**Heat-Related Climate Change Impacts on a Small Island Developing State (SIDS): A Case Study of Trinidad, W.I.**

**Kerresha Khan, Ryan Assiu, Vrijesh Tripathi, Azad Mohammed, Ameerah Ali, Aashrita Mohess, Anand Mahabir and John Agard**

**Abstract:** Small Island Developing States (SIDS) have high levels of vulnerability to climate change due to their inherent physical and socio-economic characteristics. Levels of heat within urban areas in the Caribbean are not well-understood or studied. Consequently, heat-related human health impacts can be underestimated or exaggerated. The main objective of this chapter is to determine the extent of temperature variations in Trinidad. Investigations were conducted regarding the temporal variations in land surface temperatures, heat indices, and projected heat accumulation in Trinidad. Analyses showed that urban regions in Trinidad are prone to experiencing higher temperatures and heat due to dense urban infrastructure that absorbs and radiates greater amounts of heat. Heat Index (HI) analyses showed that there were significant (*p* ≤ 0.001) increases in the maximum HI in Trinidad from 1976 to 2015. Projected Heat Accumulation (HA) analyses showed that the western and southwestern regions of Trinidad were most prone to heat risks. These findings suggest significant adverse implications for human and ecological health as well as to the broader socio-economic sectors of Trinidad and Tobago.

#### **1. Introduction**

Climate change is considered an issue of major concern globally, and there is even greater concern for disproportionately vulnerable groups such as Small Island Developing States (SIDS) within the Caribbean region. Even though the contribution towards greenhouse gas (GHG) emissions from SIDS is negligible (compared to developed countries), the impacts of climate change can be even more severe due to the inherent physical characteristics of SIDS which make them more vulnerable to multiple climate change stressors (IPCC 2021). Climate change vulnerability and adaptation is variable between the islands in the Caribbean due to the high diversity of physical and human attributes such as geophysical characteristics as well as socio-economic structures (Leal Filho et al. 2021). Recent available projections show that climate change is already affecting the growth and development of SIDS, and further effects are inevitable in the near future. According to the Intergovernmental Panel on Climate Change (IPCC), current and future risk drivers for climate change in SIDS include sea level rise (SLR), tropical cyclones, increasing air and sea surface temperatures, disease prevalence, and changing rainfall patterns (IPCC 2021). The Special Report on Emissions Scenario (SRES A2 and B2)

scenarios as well as Representative Concentration Pathway (RCP) models project that there will be increases in temperature across the Caribbean with drier conditions and an increasing frequency of droughts, increased sea level and coastal flooding, as well as increased sea surface temperatures (IPCC 2021).

Trinidad and Tobago is an archipelagic republic in the southern Caribbean, located between the Caribbean Sea and the North Atlantic Ocean. Trinidad is split into 14 regional corporations and municipalities (Figure 1 below).

**Figure 1.** Map of Trinidad. Source: Figure by the authors. **Figure 1.**Map of Trinidad. Source: Figure by the authors.

Trinidad is a Caribbean SIDS, and one of the current and future climate-related risk drivers for SIDS is increasing temperatures (both ambient and surface temperatures). This can result in impacts such as a loss of ecosystem services and adaptive capacities, which are essential to lives and livelihoods in many small islands (IPCC 2021). There is a consensus that small islands do not have uniform climate risk profiles due to variations in both the physical and human attributes of each island. In particular, small islands are by no means the same when it comes to physical size, character, or economic development, creating variations in adaptive capacities (IPCC 2021). Therefore, approaches to mitigate and adapt to climate change would differ among these islands, particularly tropical SIDS within the Caribbean. Persons that live and work within highly urbanized regions will also be greatly impacted by increased heat since urban areas trap and retain heat due to the urban heat island effect (Shi et al. 2021). It is therefore imperative that urban populations be targeted for adaptation and mitigation so that relevant precautions Trinidad is a Caribbean SIDS, and one of the current and future climate-related risk drivers for SIDS is increasing temperatures (both ambient and surface temperatures). This can result in impacts such as a loss of ecosystem services and adaptive capacities, which are essential to lives and livelihoods in many small islands (IPCC 2021). There is a consensus that small islands do not have uniform climate risk profiles due to variations in both the physical and human attributes of each island. In particular, small islands are by no means the same when it comes to physical size, character, or economic development, creating variations in adaptive capacities (IPCC 2021). Therefore, approaches to mitigate and adapt to climate change would differ among these islands, particularly tropical SIDS within the Caribbean. Persons that live and work within highly urbanized regions will also be greatly impacted by increased heat since urban areas trap and retain heat due to the urban heat island effect (Shi et al. 2021). It is therefore imperative that urban populations be targeted

can be taken to reduce health impacts in urban areas within Trinidad and the

Caribbean region.

2

effects of heat on human health and to predict heat waves (Dahl et al. 2019).

impact of humidity was often neglected in climate research (Marx 2021). However, in recent years, this has changed with the introduction of various heat stress indications such as the Heat Index, which are now frequently used to determine the for adaptation and mitigation so that relevant precautions can be taken to reduce health impacts in urban areas within Trinidad and the Caribbean region.

Traditionally, heat-related impacts on humans and the environment have only been attributed to increases in temperature, and due to a lack of long-term data, the impact of humidity was often neglected in climate research (Marx et al. 2021). However, in recent years, this has changed with the introduction of various heat stress indications such as the Heat Index, which are now frequently used to determine the effects of heat on human health and to predict heat waves (Dahl et al. 2019).

Increased heat levels are a major cause for concern globally, as most biological life is sensitive to small variations in temperatures and function optimally within a narrow range of temperatures (Alinejad et al. 2020). Threats can be even more pronounced in humid regions such as the Tropics (Matthews 2018). It is therefore crucial that the heat in tropical regions such as the Caribbean should be closely monitored and evaluated in order to prevent and lessen potential negative impacts (Matthews 2018; Di Napoli et al. 2022).

#### **2. Materials and Methods**

Three different types of heat data were collected and analysed. These included Landsat thermal imagery, heat index variations, and projected heat accumulation. Descriptions of data collection and analysis for each type of heat data are provided below. Additionally, an impact analysis on various sectors was carried out through a literature review and synthesis and described in the discussion.

On 9 January 2014, 25 January 2014, and 28 January 2015, Landsat thermal infrared data were obtained online from the United States Geologic Survey (USGS) Global Visualization (GloVis) tool. Thermal band 10 was used to create a mosaic of Trinidad at 30 m resolution. These years and days were selected because they were easily available, and the mosaic pictures at the site on those dates were cloud-free, allowing for full visibility of the study areas. The temperature scale on the map was times 100 to eliminate decimal values. Maps were displayed in ArcGIS 10.2.

In order to calculate the heat index (HI), temperature and relative humidity data are needed. The Trinidad and Tobago Meteorological Services (TTMS) is the main meteorological and forecasting facility for Trinidad's weather and is considered the most reliable data source. Therefore, hourly temperature and relative humidity data for this study were acquired from the Trinidad and Tobago Meteorological Services (TTMS). Intermittent data from 1976 to 2015 from the following years were utilized for the study: 1976, 1982, 1986, 1992, 1996, 2002, 2006, 2012, 2014, and 2015. The data consisted of hourly temperatures and relative humidity readings for 24 h days. These data were utilized because they were available and complete datasets for representative years per decade for approximately four decades. This present study confines itself specifically to the changes in maximum HI.

The heat index was calculated using the formula by Rothfusz and NWS Southern Region Headquarters (1990):

$$\rm HI = -42.379 + 2.04901523T + 10.14333127 \rm R - 0.224755417 \rm R - \\\rm \\ \rm 2.52 \times 10^{-3} \rm R^2 + 1.22874 \times 10^{-3} T^2 \rm R + \\\ \rm \\ \rm 8.5282 \times 10^{-4} T \rm R^2 - 1.99 \times 10^{-6} T^2 \rm R^2$$

where *T* = temperature (◦ F); *R* = relative humidity (integer percentage).

The HI was calculated in Fahrenheit and converted to Celsius. The HI calculations were made for every hour of every day for the ten years used in the study. The maximum HI that occurred per day was then determined from the data using the hourly temperature and relative humidity as well as the HI formula. The formula was applied to data for every hour of every day, and the maximum HI per day was obtained. The maximum HI per day was used to calculate the average maximum HI per month and year. Calculations were also made based on seasonal variations. The dry season in Trinidad occurs in the months of January to May, while the wet season occurs from June to December (TTMS 2023).

Statistical analyses were carried out using SPSS version 23. The raw dataset was also examined under quality control measures to remove any erroneous data. The datasets obtained did not contain any missing data. There were also no negative values (which could be an indication of errors).

In order to determine if there were significant changes in maximum HI, statistical analyses were completed to determine significant changes. These included Student's T tests and one-way ANOVA tests as well as additional post hoc Tukey tests, which were used to analyse the data in greater detail.

The Heat Accumulation for Trinidad was calculated and mapped using the SimCLIM Desktop 4.0 degree day site-specific model that calculates degree day based on daily time series of maximum and minimum temperatures. The degree day estimates were then calculated using the area under the diurnal temperature curve and between the thresholds using a double sine estimation method, as shown in Figure 2 below.

formula was applied to data for every hour of every day, and the maximum HI per day was obtained. The maximum HI per day was used to calculate the average maximum HI per month and year. Calculations were also made based on seasonal variations. The dry season in Trinidad occurs in the months of January to May, while

Statistical analyses were carried out using SPSS version 23. The raw dataset was

In order to determine if there were significant changes in maximum HI,

The Heat Accumulation for Trinidad was calculated and mapped using the

also examined under quality control measures to remove any erroneous data. The datasets obtained did not contain any missing data. There were also no negative

statistical analyses were completed to determine significant changes. These included Student's T tests and one-way ANOVA tests as well as additional post hoc Tukey

SimCLIM Desktop 4.0 degree day site-specific model that calculates degree day based on daily time series of maximum and minimum temperatures. The degree day estimates were then calculated using the area under the diurnal temperature curve and between the thresholds using a double sine estimation method, as shown in

the wet season occurs from June to December (TTMS 2023).

tests, which were used to analyse the data in greater detail.

values (which could be an indication of errors).

**Figure 2.** Thresholds and accumulated degree days. Source: Adapted from Wilson and Barnett (1983). **Figure 2.** Thresholds and accumulated degree days. Source: Adapted from Wilson and Barnett (1983).

The degree day impact model was visualized for Trinidad using a base temperature of 25 °C. This value was used based on similar average historical temperatures for Trinidad. The degree day impact model was run using the IPCC's four low to high global warming Representative Concentrations Pathways (RCPs) (2.6, 4.5, 6.0, and 8.5) for a time series (2014, 2015, 2016, 2017, 2018, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, and 2100). Maps of the years 2014, 2018, 2030, 2050, and 2090 were used for analyses to visualize trends in the heat accumulation based on the highest degree day values. These changes were calculated for each The degree day impact model was visualized for Trinidad using a base temperature of 25 ◦C. This value was used based on similar average historical temperatures for Trinidad. The degree day impact model was run using the IPCC's four low to high global warming Representative Concentrations Pathways (RCPs) (2.6, 4.5, 6.0, and 8.5) for a time series (2014, 2015, 2016, 2017, 2018, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, and 2100). Maps of the years 2014, 2018, 2030, 2050, and 2090 were used for analyses to visualize trends in the heat accumulation based on the highest degree day values. These changes were calculated for each representative concentration pathway (RCP). The representative scale on each map is measured in units of accumulated degree days.

4 representative concentration pathway (RCP). The representative scale on each map is measured in units of accumulated degree days. The year 2014 was used as a base for analyses and comparison to future projected changes as this was the earliest year used for analyses. Heat accumulation (HA) was simulated with an ensemble of the same 40 General Circulation Model The year 2014 was used as a base for analyses and comparison to future projected changes as this was the earliest year used for analyses. Heat accumulation (HA) was simulated with an ensemble of the same 40 General Circulation Model (GCM) patterns used by the IPCC and applied with high sensitivity. High climate sensitivity was chosen so that corresponding low and high bounds of the climate uncertainty ranges could be accounted for in Trinidad. The median projection of the GCM ensemble was used for each scenario.

#### **3. Results**

Figure 2 below.

#### *3.1. Thermal Imagery*

Comparative maps were completed using ArcGIS 10.2. The maps in Figure 3 show thermal imagery of Trinidad on the left and the distribution of urban infrastructure on the right.

**Figure 3.** Trinidad (**a**) Landsat thermal imagery; (**b**) distribution of buildings. Source: Authors' compilation based on data from the United States Geologic **Figure 3.** Survey. Trinidad (**a**) Landsat thermal imagery; (**b**) distribution of buildings. Source: Authors' compilation based on data from the United States Geologic Survey.

#### *3.2. Heat Index 3.2. Heat Index*

#### 3.2.1. Heat Index Variations 3.2.1. Heat Index Variations

The average monthly maximum HI for the time period (1976–2015) was calculated using daily maximum HI values (Figure 4). The maximum HIs in August (37.4), September (38.1), and October (38.1) were significantly higher compared to other months (*p* ≤ 0.001). The average monthly maximum HI for the time period (1976–2015) was calculated using daily maximum HI values (Figure 4). The maximum HIs in August (37.4), September (38.1), and October (38.1) were significantly higher compared to other months (*p* ≤ 0.001).

**Figure 4.** Monthly average maximum HI (1976–2015). Source: Authors' compilation based on data from the Trinidad and Tobago Meteorological Services. **Figure 4.** Monthly average maximum HI (1976–2015). Source: Authors' compilation based on data from the Trinidad and Tobago Meteorological Services.

The average yearly maximum HI for the time period 1976 to 2015 is shown below in Figure 5. Statistical analyses showed that there was a significant increase (*p* ≤ 0.001) in the average maximum HI (from 1976 to 2015). The average yearly maximum HI for the time period from 1976 to 2015 is shown below in Figure5. Statistical analyses showed that there was a significant increase (*p* ≤ 0.001) in the average maximum HI (from 1976 to 2015).

7

**Figure 5.** Yearly average maximum heat index (1976–2015). Source: Authors' compilation

1976 1982 1986 1992 1996 2002 2006 2012 2014 2015

**Year**

based on data from the Trinidad and Tobago Meteorological Services.

33

34

35

36

37

**Max HI (°C)**

38

39

**Figure 4.** Monthly average maximum HI (1976–2015). Source: Authors' compilation based

below in Figure 5. Statistical analyses showed that there was a significant increase

The average yearly maximum HI for the time period 1976 to 2015 is shown

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

**Months**

on data from the Trinidad and Tobago Meteorological Services.

(*p* ≤ 0.001) in the average maximum HI (from 1976 to 2015).

The average monthly maximum HI for the time period (1976–2015) was

calculated using daily maximum HI values (Figure 4). The maximum HIs in August (37.4), September (38.1), and October (38.1) were significantly higher compared to

**Figure 5.** Yearly average maximum heat index (1976–2015). Source: Authors' compilation based on data from the Trinidad and Tobago Meteorological Services. **Figure 5.** Yearly average maximum heat index (1976–2015). Source: Authors' compilation based on data from the Trinidad and Tobago Meteorological Services.

#### 3.2.2. Seasonal Variation in Maximum Heat Index 3.2.2. Seasonal Variation in Maximum Heat Index

*3.3. Heat Accumulation*

*3.2. Heat Index*

3.2.1. Heat Index Variations

other months (*p* ≤ 0.001).

**Max HI (°C)**

7 The seasonal variation in the yearly average maximum HI is shown below in Figure 6. The maximum HI was higher in the wet season compared to the dry season. A Mann–Whitney U test was carried out, comparing the average maximum HI during the wet and dry season from 1976 to 2015. The analyses showed that the average maximum HI was significantly higher (*p* ≤ 0.001) in the wet season compared to the dry. The seasonal variation in the yearly average maximum HI is shown below in Figure 6. Themaximum HI was higherin the wet season compared to the dry season. A Mann–Whitney U test was carried out, comparing the average maximum HI during the wet and dry season from 1976 to 2015. The analyses showed that the average maximum HI was significantly higher (*p* ≤ 0.001) in the wet season compared to the dry.

**Figure 6.** Seasonal variations in average maximum HI (1976–2015). Source: Authors' compilation based on data from the Trinidad and Tobago Meteorological Services. **Figure 6.** Seasonal variations in average maximum HI (1976–2015). Source: Authors' compilation based on data from the Trinidad and Tobago Meteorological Services.

there were even larger areas of the warmest regions (purple and deep purple) in 2090 under RCP 8.5 compared to all other RCPs. The highest HA values were

approximately 2319-degree days in 2090 under RCP 8.5 (Figures 7–11 below).

The HA maps for RCP 8.5 show that the areas with the highest HA (purple and
