Climate change has been a matter of discourse for the last several decades. Much research has been conducted regarding the causes and impacts of climate change around the world. The current research contributes to the knowledge of the influence of climate change on our environment, with emphasis on earthquake occurrences in the region of Indonesia. Using global temperature anomaly as a measure of climate change, and earthquake data in Indonesia for the period 1900-2022, the paper seeks to find a relationship (if any) between the two variables. Statistical methods used include normal distribution analysis, linear regression and correlation test. The results show peculiar patterns in the progression of earthquake occurrences as well as global temperature anomaly occurring in the same time periods. The findings also indicated that the magnitudes of earthquakes remained unaffected by global temperature anomalies over the years. Nonetheless, there appears to be a potential correlation between temperature anomalies and the frequency of earthquake occurrences. As per the results, an increase in temperature anomaly is associated with a higher frequency of earthquakes.
Indonesia is recognized as a region vulnerable to earthquakes. This is because of its location at the intersection of three significant tectonic plates: the Indian-Australian, Eurasian, and Pacific plates. The Indian-Australian plate tends to shift northward, leading to a subduction zone beneath the Eurasian plates, while the Pacific plate moves in a westward direction [
Climate change has been known to have caused a great deal of natural disasters and extreme weather events, including; floods, droughts, and in other parts of the world, hurricanes. It’s crucial to emphasize that climate change manifests various impacts worldwide and the impacts vary across different regions and climatic conditions. Due to its geography and topography, Indonesia has often been afflicted with shifts in rainfall and temperature [
When earthquakes surpass a magnitude of 9.0, they can cause destruction within a range of up to 1000 kilometers [
Few research works have tackled the question of whether Indonesia is at a greater risk of earthquakes due to increasing temperature caused by global warming. The purpose of the current research is to make an attempt to answer this question by understanding the relationship between global temperature anomaly caused by climate change, and frequency of earthquake occurrences in Indonesia.
Global temperature anomaly data was collected from NOAA (National Oceanic and Atmospheric Administration) data-base [
Research methods can be summarized as follows:
1) Conduct normal distribution analysis.
2) Conduct stability analysis.
3) Calculate linear closeness using linear regression equations.
4) Use a correlation test to determine the relationship between climate and earthquakes.
Normal Distribution will involve the calculation of the mean, mode, median, standard deviation and probability distribution in order to obtain a graphical representation of the data.
f ( x | μ , σ 2 ) = 1 σ 2 π e − ( x − μ ) 2 2 σ 2 (1)
Stability analysis is used to describe how well the data can be managed and involves the determination of critical values based on the standard deviation. Critical values for a two-tailed test at a significance level α can be calculated using the inverse cumulative distribution function of the standard normal distribution (Z-distribution). In this paper, with a 95% confidence level ( α = 0.05 ), the critical values are:
Critical value for the right tail:
Z α / 2 = Φ − 1 ( 1 − α / 2 ) z (2)
Critical value for the left tail:
Z α / 2 = Φ − 1 ( α / 2 ) (3)
where Φ − 1 represents the inverse cumulative distribution function of the standard normal distribution.
Linear regression is used to determine the general trend of the data set. Consider a linear regression model for a set of data points ( x i , y i ) where i = 1,2, ⋯ , n : The simple linear regression equation:
y = m x + c (4)
where m is the slope and c is the y-intercept. The slope m (coefficient) is calculated using:
m = n ∑ ( x i y i ) − ∑ x i ∑ y i n ∑ x i 2 − ( ∑ x i ) 2 (5)
The y-intercept c is calculated using:
c = ∑ y i − m ∑ x i n (6)
The predicted value y ^ based on a given x can be calculated as:
y ^ = m x + c (7)
The coefficients m and c can also be computed using matrices:
[ m c ] = ( X T X ) − 1 X T Y (8)
where,
X = [ 1 x 1 1 x 2 ⋮ ⋮ 1 x n ] (9)
and,
Y = [ y 1 y 2 ⋮ y n ] (10)
A correlation test used to express statistically how the two variables—Global Temperature Anomaly and Earthquake occurrence in Indonesia for the specified time period. The process involves calculating the Pearson correlation coefficient and determining P-values. Scatter-plots are used to help visualize the result.
Consider two variables X and Y for which we want to calculate Pearson’s correlation coefficient (r) and its associated P-value. Let X = { x 1 , x 2 , ⋯ , x n } and Y = { y 1 , y 2 , ⋯ , y n } be the sets of values for each variable. The Pearson correlation coefficient (r) is given by:
r = n ∑ x y − ∑ x ∑ y ( n ∑ x 2 − ( ∑ x ) 2 ) ( n ∑ y 2 − ( ∑ y ) 2 ) (11)
To calculate the P-value associated with r, we can use statistical methods, such as:
Let r be the calculated correlation coefficient and df be the degrees of freedom.
p-value = P ( | R | > | r | ) (12)
where R ~ t ( d f ) .
In order to obtain a more in-depth understanding of the data, the various statistical—normal distribution analysis, stability analysis and linear regression analysis were carried out on both data sets for the period 1900-2022 i.e. Global Temperature Anomaly data set and the data set of Earthquake Occurrences in Indonesia. All calculations were made using python and the results are summarized in the following tables and images.
The following are the statistical results in the Global Temperature anomaly for the period 1900-2022 and they collectively offer a summary of the distribution, central tendency, and variability of data, allowing better understanding of the characteristics of the data:
Number of samples in Anomaly: 123
Mean Global Temperature Anomaly: 0.16341463414634147
Mode of Global Temperature Anomaly: 0.1
Median of Global Temperature Anomaly: 0.02
Standard deviation of Global Temperature Anomaly: 0.45355034652535176
A standard deviation of 0.5 with a mean of 0.2 indicates that the data is relatively tightly clustered around the mean of 0.2. Almost all of the data would falls within the range of approximately −0.8 to 1.2. The mode which is the most frequent value is 0.1, and the mid most value is 0.02.
Critical value for the right tail: 1.0523569785116922
Critical value for the left tail: −0.7255277102190094
Slope (Coefficient): 0.010538623260373269
Intercept: −20.502825579445638
The collective summary of the distribution, central tendency, and variability of data is as follows: Number of samples in Mag: 279
Mean: 6.640860215053763
Mode: 6.5
Median: 6.6
Standard deviation of Mag: 0.9344220572681842. A standard deviation of 0.9 with a mean of 6.6 indicates a high level of variation in the data around the mean. Majority of the data falls within the range of 3.9 to 9.3. The mode which is the most frequent value is 6.5, and the mid most value is 6.6.
Pearson’s correlation coefficient: 0.2519665863046944
P-value: 0.00493231758965154
Correlation Coefficient: −0.39
P-value: 0.0000
The correlation is statistically significant.
The line graph (
The Normal Distribution Analysis (
The linear regression equation is given by:
Y = − 20.503 + 0.012 X + ε (13)
where, Intercept: −20.502825579445638.
Slope: 0.010538623260373269. A positive slope means that as X increases, Y is expected to increase. That is, as the years increase, temperature anomaly is expected to increase. However, large negative intercept suggests that even when the independent variable X is zero, the expected value of Y is substantially lower (
The R-squared value of 0.6862183120507789 lies closer to 1 than 0, which indicates that the independent variable has a strong explanatory power on the dependent variable. Aside from potential bias caused by outliers, no other potential sources of bias were detected.
According to the Scatter Plot (
The Normal distribution analysis (
The standard deviation was 0.943 which indicates that most of the earthquakes were close to the mean in magnitude (6.64) Magnitudes between 6.0 and 6.9 are considered strong and could cause damage to infrastructure and could also be life threatening if they occur in highly populated areas. Kurtosis of −0.6279336419464818 which means the data have fewer extreme values (
The linear regression equation is given by:
Y = 27.674 − 0.011 X + ε (14)
where the intercept = 27.67431054034266 and the slope = −0.010599392828576283. A negative slope indicates a downward trend of earthquake magnitudes over time.
R-squared value of 0.14565349637741237 which means that only about 20 percent of the fluctuations in earthquake magnitudes are accounted for by the variables in the model. The other 80 percent is not explained by the model.
The Correlation coefficient of −0.39 indicates a negative relationship between the two variables. As global temperature anomaly increases, the earthquake magnitudes in Indonesia tend to decrease, and vice versa. A P-value of 0 indicates that the observed result is extremely unlikely under the assumption that the null hypothesis is true. Therefore, the result is considered statistically significant.
The purpose of this research was to investigate the relationship between climate change (measured through increase in global temperature) and earthquake occurrences in Indonesia. Based on the results, it is evident that the relationship between temperature anomaly and earthquake occurrences is not a straightforward one. The results indicate that increase in global temperatures over the past century may have had an impact on the frequency of earthquakes. This conclusion was drawn from the observation that the highest global temperature recorded coincided with the most earthquakes occurrences in Indonesia. Another likelihood is that there perhaps exists a third factory responsible for both scenarios (high temperature and high earthquake frequency) to occur at the same time. The available evidence does not definitively indicate whether Indonesia faces a heightened risk of earthquakes due to rising temperatures attributed to global warming. Further research can be conducted to investigate this, as well as to understand different ways in which thermal stress could have geological impacts that lead to earthquakes.
The authors declare no conflicts of interest regarding the publication of this paper.
Mtambo, L. and Tao, X.X. (2024) Seismology and Climatology: A Study of Seismological Impacts of Climate Change in Indonesia. Atmospheric and Climate Sciences, 14, 210-220. https://doi.org/10.4236/acs.2024.142013
NOAA: National Oceanic and Atmospheric Administration
NCEI: National Centers for Environmental Information