Extreme Climate Events Research Articles

Longitudinal assessment of extreme climate events in Kinnaur district, Himachal Pradesh, north-western Himalaya, India.

It is vital to keep an eye on changes in climatic extremes because they set the stage for current and potential future climate, which usually have a reasonable adverse impact on ecosystems and society. The present study examines the variability and trends in precipitation and temperature across seasons in the Kinnaur district, offering valuable insights into the complex dynamics ofthe Himalayan climate. Using Climatic Research Unit gridded Time Series (CRU TS) datasets from 1951 to 2021, the study analyzes the data to produce 28 climate indices based on India Meteorological Department (IMD) convention indices and Expert Team on Climate Change Detection and Indices (ETCCDI). Although there may be considerable variation in climate indices in terms of absolute values within different products, there is consensus in both long-term trends and inter-annual variability. Analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Initially, the datawere subjected to rigorous quality control procedures, which involved identifying anomalies. Statistical analysis like trend analysis, employing Mann-Kendall test and Sen's slope estimator, reveal significant (p < 0.05) increase in consecutive dry days (CDD) at 0.03days/year and decrease in consecutive wet days (CWD) at 0.02days/year. Notably, the frequency of heavy precipitation occurrences showed an increasing trend. Changes in precipitation in the Western Himalaya are driven by a complex interplay of orographic effects, monsoonal dynamics, atmospheric circulation patterns, climate change, and localized factors such as topography, atmospheric circulation patterns, moisture sources, land-sea temperature contrasts, and anthropogenic influences. Moreover, in case of temperature indices, there is significant increasing trend observed. Temperature indices indicate a significant annual increase in warm nights (TN90p) at 0.06%/year and warm days (TX90p) at 0.11%/year. Extreme temperature events have been trending upward, with monthly daily maximum temperature (TXx) increasing by 1.5°C yearly. This study enhances our comprehension of the global warming phenomenon and underscores the importance of acknowledging alterations in the water cycle and their repercussions on hydrologic resources, agriculture, and livelihoods in the cold desert of the northwestern Indian Himalaya.

Identifying protected areas in biodiversity hotspots at risk from climate and human-induced compound events for conserving threatened species.

Anthropogenic pressure in areas of biodiversity importance erodes the integrity of the ecosystems they harbour, making features of biodiversity less buffered against extreme climatic events. We define the combination of these disturbances as compound events. We assessed compound event risk in protected areas (PAs) applying a spatial framework guided by criteria and quantitative thresholds associated with exposure to cyclones, drought, and intense human pressure. This assessment was used in a relational matrix to classify PAs with different risk of compound event occurrence. We identified PAs of higher conservation concern by quantifying the extent of human pressure in their surrounding landscape while harbouring large numbers of threatened vertebrate species. Of the 39,694 PAs assessed, very high risk of compound events was determined for 6965 PAs (17.5 %) related to cyclones and human pressure (mainly island hotspots), 6367 PAs (16 %) related to droughts and human pressure (island and continental hotspots), and 2031 PAs (5 %) to cyclones, drought and human pressure (mainly in island hotspots). From the subset of 2031 PAs assessed at very high risk, we identified 239 PAs of higher conservation concern distributed predominantly in the Caribbean Islands, Japan, North America Coastal Plain, Philippines, and Southwest Australia. Our work highlights PAs in the biodiversity hotspots where high risk of compound event occurrence poses a greater threat to species. We encourage researchers to adapt and apply this framework across other globally significant sites for conserving biodiversity to identify high risk-prone areas, and prevent further biodiversity decline.

Feasibility of Urban-Based Climate Change Adaptation Strategies in Urban Centers of Southwest Ethiopia: From Local Climate Action Perspective

This study identified the practices of adaptation strategies to climate change in Jimma, Bedelle, Bonga, and Sokorru urban centers using a survey of 384 households, 55 key informant interviews, 4 focus group discussions, and field observations. A cross-sectional study design was employed from 2019 to 2021. The adaptive capacity of municipalities to reduce climate extreme events was rated as poor by the majority (51%), mostly reactive measures (76%). The climate hazards identified in four urban centers were riverine and flash floods, urban heat waves, landslides, and windstorms. The urban households practiced lifestyle modification, reduce paved surfaces, the use of air conditioner, planting trees, and multiple windows. The adaptation strategies practiced by municipalities include the relocation of prone areas, the support of basic amenities, the construction of protection walls, diversion ditches, the clearance of waterways and rivers, greenery, and park development. The adaptation actions were constrained by a lack of awareness, commitment, cooperation and coordination, adaptive capacity, and participation. Gray/physical infrastructures (costly but important) as adaptation actions were hampered by the low municipal capacity. We recommend that urban authorities should incorporate climate change adaptation strategies into urban planning and development proactively to ensure future resilient climate smart urban centers of southwest Ethiopia.

Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

The Himalayas are highly susceptible to various natural disasters, such as the tectonically induced land deformation, earthquakes, landslides, and extreme climatic events. Recently, the Joshimath town witnessed a significantly large land subsidence activity. The phenomenon resulted in the development of large cracks in roads and in over 868 civil structures, posing a significant risk to inhabitants and infrastructure of the area. This study uses a time-series synthetic aperture radar (SAR) interferometry-based PSInSAR approach to monitor land deformation utilizing multi-temporal Sentinel-1 datasets. The line of sight (LOS) land deformation velocity for the Joshimath region, calculated for the year 2022–2023 using a PSInSAR-based approach, varies from − 89.326 to + 94.46 mm/year. The + ve sign indicates the LOS velocity/displacement away from the SAR sensor, whereas − ve sign signifies the earth's movement towards the SAR sensor in the direction of LOS. In addition, the study investigates feature tracking land displacement analysis using multi-temporal high-resolution Planet datasets. The result of this analysis is consistent with the PSInSAR results. The study also estimated the land deformation for the periods 2016–2017, 2018–2019, and 2020–2021 separately. Our results show that the Joshimath region experienced the highest land deformation during the year 2022–2023. During this period, the maximum land subsidence was observed in the north-western part of the town. The maximum LOS land deformation velocity + 60.45 mm/year to + 94.46 mm/year (2022–2023), occurred around Singhdwar, whereas the north and central region of the Joshimath town experienced moderate to high subsidence of the order of + 10.45 mm/year to + 60.45 mm/year (2022–2023), whereas the south-west part experienced an expansion of the order of 84.65 mm/year to − 13.13 mm/year (2022–2023). Towards the south-east, the town experienced rapid land subsidence, − 13.13 mm/year to − 5 mm/year (2022–2023). The study analyzes the causative factors of the observed land deformation in the region. Furthermore, this work assesses the ground conditions of the Joshimath region using UAV datasets acquired in the most critically affected areas such as Singhdhaar, Hotel Mountain View, Malhari Hotel, and Manoharbagh. Finally, the study provides recommendations and future prospects for the development policies that need to be adopted in the critical Himalayan regions susceptible to land deformation. The study suggests that land deformation in the region is primarily attributed to uncontrolled anthropogenic activities, infrastructural development, along with inadequate drainage systems.

Reservoir Filling Up Problems in a Changing Climate: Insights From CryoSat‐2 Altimetry

AbstractRecent droughts have severely threatened water security in many regions worldwide. Reservoirs, designed to combat droughts and secure water supply partially, are reported failing to fill up to the total capacity due to severe droughts. How bad is climate affecting reservoir filling up on a global scale? This issue has not been studied. We present a big picture of reservoirs in crisis using satellite altimetry. Thanks to the unique characteristics of CryoSat‐2, 525 reservoirs worldwide were investigated during 2010–2022. Results show that most reservoirs (93%) are found not fully filled up at least once. About 21% of reservoirs, which are mainly located in the Southern Hemisphere, show a significant decline in water levels. Moreover, about 20% of reservoirs with larger level fluctuations (&gt;3 m) are located in less developed economies, indicating informed operation rules are needed. Further analyses indicate reservoirs are largely affected by extreme climate events, such as ENSO.

Aging in climate change: Unpacking residential mobility and changes of social determinants of health in southern United States

The southern coastal states of the United States are susceptible to extreme weather and climate events. With growing move-in and -out older populations in the region, health implications of their residential mobility lack sufficient knowledge. Using 126,352 person-level records from 2012 to 2021, we examined geospatial and temporal patterns of older populations’ residential mobility, considering the changing social determinants of health and disparities. We found the moves of older populations with socioeconomic or health disadvantages were related to increased exposure to environmental hazards and reduced access to health resources. The findings inform targeted strategies for climate adaptation that address the needs of vulnerable aging populations.

Predicting the hydraulic response of critical transport infrastructures during extreme flood events

Understanding the effects of extreme floods on critical infrastructures such as bridges is paramount for ensuring safety and resilient design in the face of climate change and extreme events. This study develops robust computational and predictive modeling tools for assessing the impacts of extreme floods on the hydraulic response and structural resilience of bridges. A computational fluid dynamic (CFD) model utilizing RANS equations and k-ω Shear Stress Transport (SST) for simulating supercritical flows is adopted to compute hydrodynamic pressures and the water levels on bridge piers of cylindrical and rectangular shapes during a flood event. The CFD model is validated based on the case study data obtained from the Haj Omran Bridge, built on the Khorramabad River in Iran. The numerical simulations consider hydrological conditions and exclude geotechnical parameters and abutment damages. The numerical results are evaluated based on well-established design guidelines. Machine learning techniques, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized with Grid Search Cross-Validation (GSCV), are adopted to enhance the accuracy of hydrodynamic pressure forecasting at bridge piers. The XGBoost model exhibits superior performance (R2 = 0.908, RMSE = 0.0279, and E = 3.41%) compared to the RF and SVR models. All the estimated pressure data by XGBoost falls within ±6 percent error lines, highlighting the model's robustness for out-of-range hydrodynamic pressure prediction. Additionally, an optimized Long Short-Term Memory (LSTM) model is adopted to effectively predict free surface flow profiles (i.e. flood depth) over the bridge (R2 = 0.937 and RMSE = 0.083), demonstrating its potential for practical applications of flood depth predictions over bridge infrastructures. The proposed methodological framework outlined in this study can facilitate modeling the impacts of extreme floods on bridges, enabling robust climate resilience assessment of critical infrastructures.

Extreme Weather Events and Climate Change: Economic Impacts and Adaptation Policies

This article reviews the literature on the economic impacts of disasters caused by extreme weather and climate events to draw lessons on how societies can better manage these risks. While evidence that richer, better-governed societies suffer less and recover faster from climate extremes suggests adaptation, knowledge gaps remain, and little is known about the efficiency of specific adaptation actions. I review various “no or low” regrets adaptation options that are recommended when uncertainties over climate change impacts are high. I discuss how governments can play an important role in adaptation by directly providing public goods to manage disaster risks or by facilitating private agents’ adaptation responses but also highlight the political economy of policy and coordination failures.

Utilizing Machine Learning Framework to Evaluate the Effect of Climate Change on Maize and Soybean Yield

In recent years, climate change patterns and extreme weather events have adversely affected agricultural production and have raised concerns about its effect on crop yields. These changes can affect the crop yield in many ways including the changes in the length of the growing season, planting and harvest time windows, precipitation amount and frequency, and the growing degree days, etc. So, it is important to analyze the effect of climate change on yield variability for a better understanding of its effect on the crops. This study aims to analyze the historical monthly county-level data for the state of Ohio to quantify maize (Zea mays) and soybean (Glycine max) yield temporal variability and to study the impact of proposed climate change scenarios on the yield. Machine learning algorithms were used to model the effect of monthly temperature and precipitation levels on yield variability, and to study the effect of climate change scenarios on yield. In addition, yield prediction models were integrated with proposed climate change scenarios for higher and lower emissions scenarios to predict the maize and soybean yield for the year 2100. To model the effect of weather parameters on temporal yield variability, the Random Forest model outperformed with RMSE of 0.61 Mt/ha and R2 of 0.73 for maize and with RMSE of 0.21 Mt/ha, R2 of 0.64 for soybean crop. Maximum temperature for July which has a negative correlation with yield was found to be the most dominant parameter for maize yield followed by the precipitation in August, July, and June which have a positive correlation with maize yield. Precipitation in August was found to be the most dominant weather parameter for soybean yield followed by maximum temperature in July. Based on the projected average increase in Ohio’s temperature and precipitation levels alone, maize yield was predicted to drop by 13.2% or 18.5% respectively due to lower emissions and higher emissions climate change scenario of Ohio. Similarly, a 6.64% and a 9.63% drop is expected in the soybean yield by the year 2100 for both lower emissions and higher emissions climate change scenarios. In economic terms based on current commodity values, this is an astounding loss of 254.7 to 369.7 million USD for maize and 535.9 – 751.1 million USD for soybean crop to the state of Ohio.

Integrating Remote Sensing and Ground-Based Data for Enhanced Spatial–Temporal Analysis of Heatwaves: A Machine Learning Approach

In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature (Tair) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis (p&lt;0.05) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between Tair and LST in rural (HWN, HWF, HWD, r&gt;0.90) and peri-urban (HWM, HWA, r&gt;0.65) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability.

Assessing water storage variations in La Plata basin and sub-basins from GRACE, global models data and connection with ENSO events

The La Plata basin is one of the largest river basins in the world, draining about a fifth of the South American continent. It is a system of great hydrological significance, sensitive to extreme events, and also has the most economic development in the region. This work analyses the terrestrial water storage (TWS) changes over the La Plata basin from the GRACE mission and compares it with TRMM rainfall data. Subsequently, a study was conducted on the main sub-basins for the period 2003-2017 to identify regional-scale variations in TWS. Precipitation and GLDAS run-off data were evaluated in these areas, along with their connections to ENSO episodes. The results suggest that the majority of TWS variations are associated with extreme climatic events, such as the 2009 drought, and are linked to ENSO periods. The statistical analysis indicates a significant correlation between severe Niño/Niña episodes and changes in TWS in the sub-basins.

The Physiological Adjustments of Two Xerophytic Shrubs to Long-Term Summer Drought

Adaptive characteristics of plants, such as those associated with photosynthesis and resource use efficiency, are usually affected by synthesis costs and resource availability. The impact of extreme climate events such as long-term drought on plant physiological functions needs to be examined, particularly as it concerns the internal management of water and nitrogen (N) resources. In this study, we evaluated the resource management strategies for water and N by xerophytic shrubs, Artemisia ordosica and Salix psammophila, under extreme summer drought. This was carried out by comparing the plants’ physiological status during periods of wet and dry summer conditions in 2019 and 2021. Compared with the wet period, A. ordosica and S. psammophila both decreased their light-saturated net carbon (C) assimilation rate (Asat), stomatal conductance (gs), transpiration rate (E), leaf N content per leaf area (Narea), and photosynthetic N use efficiency (PNUE) during the summer drought. Whether in wet or dry summers, the gas-exchange parameters and PNUE of A. ordosica were generally greater than those associated with S. psammophila. The instantaneous water use efficiency (IWUE) response to drought varied with species. As a drought-tolerant species, the A. ordosica shrubs increased their IWUE during drought, whereas the S. psammophila shrubs (less drought-tolerant) decreased theirs. The divergent responses to drought by the two species were largely related to differences in the sensitivity of gs, and as a result, E. Compared with A. ordosica, S. psammophila’s inferior plasticity regarding gs response affected its ability to conserve water during drought. Our research illustrates the need for assessing plasticity in gs when addressing plant adaptation to long-term drought. A high dry-season IWUE in xerophytic shrubs can benefit the plants by augmenting their C gain.

Effects of an extreme weather event over reproduction and survival of Great Tits (Parus major) in eastern Spain

AbstractThe frequency of extreme climatic and weather events has increased since 1950 due to global changes induced by human influence. These events can have significant impacts at the individual, population, and community levels across various taxonomic groups. They can be particularly detrimental to bird populations during their breeding season, affecting specific breeding parameters. This study originated from an exceptionally rare breeding season of a resident Great Tit population recorded in spring 2002 when an unusual mortality of nestlings was observed. We quantified weather conditions (temperature, rainfall) from the end of April, a few days before we started detecting failed nests, and compared them with previous and subsequent years. In early May, when many pairs were incubating or caring for newly hatched chicks, we detected unusually low ambient temperatures and unusually high rainfall. This event likely caused that many first clutches failed completely, and that, compared with previous and subsequent years, fledging and breeding success was relatively low in those which were successful. There was also an increased proportion of replacement clutches that year. Anyway, the overall production of fledglings per breeding pair over the breeding season was 2.39, lower than other years. Although recruitment rate in 2003 was similar to other years, the lower production of fledglings in 2002 probably resulted in a decrease in the number of breeding pairs in the following years.

The effects of climate extreme events on selected food crop yields in Sub-Saharan Africa

The agricultural sector is essential for economic growth. However, agricultural performance can be limited by factors such as climatic risks. This paper aims to analyse the effect of climate extreme events on selected food crop yield in sub-Saharan Africa (SSA). The study uses data from the Food and Agriculture Organisation (FAO) database for maize, rice, and sorghum yields. Also, we used data obtained from the International Disaster Database of the Centre for Research on the Epidemiology of Disasters (CRED) for floods and droughts over the period 1990–2020. The data were analysed based on the Fully Modified Ordinary Least Squares (FMOLS). The results showed that climate extreme events negatively affected maize, rice and sorghum yields. Also, the findings showed that floods and droughts in past years negatively influence current yields of maize, rice and sorghum. Moreover, agricultural labour force, fertilizer and financial development are the main transmission channels through which floods and droughts can affect maize, rice and sorghum yields. The study concludes by recommending that policies aimed at promoting climate change adaptation measures as well as agricultural insurance could make the agriculture sector more resilient to climate extreme events and in turn that could improve agricultural productivity and reduce food insecurity.

Differential responses of vegetation phenology to climatic elements during extreme events on the Chinese loess plateau

Extreme, dry events have major impacts on vegetation phenology worldwide. However, the differential responses of vegetation phenology to climatic elements during these extreme events remain unclear. We investigated the response of vegetation phenology to climatic factors during extreme events in arid and semi-arid regions of the Chinese Loess Plateau, using the climate water deficit method, to identify extremely dry and wet events. The results revealed that extremely wet events extended the vegetation growth periods in addition to global warming, whereas extremely dry events did not completely counteract this effect. During different extreme events, phenological changes in vegetation on the Loess Plateau were primarily influenced by the interactive effects of climatic factors. During extremely dry events, a 100 mm increase in precipitation advanced the start of the season by 3.0, 9.2, and 16.7 days in forest, shrubland, and grassland, respectively. During extremely wet events, a 1 °C rise in temperature delayed the end of the season by 1.6, 0.6, and 3.8 days in forest, shrubland, and grassland, respectively. These findings provide crucial guidance for improving predictions of plant phenology changes under extreme climatic events and unraveling biosphere–atmosphere feedback cycles.

Evaluation of potential increase in photosynthetic efficiency of cassava (Manihot esculenta Crantz) plants exposed to elevated carbon dioxide.

Cassava (Manihot esculenta Crantz), an important tropical crop, is affected by extreme climatic events, including rising CO2 levels. We evaluated the short-term effect of elevated CO2 concentration (ECO2 ) (600, 800 and 1000ppm) on the photosynthetic efficiency of 14 cassava genotypes. ECO2 significantly altered gaseous exchange parameters (net photosynthetic rate (P n ), stomatal conductance (g s ), intercellular CO2 (C i ) and transpiration (E )) in cassava leaves. There were significant but varying interactive effects between ECO2 and varieties on these physiological characteristics. ECO2 at 600 and 800ppm increased the P n rate in the range of 13-24% in comparison to 400ppm (ambient CO2 ), followed by acclimation at the highest concentration of 1000ppm. A similar trend was observed in g s and E . Conversely, C i increased significantly and linearly across increasing CO2 concentration. Along with C i , a steady increase in water use efficiency [WUEintrinsic (P n /g s ) and WUEinstantaneous (P n /E )] across various CO2 concentrations corresponded with the central role of restricted stomatal activity, a common response under ECO2 . Furthermore, P n had a significant quadratic relationship with the ECO2 (R 2 =0.489) and a significant and linear relationship with C i (R 2 =0.227). Relative humidity and vapour pressure deficit during the time of measurements remained at 70-85% and ~0.9-1.31kPa, respectively, at 26±2°C leaf temperature. Notably, not a single variety exhibited constant performance for any of the parameters across CO2 concentrations. Our results indicate that the potential photosynthesis can be increased up to 800ppm cassava varieties with high sink capacity can be cultivated under protected cultivation to attain higher productivity.

Exploring the impact of the recent global warming on extreme weather events in Central Asia using the counterfactual climate data ATTRICI v1.1

We study the impact of recent global warming on extreme climatic events in Central Asia (CA) for 1901-2019 by comparing the composite representation of the observational climate with a hypothetical counterfactual one that does not include the long-term global warming trend. The counterfactual climate data are produced based on a simple detrending approach, using the global mean temperature (GMT) as the independent variable and removing the long-term trends from the climate variables of the observational data. This trend elimination is independent of causality, and the day-to-day variability in the counterfactual climate remains preserved. The analysis done in the paper shows that the increase in frequency and magnitude of extreme temperature and precipitation events can be attributed to global warming. Specifically, the probability of experiencing a +7 K temperature anomaly event in CA increases by up to a factor of seven in some areas due to global warming. The analysis reveals a significant increase in heatwave occurrences in Central Asia, with the observational climate dataset GSWP3-W5E5 (later called also factual) showing more frequent and prolonged extreme heat events than hypothetical scenarios without global warming. This trend, evident in the disparity between factual and counterfactual data, underscores the critical impact of recent climatic changes on weather patterns, highlighting the urgent need for robust adaptation and mitigation strategies. Additionally, using the self-calibrated Palmer drought severity index (scPDSI), the sensitivity of dry and wet events to the coupled precipitation and temperature changes is analyzed. The areas under dry and wet conditions are enhanced under the observational climate compared to a counterfactual scenario, especially over the largest deserts in CA. The expansion of the dry regions aligns well with the pattern of desert development observed in CA in recent decades.

Resilience of finite clusters of carbon flux network under localized attack.

The investigation into the resilience of the carbon flux network regarding its capability to sustain the normal flow and transformation of carbon under extreme climatic events, pollutant emissions, biological invasions, and other factors, and the stability of connections between its nodes, has not yet been deeply studied. In this study, we developed carbon flux network models for various regional lands using complex networks, percolation theory, and introducing time delay effects using carbon flux daily data from 2000 to 2019 for three regions: China, the mainland United States, and Europe, to measure the resilience of finite clusters with sizes greater than or equal to s of the carbon flux network under localized attack. The analysis revealed that the carbon flux networks in different regions are characterized by a degree distribution consistent with the Poisson distribution. The carbon flux network demonstrated continuous phase transition behavior under localized attack. Interestingly, numerical simulation revealed a consistent relationship between the carbon flux network and the theoretical Erdős-Rényi network model. Moreover, the carbon flux network becomes more vulnerable as s increases. In addition, we discovered that there is a general scaling relationship of critical exponent δ≈-2 between the fraction of finite clusters and s. Therefore, investigating the resilience of carbon flux networks can enable us to predict and respond to the various risks and challenges, which will help policy designers formulate appropriate response strategies and enhance carbon flux systems' stability and resilience.

Extreme precipitation events trigger abrupt vegetation succession in emerging coastal wetlands

Climate change is projected to increase extreme precipitation events in coming decades. Although extreme precipitation events have been reported to cause vegetation degradation, how they affect replacement of dominant species in vegetation succession remains poorly understood. By analyzing two decades’ vegetation dynamics in emerging coastal wetlands on four fluvial islands, we investigate the roles of extreme precipitation events in the shifts of dominant vegetation. We found that shifts in the dominant vegetation from Suaeda salsa to Phragmites australis were abrupt, rather than gradual. Phragmites coverage varied in a nonlinear, threshold manner along flooding gradients across the islands, being very high where flooding frequency was below the threshold flooding frequency (18–40%, depending on island) and minimal elsewhere. Although with time sedimentation lowered flooding frequency to below these thresholds, abrupt shifts from Suaeda to Phragmites did not take place until extremely wet years occurred, likely because extreme precipitation was necessary to decrease salinity stress and to allow this relatively salt-intolerant plant to proliferate. Our findings reveal that extreme precipitation events may play a key role in triggering abrupt vegetation succession and provide a novel addition to previous views that vegetation succession in emerging coastal wetlands is driven primarily by sedimentation-mediated flooding changes and plant-plant interactions. Combining extreme climate events with local hydrological conditions is thus critical for vegetation management in coastal wetlands (including those under restoration) in an increasingly anthropogenic hydrosphere.

How does heavy air pollution impact operational efficiency? An empirical study of Chinese manufacturing firms

Global warming has escalated the frequency of extreme climate events. Considering the escalating severity of air pollution, investigating its potential to cause operational problems is of value. Some studies have explored air pollution’s effects from individual perspectives, including emotions, attitudes, and decision-making behaviors. Yet, the influence of heavy haze weather on firm performance from an operations management viewpoint remains uninvestigated. This study seeks to address this research gap. Utilizing a longitudinal dataset from 2,255 manufacturing firms between 2014 and 2020, this study empirically examines heavy haze weather’s impact on operational efficiency. Three contingency categories are identified: strategic positioning, structural contingency, and institutional factors. The findings suggest that heavy haze weather adversely impacts operational efficiency, notably for firms with cost leadership strategies and high labor productivity, whereas such effect can weaken in the firms with process standardization and governmental environment inspection. Enhancing the understanding about the effect of heavy haze weather on operational performance, these findings offer strategic insights for firms to mitigate heavy haze weather and provide empirical support for government to make environmental policy-making.