Search Results (151)

As local risk assessments are fundamental for risk management and mitigation strategies, this work introduces a methodology for assessing multi-hazard scenarios of extreme compounded events and their duration using daily time series of surface variables from high-resolution climate simulations during historical and future periods under RCP8.5. The aim was to investigate the return level extremes of 20- and 50-year periods of hazards occurring within specific durations and concurrent extreme values of other surface variables, for selected locations in Greece. In addition, future changes in the temporal occurrence of compounded hazards involving precipitation and wind with temperature extremes were performed based on temperature extreme percentiles. The assessment revealed the geographical dependence in the projected occurrence, intensity, and duration of compounded multi-hazard extremes, emphasising the need for high spatial resolution climate data for their investigation. The highlights of the findings include a significant increasing trend of compounded multi-hazard extremes, e.g., hot days and tropical nights, milder winter minimum temperatures with lower rainfall extremes, hotter and windier events of shorter duration, and longer precipitation extremes with increased extreme temperatures. The projections showcased the impact of climate change on extreme compounds with a multitude of interesting findings associated with significant changes in their duration, intensity, and temporal occurrence. Full article

Climate change impacts on vegetation phenology, especially under extreme climate events, remain inadequately understood. Based on the Fraction of Photosynthetically Active Radiation (FPAR) from MODIS, this study extracted and investigated the end of the growing season (EOS) dynamics in semi-arid grassland of Inner Mongolia from 2003 to 2020. The relationship between the EOS and extreme climate events was examined, and the coincidence rate (CR) between these events and EOS standardized anomaly (EOS_SA) was quantified. The results showed that the EOS exhibited a significant delaying trend (1.48 days/year, p < 0.05) after 2011, with its spatial distribution patterns strongly correlated with climatic gradients. Compound dry–warm events exhibited the widest spatial extent and highest frequency among all compound extreme climate events (CECEs). The impact of extreme climate events on EOS_SA varied depending on climatic background. Extreme dry delayed EOS_SA in colder regions but advanced it in warmer regions. CECEs exerted a stronger regulatory effect on EOS_SA. Compound dry–warm events showed high CR with EOS_SA (CR > 0.4), which was higher under low temperature gradients but decreased under high gradients. The result enhances our understanding of how semi-arid grassland respond to extreme climate events, aiding the improvement of phenology models. Full article

Compound dry and hot events or extremes (CDHEs) have emerged as major climatic threats to agricultural production and food security in the middle reaches of the Yangtze River Basin (MRYRB), a critical grain-producing region in China. However, agricultural risks associated with CDHEs, incorporating both natural and socio-economic factors, remain poorly understood in this area. Using a Hazard-Exposure-Vulnerability (HEV) framework integrated with a weighting quantification method and supported by remote sensing technology and integrated geographic data, we systematically assessed the spatiotemporal dynamics of agricultural CDHE risks and corresponding crop responses in the MRYRB from 2000 to 2019. Results indicated an increasing trend in agricultural risks across the region, particularly in the Poyang Lake Plain (by 21.9%) and Jianghan Plain (by 9.9%), whereas a decreasing trend was observed in the Dongting Lake Plain (by 15.2%). Spatial autocorrelation analysis further demonstrated a significant negative relationship between gross primary production (GPP) and high agricultural risks of CDHEs, with a spatial concordance rate of 52.6%. These findings underscore the importance of incorporating CDHE risk assessments into agricultural management. To mitigate future risks, we suggest targeted adaptation strategies, including strengthening water resource management and developing multi-source irrigation systems in the Poyang Lake Plain, Dongting Lake, and the Jianghan Plain, improving hydraulic infrastructure and water source conservation capacity in northern and southwestern Hunan Province, and prioritizing regional risk-based adaptive planning to reduce agricultural losses. Our findings rectify the longstanding assumption that hydrological abundance inherently confers robust resistance to compound drought and heatwave stresses in lacustrine plains. Full article

Climate change has led to increased extreme weather events, such as severe droughts and intense rainfall, with regions in Portugal, like Alentejo and Algarve, being particularly affected. Understanding the influence of water availability in the concentration of phenolic compounds in autochthonous varieties could be an important tool to know how these varieties adapt to water scarcity. This work has been carried out with the aim to analyze the profile of phenolic compounds by HPLC-DAD in four Portuguese grape varieties (Tinta Gorda, Tinta Miúda, Tinta Caiada, and Moreto), cultivated under three irrigation regimes (water comfort, moderate water deficit, and rainfed). The results reveal that Tinta Gorda, Tinta Miúda, and Tinta Caiada varieties exhibit the higher concentrations of phenolic compounds under rainfed conditions. Among these, Tinta Miúda and Tinta Caiada stand out as the most promising varieties in terms of adaptability to water scarcity. Full article

The compound effects of extreme climate change and intensive urban development have led to more frequent urban inundation, highlighting the urgent need for the fine-scale evaluation of stormwater drainage system performance in high-density urban built-up areas. A typical basin, located in Shenzhen, was selected, and a pipe–river coupled SWMM was developed and calibrated via a genetic algorithm to simulate the storm drainage system. Design storm scenario analyses revealed that regional inundation occurred in the central area of the basin and the enclosed culvert sections of the midstream river, even under a 0.5-year recurrence period, while the downstream open river channels maintained a substantial drainage capacity under a 200-year rainfall event. To systematically identify bottleneck zones, two novel metrics, namely, the node cumulative inundation volume and the conduit cumulative inundation length, were proposed to quantify the local inundation severity and spatial interactions across the drainage network. Two critical bottleneck zones were selected, and strategic improvement via the cross-sectional expansion of pipes and river culverts significantly enhanced the drainage efficiency. This study provides a practical case study and transferable technical framework for integrating hydraulic modeling, spatial analytics, and targeted infrastructure upgrades to enhance the resilience of drainage systems in high-density urban environments, offering an actionable framework for sustainable urban stormwater drainage system management. Full article

The Mw 7.8 Gorkha Earthquake of 25 April 2015 triggered over 25,000 landslides across central Nepal, with 4775 events concentrated in Gorkha District alone. Despite substantial advances in landslide susceptibility mapping, existing studies often overlook the compound role of post-seismic rainfall and lack robust spatial validation. To address this gap, we validated an ensemble machine learning framework for co-seismic landslide susceptibility modeling by integrating seismic, geomorphological, hydrological, and anthropogenic variables, including cumulative post-seismic rainfall. Using a balanced dataset of 4775 landslide and non-landslide instances, we evaluated the performance of Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) models through spatial cross-validation, SHapley Additive exPlanations (SHAP) explainability, and ablation analysis. The RF model outperformed all others, achieving an accuracy of 87.9% and a Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) value of 0.94, while XGBoost closely followed (AUC = 0.93). Ensemble models collectively classified over 95% of observed landslides into High and Very High susceptibility zones, demonstrating strong spatial reliability. SHAP analysis identified elevation, proximity to fault, peak ground acceleration (PGA), slope, and rainfall as dominant predictors. Notably, the inclusion of post-seismic rainfall substantially improved recall and F1 scores in ablation experiments. Spatial cross-validation revealed the superior generalizability of ensemble models under heterogeneous terrain conditions. The findings underscore the value of integrating post-seismic hydrometeorological factors and spatial validation into susceptibility assessments. We recommend adopting ensemble models, particularly RF, for operational hazard mapping in earthquake-prone mountainous regions. Future research should explore the integration of dynamic rainfall thresholds and physics-informed frameworks to enhance early warning systems and climate resilience. Full article

As global climate change intensifies, extreme climate events are becoming more frequent, presenting significant challenges to socioeconomic systems and ecosystems. Northeast China, a region highly sensitive to climate change, has been profoundly impacted by compound drought and heat extremes (CDHEs), affecting agriculture, society, and the economy. To evaluate the characteristics and evolution of summer CDHEs in this region, this study analyzed observational data from 81 meteorological stations (1961–2020) and developed a Standardized Temperature–Precipitation Index (STPI) using the Copula joint probability method. The STPI’s effectiveness in characterizing compound drought and heat conditions was validated against historical records. Using the constructed STPI, this study conducted a comprehensive analysis of the spatiotemporal distribution of CDHEs. The Theil–Sen median trend analysis, Mann–Kendall trend tests, and the frequency of CDHEs were employed to examine drought and heatwave patterns and their influence on compound events. The findings demonstrated an increase in the severity of compound drought and heat events over time. Although the STPI exhibited a slight interannual decline, its values remained above −2.0, indicating the continued intensification of these events in the study area. Most of the stations showed a non-significant decline in the Standardized Precipitation Index and a significant rise in the Standardized Temperature Index, indicating that rising temperatures primarily drive the increasing severity of compound drought and heat events. The 1990s marked a turning point with a significant increase in the frequency, severity, and spatial extent of these events. Full article

Compound drought and heatwave (CDHW) events have shown a marked increase under global warming, posing significant challenges to crop productivity. This review systematically categorizes key input and output datasets utilized across diverse research frameworks that investigate the impacts of CDHW stress on crops. The data are organized across multiple spatial scales—from site-specific and field-level measurements to regional and global assessments—and span various temporal dimensions, including historical records, present conditions, and future projections. These datasets include laboratory experiments, field trials, Earth system observations, statistical records, and model simulations. By employing a structured and integrative approach, this review aims to facilitate efficient data access and utilization for researchers. Ultimately, it supports improved data integration, cross-study comparability, and cross-scale synthesis, thereby advancing the assessment of climate change impacts on agricultural systems. Full article

Refugee settlements are often positioned around natural borders, which often have a heightened danger of environmental hazards. Here, we aim to better understand why settlements are in environmentally vulnerable land and what social and physical factors contribute to this phenomenon. To do this, we present a holistic narrative that maps climate threats among displaced populations in Cox’s Bazar district, Bangladesh, while contextualizing environmental vulnerability by incorporating historical and social constraints. Using ArcGIS, an online mapping program, we illustrate the overlap between different climatic events and how these vulnerabilities compound and intensify one another. We also discuss the history of natural migration and settlement pertaining to the physical landscape and the sociopolitical reasons refugees remain in environmentally vulnerable areas. Overall, we find an emerging trend that may be broadly applicable to instances of forced displacement; physical settlement locations near international borders demarcated by landforms may be more vulnerable to the effects of climate change and extreme climate events. However, physical, social, and political reasons often cement these locations. Recommendations include enhancing the resilience of refugee camps through infrastructure improvements, sustainable land management, and reforestation efforts, which would benefit both the environment and local and refugee communities. Full article

Global climate change has increased the frequency of compound drought–heatwave events (CDHEs), seriously threatening tropical forest ecosystems. However, due to the complex structure of natural tropical forests, related research remains limited. To address this, we focused on rubber plantations on Hainan Island, which have simpler structures, to explore the impacts of CDHEs on their primary productivity. We used Pearson and Spearman correlation analyses to select the optimal combination of drought and heatwave indices. Then, we constructed a Compound Drought–Heatwave Index (CDHI) using Copula functions to describe the temporal patterns of CDHEs. Finally, we applied a Bayes–Copula conditional probability model to estimate the probability of GPP loss under CDHE conditions. The main findings are as follows: (1) The Standardized Precipitation Evapotranspiration Index (SPEI-3) and Standardized Temperature Index (STI-1) formed the best index combination. (2) The CDHI successfully identified typical CDHEs in 2001, 2003–2005, 2010, 2015–2016, and 2020. (3) Temporally, CDHEs significantly increased the probability of GPP loss in April and May (0.58 and 0.64, respectively), while the rainy season showed a reverse trend due to water buffering (lowest in October, at 0.19). (4) Spatially, the northwest region showed higher GPP loss probabilities, likely due to topographic uplift. This study reveals how tropical plantations respond to compound climate extremes and provides theoretical support for the monitoring and management of tropical ecosystems. Full article

High ozone (O₃) concentrations are frequently recorded in São Paulo Megacity, with extreme O₃ levels often linked to high temperatures and heatwaves, phenomena expected to intensify with climate change. The co-occurrence of extreme O₃ and heatwaves poses amplified risks to environmental and human health. Hence, this study aims to analyze individual and compound extreme O₃ and heatwave events and assess the associated atmospheric patterns. Hourly O₃ and temperature (T) data from 20 sites (1998–2023) were used to calculate the maximum daily 8 h average O₃ (MD8A-O₃) and maximum daily temperature (Tmax). The Mann–Kendall test identified trends for these variables. The 90th percentile of data from September to March defined thresholds for extreme events. Events were classified as extreme when MD8A-O₃ and Tmax exceeded their thresholds for at least six consecutive days. ERA5 data were used to evaluate atmospheric patterns during these events. The results show positive trends in MD8A-O₃ in 62% of sites, with values exceeding WHO Air Quality Guidelines, alongside positive Tmax trends in 90% of sites. Over the study period, four compound events, seven heatwaves, and four extreme O₃ events were identified. Compound and individual events were associated with the South America Subtropical Anticyclone and positive temperature anomalies. Individual O₃ events were linked to cold anomalies south of 30° S and positive geopotential height anomalies at 850 hPa. These findings highlight the increasing occurrence of extreme O₃ and heatwaves in São Paulo and their atmospheric drivers, offering insights to enhance awareness, forecasting, and policy responses to mitigate health and environmental impacts. Full article

Climate change is reducing agricultural productivity through altered weather patterns and extreme events, potentially decreasing yields by 10–25%. Biostimulants like pyroglutamic acid can enhance plant tolerance to water stress, but their rapid degradation in the soil limits effectiveness. Encapsulation in alginate matrices promises to be a good solution, protecting the compound and enabling controlled release. This study reports, for the first time, that encapsulated pyroglutamic acid markedly enhances drought tolerance in tomato and maize plants. The encapsulation strategy reduces effective concentration by an order of magnitude while significantly improving water use efficiency, photo-synthetic performance, and overall stress resilience. These findings demonstrate that alginate-based encapsulation substantially increases biostimulant uptake and efficacy, providing a novel and efficient strategy to mitigate water stress in crops, with important implications for climate-resilient agriculture. Two encapsulation methods for generating the alginate microcapsules are compared: ionic gelation with Nisco^® system and the electrospray technique. Full article

Reservoir operations play a pivotal role in shaping the flow regime of the Chao Phraya River Basin (CPRB), where two major reservoirs exert substantial hydrological influence. Despite ongoing efforts to manage water resources effectively, current operational strategies often lack the adaptability required to address the compounded uncertainties of climate change and increasing water demands. This research addresses this critical gap by developing an optimization model for reservoir operation that explicitly incorporates climate variability. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed using four fundamental inputs: reservoir inflow, storage, rainfall, and water demands. Daily resolution data from 2000 to 2012 were used, with 2005–2012 selected for training due to the inclusion of multiple extreme hydrological events, including the 2011 flood, which enriched the model’s learning capability. The period 2000–2004 was reserved for testing to independently assess model generalizability. Eight types of membership functions (MFs) were tested to determine the most suitable configuration, with the trapezoidal MF selected for its favorable performance. The optimized models achieved Nash-Sutcliffe efficiency (NSE) values of 0.43 and 0.47, R² values of 0.59 and 0.50, and RMSE values of 77.64 and 89.32 for Bhumibol and Sirikit Dams, respectively. The model enables the evaluation of both dam operations and climate change impacts on downstream discharges. Key findings highlight the importance of adaptive reservoir management by identifying optimal water release timings and corresponding daily release-storage ratios. The proposed approach contributes a novel, data-driven framework that enhances decision-making for integrated water resources management under changing climatic conditions. Full article

Integrated foliar spraying has been proposed as an effective measure to mitigate the increasingly severe impacts of hot–dry–windy (HDW) events on winter wheat yield under ongoing climate change, and its physiological effectiveness has been mechanistically validated. However, there are still few quantitative assessments of the application of this technology at the regional scale. First, hourly meteorological data from the ERA5-Land reanalysis (1981–2020) were matched to the centroids of 599 counties within China’s major winter wheat-producing regions, allowing precise alignment with county-level yield data. Subsequently, spatial and temporal trends of sub-daily HDW events were analyzed. These HDW events were classified according to daily duration into three categories: short-duration (HDW_sd1, 1 h d⁻¹), moderate-duration (HDW_sd2, 2–3 h d⁻¹), and prolonged-duration (HDW_sd3, 4–8 h d⁻¹). Finally, a difference-in-differences (DiD) approach combined with panel matching methods was employed to quantitatively assess the effectiveness of integrated foliar spraying technology—comprising plant growth regulators, essential nutrients, fungicides, and insecticides—on wheat yield improvements under varying irrigation conditions. The results indicate that HDW is a major compound event threatening high-yield and stable-yield regions within the main winter wheat production areas of China, and in the study area, the annual average number of HDW days ranges from 3 to 13 days, increasing by 1–4 days dec⁻¹. While HDW events continue to intensify, the integrated foliar spraying technology effectively mitigates yield losses due to HDW stress. Specifically, yield increases of up to 18–20% were observed in counties with sufficient irrigation infrastructure since the large-scale implementation began in 2012, particularly in regions exposed to more than 2 days of HDW stresses annually. However, the effectiveness of integrated foliar spraying was notably compromised in areas lacking adequate irrigation infrastructure, highlighting the necessity of reliable irrigation conditions. In these poorly irrigated areas, yield improvements remained limited and inconsistent, typically fluctuating around negligible levels. These findings underscore that robust irrigation infrastructure is pivotal to unlock the yield benefits of integrated foliar spraying technology, while also highlighting its transformative potential in advancing climate-smart agriculture globally—particularly in regions grappling with intensifying compound stress events driven by climate change, where this innovation could foster resilient and adaptive food systems to counter escalating environmental extremes. Full article

Climate change significantly impacts agricultural infrastructure, particularly in communal land farming systems, where socio-economic vulnerabilities intersect with environmental stressors. This study examined the effects of extreme weather events (floods, droughts, strong winds, frost, and hail) on various agricultural infrastructures—including bridges, arable land, soil erosion control structures, dipping tanks, roads, and fences—using an ordered probit model. A survey was conducted using structured questionnaires between August and September 2023, collecting data from communal farmers (n = 60) in oKhahlamba Municipality, Bergville. Key results from respondents showed that roads (87%), bridges (85%), and both arable land and erosion structures were reported as highly affected by extreme weather events, especially flooding and frost. Gender, the type of farmer, access to climate information, and exposure to extreme weather significantly influenced perceived impact severity. The ordered probit regression model results reveal that drought (p = 0.05), floods (p = 0.1), strong winds (p = 0.05), and frost (p = 0.1) significantly influence the perceived impacts on infrastructure. Extreme weather events, including flooding (p = 0.012) and frost (p = 0.018), are critical drivers of infrastructure damage, particularly for smallholder farmers. Cumulative impacts—such as repeated infrastructure failure, access disruptions, and increased repair burdens—compound over time, further weakening resilience. The results underscore the urgent need for investments in flood-resilient roads and bridges, improved erosion control systems, and livestock water infrastructure. Support should also include gender-sensitive adaptation strategies, education on climate risk, and dedicated financial mechanisms for smallholder farmers. These findings contribute to global policy discourses on climate adaptation, aligning with SDGs 2 (Zero Hunger), 9 (Industry, Innovation, and Infrastructure), and 13 (Climate Action), and offer actionable insights for building infrastructure resilience in vulnerable rural contexts. Full article