HDR utilizes an extensive set of customized tools and interfaces to expedite water/wastewater planning. The City of Atlanta is under a consent order with strict deadlines to upgrade the entire city’s system. This session will focus on tools developed for the sewer separation part of the program. One invaluable tool is Update Topology, which checks for and corrects redundant structure names and runs a macro level CADD import snapping cleanup. This session will demonstrate how Update Topology and the other tools have been key in compiling and utilizing data more accurately and efficiently to assist Atlanta in meeting the deadlines set by the EPA.
The City of Atlanta is facing a billion dollar program to upgrade its sewerage system. Renovations at the wastewater treatment plants are nearing completion; however, the collection system improvements are proving to be more expensive and challenging. While the treatment plant costs are shared with a number of neighboring jurisdictions, the cost of the collection system upgrades will be basically borne by the City’s residents.
Atlanta’s original sewer system was a combined system (sanitary and storm flows); parts of it are over 100 years old. As the city grew geographically, a separate sanitary sewer system was installed, but the original combined system remained in service. Dry weather flow in the combined system was diverted to separate sewer interceptors and conveyed to the treatment plants, but combined sewer overflows occurred with even moderate rainfall.
As the City of Atlanta continues to grow and the collection system ages, even the separate sanitary sewers are being taxed by dry weather peaks and surcharged by wet weather flows. The City is now operating under a consent order from the Georgia Environmental Protection Division and U.S. EPA to reduce the annual number of combined sewer overflow events and to completely eliminate sanitary sewer overflows. The program is projected to be a 20-year effort, but the mandate contains many stringent completion deadlines for intermediate milestones.
The complexity of the CSO project comes from the collection of a vast amount of data in a short time followed by the customization and development of tools so that all of the information available could be modeled and used to develop alternative solutions to the city’s CSO problems. The customization of the editing and modeling tools took not only exceptional engineering experience, knowledge, and skills, but also required advanced programming and technological foresight.
One main objective of the scope of the project was to collect and model the data for the area. The tools developed and customized for the project not only made it possible to enter and update all data that came in, but allowed for the link to modeling rather than taking weeks for a team to convert different sources of data for the model into it’s particular input format. The project, therefore, came in on time under extreme deadline conditions, and on budget. Realizing how useful these tools could be throughout public works for updating information and how the product would make an excellent foundation for incoming information to the City, the client was able to engage many City officials and private participants for maximum coordination of information, an innovation in itself. In addition, the resulting decision support framework allowed the client to more easily address many issues often brought to the table by the citizens and City Council that previously could not easily be resolved.
East Area Combined Sewer Overflow (CSO) consists of seven sub-basins covering 5600 acres and 1.14 million feet of pipe. Approximately 340,000 feet of pipe in six of the sub-basins were modeled.
The major data sources for the East Area CSO project and for the GIS Decision Support System were the City of Atlanta planning and public works departments. The City provided paper documents that provided information on the existing combined system. These documents included archive plans, field books and files dating back to the 1920s, City sewer maps, drainage maps and design plans prepared by various firms contracted by the City over the years. Some information existed in electronic form (excel spreadsheets and sewer map Microstation files) and represented partial sewer/CSS pipeline and manhole data. The City’s sewer maps were accurate circa 1996 (before Olympic construction). Some of these changes are significant as they typically involve the replacement of major trunk lines.
The majority of the attribute data had to be converted from hard copy maps and hard copy tables to digital data before any data manipulation or analysis could occur. In addition to compiling existing wastewater pipe and facility infrastructure data, existing and future land use data had to be converted into GIS themes. This was a time consuming but necessary process in order to use HDRLink.
Existing land use maps were based on digital building footprint data retrieved from the public works department. A field survey was then conducted to verify building attributes like the number of floors. The field survey was performed in lieu of accurate tax data. Time/budget constraints prohibited the development of building data based on information from the tax assessor’s office. There are generally other proprietary sources of building information available through private software development.
To generate numbers for existing sanitary sewage flow rates, a base building footprint coverage and building database or attribute table was used. Each building footprint was given an initial attribute of either commercial or residential buildings. This was based on the parcel level current land use maps, which were also obtained from the Bureau of Planning. The buildings and the current land use layers were overlaid in the GIS, and a selection process was employed that first chose the buildings that fall within commercial parcels to receive a commercial attribute, and then chose the buildings that fall within residential parcels to receive a residential attribute. This was the initial basis for the attribution of buildings.
In addition to that process, another attribute was added to each of the buildings, in the form of a field, which contains a unique number. This number is used to join to a database, which was developed from field surveys conducted explicitly for this project. The database contains specific information about large commercial buildings, multi-family residences, and single-family residences in the study area. The information gathered about the commercial buildings includes the number of floors in each building. In cases of multi-family residences, the data gathered includes the number of units in each building. Finally, a count of single-family residences can be generated from the data gathered. As this data was entered the building footprint shapefile was updated and cleaned. Using the 1998 Digital Orthophotography from the City of Atlanta Public Works Department (flown by Hoffman & Co.) major buildings that were surveyed and missing from the footprint shapefile were added. Also, buildings that have been demolished since the building footprint shapefile was developed and that were field verified as no longer existing were deleted from the footprint shapefile.
The future land use maps were digitized into the GIS system from hard copy Neighborhood Planning Unit (NPU) maps stored in the city planning department. The land use maps define the maximum development densities for each parcel in the city. The land use densities allow the engineering team to determine what the future capacity requirements will be for storm water runoff and additional wastewater. The percentage of impervious surface was based on land use. The acres of impervious surface are calculated in the GIS using the future land use density data within particular storm catchment areas. In addition, the future land uses offer insight into potential BMP options like water reuse and buffering
The combined sources of data were used to generate square footage of commercial buildings, unit numbers of multi-family housing and total numbers of single-family dwelling units. These were generated for each sewer service area and transmitted to the Engineering department for use in their spreadsheet model.
The existing square footage of commercial area was calculated by finding the square footage of the building footprint using a GIS calculation and then multiplying by the total number of floors. In cases where a commercial building had not been field surveyed the building was assumed to have only one floor and the initial square footage calculation was used. The square footage for all commercial buildings within each sewer service area was added together to get a total square footage of commercial buildings in the area. The number of units of multi-family housing, and the number of single family residences were both calculated by selecting the multi-family or single family buildings within the service area and summing the units.
The ArcView .apr files from which these flow rate numbers were generated were saved so that as more building data is collected and the database is updated the numbers can be regenerated using more specific data.
The future estimated flow rates were calculated based on the future land use map. This was accomplished by using the delineated service areas to select a section of parcels. The land use, zoning, vacancy, and acreage are kept in an inventory database for each of the parcels. Using this information, a total for each of the categories was determined by finding the existing possible combinations. This was done by selecting a service area and the parcels with their centers in the area. Then an ArcView Script was run that summarizes the existing data into one table. This table contains the available information about the summarized parcels in that service area.
GIS data sources are seldom designed for hydraulic model. Typically, facility maintenance programs, land use planning, or some other program are responsible for the development and maintenance of the data sources used for model development. The object of the GIS/Model interface is to translate the GIS information to the format required by the hydraulic model. The design might also include some form of model output processing.
The focus in the development of a GIS interface for a hydraulic model should be to utilize existing data sources that are typically maintained or will be maintained by other departments of disciplines. If GIS data sources are used, the modelers should have access to use these sources, but should not be able to edit the data. This precaution is essential to protect the data integrity of the source. However, a data management protocol should be defined for updating the data source, and to ensure that the data requirements of the modeler are met too.
The Hydra interface requires three data sources: a base sewer coverage populated with system inventory data, a service area coverage that defines the areas served by each inflow node, and set of land use plan coverages. The latter might include several coverages and lookup tables that define the land use zoning, growth patterns, and current development levels.
To run a computer model of a sewer system, two things are necessary: a collection system and the wastewater flows that run through it. Data collection provides information about the collection system. Wastewater flow assignment is accomplished based on land use patterns. With information about the land use in a given area, the user can estimate the amount of wastewater that enters the system at a given point. Point flows (industrial facilities) are identified and can be added to the model on an individual basis. The model can also account for wet weather flows (infiltration and inflow due to high groundwater levels and rainfall). The input information necessary to develop a sewer collection system model can be divided into three broad categories:
Collection System Information: Information about the collection system itself (pipe sizes and lengths, manhole elevations and locations, pump sizes etc.). Land Use Information: Information about the current and future Landuse in the area of study. Flow Assignment Data: Includes information to calculate land use based flows, point flows and wet weather flows.
HDRLink flow assignment options include assignment of flows with diurnal flow patterns. Supported flow sources include land use based flows, point source flows, wet weather flows and storm-related infiltration. The “Flow Assignment Wizard” allows the same base data (such as land use) to be used to generate model scenarios for different growth conditions and design events, includes a flow factor calibration option, translates the land use data directly to flows and at the same time assigns a service area-specific diurnal curve to the inflow. This assigned curve is selected based on the land use that contributes to the flow. All assigned flows consist of two components: a daily average flow and a diurnal pattern for that flow.
After a model is run and calibrated, the calibrated HYDRA output file is loaded into the HDRLink HYDRA Interface and an output file is created that contains hydrographs for every pipe in the system, as well as a maximum hydraulic grade line (HGL) elevation for each pipe. The HGL can be recalculated for each 15-minute time step in the hydrograph giving a more detailed hydraulic condition assessment of the system.
HDRlink was also used to create the base models, and associated landuse based parameters, to be imported from the GIS into XP-SWMM. HDRlink has a set of ArcView extensions created by HDR that allows information pertinent to stormwater hydraulic modeling to be extracted from a GIS and formatted for use with XPSWMM. HDRLink creates relationships between landuse data and pipe information for the existing system as well as an unlimited number of design alternatives. In this case, only the existing system was modeled. Actual rainfall data was entered into the models to recreate three different storms. Flow meter data was then used to calibrate the models so that the volume reported by SWMM was within 20% of the volume recorded by the meter. Finally, a theoretical 25-year storm (SCS Type II) was used to predict potential flooding locations. COA will use the finished product to determine where storm relief lines are needed.
Similar to HDRLink’s Hydra modeling link, the XP-SWMM link has a strong landuse component. The link for the pipe system is essentially the same as for Hydra link. The landuse extraction process calculates the pre-delineated catchment area, clips the landuse parcel coverage to the delineation, calculates how much of certain landuse categories lie within the catchment area, and lookup an associated impervious percentage based on the weighted value of how much of each particular landuse categories is found within the delineation. Each catchment area is graphically associated with an input node on the pipe system. The pipe system, catchment area, the automatically calculated %imperviousness, manually calculated catchment slope and width are all formatted into an XPX file for import into XPSWMM.
The development of this wide range of spatial data combined with the HDRLink tools has added a substantial increase in the utility of the data and the GIS. Data updates can be made relatively quickly and easily. The results from the changes or additional information can also be updated much easier than with traditional methods of using separate software for graphics, data storage, and modeling.
Koos Prins, HDR, Sacramento, has developed all HDRLink extensions and concepts. Through his tireless efforts and creative genious, I have a job.