Future Extreme Events Research Articles

Influence of Regional Temperature Anomalies on Strawberry Yield: A Study Using Multivariate Copula Analysis

A thorough understanding of the impact of climatic factors on agricultural production is crucial for improving crop models and enhancing predictability of crop prices and yields. Fluctuations in crop yield and price can have significant implications for the market sector and farming community. Given the projected increase in frequency and intensity of extreme events, reliable modelling of cropping patterns becomes essential. Temperature anomalies are expected to play a prominent role in future extreme events, emphasizing the need to comprehend their influence on crop yield. Forecasting extreme yield, which encompasses both the highest and lowest levels of agricultural production within a given time period, along with peak crop prices representing the highest market values, poses greater challenges in forecasting compared to other values. Probability-based predictions, accounting for uncertainty and variability, offer a more accurate approach for extreme value estimation and risk assessment. In this study, we employ a multivariate analysis based on vine copula to explore the interdependencies between temperature anomalies and daily strawberry yield in Santa Maria, California. By considering the maximum and minimum daily yields each month, we observe an increased probability of yield loss with rising temperature anomalies. While we do not explicitly consider the specific impacts of temperature anomalies under individual Representative Concentration Pathway (RCP) scenarios, our analysis is conducted within the broader context of the current global warming scenario. This allows us to capture the overall anticipated effects of regional temperature anomalies on agriculture. The findings of this study have potential impacts and consequences for understanding the vulnerability of agricultural systems and improving crop model predictions. By enhancing our understanding of the relationships between temperature anomalies and crop yield, we can inform decision-making processes related to the impact of climate change on agriculture. This research contributes to the ongoing efforts in improving agricultural sustainability and resilience in the face of changing climatic conditions.

Dynamical Downscaling of Daily Extreme Temperatures over China Using PRECIS Model

As global warming intensifies and the frequency of extreme weather events rises, posing a major threat to the world’s economy and sustainable development, accurate forecasting of future extreme events is of great significance to mankind’s response to extreme weather events and to the sustainable development of society. Global Climate Models (GCMs) have limitations in their applicability at regional scales due to their coarse resolution. Utilizing dynamical downscaling methods based on regional climate models (RCMs) is an essential approach to obtaining high-resolution climate simulation information in future. This study represents an attempt to extend the use of the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model by employing the BCC-CSM2-MR model from the Beijing Climate Center to drive it, conducting downscaling experiments over China at a spatial resolution of 0.22° (25 km). The simulation and prediction of daily maximum and minimum temperatures across the China region are conducted, marking a significant effort to expand the usage of PRECIS with data from alternative GCMs. The results indicate that PRECIS performs well in simulating the daily maximum and minimum temperatures over the China region, accurately capturing their spatial distribution and demonstrating notable simulation capabilities for both cold and warm regions. In the annual cycle, the simulation performance of PRECIS is superior to its driving GCM, particularly during cold months (i.e., December and from January to May). Regarding future changes, the daily extreme temperatures in most regions are projected to increase gradually over time. In the early 21st century, the warming magnitude is approximately 1.5 °C, reaching around 3 °C by the end of the century, with even higher warming magnitudes exceeding 4.5 °C under the SSP585 scenario. Northern regions will experience greater warming magnitudes than southern regions, suggesting faster increases in extreme temperatures in higher latitudes. This paper provides forecasts of extreme temperatures in China, which will be useful for studying extreme events and for the government to make decisions in response to extreme events.

Marine heatwaves redistribute pelagic fishing fleets

AbstractMarine heatwaves (MHWs) have measurable impacts on marine ecosystems and reliant fisheries and associated communities. However, how MHWs translate to changes in fishing opportunities and the displacement of fishing fleets remains poorly understood. Using fishing vessel tracking data from the automatic identification system (AIS), we developed vessel distribution models for two pelagic fisheries targeting highly migratory species, the U.S. Atlantic longline and Pacific troll fleets, to understand how MHW properties (intensity, size, and duration) influence core fishing grounds and fleet displacement. For both fleets, MHW size had the largest influence on fishing ground area with northern fishing grounds gaining and southern fishing grounds decreasing in area. However, fleet displacement in response to MHWs varied between coasts, as the Atlantic longline fleet displaced farther in southern regions whereas the most northern and southern regions of the Pacific troll fleet shifted farther. Characterizing fishing fleet responses to these anomalous conditions can help identify regional vulnerabilities under future extreme events and aid in supporting climate‐readiness and resilience in pelagic fisheries.

Modeling the impact of extreme summer drought on conventional and renewable generation capacity: Methods and a case study on the Eastern U.S. power system

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation’s energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. This paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events.

Contrasting patterns of water use efficiency and annual radial growth among European beech forests along the Italian peninsula

Tree mortality and forest dieback episodes are increasing due to drought and heat stress. Nevertheless, a comprehensive understanding of mechanisms enabling trees to withstand and survive droughts remains lacking. Our study investigated basal area increment (BAI), and δ13C-derived intrinsic water-use-efficiency (iWUE), to elucidate beech resilience across four healthy stands in Italy with varying climates and soil water availability. Additionally, fist-order autocorrelation (AR1) analysis was performed to detect early warning signals for potential tree dieback risks during extreme drought events. Results reveal a negative link between BAI and vapour pressure deficit (VPD), especially in southern latitudes. After the 2003 drought, BAI decreased at the northern site, with an increase in δ13C and iWUE, indicating conservative water-use. Conversely, the southern sites showed increased BAI and iWUE, likely influenced by rising CO2 and improved water availability. In contrast, the central site sustained higher transpiration rates due to higher soil water holding capacity (SWHC). Despite varied responses, most sites exhibited reduced resilience to future extreme events, indicated by increased AR1. Temperature significantly affected beech iWUE and BAI in northern Italy, while VPD strongly influenced the southern latitudes. The observed increase in BAI and iWUE in southern regions might be attributed to an acclimation response.

Local food system and household responses to external shocks: the case of sustainable coffee farmers and their cooperatives in Western Honduras during COVID-19

IntroductionThe COVID-19 pandemic lockdowns (people confined to home, with movement restrictions) presented an external shock to livelihoods and food systems worldwide, most severely affecting vulnerable households in low-income countries. While evidence is available regarding how COVID-19 generally affected low-income countries, the specific dynamics of local food-system responses and sustainably-certified coffee farm households has not been examined, despite them being usually deemed to be more resilient to shocks. This research examines how local food systems in Honduras changed during lockdowns, how certified coffee households coped with the shock, especially food insecurity, and the potential role of coffee cooperatives in increasing households’ resilience under future shocks and stressors.MethodsWe applied a mixed-methods approach that combined a structured household survey with semi-structured qualitative interviews with 91 households, 6 cooperative representatives, and 18 food-system representatives.ResultsWe found that coffee-income-dependent households experienced greater food insecurity during lockdown than coffee households with diversified incomes. Before lockdown the local food system was highly dependent on external fresh food from outside the state. Food suppliers changed altered fresh-food procurement strategies, mostly to maintain fresh-food availability at the beginning of the pandemic. However, more than half the interviewed households lacked confidence regarding food security, amid rising food prices and local shortages. Certified coffee cooperatives supported their members by providing food assistance, cash transfers, and credit.DiscussionSome of these strategies are difficult to maintain where crises are recurrent and that may render households more vulnerable to future extreme events. Rather, coffee cooperatives could diversify and support their members in growing and marketing additional food crops. This could be a key approach for boosting local food security and strengthening the local food system.

Visualisation of Mahalanobis Distances for Trivariate JOINT Distributions

The Mahalanobis distance is a statistical measure used to quantify the distance between elliptic distributions with distinct locations and shared shapes, while accounting for the variables' covariance structure. It is applicable to both estimative and predictive estimation approaches, where variations are limited to location, and it assesses the similarity or dissimilarity between data and the mean (centroid) of a multivariate distribution, within the family of multivariate elliptic distributions. It is thus useful for outlier identification. The aim of the study is to provide, for the first time, a three-dimensional visualisation of the Mahalanobis distance when the underlying framework comprises three jointly connected variables (rather than the standard two variables presented in textbooks). Data with Mahalanobis distances exceeding a predefined threshold, determined using a distribution, are considered outliers. This approach is analogous to identifying outliers for univariate distributions based on critical values derived from confidence levels. While the literature mainly discusses the Mahalanobis distance formulation for bivariate distributions, we extend the discussion to include one additional variable and provide a visualisation of the resulting Mahalanobis distance for a trivariate distribution. An empirical example is presented to illustrate a practical application of a trivariate Mahalanobis distance. Visualising outliers alongside other historical events within three-factor systems can offer valuable insights into the risk profile of the current environment and assess the probability of future extreme events.

Integrated assessment of future climate and land use changes on urban floods: A Markov chain and PCSWMM-based approach for Hyderabad case study.

This research examines the impact of climate change and urban expansion on urban drainage systems in Hyderabad (Zone-XII, Zone-IV&V), India. It employs a Markov chain-based framework to simulate future climate and land changes. Integrated 1D-2D PCSWMM model is used to assess the hazards posed by these changes. Present and future extreme rainfall event(s) (1-10 days) are simulated to determine maximum flooding hours, valuable for resilience studies. Future rainfall events are simulated under four SSP scenarios using CMIP6 Global Climate Models (GCMs): EC-Earth3-Veg, MPI-ESM-1-2-HR, and MPI-ESM-1-2-LR. The Markov Chain Precipitation Generator (MCPG) model downscales grid-scale precipitation data to station-scale. Future urban land expansion is simulated using the Markov Chain-Cellular Automata (MC-CA) model with Terrset. MCPG model is validated using performance measures, and it showed most increased rainfall events under EC-Earth3-Veg. The MC-CA model obtained a Kappa coefficient of 0.89, indicating an increase in imperviousness in future LULC; 6.1% of vegetation and 29.06% of barren land in 2022 will be urbanized by 2075. A significant increase in extreme flood hazard areas for the 1-day and above 7-day events in the both zones is observed from the PCSWMM results. The study highlighted the importance of Markov chains and event duration in flood hazard assessments.

The Extraordinary March 2022 East Antarctica “Heat” Wave. Part I: Observations and Meteorological Drivers

Abstract Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30°–40°C temperature anomalies across the ice sheet. This record-shattering event saw numerous monthly temperature records being broken including a new all-time temperature record of −9.4°C on 18 March at Concordia Station despite March typically being a transition month to the Antarctic coreless winter. The driver for these temperature extremes was an intense atmospheric river advecting subtropical/midlatitude heat and moisture deep into the Antarctic interior. The scope of the temperature records spurred a large, diverse collaborative effort to study the heat wave’s meteorological drivers, impacts, and historical climate context. Here we focus on describing those temperature records along with the intricate meteorological drivers that led to the most intense atmospheric river observed over East Antarctica. These efforts describe the Rossby wave activity forced from intense tropical convection over the Indian Ocean. This led to an atmospheric river and warm conveyor belt intensification near the coastline, which reinforced atmospheric blocking deep into East Antarctica. The resulting moisture flux and upper-level warm-air advection eroded the typical surface temperature inversions over the ice sheet. At the peak of the heat wave, an area of 3.3 million km2 in East Antarctica exceeded previous March monthly temperature records. Despite a temperature anomaly return time of about 100 years, a closer recurrence of such an event is possible under future climate projections. In Part II we describe the various impacts this extreme event had on the East Antarctic cryosphere. Significance Statement In March 2022, a heat wave and atmospheric river caused some of the highest temperature anomalies ever observed globally and captured the attention of the Antarctic science community. Using our diverse collective expertise, we explored the causes of the event and have placed it within a historical climate context. One key takeaway is that Antarctic climate extremes are highly sensitive to perturbations in the midlatitudes and subtropics. This heat wave redefined our expectations of the Antarctic climate. Despite the rare chance of occurrence based on past climate, a future temperature extreme event of similar magnitude is possible, especially given anthropogenic climate change.

Climate change quadruples flood‐causing extreme monsoon rainfall events in Bangladesh and northeast India

Bangladesh and northeast India are the most densely populated regions in the world where severe floods as a result of extreme rainfall events kill hundreds of people and cause socio‐economic losses regularly. Owing to local high topography, the moisture‐carrying monsoon winds converge near southeast Bangladesh (SEB) and northeast Bangladesh and India (NEBI), which produces significant extreme rainfall events from May to October. Using observed data, we find an increasing trend of 1‐day extreme event (150 mmday) frequency during 1950–2021. The extreme rainfall events quadrupled over western Meghalaya (affecting NEBI) and coastal SEB during this period. Composite analysis indicates that warm Bay of Bengal sea‐surface temperature intensifies the lower tropospheric moisture transport and flux through the low‐level jet (LLJ) to inland, where mountain‐forced moisture converges and precipitates as rainfall during extreme events. To understand the role of climate change, we use high‐resolution downscaled models from Coupled Model Intercomparison Project phase 6 (CMIP6). We find that the monsoon extreme event increase is ongoing and the region of quadrupled events further extends over the NEBI and SEB in the future (2050–2079) compared with historical simulations (1950–1979). A quadrupling of the intense daily moisture transport episodes due to increased LLJ instability, a northward shift of LLJ, and increased moisture contribute to the increased future extreme events. This dynamic process causes moisture to be transported to the NEBI from the southern Bay of Bengal, and the local thermodynamic response to climate change contributes to the increased extreme rainfall events. The CMIP6 projection indicates that more devastating flood‐causing extreme rainfall events will become more frequent in the future.

Synergies of CGE and IAM modelling for climate change implications on WEFE nexus in the Mediterranean

The Mediterranean Sea Basin (MSB) is experiencing increasing pressure on its natural resources due to climate change (CC) and demographic growth, posing challenges to water and food sustainability. In line with the United Nations Sustainable Development Goals and the Water-Energy-Food-Ecosystems (WEFE) nexus, this study projects shifts in welfare and food security under various climatic conditions. Agriculture, a sector that is highly vulnerable to climate variability, depends predominantly on rainfed croplands, which constitute 70-100% of agricultural land in most MSB countries. The remaining areas are irrigated by climate-dependent water bodies such as rivers and aquifers.A comprehensive analysis of the WEFE nexus is essential for a coherent examination of climate policy and future pathways for the economy and the natural environment. Using a dual-modeling approach, this research assesses the impacts of alternative water sources and irrigated agriculture within the MSB amidst uncertainties of CC-driven extreme events. A global computable general equilibrium (CGE) model, based on the GTAP framework, was used to examine inter-sectoral and inter-regional impacts. In tandem, the Integrated Assessment Model (IAM) based on the RICE-99 framework quantifies the uncertainties related to future extreme climatic events. This synergistic approach provides a comprehensive assessment of CC impacts, integrating adaptation strategies for alternative water sources and irrigated agriculture, as well as mitigation strategies to reduce greenhouse gas emissions from energy production.The focus on cross-sectoral and multi-scale management of water, ecosystems, and food in the MSB was embedded into the economic models - CGE GTAP-AW and IAM RICE-MED, to analyze the impacts of CC adaptation and mitigation strategies on the WEFE nexus. The results indicate a reduced impact of CC on food production, and provide a comprehensive overview of potential adaptation and mitigation measures to reduce food security risks in the MSB. These findings are crucial for policymakers to promote sustainable water and agricultural practices in the face of a changing climate.

Heat-related illness in Singapore: Descriptive analysis of a tertiary care center from 2008 to 2020

Background This study aimed to describe the clinical characteristics and epidemiology of heat-related illnesses presented at a tertiary hospital in Singapore. Method This retrospective observational study utilized electronic health records from the emergency department (ED) of Singapore General Hospital. Patients primarily diagnosed with heat-related illnesses from 2008 to 2020, were analyzed. The annual number of cases and the temporal trend of heat-related illnesses were described, along with the characteristics of patients. Results Among the 1,833,908 patients registered at the ED, 426 patients had diagnoses of heat-related illnesses. The median age (interquartile range [IQR]) was 32 (22, 43) years, with 303 (71%) being male and 201 (47%) not being Singapore citizens. The annual number of cases ranged from a minimum of 21 in 2019 to a maximum of 49 cases in 2015, except for four cases in 2020. Heat-related illnesses occurred most commonly on Sundays (233, 55%) and in December (97, 23%). A significant number of cases in December coincided with the day of an annually organized marathon (91, 21%). Approximately half of the total cases required hospitalization, with 183 (43%) admitted to the general ward and 31 (7%) admitted to the intensive care unit. The 30-day mortality was 2 (0.5%). Conclusion This descriptive study indicated the unique characteristics of heat-related illness in a tertiary care hospital in Singapore. These local contexts may be valuable to consider preventative measures for heat-related illness, in preparation for future extreme weather events related to climate change.

Resilience of transportation infrastructure networks to road failures.

Anthropogenic climate change drives extreme weather events, leading to significant consequences for both society and the environment. This includes damage to road infrastructure, causing disruptions in transportation, obstructing access to emergency services, and hindering humanitarian organizations after natural disasters. In this study, we develop a novel method for analyzing the impacts of natural hazards on transportation networks rooted in the gravity model of travel, offering a fresh perspective to assess the repercussions of natural hazards on transportation network stability. Applying this approach to the Ahr valley flood of 2021, we discovered that the destruction of bridges and roads caused major bottlenecks, affecting areas considerably distant from the flood's epicenter. Furthermore, the flood-induced damage to the infrastructure also increased the response time of emergency vehicles, severely impeding the accessibility of emergency services. Our findings highlight the need for targeted road repair and reinforcement, with a focus on maintaining traffic flow for emergency responses. This research provides a new perspective that can aid in prioritizing transportation network resilience measures to reduce the economic and social costs of future extreme weather events.

Index-based insurance to mitigate current and future extreme events financial losses for water utilities

Mitigation and management strategies for extreme weather events become increasingly important for the development and maintenance of society as these events become more frequent and intense. Drought and flood management strategies have focused on structural measures; however, they are not sufficient to prevent water supply disruptions and economic losses. In this concept, adaptation plays the role of anticipating the adverse financial impacts of extreme weather events and taking appropriate measures to minimize them. Therefore, insurance is a valuable adaptation tool to offset unexpected losses and prevent financial damage from turning into long-term economic damage. We simulated multi-year and multi-risk index-based insurance for a Water Supply System, responsible for providing water to 7.2 million people. Our methodology comprises (1) characterizing the indexed variable, (2) calculating economic losses by Loss Distribution Approach (LDA) for current and future periods, and (3) estimating risk premiums based on expected losses of the low, medium, and high coverage levels. Our findings indicate a linear relationship between premium and coverage level, i.e, the higher the premium, the higher the insurance coverage level. We suggested a premium fee for each evaluated scenario, which would be introduced into the water bill as a novel way to pool risk among customers and protect them from surcharge fluctuations.

Regional pooling in extreme event attribution studies: an approach based on multiple statistical testing

Statistical methods are proposed to select homogeneous regions when analyzing spatial block maxima data, such as in extreme event attribution studies. Here, homogeneitity refers to the fact that marginal model parameters are the same at different locations from the region. The methods are based on classical hypothesis testing using Wald-type test statistics, with critical values obtained from suitable parametric bootstrap procedures and corrected for multiplicity. A large-scale Monte Carlo simulation study finds that the methods are able to accurately identify homogeneous locations, and that pooling the selected locations improves the accuracy of subsequent statistical analyses. The approach is illustrated with a case study on precipitation extremes in Western Europe. The methods are implemented in an R package that allows for easy application in future extreme event attribution studies.

Influence and characteristics of anticyclonic eddies in active MHW years in the Northwest Atlantic

The Northwest Atlantic contains two subregions, the Gulf of Maine and the Mid-Atlantic Bight. This region is dynamically controlled by the Jet Stream, the Gulf Stream, (GS) and its eddies. Anticyclonic eddies (AEs) (warm core clockwise eddies) and cyclonic eddies (cold core counterclockwise eddies) influence oceanic properties as they are shed by the GS and dissipated in the Northwest Atlantic. Marine Heatwaves (MHWs) are extreme temperature events that cause ecological damage in the Northwest Atlantic where drivers for these events are abundant. This work focuses on four major MHW active years (2012, 2016, 2017, and 2020) and observes the characteristics of MHWs, AEs, and oceanic parameters, such as Ocean Heat Content, Mixed Layer Depth, temperature, and salinity anomalies. Depth profiles, lead-lag covariance and correlations, and comparisons of events reveal dynamics of MHW intensification in these four years. AE radius, amplitude, and eddy kinetic energy (EKE) were less variable in the MHW years of 2012, 2016, and 2017 but have a weak relationship with MHW intensity. The Gulf of Maine had the strongest (above 0.80) correlation between GS eddy heat flux convergence and MHW intensity. The 2012 MHWs were shown to be strongly influenced by the GS in addition to atmospheric forcings, as were the 2017 Gulf of Maine MHWs. The 2020 MHWs experienced a number of forcings from the Jet Stream, the GS, and AEs, with the highest intensity events occurring at depths below 100 m. We conclude that better understanding how regional dynamics and multiple drivers intensify MHWs to form MHW active years is key to modelling and forecasting future extreme events.

Resilience Design: Myths of Participation and Stewardship

Design proposals that aim to increase resilience to future extreme weather events increasingly center on equity concerns. The question of the implementation and ongoing maintenance of resilient landscapes, however, receives scant attention in planning literature despite the abundance of stewardship references in resilience proposals. Stewardship, as both concept and act, implicates equity because it is a form of adaptation labor that is invisible and unpaid. Here, I discuss California's Resilient by Design project to show that when the labor involved in stewardship projects is not acknowledged, claims for an equitable and resilient future espoused by planning proposals are diminished.

Impacts of changing climate and topography on snow cover variability of Parvati River Basin, western Himalayas, India.

Snow cover is an essential element of the Himalayan region (Third Pole), and it represents the impacts induced by climate change. Recently, studies have reported significant variations in the Himalayan snow cover area, which may impact the livelihood of a large portion of the global population. Therefore, in this study, efforts were made to estimate the association between key climate stressors (CSs), i.e., temperature and precipitation, topography, and temporal variability of snow cover area (SCA) in the Parvati River basin (PRB) of the Indian western Himalayas. In this regard, the PRB has been classified into different elevation zones, i.e., zone I to zone V, ranging from 1100 to 6200 amsl. The databases such as MODIS, MEERA-2, and ASTER DEM V2 have been used to estimate the changes in the SCA and the CSs with changes in elevation and seasons. The linear regression analysis of the dataset from 2001 to 2017 revealed a significant association and increasing trend in the SCA of zone III. However, a significant association could not be established between the elevation and the SCA for the rest of the zones. A zonal seasonal trend investigation of the SCA observed an increasing trend in zones IV and V during the summer season due to a momentous rise in snowfall and a decline in temperature. The SCA has shown a significant declining trend only during the monsoon season in zones IV and V, which is due to a strong negative relationship between the SCA and the temperature of the region. These results demonstrate the amount of SCA in zones of high elevation of the PRB has been declining at an alarming rate, which could negatively influence glaciers' retreat in the near future. Hence, it can be estimated that the outcomes of the study will act as a base for future studies, regional policy formulation, and climate modelling that can further prevent future drastic or extreme events.

After the ‘Black Summer’ fires: Faunal responses to megafire depend on fire severity, proportional area burnt and vegetation type

Abstract Climate change and human activities have disrupted historical fire regimes, leading to complex and far‐reaching impacts on global ecosystems. Despite extensive research in fire ecology, studies exploring vertebrate responses to megafires, and to nuanced fire characteristics, remain limited. We collected camera trap data 3–27 months following Australia's 2019–20 ‘Black Summer’ megafires from 30 burnt sites and 10 unburnt sites. Our data included 14 animal species/groups, encompassing mammalian predators, small and medium‐sized mammals, large herbivores, and birds. We used generalised additive mixed models to assess the influence of time‐since‐the‐fires, burn status, fire severity, proportional area burnt, and vegetation type on species' activity. Models that included fire variables were well‐supported for all species. The proportional cover of low‐moderate or high‐extreme severity fire had substantial support for five species, particularly herbivores, which generally showed a preference for burnt sites but at differing fire severities. The proportional area burnt, disregarding severity, was well supported for four species. At highly burned sites fox activity peaked shortly after the fires while small to medium‐sized mammal activity increased more gradually. Vegetation type strongly influenced the response of four species to fire; in particular, wet forest birds preferred unburnt areas. Policy implications. We document variable short‐ to medium‐term responses of a range of species to fire which could help guide management interventions. We demonstrate that animal species' responses to fire are diverse and better captured using broader landscape scale fire variables. We found that species were strongly influenced by proportional area burnt, fire severity and vegetation type. Introduced foxes were attracted to recently burnt areas, so timely predator control may benefit vulnerable prey species. Wet forest species were sensitive to fires and could benefit from preservation and restoration of these habitats. Some species exploited low‐moderate severity burnt areas, while others preferred high‐severity burns. This suggests that species will face diverse challenges and opportunities in future extreme fire events. We emphasise the importance of using multi‐faceted approaches to account for the complex responses of co‐occurring species to fire events.

Artificial neural network-empowered projected future rainfall intensity-duration-frequency curves under changing climate

With the increasing trend of greenhouse gases in the atmosphere, by 2052 the temperature is expected to rise by 1.5 °C from the Pre-industrial Period, affecting future extreme rainfall events. This necessitates quantifying extreme hydrologic events to plan and design hydrologic and hydraulic structures using rainfall Intensity-Duration-Frequency (IDF) curves to adapt to future climate scenarios. This study developed future projected IDF curves for the Southeast United States using disaggregated sub-hourly (15-, 30-, and 45-min) monthly maximum rainfall from 2030 to 2059 using five climate models under the Representative Concentration Pathway 8.5 scenario. A computationally efficient feed-forward back-propagation Artificial Neural Network (ANN)-based approach was found to be significantly superior for disaggregating rainfall to a stochastic model with an average Nash–Sutcliffe efficiency (NSE) ranging from 0.67 to 0.84. The study found that there is an increasing rate of future projected annual maximum rainfall intensities in the range of 7% to 36% with reference to the historical period. The spatial variation in future projected extreme rainfall depths showed that the Gulf-Atlantic coast and the Appalachian Mountains are expected to receive more extreme rainfalls.