Lately the planet’s climate has been constantly changing, caused mainly by global warming which has exposed a great deal of concern to the population over the years. In order to understand the possible impacts that such changes may have on the environment and society in general, the importance of the analysis of climate and hydrological events trends and their performance in a region is justified. The objective of the present work was to perform the climatic classification and to evaluate the behavior of the Climatological Hydric Balance—CHB, from the region of Machado state of Minas Gerais—MG, taking into account a historical series of 55 years of climatic season data of the National Institute of Meteorology—INMET; to verify the occurrence of climatic changes by the temporal trends of precipitation and the average temperature, using the Mann-Kendall and Pettitt method; and the influence of these possible climate changes on CHB behavior and on the region’s climate classification. Based on the results found it verified the increase in the water deficit between the months of June to September and a reduction in the water surplus from November to February. By means of the trend analysis, there was a positive trend of increase in the average temperature of 1.6 °C until the year 2100. The continuity and occurrence of these trends may have impacts on the economy, agriculture, the hydrological cycle, and consequently on the fauna, the flora and the population.
The world’s climate activity has been suffering severe changes, producing a raise in the number of environmental disasters and natural catastrophes, causing huge financial losses in many areas of the planet [
The damage to the society, to the economy and to the environment caused by climate changes is frequently showed by the press and, among the most relevant economic activities, Cecílio et al. [
In order to have an adequate planning of the tillage, the climate conditions and the soil from the different agricultural regions have to be considered. Thus, the good establishment of a crop in the tillage depends mainly on the water availability, on the soil technical features, on the amount of heat and solar energy. When a shortfall or excess of these elements occurs, it will possibly reduce the tillage’s productivity.
According to Marin et al. [
The CHB can be calculated by the accounting of the natural supply of groundwater, rainfall (P) and atmospheric demand, through the potential evapotranspiration (ETP), and with a maximum level of storage or available water capacity, appropriate to the present study. The CHB provides estimative of water deficiency (DEF), water surplus (EXC), real evapotranspiration (ETR) and storage of groundwater (ARM). The CHB can be elaborated in a daily scale, in specific days, or monthly or annual scale [
The Knowledge of the CHB elements guides the agricultural planning and management. Besides, it supports the climate and agro ecological zoning; the definition of the most appropriate times for the main processes in the crop, such as soil preparation, seeding and planting, pulverization and harvest; the estimation of the crops productivity; irrigation design and management; management of hydric resources in river basins; the selection and sizing of techniques for the conservation of water and soils [
According to the IPCC report [
Studies aiming the detection of possible climate tendencies applied satisfactorily the Mann-Kendell method in climate variations. The World Meteorological Organization (OMM) suggests this test for tendency identification in time series [
Salviano, Groppo e Pellegrino [
In the same way, the study of the climate information daily collected is of great importance to study and predict the main severe hydric phenomena such as droughts, storms and hail rains. Those phenomena are essential to understand the climate changes and the possible impacts that they may cause in a certain area [
The objective of this work was to evaluate the behavior of BHC and to determine the existence of changes in the climate due to the temporal trends of precipitation and the average temperature in the Machado-MG region, investigating the influence of possible climatic changes on BHC behavior.
The studying area is represented by the city of Machado, located in the micro region of Alfenas in the south of the Minas Gerais state. It has a territorial area of approximately 586 km2 and is located in the geographic coordinates of latitude 21˚40'30''S and longitude 45˚55'12''W. Its main economic activity is the agriculture exploitation and coffee is the main economic product and a massive generator of employment [
In order to use the statistic method determined for this research, a meteorological mean of 55 years of daily climate data was applied. The data is from to the climatological station of the INMET―National Institute of Meteorology located in Machado-MG.
The data obtained in INMET are related to the monthly and annual means of a historical series from 1961 to 2015, referring to temperature and precipitation. The organization and tabulation of the data were executed using an electronic speadsheet.
According to Marin et al. [
In order to elaborate and estimate the CHB by Thornhwaite and Mather method [
To elaborate the CHB of this research, we used a model of an electronic spreadsheet developed by Rolim, Sentelhas and Barbieri [
Firstly, monthly climatic potential evapotranspiration, in mm, was estimated through the Thornthwaite and Mather [
ETP = 16 ( 10 t / I ) a (1)
ETP = potential evapotranspiration for a 30 days month with a 12 hour insolation (mm), t is the average temperature of the month (˚C) and a is the cubic function of I (that can be calculated by the formula)
a = 6.75 × 10 − 7 I 3 − 7.71 × 10 − 5 I 2 + 1.792 × 10 − 2 I + 0.49239 (2)
I is the annual calorific value.
The value of I can be calculated by summing the 12 values of the monthly calorific indices (i), which can be calculated by the following formula:
i = ( t ′ / 5 ) 1 , 514 (3)
t’ is the common monthly average temperature (˚C).
After estimating the evapotranspiration, the calculation of Thornthwaite and Mather [
P-ETP: Calculates the difference between precipitation P and the estimated potential evapotranspiration (ETP) in order to collect positives and negative balances. In most areas, the most common is the occurrence of a rainy season followed by a drought. In the humid months P-ETP are positives, indicating excessive rainfall, while in the dry months P-ETP is negative, representing potential water loss. When the situation is the water recharge in the soil, that is, whenever the (PETP) ≥ 0, it has to be added to ARM (storage) of the previous period and through this new ARM, it is possible to calculate the new NAc (accumulated negative) by the following expression:
NAc = CAD ( ln ARM CAD ) (4)
When there is withdrawal of the water in the soil, that is, when the (P-ETP) < 0, it has to be accumulated and through the (P-ETP) we calculate the ARM, using the following expression:
First, we calculate the Nac by the equation:
NAc = NAc + NAc previous (5)
Then, we calculate the ARM:
ARM = CAD ( e − | NAc CAD | ) (6)
ALT―Storage Alteration
ALT = ARM − ARM anterior (7)
ALT > 0 There was replacement
ALT < 0 There was withdrawal of water from the soil
ETR―Real Evapotranspiration
If,
ETR = P + | ALT | (8)
If ( P − ETP ) ≥ 0 ,
ETR = ETP (9)
DEF―Hydric deficiency: refers to the amount that the soil-plant system did not evapotranspirate
DEF = ETP − ETR (10)
EXC―Water surplus: it is related to the water that the soil cannot retain or evapotranspirate
If ARM < CAD ,
EXC = 0 (11)
If ARM = CAD ,
EXC = ( P − ETP ) − ALT (12)
In order to do the climate classification by the Thornthwaite and Mather method [
I h = ( EXC / ETP ) × 100 (13)
I a = ( DEF / ETP ) × 100 (14)
I u = I h − ( 0.6 × I a ) (15)
In order to classify the thermal factor (TE), the climate types are defined based on the annual potential evapotranspiration (annual ETP). The subtypes depend on the percentage relation between the potential evapotranspiration in the
Climate types | Humidity index (Iu) |
---|---|
A → super humid | Iu ≥ 100 |
B4 →humid | 80 ≤Iu < 100 |
B3 → humid | 60 ≤Iu < 80 |
B2 → humid 40 ≤ Iu < 60 | 40 ≤Iu < 60 |
B1 → humid 20 ≤ Iu < 40 | 20 ≤ Iu < 40 |
C2 → semi-humid 0 ≤ Iu < 20 | 0 ≤ Iu < 20 |
C1 → dry semi-humid −20 ≤ Iu < 0 | −20 ≤ Iu < 0 |
D → semi-arid −40 ≤ Iu < −20 | −40 ≤ Iu < −20 |
E → arid −60 ≤ Iu < −40 | −60 ≤ Iu < −40 |
Humid climes (A, B, C2) | Arid index (Ia) |
---|---|
r → without or with a small hydric deficit | 0 ≤ Ia < 16.7 |
s → moderate hydric deficit in the summer | 16.7 ≤ Ia < 33.3 |
w → moderate hydric deficit in the winter | 16.7 ≤ Ia < 33.3 |
s2 → big hydric deficit in the summer | Ia ≥ 33.3 |
w2 → big hydric deficit in the winter | Ia ≥ 33.3 |
Dry climes (C1, D, E) | Arid index (Ia) |
d → small or null water surplus | 0 ≤ Ih < 10 |
s → moderate water surplus in the summer | 10 ≤ Ih < 20 |
w → moderate water surplus in the winter | 10 ≤ Ih < 20 |
s2 → big water surplus in the summer | Ih ≥ 33.3 |
w2 → big water surplus in the winter | Ih ≥ 33.3 |
summer and the annual potential evapotranspiration (
TE = ETP anual (16)
TE = ( ETP nover a ˜ o / ETP anual ) × 100 (17)
The non-parametric tendency test of Mann-kendall―MK [
Climate types | ETP annual (mm) | Climate Subtypes | (ETP in the summer /ETP annual) *100 |
---|---|---|---|
A ′ → megathermic | ETP ≥ 1140 | a ′ | Less than 48.0% |
B ′ 4 →mesothermic | 1140 >ETP ≥ 997 | b ′ 4 | between 48.0% and less than 51.9% |
B ′ 3 →mesothermal | 997 > ETP ≥ 885 | b ′ 3 | between 51.9% and less than56.3% |
B ′ 2 →mesothermal | 885 > ETP ≥ 712 | b ′ 2 | between 56.3% and less than 61.6% |
B ′ 1 → mesothermal | 712 > ETP ≥ 570 | b ′ 1 | between 61.6% and less than 68.0% |
C ′ 2 → microthermal | 570 > ETP ≥ 427 | c ′ 2 | between 68.0% and less than 76.3% |
C ′ 1 → microthermal | 427 > ETP ≥ 287 | c ′ 1 | between 76.3% and less than 88.0% |
D ′ → tundra | 287 > ETP ≥ 142 | d ′ | equal or more than 88.0% |
E ′ → perpetual ice | ETP < 142 |
[
In MK test, the S statistic is calculated by the summing of all counts, as follows:
S = ∑ i = 1 n − 1 ∑ j = i + 1 n sgn ( x j − x i ) (18)
In which,
sgn ( x j − x i ) = { + 1 ; if x j ≥ x i 0 ; if x j = x i − 1 ; if x j < x i (19)
The S statistic tends to normality for a large n, with mean and variance given by:
E [ S i ] = 0 V a r ( s ) = n ( n − 1 ) ( 2 n + 5 ) − ∑ j = 1 p t j ( t j − 1 ) ( 2 t j + 5 ) 18 (20)
In which n is the size of the time series. Therefore, the statistic test Z is given by:
Z = { S − 1 ( V a r ( S ) ) 1 2 s e S > 0 0 s e S = 0 S + 1 ( V a r ( S ) ) 1 2 s e S < 0 (21)
The considerable statistic tendency in the temporal series is measured by the Z value. This statistic is used to test the null hypothesis that the tendency does not exist. In Mann-Kendall test, a tendency is considered positive or negative, indicating a decrease or increase in the elements of the analyzed series, the case of Kandall’s Tau is negative or positive. The statistical significance was analyzed by the p-value test. The null hypothesis is not reject if p value is more or equal a; if p is less than a, the null hypothesis is rejected [
In addition to the MK test, I did the Pettitt non parametric statistic test in order to evaluate the occurrence of abrupt changes in the means of the historical series. According to Pettitt [
The Petit test verifies two samples, X 1 , X 2 , ⋯ , X t e X t + 1 , X t + 2 , ⋯ , X T belonging to the same population, providing also information about the data homogeneity from the historical series analyzed. This statistic finds the point where an abrupt change in a temporal series occurred [
The Ut,T statistic counts the times that a member of the first sample is higher than a member of the second sample. It can be written as:
U t , T = U t − 1 , T + ∑ j = 1 T sgn ( X i − X j ) (22)
for t = 2 , ⋯ , T
where: sgn(x) = 1 para x > 0; sgn(x) = 0 for x = 0; sgn(x) = −1 for x < 0.
The Ut,T statistic is calculated for the 1 < t < T values and the K(t) statistic from Pettitt test is the maximum absolute value for Ut,T. This statistic locates the changing point of a temporal series and its meaning. It can be described as:
k ( t ) = M A X 1 ≤ t ≤ T | U t , T | (23)
p ≅ 2 exp { − 6 k ( t ) 2 / T 3 + T 2 } (24)
The abrupt changing point is the time (t) where there is the maximum k(t). We can calculate the critical K values by the equation:
K c r i t = ± − ln ( p / 2 ) ( T 3 + T 2 ) 6 (25)
The significance level used was 5%.
The software extension XLSTAT 2014.5.03, for Microsoft Office Excel, was used in order to analyse and organize the data.
From of the graphs of surplus and water deficit we can precisely establish the driest periods, the rainy seasons, the traffic conditions for supplies and machines, the best seasons for the development of vegetation and for the beginning of a recovery process from degraded areas through the hydric surplus and deficiency graphics. The CHB analysis shows that, in the region of Machado, the dry season, when the highest hydric deficits are observed, maintains close values for the different periods analyzed: a) Period (1961-1979), b) Period (1979-1998); c) Period (1998-2015); d) Period (1961-2015), varying between 9 to 16 mm in August, the most critical period regarding hydric deficiency (
According to Matielo et al. [
The CHB shows a reduction in the hydric surplus for the specific a) Period (1961-1979), b) Period (1979-1998); c) Period (1998-2015); d) Period (1961-2015), mainly in February, October and November In August, the hydric deficit was more accentuated (
According to Cunha and Martins [
I calculated the index to do the climate classification through the information obtained in the CHB. The hydric index was (Ih = 66.65), the aridity index was (Ia = 1.72), and the humidity index was (Iu= 65.62). Regarding the thermal factor (TE), the climate types were defined by the potential annual evapotranspiration (ETPanual = 930.77 mm) and by the percentual relation between the potential evapotranspiration in the summer and the potential annual evapotranspiration ((ETP in the summer/ETP annual) × 100 = 33.60%).
Thus, the climate classification of the region of Machado is a humid mesothermic clime, with little hydric deficit (B3 r B ′ 3 a ′ ).
Craparo et al. [
According to the climatic data from region of Machado-MG, there is only a significant reduction tendency in October, with a rate of 1.7 mm per year, which is significant for two tests (
MONTH | PERIOD ANALYZED IN CHB | |||||||
---|---|---|---|---|---|---|---|---|
1961-1979 | 1979-1998 | 1998-2015 | 1961-2015 | |||||
DEF | EXC | DEF | EXC | DEF | EXC | DEF | EXC | |
JANUARY | 0.0 | 159.0 | 0.0 | 182.6 | 0.0 | 181.0 | 0.0 | 179.7 |
FEBRUARY | 0.0 | 111.5 | 0.0 | 109.1 | 0.0 | 104.4 | 0.0 | 109.0 |
MARCH | 0.0 | 63.8 | 0.0 | 117.6 | 0.0 | 70.3 | 0.0 | 84.2 |
APRIL | −0.1 | 0.0 | 0.0 | 6.5 | −0.3 | 0.0 | 0.0 | 0.0 |
MAY | −0.1 | 0.0 | 0.0 | 11.3 | −0.3 | 0.0 | 0.0 | 0.0 |
JUNE | −1.6 | 0.0 | −0.7 | 0.0 | −3.6 | 0.0 | −1.1 | 0.0 |
JULY | −1.8 | 0.0 | −3.8 | 0.0 | −6.9 | 0.0 | -3.3 | 0.0 |
AUGUST | −8.0 | 0.0 | −14.3 | 0.0 | −16.7 | 0.0 | −11.6 | 0.0 |
SEPTEMBER | −3.7 | 0.0 | 0.0 | 0.0 | −3.6 | 0.0 | 0.0 | 0.0 |
OCTOBER | 0.0 | 20.4 | 0.0 | 20.1 | 0.0 | 0.0 | 0.0 | 0.0 |
NOVEMBER | 0.0 | 107.6 | 0.0 | 81.8 | 0.0 | 11.4 | 0.0 | 82.3 |
DECEMBER | 0.0 | 172.2 | 0.0 | 210.0 | 0.0 | 131.2 | 0.0 | 165.4 |
MONTH | MANN-KENDALL | PETTITT | |||
---|---|---|---|---|---|
p-valor | Kendall’s Tau | p-valor | K | Changing point (t) | |
JANUARY | 0.5594 | −0.0601 | 0.8418 | 102.0 | 8 |
FEBRUARY | 0.7039 | −0.0390 | 0.9131 | 95.0 | 11 |
MARCH | 0.6020 | 0.0532 | 0.6738 | 126.0 | 14 |
APRIL | 0.8252 | 0.0230 | 0.6346 | 130.0 | 8 |
MAY | 0.6778 | 0.0426 | 0.3918 | 159.0 | 21 |
JUNE | 0.9929 | 0.0018 | 0.4385 | 154.0 | 22 |
JULY | 0.7621 | 0.0313 | 0.9691 | 82.0 | 8 |
AUGUST | 0.2122 | −0.1266 | 0.4439 | 152.0 | 16 |
SEPTEMBER | 0.5574 | 0.0595 | 0.6073 | 133.0 | 21 |
OCTOBER | 0.0059 | −0.2730 | 0.0261 | 265.0 | 31 |
NOVEMBER | 0.7105 | −0.0541 | 0.7936 | 108.0 | 19 |
DECEMBER | 0.6800 | 0.0598 | 0.1156 | 208.0 | 25 |
Santos [
of this variation, stating that this fact may be reflections of natural fluctuations and random behaviors inherent to the historical series itself.
However, Salviano, Groppo and Pellegrino [
We can observe throught the climate data in
MONTH | MANN-KENDALL | PETTITT | |||
---|---|---|---|---|---|
p-valor | Kendall’s Tau | p-valor | K | Changing point (t) | |
JANUARY | 0.0323 | 0.2241 | 0.0147 | 249.0 | 25 |
FEBRUARY | 0.0363 | 0.2173 | 0.1532 | 183.0 | 25 |
MARCH | 0.1113 | 0.1657 | 0.1795 | 178.0 | 26 |
APRIL | 0.0007 | 0.3455 | 0.0007 | 320.0 | 19 |
MAY | 0.1879 | 0.1374 | 0.1823 | 176.0 | 16 |
JUNE | 0.3064 | 0.1071 | 0.5350 | 128.0 | 31 |
JULY | 0.0248 | 0.2347 | 0.1293 | 186.0 | 14 |
AUGUST | 0.4775 | 0.0747 | 0.6033 | 120.0 | 31 |
SEPTEMBER | 0.1100 | 0.1682 | 0.0844 | 199.0 | 32 |
OCTOBER | 0.0003 | 0.3700 | 0.0010 | 311.0 | 19 |
NOVEMBER | 0.0350 | 0.2265 | 0.0590 | 195.0 | 15 |
DECEMBER | 0.0178 | 0.2474 | 0.0036 | 248.0 | 20 |
for Mann-kendall and not significant for Pettitt. However, the test indicates that the tendency started in 1976. October presents an increasing tendency of 0.029˚C per year, significant for Mann-kendall and not significant for Pettitt. However, the test indicates that the tendency started in 1982. November presents an increasing tendency of 0.0016˚C per year, significant for Mann-kendall and not significant for Petit, considering that the tendency started in 1977. And for the month of December there is a trend of increase of 0.019˚C per year, significant for both tests, and the trend occurs from 1985.
In accordance with to the IPCC report [
According to the National Supply Company―CONAB [
Avila et al. [
Salviano, Groppo and Pellegrino [
By simulating the increase in temperature from the tendency data obtained, a considerable increase in temperature is observed for the next century. The results reinforce the data obtained by the IPCC surveys, with an average increase of 1.6˚C (
In
In
MONTH | REGRESSION MODEL | R2 | TEMPERATURE INCREASE YEAR 2100 (°C) |
---|---|---|---|
JANUARY | −15.59717 + 0.01917*Ano | 0.1679 | 1.577 |
FEBRUARY | −10.03567 + 0.01647*Ano | 0.1093 | 1.328 |
MARCH | - | - | - |
APRIL | −21.16785 + 0.2087*Ano | 0.1895 | 1.743 |
MAY | - | - | - |
JUNHO | - | - | - |
JULY | −16.59154 + 0.01644*Ano | 0.1074 | 1.328 |
AUGUST | - | - | - |
SEPTEMBER | - | - | - |
OCTOBER | −38.29537 + 0.0299*Ano | 0.2180 | 2.407 |
NOVEMBER | −9.66397 + 0.01575*Ano | 0.0988 | 1.328 |
DECEMBER | −17.47673 + 0.01993*Ano | 0.1933 | 1.577 |
MONTH | PERIOD ANALYZED AT BHC | |||
---|---|---|---|---|
1962-2015 | 1962-2100 | |||
DEF | EXC | DEF | EXC | |
JANUARY | 0.0 | 179.7 | 0.0 | 164.2 |
FEBRUARY | 0.0 | 109.0 | 0.0 | 97.7 |
MARCH | 0.0 | 84.2 | 0.0 | 86.9 |
APRIL | 0.0 | 0.0 | −0.7 | 0.0 |
MAY | 0.0 | 0.0 | 0.0 | 0.0 |
JUNE | −1.1 | 0.0 | −1.5 | 0.0 |
JULY | −3.3 | 0.0 | −5.1 | 0.0 |
AUGUST | −11.6 | 0.0 | −11.7 | 0.0 |
SEPTEMBER | 0.0 | 0.0 | 0.0 | 0.0 |
OCTOBER | 0.0 | 0.0 | −52.5 | 0.0 |
NOVEMBER | 0.0 | 82.3 | 0.0 | 0.0 |
DECEMBER | 0.0 | 165.4 | 0.0 | 139.6 |
The climate classification in the region of Machado would continuous to be a humid mesothermic clime, with little hydric deficiency. However, its category would change from B3 r B’3 a’, to B2 r B’4.
The climate classification of the region of Machado is a humid mesothermic clime, with little hydric deficit (B3 r B ′ 3 a ′ ).
The Mann-kendall test and the Pettitt test show an agreement in their results and can be used in order to identify time tendencies. There is tendency of reduction in the average volume of precipitation for October in the average of 1.7 mm per year.
There is a tendency of average temperature increase for the months of January, February, April, July, October, November and December in the average of 1.6˚C until the year 2100.
The significant tendencies in the climate variables studied show that important changes are happening, mainly in the average temperature.
The occurrence of these tendencies over the years may have impacts on agriculture, on the hydrological cycle and, consequently, on the fauna and flora and the population.
The authors declare no conflicts of interest regarding the publication of this paper.
Rodrigues, G.S., Putti, F.F., da Silva, A.C., de Oliveira, A.S. and Filho, L.R.A.G. (2018) Climatological Hydric Balance and the Trends Analysis Climatic in the Region of Machado in Minas Gerais State, Brazil. American Journal of Climate Change, 7, 558-574. https://doi.org/10.4236/ajcc.2018.74034