GIS Applications In Environmental & Water
Resources Engineering
March 22, 2002
Abstract
This paper is an extensive
overview of the recent applications of Geographic Information Systems
(GIS) in the domain of environmental and water resources engineering.
The reader is first concisely acquainted with the fundamentals of any GIS to
help him develop an appreciation of its basic technical aspects, which are
prerequisite to understanding the other application-oriented sections of this
paper. Such applications are presented in the form of tailored case studies
demonstrating various GIS analysis techniques/approaches applied to a diversity
of real-life engineering endeavors in the environmental and water resources
sector.
Complying with the ‘Water Supply
& Sewage Works Design’ course requirements, this paper is specifically
addressed to the course professor, Dr. G. Ayoub, and my fellow colleagues.
Neither does it address a totally lay audience nor a team of GIS specialists,
but rather a group of interested senior students with basic engineering
knowledge, sufficient computer literacy, and minimal GIS know-how. This also
applies to the assortment of applications yet to be discussed. The selection
criterion was their relevance to familiar topics and problems of interest
rather than representing the full scale of GIS applications.
q Data Acquisition & Registration
Map
Projections & Coordinate Systems
q Water Utilities’ Primary Functions
Case Study: Vulnerability Assessment of Missouri Drinking Water to Chemical Contamination
Case Study: Integration of GIS and Hydrologic Models for Nutrient Management Planning
q Hydrologic/Water Quality Models
& GIS
q Approaches for Integrating
Hydrologic Models & GIS
Case Study: Predictive Modeling for Sewer & Water Lines Maintenance in the City of Houston
Case Study: Wastewater Treatment Plant Siting in the Town of Plymouth, Massachusetts
Case Study: Hydraulic Network Analysis for the City of Livermore, California
q Network Allocation & Hydraulic
Analysis
The Merger: Hydraulic Network Modeling in GIS
q Overview of Model Needs & Results
Table 2: Cost
Savings for Public Drinking Systems
Table 3: Failures to
be Modeled
Table 4: Possible
Model Input Needs & Results
Figure
1: The 3 elements of geographic data
Figure 2: Depiction
of the same in raster & vector format
Figure 3: An
example of overlaying various layers in a GIS
Figure 4: User
waiver search radius distances
Figure 5: Loose
or shallow coupling through common files
Figure 6: Deep
coupling in
a common framework
Figure 7: Land
use by grid cells (acres)
Figure 8: Subarea
delineation for each modeled sewer node
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Prelude
“In science and engineering, there are a number of roles that can be
served by GIS technologies. As with any class of technologies, there are a
variety of ways to employ the tools. The goal, of course, is to be innovative
with the application of tools. Therein lies the challenge and the reward for
successful work.”
- Lyon
& McCarthy (1995)
At the lowest level of effort, the GIS data can be used to supply inventory information. The presence or absence of given land cover or water classes, or change in these variables on a spatial basis, can be valuable in planning and management. The variety or quantity of certain land cover or water types can be summarized by a certain watershed to produce statistics of interest. The capability to store, quantify, and present data on a spatial basis is an inherent characteristic of GIS technologies.
At the highest level of
technology, a GIS can provide a spatial database of information to support
modeling of phenomena. The GIS supplies the spatial
data in a form that can be input to deterministic or statistical models. The spatial power of the GIS database
is used in full by the model, and more detailed and spatially averaged results
are produced. This represents a high level of integration and achievement that
is now seen in the industry. It has taken a while for such applications to
develop, however. This is due to the absence of spatially integrated models for
water resource phenomena. Many models use spatial data but average or summarize
these data by watershed and/or subwatershed, and thereby lose much of the
detail of spatial variability that often influences phenomena. This is the same
level of detail necessary to provide high quality model simulations. In
general, the strength of GIS is that it is possible to process the data sets
using any type of numerical analysis procedure. In particular, certain
procedures are valuable for data visualization and analysis, including image
processing techniques, virtual reality, and simulation modeling. The digital approach to
storing and processing spatial or image data is a fantastic boon to these
analyses of data, and the capabilities have yet to be fully realized.
Of particular interest is also
the application of a GIS in the automation of infrastructure modeling and information management using modern
computer techniques and graphics technology to build what is called
‘intelligent infrastructures’. However, this along with the other rewarding
applications of a GIS in environmental and water resources engineering cannot
be fully conceived by the reader before a terse overview of the basic components
and features of any GIS.
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This section introduces the concepts
of a geographic information system (GIS). A discussion of the sources of data,
analysis techniques (with an emphasis on modeling), software and hardware requirements, and some example
applications are presented. This introduction will acquaint the reader with the
very useful tool of GIS and help develop an appreciation of its basic technical
aspects required for understanding the other application-oriented sections of
this paper.
A Geographic Information System (GIS) can be defined as a system for entering, storing, manipulating, analyzing, and displaying geographic or spatial data. These data are represented by points, lines, and polygons (Figure 1) along with their associated attributes (i.e., characteristics of the features which the points, lines, and polygons represent). For example, the points may represent hazardous waste site locations and their associated attributes may be the specific chemical dumped at the site, the owner, and the date the site was last used. Lines may represent roads, streams, pipelines, or other linear features while polygons may represent vegetation types or land use.
Figure 1: The 3 elements of geographic data
In addition, geographic data can be represented in two data formats. The first of these formats is called raster or grid structure and is the older of the two data formats. Raster data are stored in a grid or pixel format which is referenced to some coordinate system (i.e., latitude and longitude). The size of the grid can vary and therefore the spatial resolution of the data is determined by the size of the grid. Raster data are computationally easier to manipulate but typically require larger amounts of storage. Digital remotely sensed data (i.e., satellite imagery) is a good example of raster or grid data.
Vector or polygon data are the second way geographic data may be represented. Vector data uses a series of points (i.e., x, y coordinates) to define the boundary of the object of interest. These types of data may require less storage and are preferred for display purposes because a truer rendition of the shape of the object of interest is maintained. However, some computations are especially difficult and time consuming if performed on vector data. The example below shows what an object would look like in both raster and vector formats (Figure 2). Note that the raster image has lost some of its true shape due to the ‘gridding’ process. (In this example, a large grid cell size was used to emphasize the differences between the two formats.)
Figure 2: Depiction of the same polygon in raster & vector format
Recent technological developments and refinements in GIS computer hardware and software, and data acquisition techniques have revolutionized land management and planning. Today, using GIS, land managers, planners, resource managers, engineers, and many others can use geographic data more efficiently than ever before to analyze management and policy issues. Geographic information systems link computerized maps (location data) to computerized data bases which describe the attributes of a particular location. This linkage makes it possible for decision makers to access location and attribute data simultaneously to simulate the effects of management and policy alternatives. In addition, maps and other data can be updated quicker and more accurately using GIS than with conventional methods.
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q
Data Acquisition &
Registration
Spatial data are the “life blood” of any GIS. Somewhere between 80% and 90% of the effort and money required to run a GIS is used to acquire, input, update, and manipulate data. Therefore, it is critical that the GIS user have a thorough understanding of the many aspects of data acquisition and manipulation even before the system is turned on.
Map Projections & Coordinate Systems – A very important aspect of spatial data is ground registration. Registering spatial data to the ground requires transforming the original data which is recorded in digitizer inches to some ground-based coordinate system. (A digitizer is an instrument that permits the electronic transfer of x, y coordinates into a computer by manually tracing a map and sending the coordinate location to the computer.) A coordinate system is simply the two-dimensional (x, y) values that designate the position of a given point on the ground. Common coordinate systems include latitude-longitude, Universal Transverse Mercator (UTM), and state plane coordinates.
Failure to consider registering the spatial database to the ground can cause serious problems in the analysis and assessment stages. Surveyors understand well this registration process. A failure to consider registration before beginning any GIS project can lead to serious problems during the later stages of GIS analysis.
The procedure for registering spatial data to the ground is quite simple and would be even simpler if the earth was flat. However, because the earth is round, a projection system must be used to make a map flat. A map projection is an orderly, mathematical system of parallels and meridians used in creating a map. Projections allow us to flatten the earth only at the cost of one or more spatial attributes. Depending on the projection used, the attributes affected could be distance, shape, and/or direction. Knowledge of map projections and coordinate systems is critical because overlay analysis is such an important tool in GIS. Obviously, maps of the same area generated with different projections will not overlay. In addition, the ability to locate yourself precisely on the ground is very important. An assessment of the accuracy of any spatial data layer depends on being able to locate the necessary points on the ground (called ground control points). Without the ability to precisely locate ground positions, it is impossible to assess the accuracy of the data layer.
Sources of Spatial Data – As mentioned earlier, in a GIS spatial data are expressed as points, lines, or polygons. The spatial relationship of the points, lines, and polygons to one another is called topology. All landscape features can be reduced to one of these three data types using x, y coordinate pairs. The data can then be entered into a computer where it is stored as topology for future analysis.
As previously discussed, collecting, importing, verifying, and updating spatial data is the most expensive component of any GIS. Knowledge of how each data layer is obtained is critical to the ultimate economic success of the GIS. Before any new data are collected an exhaustive search should be performed to verify that no substitute data exists. Such data can be in the form of hard copy maps (to be manually digitized and registered), digital format maps (to be readily input into a computerized GIS), digital line graph (DLG) data consisting of vector format or CAD information about certain characteristics found on maps including land use, land cover, transportation, ownership boundaries, hydrography, etc…, and digital terrain/elevation models (DTM or DEM) that contain a grid pattern of point elevations that can be used to simulate the topography of an area and generate three-dimensional information about an area such as slope, aspect, volume, and surface profiles.
It is very easy in this age of satellite imagery, video cameras, and global positioning satellites (GPS) to overlook the many advantages and uses of aerial photography as a source of spatial information. However, to do this would be to make a huge mistake. The accuracy, precision, and detail required for many mapping projects dictates the use of aerial photographs. Furthermore, the ease of use and simplicity of aerial photography with respect to other remotely sensed data must be considered. Aerial photography can be as simple as using the photograph to record some historical event (i.e., a picture is worth a thousand words), or as complicated as digitizing the photo and entering it into a computerized image processing system. Anyone can pick up an aerial photograph and begin to glean some information from it. However, photographs do have some disadvantages with positional accuracy due to distortion and displacement. Techniques are available to correct these problems and should be used to take advantage of this very common source of spatial data.
The most exciting development in the area of sources of spatial data is the ability to geocode and/or terrain process digital satellite data. Geocoding refers to a process by which the remotely sensed data is very accurately registered to a ground coordinate system. This process is possible because knowing the precise location of the satellite in space is now feasible. Now, not only can digital data be very easily added into a GIS, but it can also be a source of one of the most important GIS data layers—elevation. This has far reaching implications in the ability to quickly and efficiently update and revise GIS databases.
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Once all the necessary existing and new data have been collected then it can all be registered to a common base map. As mentioned, the collection and registering of all these data from various sources can be an expensive, time consuming, and frustrating process. Once completed, however, the analysis can begin. Basic GIS analysis techniques include overlay analysis, modeling, buffering, and network analysis.
Overlay Analysis – The concept of overlaying data layers to obtain certain information is
not a new concept to GIS. Many of us have used tracing paper and colored
pencils to produce transparent maps that could lay over the top of each other
in order to derive some information; that could be considered a ‘primitive’
GIS. The ability to analyze spatial data separates GIS from mere spatial
databases. In early computer-aided design/computer-aided mapping (CAD-
Figure 3: An example of overlaying various
layers in a GIS
The ability to extract specific information from a data layer and combine it with other information from that same or some other data layer depends on the use of Boolean algebra. This procedure involves the use of the operators AND, OR, XOR, and NOT to manipulate spatial data by testing to see if a given condition or statement is true or false. It is then possible to combine data layers to form a new layer.
Overlay analysis can be divided into two general
categories: point operations and neighborhood or region operations. Point operations can be
anything from the Boolean operators just discussed to simple weighting functions
such as multiplication by some factor. In addition, point operations can
involve more complex functions such as clustering, discriminant analysis,
principal components analysis, and other such statistical techniques.
Region operations differ from point operations in that
they relate a point to its neighbors or to a specified region. This process is
much more complicated than point operations and
involves the use of the spatial component of the data in order to operate. It
is in these neighborhood operations that the true utility of a GIS shines
through. Measures of correlation and diversity as well as slope and aspect are
common examples of neighborhood operations.
Modeling – Primarily,
GIS technologies can facilitate input of data sets to simulation models. This
may take the form of one or multiple variable or GIS ‘layer’ inputs to models.
However, we still need to develop or identify models in application areas that
are already amenable to the entry of spatially distributed data. Models must be
formulated in the future to receive spatial data directly from the GIS (see
concluding section of this paper, The Merger: Hydraulic Network
Modeling in GIS.)
Many current and traditional model procedures use summary information in the
form of model coefficients. These types of models can also be improved by GIS
or remote sensor technologies by conducting tests so that model coefficients
can be further defined or refined through improved measurements of their
characteristics. A more ‘natural’ coefficient better defines the behavior of
model variables, and allows the modeler to achieve high fidelity between
natural systems and their model simulations. Four aspects of modeling spatial information will be discussed here:
cartographic modeling, simulation/deterministic
modeling, statistical/predictive modeling, and model calibration:
Cartographic Modeling: When
the user of spatial information is presented with a problem, the response
should be a careful plan of what should be done. A more common response is to
rush to the computer and start to work. Instead, cartographic modeling suggests detailed flow charts and careful
planning to decide what data are
important and how they should be used.
Simulation Modeling: Another aspect of modeling is the simulation approach. In this case, the user tries to simulate some complex phenomenon using a combination of spatial and nonspatial information. This approach typically requires an expert who is knowledgeable enough to build such a simulation or model. It should be noted that rarely in these cases do any two experts agree on exactly how the model should be built. A good example of this type of modeling is evaluating wildlife habitat suitability. In this example, one might use the following spatial information layers: vegetation, elevation, aspect, slope, ownership, roads, and streams. This information could then be combined in some model with weights used to prioritize important layers. In addition, calculations of distances (i.e., distance from roads, distance to streams) and measures of diversity may be included in the model. This model is then used to evaluate areas of good habitat and determine where the habitat can be improved.
Predictive Modeling: In
this approach, statistical techniques are used to build a model that will
be able to predict using the spatial information. The statistical tool used for
building such models is most commonly regression analysis. The first step in
this process is to collect information about the phenomena one wishes to model.
A subset of this information is then used to statistically build the model.
This model building is performed by looking at each layer of spatial
information and each component of nonspatial information to see which are
correlated to the phenomena one wishes to predict. Once the model is built, the
model is tested using the remaining information.
An example will elucidate this explanation. Suppose one wants to predict the
amount of snowmelt runoff from a forested watershed. These predictions
currently are being made by point samples taken throughout the watershed. The
predictions can be compared to the actual runoff statistics collected by stream
gauges. One might hypothesize that using spatial data that completely covers
the area should lead to better predictions than point samples. Therefore, one
would put together a GIS with the necessary layers to predict runoff. These
layers might include: vegetation, slope, aspect, snow extent, elevation, and soil type. In addition,
the point sample data which includes snow depth and the amount of water in the
snow may also be included. Some of this information remains constant over time
while some changes daily. Therefore, a variety of conditions (i.e., years)
should be represented in the information collected. In this example, one would
collect runoff data for dry years, wet years, and average years. A subset of
this runoff data would then be used to develop the model and select the necessary
spatial and point sample data to be included in it. The remainder of the data
would then be used to test the model. Once the model has been proved effective,
it can be used to predict snowmelt runoff in future years.
Statistical models can also be applied later to predict other conditions at
other places and times if the model represents the behavior of the variable.
When statistical models of physical, chemical, or biological
systems truly predict variables, users can apply the model to new situations.
Good applications include: the evaluation of suspended sediments in freshwater
and coastal ecosystems, temperature of water bodies, crop residue and tillage
practices for evaluation of nonpoint pollution, concentrations of chlorophyll
in water and plants, and a variety of other detailed applications. The use of
statistical analyses has proven of great value in water resources studies over
large areas.
Model Calibration: Another valuable use of GIS and related technologies is the calibration of model coefficients in statistical models. Both statistical and deterministic models often consist of a number of submodel units. Coefficients used in either approach reflect the characteristics of nature, and they will adjust the contribution of variables or submodels to the overall model results.
To optimize the model simulation of natural phenomenon, the coefficients need
to reflect the reality of the situation. As a given model begins to approximate
nature, its further development often takes the path of improving the quality
of coefficients. Many times, a number of experiments will be executed to better
measure the level of a coefficient and thus to better have it mimic nature and
help supply better model predictions.
In GIS and remote sensor experiments using statistical models can be greatly facilitated by the
analysis of these individual coefficients. These analyses are driven to find
the ‘sensitivity’ of the overall model result or simulation to a given
variable. Sensitivity analyses are part of a good modeling strategy because it is very desirable to
understand the contributions of model coefficients to the overall results, and
to ensure that each variable and/or submodel contribution is appropriate or
similar to that of nature. On many occasions, deterministic or statistical
models are ‘run’ engineers that often have no model inputs from GIS or remote
sensor technologies. However, the model results are usually some
variable that can be measured or evaluated using GIS and remote sensors. The
use of separate results from GIS and/or remote sensor technologies supplies an
independent verification of modeling results. A number of verification checks
between model results and those of GIS or remote sensors will allow a
validation of the model.
Buffering – Buffering
is a technique by which a boundary of known width is drawn around a point or
linear feature. Some examples of point buffers may be a zone around a hazardous waste site or
around a tree that is a nest for a particular endangered bird. Examples of
linear buffers may be an area around a stream to prevent logging or an area
around a utility pipeline to prevent digging. A related function is generally
referred to as “proximity searches”. Proximity
searches can be used to identify adjacencies between particular features or
data classifications. Examples might include identifying all groundwater
pollution cases within a specific radius of a given drinking supply well (see Case Study:
Vulnerability Assessment of Missouri
Drinking Water to Chemical Contamination on p.23–p.28) Proximity
search can be used to locate potential sources of contamination, to schedule
inspections, to identify monitoring wells or sampling points which might
provide relevant data from nearby sites, to identify specific residents or the
size exposed populations, or to generate mailing lists for further
investigation.
Network Analysis – Network analysis is a technique by which a linear path is identified that represents the flow of some object through the area. Network analysis is especially useful in hydrology, water resources, transportation, and other disciplines that study the flow of an object. This flow is not limited to water but can also be used for vehicles, utility and communication lines, and animals.
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A particularly valuable use of a GIS is in project presentations. The managers or decision makers on any project appreciate the quantitative information derived from GIS analyses. Naturally, they also appreciate the high quality graphical products that support the project, and demonstrate the results of project analyses. Another valuable use is the public presentation of the results of project analyses using GIS. This is appropriate for public meetings and audiences beyond those of the immediate group. Current land and water planning efforts involve a wealth of information. GIS products can help to display the site conditions, and to show some of the variables that must be weighed to make a decision. The GIS displays can also help lead the audience through the same decision-making process, and help lend insight into the results of the planning study. This is how a GIS was utilized in the preliminary Qalamoun Water & Wastewater Study to share results and discuss location and component recommendations with other groups before narrowing down on the optimum alternatives.
The concept of “thematic maps” maps generated to visually demonstrate the distribution of specific data results is very important. Symbols or colors can be designated for locations which have or exceed particular values or fall within defined value ranges. They might indicate all monitoring wells having a given pollutant at or above a specified level of concentration. Or, they might be used to show the groundwater elevation for wells within a given geographical area. Thematic mapping offers virtually an unlimited number of map products; hence the number of maps we were able to derive from the few maps and spatial data accessible to us during the Qalamoun Water & Wastewater Study. Such maps are far more readily-interpreted than tabular data alone. In fact the transfer of data from tables to maps or site plans for interpretation has historically been a major and time consuming component of data analysis for virtually all environmental sampling studies.
Specific uses of GIS products for presentation of projects include briefings for management
or boards, presentations at group or public meetings, environmental analyses or
environmental impact statements, book, article, or web illustrations, and other
efforts at communicating the results of a project.
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In order to effectively use any GIS, one must understand the errors associated with spatial information. Errors associated with spatial information can be divided into three groups: user errors, measurement/data errors, and processing errors. User errors are those errors which are probably most obvious and are more directly controlled by the user. Measurement/data error deals with the variability in the spatial information and the corresponding accuracy with which it was acquired. Finally, processing error involves errors inherent to the techniques used to input, access, and manipulate the spatial information.
Included within the user errors are the age, scale, coverage, and relevance of
the data. Errors result when outdated data are used because of
a lack of cur rent information. A good example of this problem is the use of
old aerial photography because new photography is not available.
Error also results when data of the wrong scale are applied to meet some
objective. This situation is especially dangerous when small scale data are
used to meet the objectives of some large scale project. Finally, error can be caused
by using indirect data layers as input into the GIS. An indirect layer is one
that has been derived from some primary information. Probably the best example
of this is a vegetation classification generated from satellite data or aerial photography.
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The GIS software links the attribute data to the geographic features (represented by points, lines, and polygons and their topology), using a database management system. It also provides for the input, editing, and analysis functions previously discussed. The capabilities and costs of GIS software vary greatly from vendor to vendor and the selection of such software depends on the needs of the user. The following factors should be considered when choosing GIS software:
§ Data input and editing functions
§ Analysis functions
§ Flexibility of the system (compatibility with other software/hardware)
§ Risk associated with the vendor
§ Cost to purchase, operate, and maintain the system
§ Type of database management system
As with software, the hardware should be matched to the user’s needs. With
the continuing development of hardware, GIS is possible in almost
every work setting. Computer sizes range from microcomputers and advanced
workstations to mini- and mainframe computers. Size is mainly a function of speed,
disk space, random access memory (RAM), number of users, types of input/output
devices, and cost. There always seems to be a trade-off between buying the
right computer and then being able to afford the necessary peripherals. This
dilemma is similar to the one we face when buying a car. Do we buy a basic
model with a big engine or do we settle for a smaller engine and get the
stereo, air conditioner and sun roof? Careful consideration of the specific GIS
application is critical before deciding on the right software and hardware.
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An integral and yet largely forgotten and unnoticed component of a GIS is people. Geographic information systems need people in order to operate. Without well-trained people and an adequate staff, it is likely that an investment of thousands of dollars on state-of-the-art equipment and data will be wasted. Continued funding is critical to provide the necessary training and database maintenance. All in all, people are the most important resource; failure to make a strong commitment to the people operating the GIS dooms it to failure.
Geographic
Information Systems have become a popular item on the wish list of
municipalities and water agencies. This section considers the
traditional activities of water utilities,
and the solutions that will yield short and longer term requirements. These
needs are then contrasted with the capabilities and strengths of GIS
technologies.
Although a GIS is often seen as an important technology for water utilities, it is sometimes hard to see the real benefits of a GIS when compared to the high hardware, software, and human implementation costs. ‘Serious’ GIS systems require a significant investment in mainframe, minicomputer, or engineering workstation hardware and software, followed by additional investments in dollars and time to develop applications to address the actual problems faced by the agencies who are making the investment. GIS software, and computer technology in general, clearly poses a dilemma to water utility managers. Unlike traditional civil engineering technology, the hardware, software, and available techniques for computerized facility management and automated mapping change almost daily. It is difficult to make rational decisions regarding long term investments in such an environment; this year’s ‘hot’ system may not be capable of growing to meet tomorrow’s integration and data distribution requirements.
q
Water Utilities’ Primary
Functions
GIS technology can be utilized by each of the following water utility function groups, but there are considerable differences in the uses and relative advantages of such technology:
§ Planning — estimating future demands and planning the timed expansion of the water system.
§ Engineering — detailed design and construction of water facilities.
§ O & M — operating and maintaining water transmission and supply facilities.
§
Administration — managing
the paperwork and dollars associated with operating the water system.
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To start with, let us go through a quick overview of the components/functions of a GIS discussed earlier:
§ Database — At the core of every GIS is information. All true GIS systems have a database management system in some form.
§ Graphics — The visible portion of every GIS is geography. This graphical interface serves two functions: first as an environment for maintaining geographic data in an intuitive fashion, and second as a window into the database for any number of informative views.
§ Environment — One cannot consider a GIS without evaluating the system in which it operates. To be effective, a GIS must include applications that address the needs of its users, and the overall system must be designed in a way that serves these needs.
Consider now the functions of every water utility with regards to GIS technology:
Planning – A water utility’s planning group generally performs estimates of future
water demands, evaluates the transmission system utilizing these estimates,
specifies the required system improvements, and structures a long term capital
investment program around these improvements.
What is required by planners is a modeling and analysis application that provides a
working environment to study the transmission system. Solutions are generally
built around skeletonized hydraulic models of the current and projected
network/system. A GIS system, although not central to the analysis process, is
useful as a source of information for calculating existing and future water
demand as well as data on transmission facilities. The GIS might also serve a
role as a tool to view modeling and analysis results. One must consider,
however, that planning analysis of water facilities generally is
performed with a simplified or skeletonized version of
the actual system.
A crucial issue in most
planning activities is presentation of analysis and recommendations to lay groups
(i.e., water system boards, city council). As discussed earlier, a GIS system
can be a powerful presentation tool that can show both results and the
reasoning on which results are based. The Case Study: Wastewater Treatment
Plant Siting in the Town of
Engineering – The engineering group within a water utility is generally responsible for facility design, construction, and mapping. Unlike the planning function, engineering deals with facilities as they are actually built.
For the design and construction activities, CAD software technology yields an invaluable tool for
increasing productivity and aiding the long term maintenance of as-built drawings. GIS systems are not
intended to handle the details and nuances of construction documents. However,
conversion and import/export of data from GIS to CAD systems and vice versa has
become so simple; it can be executed with the click of a button.
Facility mapping, on the other hand, is best achieved using GIS technology.
Unlike CAD systems,
where individual mapsheets are all treated as separate documents, a GIS
environment can manage the information for an entire service area, and provide
any number and variety of maps and mapsheets as required. Further information
is provided towards the end of this paper on the merger of such systems and the capability of using a
GIS in preliminary design (The Merger: Hydraulic Network
Modeling in GIS on p. 44 - p. 49) Moreover, Case Study:
Hydraulic Network Analysis for the City of Livermore, California on
p. 40 – p. 43 provides a real-life example on attempting such an endeavor.
Operation & Maintenance – Operations and maintenance performs work on geographically distributed facilities. The primary need on a daily basis is to manage work crews. The technology required to satisfy this need is a database application that provides work order management, work scheduling, and work history logging.
In addition, 0 & M activities can have a direct impact on the other phases
of water system management. A crucial problem is how this information (for
example new facility data) is passed in a timely fashion to other departments.
With their own database application, the efficiency of such transfers could be
improved. Note that in this context, 0 & M would also function as a data
source for a GIS.
Assigning and locating work fronts on a daily basis can also be highly optimized by a GIS. For example, through access to ‘shortest route’ and ‘path with least traffic’ information obtained through running a network analysis on a road network, a lot of daily time and resources can be saved and work front overlaps can be avoided. Furthermore, over the long term, 0 & M needs a way to evaluate the operation of the system with respect to maintenance activities. For those activities, a GIS would serve as an ideal analytical tool. Refer to Case Study: Predictive Modeling for Sewer & Water Lines Maintenance in the City of Houston on p. 34 – p. 36 for the use of a GIS in the management and maintenance of a sprawling infrastructure system of distribution and wastewater collection lines in the City of Houston.
Administration – Administrative functions have been computerized for many years and a wide variety of standard applications now exist to serve administrative needs. Although it is certainly possible to integrate administrative data within a GIS, GIS technology does not yield many tangible benefits for water utility administrators.
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As the above discussion shows, GIS technology can provide a water utility with a flexible capability to store and present geographic information. These features are complementary to applications that solve the problems of specific activities within a utility. The key requirement, prior to making any investment in technology, is to establish and maintain a long-term vision and plan while focusing on short-term benefits. A rational approach to technology investment for water utilities has to focus on two critical points: data maintenance and system modularity.
Data Maintenance – Consider the following guidelines
with regards to cost and longevity:
|
Table 1: The
Value of Data |
||
|
Item |
Relative Cost |
Typical Longevity |
|
Hardware |
Moderate to high |
5 to 10 years |
|
Software |
Low to moderate |
10 to 20 years |
|
Data |
Very high |
50 years or more |
The aim of this table is to prove that regardless of what hardware and software environment is chosen for any water utility application, the cost and value of the data is much greater. Therefore, to protect one’s investment, evaluation of each application with a view toward insuring that the data is stored in an environment where it can and will be accurately maintained is most crucial.
System Modularity – It is common belief that it is possible to build or purchase a single software application that addresses everyone’s needs; nowadays GIS is portrayed as such an application. The reality is that there are a wide variety of products available that provide the appropriate solution to specific problems, and a system that effectively integrates these individual solutions will provide more useful functionality sooner and at a far lower cost.
The key is to build a modular system from independent applications, with well
defined rules for data exchange between applications. A system designed along
these lines yields several advantages:
§
A choice of products will be
available for every key application. It’s not necessary to select only what a
single application vendor can offer.
§
The operation of the system is
not dependent on a single component. Any application could be replaced when
defective or upgraded when a newer version is available without disrupting the
overall system.
§
The initial cost is lower, and
the applications are useful immediately.
Before bringing a GIS into a water utility, several questions must be addressed. Can any individual application within the plan be replaced without disrupting other applications and users? Does the architecture avoid data redundancy? (In other words, is each type of data maintained in only one place?) Is the long term maintenance of all types of data assured? Are the appropriate users maintaining their own data? Are there immediate benefits and what are they?
All in all, water utilities – especially smaller ones – should adopt a
slightly conservative approach to acquiring GIS technologies. Emphasis should
be placed first on the utility’s immediate needs while stressing the issues of
long term investment with regards to data collection and management, and then
on following a growth path towards a full GIS environment.
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Case
Study: Vulnerability Assessment of
C. Barnett, S.
Vance, & C. Fulcher – Center for Agricultural, Resource, &
Environmental Systems,
This section
demonstrates the use of a GIS (mainly though buffer analysis) in determining which water sources in
Missouri are least susceptible to chemical contamination and can be granted use
waivers. This resulted the
state saving several million dollars in testing costs and developing several
spatial and nonspatial databases that will have many uses. In addition, the
project established a basic framework for future
assessments, which EPA requires on a regular basis.
The Missouri Department of Natural Resources (MDNR) implemented the Vulnerability Assessment of Missouri Drinking Water to Chemical Contamination project. They designed the project to determine which, if any, public water supplies are threatened by chemicals being tested under the Safe Drinking Water Act under which the United States Environmental Protection Agency (EPA) required that all public drinking water systems be routinely monitored for 79 contaminants. If a selected chemical parameter is not detected in an area that would affect a water supply (where ‘detected’ is defined as used, stored, manufactured, disposed of, or transported regardless of amount), then the water supply need not be tested for that chemical. Instead, that system would be granted a use waiver, meaning that the state would not test for that chemical. EPA granted use waivers for 43 of the 79 contaminants. Use waivers can result in considerable cost savings.
Because use waivers are granted based on the spatial relationship
between drinking water sources and contaminant sources, accurate positional
data is needed to be collected for those items. A GIS was used to store and
analyze this information in a spatial context.
Water sources, as defined for this study, are the points where water is
drawn from a river, lake, or aquifer for use in a public water supply.
Preliminary efforts focused primarily on the development of the water source
layers for the GIS. These layers, containing wellheads, impoundment intakes,
and river intakes, were created in house or obtained from state and federal
agencies. Since these MDNR regional office personnel routinely inspect
The information is stored in the GIS in the form of geographic data sets or
layers. These layers offered the most accurate and current information available.
The wellhead layer contains 2,327 public wells and their attributes (e.g.,
well depth, casing type). The surface water impoundment layer contains 105
points representing the intake locations for systems that rely on lake water.
Additionally, the drainage basin and lake area are mapped for these systems.
The final layer represents the systems that use river water.
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Contaminant sources, as defined for this study, are the points or areas where existing databases indicate the presence of a chemical contaminant. Incorporation of contaminant data into the GIS proved to be the most difficult task. These data usually contained very precise information about what contaminants were found at a site and who was responsible, but the quality of the locational information was often poor.
Ninety-three state and federal databases were reviewed for contaminant
information before performing the final use waiver analysis. The contaminant
information was broken into two separate types, contaminant sites and pesticide
dealerships. The contaminant sites were locations at which certain chemicals
were known to exist. The pesticide dealerships were dealerships licensed to
distribute restricted use pesticides. Information about contaminant sites was
extracted from the databases and entered into Microsoft Excel, a spreadsheet
program. The small amount of data with coordinate (latitude/longitude) or map
information was readily converted to the GIS. The majority of the contaminant
records, however, contained only address information, often appearing as a
rural route address or post office box number.
Of more than 2,800 contaminant sites found in the databases, 88 percent were
geographically located and used in the study. The contaminant site layer
contains 2,493 points representing the information collected on the 43 chemical
contaminants required by MDNR. Each point contains a seven-digit chemical code
indicating the chemical it represents and serving as a link to the chemical
contaminant files. The contaminant sites tend to be concentrated more in urban
areas than rural areas. Even though this layer is being continually updated,
the basic distribution of contaminant sites remains the same.
A second contaminant source layer represents
The final parameters for the use waiver analysis were developed from EPA and MDNR guidelines and accounted for the capabilities of the GIS. These parameters were designed to present a conservative list of the systems that needed to be tested for the possible presence of studied chemicals. Parameters for the wellhead analysis are as follows:
§ A Ľ-, ˝-, and 1-mile radius around each wellhead was searched for contaminant sites and pesticide dealerships (Figure 4). Any contaminant sources found within those radii were reported.
§ Any wellheads found within a contaminant area were denied a use waiver for that contaminant.
§ Each highway and railroad within 500 feet of a wellhead was recorded. This indicates the threat posed by the transport of chemicals near wellheads.
§ Additionally, the percentage of the county planted in corn, soybeans, wheat, sorghum, tobacco, cotton, and rice was listed for each well to indicate the threat posed by agricultural chemical use within that county.
The parameters for the systems
relying on lake water are as follows:
§ Any contaminant sources found within a surface water impoundment drainage basin caused the associated intake(s) to fail use waiver analysis for those contaminants.
§ Any area of contamination overlapping a drainage basin caused the associated intake to fail use waiver analysis for that contaminant.
§ Transportation corridors passing through a drainage basin were noted to indicate the threat posed by transport of chemicals within the basin.
§ The percentage of the county planted in the seven crops mentioned before was listed to indicate agricultural chemical use within the drainage basin.
Many of the rivers that supply water to systems in
The GIS searches around each wellhead for each radius and notes which
contaminant sites affect which wellheads. If a contaminant falls within that
radius, the wellhead is to be monitored. In Figure 4, the well is
affected by one contaminant within the Ľ- radius, two within the ˝- radius, and
four within the 1-mile radius.
Figure 4: User waiver search radius distances
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The results of the use waiver analysis indicate which systems may be affected by the use of a chemical near a water source. Several results show the substantial savings realized from the analysis. For example, the analysis showed that only five wells serving four public drinking water systems were potentially affected by dioxin and should be monitored. By not testing the remaining systems for dioxin, the state can realize a considerable cost savings, as the test for dioxin is the most expensive test to perform.
The final wellhead system analysis shows that the ˝-mile buffer analysis affected a total of 447 wellheads in 241 systems. That is, a chemical site or pesticide dealership was found within ˝-mile of 447 public wellheads. A result form was generated for each of the 1,340 systems in the state listing each well or intake and the potential threat posed by nearby contaminant sources.
The cost of testing all wellhead systems for all 43 contaminants without
issuing use waivers is more than $15 million (Table 2).
According to this analysis, only $1.8 million need be spent to monitor
vulnerable wells. Therefore, the state can save more than $13.5 million in
monitoring costs.
|
Table 2: Cost
Savings for Public Drinking Systems |
|||
|
Method |
Estimated Total Cost |
Estimated Mean Cost per
System |
Estimated Total Cost
Savings |
|
No use waiver |
$15,533,100 |
$12,200 |
$0 |
|
With use waiver |
$1,813,900 |
$1,400 |
$13,719,200 |
To date, the investment the state made in the vulnerability assessment project has provided many benefits. The state saved several million dollars in testing costs and developed several spatial and nonspatial databases that will have many uses. In addition, the project established a basic framework for future assessments, which EPA requires on a regular basis.
The basic data required for use waiver analysis are the locations of water
sources and the locations of potential contamination sources. It was realized,
however, that the available data did not contain the information necessary to
map these locations or that the data were of questionable quality. Many layers
required updating and correction. Considerable effort was necessary to improve
existing locational information for both water source layers and chemical
contaminant files. Local knowledge of an area was heavily relied upon to
determine accurate locations, particularly contaminant sites. The vast majority
of these sites contained only the address as the geographic reference. An
address is not a coordinate system; it does not indicate a fixed location on a
map. Because the location of any chemical detection site is of vital
importance, state and federal agencies that collect these data need to record
more complete geographic information. Ideally, a global positioning system (GPS) could be employed to generate coordinates.
Realistically, however, the recording of legal descriptions or directions from
an easily located point would substantially improve the quality of the current
databases.
In many cases, data resided in digital format; however, due to regulations or
lack of agency cooperation, they could only be distributed in paper
format. Reentering data from paper format into digital format required
considerable time and expense. Interagency cooperation should be emphasized to
reduce unnecessary data entry.
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Case
Study: Integration of GIS and Hydrologic Models for Nutrient
Management Planning
C. Fraisse, K.
Campbell, J. Jones, W. Boggess, B. Negahban – Agricultural Engineering
Department, University of Florida, Gainesville, Florida
This section
emphasizes issues related to the integration of hydrologic/water quality models
and GIS programs by simulating the impact of animal waste production, storage, treatment, and use on
water resources in southwest
Recent evidence that agriculture in general, and animal waste in
particular, may be an important factor in surface and ground-water quality
degradation has increased the interest in nutrient management research. The
presence of nitrogen and phosphorus in surface water bodies and ground water is
a significant water quality problem in many parts of the world. Increases in
nutrient loadings to water resources have recently been observed in the
southeastern
Animal waste management has always been a part of farming, but historically has
been relatively easy due to the buffering capacity of the land. In fact, land
application of animal waste at acceptable rates can provide crops with an
adequate level of nutrients, help reduce soil erosion, and improve water
holding capacity. As the animal industry attempts to meet the food requirements
of a growing population, however, it applies new technologies that reduce the
number of producers, but create larger, more concentrated operations. That, in
addition to the decreasing amount of land available for waste application, has
increased the potential for water quality degradation.
Successful planning of an animal waste management system requires the ability to simulate the impact of waste production, storage, treatment, and use on water resources. It must address the overall nutrient management for the operation, including other nutrient sources such as supplemental fertilizer applications. Livestock operations are highly variable in their physical facilities, management systems, and the soil, drainage, and climatic conditions that affect the risk of water pollution from animal wastes. Linkage between geographic information systems (GIS) and hydrologic models offers an excellent way to represent spatial features of the fields being simulated and to improve results. In addition, a GIS containing a relational database is an excellent way to store, retrieve, and format the spatial and tabular data required to run a simulation model. This case study examines some of the issues related to the integration of hydrologic/water quality models and GIS programs.
q
Hydrologic/Water Quality
Models & GIS
A hydrologic model is a mathematical representation of the transport of water and its constituents on some part of the land surface or subsurface environment. Hydrologic models can be used as planning tools for determining management practices that minimize nutrient loadings from an agricultural activity to water resources. The results obtained depend on an accurate representation of the environment through which water flows and of the spatial distribution of rainfall characteristics. These models have successfully dealt with time, but they are often spatially aggregated or lumped-parameter models.
Recently, hydrologists have turned their attention to GIS for assistance in
studying the movement of water and its constituents in the hydrologic cycle. A GIS can represent the spatial
variation of a given field property by using a cell grid structure in which the area is partitioned
into regular grid cells (raster data) or by using a set of points, lines, and
polygons (vector data) as discussed in the The ABCs of any GIS
section of this paper.
A close connection obviously exists between GIS and hydrologic models, and integrating them produces
tremendous benefits. Parameter determination is currently one of the most
active hydrology-related areas in GIS. Parameters such as land surface slope,
channel length, land use, and soil properties of a watershed
are being extracted from both raster and vector GIS programs. The spatial nature of
GIS also provides an ideal structure for modeling. A GIS can be a substantial
time saver that allows different modeling approaches to be tried, sparing manual
encoding of parameters. Further, it can provide a tool for examining the
spatial information from various user-defined perspectives. It enables the user
to selectively analyze the data pertinent to the situation and try alternative
approaches to analysis.
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q
Approaches for
Integrating Hydrologic Models & GIS
A significant amount of work has been done to integrate raster and vector GIS with hydrologic/water quality models. Several strategies and approaches for the integration have been tried. Initial work tended to use simpler models. These studies attempted to develop GIS-based screening methods to rank nonpoint pollution potential. The use of more complex models requires that the GIS be used to retrieve, and possibly format, the model data. The model itself is implemented separately and communicates with GIS via data files. This model-GIS integration mode is referred to as ‘loose’ or ‘shallow’ coupling (Figure 5), implying that the GIS and modeling software are coupled sufficiently to allow only the transfer of data and perhaps also of results, in the reverse direction. Only the file formats and the corresponding input and output routines, usually of the model, must be adapted.
Figure 5: Loose or shallow coupling through common files
Higher forms of connection use a common interface and transparent file or information sharing and transfer between the respective components (Figure 6). The dairy model, currently under development for this nutrient management planning case study, is an application of this kind. It will link the Ground-Water Loading Effects of Agricultural Management Systems (GLEAMS) model and GIS to evaluate potential leaching and runoff of both nitrogen and phosphorus. LOADSS (Lake Okeechobee Agricultural Decision Support System), however, is an extension of this type of application because it includes an optimization module that enables the system to select the best phosphorus control practices (PCPs) at the regional scale, based on the goals and constraints defined by the user.
The first part of LOADSS (Version 2.2) is fully functional and currently available at the South Florida Water Management District (SFWMD). Preliminary results show that LOADSS behaves consistently with measured data at the lake basin scale. Some of this, however, is due to offsetting errors in model behavior at the subbasin scale, particularly in subbasins that are adjacent to or very far from the lake. Currently, projects are underway to further verify and calibrate the model at the subbasin level to improve its performance at smaller scales. Initial results of the optimization component are currently being evaluated.
Figure 6: Deep coupling in a common framework
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The search for solutions to the many problems concerning nutrient management that affect water resources implies a continued demand for the development of modeling systems that can be used to analyze, in a holistic approach, the impact of alternative management policies.
The development of LOADSS exemplifies how the integration of hydrologic models and a GIS can be used for analyzing nutrient control practices at different scales. The addition of optimization algorithms further enhances the ability of policy- and decision-makers to analyze the impact of alternative management practices and land uses at the regional level.
All in all, the dairy model represents a different approach in integrating water quality models and GIS in that it is de signed to be generic an