Global concurrent climate extremes exacerbated by anthropogenic climate change

Plant Functional Traits, but Not Community Composition, Are Affected by Summer Precipitation and Herbivory in an Old-Field Ecosystem

Both precipitation and herbivores can independently control plant community composition and ecosystem function. However, few studies have experimentally examined the potential interactive effects of altered precipitation and herbivores on plant communities and plant traits. Here, we manipulated summer precipitation and insect presence in an old-field ecosystem and quantified their interactive effects on plant community structure and functional traits. Overall, the effect of an insect herbivore on the plant community was contingent on the precipitation treatment. There were no experimental effects on total plant biomass or plant species richness, but grass biomass was higher in the absence of insect herbivores only in reduced summer precipitation plots. Furthermore, plant functional diversity and the community-averaged trends of several plant functional traits related to resource use and herbivore resistance varied systematically with reduced precipitation and insect presence. We demonstrate that the effect of reduced precipitation on plant biomass, functional diversity, and the community-averaged trends of plant functional traits can be mediated by the presence of insects. Our findings further suggest that the functional traits of the common plant species in the community are the most affected by the combined manipulation of altered summer precipitation and insect presence.

Frequent land-ocean transboundary migration of tropical heatwaves under climate change

Anthropogenic warming has exacerbated atmospheric heatwaves globally, yet the transboundary migration of heatwaves between land and ocean, along with the anthropogenic influence on this process, remain unclear. Here, we employ a Lagrangian tracking approach to identify and track spatiotemporally contiguous warm-season heatwaves in both reanalyses and simulations. This way, we show that land-ocean transboundary heatwaves, especially in the tropics, exhibit longer persistence, wider areal extent, and greater intensity than those confined to land or ocean. These transboundary migrations are primarily driven by the movement of high-pressure systems (such as the westward extension of subtropical highs) and the propagation of Rossby waves. Associated with increasing greenhouse gas concentrations, the frequency of tropical heatwave migrations has increased over the past four decades, and is projected to accelerate further in the twenty-first century under the high-emissions scenario. Anthropogenically-driven landward migrations are amplified by stronger landward winds that drive heat advection, while oceanward processes are likely intensified by increased land-ocean temperature gradient. These intensified transboundary heatwaves not only accentuate humid heat risks for humans but also threaten ecosystems.

Reconciling the Discrepancy in Projected Global Dryland Expansion in a Warming World

Continental drying and associated dryland expansion would accelerate environmental degradation and desertification. However, the rate of continental drying commonly assessed with an aridity index is inconsistent with observations and projections of widespread greening and increased global runoff. This raises questions about the accuracy of assessment methods and projections of continental drying and dryland dynamics in a warming world. Here we show that the continental drying trend has been exaggerated because the potential evapotranspiration (PET) and its rate of increase over time are grossly overestimated with the widely used Penman equations. Using an energy-based PET estimator, we bias correct the aridity index and find considerably weaker and less extensive continental drying (47% of the global land area) than the 61%-65% based on Penman equations. Dryland expansion is projected to occur over only 2.1% of global land area in a high-emission scenario in the 21st century. Moreover, the corrected aridity index and ecohydrological and hydroclimate projections all show no change in significance consistently in the extent of global drylands based on 32 climate models. These findings resolve the ongoing debate about global dryland expansion and have far-reaching implications for understanding long-term changes in the climate system and their impacts on terrestrial ecohydrological processes.

Assessing climate sensitivity of the Upper Indus Basin using fully distributed, physically-based hydrologic modeling and multi-model climate ensemble approach

The Upper Indus Basin (UIB) of Pakistan is home to three largest mountain ranges: Karakoram, Hindukush, and the Himalayas. There's a hovering danger of reduced water resources due to climate-induced warming, since the Indus Basin relies mostly on snow and glacier melt runoff from these mountains. In this study, the hydrology of these areas is studied to estimate variability in water resources under changing climate. A coupled hydrologic/hydraulic model, MIKE SHE/MIKE HYDRO RIVER, was used to calibrate and validate the model using streamflow data at the Shatial gauge station. The bias-corrected multi-model ensemble dataset accessed from the coordinated regional climate downscaling experiment (CORDEX) for two scenarios RCP 4.5 and RCP 8.5 were used up to the end of twenty-first century for the projection of stream flow. The calibrated model depicts an increase of 86% for RCP4.5 and 97% for RCP8.5, dominated by increased snow melt contribution due to consistent warming trends (average increase of 2.3 and 4 °C, annually for RCP 4.5 and 8.5, respectively). The most prominent increase was observed in winter months when predicted flows increased by 300% from historic. With predicted annual average available water of 4500 m3/s under RCP8.5, the study concludes that the available water would relieve some of the water stress in the country. However, the increased availability of water can also cause catastrophic floods. More flood defense and storage structures are needed to improve management practices in the downstream areas, particularly during wet and dry years.

Evolutionary analysis of CBFs/DREB1s in temperate and tropical woody bamboos and functional study of PeDREB1A3 under cold and drought stress

Bamboo forests are vulnerable to extreme cold, as well as drought caused by declining rainfall or persistent hot, under global climate change. The C-repeat binding factors/dehydration-responsive element binding protein 1s (CBFs/DREB1s) are vital to acquiring tolerance to deal with the changing climate in plants. Herein, we investigated the evolution of CBFs/DREB1s in four temperate or tropical woody bamboos. In Phyllostachys edulis, Hsuehochloa calcarea, Dendrocalamus latiflorus, and Dendrocalamus brandisii, a total of 16, 12, 24, and 22 putative DREB1s were identified and were categorized into nine subclades, from DREB1A to DREB1I. DREB1s members increased with bamboo polyploidization, coinciding with the presence of at least two collinear DREB1s orthologs in different bamboos. It indicates the importance of polyploidization in driving the expansion of DREB1s. Except for the DREB1F, DREB1s of the other subclades showed direct collinearity with their orthologs in Poaceae. Tandemly linked loci of DREB1A, DREB1H, and DREB1B were of concern due to their conserved and inherited relationship in bamboo, and a recent duplication of DREB1A occurred during bamboo speciation. In P. edulis, PeDREB1A3/PeDREB1H1/PeDREB1B3 locus showed sensitivity to cold stimulation, especially for PeDREB1A3 rapidly induced after 0.5-h cold stimulation. PeDREB1A3 was proved as a nuclear-located transcription activator recognizing DRE cis-element. Moreover, overexpression of PeDREB1A3 improved both cold and drought tolerance of Arabidopsis thaliana. It suggested that the neoteric duplication of DREB1As might contribute to bamboo adaptability. DREB1A represents the potential locus for improving agronomic traits in the future. This research provides valuable information for excavating potential genes for bamboo adaptation and will facilitate the research on bamboo breeding for stress tolerance.

Spatial-temporal characteristics and drivers of urban built-up areas land low-carbon efficiency in China

Understanding the evolution of low-carbon efficiency in urban built-up areas is essential for developing countries striving to meet sustainable development goals. However, the mechanisms driving low-carbon efficiency and the associated development pathways remain underexplored. This study applies the Global Data Envelopment Analysis (DEA) model, the Global Malmquist-Luenberger Index, and econometric models to evaluate low-carbon efficiency and its determinants across China's urban built-up areas from 2010 to 2022. The findings reveal a significant increase in efficiency, from 0.555 in 2010 to 0.785 in 2022, reflecting an overall improvement of 41.4% (P < 0.05). Spatially, low-carbon efficiency demonstrates a pronounced "east-high and west-low" distribution, highlighting regional disparities and spatial correlations. Temporal changes in low-carbon efficiency are primarily driven by technological advancements and shifts in the technological frontier. However, the disproportionate efficiency gains during periods of high resource input suggest inefficiencies in production factor allocation, particularly in densely populated urban centers. Unlike natural endowments, concentrated factor inputs in such cities often impede efficiency improvements. The findings underscore the need for context-sensitive development strategies, as a one-size-fits-all model may not address the diverse challenges posed by regional disparities. By leveraging market mechanisms to optimize resource allocation and strengthen interregional spatial connectivity, this study provides actionable insights for promoting sustainable land development in developing economies.

Relationships among vegetation restoration, drought and hydropower generation in the karst and non-karst regions of Southwest China over the past two decades

To curb the intensification of desertification, China has implemented a series of measures to control rocky desertification. However, the interaction between vegetation restoration and the frequent occurrence of extreme weather events has complicated the drought situation in Southwest China. Therefore, in this study, the vegetation health index (VHI) was used to analyze the spatiotemporal variations in drought. Additionally, the fractional vegetation cover (FVC), VHI, vegetation condition index (VCI), and temperature condition index (TCI) were compared between karst and non-karst regions. Additionally, the driving factors of drought and the response of hydropower generation (HG) to drought conditions were explored. The results revealed that (1) after the implementation of measures to combat desertification, the FVC and VHI increased annually by 0.37 % and 0.801, respectively, from 2002 to 2022. In Southwest China, the increase rates of the VCI and TCI were 1.993 and 0.349 yr-1, respectively, with VCI increase as a key factor in enhancing the VHI. (2) VHI improvement in karst regions was significantly greater than that in non-karst areas, indicating effective rocky desertification control efforts. (3) The geodetector analysis results indicated that the topography is the primary factor influencing the spatial distribution of drought in Southwest China, followed by climatic factors. (4) During drought occurrence, the gap between HG and the total electricity consumption in Southwest China increased, leading to increases in fossil fuel-based power generation and pollutant emissions.

Thermal response of deep monomictic reservoir under different selective withdrawal types

Selective withdrawal is an effective measure to mitigate the adverse effects caused by reservoir construction. The main types of selective withdrawal include multi-level withdrawal and internal weir withdrawal, each with distinct characteristics. It is urgent to elucidate the thermal response differences between these two types of selective withdrawal to improve scheduling accuracy. Taking the Xiluodu Reservoir (XLDR) as a representative of monomictic reservoirs in this study, a hydrodynamic-thermal numerical model is employed to investigate the thermal response of large deep monomictic reservoirs under different selective withdrawal types and schemes. Additionally, we also explore the regulatory mechanisms of selective withdrawal on water temperature and its applicability under different hydrological conditions. The results indicated an asynchrony in the variations of the vertical temperature gradient (VTG) and the Schmidt stability index (SSI). SSI is identified as the optimal index for comprehensively reflecting the characteristics of water temperature stratification. The multi-level withdrawal generates a strong velocity zone and a strong temperature gradient zone near the intake, enhancing the overall stratification strength and surface water temperature and increasing the thermocline thickness but weakening its strength. In contrast, internal weir withdrawal accelerates the downward movement of the thermocline and strengthens it but reduces the thermocline thickness. As the withdrawal depth increases, the surface water temperature gradually increases, the thermocline position moves downward, and the duration of strong stratification extends. Different selective withdrawal schemes should be matched with different water demands, based on water quality health and ecological temperature requirements, we provided selective withdrawal strategy recommendations for large monomictic reservoirs, considering the thermal response characteristics of each withdrawal scheme, including changes in temperature structure and outflow temperature characteristics. The findings of this study provide a theoretical basis and technical support for reservoir optimal management and the design of selective withdrawal engineering.

Future increase in compound soil drought-heat extremes exacerbated by vegetation greening

Compound soil drought and heat extremes are expected to occur more frequently with global warming, causing wide-ranging socio-ecological repercussions. Vegetation modulates air temperature and soil moisture through biophysical processes, thereby influencing the occurrence of such extremes. Global vegetation cover is broadly expected to increase under climate change, but it remains unclear whether vegetation greening will alleviate or aggravate future increases in compound soil drought-heat events. Here, using a suite of state-of-the-art model simulations, we show that the projected vegetation greening will increase the frequency of global compound soil drought-heat events, equivalent to 12-21% of the total increment at the end of 21st century. This increase is predominantly driven by reduced albedo and enhanced transpiration associated with increased leaf area. Although greening-induced transpiration enhancement has counteracting cooling and drying effects, the excessive water loss in the early growing season can lead to later soil moisture deficits, amplifying compound soil drought-heat extremes during the subsequent warm season. These changes are most pronounced in northern high latitudes and are dominated by the warming effect of CO2. Our study highlights the necessity of integrating vegetation biophysical effects into mitigation and adaptation strategies for addressing compound climate risks.

Predicting the potential distribution of Astragali Radix in China under climate change adopting the MaxEnt model

Introduction

Astragali Radix is the dried root of Astragalus mongoliae or Astragalus membranaceus, a leguminous plant. Since ancient times, Astragali Radix has been widely used in Chinese traditional Chinese medicine. As people become more health-conscious, the market demand for Astragali Radix grows and its popularity is increasing in the international market. As an important medicinal plant, the growth of Astragali Radix is strongly influenced by environmental conditions. In order to meet the market demand for high quality Astragali Radix herbs, it is necessary to search and find areas suitable for the growth of Astragali Radix.

Methods

In this study, we assessed the potential impacts of climate change on the distribution of the Chinese medicinal plant Astragali Radix using the maximum entropy (MaxEnt) model in combination with a geographic information system(GIS). Distribution data and environmental variables were analyzed to predict suitable areas for Astragali Radix under the SSP126, SSP245 and SSP585 scenario for current and future (2041-2060, 2061-2080, 2081-2100). Jackknife is used to assess the importance of environmental variables, and environmental variables with a model contribution greater than 5% were considered to be the main drivers.

Results

The results showed that the current area of suitable area for Astragali Radix is 188.41 km2, and the three climate scenarios show an increasing trend in the future, with a maximum of 212.70 km2. North China has always been the main suitable area, while the area of suitable area in Southwest China is decreasing, and Xinjiang will be developed as a new suitable area in the future. Annual precipitation (41.6%), elevation (15.9%), topsoil calcium carbonate (14.8%), annual mean temperature (8.3%), precipitation seasonality (8%) and topsoil pH (6%) contributed more to the model and were the main environmental influences on the distribution of Astragali Radix. In addition, the centroids of the suitable areas shifted northward under all three climate scenarios, indicating a migratory response to global warming.

Discussion

Our study found that suitable area of Astragali Radix has been expanding for most of the time in each period of the three climate scenarios compared with the current situation. In the future, humans can focus on enhancing the cultivation techniques of Astragali Radix in these suitable areas. This study provide a scientific basis for the development of planting strategies and spatial distribution management of Astragali Radix. It helps to optimize the selection of planting areas and resource conservation of Chinese herbs.

Declined nutrients stability shaped by water residence times in lakes and reservoirs under climate change

Water quality stability in lakes and reservoirs is essential for drinking water safety and ecosystem health, especially given the frequent occurrence of extreme climate events. However, the relationship between water quality stability and water residence time (WRT) has not been well elucidated. In this study, we explored the relationship based on nitrogen (N) and phosphorus (P) concentrations data in 11 lakes and 49 reservoirs in the Yangtze-Huaihe River basin from 2010 to 2022. Additionally, we examined the effects of hydrometeorological characteristics, the geomorphology of water bodies and catchments, and land use on the WRT, establishing a link between climate change and the stability of N and P in these water bodies. The results showed that a significant correlation between the stability of N and P in lakes and reservoirs and their WRT. The longer WRT tends to coincide with decreased stability and higher nutrient concentrations. Hydrometeorological factors are the primary factors on the WRT, with precipitation exerting the greatest effect, particularly under extreme drought. In recent years, extreme climatic events have intensified the fluctuations of WRT, resulting in a renewed increase in N and P concentrations and deterioration in stability. These findings highlight the importance of incorporating meteorological and hydrological factors alongside reinforcing ecological restoration into lake and reservoir management strategies, and providing a scientific basis for future efforts aimed at enhancing lake and reservoir water quality stability and safeguarding aquatic ecosystems.

Protists and fungi: Reinforcing urban soil ecological functions against flash droughts

Rising instances of flash droughts are contributing to notable variability in soil moisture across terrestrial ecosystems. These phenomena challenge urban ecosystem services, yet the reaction of soil ecological functions (SEFs) to such events is poorly understood. This study investigates the responses of SEFs (about nutrient metabolism capacity and potential) and the microbiome under two specific scenarios: a flooding-drought sequence and a direct drought condition. Using quantitative microbial element cycling analysis, high-throughput sequencing, and enzyme activity measurements, we found that unlike in forests, the microbial composition in urban soils remained unchanged during flash drought conditions. However, SEFs were affected in both settings. Correlation analysis and Mantel test showed that forest soils exhibited more complex interactions among soil moisture, properties, and microbial communities. Positive linear correlation revealed that bacteria were the sole drivers of SEFs. Interestingly, while multi-threshold results suggested bacterial α diversity impeded the maximization of SEFs in urban soils, fungi and protists had a beneficial impact. Cross-domain network of urban soils had higher number of nodes and edges, but lower average degree and robustness than forest soils. Mantel test revealed that fungi and protist had significant correlations with bacterial composition in forest soils, but not in urban soils. In the urban network, the degree and eigenvector centrality of bacterial, fungal and protistan ASVs were significantly lower compared to those in the forest. These results suggest that the lower robustness of the microbial network in urban soils is attributed to limited interactions among fungi, consumer protists, and bacteria, contributing to the failure of microbial-driven ecological functions. Overall, our findings emphasize the critical role of fungi and protists in shielding urban soils from drought-induced disturbances and in enhancing the resistance of urban ecological functions amidst environmental changes.

Dynamic evolution characteristics and hazard assessment of compound drought/waterlogging and low temperature events for maize

Hazard assessment is fundamental in the field of disaster risk management. With the increase in global warming, compound water and temperature events have become more frequent. Current research lacks risk assessments of low temperatures and their compound events, necessitating relevant hazard assessment work to improve the accuracy and diversity of maize disaster prevention and mitigation strategies. This study comparatively analyzed the dynamic evolution characteristics and hazards of compound drought/waterlogging and low temperature events (CDLEs and CWLEs) for maize in the Songliao Plain during different growth periods from 1981 to 2020. First, composite drought/waterlogging and low temperature magnitude indices (CDLMI and CWLMI) were constructed to quantify the intensity of CDLEs and CWLEs by fitting non-exceedance probabilities. Next, static and dynamic hazard assessment models were developed by fitting probability density and cumulative probability density curves to CDLMI and CWLMI. The results showed that the correlations between SPRI and LTI across different decades were mainly negative during the three growth periods. The hazard ratings for both CDLEs and CWLEs were relatively high in the northern part of the study area, consistent with the higher occurrence, duration, and severity of both CDLEs and CWLEs at higher latitudes. Relative to 2001-2010, the center of gravity of hazard shifted southward for CDLEs and northward for CWLEs in 2011-2020. The mean duration, frequency, and hazard were generally higher for CWLEs, but CDLEs were associated with more severe maize yield reductions. This study provides new insights into compound disaster risk assessment, and the research methodology can be generalized to other agricultural growing areas to promote sustainable development of agricultural systems and food security.

Global synthesis indicates widespread occurrence of shifting baseline syndrome

As environmental degradation continues at local, regional, and global levels, people's accepted norms for natural environmental conditions are likely to decline. This phenomenon, known as shifting baseline syndrome (SBS), is increasingly recognized as a likely major obstacle to addressing global environmental challenges. However, the prevalence of SBS remains uncertain. We conducted an extensive systematic review, synthesizing existing research on people's perceived environmental baselines. Our analysis, based on 73 case studies, suggests that SBS is a widespread global phenomenon, occurring across diverse socioeconomic, environmental, and cultural settings. We observed that younger individuals tend to hold lower environmental baselines across various environmental contexts, including climate change, natural resource depletion, biodiversity loss, and pollution. An upward shift in perceived environmental baselines among younger generations was rarely observed. These results underscore the challenge that SBS poses when policy and management responses to environmental degradation are influenced by perceived natural environmental norms.

The effect of heavy precipitation on the leaching of heavy metals from tropical coastal legacy tailings

The continued growth in demand for mineral resources has led to a large amount of mining wastes, which is a major challenge in the context of carbon neutrality and climate change. In this study, runoff migration, batch leaching, and column experiments were used to investigate the short-, medium-, and long-term leaching of heavy metals from legacy tailings, respectively; the cumulative metal release kinetic equations were established, and the long-term effects of tailings leaching were verified by HYDRUS-1D. In runoff migration experiments, surface dissolution of tailings and the co-migration of adsorbed soil particles by erosion were the main carriers in the early stages of leachate formation (Mn ∼ 65 mg/L and SO42- up to 2697.2 mg/L). Batch leaching tests showed that the concentration of heavy metals in soil leached by acid rain were 0.1 ∼ 22.0 μg/L for Cr, 0.7 ∼ 26.0 μg/L for Cu, 4.8 ∼ 5646.0 μg/L for Mn, 0.3 ∼ 232.4 μg/L for Ni, and 1.3 ∼ 448.0 μg/L for Zn. The results of column experiments indicated that some soluble components and metals with high mobility showed a significant decreasing trend at cumulative L/S ≤ 2. Additionally, the metals have higher leaching rates under TCLP conditions, as shown by Mn > Co > Zn > Cd > Ni > Cu > Pb > Cr. The fitting results of Langmuir equation were closer to the cumulative release of metals in the real case, and the release amounts of Mn, Zn, Co, and Ni were higher with 55, 5.84, 2.66, and 2.51 mg/kg, respectively. The water flow within tailings affects the spatial distribution of metals, which mainly exist in relatively stable chemical fractions (F3 + F4 + F5 > 90 %) after leaching. Numerical simulation verified that Mn in leachate has reached 8 mg/L at a scale of up to 100 years. The research results are expected to provide technical basis for realizing the resource utilization of tailings in the future.

An unprecedented fall drought drives Dust Bowl-like losses associated with La Niña events in US wheat production

Unprecedented precipitation deficits in the 2022-2023 growing season across the primary wheat-producing region in the United States caused delays in winter wheat emergence and poor crop growth. Using an integrated approach, we quantitatively unraveled a 37% reduction in wheat production as being attributable to both per-harvested acre yield loss and severe crop abandonment, reminiscent of the Dust Bowl years in the 1930s. We used random forest machine learning and game theory analytics to show that the main driver of yield loss was spring drought, whereas fall drought dominated abandonment rates. Furthermore, results revealed, across the US winter wheat belt, the La Niña phase of the El Niño Southern Oscillation (ENSO), increased abandonment rates compared to the El Niño phase. These findings underscore the necessity of simultaneously addressing crop abandonment and yield decline to stabilize wheat production amid extreme climatic conditions and provide a holistic understanding of global-scale ENSO dynamics on wheat production.

Monthly climate prediction using deep convolutional neural network and long short-term memory

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951-31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy than other compared models. The hybrid CNN-LSTM model significantly reduces the forecasting error compared to the models for the one month time step ahead. For instance, the RMSE values of the ANN, RNN, LSTM, CNN, and CNN-LSTM models for monthly average atmospheric temperature in the forecasting stage are 2.0669, 1.4416, 1.3482, 0.8015 and 0.6292 °C, respectively. The findings of climate simulations shows the potential of CNN-LSTM models to improve climate forecasting. Climate prediction will contribute to meteorological disaster prevention and reduction, as well as flood control and drought resistance.

Extended MOF-74-Type Variant with an Azine Linkage: Efficient Direct Air Capture and One-Pot Synthesis

Direct air capture (DAC) shows considerable promise for the effective removal of CO2; however, materials applicable to DAC are lacking. Among metal-organic framework (MOF) adsorbents, diamine-Mg2(dobpdc) (dobpdc4- = 4,4-dioxidobiphenyl-3,3'-dicarboxylate) effectively removes low-pressure CO2, but the synthesis of the organic ligand requires high temperature, high pressure, and a toxic solvent. Besides, it is necessary to isolate the ligand for utilization in the synthesis of the framework. In this study, we synthesized a new variant of extended MOF-74-type frameworks, M2(hob) (M = Mg2+, Co2+, Ni2+, and Zn2+; hob4- = 5,5'-(hydrazine-1,2-diylidenebis(methanylylidene))bis(2-oxidobenzoate)), constructed from an azine-bonded organic ligand obtained through a facile condensation reaction at room temperature. Functionalization of Mg2(hob) with N-methylethylenediamine, N-ethylethylenediamine, and N,N'-dimethylethylenediamine (mmen) enables strong interactions with low-pressure CO2, resulting in top-tier adsorption capacities of 2.60, 2.49, and 2.91 mmol g-1 at 400 ppm of CO2, respectively. Under humid conditions, the CO2 capacity was higher than under dry conditions due to the presence of water molecules that aid in the formation of bicarbonate species. A composite material combining mmen-Mg2(hob) and polyvinylidene fluoride, a hydrophobic polymer, retained its excellent adsorption performance even after 7 days of exposure to 40% relative humidity. In addition, the one-pot synthesis of Mg2(hob) from a mixture of the corresponding monomers is achieved without separate ligand synthesis steps; thus, this framework is suitable for facile large-scale production. This work underscores that the newly synthesized Mg2(hob) and its composites demonstrate significant potential for DAC applications.

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

Temperature fluctuation in soil alters the nanoplastic sensitivity in wheat

Despite the wide acknowledgment that plastic pollution and global warming have become serious agricultural concerns, their combined impact on crop growth remains poorly understood. Given the unabated megatrend, a simulated soil warming (SWT, +4 °C) microcosm experiment was carried out to provide a better understanding of the effects of temperature fluctuations on wheat seedlings exposed to nanoplastics (NPs, 1 g L-1 61.71 ± 0.31 nm polystyrene). It was documented that SWT induced oxidative stress in wheat seedlings grown in NPs-contaminated soil, with an 85.56 % increase in root activity, while decreasing plant height, fresh weight, and leaf area by 8.72 %, 47.68 %, and 15.04 % respectively. The SWT also resulted in reduced photosynthetic electron-transfer reaction and Calvin-Benson cycle in NPs-treated plants. Under NPs, SWT stimulated the tricarboxylic acid (TCA) metabolism and bio-oxidation process. The decrease in photosynthesis and the increase in respiration resulted in an 11.94 % decrease in net photosynthetic rate (Pn). These results indicated the complicated interplay between climate change and nanoplastic pollution in crop growth and underscored the potential risk of nanoplastic pollution on crop production in the future climate.

Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash-Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling.

California's 2023 snow deluge: Contextualizing an extreme snow year against future climate change

The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term "snow deluge" and use anomalously high snowpack in California's Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California's 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges-which we define as the 20 y April 1 SWE event-are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation.

Declined terrestrial ecosystem resilience

Terrestrial ecosystem resilience is crucial for maintaining the structural and functional stability of ecosystems following disturbances. However, changes in resilience over the past few decades and the risk of future resilience loss under ongoing climate change are unclear. Here, we identified resilience trends using two remotely sensed vegetation indices, analyzed the relative importance of potential driving factors to resilience changes, and finally assessed the risk of future resilience loss based on the output data of eight models from CMIP6. The results revealed that more than 60% of the ecosystems experienced a conversion from an increased trend to a declined trend in resilience. Attribution analysis showed that the most important driving factors of declined resilience varied regionally. The declined trends in resilience were associated with increased precipitation variability in the tropics, decreased vegetation cover in arid region, increased temperature variability in temperate regions, and increased average temperature in cold regions. CMIP6 reveals that terrestrial ecosystems under SPP585 are expected to experience more intense declines in resilience than those under SSP126 and SSP245, particularly in cold regions. These results highlight the risk of continued degradation of ecosystem resilience in the future and the urgency of climate mitigation actions.

Fungi beyond limits: The agricultural promise of extremophiles

Global climate changes threaten food security, necessitating urgent measures to enhance agricultural productivity and expand it into areas less for agronomy. This challenge is crucial in achieving Sustainable Development Goal 2 (Zero Hunger). Plant growth-promoting microorganisms (PGPM), bacteria and fungi, emerge as a promising solution to mitigate the impact of climate extremes on agriculture. The concept of the plant holobiont, encompassing the plant host and its symbiotic microbiota, underscores the intricate relationships with a diverse microbial community. PGPM, residing in the rhizosphere, phyllosphere, and endosphere, play vital roles in nutrient solubilization, nitrogen fixation, and biocontrol of pathogens. Novel ecological functions, including epigenetic modifications and suppression of virulence genes, extend our understanding of PGPM strategies. The diverse roles of PGPM as biofertilizers, biocontrollers, biomodulators, and more contribute to sustainable agriculture and environmental resilience. Despite fungi's remarkable plant growth-promoting functions, their potential is often overshadowed compared to bacteria. Arbuscular mycorrhizal fungi (AMF) form a mutualistic symbiosis with many terrestrial plants, enhancing plant nutrition, growth, and stress resistance. Other fungi, including filamentous, yeasts, and polymorphic, from endophytic, to saprophytic, offer unique attributes such as ubiquity, morphology, and endurance in harsh environments, positioning them as exceptional plant growth-promoting fungi (PGPF). Crops frequently face abiotic stresses like salinity, drought, high UV doses and extreme temperatures. Some extremotolerant fungi, including strains from genera like Trichoderma, Penicillium, Fusarium, and others, have been studied for their beneficial interactions with plants. Presented examples of their capabilities in alleviating salinity, drought, and other stresses underscore their potential applications in agriculture. In this context, extremotolerant and extremophilic fungi populating extreme natural environments are muchless investigated. They represent both new challenges and opportunities. As the global climate evolves, understanding and harnessing the intricate mechanisms of fungal-plant interactions, especially in extreme environments, is paramount for developing effective and safe plant probiotics and using fungi as biocontrollers against phytopathogens. Thorough assessments, comprehensive methodologies, and a cautious approach are crucial for leveraging the benefits of extremophilic fungi in the changing landscape of global agriculture, ensuring food security in the face of climate challenges.

Precipitation variation: a key factor regulating plant diversity in semi-arid livestock grazing lands

Livestock presence impacts plant biodiversity (species richness) in grassland ecosystems, yet extent and direction of grazing impacts on biodiversity vary greatly across inter-annual periods. In this study, an 8-year (2014-2021) grazing gradient experiment with sheep was conducted in a semi-arid grassland to investigate the impact of grazing under different precipitation variability on biodiversity. The results suggest no direct impact of grazing on species richness in semi-arid Stipa grassland. However, increased grazing indirectly enhanced species richness by elevating community dominance (increasing the sheltering effect of Stipa grass). Importantly, intensified grazing also regulates excessive community biomass resulting from increased inter-annual wetness (SPEI), amplifying the positive influence of annual humidity index on species richness. Lastly, we emphasize that, in water-constrained grassland ecosystems, intra-annual precipitation variability (PCI) was the most crucial factor driving species richness. Therefore, the water-heat synchrony during the growing season may alleviate physiological constraints on plants, significantly enhancing species richness as a result of multifactorial interactions. Our study provides strong evidence for how to regulate grazing intensity to increase biodiversity under future variable climate patterns. We suggest adapting grazing intensity according to local climate variability to achieve grassland biodiversity conservation.

Research Progress on Plant Responses to Stress Combinations in the Context of Climate Change

In the context of climate change, the frequency and intensity of extreme weather events are increasing, environmental pollution and global warming are exacerbated by anthropogenic activities, and plants will experience a more complex and variable environment of stress combinations. Research on plant responses to stress combinations is crucial for the development and utilization of climate-adaptive plants. Recently, the concept of stress combinations has been expanded from simple to multifactorial stress combinations (MFSCs). Researchers have realized the complexity and necessity of stress combination research and have extensively employed composite gradient methods, multi-omics techniques, and interdisciplinary approaches to integrate laboratory and field experiments. Researchers have studied the response mechanisms of plant reactive oxygen species (ROS), phytohormones, transcription factors (TFs), and other response mechanisms under stress combinations and reached some generalized conclusions. In this article, we focus on the research progress and methodological dynamics of plant responses to stress combinations and propose key scientific questions that are crucial to address, in the context of plant responses to stress assemblages, conserving biodiversity, and ensuring food security. We can enhance the search for universal pathways, identify targets for stress combinations, explore adaptive genetic responses, and leverage high-technology research. This is in pursuit of cultivating plants with greater tolerance to stress combinations and enabling their adaptation to and mitigation of the impacts of climate change.

Exploring changes of precipitation extremes under climate change through global variable-resolution modeling

Understanding the responses of precipitation extremes to global climate change remains limited owing to their poor representations in models and complicated interactions with multi-scale systems. Here we take the record-breaking precipitation over China in 2021 as an example, and study its changes under three different climate scenarios through a developed pseudo-global-warming (PGW) experimental framework with 60-3 km variable-resolution global ensemble modeling. Compared to the present climate, the precipitation extreme under a warmer (cooler) climate increased (decreased) in intensity, coverage, and total amount at a range of 24.3%-37.8% (18.7%-56.1%). With the help of the proposed PGW experimental framework, we further reveal the impacts of the multi-scale system interactions in climate change on the precipitation extreme. Under the warmer climate, large-scale water vapor transport converged from double typhoons and the subtropical high marched into central China, enhancing the convective energy and instability on the leading edge of the transport belt. As a result, the mesoscale convective system (MCS) that directly contributed to the precipitation extreme became stronger than that in the present climate. On the contrary, the cooler climate displayed opposite changing characteristics relative to the warmer climate, ranging from the large-scale systems to local environments and to the MCS. In summary, our study provides a promising approach to scientifically assess the response of precipitation extremes to climate change, making it feasible to perform ensemble simulations while investigating the multi-scale system interactions over the globe.

The impact of extreme heat on lake warming in China

Global lake ecosystems are subjected to an increased occurrence of heat extremes, yet their impact on lake warming remains poorly understood. In this study, we employed a hybrid physically-based/statistical model to assess the contribution of heat extremes to variations in surface water temperature of 2260 lakes in China from 1985 to 2022. Our study indicates that heat extremes are increasing at a rate of about 2.08 days/decade and an intensity of about 0.03 °C/ day·decade in China. The warming rate of lake surface water temperature decreases from 0.16 °C/decade to 0.13 °C/decade after removing heat extremes. Heat extremes exert a considerable influence on long-term lake surface temperature changes, contributing 36.5% of the warming trends within the studied lakes. Given the important influence of heat extremes on the mean warming of lake surface waters, it is imperative that they are adequately accounted for in climate impact studies.

A novel framework for investigating the mechanisms of climate change and anthropogenic activities on the evolution of hydrological drought

Climate change and anthropogenic activity are the primary drivers of water cycle changes. Hydrological droughts are caused by a shortage of surface and/or groundwater resources caused by climate change and/or anthropogenic activity. Existing hydrological models have primarily focused on simulating natural water cycle processes, while limited research has investigated the influence of anthropogenic activities on water cycle processes. This study proposes a novel framework that integrates a distributed hydrological model and an attribution analysis method to assess the impacts of climate change and anthropogenic activities on hydrological drought The distributed dualistic water cycle model was applied to the Fuhe River Basin (FRB), and it generated a Nash-Sutcliffe efficiency coefficient > 0.85 with a relative error of <5 %. Excluding the year with extreme drought conditions, our analysis revealed that climate change negatively impacted the average drought duration (-105.5 %) and intensity (-23.6 %) because of increasing precipitation. However, anthropogenic activities continued to contribute positively to the drought, accounting for 5.5 % and 123.6 % of the average drought duration and intensity, respectively, because of increased water consumption. When accounting for extreme drought years, our results suggested that climate change has contributed negatively to the average duration of drought (-113.2 %) but positively to its intensity (7.8 %). Further, we found that anthropogenic activities contributed positively to both the average drought duration and intensity (13.2 % and 92.2 %, respectively). While climate change can potentially mitigate hydrological drought in the FRB by boosting precipitation levels, its overall effect may exacerbate drought through the amplification of extreme climate events resulting from global climate change. Therefore, greater attention should be paid to the effects of extreme drought.

Spatiotemporal change in ecological quality and its influencing factors in the Dongjiangyuan region, China

It is of great significance for regional ecological protection and sustainable development to quickly and effectively assess and monitor regional ecological quality and identify the factors that affect ecological quality. This paper constructs the Remote Sensing Ecological Index (RSEI) based on the Google Earth Engine (GEE) platform to analyze the spatial and temporal evolution of ecological quality in the Dongjiangyuan region from 2000 to 2020. An ecological quality trend analysis was conducted through the Theil-Sen median and Mann-Kendall tests, and the influencing factors were analyzed by using a geographically weighted regression (GWR) model. The results show that (1) the RSEI distribution can be divided into the spatiotemporal characteristics of "three highs and two lows," and the proportion of good and excellent RSEIs reached 70.78% in 2020. (2) The area with improved ecological quality covered 17.26% of the study area, while the area of degradation spanned 6.81%. The area with improved ecological quality was larger than that with degraded ecological quality because of the implementation of ecological restoration measures. (3) The global Moran's I index gradually decreased from 0.638 in 2000 to 0.478 in 2020, showing that the spatial aggregation of the RSEI became fragmented in the central and northern regions. (4) Both slope and distance from roads had positive effects on the RSEI, while population density and night-time light had negative effects on the RSEI. Precipitation and temperature had negative effects in most areas, especially in the southeastern study area. The long-term spatiotemporal assessment of ecological quality can not only help the construction and sustainable development of the region but also have reference significance for regional ecological management in China.