Flux Estimates Research Articles

Influence of grazing management strategy and data time scales on estimates of sensible heat flux in grasslands

Study regionInner Mongolia Autonomous Region, North China Study focusSensible heat flux (H) quantifies the intensity of water evapotranspiration, making the factors influencing H pivotal to water consumption. North China's semi-arid grasslands are characterized by widespread grazing and strong wind conditions. Grazing strategy modifies surface air heat transfer resistance via vegetation and consequently alters H. Nevertheless, the majority of studies overlook the impact of the observed variables' time scale on H estimation. This paper employs a "big-leaf" model to estimate H, incorporating both grazing management strategy and data time scale, validated using the Bowen ratio, and attempted to analyze the factors that affect H. New hydrological insights for the regionAerodynamic characteristics estimated from daily data typically exceed estimates derived from hourly data. The influence of surface radiative temperature and air temperature on H outweighs that of aerodynamic resistance, whereas aerodynamic resistance accounts for the disparity in estimating H between daily and hourly data. In grazing-prohibited grasslands, the daily H values estimated by the Big Leaf model closely match those obtained from the Bowen ratio. When estimating the hourly H value for grazing prohibited and grazing grasslands, the increase was 25.95 w/m2 and 52.27 w/m2 compared to the daily scale, respectively. For the estimation of water and heat fluxes across different regions, the temporal scale of input data is a pivotal factor.

A consistent budgeting of terrestrial carbon fluxes

Accurate estimates of CO2 emissions from anthropogenic land-use change (ELUC) and of the natural terrestrial CO2 sink (SLAND) are crucial to precisely know how much CO2 can still be emitted to meet the goals of the Paris Agreement. In current carbon budgets, ELUC and SLAND stem from two model families that differ in how CO2 fluxes are attributed to environmental and land-use changes, making their estimates conceptually inconsistent. Here we provide consistent estimates of ELUC and SLAND by integrating environmental effects on land carbon into a spatially explicit bookkeeping model. We find that state-of-the-art process-based models overestimate SLAND by 23% (min: 8%, max: 33%) in 2012–2021, as they include hypothetical sinks that in reality are lost through historical ecosystem degradation. Additionally, ELUC increases by 14% (8%, 23%) in 2012–2021 when considering environmental effects. Altogether, we find a weaker net land sink, which makes reaching carbon neutrality even more ambitious. These results highlight that a consistent estimation of terrestrial carbon fluxes is essential to assess the progress of net-zero emission commitments and the remaining carbon budget.

Estimation of Surface Temperature and Heat Flux over a Hollow Cylinder and Slab using an Inverse Heat Conduction Approach

A non-iterative approximation of the inverse heat conduction problem has been developed through energy balance between the control volumes. The heat flow domain along the surface normal was divided into three control volumes and studied in three cases, specifically on a heated hollow metallic cylinder cooled in crossflow of air, a heating flat plate in still air, and a heated flat plate when cooled in still air with random air blows. The time derivatives of temperature and heat flux estimates at the surface appearing in derived equations were evaluated by approximate expressions. Both estimates were obtained from the derived equations by simultaneous measurement of temperature-time histories at two locations: one at the inner surface and the other anywhere within the control volume along the surface normal. The deviation was checked by real-time simultaneous measurement of temperature-time history at the outer surface along the normal surface. Comparison between the estimated surface temperature-time history using the derived equations and the real-time measurement showed deviations within 0.5 % for the cylinder, within 0.03 % for the plate in still air and within 0.5 % when air blows were given. Heat fluxes estimated using these time histories were correspondingly arrived at consistent and close approximations for all cases. Estimates from the derived equations were compared with reported equations from the literature, and a wind tunnel experiment for the validation of circumferential distribution of heat transfer was conducted, for which they also showed reasonably good agreements.

A Census of the Deep Radio Sky with the VLA. I. 10 GHz Survey of the GOODS-N Field*

Abstract We present the first high-resolution, high-frequency radio continuum survey that fully maps an extragalactic deep field: the 10 GHz survey of the Great Observatories Origins Deep Survey-North (GOODS-N) field. This is a Large Program of the Karl G. Jansky Very Large Array (VLA) that allocated 380 hr of observations using the X-band (8–12 GHz) receivers, leading to a 10 GHz mosaic of the GOODS-N field with an average rms noise σ n = 671 nJy beam−1 and angular resolution θ 1/2 = 0.″22 across 297 arcmin2. To maximize the brightness sensitivity we also produce a low-resolution mosaic with θ 1/2 = 1.″0 and σ n = 968 nJy beam−1, from which we derive our master catalog containing 256 radio sources detected with peak signal-to-noise ratio ≥ 5. Radio source size and flux density estimates from the high-resolution mosaic are provided in the master catalog as well. The total fraction of spurious sources in the catalog is 0.75%. Monte Carlo simulations are performed to derive completeness corrections of the catalog. We find that the 10 GHz radio source counts in the GOODS-N field agree, in general, with predictions from numerical simulations/models and expectations from 1.4 and 3 GHz radio counts.

A benthic source of isotopically heavy Ni from continental margins and implications for global ocean Ni isotope mass balance

Nickel (Ni) is a bio-essential element for phytoplankton in the modern oceans, and yet, the global ocean mass balance of Ni has puzzled scientists for decades. Many estimates of total Ni output flux are larger than the total Ni input flux to the ocean. The measurement of Ni stable isotopes (δ60Ni) in earth materials may inform our understanding of ocean biogeochemistry and redox conditions in both the modern ocean and the geological past. An ocean Ni stable isotope imbalance has also concerned scientists, with a global ocean δ60Ni of +1.4 ‰ which is notably heavier than the major source of Ni to the oceans from rivers. Recent efforts to resolve Ni and δ60Ni imbalances have focused on the role which manganese (Mn) oxides play in marine Ni cycling, both in the water column and in marine sediments. Manganese oxide rich marine sediments can supply isotopically heavy dissolved Ni to porewater fluids and seawater, but the source of the isotopically heavy porewater Ni remains unclear. Here we present porewater trace metal concentrations and δ60Ni from two sites from the continental margin off southern California. Porewater Ni concentrations are up to roughly 100-fold higher than deep seawater concentrations (∼1 μM compared to 10 nM, respectively). Porewater δ60Ni is near 0 ‰ in the subsurface region where Ni concentrations are highest, suggesting dissolution of lithogenic material from sediments. Porewater δ60Ni increases dramatically towards the sediment-water interface with values of up to +2.66 ‰, which to our knowledge are the heaviest Ni isotope compositions ever reported for natural materials. Measurements of porewater and sediment Ni and Mn concentrations suggest that Ni is released to porewaters in the Mn-reducing zone, and then removed by newly precipitated Mn oxides as Mn and Ni move towards the oxygenated zone of sediments. A simple Rayleigh model suggests a Ni isotope fractionation of factor of -0.61 ‰ for Ni sorption onto Mn oxides, while a diffusion-reaction model suggests a Ni isotope fractionation of -1.80 to -0.96 ‰, always with preferential adsorption of lighter Ni isotopes. This flux of Ni toward the sediment-water interface, coupled with preferential removal of lighter Ni isotopes in marine sediments, provides evidence supporting an isotopically heavy benthic source of new Ni to the oceans, which we estimate has a magnitude of 0.65 × 108 to 1.66 × 108 moles/year. We find that a benthic flux such as measured here over just 0.27-2.70 % of the ocean seafloor could mix with the river δ60Ni of +0.8 ‰ to achieve the observed deep ocean δ60Ni of +1.4 ‰. This study reveals the similarities in how rivers and continental margin sediments supply heavy Ni isotopes to the ocean. In both continental rivers and margin sediments, terrigenous Ni is released with an δ60Ni near 0.1 ‰, but lighter isotopes are captured by precipitation onto oxides so that the dissolved flux to the ocean is isotopically heavier than the terrigenous material from which Ni was released, and in the case of margin sediments may be heavier than even bulk seawater δ60Ni. We thus recognize continental margin sediments with active Mn cycling as a ‘new’ source of isotopically heavy dissolved Ni to the ocean.

To improve estimates of neotropical forest carbon stocks more direct measurements are needed: An example from the Southwestern Amazon

Tropical forests play a critical role in the global carbon cycle, storing 40–55 % of terrestrial plant carbon and significantly contributing to primary productivity. However, uncertainties persist in estimating carbon stocks and fluxes, exhibiting variation across the Neotropics, Africa, and Asia tropical forest regions. Despite hosting some of the most densely sampled forests, significant uncertainties persist in biomass and forest carbon stock estimates in the Neotropics. Although the Southwestern Amazon (SWA) forests span over 20 million hectares, no specific biomass or above- and below-ground carbon model has been calibrated for this region thus far. In our study, we conducted direct forest inventories in the SWA to address the following question: Do the allometric patterns, biomass, and carbon stocks observed in the Southwestern Amazon differ from those found in other regions of the Amazon or Pantropical? Our research reveals substantial differences in water and carbon content, biomass stocks, above- and below-ground oven-dry biomass ratios, and allometric patterns between SWA forests and other Amazonian and Pantropical forests. We have demonstrated that these differences result in overestimations of forest biomass when applying allometric equations developed for other Amazonian and Pantropical regions to the open forests of Southwestern Amazonia. This overestimation can reach up to 37 % when using equations from the eastern Amazon, and between 26 % and 46 % depending on the applied Pantropical equation. The use of an inappropriate factor for the root-to-shoot ratio in the Southwestern Amazon (SWA) can lead to overestimates of belowground oven-dry biomass by up to 20 %. To reduce uncertainties related to estimates of forest carbon stock and flux in Neotropical forests, it is necessary to enhance the density of direct biomass measurements, particularly in southwestern Amazonia.

Spatial dynamics of pCO2 and CO2 emissions from eutrophic lakes

Eutrophication of lakes exhibits a more serious trend in a global scale under the climate change. Eutrophic lakes also exhibit a unique role in global carbon, yet, data on carbon dioxide (CO2) emissions from these eutrophic lakes are too scarce to accurately upscale CO2 emissions for large scale or global estimation. This study presented the results of a partial pressure of carbon dioxide (pCO2) survey covering 43 eutrophic lakes in China in the same season across diverse ecosystem types and climate zones. The first estimation of CO2 flux from Chinese eutrophic lakes was reported as − 0.7 to 1.0 Tg C a−1 based on the surveyed pCO2 values, indicating some lakes act as CO2 sinks while others are sources. The results showed that pCO2 in Chinese eutrophic lakes ranged from 0.9 to 1366.4 μatm with the mean of 356.9 ± 310.3 μatm, almost 69.3 % of the sampling sites in eutrophic lakes were undersaturated with CO2 to the atmosphere. The mean pCO2 variations in eutrophic lakes were consistent with the amount of precipitation across different climate zones, with the trend of semiarid > semihumid > humid regions, and all lower than that for atmosphere. Meanwhile, both the mean and median values of pCO2 went down with the decrease of the lake mean depth and the increasing lake area, and the highest value in deep lakes (mean depth > 10 m, 548.3 ± 353.7 μatm) and small lakes (376.6 ± 329.8 μatm). In all the studied eutrophic lakes, pCO2 values showed significant seasonal variations, and in summer and autumn showed an under-saturated seasonal mean value. This study proved that CO2 undersaturation was prevalent in the eutrophic conditions. Due to the CO2 source-sink conversion role of eutrophic lakes, it is recommended to focus on CO2 fluxs from eutrophic lakes in the first place during the research of inland waters carbon emissions.

Accurate heat flux estimation in continuous casting Molds via MH-MCMC Bayesian Inverse Method

The present work focuses on accurately estimating heat flux variations on the hot faces of the billet and slab molds during continuous casting using the Metropolis Hastings–Markov Chain Monte Carlo (MH-MCMC) Bayesian inverse method and experimental data. Accurate estimation of heat flux variation is crucial for operational efficiency and ensuring high-quality steel products. The variation in the hot face heat flux (qn(y)) of the billet mold is estimated using the proposed MH-MCMC Bayesian inverse method and experimental data for various casting speeds. The study extends this methodology to the slab mold for estimating the variations in the heat fluxes (qnr(y), qw(x,y)) on the hot narrow and wide faces of the slab mold. The estimated heat fluxes for both molds are used in respective forward models to obtain temperatures. The temperatures obtained excellently match experimental temperatures and show superior accuracy in estimating the heat fluxes. From the present methodology, the errors between these simulated and experimental temperatures are much less compared with the literature. The predicted heat flux variations on the hot faces of the billet and slab molds are also compared with the heat flux obtained from the literature.This study provides valuable insights into accurately estimating heat fluxes in continuous casting molds (billet and slab) using the MH-MCMC Bayesian inverse method. The accurately estimated heat fluxes of the molds are essential for ensuring the quality, efficiency, and innovation of continuous casting processes.

On the intrusiveness of phosphor thermometry for metal surface temperature measurements in reciprocating engines: an experimental and heat conduction modeling study

Abstract Lifetime-based phosphor thermometry has been applied in a wide variety of surface temperature measurement applications due to its relative ease of implementation and robustness to background interference when compared to other optical temperature measurement methods. It is often assumed that the technique is minimally intrusive if the thickness of the applied phosphor coating is < 20 µm. To evaluate this assumption, high-speed phosphor surface temperature and thermocouple measurements were performed on 4140 steel substrates installed in the cylinder head of an optically-accessible internal-combustion engine for four operating conditions. For phosphor thermometry measurements, four substrates were studied, each coated with a phosphor layer of different thickness ranging between 6 µm and 47 µm. The phosphor thermometry measured temperature swings during combustion were shown to be heavily impacted by the presence of the phosphor coatings, increasing by roughly a factor of 2 - 2.5 when increasing the thickness from 6 µm to 30 µm. A technique was implemented which utilizes the temperature data in combination with a heat conduction model to provide estimates of the temperature swing and heat transfer flux in the absence of the phosphor coating. It was shown that even the 6 µm phosphor coating could lead to an order of magnitude increase in the temperature swing relative to the uncoated 4140 steel substrate. Despite the intrusiveness of phosphor thermometry for the surface temperature measurements, reasonable agreement was demonstrated between heat flux estimates determined with the heat transfer modeling technique and those deduced from temperature-swing measurements using two different high-speed thermocouples. The results indicate that phosphor surface thermometry can be a reliable surface temperature and heat flux diagnostic for transient high heat flux environments, as long as proper care is taken to account for the impact of the phosphor layer on the measurement.

Variations and driving factors for concentrations and carbon isotopes of dissolved CO2 in lake water across different Chinese lakes

Carbon dioxide (CO2) stands as the primary driver of Earth's greenhouse effect, and it's suggested that the global contribution of CO2 emissions from lakes cannot be ignored. Despite the numerous estimations of CO2 fluxes from lakes, limited focus has been directed towards the carbon isotopes (δ13C) of dissolved CO2 in lake water. Particularly, the potential use of δ13C values in tracing the CO2 concentrations in lake water remains as an understudied area, warranting further exploration and investigation. In this study, we conducted an analysis of the concentrations and δ13C values of dissolved CO2 in 33 lakes located at the Tibetan Plateau, Chinese Loess Plateau, and Yangtze Plain (among which high-resolution spatial investigations were performed in 6 lakes through in-situ continuous monitoring). Our findings revealed spatial variations in both the CO2 concentrations and δ13C values in lakes. Additionally, notable differences are observed among lakes in different regions of China, with lakes in the Yangtze Plain exhibiting considerably higher CO2 concentrations, and the overall CO2 δ13C values in lakes on the Tibetan Plateau tend to be more positive, while those in lakes on the Chinese Loess Plateau tend to be more negative. The pH values, dissolved oxygen, and dissolved organic carbon are likely crucial factors influencing the CO2 concentrations and δ13C values in the lakes. Furthermore, lake water CO2 concentrations are negatively correlated with δ13C values of CO2 and dissolved inorganic carbon (DIC) both within a single lake with high spatial resolutions or in lake groups across different regions. These results highlight that the CO2/DIC δ13C values can be applied to trace the concentration variations of dissolved CO2 in lakes.

Experimental investigation of predictive control for PMSM-based wind turbine generation system

The theoretical analysis of permanent magnet synchronous machine (PMSM)-based wind turbine generation system was presented by many researchers; nevertheless, although the practical setup is still quite difficult, it is more valid. Moreover, the main challenge facing wind power systems is figuring out how to extract the most energy from varying wind speeds. Since wind turbine maintenance is hard and expensive, using wind speed sensors is a challenging operation. Therefore, this research suggests an effective and practical control system based on the Model Predictive Control Scheme (MPCS) for both the grid-side converter (GSC) and the machine-side converter (MSC) to optimize power extraction from wind energy. Detailed exploration of the MPCS for the PMSM and predictive power control (PPC) for the GSC involves stages such as flux estimation, torque prediction, and cost function minimization. The results obtained validate the higher performance of MPCS in comparison to the direct torque control (DTC) method. Furthermore, excellent tracking with high accuracy is achieved in relation to simulation and experimental data. Additionally, an evaluation indices based on root mean square error (RMSE) and gross system efficiency are used for wind turbine performance. These indices are used to show that, the feasibility of the MPCS approach compared with DTC under the same WT conditions. This approach offers a promising avenue for cost-effective wind energy conversion. The control systems were designed and implemented utilizing a DSPACE 1104 card. Experimental results confirm the effectiveness of the proposed mechanism in achieving maximum power point tracking (MPPT).

Extending our understanding on the retrievals of surface energy fluxes and surface soil moisture from the “triangle” technique

The present study demonstrates the capability of an inversion modelling scheme so-called the “triangle” to retrieve spatiotemporal estimates of surface energy fluxes and soil surface moisture (SSM) at high resolution using ASTER satellite imagery synergistically with SimSphere land biosphere model. In addition, as a further objective of this study is to examine the use of the technique for retrieving the Evaporative (EF) and the Non-Evaporative (NEF) Fractions as representations of the daytime average fluxes. The applicability of the investigated technique, is demonstrated for sixteen calendar days of year 2011 using in-situ data acquired from nine CarboEurope sites representing a variety of climatic, topographic and environmental conditions. Results indicated a close agreement between all the inverted parameters and the corresponding in-situ data. SSM predicted maps showed a small bias of 0.08 vol vol−1, a scatter of 0.18 vol vol−1 and a RMSD of 0.19 vol vol−1. The predicted LE fluxes showed a relatively low overall agreement (RMSD = 65.10 Wm-2), whereas for H flux reported RMSD was 85.02 Wm-2. The results also confirmed the ability of the investigated technique to provide meaningful estimates of the NEF and EF. All in all, the present study findings were at least comparable, or of higher accuracy, to those reported in other similar verification studies of the “triangle” using both high resolution (airborne) and low resolution (satellite) data. To our knowledge, this study represents the first comprehensive evaluation of the performance of this particular methodological implementation at a European setting combining the SimSphere SVAT model and ASTER EO datasets.

A deep learning-based model for estimating pollution fluxes from rivers into the sea and its optimization

Pollution fluxes from rivers into the sea are currently the main source of pollutants in nearshore areas. Based on the source-sink process of the basin-estuary-coastal waters system, the pollution fluxes into the sea and their spatiotemporal heterogeneity were estimated. A deep learning-based model was established to simplify the estimation of pollution fluxes into the sea, with socio-economic drivers and meteorological data as input variables. A method for estimating the contribution rate of pollution fluxes from different spatial gradient was proposed. In this study, we found that (1) the pollution fluxes into the sea of total nitrogen (TN) and total phosphorus (TP) from the Bohai Sea Rim Basin (BSRB) in 1980, 1990, 2000, 2010, and 2020 were 25.38 × 104, 26.12 × 104, 27.27 × 104, 29.82 × 104, 25.31 × 104 and 1.32 × 104, 2.14 × 104, 2.09 × 104, 1.87 × 104, 1.68 × 104 tons, respectively. (2) The proportion of rural life and livestock to the TN was the highest, accounting for 39.18 % and 21.19 %, respectively. The proportion of livestock to the TP was the highest, accounting for 39.20 %, followed by rural life, accounting for 24.72 %. The results indicated that the pollution fluxes in the BSRB were related to human economic activities and relevant environmental protection measures. (3) The deep learning-based model established to estimate runoff pollution fluxes into the sea had the accuracy of over 90 %. (4) As for contribution rate, in terms of the elevation, the range of 0–100 m had the highest proportion, accounting for 39.65 %. The range of 50–100 km from the coastline had the highest proportion, accounting for 18.11 %. In terms of the district, coastal area has the highest proportion, accounting for 38.00 %. This study revealed the changing trends and driving mechanisms of pollution fluxes into the sea over the past 40 years and established a simplified deep learning-based model for estimating pollution fluxes into the sea. Then, we identified regions with high pollution contribution rate. The results can provide scientific references for the adaptive management of the nearshore areas based on the ecosystem.

Improving carbon flux estimation in tea plantation ecosystems: A machine learning ensemble approach

Tea plant (Camellia sinensis) is a major global crop consumed as a drink after water. Quantifying carbon flux, specifically the net ecosystem exchange (NEE), in tea plantations is essential for determining carbon sequestration and ecosystem carbon balance. The Eddy covariance (EC) system is widely used for continuous monitoring of carbon flux but high costs associated with installation and maintenance limit its widespread adoption. In addition, EC flux data is often discarded due to malfunction of instruments caused by adverse weather conditions. Therefore, additional approaches for estimating NEE are necessary to overcome these challenges and ensure accurate NEE measurement. For this purpose, three standalone tree-based machine learning (ML) models were used for NEE estimation using EC flux data collected from tea ecosystem located in subtropical region (Danyang county of Zhenjiang city) of China. To address the accuracy limitations inherent in standalone ML models, the ensemble mechanism based on voting regressor method was proposed. In addition, k-fold cross-validation based on early stopping process was also used to enhance the performance of standalone ML models. Based on visual plots (scatter diagram, heatMap, Taylor diagram) and performing indices (root-mean-square error (RMSE), determination coefficient (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), correlation coefficient (r), Kling Gupta Efficiency (KGE) and index of agreement (d)), the findings indicated that non-linear ensemble-generalized regression neural network (NLE-GRNN) significantly improved standalone ML model's results. In current study, the highest NSE, r and d in case of standalone ML model (DT) achieved 0.49, 0.73 and 0.75 respectively while our proposed NLE-GRNN model improved 48 % in NSE value (NSE = 0.97), 25 % in r value (r = 0.98) and 24 % in d value (d = 0.99). Likewise, NLE-GRNN significantly reduce errors (MAE, MAPE and RMSE) and provides NEE estimate closet to the observed value. The impact of climatic variables on NEE using shapley additive explanations (SHAP) analysis revealed that Rg (solar radiation) and Tair (air temperature) were the prime factors controlling NEE variation in the tea ecosystem. Considering the high accuracy and stability of the studied ML models, it is recommended to apply developed ensemble ML model (NLE-GRNN) for significant improvement of NEE estimate in the tea biomes or other ecosystems.

The importance of natural land carbon sinks in modelling future emissions pathways and assessing individual country progress towards net-zero emissions targets

Nature-based solutions (NBS), in the form of active ecosystem conservation, restoration and improved land management, represent a pathway to accelerate net-zero emissions (NZE) strategies and support biodiversity. Meaningful implementation and successful accounting depend on the ability to differentiate between anthropogenic and natural carbon fluxes on land. The United Nations Framework Convention on Climate Change (UNFCCC) land carbon accounting methods currently incorporate all CO2 fluxes on managed land in country inventories without distinguishing between anthropogenic and natural components. Meanwhile, natural land carbon sinks are modelled by earth system models but are mostly reported at global level. Here we present a simple yet novel methodology to estimate the present and future progression of natural land sinks at the country and regional level. Forests dominate the uptake of CO2 on land and as such, our analysis is based on allocating global projections of the natural land carbon flux to individual countries using a compilation of forest land areas for a historic and scenario range spanning 1960–2100. Specifically, we use MIT’s carbon cycle model simulations that are set in the context of emissions pathways from the Shell Energy Security Scenarios (2023). Our natural land carbon flux estimates for individual countries and regions such as the European Union (EU) show generally good agreement with independent estimates from recent land-use harmonisation studies for 2000–2020. Hence, our approach may also provide a simple, first-order exploration of future natural land fluxes at country level—a potential that other studies do not yet offer. In turn, this enables better understanding of the anthropogenic and natural components contributing to country NZE targets under different scenarios. Nevertheless, our findings also suggest that models such as the Shell World Energy Model (WEM) would benefit from further improvements in the apportionment of land carbon sources and sinks to evaluate detailed actions to meet country targets. More importantly, uncertainties remain regarding the resilience of land ecosystems and their capacity to store increasing amounts of carbon under progressive global warming. Therefore, we recommend that the carbon cycle modelling and energy modelling research communities continue to collaborate to develop a next generation of relevant data products to distinguish anthropogenic from natural impacts at local, regional and national levels.

Unsteady radiative-convective Casson hybrid nanofluid flow over an inclined disk with Cattaneo–Christov heat flux and entropy estimation

Unsteady radiative-convective Casson hybrid nanofluid flow over an inclined disk with Cattaneo–Christov heat flux and entropy estimation

The muon beam monitor for the FAMU experiment: design, simulation, test, and operation

FAMU is an INFN-led muonic atom physics experiment based at the RIKEN-RAL muon facility at the ISIS Neutron and Muon Source (United Kingdom). The aim of FAMU is to measure the hyperfine splitting in muonic hydrogen to determine the value of the proton Zemach radius with an accuracy better than 1%. The experiment has a scintillating-fibre hodoscope for beam monitoring and data normalisation. In order to carry out muon flux estimation, low-rate measurements were performed to extract the single-muon average deposited charge. Then, detector simulation in Geant4 and FLUKA allowed a thorough understanding of the single-muon response function, which is crucial for determining the muon flux. This work presents the design features of the FAMU beam monitor, along with the simulation and absolute calibration measurements in order to enable flux determination and enable data normalisation.

Carbon dioxide emissions from temperate reservoirs and pit lakes of different trophic states

The abundance of human-made inland waters, such as artificial ponds and reservoirs, has multiplied over the last decades. Compared to natural lakes, reservoirs and retention basins can exhibit a higher accumulation of organic carbon and a higher emission of greenhouse gases. Magnitude and dynamics of related emission fluxes are important to understand the role of inland waters in the global carbon cycle. However, origin and use of artificial waterbodies are diverse, and it is unclear whether current gas flux estimates adequately represent all important types and conditions of artificial inland waters. This study compares drivers of CO2 and CH4 emissions from two types of temperate artificial waters that are widespread in central Europe: reservoirs and artificial lakes in former open-pit lignite mines (pit lakes). We measured CO2 exchange at the water surface, calculated surface water CO2 from long-term DIC and pH data, and measured CO2 and CH4 concentrations throughout the water column. We found that an older, eutrophic pit lake showed seasonal variations in CO2 fluxes including uptake during summer and release during turnover, similar to three studied freshwater reservoirs. In contrast, reacidification and repeated addition of carbonate (liming) led to sustained CO2 supersaturation in a young, oligotrophic pit lake throughout the year, potentially making it a steady source of CO2 to the atmosphere. This implies that on an annual level, pH regulation and lake maturity largely determine whether pit lakes act as sources of CO2 (young, slightly acidic) or sinks of CO2 (older, neutral pH).

The Unmanned Aerial Vehicle-Based Estimation of Turbulent Heat Fluxes in the Sub-Surface of Urban Forests Using an Improved Semi-Empirical Triangle Method

The Unmanned Aerial Vehicle-Based Estimation of Turbulent Heat Fluxes in the Sub-Surface of Urban Forests Using an Improved Semi-Empirical Triangle Method

Ion-temperature- and density-gradient-driven instabilities and turbulence in Wendelstein 7-X close to the stability threshold

Electrostatic gyrokinetic instabilities and turbulence in the Wendelstein 7-X stellarator are studied. Particular attention is paid to the ion-temperature-gradient (ITG) instability and its character close to marginal stability [Floquet-type turbulence (Zocco et al., Phys. Rev. E, vol. 106, 2022, p. L013202) with no electron temperature gradient]. The flux tube version of the $\delta f$ code stella (Barnes et al., J. Comput. Phys., vol. 391, 2019, pp. 365–380) is used to run linear and nonlinear gyrokinetic simulations with kinetic electrons. The nature of the dominant instability depends on the wavelength perpendicular to the magnetic field, and the results are conveniently displayed in stability diagrams that take this dependence into account. This approach highlights the presence of universal instabilities, which are less unstable but have longer wavelengths than other modes. A quasi-linear estimate of the heat flux suggests they are relevant for transport. Close to the stability threshold, the linear eigenmodes and turbulence form highly extended structures along the computational domain if the magnetic shear is small. Numerical experiments and diagnostics are undertaken to assess the resulting radial localisation of the turbulence, which affects the interaction of the latter with zonal flows. Increasing the amplitude of the magnetic shear (e.g.through current drive) has a stabilising effect on the turbulence and, thus, reduces the nonlinear energy transport.