4.1. Influence of ENSO Events on LST (Maximum, Minimum, and Mean) in Land Cover
Milica et al. [
25] stated that humans start to feel discomfort when the land surface temperature exceeds 30 °C for residents in Kuching City.
Figure 3 describes the land surface temperature (LST) and land cover map during El Niño and La Niña events in the study areas. It illustrates the changes in temperature patterns and land cover types during these climatic events. According to the findings depicted in
Figure 8, during the La Niña event, hotspots were observed in specified areas (e.g., a zone near area G) to a greater extent compared to other events. For instance, during the El Niño event in 2016, hotspots were identified in areas A, B, C, D, E, and F (urban areas) and G, H, I, and J (industrial areas). Similarly, during the La Niña event in 2018, hotspots were observed in areas B, C, D (urban areas), and G (industrial area). Another example mentioned is the El Niño event in 1998, where additional hotspots were found in areas B, C, D, E, F (urban areas), and G, H, and I (industrial areas) compared to the time of the La Niña event. In summary,
Figure 7 provides visual representations of temperature and land cover changes during El Niño and La Niña events in Kuching City. The information suggests that specific areas, particularly urban and industrial zones, tend to exhibit hotspots during these climatic events, with variations observed between El Niño and La Niña conditions.
Urban areas typically experience higher temperatures compared to surrounding rural or natural areas due to the urban heat island effect. This effect is primarily caused by the extensive use of impervious surfaces such as asphalt, concrete, and buildings in urban environments. These surfaces absorb and retain heat from the sun, leading to elevated temperatures. Additionally, the lack of vegetation and green spaces in urban areas reduces the cooling effect of evapotranspiration. Urban and industrial areas are characterized by high levels of human activities and infrastructure. These activities release significant amounts of heat into the environment. Heat-generating sources such as factories, power plants, vehicles, and air conditioning systems contribute to localized heating.
Urban areas often have limited vegetation cover compared to natural or rural areas. Trees and plants play a crucial role in regulating temperatures by providing shade and evaporative cooling through transpiration. The absence of vegetation results in reduced shading and less evapotranspiration. The presence of tall buildings and narrow streets can create a canyon-like effect that traps heat within urban areas. This can limit airflow and prevent heat dissipation. Moreover, urban areas tend to have higher levels of air pollution which can contribute to increased temperatures. Overall, factors such as the urban heat island effect, human activities and infrastructure, reduced vegetation cover, and heat-trapping contribute to urban and industrial areas becoming hotspots with elevated temperatures. Mitigation strategies can help alleviate the heat island effect and create more comfortable and sustainable urban environments. The figure clearly shows more hot spot areas in the study area at the time of the El Niño event. This result is supported by Melo et al. [
15], Kanghyun et al. [
26], and Jin et al. [
27], who show that during El Niño, the temperature increased compared to La Niña. This explains the statistical value and distribution of high temperatures around Kuching City during El Niño.
Figure 9 shows that the maximum value of land surface temperature during El Niño in 1997 and 1998 was higher than during La Niña events. For example, during El Niño 1998, the maximum value was 33 °C compared to 30 °C during La Niña 2011. Kemarau and Eboy [
2] showed that during El Niño events, rainfall decreases, which can cause temperatures to rise, while during La Niña, rainfall is high, which can cause temperatures in Kuching City to decrease. Kemarau and Eboy [
28] reported that the El Niño phenomenon can cause temperatures to rise between 0.5 °C and 1.5 °C, while the La Niña phenomenon can cause temperatures to drop between 0 °C and 1.2 °C.
This study discovered that the value of the absolute maximum, mean, and absolute minimum LST was higher during El Niño events compared to La Niña events for each type of land cover (
Figure 7). The study also found that urban and industrial areas had higher LST values compared to wetlands, water bodies, and vegetation during El Niño and La Niña events. This study also found that the center’s urban and industrial activities experienced a decrease in LST value during the La Niña event in 2018 compared to during the El Niño event in 2016. The study showed that the effect of ENSO on the absolute maximum temperature of each type of land cover during the El Niño event was higher than during the La Niña event. For example, as shown in
Figure 4, during the El Niño phenomenon in 2016, the absolute maximum LST in an urban area was 35.81 °C, while during La Niña in 2018, it was 31.89 °C. A second example is that industrial zones during El Niño 2016 had an absolute maximum LST value of 35.9 °C, while during the La Niña event in 2018, it was 31.99 °C. In addition to other areas surveyed during the El Niño event, the vegetation area reported a temperature in 2016 with an absolute maximum LST of 26.29 °C, while during La Niña in 2011, it was 24.1 °C. The figure also shows that during El Niño in 1998, wetland areas had an absolute maximum LST of 26.77 °C which was higher compared to the absolute maximum LST of 23.14 °C during La Niña in 2011. The values of the absolute minimum and mean LST have similar patterns to the absolute maximum LST values in each type of cover during El Niño events, being higher than during La Niña events, as shown in
Figure 9.
Melo et al. [
15], Kemarau and Eboy [
2] and Jin et al. [
28] support this result based on
Figure 4 and report that rainfall plays an important role in LST variability in study areas during La Niña and El Niño. Melo et al. [
15], Kemarau and Eboy [
2], Jin et al. [
28] and Mahmud [
29] reported that there were times when La Niña caused temperatures to drop from 0 °C to 1.2 °C while El Niño increased temperatures from 0.5 °C to 1.5 °C based on data provided by a meteorological station in Kuching discussed in
Section 3.3. Overall, industrial areas are typically found to be the hottest areas within urban environments due to high heat generation [
30,
31].
Table 5 describes in-depth surface temperature patterns with different types of coverage. There are numerous studies on the impact of land cover change on land surface temperature. For instance, in the study conducted in the Kuala Lumpur metropolitan city, researchers [
30] found that there was an increase in non-evaporating surfaces (such as concrete or asphalt) and a decrease in vegetation area. This change in land cover resulted in elevated surface temperatures and modified the overall temperature of the study area. In another study conducted in Omeno et al. [
31], the capital of Botswana, researchers discovered that there was a net gain in built-up areas and tree-covered areas. This means that these areas increased in size or density. However, cropland and grassland experienced a net loss, meaning they decreased in size or density. The study also observed that there was an increase in mean land surface temperature (LST) values across all villages between the years 2000 and 2018. The sentence also mentions a specific El Niño event. It states that during this event, there was a higher temperature increase in 2016 compared to 1998. This increase in temperature was mainly attributed to factors related to land cover changes, particularly in urban areas affected by the urban heat island effect [
16,
28,
32]. Overall, these studies highlight the impact of land cover changes on surface temperatures and demonstrate the significance of factors such as non-evaporating surfaces, vegetation area, built-up areas, and the urban heat island effect in modifying the temperature patterns of the respective study areas.
The significant increase in the time gap between the two periods (1998 and 2016) highlights the potential influence of global warming on the observed higher land surface temperature (LST) values. It is important to note that while global warming and El Niño events can both contribute to temperature increases, they are separate phenomena with distinct impacts. In the given statement, the specific relation to the El Niño event may not be explicitly addressed. However, it is possible to infer that the El Niño event, in combination with long-term global warming trends, could have exacerbated the already high temperatures observed in 2016. El Niño events often lead to regional climate anomalies, including altered precipitation patterns and changes in atmospheric circulation, which can influence local temperature conditions. Therefore, while global warming is a broader and ongoing phenomenon, the occurrence of an El Niño event can contribute to short-term temperature variations within a specific period.
To provide a more comprehensive analysis of the relation between El Niño events and the observed temperature increases, additional information about the specific El Niño conditions during 1998 and 2016, as well as their influence on regional climate patterns and land surface temperature, would be needed. Similarly, in the case of La Niña events, such as those that occurred in 2011 and 2018, it was found that maximum, average, and minimum temperatures were lower compared to the El Niño event in 2016, as indicated by the findings in
Figure 7. This is despite changes in land cover, particularly the increase in urban areas and the decrease in wetlands and vegetation. This study conducts correlation and regression analyses to identify the effects of the ENSO event on LST.
Figure 3 illustrates a significant positive correlation between the Oceanic Niño Index (ONI) and land surface temperature obtained from Landsat satellite data, with a correlation coefficient of 0.71. This strong positive correlation indicates a direct relationship, indicating that an increase in ONI results in an increase in land surface temperature. Additionally, this study utilizes linear regression to understand both parameters.
Table 6 presents the findings of the linear regression analysis, aiming to comprehend the influence of the El Niño-Southern Oscillation (ENSO) on land surface temperature in Kuching, Sarawak, derived from Landsat remote sensing data.
Table 7 presents the regression coefficient R and R
2. The R-value of 0.75 indicates a high correlation between the effect of ONI on land surface temperature from Landsat satellite data. The R
2 value of 0.57 indicates that 57% of the variation in the dependent variable (temperature) can be explained by the independent variable (ONI). In this case, 57% of the effect of ONI on temperature can be explained by ONI.
Table 6, which is the analysis of the variance table, reports the extent to which the relationship between the dependent and independent variables is significant. ANOVA is used to test the adequacy of understanding the dependent variable.
Table 7 demonstrates a well-fitting regression model that supports the importance of regression analysis in understanding the dependent variable. A
p-value of < 0.0005, which is less than 0.05, is reported. Overall, the regression model is consistent with the study’s data.
Table 7 is the ANOVA table, which reports the extent to which the regression equation aligns with the data in understanding the dependent variable.
4.2. Impact of Land Cover Changes on Maximum during El Niño
Figure 5 illustrates the land cover map during the El Niño events in 1998 and 2016. The figure reveals changes in land cover, including the clearance of vegetation areas for the construction of commercial areas in the western and southern regions of the study area. Moreover, an increase in commercial and industrial areas can be observed in the northeastern part of the study area. A detailed description of the land cover changes in 1998 and 2016 is provided in
Table 8.
Table 8 presents the land cover distribution for the years 1998 and 2016. In 2016, there was an increase in the municipal area, which expanded by 50.7 km
2, from 112.06 km
2 to 162.82 km
2. This increase is the only one observed among the different land use types, as the remaining areas experienced a decrease in size.
The most significant decrease occurred in the vegetation area, which decreased by 50.76 km2. In 1998, the vegetation area covered 234.34 km2, but by 2016, it had decreased to 183.58 km2. The next land use type that reported a decline is the wetland area, which decreased from 216 km2 to 193 km2, representing a reduction of 23.1 km2 between 1998 and 2016. Lastly, the water area experienced a decrease of 2.30 km2. This reduction is attributed to reclamation activities in the river area for the construction of a port in the Sama Jaya industrial area.
This study found that changes in the land cover area had an influence on the maximum Land Surface Temperature (LST) during El Niño events. The study compared the maximum LST temperature during El Niño at the same ONI 1 scale value for two selected years. In 2016, the maximum LST temperature reached 35.81 °C, which showed an increase of 2.5 °C compared to the maximum LST of 33.31 °C observed in the urban area during the El Niño event in 1998. Furthermore, the study revealed that the maximum LST value also increased in the ground cover vegetation (0.7 °C), wetland area (0.12 °C), and water body area (0.17 °C). This increase was influenced by the decrease in the area of the water body, vegetation, and wetland. The rise in maximum temperature values in 2016 was attributed to the expansion of urban areas (commercial, residential, and industrial). The increase in urban areas indirectly modified the natural ecosystem by reducing cooling elements such as green plants and water bodies. This indirectly led to an increase in the urban heat island effect, contributing to the upward trend in maximum LST values in 2016 compared to 1998.
These findings are consistent with the research conducted by Kemarau and Eboy [
33], which showed that El Niño events lead to an expansion of heat island areas compared to La Niña and neutral events. Additionally, the increase in maximum temperature in each land cover type is also influenced by the presence of urban land cover, especially the changes in urban areas, which contribute to local temperature increases due to the lack of green plants and water areas that help moderate the temperature in the environment [
33]. A visual representation of the temperature distribution in each land cover is presented in
Figure 10.
Based on
Figure 10, it is evident that the water body area (Sarawak River) and vegetation exhibit lower temperatures compared to the urban area. For instance, the water body temperature is around 26 °C, while vegetation ranges from 27.5 °C to 29 °C, whereas urban areas exceed 29 °C. The temperature variations depend on factors such as vegetation volume, quantity, and area. Additionally, the presence of human-made structures and impervious surfaces tends to increase surface temperatures, making urban areas hotspots [
29,
30]. Human activities, especially indoor air conditioning usage in urban areas, significantly contribute to the urban heat island effect [
16,
30,
31]. The urban area within the black square comprises areas B, C, D, E, F, and G [
29,
30]. Moreover, Kemarau and Eboy [
16] demonstrated that emissions of carbon dioxide (CO2) and carbon monoxide (CO) from industrial zones contribute to elevated temperatures in the surrounding areas.
Vegetation and water bodies play critical roles in mitigating high temperatures in urban environments by providing cooling effects and offsetting the heat generated by human activities and infrastructure. Vegetation, including trees, plants, and green spaces, offers a natural cooling effect through a process called evapotranspiration. During evapotranspiration, plants release water vapor through their leaves, which helps cool the surrounding atmosphere. The evaporation of water from vegetation surfaces absorbs heat from the environment, thereby reducing the temperature of the surrounding air. This process creates a more comfortable microclimate in urban areas and helps alleviate the heat buildup caused by human activities and urban infrastructure.
Water bodies, such as rivers, lakes, and ponds, also contribute to heat mitigation. Water possesses a high heat capacity, allowing it to absorb and store significant amounts of heat energy. In urban areas, water bodies act as heat sinks, absorbing excess heat from the surroundings. This helps lower the overall temperature and provides a cooling effect in the immediate vicinity. Overall, vegetation and water bodies offer crucial mechanisms for mitigating high temperatures in urban environments. Their cooling effects, capacity to offset heat from human activities, and ability to absorb and dissipate heat from infrastructure contribute to creating more comfortable and sustainable urban spaces. Encouraging the presence of green spaces, promoting urban greening initiatives, and preserving water bodies are vital strategies for combating the heat island effect and enhancing the livability of cities.