Long-term Changing Research Articles

Combining tree-ring growth and carbon isotope data enhances the understanding of climate sensitivity and physiological responses for Chinese fir in a common garden

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is one of China's most important tree species, and possible adverse factors affecting its growth and physiology is of particular concern for climate change adaptation. In this context, growth performance (BAI: basal area increment and MAXD: maximum density), climate sensitivity, and the relative contributions of climatic and physiological determinants to growth were evaluated across the 30 Chinese fir provenances using a combination of dendroclimatology and carbon isotope analysis (δ13C). Over the past 27 years, intrinsic water-use efficiency (iWUE) increased significantly by 12.71–33.56 %, while radial growth decreased (−63.38 % to −88.93 %). Strong growth decreases reflected increasing water stress due to climate warming, which was not offset by greater iWUE. A similar trend was observed in the theoretical gas exchange scenario as a response to increasing Ca as stomata opened and Ci increased proportionally to Ca. This study identified temperature and relative humidity as determinants of growth and physiology. However, meteorological factors (temperature, relative humidity, and sunshine hours) contributed much less to growth than physiological factors (iWUE). Variation in performance and climate sensitivity among populations significantly correlated with the mean annual temperature of the seed source origin. In general, seed sources from warmer and more humid climates in the center region grew faster and had higher iWUE. Provenances from drier climates had slower growth, higher wood density, and higher carbon isotope discrimination (Δ13C) compared to those from wetter climate conditions. Compared to provenances from cooler regions, the seed sources from warmer climates were less sensitive to temperature but more sensitive to sunshine hours for BAI and MAXD. These results contribute to a better understanding of the climate sensitivity and physiological responses of the Chinese fir provenances to long-term changing climate.

The importance of global synchronous observation for estimating oceanic chlorophyll-a

Over the last three decades, ocean color satellites have recorded continuous change in phytoplankton concentration, and in doing so, the satellites have helped marine remote sensing to become an emerging discipline. However, ocean color satellite has provided the state of global phytoplankton only around midday whereas phytoplankton have significant diurnal variation characteristics over an entire day-night cycle, which would lead to large estimation uncertainty in downstream products. A large global field observations show that the diurnal variation curves can be constructed as the sum of an exponential and a polynomial function. After correction with our diurnal variation curves, the intermission difference between MODISA and MODIST phytoplankton product could be decreased from 7.57% to 6%, which is critical to monitor the long-term changing trend with multi-mission data. When the satellite-recorded chlorophyll-a concentration is simply equal to a daily average value, it results in overestimating uncertainties of 16% and 54% for the northern and southern hemispheric oceans, respectively. Moreover, as a typical example of downstream product of the chlorophyll-a, the coverage of oligotrophic waters can oscillate 76% from day to night simulated from satellite images with our diurnal variation curves, further confirming the importance of global synchronous observation. The geostationary satellite might be an efficient regional tool for diurnal observation, but it must account for its limitations under the weak light conditions of early morning and late afternoon. In the real world, the effects of diurnal variations in ocean properties reach far beyond chlorophyll-a in the global oceans. We anticipate these quantitatively findings to be a starting point for quantitatively re-examining the proper application of ocean color products in scientific communication.

Long-term declines in chlorophyll a and variable phenology revealed by a 60-year estuarine plankton time series

Long-term ecological time series provide a unique perspective on the emergent properties of ecosystems. In aquatic systems, phytoplankton form the base of the food web and their biomass, measured as the concentration of the photosynthetic pigment chlorophyll a (chl a), is an indicator of ecosystem quality. We analyzed temporal trends in chl a from the Long-Term Plankton Time Series in Narragansett Bay, Rhode Island, USA, a temperate estuary experiencing long-term warming and changing anthropogenic nutrient inputs. Dynamic linear models were used to impute and model environmental variables (1959 to 2019) and chl a concentrations (1968 to 2019). A long-term chl a decrease was observed with an average decline in the cumulative annual chl a concentration of 49% and a marked decline of 57% in winter-spring bloom magnitude. The long-term decline in chl a concentration was directly and indirectly associated with multiple environmental factors that are impacted by climate change (e.g., warming temperatures, water column stratification, reduced nutrient concentrations) indicating the importance of accounting for regional climate change effects in ecosystem-based management. Analysis of seasonal phenology revealed that the winter-spring bloom occurred earlier, at a rate of 4.9 ± 2.8 d decade-1. Finally, the high degree of temporal variation in phytoplankton biomass observed in Narragansett Bay appears common among estuaries, coasts, and open oceans. The commonality among these marine ecosystems highlights the need to maintain a robust set of phytoplankton time series in the coming decades to improve signal-to-noise ratios and identify trends in these highly variable environments.

The long-term changing patterns of suicide mortality in China from 1987 to 2020: continuing urban-rural disparity

BackgroundOver the past three decades, China has experienced significant changes in urban-rural, gender, and age-specific suicide mortality patterns. This study aimed to investigate the long-term trends in suicide mortality in China from 1987 to 2020.MethodsSuicide mortality data were obtained from China’s National Health Commission. Joinpoint regression analysis was used to examine changes in trends and age-period-cohort modeling to estimate age, period, and cohort effects on suicide mortality from 1987 to 2020. Net drift, local drift, longitudinal age curves, and period relative risks were also calculated.ResultsCrude and age-standardized suicide mortality in China showed continuing downward trends from 1987 to 2020, with a more pronounced decrease in rural areas (net drift = -7.07%, p<0.01) compared to urban areas (net drift = -3.41%, p<0.01). The decline curve of urban areas could be divided into three substages. Period and cohort effects were more prominent in rural areas. Suicide risk was highest among individuals aged 20–24 and gradually increased after age 60. Females, particularly those of childbearing age, had higher suicide risk than males, with a reversal observed after age 50. This gender reversal showed distinct patterns in urban and rural areas, with a widening gap in urban areas and a relatively stable gap in rural areas.ConclusionsSuicide mortality in China has consistently declined over the past three decades. However, disparities in age, gender, and urban-rural settings persist, with new patterns emerging. Targeted suicide prevention programs are urgently needed for high-risk groups, including females of childbearing age and the elderly, and to address the slower decrease and reversing urban-rural gender trends.

Enhanced Role of Streamflow Processes in the Evolutionary Trends of Dissolved Organic Carbon.

Investigating dissolved organic carbon (DOC) dynamics and drivers in rivers enhances the understanding of carbon-environment linkages and support sustainability. Previous studies did not fully consider the dynamic nature of key drivers that influence the long-term changing trends in DOC concentration over time (the controlling factors and their roles in DOC trend can undergo alterations over time). We analyzed 42 years (1979-2018) of hydrometeorology, sulfate SO4, and DOC data from a 5.42 km2 watershed in central-southern Ontario, Canada. Our findings reveal a significant (p ≤ 0.01) overall increase in DOC concentrations, mainly due to the coevolution of SO4 and streamflow trends, especially the extreme flows. Over the 42-year period, the changing trend of streamflow (especially the extreme high or low flows) have significantly (p < 0.05) intensified their influence on DOC trends, increasing by an average of 30%. Conversely, the impact of SO4 has weakened, experiencing an average decrease of 32.6%. The upward trend in the annual average DOC concentration is attributed to the increasing number of maximum flow days within a year, while the decreasing trend in the number of minimum flow days has a contrasting effect. In other words, changes in maximum and minimum flow days have a counteracting effect on the DOC concentration trends. These results underscore the importance of considering the effects of altered streamflow processes on carbon cycle changes under evolving environmental conditions.

An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong

&lt;p&gt;Identifying epidemic-driving factors through epidemiological modeling is a crucial public health strategy that has substantial policy implications for control and prevention initiatives. In this study, we employ dynamic modeling to investigate the transmission dynamics of pneumonic plague epidemics in Hong Kong from 1902 to 1904. Through the integration of human, flea, and rodent populations, we analyze the long-term changing trends and identify the epidemic-driving factors that influence pneumonic plague outbreaks. We examine the dynamics of the model and derive epidemic metrics, such as reproduction numbers, that are used to assess the effectiveness of intervention. By fitting our model to historical pneumonic plague data, we accurately capture the incidence curves observed during the epidemic periods, which reveals some crucial insights into the dynamics of pneumonic plague transmission by identifying the epidemic driving factors and quantities such as the lifespan of flea vectors, the rate of rodent spread, as well as demographic parameters. We emphasize that effective control measures must be prioritized for the elimination of fleas and rodent vectors to mitigate future plague outbreaks. These findings underscore the significance of proactive intervention strategies in managing infectious diseases and informing public health policies.&lt;/p&gt;

Future indoor overheating risk for urban village housing in subtropical region of China under long-term changing climate

Over the last two decades, China has witnessed a surge in the emergence of urban villages characterized by high building density and significant migrant populations, primarily due to shifts in land use during urbanization. Housings in urban villages with low-cost design strategies, potentially exposing them to indoor overheating risks under climate changes. Given the scarcity of literature analyzing thermal performance of these houses, this study aims to assess the future indoor overheating risk of common urban village house types based on different construction material in subtropical China with two metrics which are Indoor Overheating Hours (IOH) and Indoor Overheating Degree (IOD), and explore effective methods to counter outdoor thermal stress. A four-month on-site measurement was conducted to calibrate the building thermal environment calculation model. Results demonstrate pronounced indoor overheating risks for both selected houses, Traditional Urban Village House (TUVH) and Contemporary Urban Village House (CUVH), particularly under the Representative Concentration Pathways (RCPs) 8.5 scenario. TUVH and CUVH may experience up to 2111 and 4325 overheating hours, respectively, with the IOD reaching 7.1 °C and 10.9 °C. Moreover, employing a global sensitivity analysis, we found that adding opaque shading for external wall is an effective retrofit strategy for TUVH and CUVH. This study contributes to evaluating the thermal performance of urban village houses under long-term climate changes with the findings promoting future retrofit strategy research for urban village houses focusing on housing types and usage pattern of the housings.

Longitudinal Changes of Cognition and Frailty With All-Cause and Cause-Specific Mortality in Chinese Older Adults: An 11-Year Cohort Study.

Physical function deterioration is always accompanied by a cognitive decline in older adults. However, evidence is lacking for the long-term simultaneous changing patterns of cognition and physical frailty and their associations with mortality among older adults. This study included 8,231 adults aged ≥65 with a baseline and at least one follow-up assessment of both cognition and physical frailty from the 2007-2018 Chinese Longitudinal Healthy Longevity Survey. Physical frailty (FRAIL phenotype) and cognition (Mini-Mental State Examination) were applied. Group-based joint trajectory modeling was used to fit the joint trajectories of cognition and physical frailty. Cox proportional hazards model was used to evaluate the trajectory-mortality associations. Three distinct joint trajectories were identified: no joint progression (34.4%), moderate joint progression (47.0%), and rapid joint progression (18.6%). During a median follow-up of 8.3 years, the rapid joint progression group, compared to the no joint progression, had the highest risk for all-cause mortality (hazard ratio (HR), 3.37 [95% CI: 2.99-3.81]), cardiovascular (CVD) mortality (3.21 [2.08-4.96]) and non-CVD mortality (2.99 [2.28-3.92]), respectively. Joint trajectory was found to be more predictive of mortality as compared to baseline measures of cognition and/or frailty (C-statistic ranged from 0.774 to 0.798). Higher changing rates of cognition and frailty were observed among all-cause decedents compared to CVD and non-CVD decedents over a 45-year span (aged 65-110) before death. Our study suggested that subjects with the worst cognitive decline and severest physical frailty progression were at the highest risk for all-cause and cause-specific mortality. Our findings expand the limited prior knowledge on the dynamic course of cognition and frailty.

Sexual health problems of patients with cancer: A bibliometrics study and visualization analysis via CiteSpace

PurposeThis study aimed to conduct a bibliometric analysis of the data acquired and clarified the current research status of sexual health problems in patients with cancer, to provide a comprehensive visual perspective suitable as a reference for subsequent research. MethodsWe searched the Web of Science Core Collection (WoSCC) up to April 30, 2023 to identify studies associated with sexual health problems in patients with cancer. CiteSpace was used to create visualization networks for countries, institutions, authors, and journals. ResultsA total of 3183 publications related to sexual health problems in patients with cancer were collected from the WoSCC. In terms of volume, the USA (1259 papers) was the leading country, the Memorial Sloan Kettering Cancer Center (119 papers) was the leading institution, and Carter (39 papers) was the author with the most publications. The top-cited references and keywords were related to quality of life. The top five clusters of reference cocitation were ‘brachytherapy’, ‘prostate cancer’, ‘radical prostatectomy’, ‘hypogonadism’, and ‘breast cancer’. Meanwhile, the top five clusters of keyword cocitation were ‘breast cancer’, ‘prostate cancer’, ‘rectal cancer’, ‘testicular cancer’, and ‘sexual function’. The analysis of the top 25 references and keywords with the strongest citation bursts of published papers on sexual health problems in patients with cancer to reveal the research hotspots and trends. ConclusionsResearch on sexual health among patients with cancer is constantly developing. The current research focuses on the impact of different treatment options for sexual health and quality of life of patients with breast, rectal, and genitourinary neoplasms. Exploring the long-term changing regularities of sexual function among cancer survivors and formulating sexual health interventions toward patient-reported outcomes and needs are key research directions.

Changing patterns in the highly contributing aerosol types/species across the globe in the past two decades

With the rapidly changing aerosol emissions due to the increase in urbanization, energy consumption, population density, and industrialization in the past two decades across the globe, there is an evolution of different chemical properties of aerosols that are yet not quantified properly. Therefore, a rigorous attempt is made in this study to obtain the long-term changing patterns in the contribution of different aerosol types/species, to the total aerosol loading. This study is carried out only over those regions exhibiting either increasing or decreasing trends in the aerosol optical depth (AOD) parameter on a global scale. Applying the multivariate linear regression trend analysis on Modern-Era Retrospective Analysis for Research and Application version 2 (MERRA-2) aerosol species dataset obtained between 2001 and 2020, we found that despite the overall statistically significant decrease in total columnar AOD trend values over North-Eastern America, and Eastern and Central China regions, an increase in the dust and organic carbon aerosols is observed, respectively. As the uneven vertical distribution of aerosols can alter the direct radiative effects, the extinction profiles of different aerosol types obtained using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) dataset between 2006 and 2020, are further partitioned, for the first time, based on their presence in different altitudes (i.e., within the atmospheric boundary layer and free-troposphere) as well as measurement timing (i.e., daytime and night-time) regimes. The detailed analysis showed that there exists an overall higher contribution of aerosols persisting in the free troposphere region which in turn can have a long-term effect on climate due to their higher residence time, particularly absorbing aerosols. As the trends are mostly associated with the changes in energy use, regional regulatory policies, and/or changing background meteorology conditions, therefore this study also elaborates on the effectiveness of these factors with the changes obtained in different aerosol species/types over the region.

Dreissena polymorpha responses under thermal and hypoxic stress: New insights in the tolerance of this freshwater sentinel species

Dreissena polymorpha is a sentinel freshwater mussel providing key functional ecosystemic services like nutrient recycling and suspended matter filtration. Global warming and especially extreme events imply rapid fluctuations of environmental parameters that sessile organisms could not escape. The increase occurrence of heat waves and the subsequent expansion of hypoxic areas could challenge the survival of mussels. This study provided a deeper knowledge of energy management and cellular function during thermal (+15 °C) or hypoxic (30% of dissolved oxygen saturation) stress for 7 days. A potential metabolic rate depression was highlighted in D. polymorpha under hypoxia through a decline in the mitochondrial activity and a constant AMP content over time. A contrasted pattern of response was observed in thermal-stressed mussels between 24 h and 7 days of exposure. A global increase of metabolic activity was noticed in mussels after 24 h while a return to control level was noticed at the end of the experiment. Although D. polymorpha is considered as a temperature tolerant species, a significant increase of ADP:ATP ratio, related to a decrease of mitochondrial activity and density, suggested an overwhelming of organisms. This study pointed to the importance of considering time of exposure to natural factor variations in tolerance window of organisms in a long-term changing environment. The apparent short-term tolerance of D. polymorpha could hide much more deleterious consequences, i.e. mortality, if abiotic stresses persist, as suggested by climate change models.

Learning a Novel LiDAR Submap-Based Observation Model for Global Positioning in Long-Term Changing Environments

In complex environments with long-term changes such as light, seasonal, and viewpoint changes, robust, accurate, and high-frequency global positioning based on light detection and ranging (LiDAR) map is still a challenge, which is crucial for autonomous vehicles or robots. To this end, a novel observation model that relies on the siamese multitask convolutional neural networks (CNNs) with multimodule cascaded is creatively presented in this article. In particular, a new pseudoimage representing LiDAR submap is designed to enrich scene texture and enhance rotation invariance. Besides, novel siamese CNNs that are coupled by NeXtVLAD and long short-term memory is designed for the first time, which can reliably predict similarity and quaternion at the same time. Finally, the predicted quaternion observation is integrated into the extended Kalman filter framework for multisensor fusion to achieve robust high-frequency global pose estimation. Extensive evaluations on KITTI, NCLT, and real-world datasets suggest that the proposed method not only obtains the remarkable precision-recall performance, but also effectively enhances and improves the robustness and accuracy of long-term positioning.

Long-Term Visual Simultaneous Localization and Mapping: Using a Bayesian Persistence Filter-Based Global Map Prediction

With the rapidly growing demand for accurate localization in real-world environments, visual simultaneous localization and mapping (SLAM) has received significant attention in recent years. However, those existing methods still suffer from the degradation of localization accuracy in long-term changing environments. To address these problems, we propose a novel long-term SLAM system with map prediction and dynamics removal. First, a visual point-cloud matching algorithm is designed to efficiently fuse 2D pixel information and 3D voxel information. Second, each map point is classified into three types: static, semistatic, and dynamic based on the Bayesian persistence filter (BPF). Then we remove the dynamic map points to eliminate the influence of those map points. We can obtain a global predicted map by modeling the time series of semistatic map points. Finally, we incorporate the predicted global map into a state-of-the-art SLAM method, achieving an efficient visual SLAM system for long-term, dynamic environments. Extensive experiments are carried out on a wheelchair robot in an indoor environment over several months. The results demonstrate that our method has better map prediction accuracy and achieves more robust localization performance.

Changes of plant biomass, species diversity, and their influencing factors in a desert steppe of northwestern China under long-term changing precipitation

Changes of plant biomass, species diversity, and their influencing factors in a desert steppe of northwestern China under long-term changing precipitation

Dynamic patterns of blood lipids and DNA methylation in response to statin therapy

IntroductionStatins are lipid-lowering drugs and starting treatment has been associated with DNA methylation changes at genes related to lipid metabolism. However, the longitudinal pattern of how statins affect DNA methylation in relation to lipid levels has not been well investigated.MethodsWe conducted an epigenetic association study in a longitudinal Swedish twin sample in previously reported lipid-related CpGs (cg10177197, cg17901584 and cg27243685). First, we applied a mixed-effect model to assess the association between blood lipids (total cholesterol (TC), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), total triglyceride (TG)) and DNA methylation. Then, we performed a piecewise latent linear–linear growth curve model (LGCM) to explore the long-term changing pattern of lipids and methylation in response to statin treatment. Finally, we used a bivariate autoregressive latent trajectory model with structured residuals (ALT-SR) to analyze the cross-lagged effects in different lipid-CpG pairs in statin users and non-users.ResultsWe replicated the associations between TC, LDL, HDL and DNA methylation level in cg17901584 and cg27243685 (P values ranged from 4.70E−12 to 1.84E−04). From the piecewise LGCM, we showed that TC and LDL significantly decreased in statin users before treatment started and then remained stable. For non-statin users, we only found a slightly significant decreasing trend for TC and TG. We observed a similar dynamic pattern for methylation levels at cg27243685 and cg17901584. Before statin initiation, cg27243685 showed a significantly increasing trend and cg17901584 a decreasing trend, but post-treatment, there were no additional changes. From the ALT-SR model, we found TG levels to be significantly associated with the DNA methylation level of cg27243685 at the next measurement in statin users (estimate = 0.383, 95% CI: 0.173, 0.594, P value < 0.001).ConclusionsLongitudinal blood lipid and DNA methylation levels change after statin treatment initiation, where the latter is mostly a response to alterations in lipid levels and not vice versa.

QoS Guaranteed Network Slicing Orchestration for Internet of Vehicles

To support the diversified Quality of Service (QoS) requirements of application scenarios, network slicing has been introduced in the mobile cellular network. It allows mobile cellular network operators to accomplish the creation of multiple logically isolated networks on common network infrastructure flexibly depending on specified demands. Meanwhile, in Internet of Vehicles (IoV), it is very intractable to supply a stable QoS for the vehicles, especially for the dynamic vehicular environments. Thus, we investigate the IoV slicing problem in this article, and propose a QoS guaranteed network slicing orchestration, namely, the long short-term memory-based deep deterministic policy gradient algorithm (LSTM-DDPG), to ensure the stable performance for the slices. Specifically, we first decouple the resource allocation problem into two subproblems. After that, the deep learning and reinforcement learning (RL) are used to allocate resources collaboratively to solve these two questions. We use deep learning LSTM to track the characteristic of the long-term vehicular environment changing, and the RL algorithm DDPG is utilized for online resource tuning. Extensive simulations have proved the effectiveness of the LSTM-DDPG, which can offer stable QoS to the vehicles with a probability greater than 92%. We also demonstrated the adaptiveness of the proposed orchestration with different slicing environments, and the performance is always optimal compared to that of other algorithms.

Predicting the At-Risk Online Students Based on the Click Data Distribution Characteristics

High fail and dropout rates are the major problems in distance education. Due to a large number of online learners and limited teacher resources, it is essential to accurately identify these potential at-risk students in advance and provide timely aids, which will help to improve the educational outcome. In the online learning environment, students’ online learning behaviors can be recorded easily, with the click data being the most common one. Students’ learning behavior can reflect their learning situation and may differ among different students and periods. This paper proposed a model that uses the short-period activity characteristic and long-term changing pattern to predict the potential at-risk students. The model contains two stages: information extraction and information utilization. The first stage extracts data from the log files and organizes it in a form suitable for the model. In the second stage, according to the different characteristics of students’ short-term and long-term learning behavior, a convolution residual recurrent neural network (CRRNN) model is proposed. The convolutional neural network is used to obtain the representation of the student’s learning behavior in a certain period. Then, the residual recurrent neural network is used to get the behavior changing pattern over the periods. The experimental results indicate that the proposed model has higher performance than the three widely used baseline methods on the OULA dataset and has good practical application value for teaching and management.

Changes in global heat waves and its socioeconomic exposure in a warmer future

Heat waves are continuous high temperature processes, which are defined as the daily maximum temperature exceeding the absolute-relative combined high temperature thresholds for more than three consecutive days in this study. Because of its unprecedented casualties, devastating compound disasters and irreversible deterioration trends, heat waves have attracted worldwide concern, while its global changes and socioeconomic impacts still need further study. Using three historical reanalysis data and multi scenario CMIP6 modeled data, Global Heat Wave Toolbox (GHWT) was developed to generate heat wave matrix from 1971 to 2100. The long-term changing characteristics of global heat waves were also analyzed. Next, population and GDP projections are employed to estimate future socioeconomic exposure and risks. The results show that except for high latitudes (latitude greater than 60°), high elevations and some coastal areas, heat waves have visited about 45% (±3%) of the global land area (excluding Antarctica). North Africa, North Australia, South Asia, and the Arabian Peninsula are detected as high heat wave area, where experience more than three heat waves with total duration over 15 days annually. Additionally, the average growth rate of global heat waves under SSP2 4.5 and SSP5 8.5 may be 2–3 times and 3–5 times of that under SSP1 2.6, respectively. Under SSP2 4.5, there may be about 7.32 billion people exposed to heat waves in 2100, accounting for 82.65% of the global population. Economic exposure may reach $433.37 trillion in 2100, accounting for 82.23% of the total global economy in that year. India could have the highest population and economic exposure, follow by populous countries, such as Pakistan and Bangladesh, and major economies, such as China and the United States. Under SSP1 2.6, the increase in heat waves may slow down significantly. Moreover, North Africa, Australia, and Brazil are identified as rapid heat wave growth area. Our study highlights the heat wave of future growth and its socioeconomic impacts, as well as the necessity for climate mitigation and adaptation measures.

Particle segregation in a rotating drum with inner segmentation ring pair

A pair of the inner segmentation (IS) rings is inserted in a rotating drum. The effects of the location and size of the rings on the axial and radial segregation patterns are studied. When the IS ring pair locates outside the end wall shearing zone of 10 particle diameter, the ring shearing independently affects the local mixing. The rings concentrate the larger particles at the bed periphery and smaller particles in the core, increasing the band number and degree of mixing. The drum mixing index monotonically increases with the ring width. There exists a critical IS ring width causing a 2-h long-term segregation pattern changing. The critical ring width in a 40% fill drum is ca. 30 particle diameter, covering 52% of the transverse bed area.

Management of Global Warming Effects in the European Water Framework Directive: Consideration of Social–Ecological System Features in the Elbe River Basin District

In this study, we examined the extent to which global warming management is currently integrated into the European Water Framework Directive (WFD), the central legal framework for water management in the EU. We focused on the Elbe River Basin District and how global warming is addressed in its water management. We used the social–ecological systems (SES) approach as our theoretical framework, representing an eminent analytical frame of biosphere-based sustainability science. In our study, we analysed core characteristics of SES in the context of global warming to evaluate the effectiveness of current water management in the Elbe River basin concerning long-term changing climate conditions. To determine to what extent each SES feature is considered in the Elbe water management, we applied a scale of 1 to 5. Our results show that the SES feature “scale and openness” is best addressed (score 4.0) by the Elbe River basin management, followed by “context dependency” (score 3.9); however, “non-linearity, uncertainty, unpredictability” (score 3.2), “self-organisation and adaptability” (score 3.1), and “dynamics” (score 3.0) have only moderate impacts. SES features can only be considered comprehensively if global warming is accounted for in an integrated way at a European level. In order to ensure effective implementation, explicit regulations and legally binding obligations are most likely required.