lab 7 snwo accumulation and abalation
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Feb 20, 2024
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Lab 7: Snow accumulation and ablation
Q.1 Table1.
Average values of the core depth compression ratio for each site and each snow survey
Average of Compression ratio
Site
Survey
Forest (HI-
GY)
Open (HI-
CC)
Grand Total
1
0.79
0.81
0.80
2
0.84
0.81
0.82
3
0.79
0.75
0.77
Grand Total
0.81
0.79
0.80
Q.2
Feb
Mar
May
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
Forest (HI-GY)
Open (HI-CC)
Month
Average of Real Depth (cm)
Figure 1: Graph showcasing the average of real depth for each site each site.
Q.3
2009-02-22
2009-03-29
2009-05-05
0.00
50.00
100.00
150.00
200.00
250.00
300.00
Forest (HI-GY)
Open (HI-CC)
Date
Average of Estimated SWE (mm)
Figure 2: Graph showcasing the average of the estimated SWE (mm) vs date for each site.
Q.4
Feb
Mar
May
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
Forest (HI-GY)
Open (HI-CC)
Month Average of snow density (kg/m3)
Figure 3: A graph representing the average of snow density (kg/m3) vs month for each site.
Q.5
Table 2.
Coefficient of variation percentages for depth, SWE, and density for SWE tube samples.
Site
Depth
SWE
Density
Coefficient of Variation (%)
Forest ( HI-GY)
22.06
22.32
15.47
Open (HI-CC)
30.25
32.45
20.87
Q.6 Table 3.
Ablation rates in (mm/day) for the open and forested site between late March and early May.
Parameters
Forest (HI-GY)
Open (HI-CC)
Peak SWE
193.33
245.92
Ablation rate(mm/day)
1.55
2.17
Discussion Questions
Q7. The manual snow surveys were obtained by SWE tubes that extract samples from the ground. Sources of error that could potentially rise are the compression of snow when taking the sample, systematic errors such as scale calibration, scale reading, retention of the snow in sampler and subjective measurement of soil depth and debris at the bottom of the pit all leading up to a bias of 12% (Varhola et al., 2010). Therefore, these biases may have compromised the quality of the snow surveys therefore I am not satisfied with the accuracy of our results.
Q8. The snow accumulation peak (Peak SWE) of the open site is 27.20% greater than the forested site. The snow accumulation is explained by the combined effect of the clearcut size and
wind. The greater amount of snow accumulation in the openings is due to the lack of canopy interception and the redistribution of the snow via wind from the forest towards the clearing. Incase of the forested site, the intercepted snow is lost via sublimation and is not accumulated on the ground
(Varhola et al., 2010).
Q.9
The snow ablation rates of the open is 40.39 % greater than the forested site. Forest canopy shades the snowpack by reducing the incoming solar radiation. In the clearcut, both incoming shortwave radiation and exposure to wind
can lead to the sublimation of the snowpack.
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Q10. Snow density can change due to diurnal temperature fluctuations and localized impacts of wind that contribute to snow metamorphism. For example, during the day relatively warm temperatures can cause the melting of the snow. However, if the temperatures drop down drastically it can cause the melted snow to freeze which can increase the density of the snowpack. Warmer temperatures facilitated by increased solar radiation generally contribute to increasing the density of snowpack because additional energy input causes surface metamorphism resulting in larger grains and decreased pore space. Generally, the open site is more prone to metamorphism which is changes in physical and chemical properties over time. During the day, the snow can be more exposed to the incoming solar radiation which can introduce more liquid water to the snowpack. However, refreezing at night can result in a compacted high-density snowpack. Open sites are more susceptible to wind which results in the redistribution of snow and compact the snow at its surface resulting in a higher density snowpack (wind-driven compaction).
Q11. The snow depth and SWE display a very high variability in the site which is justified by the high coefficient of variation. Snow depth is highly variable on the site as the depth can change due to temperature, compaction, sublimation processes. The estimated SWE was computed by the average density ratio of the site multiplied by the real depth. The real depth is the difference between raw depth and the plug which could result in errors leading to high variability. As per the results computed, the snow density is a relatively stable variable due to low coefficient of variation. A low coefficient of variability implies that the values in the dataset are more closely packed around the mean. Q.12.
Briefly discuss the limitations and sources of error of estimating peak SWE and ablation rates using manual snow surveys (hint: is peak SWE real peak SWE?).
The variable (Peak SWE) is the maximum SWE accumulated prior to the snow starts melting. The standardization of determing the Peak SWE on 1
st
of April is one of the limitations and potential sources of error. The snow accumulation varies from year to year and is highly variable within a site due to which peak SWE can occur at different times for different sites. Therefore, additional biases arise when considering the ablation period which is the peak SWE divided by the time of the snow disappearance.
Snow melting is also subjected to variability in a plot and does not occur simultaneously.
The estimation of when the ablation period can be limited by the number of site visits which could also be a potential source of error.
Q13. FOPR 388 questions
Mountainous watersheds have high variability in elevation and aspect distribution
which could result in the differentiated onset and rates of melting along the gradient. Therefore, there is a desynchronization of melt as when the temperature rises the lower elevations melt first and then the mid to high elevations. In terms of aspects, southern aspects and eastern aspects melt quickly (due to high solar radiation) whereas northern and western aspects accumulate more snow and melt slowly (due to reduced melting and reduced sublimation). Due to this desynchronization. Mountainous watersheds are less sensitive if
a significant proportion of forest cover is lost. Therefore, the rising limb of the hydrograph would be less steep. However, that depends upon where the forest was cut. If it was cut in a lower elevation of a south facing aspect it may amplify the rates of snow melt leading to increased runoff and steeper stope of the rising limb spring melt hydrograph.
Generally, flat watersheds are more sensitive to changes of forest loss. The loss of forests can reduce interception and expose the snowpack contributing to a synchronized melt as the topography is flat.
Q14.
When extrapolating plot-level results to a larger area the most important factor that should be considered is landscape heterogeneity. If the geographical scope of the study is larger, then the terrain variations should be proceeded with extreme caution. This is because the combination of factors such as elevation, aspect and slope, landscape interactions and can influence snowpack energy balance at any given point to understand the snow accumulation and snow ablation rates. A study conducted by Varhola et al. (2010) drawing upon the research of (Murray and Buttle, 2003) emphasized the need to consider the confounding effects of wind that can distribute snow accumulation. The study explained the differences in snow accumulation on a ridge crest and a south facing slope in a clearcut due to redistributing effects of northerly winter winds.
Sample calculations
1.
Coefficient of variation = (standard deviation/ mean) *100
Average of the real depth of a forest site
= 13.68\62.03= 22.06
2.
Ablation rate (mm/day)
Forest – 193.33-135.92/37= 1.55 mm/day (where 193.33 is the peak SWE and 37 number of days is the period between 29
th
March and 5 May.
Open= 245.92-165.31/37= 2.17nmm/day.
References
Varhola, A., Coops, N. C., Weiler, M., & Moore, R. D. (2010). Forest canopy effects on snow accumulation and ablation: An integrative review of empirical results. Journal of Hydrology
, 392
(3-4), 219–233. https://doi.org/10.1016/j.jhydrol.2010.08.009
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