Search Results (2,290)

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting. Full article

This study compared the effectiveness of the Sequencing Batch Biofilter Granular Reactor (SBBGR) plant with and without the integration of ozone (BIO-CHEM process) in the remediation of medium-aged landfill leachate. Special attention is given to the removal of per- and polyfluoroalkyl substances (PFAS) as a group of bioaccumulative and persistent pollutants. The findings highlight the high SBBGR performance under biological process only for key wastewater contaminants, with 82% for chemical oxygen demand (COD), 86% for total nitrogen, and 98% for ammonia. Moderate removal was observed for total (TSS) and volatile (VSS) suspended solids (41% and 44%, respectively), while phosphorus and colour removal remained limited. Remarkably, the SBBGR process achieved complete removal of long-chain PFAS, while its performance declined for shorter-chain PFAS. BIO-CHEM process significantly improved COD (87.7%), TSS (84.6%), VSS (86.7%), and colour (92–96%) removal. Conversely, ozonation led to an unexpected increase in the concentrations of several PFAS in the effluent, suggesting ozone-induced desorption from the biomass. SBBGR treatment was characterised by a low specific sludge production (SSP) value, i.e., 5–6 times less than that of conventional biological processes. SSP was further reduced during the application of the BIO-CHEM process. A key finding of this study is a critical challenge for PFAS removal in this combined treatment approach, different from other ozone-based methods. Full article

Urban flooding and the management of stormwater present significant challenges that necessitate innovative and sustainable solutions. This research examines the effectiveness of green stormwater infrastructure (GSI) for resilient storm sewer systems using the Storm Water Management Model (SWMM), based on customized local climate scenarios. Daily climate data downscaled by four CMIP6 models—CESM2, GFDL-CM4, GFDL-ESM4, and NorESM2-MM—was used. The daily data was disaggregated into 15 min temporal resolution using the HyetosMinute R-package. Two GSI types—bio-retention and rain gardens—were evaluated with a maximum coverage of 30%. The analysis focuses on two future climate scenarios, SSP2-4.5 and SSP5-8.5, predicted under the Shared Socioeconomic Pathways (SSPs) framework. The performance of the stormwater network was assessed for mid-century (2041–2060) and late century (2081–2100), both before and after integration of GSI. Three performance metrics were applied: node flooding volume, number of nodes flooded, and pipe surcharging duration. The simulation results showed an average reduction in flooding volumes ranging between 86 and 98% over the area after integration of GSI. Similarly, reductions ranging between 78 and 89% and between 75 and 90% were observed in pipe surcharging duration and number of nodes vulnerable to flooding, respectively, following GSI. These findings underscore the potential of GSI in fostering sustainable urban water management and enhancement of sustainable development goals (SDGs). Full article

The Amur tiger (Panthera tigris altaica) is a flagship and umbrella species of forest ecosystems in northeastern Asia. Climate change is profoundly and irreversibly affecting wildlife habitat suitability, especially for large mammals. To effectively protect the Amur tiger, it is necessary to understand the impact of climate change on the quality and the connectivity of its habitat. We used the species distribution models combined with the latest Shared Socioeconomic Pathway (SSP) climate scenarios to predict current and future changes in habitats and corridors. We found the following: (1) The total area of the Amur tiger’s suitable habitat currently amounts to approximately 4941.94 km², accounting for 27.64% of the study area represented by two adjacent national parks. Among these habitats, the highly suitable areas are mainly located on the both sides of the Sino-Russian border. The landscape connectivity remains relatively stable, and the degree of fragmentation in highly suitable habitats is low. (2) Although the suitable habitat of the Amur tiger shows an expansion trend under most climate scenarios (excluding SSP3-7.0), the area of suitable habitat within the entire study region does not increase significantly. Therefore, we should implement conservation measures to facilitate the continued expansion of suitable habitat for the Amur tiger. The quantity and length of landscape connectivity corridors are expected to vary in response to changes in core habitat patches, while the centroid of highly suitable habitats is also expected to shift to different extents. In such circumstances, new ecological corridors need to be constructed, while existing natural ecological corridors should be preserved. Full article

Ogura cytoplasmic male sterility (CMS) in Chinese cabbage (Brassica rapa) is characterized by complete pollen abortion, wherein stamens fail to produce viable pollen while pistils retain normal fertility. This maternally inherited trait is valuable for hybrid breeding. This study employed integrated analysis of miRNA, transcriptome, and degradome sequencing data aligned to the Chinese cabbage reference genome to elucidate the molecular function of bra-miR9569 in Ogura CMS pollen fertility and explore its associated pathways. Subsequently, a bra-miR9569 overexpression vector was constructed and transformed into Arabidopsis thaliana. Phenotypic characterization of transgenic Arabidopsis lines, combined with anther viability assessment and quantification of ATP content and reactive oxygen species (ROS) levels in Chinese cabbage, was performed to analyze the effects of bra-miR9569. Our findings demonstrate that mutation of the mitochondrial gene orf138 in Ogura CMS lines leads to upregulation of bra-miR9569. This microRNA negatively regulates the expression of the ATP-related gene AHA6, resulting in reduced H⁺-ATPase activity. The consequent energy deficiency triggers cellular content degradation, ultimately causing failure of pollen wall formation and pollen abortion. Full article

Biodiversity in arid river basins is highly climate-sensitive, yet the multi-pathway relations among the environment, landscape structure, connectivity, and plant diversity remain unclear. Framed by a scale–place–space sustainability perspective, we evaluated, in the Hotan River Basin (NW China), how the environmental factors affect plant diversity directly and indirectly via the landscape configuration and functional connectivity. We integrated Landsat images (2000, 2012, and 2023), 57 vegetation plots, topographic and meteorological data; computed the landscape indices and Conefor connectivity metrics (PC, IIC); and fitted a partial least squares structural equation model (PLS-SEM). From 2000 to 2023, the bare land declined, converted mainly into shrubland and cropland; the construction land is projected to expand under SSP1-2.6/SSP2-4.5/SSP5-8.5 by 2035 and 2050. The landscape metrics showed a rising PD, DIVISION, and SHDI/SHEI, and a declining AI and CONTAG, indicating finer, more heterogeneous mosaics. Plant diversity peaked on low–moderate slopes and with ~32–36 mm annual precipitation. The PLS-SEM revealed significant direct effects on diversity from environmental factors (positive), landscape structure (negative), and connectivity (positive). The dominant chained mediation (environment → structure → connectivity → diversity) indicated that environmental constraints first reconfigure the spatial structure and then propagate to community responses via connectivity, highlighting connectivity’s role in buffering climatic stress and stabilizing communities. The findings provide a quantitative framework to inform biodiversity conservation and sustainable landscape planning in arid basins. Full article

This study evaluated the effects of incorporating spent mushroom substrates (SMS) derived from Agaricus bisporus, Pleurotus ostreatus, and Lentinula edodes into peat-based growing media on the morphological traits, photosynthetic parameters, and mineral composition of radish and black radish microgreens. Six substrate mixtures were tested, with 2.5–30% SMS and two composting durations (97 and 153 days). The results showed that a low proportion of A. bisporus SMS (2.5–5%) significantly enhanced biomass production, plant length, and leaf area, particularly in radish. In contrast, higher proportions (20–30%) of P. ostreatus and L. edodes SMS, especially when short-time composted, inhibited plant growth and photosynthetic performance (Fv/Fm, PIabs), likely due to phytotoxic compounds, high salt content, or nutrient imbalances. Mineral analysis revealed substantial increases in K, Fe, and Zn accumulation in microgreens grown on selected SMS media, particularly Agaricus 5% and Lentinula 30, while also highlighting the risk of excessive Na or heavy metal content in some treatments. Differences between the species were observed: black radish produced higher dry mass and accumulated more minerals, suggesting greater adaptability to suboptimal substrates. These findings support the potential use of well-composted SMS as a sustainable growing media component for microgreens, provided proper substrate selection, composting, and dosage control are applied. Full article

Palaemon gravieri is an ecologically important shrimp species that plays a vital role in depressing jellyfish blooms in the southern Yellow and East China Seas of China. However, information on its distribution pattern and migration route related to environmental variables is fragmented. We conducted independent trawling surveys of P. gravieri between 2018 and 2019. Its sea surface temperature and sea surface salinity lower limits were 8 °C and 30‰, respectively. It showed the highest abundance at sea bottom temperatures and salinities of 12–14 °C and 32–33‰, respectively, in spring; 11–12 °C and 32.5‰ in autumn; and 10.5–14 °C and 31–33‰ in winter. Mean biomass, abundance, and size were ranked seasonally as follows: autumn > winter > spring > summer; autumn > winter and spring; and summer > spring > autumn > winter, respectively. Under the current climate scenario, P. gravieri is mainly concentrated in the inshore areas of the southern Yellow and northern East China Seas. Under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios in 2100, P. gravieri was mainly concentrated in the southern Yellow and northern East China Seas, and in inshore areas of the East China Sea. This species is therefore expected to benefit from climate warming. The findings of this study can facilitate the development of climate-induced fishery strategies and management schemes. Full article

Climate change is altering environmental conditions, which can, in turn, change the geographic distribution and flowering patterns of plant species, affecting both the plants themselves and their pollinators. The responses of melliferous plant species to climate change in southeastern Mexico are poorly understood, which hinders an accurate assessment of their vulnerability and the resulting ecological impacts. As understanding the mechanisms that influence the distribution and susceptibility of these species is essential, the present study examined how climate change affects their potential distribution areas and spatial distribution patterns. This information will serve as reference data for the implementation of conservation strategies and inform the selection of species for reforestation. Ecological niche models were used to estimate the potential distributions of 92 melliferous species under both current environmental conditions and two climate change scenarios projected for the 2041–2060 period (SSP245 and SSP585). Changes in distribution patterns were then assessed by evaluating the range size of each species and analyzing the spatio–temporal trends in species richness. The results revealed that suitable habitats shifted for approximately 80% of melliferous species, with more significant habitat loss observed under the SSP585 scenario than under SSP245. Although a significant decrease in melliferous plant species richness was expected in future scenarios, richness was slightly higher (by 10% for SSP245 and 5% for SSP585) than that observed under current environmental conditions. Under SSP245 conditions, species richness areas expanded to encompass almost the entire region, although this contrasted drastically with the SSP585 scenario, where areas with the highest concentration of species richness contracted significantly and areas with low species richness expanded. These projections are of potential use for conservationists and environmental management authorities, providing crucial insights into the future distributions of several melliferous plant species in the region, the potential impacts of climate change on their habitats, and the vulnerability of threatened species to changing climatic conditions. Full article

The Euphrates–Tigris Basin is experiencing significant environmental transformations due to climate change, Land Use and Land Cover Change (LULCC), and anthropogenic pressures. This study employs Earth Map, an open-access remote sensing platform, to comprehensively assess climate trends, vegetation dynamics, water resource variability, and land degradation across the basin. Key findings reveal a geographic shift toward aridity, with declining precipitation in high-altitude headwater regions and rising temperatures exacerbating water scarcity. While cropland expansion and localized improvements in land productivity were observed, large areas—particularly in hyperarid and steppe zones—show early signs of degradation, increasing the risk of dust source expansion. LULCC analysis highlights substantial wetland loss, irreversible urban growth, and agricultural encroachment into fragile ecosystems, with Iraq experiencing the most pronounced transformations. Climate projections under the SSP245 and SSP585 scenarios indicate intensified warming and aridity, threatening hydrological stability. This study underscores the urgent need for integrated water management, Land Degradation Neutrality (LDN), and climate-resilient policies to safeguard the basin’s ecological and socioeconomic resilience. Earth Map is a vital tool for monitoring environmental changes, offering rapid insights for policymakers and stakeholders in this data-scarce region. Future research should include higher-resolution datasets and localized socioeconomic data to improve adaptive strategies. Full article

Extreme sea surface temperature (SST) events, such as marine heatwaves (MHWs) and marine cold spells (MCSs), severely affect warm water coral reefs. However, further study is required on their historical and future spatiotemporal patterns, driving mechanisms, and impacts in coral reef regions. This study analyzed the spatiotemporal patterns in MHWs/MCSs for the periods 1982–2022 and 2023–2070 using ten indices based on OISSTv2.1 and CMIP6 data, respectively, identified key MHW drivers via four machine learning methods (Random Forest, Extreme Gradient Boosting, Light Gradient Boosting Machine, and categorical boosting) and SHAP values (Shapley Additive Explanations), and then examined their relationship with coral coverage across ten global marine regions. Our results revealed that (1) MHWs are not only increasing in their average intensity but also becoming more extreme, while MCSs have declined. More MHW days are observed in regions like the Red Sea, the Persian Gulf, and the South Pacific Islands, with increases of up to 28 days per decade. (2) Higher-latitude coral reefs are experiencing more severe MHWs than equatorial regions, with up to 1.24 times more MHW days, emphasizing the urgent need to protect coral refuges. (3) MHWs are projected to occur nearly year-round by 2070 under scenario SSP5–8.5. The area ratio of MHWs to MCSs is expected to rise sharply from 2040 onward, reaching approximately 100-fold under the SSP2–4.5 scenario and 196-fold under the SSP5–8.5 scenario, particularly in the Marshall Islands and Caribbean Sea regions. (4) The coefficient of variation (CV) of annual temperature, annual ocean heat content, and monthly temperature were the top three factors driving MHW intensity. We emphasize that future MHW predictions should focus more on the CV of forecasting indicators rather than just the climate means. (5) Coral coverage exhibited post-mortality processes following MHWs, showing a strong negative correlation (r = −0.54, p < 0.01) with MHWs while demonstrating a significant positive correlation (r = 0.6, p < 0.01) with MCSs. Our research underscores the sustained efforts to protect and restore coral reefs amid escalating climate-induced stressors. Full article

Spatholobus suberectus, a valuable Chinese medicinal plant, faces habitat shifts under climate change. In order to better utilize the medicinal properties of S. suberectus and conduct further investigations, this study utilized the Biomod2 ensemble model to predict and analyze the potential expansion and contraction of suitable habitat areas for S. suberectus in China under changing climatic and environmental conditions. The results showed that, compared to the pre-screened models, the ensemble model significantly improved the prediction accuracy. Currently, S. suberectus is primarily distributed in southern China. Under the projected scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5, its suitable habitat is expected to expand overall, with the increased areas concentrated mainly in southwestern, central, and eastern China. As climatic factors shift, the high-suitability center of S. suberectus is predicted to shift slightly southward under the SSP1-2.6 scenario, while under the SSP2-4.5 and SSP5-8.5 scenarios, it is projected to move northwestward. In the future, it will be necessary to optimize the warm and humid growth environment for cultivated S. suberectus in China. Meanwhile, wild S. suberectus populations should be closely monitored for the impact of climate change to prevent potential partial reductions in suitable habitats, thereby ensuring ecological balance and sustainable development. Full article

The Tibetan Plateau’s lakes, serving as critical water towers for over two billion people, exhibit divergent responses to climate change that remain poorly quantified. This study develops a deep learning framework integrating Synthetic Aperture Radar (SAR) altimetry from Sentinel-3A with bias-corrected CMIP6 (Coupled Model Intercomparison Project Phase 6) climate projections under Shared Socioeconomic Pathways (SSP) scenarios (SSP2-4.5 and SSP5-8.5, adjusted via quantile mapping) to predict lake-level changes across eight Tibetan Plateau (TP) lakes. Using a Feed-Forward Neural Network (FFNN) optimized via Bayesian optimization using the Optuna framework, we achieve robust water level projections (mean validation R² = 0.861) and attribute drivers through Shapley Additive exPlanations (SHAP) analysis. Results reveal a stark north–south divergence: glacier-fed northern lakes like Migriggyangzham will rise by 13.18 ± 0.56 m under SSP5-8.5 due to meltwater inputs (temperature SHAP value = 0.41), consistent with the early (melt-dominated) phase of the IPCC’s ‘peak water’ framework. In comparison, evaporation-dominated southern lakes such as Langacuo face irreversible desiccation (−4.96 ± 0.68 m by 2100) as evaporative demand surpasses precipitation gains. Transitional western lakes exhibit “peak water” inflection points (e.g., Lumajang Dong’s 2060 maximum) signaling cryospheric buffer loss. These projections, validated through rigorous quantile mapping and rolling-window cross-validation, provide the first process-aware assessment of TP Lake vulnerabilities, informing adaptation strategies under the Sustainable Development Goals (SDGs) for water security (SDG 6) and climate action (SDG 13). The methodological framework establishes a transferable paradigm for monitoring high-altitude freshwater systems globally. Full article

Over the past 70 years, Central Asia has emerged as a globally recognized water security hotspot due to its unique geographic location and uneven distribution of water resources. In arid and semi-arid regions, understanding runoff dynamics under climate change is essential for ensuring regional water security. This study addresses the data-sparse Central Asian region by applying the ISIMIP3b multi-scenario analysis framework, selecting three representative global hydrological models. Using model intercomparison, trend analysis, and geographically weighted regression, we assess the spatiotemporal evolution of runoff from 1950 to 2080 and investigate the spatial heterogeneity of runoff responses to precipitation and temperature. The results show that under the historical scenario, all models consistently identify similar spatial pattern of runoff, with higher values in southeastern mountainous regions and lower values in western and central regions. However, substantial differences exist in runoff magnitude, with regional annual means of 10, 26, and 68 mm across the three models, respectively. The spatial disparity of runoff distribution is projected to increase under higher SSP scenarios. During the historical period, most of Central Asia experienced a slight decreasing trend in runoff, but the overall trends were −0.022, 0.1, and 0.065 mm/year, respectively. In contrast, future projections indicate a transition to increasing trends, particularly in eastern regions, where trend magnitudes and statistical significance are notably greater than in the west. Meanwhile, the spatial extent of significant trends expands under high-emission scenarios. Precipitation exerts a positive influence on runoff in over 80% of the region, while temperature impacts exhibit strong spatial variability. In the WaterGAP2-2e and MIROC-INTEG-LAND models, temperature has a positive effect on runoff in glaciated plateau regions, likely due to enhanced snow and glacier melt under warming conditions. This study presents a multi-model framework for characterizing climate–runoff interactions in data-scarce and environmentally sensitive regions, offering insights for water resource management in Central Asia. Full article

Global warming has made water resources more uneven in space and time, making water management harder. This study used the HBV-XGBoost model to see how climate change affects runoff in the Xiangxi River Basin. The HBV model simulated water processes, and XGBoost improved predictions by handling complex relationships. This study used the SDSM to create climate data for 2025–2100 and looked at runoff trends under different emission scenarios. The HBV-XGBoost model performed better than the HBV model in simulating runoff. Future predictions showed big differences in runoff trends under various SSP scenarios in the 2040s and 2080s. For example, under SSP585, the ACCESS-CM2 model projected a May runoff increase from 1527.52 m³/s to 2344.42 m³/s by the 2080s, and ACCESS-ESM1-5 projected an increase from 1462.11 m³/s to 2889.58 m³/s. All GCMs predicted a large rise in annual runoff under SSP585 by the 2080s, with FGOALS-g3 showing the highest growth rate of 76.54%. The model accurately simulated runoff changes and provided useful insights for adapting water management to climate change. However, this study has limitations, including uncertainties in machine learning models, potential input data biases, and varying applicability under different conditions. Future work should explore more climate models and downscaling methods to improve accuracy and consider local policies to better address climate impacts on water resources. Full article