Search

The unique role of the spatial distribution of crystallizable emulsifiers in regulating the structure and properties of double emulsions has been gradually recognized. Herein, we utilized crystallizable monoglycerides of different carbon chain length (GMS/GMP/GML) to "structuring" the intermediate oil phase of double emulsions during a two-stage emulsification process followed by a cooling treatment. A ternary eigenvector (I, M, E) based on the numerical processing of polarization images was invented to quantitatively characterize the distribution pattern of monoglycerides. Crystallization kinetics analysis and dissipative particle dynamic simulation were then employed to reveal the regulatory mechanism for the site-specific interface distribution behavior. Results suggested that the distribution pattern of monoglycerides could be pricesly tuned as the internal interface-, external interface- or oil-phase dominated one in double emulsions. The surface activity as well as crystallization rates of monoglycerides dominated the interfacial distribution kinetics, and the cooling gradient along the interface region further regulated their interfacial distribution potential. Specificly, shorter crystallization time (t

Climate change leads to a change of precipitation frequency and quantity as well as to increased temperature inducing extreme weather events like floods but also more intensive and longer drought periods.The response of the vegetation to these trends is of high interest because vegetation regulates interception, transpiration and is a water storage which is important for plant productivity, agriculture, carbon cycling and the danger of wild fire occurrence. Reduced precipitation in combination with increased temperature lead to water stressed vegetation which might not only behave different in regards of evapotranspiration but are also prone to wildfires. However, currently we don’t know how the water status changes in the long-term. A long-term time series of the vegetation leaf moisture content can help to understand the consequences of changing environmental conditions on the vegetation layer as part of the water as well as the carbon cycle.Measurements of vegetation leaf moisture are usually only available for single test-sites (missing spatial coverage), often measured for a short time span and might hold missing data. Estimations of vegetation leaf moisture are able to provide consistent time series but are mostly done on regional scale which are also missing spatial transferability. However, long-term data with a consistent time series and large spatial coverage are necessary to address a reliable time series analyses in the context of climate change.Our trend analysis will focus on the live-fuel moisture content (LFMC) which is based on the vegetation optical depth (VOD) and Leaf Area Index (LAI). LFMC is defined as the water mass of living vegetation to the dry mass of the vegetation, usually expressed in percentage. LFMC is an important variable in the field of wild fire analyses as it is one of the key predictors for risk and development of a fire. LFMC can be estimated on ecosystem level due to its independence of plant type. Here we use VODCA VOD and GLOBMAP LAI data to create a longer time series of LFMC for the period 1988-2017 on global scale to analyse temporal changes in LFMC. Initial results indicate a heterogeneous pattern of LFMC trends which depend on land cover type, e.g., with a decreasing trend for shrublands but an increasing trend for needle-leaved forests. We compare the trends in LFMC with trends in heat and drought events as well as fire weather indices. Inter-annual changes in LFMC correspond to multi-year drought events.

The ongoing and projected climate change involves changes in temperatures and precipitation in many regions. These changes in turn affect terrestrial ecosystems that require sufficient water and energy to provide essential services such as food security and the uptake of human-caused CO2 emissions.This presentation will introduce the concept of ecosystem water and energy limitation, and identify areas where each limitation prevails. These areas are characterised by different sensitivities of evapotranspiration and vegetation productivity to long-term changes in temperature and precipitation. A special focus will be on the global trends of ecosystem water limitation through time, where our results show increased water sensitivity across recent and future decades in many regions. This implies an increasing ecosystem vulnerability to water availability which can lead to reductions in vegetation carbon uptake in the future, consequently amplifying climate change. In this context, near-surface soil moisture is found to be the most relevant water reservoir for vegetation functioning, while deeper soil moisture is less relevant for the investigated multi-decadal time periods.The presentation will also illustrate that the increasing water limitation can affect the consequences of droughts in related regions. These ecosystems become more vulnerable to droughts such that disruptions in vegetation functioning are more pronounced. Also evaporative cooling will decrease more strongly which promotes hotter temperatures during drought. At the same time, decreased vegetation productivity could lead to reduced availability of fuel for wildfires.These analyses are based on (i) observation-based data including reanalyses, satellite-based datasets and gridded data derived from upscaling in-situ observations, and (ii) simulations from land surface and Earth system models. Building upon this, the presentation will discuss the related model performance as well as opportunities for model development to more accurately capture and predict ecosystem water limitation.

High streamflow in rivers can lead to flooding, which may have severe impacts on economy, society and ecosystems. Therefore it is imperative to understand their underlying physical mechanisms. Previous research has illustrated the relevance of several hydrological drivers, such as precipitation, snowmelt and soil moisture. However, the relative importance of these drivers compared with each other is unclear. Moreover, the role of vegetation-related drivers is not well studied. In this study, we focus on high river flows and consider a comprehensive set of potential drivers and analyze their relative importance. This is done with streamflow observations from over 250 near-natural catchments located across Europe during 1984–2007, which are matched with driver data from various observation-based sources. Not surprisingly, we find that precipitation is the most relevant driver of high river flows in most catchments. In addition, and more interestingly, we show that next to precipitation a diversity of other drivers is relevant for high flows, including shallow soil moisture, deep soil moisture, snowmelt, evapotranspiration and leaf area index. These non-precipitation drivers tend to be even more relevant for more extreme high flows. The relative importance of most considered drivers is similar across daily, weekly and monthly time scales. The spatial patterns of the relevance of precipitation, snowmelt and soil moisture for supporting high river flows are controlled by vegetation types and terrain characteristics, while climate and basin area are less important. By analyzing a comprehensive selection of drivers of high river flow in a powerful framework which accounts for co-linearities between drivers, this study advances the understanding of flood generation processes and informs respective model development.

Terrestrial vegetation is a key component of the Earth system as it mediates the exchange of carbon, water and energy between the land and the atmosphere. Thereby, the vegetation affects the climate through changes in its structure (such as leaf area index, LAI) and its physiology (such as stomatal conductance); However, their relative contributions and respective processes on the land-atmosphere coupling are not yet understood. For instance, increased LAI, referred to as structural changes, promotes transpiration and vegetation productivity, and increases the surface albedo in most cases. In contrast, decreased surface conductance, referred to as physiological changes, could reduce transpiration and productivity. Therefore, the overall feedback of vegetation to climate change via water, carbon and energy exchange will depend on the relative importance of structural and physiological responses. Here we study to what extent dynamic changes in global vegetation structure and physiology modulate land-atmosphere coupling using satellite remote-sensing, data-driven, and earth system modelled vegetation data, as well ashydro-meteorological reanalysis. The land-atmosphere coupling is quantified through the correlation between soil moisture and lagged vapor pressure deficit determined with a moving time window. We employ random forests to quantify vegetation physiology by accounting for functional variability (e.g. GPP and ET) explained by hydro-meteorological data but not by the vegetation structure. Then using an explainable machine learning approach (SHAP), we determine the contributions of vegetation structure and physiology where we find overall larger contributions of structure on regulating land-atmosphere coupling during the growing season. The relative importance of vegetation structure differs across ecosystems, with stronger contributions in dry ecosystems. Furthermore, we analyze the variations of the relevance of vegetation structure over time and in particular during warm and dry periods. The results are partially backed up by using in-situ measurements of physiological traits to interpret the large-scale observed physiological patterns.

Due to the complex radiative transfer process, the retrieval time of the physical retrieval algorithm is significantly increased compared with that of the statistical retrieval algorithm. The calculation of the Jacobian matrix is the most computationally intensive part of the physical retrieval algorithm. Further analysis showed that the changes in Jacobians had little effect on the performance of the physical retrieval algorithm. On the basis of the above findings, a fast physical-iterative retrieval algorithm was proposed by adaptively updating the Jacobian in keeping with the changes of the atmospheric state. The performance of the algorithm is evaluated using synthetic ground-based infrared spectra observations. The retrieval speed is significantly improved compared with the traditional physical retrieval algorithm under the condition that the parameters of the computing platform remain unchanged, with the average retrieval time reduced from 8.96 min to 3.69 min. The retrieval accuracy of the fast retrieval model is equivalent to that of the traditional algorithm, with maximum root-mean-square errors of less than 1.2 K and 1.0 g/kg for heights below 3 km for the temperature and water vapor mixing ratio (WVMR), respectively. The Jacobian updating strategy has a certain impact on the convergence of the retrieval algorithm, whose convergence rate is 98.7 %, which is lower than that of the traditional algorithm to some extent. However, reliable retrieval results can still be obtained by adjusting the convergence criteria.

Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of their rooting system and soil moisture availability across depth. The depth of vegetation water uptake is largely unknown across large spatial scales as a consequence of sparse ground measurements. At the same time, emerging satellite-derived observations of vegetation functioning, surface soil moisture and terrestrial water storage, present an opportunity to assess the depth of vegetation water uptake globally. In this study, we characterise vegetation functioning through the Near-Infrared Reflectance of Vegetation (NIRv), and compare its relation to (i) near-surface soil moisture from ESA-CCI and (ii) total water storage from GRACE at the monthly time scale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy-related variables. We find that vegetation functioning is generally more strongly related to near-surface soil moisture, particularly in semi-arid regions and areas with low tree cover. In contrast, in regions with high tree cover and in arid regions, the correlation with terrestrial water storage is comparable to or even higher than with near-surface soil moisture, indicating that trees can and do make use of their deeper rooting systems to access deeper soil moisture, similar to vegetation in arid regions. In line with this, an attribution analysis that examines the relative importance of these soil water storages for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of ecosystems to local water resources. Next to variations in space, the vegetation water uptake depth also varies in time. During dry periods, the relative importance of terrestrial water storage increases, highlighting the relevance of deeper water resources during rain-scarce periods. Overall, the synergistic exploitation of state-of-the-art satellite data products to disentangle the relevance of near-surface vs. terrestrial water storage for vegetation functioning can inform the representation of vegetation-water interactions in land surface models to support more accurate climate change projections.

Double emulsions (DEs) are promising delivery vehicles for the protective and programmed release of bioactive compounds. Herein, DEs with monoglycerides crystallized at the internal- or external interface or oil phase were fabricated. The results suggested that the crystallization site of monoglycerides exerts a significant role in retarding the structural degradation and lipid digestion of DEs by affecting the available contact area of lipase. At the initial stage of intestinal digestion, compared with noncrystalline DEs (82.1%, 3.7 min), the burst release of internal markers in the internal interface crystallized emulsions was decreased by 42.4% and the lag time of free fatty acid (FFA) release was delayed by 5.8 min in the external interface crystallized emulsions. The structural integrity and digestion kinetics of the external interface crystallized DEs were synchronized with the retention time of the interfacial crystals. Therefore, crystallizable emulsifiers exhibit unique and fine regulatory effects on the digestive properties of emulsions.

Droughts cause serious environmental and societal impacts, often aggravated by simultaneously occurring heat waves. Climate and vegetation play key roles in the evolution of drought-associated temperature anomalies, but their relative importance is largely unknown. Here, we present the hottest temperature anomalies during drought in subhumid and tree-dominated regions using observation-based, global data over 2001–15. These anomalies are mainly driven by a drought-related net radiation surplus and further amplified by forests’ water-saving strategies that result in diminished evaporative cooling. By contrast, in semiarid and short-vegetation regions, drought-related temperature increases are smaller. The reduction of evaporative cooling is weak and net radiation increases only marginally due to high albedo over drought-stressed vegetation. Our findings highlight the importance of considering all interacting factors in understanding diverse mechanisms of concurrent drought–heat extremes across different climate regimes. Significance Statement Climate and vegetation have a strong influence in regulating temperature anomalies during drought. However, the physical mechanisms behind drought–heat events across different climate–vegetation regimes are not always accurately described in physically based models. Here we use global-scale, observation-based datasets to show the spatial variation of temperature anomalies during drought, with the largest anomalies in subhumid and tree-dominated regions. Further, we present observational evidence for the relative roles of climate and vegetation in shaping drought–heat extremes across space. Our study provides valuable inputs to better understand the drought–heat pathways and their spatial variations, which can inform drought adaptation and mitigation efforts.

Air pollution (AP) is a significant environmental issue that poses a potential threat to human health. Its adverse effects on human health are diverse, ranging from sensory discomfort to acute physiological reactions. As such, air quality evaluation (AQE) serves as a crucial process that involves the collection of samples from the environment and their analysis to measure AP levels. With the proliferation of Internet of Things (IoT) devices and sensors, real-time and continuous measurement of air pollutants in urban environments has become possible. However, the data obtained from multiple sources of IoT sensors can be uncertain and inaccurate, posing challenges in effectively utilizing and fusing this data. Meanwhile, differences in opinions among decision-makers regarding AQE can affect the outcome of the final decision. To tackle these challenges, this paper systematically investigates a novel multi-attribute group decision-making (MAGDM) approach based on hesitant trapezoidal fuzzy (HTrF) information and discusses its application to AQE. First, by combining HTrF sets (HTrFSs) with multi-granulation rough sets (MGRSs), a new rough set model, named HTrF MGRSs, on a two-universe model is proposed. Second, the definition and property of the presented model are studied. Third, a decision-making approach based on the background of AQE is constructed via utilizing decision-making index sets (DMISs). Lastly, the validity and feasibility of the constructed approach are demonstrated via a case study conducted in the AQE setting using experimental and comparative analyses. The outcomes of the experiment demonstrate that the presented architecture owns the ability to handle multi-source IoT sensor data (MSIoTSD), providing a sensible conclusion for AQE. In summary, the MAGDM method presented in this article is a promising scheme for solving decision-making problems, where HTrFSs possess excellent information description capabilities and can adequately describe indecision and uncertainty information. Meanwhile, MGRSs serve as an outstanding information fusion tool that can improve the quality and level of decision-making. DMISs are better able to analyze and evaluate information and reduce the impact of disagreement on decision outcomes. The proposed architecture, therefore, provides a viable solution for MSIoTSD facing uncertainty or hesitancy in the AQE environment.

With the purpose of the design problem of electromagnetic launch distributed measurement and control system, this paper proposes a design method of scalable large-scale distributed interconnected measurement and control system based on FlexRay bus. The task execution time deviation of each node in the distributed system, including phase deviation and frequency deviation, is reduced by using the macro tick generation process and clock synchronization process to achieve task synchronization in the distributed system. The synchronization of timing generation is realized based on the universal clock technology. When the timing interval is zero, the pulse power units are guaranteed to be close to the ideal full synchronization, thus ensuring that the released energy during transmission is close to the design value. In the state of adjustable timing, when the pulse power supply module conducts pulse discharge, the trigger discharge times of the modules are set according to the load needs. The flat top, sawtooth, and other characteristic current waveforms are adjusted accurately. At the same time, a combined multi-level protection structure based on hardware and software is realized. The verification results show that the distributed electromagnetic launch measurement and control system implemented in this paper can meet the high efficiency, fast and continuous launch target.

The frequency and the intensity of drought events have increased during the past decades in some regions, yet the implications of drought for the terrestrial vegetation functioning are not fully understood. In particular, drought in related studies has often been characterized by meteorological conditions rather than the actual soil moisture deficit. Further, previous research focused predominantly on the structural vegetation response, such that the large-scale physiological response remains poorly understood.Here, we analyze and compare the vegetation’s physiological and structural responses to drought across the globe using high-resolution daily TROPOMI sun-induced fluorescence (SIF) as a proxy for productivity, short-wavelength vegetation optical depth (VOD) as a proxy for canopy water conditions and biomass, and near-infrared reflectance of terrestrial vegetation (NIRv) during the period March 2018 - August 2021. Taking advantage of an extended soil moisture record (1979-2021, ERA5-Land reanalysis) we identify and focus on regions where severe soil moisture droughts occurred during our relatively short analysis period. Therein, we quantify and compare the amounts of SIF, VOD and NIRv changes during the early and late drought stages as well as for the recovery period. We also compute the vegetation response to short-term soil moisture changes, i.e. the vegetation sensitivity to short-term soil moisture and the respective changes during the course of droughts. The absolute changes of vegetation indices allow to disentangle physiological and structural responses, while the sensitivity analysis can quantify vegetation responses straightforward to water limitation by accounting for meteorological forcings. To infer vegetation sensitivity, we train random forest regression models at each grid cell, and apply the SHapley Additive exPlanations (SHAP) method to isolate the influence of soil moisture on vegetation from that of other meteorological variables such as temperature, solar radiation, precipitation and vapor pressure deficit. Analyzing the absolute value changes of NIRv, SIF and VOD during the 2018 European drought and the 2020 Russian drought events, we find asynchronous responses of vegetation productivity and vegetation water content. This indicates different hydraulic regulation strategies in response to drought. Moving beyond these case studies, we quantified and averaged the vegetation sensitivity to soil moisture across many severe droughts across the globe, which reveals systematic sensitivity increases during the course of the drought. From an ecological perspective, this indicates increases in ecosystem vulnerability to drought, and can induce feedback in climate and ecosystem services. Vegetation responses to drought differ depending on different vegetation types, climate and soil conditions. In addition, we conduct carbon fluxes from eddy covariance measurements to confirm the vegetation responses to drought derived from Earth observation satellites. In summary, this study provides insights into vegetation responses to droughts at large spatial scales which can help to accurately quantify respective anomalies in terms of land carbon uptake to consequently improve climate projections.