APPLICATION OF STORM WATER MANAGEMENT MODEL TO AN URBAN CATCHMENT

APPLICATION OF STORM WATER MANAGEMENT MODEL TO AN URBAN CATCHMENT V Swathi 1, K Srinivasa Raju 2, Department of Civil Engineering BITS Pilani- Hyderabad Campus, Hyderabad- 500 078 email: p201244029@hyderabad.bits-pilani.ac.in; ksraju@hyderabad.bits-pilani.ac.in; ABSTRACT Ajit Pratap Singh 3 Department of Civil Engineering, BITS Pilani Dubai Campus email: aps@pilani.bits-pilani.ac.in In the present study Storm Water Management Model (SWMM) has been explored for the catchment of BITS Pilani Hyderabad campus, India. The catchments have been divided into various sub catchments and are modeled for 2006 rainfall event. The study deals with a flexible set of hydraulic modeling capabilities, in particular it is used to assess infiltration using Horton method and flow routing analysis using dynamic wave method. The external inflows through the drainage system network of pipes, channels, storage works and diversion structures were also considered. Finally, the critical locations of overflow are identified, if any. The results from the model show that there are no nodes flooded and overflow sections in the entire catchment, thus the campus storm network system has been well planned and has sufficient carrying capacity to cater the simulated rainfall event. INTRODUCTION Urban Flooding is one of the disasters effecting mankind especially in urban areas. Metropolitan cities like Mumbai, Hyderabad, Chennai and Kolkata in India are frequently witnessing urban floods. This may occur due to various reasons such as encroachment of water bodies, insufficient carrying capacity of storm drains, blockage of storm drains due to solid waste, improper planning of storm network and changes in rainfall pattern. Thus it is very much necessary to perform the capacity analysis of the existing storm network to identify the locations of overflow so as to come up with preventory and mitigation measures. Keeping the necessity to model urban floods, several modeling software's are developed (Borah, 2011), namely, Storm Water Management Model (SWMM) (Liong et al., 1991), Hydrologic Engineering Centers-Hydrologic Modeling System (HEC-HMS) (Knebl et al., 2005), Hydrologic Engineering Centers-River Analysis System (HEC-RAS) (Tate and David, 1999). The present study explored the applicability of SWMM to a case study of Birla Institute of Technology & Science, Pilani-Hyderabad Campus (BPHC). The terrain of the campus consists of small hillocks and urban forests of Shamirpet which spreads across about 200 acres of land (Swathi, 2014). Conference Speaker

This paper covers literature review, description of SWMM, results followed by conclusions and acknowledgments. LITERATURE REVIEW This section deals with a brief discussion on SWMM along with review of literature on the topic made by several researchers. Rossman and Supply (2006) compared dynamic flow routing in both SWMM 5 and SWMM 4 for 20 sewer patterns. They found SWMM 5 produced stable results for a larger time steps than SWMM 4. Peterson and Wicks (2006) explored SWMM to a catchment in Missouri, USA. They performed various simulations with increase in dimension of conduits. Increase in dimension of conduits by 10% effected fluid flow responses. Bedient et al., (2007) modeled urban flood using two software's HEC-HMS and SWMM. They created hydrographs in HEC-HMS which were treated as input to nodes for storm drain network in SWMM. The results obtained from this study have clearly demonstrated usefulness and understanding of the storm network. Barco et al., (2008) coupled Geographic Information System (GIS), SWMM and optimization procedures to model a catchment in California. The time required for data management and calibration was drastically reduced. Aad et al., (2009) modeled two Best Management Practices (BMPs) rain garden and rain barrel within the sub catchments. They simulated a rainfall event to assess the reduction in peak runoff. Rain garden was comparatively effective in reducing peak runoff and volume. Freni et al., (2010) used SWMM to simulate pressurization effect caused due to reduction in discharge at downstream. The results were compared with an experimental surge measured in a laboratory pipe. SWMM was able to model with reasonable accuracy. Gambi et al., (2011) modeled BMPs such as retention basin, green roof and infiltration tanks in catchment of Italy to reduce peak runoff. The simulation was performed using 15 years rainfall data. Green roof was comparatively more effective in reducing peak runoff. Ouyang et al., (2012) modeled rainfallrunoff and pollutant discharge from catchment of Beijing. The results show that impervious area is linearly proportional to runoff coefficient. Gulbaz and Kazezyilmaz-Alhan (2013) calibrated SWMM model for Sazlidere Watershed. Calibration was done using extreme rainfall event to find sensitive parameters of SWMM. They inferred that Manning s roughness and infiltration parameters were sensitive parameters. Weilin et al., (2014) modeled storm water network of Dongguan city, China using SWMM. They have modeled for 1 year and 2 year rainfall return periods. They also modeled for all critical locations of node overflow. Results show that increase in conduit diameter could cater for excess runoff. Scieranka (2013) performed hydraulic modeling of car park drainage in SWMM 5. They used dynamic wave routing approach. They concluded that introducing sewer retention could be an effective method to reduce runoff. Walsh et al., (2014) proposed SWMM modeling for rainfallrunoff simulation of Rain Water Harvesting (RWH) for a catchment in California, USA. RWH could significantly reduce runoff and it is inferred that runoff reductions and drain capacity are directly related to each other. Mikovitsa et al., (2014) used GIS custom tool box with SWMM to simulate city growth patterns. They analyzed differences in runoff pattern for both simulated and existing catchment of Innsbruck, Austria. Increase in impervious area resulted in twice increase in runoff volume. Zhang and Guo (2015) used SWMM for Low Impact development (LID). They modeled permeable pavement system for catchment of Atlanta. They concluded that SWMM needs to further improve its LID module to take into account effect of storage capacity of under drains on infiltration. Park et al., (2015) applied SWMM and linear programming to model rainwater detention system for a rainfall of 30 years frequency. Cost related objectives of the detention system were analyzed using

linear programming. The modeling results show significant reduction in flood damage. Pathak and Chaudhari (2015) have applied SWMM to model rain garden as BMP for Mithi River sub catchment, Mumbai, India. They calibrated and validated the model for 2009 rainfall event. Introducing rain garden resulted in significant reduction in peak runoff. Some of the observations emanated from the literature are discussed below: Most of the above studies dealt with modeling BMPs and LIDs than performing the adequacy analysis of the existing system. Limited studies are reported for identifying critical location of overflows in the storm network and analyzing the same. The response of a single catchment to various rainfall events have not been discussed. In the following section, SWMM has been described along with its modules. DESCRIPTION OF SWMM SWMM is an open source model by United States Environmental Protection Agency (US-EPA). It is a dynamic rainfall-runoff model, used to model hydraulic and hydrological components of a catchment. It models and simulates both single and continuous rainfall events. It is also used to model various BMPs and LIDs in the catchment. It analyses the catchment response for various scenarios of rainfall events. The model also allows snowmelt calculations and hydraulic modeling. There are four blocks in SWMM namely, runoff block, transport block, extran block and storage treatment block. Runoff block deals with the evaluation of both surface and sub surface runoff for a given rainfall event whereas transport block incorporates effects of inflow hydrograph in a drainage system through kinematic routing. Extran is the extended transport block which performs dynamic routing. Storage/treatment block accounts for various storage, treatment functions and pollutant loadings defined by the user (SWMM Reference Manual, 2014). Thus SWMM not only contributes in routing of flow quantities but also functions such as routing of quality parameters, estimating dry-weather flow, estimating infiltration, assessing storage, treatment functions and pollutant loadings. In the present study an application of SWMM has been explored by identifying critical locations across BPHC. The methodology adopted and results obtained from the study have been discussed in the following section. METHODOLOGY AND RESULTS The watershed of BPHC has been considered in this study. The entire watershed is divided into sub catchments and the storm network has been represented by conduits, junctions and outfall. The Rainfall-Runoff block of SWMM has been applied to model on the basis of rainfall event for 2006. Infiltration and flow routing of the watershed have been evaluated using Horton method and dynamic wave method respectively. Storm network map of the campus was obtained from the Google Earth as shown in Figure 1. The maps are geo-referenced in Arc-GIS Version 9.1 to perform complete analysis.

Figure 1: BPHC Google Earth Image (source: https://earth.google.com/) Using the load image option, the back drop image of storm network is loaded. The complete storm network is drawn as line diagram in SWMM. All the data pertaining to conduits and junctions are treated as input variables. The input parameters for junction are invert elevation and maximum depth. The data required for conduits are shape, dimension, maximum depth, length, roughness, inlet offset, outlet offset and maximum flow as shown in figures 2(a), 2(b). The required data is obtained from the storm network plan of the campus. The catchment is divided into 18 sub catchments based on their discharge point, storm network and drainage pattern as shown in Figure 3. The model is run successfully after all the inputs are given. (a) (b) Figures 2(a), 2(b): The data entry option in SWMM at various junctions and conduits

Figure 3: Catchments visualization in SWMM (C denotes Catchment) The maximum peak runoff from sub catchments 1 to 18 are 32.40, 33.42, 33.42, 33.42, 33.45, 33.45, 33.48, 1.47, 32.72, 32.13, 33.37, 33.00, 29.86, 29.36, 3.45, 29.86, 3.45, 33.43 m 3 /sec as shown in Figures 4(a),4(b) and 4(c). The peak runoff is obtained from sub catchment 7 with 33.48 m 3 /sec and least peak runoff was obtained from sub catchment 8 with 1.47 m 3 /sec. The peak runoff obtained and respective peak time for all catchments are summarized in Figure 5(a) and 5(b). Node flooding s were nil in the obtained model results for the rainfall event simulated. This implies no flooding scenario.

Figure 4(a): Runoff pattern from sub catchment 1 to sub catchment 6 Figure 4(b): Runoff pattern from sub catchment 7 to sub catchment 12 Figure 4(c): Runoff pattern from sub catchment 13 to sub catchment 18

Figure 5(a): Graph showing peak runoff from catchments Figure 5(a) shows the peak runoff from all the catchments. It can be inferred that catchment 7 has highest peak runoff of 33.48 m 3 /sec, catchment 8 has a least runoff of 1.47 m 3 /sec. Figure 5(b): Graph showing time taken to attain peak runoff from catchments Figure 5(b) shows the time taken by catchments to obtain peak runoff, it is inferred that all the catchments have attained peak runoff at 2.5 hr.

CONCLUSIONS The SWMM model is applied for the catchment of BPHC. Dynamic wave routing and Horton approaches were applied to analyze flow routing and infiltration processes. The case study of BPHC presented herein showed that SWMM is well suited for urban catchments especially when it is modeled as a single watershed under uncalibrated condition. The results show that there are no nodes flooded in the entire catchment and there are no overflow sections. Thus the campus storm network system has been well planned and has sufficient carrying capacity to cater the simulated rainfall event. ACKNOWLEDGMENTS This work is supported by Information Technology Research Academy (ITRA), Government of India under, ITRA-Water grant ITRA/15(68)/Water/IUFM/01 dated Sep 20, 2013, Integrated Urban Flood Management in India: Technology Driven Solutions. Special acknowledgements to Sri K.V.Rao, Facilites Manager BPHC, for providing the necessary data in the form of drainage plan of the campus. First author thanks Google Earth for enabling to download Google images. The authors thank EPA for providing SWMM as open source software and also India Meteorological Department for providing rainfall data. REFERENCES Aad, M. P. A., Suidan, M. T., and Shuster, W. D. (2009). Modeling techniques of best management practices: Rain barrels and rain gardens using EPA SWMM-5. J. Hydrol. Eng., 15(6), 434-443. Barco, J., Wong, K. M., and Stenstrom, M. K. (2008). Automatic calibration of the US EPA SWMM model for a large urban catchment. J. Hydrol. Eng., 134(4), 466-474. Bedient, P. B., Holder, A. W., Thompson, J. F., and Fang, Z. (2007). Modeling of Storm-Water Response under Large Tail water Conditions: Case Study for the Texas Medical Center. J. Hydrol. Eng., 12(3), 256-266. Borah, D. K. (2011). Hydrologic procedures of storm event watershed models: a comprehensive review and comparison. Hyd. Pros., 25(22), 3472-3489. Freni, G., Ferreri, G. B., and Tomaselli, P. (2010). Ability of software SWMM to simulate transient sewer smooth pressurization. Novatech.., France, 2010. Gambi, G., Maglionico, M., and Tondelli, S. (2011). Water management in local development plans: the case of the old Fruit and Vegetable Market in Bologna. Pro. Engg., 21, 1110-1117.

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