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Global hydrological models enhance our understanding of the Earth system and support the sustainable management of water, food and energy in a globalized world. They integrate process knowledge with a multitude of model input data (e.g., precipitation, soil properties, and the location and extent of surface waterbodies) to describe the state of the Earth. However, they do not fully utilize observations of model output variables (e.g., streamflow and water storage) to reduce and quantify model output uncertainty through processes like parameter estimation. For a pilot region, the Mississippi River basin, we assessed the suitability of three ensemble-based multi-variable approaches to amend this: Pareto-optimal calibration (POC); the generalized likelihood uncertainty estimation (GLUE); and the ensemble Kalman filter, here modified for joint calibration and data assimilation (EnCDA). The paper shows how observations of streamflow (Q) and terrestrial water storage anomaly (TWSA) can be utilized to reduce and quantify the uncertainty of model output by identifying optimal and behavioral parameter sets for individual drainage basins. The common first steps in all approaches are (1) the definition of drainage basins for which calibration parameters are uniformly adjusted (CDA units), combined with the selection of observational data; (2) the identification of potential calibration parameters and their a priori probability distributions; and (3) sensitivity analyses to select the most influential model parameters per CDA unit that will be adjusted by calibration. Data assimilation with the ensemble Kalman filter was modified, to our knowledge, for the first time for a global hydrological model to assimilate both TWSA and Q with simultaneous parameter adjustment. In the estimation of model output uncertainty, we considered the uncertainties of the Q and TWSA observations. Applying the global hydrological model WaterGAP, we found that the POC approach is best suited for identifying a single “optimal” parameter set for each CDA unit. This parameter set leads to an improved fit to the monthly time series of both Q and TWSA as compared to the standard WaterGAP variant, which is only calibrated against mean annual Q, and can be used to compute the best estimate of WaterGAP output. The GLUE approach is almost as successful as POC in increasing WaterGAP performance and also allows, with a comparable computational effort, the estimation of model output uncertainties that are due to the equifinality of parameter sets given the observation uncertainties. Our experiment reveals that the EnCDA approach performs similarly to POC and GLUE in most CDA units during the assimilation phase but is not yet competitive for calibrating global hydrological models; its potential advantages remain unrealized, likely due to its high computational burden, which severely limits the ensemble size, and the intrinsic nonlinearity in simulating Q. Partitioning the whole Mississippi River basin into five CDA units (sub-basins) instead of only one improved model performance in terms of the Nash–Sutcliffe efficiency during the calibration and validation periods. Diverse parameter sets achieved comparable fits to observations, narrowing the range for at least three parameters. Low coverage of observation uncertainty bands by GLUE-derived model output bands is attributed to model structure uncertainties, especially regarding artificial reservoir operations, the location and extent of small wetlands, and the lack of representation of rivers that may lose water to the subsurface. These uncertainties are also likely to be responsible for significant trade-offs between optimal fits to Q and TWSA. Calibration performed exclusively against TWSA in regions without Q observations may worsen the Q simulation as compared to the uncalibrated model variant. We recommend that modelers improve the realism of the output of global hydrological models by calibrating them against observations of multiple output variables, including at least Q and TWSA. Further work on improving the numerical efficiency of the EnCDA approach is necessary.

Participatory processes for identifying local climate change adaptation measures have to be performed worldwide. As these processes require information about context-specific climate change hazards, we show in this study how to quantify climate change hazards with their uncertainties in regions all around the globe and how to best communicate the potential hazards with their uncertainties in order to identify local climate change adaptation strategies. In a participatory process on water-related adaptation in a biosphere reserve in Germany, we used the freely available output of a multi-model ensemble provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) initiative, which provides global coverage, to quantify the wide range of potential future changes in (ground)water resources. Our approach for quantifying the range of potential climate change hazards can be applied worldwide for local to regional study areas and also for adaptations in agriculture, forestry, fisheries, and biodiversity. We evaluated our approach to communicating uncertain local climate change hazards by means of questionnaires that the stakeholders in the participatory process and the audiences from the general public of two project result presentations answered. To support the stakeholders in participatory climate change adaptation processes, we propose the use of percentile boxes rather than boxplots for visualizing the range of potential future changes. This helps the stakeholders identify the future changes they wish to adapt to, depending on the problem (e.g., resource scarcity vs. resource excess) and their risk aversion. The general public is best informed by simple ensemble averages of potential future changes together with the model agreement on the sign of change. Using or adapting our quantification and communication approach, flexible climate change adaptation strategies can and should be developed worldwide in a participatory and transdisciplinary manner, involving stakeholders and scientists.

Abstract In recent sea level studies, discrepancies have arisen in ocean mass observations obtained from the Gravity Recovery and Climate Experiment and its successor, GRACE Follow-On, with GRACE estimates consistently appearing lower than density-corrected ocean volume observations since 2015. These disparities have raised concerns about potential systematic biases in sea-level observations, with significant implications for our understanding of this essential climate variable. Here, we reconstruct the global and regional ocean mass change through models of ice and water mass changes on land and find that it closely aligns with both GRACE and density-corrected ocean volume observations after implementing recent adjustments to the wet troposphere correction and halosteric sea level. While natural variability in terrestrial water storage is important on interannual timescales, we find that the net increase in ocean mass over 20 years can be almost entirely attributed to ice wastage and human management of water resources.

Almost no hydrological model takes into account that changes in evapotranspiration are affected by how vegetation responds to changing CO2 and climate. This severely limits their ability to quantify the impact of climate change on evapotranspiration and, thus, water resources. As the simulation of vegetation responses is both complex and very uncertain, we recommend a simple approach to considering (in climate change impact studies with hydrological models) the uncertainty that the vegetation response causes with respect to the estimation of future potential evapotranspiration (PET). To quantify this uncertainty in a simple manner, we propose running the hydrological model in two variants: with its standard PET approach and with a modified approach to compute PET. In the case of PET equations containing stomatal conductance, the modified approach can be implemented by adjusting the conductance. We introduce a modified approach for hydrological models that computes PET as a function of net radiation and temperature only, i.e., with the Priestley–Taylor (PT) equation. The new PT-MA approach is based on the work of Milly and Dunne (2016) (MD), who compared the change in non-water-stressed actual evapotranspiration (NWSAET) as computed by an ensemble of global climate models (GCMs), which simulate vegetation response as well as interactions between the atmosphere and the land surface, with various methods to compute PET change. Based on this comparison, MD proposed estimating the impact of climate change on PET as a function of only the change in net energy input at the land surface. PT-MA retains the impact of temperature on daily to interannual as well as spatial PET variations but removes the impact of the long-term temperature trend on PET such that long-term changes in future PET are driven by changes in net radiation only. We implemented PT-MA in the global hydrological model WaterGAP 2.2d and computed daily time series of PET between 1901 and 2099 using the bias-adjusted output of four GCMs. Increases in GCM-derived NWSAET between the end of the 20th and the end of the 21st century for Representative Concentration Pathway 8.5 (RCP8.5) are simulated well by WaterGAP if PT-MA is applied but are severely overestimated with the standard PT method. Application of PT-MA in WaterGAP results in smaller future decreases or larger future increases in renewable water resources (expressed as the variable RWR) compared with the standard PT method, except in a small number of grid cells where increased inflow from upstream areas due to increased upstream runoff leads to enhanced evapotranspiration from surface water bodies or irrigated fields. On about 20 % of the global land area, PT-MA leads to an increase in RWR that is more than 20 % higher than in the case of standard PT, while on more than 10 % of the global land area, the projected RWR decrease is reduced by more than 20 %. While the modified approach to compute PET is likely to avoid the overestimation of future drying in many if not most regions, the vegetation response in other regions may be such that the application of the standard PET leads to more likely changes in PET. As these regions cannot be identified with certainty, the proposed ensemble approach with two hydrological model variants serves to represent the uncertainty in hydrological changes due to the vegetation response to climate change that is not represented in the model.

Streamflow drought hazard indicators (SDHIs) are mostly lacking in large-scale drought early warning systems (DEWSs). This paper presents a new systematic approach for selecting and computing SDHIs for monitoring drought for human water supply from surface water and for river ecosystems. We recommend considering the habituation of the system at risk (e.g., a drinking water supplier or small-scale farmers in a specific region) to the streamflow regime when selecting indicators; i.e., users of the DEWSs should determine which type of deviation from normal (e.g., a certain interannual variability or a certain relative reduction of streamflow) the risk system of interest has become used to and adapted to. Distinguishing four indicator types, we classify indicators of drought magnitude (water anomaly during a predefined period) and severity (cumulated magnitude since the onset of the drought event) and specify the many relevant decisions that need to be made when computing SDHIs. Using the global hydrological model WaterGAP 2.2d, we quantify eight existing and three new SDHIs globally. For large-scale DEWSs based on the output of hydrological models, we recommend specific SDHIs that are suitable for assessing the drought hazard for (1) river ecosystems, (2) water users without access to large reservoirs, and (3) water users with access to large reservoirs, as well as being suitable for informing reservoir managers. These SDHIs include both drought magnitude and severity indicators that differ by the temporal averaging period and the habituation of the risk system to reduced water availability. Depending on the habituation of the risk system, drought magnitude is best quantified either by the relative deviation from the mean or by the return period of the streamflow value that is based on the frequency of non-exceedance. To compute the return period, we favor empirical percentiles over the standardized streamflow indicator as the former do not entail uncertainties due to the fitting of a probability distribution and can be computed for all streamflow time series. Drought severity should be assessed with indicators that imply habituation to a certain degree of interannual variability, to a certain reduction from mean streamflow, and to the ability to fulfill human water demand and environmental flows. Reservoir managers are best informed by the SDHIs of the grid cell that represents inflow into the reservoir. The DEWSs must provide comprehensive and clear explanations about the suitability of the provided indicators for specific risk systems.

Abstract Purpose An experimental approach was designed to measure the preload force, the coefficient of friction and the component of the tightening torque that is needed to surmount the thread-friction in an implant–abutment–screw complex that includes a carbon-coated screw. With the determined preload values the coefficient of friction was calculated. Methods 25 unused complexes, containing an implant, an abutment and a carbon-coated titanium alloy abutment screw, were tested. A custom load frame with two load cells and associated electronics was used. The threads were not lubricated. All abutment screws were torqued ten times to 25 Ncm. The produced preload values and a force that was proportional to the thread-friction component of the tightening torque were recorded. Results Mean preload values decreased significantly with the number of repetitions (p

Continental-to global-scale hydrologic and land surface models increasingly include representations of the groundwater system. Such large-scale models are essential for examining, communicating, and understanding the dynamic interactions between the Earth system above and below the land surface as well as the opportunities and limits of groundwater resources. We argue that both large-scale and regional-scale groundwater models have utility, strengths, and limitations, so continued modeling at both scales is essential and mutually beneficial. A crucial quest is how to evaluate the realism, capabilities, and performance of large-scale groundwater models given their modeling purpose of addressing large-scale science or sustainability questions as well as limitations in data availability and commensurability. Evaluation should identify if, when, or where large-scale models achieve their purpose or where opportunities for improvements exist so that such models better achieve their purpose. We suggest that reproducing the spatiotemporal details of regional-scale models and matching local data are not relevant goals. Instead, it is important to decide on reasonable model expectations regarding when a large-scale model is performing "well enough"in the context of its specific purpose. The decision of reasonable expectations is necessarily subjective even if the evaluation criteria are quantitative. Our objective is to provide recommendations for improving the evaluation of groundwater representation in continental-to global-scale models. We describe current modeling strategies and evaluation practices, and we subsequently discuss the value of three evaluation strategies: (1) comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation), (2) comparing several models with each other with or without reference to actual observations (model-based evaluation), and (3) comparing model behavior with expert expectations of hydrologic behaviors in particular regions or at particular times (expert-based evaluation). Based on evolving practices in model evaluation as well as innovations in observations, machine learning, and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches, while accounting for commensurability issues, may significantly improve the realism of groundwater representation in large-scale models, thus advancing our ability for quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on this quest, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.

Abstract Nanoemulsions (NEs), emulsified systems with diameter between 20 and 500 nm, are promising in cosmetic science due to stability and efficient transportation of lipophilic substances, such as volatile oils, favoring the skin penetration to carry out the biological activity. Volatile oil of Matricaria recutita L. (VOMR) has several therapeutic effects, but the lability of the volatile oils requires appropriate vehiculation in NEs. Besides, the hydrophilic-lipophilic balance (HLB) parameter for VOMR is not described in the literature. Therefore, NEs composed of Span® 80, Tween® 80, PEG-7 glyceryl cocoate, glycerin and water were developed by the high-energy method. The NEs were characterized by organoleptic, physicochemical and morphological evaluations as well as the stability evaluation was conducted at different temperatures for 60 days. The NEs obtained with and without VOMR exhibited translucent aspect, pH of 4.82 and 5.62; average diameter of 104.97 ± 4.03 and 92.32 ± 6.17 nm; polydispersity index of 0.27 ± 0.06 and 0.31 ± 0.04, and zeta potential of -28.37 ± 3.06 and -36.48 ± 6.56 mV, respectively. The encapsulation of VOMR was around 83% into the NE and confirmed by visualization through scanning electron microscopy - field emission gun. The stability study demonstrated that refrigeration is the optimal storage condition because the conservation of the NEs characteristics. As a result, the present study describes for the first time HLB value near 7 for VOMR. Thus, NE with this volatile oil was developed with suitable characteristics, contributing to technology development of phytopharmaceuticals for topical application to the skin.

Abstract Silver nanoparticles (AgNPs) have extensive applications in nanomedicine due to their physicochemical properties and interactions with biomolecules. One way of obtaining them is through green synthesis with plant extracts containing reducing and capping agents, such as Catharanthus roseus (L.) G Don. This plant has pharmacological applications due to its phytocompounds with biological activity. The C. roseus flowers are rich in alkaloids and phenolic compounds, and have the potential to synthesize AgNPs but have never been used with this purpose. In this study, AgNPs were synthesize with the aqueous extract from the C. roseus flowers, and their cytotoxicity evaluated on A549 lung tumor cells through MTT assay. The AgNP synthesis could be seen visually from the solution's color change and through UV-visible spectroscopy to detect a peak between 400 - 530 nm. FEG microscopy showed AgNPs with variable shapes and sizes. In vitro cytotoxicity assay against A549 cells showed that the C. roseus extract have low toxicity to the lineage, and AgNPs reduced half the viable cells at the 1.5% concentration, demonstrating high cytotoxicity. Therefore, AgNPs from C. roseus were successfully obtained and showed significant cytotoxic activity on A549 lung cancer cells.

Abstract In the last few years, the world has experienced numerous extreme droughts with adverse direct, cascading, and systemic impacts. Despite more frequent and severe events, drought risk assessment is still incipient compared to that of other meteorological and climate hazards. This is mainly due to the complexity of drought, the high level of uncertainties in its analysis, and the lack of community agreement on a common framework to tackle the problem. Here, we outline that to effectively assess and manage drought risks, a systemic perspective is needed. We propose a novel drought risk framework that highlights the systemic nature of drought risks, and show its operationalization using the example of the 2022 drought in Europe. This research emphasizes that solutions to tackle growing drought risks should not only consider the underlying drivers of drought risks for different sectors, systems or regions, but also be based on an understanding of sector/system interdependencies, feedbacks, dynamics, compounding and concurring hazards, as well as possible tipping points and globally and/or regionally networked risks.

Butylated hydroxyanisole (BHA) is a synthetic phenolic antioxidant widely used in various food matrices to prevent oxidative rancidity. However, its presence has been associated with liver damage and carcinogenesis in animals. Thus, an electrochemical sensor was built using a composite of gold nanoparticles synthesized in peach extract (Prunus persica (L.) Batsch) and graphene. Peach extract served as a reducing and stabilizing agent for gold nanoparticles, as a dispersing agent for graphene, and as a film former to immobilize the composite on the surface of a glassy carbon electrode. The gold nanoparticles were characterized using spectroscopic and microscopic techniques, and the electrodes were electrochemically characterized using electrochemical impedance spectroscopy and cyclic voltammetry. The sensor provided higher current responses and lower charge transfer resistances compared to the unmodified glassy carbon electrode. Under the established optimized working conditions (0.1 mol L−1 Britton–Robinson buffer, pH 4.0, and differential pulse voltammetry), the calibration curve exhibited a linear range from 0.2 to 9.8 µmol L–1, with a detection limit of 70 nmol L−1. The proposed sensor represented a sensitive and practical analytical tool for the accurate determination of BHA in mayonnaise samples.

IntroductionPlants release a large variety of metabolites via their roots to shape physico-chemical soil properties and biological processes in the rhizosphere. While hydroponic growth conditions facilitate accessibility of the root system and recovery of root exudates, the natural soil environment can alter root metabolism and exudate secretion, raising the question to what extent the quantity and composition of root exudates released in hydroponic growth systems reflect those recovered from soil-grown roots.MethodsUsing a root washing method, we sampled root exudates from four field-grown cover crop species with wide taxonomic distance, namely white mustard, lacy phacelia, bristle oat, and Egyptian clover. A set of primary metabolites and secondary metabolites were analysed in a targeted and untargeted LC-MS-based approach, respectively, for comparison with exudates obtained from hydroponically cultured plants.Results and discussionWe found that hydroponically cultivated plants released a larger amount of total carbon, but that the recovery of total carbon was not indicative for the diversity of metabolites in root exudates. In the field, root exudates from phacelia and clover contained 2.4 to 3.8 times more secondary metabolites, whereas carbon exudation in hydroponics was 5- to 4-fold higher. The composition of the set of metabolites identified using the untargeted approach was much more distinct among all species and growth conditions than that of quantified primary metabolites. Among secondary metabolite classes, the presence of lipids and lipid-like molecules was highly indicative for field samples, while the release of a large amount of phenylpropanoids, organoheterocyclic compounds or benzenoids was characteristic for clover, mustard or oat, respectively, irrespective of the cultivation condition. However, at the compound level the bulk of released metabolites was specific for cultivation conditions in every species, which implies that hydroponically sampled root exudates poorly reflect the metabolic complexity of root exudates recovered from field-grown plants.

Studies of the global sea-level budget (SLB) and the global ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributors. Here we present datasets for times series of the SLB and OMB elements developed in the framework of ESA's Climate Change Initiative. We use these datasets to assess the SLB and the OMB simultaneously, utilising a consistent framework of uncertainty characterisation. The time series, given at monthly sampling and available at https://doi.org/10.5285/17c2ce31784048de93996275ee976fff (Horwath et al., 2021), include global mean sea-level (GMSL) anomalies from satellite altimetry, the global mean steric component from Argo drifter data with incorporation of sea surface temperature data, the ocean-mass component from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry, the contribution from global glacier mass changes assessed by a global glacier model, the contribution from Greenland Ice Sheet and Antarctic Ice Sheet mass changes assessed by satellite radar altimetry and by GRACE, and the contribution from land water storage anomalies assessed by the global hydrological model WaterGAP (Water Global Assessment and Prognosis). Over the period January 1993–December 2016 (P1, covered by the satellite altimetry records), the mean rate (linear trend) of GMSL is 3.05 ± 0.24 mm yr−1. The steric component is 1.15 ± 0.12 mm yr−1 (38 % of the GMSL trend), and the mass component is 1.75 ± 0.12 mm yr−1 (57 %). The mass component includes 0.64 ± 0.03 mm yr−1 (21 % of the GMSL trend) from glaciers outside Greenland and Antarctica, 0.60 ± 0.04 mm yr−1 (20 %) from Greenland, 0.19 ± 0.04 mm yr−1 (6 %) from Antarctica, and 0.32 ± 0.10 mm yr−1 (10 %) from changes of land water storage. In the period January 2003–August 2016 (P2, covered by GRACE and the Argo drifter system), GMSL rise is higher than in P1 at 3.64 ± 0.26 mm yr−1. This is due to an increase of the mass contributions, now about 2.40 ± 0.13 mm yr−1 (66 % of the GMSL trend), with the largest increase contributed from Greenland, while the steric contribution remained similar at 1.19 ± 0.17 mm yr−1 (now 33 %). The SLB of linear trends is closed for P1 and P2; that is, the GMSL trend agrees with the sum of the steric and mass components within their combined uncertainties. The OMB, which can be evaluated only for P2, shows that our preferred GRACE-based estimate of the ocean-mass trend agrees with the sum of mass contributions within 1.5 times or 0.8 times the combined 1σ uncertainties, depending on the way of assessing the mass contributions. Combined uncertainties (1σ) of the elements involved in the budgets are between 0.29 and 0.42 mm yr−1, on the order of 10 % of GMSL rise. Interannual variations that overlie the long-term trends are coherently represented by the elements of the SLB and the OMB. Even at the level of monthly anomalies the budgets are closed within uncertainties, while also indicating possible origins of remaining misclosures.

Since the seminal work of Spearman, exploratory and confirmatory factor analysis represents the standard method of examining the dimensionality of psychological instruments. Recently, within the network psychometrics approach, a new procedure was proposed to estimate the dimensionality of psychological instruments: exploratory graph analysis (EGA). This study investigated the structure of the French Wechsler Intelligence Scale for Children–Fifth Edition (WISC–VFR) with five standardization sample age groups (6–7, 8–9, 10–11, 12–13, and 14–16 years) using EGA. The primary research questions include (a) how many WISC–VFR dimensions are identified in each age subgroup? (b) how are subtest scores associated within the dimensions? Because the number and the content of the dimensions identified by EGA could vary with samples, the secondary research questions include (c) is there evidence of reproducibility and generalizability of the dimensions identified by EGA? We used another procedure called bootstrap exploratory graph analysis (bootEGA). EGA and bootEGA suggested only three dimensions, which are consistent with processing speed, verbal comprehension, and the “old” perceptual reasoning factor. Results did not support the distinction between visual–spatial and fluid reasoning dimensions. EGA and bootEGA represent new tools to assess the construct validity of psychological instruments, such as the WISC–VFR.

Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system. Such large-scale models are essential for examining, communicating, and understanding the dynamic interactions between the Earth system above and below the land surface as well as the opportunities and limits of groundwater resources. We argue that both large-scale and regional-scale groundwater models have utility, strengths, and limitations, so continued modeling at both scales is essential and mutually beneficial. A crucial quest is how to evaluate the realism, capabilities, and performance of large-scale groundwater models given their modeling purpose of addressing large-scale science or sustainability questions as well as limitations in data availability and commensurability. Evaluation should identify if, when, or where large-scale models achieve their purpose or where opportunities for improvements exist so that such models better achieve their purpose. We suggest that reproducing the spatiotemporal details of regional-scale models and matching local data are not relevant goals. Instead, it is important to decide on reasonable model expectations regarding when a large-scale model is performing “well enough” in the context of its specific purpose. The decision of reasonable expectations is necessarily subjective even if the evaluation criteria are quantitative. Our objective is to provide recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We describe current modeling strategies and evaluation practices, and we subsequently discuss the value of three evaluation strategies: (1) comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation), (2) comparing several models with each other with or without reference to actual observations (model-based evaluation), and (3) comparing model behavior with expert expectations of hydrologic behaviors in particular regions or at particular times (expert-based evaluation). Based on evolving practices in model evaluation as well as innovations in observations, machine learning, and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches, while accounting for commensurability issues, may significantly improve the realism of groundwater representation in large-scale models, thus advancing our ability for quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on this quest, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.

River networks are among Earth’s most threatened hot-spots of biodiversity and provide key ecosystem services (e.g., supply drinking water and food, climate regulation) essential to sustaining human well-being. Climate change and increased human water use are causing more rivers and streams to dry, with devastating impacts on biodiversity and ecosystem services. Currently, more than a half of the global river networks consist of drying channels, and these are expanding dramatically. However, drying river networks (DRNs) have received little attention from scientists and policy makers, and the public is unaware of their importance. Consequently, there is no effective integrated biodiversity conservation or ecosystem management strategy of DRNs.A multidisciplinary team of 25 experts from 11 countries in Europe, South America, China and the USA will build on EU efforts to assess the cascading effects of climate change on biodiversity, ecosystem functions and ecosystem services of DRNs through changes in flow regimes and water use. DRYvER (DRYing riVER networks) will gather and upscale empirical and modelling data from nine focal DRNs (case studies) in Europe (EU) and Community of Latin American and Caribbean States (CELAC) to develop a meta-system framework applicable to Europe and worldwide. It will also generate crucial knowledge-based strategies, tools and guidelines for economically-efficient adaptive management of DRNs. Working closely with stakeholders and end-users, DRYvER will co-develop strategies to mitigate and adapt to climate change impacts in DRNs, integrating hydrological, ecological (including nature-based solutions), socio-economic and policy perspectives. The end results of DRYvER will contribute to reaching the objectives of the Paris Agreement and placing Europe at the forefront of research on climate change.

ABSTRACT: Corporate Venture Capital (CVC) has been receiving increasingly more attention all over the world as a special way for accessing new ideas and innovative opportunities through minor-share investing in established companies. The purpose of CVC investments may either be purely financial or to pursue strategic goals. Organisations often seek to take actions that impact positively on sustainability by assembling related knowledge and technologies. These resources may come from invested startups through the use of a CVC programme. This research aims to measure and analyse the Corporate Venture Capital programmes of companies listed in the ISE B3 Corporate Sustainability Index. To this end, a three-step methodology was conducted. First, a systematic review of the literature took place, followed by a review of companies based on secondary sources such as their websites. Finally, a survey was developed and was opted to survey companies through their Investor Relationship (IR) public channels. Results show that 27 of the companies listed in the ISE B3 Index have CVC programmes that contemplate organizational initiatives besides investment. In this sense, one can claim that at least 70% of the ISE B3 Index companies are somehow involved in CVC operations. The results contribute to the literature on corporate venture capital and sustainability by showing that companies spend from 10% to 15% of their capital in sustainable businesses in order to remain competitive.

Global water models (GWMs) simulate the terrestrial water cycle on the global scale and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modelling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improve existing hydrological processes, and integrate new processes.

Drought is understood as both a lack of water (i.e., a deficit compared to demand) and a temporal anomaly in one or more components of the hydrological cycle. Most drought indices, however, only consider the anomaly aspect, i.e., how unusual the condition is. In this paper, we present two drought hazard indices that reflect both the deficit and anomaly aspects. The soil moisture deficit anomaly index, SMDAI, is based on the drought severity index, DSI (Cammalleri et al., 2016), but is computed in a more straightforward way that does not require the definition of a mapping function. We propose a new indicator of drought hazard for water supply from rivers, the streamflow deficit anomaly index, QDAI, which takes into account the surface water demand of humans and freshwater biota. Both indices are computed and analyzed at the global scale, with a spatial resolution of roughly 50 km, for the period 1981–2010, using monthly time series of variables computed by the global water resources and the model WaterGAP 2.2d. We found that the SMDAI and QDAI values are broadly similar to values of purely anomaly-based indices. However, the deficit anomaly indices provide more differentiated spatial and temporal patterns that help to distinguish the degree and nature of the actual drought hazard to vegetation health or the water supply. QDAI can be made relevant for stakeholders with different perceptions about the importance of ecosystem protection, by adapting the approach for computing the amount of water that is required to remain in the river for the well-being of the river ecosystem. Both deficit anomaly indices are well suited for inclusion in local or global drought risk studies.

The human gut acts as the main reservoir of microbes and a relevant source of life-threatening infections, especially in immunocompromised patients. There, the opportunistic fungal pathogen Candida albicans adapts to the host environment and additionally interacts with residing bacteria. We investigated fungal-bacterial interactions by coinfecting enterocytes with the yeast Candida albicans and the Gram-negative bacterium Proteus mirabilis resulting in enhanced host cell damage. This synergistic effect was conserved across different P. mirabilis isolates and occurred also with non-albicans Candida species and C. albicans mutants defective in filamentation or candidalysin production. Using bacterial deletion mutants, we identified the P. mirabilis hemolysin HpmA to be the key effector for host cell destruction. Spatially separated coinfections demonstrated that synergism between Candida and Proteus is induced by contact, but also by soluble factors. Specifically, we identified Candida-mediated glucose consumption and farnesol production as potential triggers for Proteus virulence. In summary, our study demonstrates that coinfection of enterocytes with C. albicans and P. mirabilis can result in increased host cell damage which is mediated by bacterial virulence factors as a result of fungal niche modification via nutrient consumption and production of soluble factors. This supports the notion that certain fungal-bacterial combinations have the potential to result in enhanced virulence in niches such as the gut and might therefore promote translocation and dissemination.

Billions of people rely on groundwater as being an accessible source of drinking water and for irrigation, especially in times of drought. Its importance will likely increase with a changing climate. It is still unclear, however, how climate change will impact groundwater systems globally and, thus, the availability of this vital resource. Groundwater recharge is an important indicator for groundwater availability, but it is a water flux that is difficult to estimate as uncertainties in the water balance accumulate, leading to possibly large errors in particular in dry regions. This study investigates uncertainties in groundwater recharge projections using a multi-model ensemble of eight global hydrological models (GHMs) that are driven by the bias-adjusted output of four global circulation models (GCMs). Pre-industrial and current groundwater recharge values are compared with recharge for different global warming (GW) levels as a result of three representative concentration pathways (RCPs). Results suggest that projected changes strongly vary among the different GHM–GCM combinations, and statistically significant changes are only computed for a few regions of the world. Statistically significant GWR increases are projected for northern Europe and some parts of the Arctic, East Africa, and India. Statistically significant decreases are simulated in southern Chile, parts of Brazil, central USA, the Mediterranean, and southeastern China. In some regions, reversals of groundwater recharge trends can be observed with global warming. Because most GHMs do not simulate the impact of changing atmospheric CO2 and climate on vegetation and, thus, evapotranspiration, we investigate how estimated changes in GWR are affected by the inclusion of these processes. In some regions, inclusion leads to differences in groundwater recharge changes of up to 100 mm per year. Most GHMs with active vegetation simulate less severe decreases in groundwater recharge than GHMs without active vegetation and, in some regions, even increases instead of decreases are simulated. However, in regions where GCMs predict decreases in precipitation and where groundwater availability is the most important, model agreement among GHMs with active vegetation is the lowest. Overall, large uncertainties in the model outcomes suggest that additional research on simulating groundwater processes in GHMs is necessary.

WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth. Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has improved our understanding of continental water storage variations, with a focus on overexploitation and depletion of water resources. In this paper, we describe the most recent model version WaterGAP 2.2d, including the water use models, the linking model that computes net abstractions from groundwater and surface water and the WaterGAP Global Hydrology Model (WGHM). Standard model output variables that are freely available at a data repository are explained. In addition, the most requested model outputs, total water storage anomalies, streamflow and water use, are evaluated against observation data. Finally, we show examples of assessments of the global freshwater system that can be achieved with WaterGAP 2.2d model output.

Glyphosate, the most successful herbicide in history, specifically inhibits the activity of the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS; EC 2.5.1.19), one of the key enzymes in the shikimate pathway. Amaranthus palmeri is a driver weed in agriculture today that has evolved glyphosate-resistance through increased EPSPS gene copy number and other mechanisms. Non-targeted GC–MS and LC–MS metabolomic profiling was conducted to examine the innate physiology and the glyphosate-induced perturbations in one sensitive and one resistant (by EPSPS amplification) population of A. palmeri. In the absence of glyphosate treatment, the metabolic profile of both populations was very similar. The comparison between the effects of sublethal and lethal doses on sensitive and resistant populations suggests that lethality of the herbicide is associated with an amino acid pool imbalance and accumulation of the metabolites of the shikimate pathway upstream from EPSPS. Ferulic acid and its derivatives were accumulated in treated plants of both populations, while quercetin and its derivative contents were only lower in the resistant plants treated with glyphosate.

Ocean mass and thus sea level is significantly affected by water storage on the continents. However, assessing the net contribution of continental water storage change to ocean mass change remains a challenge. We present an integrated version of the WaterGAP global hydrological model that is able to consistently simulate total water storage anomalies (TWSAs) over the global continental area (except Greenland and Antarctica) by integrating the output from the global glacier model of Marzeion et al. (2012) as an input to WaterGAP. Monthly time series of global mean TWSAs obtained with an ensemble of four variants of the integrated model, corresponding to different precipitation input and irrigation water use assumptions, were validated against an ensemble of four TWSA solutions based on the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry from January 2003 to August 2016. With a mean Nash–Sutcliffe efficiency (NSE) of 0.87, simulated TWSAs fit well to observations. By decomposing the original TWSA signal into its seasonal, linear trend and interannual components, we found that seasonal and interannual variability are almost exclusively caused by the glacier-free land water storage anomalies (LWSAs). Seasonal amplitude and phase are very well reproduced (NSE=0.88). The linear trend is overestimated by 30 %–50 % (NSE=0.65), and interannual variability is captured to a certain extent (NSE=0.57) by the integrated model. During the period 1948–2016, we find that continents lost 34–41 mm of sea level equivalent (SLE) to the oceans, with global glacier mass loss accounting for 81 % of the cumulated mass loss and LWSAs accounting for the remaining 19 %. Over 1948–2016, the mass gain on land from the impoundment of water in artificial reservoirs, equivalent to 8 mm SLE, was offset by the mass loss from water abstractions, amounting to 15–21 mm SLE and reflecting a cumulated groundwater depletion of 13–19 mm SLE. Climate-driven LWSAs are highly sensitive to precipitation input and correlate with El Niño Southern Oscillation multi-year modulations. Significant uncertainty remains in the trends of modelled LWSAs, which are highly sensitive to the simulation of irrigation water use and artificial reservoirs.

Abstract Background Elevated blood lactate levels were reported as useful predictors of clinical outcome and mortality in critically ill patients. To identify higher-risk patients, this investigation evaluated the relationship between patient mortality and admission lactate levels during the management of non-trauma critically ill patients in the emergency department (ED). Methods In this prospective, single centre observational study in a German university ED, all adult patients who were admitted to the ED resuscitation room were evaluated between September 1, 2014 and August 31, 2015. Blood samples for blood gas analysis, including lactate levels, were obtained immediately at admission. Study endpoint was 30-day mortality. Results During the study period, 532 patients were admitted to the resuscitation room of the ED. The data of 523 patients (98.3%) were available. The overall 30-day mortality was 34.2%. Patients presenting to the resuscitation room with admission lactate levels

During the last decades, the endorheic Lake Urmia basin in northwestern Iran has suffered from declining groundwater tables and a very strong recent reduction in the volume of Lake Urmia. For the case of Lake Urmia basin, this study explores the value of different locally and globally available observation data for adjusting a global hydrological model such that it can be used for distinguishing the impacts of human water use and climate variations. The WaterGAP Global Hydrology Model (WGHM) was for the first time calibrated against multiple in situ and spaceborne data to analyze the decreasing lake water volume, lake river inflow, loss of groundwater, and total water storage in the entire basin during 2003–2013. The calibration process was done using an automated approach including a genetic algorithm (GA) and non-dominated sorting genetic algorithm II (NSGA-II). Then the best-performing calibrated models were run with and without considering water use to quantify the impact of human water use. Observations encompass remote-sensing-based time series of annual irrigated areas in the basin from MODIS, monthly total water storage anomaly (TWSA) from GRACE satellites, and monthly lake volume anomalies. In situ observations include time series of annual inflow into the lake and basin averages of groundwater level variations based on 284 wells. In addition, local estimates of sectoral water withdrawals in 2009 and return flow fractions were utilized. Calibration against MODIS and GRACE data alone improved simulated inflow into Lake Urmia but inflow and lake volume loss were still overestimated, while groundwater loss was underestimated and seasonality of groundwater storage was shifted as compared to observations. Lake and groundwater dynamics could only be simulated well if calibration against groundwater levels led to an adjustment of the fractions of human water use from groundwater and surface water. Thus, in some basins, globally available satellite-derived observations may not suffice for improving the simulation of human water use. According to WGHM simulations with 18 optimal parameter sets, human water use was the reason for 52 %–57 % of the total basin water loss of about 10 km3 during 2003–2013, for 39 %–43 % of the Lake Urmia water loss of about 8 km3, and for up to 87 %–90 % of the groundwater loss. Lake inflow was 39 %–45 % less than it would have been without human water use. The study shows that even without human water use Lake Urmia would not have recovered from the significant loss of lake water volume caused by the drought year 2008. These findings can support water management in the basin and more specifically Lake Urmia restoration plans.

Droughts continue to affect ecosystems, communities and entire economies. Agriculture bears much of the impact, and in many countries it is the most heavily affected sector. Over the past decades, efforts have been made to assess drought risk at different spatial scales. Here, we present for the first time an integrated assessment of drought risk for both irrigated and rainfed agricultural systems at the global scale. Composite hazard indicators were calculated for irrigated and rainfed systems separately using different drought indices based on historical climate conditions (1980–2016). Exposure was analyzed for irrigated and non-irrigated crops. Vulnerability was assessed through a socioecological-system (SES) perspective, using socioecological susceptibility and lack of coping-capacity indicators that were weighted by drought experts from around the world. The analysis shows that drought risk of rainfed and irrigated agricultural systems displays a heterogeneous pattern at the global level, with higher risk for southeastern Europe as well as northern and southern Africa. By providing information on the drivers and spatial patterns of drought risk in all dimensions of hazard, exposure and vulnerability, the presented analysis can support the identification of tailored measures to reduce drought risk and increase the resilience of agricultural systems.

In global hydrological models, groundwater storages and flows are generally simulated by linear reservoir models. Recently, the first global gradient-based groundwater models were developed in order to improve the representation of groundwater–surface-water interactions, capillary rise, lateral flows, and human water use impacts. However, the reliability of model outputs is limited by a lack of data and by uncertain model assumptions that are necessary due to the coarse spatial resolution. The impact of data quality is presented in this study by showing the sensitivity of a groundwater model to changes in the only available global hydraulic conductivity dataset. To better understand the sensitivity of model output to uncertain spatially distributed parameters, we present the first application of a global sensitivity method for a global-scale groundwater model using nearly 2000 steady-state model runs of the global gradient-based groundwater model G3M. By applying the Morris method in a novel domain decomposition approach that identifies global hydrological response units, spatially distributed parameter sensitivities are determined for a computationally expensive model. Results indicate that globally simulated hydraulic heads are equally sensitive to hydraulic conductivity, groundwater recharge, and surface water body elevation, though parameter sensitivities vary regionally. For large areas of the globe, rivers are simulated to be either losing or gaining, depending on the parameter combination, indicating a high uncertainty in simulating the direction of flow between the two compartments. Mountainous and dry regions show a high variance in simulated head due to numerical instabilities of the model, limiting the reliability of computed sensitivities in these regions. This is likely caused by the uncertainty in surface water body elevation. We conclude that maps of spatially distributed sensitivities can help to understand the complex behavior of models that incorporate data with varying spatial uncertainties. The findings support the selection of possible calibration parameters and help to anticipate challenges for a transient coupling of the model.

In global hydrological models, groundwater (GW) is typically represented by a bucket-like linear groundwater reservoir. Reservoir models, however, (1) can only simulate GW discharge to surface water (SW) bodies but not recharge from SW to GW, (2) provide no information on the location of the GW table, and (3) assume that there is no GW flow among grid cells. This may lead, for example, to an underestimation of groundwater resources in semiarid areas where GW is often replenished by SW or to an underestimation of evapotranspiration where the GW table is close to the land surface. To overcome these limitations, it is necessary to replace the reservoir model in global hydrological models with a hydraulic head gradient-based GW flow model. We present G3M, a new global gradient-based GW model with a spatial resolution of 5′ (arcminutes), which is to be integrated into the 0.5∘ WaterGAP Global Hydrology Model (WGHM). The newly developed model framework enables in-memory coupling to WGHM while keeping overall runtime relatively low, which allows sensitivity analyses, calibration, and data assimilation. This paper presents the G3M concept and model design decisions that are specific to the large grid size required for a global-scale model. Model results under steady-state naturalized conditions, i.e., neglecting GW abstractions, are shown. Simulated hydraulic heads show better agreement to observations around the world compared to the model output of de Graaf et al. (2015). Locations of simulated SW recharge to GW are found, as is expected, in dry and mountainous regions but areal extent of SW recharge may be underestimated. Globally, GW discharge to rivers is by far the dominant flow component such that lateral GW flows only become a large fraction of total diffuse and focused recharge in the case of losing rivers, some mountainous areas, and some areas with very low GW recharge. A strong sensitivity of simulated hydraulic heads to the spatial resolution of the model and the related choice of the water table elevation of surface water bodies was found. We suggest to investigate how global-scale groundwater modeling at 5′ spatial resolution can benefit from more highly resolved land surface elevation data.

Abstract In the rhizosphere, plants are exposed to a multitude of different biotic and abiotic factors, to which they respond by exuding a wide range of secondary root metabolites. So far, it has been unknown to which degree root exudate composition is species‐specific and is affected by land use, the local impact and local neighborhood under field conditions. In this study, root exudates of 10 common grassland species were analyzed, each five of forbs and grasses, in the German Biodiversity Exploratories using a combined phytometer and untargeted liquid chromatography‐mass spectrometry (LC‐MS) approach. Redundancy analysis and hierarchical clustering revealed a large set of semi‐polar metabolites common to all species in addition to species‐specific metabolites. Chemical richness and exudate composition revealed that forbs, such as Plantago lanceolata and Galium species, exuded more species‐specific metabolites than grasses. Grasses instead were primarily affected by environmental conditions. In both forbs and grasses, plant functional traits had only a minor impact on plant root exudation patterns. Overall, our results demonstrate the feasibility of obtaining and untargeted profiling of semi‐polar metabolites under field condition and allow a deeper view in the exudation of plants in a natural grassland community.

The present study aimed to assess metabolites heterogeneity among four major Cinnamomum species, including true cinnamon (Cinnamomum verum) and less explored species (C. cassia, C. iners, and C. tamala). UPLC-MS led to the annotation of 74 secondary metabolites belonging to different classes, including phenolic acids, tannins, flavonoids, and lignans. A new proanthocyanidin was identified for the first time in C. tamala, along with several glycosylated flavonoid and dicarboxylic fatty acids reported for the first time in cinnamon. Multivariate data analyses revealed, for cinnamates, an abundance in C. verum versus procyandins, dihydro-coumaroylglycosides, and coumarin in C. cassia. A total of 51 primary metabolites were detected using GC-MS analysis encompassing different classes, viz. sugars, fatty acids, and sugar alcohols, with true cinnamon from Malaysia suggested as a good sugar source for diabetic patients. Glycerol in C. tamala, erythritol in C. iners, and glucose and fructose in C. verum from Malaysia were major metabolites contributing to the discrimination among species.

The objective of this work was to develop and characterize liposomes loaded with silver nanoparticles (LAgNPs) to show improvement in stability characteristics. AgNPs were prepared by the green synthesis method with Aloe vera gel extract and exposure to sunlight. Liposomes were prepared by the modified reverse phase method. Particle size, polydispersity index, zeta potential, as well as the scanning electron microscopy (SEM) morphological aspects of AgNPs and LAgNPs were evaluated. In addition, was used flame atomic absorption spectroscopy to determine the amount of AgNP that was encapsulated in liposomes. The AgNPs presented as amorphous and polydisperse structures, with a mean diameter of 278.46 nm and zeta potential of -18.3 mV. LAgNPs had a mean diameter between 321 and 373 nm, the polydispersity index close to 0.2 and a zeta potential around -40 mV, which indicates greater stability to the AgNPs. The images obtained by SEM show semicircular structures for AgNPs and well-defined spherical shape for LAgNPs. The percentage of encapsulation was between 51.81 to 58.83%. These results showed that LAgNPs were obtained with adequate physicochemical characteristics as a release system.