Briana Seapy
Assignment #5
UEP 232
4/1/2011
Project & Location:
Dam Analysis; Gunnison River Basin, CO
Goal of analysis:
The goal of this report is to explore the potential of ARC GIS in performing basic environmental queries
as they relate to existing dams within the Gunnison River Basin, CO. Dams, though necessary to our
modern existence, pose significant threats to the environment and to human safety. It is in our best
interest to firmly understand our anthropogenic influence on the hydrological processes of rivers via
environmental monitoring, as well as to grasp both the potential risks and benefits that dams offer us.
This project is an introductory query practice that explores via ARC GIS three areas of environmental,
dam-related interest: water quality and quantity monitoring, flood vulnerability & dam reliability, and
human usage & dam benefits. These queries barely scratch the surface of potential GIS dam analysis.
Query Process:
1) Monitoring Water Quality & Quantity near Gunnison Dams
To begin, I ‘selected by location’ all dams (orange dots) and USGS stream gages within the Gunnison
River Basin using the ‘are within’ command and the ‘CO Watershed Divisions--Gunnison Watershed’ as
my encompassing layer. I anticipated using these selections frequently, so I chose to create two new
layers, ‘Dams Gunnison’ and ‘USGS Streamflow gages Gunnison,’ in an effort to simplify future queries.
I then used a similar ‘select by location’ process to identify USGS stream gages (green triangles) within 3
miles of dams in the Gunnison River Basin; the identification of these specific, dam-proximate gages is
important to accessing preexisting USGS data on upstream and downstream water quality and quantity
measurements. This historical data can then be compared to data from comparable stream gages to
determine the possible effects of anthropogenic influence (such as dams and reservoirs) on rivers.
2) Flooding Vulnerability as determined by Dam Reliability
To determine areas of high flood-damage vulnerability (as determined by dam-reliability), I first
selected all Gunnison Dams that had a ’High Hazard’ Rating,* because these are the dams that
would cause the greatest harm if flooded. Next, from that selection, I selected all the dams that
met either of the following two conditions: dams built prior to 1960 (approximately 50 years
ago), or dams with a surplus-storage: drainage-area ratio < 100 ft. The less reliable dams as
decided by these three conditions (high hazard, and old, or low surplus-drainage ratio) are
indicated by the turquoise circles on the map below.
I digress. To find each reservoir’s surplus-storage, I created a new field and used the field
calculator to subtract normal storage from maximum storage which gave me what I call surplus
storage. I then calculated the surplus-storage to drainage-area ratio by creating the new field,
‘surp_drain,’ and dividing the surplus values by the drainage area to fill the field. This process
left me with a ratio of reservoir storage that exceeds the normal required storage ( acre-feet) to
the area of land that drains into the dammed reservoir (acres). Thus, the lower the surplusstorage to drainage-area ratio, the more susceptible the reservoir is to flooding.
Even though I did not research specific flood plain areas around the dams, this query serves to
narrow down potentially vulnerable regions by identifying specific low-reliability dams as flood
risk factors. The land near these dams, especially land at slightly lower elevations, could be
considered especially vulnerable to flooding damage due to the high Hazard ratings and low
dam reliability (please see ‘Caveats’ for logical fallacies).
* High Hazard: Dam failure may cause loss of life, serious damage to homes, industrial or commercial
buildings, important public utilities, main highways or railroads. Dams constructed in existing or
proposed residential, commercial or industrial areas will be classified as high hazard dams, unless the
applicant presents clear and convincing evidence to the contrary (Definition Source: Mississippi
Department of Environmental Security).
3) Human Usage & Dam Benefits
To determine primary dam usage and human-benefits I used the ‘summarize by attribute field’ tool.
I summed, separately, the surface areas (acres) and normal storage (acre-feet) for reservoirs
aggregated by purpose.* The field labeled ‘Count_PURPOSES’ tallies the number of dams by their
purpose or set of purposes, and the fields ‘Sum_SURF_AREA’ and ‘Sum-NORMAL_ST’ shows the sum
of the surface area and normal storage for all dams with the same purpose or set of purposes.
The summary tables below show, in descending order, the sums for both surface are and normal
storage for each dam-purpose category. The tables give us an idea of dam-building priority for the
Gunnison River Basin. Eleven out of the sixteen unique purpose values include the letter ‘I’ for
irrigation and the purpose category ‘I’ has the highest dam tally of 14, exceeding the next highest
tally by 11 dams. These numbers suggest that dams are crucial to basin agriculture and thus
important to human and/or animal food-supply. Furthermore, the majority of dams serve more than
one purpose; in fact, the highest storage capacity by ‘purpose’ is for the HIRC category which
encompass dams that not only irrigate, produce hydroelectric power, and manage
floods/stormwater, they also offer human recreation space.
(As a side note, where there are discrepancies in order between the two tables, there are
discrepancies in potential evaporation. Overall, the two charts tend to follow the same pattern (i.e.
the same storage: surface-area ratio), but the few ranking differences indicate that some (purposeaggregated) reservoirs have smaller storage: surface-area ratios suggesting higher rates of
evaporation.)
*Purpose Key (U.S. Army Corps of Engineers):
C: Flood control and storm water management
F: Fish and wildlife pond
H: Hydroelectric
I: Irrigation
P: Fire protection, stock, or small farm pond
R: Recreation
S: Water supply
Caveats:
As previously mentioned, this dam analysis is meant to represent a basic and experimental investigation
of GIS potential. Therefore, many of the preliminary results found in this report are flawed for many
reasons, and the depth and breadth presented by this report is greatly limited in terms of what analysis
is possible with the same data and software. I will now discuss a number of caveats to this brief report,
but I am positive there exist even more that I have overlooked.
To begin, the measures of dam reliability (age and storage-surplus to drainage ratio) were arbitrarily
given cutoffs to determine whether or not a dam should be considered reliable or a flood risk-factor.
Some logical thought processes were put into determining the cutoffs, but in reality, neither the 50 year
condition nor the 100ft ratio value are grounded in any hard scientific data or pre-existing standards. I
believe that the risk-factor concepts, on a basic level, retain their analytical value. However, there are
many more factors to consider; a dam, for example, may be old, but may receive a check up every 5
years. A younger dam, however, may go decades without a checkup, thus posing as a greater flood risk
factor. The frequency of dam check-ups would serve as a better measure of dam reliability than the
dam’s age. There are also, obviously, many other factors that influence dam reliability (surrounding
soils/environment, annual hydrological stresses, engineering structure, etc).
The 3-mile radius cutoff for monitoring-stations, those that could serve as evaluators of anthropogenic
influence, was also a quasi-arbitrary value. However, visually, when compared with the other USGS
streamflow gages, the 3-mile division seemed an appropriate measure against which to bisect the set of
gages.
Lastly, there were at least two major data issues involving attribute table records: streamflow and
surplus-storage. The first I chose not to integrate in the analysis because records were so poor; many of
the USGS streamflow gages had a value of ‘zero’ under flow. This zero value could mean many things (I
was struggling to access the metadata that should detail what the ‘flow’ value in the attribute table
summarizes and what a value of zero really means). It could mean that no streamflow records were
attained, the river has dried up, there is negligible flow due to low water, the gage is on an intermittent
stream, etc. The frequent value of zero found in the ‘flow’ column discouraged me from trying to work
too heavily with the USGS streamflow gage layer.
The second major issue came about when I calculated the difference between maximum storage and
normal storage in an effort to derive surplus storage. For several of the dams, the resulting value turned
out to be ‘0’ because the normal and maximum storages were equal. In theory, this value of zero could
be acceptable if it represents reality, but I have my doubts. More importantly, I also found one negative
value for surplus storage (see ‘surp_storag’ below). This is an impossible value that skews statistics by
offering up a false minimum and range for surplus storage, and an improbable mean. Although the zeros
also skew the mean, they may reflect reality, whereas the negative surplus value certainly does not
reflect reality.