Ex 4. Effects of clear-cutting and plant plantation on evapotranspiration rate.
Sandy Skolochenko
Task 1:
Q1:
Q(WS17) = a * Q(WS18) + b
a = 0.8476
b = -1.6015
Q(WS17)=0.8476 * Q(WS18) – 1.6015
Correct.Correct.
The linear regression fits the monthly stream flow data very well.
Overall, the linear regression line fits the observed data well, but the coefficients of the regression line are determined dominantly by high flow data. As a result, this graph shows a systematic bias for low flow less than 50 mm/month. The bias can introduce serious errors in calculating monthly stream discharge of undisturbed WS17 during dry season.
2.5/3
Correct.
Q2:I think this assumption is acceptable to a certain extent. Since the undisturbed WS17 data is based on a linear regression of WS18, the entire collection period is linear period is linear? In addition, you got the linear line in the 2nd graph because you re-use the linear regression line derived from the 2st graph. This is obviously not entirely practical, because as the first figure shows, there is still variation from the regression line because of natural imbalances what do you mean by imbalances?. Overall, the assumption can be useful for an approximation of the trends of WS17, were it not disturbed. A better estimate would probably be derived if there were more than just two years of data for the undisturbed WS17. Although the watersheds are right next to each other, they could experience different precipitation valuesminor influence on the following analysis or have different soil properties soils change in the geological time scale from one another that would affect the stream discharge. There is a chance that in the two years before the clear-cutting, there could have been some uncommon climate or precipitation patternsgood. which would alter the data, and therefore this entire model for undisturbed WS17 would be obsolete. The range of monthly stream flow for the undisturbed period does not cover the range of the entire data collection period. Therefore, there is a possibility that the regression relationship derived from the 3 year data does not represent well the relationship of the two watersheds, particularly when they experience extreme climate conditions, such as hurricanes or severe droughts.
If natural disturbance such as hurricanes or insect infection influences the hydrologic response of one of the two watersheds after the clear-cutting, the derived regression relationship is no longer valid. In fact, the hemlock trees of WS18 were seriously infected by the introduction of hemlock woolly adelgid (Adelges tsugae), an exotic invasive insect, in 2002 (
1.5/2
Task 2
Q3:
Put a horizontal line at Y=0 to clearly show the signs of difference. And, lines between samples to show the temporal trend of the data. 3/3
Q4: After the clear cutting of 1940, we see a dramatic drop in the graph. The y axis is undisturbed WS17-disturbed WS17 meaning that a negative value indicates the actual stream discharge of disturbed WS17is much higher than the predicted discharge if WS 17 were not disturbed. By clear cutting, runoff increases a great deal, leading about a 400 mm increase it is just a short-term response driven by unknown disturbance. Fluctuates around 250 mm in stream discharge the year after.
After the pine plantation in 1955, it took nearly twenty years for the water yields to stablilze. Directly after the clear-cutting in 1940, the discharge decreased a bit, varying from 100 to 300 mm more than undisturbed data. This is probably due to small plants developing, but maybe being washed away in a major precipitation event, leading to a fairly unstable period. Good observation. It takes time for pine trees to establish their roots. In addition, the initial increase of leaf area after plantation is small. After the pine plantation in 1955, there is a steady increase in the graph, corresponding to a steady decrease in stream discharge as the pines grow. As the trees grow, they expand their root systems, taking in more and more water Not main factor and probably also improving soil condition Again, not main factor. , allowing it to retain more water and further decreasing runoff. Also, we can account for interception as LAIs expand and catch more precipitation before it even reaches the soil This is major.
.
3/4
Q5:
Yes, the evaporation rate of a mature pine forest is higher than that of a mature deciduous oak forest. We can see this because from 1970 on, we see data points on the graph greater than zero. This means that the stream discharge from the undisturbed WS17 (deciduous oak forest) is higher than the pine forest, meaning more water is evaporated or transpired in the pine forest.
Because:
More effective leaf structure of coniferous forest for interception than deciduous tree.
Deciduous tree has dormant season.
Broader leaf area of coniferous, even during summer.
1.5/3
Q6:
Correct.
From the plot, we see that as a trend from 1980-2001, the stream discharge difference is highest in the winter in spring months, and lowest in the late summer and fall. There is also the least variation in the late summer and fall months, while the winter and spring have a much wider distribution. From the article by Swank and Douglass, they explain what characteristics cause these seasonal variations. In the winter and early spring months, the interception is much greater for the white pine forest because they retain their needles all year, while the oaks shed their leaves, allowing almost all precipitation to reach the surface. Also, in the spring season, the deciduous oaks are allocating most of their energy to producing new leaves. During this period, the pines are stable and are respiring. At this time, stronger solar radiation increases ET, which makes the difference larger. They are still contributing to interception and transpiration and have higher evapotranspiration rates.
3.5/4
Total:
15/19= / 7.9Reference:
Swank, and Douglas. “Streamflow Greatly Reduced by Converting Deciduous Hardwood Stands to Pine.”Science. 185.4154. 6 Sept 1974. 857-859/