Amsterdecks - Waternet Innovatie

Transcription

Amsterdecks
Visualizing the water flow in the canals
of Amsterdam
Visualizing the water flow in the canals of Amsterdam
Group 4 ‘Amsterdecks’
Period 6 2015
RS&GIS Integration (GRS-60312)
Wageningen University
Commissioners
Ing. Jan Willem Voort (Waternet)
Christopher de Vries MSc (RVDA)
Drs. Tom Demeyer (Waag Society)
Coach and experts
Prof. Dr. Ir Arnold Bregt
Dr. Ir. Sytze de Bruin
Corné Vreugdenhil MSc
Team members
Hugo van Meijeren 901105568090
Janna Jilesen 900102400080
Michiel Oliemans 901102619040
Vera van Zoest 920805988050
Michiel Blok 880902074130
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Executive summary
In the nineteenth and twentieth century, the canals of Amsterdam were used as a sewerage system. Although the
water quality has improved since, the canals still suffer from a bad image. Amsterdecks, a project by Rademacher
De Vries Associates (RDVA), Waternet, and Waag Society, aims to improve the image of the canals by good communication and visualisation of the water quality. The water quality is partly determined by water flow (here defined as
water speed and water direction). However, information about the water flow in the canals of Amsterdam is limited
as data is only available from sensors at a few static locations, and calculated with the SOBEK model at other locations. Global Navigation Satellite Systems (GNSS) tracking devices can possibly be used as low-cost dynamic flow
sensors and have the potential to obtain a large amount of data in a short period of time.
The objective of this study is to obtain data about the water flow in the canals of Amsterdam, at multiple locations
and multiple points in time, using low-cost sensors. This data will be used for visualizing the water flow in the canals of Amsterdam in such a way that patterns in the water can be explained to inhabitants and recreationists. Two
specific research questions have been formulated in relation to the kind of data required: What are the similarities
and differences in water flow between the measurements by Waternet (flow sensors) and measurements using
floats mounted with GPS trackers? And what patterns can be found in the water flow?
Floating rods equipped with GPS trackers were used for deriving water speed and direction at different locations
and different points in time in the canals of Amsterdam. The tracking devices sent their location at a time interval of two minutes to a server, which was set up specifically for this study. Measurements were done during two
different sessions. The first session took place on the 10th and 11th of June 2015. A total of 18 floating rods was
dropped in the water and left there for 24 hours. The second session took place from the 15th till the 17th of June
2015, when a total of 17 floating rods was used. The locations and times sent to the server were stored in log files,
and imported in Excel . ArcMap and CartoDB were used to visualize the data.
Some of the floating rods showed interesting patterns. For example, the velocity of one of the measurement devices was compared with the velocity of the Amstel according to the static flow sensor at Amstel Omval (Berlagebrug).
The measurements of the tracking device followed approximately the same pattern as the measurements of the
static flow sensor. The largest differences were found when the velocity of the tracking device was 0. In this case,
the device was stuck in the water for some time. The correlation between the two types of measurement is tested
using correlation tests. A Spearman’s rank correlation test resulted in a correlation of ρ(283)=0.290 (p=0.000),
which indicates that the measurements of the tracking devices correspond to the measurements of the static
water flow sensor.
Concerning the water flow, specific attention has been paid to exploring the influence of the tide at sea (near
IJmuiden) on the water flow in the canals of Amsterdam. A Spearman’s rank correlation test was performed using
the height of the tide and water flow measured by the static water flow sensor as inputs, showing a strong and
significant correlation (ρ(283)=0.637 P=0.000). A Spearman’s rank correlation test was also done using the water flow measured by the tracking device, in relation to height of the tide. This test however showed no significant
correlation between the measurements and tide height (ρ(283)=0.097 P=0.103). This result is remarkable, since
a significant correlation between the measurements with the tracking devices and measurements with the static
flow sensor was already found. This result may be caused by the device being stuck at some moments in time and
therefore having a velocity of 0 for a while.
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A large part of the tracking devices got stuck behind houseboats, in reed, or in shallow water. However, the tracking
devices that did not get stuck too quickly, show patterns in water flow similar to the patterns observed in the data
of the static water flow sensor at Amstel Omval. Even the influence of pseudo-tide can be seen in the water flow
measured by the tracking devices, although no significant correlation was found.
In short, the tracking devices used were well capable of measuring the water flow, and even seemed capable of
showing the influence of the pseudo-tide on the water flow direction. At least, they can if they do not get stuck behind houseboats or reed. For future studies it would be interesting to investigate the effect of heavy rain showers
on the water flow.
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Table of contents
Executive summaryII
PrefaceV
1. Introduction1
1.1 Context
1.2 Problem definition
2
1.3 Research objective and research questions
2. Methodology3
2.1 Sample
2.2 Measurement instruments
2.3 Design of the experiment5
2.4 Data analysis6
3. Results8
3.1 Space-time cube
3.2 Comparing tracker data to static water flow sensor data
3.3 Patterns in water flow
10
4. Discussion14
4.1 Results in perspective
4.2 Methodology revisited
4.3 Recommendations for futher research15
5. Conclusion16
References17
Appendix 1: R script for creating space-time cube
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Preface
This report is part of our ACT (Academic Consultancy Training) project, which is performed in the context of the Remote Sensing and GIS Integration course. This RGIC course is compulsory within the MSc Geo-Information Science
at Wageningen University.
Our ACT project is commissioned by Rademacher De Vries Associates (RDVA), Waternet, and Waag Society, as part
of the Amsterdecks project which is initiated by the same organizations. The Amsterdecks project has two main
goals: firstly to increase the engagement of the inhabitants of Amsterdam with their water through visualisations
of the hydrologic system, and secondly to stimulate swimming and increase the safety of swimmers through the
development of decks. Within the Amsterdecks project, our aim was to collect data on the water flow in the Amsterdam canals using tracking devices, detect patterns in the water flow, compare the data with the measurements of
a static water flow sensor, and create a visualisation which can inform the general public about the water flow in
the canals.
We want to thank our commissioners Waternet, Waag Society, and RDVA for the good cooperation and support.
Jan Willem Voort and Benno, it has been great to work with you as you were as enthusiastic as we were. As the
budget of the project was limited, we highly appreciate the support of some sponsors: KPN for providing SIM cards
for communication with the tracker devices, and SoundLink for technical realization of the movie clip. Our gratitude
also goes to some very enthusiastic and supportive people at the university: Philip Wenting and Eef Veldhorst for
providing materials, and Bart Vermeulen and Paul Torfs from the hydrology department for thinking along with us
about our hydrological questions. Last but not least we would like to thank our supervisor Sytze de Bruin and our
experts Arnold Bregt and Corné Vreugdenhil for their feedback, commitment and encouraging enthusiasm.
The results of this study have also been presented on a poster and in a movie clip.
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1. Introduction
1.1 Context
In the nineteenth century, the canals of Amsterdam were still used as an open sewerage system. Until 1987, houses near the canals dumped their sewage into the canals (NPO, 2015). Ten years ago, swimming in the canals of
Amsterdam was unthinkable. A report on the microbiological quality of the surface water in Amsterdam in 2007
shows that there were pathogens present in the water and that “people that are exposed to this water are not free
from health risks” (RIVM, 2007, p. 2, translated). At the time of this RIVM report, houseboats still flushed their
sewage into the canal water. In 2008, new regulations were made considering the sewage of houseboats (Schoonschip, 2009). Currently, all houseboats are being connected to the sewerage system, which should be finished
by 2018 (Het Parool, 2008). Swimming in the canals is still not allowed, except during the yearly Amsterdam City
Swim (Tienkamp, 2013). However, the organisation of Amsterdam City Swim also warns for health risks related to
swimming in the canals (Amsterdam City Swim, s.a.). Events such as the Amsterdam City Swim however indicate
that water quality has improved over the years.
Although the water quality has improved, the canals of Amsterdam still suffer from a bad image as sewerage system, according to a spokesman of Waternet (NPO, 2015). While Waternet is busy improving the water quality in the
canals of Amsterdam, other organizations are busy trying to improve the image of the canals to make swimming
attractive when water quality meets the regulations. One of the projects to improve the image of the canals is Amsterdecks, a project by Rademacher De Vries Associates (RDVA), Waternet, and Waag Society. One of the goals of
this project is to design decks which will be located in the canals, and which can be used for swimming and other
forms of water recreation. The decks are also meant for communication with inhabitants and recreationists, by
showing near real-time information about the water quality and water flow on displays (RDVA, s.a.).
In order to enhance communication with inhabitants and recreationists in Amsterdam, and to create such near
real-time visualisations, more data is needed on the water quality in the canals of Amsterdam. The water quality
is partly determined by water flow (velocity and direction). Waternet has one flow sensor (Amstel Omval) and a few
water level sensors at different locations in the canals of Amsterdam (Figure 1).
Figure 1. Locations of the water flow sensor and water level sensors of Waternet.
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The data of the water level sensors is used in a SOBEK model to estimate water flow at different locations. SOBEK
is a piece of modelling software developed in The Netherlands and widely used in hydrological studies (see for
example Augustijn et al., 2011; Driessen & Van Ledden, 2013; Prinsen & Becker, 2011; Prinsen et al., 2014).
1.2 Problem definition
The water quality of the canals in Amsterdam needs improvement. One way of improving water quality is by improving the image of the canals and creating involvement amongst the inhabitants. There is a lack of communication
with water recreationists and inhabitants. This could be improved by the Amsterdecks project, for which visualisations are needed. More data about the water flow in the canals of Amsterdam is required for such visualisations.
Since data is only available from sensors at a few static locations, information about the water flow in the canals of
Amsterdam is limited. A more dynamic approach is needed to measure the water flow at multiple locations at different points in time. Global Navigation Satellite Systems (GNSS) could possibly be used for measuring water flow in
a more dynamic way. Until now, GNSS have not often been used for measuring water flow, although examples exist
in which GPS trackers were successfully used in measuring water flow (see for example Ravazzolo et al, 2015).
Schneider and Henneberger (2014) tested several types of commonly used GPS tracking devices. In terms of location accuracy and time to first fix, most of the devices are suitable for measuring water flow. Such tracking devices
can be used as low-cost dynamic flow sensors and therefore have the potential to obtain a large amount of data
in a short period of time.
1.3 Research objective and research questions
The objective of this study is to obtain data about the water flow in the canals of Amsterdam, at multiple locations
and multiple points in time, using low-cost sensors. This data will be used for visualizing the water flow in the canals of Amsterdam in such a way that patterns in the water can be explained to inhabitants and recreationists. Two
specific research questions have been formulated in relation to the kind of data required:
1.
What are the similarities and differences in water flow between the measurements by Waternet (flow sensors) and measurements using floats mounted with GPS trackers?
2.
What patterns can be found in the water flow?
Within research question 2, the focus will be on patterns in water flow caused by pseudo-tide. A pseudo-tide is
caused by periodically flushing of water from a river or canal into the sea by opening sluices. This flushing happens
within a period of time which equals the tide at sea (12 hours) and causes fluctuations in water height in the canal.
Next to fluctuations in water height, the pseudo-tide also causes periodically changes in the direction of the water
current (Rijkswaterstaat, 1998).
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2. Methodology
2.1 Sample
In order to answer both research questions, the water flow in the canals of Amsterdam is measured using floating
rods equipped with GPS trackers (section 2.2). In the current study, two canals in Amsterdam have been sampled
to study the water flow. These canals have been chosen because of the presence of static water flow sensors.
The measurements of the current study can therefore be compared to the measurements of the sensors. Figure
2 shows the parts of the canals that have been studied using rod floats, and the locations of the static water flow
sensors. One of the static water flow sensors measures water level (Amstel Omval, north), the other measures water flow (Amstel Amstelpark, south) (Figure 1).
Figure 2. Study area and positions of the static flow sensors.
In consultation with the province of Noord-Holland, an authorized exemption for putting the measurement instruments in the canals was not required within this study area and with the measurement instruments used.
2.2 Measurement instruments
To measure the average water flow over the entire water depth, rod floats have been used in line with Boiten
(2000). To measure the water flow at multiple locations at the same time, 17-18 rod floats (depending on the
session, see section 2.3) have been used at the same time, spread throughout the study area. The rod floats were
constructed specifically for this experiment using low-cost materials. Each rod consists of a 1 metre PVC tube (diameter 40 millimetres). Each tube was filled with 1170 grams of sand at the bottom of the tube and filled up with
polyurethane foam, to keep it in the water at upright position (Figure 3).
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In previous studies using rod floats, the time is measured between two measurement points which are passed by
the rod float (Ravazzolo et al., 2015). This method can be used in rivers with a strong water flow, in which case
the data is collected within a short time period. As this method is too labour-intensive for longer periods of data
collection, as is the case in the current study, a new method of data collection has been developed for the purpose
of this study. For the current study, GPS tracking devices have been used for tracking the locations of the floating
rods. This data was used for deriving water velocity and direction at the different locations of the floating rods, at
different moments in time.
In total, 24 GPS tracking devices were available for this study. Of these 24 tracking devices, 3 devices were original
Xexun TK102-2 devices and 21 devices were imitation TK102-2 devices, consisting of two different types. These
devices are able to send an update of their location to a central server by means of a GPRS connection, with a
set time interval. According to Schneider and Henneberger (2014), the standard deviation of location accuracy of
the Xexun TK102-2 devices is about 10 metres, which is suitable for taking measurements in the canals. As not
all devices were working properly, not all devices could be used. In total, 18 devices could be deployed in the first
measurement session (see section 2.3 for further details).
Some adjustments were made to the GPS trackers to make them suitable for the current study. The battery life
of the tracking devices was originally 5-12 hours, depending on the brand of the device. This was too low for the
current study, for which a battery life of at least 48 hours was required. Therefore, the original batteries were replaced by rechargeable batteries of 3.7 V with a capacity of 4500 mAh. These batteries should be able to last 45
hours based on a measured average current consumption of approximately 100 mA. In practice they lasted over
50 hours, which may be due to the batteries having a higher capacity when used for the first time. Both the tracking devices and battery chargers were modified and equipped with Japan Solderless Terminals (JST B3B-XH-A) to
fit the rechargeable batteries. These terminals allowed for quick mounting and dismounting of the batteries to the
trackers and chargers.
Figure 3. Technical design of the
measurement device.
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The GPS trackers were attached to the rod floats by means of a waterproof container on top of the tube (Figure
4). This waterproof container remained partly above the water level to prevent loss of satellite signals (Figure 5).
The construction was tested in a pond in Wageningen, to ensure stability of the rod in the water and the container
to stay above the water level. Each waterproof container was provided with a sticker, on which people were kindly
asked to leave the measurement device in the water for the experiment. This information included logo’s and
phone numbers of Waternet and Wageningen University.
The measurement devices provided data by means of a GPRS connection between the tracking device and a server. Each tracking device was equipped with a SIM card, sending the coordinates of the tracking device to Traccar,
a software package installed on the server. This server was set up specifically for the current study. By means of
an SMS message, the tracking devices were instructed to send their location at a time interval of 2 minutes to the
server (port 5023). Traccar uses port 8082 for a user interface, which can be used to view the locations of the
tracking devices and the location history of each device. The ability to view the locations of the tracking devices using this interface only works for the imitation TK102-2 devices. However, the locations of all devices (both imitation
and Xexun devices) were stored in a log file on the server. Exporting the coordinates from the log file is explained
in further detail in section 2.4.
Figure 5. The container with the GPS tracking device inside stays
above water level.
Figure 4. Measurement device consisting of a rod filled with sand, and a water
proof container on top.
2.3 Design of the experiment
In order to obtain as much data as possible within the time limits of this study, measurements were taken during
two different sessions. For the first session the rod floats were dropped into the water on the 10th of June 2015,
spread throughout the study area. One day later the rod floats were picked out of the water again, after collecting
data for about 24 hours. A boat from Waternet was used to drop the rod floats in the middle of the canal and to
pick up the rod floats again after the experiment. In total 18 rod floats were dropped into the water, based on the
availability of working tracking devices: 9 of these rod floats were dropped in the Amstel, 8 were dropped in the
Weespertrekvaart, and 1 at the crossing of Amstel and Weespertrekvaart (Figure 1). After the first session, the rod
floats were picked out of the water again, using the data of the last known locations.
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The second session took place on the 15th of June 2015. To explore the patterns in water flow as well as possible,
this time the measurement devices collected data for about 48 hours. On the 17th of June, the rod floats were taken out of the water again. During this session, 17 rod floats were dropped into the water based on the availability
of working tracking devices. Based on the results of the first session, the rod floats were dropped into the water at
different locations within the study area: 6 rod floats into the south-western part of the study area (Amstel), 3 rod
floats into the Weespertrekvaart, 1 south of the Berlagebrug and 7 north of the Berlagebrug (Amstel). At the end
of the second session all rod floats were taken out of the water again.
2.4 Data analysis
All locations and times sent by the trackers were collected by the server and saved into a log file. This resulted into
one log file per day and hence 5 log files in total. The data in these log files was imported into Excel. Measurement
points taken during the boat trip were removed, as well as incorrect locations. An incorrect location was for example at latitude 0 / longitude 0 degrees, which means that the tracker could not connect properly to satellites. An
example of part of a log file is shown in Figure 6.
INFO: device: 1, time: Thu Jun 11 00:05:15 CEST 2015, lat: 52.34267166666667, lon: 4.921226111111111
2015-06-11 00:05:26 DEBUG: [0C3DAC5B: 5023 <- 188.207.104.135] - HEX:
78781f120f060a160519ca059d83f00086c68100356100cc080bf400b3e602fd5b670d0a
2015-06-11 00:05:26 DEBUG: [0C3DAC5B: 5023 -> 188.207.104.135] - HEX: 7878051202fdbd7e0d0a
2015-06-11 00:05:26
Figure 6. Piece of log file which represents one message of the tracker to the server.
The Excel files were used as input for multiple products. At first, the Excel files were imported in ArcMap 10.2.1
and CartoDB for visualisation. These visualisations were used to detect patterns, support results and determine
the travel direction of the trackers (towards or away from the city). ArcMap was used to produce maps showing the
path covered by the trackers, while CartoDB was used to create the animation. One of the visualisations produced
is a space-time cube. This space-time cube was created in scripting language R, using packages sp, rgdal, rgl and
rgeos. The R script can be found in Appendix 1. The extent is determined by taking the minimum and maximum
latitude and longitude of the input data.
Secondly, the Excel files were used for the data analysis. As the tracking devices sent data to the server every 2
minutes or even 10 seconds, a lot of data was included in these files. Travel distance is very short at a 10 second
time interval; for this reason, only one location every 10 minutes was used. This enabled comparison with the water
velocity measured by Waternet at Amstel Omval, which is measured with a 10 minute interval as well. From the
locations and time interval, the water velocity was calculated. In order to calculate the water velocity, the distance
travelled was calculated with the Haversine function:
Distance = ACOS(COS(RADIANS(90-Lat1)) * COS(RADIANS(90-Lat2)) +SIN(RADIANS(90-Lat1)) *SIN(RADIANS(90-Lat2)) * COS(RADIANS(Lon1-Lon2))) *6371
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This distance was divided by the time interval, resulting in the velocity in metres per second (m/s). As the rod floats
had a length of about 30% of the water depth in the canals, a correction factor of approximately 0.90 was required
to obtain the average water velocity over the entire depth of the canal (Boiten, 2000). Therefore, the measured
velocity was multiplied by 0.90. The water velocity of one of the trackers in the second session was compared with
the water velocity measured by the static water flow sensor at Amstel Omval. This particular tracker was selected
since it was the tracker which passed closest to the measurement point. To compare the velocity of the tracker
and the water velocity measured by Waternet, statistical tests were performed within SPSS 20. First, the data was
tested for normality with a Shapiro-Wilkinson test. Depending on the distribution of the data, a choice was made
for a Pearson correlation test or Spearman’s rank correlation test, to test whether the velocities of the tracker and
the model were correlated. The Pearson correlation test is applied when the data is normally distributed, otherwise
a Spearman’s rank correlation test is more appropriate.
After the correlation test with the static water flow sensor, a correlation test was performed between the water
velocity measured by the trackers and the water height of the sea close to IJmuiden. The water height at the sea
determines the pseudo-tide in the Amstel. For this reason, this correlation test was done to assess whether the
pseudo-tide could be recognized in the data of the tracking devices.
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3. Results
3.1 Space-time cube
In order to explore the patterns in water flow, the velocity and direction of the measurement devices have been put
into graphs and the paths of the tracking devices have been mapped (Figures 8-12). For visualisation of the path
travelled in time, a selection of the tracks has also been visualized in a space-time cube (Figure 7). Four trackers
which show the most interesting patterns have been selected for this purpose; most other trackers got stuck after
several hours.
A space-time cube is basically a 3D display where the spatial component is plotted in the xy-plane and the temporal
domain is represented on the vertical z-axis (Kratochvílová, 2012). The trajectory per tracking device is determined
by connecting all the measurement points to form a path. The space-time cube displays the trajectory in 3D in
space (2D) and time for four tracking devices. Places where lines are almost horizontal advert to trajectories where
the flow velocity was high, hence moments in time where they cover large distances in a short period of time. Lines
going straight down indicate places where the tracking devices got stuck. The jerky pattern along the vertical lines
is likely the result of inaccuracies of the GPS. One tracking device (20) lost signal after several hours. The thin lines
going straight down indicate intermediate locations where the trackers have come along. Two tracking devices in
the middle (14 and 16) show more or less clearly a path going back and forth several times, suggesting changes
in flow direction.
Figure 7. Space-time cube with the tracks of 4 tracking devices.
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3.2 Comparing tracker data to static water flow sensor data
To compare the observations of the tracking devices with the data of the static flow sensors (research question 1),
the data of both measurement sessions was analysed. Only one of the trackers was observed close enough to the
static flow sensor at Amstel Omval (Berlagebrug). This measurement device was dropped into the Amstel south of
the Berlagebrug, in the second measurement session. The velocity of this tracking device was compared with the
velocity of the Amstel according to the static flow sensor at Amstel Omval. This comparison is shown in Figure 8. In
this graph, a negative value for velocity refers to water flow in the direction of the city of Amsterdam (north/east).
A positive value for velocity refers to water flow out of the city (south/west).
Velocity (m/s)
Water velocity measurement Amstel (m/s)
Velocity tracker 14
Date and time
Figure 8. Comparison velocity according to tracker data and velocity according to static flow sensor at Amstel Omval.
As can be derived from this graph, the observations of the tracking device follow approximately the same pattern
as the measurements of the static flow sensor. The largest differences are found when the velocity of the tracking
device is 0 m/s. In this case, the device was stuck in the water for some time. At some moments, the tracker has
some delay compared to the measurements of the static flow sensor.
The relation between the two types of measurement was tested using correlation tests. For these correlation tests,
Spearman’s rank correlation test was preferred over Pearson. This choice was made after a test for normality using
the Shapiro-Wilkinson test, which showed that the data was not normally distributed (p=0.000). The Spearman’s
rank correlation test resulted in a correlation of ρ(283)=0.290 (p=0.000). For this test, 285 measurement points
were used with a 10 minute interval.
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3.3 Patterns in water flow
Concerning the water flow, specific attention has been paid to exploring the patterns in water flow in the canals of
Amsterdam (research question 2). To explore whether pseudo-tide is visible in the data of the tracking devices, the
data is compared to tide height at sea. Figure 9 shows the velocity measured by one of the tracker devices and the
velocity measured by the static water flow sensor at Amstel Omval (both in m/s), compared to the tide height at
IJmuiden (in cm*10-3). Mind that for the velocity measurements, a negative value for velocity refers to water flow
in the direction of the city of Amsterdam (north/east). A positive value for velocity refers to water flow out of the
city (south/west).
Velocity tracker 14 (m/s)
15-06-2015
Flow speed Amstel (m/s)
16-06-2015
Tide (cm*10-3)
17-06-2015
Date and time
Figure 9. Velocity measured by one of the tracking devices (pink, in m/s), velocity measured by the static water flow sensor
at Amstel Omval (purple, in m/s), and tide height at IJmuiden (blue, in cm*10-3).
As can be derived from this graph, the water flow (velocity and direction) in the canals follows the same pattern
as the tide at sea. To test if the water flow measurements and tide are significantly correlated, correlation tests
have been performed. The Spearman’s rank correlation test was performed using tide height and water flow measured by the static water flow sensor as inputs, showing a strong and significant correlation between water flow
and height of tide (ρ(283)=0.637, p=0.000). For this test, 285 measurement points were used with a 10 minute
interval. The same test was done using the water flow measured by the tracking device, in relation to tide height.
However, this test showed no significant correlation between the measurements and tide height (ρ(283)=0.097,
p=0.103). This result is remarkable, as a significant correlation between the measurements with the tracking devices and measurements with the static flow sensor was already found before. This result may be caused by the
device being stuck at some moments in time and therefore having a velocity of 0 m/s for a while.
Figures 10 shows the path of the tracker device analysed in Figure 9. All points of interest have been highlighted.
These points are moments in which the device was either stuck or changed direction. These moments very well
correspond to the water flow measured by the static water flow sensor (“water movements”) and to changes in the
tide. The path of another tracking device also corresponds well to changes in tide (Figure 11).
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In Figure 10, one particular change in direction can be observed which does not correspond to the changes in tide
(point 3). In this case, the tracking device changes direction two hours before tide changes. This may be caused by
pumping stations within the area, which regularly pump water into the Amstel. For example, the pumping station
Winkel of Groot-Mijdrecht pumped water into the Amstel around the time of this change of direction.
Figure 12 shows the water flow data of two tracking devices during the first measurement session (m/s), in comparison to the water flow measurement of the static flow sensor (m/s) and the tide at IJmuiden (cm*10-3). This graph
initially shows a similar pattern for the velocity measurements of the static flow sensor and the tide at IJmuiden,
but the tracking devices get stuck very quickly after they were dropped, and therefore stop following this pattern.
Figure 10. Path of tracker 14, water movement, and tidal
movements.
Figure 11. Path of tracker 16, water movement, and tidal
movements.
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Tracker 11 (session 1) (m/s)
Tracker 12 (session 1) (m/s)
Velocity measurement (m/s)
Tide (cm*10-3)
10-06-2015
11-06-2015
Date and time
Figure 12. The water flow data of two tracking devices during the first measurement session (m/s), in comparison to the
water flow measurement of the static flow sensor (m/s) and the tide at IJmuiden (cm*10-3).
Correlation tests have been performed over the time period before the tracking devices got stuck (tracking device
12 until 12:20h, tracking device 11 until 15:10h). Table 1 shows the results of the correlation tests. These results
show a strong correlation between the water flow measurements and tide height at IJmuiden.
Table 1. Results of the Spearman’s rank correlation test between different water flow measurements in the Amstel canal
and tide at IJmuiden.
Tracking device 11
Tracking device 12
Tracking device 11
Tracking device 12
Static flow sensor
Tide
* significant at 0.01 level (two-tailed)
** significant at 0.001 level (two-tailed)
Static flow sensor
ρ(29)=0.753**
ρ(11)=0.191
Tide
ρ(29)=0.889**
ρ(11)=0.738*
ρ(141)=0.604**
12
Figure 13. Water flow out of the city (from sea inland).
30-06-2015
Figure 14. Water flow towards the city (from inland to sea).
The paths travelled by the tracking devices can be used to detect the direction in which the devices travel, which
was used to determine the direction of the water flow. As can be seen in Figures 9-12, two directions can be distinguished: away from the city (direction south-west, depicted as positive velocities in the graphs) and towards
the city (direction north-east, depicted as negative values in the graphs). These two directions of the water flow
are shown in Figures 13 and 14. The water flow towards the city (Figure 14) was only detected during the second
measurement session. At the moment the water moved towards the city, the tracker which was situated in the
Weespertrekvaart was already stuck. For this reason, the water direction in the Weespertrekvaart at the moment
at which the water in the Amstel is moving towards the city is not known, and hence not depicted in Figure 14.
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4. Discussion
4.1 Results in perspective
In this study, data about the water flow in the canals of Amsterdam was obtained using tracking devices attached
to floating rods. Data about location and time of the tracking devices was obtained during two measurement sessions. From these data, water velocity was calculated. The water velocity calculated with the data of one of the
tracking devices was tested for a correlation with the water velocity measured by a static flow sensor, and found to
be significant. This means that both measurement methods are correlated, and hence that the tracking devices
are suitable for measuring water velocities.
The results not only show that the patterns of water flow (direction and velocity) measured by the static water flow
sensor and trackers are similar, but also that these patterns show strong resemblance with the tidal height at
IJmuiden. This resemblance is caused by the sluices at IJmuiden, which form the connection between the canal
(North Sea Canal) and the sea. At low tide, the water is being flushed from the canal into the sea at the sluices of
IJmuiden. This causes the water to move into the direction of the city. At high tide there is no water being flushed.
Therefore, the water bumps against the sluices and moves back from the sluices to the city, and away from the city
at the study area. This back-and-forth movement is called pseudo-tide (Rijkswaterstaat, 1998; RIZA, 2001).
The pseudo-tide can be observed in the path of some of the trackers of session two. In session one, all trackers got
stuck within a few hours. This time was too short to show the tidal pattern in the path travelled by the trackers. The
part which was travelled, and for which the velocity was calculated, still shows close resemblance with the height
of the tide. Although the patterns of the velocity of the trackers show close resemblance with the tidal height, not
all tracker measurements were significantly correlated to tidal height. The trackers were stuck part of the time,
which may explain why the velocity is not significantly related to the height of the tide, even though the patterns
look similar.
4.2 Methodology revisited
The trackers mentioned above are not the only ones which got stuck (part of the time). At the end of each measurement session, all trackers were found stuck. For some (like tracker 14 and 16), it took a relatively long time
before they got stuck. Others stayed behind reed, houseboats or in shallow water already after a few hours. For
this reason, a large number of tracking devices is needed in order to obtain interesting results. Getting stuck will
influence the velocity measured by the trackers. For this reason, a validation measurement such as the static water
flow sensor at Amstel Omval, is always needed to validate the results. However, the measurement devices which
did not get stuck too soon were well capable of measuring the water flow (velocity and direction).
The chance of devices getting stuck could be decreased by decreasing the length of the measurement devices.
For the current study, a device with a rod length of 1 metre was used. Shortening this will decrease its capabilities
of measuring water flow over the entire depth of the canal. In the current study a rod of 30% of the entire water
depth was used, for which the speed was corrected with a factor of 0.90. In case of shortening the rod float, a lower
correction factor is required according to Boiten (2000). This correction factor however does not take into account
the influence of wind. Shortening the measurement device will increase the influence of wind and therefore make
the results less reliable.
The use of tracking devices in watertight containers on top of the floating rods was a good method for collecting
spatio-temporal data. The containers kept floating and stayed high enough above the water in order to avoid losing
GPS signal. The accuracy of the tracking devices was suitable for the purpose of this study. According to Schneider
and Henneberger (2014), the standard deviation of location accuracy of the Xexun TK102-2 devices is about 10
metres. Although we have not tested the accuracy during this study, the results of the current study suggest an
even better accuracy. This may be due to the open water area in which the tracking devices were used. The tracking
devices of this type were however not always responding properly to requests send by SMS messages. For example,
the tracking devices kept sending updates about their location every 2 minutes or even 10 seconds, while they
were requested to send updates every 10 minutes. This however did not result in any problems in the current study.
14
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The use of stickers on the measurement devices turned out to be very useful and is recommended in further
studies as well. As observed by eye witnesses, the stickers containing the logo of the local water authorities made
people aware that the devices are used for a study and should not be taken out of the water. There were reports
of some houseboat owners and people working in proximity of the water who were concerned with the floating
devices, which indicates the close relation of some citizens with the water.
During this study, data was collected for 1-2 days. Especially the session of 2 days was useful for obtaining data
about tidal influences. For other studies, other session lengths may be useful. Of course, suitable battery packs
are required in order to obtain data for a longer period of time and to be able to trace back the tracking devices. In
the current study, all tracking devices were stuck in the water at the end of each session. It is therefore not recommended to use these measurement devices for a longer period of time in this study area.
4.3 Recommendations for further research
Recommendations for further research include studying the correlation between the GPS tracker measurements
and data of other water flow sensors. Besides the static water flow sensor at Amstel Omval, there are two other
static water sensors. Those two sensors only measure water level instead of water flow, and are located at Amstel
Amstelpark and Weespertrekvaart (Figure 1). As explained in the introduction, the data of these sensors is used as
input for a model in SOBEK. This model is used to estimate water flow. It would be of interest to compare the output
of this model with the measurements of the tracking devices near these locations. Unfortunately, the output of the
model was not available in time in order to make this comparison within the current study. Comparing this data is
however still an interesting suggestion for further research.
Another suggestion for further research encompasses studying the influence of weather differences on water flow.
Weather circumstances like heavy wind or rainfall may influence water flow. For such a study, the moment of data
collection needs to be scheduled in detail and measurement devices may need adjustments.
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5. Conclusion
The aim of this study was to obtain data which could be used for creating a visualisation which can inform the
general public about the water flow in the canals. In order to obtain this data, a new data collection method was
tested. GPS tracking devices were attached to floating rods and dropped in the Amstel in two measurement sessions. The paths travelled by these devices were mapped and analysed. Water velocity was calculated using the
GPS coordinates and the time stamps. The water velocity from the devices was compared to measurements done
with a static flow sensor, and found to be correlated. The most important pattern visible in the paths travelled by
the devices was the effect of pseudo-tide. At low tide, water is flushed from the canal into the sea at the sluices
in IJmuiden. This causes the water flow in the study area to go into the direction of the city during flushing, and
change direction and move away from the city when there is no flushing. These changes in water flow caused by
the pseudo-tide were clearly visible in the travel direction and speed of the tracking devices. This means that these
devices are suitable to recognize and visualize patterns in water flow. The effect of pseudo-tide implies that for
example thrash will stay in the canals. Visualizing the water flow and pseudo-tide may be useful to create a better
understanding of the water movements in Amsterdam and may be a first step towards improving the water quality.
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References
Amsterdam City Swim (s.a.). Veiligheid en gezondheid: gezondheid en de Amsterdam City Swim. Retrieved
25/06/2015: http://www.amsterdamcityswim.nl/info/veiligheid-en-gezondheid/ (last update unknown).
Augustijn, D.C.M., van den Berg, M., de Bruine, E., Korving, H. (2011). Dynamic Control of Salt Intrusion in the
Mark-Vliet River System, The Netherlands. Water Resources Management, 25 (3), pp. 1005-1020.
Boiten, W. (2000). Hydrometry. IHE|Delft Lecture Note Series. Rotterdam: A.A. Balkema Publishers.
Driessen, T.L.A., Van Ledden, M. (2013). The large-scale impact of climate change to Mississippi flood hazard in
New Orleans. Drinking Water Engineering and Science, 6 (2), pp. 81-87.
Kratochvílová, A. (2012). Visualization of Spatio-Temporal Data in GRASS GIS. Prague, Czech Republic: Faculty of
Civil Engineering, Czech Technical University.
NPO (2015). De IJzeren Eeuw: Amsterdam, een kolerestad. Documentary 15/05/2015. Retrieved 25/06/2015:
http://www.npo.nl/de-ijzeren-eeuw/15-05-2015/VPWON_1226676 (last update 16/05/2015).
Het Parool (2008). Woonboten voor 2018 aangesloten op riool. Retrieved 25/06/2015: http://www.parool.nl/
parool/nl/6/WONEN/article/detail/84701/2008/12/18/Woonboten-voor-2018-aangesloten-op-riool.dhtml (last
update 18/12/2008).
Prinsen, G.F., Becker, B.P.J. (2011). Application of sobek hydraulic surface water models in the Netherlands hydrological modelling instrument. Irrigation and Drainage, 60 (SUPPL. 1), pp. 35-41.
Prinsen, G., Sperna Weiland, F., Ruijgh, E. (2014).The Delta Model for Fresh Water Policy Analysis in the Netherlands. Water Resources Management, 29 (2), pp. 645-661.
Ravazzolo, D., Mao, L., Picco, L. & M.A. Lenzi (2015). Tracking log displacement during floods in the Tagliamento
River using RFID and GPS tracker devices. Geomorphology, 228, pp. 226-233.
RDVA (s.a.). Amsterdecks: dichterbij een schone leefomgeving.
Rijkswaterstaat (1998). MER Amsterdam-Rijnkanaal: Verruiming Passage Zeeburg. Basisdocument zoutindringing.
Rijkswaterstaat directie Utrecht.
RIVM (2007). De microbiologische kwaliteit van het grachtenwater in Amsterdam. RIVM rapport 330000011/2007.
RIZA (2001). Systeem- en procesbeschrijving Noordzeekanaal; een kennisinventarisatie. Ministerie van Verkeer en
Waterstaat, Directoraat-Generaal Rijkswaterstaat, RIZA Rijksinstituut voor Integraal Zoetwaterbeheen en Afvalwaterbehandeling. RIZA rapport 2001.025.
Schoonschip (2009). Programma van Eisen: Aansluiten woonboten op de riolering. Version 5, June 2009. Haarlem:
Projectbureau Schoonschip.
Tienkamp, P. (2013). Zwemmen in de grachten: waarom niet? Article in Dichtbij, published 24/07/2013. Retrieved
25/06/2015: http://www.dichtbij.nl/amsterdam-centrum/regionaal-nieuws/artikel/2942636/zwemmen-in-degrachten-waarom-niet-.aspx (last update 24/07/2013).
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Appendix 1: R script for creating space-time cube
library(sp)
library(rgdal)
library(rgl)
library(rgeos)
# Set the working directory
setwd(“C:/Users/Scripting”)
# Read the data from the file
Trackers <- read.table(“Trackers.txt”, header = TRUE, sep = “\t”, na.strings = “NA”, colClasses = NA, blank.lines.skip = TRUE)
Trackers$DateTime <- as.POSIXct(Trackers$DateTime, format = ‘%d-%m-%Y %H:%M:%S’)
Tracker9 <- subset(Trackers, Device == “9”)
# Sort the date and time
Tracker9 <- Tracker9[order(Tracker9$DateTime),]
# Determine the time when the measurements started
timeStart <- min(min(Tracker9$DateTime), min(Tracker14$DateTime),
min(Tracker16$DateTime), min(Tracker20$DateTime))
Tracker9$timeSpan <- difftime(Tracker9$DateTime, timeStart, units =
Tracker14$timeSpan <- difftime(Tracker14$DateTime, timeStart, units
“hours”)
= “hours”)
= “hours”)
= “hours”)
# Determine the range of the axis
xscale <- c(min(Tracker9$Latitude, Tracker14$Latitude, Tracker16$Latitude,
Tracker20$Latitude), max(Tracker9$Latitude, Tracker14$Latitude,
Tracker16$Latitude, Tracker20$Latitude))
yscale <- c(min(Tracker9$Longitude, Tracker14$Longitude, Tracker16$Longitude, Tracker20$Longitude), max(Tracker9$Longitude, Tracker14$Longitude,
Tracker16$Longitude, Tracker20$Longitude))
zscale <- c(0, max(Tracker9$timeSpan, Tracker14$timeSpan, Tracker16$timeSpan, Tracker20$timeSpan))
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# Plot the space-time cube
open3d(windowRect = c(100, 100, 700, 700))
plot3d(Tracker9$Latitude, Tracker9$Longitude, Tracker9$timeSpan, type = “l”,
col = “red”, lwd = 4.0, xlab = “Latitude”, ylab = “Longitude”, zlab = “Time
(hours)”, xlim = xscale, ylim = yscale, zlim = zscale)
lines3d(Tracker14$Latitude, Tracker14$Longitude, Tracker14$timeSpan, col =
“blue”, lwd = 4.0, add = TRUE)
“green”, lwd = 4.0, add = TRUE)
“purple”, lwd = 4.0, add = TRUE)
# Draw the lines from the path to the map
range <- (1:(nrow(Tracker9)*0.005)*500)
for (i in range) {
lines3d(Tracker9$Latitude[i], Tracker9$Longitude[i], c(Tracker9$timeSpan[i], max(zscale)), col = “red”, add = TRUE)
}
for (i in range) {
lines3d(Tracker14$Latitude[i], Tracker14$Longitude[i], c(Tracker14$time
Span[i], max(zscale)), col = “blue”, add = TRUE)
}
for (i in range) {
Span[i], max(zscale)), col = “green”, add = TRUE)
}
for (i in range) {
Span[i], max(zscale)), col = “purple”, add = TRUE)
}
19

Amsterdecks - Waternet Innovatie

Transcription

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