Heatwaves are among the most impactful climate extremes, yet their detection and quantification depend strongly on methodological choices, particularly the definition of the baseline climate. In this study, we conduct a global analysis of atmospheric heatwaves from 1981 to 2023 using ERA5-Land reanalysis data to evaluate how baseline selection influences the characterization of heatwave frequency, duration, and intensity. We compare two commonly used approaches: a traditional fixed baseline (1951-1980) and a running baseline defined by the preceding 30-year climatology. In addition, we introduce a ratio-based intensity metric that normalizes temperature anomalies by local background variability to better account for differences in climatic regimes. Results show that baseline choice substantially affects the apparent magnitude of heatwave trends. Using a fixed baseline, global heatwave frequency, duration, and intensity exhibit strong and widespread increasing trends. In contrast, the running baseline attenuates these trends by adjusting for long-term warming, highlighting deviations relative to evolving climatic conditions rather than historical climatology. Despite these differences in trend magnitude, the overall spatial distribution and frequency patterns of heatwaves remain broadly consistent between the two approaches. When heatwave intensity is expressed relative to local variability, the pronounced latitudinal gradient observed with traditional absolute anomalies becomes less distinct, suggesting that relative thermal exposure may be more evenly distributed across climatic zones than previously inferred. Running baselines and variability-normalized metrics provide complementary perspectives for interpreting extremes under a non-stationary climate. These findings highlight the importance of methodological transparency when comparing heatwave studies and assessing ecological and societal risks associated with extreme heat.

Despite growing evidence that heatwaves and physical mixing jointly regulate phytoplankton dynamics, their interactive effects during early-season warming events remain poorly understood. To address this gap, we conducted a mesocosm experiment simulating a short-term spring heatwave under contrasting mixing regimes. Our results showed that phytoplankton growth rates declined sharply during the first two days of the heatwave (mean: − 0.38 d−1), indicating that acute thermal stress imposed immediate constraints on population growth. In the absence of mixing, heatwave conditions led to a sustained decline in cell concentration. In contrast, physical mixing significantly alleviated this suppression, increasing growth rates over the 0–6 day period from − 0.33 to − 0.12 d−1. Multivariate regression further showed that the accumulation of total biovolume was primarily promoted by phosphorus availability under intermediate mixing conditions, while being negatively related to surface warming intensity. These findings suggest that physical mixing acts as a critical buffering mechanism against heatwave-induced suppression of phytoplankton growth by weakening thermal stratification and promoting vertical nutrient exchange. Overall, this study highlights the importance of considering interactions between physical mixing and thermal stress, particularly within a seasonal context, when assessing ecosystem responses to increasingly frequent heatwaves under climate change.

Greenhouse gas (GHG) emissions from freshwater ecosystems contribute significantly to global carbon budgets, yet they remain poorly constrained due to limited high-frequency measurements. We tested a low-cost, high-frequency GHG measurement system in a long-term mesocosm experiment in Lemming, Denmark, over a 7-month period, focusing on CO2 and CH4 fluxes. We deployed a methodology for calculating CH4 diffusive fluxes using high-frequency sensor data and tested the effects of sampling intervals on emission upscaling. Our findings reveal substantial temporal variability in GHG emissions, particularly for CH4, with ebullitive fluxes dominating and exhibiting large variation. Pronounced diurnal fluctuations were also observed for CO2 and diffusive CH4 fluxes, whereas ebullitive CH4 emissions showed no significant diurnal pattern. Relying solely on daytime measurements led to a significant overestimation of overall CO2 fluxes and CH4 diffusive fluxes. Resampling data at lower frequency showed that reduced sampling frequency leads to an underestimation of total emissions, especially for CH4 ebullitive fluxes. Increasing the sampling interval from daily to monthly markedly increased uncertainty, while weekly sampling better captured overall GHG flux patterns and reduced the uncertainty compared with more infrequent sampling. These results underscore the value of high-frequency GHG measurements in capturing both diurnal and seasonal variations, improving the accuracy of flux estimates, and reducing uncertainties in upscaling emission. We emphasize the need to optimize sampling intervals and incorporate diurnal cycle measurements to enhance the accuracy and reliability of freshwater GHG assessments.

Dissolved oxygen concentrations regulate diverse aquatic ecosystem processes from organismal respiration to biogeochemical cycles, yet concentrations have declined markedly in flowing and standing waters in Europe and North America during recent decades. While atmospheric warming partially explains this deoxygenation, land-use changes such as farming and urbanization have also intensified depletion of dissolved oxygen, which suggests that improved land management could prevent widespread oxygen loss from freshwaters. Here we analysed monthly dissolved oxygen concentration, percent oxygen saturation and coeval environmental variables in 972 river and 354 lake sites in China from January 2005 to December 2022 to assess how extensive restoration efforts to control nutrient pollution correspond with changes in freshwater oxygen levels. Despite surface water warming of +1.2 degrees C per decade, dissolved oxygen concentration and percent saturation increased markedly in rivers and lakes resulting in a rapid and substantial decrease in the incidence of hypoxia and anoxia. Long-term increases in dissolved oxygen were correlated negatively with biochemical oxygen demand, but not with phytoplankton abundance. We conclude that effective nutrient management at the sub-continental scale can reverse widespread freshwater deoxygenation, thereby improving fisheries, biodiversity and ecosystem health, while reducing the risk of exceeding critical planetary boundaries even in the face of global warming.

Shallow lakes are hotspots for carbon dioxide (CO2) and methane (CH4) emissions, and they are highly sensitive to temperature variations. Recently, heatwaves that occur in winter have become more frequent and prolonged compared to other seasons. However, the impact of winter heatwaves on CO2 and CH4 emissions from shallow lakes remains unclear. To address this gap, we conducted a mesocosm experiment during winter, comparing an unheated control group with a heated group, where surface water temperature was maintained at a constant 8 degrees C higher than the control, based on meteorological data from Lake Taihu, China. Our results showed that the first three days of the heatwave induced pronounced pulses of CO2 and CH4 fluxes, with CH4 contributing disproportionately to total CO2-equivalent emissions. CO2 fluxes were primarily regulated by water temperature and chlorophyll a concentration, whereas CH4 fluxes were strongly associated with dissolved carbon concentrations, highlighting the differential sensitivity of carbon pathways to episodic thermal disturbances. Observed changes in dissolved oxygen and chlorophyll a concentration suggested potential cascading effects on microbial communities and trophic interactions, indicating that winter heatwaves can influence both biogeochemical processes and ecosystem structure. Our results highlight the importance of high-resolution winter monitoring and the incorporation of episodic warming events in predictive models of lake carbon dynamics.

Monitoring heavy metals and other pollution in drinking water sources is protecting public health, ensuring social stability, and promoting sustainable ecological development. Traditional fixed-point sampling approaches suffers from insufficient spatial coverage, weak remote sensing signals for heavy metals, and considerable challenges in achieving robust long-term inversion. To address these limitations, this study develops a multi-dimensional time-series feature fusion model (CNN-BiLSTM-Attention) for water quality parameter inversion. This model integrates a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism to enable monthly inversion of eight indicators, including dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), fluoride (F−), permanganate (CODMn), zinc (Zn), selenium (Se), and mercury (Hg). This paper investigates the spatiotemporal evolution of both heavy metal and non-heavy metal water quality parameters during normal, dry and flood seasons. A partial least squares structural equation model (PLS-SEM) is constructed to quantify the direct and indirect effects of climate, human activities, and hydrological characteristics on the dynamic changes of the eight water quality parameters, thereby identifying their dominant drivers. The results showed that the CNN-BiLSTM-Attention model substantially improves inversion performance by capturing nonlinear spatiotemporal relationships among water quality parameters (mean R2 = 0.791), representing a 32.05% increase over the conventional CNN-LSTM model (mean R2 = 0.599). From 2018 to 2024, the overall water quality exhibited an improving trend, with notable decreases in Hg, Zn, Se, and COD concentrations (maximum reduction of 68.3%), while DO and NH3-N increased slightly. Spatially, pollutants accumulated at the reservoir edge and tributary confluences during the dry season and migrated toward the reservoir center in the flood season. Notably, the low-DO area largely overlapped with the peak CODMn and COD region in the southwestern tributary of the reservoir. PLS-SEM revealed that the organic pollution load (CODMn, COD) serves as the central hub driving heavy metal evolution, exhibiting a strong positive correlation with heavy metal indicators (Hg, Se; β = 0.946, P < 0.001). Human activities exert a direct negative effect on oxygen-consuming pollutants (DO, NH₃-N; β = −0.359, P < 0.01) and indirectly modulate heavy metal dynamics through the organic pollution load pathway (β = −0.432, P < 0.05). Climatic factors (primarily rainfall, β = 0.534, P < 0.1) and water temperature (β = −0.562, P < 0.05) constitute the dynamic basis of heavy metal evolution. The proposed time-series inversion and driving mechanism analysis method enables both temporal monitoring and attribution analysis of heavy metals and non-heavy metals in water bodies, providing reliable scientific support for water quality management in drinking water sources.

The extent of the differences between atmospheric and lake heatwaves remains unclear. Here we analysed daily surface water and air temperature data from 265 lakes worldwide (2000–2022) to compare heatwave trends, spatial distributions, and key differences. We find that lake heatwaves are more severe than atmospheric heatwaves, with longer accumulated heatwave days (29.7 days vs. 18.0 days), a shorter reoccurrence period (86.9 days vs. 121.4 days), and greater accumulated heat. Additionally, the frequency and total heatwave days have increased faster for lake heatwaves than for atmospheric heatwaves. When both types co-occur, heatwave severity intensifies. From a long-term perspective, reduced wind speed is the key driver of the differences between lake heatwaves and atmospheric heatwaves. Spatially, lake location is the primary determinant, followed by lake area and depth. Under a fixed-baseline high-emission scenario, by 2100, the difference is expected to diminish as air temperatures rise faster than water temperatures. Over the past two decades, lake heatwaves have become more intense than atmospheric ones due to declining wind speeds, which stabilise water column stratification and increase surface heating, according to analysis of daily surface water and air temperature from 265 lakes during 2000–2022.

The Eastern Lake Region is the most eutrophic in China and is most affected by human activities.In recent years,phytoplankton have proliferated in most lakes in the lake region,with the frequent occurrence of water blooms,and the driving mechanisms and spatial differences for long-term changes in the phytoplankton community of lakes at the regional scale remain unclear.Among them,Lake Taihu,Lake Hongze,and Lake Luoma are located in the Yangtze River Economic Zone and have important ecological functions such as storage,drinking water,and irrigation.They are greatly affected by human activities and are typical lakes in the Eastern Lake Region.We used hydro-meteorological data,physical and chemical index data,and phytoplankton biomass data from 2016 to 2021 to study the phytoplankton community changes in typical lakes in the Eastern Lake Region based on redundancy analysis and combined hierarchical partitioning and variance decomposition to identify the main drivers of phytoplankton community changes.The results showed that the long-term trends of climate background were generally consistent among typical lakes in the Eastern Lake Region,but their nutrients,phytoplankton community,and environmental driving factors were different.The dominant phytoplankton phyla and genera in Lake Taihu,Lake Hongze,and Lake Luoma were significantly different.The lake characteristic,mainly characterized by water depth,was the main driving factor that led to spatial differences in phytoplankton communities among typical lakes in different seasons.The explanatory rates of water depth in spring,summer,autumn,and winter were 46.32%,30.79%,26.92%,and 35.80%,respectively.However,the secondary driving factors had seasonal differences.Among them,in spring,the secondary driving factors were conductivity(13.48%)and total nitrogen(12.74%).In summer,the secondary driving factors were total phosphorus(19.02%)and conductivity(14.71%).In autumn,the secondary driving factors were total phosphorus(19.43%)and dissolved total nitrogen(15.86%).In winter,the secondary driving factors were total phosphorus(23.53%)and the daily minimum temperature(14.91%).Quantifying the contribution of different drivers was important for future lake eutrophication management and policy formulation.

Global warming and accompanying heatwaves are increasing because of global climate change and will result in stronger lake stratification and hypoxia. Meanwhile, climate warming also causes atmospheric stilling, a phenomenon of the significant decrease of surface wind speed, which reduces the water column mixing and exacerbates hypoxia in lake bottom water. However, the relative importance and compound impacts of heatwaves and atmospheric stilling on lake deoxygenation and hypoxia remain poorly understood, inhibiting our deep understanding of aquatic ecosystem response to global climate change. Here we use the long-term meteorological and hydrological observations, combined with high-frequency dissolved oxygen profile measurements to explore compound processes and their impact on deoxygenation in Lake Taihu, a large shallow lake. Our findings reveal a rapid increase in the frequency and duration of lake heatwaves and atmospheric stilling extremes in Lake Taihu from 1980 to 2023, with more frequent and prolonged compound heatwaves and stilling extremes (CHSEs). The linear increasing rates of heatwaves, stilling extremes and CHSEs duration are 14.8, 6.6, and 1.9 day/decade, respectively. Quadratic models outperform the linear fitting for heatwaves and CHSEs demonstrating accelerating trends. Hypoxia (DO <= 2.0 mg/L at 7:00 a.m.) occurred 14.1 % vs 11.8 %, 21.6 % vs 12.3 %, and 30.4 % vs 11.9 % of the time in bottom waters during heatwave vs non-heatwave periods, stilling extremes vs no-stilling periods, and CHSEs vs non-CHSEs, respectively, in the summer half year (May-October). Our results highlight that atmospheric stilling extremes drive stronger deoxygenation and hypoxia than heatwaves, while CHSEs exacerbate this trend in large shallow lake.

There is a long history of observation and monitoring at local scales with a focus on meteorological modulation of lakes, however, the features at the local scale are heavily influenced by large-scale atmospheric circulation. Harmful algal blooms (HABs) in large shallow eutrophic lakes are susceptible to local meteorological conditions, but their response to climate system variability is rarely considered. This study reveals that HABs in Lake Taihu, the third largest freshwater lake in subtropical China, are promoted by the weakened East Asian monsoon system in precursory early winter, but show no response in late winter with the low temperature unfavorable for algal metabolism. The weak early winter monsoon system is characterized as the northward-shifted polar front jet and eastward-tilted East Asian Trough. The accompanied anomalous descending motion decelerates surface winds over the middle to lower reaches of the Yangtze River including Taihu basin by strengthening atmospheric stratification and decreasing turbulent activity in the lower troposphere. The weakened surface winds reduce sediment resuspension and improve underwater light availability in Lake Taihu, providing favorable light condition for phytoplankton growth. It is indicated that the early-winter surface winds rather than simultaneous nutrient supply, play a dominant role on the interannual variability of algal biomass in Lake Taihu. The positive algal biomass anomaly in early winter tends to persist and promote HABs in the following warmer seasons. Our finding validates the interannual teleconnection between climate system and HAB variation in Lake Taihu, and provides insights on predicting HABs in subtropical East Asian shallow lakes. The algae in Lake Taihu are more susceptive to meteorological condition in early winter than in late winter Early winter monsoon system modulates algal biomass by impacting local wind speed and underwater light condition Extreme meteorological condition driven by remote climate variability could impact harmful algal blooms in East Asian shallow lakes

To meet the Sustainable Development Goal (SDG) target 6.1, China has undertaken significant initiatives to address the uneven distribution of water resources and to enhance water quality. Since 2000, China has invested heavily in the water infrastructure of numerous reservoirs, with a total storage capacity increase of 4.704 × 1011 m3 (an increase of 90.8%). These reservoirs have significantly enhanced the available freshwater resources for drinking water. Concurrently, efforts to improve water quality in lakes and reservoirs, facilitated by nationwide water quality monitoring, have been successful. As a result, an increasing lakes and reservoirs are designated as centralized drinking water sources (CDWSs) in China. Among the 3,441 CDWSs across all provinces, 40.8% are sourced from lakes and reservoirs, 32.6% from rivers, and 26.6% from groundwater in 2023. Notably, from 2016 to 2023, the percentage of lakes and reservoirs categorized as CDWSs has increased consistently across all 29 provinces. This progress has enabled 561.4 million urban residents to access improved drinking water sources in 2022, compared to 303.4 million in 2004. Our findings underscore the pivotal role of water infrastructure construction and water quality improvement jointly promoting lakes and reservoirs as vital drinking water sources. Nevertheless, the nationwide occurrence of algal blooms has surged by 113.7% from the 2000s to the 2010s , which is a considerable challenge to drinking water safety. Fortunately, algal blooms have been markedly alleviated in past four years. However, it is still crucial to acknowledge that lakes and reservoirs face the challenges of algal blooms, and associated toxic microcystin and odor compounds.

>Cyanobacteriabloomsandtheirsecondary hazards(cyanotoxins,tasteandodorcompounds)continuetoharmtheecologicalenvironmentof naturalandsemi-artificiallyregulatedwaterbodies intheworld,thusaffectingthesafetyofwater supplyandaquaticproductquality. The8thNational CyanobacteriaBloomForumwassuccessfullyheld onJuly14–16,2023,inTianjin,China.Theforum establishedanacademicexchangeplatformfor nearly300waterecologyexperts,reservoirmanagers,andaquaculturetechnicians.Thisspecialissue,“Scientific control of cyanobacterial blooms and their secondary hazards” in Journal of Oceanography and Limnology, presents a collection of 10 papers on the occurrence regularity, control strategy, and prevention and control technology of related secondary hazards(cyanotoxins,tasteandodorcompounds)of cyanobacteriabloomsintypicalnaturalwatersof Southeast Asian countries.

Global warming has been reported to enhance thermal stratification and decrease the mix-layer depth (MLD) in waters due to higher surface water temperatures, especially in summer. Previous studies were conducted for individual cases or specific periods. At present, there is a lack of global assessments on the influence of climate warming in different seasons on thermal stratification. The ECMWF Reanalysis v5 (ERA5) dataset was used to estimate the variability of water body mixing and its drivers in different seasons and regions. Results indicate that global warming could enhance thermal stratification and decrease the MLD globally in summer. Wind speed was the primary driver of MLD changes, followed by temperature. However, ice melt due to global warming enhanced the mixing in ice-covered waters in the Northern Hemisphere, and early ice melt led to early mixing. Ice depth was the primary driver of MLD changes in the Northern Hemisphere due to delayed ice formation and earlier melting, while wind speed was the primary driver in other regions or during ice-free seasons. The enhanced mixing due to earlier ice melt out in late winter and early spring could promote water circulation and nutrient turnover, and replenish dissolved oxygen in deep water, thereby promoting the maximum biomass of cyanobacteria and advance harmful algal blooms.

Polycyclic aromatic hydrocarbons (PAHs) are of great concern because they threaten primary productivity, but their specific effects on ecosystem functioning are scarce, hindering a comprehensive understanding of their ecological risks, especially in eutrophic waters. The present study was conducted by adding PAHs to four marine phytoplankton species and showed that naphthalene (Nap) and phenanthrene (Phe) induced both stimulatory and inhibitory effects (>50 %) on urea and NO3- uptake by phytoplankton species. In addition, the apparent stimulative effects (>50 %) for NH4+ were also observed. Overall, 38.9 % of the samples exhibited stimulation effects after 24 h exposure, which increased to 61.1 % after 96 h exposure. This suggested the existence of a lag period, during which a tolerant cell population could adapt to PAHs. Significant positive correlations (P < 0.01) between low and high concentrations of PAH individuals demonstrated that the mode of action for both pollutants on nitrogen uptake by phytoplankton was the same. Species-specific responses were also observed, with 19.0 % of Thalassiosira sp. and 24.0 % of Tetraselmis sp. exhibited inhibition effects greater than 50 %, while 40.9 % of Karlodinium veneficum and 27.3 % of Rhodomonas salina demonstrated stimulation effects exceeding 50 %, providing a unique perspective for exploring the harmful algal bloom of the mixotrophic K. veneficum, in addition to the original consideration of nutrients. The internal mechanisms may lie in differences in energy consumption between N-forms, exposure time and chemical concentrations, as well as morphological characteristics and biochemical structures of the species, which require further investigation.

Keywords: macrophyte, microalgae, aquatic habitat, habitat fragmentation, adaptation strategies, genetic diversity, antibiotics degradation, eutrophic lake

Phytoplankton communities are crucial components of aquatic ecosystems, and since they are highly interactive, they always form complex networks. Yet, our understanding of how interactive phytoplankton networks vary through time under changing environmental conditions is limited. Using a 29-year (339 months) long-term dataset on Lake Taihu, China, we constructed a temporal network comprising monthly sub-networks using "extended Local Similarity Analysis" and assessed how eutrophication, climate change, and restoration efforts influenced the temporal dynamics of network complexity and stability. The network architecture of phytoplankton showed strong dynamic changes with varying environments. Our results revealed cascading effects of eutrophication and climate change on phytoplankton network stability via changes in network complexity. The network stability of phytoplankton increased with average degree, modularity, and nestedness and decreased with connectance. Eutrophication (increasing nitrogen) stabilized the phytoplankton network, mainly by increasing its average degree, while climate change, i.e., warming and decreasing wind speed enhanced its stability by increasing the cohesion of phytoplankton communities directly and by decreasing the connectance of network indirectly. A remarkable shift and a major decrease in the temporal dynamics of phytoplankton network complexity (average degree, nestedness) and stability (robustness, persistence) were detected after 2007 when numerous eutrophication mitigation efforts (not all successful) were implemented, leading to simplified phytoplankton networks and reduced stability. Our findings provide new insights into the organization of phytoplankton networks under eutrophication (or re-oligotrophication) and climate change in subtropical shallow lakes.

Phytoplankton are primary producers in aquatic ecosystems and their diversity directly affects the community stability and primary productivity. However, the commonly used diversity indices (such as Shannon and Pielou indices) were originally derived from other fields rather than ecology and did not have a direct biological explanatory function. There is still a need to incorporate biological explanatory functions into diversity evaluation methods and theories to bridge the gap between phytoplankton biodiversity and biological characteristics. This study aimed to explicate the intrinsic distribution patterns of phytoplankton relative abundance and biomass. Our study demonstrated an exponential distribution pattern of phytoplankton relative abundance and biomass ranking through field investigations of 367 phytoplankton samples in China and microcosm experiments, respectively. Microcosm experiments illustrated that the linear distribution of the specific growth rate ranking resulted in an exponential distribution of the relative phytoplankton biomass ranking due to exponential growth patterns. Through mathematical deduction, it was found that the three indices a, k and N in the exponential distribution could be considered as the critical relative abundance of extinction, competition coefficient and the environmental taxa capacity, respectively. We found that a was positively correlated with Shannon index and Pielou index, k was negatively correlated with Shannon index, Pielou index and Chao1 index. In addition, N and Chao1 index were almost exactly the same. Our study obtained these indices based on the distribution pattern of phytoplankton, enabling a comprehensive analysis of the phytoplankton community and providing novel insights for further evaluating the health of aquatic ecosystems.