The
Dams of California as a Case Study
In
Using
Geographic Information Systems in Environmental History and Politics
Kenneth Worthy
ESPM 275
5 December 2000
Water
has always played a crucial role in the formation of the modern California
state (see Marc Reisner, Cadillac Desert). It is evident that none of
the large cities of the state, particularly Los Angeles, could exist in
anything like their current form without a remarkably extensive control of
water resources. The entire current population of the state depends on a
controlled source of water. To that end, there are approximately 1300 dams in
California, ranging in size from very small to extremely large, such as the one
creating Lake Shasta. Dams regulate the flow of water, providing a buffer
against seasonal and other variations, in addition to providing a large,
perpetual on-demand supply for fire fighting and other urgencies. The vast
majority of flowing water in the state has come under the control of public and
private water systems of all sorts, with dams as the central feature of those
water systems. Little water reaches the ocean without having been held back by
several of these dams.
Yet,
few people have a realistic, informed conception of the extensive nature of the
dams (and their own dependence on them). Because of their great number,
distribution and range of factors in use, construction size, etc., it is
difficult to get a strong grasp of the overall characteristics of the dams of
California without the use of GIS tools. These tools allow for the spatial
representation of geographic distribution, size, time period, drainage area
size, etc. The rarity of the use of these tools for education and policy making
concerning the dams probably contributes to a general ignorance about the
extent of dams in California; this is probably also the case with most of the
other public works projects upon which citizens depend.
The
purpose of this project is to demonstrate the use of GIS in elucidating general
trends, scope and other characteristics of dams in California for the purpose
of general education and policy making and to spawn questions for further
research. Such data in an aggregate state is either inaccessible to the public
because it is in a dense tabular form or because it is presented in
interspersed, discontinuous form throughout the public media. This study hopes
to provide an example of presenting such data in ways that enables a “gestalt”
view of the overall system of dams in the state, allowing the perusal of the
information in meaningful aggregate forms. In other words, rather than
providing many abstract, numeric statistics, or decontextualized, partial views
of the system of dams in the state, these methods are meant to allow for the
presentation of the dam data in such a way that general characteristics of the
dams can begin to emerge. Thus, this project will hopefully work as an example
and prototype for using GIS data about large-scale public works projects to
create a broader understanding among the public and politicians about the
control over, and environmental history of, the natural resources upon which
they depend.
The
core data for the project was initially found in the extensive University of
California Digital Library dam database, which was constructed via an optical
character recognition conversion of a database of the dams of California
published by the California Department of Water Resources. The site for that
database can be found at: http://elib.cs.berkeley.edu/kopec/b17/html/home.html. Because the published schema for that online
database was found to be error prone (fields described in the wrong order,
etc.), I used instead the California state section of the National Inventory of
Dams (NID), provided free online by the United States Geological Survey. The
site for this database can be found at http://crunch.tec.army.mil/nid/webpages/nid.cfm. The NID also provided a more extensive set of data
for each dam, including lat-long coordinates for each dam, owner, county, and
various dam metrics such as maximum storage, drainage area feeding the dam,
etc. The full data dictionary and schema of the NID database can be found at: http://www.nws.noaa.gov/oh/hod_whfs/documentation/damcat_dict.html.
Arcview
was used as the primary GIS tool. The California dams database was added as a
table and then processed in numerous ways described below to extract data
presentations. The dams database was supplemented by stock Arcview data such as
California state and county maps and databases, etc. Because of the limitations
of Arcview, I also used Excel, PowerPoint and RealPlayer as data processing and
presentation tools. These tools together were used to present the dams data in
multiple forms: 1) as static themes in Arcview views, 2) as Arcview and Excel
charts, 3) as an animated display in PowerPoint.
Several views of the dam database were produced in the
study. A dynamic view was produced with an Excel animation; the rest were
static views which could be manipulated by an Arcview user. Examples of all of
the views have been printed out in color and attached as an appendix to this
document. Arcview views were formatted using the layout facility. For a more
professional presentation, additional information might be printed on these
displays, including additional legend values, etc. For example, the legend
values for the “dams by year completed” view were difficult to include in full
in the printout due to their great number; the range of legend values is dark
blue at 1850 to dark red at 2000.
The following is an abbreviated list of views, with
description, produced for this study. Unless otherwise noted, the view includes
a plain gray base map of California.
1.
100 biggest dams by maximum storage, with DEM (Digital
Elevation Model) base map: a point for each of
the 100 biggest dams by maximum storage volume in acre-feet, sized
proportionally to the maximum storage volume.
2.
Dams by year complete, with DEM base map: a point for each dam, with color dependent on year.
Colors range from deep blue for the oldest dams (1850 being the oldest) through
yellow to deep red for the newest.
3.
Dams by height, with DEM base map: a point for each dam, with color dependent on dam
height. Colors range from the shortest in blue, through yellow, to the tallest
in red.
4.
Dams by maximum storage:
a point for each dam, with size proportional to the maximum storage volume of
the dam in acre-feet.
5.
Dams by year completed:
a point for each dam, with color dependent on year. Colors range from deep blue
for the oldest dams (1850 being the oldest) through yellow to deep red for the
newest.
6.
Dams by height: a
point for each dam, with color dependent on dam height. Colors range from the
shortest in blue, through yellow, to the tallest in red.
7.
100 biggest dams by maximum storage: a point for each of the 100 biggest dams by
maximum storage volume in acre-feet, sized proportionally to maximum storage
volume.
8.
Number of dams per county:
Counties shaded according to the number of dams in the county (darker = greater
volume), overlaid with dams sized proportionally to maximum storage.
9.
Total dam volume per county: Counties shaded according to total dam volume, overlaid with dams
sized proportionally to maximum storage.
10.
Total dam drainage area as a percentage of total county
area: Counties shaded by percentage of total
dam drainage area over county area. Drainage area is defined as the total area
feeding into the dam. Percentages range from 0 to 1,436.
11.
A chart of the
top 15 dam owners by total volume of water dammed.
In acre-feet.
12.
A chart of the
number of dams built per decade from 1850 to
2000.
13.
A chart comparing the
number of dams built for each combination of purposes. A table defining the set of single-letter purpose codes follows
below.
14.
The PowerPoint slide
of dams completed by the
1880’s. Color-coded by decade.
15.
The PowerPoint slide
of dams completed by 2000. Color-coded
by decade.
The
creation of Arcview views was accomplished primarily using the NID data as well
as stock data of U.S. states and counties provided with Arcview. The NID
database is rich, with up to fifty-seven fields provided for each dam. These
fields include such things as geographic location, owner, purpose, construction
specifics, storage size, drainage areas, regulatory specifics, and emergency
action plan. Only a small (but interesting) subset of the data was included in
this study. In each case, the dam data was brought in to a view as an event
theme—a possibility enabled by the database’s geographic orientation, with its
inclusion of latitude and longitude. (See the results section for discussion of
invalid coordinates.) For some images, a Digital Elevation Model of California
was used as a base map, allowing the viewer to ascertain dam locations relative
to geographic features, such as the central valley, mountain ranges and lakes.
The resultant static views of dam data, which are listed above, can be viewed
by users of Arcview; eventually, these could be exported as web pages for
access to a broader audience.
Charts,
which were used to augment the GIS data, were produced primarily in Excel due
to the better chart production facility in that program compared with Arcview.
In several cases, data were selected and then exported from the tables in
Arcview, read into Excel, and then formatted into charts and tables. Charts are
useful as an auxiliary to GIS for data which is not immediately spatially
oriented. They are particularly useful for direct comparison of quantities such
as number of dams built per decade.
An
animation of dam construction rates and locations was produced by successively
layering themes containing dams from individual decades and then exporting the
resulting graphical representations from the Arcview view as JPEG files.
Initially, RealPlayer was used as a viewer for the images, by creation of a
“playlist” file which lists the source files and timing, but there was a
significant problem with this method: RealPlayer unfortunately displayed a
black screen between each image, disrupting the animation. The problem was
solved by importing each image into PowerPoint and then setting up an automated
“transition” of two seconds between slides. By ensuring that the images were
placed in identical locations on each page, a reasonable animation of the dam
completion over the years was created. Animating the process of the addition of
dams to the landscape can give an audience a feeling for the history of dam
building, in as much as the change in rate of dam construction over time is
readily perceptible. As an extension of this project, the views for the
animation could be changed to incorporate dam volumes, and the animation could
perhaps be accomplished directly in Arcview using Avenue, with resolution at
the year, rather than decade, level.
The following table specifies
the dam purposes encoded in the National Inventory of Dams:
Dam Purpose Code |
Purpose |
I |
Irrigation |
H |
Hydroelectric |
C |
Flood
Control And Storm Water Management |
N |
Navigation |
S |
Water
Supply |
R |
Recreation |
P |
Fire
Protection, Stock, Or Small Farm Pond |
F |
Fish
and Wildlife Pond |
D |
Debris
Control |
T |
Tailings |
O |
Other |
These values and combinations of them are used in the chart which
analyzes the major combination of purposes of dams.
The
results are basically as intended, though there were several problems in the
production of the various types of views, most of which could be circumvented.
The worst problem occurred when the animation attempt using RealPlayer was
thwarted by that program’s display of a black screen between images. The
problem was solved by using PowerPoint to display the animation. The
JPEG-formatted images were each read into an individual slide, with title, and
then an automated “slide transition” with a period of two seconds was set up.
When the slide show is started, the slides automatically proceed. Using a common alignment of the JPEG image among
all of the slides ensured a realistic animation.
Another
problem with the animation is the quality of the converted JPEG photos. Due to
image compression, a blurring of the dam points occurred. Since the maps
include a very large number of points, this makes the view less clear. In
future developments, this problem could perhaps be circumvented by the use of
an alternative image format, which may be facilitated by the use of alternative
viewer software, as well.
Some
problems were encountered with the use of the lat-long data from the NID dams
database. Several dams in California were found outside of the land area of
California, including a few which were found in the Pacific Ocean. There were
two classes of data error that contributed to this result. One is that there
were significant rounding errors in the latitude and longitude values. The
other is that the latitude and longitude data in some cases were found to be
far off from expected values, possibly due to data corruption or capture
problems. With over 1300 entries in the database, it is perhaps not surprising
that several location fields were invalid, given the wide range of source types
for the data. A related problem was the mismatch of projection types between
the dam data and the DEM base map; this was solved by converting the projection of the DEM base map to
geographic, with matching scale measures.
Another
minor problem was the lack of accessibility in the legend editor to joined
fields in an Arcview table. This was circumvented by exporting the table, quitting
Arcview, copying over the original table with the exported table and restarting
Arcview. The fields could then be used for legend construction. It is possible
that further learning of Arcview techniques would present other possibilities,
as well. Also, it was difficult to construct a legend for the DEM theme of
California which clearly portrayed the contours of the geography. This is
perhaps due to the very large scale of pixel size used in the only DEM that I
was able to find; it would perhaps require extensive work to aggregate a more
precise DEM of the entire state, though perhaps this has been done and could be
found with more extensive searching. In addition, Arcview occasionally crashed.
These
setbacks were minor compared to the amount of success in producing views with
these methods. The tools used—Arcview, Excel and PowerPoint—are powerful in
spite of the occasional difficulties in using them. They enabled the
construction of several methods of graphically viewing what is otherwise merely
alphanumeric data in a large database.
The
intent in producing these graphical views of the dam data is to provoke
thought—questions and observations—about the general topic of dams in
California. Through this process, it is hoped that new knowledge pertaining to
policy, ethics and environmental history can come to light. The following are
some examples of questions and ideas catalyzed by viewing this data.
For
instance, viewing the map of all California dams by size, one notices that not
only are the largest dams predominantly in the mountainous regions, as one
would expect, but they are also primarily in Northern California. This perhaps
has implications in the old controversy about the movement of water from the
north to the south. Viewing the map of the 100 biggest dams by volume with DEM
base map shows a distinct concentration of the largest dams at the periphery of
the central valley; this perhaps highlights the ultimate purpose of many of
those dams—the collection of water for use in agriculture in the central
valley. The map of all dams color-coded by year similarly shows a large
concentration of recent dams in the mountains and hills just East of the middle
of the central valley.
The
map of dam completion years, with its predominance of red, demonstrates also
the accelerated dam building in the last fifty years. The dam heights view
demonstrates clearly that virtually all very large and very small dams tend
toward the northern half of the state, with predominantly only mid-sized dams
in the southern half of the state; this is perhaps related to topology or maybe
to geography of water use. The top 100 big dams view clearly shows them mainly
in the mountainous regions, confirming that big dams require valleys.
Presenting only the top 100 dams shows how a simplified view can lend
additional clarity to the presentation.
The
number of dams per county and the dam volume per county maps show a clear
preponderance of the latter, but not the former, in the northern half of the
state; while there are many dams in Los Angeles county (with one of the highest
dam densities), one can see that its total storage of dam water is relatively
low, further exemplifying the dependence of the south on the north. Also of
note is the great number of counties, particularly in the north, whose dam
drainage areas greatly outstrip the amount of land in the county; i.e.,
counties are collecting water from much greater areas than they have in their
jurisdiction. Although this is surely partly due to the iterative damming of
waterways, it may lead one to inquire into the politics of collecting water
which ultimately came from distant areas. In addition, a database query to
count the total dam drainage area for all dams in California, 211005 square
miles, is significantly greater than the total land area of California, 155,973
square miles. These observations signify one of the salient characteristic of
the collection and use of water, which will no doubt be central to the water
wars of the future: it is an inherently mobile resource; projects such as the
Colorado River system aside, California certainly collects water that never
precipitated in the state.
Dam
ownership is interestingly portrayed by the Excel chart. The top owner, DOI BR,
the U.S. Bureau of Reclamation, owns more stored water in California than the
next fourteen top owners combined. This perhaps demonstrates vividly the
immensity of the California Water Project, built mainly to irrigate the state’s
farms. Another interesting observation from this chart is that one of the top
fifteen owners appears to be a private individual: Who is “C. Bruce Orvis” and
why does he own about a million acre-feet of water in the state? This
observation could lead to further inquiry on large-scale private water
ownership.
The
Excel chart of dam building supports the Arcview view of dams by decade as well
as the animation. Large peaks of dam completion are evident in the 1910’s,
1920’s, 1950’s and 1960’s, with a valley in the 1930’s and 1940’s. These peaks
perhaps follow economic cycles to some extent, with the valley coinciding with
the great depression, and the 50’s and 60’s peaks coinciding with the post-war
economic expansion. The chart of dam purposes is complicated by the large
number of purpose combinations and would need further refinement and
simplification to be of much use; however, it does show the vague qualitative
aspect of the top seven or so combinations (of several dozen) taking up the
majority of dams. Producing this chart and looking at the database did bring up
the interesting fact that the dam in the Hetch Hetchy valley, constructed by
San Francisco in 1923, does not have listed as one of its purposes fire
control; this is curious, since in the immense political struggle over its
construction—exemplified by the war between John Muir and Gifford Pinchot—fire
protection was cited as one of the primary motivations for its construction.
Indeed, the issue of the damming of Hetch Hetchy valley only arose after the
huge earthquake-induced fires which devoured San Francisco in 1905.
The
previously mentioned dam building (by decade) animation allows the user a
time-oriented intuitive feel for the progress of dam construction, as also
reflected in the dams built per decade chart. Such a visualization tool helps
to broaden viewers’ conceptualization of the evolution of dam construction
through history. Upon viewing, the user may perhaps be startled by the large
number of dams appearing in certain decades.
This
project demonstrates the utility of GIS in public awareness, and hence policy
and education, about natural resource use. The case examined, California dams,
has been a rich subject matter because of the availability of extensive data
about the dams in the National Inventory of Dams. The view examples produced
for the project illustrate only part of the range of possibilities available
for the presentation of historical and other complex data about large
resource-oriented public projects. These views enable the user to develop an
initial, intuitive understanding of the scope, scale and extent of dams in the
state as well as some of the politics involved in the dams and water system.
This information could be crucial as water continues to become a growing
concern for California residents. Similar projects could be undertaken to
demonstrate emergent aspects of other resource-oriented public works.
The California
Department of Water Resources dam database:
http://elib.cs.berkeley.edu/kopec/b17/html/home.html.
The National Inventory
of Dams:
http://crunch.tec.army.mil/nid/webpages/nid.cfm.
Schema for the National
Inventory of Dams:
http://www.nws.noaa.gov/oh/hod_whfs/documentation/damcat_dict.html.
Marc Reisner, Cadillac Desert.