Results
Tree Canopy Cover
As it is stated 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 the 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 of 61.22 mi2 was observed in 2005. Then it was a huge reduction to 39.27mi2 in 2009. However, in the following years, 2011-2014, it dropped from 57.82 to 55.92.
Figure 8. Tree canopy cover in the 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 the percentage of tree canopy cover increased steadily, reaching its maximum in 2005. Fig 10 shows that there was a small drop in tree canopy cover from 2005 to 2014.
Figure 9. Tree canopy cover in upper Gallinas Figure 10. Tree canopy cover in the upper Gallinas
watershed from 1939 to 2005. watershed from 2005 to 2014
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 was 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 inconclusive.
Figure 11. Average annual discharge (cfs) of the Gallinas River from 1930 to 2015. The dashed line indicates the regression line that presents the best-fitted line to the data.
The figure indicates that temperature during the 1930-2014 era mildly increased from 1939 to 1955, and it decreased later on. Again, it increased to 51 in 1980; then the temperature was constant between 1995 and 2005. The highest temperature with little variation from the last one was observed in 2014, and it dropped in 2015. It is noticed that temperature 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 20th and 21st centuries. However, almost the same pattern of precipitation was observed in 1980.
Figure 13. Average annual precipitation (in) in the upper Gallinas watershed from 1930 to 2015.
Correlation between Tree Canopy Cover, Discharge & Weather data
It is represented that both the discharge water from the 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 by tree canopy, and discharged water led to improved vegetation in 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 helps to modify plantations in that 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 leads to an increase in the precipitation rate; which positively affects 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 in Fig 17 that precipitation and tree canopy cover both increased in the 20th century and dropped in the 21st century.
Figure 17. Tree canopy cover percentage and precipitation of the upper Gallinas watershed from 1939 to 2015.
It is represented in the graph that annual temperature variation in Gallinas watershed led to enhancement ofment of the tree cover area.
Figure 18. Tree canopy cover percentage and temperature of the upper Gallinas watershed from 1939 to 2015.
As shown in the figure the annual discharge was stopped after a specific time and it enhanced plantation that’s why the tree canopy cover percentage was increased. This regression analysis was performed with an R2 of 0.02 and a standard deviation of 14.87. the significance of the 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 the mean annual discharge and precipitation of Gallinas watershed were parallel to each other because they increased in almost similar patterns.
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 the 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 was reported in the second Intergovernmental Panel on Climate Change (IPCC) in 1995 that in the past 100 years, the worldwide mean surface air temperature has amplified from about 0.54 to 1.08 degrees Fahrenheit (R.J Georgea, 1990). The comprehensive sea level has increased by around 8 inches or 20 cm in the previous century end to end with the bitterness of surface ocean waters that has improved by about 30 per cent 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 is somehow different from the mean worldwide temperature; this condition results in extreme weather conditions (Parmesan, et al, 2000). In climate change and environmental health, various factors are involved and consequently affect several things. Tree canopy covers areas, water discharge from rivers comes to areas that develop temperature, and precipitation variation. These factors may positively or negatively influence a specific area’s climate or weather conditions. Tree canopy cover areas are meant to enhance plantation and vegetation in that areas that need friendly environments with specific temperatures and precipitation. This study revealed that total canopy cover was varied such that the highest canopy cover percentage was observed to be 61.22%. In another study, half of the city core (HCC) research area had a total canopy area of 4.09 acres, and total canopy cover was only 13.09% in 2007 when it was estimated during the 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 how much of the Gallinas watershed was covered by tree canopy and what rate of discharge was in the Gallinas River. It was meant to find out how much water from the Gallinas River was transported to the area and how it resulted in temperature and precipitation variation.
The discharge of water from a river to an area can affect the environment’s health 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 environments etc. Owing to the high discharge of water, the weather on the river may result in storms or rainfall. Similarly, if the discharge is the 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 the least discharge was observed in 1940. However, another author reported the discharge value in the 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 correlate such that the discharge rate was high earlier in the mid-2teenth century, later it dropped in a continuous manner and dropped in 2017. So, this study also suggests that discharge may further drop in the 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, annual temperature was estimated to find out how discharge affects temperature. Thus, it was revealed that temperature was comparatively high in the 21st century, whereas discharge was lower than in the 20th century. This result correlates with another finding, where the author reported that discharge actually affects the temperature variation in a region. It is illustrated in another research report that the temperature was maximum in 1960 during the estimated period of 1950-1980; this positive discharge trend was attributed to the rise in discharge (Kundzewic WZ, 2015). However, Manning contributed to this finding by stating that an extreme increase in temperature of the Aksu River basin would be expected via the 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 conditions of heavy snowfall or storms in the Gallinos watershed area. Trends in complete yearly precipitation are chiefly irrelevant for total sub-basins for the various periods, they becoming 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 from 1950 to the late ~2000s (Kundzewic WZ, 2015). Increased precipitation is less and higher than normal in Central Asia and Western China, which is examined using the uniform precipitation index (Bothe O, 2012).
Then, a statistical correlation between tree canopy cover and discharge was determined. It is represented that the discharge of water from the Gallinos River to the Gallinos watershed area results 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 separate regression analysis of the correlation between temperature, precipitation, and discharge and tree canopy cover. It was observed that all these factors were correlated such that they worked to enhance the vegetation on land while reducing irrigation. This is also proved in another article. This statistical analysis of canopy cover and discharge in NorthWest China was relevant to this study’s findings (Chen YN, 2007).
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