Important Source Of Uncertainty Research Articles

Stereotactic body radiation therapy (SBRT) for prostate cancer: Improving treatment delivery efficiency and accuracy

PurposeIn stereotactic body radiation therapy (SBRT) for prostate cancer, intrafraction motion is an important source of treatment uncertainty as it could not be completely smoothed through fractionation. Herein, we compared different arrangements and beam qualities for extreme hypofractionated treatments to minimize beam delivery time and so intrafractional errors. MethodsA retrospective dataset of 11 patients was used. Three volumetric modulated arc therapy (VMAT) beam arrangements were compared for a prescription dose of 40 Gy/5 fractions: two full arcs, 6 MV flattening filter free (FFF); one full arc, 6 MV FFF; one full arc, 10 MV FFF. A plan quality index was defined to compare achievement of the planning goals. Plan complexity was evaluated with the modulation factor. Dose delivery accuracy and efficiency were measured with patient-specific quality assurance plans. ResultsAll treatment plans fulfilled all dose objectives. No statistical differences were found both in plan quality and complexity. Very accurate dose delivery was achieved with the three arrangements, with mean γ passing rates >96.5 % (2 %/2 mm criteria). Slightly but significantly higher γ passing rates were observed with single-arc 6 MV FFF. Contrariwise, statistically significant reductions of the delivery time were obtained with single-arc geometries: the average delivery times were 1.6 min (−46.1 %) and 1.3 min (−56.2 %) for 6 and 10 MV FFF respectively. ConclusionsThe high-quality, very fast and accurate dose delivery of single-arc plans confirmed the suitability of this arrangement for prostate SBRT. In particular, the significant reduction of delivery time would improve treatment robustness against intrafraction prostate motion.

Weather variability risks slow climate adaptation: An empirical analysis of forestry

The timing of climate adaptation decisions can have substantial consequences for the assessment of climate damages. Since weather variability can create risks for natural resource management that differ across adaptation choices, such variability has the potential to alter the speed of climate adaptation. This paper estimates the effect of weather variability on the timing of adaptation decisions of forest landowners in the Eastern United States. A discrete-choice econometric model of forest management is estimated and used in a bio-economic simulation that shows how variability in cold temperatures can significantly slow the rate of adapting from cold-tolerant natural hardwood forests to cold-sensitive, but highly valuable pine plantations. The range of weather variability in climate projections and across the landscape generates large differences in adaptation timing. Ignoring projected future decreases in weather variability results in a large downward bias in estimating future paths of climate adaptation. Since pine plantations produce fewer non-market ecosystem services than natural hardwood forests, an important source of future conservation uncertainty is the economic response of private forest landowners to changing weather variability.

Uncertainty quantification for high-temperature flame sensing using wavelength modulation spectroscopy

In this study, we explore the uncertainty of the wavelength modulation spectroscopy (WMS) technique applied to water profile measurement in a flat flame. The scanned WMS-2f/1f method is used to measure the water profile in a C2H4-O2-Ar flame, using an absorption transition line near 1391.7 nm. The Monte Carlo sampling method is used for the uncertainty quantification (UQ) analysis. The results show a non-negligible uncertainty on the water concentration with an uncertainty range from 10% to 30%, depending on the uncertainty level considered for the input parameters of the spectroscopic model. Further sensitivity analyses show that the lower state energy (E′′), the line strength, and the temperature are the most important uncertainty sources. When multiple transition lines are involved, the line strength value controls the overall influence of the other parameters associated with these specific lines. We also compared the uncertainty obtained when applying the direct absorption spectroscopy (DA) and WMS techniques for a similar flame configuration. Only a slight advantage is observed for the WMS.

Inconsistent evolutionary analysis of multi-level physical model for lithium-ion batteries considering calendering process uncertainties

Due to the variation in the calendering process, there are inconsistencies between battery cells that cannot be eliminated, which affects the energy utilization efficiency, safety, and reliability of the battery system. The deviations occur in the calendering process resulting in uncertainties in the structural parameters of the electrode. To analyze the evolutionary mechanism of inconsistency arising from the uncertainties in the calendering process, a stochastic, multi-level physical model approach combined with sparse polynomial chaos expansions is presented. The physical model is verified by voltage and temperature experiments. This approach enables to establish the relationship between calendering parameters variation and the inconsistency performance between cells. The performance of battery inconsistency has been quantified as a function of calendering process deviations at the manufacturing level. The most important uncertainty sources are identified by global sensitivity analysis. Based on the multi-level physical model approach, the battery module connected in series is constructed to study the effect of the calendering variations on the energy utilization efficiency of the battery module. The results suggest that the electrode thickness has the most significant effects on the battery capacity, resistance, and temperature. Meanwhile, the deviations of the mass load and electrode thickness need to be effectively controlled to achieve a better consistent performance between cells. Such information allows for more efficient design and targeted quality control, thereby reducing production costs.

Biofuels Induced Land Use Change Emissions: The Role of Implemented Land Use Emission Factors

Biofuels’ induced land-use change (ILUC) emissions have been widely studied over the past 15 years. Many studies have addressed uncertainties associated with these estimates. These studies have broadly examined uncertainties associated with the choice of economic models, their assumptions and parameters, and a few bio-physical variables. However, uncertainties in land-use emission factors that represent the soil and vegetation carbon contents of various land types across the world and are used to estimate carbon fluxes due to land conversions are mostly overlooked. This paper calls attention to this important omission. It highlights some important sources of uncertainty in land-use emissions factors, explores the range in these factors from established data sources, and compares the influence of their variability on ILUC emissions for several sustainable aviation fuel (SAF) pathways. The estimated land-use changes for each pathway are taken from a well-known computable general equilibrium model, GTAP-BIO. Two well-known carbon calculator models (CCLUB and AEZ-EF) that represent two different sets of emissions factors are used to convert the GTAP-BIO estimated land-use changes to ILUC emissions. The results show that the calculated ILUC emissions obtained from these carbon calculators for each examined SAF pathway are largely different, even for the same amortization time horizon. For example, the ILUC emissions values obtained from the AEZ-EF and CCLUB models for producing jet fuel from corn ethanol for a 25-year amortization period are 24.9 gCO2e/MJ and 15.96 gCO2e/MJ, respectively. This represents a 60% difference between the results of these two carbon calculators for the same set of land-use changes. The results show larger differences for other pathways as well.

Hydroclimatic Trends and Streamflow Response to Recent Climate Change: An Application of Discrete Wavelet Transform and Hydrological Modeling in the Passaic River Basin, New Jersey, USA

The exigency of the current climate crisis demands a more comprehensive approach to addressing location-specific climate impacts. In the Passaic River Basin (PRB), two bodies of research—hydroclimatic trend detection and hydrological modeling—have been conducted with the aim of revealing the basin’s hydroclimate patterns as well as the hydrologic response to recent climate change. In a rather novel application of the wavelet transform tool, we sidelined the frequently used Mann–Kendal (MK) trend test, to identify the hidden monotonic trends in the inherently noisy hydroclimatic data. By this approach, the use of MK trend test directly on the raw data, whose results are almost always ambiguous and statistically insignificant in respect of precipitation data, for instance, no longer poses a challenge to the reliability of trend results. Our results showed that, whereas trends in temperature and precipitation are increasing in the PRB, streamflow trends are decreasing. Based on results from the hydrological modeling, streamflow is more sensitive to actual evapotranspiration (ET) than it is to precipitation. In periods spanning decades with sufficient water availability, energy governs actual evapotranspiration rates, rendering streamflow more sensitive to increases in precipitation. Conversely, during meteorologically stressed decades, water availability dictates actual evapotranspiration, consequently amplifying streamflow sensitivity to fluctuations in actual evapotranspiration. We found that the choice of baseline condition constitutes an important source of uncertainty in the sensitivities of streamflow to precipitation and evapotranspiration changes and should routinely be considered in any climate impact assessment.

Numerical impact of variable volumes of Monte Carlo simulations of heterogeneous conductivity fields in groundwater flow models

The knowledge of aquifer systems, their geological setting, their structure, and subsequent modeling is highly uncertain and is usually faced through Monte Carlo-based methods in hydrogeology. One of the most important uncertainty sources for groundwater models is represented by input hydraulic conductivities, related to the aquifer’s structure. There are no specific rules when simulating hydraulic conductivity fields within Monte Carlo frameworks to instruct numerical models, and information about employed conductivity fields and their numerical convergence is often not given. This technical work aims to fill this gap by investigating the impact of employing conductivity information upon different volumes of Monte Carlo simulations applied to a real case study. Thus, this work estimates the minimum volumes of Monte Carlo hydraulic conductivity fields to be employed in groundwater flow models for achieving numerically stable (i) boundary conditions, (ii) global model performances, and (iii) local ones such as simulated hydraulic heads. The present results aim to be indicative of similar hydrogeological settings and will serve as a basis for more complex ones and for investigating transport problems.

Temperature‐Dependence Assumptions Drive Projected Responses of Diverse Size‐Based Food Webs to Warming

AbstractFood web projections are critical for evaluating potential risks to ecosystems and fisheries under global warming. The temperature dependence of biological processes and regional differences in food web structure are two important sources of uncertainty and variation in climate forced projections of fish communities, but we do not know their magnitude or relative contribution. Here we systematically evaluated a range of different assumptions about temperature‐dependence on rates, including size‐dependent effects, controlling food intake, metabolism, and non‐predation mortality in fishes using species‐resolved size spectrum food web models that link individual‐level physiological processes to population and community dynamics. We simulated the physiological effect of warming in a range of size‐structured food web models calibrated to different marine ecosystems and in simplified trait‐based models. Higher food intake in warmed conditions increased total fish biomass, catches, and mean body weight, but these effects were offset by the negative effects of warming on metabolism and mortality, which combined resulted in lower total biomasses and catches for most food webs. These effects were enhanced when warming increased metabolic rates more than food intake, and the outcomes were also sensitive to size dependency of temperature responses. Importantly, these general patterns were not uniform across all food webs—individual functional groups and fish species within food webs responded to warming in different ways depending on their position in the food web and its structure. Hence, caution is warranted when generalizing food web or species outcomes to warming because they are mediated by community interactions. Uncertainty related to temperature dependence and ecological interactions will impact food web projections and should be represented in climate change projections.

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

Decomposing Uncertainty in Macro-Finance Term Structure Models

Abstract This paper studies the extent to which macro-finance term structure models are susceptible to predictive uncertainty. We propose a general form of arbitrage-free models and quantify the relative importance of unpredictable priced risk variance, as well as macro-finance model uncertainty and learning uncertainty in predictability. Predictive performance and relative contributions of uncertainty sources are dynamically measured based on Bayesian methods, revealing dominating priced risk variance and other important uncertainty sources at different points in time. Macro-finance model uncertainty is high for near-term forward spread forecasts and contributes up to 87% of predictive uncertainty prior to recessions, implying strong dispersion in the information content of macro variables when forming near-term monetary policy expectations. (JEL C1, C3, C5, D8, E4, G1)

Review of Smog Chamber Experiments for Secondary Organic Aerosol Formation

In this study, we reviewed smog chamber systems and methodologies used in secondary organic aerosol (SOA) formation studies. Many important chambers across the world have been reviewed, including 18 American, 24 European, and 8 Asian chambers. The characteristics of the chambers (location, reactor size, wall materials, and light sources), measurement systems (popular equipment and working principles), and methodologies (SOA yield calculation and wall-loss correction) are summarized. This review discussed key experimental parameters such as surface-to-volume ratio (S/V), temperature, relative humidity, light intensity, and wall effect that influence the results of the experiment, and how the methodologies have evolved for more accurate simulation of atmospheric processes. In addition, this review identifies the sources of uncertainties in finding SOA yields that are originated from experimental systems and methodologies used in previous studies. The intensity of the installed artificial lights (photolysis rate of NO2 varied from 0.1/min to 0.40/min), SOA density assumption (varied from 1 g/cm3 to 1.45 g/cm3), wall-loss management, and background contaminants were identified as important sources of uncertainty. The methodologies developed in previous studies to minimize those uncertainties are also discussed.

Incorporating clinical trial outcomes in health economic evaluations of disease‐modifying therapies for AD

AbstractBackgroundEvidence on cost‐effectiveness of novel therapies for Alzheimer’s disease (AD) is of increasing importance for patient access. Challenges involved in implementing treatment effects in health economic (HE) models is an important source of uncertainty around the value of new AD therapies. HE models are developed from observational studies that often include different patient populations and different endpoints and scales from clinical trials.MethodsThe International Pharmacoeconomic Collaboration on Alzheimer’s Disease (IPECAD) conducted a series of workshops with research groups developing HE models in AD, to systematically characterize methodological approaches to modelling and benchmark results from these models in standardized scenarios. We reviewed published models and contrasted the characteristics of these models with study design specifications of recent late‐stage clinical trials of AD DMT to highlight potential issues with incorporating efficacy data from clinical trials in existing HE models. Identified issues included: need for mapping between different scales used to assess cognition, function and other efficacy domains, incorporating effects on multiple domains, differences in trial populations and expected patient populations treated in routine care, and statistical methods for generating efficacy estimates for use in HE models.ResultsThree options were identified for translating trial efficacy estimates into HE models: 1) use actual trial data and model disease course post trial, 2) assume treatment reduces transition probabilities to more severe states, and 3) implement treatment effect as a proportional reduction of progression on the time scale.ConclusionsThe design of future clinical trials of DMT need to take into account the need to generate data to populate HE models. Pre‐specifying analyses of trial data for HE modeling purposes can reduce uncertainty and improve confidence in results.

A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land-use change.

Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi- or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within-time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site-specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long-term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.

Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space.

Carbon dioxide and methane emissions are the two primary anthropogenic climate-forcing agents and an important source of uncertainty in the global carbon budget. Uncertainties are further magnified when emissions occur at fine spatial scales (<1 km), making attribution challenging. We present the first observations from NASA's Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer showing quantification and attribution of fine-scale methane (0.3 to 73 tonnes CH4 hour-1) and carbon dioxide sources (1571 to 3511 tonnes CO2 hour-1) spanning the oil and gas, waste, and energy sectors. For selected countries observed during the first 30 days of EMIT operations, methane emissions varied at a regional scale, with the largest total emissions observed for Turkmenistan (731 ± 148 tonnes CH4 hour-1). These results highlight the contributions of current and planned point source imagers in closing global carbon budgets.

Effects of Past Nd Seawater Concentrations on Nd‐Isotope Paleocirculation Reconstructions: A Bayesian Approach

AbstractNeodymium (Nd) isotope ratios are potentially valuable for paleo‐ocean circulation reconstructions because they trace water‐mass transport and mixing in the present‐day oceans. Moreover, the Atlantic and Pacific global end‐member Nd‐isotope values can be constrained through time, and at in‐between sites their proportions vary with past climate changes. However, an important source of uncertainty about Nd‐isotopes' applicability for paleo‐circulation studies arises from the inability to constrain past Nd‐concentrations of the end‐members. Here we address this “paleo‐[Nd] problem” through a Bayesian analysis. Results show that even large variability in end‐member Nd‐concentrations over the Pleistocene is unlikely to significantly impact their concentration ratio, therefore end‐member concentration changes likely have only small impacts on Nd‐isotope ratios at in‐between locations. The results support their applicability to reconstruct past Atlantic paleo‐circulation. In addition, a Nd‐isotope mass balance for Antarctic Bottom Water shows that its present‐day Nd‐isotope values are consistent with the input from its sources.

Spatio-Temporal Variation of Critical Relative Humidity Based on Multiple Datasets

Clouds remain an important source of uncertainty in climate simulations, in large part because subgrid processes are not well represented. Critical relative humidity (RHc) is an important metric for subgrid-scale variability in humidity in cloud parameterization. Based on CloudSat and CALIPSO satellite data, we explored the spatial and temporal distribution characteristics of RHc, assessed the ability of ERA-5 and MERRA-2 reanalysis and CMIP-6 climate models to characterise humidity subgrid variability and further explored the influence of meteorological factors and aerosols. The statistical results showed that there was significant variation in the spatial distribution of RHc, with large variations in both latitude and altitude, as well as more pronounced monthly variations, and that there were differences in monthly variations between regions. Both the reanalysis data and the climate models were able to reproduce similar spatial and temporal distribution patterns but differed significantly in their specific values. The temporal correlations with satellite observations were also relatively poor. In addition, aerosols and meteorological conditions affected the distribution of RHc by influencing the cloud fraction at a certain relative humidity level, indicating that their influence needs to be considered in future parameterization schemes.

Recall of residential history and dietary habits during pregnancy and lactation in the distant past: reliability of questionnaire-based radiation doses for persons exposed in utero and early life.

This study evaluates the reliability of information obtained by standardized questionnaires used in by personal interviews for estimation of radiation thyroid doses of 1065 individuals in the Belarusian cohort of individuals who were exposed in utero and early life following the Chernobyl accident in April 1986. Data from two interviews conducted in 2012-2017 and in 2018-2022 with mothers, who were pregnant or gave birth shortly after the Chernobyl accident, were analysed. The most reliable answers dealt with various attributes related to residential history. In contrast, the reliability of answers regarding consumption rates of milk from privately owned cows or trade network was moderate, while the agreement in responses for consumption of milk products and leafy vegetables was fair. Information from the two interviews was used to calculate thyroid doses received by the cohort members. Specifically, 'model-based' thyroid doses due to 131I were estimated using input data on individual residential history and food consumption reported during the personal interviews and ecological data (131I ground deposition in the corresponding settlements). In addition, for a subset of cohort subjects (n = 205) whose mothers were measured for 131I thyroid activity, 'measurement-based' thyroid doses were calculated by adjusting the model-based dose using a scaling factor that is defined as the ratio of measured 131I thyroid activity to model-based 131I thyroid activity calculated for the date of measurement. A moderate agreement was observed for total (prenatal and postnatal) model-based thyroid doses due to 131I intake, the arithmetic mean ± standard deviation for the Jaccard similarity coefficient ([Formula: see text]) was 0.45 ± 0.34 (median = 0.39), while measurement-based doses showed a much better agreement with a [Formula: see text] of 0.78 ± 0.29 (median = 0.93). For model-based thyroid doses from external irradiation and from ingestion of 134Cs and 137Cs, [Formula: see text] was 0.82 ± 0.23 (median = 0.90) and 0.84 ± 0.24 (median = 0.96), respectively. Measurement-based doses due to ingestion of radiocaesium isotopes resulted in an almost perfect agreement, [Formula: see text] was 0.91 ± 0.19 (median = 1.0). The present findings suggest that long-term memory recall can be reliable, if a person is asked about unique or important life events, such as pregnancy and childbirth occurring around the time of a nuclear reactor accident. However, the substantial difference (more than 10 times) observed for model-bases doses calculated using the two questionnaires represents an important source of human factor uncertainties that needs to be considered in any dose response analyses. Other lessons learned from this study are that (i) individual measurements of radionuclides in the human body are the most valuable source of information for estimating radiation doses, and (ii) whenever a radiation accident occurs, a sample of affected people should be asked to keep a diary, if at all possible.

High uncertainty in fish bioenergetics impedes precision of fish-mediated carbon transport estimates into the ocean’s twilight zone

Mesopelagic fishes may contribute substantially to marine carbon transport by consuming organic carbon near the surface at night and releasing it in the mesopelagic zone during the day. However, the magnitude and uncertainties associated with this transport are not well understood; fish-mediated carbon flux estimates range from less than 1 % to greater than 30 % of biologically-driven carbon export out of the epipelagic zone. While total mesopelagic fish biomass is an important source of uncertainty, information on fish bioenergetics and movement might also limit the precision of carbon transport estimates. Here, we ask how uncertainties in fish bioenergetics and behaviors affect carbon flux estimation, and which processes contribute most to uncertainty. We used sensitivity analyses to reveal that modeled carbon flux was most sensitive to respiration-related parameters, and per capita fish carbon flux estimates varied six-fold over the range of plausible parameter values. Biomass estimation can add at least ten-fold variation in ecosystem-scale carbon flux estimates. We conclude that it is not currently possible to estimate fish-mediated carbon flux precisely, but estimates may be constrained through future empirical work on the most influential parameters.

Spatio-temporal modelling of the effect of environmental and land-use factors on species-rich calcareous grasslands

The abundance of sensitive plant species in calcareous grasslands is threatened by agricultural intensification with nutrient addition and increased livestock densities as well as by land abandonment. The Hill-Shannon diversity is decreasing, and the species composition of Danish calcareous grassland is generally unstable and is expected to experience further change in the coming years. In order to quantify the effects of selected environmental and land-use factors on the observed variation and changes in the vegetation of calcareous grasslands, large-scale spatial and temporal pin-point plant cover monitoring data are fitted in a structural equation model. The analyzed vegetation data come from 100 Danish sites monitored over an eight-year period. The important sources of measurement and sampling uncertainties have been included using a hierarchical model structure. Furthermore, the measurement and sampling uncertainties are separated from the process uncertainty, which is important when generating ecological predictions that may feed into local conservation management decisions. There were significant negative effects of grazing and nitrogen deposition on the change in cover of sensitive plant species. Whereas the negative effect of nitrogen deposition on the cover of sensitive species was expected, it was surprising that the model results suggest that sensitive species may be subjected to overgrazing by the grazing regimes that are currently applied at protected Danish calcareous grasslands. The standardized regression coefficients suggest that the effects of both grazing and nitrogen deposition are relatively benign compared to the effects of soil type, soil pH and precipitation. The relatively large effect of precipitation is interesting, since it is predicted that the amount of precipitation and its distribution over the seasons will change due to climate change, and the results suggest that climate change may lead to important species compositional changes in calcareous grasslands. The fitted model may be used to quantify the uncertainties when generating ecological forecasting and local adaptive management plans. More specifically, this study suggests that it is relevant to rethink the currently used criteria for vegetation height when assessing the conservation status of calcareous grasslands.

Dealing with the uncertainty of technical changes in the CORINE Land Cover dataset: The Portuguese approach

Land Use and Land Cover (LULC) datasets are widely used across disciplines, with many users demanding more and better information. Understanding the uncertainties and errors associated to the main LULC datasets is a required step to facilitate their correct use, as well as to identify what could be improved in the future production of these products. CORINE Land Cover is probably the most well-known and used LULC dataset in Europe, especially valuable for the rich time-series that it provides. Despite being produced through a change mapping first approach, which tries to avoid technical errors and uncertainties in the temporal analysis of LULC changes, the Copernicus Land Monitoring Service distributes status layers of CORINE (CSL), which are not valid for change analysis because of their associated errors and uncertainties. The CORINE layers of changes (CHA) remove a lot of these issues, but do not meet the needs of many users. In Portugal, the national authority in charge of producing CORINE, the DGT, has implemented a backdating approach to produce consistent CSL layers that allow change analysis with low levels of uncertainty. Throughout this paper, we evaluate the changes that can be analyzed through all available CORINE layers in Portugal: Copernicus CSL layers; the national DGT CSL layers; and CHA layers. To this end, we aim to assess what type of changes can be studied through each type of layer, their associated sources of uncertainty and the relevance and utility of the Portuguese backdating approach to produce a consistent time-series of LULC maps. The results prove how the Portuguese CORINE layers distributed by the Copernicus Land Monitoring Service contain important sources of uncertainty, which however have been removed through the national backdating methodology. This methodology can be therefore exported for the production of CORINE in other European countries.