examples (Figure 1):

- Research question 3 is investigated by expanding the observational space

to include smart meter consumption data; hereby linking the water supply

and urban drainage systems.

- Research question 4 is investigated by expanding the application area for

traditionally observed level and flows to include data assimilation of ur-

ban drainage models.

- Research question 5 is investigated by expanding the model boundaries of

an internal MPC model beyond the underground system; hereby including

and activating the link between the sewer system and the terrain.

Each example of digitalisation places a set of requirements on the case areas

in terms of the physical layout of the areas and the availability of data and

models. The case areas have therefore all been carefully chosen. It has been

important to work with real case areas (in one case including planned cloud-

burst infrastructure) and real data as well as technologies that can relatively

5

easily be applied by utilities and consultants in an operational setting. The

modelling therefore takes its point of departure in water quantity-based 1D

hydrodynamic MIKE URBAN models (DHI, 2016b) that are already widely

used by utility companies:

- In research question 3, MIKE URBAN was used directly as monitoring

model. Furthermore, the MIKE 1D API (DHI, 2019) was used in C# to

connect smart meter data and the MIKE URBAN model, and to subse-

quently run the model.

- In research question 4, the forecast model was constructed in Matlab

based on MIKE URBAN simulation results. This forecast model was run

using compiled, open source C# code and updated using the EnKF library

in Matlab developed by Sakov (2013).

- In research question 5, the construction of the internal MPC model was

inspired by a MIKE URBAN model, and key model parameters were cho-

sen based on MIKE URBAN simulation results. The internal MPC model

and control optimisation was set up in the MIKE OPERATIONS software

(DHI, 2016a; Madsen et al., 2018b).

1.4 Thesis structure This thesis is structured as follows. Chapter 2 provides a general introduction

to the management objectives of urban drainage and to structural and real-

Figure 1. Connection between research questions (RQs), papers (only their main

contribution is shown), thesis structure and methodology.

6

time solutions. It also presents the traditional view of integrated urban drain-

age management and introduces the interlinked urban drainage system as a

subset of the integrated urban water system. Chapter 3 introduces terms and

concepts related to digitalisation, especially in the context of real-time mod-

elling (Paper I), and discusses how to ensure that data and models are ade-

quate for the task at hand. Chapter 4 connects the two previous chapters and

exploits data flows and system links through three digitalisation examples: a

monitoring model (Paper II), a forecast model (Paper III) and a control model

(Papers IV and V). Chapter 5 discusses real-time models, institutional silos,

data silos and additional management objectives, before Chapter 6 concludes

the thesis and Chapter 7 presents further perspectives.

7

2 The interlinked urban drainage system

2.1 Management objectives The first priority of modern urban drainage management is to ensure public

health. However, once this has been resolved, several other aspects, ‘critical

states’, can be the driver for change, such as public health hazards, poor eco-

logical status and large economic costs (Figure 2). Decisions on how to re-

spond to these critical states should be made by first defining the scope of the

project. Besides the critical state, project scoping includes the definition of

the response category (the type of solution that should be employed), spatial

scale (the size of the area under consideration) and the domain (the amount of

rain that the solution is targeting) (Lund et al., in prep.; Madsen et al.,

2018a). In this thesis, the latter is differentiated into minor, medium and ma-

jor rain events. The project scope may be further decomposed into more con-

crete objectives that describe how to mitigate the critical state (Figure 2).

Projects aiming at targeting multiple objectives may find that these are coun-

teractive. For example, the reduction of flooding will often lead to an in-

crease in CSO, and the reduction in CSO by activating pumps and gates will

Figure 2. Critical states (outside circles) and objectives (inside circles) in urban

drainage management. The list is not exhaustive. CSO = combined sewer overflow.

Partly adapted from Paper I and Lund et al. (in prep.).

8

lead to increased energy costs. These trade-offs between different objectives

are quantified though ‘penalties’ (also called ‘weights’ (Paper I) or ‘costs’) to

allow finding a balanced compromise. After defining the objectives, these

should subsequently be broken down into tangible, quantifiable indicators

which allow one to compare the effects of different potential solutions (Ber-

trand-Krajewski et al., 2002; Gregory et al., 2012; Lahtinen et al., 2017).

Sometimes, it is not possible to quantify the actual indicator, and Gregory et

al. (2012) therefore introduced ‘proxy measures’ as substitutes for the indica-

tors. Likewise, Paper I divides objectives into ‘operational goals’ (the quanti-

ty that matters) and ‘objectives’ which either reflect the operational goal di-

rectly or describe how this indirectly is accounted for in the objective func-

tion. The critical state is also referred to as ‘objective’, objectives are referred

to as ‘attributes’ and indicators are referred to as ‘measurement units’

(Lahtinen et al., 2017), ‘performance indicators’ (Bertrand-Krajewski et al.,

2002), ‘performance measures’ (Gregory et al., 2012) and ‘metrics’ (Paper I).

It is thus evident that there is linguistic uncertainty related to these defini-

tions, which in general is a common phenomenon within urban drainage (for

example, Paper I; Fletcher et al., 2015).

Much literature on decision-making focuses solely on the decision process

and leaves the quantification of the indicator to experts’ assessment of meas-

urements and models (for example, Gregory et al., 2012; Lahtinen et al.,

2017). Lund et al. (in prep.) focus on model-aided decision-making and dive

into this indicator quantification and assess the role decision-makers play in

specifying the indicator in an unambiguous way for the model output to be

suitable for decision-aid. Gregory et al. (2012) and Lahtinen et al. (2017) ex-

emplified the indicator with, for example, tonnes/year or percentage reduc-

tion. Lund et al. (in prep.) argue that these example indicators are insuffi-

ciently specified and that there is a risk that the modelling task given to the

modeller is placed in an ambiguous way, which may cause the model output

to be unsuitable for decision-making. Instead, a well-specified indicator

should contain information on ‘type’, ‘uncertainty description’, ‘spatial reso-

lution’ and ‘temporal resolution’, which are further defined and elaborated in

Section 3.4. Once the indicator has been specified, the subsequent model-

based quantification can assist in choosing between potential structural and

real-time solutions.

9

2.2 Solution types

2.2.1 Structural solutions

Structural solutions are ‘static’ solutions, traditionally including the construc-

tion of storage basins, sewer pipe expansion and sewer system separation.

The construction of SCMs stands as an alternative to these traditional ap-

proaches (Kerkez et al., 2016). SCMs include both grey and green surface

and near-surface elements such as bioretention units, rain gardens, soaka-

ways, permeable pavement, dry and wet detention ponds, rainwater harvest-

ing systems, green roofs and vertical green walls, as well as cloudburst roads,

retention roads, retention spaces and green roads, which are all four part of

Copenhagen’s strategy on how to manage major rain events (‘cloudbursts’)

(City of Copenhagen, 2015). SCMs aim at increasing the retention of storm-

water, hereby smoothing and delaying the peak, and at increasing the infiltra-

tion and evapotranspiration of stormwater runoff, hereby minimising the

stress on the sewer system. In some instances, SCMs additionally treat the

stormwater by settling or filtration. Some SCMs, such as green roofs, may

have outlets to the sub-surface sewer system whereas others, such as deten-

tion ponds, may discharge stormwater directly to receiving waters.

Especially in Europe, the predominant sewer system type is still the com-

bined version as the cost and logistics prohibit exchanging the old, combined

sewer system with a separate version (Semadeni-Davies et al., 2008); howev-

er, most newer cities and suburban areas have separate sewer systems. Un-

derground structural solutions and SCMs may be challenging to implement in

densely populated cities. Uncertainty related to future precipitation also

means that static design solutions can prove non-optimal (Gregersen and

Arnbjerg-Nielsen, 2012). Furthermore, studies show that the construction of

grey infrastructure has higher environmental life cycle impacts than using

green or partially green solutions (Brudler et al., 2016, 2019; De Sousa et al.,

2012). Green SCMs may on the other hand come with a range of added bene-

fits, such as amenity values and increased biodiversity (Fletcher et al., 2015).

2.2.2 Real-time solutions

Real-time solutions are non-structural and do not alter the physical system at

large. One option is to warn about the occurrence of a negative outcome, for

example, decreased bathing water quality or flooding. On the other hand,

control can be implemented to prevent a given outcome from happening. For

example, control can be applied to fully use the underground system capacity

and thereby minimise or even avoid CSOs. This is normally done ‘passively’

10

by installation of static flow regulators, such as an internal weir with a fixed

height. Contrarily, real-time control allows for a dynamic control of these

flow regulators (‘actuators’) through the installation of controllers which ad-

just the actuators based on sensor measurements. In an urban drainage con-

text, the sensor will normally measure flow or water level. Common actuators

include pumps, gates, weirs and valves, and the controller either adjusts the

actuator to a pre-defined setting (for example, a two-point controller specify-

ing on/off or open/closed), or tries to meet a specified set-point such as a tar-

get flow or water level (for example, a PID (proportional-integral-derivate)

controller) (Schütze et al., 2004). The setting of the actuator or the set-point

for the PID controller may change with time. Most often, this dynamic con-

trol is implemented by specifying static rules (denoted ‘rule-based control’),

but it can also be implemented through more advanced and adaptive control

schemes such as MPC that continually dynamically optimise the control of

the entire system based on model simulations and rain forecasts (Garcia et al.,

2015). Despite an increasing research focus, MPC is, according to the open

literature, only implemented in a few urban drainage systems world-wide

(Paper I).

Passive and real-time control are normally carried out for underground sewer

systems. There are, however, also literature examples of the passive control

of street inlets to the sewer system, allowing for storage of stormwater on the

surface (Walesh, 2000), and the use of real-time control to regulate inflow to

or outflow from SCMs to either natural water recipients or to the urban drain-

age system (Gaborit et al., 2016; Quigley and Brown, 2014). Since real-time

solutions do not require major alterations of the existing system it is assumed

that this option has lower environmental life cycle impacts compared to static

solutions, even though electricity is needed to run the control setup (Paper

IV). The exploitation of the existing system capacity also means that real-

time control solutions are often found to be cheaper than structural solutions

(Meneses et al., 2018; Walesh, 2000).

2.3 The traditional meaning of integrated urban

drainage management The traditional meaning of ‘integrated urban drainage management’ compris-

es the joint management of the sewer system and WWTP based on the eco-

logical status of the recipient, which has received extensive attention over the

last three decades. It was the main topic of both the INTERURBA I (Lijkle-

ma et al., 1993) and INTERURBA II conferences (Harremöes, 2002), and its

11

importance has been emphasised by the implementation of the EU water

framework directive on integrated river basin management (European Com-

mission, 2000).

The discharge of sewage through CSOs, bypasses and WWTP effluents may

exhibit different acute and chronic effects on the ecological status of the re-

cipients. Furthermore, WWTPs may be run more efficiently by storing sew-

age in the sewer system for a prolonged period of time; however, this will

increase the risk of CSOs. The joint operation of the sewer system and

WWTP thus includes a range of trade-offs. These are optimally accounted for

in the decision-making process by using models to assess how different com-

pounds move and transform through the urban drainage system and WWTP,

and how these compounds subsequently affect the recipient. The dynamics of

many water quality aspects are inherently complex, and real-time models that

can simulate the movement and transformation from the source, through the

urban drainage system and WWTP, and the subsequent effect on receiving

waters are often still not accurate enough to replicate important processes

within the limited time available in a real-time context (Bertrand-Krajewski,

2007; Bonhomme and Petrucci, 2017). As an alternative to modelling the wa-

ter quality processes, hydraulic simulations with the addition of mean con-

centrations from measurements can be used (Langeveld et al., 2013), which

may however ignore important details and lead to less optimal management.

Instead of representing all three sub-systems explicitly in the model, man-

agement objectives related to CSO, bypass and WWTP discharge may be

used as a proxy for including the recipient (see, for example, Brdys et al.,

2008; O’Brien et al., 2005). In the most extreme case, only hydraulic simula-

tions of the urban drainage system are performed. Here, the WWTP may be

substituted with an objective of keeping the inflow below or at the WWTP

inlet capacity, while the recipient may be substituted with an objective re-

garding CSO volume minimisation; however, research shows that the correla-

tion between CSO quantity and recipient water quality may be poor (Rauch

and Harremöes, 1998). In recent years, integrated control of the urban drain-

age system and WWTP has also given rise to new objectives in addition to

increased ecological status, including the combined control of, for example,

pumps in the sewer system and the aeration tank in the WWTP based on the

fluctuation in electricity prices (Bjerg et al., 2015; Kroll et al., 2018).

12

The joint management of the sewer system, WWTP and recipient is thus

well-studied; however, the uptake of such solutions into practice is in general

slow (Bach et al., 2014).

2.4 The interlinked system – a subset of integration Solutions that are found through ‘integrated management’ are increasingly

being advocated in urban drainage literature where ‘integration’ is used as a

wider concept than traditional integrated urban drainage management. Gener-

ally, ‘integrated’ is an ambiguous term often perceived as equivalent to

‘complete’ (Bach et al., 2014; Rauch et al., 2005). However, this utopian ap-

proach is never fully implemented due both to lack of knowledge but also to

lack of need. Instead, we use abstractions to leave out parts of reality simply

because it serves no purpose for the decision (Lund et al., in prep.), and one

can thus talk about levels of integration (Rauch et al., 2005). This thesis dif-

ferentiates between two forms of integration, namely of systems and of objec-

tives.

Systems. In a comprehensive review of integrated urban water modelling,

Bach et al. (2014) proposed four overall levels in respect to system integra-

tion. Assuming that these levels of integration can also be used for urban

water management in general, they include: 1) integration of an individual

component within a single sub-system; 2) integration of the urban drainage

system, WWTP and recipient; 3) integration of the urban drainage and wa-

ter supply systems; and 4) integration of the urban drainage system with

non-physical systems such as climate, society, economy, etc. Examples of

the latter level include the water-food-energy nexus (Grigg, 2016) or man-

agement considering how education of the public affects the amount of

pollutants entering the sewer system (Rauch et al., 2005).

Objectives. Management strategies can also vary in integration when it

comes to objectives and their trade-offs within and between human, tech-

nological, economic and environmental domains (Grigg, 2016; Pahl-Wostl,

2007). These objectives may also integrate effects that span different time

scales as, for example, the minimisation of both acute and chronic effects

of pollution in receiving water bodies (Bach et al., 2014; Grigg, 2016). The

more objectives included, the larger the level of integration. For example,

the minimisation of both CSO and electricity costs increases the integra-

tion of objectives compared to solutions targeting only one of the two.

Some management strategies will explicitly include all important systems and

all relevant objectives. Other strategies will only include parts of the system

13

explicitly and instead include other sub-systems implicitly by the specifica-

tion of objectives. This does not qualify for integration from a system per-

spective (Rauch et al., 2005), but instead increases the level of integration in

respect to objectives; thus integration in respect to objectives may be used as

a proxy for system integration. In Section 2.3, this was exemplified by ex-

changing the recipient and WWTP with objectives such as CSO minimisa-

tion.

System-integration levels 2 and 3 in Bach et al. (2014) consider that the ur-

ban drainage system is physically linked to the WWTP, recipient and water

supply system through the exchange of water. However, the urban drainage

system is physically linked to many additional water sub-systems (Gessner et

al., 2014; Salvadore et al., 2015). Some of these links are one-way effects

(only one sub-system affects the other), while others are two-way interactions

(both sub-systems affect each other). The flow of water may change direction

or transform from a one-way to a two-way pathway, depending on the rain

domain. Figure 3 shows examples of links relevant for urban drainage man-

agement (the numbers refer to the numbers in Figure 3):

1. The one-way effect of the atmosphere on the terrain through rainfall.

2. The one-way effect of the urban drainage system on the WWTP and the

subsequent effect on the recipient.

3. The two-way interaction between upstream rural areas and rivers.

Figure 3. Interlinkages relevant for the management of the urban drainage system.

14

4. The two-way interaction between rivers and the urban landscape.

5. The two-way interaction between SCMs and groundwater through infil-

tration/exfiltration.

6. The two-way interaction between the urban drainage systems and

groundwater through infiltration/exfiltration.

7. The one-way effect of the sewer system on the recipient, which during

increased water levels in the recipient may be converted to a two-way

interaction.

8. The one-way effect of consumed water on the urban drainage system.

9. The two-way interaction between SCMs and the urban drainage system.

10. The two-way interaction between the urban drainage system and the ur-

ban landscape.

11. The two-way interaction between the urban landscape and SCMs.

The urban drainage system is thus inherently ‘interlinked’ to other sub-

systems. Like integration, it will rarely be valuable to include all of these

links in the decision-making process, and it is therefore not a goal in itself to

create one model that reflects the entire system. Instead, these links should be

taken into account when their inclusion is meaningful.

15

3 Digitalisation: real-time models and data

3.1 A new boy in class or an old friend? Digitalisation of the urban water sector is also referred to as ‘smart water’,

‘digital water’, ‘internet of water’, ‘data-driven urban water management’

and ‘water 4.0’ (German Water Partnership, 2018; Gourbesville, 2016; Eg-

gimann et al., 2017; Sarni et al., 2019). In general, these terms are diffuse and

used in different ways by different people and in different contexts. Some

distinguish between ‘digitalisation’ and ‘digital transformation’, but the defi-

nitions of these two words are still so diverse that this thesis solely uses the

term ‘digitalisation’.

Digital technologies are sometimes presented as new concepts, which is also

visible from the recent increase in the use of the terms ‘digitalisation’, ‘digi-

talization’ and ‘digital transformation’ within the water sector (Figure 4).

However, digital solutions to urban water problems were already discussed in

the 1980s and 1990s, albeit under different and more diffuse terms. For ex-

ample, Loke (1999) researched the use of neural network applications within

urban drainage management and talked about the electronic revolution which

allowed for in-sewer sensing and numerical modelling of the urban drainage

system. Adaptive real-time control is another example of technology that has

been researched since the 1980s (Paper I). Many of the topics are, however,

still not state-of-the-art today. There may be multiple reasons for that. The

operationalisation of digital technologies has only recently been made possi-

ble by advancements in computational power, the development of easier-to-

use open source software such as PyTorch for modelling of deep neural net-

works, the lower cost of communication and data management platforms,

cheaper sensors and more robust actuators (Campisano et al., 2013; Kerkez et

Figure 4. Number of hits using (“digital transformation” OR “digitalisation” OR

“digitalization”) AND “water” as search words on Scopus on August 4, 2019.

0

5

10

15

20

25

30

35

1949 1959 1969 1979 1989 1999 2009 2019

Hits

16

al., 2016), as well as improved rain forecasts (Paper I). The review conducted

in Paper I shows that, at least in the field of MPC, there is still a lack of fur-

ther research to conclude on best practices regarding, for example, the use of

linear versus non-convex optimisation and research that replicates real opera-

tional implementations, hereunder the use of input models and data assimila-

tion as well as methods for handling uncertainty, missing sensor values and

defect actuators. The fear of lacking methods for managing customer privacy

and cyber security also impedes the uptake of digital technologies (Blumen-

saat et al., 2019; Eggimann et al., 2017; Sarni et al., 2019).

The implementation of digital solutions not only depends on technological

advances but also requires a change of human behaviour in the usually con-

servative urban water sector. An important part of the implementation of

more automated solutions is to convince utility companies and operators that

new technologies can be trusted, even though these may be so advanced that

it is difficult to understand the underlying logic (Paper I, Eggimann et al.,

2017; Kerkez et al., 2016; Sarni et al., 2019). Operator reluctance may be

overcome by involving operators in the setup of the systems, operator train-

ing, development of better human-computer interfaces, and transition periods

where the new technologies are run in real time, but offline (Paper I). Fur-

thermore, digital adeptness will increase with a generational change in the

workforce (Sarni et al., 2019). The focus on ‘smart cities’ within other fields

as well as an urgency within urban drainage to tackle water-related issues due

to climate change and an increased urban population seems to have kick-

started the discussion and implementation of digital technologies within wa-

ter utilities (Sarni et al., 2019).

Another barrier to the implementation of digital technologies is linguistic un-

certainty. The terminology used for technologies such as MPC is dependent

on the professional background of the modeller as well as the area it is ap-

plied in. For example, in the open literature, MPC is also referred to as ‘mod-

el-based predictive control’, ‘receding horizon control’, ‘moving horizon op-

timal control’, ‘optimisation with rolling horizon’, ‘certainty equivalent open-

loop feedback control’ and ‘global optimal control’. Model inputs are also

known as ‘disturbances’, ‘loadings’ and ‘forcing’, and technical MPC details

are represented with up to 13 different terms in urban drainage literature (Pa-

per I). These linguistic uncertainties hinder the consolidation of the technolo-

gy into best practices. Paper I gives a structured overview of MPC terminolo-

gy and concepts to reduce this uncertainty. Likewise, this thesis provides a

language and structure for discussing digitalisation in general (Section 3.2),

17

real-time modelling (Section 3.3) and fit-for-indicator modelling (Section

3.4), which hopefully will enable a better communication and accelerate the

uptake of digital technologies into practice.

So is digitalisation a new boy in class or an old friend? It seems to be both,

and it may be seen as an ongoing process rather than a static concept. Some

technologies have been around for decades, but their implementation has

been impeded due to technical, human and linguistic barriers. The structure

and language provided from bundling old and new technologies under the

term ‘digitalisation’ has facilitated more clarity about the concepts, better

communication, and a hype around the topic that spurs more research as well

as operational implementations.

3.2 Concept and terminology Data that has been converted into a digital format (‘digitisation’) is the cor-

nerstone of digitalisation. Digital technologies can be classified according to

the ‘sense-think-act’ cycle, which is a well-known concept within robotics

and artificial intelligence communities. Data is first collected and handled

(‘sense’), then analysed (‘think’, with the potential inclusion of ‘learn’

through data assimilation) and finally used for decision-making (‘act’). This

thesis also applies this line of thinking to urban drainage management by di-

viding the ‘sense-think-act’ process into four steps. The steps are shown in

Figure 5, which furthermore provides an overview of how different method-

ologies (Table 1) (which, as described previously, may not yet be state-of-

the-art) belong to different digitalisation phases.

The four steps include ‘infrastructure’, ‘data handling’, ‘analysis’ and ‘deci-

sion-making’. First, data is collected from and by various types of infrastruc-

ture including, for example, roads, pipes, actuators, controllers and devices

used for sensing the system. The data is subsequently handled in the data

handling step, which includes the initial transfer, storage and sharing of data.

Here, open data standards for how to make data publicly available, data pri-

vacy and cyber security become important. The analysis step includes data

validation and possibly re-estimation of erroneous or missing data, and the

subsequent use of data to describe and potentially forecast the future state of

the system. Lastly, decisions are reached in the decision-making step and

may result in structural solutions, such as asset management based on neural

network models trained on CCTV footage (Meijer et al., 2019), and real-time

solutions, which are the focus of the remaining part of the thesis.

18

3.3 Real-time modelling There is much research on different applications of real-time models and data

for the analysis and decision-making steps in Figure 5. This thesis synthesiz-

es this data and models into different real-time decision pathways (Figure 6).

As a general rule of thumb, the solutions become more advanced – and more

digitally mature – the further to the right in Figure 6 they are placed. In all

cases, different types of data (‘sense’, first row) may be used. In this context,

‘sense’ includes information about the system attributes, the observed and

forecast input to the system and in-sewer observations revealing the current

state of the system. Four different pathways are possible, depending on

whether or not the data is accompanied by real-time models for automatic

‘thinking’. If not, the decision becomes ‘observation-based’. The addition of

a monitoring model for replication of the physical system (‘think’ , second

Figure 5. The steps for implementation of digital technologies within urban drain-

age. Methods (written in italics) are described in Table 1 and/or in Section 3.3.

19

row) allows operators to gain an overview and transforms the decision into a

‘monitoring model-based’ decision. The addition of a forecast model (‘think’,

third row) to predict the future state of the system transforms it to a ‘forecast

model-based’ decision. It is also possible to use both a monitoring and a fore-

cast model, which will be denoted ‘multi-model-based decision-making’. The

knowledge obtained from sensing and thinking is subsequently used for deci-

Figure 6. Real-time decision-making pathways that use different combinations of

data and models for the analysis and decision-making steps.

Table 1. Terms used within digitalisation of the urban water sector.

Term Full word Definition used in this thesis Relation to Figure 5

IoT Internet of things Devices that are connected via

the internet Data handling

Cloud storage

----- Storage of data on remote servers Data handling

Block-chain

----- A digital ledger in which transac-tions or other data are recorded

chronologically and publicly Data handling

ML Machine learning The use of mathematical algo-

rithms to learn from data for clas-sification, forecasting, etc.

Analysis and deci-sion-making

AI Artificial intelligence The use of ML to perform tasks normally requiring human intelli-gence, such as visual perception

Analysis and deci-sion-making

MPC Model predictive

control

Using the receding forecast hori-zon principle and optimisation to control a given system (Paper I)

Analysis and deci-sion-making

DA Data assimilation Updating the model with system observations to adapt it to reality

Analysis and deci-sion-making

20

sion-making, which can either be done manually by an operator or automati-

cally by a piece of software such as a control model (‘act’, fourth row). The

thesis thus proposes to categorise real-time models into three types (monitor-

ing, forecast and control models), which should all be able to run at, or faster

than, real time. Collectively, they may be referred to as ‘online models’. Dif-

ferent types of data and online models will be discussed in greater detail in

subsequent sections.

3.3.1 Data

Data can be divided into system attribute data, input data and in-sewer data.

System attribute data includes information about the physical system

such as number of inhabitants, degree of imperviousness, digital elevation

models, asset data (with, for example, pipe diameters and locations), etc.

Such data is especially important for monitoring models.

Input data is used to run the models. Decisions about stormwater man-

agement are often based on situations where the system is under stress, and

rain-runoff data is therefore often conceived as the most important model

input. Besides this, combined sewers also receive wastewater from indus-

try, institutions and households, and are potentially also affected by infil-

tration from groundwater and rainfall-derived infiltration. There may also

be exfiltration from the system. Collectively, the flow during dry periods is

known as the DWF. In areas with low water consumption during the night,

the infiltration and wastewater components may be individually estimated

from flow measurements. However, if the night wastewater flow is signifi-

cant, as in areas affected by industry, it may be difficult to distinguish

wastewater and infiltration unless the water consumption is known (US

EPA, 2014). Predefined patterns are normally used to mimic the DWF.

However, research has shown that this flow varies greatly from day to day

(Métadier and Bertrand-Krajewski, 2011). More accurate descriptions of

the variability of the DWF may be valuable for real-time modelling.

In-sewer data most commonly includes levels and sometimes also flows.

Level measurements can be obtained with, for example, pressure inductive

or ultrasonic sensors, whereas flow observations are normally obtained ei-

ther by combining velocity and level measurements through equations de-

scribing the geometry of the pipe, or by measuring the flow directly with

an electromagnetic sensor (Campisano et al., 2013; Schütze et al., 2004).

Urban drainage systems carry dirty water, which complicates the sensing

and makes maintenance expensive; thus, sensors are often scarcely distrib-

21

uted. Digitalisation of the urban drainage sector is expected to increase the

amount of level and flow sensors installed in the urban drainage system.

Observations of water quality parameters, such as organic pollutants and

nutrients, can also be performed, but cheap and robust sensors for this pur-

pose are still lacking (Eggimann et al., 2017; Kerkez et al., 2016). In-sewer

data is used for model calibration, data assimilation and control.

Traditional data as presented above is expected to be accompanied by data

from untraditional sources from the urban drainage system as well as other

sub-systems as part of digitalisation. Examples of alternative input data from

the urban drainage literature include the use of microwave links to estimate

rain intensities (Fencl et al., 2017). Alternative in-sewer data includes the use

of temperature data to detect CSOs from the urban drainage system by com-

paring observations from a sensor placed in the CSO chamber and one in the

overflow outlet (Hofer et al., 2018), the localisation of infiltration by in-

stalling optic fibres along the inside of sewage pipes to measure the sewage

temperature (Beheshti et al., 2018) and the use of pump electricity data as a

substitute for in-sewer flow measurements (Fencl et al., 2019). These are all

examples of proxy measurements, and the conversion into quantities relevant

for urban drainage may require the development of new model types.

3.3.2 Monitoring models

Monitoring models can be used as online ‘software sensors’ to interpolate

between observed states, and help operators and urban water managers under-

stand what is going on in the otherwise hidden parts of the system. These

models are normally based on system attribute data, which can be embedded

in detailed hydrodynamic (high-fidelity (HiFi)) models of the system; thus,

they may also be referred to as ‘digital twins’. 2D HiFi models are computa-

tionally heavy and may therefore not be able to run in real time with a large

spatial and/or temporal resolution. On the other hand, 1D HiFi models con-

structed in software such as MIKE URBAN, Infoworks or SWMM are nowa-

days often capable of running in real time.

The uncertainty related to monitoring models can, to some degree, be dimin-

ished by calibration. The lack of historical time series of flow can prohibit

this calibration, at least in more than a few points, and the calibration will

only ensure a fit to the data available at the time of calibration (Bertrand-

Krajewski, 2007). The real-world appearance of the model may, however,

acquire trust from users even in un-calibrated parts of the model. If the hy-

drology of the catchment is simple, the HiFi models may provide valuable

22

knowledge about the system even in a fairly un-calibrated state (Borup et al.,

2016), but results from un-calibrated model parts subject to complex dynam-

ics should only be applied along with sound scepticism. The uncertainty of

the monitoring models can also be reduced by updating them with real-time

observations from the system. The models thus ‘learn’ from reality along the

way. The underlying concept is that a better estimate of the true system state

may be obtained by combining knowledge from the uncertain model with

knowledge from uncertain in-sewer observations. Literature on data assimila-

tion of urban drainage models is scarce, but successful implementations of

data assimilation of HiFi models were reported in Borup et al. (2011, 2014,

2018). With increased in-sewer sensing as part of the digitalisation trend, da-

ta assimilation will likely become an intrinsic part of real-time modelling

since it provides valuable information about the current state of the system.

3.3.3 Forecast models

Simulation into the future can make the decision-making pro-active. For ex-

ample, the forecast of flow to the WWTP allows for a manual or automatic

switch between dry and wet weather operation (Sharma et al., 2013). In a

forecast setting, the applied models have to run many times faster than real

time, which can prohibit the use of HiFi models. Forecast models are thus

often simpler in their structure than monitoring models. These models may be

constructed and/or calibrated on the basis of measurements, such as stochas-

tic grey-box models (for example, Löwe et al., 2014) or linear reservoir mod-

els (for example, Pedersen et al., 2016), but the required flow time series are

often not available for calibration. Alternatively, the models can be con-

structed based on already existing HiFi models, and are then called ‘surrogate

models’. Surrogate models can either be ‘response surface surrogates’ or

‘lower-fidelity surrogates’ (Razavi et al., 2012). Response surface surrogates

fit functions to the results of the HiFi models and, for example, include the

use of neural network models trained on HiFi model results (for example,

Darsono and Labadie, 2007). Lower-fidelity surrogates simplify HiFi models,

for example through trimming, pruning and merging, while maintaining the

physical representation (for example, Leitão et al., 2010).

Like monitoring models, forecast models benefit from continual updating

with in-sewer observations (for example, Branisavljevic et al., 2014; Hutton

et al., 2014). Since forecast models are simpler in their structure, data assimi-

lation may arguably be even more important. The data assimilation can result

in the adjustment of, for example, parameters such as the area and transport

time (Pedersen et al., 2016) or states such as volumes (Paper III). State updat-

23

ing with the Ensemble Kalman filter (EnKF) is commonly used in hydrologi-

cal forecast models (Liu et al., 2012). Basically, the EnKF assumes that the

uncertainty of input, model and observations can be represented by ensem-

bles. This allows for faster computations than using methods where the full

uncertainty distribution has to be described, making it ideal for real-time

forecasting purposes. Furthermore, the EnKF allows for updating of non-

linear models, which is an essential feature for urban drainage models repre-

senting highly non-linear behaviour such as backwater effects, pumps and

internal/external overflows.

3.3.4 Control models

Automated rule-based control bases decisions on pre-defined if-then-else

statements and observations from the system, and it thus belongs to ‘observa-

tion-based decision-making’. The rules may also be dependent on model-

based flow forecasts (Hutton et al., 2014), converting it to a ‘forecast model-

based’ decision. These types of control schemes using model predictions and

static rules could be referred to as rule-based MPC (Paper I). Rule-based con-

trol is normally carried out at a local scale. The use of global control schemes

that optimise the entire system at once can be applied to obtain a better per-

formance. This global control may be optimised offline using a HiFi model

and implemented online by using a neural network model that is trained on

the HiFi model results of optimal gate settings given specific rain forecasts

(Darsono and Labadie, 2007). Alternatively, the global control can be adap-

tive, and thus potentially be more optimal, by using MPC to re-calculate the

optimal control in real time using dynamic optimisation.

Figure 7 shows the concept of MPC and its dependence on different data and

model products, using the framework presented in Figure 6. Furthermore,

Figure 8 categorises MPC into the ‘receding horizon principle’ and ‘optimisa-

tion’ (and further sub-categories) to diminish the linguistic uncertainties sur-

rounding MPC (Paper I). In MPC, information from rain forecasts may first

be propagated through an input model, describing the uncontrolled part of the

sewer system. The forecast horizon here describes the period in which the

rain inputs are trusted. The resulting inputs are subsequently run through the

internal MPC model, which simulates the control-affected part of the system

over a given prediction horizon. The input and internal MPC model are thus

both forecast models. The model structure of the internal MPC model can

either be linear or non-linear. The former makes the optimisation faster and

thus provides the opportunity of controlling large scale systems, while the

latter allows for the explicit simulation of non-linearities in the urban drain-

24

age system, such as backwater effects and threshold dynamics, but at the cost

of speed (Paper I). An optimisation solver finds the optimal control actions

over the control horizon by combining the internal MPC model with the op-

timisation model. The latter includes the system constraints, optimisation var-

iables (for which control actions should be computed) and an objective func-

tion describing relevant management objectives. The control actions come at

a given resolution (the setting duration) and are after each optimisation usual-

ly sent to local PID controllers, which convert the signal into actuator set-

tings. When testing the control concept, the control actions can also be sent to

a HiFi model representing a virtual reality. The optimisation is repeated re-

cursively (indicated by the sampling interval), for example every 5 minutes,

to ensure that new information about weather forecasts and the system state is

included in the control optimisation. Information about the system state can

be included by updating the initial conditions of the applied models. Observa-

tions may be used directly to update states and flows in the input and internal

MPC models (left part, Figure 7). Examples of data assimilation of the inter-

nal MPC model are rare but can be found in, for example, Löwe et al. (2016)

and Pleau et al. (1996). Alternatively, the observations can be used to update

a monitoring model which is then, in turn, used to update the input and inter-

Figure 7. The concept of model predictive control (MPC) where the system states

in the input model and internal MPC model are updated without (orange) and with

(purple) using a monitoring model. The orange path is used in Paper III, while the

purple pathway is followed in Papers IV and V albeit without updating the high-

fidelity (HiFi) model. PID controller = proportional-integral-derivate controller.

25

Figure 8. Categorisation of model predictive control (MPC) into the receding hori-

zon principle and optimisation. A solid line indicates that the component is a direct

part of MPC, while a dashed line indicates that the component is external. Adapted

from Paper I.

nal MPC models (right part, Figure 7), allowing for the updating of these two

models even in locations without observations. This requires that the control

actions are also sent to virtual PID controllers in the monitoring model. Ex-

amples of HiFi model updating are, as stated previously, rare and there are

also only a few examples of the updating of the internal MPC model from

HiFi models (for example, Paper V, Cembrano et al., 2004; Joseph-Duran et

al., 2014; Marinaki and Papageorgiou, 2001). Depending on which of these

two pathways is taken, the MPC scheme becomes either ‘forecast model-

based’ or ‘multi-model-based’.

3.4 Choosing data and models for the task at hand When going through a given modelling task it is important to consider which

indicator the model and data should be used to quantify (Section 2.1). An

ambiguously defined indicator will leave too many degrees of freedom to the

modeller and increase the risk that the model provides an output that is unus-

able for decision-aid. The first step is thus to define a well-specified indica-

tor, which is obtained by specifying the four features (‘type’, ‘uncertainty

description’, ‘spatial resolution’ and ‘temporal resolution’). These features

can also be used to describe each aspect of the subsequent modelling process.

To assess whether each modelling choice is adequate given the indicator,

Lund et al. (in prep.) introduce the term ‘granularity’, which describes the

amount of information embedded in the features of the indicator and of each

modelling aspect. The granularity scale goes from ‘finely grained’ (indicating

26

a lot of information) to ‘coarsely grained’ (indicating little information), and

can be seen as a common measure for previously defined terms such as

‘lumped’, ‘distributed’, ‘detailed’, ‘simplified’, ‘deterministic’, ‘stochastic’,

‘conceptual’, ‘black box’, ‘grey box’, ‘white box’, etc. Table 2 shows exam-

ples of the granularity scales for the four features in respect to different as-

pects, including the indicator and modelling choices of data, model structure

and flux description. For example, in-sewer flow data may take continuous

values as type (finely grained) with a temporal resolution of minutes (finely

grained) whereas the type of the indicator may be binary if it is only desired

to know whether the inflow to the WWTP is above or below a given thresh-

old (coarsely grained). Besides the indicator, the examples shown in Table 2

only serve as inspiration for the modeller, who would come up with aspects

relevant for the modelling task at hand. Other relevant aspects may, for ex-

ample, include the parameter estimation method, choice of numerical solver,

optimisation algorithm, etc. Once the granularity of the indicator has been

defined, the determination of the granularity of the four features for each

modelling-related aspect can be evaluated by comparing it with the granu-

larity of the indicator, as well as with the granularity of the other modelling

Table 2. Examples of different modelling aspects and the related granularity going

from fine to coarse for the four features. Based on Lund et al. (in prep.).

Aspect Indicator Data Model structure

Flux description Feature

Type

How the aspect is represented

Continuous to categorical to binary values

Continuous to categorical to binary values

Continuous to categorical to binary values

From dynamic wave to linear reservoir equa-tions

Uncertainty description

How uncer-tainty is managed

Stochastic de-scription over scenario analysis and a determinis-tic value to quali-tative description

Stochastic de-scription, over ensemble repre-sentations to a deterministic val-ue

Ensemble simula-tions of model structure layouts to a single deter-ministic layout

Stochastic equa-tions, over en-semble simula-tions to a single deterministic equation

Spatial reso-lution

The applied spatial reso-lution

Thousands of points down to a single point

Values in thou-sands of points/small grid to single point values/large grids

A large number of state variables to simple input-output relations with a single state

Detailed equa-tions used in parts of the model to the same, less detailed, equa-tions in the entire model

Temporal resolution

The applied temporal resolution

Seconds to days, years and dec-ades

Seconds to days, years and dec-ades

Short time steps to large time steps

Time-varying equations to static choice of equa-tions

27

choices. Intuitively it seems appropriate that the granularity of the features

should align across the different aspects. For example, it makes sense that

coarsely grained rainfall data is used in lumped linear reservoir models for

forecasting whether the inflow to the WWTP exceeds a given threshold, and

that flow and/or level data from the system may only be needed in few loca-

tions in the system for model calibration. There may, however, be exceptions

where, for example, detailed descriptions of processes are important to quan-

tify even coarsely grained indicators, such as when backwater effects are

dominating the flows in the system. The concept, therefore, does not elimi-

nate the need for careful thinking and evaluation by the modeller, but instead

seeks to facilitate structured thinking and evaluation of modelling choices

(Lund et al., in prep.).

When constructing and using real-time models and data, it is thus important

to first evaluate the granularity of the indicator and subsequently ensure that

the applied model and data match this granularity, thus ensuring ‘fit-for-

indicator’ modelling.

28

29

4 Real-time exploitation of data and links

Urban drainage interlinkages represented in current real-time models include

rainfall-runoff descriptions spanning detailed representations in monitoring

models to less detailed representations in forecast and control models. Alt-

hough we rarely think about it, the observational space in rainfall-runoff

models is actually expanded beyond the underground urban drainage system

as rain observations are used instead of modelling the complex atmospheric

system. Furthermore, real-time flood modelling is an on-going field of re-

search (for example, Thrysøe et al., 2019). The link between the urban drain-

age system and tunnels has also been considered in literature concerning real-

time modelling, where the link is controlled to meet a wide range of objec-

tives, hereunder greywater reuse, CSO reduction, flood prevention, convey-

ance of traffic, etc. (Palmitessa et al., 2018). This control may be dependent

on online models for rule-based MPC, as is the case for the SMART tunnel in

Kuala Lumpur, Malaysia (Cohen et al., 2017). In general, however, real-time

models representing and fully exploiting the interlinked nature of the urban

drainage system are not common.

The use of digital technologies to exploit traditional in-sewer and emerging

data sources, as well as the links with the water supply system and with the

terrain in real time, will be elaborated in the following sections through three

digitalisation examples considering a monitoring model (Section 4.1), a fore-

cast model (Section 4.2) and a control model (Section 4.3).

4.1 Smart meter data-based wastewater flow in

monitoring models Real-time information about the magnitude, timing and distribution of DWF

may enable a more optimal control of sewer systems during dry weather in

terms of energy optimisation, the setup of heat exchange between the urban

drainage and district heating systems (creating yet another link) and more

optimal data assimilation of urban drainage models (Paper II). Obtaining such

real-time information would require an immense number of flow sensors to

be installed in the sewer system, which, at least with current technology, is

not feasible. Instead, the link between the water supply and urban drainage

systems may be exploited to obtain knowledge of the wastewater component

of the DWF. There are already literature examples where the water consump-

tion itself is modelled and subsequently used as input to an urban drainage

model (for example, Butler and Graham, 1995; Elías-Maxil et al., 2014).

30

However, these estimations will, like standard DWF curves, not give a real-

time picture of the in-sewer flow. Alternatively, real-time observations from

the water supply system may be used as input to an urban drainage monitor-

ing model to improve the wastewater flow representation. To investigate this

possibility, filtered hourly data from smart meters installed in houses and in-

dustry in Elsinore was obtained from Utility Elsinore from 2018, as well as 1-

minute resolution in-sewer flow observations from five locations in the sewer

system, a HiFi urban drainage model of the catchment, and the wastewater

plans showing the wastewater sub-catchments. Furthermore, daily WWTP

inflow values and daily waterworks outflow values were available for specif-

ic days in 2018, as well as annual household consumption values from a da-

tabase from 2012. Figure 9 shows the location of the urban drainage system,

WWTP, smart meters, in-sewer flow sensors and their respective sub-

catchments. Only few cities in Denmark have such high-resolution data from

both the water supply and urban drainage systems overlapping in time. How-

ever, the data and models were stored by different in-house staff members as

Figure 9. Elsinore city centre with smart meters, the sewer system and wastewater

treatment plant (WWTP) as well as in-sewer flow sensors and their catchments.

Adapted from Paper II.

31

well as by different contractors, and the fact that no single person could ac-

cess and extract all the data and models complicated the linking of the sub-

systems (Paper II).

The reliability and completeness of the smart meter data were verified by

comparing the average smart meter consumption in October and November,

2018, with the average water consumption from the database from 2012, as

well as by comparing the total outflow from the waterworks with the smart

meter data for single days in October and November (Paper II). The smart

meter data was thus deemed representative for the wastewater flow.

The smart meter data was used as input data in the HiFi monitoring model

and compared to the observed in-sewer flow in dry periods in October and

November. These flows may differ since the smart meter data will only repre-

sent the wastewater part of the DWF. Potential reasons for deviations (Table

3), hereunder additional DWF components, were assessed. Figure 10 shows

that the observed flow was smaller than the simulated flow in the ‘east up-

stream’ catchment in October; however, in November, the simulated and ob-

served flow fitted well at this location. Furthermore, the observed flow in the

‘east downstream’, ‘central downstream’ and ‘west downstream’ catchments

were all significantly larger than the simulated flow in October. In Novem-

ber, only ‘west downstream’ exhibited this behaviour. The most likely reason

for these deviations was found to be erroneous in-sewer flow observations.

This was also validated by the fact that observed daily inflow values to the

WWTP were lower than the simulated WWTP inflow. Since the observed

Table 3. Potential reasons for deviations between observed and simulated in-sewer

flows. Adapted from Paper II.

Deviation type Potential reason

Observed flow smaller than simulated flow

Consumed water not discharged to the sewer system

Exfiltration

Observed flow larger than simulated flow

Pumping of groundwater to the sewer system

Re-use of rainwater

Melting of snow (only in winter)

Unaccounted for consumers

Infiltration

Sedimentation

General reasons

Erroneous smart meter sensors

Wrong conceptualisation of the sewer system

Erroneous in-sewer sensors

32

sewage flow was even higher than the simulated flow, the difference to the

observed WWTP inflow would become even larger. The inaccuracy of the in-

sewer sensors could, however, not be proven since the measurement cam-

paign had been terminated. This also meant that the other components of the

DWF could not be estimated (Paper II).

Figure 10 furthermore shows that the monitoring model captured the timing

of both peak and low flows better than simply summing the smart meter data

upstream of each flow sensor based on the utility’s wastewater plans (Paper

II) and thus decreased the uncertainty of the timing, leading to a finer tem-

Figure 10. Measured wastewater flow, summed smart meter data and modelled

flow using smart meter data as input (all three filtered with a 30-minute moving

average) for October 2018 (top) and November 2018 (bottom). The arrows indicate

the flow direction. Adapted from Paper II.

33

poral resolution of the model output. The type of the data, model structure

and flux descriptions are all finely grained as they take continuous values,

and the large spatial resolution of the smart meter data will provide the option

to calibrate the monitoring model in a large number of points. This means

that the monitoring model may be used to quantify an indicator with a simi-

larly finely grained type, spatial resolution and temporal resolution, such as

in relation to storing sewage in the sewer system for a more optimal control

of the WWTP. If the model should be used to quantify an indicator with a

coarser spatial resolution, the water consumption data from fewer district me-

tering area flow sensors may be sufficient.

The processes of linking smart meter data with the monitoring model re-

vealed that one of the sub-catchments discharging water to the ‘east up-

stream’ catchment was, falsely, not included in the wastewater plans, which

formed the basis for the simple summation of smart meter data. On the other

hand, monitoring models constitute an easy way of linking smart meter con-

sumption data with in-sewer sensor locations. In general, we will likely get

an increased amount of data in the future, and monitoring models may repre-

sent a viable way of extracting knowledge from this data. Furthermore, mod-

elling the wastewater flow in real time using smart meter data most likely

presents a more robust method than obtaining wastewater flow information

from in-sewer sensors, since the failure of a single smart meter will not influ-

ence the results significantly unless the flow is highly dominated by a few

consumers (Paper II).

Smart meter data may pose a viable path for representing the wastewater

component of the DWF in monitoring models, and may furthermore be used,

together with other data, to assess the quality of in-sewer observations; how-

ever, infiltration and exfiltration cannot be estimated without reliable obser-

vations from the sewer system.

4.2 Updating forecast models with in-sewer levels

and flows Forecast models may benefit from being updated in real time. This possibility

was investigated by updating an urban drainage surrogate model with in-

sewer data from the system. The Greater Utility of Copenhagen, HOFOR,

provided a HiFi model of the Damhusåen catchment in Copenhagen, Den-

mark, and a colleague at DTU Environment set up a piece-wise linear surro-

gate model of the response surface type for the area by conceptually dividing

34

the HiFi model into a number of compartments. The relationship between the

total volume of water within the collection of pipes and basins and the flow

out of the compartment was registered at different stages, forming a storage-

discharge curve for each compartment (Borup et al., 2017; Thrysøe et al.,

2019). HOFOR furthermore provided 1-minute resolution level and flow data

from various locations in the Damhusåen catchment from 2016. A smaller

part of the surrogate model consisting of five network compartments (Figure

11) was used, where level observations were available from a downstream

CSO structure as well as flow observations from the throttle pipe discharging

water southwards out of the CSO structure. To be applicable in a forecast set-

ting, the surrogate model was altered to also include runoff compartments

(one per network compartment) to represent the surface module of the HiFi

model. A DWF component fitted to the HiFi model DWF was furthermore

added to the modelled throttle outflow from the CSO structure in the very

downstream point of the model (Paper III).

The surrogate model was updated every minute using the EnKF. If the model

Figure 11. Part of the Damhusåen catchment showing the flow direction and the

division of the urban drainage system into compartments (Paper III) as well as the

cloudburst infrastructure and the controllable area (Papers IV and V). CSO =

combined sewer overflow.

35

should be used to quantify an indicator with a coarser temporal resolution,

this frequency could likely be lowered. To determine whether utilities should

invest in level or flow sensors, both types of measurements were used to up-

date the surrogate model. The model contained volumes as states and routed

water through flows; only volumes and flows could thus be used to update the

model. Four different experiments were therefore tested. Three of these in-

cluded conversions of the observations before updating: level observations

were converted to volumes and flows, and flow observations to volumes. Rat-

ing curves to perform these conversions were obtained from HiFi model re-

sults. These results were also used to estimate the observation noise. Flow

observations were also used to update the surrogate model directly. Here, a

proportional observation noise of 10% of the observed flow value was chosen

based on experience from the literature (Paper III). Section 4.1, however,

highlighted that the uncertainty, at least during dry weather, may be larger.

Noise was also added to the rain input and model states. Updating with level

and flow data both improved the prediction of the throttle flow out of and

overflow from the CSO structure during rain events compared to the baseline

surrogate model (no update). The use of level measurements converted to

volumes overall performed the best and improved deterministic throttle flow

and overflow forecasts up to two hours into the future. The 15- and 60-

minutes forecasts (Figure 12) show that the updated surrogate model captured

peaks and low throttle flows better than the baseline surrogate model, and

that it also had a better timing for the prediction of CSO events. Since the

uncertainty of the rain and model structure was represented as ensembles

(and thus was relatively finely grained), it would also be possible to quantify

the uncertainty of the flow and overflow forecasts.

The data assimilation scheme was set up solely using information provided

by the utility in form of the HiFi model and technical drawings showing sys-

tem attribute data for the CSO structure. It is fortunate that updating with

levels performed best since these are the predominant observation type in

most urban drainage systems and also the type of sensor that requires the

least maintenance. Furthermore, using levels converted to volumes was found

to be insensitive to changes in the noise settings of the data assimilation

scheme. These aspects are a large advantage in operational settings where

long historical time series of in-sewer data, money and time may be scarce

(Paper III).

In the technical drawings used to create the results in Figure 12, the CSO

structure had an invert level of 5.82 m and a crest level of 7.14 m. After the

36

completion of Paper III, the DWF, which had not been the focus of the paper,

was inspected more closely. This inspection showed that the water level dur-

ing the night at times remained constant at 5.82 m. This caused suspicion and

the utility company subsequently confirmed that the level sensor had mistak-

enly been set to have a minimum detection level of 5.82 m. Thus, the tech-

nical drawings were faulty; the true invert level was at 5.61 m, while the crest

level was actually situated in 7.17 m. This is likely the reason for the poor

dry weather throttle flow forecast in Figure 12. Simulations with these new

Figure 12. Throttle flow (upper panels) and combined sewer overflow (CSO) (low-

er panels) forecasts for the baseline surrogate model and updated surrogate model

using level observations converted to volumes before updating. Adapted from Pa-

per III.

37

values have not been performed. These errors should, however, also be seen

in the light of other sources of uncertainty related to the model, rain and in-

sewer observations. The fact that the data assimilation scheme improved the

forecasts (as presented in Figure 12) even with faulty information confirms

the robustness of the method developed in Paper III.

The DWF constituted around 10% of the flow capacity of the throttle flow

out of the CSO structure. The DWF was added to the simulated throttle flow

to generate the results in Figure 12. The effect of a distributed DWF repre-

sentation was establish by allocating the DWF curve equally on the five run-

off compartments, while taking into account that on average it takes the DWF

69 min to arrive at the CSO throttle pipe. The data assimilation scheme using

levels converted to volumes was re-run with these settings. Distributing the

DWF provided better results in respect of throttle outflow (Figure 13, here

represented by the Nash-Sutcliffe efficiency (NSE)), which was true both

when comparing rain events (left panel) and for all flow regimes (right pan-

el). The surrogate model with distributed DWF also predicted more of the

observed CSOs (not shown). This was expected since the simple addition of

the DWF to the modelled throttle outflow may lead to an outflow value that is

larger than the real maximum capacity of the throttle pipe if the system state

is close to or already at full capacity. This will cause the simulated thrott le

flow to exceed the observed flow, and the data assimilation scheme will

therefore extract water from the model, thus leading to fewer CSO predic-

tions. Distributing the DWF, however, also predicted more CSOs that did not

Figure 13. Nash-Sutcliffe efficiency (NSE) for the baseline surrogate model and the

surrogate model updated with levels converted to volumes when adding the dry

weather flow (DWF) to the throttle outflow versus distributing it to the compart-

ments.

38

occur in reality. This led to a lower trust in CSO prediction compared to add-

ing the DWF curve directly to the throttle flow. Overall, distributing the

DWF seems to be better than adding the DWF to the downstream throttle out-

flow. It is expected that the inclusion of the true DWF distribution may im-

prove the results even more, especially in larger models. The installation of

smart meters in the water supply system could pose an important first step for

obtaining such a distributed DWF representation.

Updating surrogate models with level and flow observations thus improves

the forecast of flows and overflows up to two hours ahead, benefits from a

distributed DWF representation, and can be set up independent of long histor-

ical data records.

4.3 Model-based integrated stormwater inflow

control Controlling the link between the urban drainage system and terrain can be

used to keep stormwater temporarily on the surface in designated areas.

These designated areas may include SCMs that are designed for minor to me-

dium-sized rain events, such as rainwater harvesting systems, SCMs that are

designed to manage major rain events, such as cloudburst roads and retention

spaces, or other parts of the urban landscape capable of conveying or storing

stormwater such as parking lots, skate parks, regular streets with a high curb,

etc. The concept thus expands beyond traditional stormwater management

concepts, hereunder sewer system control and single SCM control (Figure

14), which both operate solely within the technical grey and green stormwater

infrastructure regime (Paper IV). The control can be used for a wide range of

the objectives shown in Figure 2, for example, for minimisation of CSO and

bypass as well as reduction of flooding of undesignated areas. If designed

optimally, the presence of stormwater on terrain can, at least for minor rain

events, even be used to create liveability in the form of blue-green-grey rec-

reational spaces such as channels in the roadside, storage of stormwater in

park sections, etc. In the case of flooding, the option of keeping cleaner

stormwater on terrain poses a large benefit over the surcharge of mixed

stormwater and wastewater into basements and streets. Furthermore, the use

of already existing infrastructure is assumed to pose a more environmentally

and economically sustainable option compared to construction of new, espe-

cially grey, infrastructure. These benefits, however, come with a range of

trade-offs. Medium-sized rain events may be too big to be conveyed in small

channels along the roadside and stormwater may instead claim space on the

39

road itself, disturbing the movement of cars, bicycles and pedestrians. Fur-

thermore, the use of the terrain for storage of stormwater may lead to un-

designated flooding due to uncertain rain forecasts. Discussing these trade-

offs with the public and allowing them to experience and learn about storm-

water movements may increase their resilience. Furthermore, the inclusion of

trade-offs ensures that these become negotiable across different stakeholder

groups instead of being ignored and hence excluded from any systematic

analyses. The joint control of the sewer system, SCMs and urban landscape

integrates both different interlinked sub-systems as well as multiple objec-

tives and is therefore denoted ‘integrated stormwater inflow control’ (Paper

IV).

Figure 14. Control of sewer systems (top panel) and single stormwater control

measures (SCMs) (middle panel) compared to integrated stormwater inflow con-

trol (lower panel). From Paper IV.

40

As mentioned in Section 2.2, Copenhagen is planning to construct a range of

different above-ground cloudburst infrastructure to increase the city’s ability

to manage major rain events. This infrastructure will thus also have sufficient

capacity to be used during minor to medium-sized rain events. A single

cloudburst project in the Damhusåen sub-catchment comprising a cloudburst

road and a green SCM element functioning as retention space was used as

case area (Figure 11) to investigate the ability of integrated stormwater in-

flow control to minimise CSO. MPC was used as control method to regulate

the stormwater inflow from the road and the retention space. This controlla-

ble above-ground cloudburst infrastructure contributed 7% of the stormwater

entering the CSO structure from the entire catchment; the remaining 93% of

the contributing area was left uncontrolled. The control should prevent

stormwater inflow if a CSO was forecast. Instead, the stormwater should be

re-directed on the cloudburst road and stored in the retention space from

where it should be pumped to the CSO structure when the risk of overflow

had ceased. Besides CSO minimisation, the control also sought to minimise

the nuisance of having stormwater on terrain when no overflow was expected

(Paper V).

MPC calculated the optimal control actions for four actuators (three road in-

lets and one pump from the retention space to the CSO structure) as a contin-

uous, deterministic control signal with a 1-minute setting duration. The opti-

misation was re-done every 5 minutes. After each optimisation, the control

actions were sent to the HiFi model (see Section 4.2), which was used as

monitoring model to update the water levels and flows in the internal MPC

model before each new optimisation (following the purple pathway in Figure

7). This HiFi model was altered to include the above-ground cloudburst road

and green retention space additionally to the standard time-area surface mod-

ule (Paper V); thus, the granularity of the surface part of the model was in-

creased to match the granularity of the network part of the model. PID con-

trollers and sensors were also included in the HiFi model, which was not

straightforward since the actuators in the model are not meant to control ver-

tical flows (Paper V). These controllers and sensors were placed right next to

each other. This may not always be the case in reality, in which case it may

not be meaningful to have a finely grained temporal resolution of the setting

duration and sampling interval. The internal MPC model included the under-

ground sewer system as well as the terrain; thus, the boundaries were ex-

panded beyond usual internal MPC models, which only simulate the urban

drainage system (Paper I). The internal MPC model was constructed with in-

41

spiration from the HiFi model. However, the internal MPC model was linear

and could thus not represent, for example, backwater effects and the overflow

dynamics directly. It thus had a less granular spatial resolution of the model

structure and a less granular flux description than the HiFi model.

In this offline proof of concept, the HiFi model also functioned as ‘virtual

reality’ to evaluate the control performance. Here, a total of 32 rain events

was applied, which is more than what is normally included in urban drainage

MPC literature (Paper I). Since CSO minimisation was the main objective,

these 32 rain events only included minor to medium-sized rain events. The

control actions were optimised over prediction horizons spanning 10–120

minutes. The input for the internal MPC model over these horizons was gen-

erated using the HiFi model with perfect rain information to allow for the

isolated investigation of the control. The performance of MPC was compared

to a baseline scenario (no control) and to the maximum potential reduction,

which was found by completely decoupling the controllable area from the

sewer system. The length of the prediction horizon was found to have a large

impact on the ability of MPC to reduce the CSO volume (Figure 15). The

prediction horizon should at least cover the travel time from the terrain to the

CSO structure, and thus a prediction horizon of 10 minutes did not reduce the

CSO volume. A maximum prediction horizon of 120 minutes was used, since

this is the typical forecast length for radar rainfall; 120 minutes was also

Figure 15. Reduction in combined sewer overflow (CSO) volume when using model

predictive control (MPC) with prediction horizons of 10, 30, 60 and 120 min

(MPC-10, -30, -60 and -120) compared to the maximum potential reduction

(MaxRed) as a function of the baseline scenario without control of stormwater in-

flow. The brackets show the average reduction for the 18 CSO-producing events.

From Paper V.

42

close to the retention time in the sewer system. Using this prediction horizon

reduced the CSO with 98.4% of the maximum potential reduction, confirming

that there is little to gain from expanding the prediction horizon much above

the maximum retention time in the system. With increasing baseline CSO

volume, the reduction in CSO volume approached 7%, which corresponds to

the relative size of the controllable area. On only one occasion did the control

unnecessarily keep stormwater on terrain because the lower granularity of the

internal MPC model led to a false prediction of CSO (Paper V).

The implementation of integrated stormwater inflow control would require

the invention of new types of actuators to be placed in the links, including

actuators to decouple downspouts and close stormwater inlets on streets. Pas-

sive versions of these are, however, already available; thus, the conversion

into dynamic versions are likely a natural next step (Paper IV). Furthermore,

a faster input model should be applied instead of the HiFi model. Since the

control model and the surrogate model from Section 4.2 cover the same area

(Figure 11), the boundaries of the surrogate model may potentially be re-

configured to be used as input model to the MPC. This would make the MPC

scheme ‘forecast model-based’ (following the orange pathway in Figure 7).

However, the control of the links between the terrain and sewer system would

affect the observations in the CSO structure, and the surrogate model could

thus not have been updated. Since data assimilation is important for the per-

formance of surrogate models, this coupling has not been done.

Model predictive control can thus be used for integrated stormwater inflow

control to meet a range of different management objectives (herunder CSO

reduction), albeit at the cost of nuisance. Discussing these trade-offs with the

local community is an important part of a succesful implementation of the

control scheme.

43

5 Discussion

This discussion will focus on real-time modelling, the need for breaking

down institutional and data silos, the importance of data quality, and how ur-

ban drainage interlinkages can be included in real-time models to meet new

management objectives.

5.1 Data-driven or HiFi model-based online models Real-time modelling is still not state-of-the-art within the urban water sector

(Borup et al., 2014), and data collection has thus not yet been adjusted to

meet the requirements for online modelling. This means that data-driven real-

time models, such as stochastic grey-box and neural network models, may be

difficult to construct based on data alone. Data-driven monitoring models

may be used as ‘software sensors’ to interpolate between model states (for

example, calculate the intermediate flow based on the upstream and down-

stream water levels); however, the construction of system-wide data-driven

monitoring models with a finely grained spatial resolution would require im-

mense numbers of in-sewer sensors for the collection of training data, which

currently does not seem feasible. Forecast models generally have a more

coarsely grained spatial resolution than monitoring models and thus require

fewer in-sewer data points for calibration. It is therefore more likely that

purely data-driven approaches can pose a viable alternative to surrogate fore-

cast models. Internal MPC models also have a more coarsely grained spatial

resolution than monitoring models. Here, the minimum resolution will be de-

termined by the number of PID controllers in the system, since the MPC

transmits control actions to these locations. In-sewer observations must be

present for the PID controllers to calculate the optimal settings of the actua-

tors. Since continual in-sewer sensing is already required here, there may be a

larger basis for the construction of data-driven internal MPC models. Howev-

er, valid for all data-driven models are that they may require re-

calibration/training every time changes are made to the physical system,

which requires just as frequent measurement campaigns to be carried out.

Furthermore, they may only be able to represent the dynamics that are present

in the training data, and the measurement campaigns would thus need to be

(sometimes unrealistically) long to ensure that also medium-sized and major

rain events may be simulated with the models.

Instead of using observations as the starting point for model building, this

thesis took HiFi models as the starting point for the discussion on real-time

44

models in Section 3.3 and for the models applied in the three digitalisation

examples in Chapter 4. Many utilities already have urban drainage HiFi mod-

els based on asset databases for offline uses, and HiFi models may therefore

pose a more approachable way of constructing online models. Using HiFi

models as the basis may even allow for the setup of an entire model complex,

where an offline ‘mother’ HiFi model is continually kept up to date by updat-

ing the underlying asset database when changes to the system are performed.

This ‘mother’ model may then be used to automatically construct monitoring,

forecast and control ‘child’ models, which may either be equivalent to the

‘mother’ model or simplified into lower fidelity or response surface surrogate

models. The latter also includes otherwise data-driven model types that are

trained on ‘simulated data’ from HiFi models, which allows for the training

on more extreme events than what may be present in historical in-sewer data

records.

5.2 Silo-thinking and data quality Even when the inclusion of interlinkages (such as that between the urban

drainage system, the WWTP and the recipient) is well-studied, they are only

rarely implemented in online models in practice. One explanation is that the

sub-systems are often managed by different utilities and authorities with dif-

ferent mind-sets, management objectives, budgets and regulatory require-

ments. Joint management may be facilitated by having one utility managing

multiple sub-systems (Grigg, 2016) but, even within a single utility, different

departments have their own objectives, organisational layers, physical assets

and data silos (i.e. repositories of data which are not easily accessed by oth-

ers), which can limit cooperation (Paper I, Sarni et al., 2019). Furthermore,

the implementation of digital technologies also requires interdisciplinary col-

laboration between different experts, including computer scientists, manag-

ers, urban water engineers, etc. (Blumensaat et al., 2019). The implementa-

tion of integrated stormwater inflow control (Section 4.3) would require insti-

tutional silos to be broken down, but also calls for collaboration with com-

puter scientists, urban planners, landscape architects and local citizens. Link-

ing the water supply and urban drainage systems (Section 4.1) also requires

the breakdown of data silos both between and within each of the two sub-

systems, which can be complicated by the dependence on different contrac-

tors for the export of data.

Sharing of data between data silos is only meaningful if this data adds value.

Recent literature on digitalisation of urban drainage (for example, Eggimann

45

et al., 2017; Kerkez et al., 2016) reports on advances within in-sewer sensors.

However, this thesis has shown that in-sewer flow data (Section 4.1) as well

as system attribute data from technical drawings (Section 4.2) and wastewater

plans (Section 4.1) are prone to errors and uncertainty related both to the

measurement equipment and to human mistakes. Digitalisation is sometimes

presented as an easy solution, but projects that initially seemed straightfor-

ward can turn out to be very complex due to faulty data. Data is the backbone

of digitalisation, and the lack of trustworthy input data, in-sewer data and

system attribute data may complicate the transition towards digital solutions

and management across silos. Furthermore, there is a risk of drowning in data

that the urban water sector does not know how to handle (Blumensaat et al.,

2019). Thus, there is a need for the development of more accurate sensors,

data validation methods and independent data sources, as well as methods for

managing and extracting useful information from large amounts of data, in-

cluding where this data is of a lower quality.

5.3 Management objectives The United Nations estimates that almost 70% of the global population will

be living in cities by 2050 (United Nations, 2018), leading to an increased

fight for space. Optimally, applied solutions should therefore be multifunc-

tional to address different objectives, which are not necessarily all related to

water management (Fratini et al., 2012), including added benefits such as

amenity value and liveability. At the same time, the United Nations sustaina-

ble development goals have increased the focus on environmental, economic

and social sustainability, also within the urban drainage sector. Traditional

stormwater and wastewater infrastructure typically lasts between 25 and 100

years (Rauch and Kleidorfer, 2014; Spiller et al., 2015), but digital technolo-

gies are expected to develop much more rapidly. The urban water sector

therefore needs to implement flexible solutions that are capable of taking up

these innovative digital technologies and adapt to new objectives along the

way. The implementation of such technologies makes it easier to adjust the

operation to respond to changes in weather, etc. (Sarni et al., 2019) but at the

same time introduces new types of vulnerabilities related to the real-time op-

eration of the system. Resilience must therefore be implemented in the form

of cyber security as well as fail-safety measures in the case of missing obser-

vations or faulty actuators. This may be challenged by the fact that operators

lose critical knowledge about the system once it is operated automatically

most of the time, and they thus have a harder time taking over manually

(Blumensaat et al., 2019). Diffuse objectives such as multifunctionality, sus-

46

tainability, liveability, flexibility, security and resilience are thus some of the

aspects that urban drainage managers will have to consider on top of the usu-

al management objectives (Figure 2). These diffuse objectives may, however,

be difficult to quantify in monetary values, which can prohibit their explicit

inclusion in the decision-making process and thus lead to sub-optimal solu-

tions (Eggimann et al., 2017). Furthermore, these more diffuse objectives

may be difficult to obtain if each part of the city is managed separately. Digi-

talisation and interdisciplinary collaboration across sub-systems may, howev-

er, enable these goals to be reached; this can be exemplified by integrated

stormwater inflow control (Section 4.3) which uses flexible control to mini-

mise local environmental impacts while tapping into liveability, resilience

and sustainability agendas.

47

6 Conclusions

Integrated management of urban drainage systems seeks to include multiple

sub-systems and/or management objectives. Some of the sub-systems are

connected by physical flows of water and are thus physically ‘interlinked’;

hence, the interlinked urban drainage system is a subset of the integrated ur-

ban drainage system. The interlinked urban drainage system includes the tra-

ditional perception of ‘integrated urban drainage management’ comprising

the urban drainage system, WWTP and recipients as well as the link with

SCMs, groundwater, water supply, the atmosphere, urban landscape and up-

stream rural areas.

Implementation of digital technologies depends on data and often also models

for real-time analysis and decision-making. While data can be subdivided

into ‘system attribute data’, ‘input data’ and ‘in-sewer data’, real-time models

may be categorised into ‘monitoring models’, ‘forecast models’ and ‘control

models’, depending on whether they are used for system replication, system

state forecasting or decision-making, respectively. The adequate setup of

each of these model types and the related data may be facilitated by evaluat-

ing the granularity (i.e. the amount of contained information) of these model-

ling-related aspects with the granularity of the indicator (the tangible unit

used to evaluate solutions), hereby ensuring ‘fit-for-indicator’ modelling.

To obtain an improved urban drainage management, traditional and emerging

data flows and urban drainage system links have been exploited in urban

drainage monitoring, forecast and control models:

Monitoring models. Linking the water supply and urban drainage systems

by expanding the observational space to include emerging smart meter data

was not straight-forward since data from the two sub-systems was hidden

in different data silos (i.e. repositories inaccessible to others). Using smart

meter data as input to a HiFi monitoring model could be used to estimate

the wastewater flow component of the DWF, and provided an easy way of

extracting spatially and temporally distributed information from large

amounts of data. Furthermore, the results showed that the in-sewer obser-

vations were most likely erroneous; thus, smart meter data may pose an in-

dependent data source that, together with other data sources, can validate

or question in-sewer flow observations.

Forecast models. Expanding the application area of traditional flow and

level observations to include data assimilation procedures improved the

48

flow and overflow forecast performance of surrogate models significantly.

The data assimilation scheme was set up independently of in-sewer data

records, instead using only HiFi model results and technical drawings. Fur-

thermore, low-maintenance level sensors provided the best results. Updat-

ing with these data was also insensitive to changes in the filter noise speci-

fications. The applied scheme is thus attractive for utilities where data,

time and money for fine tuning and maintenance of models and sensors

may be scarce.

Control models. ‘Integrated stormwater inflow control’ can be facilitated

by expanding the internal MPC model boundaries to include both the ter-

rain and sewer system. The control may be used to minimise CSO (validat-

ed by a simulation study), to increase amenity value from water being visi-

ble in the urban space, and to increase the resilience of the population

through education of cause-effects. The control scheme also comes with

lower environmental impacts than construction of new infrastructure. Nui-

sances include increased flood risk and disturbance of people’s daily lives.

These trade-offs may explicitly be considered in the control.

Low data quality, a fragmented sector and linguistic uncertainty are some of

the aspects impeding the use of digitalisation to exploit the interlinked urban

drainage system. This thesis reduces this linguistic uncertainty by providing a

common language and structures for how to implement digital technologies,

how to categorise different types of real-time models, and how to ensure fit-

for-indicator modelling. The thesis furthermore shows that extending the

view beyond the urban drainage system, exploring new application areas,

breaking down data silos and engaging in interdisciplinary collaboration

across sub-systems may not only lead to more efficient urban drainage man-

agement but also to the exploration of new agendas such as sustainability,

resilience and liveability.

49

7 Perspectives

There is still some way to go before the three digitalisation examples present-

ed in this thesis can be realised. In respect to the example that included the

link with water supply, it would be necessary to perform additional independ-

ent flow measurements to determine whether the discrepancies between

measured and simulated flow stem from erroneous in-sewer observations, and

CCTV footage to assess the potential downstream infiltration. In the example

where in-sewer observations were used for updating, the surrogate model

could be expanded with compartments having a longer time constant, which

may be used to estimate infiltration from rain and groundwater. Furthermore,

the model could have been expanded to cover the area all the way down to

the WWTP, which would also allow the inclusion of multiple data points for

updating. In the digitalisation example that linked the terrain and the urban

drainage system, the concept could be expanded to a larger area, and further

objectives could also have been included. The surrogate forecast model could

have been used as input model, despite the fact that it could not have been

updated. The practical development of actuators to be placed in the storm-

water inlets could also be undertaken.

In more general terms, establishment of best practices for real-time model-

ling, data assimilation and MPC requires more research, benchmarking case

studies where different methods can be compared as well as real-world im-

plementations where experiences are documented to serve as inspiration for

other utilities in the otherwise conservative urban drainage sector. Further-

more, awareness of the fact that we communicate differently depending on

backgrounds, and the minimisation of this linguistic uncertainty through in-

terdisciplinary collaboration and the establishment of structures (from, for

example, review articles) will make it easier to communicate across depart-

ments, institutions, systems and professions.

The investigation of real-time approaches related to other aspects of the inter-

linked urban drainage system than those presented in this thesis could be un-

dertaken. A natural starting point would be to exchange some of the offline

models that already reflect the interlinked nature of the urban drainage sys-

tem with online versions. This could include, for example, the control of ad-

ditional SCMs or the control of gates to keep stormwater on upstream agri-

cultural fields during heavy rain. Expansions of the observational space could

include the use of groundwater levels for estimating infiltration/exfiltration,

and the use of soil moisture sensors originally installed to plan municipal

50

workers’ watering schedule to estimate runoff from green areas . Looking be-

yond physical links, the application of real-time cellular phone signals could

be used as a proxy for the number of people to estimate DWF or as a basis for

assessing the nuisance caused by deliberate flooding of a given area (for ex-

ample, as an outcome of integrated stormwater inflow control). Information

on flooding of roads may also be used to redirect traffic in real time.

51

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59

9 Papers

I Lund, N.S.V., Falk, A.K.V., Borup, M., Madsen, H., and Mikkelsen,

P.S., 2018. Model predictive control of urban drainage systems: A re-

view and perspective towards smart real-time water management. Crit.

Rev. Env. Sci. Tech., 48(3), 279–339. doi:10.1080/10643389.2018.1455

484.

II Lund, N.S.V., Kirsten, J.K., Madsen, H., Mark, O., Mikkelsen, P.S.,

and Borup, M., manuscript in preparation. Using an urban drainage

model to link smart meter consumption data with in-sewer observa-

tions to estimate wastewater flows and identify system anomalies.

III Lund, N.S.V., Madsen, H., Mazzoleni, M., Solomatine, D., and Borup,

M., 2019. Assimilating flow and level data into an urban drainage sur-

rogate model for forecasting flows and overflows. J. Environ. Man-

age., 243(109052), 1–13. doi:10.1016/j.jenvman.2019.05.110.

IV Lund, N.S.V., Borup, M., Madsen, H., Mark, O. Arnbjerg-Nielsen, K.,

and Mikkelsen, P.S., 2019. Integrated stormwater inflow control for

sewers and green structures in urban landscapes. Nat. Sustain., accept-

ed.

V Lund, N.S.V., Borup, M., Madsen, H., Mark, O., and Mikkelsen, P.S.,

submitted. Model predictive control of stormwater inflows using a lin-

ear surrogate model to integrate the operation of above- and below-

ground infrastructure.

In this online version of the thesis, Papers I–V are not included but can be

obtained from electronic article databases e.g. via www.orbit.dtu.dk or on

request from

DTU Environment

Technical University of Denmark

Miljoevej, Building 113

2800 Kgs. Lyngby

Denmark

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