Review of applications for SIMDEUM, a stochastic drinking water demand model with a small temporal and spatial scale

Abstract

Many researchers have developed drinking water demand models with various temporal and spatial scales. A limited number of models is available at a temporal scale of 1 s and a spatial scale of a single home. The reasons for building these models were described in the papers in which the models were introduced, along with a discussion on their potential applications. However, the predicted applications are seldom re-examined. SIMDEUM, a stochastic end-use model for drinking water demand, has often been applied in research and practice since it was developed. We are therefore re-examining its applications in this paper. SIMDEUM's original purpose was to calculate maximum demands in order to design self-cleaning networks. Yet, the model has been useful in many more applications. This paper gives an overview of the many fields of application for SIMDEUM and shows where this type of demand model is indispensable and where it has limited practical value. This overview also leads to an understanding of the requirements for demand models in various applications.

Full text

Drink. Water Eng. Sci., 10, 1–12, 2017
www.drink-water-eng-sci.net/10/1/2017/
doi:10.5194/dwes-10-1-2017
© Author(s) 2017. CC Attribution 3.0 License.
Review of applications for SIMDEUM, a stochastic
drinking water demand model with a small temporal
and spatial scale
Mirjam Blokker1, Claudia Agudelo-Vera1, Andreas Moerman1, Peter van Thienen1, and
Ilse Pieterse-Quirijns2
1KWR Watercycle Research Institute, 3433 PE Nieuwegein, the Netherlands
2Amsterdam University of Applied Science, Faculty of Technology, Hogeschool van Amsterdam,
Amsterdam, the Netherlands
Correspondence to: Mirjam Blokker (mirjam.blokker@kwrwater.nl)
Received: 25 January 2017 – Discussion started: 27 January 2017
Accepted: 3 April 2017 – Published: 26 April 2017
Abstract. Many researchers have developed drinking water demand models with various temporal and spatial
scales. A limited number of models is available at a temporal scale of 1 s and a spatial scale of a single home.
The reasons for building these models were described in the papers in which the models were introduced, along
with a discussion on their potential applications. However, the predicted applications are seldom re-examined.
SIMDEUM, a stochastic end-use model for drinking water demand, has often been applied in research and prac-
tice since it was developed. We are therefore re-examining its applications in this paper. SIMDEUM’s original
purpose was to calculate maximum demands in order to design self-cleaning networks. Yet, the model has been
useful in many more applications. This paper gives an overview of the many fields of application for SIMDEUM
and shows where this type of demand model is indispensable and where it has limited practical value. This
overview also leads to an understanding of the requirements for demand models in various applications.
1 Introduction
Many researchers have developed drinking water demand
models with various temporal and spatial scales. Several
models are available at a temporal scale of 1 s and a spa-
tial scale of a single home (Creaco et al., 2017). The mod-
els were introduced in papers, along with a discussion on
their potential applications. For the small temporal and spa-
tial scale models, the main application was assumed to be
in water quality modelling (Blokker et al., 2008b) related to
discolouration, chlorine decay and contaminant propagation.
However, the predicted applications are rarely re-examined.
The applications are reported in papers, but an overview of
which applications are of interest and which are not has not
yet been described.
An example of a drinking water demand model is
SIMDEUM (Blokker et al., 2011b, 2010c). SIMDEUM dif-
fers from traditional demand models in various aspects:
firstly, SIMDEUM is based on parameters with a physical
meaning instead of statistical parameters determined from
measurements. Secondly, SIMDEUM is a stochastic model,
while traditional models are deterministic (for an overview,
see Donkor et al., 2012). Third, SIMDEUM has a small spa-
tial scale (the customer tap). Fourth, SIMDEUM has a small
temporal scale (1 s). There are some other non-traditional de-
mand models that are stochastic and have a small spatial and
temporal scale (see e.g. Creaco et al., 2017). However, these
models usually only go as far as the household connection,
and they rely on extensive demand measurements.
In contrast, SIMDEUM has often been applied in re-
search and practice since it was developed, and many pa-
pers have been published about it. SIMDEUM’s original pur-
pose was to calculate maximum demands in order to design
self-cleaning networks. However, SIMDEUM has been used
for many more applications, some of which have led to in-
sight into the appropriate temporal and spatial scales for the
Published by Copernicus Publications on behalf of the Delft University of Technology.

2 M. Blokker et al.: Review of applications for SIMDEUM
Figure 1. A basic explanation of how SIMDEUM DW works.
different applications. This paper gives an overview of the
many fields of applications for SIMDEUM and shows where
this type of demand model is indispensable and where it is
of limited practical value for the use of the full details of
SIMDEUM. In these applications, it is always possible to use
SIMDEUM at a higher aggregation level.
This paper is structured as follows. Section 2 ex-
plains how SIMDEUM works and discusses the valida-
tion of the SIMDEUM results with measurements. Each
of SIMDEUM’s unique modelling aspects provides a dif-
ferent modelling opportunity and leads to a different field
of application. Section 3 gives an overview of the various
SIMDEUM applications. Section 4 reviews the various as-
pects and the areas in which they are particularly valid or
useful. Some applications are relevant for multiple aspects,
which means that previous studies may be mentioned multi-
ple times in Sects. 3 and 4.
2 Introduction of SIMDEUM
2.1 Basic description of SIMDEUM
SIMDEUM (Blokker et al., 2010c; Blokker, 2010) is a
stochastic model based on input parameters with a physi-
cal meaning that is related to water-using appliances (typi-
cal flows and volumes) in the home, the household composi-
tion (number of people, ages) and consumer water-using be-
haviour (number of toilet flushes, duration of showers, pref-
erence over the day for water-using activities). SIMDEUM
is not based on flow measurements, but on surveys of
household occupancy, household appliances and people’s be-
haviour with respect to water (Foekema and Engelsma, 2001;
Van der Broek and Breedveld, 1995). SIMDEUM generates
demand patterns for cold and hot water use at the tap. Fig-
ure 1 shows some examples of the inputs and outputs of
SIMDEUM. Its spatial scale is the tap, and it can be aggre-
gated to the scale of a single household and an apartment
building or street, e.g. by applying a bottom-up approach of
demand allocation in a hydraulic model where each node has
its own unique set of stochastic demand patterns. Its temporal
scale is 1 s and can also be aggregated. SIMDEUM thus has
some unique aspects in which it differs from traditional de-
mand models. These include its physical basis, its stochastic
nature, its small spatial scale (the customer tap) and its small
temporal scale (1 s). SIMDEUM leads to realistic demands
and realistic variations in those demands, over the day as well
as between days and between demand nodes (see Sect. 2.2),
at various aggregation levels from 1 s to 1h and from a single
household to a small town.
SIMDEUM was first developed for drinking water de-
mands (SIMDEUM DW; Blokker et al., 2010c). It was then
also made applicable for non-residential water demand in
offices, hotels and nursing homes by introducing “func-
tional rooms” (Blokker et al., 2011b). Other types of non-
residential demand can easily be generated with SIMDEUM;
the required information for these non-residential buildings
is per functional room of occupants, including the water-
using appliances and an estimate of the water-using be-
haviour. A next development step was SIMDEUM HW for
hot water demand and the related energy demand (Pieterse-
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M. Blokker et al.: Review of applications for SIMDEUM 3
0 6 12 18 24
0
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SIMDEUM
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Qday (m3 h-1)
Measured
SIMDEUM
Time (h)
Figure 2. A comparison between the average measured demand pattern and the pattern generated by SIMDEUM DW. (a) The 5 min demands
of the sum of 45 homes (Blokker et al., 2010c), and (b) the 15 min demands of a nursing home (Blokker et al., 2011b).
Water age (h)
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Model (95 % c.i.)
BU
ModelBU (μ)
ModelTD
Measurements
(a) (b)
(c) (d)
Figure 3. The measured and modelled water age at four loca-
tions. ModelTD is the model with top-down demand allocation,
and ModelBU is the model with bottom-up demand allocation of
SIMDEUM patterns. The 95 % confidence interval is due to varia-
tion, not to uncertainty (Blokker et al., 2010a).
Quirijns et al., 2015). SIMDEUM HW takes into account
heat losses in the pipes and hot water devices and can show
the benefits of a shower heat recovery system. A last de-
velopment was SIMDEUM WW for waste water discharge
patterns (Pieterse-Quirijns et al., 2012). SIMDEUM WW in-
cludes the thermal energy of the waste water and the nutrient
load, which are also determined by the end use.
2.2 SIMDEUM validations
SIMDEUM has been validated on several of its unique as-
pects in various studies over the years. The maximum de-
mand in various time steps (per second, per minute, per hour,
per day), the number of pulses per day and the number of
clock hours per day when there is water use was compared to
the measurements for a single household and an aggregation
of 30–45 homes for the Netherlands (Blokker et al., 2010c)
and Ohio, USA (Blokker et al., 2008a; Sect. 5 in Blokker,
2010; Buchberger et al., 2003). For the aggregated levels, the
diurnal patterns and the variability over the day (by using the
cumulative frequency distribution of flows and flow changes
between one time step and the next) were also compared to
the measurements. A validation was also done for apartment
buildings (hot and cold water) of up to 200 units (Pieterse-
Quirijns and Beverloo, 2013) and for several streets where
a limited set of demand measurements was available off
the shelf (Blokker, 2006). Specific validation measurements
were also done for the total and hot water use in offices, ho-
tels and nursing homes (Pieterse-Quirijns et al., 2013b). In
these specific validation measurements, some of the assump-
tions in the input variables, such as number of toilets per of-
fice employee, were also validated. All of these validations
showed the power of the physically based model in generat-
ing realistic demand patterns with a realistic variability over
the day and between days, as well as a realistic prediction of
the maximum flows (see Fig. 2). The ninimum flows were
predicted less well with the original SIMDEUM version. In
the SIMDEUM drinking water (SIMDEUM DW) version of
2015 (Blokker and Agudelo-Vera, 2015), the night flow was
improved as was the midday evening demands for rural and
urban residents. This was done by adding new specific data
on the time of use for showers, washing machines and dish-
washers and a different use of the time budget data. Hot wa-
ter demand in residences (SIMDEUM HW) and waste water
discharge (SIMDEUM WW) patterns have not yet been val-
idated with specific measurements.
The stochastic aspect (i.e. the variability in the demands)
was also validated. The effect of the stochastic demands in
a hydraulic model on the residence time towards all model
nodes and the variability in residence time over the day
and between days was validated in two networks with NaCl
tracer tests (Blokker et al., 2011a, 2010a). It compared the
use of SIMDEUM in a so-called bottom-up approach of de-
mand allocation to the traditional top-down demand alloca-
tion that uses a pumping station multiplier pattern in combi-
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4 M. Blokker et al.: Review of applications for SIMDEUM
Figure 4. The fraction of time that the flow reverses in a model with
(a) bottom-up allocated SIMDEUM demands (Blokker, 2010) and
(b) top-down demand allocations. The 50 % value means that 50 %
of the time the flow is in one direction and 50 % in the other.
nation with annual water metre readings. More specifically,
with SIMDEUM the variation in the demand between days
was taken into account by analysing multiple (stochastic)
model runs. The results showed that the SIMDEUM model
approach gives a much better insight into the actual residence
time, especially in the periphery of the DWDS (drinking wa-
ter distribution system), and also in the variability of the res-
idence time (Fig. 3).
3 Discussion of SIMDEUM applications
3.1 Water quantity modelling
SIMDEUM is used to generate realistic drinking water de-
mand patterns, and these are able to provide insight into the
various aspects related to water quantity, such as hydraulic
network modelling, leakage modelling and transient mod-
elling.
Hydraulic network models are used to study the effects
on network pressures and flows through the DWDS. The re-
sults heavily depend on the demand patterns that are used.
The bottom-up approach to (SIMDEUM) demand allocation
and the comparison to the traditional top-down demand al-
location (Blokker et al., 2011a, 2010a) allowed for the study
of how more realistic demands lead to different maximum
flow velocities, flow direction reversals (Fig. 4) and resi-
dence time in the network (Sect. 8 in Blokker, 2010). In a
transition between a fully stochastic bottom-up demand al-
location and a fully deterministic top-down demand alloca-
tion (see Sect. 2.2), it was found that the use of specific
(deterministic) demand patterns for a total of 20 different
types of users, such as hotels, schools and restaurants, was a
good approach. SIMDEUM was used to generate these spe-
cific patterns (Pieterse-Quirijns, 2014), and a model study
showed that using this demand pattern library in a hydraulic
model leads to a better understanding of water quality in
the network (Pieterse-Quirijns and van de Roer, 2013). The
study showed many differences between the standard model
and the model with the multiplier patterns from the library,
such as different residence times, the hour of maximum flow,
flow direction reversals, routes of water flow and contam-
inant propagation. The effect of changing demands on the
robustness of a DWDS was also studied; Agudelo-Vera et
al. (2016) showed that although there can be a significant ef-
fect on head, maximum flow velocities and residence times,
the studied networks are able to cope.
Another important aspect of water quantity is leakage.
Leakage is not usually one of the end uses that is modelled
in SIMDEUM. By comparing the measured and SIMDEUM-
generated night flows, it may be possible to assess leakage in
a DWDS (van Thienen et al., 2012). Depending on the accu-
racy of the SIMDEUM-generated night flow (which was im-
proved in the 2015 version; see also Sect. 2.2) and the relative
amount of leakage compared to total demand, this approach
helps to identify the actual leakage, while most traditional
water balancing approaches (based on comparing measure-
ments over time) will only give insight into the change in
leakage over time. The night flow in SIMDEUM is modelled
by setting a certain expected total night flow percentage as an
input parameter. This input parameter, however, is still only
known with a high level of uncertainty and should be studied
further.
The effects of transients in the DWDS from changes in wa-
ter demand on a small temporal scale (1 s) and a small spa-
tial scale (household level) were studied (Pothof and Blokker,
2012). The study showed that the effect on the discolouration
risk was limited. The effect of transients on other water qual-
ity aspects should be studied further. This requires a large
computational capacity when each demand node has 86 400
multipliers in a diurnal pattern.
3.2 Water quality modelling
On top of the hydraulic network modelling, some water qual-
ity modelling was done. This involved residence time in the
DWDS, chlorine decay, the interpretation of water quality
sensor data, the temperature of the drinking water and bac-
terial growth, which are all potentially influenced by the dy-
namics of the hydraulics in the DWDS.
It was expected that one of the main applications for
SIMDEUM would be in modelling water quality in the
DWDS (Blokker et al., 2008b). Added value was shown in
residence time modelling (Blokker et al., 2010a, 2011a), es-
pecially in the periphery of the DWDS (Fig. 3; see also
Sect. 2.2), and also in the saddle points where the flow direc-
tions change constantly. This also suggested a large effect by
bottom-up demand allocation in modelling chlorine residue.
There was some effect, again in the periphery of the network,
but a much larger effect was notable from the uncertainty in
the temperature in relation to chlorine decay (Blokker et al.,
2013).
Backtracing contaminants that are detected on a (theoret-
ical) sensor is also influenced by the demands in a model
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M. Blokker et al.: Review of applications for SIMDEUM 5
Figure 5. A stacked origin map for a single-sample node (upper
right, green circle) for five different sets of stochastic demand pat-
terns in the reticulation part of a medium-sized city network, illus-
trating the effect of the stochastic nature of water demand on the
flow patterns in the network and its impact on calculations. Red
nodes occur in each of the five backtraces for the respective de-
mand pattern sets. The other colours indicate a lower (but non-zero)
number of occurrences (van Thienen et al., 2013).
(van Thienen et al., 2013), specifically at locations that can
receive water through multiple paths and where many flow
inversions occur (Fig. 5). With SIMDEUM, the uncertainty
in backtracing a contaminant becomes evident for some loca-
tions; for other locations, the variability in the demand is less
influential. This correlates to e.g. the changes in flow direc-
tions (Fig. 4). With the help of SIMDEUM, these potential
sensor locations may be determined with more certainty.
The drinking water temperature is largely influenced by
the temperature of the soil surrounding the DWDS. For most
locations, the residence time in the DWDS is long enough
for the drinking water temperature to be equal to the soil
temperature (Blokker and Pieterse-Quirijns, 2013). For loca-
tions where the residence time is relatively short, the temper-
ature at the tap has not yet reached the soil temperature. For
these locations, the stochastic variation in residence time (in
a street, over the day, between days) means that the temper-
ature of the tap samples may vary a lot, and the temperature
model developed by KWR would be difficult to validate in
this case (Blokker et al., 2014a). In contrast, the temperature
in the drinking water installation (DWI) is heavily influenced
by the demand pattern, and not mainly by the ambient tem-
perature (Moerman et al., 2014). In this study, SIMDEUM
enabled the setting of specific demand patterns for toilet cis-
terns, washing machines and hot and cold water for show-
ering on the respective faucets in an EPANET model of the
drinking water installation. The model study showed that in
the winter when the home is heated, the difference between
the temperature of the drinking water at the entrance of the
home (the water metre) and at the kitchen tap may be as much
as 2–4 ◦C during the withdrawal of water.
A very preliminary attempt to model bacterial growth in
the DWDS showed that validating such a model with tap
samples may be very difficult as the variation between loca-
tions within a street, the variation over time in a day and the
variation between days could be quite significant (Blokker et
al., 2014b). For Aeromonas, this variation was indeed found
in tap samples (van der Wielen, 2015).
3.3 Design of drinking water distribution systems and
drinking water installations
SIMDEUM was originally developed for an accurate predic-
tion of the maximum drinking water demand and hot water
demand. These parameters can then be used in the design of
drinking water distribution systems (DWDSs) and (domes-
tic and non-domestic) drinking water installations (DWIs).
This application requires a small temporal scale; the maxi-
mum flow per minute can be 80 % of the maximum flow per
second for a demand of 100 homes.
SIMDEUM has been used for the design of self-cleaning
networks (Vreeburg et al., 2009), which are designed in such
a way that any particulate material that accumulates over the
day is regularly resuspended due to high flow velocities dur-
ing (mostly) the morning peak in demand. The resuspended
material then leaves the system through the customer taps
by ensuring a unidirectional flow in the network. Note that
this design is only applied to isolated areas of approximately
150–200 (residential) connections. SIMDEUM provides in-
sight into the maximum daily demands that, together with
a certain maximum diameter, ensure regular resuspension.
SIMDEUM also provides insight into the maximum yearly
demands that, together with a certain minimum diameter, en-
sure enough pressure at the taps (Blokker et al., 2010b; Buch-
berger et al., 2008). Several Dutch water companies now use
SIMDEUM as the basis for designing their self-cleaning net-
works.
SIMDEUM has been used for the design of the DWIs in
apartment buildings and non-domestic buildings (Pieterse-
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6 M. Blokker et al.: Review of applications for SIMDEUM
Figure 6. The maximum design flow for hotels depending on the
number of rooms for (a) cold water and (b) hot water. The old de-
sign rule was officially the upper limit of the coloured area, but in
practice the line in the middle or even the lower limit of the coloured
area was used depending on the design expertise. The new design
rule is based on SIMDEUM (ISSO-kontaktgroep, 2015). The mea-
surements fit the new design rule well (graph based on data from
Pieterse-Quirijns et al., 2013a).
Quirijns et al., 2013b; Agudelo-Vera et al., 2013a; Pieterse-
Quirijns and Beverloo, 2013) for which insight into the max-
imum daily drinking water demand and the maximum daily
hot water demand, together with a requirement for the mini-
mum and maximum flow velocities, leads to a certain pipe
diameter; the maximum demand for hot water in 10 min,
1 h or 1 day is used to determine the most appropriate hot
water device. The design rules following from SIMDEUM
were validated (see Sect. 2.2, Fig. 6) and are now in the of-
ficial Dutch guidelines for the design of DWIs in apartment
buildings and non-domestic buildings (ISSO-kontaktgroep,
2015). A research project was also conducted for the design
of hot water devices in residential DWIs (Pieterse-Quirijns
et al., 2015). This study showed that hot water devices can
be designed much smaller, thus reducing the waste of en-
ergy, when realistic water demands are taken into account.
The current Dutch building regulation is based on floor space
only; SIMDEUM can complement this as it can also take into
account the presence and behaviour of the residents.
With SIMDEUM WW, it is also possible to determine the
discharge of (non-) domestic waste water. In studying sus-
tainability concepts for water saving by using light grey wa-
ter and rainwater for flushing toilets and doing the laundry,
SIMDEUM was used to balance the supply and demand at
both the level of a single home and an apartment building
(Agudelo-Vera et al., 2013b, 2014b; Pieterse-Quirijns et al.,
2012). The study showed that the adaptation of water-saving
appliances is the most sustainable option compared to rain-
water harvesting. It also showed that the design of systems
for the reuse of grey water or rainwater is highly sensitive to
the demand pattern. The use of realistic patterns simulated
Figure 7. The range of simulated daily water consumption for a
selection of end uses and household sizes for 12 future demand sce-
narios used in a stress test (Agudelo-Vera et al., 2016).
with SIMDEUM can lead to a smaller tank size or to a better
estimation of the harvesting potential. It also showed that in
the Netherlands, the size of the necessary storage tank can be
much smaller when light grey water is used than when only
rainwater is used as an alternative water source. SIMDEUM
was used to simulate the water cycle at the block level and the
neighbourhood level (Sects. 6 and 7 in Agudelo-Vera, 2012),
showing that the building type related to different neighbour-
hoods has an influence on the urban water cycle, the effi-
ciency of decentralised reuse and rainwater systems.
3.4 Prediction of future water demand
Due to the physical basis of SIMDEUM, it can be used
to generate future water demand patterns. For example,
SIMDEUM can model future demographic changes and
changes in household occupancy, changes in the behaviour
of people (due to legislation or awareness), increases in
the number of luxurious (water inefficient) devices or in-
creases in the number of water-saving appliances (Agudelo-
Vera et al., 2014a; Blokker et al., 2012). Because SIMDEUM
is based on the understanding of end uses, it is able to
model trend breaks in water use, unlike the models that are
based on measurements of current and historical water use.
SIMDEUM can thus simulate specific scenarios that are rel-
evant for climate change studies, for instance, the water de-
mand on a warm summer day with the increased use at the
outside tap. SIMDEUM acts as a predictive model in the de-
sign phase of new buildings as well as in the phase during
which water use changes. These forecasted demands may
then be used to design future DWDSs and DWIs or to check
the robustness of existing DWDSs under changing demands
(Fig. 7). A study showed that future demands are uncertain,
but a range of demand scenarios can be taken into account
and the uncertainty of the future can thus be incorporated
into transitions in the (design of the) DWDS (Blokker et al.,
2015).
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M. Blokker et al.: Review of applications for SIMDEUM 7
Figure 8. (a) The EPANET hydraulic model of a DWI (Moerman et al., 2014). (b) A detailed view of one tap in the DWI experimental
set-up. From left to right: the sampling tap, solenoid valve, temperature sensor, flow sensor and PVC drainage pipe (below in the picture,
behind the bend). The solenoid valves in the DWI were electronically operated through the use of SIMDEUM demand patterns; this one is
the washing machine (Vreeburg et al., 2012).
3.5 Research in the DWDS
SIMDEUM has also proven to be of use in research. It is
impossible to measure flows at all times at all locations in
the DWDS, but with the use of SIMDEUM, a hydraulic
model can provide this information. This type of modelling
has provided insight into the self-cleaning effect and shown
that the self-cleaning velocity that has to be reached at 50 %
of the days is 0.20 to 0.25 m s−1in the tertiary network
(Blokker et al., 2010b, 2011c; Schaap and Blokker, 2011).
With SIMDEUM, a better prediction of the residence time
will be available, and the study of the relation between
the residence time and the water quality may also improve
(Mounce et al., 2016; Blokker et al., 2016).
3.6 Application of SIMDEUM outside the developers’
research group
So far, SIMDEUM has mainly been used by the developers,
as can be noted by the references in the previous sections.
Others outside the developers’ research group have applied
SIMDEUM, but this has not always been published.
The Dutch installation sector does not use SIMDEUM
directly, but the design rules that were derived from
SIMDEUM are in the official Dutch guidelines for the design
of DWIs for apartment buildings and non-domestic buildings
(ISSO-kontaktgroep, 2015). Several Dutch drinking water
companies also use the design rules that were derived from
SIMDEUM to develop self-cleaning networks.
MSc and PhD students in the Netherlands, the UK and
Germany have used SIMDEUM with limited help from the
developers, and some have published their results (Agudelo-
Vera, 2012, before she joined KWR; Agudelo-Vera et al.,
2013b; Blok, 2016; Fischer, 2008). In Italy, a student devel-
oped her own version of a SIMDEUM model (Spina et al.,
2014). Other students have used the SIMDEUM results in
the hydraulic networks of drinking water installations (Mo-
erman, 2013, when he did his internship with KWR; Mali-
novskis, 2013; Zlatanovic, 2017). Most students have strug-
gled to collect country-specific or case study SIMDEUM in-
put data and have used generic Dutch data for some of the
parameters that they could not find. For the Milford, Ohio
case, we were able to determine approximately 80 % of US
or Milford data from a multitude of publications (Sect. 5. in
Blokker, 2010). SIMDEUM input data are taken from infor-
mation on (1) household occupancy, (2) household presence
(3) and household appliances and people’s behaviour with re-
spect to water. With respect to the first type of information,
the Dutch bureau of statistics publishes this on their website
(CBS); many countries have this kind of data (or census data)
available. With respect to the second type of information, a
three-yearly survey on domestic water use has been available
since 1992 in the Netherlands (van Thiel, 2014); elsewhere
this type of data is often collected on a more irregular and
ad hoc basis by alternating researchers. With respect to the
third type of information, a five-yearly survey on time use is
performed in the Netherlands; many countries have similar
surveys that are publicly available (Centre for Time Use Re-
search, University of Oxford). We believe that various input
parameters can be used from the Dutch situation (as e.g. the
same washing machines are sold in the whole of Europe) and
that country-specific SIMDEUM input data are available, but
they are not always easy to find or very recent. In order to val-
idate a country-specific SIMDEUM result, some flow mea-
surements must be taken, typically in a small DMA (district
metered area). These are not always available to students, but
water companies can help with that.
SIMDEUM is available in Matlab®(MathWorks, Nat-
ick, MA, USA) through command line control and as a
stand-alone version with a graphical user interface (Pieterse-
Quirijns, 2014). Students are invited to contact the authors
for access to SIMDEUM. We welcome contact from anyone
who wants to improve on the model.
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8 M. Blokker et al.: Review of applications for SIMDEUM
Table 1. The importance of SIMDEUM aspects for various types of applications: ++/+indicates that this aspect is either of great importance
for the application or that SIMDEUM can be used for this aspect of the application, ++ indicates that the aspect is essential and +indicates
that the aspect is important, but not to its full extent. A “0” means that this aspect has a lesser influence.
Application Aspects of SIMDEUM
Physically Stochastic Small Small Realistic
based model spatial temporal demand
parameters scale scale patterns
Water quantity Hydraulic network model: maximum 0 ++ 0++ +
modelling flow velocities
Hydraulic network model: flow 0 ++ ++ 0+
direction reversals
Demand pattern library ++ 0+0 0
Leakage 0 0 0 0 ++
Transients 0 0 ++ ++ 0
Water quality modelling Residence time 0 ++ ++ + 0
Chlorine decay 0 0 ++ 0 0
Temperature 0 ++ ++ 0 0
Bacterial growth 0 0 0 0 0
Sensor backtracing 0 ++ ++ 0++
Particulate material 0 + + ++ 0
Design of DWDS Self-cleaning network 0 ++ ++ ++ 0
Design of DWI Cold water ++ ++ + ++ ++
Hot water ++ ++ + 0++
Fit-for-purpose source ++ ++ ++ + ++
Prediction of future demands Design ++ 0 0 0 0
Water-saving effect ++ + 0 0 0
Robustness check of DWDS + ++ 0 0 0
Research in the DWDS Water quality in relation to hydraulics 0 + ++ ++ 0
Lab set-up DWI ++ 0++ + 0
Lab set-up DWDS 0 + + + 0
4 Discussion of SIMDEUM aspects
4.1 The physically based input
The fact that SIMDEUM is based on knowledge of water-
using behaviour and water appliances instead of water flow
measurements means that it can be used to describe in de-
tail and even predict the total and hot water demands, and
can thus be used in the design phase. SIMDEUM provides
insight into the influence of changes in the technical speci-
fications or the water-using behaviour on the total and max-
imum demand, and thus provides information on where to
focus water-saving campaigns.
Furthermore, SIMDEUM generates customised drinking
water demand patterns that can be revised or updated reg-
ularly without the need to perform extended demand mea-
surements. The regular survey of Dutch residential drinking
water use suffices.
4.2 The stochastic aspect
The stochastic aspect of SIMDEUM provides the user with
multiple possible demands. This is essential in designing the
DWDSs and DWIs to determine the maximum demands that
occur only once per year and has much added value in mod-
elling water quality in the DWDS to determine the true resi-
dence times and flow direction reversals (including the varia-
tion between days) and how these may influence the interpre-
tation of sensors. The important conclusion of these studies
is that the variability in the demand adds extra understanding
which is not reached with deterministic demand models.
4.3 The small spatial and temporal scale
There is a correlation between the variability in demand
(stochastic aspect), the temporal scale and the spatial scale.
Both the cross-correlation between the demand patterns and
the lag-1 autocorrelation increase with an increase in the spa-
tial scale (Sect. 2 in Blokker, 2010). This aspect is very re-
lated to the choice for the bottom-up approach of demand
allocation (each connection has its own unique demand pat-
tern) or the top-down approach of demand allocation (similar
types of connections have the same demand multiplier pat-
tern) in modelling the DWDS. The cross-correlation between
the demand patterns increases with an increase in the tempo-
ral scale; the lag-1 autocorrelation is more or less stable for
a temporal scale of 15 min or more, but it is a lot higher for a
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M. Blokker et al.: Review of applications for SIMDEUM 9
Table 2. A summary of the requirements for the demand models for various types of applications.
Applications Temporal
scale
Spatial scale Need for re-
semblance to
real-time data
Does variabil-
ity need to be in-
cluded (i.e.
stochastic de-
mand patterns)?
Is a model based
on
historical data ac-
ceptable?
Water quan-
tity mod-
elling
Hydraulic network
model: maximum
flow velocities
Hydraulic network
model: flow direc-
tion reversals
1–5 min End pipes:
single home
Loops: 10
Depends on
whether
a general or
specific view
is required.
Yes, over time
(day, season)
and type (home,
school).
Only if available
at a suitable
timescale and
with
variation over
time.
Demand pattern
library
15–60 min > 500 homes Can be lim-
ited.
No Yes
Leakage 15–60 min > 500 homes Must be high
during night
flow.
Yes, in order to
discern natural
variation from
leaks.
Only if leak-free
historical data are
available.
Transients 1 s 1–200 homes Can be lim-
ited, as long
as variation is
there.
No Only if available
at a suitable
timescale.
Water quality
modelling
Residence time
Chlorine decay
Temperature
Bacterial growth
Sensor backtracing
Particulate material
5 min 1–20 homes Depends on
whether
a general or
specific view
is required.
Yes, over time
(day, season)
and type (home,
school).
Only if available
at a suitable
timescale and
with variation
over
time.
Design of
DWDS
Self-cleaning net-
work
1–10 s 1–200 homes Must be high
during peak
hours.
Yes, 50th per-
centile or 99th
percentile of
maximum flows.
No, during the
design phase no
historical data are
available. Also,
the design is for
future demand.
Design of
DWI
Cold water
Hot water
Fit-for-purpose
source
The tap; dis-
cern cold and
hot water
Prediction of
future
demands
Design
Water-saving ef-
fect
Robustness check
of DWDS
15–60 min The tap; to
see trend
breaks
Unknown by
definition.
Recommended to
aim for a band-
width around pre-
diction.
Possibly, but not
when trend breaks
need to be consid-
ered.
Research in
the DWDS
Water quality in
relation to
hydraulics Lab
set-up
DWI Lab set-up
DWDS
1–10 s Tap and water
metre
Can be lim-
ited, as long
as variation is
there.
Yes, for sensitiv-
ity studies.
Possibly
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10 M. Blokker et al.: Review of applications for SIMDEUM
temporal scale of less than 5 min (Sect. 2 in Blokker, 2010). It
does not seem to make sense to model with a small temporal
scale in a DWDS when the spatial scale is large.
SIMDEUM can provide insight into when a bottom-up ap-
proach of demand allocation in a hydraulic network is re-
quired and when a top-down approach is good enough. It was
shown that, depending on the number of household connec-
tions that a pipe feeds, the bottom-up approach can lead to
differences in the (maximum) residence time, in the num-
ber of flow direction reversals, in the amount of stagnant wa-
ter and laminar flows and in the maximum flow velocities
(Sect. 8 in Blokker, 2010). This type of model is required in
the extremities (dead-end pipes) of the DWDS when water
quality (residence time, sensor interpretation, etc.) is mod-
elled.
The spatial scale of SIMDEUM is actually the tap level,
which is even smaller than what is used in modelling the
DWDS. This mean that hydraulics and water quality in the
DWI can also be simulated in a hydraulic network model.
The design of DWI pipes and hot water devices can be im-
proved.
SIMDEUM can provide insight into the best temporal
scale for different applications (Sect. 2 in Blokker, 2010). To
determine the maximum demand for the network design, a 1 s
time step is required; this, however, depends on the require-
ments for the design (Buchberger et al., 2008). It was shown
that for modelling residence time in the DWDS, the standard
time step of 1 h is too coarse, and a time step of 15 min or
less is required to accurately predict the residence time at lo-
cations in the DWDS with several homes connected to it. For
the locations where only a few homes are connected, a 5 min
time step seems a better choice. The fact that a model with a
1 s time step was available allowed for the study of the effect
of transients on discolouration issues.
4.4 The realistic results
All of the above aspects are only valid when SIMDEUM
leads to realistic results. The validation of the demand pat-
terns, the maximum demand values and the resulting res-
idence times shows that the SIMDEUM results are real-
istic. The advantage is that this allows for the use of the
SIMDEUM patterns in laboratory set-ups, such as the DWI
lab set-up at TU Delft (Vreeburg et al., 2012) or the Vitens
scale model of a DWDS (KWR, 2015).
Realistic results also enable the use of the model results
instead of expensive measurements to estimate maximum
flows when studying the effect of hydraulics over time on the
discolouration risk, to estimate contact times when studying
microbial growth or to have a theoretical reference for night
use when detecting leakage.
4.5 Summary
Table 1 summarises the importance of these aspects for vari-
ous applications. In modelling residence time in the DWDS,
the small spatial scale is where the SIMDEUM bottom-up
demand allocation will make a difference (++). The tempo-
ral scale required is 5 min, which is smaller than with con-
ventional demand models, but the very small time step of 1s
is not needed (+). Of course, all applications need realistic
demand patterns, but the table shows where SIMDEUM has
added value compared to other demand models; for example,
this is the case in leakage modelling as conventional models
often do not provide leak-free reference demand patterns. At
the same time, Table 1 provides an overview of the require-
ments on demand models in various applications, which are
made even more explicit in Table 2.
SIMDEUM is a very versatile model. Not all aspects
of SIMDEUM are relevant in all applications (Table 1).
SIMDEUM did allow us to determine the required aspects
for the various applications, such as the best spatial scale for
leakage assessment. For each application, it is now possible
to determine what the requirements are for a demand model
(Table 2). If it is important that the model has a physical ba-
sis rather than a measurement basis, e.g. for future demand
estimation, an end-use model is advised. SIMDEUM is such
a model, but with a temporal scale of 1 day; e.g. REUM is
available (Jacobs, 2004). If a stochastic demand model is re-
quired, e.g. to determine the variation in the residence time or
to find the maximum demand for design purposes, the PRP
model and its derivative models (for an overview, see Creaco
et al., 2017) are available next to SIMDEUM. If a spatial
scale of the tap is required, SIMDEUM is the only model that
the authors are aware of. If a spatial scale at the household
level is required, SIMDEUM, the PRP or the PRP derivatives
may be used. If a spatial scale at the street or DMA level is
required, an aggregation of SIMDEUM patterns can be used,
or a direct model at this level can be used (PRP or DMA
measurements). Creaco et al. (2017) showed that for the Mil-
ford case, the PRP and its derivatives performed slightly bet-
ter than SIMDEUM. If a temporal scale of 1 s is required,
SIMDEUM and the PRP derivatives perform equally well
(Creaco et al., 2017). If a temporal scale of 1–5 min is re-
quired, an aggregation of SIMDEUM patterns can be used,
or a direct model at this level can be used (PRP or measured
demands). If a temporal scale of 15 min or more is required,
measurement-based demands are probably good enough. If
realistic demand patterns are required, including the realistic
variability for the sensor interpretation, the selected model
must be fit for the study site. SIMDEUM has not been widely
used outside of the Netherlands. It is expected that many of
the Dutch input parameters may be reused, but a validation
of the best parameter values and the results of SIMDEUM
elsewhere is recommended (Sect. 5 in Blokker, 2010).
Drink. Water Eng. Sci., 10, 1–12, 2017 www.drink-water-eng-sci.net/10/1/2017/

M. Blokker et al.: Review of applications for SIMDEUM 11
5 Conclusions
SIMDEUM, with its stochastic approach to demand simula-
tion and the physical basis of its input parameters at a small
temporal and spatial scale, has contributed added value to un-
derstanding and simulating current and future water demand.
A decade after its first introduction, the use of SIMDEUM
has allowed for a better simulation of the processes in the
DWDS, improvement in the design of DWIs and support for
decision-making by playing a role in the development of new
design guidelines. This paper provides an overview of which
aspects are required for the various applications. Some appli-
cations rely on the physical basis of SIMDEUM, and others
rely on the stochastic nature and small temporal and spatial
scale that SIMDEUM provides. This overview leads to an
understanding of the requirements for demand models in var-
ious applications (Table 1).
SIMDEUM is a versatile model that has been used in vari-
ous fields of application, and it is expected that more is still to
come. The fields of application are not restricted to the water
cycle and extend to the water–energy nexus and the circular
economy concept.
Data availability. No data sets were used in this article.
Competing interests. The authors declare that they have no
conflict of interest.
Edited by: Abraham
Reviewed by: two anonymous referees
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