Vulnerability Assessment Framework Research Articles

Variable climatic conditions dominate decreased wetland vulnerability on the Qinghai‒Tibet Plateau: Insights from the ecosystem pattern-process-function framework

Wetlands are of global importance in providing essential ecosystem services but are also sensitive to climate change and human activities. Monitoring and assessing wetland vulnerability are crucial for ecological conservation and management strategies. However, the framework of wetland vulnerability assessment and the underlying mechanisms have not been well studied. In this study, the spatiotemporal variations in wetland vulnerability on the Qinghai‒Tibet Plateau (QTP) between 1990 and 2020 were investigated based on the ecosystem pattern-process-function framework. The key driving factors were identified by partial least squares structural equation modelling (PLS-SEM) and multiscale geographically weighted regression (MGWR) models. Our results showed that the wetland ecosystem pattern index (EPI), ecosystem process index (EPOI), ecosystem function index (EFI), and wetland vulnerability index (WVI) all demonstrated an increasing pattern from northwest to southeast. Between 1990 and 2020, the mean WVI values gradually decreased from 0.616 to 0.588, indicating a steady improvement in the wetland ecosystem on the QTP. Rapid urbanization increased the EPOI, while rugged topography increased both the EPI and EPOI, and the increase in hydrological abundance enhanced the EFI, which in turn contributed to an increase in the WVI. Conversely, climatic conditions led to a reduction in the EPI, which in turn resulted in a significant decrease in the WVI. Therefore, although urbanization and topographical and hydrological factors have somewhat exacerbated the WVI on the QTP, variable climatic conditions have driven the decline in wetland vulnerability in the last three decades. Furthermore, our results indicated that the proposed framework could provide a comprehensive approach for wetland vulnerability assessment and useful implications for wetland conservation and management.

Precondition Study of a Sponge City: Comprehensive Assessment of the Vulnerability of an Urban Rainwater System

This study introduces the concept of urban rainwater system vulnerability and identifies the indicator factors that affect the vulnerability of rainwater systems. Using the analytic hierarchy process (AHP), an index system for the vulnerability assessment of the rainwater system was established, and a vulnerability assessment model for the rainwater system was constructed. By integrating vulnerability degree, recurrence period, and water depth of ponding, a vulnerability assessment framework for urban rainwater systems was developed. Taking a newly developed urban area in the Suzhou High-Tech Zone as an example, we calculated the vulnerability degree of the urban rainwater system in this area to be 0.6497, indicating a high level of vulnerability and poor system safety. When encountering rainfall with a recurrence period p > 5 years, the city is likely to experience severe waterlogging. Through the analysis and evaluation of the rainwater system’s vulnerability, while clarifying the current state of the rainwater system, it can provide a scientific reference basis for the system’s upgrade, transformation, and optimized operation and management. Although the selection of factors may not be entirely comprehensive, this method allows for adjustments based on the composition and operation of different rainwater systems.

A novel framework for the spatiotemporal assessment of urban flood vulnerability

Considering the pressing Urban Flood (UF) challenges faced by developing countries, the assessment of Urban Flood Vulnerability (UFV) assumes paramount importance in comprehending the detrimental impacts of UF on urban environments, thereby facilitating effective UF management. Addressing a notable gap in previous UFV research, which predominantly focused on spatial characteristics while overlooking temporal dynamics, we introduce a novel framework for spatiotemporal UFV assessment, by presenting a multiplicative equation to elucidate the interaction between Urban Flood Susceptibility (UFS) and Vulnerable Entities (VE). Employing this framework in the Great Bay Area (GBA), by considering the composition of the UFV database, we leveraged Machine Learnings and the Urban Vitality Index for quantifying the UFS and VE, respectively. SHapley Additive exPlanations was employed to quantify the local contributions of various factors to UFV. We found (1) a discernible upward trajectory in UFV across the GBA from 2012 to 2020, coupled with an expansion in spatial scope, indicative of persistent challenges in the future, and (2) the impervious surface percentage emerges as the primary contributor to UFV, exhibiting spatial heterogeneity reflective of regional environments. We believe this study has the potential to significantly contribute to addressing UF challenges and fostering the development of sustainable cities.

Cyber Security of Smart-Grid Frequency Control: A Review and Vulnerability Assessment Framework

Smart Grid involves application of Information and Communication Technology (ICT) for monitoring, protection, operation, and control of interconnected power systems under various scenarios. Smart Grid Control (SGC) is an important aspect that is constantly subjected to various vulnerabilities, threats, and attacks under central and distributed control architectures. Cyber Security of smart grid control, especially, Load Frequency Control (LFC) is an important issue that is addressed in this article. The state-of-the-art in cyber security and attacks on smart grid control and intensive literature review is discussed with a comprehensive list of references on LFC. The authors present a part of their own work carried out on a systematic Vulnerability Assessment (VA) framework that can be used to identify weak points in the LFC system. The proposed methodology is explained for Vulnerability Assessment of the standard 39-bus New England test system and the 96 bus reliability test system to illustrate the concept of cyber security and Vulnerability Assessment of smart grid LFC.

Integrated SEAWAT model and GALDIT method for dynamic vulnerability assessment of coastal aquifer to seawater intrusion

Seawater intrusion (SI) has become a global issue exacerbated by intense anthropogenic activities and climate change. It is imperative to seek a synergistic strategy to reconcile environmental and economic benefits in the coastal regions. However, the intricate SI process and data scarcity present formidable challenges in dynamically assessing the coastal groundwater vulnerability. To address the challenge, this study proposed a novel framework that integrates the existing vulnerability assessment method (GALDIT) and variable-density groundwater model (SEAWAT). The future scenarios from 2019 to 2050 were investigated monthly under climate change (SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5) and human activities (80 % and 50 % of current groundwater abstraction) in Longkou city, China, a typical coastal region subject to extensive SI, compared with the status quo in 2018. Results indicated that by 2050, the high vulnerability area, is in a narrow buffer within 1.2 km from the shoreline and exhibits minor changes while the salt concentration here increased by about 2700 mg/L compared with the current situation. The moderate vulnerability zone expands by about 30 km2, and the low vulnerable area decreases proportionally. The groundwater over-abstraction is identified as a more critical factor compared to the regional precipitation under climate change. When groundwater abstraction is reduced to 80 % of the current scale, the expansion rate of the moderate-vulnerable area slows down significantly, with an expansion area of only 18 km2 by 2050. Further reducing groundwater abstraction to 50 % of the current scale shifts the evolution trend of the medium-vulnerable area from expansion to contraction, with the area shrinking by about 11 km2 by 2050. The integrated vulnerability assessment framework can be applied not only in the similar coastal regions but also provides insights into other natural hazards.

A feasibility study for the application of climate change vulnerability assessments on species in the Tallurutiup Imanga National Marine Conservation Area

Abstract Climate change is disproportionately affecting Arctic ecosystems and their resident species, but knowledge gaps complicate conservation planning. A proof‐of‐concept application of existing trait‐based vulnerability assessment frameworks were applied to nine species from three different taxa (cetaceans, pinnipeds, marine fish) to determine their vulnerability under the RCP 8.5 emissions scenario. A literature review and data gap analysis were performed to quantify vulnerability and recommend future research priorities. Each assessed species was found to be highly vulnerable to climate change, with char, cod, and walrus being the most vulnerable. The largest data gaps include outdated or missing abundance measurements mortality rates in both marine mammals and fish, and reproductive behaviours for fish specifically. Future assessments should consider multiple emissions scenarios that match the ranges of migratory species not confined to the TINMCA. Providing a method to preliminarily evaluate climate change vulnerability may help mitigate issues brought by limited species experts, such as respondent fatigue. More refined scores will require the assistance of species experts, including biologists and Indigenous knowledge holders.

A novel framework for effective structural vulnerability assessment of tubular structures using machine learning algorithms (GA and ANN) for hybrid simulations

Abstract Seismic vulnerability assessments are conventionally conducted by using sophisticated nonlinear analytical models, leading to aggressive computational demands. Previous attempts were made to reduce computational efforts for establishing vulnerability assessment of structures; however, the area of super tall and tubular structures still faces considerable lack. Advent of efficient machine learning (ML) has enabled engineering practitioners to automate the processes for fragility analysis; however, its application for high-rise tubular structures is not yet exploited, and most implementations are limited to basic ML. In this work, an attempt was made to reduce computational demand for the fragility assessment process for tubular structures by employing genetic algorithms (GAs) for nonlinear structural modeling, and development of artificial neural network (ANN) using deep learning for fragility development. Consequently, a simple lumped parameter model had been developed using open-source code of ZEUS-NL, containing parameters selected by GA to acutely account for convoluted interactive behavior of structural systems and dynamic demands. Subsequently, incremental dynamic analysis (IDA) was performed on the optimized model. A new framework has been established to develop and train ANN architecture by amalgamating Weka’s capability of data preprocessing with deep learning. The established ANN model resulted in correlation coefficient of 0.9972 and R 2 of 0.95, demonstrating adequate performance.

A Proposed Framework of Vulnerability Assessment and Penetration Testing (VAPT) in Cloud Computing Environments from Penetration Tester Perspective

Penetration testing is a process that focuses on finding security vulnerabilities in a target environment that could let an attacker penetrate the network or computer system or steal information. Due to the COVID-19 endemic, most employees still implement working from home or a hybrid approach, even though the number of new cases of COVID-19 is decreasing. However, working from home depends mainly on cloud computing applications that help employees efficiently accomplish their daily work. This situation also increased the number of data generated from various sources, so they may be exposed to different security risks. This research will propose a framework to conduct vulnerability assessment and penetration testing (VAPT) in cloud service models such as SaaS, PaaS, and IaaS from the perspective of penetration testers. This proposed framework is developed through the integration and mapping of existing frameworks and guidelines to conduct VAPT on testing components such as web applications, APIs, network testing, etc. In this proposed framework, the method of conducting VAPT for each cloud service model will be discussed in detail, from the planning and reconnaissance stage until the report is delivered to the cloud subscriber or cloud provider. An advantage of this proposed framework for the penetration tester is that there is still a lack of methods or guidelines for conducting VAPT that cover all the cloud service models in one comprehensive document

Climate Resilient Cities Assessing Differential Vulnerability to Climate Hazards in Urban India

This report introduces the Climate Hazard and Vulnerability Assessment (CHVA) framework, which is developed as an accessible tool to focus on the hazards and social factors that create differential vulnerability to climate change in cities. To analyse differential vulnerability, CHVA is divided into three parts: Hazard Identification and Assessment, Exposure Analysis, and Vulnerability Assessment. Using official city-level data that can in some cases be supplemented with alternative local data sources, the CHVA assesses both hazards and the vulnerability of people and critical infrastructure to give a robust and in-depth understanding of urban vulnerability.

Exploring the vulnerability of the coastal wetlands of India to the changing climate and their adaptation strategies.

Changing climate exerts significant pressure on coastal wetland fishery, ecosystems and livelihood at the eco-regional level. In the present study, an indicator and scoring-based vulnerability assessment framework was developed to assess climate change-induced potential impacts and vulnerability of the coastal wetlands through stakeholder perception and primary data collection on ecology and fisheries. The study was conducted in 12 coastal wetlands in the Gangetic estuarine regions of eastern India. Analysis of rainfall, temperature and extreme climatic events was also carried out. The climate variability in the region was analysed and discussed in the context of ecosystem health and sustainability indicators. The results indicated that anthropogenic stress has a greater influence on ecosystem services than climatic stress. However, the nature and degree of vulnerability of fishers to climate change vary with their capacity to adapt to such changes and alternate sources of livelihood. Altogether, 34 fish species were documented, belonging to 11 taxonomic orders, and categorized as per intensity of resilience using selected criteria. Perception-based climate-resilient adaptation strategies have also been discussed. Developing appropriate adaptation policies and implementing climate-resilient practices and management can minimize vulnerability and increase the adaptive capacity of fishers ensuring sustainable livelihood options.

Urbanization and land use regulate soil vulnerability to antibiotic contamination in urban green spaces

The presence of antibiotics in environment is an emerging concern because of their ubiquitous occurrence, adverse eco-toxicological effects, and promotion of widespread antibiotic resistance. Urban soil, which plays a noticeable role in human health, may be a reservoir of antibiotics because of intensive human disturbance. However, little is understood about the vulnerability of soil to antibiotic contamination in urban areas and the spatial–temporal characteristics of anthropogenic and environmental pressures. In this study, we developed a framework for the dynamic assessment of soil vulnerability to antibiotic contamination in urban green spaces, combining antibiotic release, exposure, and consequence layers. According to the results, soil vulnerability risks shown obvious spatial–temporal variation in urban areas. Areas at a high risk of antibiotic contamination were usually found in urban centers with high population densities and in seasons with low temperature and vegetation coverage. Quinolones (e.g., ofloxacin and norfloxacin) were priority antibiotics that posed the highest vulnerability risks, followed by tetracyclines. We also confirmed the effectiveness of the vulnerability assessment by correlating soil vulnerability indexes and antibiotic residues in urban soils. Furthermore, urbanization- and land use-related parameters were shown to be critical in regulating soil vulnerability to antibiotic contamination based on sensitivity analysis. These findings have important implications for the prediction and mitigation of urban soil contamination with antibiotics and strategies to improve human health.

A multi-hazard framework for coastal vulnerability assessment and climate-change adaptation planning

When compounded by climate change, ecosystem degradation puts coastal communities at increased vulnerability to hazards. However, there have been few quantitative assessments of coastal vulnerability to multiple hazards that synthesize climate scenarios and ecological and demographic characteristics to inform adaptive strategies. This study presents such a framework for assessing coastal vulnerability to sea level rise, storm waves and winds under present and future climate scenarios. As a case study, we applied it to Macao, one of the most densely populated and highly urbanized coastal area in the world. We found 19% of the coastline hosting 349,000 people – distributed primarily along the Macao Peninsula coast – are highly exposed to coastal hazards at present. Almost half of Macao's total coastline with about 121,000 people are protected by natural habitats. The proportion of highly-exposed coastline was estimated to double, and that of highly-threatened population was estimated to increase by 26.9–44.1%, by 2100. Our findings provide insights into the spatiotemporal heterogeneity of coastal vulnerability to multiple hazards and highlight the role played by habitats in shielding coastal communities. The results can inform prioritizing coastal areas for conservative and adaptive strategies, thereby minimizing deleterious development pressures and enhancing the resilience of coastal communities.

Dynamic assessment of postdisaster road network vulnerability using crowdsourced traffic data

Accurate identification of the spatial and temporal dynamic impacts of disasters on road networks is crucial for making informed decisions regarding postdisaster emergency response and recovery. In this paper, we propose a multidimensional vulnerability assessment framework that utilizes time series crowdsourced data. The framework enables a spatiotemporal analysis of traffic flows at the link level, allowing us to identify the primary factors that contribute to vulnerability after a disaster. The results show that there is a significant variation in link vulnerability over time, with the overall network gradually returning to a normal state. We also observe a global correlation in link vulnerability across the spatial aspect, although the autocorrelation effect is only present among directly contiguous links. Furthermore, metrics affecting link vulnerability varied during different periods of response. Graph-based metrics of road network structure, population density, and road type are key factors influencing postearthquake vulnerability.

Murky waters: Assessing the vulnerabilities of Indo‐West Pacific non‐marine elasmobranchs to inform future conservation planning priorities

Abstract Globally, freshwater environments are imperilled, with freshwater vertebrate species declining at twice the rate of marine and terrestrial populations. Non‐marine elasmobranchs (freshwater obligates and euryhaline generalists) remain understudied and overlooked by conservation efforts. This study aimed to adapt and apply a vulnerability assessment framework to understand the conservation priorities of Indo‐West Pacific non‐marine elasmobranch species. An exposure sensitivity adaptability (ESA) framework was used to assess vulnerability to environmental threats, and an exposure susceptibility productivity (ESP) framework was used to assess vulnerability to fisheries. Resulting species vulnerabilities were categorized into three conservation priority tiers. The general patterns of conservation priority tiering were as follows: (i) large‐bodied euryhaline species occurring in densely populated nations had the highest ESA and ESP vulnerabilities; (ii) freshwater obligates also had high ESA vulnerability rankings, although ESP vulnerability rankings were lower as their smaller body sizes suggest increased population productivity and higher potential for resilience; and (iii) euryhaline species with large range proportions in northern Australia had moderate to low vulnerability rankings across ESA and ESP assessments, as these species benefit from reduced fisheries mortality compared with species occurring in other regions. The outcomes from the vulnerability assessment framework for the conservation priority rankings of species corresponded with their respective International Union for Conservation of Nature (IUCN) Red List status, whereby priority 1 and 2 species also have elevated extinction risks. Environmental threats were at high or moderate levels in all nations assessed, while Cambodia, China, Malaysia, and Myanmar face the highest pressure from inland fisheries. The major knowledge gaps identified included species‐specific productivity estimates, population dynamics (population movements and habitat requirements), and information on mortality from the threats considered. The present ESA–ESP framework was effective for the broad and data‐poor context of Indo‐West Pacific non‐marine elasmobranchs, and the results will be useful for guiding future conservation planning for high‐priority species and nations.

Understanding District Heating Networks Vulnerability: A Comprehensive Analytical Approach with Controllability Consideration

District heating networks (DHNs) are critical infrastructures that ensure production and residents' living. Analyzing vulnerability characteristics of DHNs is of great significance in heating safety decision-making. In this study, we analyze the structure, function, operation, and failure characteristics of DHNs and propose vulnerability analysis methods. We develop failure simulation models under fully-controllable (FC) and uncontrollable (UC) conditions for six failure scenarios based on component importance. To verify the proposed methods' effectiveness, we applied them to a DHN in a Chinese city. Upon analyzing the appositeness of various vulnerability assessment indicators, we find that the heat source connectivity efficiency loss rate, established based on hydraulic distribution in DHNs, can effectively characterize both topological and functional vulnerability. Our analysis also reveals that controllable DHNs, which evenly distribute heat supply among connected users for fairness, can result in lower flow and greater functional vulnerability compared to UC DHNs. In particular, under large area failure (AF) scenarios, the average functional vulnerability of UC DHNs increases by 27.59% to 38.30% compared to small-AF scenarios, while that of controllable DHNs increases by 103.78% to 120.58%. The proposed vulnerability assessment framework considering topology and function from both quantitative and qualitative perspectives, can grasp heating vulnerabilities multi-dimensionally.

A co-produced national climate change risk and vulnerability assessment framework for South Africa

IntroductionThere are mounting demands to undertake climate risk and vulnerability (CRV) assessments for policy, planning, funding, insurance, and compliance reasons. In Africa, given the adaptation imperative, this is particularly important. Increasingly, it has become clear that sub-national assessments are needed to inform adaptation practice. However, there has been relatively little guidance on how to undertake these more local assessments and aggregate them making it difficult for national governments to know the extent and variability of climate vulnerability and risk across the country.MethodsIn South Africa, the national government, led by the Department of Forestry, Fisheries and the Environment (DFFE), undertook to establish a common framework to guide the development and review of CRV assessments. This paper presents the framework that was co-developed through a series of engagements with stakeholders active in implementing and supporting CRV assessments.ResultsThe framework is intended to provide guidance on what to consider when undertaking CRV assessments within diverse South African contexts in order to enable alignment, comparison, and aggregation between them and work towards an effective climate adaptation response across scales. Rather than standardizing a methodology, the framework promotes the use of a standard set of concepts as the basis for each assessment and profiles a diversity of methods, tools and data sources for applying the concepts in a contextually sensitive way. This provides a flexible yet structured sequence of three interlinked steps in a risk and vulnerability assessment process, namely: (1) Planning, (2) Scoping and (3) Assessing. The framework guides users through the choice and application of three assessment depths, depending on decision-context, resourcing and extent of pre-existing data and information. It encourages the integration of participatory and indicator-based methods through an impact chain approach, profiling more than 30 freely available tools and resources. This process builds a strong evidence base and a deepening set of engagements and shared understanding between relevant stakeholders, upon which to act.DiscussionThis South African process can provide insight and support for actors driving the climate agenda in other countries looking to develop comparable assessments as the basis to drive equitable and transformative climate action and learning.

Flood vulnerability assessment of Thailand's flood-prone Pathum Thani province and vulnerability mitigation strategies

This research assesses the flood vulnerability of Thailand's flood-prone province of Pathum Thani using a comprehensive vulnerability assessment framework. The assessment framework incorporates three key components: exposure, sensitivity, and adaptive capacity, consisting of 10, 12 and 11 flood vulnerability indicators, respectively. The flood vulnerability components and the flood vulnerability indicators are statistically validated by confirmatory factor analysis to determine the factor loadings and reliability of the components and indicators. The flood vulnerability questionnaire corresponding to the flood vulnerability indicators is subsequently developed and applied to the flood-prone districts of the province. The results show that proximity to rivers (with an indicator score of 0.685), household debt levels (0.612), land use patterns (0.617), and the proportion of low-income households (0.621) significantly contribute to the flood exposure of the province (with an exposure index score of 0.531). Larger household size (with an indicator score of 0.901), disruptions in public utility services (0.747), and workplace absenteeism due to flooding (0.741) contribute to the province's higher flood sensitivity (with a sensitivity index score of 0.633). Drainage capacity of natural and man-made waterways (0.571) contributes to low to moderate levels of flood adaptive capacity. The flood vulnerability of seven administrative districts of Pathum Thani, as measured by the flood vulnerability index scores (0.454–0.608), range from moderate to high. Local authorities need to invest in flood warning and response systems, prioritize infrastructure development and encourage community engagement to reduce the flood vulnerability.

Prediction of groundwater quality index to assess suitability for drinking purpose using averaged neural network and geospatial analysis

Groundwater quality management is pivotal for ensuring public health and ecological resilience. However, the conventional water quality indices often face challenges related to parameter selection, geographic coverage, and scalability. The integration of machine learning and spatial analysis represents a promising methodological shift, allowing for high accuracy and adaptive management strategies. The Safe Groundwater Project in Unsupplied Areas (2017–2020) employed a comprehensive Groundwater Quality Index (GQI) to evaluate potable groundwater quality across South Korea, utilizing a large dataset comprising 28 water quality parameters and 3552 wells. This study revealed that over 50 % of the evaluated wells (Total 8326 wells) were inappropriate as sources of drinking water, indicating a pressing need for policy revision. The averaged neural network model achieved a high predictive accuracy of approximately 95 % for GQI grades, outperforming other classification models. The introduction of 2D spatial analysis in conjunction with machine learning algorithms notably increased the predictive accuracy for unevenly distributed groundwater samples. Moreover, this combined approach enabled the intuitive visualization of groundwater vulnerability across various regions, which can inform targeted interventions for effective resource allocation and management. This research represents a methodologically robust, interdisciplinary approach that holds significant implications for a framework for future groundwater quality management and vulnerability assessment.

Assessing dynamics of human vulnerability at community level – Using mobility data

The comprehension of the community vulnerability levels is pivotal for understanding how communities respond to diverse hazard events and supporting community resilience management. Existing assessment approaches often treat social vulnerability as static by assuming that residents stay within their home community, which ignored the influence of human mobilities on the vulnerability distribution. To overcome these limitations, in this study, we developed a spatial-temporal community vulnerability assessment framework that integrates human mobility with demographic characteristics. Specifically, we integrated geographic data, e.g., mobile phone data, and human activity trajectory data, e.g., activity survey data, to (1) map demographic-dependent varying human trajectory, (2) identify the dynamic distributions of social vulnerable groups, and (3) based on this to more comprehensively evaluate community vulnerability. We selected Philadelphia County of Pennsylvania as a case study to validate this method. The simulation results demonstrate that the spatial-temporal distribution of some vulnerable populations undergoes significant changes over time due to individual mobility. Furthermore, we also find that different vulnerable groups within the same community show various mobility patterns. Our results highlight the importance of integrating demographic-based human mobility into community vulnerability assessment and complementing current static vulnerability with a more comprehensive understanding to support emergency management, which could enhance the effectiveness and efficiency of timely emergency planning and responses to disasters.

Revising vulnerability assessments of montane birds in the colombian páramo to account for threats from climate change

Tropical montane ecosystems are highly vulnerable to global climate change, but their species-level conservation vulnerability assessments generally do not incorporate climate threats. The Colombian páramo is a highly diverse montane ecosystem but it contains relatively few species currently identified as threatened on the IUCN Red List. We explore whether current assessments need revising to account for climate change threats for páramo species. We use a climate change vulnerability assessment framework to evaluate the risks and opportunities from climate change for 60 bird species within the Colombian páramo. We explore two potential climate change scenarios (RCP 2.6 and RCP 8.5), representing best and worst-case projections. We find that more than half (52–68%, depending on the scenario) of bird species will be threatened by climate change by the end of the century, but a very poor agreement between the current conservation status of species and their climate vulnerability score. We find ~ 90% of species identified as climate-threatened are not currently identified as being of conservation concern, representing a substantial new suite of priority species within the Colombian páramo. Overall, we identify up to 34 páramo bird species that are threatened by climate change but not currently listed as being of conservation concern. Similar mismatches may also occur in other montane taxa and so updating species risk assessments to account for climate change is urgently needed. Implementation of appropriate conservation actions is also needed to reduce species’ risks from climate change.