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Effects of vegetation cover on baseflow in the Mica Creek Experimental Watershed
By: Wesley Green, William Drier
Advisors: Tim Link, John Gravelle
ENVS 497: Senior Thesis
March 24, 2013
Abstract
The Mica Creek Experimental Watershed (MCEW), located in northern Idaho, is a cooperative
experiment with the University of Idaho and Potlatch Corporation. One goal of MCEW is to
monitor the effects of vegetation cover and annual water yield. The focus of our research is to
examine the effects of vegetation cover on baseflow. More specifically, the effects of
vegetation cover on baseflow yield in sub-basins of within the main watershed. Data were

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collected on discharge measurements from eighteen tributaries in the watershed using a direct
catch method with tubs and a stopwatch to minimize error. Analyzing the data required using
geographic information systems (GIS) to determine the area of the sub-basins, in addition to
land coverage, slope, elevation, and aspect in the sub-basins examined. Gaining information on
the effects of vegetation cover on baseflow will help us understand an important, complex, and
newly researched subject.
Introduction
The Mica Creek Experimental Watershed (MCEW) is a cooperative effort with the
University of Idaho and Potlatch Corporation to study the effects of different timber harvesting
practices on watersheds. MCEW is an excellent location for gathering information on watershed
changes due to harvest. It started in 1990 and has been set up as paired catchments with three
treatment types: clear cut, partial cut, and no harvest. A paired catchment study is an
experiment that, “…involve[s] the use of two catchments with similar characteristics in terms of
slope, aspect, soils, area, precipitation and vegetation located adjacent to each other. Following
a calibration period, where both catchments are monitored, one of the catchments is subjected
to treatment and the other remains as a control.” (Best et al., 2003) Calibration data at MCEW
were collected six years prior to road treatment, and four more years of preharvest (Hubbart et
al., 2007). Data are still being collected and analyzed. There are seven flume stations, which are
used to find evidence of correlation between timber harvest and water yield.
Research on the effects of timber harvests on water yield is not new to the scientific
community. Hibbert (1967) established in Wagon Wheel Gap, CO that a reduction of forest

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cover increases yield. When the forest cover begins to return, water yield begins to decrease.
Stednick (1996) studied the effects of timber harvests on annual water yield in the Pacific
Northwest, in which he documented that a decrease to no increase were seen after logging
occurred. Hicks et al. (1993) who also studied harvest effects in the Pacific Northwest, in which
summer flow(which is similar to baseflow) was measured, noted that increases in summer flow
were “relatively short lived (8-16 years).” MCEW has data for water yield from previous years.
Hubbart et al. (2007) studied the effects of logging at MCEW and noted that the harvest of
timber will increase water yield in the short term prior to regrowth of vegetation. However,
Hubbart did not discuss the continued water yield decrease below pre-harvest levels that are
expected. Hubbart also notes that, while there has been research on logging effects, very little
of it has been in Inland Rocky Mountain areas such as MCEW. Unlike the Pacific Northwest,
where a lot of data has been collected. Du (2010) researched timber removal and water yield at
MCEW and noted, “The amount of flow alteration due to harvesting 50% of the watershed area
in relatively extreme patterns defined by aspect, elevation and distance to the stream network
produced similar effects, suggesting that flow regime is more sensitive to the amount of
alteration rather than the location.”
Baseflow is the groundwater contribution to a stream and is always present, unlike
snowmelt, precipitation, and other seasonal discharge. Understanding baseflow is important
for water resource management as well as environmental protection, and should be considered
when making decisions that affect the environment. It has been shown water yield is affected
by timber harvest.

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Within MCEW, the research focused on the tributaries upstream from the flume
stations. At a smaller scale of discharge and by breaking the large basins into smaller, tributary
specific discharge basins, potential correlations between timber harvest and baseflow yield
were examined.
The research examines a specific aspect of the question at large; is there a general
correlation between water yield and timber harvest? The reason for this work was to determine
what, if any changes in baseflow occur from timber harvest, and if the Best Management
Practices that are implemented are sufficient for the Rocky Mountain climate that has warm
summers and cold winters with heavy precipitation.
The objective of this research is to find out what, if any, affect on yield is on baseflow
discharge, as none of the previous research has examined specifically the effects of harvest on
baseflow. Since previous research of water yield exist, this research analyzes the data to
examine if there is a correlation between baseflow yield and timber practices: which are partial
cut, clear cut, and unharvested. The general hypothesis is the harvest of timber will increase
baseflow yield in the short term, prior to the regrowth of vegetation. This is both analogous
with studies of other geographic areas as well as with Hubbart and Du’s research on MCEW.
Methods
Our data were collected in August and at the end of September. The discharge from
eighteen class II (non-fish bearing) tributaries were measured. Data from a study performed by
Madeline Olszewski measured the diel fluctuations on baseflow in July 2010 were also
incorporated in our analysis on the same tributaries sampled in August 2012 by Wes Green and
John Gravelle, and in September 2012 by Wes Green and Will Drier. A direct catch method was

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used for collecting data, consisting of two tubs (a 27-liter and 32-liter) and a stopwatch. Which
tub was used was determined by the space underneath the each culvert. The 27-liter tub was
not as tall as the 32-liter tub, which allowed for smaller areas to perform the catchment.
Currently, discharge is measured at flume stations installed at MCEW, and data from
these flumes will assist in future analysis (Hubbart, et al., 2007). Our data will suffice for our
analysis because we have calibrated stream data for the tributaries pre-harvest, which is
important to know the increase or decrease of baseflow yield relative to the pre-harvest
benchmark. Other experiments, such as Wagon Wheel Gap, CO have used pre-harvest data to
calibrate to new, post-harvest flows (Bates & Henry, 1928).
Our research is complementary to the effective paired catchment method that MCEW
possesses. Following a calibration period, where both catchments are monitored, one of the
catchments is subjected to treatment and the other remains as a control (Best, et al., 2003).
The steps taken in this analysis compared past years of data at the tributaries, which have been
taken from the location and specific downstream end of the culvert, to provide control
measurements from previous years.
Data were gathered by taking up to ten replicate flow measurements, depending on the
relative closeness with each other, and took the highest and lowest values out until we had a
final number of five or seven measurements for each tributary. All data were converted to liters
per second (L/s) and analyzed to determine the confidence interval at 95 percent and the
statistical significance. Analyzing the statistical significance of data helped provide reassurance
that the data were taken with accuracy and error was minimal to a point, or it could not be
accepted. Further analyzing included finding the square area (Ha2) of each relative discharge

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basin for the tributary measured. In addition, the flow by basin area (L/s/Ha2) was normalized
to control for flow differences that resulted from catchment size.
Gaining a visual understanding of differences in baseflow temporally in the MCEW was
the first step before analyzing the other variables. This presented us with information on which
sub-basins have experienced treatment as well as drastic or minimal differences in baseflow.
This was accomplished by bringing the data into excel and using XY scatter plots to graph
individual basins and their respective temporal data points. In addition, a graph with the X-axis
as time and the Y-axis with all data points was constructed to bring all basins into the same
graph, and compare differences with data both spatially and temporally.
ArcGIS was used to delineate and characterize the basins within the MCEW, normalize
our sub-basin data, and visualize changes in baseflow and vegetation coverage. Other variables
examined with ArcGIS that were important to analyze for conducting exploratory data analysis
were aspect, slope, elevation and geology, as some studies (Du 2010) suggest that these
variables may play a more significant role in understanding water yield and baseflow than
previously assumed.
Our first step in analyzing data of these variables was done by using ArcGIS to create
individual basins for each flow point that was sampled out in the field. This was done by using
ArcGIS to calculate the basin areas based on the flow points and a digital elevation model(DEM)
of MCEW. After these polygons were created, they were adjusted to match up with USGS
topographic maps. The areas were then calculated through ArcGIS in hectares. Discharge was
normalized for the basins so they correlated with each other, thus having normalized and
comparable data.

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We began our exploratory data analysis through ArcGIS using the basins created and
overlaying them on aerial images of MCEW. We then cut out polygons within the basins where
trees had been removed, and used the sizes of these new polygons to calculate a rough
percentage of coverage. This was done on every year aerial imaging data was available, as to
show the estimated changes in coverage over time. This was the most accurate method
available for estimating coverage, as LiDAR data was only collected during one year, and can be
problematic and expensive. Using aerial coverage allowed the use of what was readily and
historically available to compare the harvests between 1998 and 2012.
Slope and aspect values were calculated using the DEM of the MCEW to create slope
and aspect profiles. After this was achieved, all of the values within each basin were averaged,
which would then be compared to other variables to see which would influence changes in
baseflow more. The aspect profile was also used to calculate what percentage of each basin
was south and west facing (%S+W facing).
All basins and vegetation cover polygons were also converted to square kilometers to
assist with comparing data across studies. Exploratory data analysis was conducted on these
variables that were derived from GIS to the normalized flow data that was calculated from our
field measurements. Aspect, slope, and percent vegetation cover were plotted against
normalized flow from all basins to look for correlations or differences between basins. In
addition, Q/A/unharvested average was plotted against time to examine temporal changes in
baseflow yield. Dividing the normalized flow by the average undisturbed (100% cover) yield of
basin T2 Trestle and H14T allowed us to visualize differences in flow temporally in relation to a
common yield. According to Bond, “Plotting normalized flow against time gave us further

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insight into where baseflow differences are occurring, thus providing us basins to investigate
further and try establishing a correlation with one of the many variables being analyzed.
The sub-catchments were categorized by the percent vegetation cover at the site and
the year from last disturbance. The sub-basins were first split into two categories; disturbed
and undisturbed. Undisturbed sub-catchments had at least 60% vegetation cover, while
disturbed sub-catchments had less than 60% vegetation cover. The reason for the division is
most basins included in the study are near both sides of the spectrum. The percent vegetation
cover of the sub-catchments were near somewhat skewed, with few in the middle range. This
provided a good break to split the two categories up, because few are around the 60% range.
The disturbed category has data points that range from 2-58% vegetation cover, while the
undisturbed category has vegetation cover values ranging from 87-100%. The undisturbed
category consists of the following data points; MicaCCNew, 08R, T7, H14C, T2 Trestle, Upper
504 Control, H14T, and CC1 Control. In addition, the disturbed sites were further split into two
sub-categories; < 5 and > 5 years from disturbance. This helped distinguish with the disturbed
areas that may already be experiencing recover from within the system. The > 5 year disturbed
category may be experiencing recover in the basin, while the < 5 year disturbances would be
experiencing the effects of vegetation loss. The > 5 year old category includes H11 and H11T,
which were disturbed 11 years ago. The five other data points in the disturbed category are
under 5 years from disturbance. These include 5T control, 6T Xing, T7E, and CC1T.
Though there is a large quantity of data that was used in the exploratory data analysis,
but the September 2012 data was used for the remainder of the research. According to Bond,
the discharge rate throughout the month of August is consistent. The flow during September 9,

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when sampled, is the closest measurement to baseflow available. Focusing the research on
September data enabled us to have a better understanding of what factors are controlling
baseflow yield, because flow is lower at this time than in August, signifying more of the
baseflow condition were hoped for. The exploratory data analysis involved looking at data from
primarily July 2012 and August, September 2012. This analysis helped indicate what variables
were showing a relationship with area normalized flow. By narrowing down on one dataset for
the rest of the analysis, it allowed the research to fit trendlines between variables and focus on
differences between the categories at one date in time.
Results
The average area normalized value for the disturbed sub-catchment in figure 1.1 is 8.23
L/s/Km^2, while the undisturbed
average flow is 5.51 L/s/Km^2. The
sample size of the undisturbed data is
n=5, and the disturbed data is n=8.
The p value is marginally statistically
significant at .056837. The error bars
are placed at a 95% confidence
interval, and do overlap between the
two categories. There is weak evidence that the average area normalized flow at < 5 year
disturbances is greater than the area normalized flow at undisturbed sub-catchments. When
comparing the average area normalized yield of the disturbed and <5 year disturbances in
relation to each other, the results are what were expected.

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The area normalized flow (L/s/Km^2) vs percent cover of all three categories;
undisturbed, <5 Year and > 5 year old disturbances were plotted against each other in figure
1.2. The values of the undisturbed category
are clumped in the lower right corner of the
figure, while the <5, >5 year old disturbances
are located to the left -hand portion of the
graph. A trendline was added to this figure,
along with the R^2 value. The trendline
includes all data points besides the two that are in the >5 year old disturbance category.
Including them would skew the graph downward since they may be in the recovery stage. The
R^2 value of .1626 indicates that percent coverage explains ~16% of the difference between
baseflow sub-catchments. The undisturbed category included 8 samples ranging from 87-100%
vegetation cover and area normalized flows ranging from 2.5-8.6L/s/Km^2. The 5 samples in
the <5 year disturbances category had a range of 3-58% vegetation cover. The area normalized
flow ranged from 5.2-11.8 L/s/Km^2. The other category, >5 year disturbances, contains two
samples. These two samples have vegetation coverage percentages of 2 and 25%, respectively.
In addition, the normalized flow of these two samples are 2.49 and 6.13 L/s/Km^2, respectively.
The mean elevation of each sub-catchment was plotted against the area normalized
flow of each corresponding sub-catchment in figure 1.5. The data was further evaluated to look
for a correlation with the two variables. The elevational range of all data points in the figure is
1238-1519 meters. Data points of all categories were fit with a trendline that has a R^2 value of
.00004 which indicates the heterogeneous pattern in the figure 1.4. Percent S+W aspect was

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also examined versus normalized flow of the data collected in September 2012. Trendlines
were added to visually represent differences in the groups of data, and R^2 values were
calculated to determine the responsibility of
aspect on the variability observed. The
highest flow observed in undisturbed basins
was 8.63 L/s/km^2, and the lowest was 2.52
L/s/km^2. In disturbed basins, the highest
was 11.83 L/s/km^2 and lowest was 5.29
L/s/km^2. The R^2 value for the undisturbed
basins with aspect was .2317, while the R^2 value for disturbed basins was .0159.
Normalized flow was analyzed against
years from last harvest. All of the disturbed basins
had between two and 11 years old. The added
trendline shows as age of disturbance increases,
normalized flow decreases. The R^2 value of this

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comparison was .3799 showing a strong correlation between the two variables.
Slope was analyzed against normalized flow. The most flat slope observed was 16.55
degrees, and the steepest was 21.07 degrees, making the spread less than 5 degrees.
Discussion
(Bar Graph) An average increase in baseflow discharge was observed between
the disturbed and undisturbed categories, though only weakly statistically significant (p=.057).
The overlapping confidence intervals at 95% between the two categories indicate the small
sample size with this research. Although this is the trend expected to see between disturbed
and undisturbed areas, it is only weakly statistically significant. This complies with the concept
that Hibbert (1967) set forth, “ a reduction in forest cover increases water yield.” In addition,
less flow with increased vegetation has been seen in the article by Bosch & Hewlett, 1982.
Establishing this relationship progressed further by analyzing the relationship with the percent
vegetation cover and normalized flow, with respect to the two categories and sub-categories. In
figure “BLAG BLAH” there is a weakly significant (p=0.1626) relationship between all data
points, excluding the > 5 year old disturbance. This category was excluded from the trendline
because they are both 11 years from disturbance; their low normalized flow values indicate

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they may be experiencing recover. This was based on work from Hicks et al., 1993, which noted
that “increases in summer flow were relatively short lived (8-16 years).” The data at this point is
not as relevant as initially predicted in the hypothesis. This variable, percent coverage, explains
~16% of the difference in the subcatchments area normalized flow. This prompted the research
to analyze other effects that different variables may have on baseflow discharge.
(discussion for age from disturbance) The data shown in figure 1.5 illustrates the trend
that with increasing age, baseflow discharge decreased, which was consistent with the
expectation of the impact vegetation regrowth would have on flow rates meaning years since
disturbance explains a large portion (~38%) of the variability within changes in normalized flow
of disturbed basins. This has relevance according to Hubbart et al, “Increases in annual water
yield were shown to be one of the immediate results of timber harvest.”analyzing the time
from disturbance variable with respect to disturbed categories gave us insight into the broader
picture of how baseflow functions, and what many others have concluded.
(discussion for SW%) It was expected that increasing S+W facing percent would show
lower normalized flow, as these slopes would be subject to more solar radiation and higher
evapotranspiration rates. When the data was analyzed, as shown in figure 1.4, the expected
trend held true for the undisturbed basins, whereas the disturbed basins actually saw a slight
increase in discharge with increasing S+W% aspect. In undisturbed basins, increasing S+W
facing percent was ~23.17% responsible for the variation with decreasing normalized flow.
However, such variability within the disturbed basins cannot be explained by S+W% facing
slope, as the extremely low R^2 value of .0159 shows. This shows what a critical role vegetation
plays in the water cycle and how visible of an impact evapotransporation has on a basin.

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Further research may include evaluating the disturbed land in each sub-catchment that has a
S+W aspect, thus having more precise measurements that the effect of aspect may have on the
whole basin, instead of the whole basins S+W Aspect.
(Slope discussion) Slope was an important variable that needed to be examined to see if
it impacted different basin flows, and was analyzed versus normalized flow. It is visually
apparent that there is very little, if any, relationship between slope and the amount of baseflow
discharge from a basin, and the R^2 value of .0035 shows this quantitatively. This is important
because it helps discount this variable as being influential in baseflow discharge. It is also
important to note that the range between sub-catchments is 5 degrees, and a larger spread
could show some pattern that is not visible in this case.
(Elevation discussion) This figure indicates that the elevation of these data points
analyzed doesn’t play a role in the area normalized flow between the categories. Observations
of this graph indicate that the points of all three categories are sporadic, and do not follow any
type of linear trend. The figure indicates there are other factors responsible for the variability
observed in this research.
Conclusion
There tends to be slight statistical significance with vegetation coverage (~16%) and
time from disturbance (~38%), respectively. When these were analyzed against area normalized
flow, there was some correlation. The %S+W aspect was an important factor (~23%) in
explaining variation in baseflow within undisturbed basins, however was not significant for
disturbed basins. Other variables, including mean elevation andslope, were highly variable and

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were not statistically significant. The discharge of baseflow in the sub-catchments of the MCEW
tend to be highly variable, and this variability could occur from subsurface physical
interactions.Fractures in the geology and/or different soil characteristics may be the cause of
differences in baseflow. “These processes may be quite variable for areas with different
physiographic characteristics...” (Uhlenbrook 2002 & Du 2010). This concept tends to be what
was established in the MCEW while trying to evaluate which variable has the biggest control
over baseflow discharge. Time from disturbance is statistically significant when plotted against
area normalized flow, and fits the concept set forth by Tallaksen 1994. The baseflow decreases
with increasing time from disturbance because of increasing vegetation and
evapotranspiration. While some variables are more influential than others, there is a vast
amount of variability present in these systems and between each of the variables and it’s
complexity cannot be explained solely by one variable.
Wes performed exploratory data analysis on the data delineated from the GIS and
discharge data. This required plotting variables (slope, aspect, elevation, % cover) against a
variety of discharge rates (L/s/Km^2, L/s, L/s/UH Average) to find, if any, a correlation with one
of the many variables were discussing, and if they had any relation with their normalized flow. I
worked with excel mainly throughout this research project, and collaborated with Will
consistently to keep ourselves on track and progressing.
Will Drier worked with ArcGIS to analyze and create GIS layers in order to derive data to
be used for exploratory data analysis. This included delineating watersheds and calculating
area, analyzing canopy cover using a LiDAR data layer, calculating coverage from aerial

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photography, creating slope and aspect layers derived from a DEM, examining the data of the
underlying geology of MCEW, and created visual aids and maps used in the paper and poster.
Wes and Will came together to collect samples at MCEW in September 2012. After
splitting work to gather more specific data, the two came together and combined and analyzed
to complete the exploratory data analysis portion.

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Works Cited
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