CSO Modelling Considering Moving Storms and Tipping Bucket Gauge Failures M. Hochedlinger 1 *, W. Sprung 2,3, H. Kainz 3 and K.

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1 CSO Modelling Considering Moving Storms and Tipping Bucket Gauge Failures M. Hochedlinger 1 *, W. Sprung,, H. Kainz and K. König 1 Linz AG Wastewater, Wiener Straße 151, A-41 Linz, Austria Municipality Graz Wastewater Department, Europaplatz, A-81 Graz, Austria Institute of Urban Water Management and Landscape Water Engineering, Graz University of Technology, Stremayrgasse 1/I, A-81 Graz, Austria *Corresponding author, ma.hochedlinger@linzag.at ABSTRACT The simulation of combined sewer overflow volumes and loads is important for the assessment of the overflow and overflow load to the receiving water to predict the hydraulic or pollution impact. Hydrodynamic models are very data-intensive and time-consuming for long-term quality modelling. Hence, for long-term modelling, hydrological models are used to predict the storm flow in a fast way. However, in most cases, a constant rain intensity is used as load for the simulation, but in practice even for small catchments rain occurs in rain cells, which are not constant over the whole catchment area. This paper presents the results of quality modelling considering moving storms depending on the rain cell velocity and its moving direction. Additionally, tipping bucket gauge failures and different corrections are considered. The results evidence the importance of these considerations for precipitation due the effects on overflow load and show the difference up to 8% of corrected and uncorrected data and of moving rain cells instead of constant raining intensities. KEYWORDS Combined sewer overflow (CSO), modelling, tipping bucket gauge, storm events INTRODUCTION The tipping bucket rain gauges have become probably the most popular recording rain gauge, used by many weather service agencies. The reason for such wide spread popularity comes from the very simple mechanics exploited for direct measurement of rainfall and the reliability of the instrument (La Barbera et al., ). Fankhauser (1998) has already pointed out the effect of systematic errors for precipitation data of tipping bucket rain gauges. The tipping bucket gauge can easily be updated in its data acquisition and storage components as long as new electronic devices become operationally available. Finally, maintenance work is reasonable and the cost is affordable even in the case of rather extended networks. Generally, there are different kinds of precipitation errors from different sources. The apparent differences between gauges and measurement stations originate after Mikkelsen et al. (1997) from following sources: sampling errors, difference in physiography and micro-climate, and measurement errors. The precipitation measurement error sum can reach an error up to % (Rauch et al., 1998). The measurement of liquid precipitation near the ground surface is affected by different sources of both systematic and random errors, mainly due to wind, wetting and evaporation induced losses resulting in measurements of moderate rainfall that are barely reliable in the absence of an accurate tipping bucket gauge calibration. Hochedlinger et al. 1

2 A lot of uncertainties have to be considered in overflow emissions modelling like rainfall input, dry and wet weather concentrations and the runoff coefficient. These uncertainties result from simplifications of the real sewer system to the used sewer model, the uniform rainfall input instead of a spatial one and the pollution concentrations used for modelling. Willems and Berlamont (1999) have shown the importance of the consideration of uncertainties and the resulting risk for designs. Vaes et al. () consider moving design storms for a hydrodynamic model of the combined sewer system of Dessel in Belgium. For this example the relation between the main flow direction of the sewer system and the predominant direction of the storm has an important influence on the maximum flow in the sewer system. MATERIALS AND METHODS Research area Graz-West The Graz-West catchment has a total area of 51 ha and a corresponding impervious area of ha. Figure 1. Overview of the research area Graz-West with the seven corresponding sub-catchments of the quality model Figure 1 displays the seven sub-catchments which are used for quality modelling. Additionally, the combined sewer overflow (CSO) of this area, equipped with an online monitoring station to gain concentration and flow data (Hochedlinger et al., 5), and the only tipping bucket gauge of the Graz-West catchment, situated at the south catchment boundary, can be seen. The main flow direction of the combined sewer system is from West to East. Table 1 shows the most important facts of the seven sub-catchments for the model. In the western part the catchment (, ) is similar to a rural area due to its peripheral characteristics and an almost completely residential zone without large industries. The very west areas ( ) is a steep part with a sewer slope from 4 to 1%. The middle part (, ), a mixture of residential and industrial zone, is terraced-shaped and has only a sewer slope of approximately.5%. The catchments and have a sewer system with a slope from 1 to 4%. A characteristic of the catchment is a wide derelict land of a former brewery. The eastern part is highly built-up with some industry and a very flat sewer system. CSO Modelling Considering Moving Storms

3 Table 1. Graz-West research area with the seven sub-catchments for quality modelling Catchment area Impervious area x-coordinate (CG) y-coordinate (CG) Average flow time from CG to the CSO [No.] [ha] [m] [m] [min] Tipping bucket gauge and its data correction The tipping bucket rain gauge used in the research area Graz-West consists of four main components: collector funnel, tipping bucket, data recorder, and collecting receptacle. The collector funnel has an area of cm² and the volume of the tipping buckets amounts to cm³, which corresponds to a precipitation depth of.1 mm. The data recording interval is time-variable; the time of each individual tip is registered as a binary signal in a permanent memory. The measured rainwater ends up in the collecting receptacle. A simple calibration device (figure ) developed especially for field calibration consists of a peristaltic pump with a transparent hose (Vavári, 5). The calibration equipment serves for adjustment and continuous supply of a constant flow rate. The core of the calibration device is a peristaltic pump with a tubing of different diameters which have to be calibrated first either in the laboratory or in the field in spite of the available rating curves given by the pump manufacturer. Figure. Sketch of the tipping bucket dynamic calibration device (Vasvári, 5) Two different kinds of tubes have been used for calibration; the smaller diameter suitable for rain intensities up to 1 mm/min and the larger one allows intensities up to 7 mm/min. Rain intensities with values of mm/min and more occur extremely rarely in this Graz-West catchment area. Hochedlinger et al.

4 The flow is fed via transparent tubing to the funnel of the rain gauge. The water (for the calibration tap water is used) flows into the tipping buckets and is collected in a receptacle below. During the measuring process, the electronic system records the signals produced by the tipping buckets. The flow rate of the pump and the measured intensity of the simulated precipitation in two independent measuring processes are recorded. The flow rate can be converted into a definite intensity in relation to the funnel area of cm². Therefore, the reference value of the intensity is known on the basis of the number of rotations of the peristaltic pump and a variance comparison between recorded and actual intensity can be determined which is basis for the tipping bucket gauge data correction. Three different possibilities for data correction have been developed for the tipping bucket gauge: linear correction, correction with a polynomial function, and a correction with a power function (Hochedlinger et al., 5). The linear correction equation has been adjusted by five linear equations. The general formula of this linear equation is given by: i = a i + b. The coefficients a and b of the linear function and the five different intensity ranges are displayed in table. So, the actual (corrected) intensity i can be determined knowing the recorded intensity i and the corresponding coefficients for the present recorded intensity. Table. Linear equation coefficients of different intensity ranges and resulting correlation coefficient R of developed equation and recorded values (after Hochedlinger, 5) Recorded intensity [mm/min] From To a b R The equation of the power function resulted in i = i with a corresponding correlation coefficient of.999. Further analysis showed an underestimation of this correction at high intensities for the analysed tipping bucket gauge. The analysed squared polynomial function can be expressed with the equation i =.1757 i i. The resulting correlation coefficient of.9995 seems to be also satisfying. Modelling and development of moving storms The input data for model calibration and validation are measured flow and quality data (Hochedlinger, 5). These are validated and, if necessary, corrected data. For the overflow load simulation two different parameters are considered: Total Suspended Solids (TSS) and Chemical Oxygen Demand (COD). Thus, flow and dry weather pollution curves (4-hours and 7-days curves based on measured flow and pollution data) are used in the model. By means of a mean dry weather concentration and the corresponding daily and weekly concentration trend the varying dry weather concentration is considered for modelling. The quality model is based on a TSS mean rain weather concentration of 195 mg/l and on a TSS dry weather concentration of 4 mg/l. For COD quality modelling a mean rain and dry weather concentration of 85 mg/l and 68 mg/l are considered. These concentrations differ from typical literature values and its calculation is explained in detail by Hochedlinger (5). 4 CSO Modelling Considering Moving Storms

5 For the long-term verification 14 overflow events are used to quantify the quality of the calibrated model. Differences of.% of the overflow, 1.% of COD overflow loads, and.5% of TSS overflow loads between measured and simulated values resulted. This model has been further used for a long-term simulation over a six-month modelling period. The model of a strong-single-storm event has been modified to reproduce reliable simulation results. The creation of moving storms (against and in main sewer direction) for different velocities (.5, 1,, 5, 1,, km/h) is based on calculated time differences between the several seven sub-catchments. By means of distances (calculated with the coordinates X Pn+1, X Pn, Y Pn+1, Y Pn ) and velocity v between the centre of gravity (CG) of each single catchment area the time difference T can be calculated: T = ( X X ) + ( Y Y ) Pn+ 1 Pn v Pn+ 1 Pn Every catchment gets fictive rain data for the centre of gravity by means of the time difference which is valid for the whole single catchment. The assumption of a constant fictive rain for every single cachtment is considered in the model. RESULTS AND DISCUSSION 7 OVERFLOW DIFFERENCE [%] UNCORRECTED RAIN DATA a OVERFLOW DIFFERENCE [%] POWER FUNCTION b TSS OVERFLOW LOAD DIFFERENCE [%] POLYNOMIAL FUNCTION c TSS OVERFLOW LOAD DIFFERENCE [%] POLYNOMIAL FUNCTION POWER FUNCTION Figure. Results of quality modelling of a single storm (-7-17) under consideration of moving storms of (a) the overflow of uncorrected rain data compared with unmoved storms and of (b) the overflow of corrected rain data (linear function, power function) compared with uncorrected data and unmoved storms and of (c) TSS overflow load of corrected rain data (polynomial function) compared with unmoved storms and of (d) TSS overflow loads of corrected rain data (linear, polynomial, and power function) compared with uncorrected data and unmoved storms STORM VELOCITY = 1 km/h d Hochedlinger et al. 5

6 Two different kind of quality modelling a six month long-term and a single storm event - have been analysed under consideration of the development moving storms and its velocities. The analysed single storm event took place on the 17 th of July with an uncorrected precipitation height of 8.1 mm. A significant deviation can be seen of the precipitation depth for this single storm event compared with the three different correction types. The linear function results in 46.1 mm, the polynomial function shows a value of 44.5 mm, and the rain height for the power functions is 44.6 mm. This big deviation also demonstrates the need for a precipitation correction, especially, for storm events with high rain intensities like this analysed storm. OVERFLOW DIFFERENCE [%] UNCORRECTED RAIN DATA e OVERFLOW DIFFERENCE [%] POWER FUNCTION AGAINST MAIN SEWER IN MAIN SEWER f 5 COD OVERFLOW LOAD DIFFERENCE [%] g TSS OVERFLOW LOAD DIFFERENCE [%] AGAINST MAIN SEWER C O POWER FUNCTION VELOCTIY OF STORMS [km/h] Figure 4. Results of long-term quality modelling of a six month period under consideration of moving storms of (e) the overflow of uncorrected rain data compared with unmoved storms and of (f) the overflow of corrected rain data (linear function, power function) compared with uncorrected data and unmoved storms and of (g) COD overflow load of corrected rain data (linear function) compared with unmoved storms and of (h) TSS overflow loads of corrected rain data (linear and power function) compared with uncorrected data and unmoved storms h The results of the single storm modelling, presented in figure, show CSO overflows and TSS and COD overflow loads for the Graz-West research area. Diagram (a) displays the differences of simulated overflows compared with a constant rain over all seven catchments at the same time (v = km/h). The results of the moving storms with a moving direction against the main sewer direction result in lower overflows due to a minor superposition of the maximum flows. Catchment 1 is already partly drainaged on catchment 7 the precipitation e.g. did not start. The behaviour of TSS overflow load with corrected data of a polynomial function (diagram c) is similar. The maximum difference of -17.% can be seen at a storm moving velocity of 1 km/h (against sewer direction). A comparison - diagram (b) and (d) - of corrected data and considered moving storms with the uncorrected value of an unmoved storms show higher deviations which are in the same value range as results modelled with a 6 CSO Modelling Considering Moving Storms

7 constant rain over all catchments. The deviations decrease by a reduction of the moving velocity of the corrected data. Figure 4 displays the results of the long-term quality modelling for a period of six months. The precipitation depths for this period have following values: 84.9 mm (uncorrected data), 4. mm (linear function), 99. mm (polynomial function), and 45. mm (power function). These values show the small depth for the correction with the power function due to its underestimation at high rain intensities which can also be seen in diagram (f) and (h). The comparison (e) of uncorrected overflow data by means of different moving velocities for the rain cells compared with an uncorrected and unmoved storm (constant rain over all seven catchments) shows small differences for moving velocities higher than 1 km/h. Velocities less than 1 km/h for a moving direction against the main sewer direction and less than km/h in sewer direction result in significant overflow differences and a maximum difference of -8%. The COD overflow load differences (g) of linear corrected rain data show a similar behaviour, but the maximum deviation is only -1.4%. Diagram (f) and (h) display the comparison of overflow and TSS overflow load differences compared to the uncorrected simulated overflow and overflow load by means of an unmoved storm. Only at a velocity of ±1 km/h a significant difference can be recognised. The results long-term and single storm simulation of the quality model show the reduction of the overflows at low velocities of moving storms. In the case of a slowly moving storm the maximum flows and loads of the different catchments will not be superposed due to its time shift of the maximum values. A significant increase of the overflow or the TSS or COD overflow load of quality modelling could not be recognised for the catchment area Graz-West. CONCLUSION AND FURTHER RESEARCH Quality modelling of overflows, TSS overflow loads, and COD overflow loads has been carried out for the Graz-West research area by means of online-measurement data considering moving storms and precipitation data correction of a tipping bucket gauge. Especially, for the single storm event the need of such rain correction of the tipping bucket gauge data compared with uncorrected data can be presented. Three different kinds of corrections have been presented in the paper: linear function, polynomial function and power function. The loss of data during the moving of the bucket resulted in differences from 8.1 mm (uncorrected) to 46.1 mm (linear correction). The consideration of moving storm for quality modelling resulted for low moving velocities of the storms in differences of the overflow up to 8%. This reduction of the CSO overflow can be explained with non-superposition of flows from the different sub-catchments. Storms with a moving velocity 1 km/h and higher have in this research the same effects as constant rain load modelling for all sub-catchments and no time shift. The knowledge of these analysis and modelling work evidence the need of further research of hydrological data, further rain gauges and of modelling. The characterisation of recorded storm events in its main moving direction and the mean moving storm velocity gains imported data for further simulations. The consideration in hydrodynamic models will affirm or abolishe the assumption of the decrease of overflows for at least slowly moving storms. In the case of an affirmation an economical saving potential for the municipality Graz can be made Hochedlinger et al. 7

8 available. Currently the municipality analyses strategies for a RTC concept by means of huge underground reservoir volumes which maybe can be reduced by an in-depth modelling analysis. REFERENCES Fankhauser, R. (1998). Influence of systematic errors from tipping bucket rain gauges on recorded rainfall data, Wat.Sci.Tech. 7(11), 119. Hochedlinger, M. (5). Assessment of Combined Sewer Overflow Emissions, PhD thesis, University of Technology Graz, Austria. Hochedlinger, M., Gruber, G. and H. Kainz (5). Assessment of spill flow emissions on the basis of measured precipitation and waste water data, Atm.Res. 77(1-4), La Barbera, P, Lanze, L. G. and L. Stagi (). Tipping bucket mechanical errors and their influence on rainfall statistics and extremes, Wat.Sci.Tech. 45(), 1-9. Mikkelsen, P.S., Arnbjerg-Nielsen, K. and P. Harremoës (1997). Consequences for established design practice from geographical variation of historical rainfall data, Wat.Sci.Tech. 6(8-9), 1. Rauch, W., Thurner, N. and P. Harremoës (1998). Required accuracy of rainfall data for integrated urban drainage modelling, Wat.Sci.Tech. 7(11), Vaes, G., Willems, P. and J. Berlamont (). Moving design storms for combined sewer systems, 9 th Int. Conf. on Urban Drainage, 8 1 September, Portland, Oregon, USA. Vasvári, V. (5). Calibration of tipping bucket rain gauges in the Graz urban research area, Atm.Res. 77(1-4), Willems, P. and J. Berlamont (1999). Probabilistic modelling of sewer system overflow emissions, Wat.Sci.Tech. 9(9), CSO Modelling Considering Moving Storms