Climate-related Variables Research Articles

Global thresholds for the climate-driven effects of vegetation restoration on runoff and soil erosion

Runoff generation is intimately related to topography, climate, and vegetation. However, the combined impacts of different vegetation restorations on runoff under global climate change have not been quantitatively assessed. When climate exceeds specific thresholds, the inhibitory effect of vegetation restoration on runoff may diminish. Here, we collected 771 observations for meta-analysis and used segmented regression to determine the global threshold response of vegetation restoration for climate-related variables on runoff and soil erosion. We found the effect of vegetation restoration in inhibiting soil erosion and runoff increased when MAP (mean annual precipitation) exceeded the thresholds of about 331 mm and 540 mm, respectively. Furthermore, at rainfall intensity above the threshold of 48 mm/h, the effect of vegetation restoration on erosion control increases. Comprehensive analysis indicates that grassland revegetation is more successful in decreasing of runoff and soil erosion in the North Temperate Zone. Temperature and precipitation directly or indirectly influenced the effects of shrubland runoff reduction, and the relationship varied with changes in temperature and precipitation. Our findings contribute to guiding regional governments and decision makers in developing and implementing rational vegetation restoration measures for sustainable water resource management in the future.

Bergmann’s rule in global terrestrial vertebrates

Abstract To date, whether Bergmann’s rule, initially developed for individual species or closely related taxa, can be applied to broader taxa remains elusive. Using global distribution data for birds, mammals, reptiles, and amphibians, both species- and assemblage-based, we examined the role of species richness and related climate variables in shaping body size patterns across the globe and the Northern Hemisphere only and whether the temperature–body size relationship had a phylogenetic signal. The results show that when species in all four classes were combined, body size increased with latitude but declined with temperature, consistent with Bergmann’s rule. Each group showed somewhat unique patterns but was mostly consistent with Bergmann’s rule. The body size–temperature relationships were not constrained by phylogenetic relatedness. Warmer places had both large and many small-bodied species, whereas cold places had proportionally more large species. The temperature-dependent variations in body size have important implications for macroecology under climate change.

From Arctic ponds to the "Northern Great Lakes": Algae as first responders of climate-driven regime shifts.

Arctic freshwater ecosystems are on the "frontline" of climate change, but due to a lack of direct long-term monitoring data, indirect approaches, such as algal-based paleolimnology, must be used to reconstruct past limnological conditions. Our understanding of the responses of small- to mid-sized Arctic lakes to climate warming has increased over the last ~30 years. However, until recently, little was known about even the basic limnological conditions of Canada's "Northern Great Lakes," such as Lake Hazen, Great Bear Lake, and Great Slave Lake. In this summary, we show that a continuum of algal changes, observable in the sedimentary archives of shallow ponds to very large Arctic lakes, signals the crossing of key aquatic thresholds linked to changing ice covers and thermal regimes, declining wind speeds, and other climate-related variables. With recent accelerated warming, even the largest and most resilient Arctic waterbodies are now fundamentally different than they were just a few decades ago. These changes will undoubtedly cascade throughout the food web leading to important changes for local Indigenous populations as well as the global community.

Integrating user- and data-driven weather forecasts to develop legitimate, credible, and salient information services for smallholders in the Global South

Climate-related risks and variability pose significant challenges to the livelihoods and food security of smallholder farmers practicing rainfed agriculture. Many smallholders have limited access to weather information from climate services, and this information is often not tailored to their specific context and needs. Therefore, they rely on local ecological knowledge. This study utilizes the second generation of climate services, which provide demand-driven forecast information systems through mobile apps. We present three cases from agricultural communities in Guatemala, Bangladesh, and Ghana where we collaborated with farmers to develop local weather forecasts (LF) and combined them with scientific weather forecasts (SF) to create hybrid weather forecasts (HF). The integration of user-driven forecasts (LF) and data-driven forecasts (SF) enhances the legitimacy of the service, thereby increasing farmers’ trust and credibility by providing skilful forecasts. Furthermore, our results demonstrate that the hybrid weather forecast approach facilitates climate-smart, adaptive agricultural decision-making, enhancing the resilience and capacity of smallholder farmers in the Global South to adapt to a changing climate.

Can Expert Prediction Markets Forecast Climate-Related Risks?

Abstract Prediction markets use a betting-like mechanism to aggregate information from disparate sources to produce probability forecasts that represent the collective view of participants. They have similarities with financial markets but are designed specifically to discover information rather than transfer assets or risks. Contracts that pay out a fixed amount if a well-defined event occurs are traded in these markets and, under certain conditions, the price at which these event-contracts trade can be interpreted as a probability estimate that the event will occur. This article examines the performance of 24 prediction markets for climate-related variables that have been run over the past 5 years. The predicted variables included the monthly temperature and rainfall for the United Kingdom, an index of El Niño–Southern Oscillation, Atlantic hurricane activity, and U.K. wheat yield. The markets had horizons of 2–12 months. Invitations to participate in the markets were extended to individuals and teams with relevant expertise. Participants did not have to pay to take part but did receive cash rewards based on their performance. Trades were made through an automated market maker overcoming the problems of low activity that have affected previous prediction markets for specialized topics. The predictions of the markets were consistent with good reliability, given the resolving power afforded by the sample size. Whether this level of reliability would persist for longer, multiyear, horizons relevant to climate change cannot be known without running markets on such time scales, something that we strongly advocate.

Vulnerability of barley yields in Québec.

Climate projections across Québec indicate increased water stress and recurrent vulnerability of cropping systems. In recent years, reports of droughts and water stress have been recorded across the province. Many parts of Québec have experienced droughts in the past few years, which have had uninvestigated impacts on crops. These droughts have been described as some of the most significant in the last 80 years. On the positive side, climate change is likely to trigger shorter winters and therefore longer growing seasons for several crops. However, for crops like maple syrup, the regions suitable for their cultivation will shift northwards. Despite these projections, studies monitoring the susceptibility of barley to environmental changes, climate variability, and adaptive capacity across Québec are still limited. This study aims to provide a provincial-scale portrait of vulnerability of barley in Québec based on historical growing season precipitation, barley yield, and socioeconomic data (literacy and poverty rates) using a composite statistical model. Growing season precipitation data were downloaded from Ouranos. Barley yield data were collected from the Institut de la Statistique du Québec, and the socio-demographic data were collected from the Advisory Council of Poverty and the Institut de la Statistique du Québec. A vulnerability index with sub-indices (sensitivity, exposure, and adaptive capacity) is deployed. It is hypothesised that (1) vulnerability is inversely associated with adaptive capacity, and (2) the peripheral regions of Québec are more vulnerable and less adaptive to climate stressors. Initial results show that when the vulnerability index for barley is more prominent, the associated index of adaptive capacity is relatively lower. A significant gradient between the peripheral and southern regions of Québec is observed, with vulnerability lowest around Montreal/Laval and gradually increasing towards the peripheral regions. A better understanding of vulnerability warrants a change in approach from focusing solely on climate-related variables to integrating socioeconomic proxies. The challenge, however, has been how to introduce these socioeconomic proxies into empirical and process-based crop models.

Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning.

Culicoides biting midges exhibit a global spatial distribution and are the main vectors of several viruses of veterinary importance, including bluetongue (BT) and African horse sickness (AHS). Many environmental and anthropological factors contribute to their ability to live in a variety of habitats, which have the potential to change over the years as the climate changes. Therefore, as new habitats emerge, the risk for new introductions of these diseases of interest to occur increases. The aim of this study was to model distributions for two primary vectors for BT and AHS (Culicoides imicola and Culicoides bolitinos) using random forest (RF) machine learning and explore the relative importance of environmental and anthropological factors in a region of South Africa with frequent AHS and BT outbreaks. Culicoides capture data were collected between 1996 and 2022 across 171 different capture locations in the Western Cape. Predictor variables included climate-related variables (temperature, precipitation, humidity), environment-related variables (normalised difference vegetation index-NDVI, soil moisture) and farm-related variables (livestock densities). Random forest (RF) models were developed to explore the spatial distributions of C. imicola, C. bolitinos and a merged species map, where both competent vectors were combined. The maps were then compared to interpolation maps using the same capture data as well as historical locations of BT and AHS outbreaks. Overall, the RF models performed well with 75.02%, 61.6% and 74.01% variance explained for C. imicola, C. bolitinos and merged species models respectively. Cattle density was the most important predictor for C. imicola and water vapour pressure the most important for C. bolitinos. Compared to interpolation maps, the RF models had higher predictive power throughout most of the year when species were modelled individually; however, when merged, the interpolation maps performed better in all seasons except winter. Finally, midge densities did not show any conclusive correlation with BT or AHS outbreaks. This study yielded novel insight into the spatial abundance and drivers of abundance of competent vectors of BT and AHS. It also provided valuable data to inform mathematical models exploring disease outbreaks so that Culicoides-transmitted diseases in South Africa can be further analysed.

Floods across the eastern United States are projected to last longer

While there is growing attention toward the changes in flood magnitude and frequency, little is known about the way climate change could impact flood duration. Here we focus on 378 streamgages across the eastern United States to develop statistical models that allow the description of the year-to-year changes in flood duration above two National Weather Service (NWS) flood severity levels (i.e., minor and moderate). We use climate-related variables (i.e., basin- and season-averaged precipitation and temperature) as predictors, and show that they can be used to describe the inter-annual variability in seasonal flood durations for both NWS flood severity levels. We then use the insights from the understanding of the historical changes to provide an assessment of the projected changes in flood durations using global climate models from the Coupled Model Intercomparison Project Phase 6 and multiple shared socio-economic pathways. Our results show that the eastern United States is projected to experience longer flood durations, especially in winter (i.e., the main flood season) and under higher emission scenarios.

Beta diversity patterns and driving mechanisms of stream bacteria and fungi on Mt. Kilimanjaro

Aquatic microbes, which are highly diverse in freshwater biofilms, play a vital role in shaping the structure and functioning of ecosystems. Nevertheless, the influence of environmental factors on aquatic microbes in different biogeographic ecosystems, especially in tropical mountains, is relatively unexplored. Here, we examined the relative influence of climate and physicochemical factors on beta diversity of stream bacteria and fungi using 16S rRNA amplicon sequencing at 40 sites on Mt. Kilimanjaro. Towards larger elevational distance, the total beta diversity and turnover of bacteria and fungi increased significantly, while nestedness decreased. The beta diversity of fungi showed greater spatial variation than that of bacteria, while the local contribution to beta diversity (LCBD) showed U-shaped elevational pattern for both taxa. Bacteria and fungi had strong positive relationships regarding the total beta diversity, LCBD and their components, except for nestedness, indicating similar environmental preferences and resource overlap. Among environmental variables, mean annual temperature, water depth and elevation predicted microbial beta diversity, while water depth, mean annual temperature and precipitation primarily affected LCBD. Overall, climate-related variables were the dominant factors for beta diversity and their effects were more pronounced for fungi than bacteria. The contrasting patterns of bacterial and fungal beta diversity and their ecological interactions in tropical aquatic ecosystems provide a rare dataset for assessing ecological functions in understudied aquatic ecosystems and predicting the impact of global change on microorganisms.

Enhancing climate resilience in northern Ghana: A stochastic dominance analysis of risk-efficient climate-smart technologies for smallholder farmers

Northern Ghana is a semi-arid region characterised by a unimodal rainfall pattern, and hot and dry weather conditions. Heavy reliance on rain-fed agriculture and the lack of resources for irrigation, makes smallholder farmers in the region increasingly vulnerable to climate-related crop failures. In recent years, climate-smart technologies (CSTs) such as changing planting dates (PD), compartmental bunding (CB), mulching (M), and transplanting (TP) have been recommended to minimise yield losses. However, there is limited information on the most risk-efficient CSTs for crops cultivated in the region. This study used a stochastic dominance approach to identify the most risk-efficient CSTs for maize, rice, and sorghum. The stochastic modelling process employed the Aqua-crop model to simulate climate-related yield variability using Ghana climate data, and gross margin variability with crop budgets from literature sources. From the study's findings, changing planting date from April to May was the most risk-efficient choice for maize and sorghum under farmers' and recommended practices. In contrast, transplanting was the most risk-efficient technology for rice farming in the study area. The study also highlights the importance of considering the risk-averse nature of smallholder farmers when selecting CSTs. By identifying the most risk-efficient CSTs, the study can help improve the resilience of smallholder farmers. These findings have important implications for the development and adoption of CSTs in northern Ghana.

Speeches in the green: The political discourse of green central banking

In this paper, we employ a keyword-assisted topic model to quantify the extent of climate-related communication of central banks. We find evidence for a significant increase in climate-related speeches by central banks, which address the topic mostly in parallel with issues related to financial stability, whereas monetary policy and price stability are less emphasized. Finally, we examine factors that can explain the extent of green communication by central banks. Controlling for macroeconomic and climate-related variables, we identify two external factors that can prompt central banks to increase green communication: First, peer pressure, as measured by membership in Network for Greening the Financial System (NGFS), increases green communication. Second, a high degree of governmental climate engagement, reflected by the scope of national climate policies, correlates positively with central banks’ green communication. Whether the central bank has an implicit or explicit sustainability mandate, however, does not explain the extent of green communication.

Geomatics-enabled Interdisciplinary Approach Based on Geospatial Data Processing for Hydrogeological Risk-analysis

Hydrogeological risks that are associated with rivers have emerged as a significant concern worldwide, impacting both natural ecosystems and human settlements. This contribution presents an interdisciplinary project that leverages many technologies for data-acquisition and modeling to comprehensively analyze and manage risks in riverine environments. The project integrates geomatics, geological, and hydrological techniques to provide a holistic understanding of river dynamics and their associated hazards. As a central component of this project, geomatics plays a pivotal role in instrumental field surveying through the deployment of photogrammetry and LiDAR instruments. Remote-sensing data from satellite imagery further enriches the project’s temporal analysis capabilities. By analyzing this data over time, researchers can monitor changes in river patterns, land use, and climate-related variables; this helps identify trends and potential triggers for hydrological events. To manage and integrate the vast amount of geospatial information that is generated, a geodatabase within a geographic information system (GIS) has been established. It enables efficient data storage, retrieval, and analysis, fostering collaboration among multidisciplinary researcher teams. This system offers tools for risk-assessment, modeling, and scenario planning; these allow for proactive measures for mitigating hydrological risks.

Habitat quality on the edge of anthropogenic pressures: Predicting the impact of land use changes in the Brazilian Upper Paraguay river Basin

Landscape changes driven by anthropogenic activities often have negative impacts on ecosystems, jeopardizing the provision of vital services that sustain biotic elements and wildlife. Predicting the effects of such transformations is crucial for developing strategies to mitigate ecological damage. This study explores the interaction of land use changes and habitat quality estimates in the Upper Paraguay River Basin in Brazil, using the InVEST Habitat Quality model to estimate spatiotemporal landscape trends and evaluate the potential impact of land use policies for the future. Under a business-as-usual scenario by 2050, potential land use changes are simulated for shaping habitat quality, habitat degradation, and habitat rarity estimates as landscape proxies for biodiversity conservation. Results show an extensive expansion of pastures through deforestation, leading to progressive habitat degradation with a significant decay in the predicted and observed habitat quality. The habitat quality index estimates show a spatial decrease from 0.78 to 0.57 for the 1989–2050 period. The same trend is observed in protected areas for the period 2019–2050, with an increase of anthropogenic land use and a decrease of the habitat quality from 0.80 to 0.57 in conservation units, and from 0.75 to 0.53 in indigenous lands. For improved conservation outcomes, this work introduces new insights for shaping environmental actions that can be flagged as sustainable land management practices and ecological perspectives towards spatial pressures at different scales. Therefore, government institutions responsible for the protection of conservation units and indigenous communities can be informed about potential land use impacts on their territories. The findings suggest that incorporating additional scenario trends and climate-related variables could be a valuable direction for future studies to extend further the modelling approaches explored in this work.

A high-resolution map of diffuse groundwater recharge rates for Australia

Abstract. Estimating groundwater recharge rates is important to understand and manage groundwater. Numerous studies have used collated recharge datasets to understand and project regional- or global-scale groundwater recharge rates. However, recharge estimation methods all have distinct assumptions, quantify different recharge components and operate over different temporal scales. We use over 200 000 groundwater chloride measurements to estimate groundwater recharge rates using an improved chloride mass balance (CMB) method across Australia. Groundwater recharge rates were produced stochastically using gridded chloride deposition, runoff and precipitation datasets. After filtering out groundwater recharge rates where the assumptions of the method may have been compromised, 98 568 estimates of recharge were produced. The resulting groundwater recharge rates and 17 spatial datasets were integrated into a random forest regression algorithm, generating a high-resolution (0.05°) model of groundwater recharge rates across Australia. The regression reveals that climate-related variables, including precipitation, rainfall seasonality and potential evapotranspiration, exert the most significant influence on groundwater recharge rates, with vegetation (the normalised difference vegetation index or NDVI) also contributing significantly. Importantly, the mean values of both the recharge point dataset (43.5 mm yr−1) and the spatial recharge model (22.7 mm yr−1) are notably lower than those reported in previous studies, underscoring the prolonged timescale of the CMB method, the potential disparities arising from distinct recharge estimation methodologies and limited averaging across climate zones. This study presents a robust and automated approach to estimate recharge using the CMB method, offering a unified model based on a single estimation method. The resulting datasets, the Python script for recharge rate calculation and the spatial recharge models collectively provide valuable insights for water resource management across the Australian continent, and similar approaches can be applied globally.

Estimating fine fuel loads in Eucalypt forests using forest inventory data and a modelling approach

Eucalypt forests are vital for biodiversity and fire dynamics, with fine fuels serving as the primary ignition source and critical factors in determining fire intensity. Accurate measurement of fine fuel via destructive field methods is location and time-restricted, hindering the tracking of their dynamic variations across diverse landscapes. In response, this study utilised forest inventory data collected by the Victorian Forest Monitoring Program (VFMP) in Victoria, southeastern Australia, along with allometric models, to estimate fine fuel loads across four vertical layers (surface, near-surface, elevated, and canopy). The average total fine fuel load in Victorian Eucalypt forests was estimated at 18.45 t ha−1 (ranging from 2.23 to 39.54 t ha−1), with surface litter contributing 58.2% of this total, followed by canopy (22.1%), near-surface (13.4%), and elevated (6.6%) fine fuels. We also conducted a comprehensive investigation and found distinct variations in fine fuel loads across different bioregions, forest covers, and vegetation types. Furthermore, stepwise multiple regression was employed to develop predictive models for estimating fine fuel loads from forest structural and environmental variables. Results show that total and canopy fine fuels can be reasonably explained by forest height, density, and climate-related variables (with R2 = 0.38 and 0.51, respectively). However, the explanatory power of these models diminished when applied to elevated, near-surface, and surface fine fuels. This study underscores the intricacies of fine fuel distribution in Eucalypt forests, emphasising the necessity for comprehensive factors in fire management planning. Further research is needed to better comprehend the relationship between forest characteristics and fine fuel dynamics to enhance wildfire risk assessment and mitigation strategies.

Climate Change Perceptions and Associated Characteristics in Canadian Prairie Agricultural Producers

Climate change (CC) poses a threat to agricultural sustainability, which is important in the Canadian Prairies, as agriculture is a major occupation and driver of the economy. Agriculture involves both the creation and mitigation of emissions related to CC. To implement adaptation and mitigation practices, producers should accept CC as fact. This study is based in Saskatchewan, Canada, where CC denial is prevalent in public comments. To assess the validity of this anecdotal impression, this study provided a snapshot of Saskatchewan agricultural producers’ perceptions and observations of CC and assessed whether views on CC are associated with characteristics of political orientation and affiliation, mental flexibility, systems thinking, time orientation, climate knowledge, climate observations, and demographic variables. A survey was developed with the following four sections: (1) individual characteristics; (2) observed changes in climate-related variables; (3) knowledge and perceptions about CC; and (4) demographic variables. The survey included multiple-choice questions and items scored on a Likert scale. The survey was completed by 330 Saskatchewan agricultural producers (i.e., farmers and ranchers). The results indicated more CC denial in Saskatchewan producers than in other Canadian samples. Individual and socioeconomic characteristics of lower levels of formal education, identifying as male, conservative political affiliation and ideation, low trust in science, and low mental flexibility were associated with less acceptance and concern of CC. It is therefore necessary to consider socioeconomic and individual characteristics of producers in measures aiming to increase the acceptance of the reality of CC. Future intervention research should target male producers with lower levels of formal education, low trust in science, low mental flexibility, and right-leaning political ideation for the improvement of CC perceptions and examine different teaching methods (e.g., lectures, workshops, webinars) and dissemination methods (e.g., online versus in-person sessions) to see how various techniques may influence learning, as well as the way the information is used by particular groups.

The Effect of Climate Change on Wheat Production: Present Patterns and Upcoming Difficulties

Climate refers to the mean weather conditions in a certain region, impacting every aspect of the environment. Urbanisation and industrialization are causing woods to be cleared for living communities. The equilibrium of the ecosystem is upset by this shift, affecting producers, consumers, and decomposers. Plants, or producers, are an important component of the ecosystem because they supply energy. Plant sustainability and production are impacted by this change. A staple grain, wheat is greatly impacted by both temperature and CO2 levels. In addition to decreasing wheat productivity, it also increases wheat's susceptibility to many illnesses. Elevated temperatures result in increased transpiration, which in turn triggers drought, which in turn causes reduced production. According to predictions, in the next 20 to 30 years, a 2° C temperature shift might result in a serious water crisis. A scarcity of water during the grain filling and milking stages can impact production. This article covers the following topics: climate-related variables, effects on wheat production and growth, influence on disease severity, temperature increase prediction model, and CO2 curve in 2050.

The Learning Transfer of Dementia Training Program Participants: Its Antecedents and Mediating Effect on the Job Competency of Geriatric Caregivers.

This study examined how learning transfer and its antecedents impacted job competency among geriatric caregivers who underwent dementia training. The dementia training program of the National Health Insurance Service of South Korea was selected for this study. The participants included caregivers who provided care to patients with dementia at 3-12 months post-training. A hierarchical regression analysis was used to verify the research model. The results are summarized as follows: First, transfer opportunity and transfer intention were antecedent variables with a statistically significant positive effect on the near transfer of learning. Second, self-efficacy, the instructor's role, and transfer intention were antecedent variables with a statistically significant positive effect on the far transfer of learning. Third, the near transfer of learning had a statistically significant effect on all six competency variables (communication, problem solving, interpersonal relationships, skills, self-development, and work ethics). Fourth, the far transfer of learning had a statistically significant static effect on all six competency variables, although the size of the influence on competency enhancement was relatively small compared with the near transfer of learning. This study confirmed the effects of various transfer climate-related variables in a training program on job competency, suggesting that the learning transfer of caregivers who underwent dementia training is a significant mediating variable. The limitations of this study and directions for future research are also discussed. The learning transfer of caregivers who underwent this training enhanced their job competencies.

What climate? The different meaning of climate indicators in violent conflict studies

This paper explores the operationalization of climate-related indicators in violent conflict research. The climate-conflict narrative gained traction in recent decades and climate change is often referred to as a ‘threat multiplier’ by both policy makers and scholars. Yet, the relationships between climate-related phenomena and violent conflict are complex and context-specific. However, limited attention has been given to the climatic indicators applied in climate-conflict research. This paper addresses that gap by analyzing 32 studies published from 2004 to 2020 on the operationalization of climatic indicators and their relationship with violent conflict. It first categorizes climate indicators operationalization into five clusters: natural disasters, basic climate variability, advanced climate variability, freshwater availability, and the ENSO. The study evaluates the climate indicators for each cluster and shows that at an aggregate level these clusters examine 68 different climate representations. When paired with their respective conflict types, it finds a total of 113 climate-conflict combinations. Most operationalizations represent various forms of climate-related phenomena and variability rather than climate change. Some indicators are advancements over time, for example moving from changes in average rainfall to standardized precipitation indices. However, other indicators refer to various natural processes, making it challenging to determine whether climatic variability impacts conflict. The paper then demonstrates a discrepancy between the pathways through which climate may affect violent conflict and the representation of these pathways in the selected climate indicators. It discusses how the selection and operationalization of climate indicators requires careful consideration, and the phenomena researched should be well-specified in research findings.

Ecological vulnerability assessment of coral islands and reefs in the South China Sea based on remote sensing and reanalysis data

Coral reefs are ecosystems that are highly vulnerable to external environmental impacts, including changes associated with ocean acidification and global warming. Assessing the vulnerability of coral reef growth environments over large areas of the sea is a difficult and complex process, as it is influenced by many variables. There are few studies on environmental vulnerability assessment of coral islands and reefs in the South China Sea. It is therefore particularly important to understand the environmental sensitivity of corals and how coral communities respond to changes in climate-related environmental variables. In this study, indicators were selected mainly from natural environmental factors that hinder the development of coral reefs. The sea surface temperature (SST), sea surface salinity (SSS), wind velocity (WV) and direction, sea level height (SL), ocean currents (OC), and chlorophyll concentration (Chl) of coral reefs in South China Sea Island were integrated to calculate the coral reef environmental vulnerability region. In a GIS environment, Spatial Principal Component Analysis (SPCA) was used to develop sensitivity models and evaluate the ecological vulnerability of coral reefs. Based on the Environmental vulnerability indicator (EVI) values, the study area was classified as 5 grades of ecological vulnerability: Potential (0.000–0.577), Light (0.577–0.780), Medium (0.780–0.886), Heavy (0.886–0.993) and Very Heavy (0.993–1.131). Sensitivity models identified regional gradients of environmental stress and found that some coral reefs in western Malaysia and southwestern Philippines have higher vulnerability. Meanwhile, the study found that the reefs of Paracel Islands and Macclesfield Bank areas of medium vulnerability. Future use of high-precision data from long time series will allow better estimates of site-specific vulnerability and allow for the precise establishment of marine protected areas so that the ecological diversity of coral reefs can be sustained.