Human-induced Changes Research Articles

Empirical evidence of alternative stable states in an estuary

Due to human activity, ecosystems are exceeding their ecological thresholds and shifting into undesired alternative stable states with new ecological configurations. Despite their purported ubiquity, it is uncertain whether estuaries can exist in multiple stable states. We use data from a 3.5-year study of invertebrate communities in an Australian estuary that is usually closed to the ocean to test for their existence. Sampling spanned a 1.5-year period of hypersalinity (>40 ppt) during a prolonged estuary closure, where salinity reached 122 ppt, and for 2 years during and after the estuary opened to the ocean when salinities were mesohaline (5–19 ppt). Two distinct community states occurred before and after the sandbar breached, with an intermediary period of invertebrate community impoverishment due to sediment scouring. During the closure, the community was simple (average of 1 taxa) and dominated by larvae of terrestrial insects, most notably the halotolerant, non-biting midge Tanytarsus barbitarsis. After opening, the richness and abundance of invertebrates increased (average of 4 taxa and 84 individuals 100 cm−2) as polychaetes, molluscs and crustaceans colonised the estuary, although recovery was incomplete according to previous species records. Duration of closure and salinity were the strongest drivers of composition. This study, together with evidence from the literature, suggests a salinity threshold of 60–65 ppt between states. These empirical data meet key criteria of alternative states, i.e. a clear transition between two distinct self-sustaining communities, indicating a regime shift triggered by an exogenous event. Our findings suggest that temporarily open and closed estuaries can exist in alternative stable states, with prolonged closures, hypersalinity, and sandbar breaching being key determinants of the switch between states. This situation may apply to other low-inflow estuarine systems, particularly in arid, semi-arid, or seasonally arid climates, and may become more frequent due to human-induced climate change.

Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. This review synthesizes cutting-edge developments, methodologies, and applications of AI-ML-DL across key hydrological processes. By critically evaluating these techniques against traditional models, we highlight their superior ability to capture complex, nonlinear relationships and adapt to diverse environments. We further explore AI applications in precipitation forecasting, evapotranspiration estimation, groundwater dynamics, and extreme event prediction (floods, droughts, and compound events), showcasing their timely potential in addressing critical water-related challenges. A particular emphasis is placed on Explainable AI (XAI) and transfer learning as essential tools for improving model transparency and applicability, enabling broader stakeholder trust and cross-regional adaptability. The review also addresses persistent challenges, including data limitations, computational demands, and model interpretability, proposing solutions that integrate emerging technologies like quantum computing, the Internet of Things (IoT), and interdisciplinary collaboration. This review charts a strategic course for future research and practice by bridging AI advancements with practical hydrological applications. Our findings highlight the importance of embracing AI-driven approaches for next-generation hydrological modeling and provide actionable understandings for researchers, practitioners, and policymakers. As hydrology faces escalating challenges due to human-induced climate change and growing water demands, the continued evolution of AI-integrated models and innovations in data handling and stakeholder engagement will be imperative. In conclusion, the findings emphasize the critical role of AI-driven hydrological modeling in addressing global water challenges, including climate change adaptation, sustainable water resource management, and disaster risk reduction.

Climate change and lethal violence: a global analysis

Purpose The study aims to uncover the relationship between rising temperatures, increased greenhouse gas emissions and the prevalence of lethal violence, encompassing suicides and homicides. It also sought to identify how climate change affects different economic strata in countries, notably in high and middle-income nations, and across Asia and Africa. Design/methodology/approach This study rigorously explored the link between global climate change and lethal violence across 201 countries from 1970 to 2020. Climate change was measured using annual surface temperature fluctuations and greenhouse gas emissions, while lethal violence was estimated using data on suicides and homicides. Findings The analysis revealed significant positive associations between escalating temperatures, heightened greenhouse gas emissions and lethal violence. These connections were evident across different economic levels and geographic regions in Asia and Africa. Originality/value This study emphasizes the urgent need for comprehensive interventions to combat human-induced climate change and mitigate its extensive negative impacts on society, particularly its association with increased violent behavior.

A Study on the Spatiotemporal Dynamics of Land Cover Change and Carbon Storage in the Northern Gulf Economic Zone of Guangxi Based on the InVEST Model

In recent years, the international community has increasingly focused on the “dual carbon” issue, as human-induced land use changes significantly impact ecosystem structure and carbon cycling. This study analyzes land use changes in the economic zone of the northern Gulf of Guangxi from 1980 to 2020, utilizing the InVEST model to simulate spatiotemporal changes in carbon storage and conducting zoning studies through spatial analysis. The findings reveal that ① forest land and arable land dominate the northern Gulf of Guangxi’s land use, with notable changes observed in forest land, unused land, and construction land areas. Forest land and construction land have increased by 1761.5 km2 and 1001.19 km2, respectively, while unused land has decreased by 1881.18 km2 from 2000 to 2020. ② The total carbon storage values in the northern Gulf of Guangxi in 1980, 2000, and 2020 were, respectively, 504.91 × 106/t, 487.29 × 106/t, and 500.31 × 106/t, with the expansion of construction land and conversion of forest land being the main reasons for the decrease in carbon storage. ③ In the northern Gulf of Guangxi, there is a slight upward trend in total carbon storage values over time. Spatially, higher carbon storage values are observed in mountainous and hilly areas at high altitudes, while the central and southern coastal areas exhibit lower carbon storage values. ④ The local spatial autocorrelation results reveal that Pu Bei County exhibits high–high clustering of carbon storage, while He Pu County undergoes a transition from high–low to low–low clustering, and several other administrative areas in Beihai demonstrates low–low clustering. Due to the imperative of economic development, the expansion of urban construction land encroaches upon ecological land, resulting in a decline in carbon storage. Therefore, in the Northern Gulf of Guangxi, it is essential to implement measures such as reforestation and establish ecological protection areas such as forests, grasslands, and wetlands to develop effective carbon sequestration methods and compensate for the carbon loss caused by the expansion of construction land.

The Impact of Climatic Characteristics on Increasing Soil Salinity in Manathira District Center

The climate plays a crucial role in shaping soil characteristics. High temperatures during summer can lead to increased evaporation of soil moisture, resulting in drier and more fragmented soils. This process often leads to higher salt concentrations, altering the soil's properties. In the study area, the soil is categorized into four types: river shoulder soil, river basin soil, riverine island soil, and desert gypsum soil. These soil types are significantly influenced by environmental factors, both natural and anthropogenic. Soil, being a fundamental component of the environment that supports life, is essential for the survival of humans, plants, and animals. In the Najaf Governorate, the soil is vital for agriculture, supporting the growth of various important crops, including vegetables and fruits. However, the soil's natural and human-induced changes have led to a noticeable deterioration in its quality, rendering much of the land unsuitable for agriculture due to high concentrations of saline elements. The natural factors impacting the soil in the study area include climate, soil properties, and water resources, while human activities have further exacerbated these effects. As a result, the soil, particularly in cultivated and uncultivated basins, has seen a rise in the levels of total dissolved salts, sodium, magnesium, calcium, potassium, sulfate, and chloride, often exceeding global standards. In contrast, the soils of river shoulders have remained within acceptable limits.

Formally combining different lines of evidence in extreme-event attribution

Abstract. Event attribution methods are increasingly routinely used to assess the role of climate change in individual weather events. In order to draw robust conclusions about whether changes observed in the real world can be attributed to anthropogenic climate change, it is necessary to analyse trends in observations alongside those in climate models, where the factors driving changes in weather patterns are known. Here we present a quantitative statistical synthesis method, developed over 8 years of conducting rapid probabilistic event attribution studies, to combine quantitative attribution results from multi-model ensembles and other, qualitative, lines of evidence in a single framework to draw quantitative conclusions about the overarching role of human-induced climate change in individual weather events.

Impact of Natural and Human Factors on Dryland Vegetation in Eurasia from 2003 to 2022.

Eurasian dryland ecosystems consist mainly of cropland and grassland, and their changes are driven by both natural factors and human activities. This study utilized the normalized difference vegetation index (NDVI), gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) to analyze the changing characteristics of vegetation activity in Eurasia over the past two decades. Additionally, we integrated the mean annual temperature (MAT), the mean annual precipitation (MAP), the soil moisture (SM), the vapor pressure deficit (VPD) and the terrestrial water storage (TWS) to analyze natural factors' influence on the vegetation activity from 2003 to 2022. Through partial correlation and residual analysis, we quantitatively described the contributions of both natural and human factors to changes in vegetation activity. The results indicated an overall increasing trend in vegetation activity in Eurasia; the growth rates of vegetation greenness, productivity and photosynthetic capacity were 1.00 × 10-3 yr-1 (p < 0.01), 1.30 g C m-2 yr-2 (p < 0.01) and 1.00 × 10-3 Wm-2μm-1sr-1yr-1 (p < 0.01), respectively. Furthermore, we found that soil moisture was the most important natural factor influencing vegetation activity. Human activities were identified as the main driving factors of vegetation activity in the Eurasian drylands. The relative contributions of human-induced changes to NDVI, GPP and SIF were 52.45%, 55.81% and 74.18%, respectively. These findings can deepen our understanding of the impacts of current natural change and intensified human activities on dryland vegetation coverage change in Eurasia.

Early emergence and determinants of human-induced Walker circulation weakening

The Walker circulation is projected to slow down in response to greenhouse gas warming. However, detecting the impact of human activities on changes in the Walker circulation is challenging due to the significant influence of internal variability. Here, based on ensembles of multiple climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we show evidence that the emergence of the human-induced weakening of Walker circulation tends to occur earlier in the middle-upper troposphere than at the surface. This earlier emergence is attributed to a more pronounced initial weakening response of the middle-upper tropospheric Walker circulation to atmospheric CO2 radiative forcing. We further reveal that the emergence time of a weaker Walker circulation varies across models. This intermodel spread is governed by an ocean thermostat that operates by modulating the zonal sea surface temperature gradient over the tropical Indo-Pacific region. Our findings address the key question of whether and how to detect human-induced large-scale atmospheric circulation changes and provide valuable insights for assessing the associated risks.

Linear infrastructure and associated wildlife accidents create an ecological trap for an apex predator and scavenger

Animals may fall into an ‘ecological trap’ when they select seemingly attractive habitats at the expense of their fitness. This maladaptive behavior is often the result of rapid, human-induced changes in their natal environment, such as the construction of energy and transportation infrastructure. We tested the ecological trap hypothesis regarding human-created linear infrastructure on a widely distributed apex predator and scavenger—the Golden Eagle (Aquila chrysaetos), whose range spans the entire Northern Hemisphere. Roads and railways offer novel and attractive feeding opportunities through traffic-induced mortality of other species, while powerline areas provide perching or nesting sites and scavenging opportunities from electrocuted or collision-killed birds. These conditions may have negative demographic consequences for eagles if these apparent opportunities turn into traps. Using step selection functions, we analyzed habitat selection of 74 GPS-tracked Golden Eagles (37 adults and 37 immatures) during eleven years in Fennoscandia. To assess habitat attractiveness, we used wildlife traffic accident statistics for dominant wild species, and to evaluate demographic consequences, we used mortality data from the GPS-tagged eagles. Our analysis revealed that eagles selected linear features such as roads, railways and powerlines at both the population and individual levels. Both adult and immature eagles consistently selected these features, and the strength of selection for linear features increased with age in immature eagles. The linear features however had 5.5 times higher mortality risk for eagles than other selected habitats indicating the presence of an ecological trap. We discuss the implications of these findings for the conservation and population ecology of apex predators and scavengers, as well as their potential demographic consequences. To mitigate this issue, we urgently recommend the removal of carcasses from roads and tracks to prevent ecological traps for raptors and scavenger species worldwide. Additionally, we advocate for the development of methods and strategies to reduce wildlife traffic accidents.

Direct and lagged climate change effects intensified the 2022 European drought

In 2022, Europe faced an extensive summer drought with severe socioeconomic consequences. Quantifying the influence of human-induced climate change on such an extreme event can help prepare for future droughts. Here, by combining observations and climate model outputs with hydrological and land-surface simulations, we show that Central and Southern Europe experienced the highest observed total water storage deficit since satellite observations began in 2002, probably representing the highest and most widespread soil moisture deficit in the past six decades. While precipitation deficits primarily drove the soil moisture drought, human-induced global warming contributed to over 30% of the drought intensity and its spatial extent via enhanced evaporation. We identify that 14–41% of the climate change contribution was mediated by the warming-driven drying of the soil that occurred before the hydrological year of 2022, indicating the importance of considering lagged climate change effects to avoid underestimating associated risks. Human-induced climate change had qualitatively similar effects on the extremely low observed river discharges. These results highlight that global warming effects on droughts are already underway, widespread and long lasting, and that drought risk may escalate with further human-induced warming in the future.

Persistently active El Niño–Southern Oscillation since the Mesozoic

The El Niño-Southern Oscillation (ENSO), originating in the central and eastern equatorial Pacific, is a defining mode of interannual climate variability with profound impact on global climate and ecosystems. However, an understanding of how the ENSO might have evolved over geological timescales is still lacking, despite a well-accepted recognition that such an understanding has direct implications for constraining human-induced future ENSO changes. Here, using climate simulations, we show that ENSO has been a leading mode of tropical sea surface temperature (SST) variability in the past 250 My but with substantial variations in amplitude across geological periods. We show this result by performing and analyzing a series of coupled time-slice climate simulations forced by paleogeography, atmospheric CO2 concentrations, and solar radiation for the past 250 My, in 10-My intervals. The variations in ENSO amplitude across geological periods are little related to mean equatorial zonal SST gradient or global mean surface temperature of the respective periods but are primarily determined by interperiod difference in the background thermocline depth, according to a linear stability analysis. In addition, variations in atmospheric noise serve as an independent contributing factor to ENSO variations across intergeological periods. The two factors together explain about 76% of the interperiod variations in ENSO amplitude over the past 250 My. Our findings support the importance of changing ocean vertical thermal structure and atmospheric noise in influencing projected future ENSO change and its uncertainty.

Exploring the unintended contribution of soil erosion research to microplastic contamination

The global scientific community, particularly soil erosion researchers, is increasingly recognizing the urgent need to address plastic pollution and the accumulation of microplastics in soil. Human activities such as urban expansion, food packaging, and the use of agricultural materials have driven a significant rise in microplastic contamination. The problem is compounded by the difficulty, expense, and time-intensive nature of analyzing soil erosion samples to accurately identify microplastic types, sources, and their effects. Soil erosion, while naturally shaping landscapes like hillslopes, deltas or river valleys, has evolved into a critical issue due to human-induced changes, threatening fertile agricultural zones and areas prone to landslides, with serious economic and human consequences. While validated methods exist to quantify soil erosion, runoff, and pollutant concentrations, these techniques inadvertently introduce microplastics into the environment by plastic materials during sampling, transport, and lab analysis. This not only skews historical and current data on microplastic levels in soils but also alters natural soil conditions around research plots. To address these challenges, this paper advocates for the refinement of measurement techniques to minimize contamination, a reassessment of current methodologies, and a concerted effort to reduce human impact on soil health, thereby safeguarding a critical natural resource.

Global change drives potential niche contraction and range shift of globally threatened African vulture

Human-induced global change poses an increasingly severe threat to biodiversity, with species having limited population sizes being particularly vulnerable. Mapping and modeling the distribution ranges of such species, along with detecting potential range shifts and contractions at both local and regional scales, are essential for developing effective conservation plans. Ruppell's vulture Gyps rueppelli, an ecologically important bird species native to Africa, is experiencing a rapid decline in its range. The purpose of this study is to map and model potential regional spatio-temporal distribution of Ruppell's vulture in Africa, alongside detecting the possibility of the species' range shifts and contractions. A total of 804 rarefied localities were identified where the Ruppell's vulture was the dominant bird species. This study employed the Maximum Entropy (MaxEnt) algorithm to perform species distribution modeling for the Ruppell's vulture. The modeling considered current climate conditions (1970s-2000s) as a baseline, along with two future climate change scenarios (Shared Socioeconomic Pathways: SSPs 245 and 585) for two future time periods (2050s and 2070s). The model's performance was evaluated by optimizing settings and examining the Area under the Receiver Operating Characteristic Curve (AUC-ROC). Among the 13 bioclimatic and anthropogenic variables included in the model, four (isothermality, cropland expansion, anthropogenic biomes, and urban expansion (in order of importance)) emerged as the most influential drivers of Ruppell's vulture regional distribution. All considered Species distribution models (SDMs) achieved high predictive performance, with AUC-ROC values exceeding 0.9.The model predicted a total of approximately 19,453 ha of suitable habitat for Ruppell's vultures in Africa, with East Africa identified as the most prominent region under the current climate scenario. Isothermality (38.8%) was the primary factor influencing Ruppell's vulture distribution, followed by agricultural expansion (29.9%) and anthropogenic biomes (7.2%) in the face of global change. The results reveal considerable future habitat loss (up to 61%) for Ruppell's vultures in the study area, alongside an eastward range shift (longitudinal axis) by the 2050s under projected climate change scenarios. These imply that Ruppell's vultures face imminent population decline and range shift due to significant habitat loss and climate change. Hence, prioritizing the development and implementation of a coordinated conservation program that incorporates captive breeding and assisted migration is critical to save this vulture species in its native African range.

Unequally distributed education impacts of ecosystem degradation: Evidence from an invasive species

Ecosystem degradation can have substantial social and economic costs, which may vary across groups in society. In this paper, we leverage variation from the introduction of the emerald ash borer beetle to explore how invasive species-induced declines in environmental quality impact education outcomes in a metropolitan setting. Exploiting the idiosyncratic and staggered spread of the ash borer throughout the Chicago Metropolitan Region from 2006 to 2014, we show that infestation led to declines in tree cover and subsequently, education outcomes. Our findings indicate that ash borer infestation reduced canopy cover in affected areas, stemming from both increased tree cover loss and declines in tree cover gain. Further, the ash borer reduced standardized test performance at exposed schools. Infestation exposure led to an average of 1 percentage point (1.22%) fewer students that met or exceeded the state’s testing benchmark at the typical school. While exposure to ash borer infestation was lower around low-income schools, education impacts were concentrated almost entirely among low-income students. This work adds to our understanding of the environmental drivers of education outcomes and the unequally distributed impacts of human-induced environmental change.

Anthropogenic influence on precipitation in Aotearoa New Zealand with differing circulation types

This study quantifies the influences of anthropogenic forcing to date on precipitation over Aotearoa New Zealand (ANZ). Large ensembles of simulations from the weather@home regional climate model experiments are analysed under two scenarios, a natural (NAT) or counter-factual scenario which excludes human-induced changes to the climate system and an anthropogenic (ANT) or factual scenario. The impacts of anthropogenic forcing on precipitation are analysed in the context of large-scale circulation types characterized using an existing Self Organizing Map classification. The combined effect of both thermodynamics and dynamics are compared with values expected from the Clausius–Clapeyron (C–C) relation. Changes in the precipitation intensity due to greenhouse gas-forced temperature rise are lower than the expected C–C value. However extreme precipitation changes approach the C–C value for some circulation types. Specifically westerly flows enhance precipitation change across ANZ relative to the C–C rate, particularly over the West Coast. Conversely, northwesterly flows reduce the change over the North Island relative to the C–C value. Moreover, the wet day frequency generally reduces in the ANT scenario relative to NAT, reductions are largest on the West Coast of the South Island for westerly flows. Additionally, the frequency of days with extreme precipitation rises over ANZ for most circulation patterns, except in Northland and for northwesterly flows. This underscores the combined influence of dynamics and thermodynamics in shaping both precipitation intensity and frequency patterns across ANZ.

How the characteristics of land cover changes affect vegetation greenness in Guangdong, a rapid urbanization region of China during 2001-2022.

To evaluate the quantitative impacts of land cover change on vegetation greenness in the significantly human-impacted subtropical region, the characteristics of land cover change were explored by land use dynamic degree, transition matrix and normalized entropy. Various methods including Standardized coefficient, LMG (Lindeman-Merenda-Gold), GEN (Genizi measure) and CAR (Correlation-Adjusted Marginal Correlation) were employed to estimate the contributions of land cover changes on vegetation greenness using MODIS data during 2001-2022 in Guangdong. The conclusions revealed that land cover changes exhibited obvious temporal characteristics in Guangdong with a significantly increasing trend of normalized entropy indicating a more balanced distribution of land cover types under human intervention. NDVI (Normalized Difference Vegetation Index) tended to increase likely due to the large-scale increase in evergreen forest. With regard to the contributions of impact factors on vegetation greenness, the contributions evaluated by LMG, GEN and CAR showed that the natural variation of NDVI accounted for the major contribution (> 33%), while the changes of evergreen forest and grassland had the highest contribution (> 37%) according to Standardized coefficient. These differences were mainly due to the characteristics of land cover changes in Guangdong, the correlations among impact factors and the inherent attributions of the methods. Moreover, the expansions of evergreen forest and urban at the expense of the reductions of grassland and cropland also had significant impacts on NDVI (> 10%) according to LMG, GEN and CAR indicating that human-induced land cover changes had remarkable influences on NDVI.

Differential response of multiple stream ecosystem processes to basin- and reach-scale drivers

Stream ecosystems are inherently dependent on their surroundings and, thus, highly vulnerable to anthropogenic impacts, which alter both their structure and functioning. Anchored in biologically-mediated processes, the response of stream ecosystem functioning to environmental conditions exhibits intricate patterns, reflecting both natural dynamics and human-induced changes. Our study aimed at determining the natural and anthropogenic drivers influencing multiple stream ecosystems processes (nutrient uptake, biomass accrual, decomposition, and ecosystem metabolism) at a regional scale. By examining 38 natural and anthropogenic variables across 63 stream reaches in Gipuzkoa (northern Iberian Peninsula), we used structural equation modeling to unravel the cascading effect of basin- and reach-scale drivers onto ecosystem process. The results reveal significant variability in ecosystem processes, with contrasting spatial patterns, suggesting that studied processes respond differently to environmental factors. Urban land-use emerged as a primary basin-scale driver, whereas reach-scale variables reflected both natural and anthropogenic influence. Nutrient uptake rates were primarily driven by nutrient concentrations in stream water, but models for biomass accrual, decomposition, and ecosystem metabolism exhibited more complex cause-effect relationships. Our findings highlight the impact of urban areas on multiple ecosystem processes and services, disproportionate when considering their small land cover. The present study emphasizes the convenience of measuring multiple ecosystem functions simultaneously to get a comprehensive diagnosis of the functional status of rivers.

A multi-objective optimization framework for regional land-use allocation: Fully utilizing terrestrial vegetation to mitigate carbon emissions

Human-induced changes in land use and land cover (LULC) considerably impact the global carbon cycle. Rational LULC optimization with full utilization of terrestrial vegetation facilitates reconciling carbon reduction and socio-economic development. To this end, this study develops a multi-objective framework for low-carbon LULC spatial optimization that incorporates carbon suitability, historical suitability, and socio-economic needs. The framework is applied to Hubei Province, China. The key findings include: (1) Considering spatial variations in vegetation carbon sinks could lead to remarkable carbon reductions of 2.75 × 106 t C by 2030 and 7.68 × 106 t C by 2060. It would also lower carbon emissions per unit of gross domestic product (GDP) and enhance carbon sequestration per unit of vegetation by 2030, signifying enhanced carbon reduction efficiency. (2) Integrating carbon sequestration potential and historical suitability can considerably minimize emissions while sustaining socio-economic growth. Our method, compared with strategies based solely on historical development laws, can reduce carbon emissions by approximately 1.3–1.6 × 106 t C·a−1. (3) We also propose targeted low-carbon development suggestions for different LULCs. This study provides optimal spatial LULC allocations and strategic advice for regional LULC planning aimed at low-carbon development, demonstrating the feasibility of reconciling the tension between carbon reduction and socio-economic growth at the regional scale.

A methodology for assessing multiple hazards applied to Sweden

Despite extensive efforts through various international initiatives to reduce global warming, it has been determined that human induced climate change is here, that the present scale of disruption of the climate is unprecedented and will continue. Increasing weather volatility can be expected, which will most likely increase exposure to weather related hazards, e.g. wildfires, flooding. The aim of this paper is to present an index-based multi-hazard risk assessment method to assess wildfires and flooding hazard for two municipalities within Sweden. The method is designed to be used by the Fire and Rescue Service (FRS) for planning purposes and can be modified to take the local FRS’s capabilities and local conditions into account, thereby improving hazard preparedness at a local level. The analysis presented indicates that, while the frequency of multi-hazard overlap from wildfires and flooding is greatest in more northern parts of Sweden, the method provides important information even when applied to areas with limited overlap. A variation of the hazard assessment using a box kernel sliding window was studied to investigate the sensitivity of the model for rapid variations of an individual hazard level. Given that resource needs will typically spread over several days for large scale natural hazards, the box kernel approach is valuable in helping to identify a span of days when resources associated with incident response might be needed. In the future, the model should be expanded to include additional single hazards, the application to additional municipalities and extension to FRS planning exercises for natural hazards.

Emerging signals of climate change from the equator to the poles: new insights into a warming world

The reality of human-induced climate change is unequivocal and exerts an ever-increasing global impact. Access to the latest scientific information on current climate change and projection of future trends is important for planning adaptation measures and for informing international efforts to reduce emissions of greenhouse gases (GHGs). Identification of hazards and risks may be used to assess vulnerability, determine limits to adaptation, and enhance resilience to climate change. This article highlights how recent research programs are continuing to elucidate current processes and advance projections across major climate systems and identifies remaining knowledge gaps. Key findings include projected future increases in monsoon rainfall, resulting from a changing balance between the rainfall-reducing effect of aerosols and rainfall-increasing GHGs; a strengthening of the storm track in the North Atlantic; an increase in the fraction of precipitation that falls as rain at both poles; an increase in the frequency and severity of El Niño Southern Oscillation (ENSO) events, along with changes in ENSO teleconnections to North America and Europe; and an increase in the frequency of hazardous hot-humid extremes. These changes have the potential to increase risks to both human and natural systems. Nevertheless, these risks may be reduced via urgent, science-led adaptation and resilience measures and by reductions in GHGs.