Tree Canopy Cover

Results

1. Tree Canopy Cover

As it is stated that tree canopy cover is determined by canopy percentage and watershed area. Various tree canopy cover has been found in a graph, and the least was observed in 1939, and it as increased to 50.2 and remained same for 1951-1952. However, then it was reduced to 37.69 for 1963-1964, again it was enhanced to 51.56 in 1986 and 52.52 in 1997. So, the highest tree canopy cover 0f 61.22 mi2 was observed in 2005. Then it was huge reduction to 39.27mi2 I 2009. But in following year of 2011-2014, it dropped from 57.82 to 55.92.

Figure 8. Tree canopy cover in upper Gallinas watershed from 1939 to 2014.

 

 

In the graphs given below, Fig 9 represents the change in the percentage of tree canopy cover during the era of 1935-2005. It was observed that percentage of tree canopy cover increased steadily and it was maximum at 2005. While fig 10 shows that there was a little drop in tree canopy cover from 2005 to 2014.

 

        Figure 9. Tree canopy cover in upper Gallinas                              Figure 10. Tree canopy cover in upper Gallinas

watershed from 1939 to 2005.                                                       watershed from 2005 to 2014

 

 

 

 

1. Discharge & Weather Data

The discharge quantities of Gallinas rivers illustrate analogous patterns of long term erraticism. It is said that discharge should be understood with thoughtfulness because of water reminiscence for irrigation, nonetheless discharge was obviously higher during 1945. Remarkably, in the advanced portion of this century (1960 ahead) a feeble increasing trend is detected; which was about to decline in 2014 and a bit of that was raised in 2015. However, these increasing trends have very low statistical significance and are not conclusive.

Figure 11. Average annual discharge (cfs) of the Gallinas River from 1930 to 2015. The dashed line indicates the regression line which presents the best fitted line to the data.

 

The Fig indicates that temperature during the era of 1930-2014 was mildly increased from 1939 to 1955, and it was decreased later on. Again, it was increased to 51 in 1980; then temperature was constant between 1995 to 2005. Highest temperature with little variation from last one was observed at 2014 which was dropped in 2015. It is noticed that temperature was dropped due to higher discharge in 1940.

Figure 12. Average annual temperature (°F) in the upper Gallinas watershed from 1930 to 2015.

 

 

It is illustrated in Fig 13 that precipitation was highest in 1940, same was the case with discharge. So, it is noticed that discharge and yearly precipitation are notably correlated in the 2oth and 21^st century. However, almost same pattern of precipitation was observed in 1980.

 

Figure 13. Average annual precipitation (in) in the upper Gallinas watershed from 1930 to 2015.

 

 

 

1. Correlation between Tree Canopy Cover, Discharge & Weather data

It is represented that both the discharge water from Gallinas river and tree canopy cover in the upper Gallinas watershed are correlated to each other. Thus, it can be stated that 55.2% of Gallinas watershed was covered tree canopy and discharged water led to improve vegetation of the area.

Figure 14. Relation between the percentage of tree canopy cover in the upper Gallinas watershed and the mean annual discharge of the Gallinas River (cfs) from 1930 to 2015.

 

 

 

Fig 15 shows that discharge and temperature of the area are correlated to influence climate. Temperature variation help to modify plantation in those area.

 

 

 

Figure 15. Correlation between the temperature of the upper Gallinas watershed and the mean annual discharge of the Gallinas River from 1930 to 2015.

 

Precipitation and discharge are highly correlated as discharge  led to increase the precipitation rate; that positively affect climate.

 

 

 

Figure 16. Correlation between the average annual precipitation (in) of the upper Gallinas river watershed and the average annual Gallinas river discharge (cfs) from 1930 to 2015.

 

It is illustrated from Fig 17 that precipitation and tree canopy cover both are increased in 20^th century and dropped in 21^st century.

Figure 17. Tree canopy cover percentage and precipitation of the upper Gallinas watershed from 1939 to 2015.

 

It is represented in graph that annual temperature variation in Gallinas watershed led to enhance the tree cover area.

Figure 18. Tree canopy cover percentage and temperature of the upper Gallinas watershed from 1939 to 2015.

 

As shown in figure that annual discharge was stopped after a specific time and it enhanced plantation that’s why tree canopy cover percentage was increased. This regression analysis was performed with R² of 0.02 and standard deviation of 14.87. the significance of test was 0.22.

Figure 19. Tree canopy cover percentage of the upper Gallinas watershed and discharge of the Gallinas River (cfs) from 1930 to 2015.

 

It is illustrated that mean of annual discharge and precipitation of Gallinas watershed was parallel to each other; because they increase in almost similar pattern.

Figure 20. Mean annual discharge of the Gallinas River (cfs) and mean annual precipitation (in) of the Gallinas watershed from 1930 to 2015.

 

It was demonstrated that mean of yearly discharge and temperature vary in an irregular pattern. However, both equally act to change climate.

 

Figure 21. Mean annual discharge of the Gallinas River (cfs) and mean annual temperature (F) of the Gallinas watershed from 1930 to 2015.

 

Statistical analysis:

Correlation between tree canopy cover (%), mean annual discharge (cfs), mean annual precipitation (in) and temperature (F)

	Canopy Area (%)	Average Q (cfs)	Precipitation (in)	Temperature (f)
Tree Canopy Cover (%)	1
Average Q (cfs)	-0.14	1
Precipitation (in)	-0.03	0.46	1
Temperature (f)	0.34	-0.20	-0.10	1

 

Regression analysis between:  Discharge and Tree Canopy Cover

Regression Statistics
Multiple R	0.14
R Square	0.02
Adjusted R Square	0.01
Standard Error	14.87
Observations	74
ANOVA
	df	SS	MS	F	Significance F
Regression	1	337.31	337.31	1.53	0.22
Residual	72	15918.18	221.09
Total	73	16255.49
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	34.78	12.25	2.84	0.01	10.37	59.20	10.37	59.20
Canopy Area (%)	-0.23	0.19	-1.24	0.22	-0.60	0.14	-0.60	0.14

 

Regression analysis between:  Discharge and Temperature

Regression Statistics
Multiple R	0.20
R Square	0.04
Adjusted R Square	0.03
Standard Error	14.73
Observations	74
ANOVA
	df	SS	MS	F	Significance F
Regression	1	625.07	625.07	2.88	0.09
Residual	72	15630.42	217.09
Total	73	16255.49
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	144.55	73.53	1.97	0.05	-2.04	291.14	-2.04	291.14
Temperature (f)	-2.51	1.48	-1.70	0.09	-5.46	0.44	-5.46	0.44

Regression analysis between:  Discharge and Precipitation

Regression Statistics
Multiple R	0.46
R Square	0.22
Adjusted R Square	0.21
Standard Error	13.31
Observations	74
ANOVA
	df	SS	MS	F	Significance F
Regression	1	3509.78	3509.78	19.83	0.00003
Residual	72	12745.71	177.02
Total	73	16255.49
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-4.30	5.63	-0.76	0.45	-15.52	6.93	-15.52	6.93
Precipitation (in)	1.51	0.34	4.45	0.00003	0.83	2.18	0.83	2.18

 

Regression analysis between:  Tree Canopy Cover and Discharge

Regression Statistics
Multiple R		0.14
R Square		0.02
Adjusted R Square		0.01
Standard Error		14.87
Observations		74
ANOVA
		df		SS		MS		F		Significance F
Regression		1		337.31		337.31		1.53		0.22
Residual		72		15918.18		221.09
Total		73		16255.49
		Coefficients		Standard Error		t Stat		P-value		Lower 95%	Upper 95%	Lower 95.0%		Upper 95.0%
Intercept		34.78		12.25		2.84		0.01		10.37	59.20	10.37		59.20
Canopy Area (%)		-0.23		0.19		-1.24		0.22		-0.60	0.14	-0.60		0.14
Regression analysis between:  Tree Canopy Cover and Precipitation
Regression Statistics
Multiple R		0.03
R Square		0.00
Adjusted R Square		-0.01
Standard Error		9.42
Observations		74
ANOVA
		df		SS		MS		F		Significance F
Regression		1		5.11		5.11		0.06		0.81
Residual		72		6389.45		88.74
Total		73		6394.57
		Coefficients		Standard Error		t Stat		P-value		Lower 95%	Upper 95%	Lower 95.0%		Upper 95.0%
Intercept		66.12		3.99		16.59		0.00		58.18	74.07	58.18		74.07
Precipitation (in)		-0.06		0.24		-0.24		0.81		-0.54	0.42	-0.54		0.42
Regression analysis between:  Tree Canopy Cover and Temperature
Regression Statistics
Multiple R		0.34
R Square		0.11
Adjusted R Square		0.10
Standard Error		8.87
Observations		74
ANOVA
		df		SS		MS		F		Significance F
Regression		1		732.81		732.81		9.32		0.003
Residual		72		5661.75		78.64
Total		73		6394.57
		Coefficients		Standard Error		t Stat		P-value		Lower 95%	Upper 95%	Lower 95.0%		Upper 95.0%
Intercept		-69.86		44.26		-1.58		0.12		-158.09	18.36	-158.09		18.36
Temperature (f)		2.72		0.89		3.05		0.003		0.94	4.50	0.94		4.50

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Discussion

Climate variation and its obvious upshot on global warming have been significant issues in current years. It is reported in second Intergovernmental Panel on Climate Change (IPCC) in 1995 that from the past 100 years the worldwide mean surface air temperature has amplified for about 0.54 to 1.08 degree Fahrenheit (R.J Georgea, 1990). The comprehensive sea level has increased for around 8 inches or 20 cm in the previous century end to end with the bitterness of surface ocean waters that has improved about 30 percent meanwhile the commencement of the Industrial Revolution in the eighteenth century as a consequence of discharging more carbon dioxide (CO₂) into the atmosphere by creatures (Rehfeldt GE, 2006).

Temperature variation in various regions are somehow different from mean worldwide temperature; this condition result in extreme weather condition (Parmesan, et al, 2000). In climate change, and environment health various factors are involved and also consequently it affects several things. Tree canopy cover area, discharge of water from river coming to areas actually develop temperature, and precipitation variation. These factors may positively or negatively influence the climate or weather conditions of specific area. Tree canopy cover area are meant to enhance plantation and vegetation on that area that need friendly environment with specific temperature and precipitation. This study revealed that total canopy cover was varied such that highest canopy cover percentage was observed to be of 61.22%.  While in another study, half city core (HCC) research area had total canopy area of 4.09 acre, total canopy cover was only 13.09% in 2007 when it was estimated during era of 1994 to 2007 (Kevin J. Stark). This difference may be interpreted such that the area of study and canopy cover area was higher.

This research study was meant to determine the how much percent area of Gallinas watershed was tree canopy covered, and what rate of discharge was in Gallinas River. It was meant to find out that how much water from Gallinas River was transported to the area and further how it results in variation of temperature and precipitation of that area.

Discharge of water from river to an area can affect health of environment in various aspects. It is the rate of discharge water that determines weather conditions, because discharge may increase or decrease temperature and precipitation. Such circumstances may lead to climate change such as rainfall, snowfall, heavy or low flood, moist or humid environment etc. Owing to high discharge of water, the weather on river may result in storm or rainfall. Similarly, if discharge is least it may have less rainfall and the temperature would be higher and precipitation is lower (Retrieved on 24 February 25, 2018 from https://water.usgs.gov/edu/streamflow2.html).

At two locations, with specific latitude and longitude the discharge was measured. The result was then analyzed. It was found that least discharge was observed in 1940. However another author reported the discharge value in Rhino River basin at about 10% of entire days in a 30 year near and far future period e.g. 2021 to 2050, 2071 to 2100 in comparison to the period of 1961-1990. So, these results correlates such that discharge rate was high earlier mid 2oth century, later it was dropped in continuous manner and dropped in 2017. So, this study also suggest that discharge may further dropped in near and far future (Retrieved on 25 February 25, 2018 from http://www.chr-khr.org/en/project/impact-regional-climate-change-discharge-rhine-river-basin-rheinblick2050).

To further evaluate climate by discharge, annually temperature was estimated to find out how discharge affects temperature. Thus, it was revealed that temperature was comparatively high in 21^st century where discharge was lesser than that of 20^th century. This result correlate with another finding, where the author reported that discharge actually affect the temperature variation in a region. It is illustrated in another research report that temperature was maximum at 1960 during the estimated period of 1950-1980, this positive trend of discharge was attributed to discharge rise (Kundzewic WZ, 2015).  However, Manning contributed in this finding such that extreme increase in temperature of Aksu River basin would be expecting via climate model of this region (Manning wt al, 2013).

Similarly, precipitation was also analyzed, and it is concluded that precipitation increases with discharge but in this study, it does not result in drastic condition of heavy snowfall or storm in Gallinos watershed area. Trends in complete yearly precipitation are chiefly irrelevant for total sub-basins for the various periods, nonetheless become meaningfully positive for the epochs during the 1970s to 2000s in Xiehela and Shaliguilanke basins. In Xidaqiao, the growing trends are important for all periods preliminary from 1950 to the late ~2000s (Kundzewic WZ, 2015). Increased precipitation lesser and higher than normal in Central Asia and Western China is examined using the uniform precipitation index (Bothe O, 2012).

Then statistically correlation between tree canopy cover and discharge was determined. It is represented that discharge of water from Gallinos river to Gallinos watershed area result in good vegetation and plantation in that area. Thus, tree canopy cover area was estimated to be higher. This phenomenon has also been proved in another study.

Then separately regression analysis of correlation between temperature, precipitation, and discharge and tree canopy cover. And it was observed that all these factors were correlated such that they worked to enhance the vegetation on land while reducing irrigation. So, this is also proved in another article. This statistical analysis of canopy cover and discharge in NorthWest China was relevant to this study’s finding (Chen YN, 2007).

 

References

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