![](media/image1.png){width="0.8152777777777778in"
height="0.7013888888888888in"}

**The 2008 GLOBE Learning Expedition (GLE)**

**Investigation Report from C.E.I. San Ignacio**

***Effects of the climatic change in the frequency and duration of the
fires in the North of the Patagonia.***

![](media/image2.jpeg){width="5.252083333333333in"
height="2.1743055555555557in"}

*Authors:*

![](media/image3.jpeg){width="0.9694444444444444in"
height="0.5819444444444445in"}

*Students 3º Polimodal: Castillo, E.; Huichaqueo, E. C.; Izaza, J. P.;
Lefín, G. L.; Painemilla, L. M. and Reinao, S. D.*

*Teachers: Prieto, A. B.; Bertossi, M. A., Krumpholz E. and Davies, C.*

### ![](media/image4.jpeg){width="0.7694444444444445in" height="0.7465277777777778in"}

### With the support of Capsa-Capex

March 2008

**Junín de los Andes -- Pcia. of Neuquén - Argentina**

**Abstract:**

The water is a scarce resource especially in summer in the steppe
Patagonian when most of the fires occur. Its use is increased by the
population\'s increase and it will continue with future urbanizations.
Considering the impacts of the climatic change added to the anthropic
effects we ask ourselves: How are we being affected at a regional scale
by global changes respecting the frequency of fires? Has the frequency
and duration of the fires changed? How do the changes of the
meteorological variables influence the meteorological index of danger of
fires (FWI)?

Atmosphere measurements were carried out using the GLOBE protocol. So as
to detect evidences of climatic change, meteorological data (provided by
governmental organisms) were used relating them with NOAA and NASA data
on global changes.

Significant differences were detected in the meteorological variables
and in the frequency of fires during the season which expanded until
April inclusive, coinciding with the hottest years at global level. The
month of October of 1998 has very significant differences with all the
other ones, coinciding with the events "El Niño" and "La Niña" in the
same year; as well as being one of the hottest years to global level.

**Introduction, Research Questions and Hypothesis:**

The quantity and the quality of the water can be affected by the
anthropic impacts at local and global scale, added to changes at global
scale such as the climatic change. According to the report of the IPCC;
the climatic change is caused by a variability of factors that influence
the global heating.

![](media/image5.wmf){width="3.7534722222222223in"
height="2.183333333333333in"}

*Figure 1. Estimation of the balance of the Earth´s global annual energy
average. Watts per square meter (Wm^2^). IPCC 2007.*

Of the solar radiation that crosses the atmosphere entirely; one part is
reflected by the clouds, returning to the atmosphere, the other part
enters in form of light and heat that then will be again reflected by
reflecting objects back to the atmosphere. This reflected light collides
with clouds and with hothouse gases (mainly dioxide of carbon and
methane) and with water vapor. These elements act as barriers impeding
the escape of the infrared radiation, generating this way an additional
heat that is retained daily. Added to this is the emission of heat by
living creatures. (IPCC, 2007).

Among the most worrying evidences are: the increase of the world average
temperature on the surface of the earth and of the ocean waters, the
decrease of the layers of ice, the increase of the level of the sea,
changes in the concentrations of water vapor and in the annual averages
of rainfalls/precipitations.

![](media/image6.png){width="3.6465277777777776in"
height="2.3895833333333334in"}

*Figure 2. Increase of the global averages of the temperatures
registered and the estimations for the future according to the different
models. IPCC, 2007.*

The models of climatic change seek to estimate the changes for the
future and for that, they are based on the available data of
registrations of temperature and precipitations (that are not
homogeneous in the entire world), they also estimate the climate in the
past by means of analysis of fossil registrations (e.g. rings of fossil
trees, air caught in the ice, pollen found in the silts of lakes, etc.).
(D´Arrigo, et.al.,2001; Delgado, 2002; Papú, 2002; Rabassa, 2005, 2007;
Tromboto, 2002; Urrutia, et.al., 2005; Villalba, 1997, 1998, 2002, 2003;
Zamaloa, et.al.,2005 and others). The models have discrepancies
according to the variables that are incorporated, but through the years,
thanks to the computer science\'s advances that a larger amount of data
can be processed; it has been possible to incorporate more variables and
to reduce the size of the tables.

![](media/image7.png){width="3.834722222222222in"
height="2.977777777777778in"}

*Figure 3. Changes in the annual average temperatures observed and
estimated by the different models. The temperatures show tendency to the
heating. IPCC, 2007.*

![](media/image8.png){width="3.9006944444444445in"
height="3.0791666666666666in"}

*Figures 4. Evolution of the climatic models. IPCC, 2007.*

The interaction between the solar energy, the atmosphere, the earth, the
water and the biota determine the climate, but the human activity has
changed this balance increasing the quantity of gases with hothouse
effects on the atmosphere this way causing the increase of temperature
at global scale. This causes the changes the temperatures of the oceans
registering an increase of the frequency of abnormal temperatures that
cause the phenomena of "El Niño" and of "La Niña." (IPCC, 2007; NOAA,
CPC, ENSO; Barros, et.al.,2000; Compagnucci, et.al.,2007; Dettinger,
et.al.,2000; Scarpati, et.al.,2001 and others).

![](media/image9.wmf){width="3.9895833333333335in"
height="2.7118055555555554in"}

*Figure 5. Climatic variables, processes and interactions. IPCC, 2007.*

As well as the increase of the temperatures the IPCC also advises that
regional variations and extreme events (of cold and heat) can occur with
more frequency. The global heating also results in the melting of the
polar ice causing the increase of the level of the sea putting in danger
of disappearing coastal areas. (Kokot, et.al.,2004 and ITDT, 2006). The
melting of the continental ice makes the sources of fresh water diminish
especially in arid areas like ours where most of the river waters in
spring come from the ice melting (Compagnucci, et.al.,2005 and 2007;
Cubero, 2001). Even on the Lanín volcano, located 50 Km. from our
school, a glacier has disappeared and the ice flow has suffered an
important cutback as from 1896. (Delgado, et.al.,2002, Leiva, 2002;
Llorens, 2002 and Rabassa, 2007).

![](media/image10.jpeg){width="4.052777777777778in" height="2.1in"}

*Figure 6. I change in the North front of the Lanín glacier during the
last 104 years. Pictures of: F. Moreno in 1896 (left) and Thin, et.al.,
2001 (right)*

For example, changes are being observed in the marine currents and the
surrounding air causing the displacement of the Atlantic anticyclone
toward the south. (Minetti, et.al.,2003).

![](media/image11.wmf){width="3.8506944444444446in"
height="2.313888888888889in"}

*Figure 7. Annual variation of the rains in Junín of the Andes.
(Direction of Coordination of Fire Management).*

In our region the data show a tendency of a temperature increase and
decrease of the precipitations. The models predict that these tendencies
will be accentuated. (Barros, et.al.,2006; CIMA-CONICET,2005 and
Labraga,1998). The annual average of precipitation is of 750mm, (INTA,
1965 and Walter, 1983), but the registrations taken in the school
starting from the year 2000 vary between 450mm and 760mm (Barrau,
et.al.,2004). The average over the last 10 years in Junín of the Andes,
10 Km. away from school, is of 642 mm with great interanual variability
concentrated in autumn-winter, with dry summers where the river flows
diminish considerably. This situation will increase the loss of ground
humidity due to the higher temperatures and in the humid border of the
Andean mountain a higher frequency of forest and grassland fires is
expected. The native forests, very slow to regenerate and very difficult
to implant, would be affected more and more by the catastrophes caused
by the fires. (Barros, et.al.,2006; Carretero, 1995; González,
et.al.,2007; Veblen, et.al.,1999 and others). Another factor that has an
influence in the fires is the occurrence of electric storms, unknown to
the old people of the town until few years ago. The first registration
of an electric storm is of 1943 in a book written by an old inhabitant.
Starting from the years 70 begin to be intensified. (Personal
communication with Tech. Simón L. Lewis)

Considering the forecasts of climatic change for the region, we ask
ourselves the following: How are the changes at global level affecting
us at regional scale regarding the frequency of fires? Has it changed
the frequency and duration of the fires? How do the changes of the
meteorological variables influence in the meteorological index of danger
of fires (FWI)? To try to answer, the following hypotheses are set out:

Hypothesis~1~: The temperatures, thermal amplitude, humidity, rains and
followed days without rains in the last years in Junín of the Andes,
don\'t coincide with the hottest years to global scale, neither with the
events of the "El Niño" and "La Niña."

Hypothesis~2~: The frequency and the duration of the fires have not
changed in the last years. Hypothesis~3~: The FWI indicates the changes
in the meteorological variables that can cause a fire.

Hypothesis~4~: The fire season of has not changed in the last years.

**Investigation methodology**

The meteorological data are produced in a meteorological station of
Davis trademark, Monitor II model, located in Junín of the Andes and
they were provided by Direction of Coordination of Fire Management; the
data of the hottest years were taken from NASA website (Hansen, 2008,
2007 and 2006); the data of the anomalies of "El Niño" and "La Niña"
were taken from the list of episodes produced by the NOAA station.

The school meteorological data correspond to the measurements that are
carried out in the GLOBE Program, using the protocols of GPS and
Atmosphere. The school data, with exception of rainfall, correspond to
year 2006 and part of 2007. Being a series of a larger number of years
the data of Junín de los Andes were used.

![](media/image12.png){width="3.966666666666667in"
height="2.8333333333333335in"}

*Figure 8. Departments of the Province of Neuquen. (INTA, 2002)*

![](media/image13.jpeg){width="3.990972222222222in"
height="2.7729166666666667in"}

*Figure 9. Places of sampling of meteorological variables.*

**Data Summary:**

In the test of normality (Kolmogorov-Smirnov), and in the test of
homogeneity of the variance (Levene), the data of temperatures (maximum,
minimum and thermal amplitude), humidity, consecutive days without rains
and rainfall were very significant (p \< 0,01).

*Table 1. Analysis of the variation due to the effects of the
meteorological variables*

+------------------+------+---+------+-----+---+------+------+-------+
| Variable         | SS   | d | MS   | SS  | d | MS   | F    | p     |
|                  |      | f |      |     | f |      |      |       |
|                  | Ef   |   | Ef   | Er  |   | E    |      |       |
|                  | fect | E | fect | ror | E | rror |      |       |
|                  |      | f |      |     | r |      |      |       |
|                  |      | f |      |     | r |      |      |       |
|                  |      | e |      |     | o |      |      |       |
|                  |      | c |      |     | r |      |      |       |
|                  |      | t |      |     |   |      |      |       |
+------------------+------+---+------+-----+---+------+------+-------+
| Maximum          | 153  | 1 | 139  | 205 | 3 | 54,  | 25,7 | 0,    |
| temperature (ºC) | 34,0 | 1 | 4,00 | 573 | 8 | 0412 | 9509 | 00000 |
|                  |      |   |      |     | 0 |      |      | 0\*\* |
|                  |      |   |      |     | 4 |      |      |       |
+------------------+------+---+------+-----+---+------+------+-------+
| Minimum          | 96   | 1 | 87   | 82  | 3 | 21,  | 40,1 | 0,    |
| temperature (ºC) | 39,1 | 1 | 6,28 | 931 | 7 | 8413 | 2045 | 00000 |
|                  |      |   |      |     | 9 |      |      | 0\*\* |
|                  |      |   |      |     | 7 |      |      |       |
+------------------+------+---+------+-----+---+------+------+-------+
| Thermal width    | 82   | 1 | 75   | 137 | 3 | 36,  | 20,7 | 0,    |
| (ºC)             | 60,5 | 1 | 0,95 | 534 | 7 | 2408 | 2113 | 00000 |
|                  |      |   |      |     | 9 |      |      | 0\*\* |
|                  |      |   |      |     | 5 |      |      |       |
+------------------+------+---+------+-----+---+------+------+-------+
| Humidity (%)     | 1348 | 1 | 1225 | 1   | 4 | 307, | 39,8 | 0,    |
|                  | 39,4 | 1 | 8,13 | 269 | 1 | 8461 | 1901 | 00000 |
|                  |      |   |      | 249 | 2 |      |      | 0\*\* |
|                  |      |   |      |     | 3 |      |      |       |
+------------------+------+---+------+-----+---+------+------+-------+
| Consecutive days | 781  | 1 | 710  | 857 | 4 | 199, | 35,5 | 0,    |
| without rains    | 08,6 | 1 | 0,78 | 681 | 2 | 8325 | 3365 | 00000 |
|                  |      |   |      |     | 9 |      |      | 0\*\* |
|                  |      |   |      |     | 2 |      |      |       |
+------------------+------+---+------+-----+---+------+------+-------+
| Rains (mm)       | 11   | 1 | 10   | 143 | 4 | 33,  | 3,0  | 0,    |
|                  | 07,1 | 1 | 0,64 | 610 | 2 | 4209 | 1143 | 00052 |
|                  |      |   |      |     | 9 |      |      | 0\*\* |
|                  |      |   |      |     | 7 |      |      |       |
+------------------+------+---+------+-----+---+------+------+-------+

> \*\* Very Significant p\<0,01

The test global F is very significant for all the variables, therefore
it is possible to carry out the comparison of averages with the test of
Tukey(HSD).

Hypothesis~1~: The temperatures, humidity, rainfall and consecutive days
without rains are the same in the last years in Junín of the Andes and
they don\'t coincide with the hottest years at global scale.

The data show that the minimum temperatures tend to increase, while the
maximums, as well as the thermal amplitude tend to diminish in the last
years, for the analyzed period.

![](media/image14.png){width="4.019444444444445in"
height="2.9027777777777777in"}

*Figure 10. Variation of the maximum, minimum temperatures and thermal
amplitude in Junín of the Andes.*

The test of Tukey for the hottest years at a global scale show
significant differences (p\<0,05) between the year 1998 and the years
1999 to 2007 for the thermal amplitude (x=19,6ºC±6,49). For the years
2002 to 2005 the thermal amplitude has a similar behavior of 16ºC
showing significant differences (p\<0,05) with the years (1996 to 1998;
2001, 2006 and 2007).

![](media/image15.wmf){width="4.403472222222222in"
height="3.1368055555555556in"}

*Figure 11. Variation of humidity to the 12 hs. in Junín of the Andes.*

The thermal amplitude (x=14ºC) of the year 2006 shows significant
differences with every year except with 2007. These differences are also
shown with some variants for the maximum and minimum temperatures.

The humidity at noon tends to increase for the considered period. In the
case of the hottest years the test of Tukey shows that 1998
(x=43,7%±17,21%) has significant differences (p\<0,05) with the years
2002 to 2007. The same as the thermal amplitude, the humidity of the
years 2002 to 2005 have a similar behavior (averaging 57%), showing
significant differences with the years 1996 to 2001. The year 2006 are
similar to the previous ones (x=52,9%) but has significant differences
with the years 2004 and 2005.

![](media/image16.wmf){width="4.075in" height="3.0527777777777776in"}

*Figure 12. Variation of the rainfall and of the consecutive days
without rains in Junín of the Andes.*

The test of Tukey doesn\'t show significant differences for the
rainfall, in the considered period. The consecutive days without rains
they show a decrease in the last years.

The test of Tukey for the year 1998 (x=11,58 days±12,11) has
significant differences (p\<0,05) with the years 1996, 1999 and 2002 to
2007. The years 2002 to 2005, alike the previous variables, they have a
similar behavior (averaging 7 days), showing significant differences
with the years 1996, 1998 and 1999. The year 2006 (x=6,1 days) shows
significant differences with the years 1996, 1998, 1999 and 2000.
Therefore the Hypothesis ipótesis~1~ is rejected.

Considering the Oceanic Index of "El Niño" and the hottest years, in the
year 1998 (showing the highest thermal amplitude, the lowest rain and
the flow of the lowest river with regard to the considered period) there
were a period of heating from January to April and then a period of
cooling from July to December.

*Table 2. Episodes of heat (red) and cold (blue) of the Oceanic Index of
"El Niño." Averages of abnormalities of three months of "El Niño"
(5ºN-5ºS, 120º-170ºW). (CPC.
<http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml>*)

  ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ----------
  **Year**   **DJF**    **JFM**    **FMA**    **MAM**    **AMJ**    **MJJ**    **JJA**    **JAS**    **ASO**    **SON**    **OND**    **NDJ**

  **1992**   **1.8**    **1.7**    **1.5**    **1.4**    **1.2**    **0.9**    **0.5**    0.2        -0.1       -0.1       0.1        0.3

  **1993**   0.4        0.4        0.5        0.7        0.7        0.7        0.4        0.3        0.3        0.3        0.3        0.3

  **1994**   0.2        0.2        0.2        0.3        0.4        0.4        **0.5**    **0.5**    **0.7**    **0.9**    **1.3**    **1.3**

  **1995**   **1.2**    **0.9**    **0.6**    0.3        0.2        0.1        -0.1       -0.2       **-0.5**   **-0.6**   **-0.8**   **-0.8**

  **1996**   **-0.8**   **-0.7**   **-0.5**   -0.3       -0.2       -0.2       -0.1       -0.2       -0.1       -0.2       -0.3       -0.4

  **1997**   -0.4       -0.3       -0.1       0.3        **0.8**    **1.3**    **1.7**    **2.0**    **2.2**    **2.4**    **2.5**    **2.5**

  **1998**   **2.3**    **2.0**    **1.4**    **1.1**    0.4        -0.1       **-0.7**   **-1.0**   **-1.1**   **-1.2**   **-1.4**   **-1.5**

  **1999**   **-1.5**   **-1.2**   **-0.9**   **-0.8**   **-0.8**   **-0.8**   **-0.9**   **-1.0**   **-1.0**   **-1.2**   **-1.4**   **-1.7**

  **2000**   **-1.7**   **-1.4**   **-1.0**   **-0.8**   **-0.6**   **-0.6**   -0.4       -0.4       -0.4       **-0.5**   **-0.7**   **-0.7**

  **2001**   **-0.7**   **-0.5**   -0.4       -0.3       -0.1       0.1        0.1        0.0        0.0        -0.1       -0.1       -0.2

  **2002**   -0.1       0.1        0.2        0.4        **0.6**    **0.8**    **0.9**    **0.9**    **1.1**    **1.3**    **1.5**    **1.4**

  **2003**   **1.2**    **0.9**    **0.5**    0.1        -0.1       0.0        0.3        0.4        0.5        0.5        0.6        0.4

  **2004**   0.4        0.2        0.2        0.2        0.3        0.4        **0.7**    **0.8**    **0.9**    **0.8**    **0.8**    **0.8**

  **2005**   **0.6**    **0.5**    0.4        0.5        0.5        0.5        0.5        0.3        0.2        -0.1       -0.4       -0.8

  **2006**   -0.8       -0.6       -0.3       -0.1       0.2        0.3        0.4        **0.5**    **0.7**    **0.9**    **1.2**    **1.1**

  **2007**   **0.8**    0.4        0.1        -0.1       0.0        -0.1       -0.2       **-0.5**   **-0.8**   **-1.1**   **-1.2**   **-1.4**
  ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ----------

In the years 2002 to 2006 (the thermal amplitudes tends to decrease, the
humidity to ascend, the consecutive days without rains to decrease, the
fires tend to increase and expand the season of fires inclusive until
the month of April) they happened periods of heating in the Ocean
Pacific during several months in all them. (Table 2)

![](media/image17.wmf){width="4.440277777777778in"
height="3.0395833333333333in"}

*Figure 13. Changes in the frequency of fires in the Province of
Neuquén.*

Hypothesis~2~: The frequency and the duration of the fires have not
changed in the last years. The data of the total of fires for the
department are significant for the test of normality but they are not
therefore for the homogeneity variance. The test of ranges of
Kruskal-Wallis is applied to establish differences among the median.

The test Kruskal-Wallis is very significant (p\<0,01) showing the
tendency to increase of the number of fires in the last years. The main
causes of fires are anthropic and caused by negligence.

![](media/image18.wmf){width="6.138888888888889in"
height="2.536111111111111in"}Figure 14. Causes of forest and grassland
fires.

![](media/image20.wmf){width="4.13125in" height="3.0833333333333335in"}

*Figure 15. Number of fires in the Department (period 1992 to 2007).*

The test of Tukey presents very significant differences (p\<0,01) for
the number of fires in the Department, being Huiliches, Lácar and The
Lakes the most affected. The Huiliches department (where the school is
located) is in a grassland area, while Lácar and The Lakes are in a
forest area.

![](media/image21.wmf){width="4.3381944444444445in"
height="3.2618055555555556in"}

*Figure 16. Duration of the fires.*

The test of Tukey presents very significant differences (p\<0,01)
between the years 1995 (x=0,72±1,49 Max: 7days) and the years 1997,
1998 and 2000 to 2007. The year 1999 (x=0,51±1,25 Max: 10days) has very
significant differences (p\<0,01) with the years 2000, 2006 and 2007.
The duration tends to diminish in the last years, several days in some
cases. Therefore the Hypothesis~2~ is rejected.

Hypothesis~3~: The FWI indicates the changes in the meteorological
variables that can cause a fire.

The data of temperature are significant for the test of normality but
they are not it for the homogeneity variance. Therefore the test
Kruskal-Wallis that is applied shows very significant differences
(p\<0,01) for all the variables. That is to say that when the humidity
and rains are high and the temperature and speed of the wind are low the
FWI is low and vice-versa.

![](media/image22.wmf){width="4.397916666666666in"
height="3.2631944444444443in"}

*Figure 17. Incidence of the meteorological variables in the FWI of
Junín of the Andes (Department Huiliches)*

![](media/image23.png){width="4.2131944444444445in" height="2.8in"}

*Figure 18. Type of vegetation and surface burned by the fires in the
region. (Period 1992-2007)*

The grassland is the most affected for the fires, followed in importance
by the heaths and lastly the forests. The largest number of burnt
hectares happened with extreme FWI. Therefore the Hypothesis~3~ is not
rejected.

![](media/image24.wmf){width="4.968055555555556in"
height="3.5520833333333335in"}

*Figure 19. State of the FWI (Fire Weather Index) during the fires.*

Although fires have occurred with FWI first section, the majority happen
with extreme FWI, high and moderate. The graph shows few fires with very
high FWI.

Hypothesis~4~: The fire season of has not changed in the last years.

![](media/image25.png){width="4.146527777777778in"
height="2.941666666666667in"}

*Figure 20. Fire Seasons in the region.*

![](media/image26.wmf){width="4.035416666666666in"
height="2.834722222222222in"}

*Figure 21. Fire Seasons in the region in different years.*

![](media/image27.wmf){width="6.304861111111111in" height="2.625in"}

*Figure 22. Months with significant differences.*

The test of Tukey presents significant differences (p \< 0,05) in the
month of April of the year 2005 (x=2,35±4,56 fires) with the years 1992
to 1994 and 2002, while it doesn\'t present differences with the last
years, that is to say that the season of fires would be extending
inclusive until the month of April.

The test of Tukey for the month of October of the year 1998 present very
significant differences (p\<0,01) with every other year. This year it
coincides with events "El Niño" (at beginning of the year) and then "La
Niña" that continued until half way through the year 2000. The other
months don\'t present significant differences, that is to say that the
fires always happen at the end of spring, summer and beginning of the
autumn. In accordance with all expressed above, the Hypothesis~4~ it is
rejected.

**Analysis**

To analyze the data the Statistica software was used that carries out
the following calculations:

*a) Descriptive statistics:* to determine the mean values and the
dispersion of each variable.

Mean=(Σxi)/n

Standard deviation =\[Σ(xi-xbar)2/n-1\]1/2

*b) Test of Normality:* to find out if the data are normal.

Kolmogorov-Smirnov
Test=$D_{\alpha} = \sqrt{\frac{- \text{ln}\left( \frac{1}{2}\alpha \right)}{2n}}$

> $\alpha = 0,\text{05}$

Significance level(p-level)=$p \leq \alpha$

> $$\alpha = 0,\text{01}$$

*c)* *Test Homocedasticity:* to determine the homogeneity of variance.

Levene Test: ![](media/image28.png){width="1.8020833333333333in"
height="0.4375in"}

*d) Analysis of variance:* Allows for the detection of a component added
due to the effects of each variable by proof F. If the F tests overall
analysis of variance is significant can be applied Tukey test.

df~effect~=s-1

df~error~=n-s

SS~Total~=$\sum_{}^{}\left( \overline{Y_{\text{ji}}} - \overline{\overline{Y}} \right)^{2} -$$$$\frac{(\sum_{}^{}{Y_{\text{ij}})^{2}}}{n}$

SS~efecto~=$\frac{Y_{1\text{.}}^{2} + Y_{2\text{.}}^{2} + Y_{3\text{.}}^{2} + Y_{4\text{.}}^{2} - \text{CF}}{r}$

SS~error~=SS~Total~-SS~effect~

CF=$\frac{(\sum_{}^{}{Y_{\text{ij}})^{2}}}{n}$

MS~effect~=$\frac{\text{SS effect }}{\text{df}}$

MS~Error~=$\frac{\text{SS error}}{\text{df}}$

F Test=$\frac{\text{MS}_{\text{effect}}}{\text{MS}_{\text{error}}}$

*e) Multiple comparison test:* determine the significant differences
between group means.

Tukey (HSD) Test: ![](media/image29.png){width="1.9375in"
height="0.4375in"}

*f) Reference of formulas:*

Σx~i~ = Summation of the measurements of the sample.

xbar = Is the average of the sample.

n = Sample size.

D= Difference absolute maximum

$\alpha$= Critical value

dg= Degrees of freedom

Z~ij~ =\|X~ij~ − X~i.~\| whereX~i.~ is the average for the i-^th^
subgroup.

Y~ij~=Effect

Y~1.~,Y~2.~,Y~3.~,Y~4.~= Mean of each sampling point

r= Replicates

$\overline{Y_{\text{ji}}}$= Mean effect

$\overline{\overline{Y}}$=Mean general

SS~=~ Sum of squares

CF= Correction factor

MS=Mean square

**Conclusions:**

The maximum temperatures have diminished and the minimum ones have
increased, diminishing the thermal amplitude in the last years.

The rainfall remains low, but the rains are distributed at more regular
intervals possibly impacting in the increase of the air humidity.

In the years 2002 to 2006 (the thermal amplitude tends to decrease, the
humidity to ascend, the consecutive days without rains to decrease, the
fires tend to increase and the season of fires expands inclusive until
the month of April) periods of heating in the Pacific Ocean occurred
during several months. (Table 2)

Most of the fires occur due to negligence and the most affected
departments are Huiliches (where grassland prevails), The Lakes and
Lácar (with grassland and important areas of native forest). The number
of fires has been increased in the last years but its duration has
diminished. This change is possibly due to the use of more technology to
combat them (e.g. Aircraft hydrants), better qualified human resource
and the establishment of Fire Management Plans at National and
Provincial level.

The FWI is since a good indicator of danger of fires most of them they
happened under the category of "extreme".

The fire season extends through the warmest months in the year, (spring
ends, summer and autumn principles). It tends to show the beginning of
several fires in October of 1998 that coincides with event of "El Niño"
followed by "La Niña." In the last years the season has extended
inclusive until the month of April coinciding with the warmest years to
global scale and with events of "La Niña.".

**Discussion**

Most of the province of Neuquén is arid with a narrow humid strip on the
mountain range. Good part of the province has a degree of important and
growing erosion (water erosion: moderate to serious; wind erosion:
light). The fires represent one of the important causes of degradation
of the soils and of biodiversity loss; thus they endanger the
sustainable use of the ecosystems and they enhance erosion. (Altieri,
1999; Barros, et.al.,2006; Carretero, 1995; Kitzberger, et.al.2005;
IPCC, 2007; Paruelo, 1998; Veblen, et.al.,1992, 1999 and 2002).

The factors that influence are the dry season (summer), the type of
vegetation, the occurrence of electric storms and mainly human action.
Most of the fires happen in proximities of cities or areas visited by
tourists. (Barros, et.al.,2002, 2006; Kitzberger, et.al.,1997, 2005;
IPCC, 2007; Rössler, et.al.,2004; Veblen, et.al.,1999; Villalba,
et.al.,2003 and 2006).

The climatic models show a scenario of important reduction of the
precipitations in almost the whole mountain range to the North of the
parallel one 46ºS. Most of the water that we consume in the area comes
from this mountain range and an increment of the danger of fires is
expected activating even more the erosion processes. (AIC, 1996 a 2007;
Camilloni, 2005; CIMA-CONICET, 2005; Da Silva, et.al.,2007; Labraga,
1998; Minetti, 2003; IPCC, 2007; Scarpatti, et.al.,2001 and Snaider,
2001).

Besides the native forests, in the areas near to the mountain range
there are implanted forests (mainly pines). It is considered that the
climatic changes will reduce their growth (especially because of the
reduction of precipitations) and they will be more exposed to fires.
(Barros, et.al.2006; Rodríguez, 2003; Urrutia, 2005; Veblen,
et.al.,1988, 1992 y 1999; Villalba, 1998, 2002 y 2003; Villalba,
et.al.,2003 and 2006).

The obtained results are important not only for the school but also for
the government and non governmental organizations that work in the area.
They are especially important for increasing the conscience of the
danger by the population in general, because although the conditions are
given, the anthropic action is the largest originator of fires.

With to the climatic change, tendencies are observed in the
meteorological data and it is recommended to diffuse in the school and
in the population of Junín de los Andes the mitigation measures proposed
by the IPCC related with the consumption of water and energy. Mainly
because the river Chimehuín integrates the basin of the river Limay that
possesses 5 dams that provide great part of the hydroelectric energy
consumed in the country.

Regarding the fires that cause the danger and the frequency of fires due
to anthropic action the following is recommended:

1.  Create alleys of easy access for the fire brigades in the implanted
    forests.

2.  Carry out seasonal gathering of the remaining organic matter between
    the pines.

3.  Avoid burning the residue, promoting treating and recycling them.

4.  Promote fire control at educational level.

5.  Publish procedures of fire prevention for residents (with emphasis
    in not burning organic residuals) and visitors (with emphasis in the
    treatment of cigarette butts, the campfires, etc).

**Acknowledgements:**

The authors thank the supply of hydrological data to the State
Subsecretary of Hidrologic Resources; the supply of meteorological data
from Junín de los Andes to Technician Simón Llewellin Lewis from the
Coordination Office for Fire Management. They also thank the information
sent by Dr. María Luz Duarte on the ENSO phenomenon, to Doctors Jorge
Rabasa, Darío Trombato, Juan Minetti, Ricardo Villalba, Rosa Compagnucci
and Inés Camilloni for the information on the impacts of climatic change
in North Patagonia.

Especially, they also thank all the students of CEI "San Ignacio" who
from the year 2001 to the present have carried out water quality
analyses in the river Chimehuín and in the watering channel; to
Agricultural Engineers Guillermo Barrau, Christian Hick for supplying
information about water quality for agriculture.

We also thank the collaboration of Capsa-Capex Company for supplying the
equipment to carry out environmental mensurations.

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