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9GEOGRAPHICAL SCIENCES
MANiFESTATiONS OF GLOBAL WARMiNG iN AZERBAIJAN
Mammadov A., Calalova V.
Baku State University, Faculty of Geography,
Baku, Azerbaijan DOI: 10.5281/zenodo.6579882
ABSTRACT
The task of researchers is to protect the Earth's ecological environment from atmospheric anomalies. Unfortunately, researchers are poor in the struggle to fulfil this mission. As a result of global warming, various atmospheric anomalies that occur in different regions every year cause significant damage to the economy, and sometimes even the loss of the life. As a result of contemporary warming, the precipitation has decreased, the drought recurrences and their sharpness criteria have increased, and as a result, agricultural productivity has declined. In some regions, especially in arid areas, the demand for drinking water has increased and there is a shortage of water. In this regard, the relationship between the drought and the productivity has been assessed on analyzing of the contemporary warming in Azerbaijan. In addition, methods and recommendations are given for the calculation of some prognostic indicators.
Keywords: productivity, periodical, anomaly, golesen, criteria, drought, optimum.
Introduction
According to researches on climate changes in historical periods, the climate optimum of the Holo-cene is currently one of the most controversial problems. According to the division prepared 1,000 years ago, that optimum, i.e. the hottest and wettest period of the Holocene, has been in the Atlantic period (84,500 years ago) [3,7].
Collected data allow us to say the opposite, that is, the thermal maximum of the Holocene is not in the Atlantic period, but in the Boreal period (10-8 thousand years ago).
Due to its composition, the Boreal period (10 -8 thousand years ago) has been colder than the Atlantic period. Atlantic forest areas are rich in broad-leaved (not tree-like) plant remains [2,3,7]. In the result of the analysis of palynological spectra of Holocene sedimentary sections, the following has been identified:
1) The "cold" nature of the boreal period due to the characteristics of the vegetative cover to which it belongs is explained that during the previous warming the vegetative cover has been formed from the flora of the previous glacial period, and the migration of the existing flora from the south has been delayed [3]. This explains the current lack of some plant species.
2) The maximum formation of broad-leaved plant remains, in fact, occurs at different times in different regions.
Relationship between the number of spots in 1
3) The maximum of wood remains indicates the relocation of forest boundaries, but the maximum amount can be considered a rarity. Thus, the extension of its borders to the north depends on the average annual temperature, the amount of the precipitation, the duration of the growing season and the melting of seasonal frosts in the depths of the soil.
4) The Atlantic period (in the subtropics) is not hot, but rather cool [3, 7].
5) Nowadays there is a lot of information that confirms: the thermal maximum of Holocene has been in the boreal period (10-8 thousand years ago). The adoption of the greenhouse effect as one of the main causes of climate changes would allow us to conclude that the concentration of carbon dioxide in the atmosphere over that period has been on the maximum level. According to such considerations, the assumption that in the boreal period, 10-8 thousand years ago, there has been the climatic optimum allows us to say that the current period is changed not to warming, but to cooling. The current short-term warming of the Earth is expected to change in the near future [2,8]. According to the research, the recent warming is due to the activation of solar cycles in 1900-1998 y.y. During that period, the number of sunspots increased in some regions of Azerbaijan. As a result, the number of droughts increased in the mentioned regions to 373819 [6,8]. This relationship is expressed in the values of the correlation coefficient (k) in Table 1.
Table 1.
Periods The sum of the wolf numbers Guba Ganja Zagatala Baku Naxchevan
1902-1913 373 4 4 9 7 9
1914-1923 447 0 5 9 17 8
1924-1933 411 8 3 9 8 15
1934-1944 611 14 13 17 14 17
1945-1954 751 14 15 17 14 14
1955-1964 956 19 17 12 15 20
1965-1976 710 22 18 15 15 13
1977-1985 819 23 16 14 12 11
k 0,84 0,91 0,6 0,72 0,58
average 635 13 11,4 12,8 12,8 13,4
Drought is one of the most dangerous atmospheric phenomena for the agriculture. There believed that the problem of the saturation deficit in the arid zones of the globe is exacerbated, and drought periods became frequently repeated. As the drought occurs gradually, there has been considered that the chances for the implementation an action plan to minimize the damage are enough high: to change the form of the land use; to use of reservoirs and wells for the irrigation; crop insurance; the protection of interests of priority water users, etc. The replacement of cultivated plant varieties with others and the construction of reservoirs may be examples of long-term measurements. The intensity of the drought has been increased in the Absheron region of the republic for the 2001-2019
The determination of the drought indexes in mountainous areas is more interesting than that in the foothills. One of the main reasons for this is that some studies show the increase of the rainfall over mountainous areas [8]. In this case, the situation could be perceived as a change in the circulatory regime. Thus, in a sense, it can be said that over the contemporary warming period, by increasing temperature, the precipitation decreases and the drought increases.
y.y. period on 16-17%; in the Greater Caucasus - 1415%; in the Lesser Caucasus - 18-19%; in the subtropical regions of the republic - 11-13%; on the southern slope of the Greater Caucasus - 8-10%; in Karabakh -10-12%; in Nakhchivan - 19-20% [8,9].
Over 1900-1949 y.y. the repetition frequency of droughts in the Guba-Khachmaz region has been 30%, but in 2000-2019 y.y. it has been increased to 71%. But in the relevant periods of the Ganja region, this growth has been increased from 38% to 60%. Thus, in the example of Guba region, located in the northeastern region of the republic, there has been determined that the drought increased approximately, 3035% every 50 years from 1900 to 2018 y.y. (Figure 1).
As a rule, the increase of the drought has a negative impact on the productivity of the brew grain. Thus, over 2000-2001 y.y., the productivity has been high (26 cents/ha) in the Guba-Khachmaz region due to years of drought. Over 2003-2018 y.y., due to the strong and very severe droughts, the productivity has been decreased (up to 24 cents / ha) (Figure 1, Figure 2).
2 1
4--
■5 6
7--
Figure 1. SPI-drought indexes in Guba over 2000-2018 y.y.
Figure 2. The productivity of brew grain in Guba-Khachmaz
Figure 3. SPI-drought indexes in Ganja over 2000-2018 y.y.
The intensity of drought years in Ganja began to decrease after 2001 y., and this decline continued later, except for 2013 y. (Figure 3). As can be seen from the graph, the droughts of 2000, 2001, 2009 and 2013 years led to a gradual decline of the productivity in
Ganja, respectively. At the same time over the noted period a slight increase in the precipitation in Ganja should be cited as the reason for the decrease in the drought rate (Figure 4).
Figure 4. The brew grain productivity in Ganja-Gazakh region
Figure 5. SPI-drought indexes in Zakatala in 2000-2015
Between 2000 and 2015 years, the intensity and the frequency of droughts in Zagatala have been decreased significantly. The trend for the drought is declining. The drought has been recorded in the region for only
Of course, as the trend for the drought decreased, the condition of the favourable line for the productivity should be directed to increase. During noted period the productivity of the grain has been increased in the
8 years over the mentioned period (Figure 5). range of 18.1-32.3 cents/ha in Zagatala.
Figure 6. The productivity of the brew grain in Zagatala over 2000-2015 y.y.
Figure 7. SPI-drought indexes in Lankaran over 2000-2015 y.y.
The most drought (11 years of drought) took 2009, 2010, 2012, 2013 years are strongest and most place in Lankaran over 2000-2015 y.y.: the droughts severe in terms of severity (Figure 7). that have been observed in 2001, 2005, 2006, 2007,
Naturally, such droughts in Lankaran affect the karan decreased from 22.7 cents/ha to 19.8 cents/ha productivity of brew grain, so the productivity in Lan- over the noted period (Figure 8).
Figure 8. The productivity of the brew grain in Lankaran over 2000-2015 y.y.
Thus, in any economically important region of the globe, the effects of the contemporary global warming can be met, and how long it will last can only be determined by predicting the process. The research done in this area cannot be considered sufficient. The development of climate forecasts, the cost of which is quite high, that would minimize the impact of atmospheric anomalies. Given the urgency of the issue, I share my suggestions with you.
About the prognostic-oriented stochastic model If we divide the causes of all events in the atmosphere into two variables, then:
F = ST + OT; where ST is the stationary part; OT are random parts.
Therefore, any F data series includes both ST-aggregate (periodic) and random OT random series. The first step in the investigation of the problem is to identify tc- the hidden periodicals within a given range. Such periodicals are included in any data series. Thus, the elements of the range are averaged by the method of sliding averaging, and the sub-ranges ob-
(2n \ y=A0 + A^sin + <Pi J + Ajifi
tained after each averaging stage are compared with the calculation function. When the sub-ranges correspond to the calculation function, the periodical of that sub-range is assumed. The solution of periodic functions can be described as follows:
(2n \
y = ,41 sin( —-t 4- <pl\ 1
:: TI, _ m
Where, Al= Va2 -f b2,
It should be noted that if the structure of the series contains many periodic functions with different periods, then all of them can be found by the above rule. In this case, the range can be approximated as follows [1,8]:
Where, 0 is a random part in a given range. The following results have been obtained during the analysis of Guba settlement located on the north-eastern slope of the Greater Caucasus region of Azerbaijan
-I- ■■■ + Ain,
2 "n
based on the temperature series of 1881-1981 y.y., and precipitation and temperature series of the Lesser Caucasus region for 1992-2018 y.y.: Calculations
Figure 9. The calculation of the precipitation in Gadabay for 1992-2045 y.y. based on the precipitation data for April 1992-2018 y.y. (1 observation data; 2 calculated data)
Figure 10. The detection of 5-year periodicals in Gadabay (for the precipitation) (1-factual; 2-calculated data)
Figure 11. The detection of 7-year periodicals in Gadabay (for the precipitation) (1-factual; 2-calculated data)
Figure 12. The detection of 13-year periodical in Gadabay (for the precipitation) (1-factual; 2-calculated data)
Figure 13. The calculation of the temperature in Gadabay for 1992-2045 y.y. based on temperature data for March 1992-2018 y.y. (1-observation data; 2-calculated data)
Figure 14.
The calculation of temperature fluctuations in Guba in 1881-230 y.y. (1 calculations; 2 preliminary data)
Figure 15. The detection of 8-year periodicals in Guba (1-factual; 2-calculated data)
Figure 16. The detection of 24-year periodical in Guba (1-factual; 2-calculated data)
1,2 -|
1 - ■ m
0,8 -
0,6 -0,4 - \ 2
0,2 -
-0,2 -< > 10 ar^ —""So 40 50
-0,4 - ■ ■ ■
-0,6 - ■ m m
-0,8 - m m
-1
Figure 17. The detection of 40-year periodicals in Guba (1-factual; 2-calculated data)
Checking the adequacy of the stochastic model
As can be seen from the graph of precipitation anomalies for April 1992-2018 y.y., there is no denying that there is a qualitative similarity between the actual and calculated data. In particular, over 19922007 y.y., both distributions are differed slightly in the price and the phase. Decreases and increases in post-2010 y. distributions also vary accordingly. According to estimates, the precipitation is expected to increase by 20-30 mm over 2024 - 2030 y.y. (Figure 9).
The situation with temperature distributions in March is closer, the correlation coefficient is more than 70%. The correspondence between both the initial and calculated series for 1992-2018 y.y. is quite high, and it is fully meeting the requirements for long-term weather forecasts (Figure 10).
According to the condition of the calculated curves, in each of the 5, 7 and 13-year periodicals, the correspondence is clearly visible in Figure 10, Figure 11, Figure 12.
It should be noted that the 11, 22, 33-year and other periodicals related to solar cycles have long been used in the meteorological research. However, these periodicals are not determined for Guba temperature data. There is no 11-year periodical. Here it is determined that there are periodicals of 5, 8, 18, 25, 40, 42, 55, 80 years. Here, in the 18-year periodical, the situation of both graphs is so closer that it is impossible to distinguish them. The parameters for this periodical
are defined as follows: a = 0.074288; b = 0.082814; A = 0.11; 9 = 0.7311764276.
Thus, the following analytical expressions can be written for the identified 5, 8, 18, 25, 42, 55, 60, 65, 72, 80-year periodicals:
Here A0 - the average value of fluctuations;
6' - indicates a random part.
Figure 15 gives 8-year-old, Figure 16 to 24-year-old, Figure 17 40-year-old periodicals. At this time the parameters are defined as: a = 0,032117; b = 0,083145; A = 0,09; y = 0,36862483286 . Due to the large number of selected periodicals, we are content to give only a few of them.
As can be seen from Figure 11, the computational data for Guba qualitatively are compatible with the actual. Calculations for the next 100 years, based on the theoretical basis of long-term weather forecasts, have been made on the basis of 100 years observation data, accepted as a starting condition. These sources provide extensive information on the impact of initial conditions on the forecast payment again [4,8]. Thus, according to the actual data, the condition of the isotherm curve shows that the temperature has increased on average in 1881-2010 y.y., and with this trend, the 4th phase of temperature changes until 2129 y. is no-
ticeable. The first of these phases occurs in 2009-2030 y.y., when the temperature fluctuates around the norm. The second phase is noteworthy in 2030 -2060 y.y., when the temperature drops below normal (-10 C). In the next third and fourth phases, after short-term reductions, the temperature is expected to rise to 10C (Figure 14).
Result
According to the research, the productivity can be reduced by about 5-8% in poor droughts, by 1012% in mild droughts, and by 10-13% in severe droughts. In mountainous and lowland areas, these distributions differ somewhat. Thus, the differences in the productivity reduction in the plains are as Lesser as 2-3%.
The results of precipitation and temperature calculations used for forecasting can be considered satisfactory, because in long-term forecasting practice, a forecast payment of more than 50% is considered as a positive case, in our calculations, this index is above 60%. During the calculations, there has been determined that the largeness and uniformity of the ranges is an important condition for paid forecasts. Therefore, the calculations made for mid-April and March 19922018 y.y. are considered more homogeneous, as there is a natural homogeneity. Thus, in each month, the earth is oriented at a certain angle to the sun, and therefore the solar energy coming to the earth in that month is given within certain limits. Therefore, it is easier to determine periodicals in areas with high homogeneity. According to the calculations given in Figure 13, the first phase of reducing temperature fluctuations in Gadabay began in March 2019 y., and the second phase is expected to take place after 2025 y. The increase in the precipitation is also expected in the area after about 2025 y., when the precipitation in Gadabay (April) is expected to increase by 70 mm. The third phase is expected after 2045 - 2048 y.y.
(Figure 14). Thus, the proposed model can be applied to drought forecasting, using it in temperature and precipitation forecasts.
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