Search Results (390)

With the rapid pace of urbanization, cities are increasingly facing severe challenges related to environmental pollution, ecological degradation, and climate change. Extreme climate events—such as heatwaves, droughts, heavy rainfall, and wildfires—have intensified public concern about sustainability, environmental protection, and low-carbon development. Ensuring environmental preservation while maintaining residents’ quality of life has become a central focus of urban governance. In this context, evaluating green indicators and predicting urban greenness is both necessary and urgent. This study incorporates international frameworks such as the EU Green City Index, the European Green Capital Award, and the United Nations Sustainable Development Goals to assess urban sustainability. The Extreme Gradient Boosting (XGBoost) algorithm is employed to predict the green level of cities and to develop multiple optimized models. Comparative analysis with traditional models demonstrates that XGBoost achieves superior performance, with an accuracy of 0.84 and an F1-score of 0.81. Case study findings identify “Greenhouse Gas Emissions per Person” and “Per Capita Emissions from Transport” as the most critical indicators. These results provide practical guidance for policymakers, suggesting that targeted regulations based on these key factors can effectively support emission reduction and urban sustainability goals. Full article

Hurricane Helene made landfall near Perry (Latitude 30.1 N) in the Big Bend area of Florida with a central pressure of 939 hPa. It moved northwards creating devastating damage and loss of life; however, the greatest damage and number of fatalities occurred well to the north around the City of Ashville (Latitude 35.6 N) where extreme rainfall fell and some of the strongest wind gusts were reported. This paper describes the change in the hurricane’s structure as it tracked northwards, how it gathered tropical moisture from the Atlantic and a turning wind profile between the 850 hPa and 500 hPa elevations, which led to such extreme rainfall. This turning wind profile is shown to be associated with extreme rainfall and loss of life from drowning and landslides around the globe. The area around Ashville suffered 157 fatalities, which is a considerable proportion of the 250 fatalities so far recorded in the whole United Stares from Helene. This is of extreme concern and should be investigated in detail as the public expect the greatest impact from hurricanes to be confined to coastal areas near the landfall site. It is another example of increased death tolls from tropical cyclones moving inland and generating heavy rainfall. As the global population increases and inland centres become more urbanised, run off from such rainfall events increases, which causes greater devastation. Full article

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article

The hydrological processes of agricultural fields during the fallow season in east-central Mississippi remain poorly understood, due to the region’s unique rainfall patterns. This study utilized long-term rainfall records from 1924 to 2023 to evaluate runoff characteristics and the runoff response to various rainfall events during fallow seasons in Mississippi by applying the DRAINMOD model. The analysis revealed that the average rainfall during the fallow season was 760 mm over the past 100 years, accounting for 65% of the annual total. In dry, normal, and wet fallow seasons, the average rainfall was 528, 751, and 1010 mm, respectively, corresponding to runoff of 227, 388, and 602 mm. Runoff frequency increased with wetter weather conditions, rising from 16 events in dry seasons to 23 in normal seasons and 30 in wet seasons. Over the past century, runoff dynamics were predominantly regulated by high-intensity rainfall events during the fallow season. Very heavy rainfall events (mean frequency = 11 events) generated 215 mm of runoff and accounted for 53% of the total runoff, while extreme rainfall events (mean frequency = 2 events) contributed 135 mm of runoff, making up 34% of the total runoff. Water table depth played a critical role in shaping spring runoff dynamics. As the water table decreased from 46 mm in March to 80 mm in May, the soil pore space increased from 5 mm in March to 14 mm in May. This increased soil infiltration and water storage capacity, leading to a steady decline in runoff. The study found that the mean daily runoff frequency dropped from 13.5% in March to 7.6% in May, while monthly runoff decreased from 74 to 38 mm. Increased extreme rainfall (R95p) in April contributed over 45% of the total runoff and resulted in the highest daily mean runoff of 20 mm, compared to 18 mm in March and 16 mm in May. The results from this century-long historical weather data could be used to enhance field-scale water resource management, predict potential runoff risks, and optimize planting windows in the humid east-central Mississippi. Full article

Typhoons are major climate disturbances that significantly impact coastal ecosystems, particularly mangrove forests. This study examines the effects of typhoons on mangrove communities at different stages of recovery, focusing on how environmental factors influence carbon storage and net ecosystem exchange (NEE). Three mangrove sites were selected based on their recovery age: young, moderately restored, and mature. The results revealed that typhoons had the most pronounced effect on young mangroves, resulting in significant reductions in both above-ground and soil carbon storage. In contrast, mid-aged and mature mangroves demonstrated greater resilience, with mature mangroves recovering most rapidly in terms of community structure and carbon storage. Key factors such as wind speed, heavy rainfall, and changes in photosynthetically active radiation (PAR) contributed to carbon storage losses, particularly in young mangrove forests. This study underscores the importance of recovery age in determining mangrove resilience to extreme weather events and offers insights for enhancing restoration and conservation strategies to mitigate the impacts of climate change on coastal carbon sequestration. Full article

In recent years, satellite products have played an increasingly significant role in monitoring and estimating global extreme weather events, owing to their advantages of an excellent spatiotemporal continuity and broad coverage. This study systematically evaluates the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for the GPM Late Run (IMERG-L) product for regional precipitation events based on the observations in Zhejiang Province from 2001 to 2020. In this study, seven typical precipitation indices with seven accuracy evaluation indexes are applied to analyze the performance of IMERG-L from multiple perspectives in terms of the precipitation intensity, frequency and spatial distribution dimensions. The results show that IMERG-L is capable of capturing the spatial distribution trends, especially in the frequency-based precipitation indices (CWD, R10mm and R20mm), which can depict the regional wetness and precipitation pattern. However, the product suffers from a systematic overestimation in capturing heavy precipitation and an extreme precipitation intensity, with a high false alarm rate and unstable accuracy, especially in heavy rainfall and above class events, where the Probability of Detection (POD) drops significantly, showing an obvious reduction in the recognition capability and risk of misclassification. Specifically, IMERG-L failed to reproduce the observed eastward-increasing trends in the annual maximum precipitation for both one-day (RX1day) and five-day (RX5day) durations, demonstrating its limitations in accurately capturing extreme precipitation patterns across Zhejiang Province. Overall, furthering the optimization and improvement of IMERG-L in reducing the intensity-dependent biases in heavy rainfall detection, increasing spatial inhomogeneity in trend representations and improving the false alarm suppression for extreme events are needed for the accurate monitoring and quantitative estimation of high-intensity extreme precipitation events. Full article

Accelerating climate change poses significant risks to water security and ecological stability in arid regions due to the increasing frequency and intensity of extreme precipitation events. As a climate-sensitive area, the inland river basin (IRB) of Northwest China—a critical water source for local ecosystems and socioeconomic activities—remains insufficiently studied in terms of future extreme precipitation dynamics. This study evaluated the spatiotemporal evolution of extreme precipitation in the IRB under a new low radiative forcing scenario (SSP1-1.9) by employing four global climate models (GCMs: GFDL-ESM4, MRI-ESM2, MIROC6, and IPSL-CM6A-LR). Eight core extreme precipitation indices were analyzed to quantify changes during the near future (NF: 2021–2050) and far future (FF: 2071–2100) periods. Our research demonstrated that all four models were capable of capturing seasonal patterns and exhibited inherent uncertainty. The annual total precipitation (PRCPTOT) in mountainous regions showed minimal variation, while desert areas were projected to experience a 2-6-fold increase in precipitation in the NF and FF. The Precipitation Intensity Index (SDII) weakened by approximately −10% in mountainous areas but strengthened by around +10% in desert regions. Most mountainous areas showed an increase in the maximum consecutive dry days (CDD), whereas desert regions exhibited extended maximum consecutive wet days (CWD). Moderate rainfall (P1025) variations primarily ranged between −5% and +20%, with greater fluctuations in desert areas. Heavy rainfall (PG25) fluctuated between −40% and +40%, reflecting stark contrasts in extreme precipitation between arid basins and mountainous zones. The maximum 1-day precipitation (Rx1day) and maximum 5-day precipitation (Rx5day) both showed significant increases, which indicated heightened risks from extreme rainfall events in the future. Moreover, the IRB region experienced increased total precipitation, enhanced rainfall intensity, more frequent alternations between drought and precipitation, more frequent moderate-to-heavy rainfall days, and higher daily precipitation extremes in both the NF and FF periods. These findings provide critical data for regional development planning and emergency response strategy formulation. Full article

Extreme weather events such as heavy rainfall significantly reduce surface salinity in coastal waters, presenting considerable challenges to the aquaculture of Japanese scallops (Mizuhopecten yessoensis) in shallow cage systems. This study investigated the effects of chronic low-salinity stress on the growth performance, antioxidant capacity, and gene expression profile of M. yessoensis using a 60-day salinity gradient experiment. S33 represents the control treatment with normal seawater salinity (33‰), while S30, S28, and S26 represent experimental groups with progressively lower salinities of 30‰, 28‰, and 26‰, respectively. A decline in salinity was accompanied by an increase in oxygen consumption. The S26 group exhibited a higher ammonia excretion rate (2.73 μg/g·h) than other groups, indicating intensified nitrogen metabolism. Growth was inhibited under low-salinity conditions. The S33 group exhibited greater weight gain (16.7%) and shell growth (8.4%) compared to the S26 group (11.6% and 6%), which also showed a substantially higher mortality rate (46%) compared to the control (13%). At 28‰, antioxidant enzyme activities (T-AOC, SOD, CAT, POD) were elevated, indicating a moderate level of stress. However, at the lowest salinity (26‰), these indicators decreased, reflecting the exhaustion of the antioxidant systems and indicating that the mollusks’ adaptive capacity had been exceeded, leading to a state of stress fatigue. NAD-MDH activity was elevated in the S26 group, reflecting enhanced aerobic metabolism under stress. Transcriptome analysis revealed 564 differentially expressed genes (DEGs) between the S33 and S26 groups. Functional enrichment analysis indicated that these DEGs were mainly associated with immune and stress response pathways, including NF-κB, TNF, apoptosis, and Toll/Imd signaling. These genes are involved in key metabolic processes, such as alanine, aspartate, and glutamate metabolism. Genes such as GADD45, ATF4, TRAF3, and XBP1 were upregulated, contributing to stress repair and antioxidant responses. Conversely, the expressions of CASP3, IKBKA, BIRC2/3, and LBP were downregulated, potentially mitigating apoptosis and inflammatory responses. These findings suggest that M. yessoensis adapts to chronic low-salinity stress through the activation of antioxidant systems, modulation of immune responses, and suppression of excessive apoptosis. This study provides new insights into the molecular mechanisms underlying salinity adaptation in bivalves and offers valuable references for scallop aquaculture and selective breeding programs. Full article

Climate change, urbanization, and extreme weather events such as heavy rainfall and drought present major challenges for urban water management. This paper proposes a framework to evaluate the effectiveness of blue–green infrastructure (BGI) as a sustainable stormwater management solution across different residential development scales. Two contrasting case studies are examined: a small terraced housing catchment and a large housing estate. A multi-criteria analysis (MCA) supports a structured comparison of BGI effectiveness, while a complementary SWOT analysis informs strategic implementation approaches. The results demonstrate the practical applicability of the framework and underscore that successful stormwater management requires both innovative technologies and reform in urban planning governance. This study offers valuable insights into building climate-resilient cities. Full article

As an important connecting channel between cities, roads are one of the main elements in urban development infrastructure. The stability evaluation of the roadbed slope runs through the entire life cycle, especially during the operation stage. However, under extreme weather conditions, especially heavy rainfall, the roadbed slope may become unstable, thus endangering operational safety. Therefore, it is necessary to conduct precise dynamic assessments of slope stability. However, due to site limitations, it is often not possible to obtain accurate mechanical parameters of a slope using traditional survey methods when deformation and failure have already occurred. In this study, building upon our existing parameter inversion model, the improved backpropagation genetic algorithm non-dominated sorting genetic algorithm II model (BPGA-NSGA-II), in-depth research was conducted on the selection of key parameters for the model. This study utilized monitoring data to perform an inversion analysis of the real-time mechanical parameters of the slope. Subsequently, the inverted parameters were applied to dynamically assess the stability of the slope. The calculation results demonstrate that the slope safety factor decreased from an initial value of 1.212 to 0.800, which aligns with actual monitoring data. This research provides a scientifically effective method for the dynamic stability assessment of slopes. Full article

Climate change aggravates the frequency of extreme rainfall events, resulting in carbon (C) loss. For the special climate of the highlands, cultivating the land underneath orchards increases C reservation. Systematic research on the impact of extreme rainfall on soil organic carbon compositions and (dissolved organic carbon) DOC leaching is limited, especially regarding the response to different cropping patterns underneath orchards, requiring a deeper understanding. The results showed that the DOC-leaching fluxes for the cropping patterns under rainstorms and heavy rainstorms were in the order Dactylis glomerata L. monocropping (13.5, 4.4 kg/hm²) > Medicago sativa L. monocropping (11.2, 3.8 kg/hm²) ≥ D. glomerata. + M. sativa. (10.4, 3.6 kg/hm²). The DOC-leaching fluxes during heavy rainstorms were reduced with D + M, and the root morphology showed a significant correlation with DOC concentration. Compared to the D, SOC in layers 40–60 cm of the M and the D + M increased by 68.36% and 64.24%, respectively. TP and POC of the D + M increased with soil depth. Relationships between cropping pattern and rainfall intensity for particulate organic carbon (POC) and mineral-associated organic carbon (MOC) were observed. Heavy rainstorms reduced MOC, including the decomposition of substances related to the MOC, such as ROC and DOC, then POC in layers 40–60 cm increased; compared with 0–20 cm of D and M, the content of readily oxidizable carbon (ROC) in layers 40–60 cm reduced by 56.90~77.64%, and the POC increased by 38.38~87.00% in the D + M. Therefore, it was suggested that the decomposition of deeper MOC due to heavy rainstorms is the main source of soil POC and leaching DOC. This will provide a reference basis for research on assessing soil carbon-leaching fluxes and carbon stocks under extreme rainfall events. Full article

Global climate change has led to an increased frequency of extreme weather events, with flooding caused by heavy rainfall posing a significant threat to plant growth and survival. Styrax japonicus, a species of ecological and economic importance, exhibits stronger flooding tolerance compared to its congener Styrax tonkinensis. Endogenous hormonal systems in plants are indispensable for integrating growth dynamics, developmental transitions, and ecological stress perception-transduction pathways. To investigate the response of S. japonicus to flooding stress at both hormonal and molecular levels, this study utilized annual seedlings of S. japonicus as experimental material. Two levels of flooding stress, waterlogging and submergence, were applied to examine the variations in endogenous hormone levels in S. japonicus roots under different stress conditions and durations. Combined with transcriptome sequencing, critical genes associated with hormone-mediated signaling and biosynthetic processes were identified. The results showed that the content of the ethylene precursor ACC exhibited a trend of “increase–decrease–increase”, with an earlier decline under submergence compared to waterlogging stress by approximately 10 days. Abscisic acid content sharply decreased at 5 d, followed by an initial increase and subsequent decrease, with higher ABA levels observed under waterlogging stress than under submergence. GA content significantly decreased after 10 d in both stress conditions. KEGG enrichment analysis revealed that the most prominently enriched pathway for DEGs was plant hormone signal transduction under both waterlogging and submergence stress, with 314 and 370 DEGs identified, respectively. Analysis of common genes indicated their association with ethylene, ABA, auxin, and BRs. After further investigation of DEGs in the ethylene and ABA biosynthesis process, we identified key enzyme genes encoding ACS, ACO, and NCED, which are critical for their biosynthesis. Full article

This study evaluates the impacts of Solar Radiation Management (SRM) on precipitation-related climate extremes in Southeast Asia. Using simulations from the Geoengineering Large Ensemble (GLENS), we assess spatial anomalies and differences in extreme precipitation indices—number of wet days (RR1), very heavy precipitation days (R20mm), maximum 5-day precipitation (Rx5day), consecutive dry days (CDD), and consecutive wet days (CWD)—relative to historical (1980–2009) and Representative Concentration Pathway 8.5 (RCP8.5) baselines. The results reveal that SRM induces highly heterogeneous precipitation responses across the region. While SRM increases rainfall frequency in parts of Indonesia, it reduces the number of wet days and lengthens dry spells over Vietnam, Thailand, and the Philippines. Spatial variations are also observed in changes to heavy precipitation days and multi-day rainfall events, with potential implications for flood and drought risks. These findings highlight the complex trade-offs in hydrological responses under SRM deployment, with important considerations for agriculture, water resource management, and climate adaptation strategies in Southeast Asia. Full article

Climate change is projected to increase the intensity and frequency of natural hazards such as heat waves, extreme rainfall, heavy snowfall, typhoons, droughts, floods, and cold waves, potentially impacting the operational safety of critical infrastructure, including nuclear power plants (NPPs). Although quantitative indicators exist to screen-out natural hazards at NPPs, comprehensive methodologies for assessing climate-related hazards remain underdeveloped. Furthermore, given the variability and uncertainty of climate change, it is realistically and resource-wise difficult to evaluate all potential risks quantitatively. Using a structured expert elicitation approach, this study systematically identifies and prioritizes climate-related natural hazards for Korean NPPs. An iterative Delphi survey involving 42 experts with extensive experience in nuclear safety and systems was conducted and also evaluated using the best–worst scaling (BWS) method for cross-validation to enhance the robustness of the Delphi priorities. Both methodologies identified extreme rainfall, typhoons, marine organisms, forest fires, and lightning as the top five hazards. The findings provide critical insights for climate resilience planning, inform vulnerability assessments, and support regulatory policy development to mitigate climate-induced risks to Korean nuclear power plants. Full article