To what extent does annual tree cover loss from wildfires (Km²) Correlate with annual precipitation (mm) and mean annual Temperatures (°C) in sicily, italy, between 2001-2021?

To what extent does annual tree cover loss from wildfires (km2) correlate with annual precipitation (mm) and mean annual temperatures (°C) in Sicily, Italy, between 2001-2021?

The environmental issue (wildfires) is defined as “an unplanned fire that burns in a natural area such as a forest, grassland, or prairie ... often caused by human activity or a natural phenomenon such as lightning” (WHO, n.d.). Wildfires have important ecological functions: as “nature's way of clearing out dead litter on forest floors'', facilitating reproduction of some plants (Supriya, 2017). Globally, wildfires are increasing in frequency and severity in recent years. Climate change and change in land-use are expected to cause an increase in extreme fires of up to 50% by 2100 (UNEP, 2022).

 

Sicily, an island in southern Italy, has a Mediterranean biome, with long, dry summers and short, mild winters (S., 2001).

Wildfires are prevalent in hot and dry conditions because “higher temperatures cause more evaporation and this dries vegetation, creating fuel for the fires” (Met Office, n.d.). There is evidence that climate change increases wildfires (WMO, 2020). With increasing global warming, it is clear that wildfires will become a greater threat to ecosystems and human societies of Sicily.

Wildfires represent a significant threat to communities and ecosystems of Sicily. Even the resources used for extinguishing fires (e.g. seawater, firefighting foam and additives) can pollute ecosystems (NFCC, n.d.). Wildfires can cause mass wildlife mortality, which is especially detrimental for more vulnerable species (IFAW, 2021). Additionally, wildfires lead to reduction in size of habitats, gene pools, and subsequently, genetic diversity. Other consequences include habitat fragmentation, desertification, and emission of particles and gases such as CO2 (European Environment Agency, 2016). After vegetation loss, soil can become hydrophobic, preventing absorption of water and facilitating transportation of sediment and debris into bodies of water (Nelson, 2020).

 

Wildfires can negatively impact local communities in many ways. Natural resources vital for humans can be destroyed (e.g. timber, recreational services, and crops). Destruction of agriculture can cause food insecurity. Air pollution from wildfires can cause respiratory and cardiovascular problems, placing strains on Sicily’s healthcare sector. Globally, wildfires cause approximately 5-8% out of 3.3 million annual premature deaths from poor air quality globally (Lelieveld et al., 2015). For the government, reparations from damage are costly. From 1900-2013, Italy spent 1.7 billion USD for damage caused by wildfires (Doerr & Santin, 2013).

 

It is clear from the impacts detailed above that wildfires are destructive to ecosystems and communities of Sicily.

 

The purpose of this IA is to investigate -

• Whether mean annual temperatures in Sicily are increasing
• Whether annual precipitation in Sicily is decreasing
• Whether there is a correlation between the above mentioned variables and annual tree cover loss from wildfires

Sicily was chosen because, due to its Mediterranean climate, it is vulnerable to wildfires. The time period of 2001-2021 was selected because it represents a crucial period which has seen change in the climate.

 

The variable of annual “tree cover loss” refers to “complete removal of tree cover for any reason, including human-caused loss and natural events” (Global Forest Review, n.d.). It was chosen for this IA because it is a wide term encompassing “forest loss as well as loss of industrial tree plantations and agricultural tree crops, which are not typically considered forests” (ibid.).

 

Variables “annual precipitation” and “mean annual temperature” were selected because they may be causing factors that lead to increased wildfires, such as dry vegetation.

The following sources were used for data collection -

• ‘Climate Change Knowledge Portal’ is “the hub for climate-related information, data, and tools for the World Bank Group'' (Climate Change Knowledge Portal, n.d.). This source was selected because it is reputable (a renowned intergovernmental organisation).
• ‘Global Forest Watch’ is an online platform that provides data on deforestation and forest fires, which “allows anyone to access near real-time information about where and how forests are
• changing around the world” (About GFW | Global Forest Watch, n.d.). This source was chosen due to its international validity.

Data relating to mean annual temperature and annual precipitation was extracted from the ‘Climate Change Knowledge Portal’ by selecting “Italy” under the “Country” section of the website (https://climateknowledgeportal.worldbank.org/country/italy). Sicily can be selected on a map, and data relating to “observed annual mean-temperature” and “observed annual mean-precipitation” is shown. This data is collected by the World Bank from reliable governmental sources and displayed on this website.

 

Data relating to tree cover loss from wildfires was taken from the “Sicily” section of the Global Forest Watch website, which can be found after selecting the “Dashboard” menu, then “Italy” (https://www.globalforestwatch.org/dashboards/country/ITA/). There is a section entitled “Fires'' which displays data solely relating to tree cover loss from wildfires. Data from Global Forest Watch is sourced from “the Global Land Analysis & Discovery (GLAD) lab at the University of Maryland” (GLAD, 2022).

My hypothesis is that -

• Trendlines for each variable will be statistically significant (>0.3).
• As annual tree cover loss from wildfires increases, temperatures will also increase (positivecorrelation). There will be, at least, a medium correlation (>0.3)
• As annual tree cover loss from wildfires increases, annual precipitation will decrease (negative correlation). There will be, at least, a medium correlation (>0.3)

This hypothesis is supported by the theory that hot and dry conditions, exacerbated by climate change, cause increased wildfires.

For these variables (annual tree cover loss from wildfires (km2), mean annual temperature in degrees celsius (°C) and annual precipitation in millimetres (mm), all between 2001-2021), the following calculations will be utilised -

• Trendline (r ² ) - to notice trends in individual variables
• Pearson product-moment coefficient (statistical test): to test correlation between mean annual temperature and annual precipitation, and tree cover loss from wildfires
• Spearman’s rank-order correlation: similarly, to test correlation; however, Spearman’s can also assess monotonic (non-linear) correlations, and is less sensitive to outliers (Laerd Statistics, n.d.).

 

The value of r (correlation) ranges between -1 and 1, and the further this number is away from zero, the stronger the correlation is. As such, r = 1 would indicate a very strong positive correlation, and r = -1 would be a very strong negative correlation (University of Miami, n.d.).

 

Two tests for correlation are being used because it is not certain whether the data is monotonic or linear, and I would like to account for outliers by using Spearman’s.

 

Finally, outliers will be examined. Outliers will be pointed out and compared to other variables in those years.

Since this IA relies on secondary data, reliable and authentic sources were used, correctly cited to avoid plagiarism. This IA has no environmental impacts.

The following materials were utilised to carry out this IA -

• Laptop
• Internet connection
• Calculator
• Google Sheets

Some initial correlations can be observed. Figure 3 demonstrates a climb in annual tree cover loss from wildfires. An increase in temperature is not as prominent in the stable line in Figure 4. Nonetheless, annual precipitation appears to be decreasing in Figure 5.

The most statistically significant trendline is that of mean annual temperature. The conclusion can be drawn that mean annual temperature in Sicily is increasing. The trendline for annual precipitation is also significant, if weaker than the former. Nonetheless, warming of the planet by 1.5 °C would have catastrophic consequences (IPCC, 2021): even the small change observed can have great impacts on wildfires in Sicily.

 

It also appears that annual tree cover loss from wildfires has some statistical significance. It can be determined from these trendlines that wildfires in Sicily are gradually increasing.

The correlation between mean annual temperature and annual tree cover loss from wildfires could be considered a medium strength positive correlation. This suggests that as the temperature of Sicily increases, so does annual tree cover loss from wildfires.

 

Mean annual temperature and annual tree cover loss from wildfires correlation have a strong negative correlation. It can be concluded therefore that as annual precipitation decreases, annual tree cover loss from wildfires increases in Sicily.

The Spearman’s rank-order correlation between mean annual temperature and annual tree cover loss from wildfires is strong and positive.

 

Annual precipitation and annual tree cover loss from wildfires also have a strong negative correlation. The conclusion can be drawn again that as annual precipitation decreases, there is an increase in annual tree cover loss from wildfires, thus an increase in wildfires.

When mean annual temperature is highest, annual precipitation is in the middle of its range. Annual tree cover loss, however, appears to be very high during the year 2021, suggesting a potential correlation.

When mean annual temperature is lowest, annual precipitation is not necessarily low. Notwithstanding, annual tree cover loss from wildfires is lowest, indicating a correlation.

When annual precipitation is high, mean annual temperature is quite low. Annual tree cover loss is in the middle range - this does not suggest any form of correlation.

As annual precipitation is highest, mean annual temperature is in the middle range. Annual tree cover loss from wildfires is also highest which indicates a strong correlation.

Likewise, when annual tree cover loss from wildfires is highest, so is annual precipitation. Mean annual temperature is in the middle.

When annual tree cover loss from wildfires is lowest, mean annual temperature and annual precipitation are also at their lowest during the same year, indicating a strong correlation.

 

Very strong correlations are present between mean annual temperature and annual tree cover loss. Correlations between annual precipitation and annual tree cover loss are not as strong, however not insignificant.

Numerous conclusions regarding my hypotheses can be made after analysing data.

• “Trendlines for each variable will be statistically significant (>0.3)” This hypothesis was true for 1 out of 3 variables. The trendline for mean annual temperature was >0.3 (0.408), whereas the other two variables were <0.3 (0.184 and 0.28). Nonetheless, trendlines were not statistically insignificant (<0.1).

 

Annual Tree Cover Loss from Wildfires and Mean Annual Temperatures

• “As annual tree cover loss from wildfires increases, temperatures will also increase (positive correlation)”. This was true, as there were positive correlations between annual tree cover loss from wildfires and mean annual temperature (Pearson’s = 0.3258, Spearman’s = 0.64328).
• There will be, at least, a medium correlation (>0.3)”. The correlations between annual tree cover loss from wildfires and mean annual temperature were medium in strength. The analysis of the outliers suggests a very strong correlation.

 

Annual Tree Cover Loss from Wildfires and Annual Precipitation

• “As annual tree cover loss from wildfires increases, annual precipitation will decrease (negative correlation)”. My hypothesis was correct: there were negative correlations between annual tree cover loss from wildfires and annual precipitation (Pearson’s = -0.5146, Spearman’s = -0.6090).
• “There will be, at least, a medium correlation (>0.3)”. Both tests determined that correlations are medium in strength. Analysis of outliers indicates a medium correlation.In summary, none of the trendlines and correlations are weak. Therefore, it could be concluded that, as annual precipitation of Sicily decreases, temperatures are increasing, correlating with increasing area burned by wildfires.

In this investigation, there may be an element of human error. Because I manually recorded the data, there may be some inadvertent mistakes in numbers due to human error. Even with regards to the data, which comes from reliable sources, there is some risk of error.

 

To make my conclusion more valid, the time period could be expanded (e.g. 1950-2021). Additionally, more factors associated with global warming could be tested (e.g. global greenhouse gas emissions).

 

One limitation of this IA was that it investigated correlation rather than causation. The fact that the three events occurred at the same time does not mean that they necessarily caused each other (i.e. it may not becorrect to make the claim: “rising temperatures and less annual precipitation is causing an increase in wildfires”). Nonetheless, it is logical in many ways to make such a claim since climate change does cause drier and hotter conditions.

The findings of this investigation can be applied to prevent wildfires. First, for communities to adapt to the ever-growing risk of wildfires, homes can be retrofitted with special materials such as “fire bricks” that are “made with clay, which can withstand high temperatures'', and “dual-paned glass” which can “offer an extra layer of protection” (Perez, 2022). However, these refurbishments can be expensive. It is unlikely that the Italian government would subsidise home retrofitting due to its many other priorities.

 

“Controlled burning” refers to “intentionally set fires that periodically clear underbrush or other fuels”, described as “key to reducing the severity of wildfires in the future” (Choi-Schagrin, 2021). This may be a viable solution for wildfires in Sicily because it reduces fuel for fires (e.g. dead leaf litter), and also promotes germination of serotinous plants (plants with cones that release seeds after being ignited by fire) (Mullen, 2017).

 

“Cloud seeding” is a technique that increases precipitation, whereby “small particles of silver iodide, a salt with a crystalline structure similar to that of ice, are added to clouds,” (Bleiker, 2022). Such a technique may be able to reduce the frequency of Sicilian wildfires.

 

The optimal solution is a reduction in global greenhouse gas emissions, keeping global temperatures at a level whereby wildfires can burn at stable levels. As long as temperatures increase and precipitation decreases in Sicily and worldwide, it appears that threats such as wildfires will not disappear.

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