Additional Thesis

The effect of the imperviousness on the hydrological response time of sewer districts Rotterdam Study based on new monitoring system by municipality of Rotterdam

by Martijn G.J. Mulder

MSc. Water Management Delft University of Technology Faculty of Civil Engineering and Geosciences

Student number: 4517962 Project duration: January 2017 – April 2017 Assessment Committee: Dr. ir. J.A.E. ten Veldhuis, TU Delft Dr. ir. A.M.J. Coenders, TU Delft

Additional Thesis The effect of the imperviousness on the hydrological response time of sewer districts Rotterdam Study based on new monitoring system by municipality of Rotterdam

MARTIJN G.J. MULDER MSc. Water Management, Faculty of Civil Engineering and Geosciences, University of Technology, Delft, the Netherlands

Abstract Due to an expected increase in rainfall intensity in the future because of climate change for the area of Rotterdam, the amount of storm water runoff will increase as well, resulting in higher stress on the sewer system. But until now we don’t fully understand the behavior of a sewer system. This additional thesis should help in a better understanding of the system, as it investigates the hydrological response time of the urban drainage system of Rotterdam and focuses on the effect of imperviousness. This research is based on data which are collected during the first seven months of operation of the monitoring system at 21 combined sewer overflow (CSO) weirs which was implemented in June 2016. As the hydrological response time gives information about the behavior of a sewage system, it’s an important parameter to investigate and the question is how it’s being influenced by parameters such as the imperviousness and to what extent the behavior of the sewer districts in Rotterdam is different from what we would expect from theory. From the results, we see that there is no single hydrological response time for both Time- to-Peak and Peak-to-Peak responses. The response times are highly variable with large standard deviations. There seems to be no clear linear relationship with the imperviousness or the connected surface area for the sewer system of Rotterdam. Furthermore, no significant relationship was found for several rainfall characteristics like intensity, rain event duration and cumulative rain volumes in the previous period with the hydrological response time. Finally, it was shown that the large variability in response times is mainly associated with assumptions on starting time of a rain event, which can produce large Time-to-Peak responses. However, Peak-to-Peak responses found in the research are also significantly larger than the responses found in theory. All in all, the results have shown that the response times are larger than we expected from theory and that variability cannot be explained by variability in rainfall characteristics nor by relations with catchment size or imperviousness for the urban drainage system of Rotterdam. For future research, it is recommended to have a further look into the system responses by doing a signal analysis for individual events in order to understand the high variability in responses. Furthermore, the rain radar, which will be implemented in Rotterdam in the summer of 2017, might help in a better understanding of the influence of local rainfall variability on the response time.

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This additional thesis is based on the data Introduction collected during the first seven months (June Due to the expected climate change in the next 2016 – December 2016) of operation of the decades, the KNMI (2011) expects rainfall monitoring system and it focusses on the events with higher intensities. For the area of hydrological response times between rainfall Rotterdam, an increase of 40-60% of rainfall events and overflow events of the urban water intensity per hour is expected between 2071- system in Rotterdam. 2100 compared to the situation of 1971-2000 The hydrological response time is an important (KNMI, 2011). According to this increase in parameter, as it gives more information about rainfall intensity, the amount of storm water the behavior of the system after a specific runoff will increase as well. This is also found rainfall event. Furthermore, it is strongly by Semadeni-Davies, et al (2008), who dependent on the imperviousness of a sewer concluded that the increased precipitation district. The question is what is the effect of the worsens the drainage problems in Helsingborg, imperviousness on the hydrological response Sweden. In similar cities equipped with times (“time-to-peak” and “peak-to-peak”), in combined sewer systems, in case of heavy the different districts of the Rotterdam sewer rainfall, the polluted storm water flows via system and to what extent this behavior is combined sewer overflow (CSO) weirs from the different from what we would expect from sewer system to the surface waters. It is theory. It is also interesting to see what the expected that these events will happen more effect is of different rain characteristics on the often in the future. And this is something the response time of the system and if the response municipality of Rotterdam wants to prevent is different after a wet or dry period. By (Stadsbeheer Rotterdam, afdeling Water, 2015). investigating these aspects and gathering more Rotterdam has a complex urban drainage information about CSO events and runoff system itself, which in case of a storm event can coefficients, this research should help the temporary store water and can transport it to the municipality of Rotterdam in a better open surface water by pumps and via the CSO maintenance of the system and improving the weirs. But, until now there is not enough system if necessary, to prevent it from flooding knowledge about the behavior of this system. in the future. What happens for example in case of a specific rainfall intensity, which districts have the 2.1 Definition of hydrological response shortest response time and will overflow earlier time and thus could be the critical points in the entire system? Or how does the imperviousness of a To characterize runoff processes in a catchment, District influence the behavior of the sewer often the hydrological response time is used, as system? If we better understand the behavior of it is an important parameter for the prediction this urban drainage system, the municipality can and management of peak flows (Ten Veldhuis make better decisions in maintenance and can & Skovgård Olsen, 2012). But, according to implement new measures to improve the system Gericke and Smithers (2014) there are multiple to prevent it from flooding and polluting surface water. To gather more insight in the system behavior and to see when these CSO events occur, the water department of the municipality of Rotterdam has introduced a monitoring plan, which was implemented in the summer of 2016 (Liefting & de Haan, 2016). The monitoring system includes sensors at the CSO weirs which gather information about water levels in the sewer system and surface waters. With the implementation of this system the municipality of Rotterdam is one step closer in acquiring more knowledge about their urban drainage system. However, until now this monitoring Figure 1 Different definitions of Hydrological response time according to Gericke and Smithers (2014) system is only used to detect overflows.

2 ways to describe the hydrological response According to Gericke and Smithers (2014) it is time. The time of concentration (TC), lag time also possible to calculate the Hydrological (TL) and Time-to-Peak (TP) are the most response time TP. They describe TP based on a frequently used time parameters (Gericke & relation between the rain event duration (PD) Smithers, 2014). Figure 1 shows some of these and the lag time (TL), based on a study of concepts. multiple catchments, in which lag time is the The hydrological response time is related to time between the centroid of the rainfall and the parameters such as meteorology (rainfall and centroid of the runoff. This leads to Equation 1: runoff distribution), catchment geomorphology, 푃퐷 푇 = + 푇 land cover, size, type of soils and storage 푃 2 퐿 (Smith, et al., 2002). The hydrological response Equation 1 Calculation of Time-to-Peak TP based on TL time of urban watersheds is significantly (Gericke & Smithers, 2014). different from natural catchments (Cantone & By assuming that TC(b) represents the critical Schmidt, 2011). As a result, a quicker storm duration (between centroid of rainfall and hydrological response time results in more peak of water levels, see Figure 1) of which the frequent and intense floods, which damages effective rainfall is constant, with the centroid nearby infrastructures and pollutes surface at PD / 2 and TL = 0.6TC(b), Gericke and Smithers water. According to Meierdiercks, Smith, (2014) derived Equation 2 from Equation 1. Baeck and Miller (2010) it can strongly depend 푇퐶(푏) 푇 = + 0.6푇 = 1.1푇 on the design of the Urban Drainage network 푃 2 퐶(푏) 퐶(푏) structure. Due to urbanization, the hydrological Equation 2 Calculation of Time-to-Peak TP based on TC response time is often affected by higher (Gericke & Smithers, 2014). imperviousness of these densely built areas ( (Bell, McMillan, Clinton, & Jefferson, 2016), However, as Gericke and Smithers (2014) (Smith, et al., 2002) & (Semadeni-Davies, explain in their paper, these equations are Hernebring, Svensson, & Gustafsson, 2008)). averaged over multiple catchments and are thus This effect, together with the expected increase approximations of the response time. For this of rainfall intensity of 40-60% per hour, for the purpose, the response times will be determined area of Rotterdam for 2070-2100 compared to in a data-driven analysis. the situation of 1971-2000 (KNMI, 2011) may cause serious trouble for the sewer system of Method Rotterdam. 3.1 Determination of response times In this additional thesis, the hydrological Based on the hyetograph of rainfall and the unit response time will be analyzed by the time-to- hydrograph of the runoff, the response time can peak, TP, and peak-to-peak, TC(c) (See Figure 1). be measured. But, instead of the traditional unit TP is the time between the start of a rain event hydrograph, which shows the discharge over and the maximum water levels in the sewer time, a hydrograph with the water levels over system (Gericke & Smithers, 2014). As the time will be used (output of monitoring system). highest water levels in the sewer system Based on these graphs, the Time-to-Peak correspond to the maximum volume in the response TP and Peak-to-Peak response TC(c) can system during a rain event, this is an interesting be estimated according to Figure 1. Besides this moment as it tells something about the behavior time parameter, also the rapidness of increase of of the sewer system during different rain events. water levels in the sewer system will be an Furthermore, with this method the entire rain interesting parameter to investigate. Based on event, from start to end, is taken into account. In literature we expect differences between rainfall this way, differences between rain events of events with long duration, low intensities (mild short and long duration will be made more slopes) and for rainfall events with short visible. The peak-to-peak, TC(c), is defined as the duration, high intensities (steep slopes). The time between the peak of a rain event and the variability of the response times for different maximum water levels in the sewer system rainfall volumes and intensities will be (Gericke & Smithers, 2014). It will be used to investigated. compare with Tp and is especially useful for The resulting response times will be compared rainfall events with a clearly defined peak. for several rainfall events between the different sewer districts. And we check if they are in the

3 same order of magnitude. If so, we can say influences of a dry or wet period, by having a something about the differences between look at the accumulated rain (mm) in the different areas and the possible influence of the previous 10 and 24 hours and 1 and 2 days. imperviousness of these areas. The runoff coefficient can be calculated according to Equation 3, in which R is the areal 3.2 Relationships between response runoff (mm) and P is the areal precipitation (mm): times and rainfall- and catchment 푅 (푚푚) 퐶 = characteristics 푅 푃 (푚푚) To understand the behavior of the sewer system Equation 3 Calculation of Runoff Coefficient CR (Blume, and differences in the response times, we take a Zehe, & Bronstert, 2007). further look into the influence of rainfall- and catchment characteristics. As we found from This method is also used by Smith et al (2002) literature, the response time is determined by and Meierdiercks, et al (2010), but instead of the variables such as the imperviousness, runoff coefficient, the term runoff ratio is used. catchment size, but also by meteorology such as They show that after a wet period, with several rain duration and intensity. By checking the rain events within a few days, the runoff ratio hydrological response times with the rainfall increases as less water infiltrates into the duration, rain sum, intensities, rain in the saturated soil and runs off into the sewer system previous days and district area and (Smith, et al., 2002). Based on these runoff imperviousness we see to what extent there are coefficients and runoff ratios it is also possible relationships for the sewer system of Rotterdam to say something about the water balance of the between these parameters. This results in a system per rainfall event or per month. better understanding of the distribution in In this paper three definitions are used for the response times. runoff coefficient: RC0, RC1 and RC2. RC0 is the runoff coefficient based on the measured 3.3 Determination of Runoff Coefficient pump volume at the pumping station (both waste water and storm water) and the measured Another indicator to take into account is the rain volume (assumed homogenous per district) runoff coefficient. Theoretically, the runoff according to Equation 3. However, within the coefficient or imperviousness coefficient can be pump volume, the dry weather flow is taken into estimated based on the characteristics of the consideration as well, so we should subtract it. surface of each district. But based on the ratio To calculate this direct runoff coefficient between the resulting runoff sum (mm) of a between rain volume and catchment runoff, rainfall event size (mm), the runoff coefficient RC1 and RC2 have been used. RC1 is the result of that specific rainfall event can be calculated after subtraction of the average dry weather and compared with other events (Blume, Zehe, flow per catchment during a dry day, while RC2 & Bronstert, 2007). This is also interesting is the runoff coefficient after subtracting the when we’re investigating the water balance and waste water production by population (120L per person per day) of the pump volume.

3.4 Selection of rainfall data In order to calculate response times, rainfall data needs to be selected. But, to do so, a rain event should be defined. The start of a rain event is found when precipitation is measured. Gaál, Molnar and Szolsgay (2014) and the Hydrological Observatory of Athens (2005) define the end of a rain event when no precipitation has been measured for two hours. Besides this definition, the criteria that the accumulated sum of precipitation is equal or larger than 5mm is used to select the rainfall events. Figure 2 Accumulated rain (mm) June 2016 - December 2016

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Rainfall is being measured in Rotterdam at several weather stations of KNMI and TU Delft. For selection of rain events, rainfall data of station Delfshaven has been used because of its central location in Rotterdam between all districts. 36 rain events have been selected between June 2016 and December 2016. Based on these rain events water level data has been selected from the sensors of the districts for the same duration until the time the water levels returned to their original state. Despite the several weather stations in Rotterdam, these stations show a large variability and are not exactly located at the sewer districts of interest. For this reason, 1x1km radardata of KNMI has been used to derive rainfall series for the specific districts in order to minimize the uncertainty and error in Figure 3 Overview Sewer system Rotterdam the accumulated rain per district. These rainfall Table 1 Sensors per District series are being used to calculate the ‘peak-to- disturbances like underground District Number of peak’ and ‘time-to-peak’ responses. The water storage systems to radardata of KNMI is corrected with KNMI prevent the city center from Monitoring weatherstations on the ground, to cope with flooding (Gemeente Sensors underestimation of accumulated rain by the Rotterdam, 2017). 3 2 radar (KNMI, 2016). An overview of the Impervious coefficients per 5 1 accumulated rain per location for the period District have been derived by 9 2 June-December 2016 based on the radar data is determining the 10 2 imperviousness for grid cells of shown in Figure 2. 13 1 100x100m, where land use 15 1 3.5 Description of study site classes such as streets and roofs are counted as impermeable 23 1 The monitoring system on the sewer system of area. Results are shown in 24 2 Rotterdam was implemented in the summer of Figure 20 in the appendix for 25 1 2016, and includes water level sensors at 21 each district. Table 2 26 1 CSO weirs, divided over 13 districts. An summarizes the 28 2 overview of these districts is provided in Table imperviousness coefficients 30 2 1 and Figure 3. This additional thesis will focus and shows that district vary 36 3 on five districts (shown in Table 2), which have 2 from 0.5-7km . been selected based on the variability in The Rotterdam area is largely flat and the imperviousness coefficients and on the criteria, system response mainly consists of filling up that the districts contain at least two monitoring the in-sewer storage. Within a district a similar sensors. Additionally, district 5 has been response is expected at different sensors, as the advised by the municipality of Rotterdam, since sewer is designed as a robust system to store as it is a small area without disturbances from much water as possible, meaning that the other districts by internal weirs (Schepers & van number of overflows is minimized. And even Engelen, 2017). Furthermore, the districts between districts as the systems are designed to around the city center are left out of be of similar magnitude relative to the consideration as these are often old and connected surface area for all districts. complicated systems which include Table 2 Characteristics of Districts of Interest District name District Number of Imperviousness Surface Area Population number sensors coefficient count (2016) Spangen 5 1 67% 0.5km2 10340 Pendrecht/Zuidwijk 24 2 46% 5.9km2 23849 Feyenoord 28 2 70% 7.0km2 73080 Lombardijen 30 2 52% 2.9km2 13588 Ijsselmonde 36 3 55% 6.1km2 33645 5

Results and discussion Table 3 Peak-to-Peak and Time-to-Peak responses based on rainfall of 4.1 Response times based on rainfall weather station Delfshaven. station Delfshaven Peak-to-Peak Time-to-Peak Figure 4 shows the Peak-to-Peak response times District Mean Std.Dev. Mean Std.Dev. for water level data of all sensors of the 13 D300915 03:08 02:20 06:44 04:53 districts, based on rainfall data of Weather station Delfshaven. As can be seen in Table 3, D303200 03:19 02:10 06:55 04:46 the average response time for these districts is D500295 02:57 01:54 06:34 04:57 largely between two and four hours, with D904098 04:10 02:57 07:46 04:28 standard deviations in the same order of 01:34 02:41 05:10 05:22 magnitude. There is not just one response time D908618 per district but a large range of response times D1000065 03:27 02:22 07:03 04:56 as shown in Figure 4. Furthermore, District 9 D1005813 03:01 02:15 06:37 05:15 (D908618) shows some distortions, as outliers D1304627 03:33 03:31 07:09 05:25 of 00:00 hours occur, due to the fact that no data was recorded within the first few months of D1500363 03:45 03:14 07:21 05:49 measuring D2402010 03:44 02:41 07:21 04:43 If we take a look at the Time-to-Peak response D2402041 03:42 02:36 07:18 05:03 of the weather station Delfshaven from Figure 5 we get similar results as the Peak-to-Peak D2510166 02:53 02:25 06:29 05:00 response. Again, the response times show high D2600480 03:59 03:55 07:35 05:21 variability according to the figure. Table 3 D2801387 03:58 03:12 07:35 05:02 shows mean Time-to-Peak responses in the D2804351 04:20 03:33 07:56 05:08 order of magnitude of six and seven hours with standard deviations of about five hours. D3002097 02:39 02:37 06:15 05:06 D3002234 03:04 03:25 06:40 05:23 D3605404 03:23 02:40 06:59 05:42 D3605642 03:49 02:48 07:25 05:09 D3605644 03:43 03:01 07:20 05:36

Figure 4 Distribution of Peak-to-Peak response per District based on Figure 5 Distribution of Time-to-Peak response per District based Weather Station Delfshaven on Weather Station Delfshaven

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4.2 Response times based on radar data maximum and average intensity, rain sum in Figure 6 shows the Peak-to-Peak responses previous days and rain duration. To do so, we focus on district 5, as it is small and has no based on basin-average rainfall data of the influence from upstream basins. KNMI radar for districts 5, 24, 28, 30 and 36.

We get similar results, with average response times in the order of magnitude of three hours, 4.3 Difference between start time of rain compared to Figure 5. Standard deviations are event Weather station and radar. slightly smaller for all districts as can be seen in To verify the Time-to-Peak responses we get Table 4, compared to Table 3. Figure 6 shows a from the weather station and radar, we check if large range of response times between zero and the difference in start time of a rain event for nine hours, which is comparable to the results both rainfall series is the same. Figure 9 and Figure 10 show the results of this analysis. First shown in Figure 4. of all, we see that for district 5 the start of a rain If we take a look at the Time-to-Peak response event is always observed at weather station of the KNMI radar data we get similar results as the responses from the weather station as well. Table 4 Peak-to-Peak and Time-to-Peak responses based on Both the response of the weather station rainfall of radar KNMI Delfshaven and the radar data show a large Peak-to-Peak Time-to-Peak variability of response times in Figure 5 and District Mean Std.Dev. Mean Std.Dev. Figure 7 respectively. D500295 02:54 01:46 07:46 05:59 Taking a look at Table 4 shows Time-to-peak D2402010 03:33 02:35 08:19 05:06 responses for the radar data for districts 5, 24, 28, 30 and 36 of seven and eight hours which is D2402041 03:24 02:18 08:09 05:16 longer compared to the weather stations. Also, D2801387 03:44 02:23 07:44 04:56 the standard deviations are larger, as outliers of D2804351 04:06 03:02 08:06 05:09 up to 33 hours occur as can be seen in Figure 7. D3002097 02:19 02:09 06:50 05:09 These outliers could be produced by measurement errors, or caused by a dry spell D3002234 02:56 02:41 07:26 05:38 due to a dry period in the week before. For that D3605404 02:35 01:55 07:24 06:34 we take a further look into the relations between D3605642 03:09 02:08 07:58 06:21 the responses and rainfall characteristics such as D3605644 02:56 02:26 07:45 06:18

Figure 6 Distribution of Peak-to-Peak response per District based Figure 7 Distribution of Time-to-Peak response per District based on radar data per district on radar data per district

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Figure 9 Differences between Start Time of Weather Figure 10 Differences between Start Time of Weather Station Station and KNMI Radar District 5 and KNMI Radar District 24 (Weather station – Radar)

Delfshaven first, before it’s being measured by For district 24 a delay of the station is found of the radar (Figure 9). This suggests a southern 1 hour and 1 minute, while the difference in approach of the rain by wind, as Delfshaven is Time-to-Peak response is 58 minutes according a few kilometers south of District 5. This is also to Table 3 and Table 4. confirmed when having a look at District 24, So, the differences between the average Time- which is south of Delfshaven and shows to-Peak responses are covered by the negative differences between the start times. differences in starting time of the rain event for This is because of the fact that rain events are the station and radar. And, we clearly see a measured at the radar at district 24 first, before South-North pattern based on these results. But being measured at the Weather station, as can the time differences between the station and be seen in Figure 10. radar location are large compared to the All in all, these differences in start time result in distance in between, which could explain the a delay of the radar for district 5 of about 1 hour large outlier in Time-to-Peak response time. and 10 minutes, which is almost equal to the difference in Time-to-Peak response according to Table 3 and Table 4 of 1 hour and 12 minutes.

Figure 8 Coefficient of Determination for Peak-to-Peak Response time with Average and Maximum Intensity, Rain event duration and Rain event sum

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Figure 11 Coefficient of Determination for Time-to-Peak Response time with Average and Maximum Intensity, Rain event duration and Rain event sum

4.4 Relationship between rainfall characteristics and response times Figure 8 shows the relationships between the Peak-to-Peak responses for district 5 (radar) and the rainfall characteristics; average and maximum intensity, rain event sum and rain event duration. Figure 11 shows the results for the Time-to-Peak responses for district 5 (radar). As can be seen from the figures there’s no linear relationship between the response times and the variables. Neither for the Peak-to- Peak and Time-to-Peak. This can be explained, as the travel time of a drop of water is not influenced by an increased intensity or rain sum, Figure 12 Relationship Imperviousness vs. Peak-to-Peak response but is more likely to be affected by the for five districts geomorphology of a catchment and sewer design. Still, high intensities will result in a higher filling rate of a sewer system. The rainfall characteristics based up on the available radar data don’t explain the large distribution in response times for the district as there is no clear linear relationship.

4.5 Relationship between responses and Imperviousness and size of the area Figure 12 and Figure 13 show a better relationship between the Peak-to-Peak response and the Impervious and Surface area size. However, with a coefficient of determination of only 0.156 and 0.305 respectively, we can’t Figure 13 Relationship Surface Area vs Peak-to-Peak response speak of a clear linear relationship for the for five districts imperviousness and the size of the area.

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The reason for this moderate relationship could be because of the fact that we’re having results 4.6 Effect of wet or dry period on for five individual districts only, resulting in response time very large area sizes per CSO sensor. As we assumed that the behavior of a sewer district is Figure 14 and Figure 15 show the amount of the same at all CSO-weirs as it reacts as a robust rain (mm) within 10hours, 24hours, 2days and system, we neglect local variabilities in rainfall 7days before the start of a rain event. But these or imperviousness. If we take into account don’t show a clear linear relationship with the multiple CSO sensors per district, we might be Peak-to-Peak (Figure 14) or Time-to-Peak able to better predict the relationship between (Figure 15) response for district 5. The outliers the hydrological response and local in Time-to-Peak response in District 5 reduce imperviousness and the connected surface area. the value of coefficient of determination. For But we need to take into account that the sewer district 5 this could mean that the given amount system is looped and it is often difficult to of rain in the previous 7 days is not sufficient to determine which area is connected to which fully saturate the pervious area and doesn’t lead CSO weir.

Figure 14 Relationship Rain sum over previous time and Peak-to-Peak Response District 5

Figure 15Relationship Rain sum over previous time and Time-to-Peak Response District 5

10 to an increased flow(speed) to the drainage system, resulting in a faster response.

4.7 Explanation of large outliers in response times To get a better understanding of the results, we’ll take a closer look into the calculated response times, by discussing some examples. The first example is shown in Figure 16 and represents Event 5. As shown there is a large difference between the start time of the rain and the time where the rain event reaches a maximum rain sum per 5 minutes. This results in a Time-to-Peak response of 8 hours and 6 minutes and a Peak-to-Peak response of 2 hours and 21 minutes. Figure 16 Determination response time District 5, Event 5 Secondly, Figure 17 shows Event 10, with a maximum measured intensity of 9mm in 5 minutes on the 23th of June 2016. This is a very short, but heavy storm, Peak-to-Peak and Time- to-Peak are fairly similar, as maximum rain intensity is reached right after the start time of the rain event. This results in the Peak-to-Peak and Time-to-Peak responses of 3 hours and 9 minutes and 3 hours and 19 minutes respectively. But, we need to consider that these are responses to a single peak. As Figure 17 shows, multiple rainfall peaks cause multiple responses in the sewer system. Finally, Figure 18 shows a rather complicated situation, involving the outlier of the Time-to- Peak response we found in Figure 7 of over 33 hours. As shown, the starting moment of the rain event is caused by some precipitation with Figure 17 Determination response time District 5, Event 10 very low intensities at the start of the event, resulting in a Time-to-Peak response of 33 hours and 21 minutes. When we take a look at the Peak-to-Peak we get a response of 5 hours and 17 minutes. Another point of interest is the second peak at the end of the time series of Figure 18. The model would find a maximum over here, if there wouldn’t be a period of at least two hours without rain, which indicates the end of the rain event. The second peak is therefore left out of consideration and not used to calculate a Peak- to-Peak response time in this case.

4.8 Monthly water balance and runoff coefficient per district Monthly water balances with runoff coefficients are provided in Table 5 and in Table 7 in the Figure 18 Determination response time District 5, Event 18 appendix. From these water balances follow that, despite some variations per district, June

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Table 5 Monthly Water Balance and Determination of Runoff Coefficient based on radar data for district 5 District 5 Pump volume Rain volume RC0 Mean DW RC1 Population DW RC2 (m3) (m3) (-) (m3) (-) (m3) (-) June 98955 98158 1,01 48820 0,51 37224 0,63 July 62234 31416 1,98 50447 0,38 38465 0,76 August 57760 39827 1,45 50447 0,18 38465 0,48 September 47482 6684 7,10 48820 -0,20 37224 1,53 October 56682 32689 1,73 50447 0,19 38465 0,56 November 71366 52122 1,37 48820 0,43 37224 0,66 December 46449 3112 14,93 50447 -1,28 38465 2,57 Averages 1,67 0,35 0,66 was by far the wettest month, followed by November. September and December were fairly dry, which also follows from RC0 in these months. As we can see from Table 5 and Table 7, the first method results in runoff coefficients which are twice as small as the second method. This is because RC2 underestimates the dry weather flow as groundwater inflow into the sewer system and wastewater production by commerce areas are not taken into account. However, this underestimation of the dry weather flow, RC2 approaches the imperviousness coefficients better than RC1 according to Table 6. But, as rainfall is assumed homogeneous over each district the rain volume Figure 19 Daily Runoff coefficient RC1 per Event is probably overestimated, resulting in low RC1 values. For this purpose, it’s better to use the before these months as can be seen in Table 8 RC1 method as it takes the actual dry weather and Figure 21 in the appendix. Although the flow into account. variation seems smaller for districts 30 and 36. Figure 19 shows the daily runoff coefficient Furthermore Table 8 shows mean runoff RC1 per event for district 5 over the period June coefficients for all district which are similar to - December. The figure, together with the the RC1 values in Table 6. In conclusion, more results for districts 24, 28, 30 and 36 in the research about this relationship is needed on a appendix, suggests that there is some relation local scale around the CSO sensors together between the runoff coefficient and the sum of with a study on the imperviousness and rain in the previous period. The daily runoff connected surface area. coefficients show that the highest runoff coefficients RC1 occur around June and July, which are the wet months. Likewise, lower Conclusion runoff coefficients are found around October We can conclude that the response time for the and November as not many rain events occurred sewer districts of Rotterdam is highly variable for all districts. From the results, we have seen Table 6 Comparison Imperviousness and Runoff coefficients that the response times are highly distributed District Imperviousness RC1 RC2 with large standard deviations. Although, we 5 67% 0,35 0,66 have seen from Figure 18 that the definition of 24 46% 0,16 0,39 the start of a rain event can have big influence on the length of the Time-to-Peak response. But 28 70% 0,24 0,63 aside from this definition we have found large 30 52% 0,24 0,53 Peak-to-Peak responses compared to urban 36 55% 0,19 0,39 drainage systems in literature. Meierdiercks,

12 smith, et al (2010), described runoff ratios of highly-distributed response times. An option about 0.4-0.5, which are in the same order of might be to do a signal analysis based on magnitude as Rotterdam, but found response manual determination of the peaks in the water times in the order of 15-30 minutes. For large levels per event. As we have seen from Figure urban drainage systems of up to 100km2 17 we’re missing out on some peaks with the Gericke and Smithers (2014) found lag times of current method and only the highest peak has 1-2 hours, which are smaller but comparable to been taken into account, while the other peaks the Peak-to-Peak values of 3 to 4 hours which might have a significant influence on flooding are found in this Additional thesis. The Time- of the system as well. to-Peak values we found are in the same order Furthermore, the assumptions on rainfall of magnitude for the natural catchment which selection have a big influence on the start time Blume, et al (2007) found. of a rain event. For this reason, it’s No direct linear relationship has been found recommended to use a larger threshold for the between response and rainfall characteristics. measured rain before a rain event starts. Figure Neither rainfall intensity, duration or rain sum 18 has shown that the assumptions used in this in the previous period had a significant linear paper may cause large outliers in the final Time- relationship with the response times, which to-Peak response times. raises the question what does affect this? We’ve The Rain radar in Rotterdam, which will be in seen that size of the District surface area and operation in the summer of 2017, might also imperviousness relate better, but the results we help in a better understanding of the have are based upon the entire district and not hydrological response time. But, for this the surface area that is connected to the purpose it’s important to know which part of the measurement sensor at the CSO weir. sewer system is connected to which surface Furthermore, we have seen large time area, so we understand where the water is differences between station and radar data coming from at each CSO sensor. In this way, which might have been caused by the fact that we can also better check to what extent there not all events are the same for every district. As really is a robust system and why response times rainfall is not homogenously distributed over are different at the CSO weirs within a district. the entire area of Rotterdam it might be possible However, we need to take into account that that at one district rain is measured, while at a we’re dealing with looped systems, which will second district there’s none. make it difficult to determine exactly which Finally, when we take a look at the monthly areas are connected to which CSO weir. This is water balances for the districts and the estimates why it might be useful in a future scenario to of the runoff coefficients, its seems that the add sensors to all weirs within one district. This runoff coefficients give a better view on the can help the municipality of Rotterdam in a effect of rainfall from the previous period. For better maintenance and prediction of where this purpose, the RC1 method is preferred as it floods may occur. Additionally, it could be uses a better estimation of the dry weather flow, useful to further investigate the influence of a although this method produces low runoff dry period on the runoff coefficient and coefficients due to the overestimation of rainfall imperviousness within a district and check the volume. relationship between these coefficients. Finally, it might be interesting to investigate the Recommendations influence of sustainable urban drainage systems For future research, it is advised to further in the future, as these systems make urban investigate the influence of rainfall variability drainage networks much more complicated, and on the response time, to better explain the have a large influence on the response times. References

Bell, C., McMillan, S., Clinton, S., & Jefferson, A. (2016). Hydrologic response to stormwater control measures in urban watersheds. Journal of Hydrology, 541(B), 1488-1500. Retrieved from http://dx.doi.org/10.1016/j.jhydrol.2016.08.049 Blume, T., Zehe, E., & Bronstert, A. (2007). Rainfall - runoff response, event-based runoff. Hydrological Sciences Journal, 52(5), 843-862. doi:10.1623/hysj.52.5.843

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Butler, D., & Davies, J. (2004). Urban Drainage. London: Spon Press. Cantone, J., & Schmidt, A. (2011). Improved understanding and prediction of the hydrologic responseof highly urbanized catchments through developmentof the Illinois Urban Hydrologic Model. Water Resources Research, 47(8). doi:10.1029/2010WR009330 Gaál, L., Molnar, P., & Szolgay, J. (2014). Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland. Hydrology and Earth System Sciences(18), 1561- 1573. doi:http://www.hydrol-earth-syst-sci.net/18/1561/2014/hess-18-1561-2014.pdf Gemeente Rotterdam. (2016). Wijkprofiel Rotterdam 2016. Retrieved March 15, 2017, from Website Gemeente Rotterdam: http://wijkprofiel.rotterdam.nl/nl/2016/rotterdam/ Gemeente Rotterdam. (2017). Ondergrondse Waterberging Museumparkgarage. Retrieved April 26, 2017, from Website of Municipality of Rotterdam: https://www.rotterdam.nl/wonen- leven/waterberging-museumparkgarage/ Gericke, O., & Smithers, J. (2014). Review of methods used to estimate catchment response time for the purpose of peak discharge estimation. Hydrological Sciences Journal, 59(11), 1935-1971. doi:10.1080/02626667.2013.866712 Hydrological Observatory of Athens. (2005). Rainfall Events. Retrieved 01 09, 2017, from Website of Hydrological Observatory of Athens: http://hoa.ntua.gr/rain_incident/ KNMI. (2011). Intensiteit van extreme neerslag in een veranderend klimaat. Retrieved December 19, 2016, from Website of KNMI: https://www.knmi.nl/kennis-en- datacentrum/achtergrond/intensiteit-van-extreme-neerslag-in-een-veranderend-klimaat KNMI. (2016). 5-minuut radaraccumulaties gecorrigeerd met regenmeters. Retrieved March 10, 2017, from Website of KNMI: https://data.knmi.nl/datasets/radar_gauge_adjust_rain_5min/1.0 Liefting, E., & de Haan, C. (2016). Basismeetnet overstorten Rotterdam. Nijmegen: Partners4UrbanWater. Meierdiercks, K., Smith, J., Baeck, M., & Miller, A. (2010). Analyses of Urban Drainage Network Structure and its impact on Hydrologic Response. Journal of the American Water Resources Association (JAWRA), 46(5), 932-943. doi:10.1111 ⁄ j.1752-1688.2010.00465.x Schepers, J., & van Engelen, M. (2017, March 23). Data Collection at Department Water, Municipality Rotterdam. (M. Mulder, Interviewer) Semadeni-Davies, A., Hernebring, C., Svensson, G., & Gustafsson, L. (2008). The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden: Combined sewer system. Journal of Hydrology, 350(1-2), 100-113. Retrieved from http://www.sciencedirect.com/science/article/pii/S0022169407002910 Smith, J., Baeck, M., Morrison, J., Sturdevant-Rees, P., Turner-Gillespie, D., & Bates, P. (2002). The regional hydrology of extreme floods in an urbanizing drainage basin. Journal of Hydrometeorology, 3(3), 267-282. doi:10.1175/1525- 7541(2002)003<0267:TRHOEF>2.0.CO;2 Stadsbeheer Rotterdam, afdeling Water. (2015). Gemeentelijk Rioleringsplan Planperiode 2016-2020. Rotterdam. Retrieved 12 19, 2016, from http://www.rotterdam.nl/gemeentelijkrioleringsplangrp Ten Veldhuis, J., & Skovgård Olsen, A. (2012). Hydrological response times in lowland urban catchments characterized by looped drainage systems. 9th International Workshop on Precipitation in Urban Areas: Urban Challenges in Rainfall Analysis. St. Moritz, Switzerland. weather.tudelft.nl. (2016). Weather TU Delft. Retrieved April 26, 2017, from http://weather.tudelft.nl/plots/

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Appendix 8.1 Determination of Imperviousness of Districts Imperviousness coefficient is determined for districts 5, 24, 28, 30 and 36 as described in the method. Imperviousness coefficient is shown in histograms in Figure 20. Every district was divided in grid cells of 100m by 100m of which the imperviousness coefficient was determined.

Figure 20 Histograms showing imperviousness coefficients for districts 5 24, 28, 30 and 36

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8.2 Monthly water balances districts 24, 28, 30 and 36

Table 7 Monthly water balances districts 24, 28, 30 and 36 District 24 Pump volume Rain volume RC0 Mean DW RC1 Population DW RC2 (m3) (m3) (-) (m3) (-) (m3) (-) June 427501 1071759 0,40 178226 0,23 85856 0,32 July 185766 154849 1,20 184167 0,01 88718 0,63 August 225674 463714 0,49 184167 0,09 88718 0,30 September 164488 102143 1,61 178226 -0,13 85856 0,77 October 231096 386587 0,60 184167 0,12 88718 0,37 November 319813 634427 0,50 178226 0,22 85856 0,37 December 170268 29116 5,85 184167 -0,48 88718 2,80 Averages 0,61 0,16 0,39

District 28 Pump volume Rain volume RC0 Mean DW RC1 Population DW RC2 (m3) (m3) (-) (m3) (-) (m3) (-) June 892186 1355707 0,66 452294 0,32 263088 0,46 July 519582 292816 1,77 467371 0,18 271858 0,85 August 560779 567987 0,99 467371 0,16 271858 0,51 September 438035 85934 5,10 452294 -0,17 263088 2,04 October 529767 410637 1,29 467371 0,15 271858 0,63 November 665993 685460 0,97 452294 0,31 263088 0,59 December 447069 37722 11,85 467371 -0,54 271858 4,64 Averages 1,18 0,24 0,63

District 30 Pump volume Rain volume RC0 Mean DW RC1 Population DW RC2 (m3) (m3) (-) (m3) (-) (m3) (-) June 310597 569598 0,55 106541 0,36 48917 0,46 July 129761 78131 1,66 110092 0,25 50547 1,01 August 144390 247737 0,58 110092 0,14 50547 0,38 September 96473 41616 2,32 106541 -0,24 48917 1,14 October 128179 164159 0,78 110092 0,11 50547 0,47 November 182198 287105 0,63 106541 0,26 48917 0,46 December 102333 15044 6,80 110092 -0,52 50547 3,44 Averages 0,78 0,24 0,53

District 36 Pump volume Rain volume RC0 Mean DW RC1 Population DW RC2 (m3) (m3) (-) (m3) (-) (m3) (-) June 546538 1084925 0,50 199097 0,32 121122 0,39 July 257716 182618 1,41 205734 0,28 125159 0,73 August 280943 524285 0,54 205734 0,14 125159 0,30 September 172554 95490 1,81 199097 -0,28 121122 0,54 October 208148 344818 0,60 205734 0,01 125159 0,24 November 308698 591601 0,52 199097 0,19 121122 0,32 December 190321 30174 6,31 205734 -0,51 125159 2,16 Averages 0,69 0,19 0,39

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8.3 Daily Runoff coefficients R1

Table 8 Daily Runoff Coefficent RC1 Event D5 D24 D28 D30 D36 # 1 0,34 0,09 0,15 0,11 0 2 0,16 0,04 0,15 0,15 0,11 3 0,20 0,01 0,06 0,14 0,04 4 0,35 0,31 0 0,41 0,28 5 0,29 0,11 0,27 0,23 0,18 6 0,09 0 0 0 0,20 7 0,42 0,09 0,18 0,28 0,15 8 0,63 0,17 0,36 0,32 0,40 9 0,39 0,11 0,20 0,25 0,12 10 0,46 0,83 0,77 0 0 11 0,17 0 0 0 0 12 0,56 0,17 0,22 0,42 0 13 0,18 0,06 0,11 0,14 0,15 14 0,57 0,28 0,44 0,37 0,36 15 0,66 0 0 0 0 16 0,10 0,10 0 0 0,04 17 0,06 0 0,04 0 0 18 0,33 0,10 0,26 0,17 0,17 19 0,13 0,06 0 0,09 0,09 20 0,24 0,09 0,19 0,13 0,11 21 0,24 0,11 0,17 0,17 0,12 22 0,30 0,05 0,12 0,09 0,10 23 0,38 0,20 0,44 0,28 0,22 24 0,00 0,00 0,00 0,00 0,00 25 0,42 0,24 0,37 0,36 0,23 26 0,13 0,09 0,11 0,08 0,07 27 0,33 0,23 0,33 0,32 0 28 0,08 0,04 0 0 0 29 0,27 0,12 0,23 0,14 0,17 30 0,36 0,24 0,31 0,25 0,23 31 0,28 0,09 0,19 0,15 0,11 32 0,37 0,29 0,44 0,27 0,26 33 0,36 0,13 0,17 0,21 0,11 34 0,40 0,17 0,25 0,21 0,15 35 0,38 0,16 0,26 0,24 0,14 36 0,40 0,14 0,19 0,16 0,10 Mean 0,31 0,14 0,19 0,17 0,12

Figure 21 Runoff Coefficients RC1 for Districts 24, 28, 30 and 36

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