Leveraging Geospatial Data to Solve Sewer Issues

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As presented at Map India 2006, January 30th – February 1st 2006. New Delhi, India.
Leveraging Geospatial Data to Solve Storm Sewer Issues
Sharavan Govindan, Thomas M. Walski, Robert Mankowski, Jack Cook, Malcolm Sharkey,
Bentley Systems Inc, Watertown, US 06795, 1-800-727-6555, www.bentley.com/haestad
Stormwater management systems are critical to urban populations, and the consequences of
storm sewer system failures can be catastrophic – ranging from damage to property and
possessions by flooding, through to the spread of disease and even death. In recent times India’s
stormwater management systems have been placed under enormous strain by flooding problems
throughout the country, and it is clear that many of these storm sewer systems require dramatic
improvement (Times of India, 2005). However the analysis and design of stormwater systems is
far from straight forward, and the planning of system improvements is further complicated by the
need to prioritize system upgrades to maximize the benefits of capital expenditure.
Fortunately, hydraulic models based on geospatial data can be leveraged as a key tool for
supporting investment decisions for storm sewer infrastructure. The advancement of strong
modeling technologies that marry rich geospatial modeling and thematic mapping environments
with proven dynamic wave solvers is expected to dramatically upgrade the scope and value of
systems planning and operations modeling efforts.
Hydraulic models can be built using a range of geospatial data, some of which may already be
available to Indian cities and utilities. Information such as CAD files, GIS files, aerial photography,
asset management information, digital elevation models and survey information can all be utilized
in the model building process. Then, once the model is built, the system can be analyzed and the
resulting information is used to assist with the following practical applications:
a) Develop comprehensive system master plans
b) Assess the impact of inflow and infiltration on sewer overflows
c) Develop sewer overflow remediation programs
d) Perform system capacity evaluations
e) Optimize lift station and system storage capacities
f) Implement real-time control strategies
g) Model relief sewers, overflow diversions, and inverted siphons
h) Accurately simulate variable-speed pumping and logical controls
i) Simulate out-of-service or proposed sewers.
This paper aims to investigate in detail the steps involved in leveraging geospatial data to create
hydraulic models of sewer systems, and also highlight the benefits that these models can offer
those responsible for stormwater management systems. This approach will be developed around
the technology embodied in Bentley System’s SewerGEMS, a geospatial-centric modeling
solution released at the end of 2004.
Geospatial Data
Cities and utilities may well have gathered geospatial information in various data formats over the
years. The commonly used formats include DXF or DWG drawings, DGN, Shapefiles,
geodatabases, coverages, geometric networks, SDE datasets, Excel spreadsheets, ODBC and
OLEDB compliant databases, MS Access, MS SQL Server, Oracle Spatial, etc.. In addition to the
above data sources, geospatial technology offers the ability to obtain external data sources such
as customer billing records, digital elevation models and high quality base maps and extract the
data needed avoiding the error-prone manual data entry process.
A city or utility that commits to developing a geospatial hydraulic model must consider several
driving factors including data quality, hardware resources, software availability, interdepartmental
cooperation, modeling/technical leadership, data development, and maintenance.
http://www.bentley.com/haestad
As presented at Map India 2006, January 30th – February 1st 2006. New Delhi, India.
To build an effective hydraulic model the Geospatial professional must work closely with the
modeler during the process of creating and transforming the Geospatial data so that the model
building process can work as seamlessly as possible. For example the Geospatial data may
contain more detailed information than is needed for modeling; for example every small service
line and lateral. This information is typically not necessary for modeling applications and can be
omitted to improve model run time, reduce file size and save cost, so the Geospatial professional
should set up the data in such a way that the modeler can easily omit this unnecessary
information from the model.
Some good practices for creating Geospatial data for later use in a model include a) “Snapping”
pipe ends to other element types b) Standardized element labeling conventions c) Customer
service lines in separate features classes from system pipes d) Wet wells, pumps and other
system components as separate feature classes.
Conversely, the following are some of the possible errors in data that need attention (and may be
driven by the capabilities of the numerical solver): Missing attributes, features not properly
connected, features digitized backwards, GIS feature type has no model counterpart, GIS
identifiers incompatible with the model, GIS contains hydraulically insignificant or “short” pipe
segments. Haestad et al (2004)
Other publications have described similar techniques on developing Geospatial information for
the water and wastewater industries, including Orne, Hammond, and Cattran (2001), Przybyla
(2002), Haestad et al (2004) and Manual of Practice titled Implementing Geographic Information
Systems (WEF, 2004).
Shamsi (2002) has presented two case studies on the application of GIS technology to sewer
systems. Greene, Agbenowoshi, and Loganathan (1999) discuss a program that was used to
automatically integrate GIS data a new sewer network design. New opportunities pose new
challenges for the smooth integration of the modeling process. Newer objects can be more
complex, with more-complicated connectivity, compared to older GIS data types (points, lines,
polygons). The data-modeling effort in an object world is more time consuming (Zeiler, 1999) and
requires that the hydraulic modeler pay more attention to the process at the design stage when
using an object-relational GIS.
Papers on modeling (such as Deagle and Ancel, 2002) typically discuss the use of GIS by
hydraulic models but few describe the incorporation of model information in the GIS. An exception
to this common approach is Indianapolis Water Company (Schatzlein and Dieterlein, 2002),
which has a separate section in its GIS for proposed projects.
Building a Hydraulic Model from Geospatial Data Sources.
Hydraulic analysis of sewer systems requires a great deal of data on hydrology, piping systems,
wastewater loading and topography. As discussed earlier, much of this information already exists
in the hands of many cities and utilities. The key is to utilize this data without the need to
manually reenter it. This requires the hydraulic model to be able to communicate with a wide
variety of data sources.
Automated model building tools give the user the ability to build complete modeling datasets
using shapefiles, coverages, geodatabases, geometric networks, SDE datasets, spreadsheets
and databases (Bentley, 2005). The modeler can map the tables and fields contained within the
data source(s) to element types and attributes in the Sewer model.
For example, storm sewer pipe information may exist in a shapefile containing line features. The
shapefile may include fields containing information such as pipe label, pipe material, pipe
diameter, pipe levels and roughness information. These fields are all mapped to the appropriate
field in the sewer hydraulic analysis model (for example, pipe diameter in the shapefile is mapped
to pipe diameter in the model) using automated model building tools. Numeric fields, like
As presented at Map India 2006, January 30th – February 1st 2006. New Delhi, India.
diameter, also require a unit of measure to be specified (i.e. does the 100 in the shapefilerepresent inches or millimeters) so that the model can perform the appropriate conversions during
analysis. The pipe label is typically the most important field during this process, since there must
be a common key between the different features in the source and target files – and a label is
often the best choice for this common key. However, if necessary, automated model building
tools will create a unique key/label field for this purpose.
This data mapping process can be repeated for additional data sources and model features, and
then automated model building tools are run to create the model topology. The steps taken at the
outset will impact how the rest of the process is developed, so the modeler must spend the time
to ensure that this process goes as smoothly and efficiently as possible. However, if the
Geospatial data is detailed and accurate, and the data mapping complete, then automated model
building tools will create a comprehensive sewer network. If not, it is still possible to construct a
model, but considerable manual data entry will be required.
Loadings.
The next step in the creation of the hydraulic model is to add the loadings, which are simply the
flows that enter the sewer system.
An accurate estimation of the flows entering your sewer system is one of the most important
steps towards trusting a model that truly reflects your real world sewer system. For most sewer
systems, there are two overall types of loading: 1. dry weather which includes sanitary and
industrial flows and dry weather infiltration, and 2. wet weather which comprises rainfall derived
infiltration and inflow. Purely stormwater systems should have virtually no dry weather flow while
purely sanitary systems should have very little wet weather induced flow. However, most real
systems have some combination of both types of flow and any hydraulic analysis model must be
able to deal with both.
The first step in determining loading would be to first understand the current year dry weather
flows and then add the complexities of wet weather and future conditions. Loading data can be
obtained manually from customer or flow meter data or automatically using software import tools.
Automated model loading tools can take loading information from a variety of GIS based sources
such as customer meter data, system flow meter or polygons with known population or land use
and assign those flows to elements. Automated model loading tools are oriented to the types of
data available to describe dry weather flows while other methods in SewerGEMS are more
amenable to wet weather flows (Bentley, 2005). Some of the loading options include
a) Water Consumption Data Loading can be leveraged in developed countries where the
flow data from each customer meter can be assigned by automatically assigning geocoded
customer water consumption data to the nearest manhole in the sewer network, instantly
allocating consumption data to the nearest pipe and then specifying how the demand will be split
among the bounding manholes, aggregating consumption data for individual service polygons
(meter routes or drainage basins) and then assigning the aggregated loading to the associated
manhole.
b) Flow Monitoring Data Loading where the flow data from the monitors can be assigned by
distributing the flow from each monitor equally across all elements in a specified area (polygon),
usually a drainage basin or sub-basin, proportionally distributing the flow to elements in a
specified area based on the actual service area(s) of the elements involved and distributing the
flow based on the population in the service area.
c) Land Use Parcel and Census Data Loading which is based on requirements, user-defined
“rule-of-thumb” flows are developed for each type of land use or per capita. LoadBuilder can
distribute the loads either equally or proportionally to the elements located within the polygons
contained in Land Use and Census Maps.
As presented at Map India 2006, January 30th – February 1st 2006. New Delhi, India.
Figure 1: Load builder technology allows the user to allocate loads based on flow monitoring, water
consumption records, land use polygons, and other GIS sources.
For master planning, users can project future loadings quickly and accurately based on data such
as phased land use projections and population projections. This allows modelers to efficiently
create multiple loading alternatives by intersecting any combination of future service area layers
with different land use and population forecast layers.
Wet weather flows that are caused by precipitation should be characterized based on the storm
event or by using hydrographs. In addition, flow from stormwater runoff should be computed at
the catchment elements based on different characteristics including catchment size, catchment
land use, loss method and hydrograph method, coupled with the hyetograph from the storm event
of interest.
There is no single "correct" method for converting precipitation events into sewer wet weather
loadings. Some methods such as the Green-ampt infiltration equation or the SCS runoff method
are more appropriate for pervious surfaces, while methods such as the RTK method are best for
more urbanized areas. The most general method would consist of a calibrated unit hydrograph for
a catchment. Storm sewer hydraulic analysis models can convert rainfall into wet weather sewer
flow using any one of a wide variety of methods selected by the user. (Haestad et al. 2004)
As presented at Map India 2006, January 30th – February 1st 2006. New Delhi, India.
Sewer System Hydraulic Model Analysis.
Storm Sewer system models calculate flow, velocities, depths, and hydraulic grade line in
systems and much more, given loading data and network hydraulic characteristic such as invert
elevations and pipe diameters. These can be used in system design and analysis of existing
systems.
Storm sewer systems can be modeled using Bentley’s SewerGEMS in a stand alone interface or
inside AutoCAD, ArcGIS or MicroStation environments allowing interoperability, geospatial
analysis, hydraulic and hydrologic calculations. The modeler is given the option to leverage the
power of the modeling application and the modeler’s favorite drafting/mapping application
simultaneously. Figure 2 illustrates a storm sewer thematic map with property-based symbology
and annotation, inside MicroStation.
Figure 2: SewerGEMS thematic map with property-based symbology and annotation, inside
MicroStation
Storm sewer analysis models have the ability to support the wide variety of elements found in the
real world. The storm sewer structures supported range from manholes, inlets, pipe networks,
channels, pumps, detention structures, control structures, and stormwater watersheds.
In addition the modeling engine supports the modeling of looped pipe networks, flow splits,
overflows, and storage capacity. Storm sewer analysis models can be used to perform capacity
and overflow analysis of existing systems over an extended period of, say, 24 hours.
SewerGEMS has a stable implicit finite difference numerical algorithm that solves the full St.
Venant equations.
Understanding Results.
As presented at Map India 2006, January 30th – February 1st 2006. New Delhi, India.
Digesting and comprehending the volume and complexity of raw numerical results from a
hydraulic model can be challenging. Reporting, visualization, and thematic mapping tools are
used to present the results to users in a variety of useful ways. Some of these tools include
tabular results, profiles, map annotations, color coding, and graphs. Tabular results are
customizable giving the user the option to view computed data selectively, sort and filter data
based on constraints.
Profiles help visualize how selected attributes, such as hydraulic grade, energy grade, ground
elevation vary along an interconnected series of pipes over time. Element annotation is used for
dynamic presentation of the values of user-selected variables in the plan view. Colorcoding
allows to perform quick diagnostics on the network by assigning colors to a range of attribute
values. Element sizing enables the modeler to change the line width (of pipes, channels etc) and
element size (of manhole, inlets etc) based on any attribute of interest (such as flow rate, velocity,
diameter etc) to the modeler.
Figure 3: Hydrographs for different storm events (back image) and Storm Sewer Line profiles (front
image to the right) create using SewerGEMS can be compared simultaneously for all time steps.
Once the model is running successfully, it is important to realize that the model is just a tool for
analysis, not the end in itself. The engineer or modeling professional should continue to spend
more time engineering after efficiently building, loading, editing, running, and understanding the
storm sewer model. In most studies, the engineer will run a large number of scenarios to analyze
a wide range of alternative designs and operating strategies to optimize system performance.
As presented at Map India 2006, January 30th – February 1st 2006. New Delhi, India.
Continuing improvements include detecting and addressing system bottlenecks, optimization of
control strategies, limit overflow occurrences, and more.
Summary.
Advances in the application of geospatial data in sewer modeling, analysis, and design keep
increasing with each new development in geospatial technology and data availability. Modelers
may not be aware of these geospatial advancements, and CAD/GIS professionals may not be
aware that such tools could be applied to hydraulic modeling. Thus, modeling and CAD/GIS
professionals must communicate regularly, constantly refining techniques to apply geospatial
technology to storm sewer modeling. (Haestad et al, 2004)
REFERENCES
Bentley Systems Incorporated, 2005, SewerGEMS Users’ Manual, 2005, Haestad Press
Watertown, Conn.
Deagle, G., and S. Ancel. 2002. Development and maintenance of hydraulic models. Kansas
City, MO: AWWA IMTech.
Greene, R., N. Agbenowoshi, and G. F. Loganathan. 1999. GIS based approach to sewer system
design. Journal of Surveying Engineering 125, no. 1: 36–57.
Orne, W., R. Hammond, and S. Cattran. 2001. Building better water models. Public Works,
October.
Przybyla, J. 2002. What stops folks cold from pursuing GIS. Public Works, April.
Schatzlein, M. and J. Dieterlein. 2002. Finding Needles in a Haystack: IWC’s Experience
Optimizing Integration with Hydraulic Models. Kansas City, Missouri: AWWA IMTech.
Shamsi, U. M. 2002. GIS Tools for Water, Wastewater and Stormwater Systems. Alexandria, VA:
American Society of Civil Engineers Press.
Times of India, July 28 2005. After the Flood news article
Water Environment Federation. 2004. Implementing Geographical Information Systems,
Alexandria, VA Water Environment Federation.
Haestad et al. 2004. Wastewater Collection System Modeling and Design, Haestad Press
Zeiler, M. 1999. Modeling Our World. Redlands, CA: ESRI Press.
Author Information:
Sharavan Govindan Sharavan.Govindan@bentley.com
Thomas M. Walski Tom.Walski@bentley.com
Robert Mankowski Rob.Mankowski@bentley.com
Jack Cook Jack.Cook@bentley.com
Malcolm Sharkey Mal.Sharkey@bentley.com
Bentley Systems Inc
27 Siemon Company Drive - Suite 200W
Watertown CT US 06795
http://www.bentley.com/haestad.com
mailto:Sharavan.Govindan@bentley.com
mailto:Tom.Walski@bentley.com
mailto:Rob.Mankowski@bentley.com
mailto:Jack.Cook@bentley.com
mailto:Mal.Sharkey@bentley.com
http://www.bentley.com/haestad.com