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Environ Eng Res > Volume 29(1); 2024 > Article
Limsakul, Kammuang, Paengkaew, Sooktawee, and Aroonchan: Changes in slow-onset climate events in Thailand

Abstract

Slow-onset climate events (SOCEs) cause substantial impacts on human and natural systems, but research on exploring SOCEs and their effects is still limited. Therefore, changes of SOCEs in Thailand were statistically analysed based on a set of data including projected mean temperature, gridded altimetry sea level, salinity, and areal forest cover. Related information from available studies and reports was also compiled to complement our analysis. The important observed changes of SOCEs included (i) Thailand’s mean temperature gradually rising towards the end of the 21st century, (ii) steady sea level rise accompanied by concomitant seawater intrusion, (iii) increasing areas of land degraded and affected by desertification, (iv) general decline in total forest cover with forest fires tending to increase over recent years, and (v) various plant and vertebrate species under multi-threats with most of them projected to considerably decline due to future climate change. Changes of SOCEs seem to have broad-ranging impacts on ecosystems and the well-being and health of Thai people. This study presented additional evidence and knowledge, responding well to the urgent needs to address SOCES in developing countries. To better avert, minimise and address loss and damage from SOCEs, however, future studies, especially quantitative assessment, are needed.

1. Introduction

Climate change poses an unprecedented risk to human and natural systems around the world, and its effects have been increasingly evident in recent decades. Recent scientific assessment has shown that the changes observed in the climate are widespread, rapid, and intensifying, and some of the changes, such as continued sea level rise, are irreversible over hundreds to thousands of years [1]. This anthropogenic-driven climate change has caused substantial impacts as well as related loss and damage beyond natural climate variability [2]. It is known that climate change and its impacts can occur on a variety of temporal and spatial scales. According to the temporal scale over which it occurs and the differing speed at which its impacts appear, climate change can generally be divided into rapid-onset and slow-onset events [35]. Rapid-onset climate events (ROCEs), typically referred to as extreme weather events and what we are more familiar with, are single, discrete, low-probability events with an identifiable beginning and/or end that occur or reoccur in a matter of hours, days, or even months [4, 68], while slow-onset climate events (SOCEs) –more commonly termed slow-onset processes, hazards or impacts – are understood as phenomena with a high probability of occurrence evolving slowly and gradually from incremental changes or an increased frequency or intensity of recurring events [45, 912]. SOCEs take place over prolonged periods – typically years, decades or even centuries – without a clear starting or ending point [45, 912].
SOCEs evolving through gradual transformations can generate severe, accumulative, and potentially irreversible impacts on human and natural systems, causing a wide range of economic and non-economic loss and damage [45, 1314]. The limited evidence available reveals that SOCEs are already exerting negative and accumulative impacts on countries around the world [10, 1519]. In addition, it is increasingly recognised that SOCEs are highly interconnected and sometimes exacerbate or trigger one another, and can occur coincidently, simultaneously, or sequentially with ROCEs [45]. This leads to even more substantial disruption of human and natural systems due to the simultaneous manifestation of compounds and multiple hazards and impacts, resulting in thresholds or tipping points of systems being crossed [45,11, 1314]. Over a longer period, the loss and damage resulting from SOCEs will affect more people than ROCEs because they are persistent and develop over time. Further, they are not amenable to many of the approaches currently under consideration for addressing extreme weather events. For example, continued sea level rise will affect 300 million people living in low-lying coastal areas [20], and economic loss from coastal flooding due to sea level rise is estimated to be more than 4% of the global Gross Domestic Product (GDP) [21].
Given the cumulative impacts and potentially irreversible changes, the Paris Agreement emphasises the importance of averting, minimising, and addressing the loss and damage associated with SOCEs [22]. However, the emerging body of studies has seen significantly greater attention for ROCEs as their effects including loss and damage are more visible than for SOCEs with the associated changes and hazards being often ignored [5, 11]. Over the last decades, there have been important new development and knowledge advances on observed and projected changes as well as attribution of ROCEs, especially regarding human influence on individual extreme events and on projections at different global warming levels [23]. Evidence at local to regional scales further provides a stronger confidence on changes in ROCEs [23]. On the other hand, research is relatively scare on exploring SOCEs and their causes and effects due to problem of detectability which can be associated with significant difficulties in perception, preparedness and adaptability compared to ROCEs [10, 18]. Only special journal issue that brought together literature review and synthesis articles to evaluate the nature and evidence of various types of SOCEs in several regions of the world including Pakistan, Southeast Asia, Asian Deltaic Megacities, Latin America and the Caribbean, and the Republic of Serbia has been recently made [10]. This situation is particularly true for Thailand, where previous studies have been focused on ROCEs [2429], but research on SOCEs is still limited. Thus, SOCEs and related loss and damage, especially at the local and national levels, are poorly understood due primarily to a lack of data and scientific evidence. These significant knowledge gaps and scientific challenges call urgently for improving our understanding to address the loss and damage associated with the adverse effects of SOCEs in a range of contexts, to support the identification of response options and mechanisms, and to build adaptive capacities especially at the sub-national and national levels.
In this study, changes of SOCEs in the context of Thailand were analysed based on available data and existing studies, reports, and papers. The potential impacts and knowledge gaps including future research needed were discussed further.

2. Materials and Methods

2.1. Types of SOCEs Considered

The Cancun Agreement under the United Nations Framework Convention on Climate Change (UNFCCC) listed eight types of SOCEs considered particularly relevant for the UNFCCC’s mandate, including ‘increasing temperature; desertification; loss of biodiversity; land and forest degradation; glacial retreat and related impacts; ocean acidification; sea level rise; and salinization’ [12]. The evidence of these eight SOCEs was assessed in the Intergovernmental Panel on Climate Change (IPCC) Special Reports on Climate Change and Land (SRCCL) and the Ocean and Cryosphere in a Changing Climate (SROCC), illustrating multiple and coincident occurrence of the SOCEs in different regions of the world [11]. For example, glacial retreat and related impacts and sea level rise are the known SOCEs occurring in many high-mountain cryosphere and low-lying coastal areas, respectively [11]. For tropical Southeast Asia, most of the SOCEs found in the literature are rising temperature, sea level rise, forest degradation, and land degradation with some studies covering salinization and ocean acidification [15]. Following the UNFCCC category and taking the recent study into account, five types of SOCEs including rising temperature, sea level rise and seawater intrusion, land degradation and desertification, forest degradation and loss of biodiversity were considered here. Ocean acidification was not included in this study because long-term data were not available for the coastal seas adjacent to Thailand.

2.2. Study Area

Study area in this study is Thailand domain (5.55°–20.55°N and 97.3°-105.7°E) which includes the Andaman Sea and the Gulf of Thailand (4.75°–14.125°N, 97.875°–104.125°E) (Fig. 1).

2.3. Data Used

To analyse and present evidence of any changes in a set of SOCEs, data and information were extracted and compiled from several sources, reports, and studies. Projected mean temperature data over the Thailand domain derived from four bias-corrected Atmosphere-Ocean General Circulation Models (AOGCMs) outputs simulated under the Representative Concentration Pathways 4.5 (RCP4.5) and 8.5 (RCP8.5) scenarios were obtained from the Hydro-Informatics Institute [30]. The AOGCMs for this study were CanESM2, GFDL-CM3, MIROC5 and NorESM1, which had participated in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) [31]. Brief description of the AOGCMs used in this study is shown in Table S1. The normal-normal transformation as a commonly used statistical technique for normally distributed data had been employed to correct temperature biases of these AOGCMs outputs with 124 stations of the observed data distributed across Thailand for the period 1979–2005 [32]. The normal-normal transformation procedure is similar to the gamma-gamma transformation method, but the parameters are assumed as normal distribution [32]. The four bias-corrected AOGCM outputs had finally been downscaled from the original spatial resolution down to that of 10 km.
The gridded, multi-mission altimetry data of sea surface height relative to the geoid as the mean ocean surface reference with a very high resolution of 0.25° during 1993–2019 were obtained from the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO), which is a service set up by the French Space Agency [3334]. The AVISO dataset was derived from multi-mission satellite altimetry measurements (TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, Saral, Envisat, ERS-1 and ERS-2). Altimetry data were processed considering numerous perturbations and correcting various physical phenomena such as propagation, ocean surface, seasonal and interannual changes, and geophysical and atmospheric errors [3537]. Mean sea level slopes for the entire period were then calculated using the least squares method at each grid point after adjusting annual and semi-annual signals [35]. More details of the AVISO altimetry data processing and corrections can be found at https://www.aviso.altimetry.fr/en/home.html [33]. In this study, the gridded sea level trends covering the Andaman Sea and the Gulf of Thailand domain were extracted from the AVISO archives. Information about projected sea level rise in the Gulf of Thailand and the Andaman Sea was also compiled from available research papers and reports.
Salinity data observed in the major rivers of Thailand were extracted from the real-time automatic water quality monitoring system of the Pollution Control Department (PCD) [38]. The system has been installed since 2007, and now there are more than 98 stations in operation in major rivers covering all regions of Thailand [38]. Conductivity was measured continuously and converted to salinity every thirty minutes. The PCD’s automatic water quality monitoring system produced high-frequency measurements with continuity and completeness of data varying from station to station. Some missing data was commonly present in the data records, and a challenge was faced in terms of quality. Therefore, the stations located in the river mouth were selected on the basis of the records being available for as long as possible (2010 to 2021) and their being as complete as possible. Based on this criterion, only four stations (Fig. 1) with missing data ranging from 15.1% to 23.0% were obtained (Table S2). To further increase the robustness and reliability of our analysis, the quality of the selected salinity data was checked for consistency, outliers, and erroneous values.
Official forest statistics during 1973–2020 produced by the Royal Forest Department (RFD) and published in the RDF statistical yearbooks [39] were utilised. Agricultural land data obtained from World Bank Open Data [40] were also used to compare with the forest-cover trend. Agricultural land refers to the share of land area that is arable, under temporary and permanent crops, and under permanent pastures, which is land used for five or more years for forage, including natural and cultivated crops. Data and information related to biodiversity such as the current status of species diversity and critically endangered and endangered species were compiled and extracted from Thailand’s Fourth, Fifth and Sixth National Report on the Implementation of the Convention on Biological Diversity [41], as well as recently available research papers and reports.

2.4. Image Processing for Forest Cover

During 1973–2020, more than 20 national forest surveys were conducted based primarily on remote sensing images, and several methodological changes occurred [42]. Key points of methodological changes included (i) the survey’s geographical coverage and spatial resolution, (ii) characteristics of forest maps and the method of areal calculation, and (iii) the definition of forest cover [42]. In 1973 and 1976, spatial resolution was relatively low, as the images were printed at smaller scales of 1:500,000 and 1:1,000,000 [4344]. Since 1989, LANDSAT5-TM imageries with a greater maximum ground resolution of 30 m were used [42]. The 2012–2013 forest survey began to use the images derived from the Thailand Earth Observation Satellite (THEOS), the first natural resource observation satellite of Thailand, with a resolution of 15 m [42]. From 2013 to 2016, THEOS and LANDSAT-8 Operational Land Imager (OLI) images were employed instead, while recent forest surveys from 2016 onwards have been based on LANDSAT-8 OLI and Sentinel-2 images [39]. Dot sampling on 1: 250,000 forest maps was used to compute forest-cover statistics from 1978 to 1998 [42]. Since 2000, 1:50,000 forest maps have been produced, digitised, and analysed in a GIS environment for areal calculation [39]. The main steps included interpretation of satellite images, establishment of GIS database, preliminary forest land us mapping, ground verification and final forest map production. Given that methodologies have greatly changed, therefore, interpretation of forest-cover trends should be made with caution.

2.5. Studied Parameters and Analytical Methods

Annual ensemble time series of mean temperature averaged from four AOGCMs and the entire Thailand domain were computed. The choice of pre-industrial reference period was defined by Intergovernmental Panel on Climate Change (IPCC) in its Fifth Assessment Report (AR5) and subsequent Special Report on Global Warming of 1.5°C (SP1.5), since the change in global mean temperature since preindustrial times has become an important metric for discussion of migration and adaptation policy in the UNFCCC negotiation process [45]. As the observations and radiative forcings in the past were uncertain, a more recent reference period (1986–2005), offset by historical observations, was selected to use in these reports to approximate pre-industrial conditions for projections of responses to emission scenarios and associated changes and impacts [45]. This approach reduces the impacts of the uncertainty in past radiative forcing and ties the projections to more recent observations [45]. The reference timespan also accounts for the effect of natural variability, which can cause global and regional temperatures to fluctuate from one year to the next [45]. For comparative purpose with other studies used projected CMIP5-based temperature data and to be consistent with the AR5 and SR1.5, we applied the reference period 1986–2005 to construct temperature anomaly series. Linear trend analysis and a 7-year running mean filter were then applied to illustrate rates of changes and a slowly rising signal of mean temperature. For salinity, monthly mean and maximum values were computed from 30-minute data, and monthly time series were constructed. Liner trends were then estimated on the basis of the non-parametric method.
To describe the area of land that is prone to land degradation and desertification, the De Martonne aridity index (DMI) is one of the oldest aridity and humidity indices still used worldwide with good results and calculations based on simulated rainfall and temperature data. The DMI is defined as the numerical indicator of the degree of climate dryness at a given location and classifies the type of climate in relation to water availability [4647]. The higher the DMI of a region, the greater the water resource variability. The DMI, originally developed by De Martonne [48], is calculated by the Eq. (1):
(1)
DMI (mm/°C)=P/(T+10)
where P is the annual mean precipitation (mm), and T is the annual mean air temperature (°C). Climatic classification based on the DMI values is shown in Table S3. Annual ensemble total precipitation and mean temperature from four AOGCMs (CanESM2, GFDL-CM3, MIROC5 and NorESM1) were used to calculate DMI for each grid covering the entire Thailand domain.

3. Results

3.1. Rising Temperature

Mean temperature is most commonly used for a general estimation of slow-onset climate change and can offer more details about how associated variables change and key sectors are affected [5, 45]. Multi-model ensemble means derived from four CMIP5-based AOGCMs show increasing trends towards the end of the 21st century with the rates of warming depending on the global greenhouse emission pathways. Under the RCP4.5 scenario, the annual mean temperature over Thailand is projected to rise at the same rate (0.19°C per decade) as the past observation-based increase reported by Kachenchart et al. [49] (Fig. 2). After adjustment of the urbanisation effects, Kachenchart et al. [49] found that the increasing trend of Thailand’s annual mean temperature driven by anthropogenic-induced climate change during 1970–2019 increased by 0.19°C per decade or 0.95°C over 50 years and had the same rate as the global mean temperature rise. By the end of the 21st century, it will increase by about 2.1°C above pre-industrial levels, slightly higher than the goal set by the Paris Agreement (Fig. 2). The nearly doubling rate (0.36°C per decade) compared to the RCP4.5 scenario is projected to increase under the high emission scenario (RCP8.5) (Fig. 2). Under this pathway, the annual mean temperature will rise by more than 4°C by the end of the 21st century, similar to the recently projected global and regional averages based on Shared Socio-Economic Pathways (SSPs) [1, 50]. Our results also indicate that Thailand’s warming is likely to reach 1.5°C in around the 2040s for both scenarios (Fig. 2). The projected multi-model ensemble means of annual mean temperature under two scenarios exhibit few spatial differences. In the far future, the annual mean temperature in all regions, except the South, will be higher than 2°C under the 4.5 scenario, while the whole of Thailand will experience substantially greater annual mean temperatures (>4°C) under the RCP8.5 scenario (Fig. S1).

3.2. Sea Level Rise and Seawater Intrusion

Our high-resolution satellite altimetry data during 1993–2019 show increases in sea levels in the coastal zones around Thailand (Fig. 3a). Over a 27-year period, the sea level in the Gulf of Thailand and the Andaman Sea increased in the range of 2.0 mm year−1 to 5.7 mm year1. These results are consistent well with previous studies based on tidal gauge and satellite altimetry data demonstrated that the sea level in the Gulf of Thailand and the Andaman Sea has been rising significantly faster than other parts of the Indian and Pacific oceans [5156]. Land subsidence due to excessive groundwater extraction and earthquakes was found to be the main contributor to such a high rise [5253, 57]. By comparison, the average rate of sea level rise in the Andaman Sea was higher than that in the Gulf of Thailand (Fig. 3b). Moreover, the average rate when the Andaman Sea and the Gulf of Thailand were combined (3.8 mm year−1) was relatively higher than the global mean sea level (3.7 mm year−1) for the recent period (2006–2018) [1]. Future sea level rise under the RCP4.5 and RCP8.5 scenarios compared to today’s level for Thailand’s coasts was projected to be 35–38 cm for the mid-century and 80–99 cm for the end of the century [58]. Moreover, the sea level in the upper Gulf of Thailand, recently projected based on the outputs of 35 climate models under the RCP4.5 and RCP8.5 scenarios, has shown a continuous rise in the 21st century at comparable rates with the prior work [57]. Further, land subsidence significantly contributes to the sea level increase for the near-future period (2021–2050) [57]. Available studies suggest that long-term sea level rise will cause shoreline recessions and could even result in the flooding of low-lying coastal areas [5960]. End-of-century beach loss along Thailand’s coastlines due to sea level rise based on CMIP5 data using the Bruun rule was projected to be 45.8% and 71.8% under the RCP4.5 and RCP8.5 scenarios, respectively [59]. Under the RCP8.5 scenario, sandy beaches in 23 out of 51 zones will completely disappear by the end of the 21st century [59].
Sea level rise is known to be an important factor exacerbating the severity of existing seawater intrusion in the coastal regions. Evidence indicates that sea level rise affects tidal currents in many estuaries, leading to increased salinity in both surface water and groundwater [6165]. Monthly salinity data for recent 12-year periods (2010–2021) at the monitoring stations located in the mouths of major rivers (Mae Klong River, Tha Chin River, Chao Phraya River, and Bang Pakong River) show a significant increase in both mean and maximum values (Figs. 4 and S2). Peaks of salinity usually occur during high tide and the dry season when there is reduced river flow from upstream. In 2016, elevated salinity was observed when the exceptionally low surface freshwater flow caused by the El Niño event-induced rainfall deficit led to seawater migrating further landwards. Relatively high levels of salinity after the 2020s were noticed for all stations (Fig. 4), indicating seawater had moved further inland in recent years. By comparison, seawater intrusion was stronger at the monitoring station located at the Bang Pakong River mouth (Figs. 4 and S2). This is consistent with previous results showing that the Bang Pakong River mouth was the area most affected by near-future sea level rise [51]. Salinity at the Samlae pumping station of water supply for Bangkok and its surrounding provinces, which is approximately 96 km upstream of the mouth of the Chao Phraya River, exhibits an increasing trend with statistical significance at a 95% confidence level. Our results are consistent with the observed situation in early 2020 when salt water climbed up the Chao Phraya River and reached the city’s main municipal water intake, threatening the quality of the raw water supply [57, 6667]. Seawater intrusions have frequently occurred in the Gulf of Thailand due to the low-lying area’s proximity to the sea and the combined effects of sea level rise, deep well pumping and land subsidence [57, 6667]. In addition to seawater intrusion in the major rivers, the underlying multi-layered Bangkok aquifer system has been growing saltier from seawater intrusion for many years [6870].

3.3. Land Degradation and Desertification

Land degradation and desertification as a pervasive and complex phenomenon primarily resulting from climatic variations and human activities are negatively impacting the well-being of people in addition to causing loss of biodiversity and ecosystem service [7173]. Thailand is situated in a tropical wet-dry climate with high precipitation and high temperature throughout the year. Some regions, particularly in the Northeast, are at a higher risk of desertification as a result of soil degradation and prolonged or repeated droughts [72]. The Thailand report to the United Nations Convention to Combat Desertification (UNCCD) showed that the area of degraded land in Thailand was 33.57 million hectares, most of which were agricultural soil problems [74]. A recent study has also demonstrated that 1.1 million hectares (2.17% of total areas) in Thailand have been severely affected by desertification [75]. There is also about 62 percent (10.4 million hectares) of the areas in the Northeastern region where the soil has low fertility and certain areas are saline [76]. The major causes of land degradation and desertification found in Thailand include the dissolution and translocation of soil minerals caused by heavy rainfall, seasonal and prolonged droughts, and land use without soil improvement, as well as over-exploitation of land, soil erosion and expansion of saline caused by steep slope land use [7779]. Furthermore, deforestation in Thailand has a substantial impact on the degradation of land resources [7980].
Our analysis based on the ensemble means of DMI calculated from four CMIP5-based AOGCMs also demonstrates spatial changes in the climatic aridity level in Thailand in the present and near-future periods under different climate change scenarios (Fig. 5). It is evident that a moderately arid zone presently covers most of the total territory, and this zone will expand to cover nearly the entire area of Thailand in the near future under both scenarios (Fig. 5). In addition, the DMI means for Thailand as a whole were shifted from the slightly arid (32.0 and 31.4 mm/°C) for the 2006–2020 period to the moderately arid (29.4 and 27.9 mm/°C) for the 2030–2050 period under the RCP4.5 and RCP8.5, respectively. Based on two-sample t-test, the DMI means were statistically different between present and near future periods under both scenarios. These results may indicate that the areas with climatic aridity or dryness in Thailand will increase under ongoing anthropogenic-driven climate change. End-of-century projections of the aridity index based on 27 CMIP5 climate models under the RCP8.5 scenario consistently illustrate that some parts of Thailand will become drier since potential evapotranspiration increases more than rainfall [81]. An obvious increase in aridity, especially in the central part of Thailand where the water deficiency is expected to increase substantially, is likely to affect agriculture and the livelihoods of people since these sectors are the main users of water. It is also expected that future increases in aridity will exacerbate the extent and level of existing soil degradation and desertification in Thailand.

3.4. Forest Degradation

Evergreen and deciduous forests are two major types of forests in Thailand, but a number of sub-types are present in different geographical locations [82]. Forests in Thailand have been under multi-pressures for many years. The history of deforestation in Thailand dates back to the mid-1890s, with forest exploitation peaking during the 1960s–1980s mainly due to agricultural expansion that replaced subsidence crops with cash crops in export-oriented agriculture [80, 82]. A nationwide ban on logging concessions was enforced in the late 1980s when the country faced several natural calamities [80, 82]. Fig. 6a demonstrates the trend in the total forest cover for all of Thailand exhibiting a declining trend from 27.36 × 106 hectares (53.31%) in 1961 to 12.97 × 106 hectares (25.28%) in 1998. A sharp loss occurred from 1976 to 1982 as a result of political conflicts, which led to more encroachment into forest land [80]. Noticeable forest expansion during 1998–2000 was observed with the forest cover increasing from 12.97 × 106 (25.3%) to 17.01 × 106 (33.2%) of the total national area (Fig. 6a), representing an annual rate of expansion of 1.35 million hectares or 2.62%. Office forest-cover statistics show little forest-cover change rate (−0.32% per year) during 2000–2006, comparable to what was found in the 1990s. Since 2013 onwards, the forest area in Thailand has been reported as being relatively stable, accounting on average for 31.2% (Fig. 6a). Deforestation has occurred nationwide at different rates (Fig. S3). The most severe deforestation during 1973–2020 was observed in the Northeast, followed by the North (Fig. S3). To reach the 40% national forest cover target (~20.7 million hectares) set in the 11th National Economic Social and Development Plan, an additional 4.32 million hectares are needed.
Data from the Department of National Parks, Wildlife and Pant Conservation [83] also demonstrate that the protected area increased from 6,888,900 hectares in 1992 to 10,473,400 hectares in 2013 [42]. The general evolution of forest cover reported by the RDF statistical yearbooks is similar to that portrayed by the land-use surveys of the Land Development Department [42, 84]. This included an increase in forest cover in the late 1990s and early 2000s. It should be noted that the long-term trend of forest cover is generally opposite to the increase in agricultural land (Fig. 6b). Leblond and Pham [42] examined a recent forest expansion in Thailand, based on (i) a detailed review of available forest statistics and the methods used to produce these statistics, (ii) evidence from a case study in northern Phetchabun Province, and (iii) an analysis of recent sub-national remote sensing surveys. Their study confirmed that reforestation had indeed increased in Thailand in the late 1990s. These results are also in line with many studies pointing out the possibility that Thailand and several other Asian developing countries are in the first phase of a forest transition or a net increase in forest cover [8588].
Population growth and poverty, agricultural expansion, infrastructure development, especially dam and road construction, illegal logging and uncontrolled forest fires are major driving forces of forest degradation and deforestation in Thailand [42, 80, 82]. Some forest loss in Thailand has resulted from economic development policies, which focus more on economic growth than environmental protection [42, 82]. The national policy that incentivizes mono-crop and rubber plantations is an example. Apart from the above-mentioned factors, forest fires play an important role in the deforestation process. Dry season fires are a significant direct cause of forest degradation in Thailand, and climate change can make many forest ecosystems more vulnerable. Evidence indicates that forest fires in Thailand have tended to increase over recent decades [8990]. The average annual area damaged by forest fire increased from more than 480,000 hectares during 1992–1999 to greater than 1,120,000 hectares during 2016–2019 [82, 89].

3.5. Loss of Biodiversity

Climate change, which is already impacting biodiversity, has the potential to interact synergistically with many other drivers of change and is projected to become even more important in the future [9193]. Thailand, one of the richest countries in Southeast Asia in terms of biodiversity, is located entirely within the Indo-Burma biodiversity hotspot, which is recognised as one of the 36 global biodiversity hotspots [76, 9395]. It is home to more than 1,000 bird species, 10,520 plant species equating to approximately 3% of the world’s plant species, and 15 distinct types of ecoregions with five of the World Wildlife Fund’s Global 200 Ecoregions of ecological significance [76, 93]. Based on the IUCN’s definition, Thailand has protected areas covering approximately 118,320 km2 or 22.8% of the country’s land area [93, 95]. They include 132 national parks, 60 wildlife sanctuaries, 80 non-hunting areas, and 114 forest parks. However, Thailand’s protected areas are geographically biased towards the North and West; they are highly fragmented, with only 8% being larger than 1,000 km2 [95].
Like other tropical countries, biodiversity in Thailand has long been under pressure from multi-threats including both human and climate-induced drivers [76]. The estimated number of 964 threatened plant species with 20 critically endangered species (Table 1) was reported in Thailand’s Sixth National Report on the Implementation of the Convention on Biological Diversity [76]. Moreover, 569 vertebrate species (12.03%) were assessed to be under threat, including critically endangered, endangered, and vulnerable (Table 1). The data compiled from Thailand’s Fourth, Fifth and Sixth Nation Report on the Implementation of the Convention on Biological Diversity also show increases in the number of vertebrates with critically endangered species and endangered species during 2006–2016 (Fig. 7). This result is in line with the most recent IUCN Red List assessment indicating 185 threatened vertebrate species in Thailand [95]. A recent study has shown that a lowland central area is the most critical biodiversity hotspot for threatened mammals in Thailand [93]. For marine and coastal ecosystems, survey data show that mangrove forest areas decreased from 367,900 hectares in 1961 to 245,533 hectares in 2014, while beach forests were destroyed and converted into tourism sites [76]. Moreover, more than 45% of the coral reef in the Andaman Sea was severely damaged. Pomoim et al. [95] used all the available data to assess the vulnerability of 866 vertebrates and 591 plant species within Thailand’s protected areas to future climate change based on species distribution models. Their study found that, under the RCP8.5, most modelled species of mammals, birds, and plants are projected to decline significantly, and 54% of modelled species will be threatened and 11 extinct by 2070.

4. Discussion

4.1. Potential Impacts of SOCEs in Thailand

Gradually rising temperature as observed in Fig. 2 appears to exert its effect on various systems in Thailand. At CO2 concentration of 330–400 ppm scenarios, mean rice yield in Northeast Thailand was predicted to decline in range of −1.24% to −5.05% for 1°C temperature increase [96]. It was documented that the average labour product of Thai manufacture sector in 2012 would decline −41,674 baht per person, as the temperature of the working place rose 1°C from the base level [97]. In case of 1.95°C temperature rise in the future; present value of the damage was estimated to increase from 95,519 million baht in 2020 to 160,335 million baht in 2050 (in current prices) [97]. A recent study has also shown that changes in ambient temperature was associated with an increased risk of mortality in Thailand, with burden of all-cause of death and respiratory mortality mainly due to higher temperature [98]. With a global mean temperature increase of 3°C above pre-industrial levels, corresponding to the warming projected for current Nationally Determined Contributions, droughts, flooding, and extreme heat in Thailand were projected to increase by 10%, 13% and 3% compared with 2.6%, 3% and 1.1% in a 1.5°C warmer world, respectively [58]. Moreover, the extremely warm temperature indices representing warm nights and days and tropical nights in Thailand were estimated to substantially increase (~50%) at global warming between 1.5°C and 2°C, highlighting that half a degree increase in global mean temperature will lead to greatly increase in Thailand’s temperature extremes [99].
Sea level rise as evidenced from this study (Fig. 3) has threatened significant physical changes to Thailand’s coastal zones, leading to accumulative and catastrophic effects on coastal ecosystems and communities. Under the high emission scenario, up to 2.5 million people in Thailand were estimated to be potentially exposed to flooding from sea-level rise by 2070–2100 [100]. Based on high-resolution elevation data, it was estimated that the affected area of sea level rise in the Bangkok metropolitan area extended to 6,140 km2, with the vulnerable population increasing by 86%, or up to 7.2 million people, under the worst-case scenario of RCP8.5 (sea level rise of +1.10 metres) [101]. A new 2030 projection illustrates that in Bangkok, sea level rise and any subsequent flooding can cause US$ 512.28 billion of GDP at Purchasing Power Parity (PPP)) [102]. The impacted GDP (PPP) accounts for 96% of Bangkok’s total GDP (PPP) and is the highest among seven major Asian cities (Hong Kong, Tokyo, Jakarta, Seoul, Taipei, and Manila) [102]. Negative impacts of seawater intrusion on soil, crop productivity and municipal water in the Lower Central Plain of Thailand were also documented [6869, 102103]. A recent study has confirmed that the groundwater quality in the Lower Chao Phraya River Basin in Thailand has deteriorated due to salinization caused by seawater intrusion [104]. This problem remains a major concern for Bangkok, the Kingdom’s capital, as a large amount of the daily water supply which is around 4.55 Mm3 with groundwater amounting to slightly over 11,000 m3 has to provide for a population of approximately 13 million [105]. Saline water supply can also cause long-term health effects on people who regularly consume it [6667, 104].
Land degradation and desertification as observed in Thailand can substantially influence Thai’s people livelihoods by limiting the availability of vital ecosystem services and increasing the risk of poverty. In 2010, 27% (about 10.2 million) of Thailand’s rural population was living on degrading agricultural land, and 1.7 million people or 5% of Thailand’s rural population resided in remote degrading agricultural areas without market access [106]. The total annual cost of land degradation in Thailand, measured in terms of changes in land productivity, was estimated at US$ 2.7 billion, equalling to 1% of the country’s GDP [106]. A recent assessment has shown that for Thailand the returns on acting against land degradation versus inaction were estimated at US$ 3 for every dollar [107].
Forest degradation and deforestation occurred in Thailand can have great consequences, because forests are an important component of the natural resource base and part of a national capital. One impact of deforestation is land degradation and soil erosion. Deforestation-induced land degradation in Thailand can be found either soil erosion in mountainous and watershed areas or desertification [80]. Soil loss in the Upper Chi Basin in Northeastern Thailand was estimated to be about 6.6 million tonnes of soil in 2006 [108]. Further deforestation and land degradation can result from clearing more forest areas by farmers to get access to better soil quality [80]. Conflicts over essential natural resources between different ethnic groups living in upland forested areas in Northern Thailand occurred as a result of forest degradation, when one group expanded agriculture to the land already established by another group [109]. In addition, loss of ecosystem services and potential future revenues and employment provided by forests especially at the local level seem to be other social-economic impacts of deforestation and forest degradation in Thailand [80].
Substantial contribution of biodiversity to wealth, prosperity and well-beings of Thailand has been long recognized [76]. Thailand strongly depends on its rich and abundant biodiversity for people’s livelihoods, social-economic development of the country, cultural and spiritual fulfilment, and main sources of national income especially the export and tourism [76, 110]. The continued loss of biodiversity induced by multi-causes including climate change as presented in this study undoubtedly affects lifestyle, well-being, and food security of a large number of Thai people. It also leads to the loss of a beauty of nature, people’s happiness, and tourism. This includes loss of ecosystem resilience to withstand extreme events and disasters.

4.2. Knowledge Gaps and Future Research Needed

Our results present additional evidence and knowledge at the national and sub-national levels in support of recent scientific findings that SOCEs are already affecting developing countries, with the resulting loss and damage likely to increase significantly [5, 1011, 111]. This study responds well to the urgent need to better understand and address SOCEs and their confounding drivers in developing countries, as most studies and currently published research have been conducted in and focused on North America and Europe, whereas developing countries face a high risk of loss and damage due to SOCEs [15, 18, 111112]. A recent review and synthesis of the literature on SOCEs in Southeast Asia has found that Vietnam is the most studied country, while research in other countries like Thailand remains scarce and needs more comprehensive and detailed SOCEs studies to inform response options [15]. SOCEs are complex, multi-dimensional, and interconnected phenomena [5, 18]. SOCEs can also have cascading effects and become increasingly compound with other anthropogenic drivers, other SOCEs or ROCEs, owing to their interdependencies [5, 111]. Therefore, study on SOCEs and their impacts is still scientifically challenging in the local-to-national contexts of developing countries, and integrated transdisciplinary research, especially the locally led method that is transdisciplinary in nature, has been suggested to be one suitable approach to deal with such complex and multi-dimensional events [5, 9, 15, 111]. This approach could allow a more accurate assessment of SOCEs as it will include insights from vulnerable communities as well as local science-policy interfaces. In addition, quantitative assessment of loss and damage especially non-economic loss is one of the prioritised research areas that are urgently needed to improve our understanding of SOCEs in Thailand. Therefore, there is no doubt that improvement of data and scientific knowledge is an essential element to avert, minimise and address loss and damage from SOCEs.

5. Conclusions

Analysis of available data and existing scientific information from several sources discloses past and future changes in several types of SOCEs in Thailand. The important changes of SOCEs observed from this study are summarised as follows:
  1. gradually rising temperature towards the end of the 21st century when the whole of Thailand will experience increasing mean temperature higher than 4°C under RCP8.5 scenario,

  2. steady sea level rise in the coastal zones around Thailand accompanied by concomitant seawater intrusion moving further inland to contaminate surface and ground water resources,

  3. increasing areas of land degraded and severely affected by desertification with tendency for expansion of a moderately arid zone in the near future,

  4. general decline in total forest cover over five or six decades with forest fires tending to increase over recent years, and (v) various plant and vertebrate species under multi-threats with most of them projected to experience a considerable decline due to future climate change.

The results also provide the clues that changes of SOCEs in Thailand have broad-ranging and cumulative impacts across environmental, ecological, and socio-economic dimensions. These impacts may lead to substantial or even irreversible changes in the well-being, health, and livelihood as well as food and water security of Thai people.

Supplementary Information

Acknowledgments

We would like to thank the Hydro-Informatics Institute and Pollution Control Department for kindly providing valuable projected temperature and precipitation data and observed salinity data. We also extend our thanks to the editor and anonymous reviewers for their insightful critiques and constructive comments to substantially improve the quality of the earlier version of the manuscript. This work was part of Technology and Innovation Research and Development for Environmental Management (FFB650074/0061) funded by Thailand Science Research and Innovation (TSRI).

Notes

Conflict-of-Interest Statement

The authors declare that they have no conflict of interest.

Author Contributions

A.L. (PhD) interpreted the results and wrote the initial version of the manuscript. A.K. (Researcher) analysed air temperature and sea level data and supported writing the initial version of the manuscript. W.P. (PhD) analysed salinity data and supported writing the initial version of the manuscript. S.S. (PhD) analysed aridity index and supported writing the initial version of the manuscript. N.A. (Researcher) analysed forest and biodiversity data and supported writing the initial version of the manuscript. All authors discussed the results and contributed to the final version of the manuscript.

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Fig. 1
Geographical location of Thailand, the Andaman Sea, and the Gulf of Thailand as study area of this study and locations of four real-time automatic monitoring water quality stations belonging to the Pollution Control Department in the mouths of major rivers (Mae Klong River, Tha Chin River, Chao Phraya River and Bang Pakong River).
/upload/thumbnails/eer-2022-784f1.gif
Fig. 2
Time series of ensemble means of projected annual mean temperature over Thailand relative to pre-industrial values averaged from four downscaled climate models (CanESM2, GFDL-CM3, MIROC5 and NorESM1). The solid lines represent 7-year moving averages.
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Fig. 3
Spatial map of sea level rise (mm/year) in the coastal zones around Thailand estimated from high-resolution satellite altimetry data from 1993–2019 (a), and boxplot of sea level rise in the Andaman Sea and the Gulf of Thailand (b).
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Fig. 4
Monthly mean values of salinity at the selected stations located in the river mouths during 2010–2021. The Amphawa, Krathum Baen, Samlae and Saichon stations are located at the mouths of the Mae Klong River, Tha Chin River, Chao Phraya River, and Bang Pakong River, respectively.
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Fig. 5
Spatial maps of the ensemble mean of De Martonne aridity index (DMI) calculated from temperature and rainfall data for present (2006–2020) and near-future (2030–2050) periods projected under RCP4.5 and RCP8.5.
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Fig. 6
Trend in total forest cover for the whole of Thailand during 1961–2020 based on the Royal Forest Department statistical yearbooks (a), and trends in total forest cover and total agricultural land for the whole of Thailand expressed as percentages of the country area (b).
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Fig. 7
Trends in critically endangered vertebrate species and endangered vertebrate species (mammal, bird, reptile, amphibian, fish). The data were extracted from Thailand’s Fourth, Fifth and Sixth National Report on the Implementation of the Convention on Biological Diversity [76].
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Table 1
Current status of vertebrate and plant species diversity in Thailand.
Vertebrate/plant species Thai species (Types) Extinct Threatened species

CR EN VU Total
1. Mammal 345 4 17 40 66 123
2. Bird 1,012 3 43 58 70 171
3. Reptile 392 - 16 17 16 49
4. Amphibian 157 - - 4 14 18
5. Fish 2,825 1 26 66 116 208
6. Plant 10,520 - 20 207 737 964

CR= Critically endangered; EN= Endangered; VU=Vulnerable

Plant and vertebrate species were assessed in 2015 and 2016, respectively. The data were extracted from Thailand’s Sixth National

Report on the Implementation of the Convention on Biological Diversity [76].

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Changes in Size Distributions of Lime Sorbents during Fluidization  1997 June;2(2)
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