Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation

Enhancing biodiversity conservation and monitoring in protected areas through efficient data management

A scientifically informed approach to decision-making is key to ensuring the sustainable management of ecosystems, especially in the light of increasing human pressure on habitats and species. Protected areas, with their long-term institutional mandate for biodiversity conservation, play an important role as data providers, for example, through the long-term monitoring of natural resources. However, poor data management often limits the use and reuse of this wealth of information. In this paper, we share lessons learned in managing long-term data from the Italian Alpine national parks. Our analysis and examples focus on specific issues faced by managers of protected areas, which partially differ from those faced by academic researchers, predominantly owing to different mission, governance, and temporal perspectives. Rigorous data quality control, the use of appropriate data management tools, and acquisition of the necessary skills remain the main obstacles. Common protocols for data collection offer great opportunities for the future, and complete recovery and documentation of time series is an urgent priority. Notably, before data can be shared, protected areas should improve their data management systems, a task that can be achieved only with adequate resources and a long-term vision. We suggest strategies that protected areas, funding agencies, and the scientific community can embrace to address these problems. The added value of our work lies in promoting engagement with managers of protected areas and in reporting and analysing their concrete requirements and problems, thereby contributing to the ongoing discussion on data management and sharing through a bottom-up approach.

Are open-source hydrodynamic models efficient in quantifying flood risks over mountainous terrains? An exhaustive analysis over the Hindu-Kush-Himalayan region

The Hindu-Kush-Himalaya is abode to numerous severely flood-prone mountainous stretches that distress vulnerable communities and cause massive destruction to physical entities such as hydropower projects. Adopting commercial flood models for replicating the dynamics of flood wave propagation over such regions is a major constraint due to the financial economics threaded to flood management. For the first instance, the present study attempts to investigate whether advanced open-source models are skillful in quantifying flood hazards and population exposure over mountainous terrains. While doing so, the performance of 1D-2D coupled HEC-RAS v6.3 (the most recent version developed by the U.S. Army Corps of Engineers) is reconnoitred for the first time in flood management literature. The most flood-prone region in Bhutan, the Chamkhar Chhu River Basin, housing large groups of communities and airports near its floodplains, is considered. HEC-RAS v6.3 setups are corroborated by comparing them with 2010 flood imagery derived from MODIS through performance metrics. The results indicate a sizable portion of the central part of the basin experiences very-high flood hazards with depth and velocities exceeding 3 m, and 1.6 m/s, respectively, during 50, 100, and 200-year return periods of floods. To affirm HEC-RAS, the flood hazards are compared with TUFLOW at 1D and 1D-2D coupled levels. The hydrological similarity within the channel is reflected at river cross-sections (NSE and KGE > 0.98), while overland inundation and hazard statistics differ, however, very less significant (<10 %). Later, flood hazards extracted from HEC-RAS are fused with the World-Pop population to estimate the degree of population exposure. The study ascertains that HEC-RAS v6.3 is an efficacious option for flood risk mapping over geographically arduous regions and can be preferred in resource-constrained environments ensuring a minimal degree of anomaly.

A new method to compile global multi-hazard event sets

This study presents a new method, the MYRIAD-Hazard Event Sets Algorithm (MYRIAD-HESA), that compiles historically-based multi-hazard event sets. MYRIAD-HESA is a fully open-access method that can create multi-hazard event sets from any hazard events that occur on varying time, space, and intensity scales. In the past, multi-hazards have predominately been studied on a local or continental scale, or have been limited to specific hazard combinations, such as the combination between droughts and heatwaves. Therefore, we exemplify our approach by compiling a global multi-hazard event set database, spanning from 2004 to 2017, which includes eleven hazards from varying hazard classes (e.g. meteorological, geophysical, hydrological and climatological). This global database provides new scientific insights on the frequency of different multi-hazard events and their hotspots. Additionally, we explicitly incorporate a temporal dimension in MYRIAD-HESA, the time-lag. The time-lag, or time between the occurrence of hazards, is used to determine potentially impactful events that occurred in close succession. Varying time-lags have been tested in MYRIAD-HESA, and are analysed using North America as a case study. Alongside the MYRIAD-HESA, the multi-hazard event sets, MYRIAD-HES, is openly available to further increase the understanding of multi-hazard events in the disaster risk community. The open-source nature of MYRIAD-HESA provides flexibility to conduct multi-risk assessments by, for example, incorporating higher resolution data for an area of interest.

Toward a framework for systemic multi-hazard and multi-risk assessment and management

In our increasingly interconnected world, natural hazards and their impacts spread across geographical, administrative, and sectoral boundaries. Owing to the interrelationships between multi-hazards and socio-economic dimensions, the impacts of these types of events can surmount those of multiple single hazards. The complexities involved in tackling multi-hazards and multi-risks hinder a more holistic and integrative perspective and make it difficult to identify overarching dimensions important for assessment and management purposes. We contribute to this discussion by building on systemic risk research, especially the focus on interconnectedness, and suggest ways forward for an integrated multi-hazard and multi-risk framework that should be beneficial in real-world applications. In this article, we propose a six-step framework for analyzing and managing risk across a spectrum ranging from single-to multi- and systemic risk.

Spatial and temporal variation of meteorological parameters in the lower Tigris-Euphrates basin, Türkiye: application of non-parametric methods and an innovative trend approach

In this study, Mann-Kendall (MK), Spearman's rho (SR), and innovative trend analysis with significance test (ITA-ST) are performed on about 53 years of meteorological parameters obtained from 23 meteorological stations located in the lower Tigris-Euphrates basin (LTEB), Türkiye. Finally, sequential Mann-Kendall (SMK) and Cusum tests are applied to detect any abrupt changes in annual time series. Results indicate that MK and SR demonstrate a significant trend in seven of the total annual precipitation series, and ITA-ST captures the existence of a significant trend in 21 of the 23 total annual precipitations. Three methods reveal that there is an increasing trend in both the annual mean temperature and the annual total evapotranspiration (EP). MK, SR, and ITA-ST capture a significant decreasing trend in the 10, 8, and 16 of the 23 annual mean relative humidity (RH) series, respectively. According to the findings, ITA-ST is more sensitive than the classical MK and SR methods. Cusum and SMK tests are detected the start of trend year 21.7 and 8.6% of annual total precipitation, 95.65 and 69.56% of annual mean temperature, 47.82 and 17.4% of total mean RH, and 95.65 and 69.56% of annual total EP time series, respectively. The Cusum test is found to be more sensitive than the SMK test.

Climate change maladaptation for health: Agricultural practice against shifting seasonal rainfall affects snakebite risk for farmers in the tropics

Snakebite affects more than 1.8 million people annually. Factors explaining snakebite variability include farmers' behaviors, snake ecology and climate. One unstudied issue is how farmers' adaptation to novel climates affect their health. Here we examined potential impacts of adaptation on snakebite using individual-based simulations, focusing on strategies meant to counteract major crop yield decline because of changing rainfall in Sri Lanka. For rubber cropping, adaptation led to a 33% increase in snakebite incidence per farmer work hour because of work during risky months, but a 17% decrease in total annual snakebites because of decreased labor in plantations overall. Rice farming adaptation decreased snakebites by 16%, because of shifting labor towards safer months, whereas tea adaptation led to a general increase. These results indicate that adaptation could have both a positive and negative effect, potentially intensified by ENSO. Our research highlights the need for assessing adaptation strategies for potential health maladaptations.

Disaster-development interface and its impact on emerging vulnerability scenario in Ladakh region of northwestern Himalayas

The Union Territory of Ladakh, located in the northwestern Himalayan region, is highly vulnerable to natural and anthropogenic hazards like earthquakes, landslides, snow avalanches, flash floods, cloud bursts, and border conflicts. Occurrences of these disasters have significantly influenced the development and vulnerability scenario of Trans-Himalayan Ladakh. Findings reveal that despite suffering losses from natural and human-induced disasters, the region has benefited by grabbing the attention of policymakers at the national level. Consequently, long-term developments were positively impacted, reflecting infrastructural upgradation, improved transportation and communication, profoundly improving the socio-economic well-being of the people. Furthermore, post-disaster developments have managed to showcase the unique physiography and adventurous terrains of Ladakh, promoting tourism as the main economic driver in the region. The exponential growth of tourism and associated sectors have influenced the vulnerability scenario, which was quantified using the multi-criterion-based analytical hierarchical processes (AHP) method, indicating an increase in climate change-related vulnerability, followed by socio-cultural, environmental, and physical vulnerabilities. Specifically, the vulnerabilities with respect to flash floods, landslides, erratic rainfall, haphazard constructions, cultural dilution, water crisis, and changes in land use patterns have been exacerbated across the study area. The study highlights the need for effective management of these emerging vulnerabilities through proper planning to ensure long-term sustainable development goals in this environmentally fragile region.

Quantitative risk assessment of typhoon storm surge for multi-risk sources

Typhoon storm surge (TSS) is a complex marine disaster affected by multi-risk sources. Quantitative risk assessment is an important prerequisite for identifying risk areas and designing risk reduction strategies. This paper aims to propose a rapid, accurate, and comprehensive quantitative risk assessment method for TSS under multi-risk sources, including disaster occurrence probability and severity. First, identify the primary risk sources according to the disaster-causing mechanism of TSS. Then, based on the official public data from 1989 to 2020, the dependence structure among multi-risk sources is constructed using Copulas to calculate the probability of each superposition scenario. Meanwhile, build visual scenario databases employing Geographical Information System (GIS) techniques. Subsequently, the extent and depth of inundation are translated into economic risk and population risk using GIS and depth-damage functions. Finally, taking the "Mangkhut" as a case study, the method's feasibility and accuracy are verified. The results show that the primary risk sources of TSS are storm tide, astronomical tide and coastal waves. The Gumbel Copula is optimal, with OLS (ordinary least squares) and D of 0.0186 and 0.1831, respectively. The probability assessment under different superposition scenarios indicates that the greatest threat of TSS in Guangzhou comes from the storm tide and the astronomical tide. As for the "Mangkhut" case study in Jiangmen City, the assesses occurrence probability is 0.0355%, the accuracy of economic risk assessment (except mariculture) is 95.28%, and the accuracy of population risk assessment is 98.60%. Residences and the disaster-bearing bodies in 0-3 m inundation depth are most severely affected by TSS disasters. Measures such as locating residential and important buildings away from the shoreline (at least 10 km) and ground (above 3 m), formulating disaster emergency plans, and developing the forecast and prevention of storm tides and astronomical tides will help ensure the safety of residents' life and property. This paper provides an efficient and accurate method, which is of great significance for disaster control, sustainable development, and decision-making.

Learning from the COVID-19 pandemic in Italy to advance multi-hazard disaster risk management

COVID-19 challenged all national emergency management systems worldwide overlapping with other natural hazards. We framed a 'parallel phases' Disaster Risk Management (DRM) model to overcome the limitations of the existing models when dealing with complex multi-hazard risk conditions. We supported the limitations analysing Italian Red Cross data on past and ongoing emergencies including COVID-19 and we outlined three guidelines for advancing multi-hazard DRM: (i) exploiting the low emergency intensity of slow-onset hazards for preparedness actions; (ii) increasing the internal resources and making them available for international support; (iii) implementing multi-hazard seasonal impact-based forecasts to foster the planning of anticipatory actions.

Rising slopes—Bibliometrics of mountain research 1900–2019

Mountain areas provide essential resources for a significant proportion of the Earth’s population. This study presents the development of mountain research between 1900 and 2019 based on peer-reviewed articles in English listed in Web of Science^TM (WOS). We analyzed the number of publications over time, journals and scientific categories, frequent topics, and geographical distributions based on 40 mountain ranges and authors’ countries as well as institutional contributions. From 1900–2019, 195k ±10% mountain research papers were published; over 50% from 2010–2019. While papers were published in more than 1000 different journals, indicating a wide range of disciplines engaged in mountain research, 94% of the papers were assigned to “Science & Technology”, only <5% to “Social Sciences” and “Arts & Humanities”. The most papers were written by researchers in the USA, followed by China. The number of papers per area or capita showed high variability across the investigated mountain ranges. Thus, geographically and disciplinarily more balanced research activities and better accessibility of knowledge about mountain regions are recommended.

Flood Management Issues in Hilly Regions of Uttarakhand (India) under Changing Climatic Conditions

Uttarakhand, an Indian Himalayan state in India, is famous for its natural environment, health rejuvenation, adventure, and a pilgrimage centre for various religions. It is categorised into two major regions, i.e., the Garhwal and the Kumaon, and geographically, the Bhabar and the Terai. Floods, cloudbursts, glacier lake outbursts, and landslides are the major natural hazards that cause the highest number of mortalities and property damage in this state. After becoming a full 27th state of India in 2000, the developmental activities have increased many folds, which has added to such calamities. This study briefly summarises the major incidents of flood damage, describes the fragile geology of this Himalayan state, and identifies the natural as well as the anthropogenic causes of the flood as a disaster. It also highlights the issue of climate change in the state and its adverse impact in the form of extreme precipitation. Besides these, it reviews the challenges involved in flood management and highlights the effective flood risk management plan that may be adopted to alleviate its adverse impacts.

Prioritization of Resilience Initiatives for Climate-Related Disasters in the Metropolitan City of Venice

Increases in the magnitude and frequency of climate and other disruptive factors are placing environmental, economic, and social stresses on coastal systems. This is further exacerbated by land use transformations, urbanization, over-tourism, sociopolitical tensions, technological innovations, among others. A scenario-informed multicriteria decision analysis (MCDA) was applied in the Metropolitan City of Venice integrating qualitative (i.e., local stakeholder preferences) and quantitative information (i.e., climate-change projections) with the aim of enhancing system resilience to multiple climate-related threats. As part of this analysis, different groups of local stakeholders (e.g., local authorities, civil protection agencies, SMEs, NGOs) were asked to identify critical functions that needs to be sustained. Various policy initiatives were considered to support these critical functions. The MCDA was used to rank the initiatives across several scenarios describing main climate threats (e.g., storm surges, floods, heatwaves, drought). We found that many climate change scenarios were considered to be disruptive to stakeholders and influence alternative ranking. The management alternatives acting on physical domain generally enhance resilience across just a few scenarios while cognitive and informative initiatives provided resilience enhancement across most scenarios considered. With uncertainty of multiple stressors along with projected climate variability, a portfolio of cognitive and physical initiatives is recommended to enhance resilience.

Flood hazards and agricultural production risks management practices in flood-prone areas of Punjab, Pakistan

Climate induced disasters, more specifically floods, have caused severe damage to the agriculture sector in Pakistan. These climatic risks have constrained the farming community to adopt risk management strategies to overcome such climate change risks. This research work attempted to examine the association of risk management tools with farmers' perception of risk, risk averse attitude, and various socioeconomic factors. The study has employed the sample data of 398 farmers from two high-risk flood-prone districts of Punjab, Pakistan. The multivariate probit model was used in this study to investigate the association of dependent and independent variables. The findings of the study indicated that small farmers consider heavy rains and floods severe risks to their agricultural production and are more risk averse than large farmers. Estimates of a multivariate probit model interpreted as age of farmer (0.036), heavy rains risk perception (0.597), and landholding size (0.114) were positively related with the risk management tool of depletion of assets. Farmers' education (0.056), off-farm income (3.47), age (0.018), and risk averse attitude of farmer (0.687) were positive, whereas experience of farming (-0.037) was negatively linked with reduction of consumption. Furthermore, experience of farming (0.005), risk averse attitude (0.493), heavy rains (0.481), and flood risk perception (0.536) were positively related with diversification adoption. The flood-prone farming community is more vulnerable to these climatic risks and rely on traditional strategies for risk management. There is a need to adopt crop diversification based on developing research capacity for innovative crop varieties having resistance to floods and climate change affects. Some significant policy measures, such as a more resilient scenario of climate change and floods, need to stimulate activities of enterprise diversification, opportunities of diversifying employment, and strengthening activities of off-farm employment for the sound livelihood of flood-prone farmers and to minimize severe affects of climatic risks.

Multirisk: What trends in recent works? - A bibliometric analysis

The issue of multirisk is coming under increasing scrutiny in the scientific literature and is of great concern for governments. Multirisk embraces different meanings: domino and cascade effects, NaTech events and the consideration of several natural hazards and their interactions. Scientific production relating to multirisk has been growing over the last 15 years. This review, based on 191 articles, proposes a new way of analyzing and presenting bibliographic results by the use of a global textual analysis. This analysis leads to identify seven main themes of research in the literature: three concern Domino Effects (46.6% of the articles), two are dedicated to the assessment of Multi-(hazard/vulnerability) Risk (28.7%), one deals with Natech issues (13.5%) and one concerns Cascade Effects in critical infrastructures (11.2%). A cross-issue analysis was performed on the basis of four criteria: objectives, hazards, the elements at risk considered, and the approaches used or developed in the articles. It provides general lessons on these items and proposes themes for future research on the topic of multirisk.

Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

A systems approach toward climate resilient livelihoods: A case study in Thai Nguyen province, Vietnam

This study aims to identify strategic actions towards climate resilient livelihoods and secure income for smallholder farmers in Thai Nguyen province of Vietnam using a systems approach and system dynamic modelling tools. Information and data for this research was collected through surveys, interviews, focus group discussions and workshops with relevant stakeholders and 187 farmers in two vulnerable districts during October 2019–April 2020. Findings of this study uncovered a number of shortcomings of the government policies and approaches in climate change adaptation. Local initiatives, community learning and ownership seem to be neglected. This research has substantiated the effectiveness and validity of systems approaches and tools in structuring and solving complex issues in agricultural research and development under the interwoven relationships between environmental and human factors. Climate resilient production models and practices are just part of the systemic interventions that need to be implemented in a coordinated manner towards a more resilient future of the farming communities. This study has addressed the current knowledge gap and the need for using integrated approaches and decision support systems for unravelling ill-structured and/or complex issues of climate change adaptation (CCA). It also provided practical recommendations for informed CCA policies and implementation.

How Vulnerable Are Urban Regeneration Sites to Climate Change in Busan, South Korea?

Research on the risks of climate change to urban regeneration projects has been insufficient to date. Therefore, this study aims to compare and analyze the degree of risk of climate change impact on areas with and without urban regeneration projects (for Eup, Myeon, and Dong regional units) in Busan, South Korea. In this study, (1) climate change risk indicators were extracted based on the concept of risk (hazard, vulnerability, and exposure), (2) a spatial analysis was performed using a graphic information system (GIS), and (3) the primary influencing factors were derived through a logistic regression analysis. The principal results show that urban regeneration areas have a higher risk of climate change impact than other areas. The results indicate that urban regeneration areas have a higher population density per area and more impermeable or flooded areas can increase the risk of climate change impacts. We also discuss strategies to develop resilient cities and climate change adaptation policies for future urban regeneration projects.

A Multi-Risk Methodology for the Assessment of Climate Change Impacts in Coastal Zones

Climate change threatens coastal areas, posing significant risks to natural and human systems, including coastal erosion and inundation. This paper presents a multi-risk approach integrating multiple climate-related hazards and exposure and vulnerability factors across different spatial units and temporal scales. The multi-hazard assessment employs an influence matrix to analyze the relationships among hazards (sea-level rise, coastal erosion, and storm surge) and their disjoint probability. The multi-vulnerability considers the susceptibility of the exposed receptors (wetlands, beaches, and urban areas) to different hazards based on multiple indicators (dunes, shoreline evolution, and urbanization rate). The methodology was applied in the North Adriatic coast, producing a ranking of multi-hazard risks by means of GIS maps and statistics. The results highlight that the higher multi-hazard score (meaning presence of all investigated hazards) is near the coastline while multi-vulnerability is relatively high in the whole case study, especially for beaches, wetlands, protected areas, and river mouths. The overall multi-risk score presents a trend similar to multi-hazard and shows that beaches is the receptor most affected by multiple risks (60% of surface in the higher multi-risk classes). Risk statistics were developed for coastal municipalities and local stakeholders to support the setting of adaptation priorities and coastal zone management plans.

Ecosystem services research in mountainous regions: A systematic literature review on current knowledge and research gaps

Mountain ecosystem services (MES) can provide a wide range of benefits for human well-being, including provisioning, regulating, supporting and cultural services. This systematic review work analyzed existed knowledge and research gaps on MES at the global level. The study used databases of science direct, Scopus and google scholar using searching, appraisal, synthesis, and analysis (SALSA) framework. Using specific keywords for the searching engine, the number of publications linked with MES were about 1252 which published between 1992 and June 2019. But, only 74 publications fulfilled the inclusion criteria. The analysis highlighted the existence of gaps in the literature including case studies from a limited geographical areas, focus on regulating and provisioning services, and lack of studies that explore the kinds of interlink between ecosystem services, and occurrence of limitation linked with data and methodology. From the 74 publications used for analysis, only seven of them were addressed mainly trade-offs and synergies, but most of them focus on quantification, qualification and economic valuation of the services. From the total case studies, the services addressed were summed up to 317 services, and the services such as climate regulation, food and fodder, fresh water, recreation and ecotourism, and erosion regulation studied more. On the contrary, photosynthesis, ornamental resources, net primary production, disease regulation, genetic resources, water purification and waste treatments were the least studied. Therefore, future research works should focus on mountainous areas of no and least studied of its ecosystem services. Critical studies are also required that indicate the link between a human being with MES, the trade-offs and synergies between MES and the influence of human beings on the quality and accessibility of ecosystem services. Besides, priority should be given by researches for methodological development and proposing management option for the mountain ecosystem and resource.

To what extent is climate change adaptation a novel challenge for agricultural modellers?

Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers' views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.

Past and Projected Weather Pattern Persistence with Associated Multi-Hazards in the British Isles

Hazards such as heatwaves, droughts and floods are often associated with persistent weather patterns. Atmosphere-Ocean General Circulation Models (AOGCMs) are important tools for evaluating projected changes in extreme weather. Here, we demonstrate that 2-day weather pattern persistence, derived from the Lamb Weather Types (LWTs) objective scheme, is a useful concept for both investigating climate risks from multi-hazard events as well as for assessing AOGCM realism. This study evaluates the ability of a Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model sub-ensemble of 10 AOGCMs at reproducing seasonal LWTs persistence and frequencies over the British Isles (BI). Changes in persistence are investigated under two Representative Concentration Pathways (RCP8.5 and RCP4.5) up to 2100. The ensemble broadly replicates historical LWTs persistence observed in reanalyses (1971–2000). Future persistence and frequency of summer anticyclonic LWT are found to increase, implying heightened risk of drought and heatwaves. On the other hand, the cyclonic LWT decreases in autumn suggesting reduced likelihood of flooding and severe gales. During winter, AOGCMs point to increased risk of concurrent fluvial flooding-wind hazards by 2100, however, they also tend to over-estimate such risks when compared to reanalyses. In summer, the strength of the nocturnal Urban Heat Island (UHI) of London could intensify, enhancing the likelihood of combined heatwave-poor air quality events. Further research is needed to explore other multi-hazards in relation to changing weather pattern persistence and how best to communicate such threats to vulnerable communities.