Climate change will pose challenges to water quality management in the st. Croix River basin

Streamflow in the United States: Characteristics, trends, regime shifts, and extremes

Long-term streamflow observations contain essential information for understanding hydrological changes and managing water resources. A continental-scale dataset or analysis of temporal streamflow change is still lacking across hydrologic gauges in the Conterminous United States (CONUS). Here, we compiled 70 years of streamflow records from 1951 to 2021 at ~ 8000 hydrologic stations across the CONUS and characterized temporal trends, regime shifts, and extreme events using a Bayesian time series analysis algorithm. We found that the occurrences of sudden streamflow changes (e.g., regime shifts and extreme events) have been increasing with time across the CONUS. In addition, we derived 181 streamflow indicators that are valuable for hydrological and biological applications, such as the duration and frequency of high or low streamflow events. The Mississippi River Basin, especially the middle and lower parts, was a hot spot of high-frequency high-flow events. Overall, we anticipate the dataset generated here offers valuable information for understanding and quantifying changes in water resources across the CONUS.

Analysis of water quality influencing factors under multi-source data fusion based on PLS-SEM model: An example of East-Liao River in China

Owing to alterations in the environment and human activities, the quality of surface water is declining. Despite a substantial number of studies on the factors that impact water quality, there is still a need for a better understanding of the major causes of water quality degradation. This study fused multi-source data using partial least squares structural equation modeling to evaluate the effects of weather, soil composition, and geographical features on the water quality of the East Liao River (ELR), Jilin Province, China. The impacts of land-use practices on water quality at different buffer scales were analyzed. The most significant correlation between land use and water quality was observed at a distance of 4 km. The severity of water pollution was significantly influenced by soil type, with a path coefficient of 0.689 (p < 0.001). Conversely, landscape factors exhibited a notable adverse effect, indicated by a path coefficient of -0.608 (p < 0.001). Additionally, meteorological factors exhibited a significant impact, with a path coefficient of 0.463 (p < 0.001). The indirect effects of landscape elements on water quality were also examined. Water quality could be indirectly influenced by landscape through soil factors, as evidenced by a path coefficient of -0.572 (p < 0.01). In this study, new ideas for studying water quality drivers using multi-source data fusion are introduced. Managers can leverage the findings of this study to improve their decision-making and effectively address water quality issues in ELR located in Jilin Province, China.

Global patterns and key drivers of stream nitrogen concentration: A machine learning approach

Anthropogenic loading of nitrogen to river systems can pose serious health hazards and create critical environmental threats. Quantification of the magnitude and impact of freshwater nitrogen requires identifying key controls of nitrogen dynamics and analyzing both the past and present patterns of nitrogen flows. To tackle this challenge, we adopted a machine learning (ML) approach and built an ML-driven representation that captures spatiotemporal variability in nitrogen concentrations at global scale. Our model uses random forests to regress a large sample of monthly measured stream nitrogen concentrations onto a set of 17 predictors with a spatial resolution of 0.5-degree over the 1990-2013, including observations within the pixel and upstream drivers. The model was validated with data from rivers outside the training dataset and was used to predict nitrogen concentrations in 520 major river basins of the world, including many with scarce or no observations. We predicted that the regions with highest median nitrogen concentrations in their rivers (in 2013) were: United States (Mississippi), Pakistan, Bangladesh, India (Indus, Ganges), China (Yellow, Yangtze, Yongding, Huai), and most of Europe (Rhine, Danube, Vistula, Thames, Trent, Severn). Other major hotspots were the river basins of the Sebou (Morroco), Nakdong (South Korea), Kitakami (Japan), and Egypt's Nile Delta. Our analysis showed that the rate of increase in nitrogen concentration between 1990s and 2000s was greatest in rivers located in eastern China, eastern and central parts of Canada, Baltic states, Pakistan, mainland southeast Asia, and south-eastern Australia. Using a new grouped variable importance measure, we also found that temporality (month of the year and cumulative month count) is the most influential predictor, followed by factors representing hydroclimatic conditions, diffuse nutrient emissions from agriculture, and topographic features. Our model can be further applied to assess strategies designed to reduce nitrogen pollution in freshwater bodies at large spatial scales.

Climate and landuse change enhance spatio-temporal variability of Dongjiang river flow and ammonia nitrogen

The adverse impacts of climate and landuse change are threatening the availability of water quantity and its quality, yet there are limited understandings in the response of water availability to changing environment at different spatio-temporal scales. Aimed at quantifying the individual and superimposed effects of climate and landuse change on streamflow and ammonia nitrogen (NH₃-N) load in the Dongjiang River Basin (DRB), we dynamically simulated the historical (1981-2010) and future (2030-2070) variation of runoff depth and NH₃-N load coupling multiple regional climate model and landuse data. The increase in runoff depth (avg. +233.9 mm) due to climate change was about 33 times greater than that caused by landuse change (avg. -7.2 mm). Especially in the downstream of DRB (Hong Kong, Shenzhen and Dongguan cities, etc.), the maximum rise of runoff depth under climate change was near twice compared with baseline period, indicating the dominant control of climate change on runoff. Also there existed higher coefficient of variation (C_v) value of runoff in the dry season of downstream DRB, contributing potential great fluctuation in runoff. Besides, the variation of NH₃-N load was jointly influenced by climate and landuse change, revealing an offset or amplification effect. Moreover, the variability of NH₃-N load (C_v value as the metric) increased from 2030, reached a maximum in 2050, following decreased to 2070. The spatial distribution of NH₃-N load, in general, presented a downward trend and concentrated near the water body, while the monthly average NH₃-N load showed distinct peaks in spring and late summer temporally. Overall, the results highlight the significance of investigating the water availability under changing environment and more adaptive strategies should be proposed for better basin water management.

Planetary Health, Climate Change, and Lifestyle Medicine: Threats and Opportunities

Global environmental degradation and climate change threaten the foundation of human health and well-being. In a confluence of crises, the accelerating pace of climate change and other environmental disruptions pose an additional, preventable danger to a global population that is both aging and carrying a growing burden of noncommunicable diseases (NCDs). Climate change and environmental disruption function as "threat multipliers," especially for those with NCDs, worsening the potential health impacts on those with suboptimal health. At the same time, these environmental factors threaten the basic pillars of health and prevention, increasing the risk of developing chronic disease. In the face of these threats, the core competencies of lifestyle medicine (LM) present crucial opportunities to mitigate climate change and human health impacts while also allowing individuals and communities to build resilience. LM health professionals are uniquely positioned to coach patients toward climate-healthy behavior changes that heal both people and the planet.

Teleconnection between climate oscillations and riverine nutrient dynamics in Southeast China based on wavelet analysis

Nonpoint source (NPS) pollution is mainly driven by hydrological processes; climate oscillation can affect regional water cycle processes. However, the relationship between climate oscillation and NPS pollution is still unclear, which increases the difficulty of water quality prediction and management. In this study, Mann-Kendall test and wavelet transform were adopted to investigate the teleconnection between ENSO (El Niño-Southern Oscillation) phenomenon and riverine NPS load dynamics in an agricultural watershed of Southeast China from 2003 to 2016. Results showed that annual precipitation, streamflow, and riverine nutrient load increased significantly during the study period. The change point for long-term riverine TN load and TP load appeared in 2009 and 2007, respectively. Rainfall, streamflow, nutrient loads, and Niño 3.4 sea temperature (SST) shared a common periodicity of 10-16 months. The southern oscillation index (SOI) and Niño 3.4 SST shared a common periodicity of 28-36 months. Moreover, Niño 3.4 SST showed a positive correlation with riverine nutrient loads at a periodicity of 10-16 months, while SOI showed a weakly negative correlation with riverine nutrient loads at a periodicity of 28-36 months. These findings indicate that the increasing frequency of warm ENSO events would enhance the risk of nutrient export to rivers in Southeast China and more attention should be paid to large-scale climate oscillation in the prediction of agricultural nutrient pollution and management of water quality in agricultural watersheds.

Fuel cells for carbon capture applications

The harmful effect of carbon pollution leads to depletion of the ozone layer, which is one of the main challenges confronting the world. Although progress is made in developing different carbon dioxide (CO₂) capturing methods, these methods are still expensive and face several technical challenges. Fuel cells (FCs) are efficient energy converting devices that produce energy via an electrochemical process. Recently varying kinds of fuel cells are considered as an effective method for CO₂ capturing and/or conversion. Among the different types of fuel cells, solid oxide fuel cells (SOFCs), molten carbonate fuel cells (MCFCs), and microbial fuel cells (MFCs) demonstrated promising results in this regard. High-temperature fuel cells such as SOFCs and MCFCs are effectively used for CO₂ capturing through their electrolyte and have shown promising results in combination with power plants or industrial effluents. An algae-based microbial fuel cell is an electrochemical device used to capture and convert carbon dioxide through the photosynthesis process using algae strains to organic matters and simultaneously power generation. This review present a brief background about carbon capture and storage techniques and the technological advancement related to carbon dioxide captured by different fuel cells, including molten carbonate fuel cells, solid oxide fuel cells, and algae-based fuel cells.

Nitrate loading projection is sensitive to freeze-thaw cycle representation

Climate change can have substantial impacts on nitrogen runoff, which is a major cause of eutrophication, harmful algal blooms, and hypoxia in freshwaters and coastal regions. We examined responses of nitrate loading to climate change in the Upper Mississippi River Basin (UMRB) with an enhanced Soil and Water Assessment Tool with physically based Freeze-Thaw cycle representation (SWAT-FT), as compared with the original SWAT model that employs an empirical equation. Driven by future climate projections from five General Circulation Models (GCMs) from 1960 to 2099 under the Representative Concentrations Pathways (RCP) 8.5 scenario, we analyzed changes in riverine nitrate loadings, as well as terrestrial surface and subsurface contributions of the UMRB in the 21st century relative to the baseline period of 1960-1999. By the end of the 21st century, the original SWAT model predicted about a 50% increase in riverine nitrate loadings which is nearly twice as much as that estimated by SWAT-FT (ca. 25%). Such a large difference in projected nitrate changes can potentially mislead mitigation strategies that aim to reduce nitrogen runoff from the UMRB. Further analysis shows that the difference between the original SWAT model and SWAT-FT led to substantial discrepancies in the spatial distribution of surface and subsurface nitrate loadings in the UMRB. In general, SWAT-FT predicted more nitrate leaching for northwestern parts of the UMRB which are more sensitive to freeze-thaw cycle, mainly because SWAT-FT simulated less frequent frozen soils. This study highlights the importance of using physically based freeze-thaw cycle representation in water quality modeling. Design of future nitrogen runoff reduction strategies should include careful assessment of effects that land management has on the freeze-thaw cycles to provide reliable projection of water quality under climate change.