Sources Of Uncertainty Research Articles

Joint Modeling of Floor Elevations and Thickness of a Bauxite Unit Considering Trend, Histogram and Variogram Uncertainty

Laterite-type bauxite deposits typically exhibit a highly irregular boundary between the bauxite and underlying ferricrete units. This irregularity cannot be accurately modeled using data collected from sparsely spaced drillholes (e.g., 76.2×76.2 m or 250×250 ft). Geological models that assume a sharp and nearly horizontal bauxite/ferricrete contact can result in significant errors when calculating the in situ bauxite resource (in volume) and in misclassifying ore and waste during mining operations. Two primary sources of uncertainty must be addressed when modeling lateritic bauxite deposits: (1) grade uncertainty associated with variations in Al2O3 and SiO2% concentrations, and (2) geometric uncertainty related to lateral variations in the bauxite/ferricrete contact. Among these, geometric uncertainty is more critical, as accurately estimating bauxite ore tonnage depends on the precise modeling of the lateral variation in the boundary between the bauxite and underlying ferricrete units. This study evaluates the uncertainty of the bauxite resource within a selected mine area in northern Queensland, Australia, particularly in cases where experimental data are sparse and limited. To address this, the position variable (bauxite floor elevations) and the thickness of the bauxite unit are jointly simulated under two scenarios. In the first scenario, the histograms, variogram model parameters, and the estimated trend of the variables of interest are assumed to be known with certainty; that is, parameter uncertainty is not considered in the modeling process. In the second scenario, the histograms, variogram model parameters, and the estimated trend are considered uncertain, and parameter uncertainty is explicitly incorporated into the modeling process using the multivariate spatial bootstrap procedure. The methodology is applied to both scenarios, showing that incorporating parameter uncertainty in geostatistical modeling results in greater dispersion of the uncertainty associated with the in situ bauxite resource. The results show that the 95% confidence intervals for the in situ bauxite ore volume, derived from bauxite thickness realizations, vary depending on whether parameter uncertainty is considered. When parameter uncertainty is incorporated, the interval is (390,123 m3 and 393,223 m3), whereas without parameter uncertainty, it is (382,332 m3 and 384,373 m3). This comparison highlights that incorporating parameter uncertainty provides a more realistic assessment of resource risk in the modeling process.

Spatiotemporal evolution of vegetation phenology and its response to environmental factors in the upper and middle reaches of the Yellow River Basin.

Spatiotemporal evolution of vegetation phenology and its response to environmental factors in the upper and middle reaches of the Yellow River Basin.

Dubious Evidence, Valuable Information.

I explore how "low-quality evidence" from program performance might still be useful in decision-making. Conceptually, a local government named "Here" is motivated to consider a program from "Elsewhere" that seems to show year-over-year exemplary performance. Here must manage five sources of uncertainty about whether and how to extrapolate from Elsewhere: chance in assessing Elsewhere's performance; illusion due to confounding variables; estimating the several powers of the program's components; substitutions in the design process made by Elsewhere and contemplated by Here; and estimating whether in the final analysis Here can meet its own breakeven criterion for going ahead. Here can begin with Elsewhere's experience, but it still must do much thinking and information-collecting on its own.

Quantifying the analysis uncertainty for nowcasting application

Abstract. This study proposes a method to quantify uncertainty represented by errors in very-high-resolution near-surface analysis, specifically for weather nowcasting applications. Gaussian distributed perturbations are used to perturb the first guess and observation with a variance equal to that of the first-guess error. This error reflects the spatial characteristics of the difference between the first guess and observations and dominates the primary sources of analysis uncertainty. However, mapping perturbations to analyse the grid mesh through interpolation results in underdispersion, particularly in areas without stations. To address this issue, Gaussian perturbations are inflated with an inflation factor to amplify the dispersion. This method was applied to high-resolution analysis and nowcasting for hourly temperature, humidity, and wind components in the Beijing–Tianjin–Hebei region to assess its effectiveness in representing uncertainty. The generated ensemble analysis exhibits reasonable spread and high reliability, indicating accurate quantification of analysis uncertainty. Ensemble nowcasting is extrapolated from ensemble analysis to evaluate the transmission of perturbation during extrapolation. Verification results of ensemble nowcasting reflect the fact that the spread increases effectively during extrapolation up to a lead time of 6 h. However, the increase in the spread is highly dependent on the persistence of numerical weather prediction. The results demonstrate that generating appropriate perturbations based on analysis errors effectively represents the analysis uncertainty and contributes to estimating uncertainty in nowcasting.

Estimation of Manning’s roughness coefficient using observational data

ABSTRACT The inability to accurately estimate the Manning roughness coefficient (n) and the use of a constant value of n is a major source of uncertainty in flood simulations and flow-depth calculations. The main purpose of present study is to more precisely determine n for the Aji-Chai River upstream of the Vanyar hydrometric station. Results show that the lowest value of n is 0.034, corresponding to the discharge of 180 m3/s, while the highest value of n, corresponding to a discharge of 2.083 m3/s, is 0.119. As the discharge decreases, the roughness coefficient increases. The functional relationship between the roughness coefficient and discharge yields R2 = 0.80. The relationship between the hydraulic radius and the discharge yields R2 = 0.944, indicating a significant relationship. The roughness coefficient and the hydraulic radius have an inverse relationship. Every flood results in a different roughness with different sedimentation depending on variations in river bed particle diameter. Usually, rivers in arid regions are temporary and in the descending limb of the hydrograph, they leave coarser materials in the bed which cause errors in estimating n. In discharge where the flow depth is lower than D 90, the Manning coefficient reaches its maximum value.

Revealing hidden sources of uncertainty in biodiversity trend assessments

Idiosyncratic decisions during the biodiversity trend assessment process may limit reproducibility, whilst ‘hidden' uncertainty due to collection bias, taxonomic incompleteness, and variable taxonomic resolution may limit the reliability of reported trends. We model alternative decisions made during assessment of taxon‐level abundance and distribution trends using an 18‐year time series covering freshwater fish, invertebrates, and primary producers in England. Through three case studies, we test for collection bias and quantify uncertainty stemming from data preparation and model specification decisions, assess the risk of conflating trends for individual species when aggregating data to higher taxonomic ranks, and evaluate the potential uncertainty stemming from taxonomic incompleteness. Choice of optimizer algorithm and data filtering to obtain more complete time series explained 52.5% of the variation in trend estimates, obscuring the signal from taxon‐specific trends. The use of penalized iteratively reweighted least squares, a simplified approach to model optimization, was the most important source of uncertainty. Application of increasingly harsh data filters exacerbated collection bias in the modelled dataset. Aggregation to higher taxonomic ranks was a significant source of uncertainty, leading to conflation of trends among protected and invasive species. We also found potential for substantial positive bias in trend estimation across six fish populations which were not consistently recorded in all operational areas. We complement analyses of observational data with in silico experiments in which monitoring and trend assessment processes were simulated to enable comparison of trend estimates with known underlying trends, confirming that collection bias, data filtering and taxonomic incompleteness have significant negative impacts on the accuracy of trend estimates. Identifying and managing uncertainty in biodiversity trend assessment is crucial for informing effective conservation policy and practice. We highlight several serious sources of uncertainty affecting biodiversity trend analyses and present tools to improve the transparency of decisions made during the trend assessment process.

Bayesian Inference for the Seismic Moment Tensor Using Regional Waveforms and Teleseismic-P Polarities with a Data-Derived Distribution of Velocity Models and Source Locations

ABSTRACT The largest source of uncertainty in any source inversion is the velocity model used in the transfer function that relates observed ground motion to the seismic moment tensor. However, standard inverse procedure often does not quantify uncertainty in the seismic moment tensor due to error in the Green’s functions from uncertain event location and Earth structure. We incorporate this uncertainty into an estimation of the seismic moment tensor using a data-derived distribution of velocity models based on complementary geophysical data sets, including thickness constraints, velocity profiles, gravity data, surface-wave group velocities, and regional body-wave travel times. The data-derived distribution of velocity models is then used as a prior distribution of Green’s functions for use in Bayesian inference of an unknown seismic moment tensor using regional and teleseismic-P waveforms. The use of multiple data sets is important for gaining resolution to different components of the moment tensor. The combined likelihood is estimated using data-specific error models and the posterior of the seismic moment tensor is estimated and interpreted in terms of the most probable source type.

Mapping subsea permafrost around Tuktoyaktuk Island (Northwest Territories, Canada) using electrical resistivity tomography

Abstract. Along much of the Arctic coast, shoreline retreat and sea level rise combine to inundate permafrost. Once inundated by seawater, permafrost usually begins to degrade. Tuktoyaktuk Island (Beaufort Sea, Northwest Territories, Canada) is an important natural barrier protecting the harbor of Tuktoyaktuk but will likely be breached within the next 2 decades. The state of subsea permafrost and its depth distribution around the island are, however, still largely unknown. We collected marine electrical resistivity tomography (ERT) surveys (vertical electrical soundings) north and south of Tuktoyaktuk Island using a floating cable with 13 electrodes in a quasi-symmetric Wenner–Schlumberger array configuration. We filtered the data with a new approach to eliminate potentially incorrect measurements due to a curved cable and inverted the profiles with a variety of parameterizations to estimate the position of the ice-bearing permafrost table (IBPT) below the seafloor. Our results indicate that north of Tuktoyaktuk Island, where coastal erosion is considerably faster, IBPT depths range from 5 m below sea level (120 m from the shoreline) to around 20 m b.s.l. (up to 800 m from the shoreline). South of the island, the IBPT dips more steeply and lies at 10 m b.s.l. a few meters from the shore, and 200 m from the shore, it is more than 30 m b.s.l. We discuss how marine ERT can be improved by accurately recording electrode positions, although choices made during data inversion are likely a greater source of uncertainty in the determination of the IBPT position. At Tuktoyaktuk Island, IBPT depths below the seafloor increase with distance from the shoreline; comparing the northern and southern sides of the island, the inclination is inversely proportional to coastline retreat rates. On the island's north side, the historical coastal retreat rate suggests a mean permafrost degradation rate of 5.3 ± 4.0 cm yr−1.

Comparison of Guide to Expression of Uncertainty in Measurement and Monte Carlo Method for Evaluating Gauge Factor Calibration Test Uncertainty of High-Temperature Wire Strain Gauge.

High-temperature strain gauges are widely used in the strain monitoring of the hot-end components of aero-engines. In the application of strain gauges, the calibration of the gauge factor (GF) is the most critical link. Evaluating the uncertainty of GF is of great significance to the accuracy analysis of measurement results. Firstly, the calibration test of the GF of the Pt-W high-temperature strain gauge was carried out in the range of 25 °C to 900 °C. The real test data required for the uncertainty evaluation were obtained. Secondly, the guide to the expression of uncertainty in measurement (GUM) and the Monte Carlo method (MCM) were used to evaluate the uncertainty of GF calibration test. The evaluation results of GUM and MCM were compared. Finally, the concept of the weight coefficient W was proposed to quantitatively analyze the influence of each input on the uncertainty of the output GF. The main uncertainty source was found, which had important engineering practical significance. The results show that the mean value of GF decreases with the increase in temperature nonlinearly. At 25 °C, GF is 3.29, and at 900 °C, GF decreases to 1.6. Through comparison and verification, the uncertainty interval given by MCM is closer to the real situation. MCM is superior to GUM, which only uses prior information for uncertainty assessment. MCM is more suitable for evaluating GF uncertainty. Among multiple uncertain sources, the weight coefficient W can effectively analyze Δε as the main uncertain source.

An alternative to landslide volume-area scaling relationships: an ensemble approach adopting a difference model to estimate the total volume of landsliding triggered by the 2016 Kaikōura earthquake, New Zealand

Abstract Evaluating the influence of earthquakes on erosion, landscape evolution, and sediment-related hazards requires quantifying the number, location, and volume of co-seismic landslides following large earthquakes. Direct measurements of individual landslide volumes are difficult to accomplish and, as a result, rarely attempted. As an alternative, power-law scaling relationships between landslide volume and area—derived from global landslide inventories—are often used to estimate landslide volumes. The accuracy of the volume estimates derived from using these global scaling relationships remain untested and could be a source of significant uncertainty. Here, an ensemble method, adopting a pre- to post-event 2 by 2 m difference model covering an area of 6875 km2, has been developed to accurately estimate the volume of the > 30,000 mapped landslides triggered by the 2016 Mw 7.8 Kaikōura earthquake New Zealand. Our ensemble method estimates the total landslide volume for the Kaikōura earthquake to be 241 (+ 100/ − 69) M m3. This newly quantified total volume estimate of co-seismic landsliding was then compared to the total volumes estimated using globally and locally derived scaling relationships, which were found to underestimate the total landslide volume by up to 52%. As an alternative, we recommend that landslide volumes be estimated by sampling directly from high-quality empirical datasets of individual landslide volumes with similar triggering and physiographic settings.

Mixing, spatial resolution and argon saturation in a suite of coupled general ocean circulation biogeochemical models off Mauritania

Abstract. Numerical coupled ocean circulation biogeochemical modules are routinely employed in Earth system models that provide projections into our warming future to the Intergovernmental Panel on Climate Change (IPCC). Previous studies have shown that a major source of uncertainties in the biogeochemical ocean component is vertical, or rather diapycnal, ocean mixing. The representation of diapycnal mixing in models is affected by several factors, including the (poorly constrained) parameter choices of the background diffusivity, the choice of the underlying advection numerics and the spatial discretization. This study adds to the discussion by exploring these effects in a suite of regional coupled ocean circulation biogeochemical model configurations. The configurations comprise the Atlantic Ocean off Mauritania – a region renowned for its complex ocean circulation driven by seasonal wind patterns, coastal upwelling and peculiar mode water eddies featuring toxically low levels of dissolved oxygen. Based on simulated argon saturation as a proxy for effective mixing, we conclude that the resolution effect beyond mesoscale on diapycnal mixing is comparable to other infamous spurious effects, such as the choice of advection numerics or a change in the background diffusivity of less than 60 %.

To BSE or not to BSE: A capital budgeting analysis of the use of the bull breeding soundness evaluation (BSE) to improve reproductive efficiency and profitability in cow-calf herds

Abstract The breeding soundness evaluation (BSE) as defined by the Society for Theriogenology is intended to identify subfertile bulls. Removing subfertile bulls and replacing them with fertile bulls is expected to produce financial benefits in the form of more pregnant cows within a designated breeding season and subsequently higher weaning rates. However, past survey results of cow-calf producers indicate low adoption rates of the BSE. To better understand the rationale of these decisions by producers, a financial evaluation by means of a capital budgeting model was developed to calculate the net present value (NPV) of two different herd management strategies; one that performs BSE to one that does not. Because there are various sources of uncertainty in cattle production, a Monte Carlo simulation analysis was employed to estimate the differences between the expected NPV of these two strategies. Simulations were conducted across a range of plausible fertility differences from using BSE and revenues were generated from the resulting calf outputs and herd replacements. Additionally, the length of the breeding season and the cow:bull ratio were varied to capture a typical range of heterogeneous production systems. For each scenario considered, the results indicated that the likelihood of improving profitability by performing BSE increases as breeding season length decreases and cow:bull ratios increase, despite the relative increase in associated costs from hiring veterinarians to perform BSE. These results are largely driven by the increase in total weight of calves weaned and a decrease in costs associated with the replacement of non-pregnant cows. Overall, these findings provide a plausible financial explanation for why cow-calf producers with different management strategies are more/less willing to perform BSE.

Determining Moisture Content in Milk Powder: Challenges in the Evaluation of Performance by Proficiency Testing Using Independent Reference Values.

The water content in milk powder must be controlled to ensure its stability. The analytical methods used for its determination can be verified through proficiency testing (PT). This study evaluated the reference values, their uncertainty, and the implications for nine PT rounds on moisture determination in milk powder conducted between 2017 and 2021 by a Chilean PT provider. An independent laboratory assigned the reference values, considering the analysis session as a source of uncertainty. Results showed that in 77.8% of the rounds, uncertainty did not meet the ISO 13528:2022 criterion, requiring the use of z'-score, which increased the risk of erroneous evaluations in 5.8% of cases, particularly near threshold values (z' ≈ 2.0 or 3.0). The robust standard deviation from participants' results exceeded 0.14 g water/100 g milk powder in 63.0% of PT items, suggesting that future evaluations should consider modifying the standard deviation for proficiency assessment. Reference values were found to be compatible with participants' consensus values, though their use affected 19.3% of performance classifications, reinforcing the need for independent reference values in PT schemes with a small number of laboratories. These findings highlight the importance of evaluating the dispersion trends in proficiency assessment and adapting standard deviation criteria for more accurate performance evaluation.

A Grid-Based Long Short-Term Memory Framework for Runoff Projection and Uncertainty in the Yellow River Source Area Under CMIP6 Climate Change

Long-term runoff projection and uncertainty estimates can provide both the changing trends and confidence intervals of water resources, provide basic information for decision makers, and reduce risks for water resource management. In this paper, a grid-based runoff projection and uncertainty framework was proposed through input selection and long short-term memory (LSTM) modelling coupled with uncertainty analysis. We simultaneously considered dynamic variables and static variables in the candidate input combinations. Different input combinations were compared. We employed LSTM to develop a relationship between monthly runoff and the selected variables and demonstrated the improvement in forecast accuracy through comparison with the MLR, RBFNN, and RNN models. The LSTM model achieved the highest mean Kling–Gupta Efficiency (KGE) score of 0.80, representing respective improvements of 45.45%, 33.33%, and 2.56% over the other three models. The uncertainty sources originating from the parameters of the LSTM models were considered, and the Monte Carlo approach was used to provide uncertainty estimates. The framework was applied to the Yellow River Source Area (YRSR) at the 0.25° grid scale to better show the temporal and spatial features. The results showed that extra information about static variables can improve the accuracy of runoff projections. Annual runoff tended to increase, with projection ranges of 148.44–296.16 mm under the 95% confidence level, under various climate scenarios.

Incorporating Eruption Source Parameter and Meteorological Variability in the Generation of Probabilistic Volcanic Ash Hazard Forecasts

AbstractExplosive volcanic eruptions can produce large masses of tephra that are transported over long distances, with potential impacts on the ground and to aircraft. Volcanic Ash Advisory Centers (VAACs) provide advice to aviation following an eruption, using atmospheric dispersion models, initialized with eruption source parameters (ESPs) and driven by forecast meteorological data. In this paper, we develop a framework for producing probabilistic forecasts incorporating uncertainty in these inputs. Meteorological uncertainty is typically provided as an ensemble of numerical weather prediction (NWP) data and ESPs include eruption plume height and mass eruption rate (MER); these are linked by atmospheric processes, and their relationship can be modeled by Bayesian regression to quantify their uncertainties. These uncertainties can be propagated through the model to compute probabilistic quantities of ash concentration. The linearity of the advection‐diffusion‐sedimentation (ADS) equation solved by dispersion models allows us to run a single simulation for each of the NWP ensemble members, and then rescale the results to any combination of MER (or height) and emission profile. This gives a computational speed‐up compared to conventional approaches of computing every combination of ESPs and NWP. We demonstrate our method in the operational setting of the London VAAC, using the UK Met Office's NAME dispersion model, although it can be applied to any eruption scenario using any dispersion model that solves the ADS equation. The major source of uncertainty for this case study arises from the MER (due to limited variation in NWP data), although both sources of uncertainty are significant.

Decomposing the total uncertainty in wheat modeling: An analysis of model structure, parameters, weather data inputs, and squared bias contributions

Decomposing the total uncertainty in wheat modeling: An analysis of model structure, parameters, weather data inputs, and squared bias contributions

To Which Degree Do the Details of Stochastic Perturbation Schemes Matter for Convective-Scale and Mesoscale Perturbation Growth?

Abstract This study compares the impacts of two stochastic parameterization schemes in a convective-scale model. The implementation of the physically based stochastic perturbation (PSP) scheme in the Weather Research and Forecasting (WRF) Model represents uncertainties arising from unresolved boundary layer processes due to the finite grid size. Uncertainty in the microphysical processes is represented by the stochastic parameter perturbation applied to the microphysics parameterization (SPPMP) scheme. We examine the regime dependence of the impacts with 48-h forecasts of days with weakly and strongly forced convection, as well as winter storms. Early in the forecasts, the two stochastic parameterizations have different effects. The PSP scheme responds to boundary layer turbulence and produces strong perturbations that grow in phase with the diurnal cycle of convection. In regions with existing precipitation, SPPMP produces perturbations that grow more slowly with lead time, independent of the time of day. Perturbation growth in the PSP experiments is stronger for convective weather and can increase the total precipitation by triggering new convection, but SPPMP can dominate when precipitation occurs under stable conditions at night or in winter storms. The differences between the two schemes are relatively short-lived, and within a day of simulation, the amplitude and structure of differences introduced by both schemes are similar. This is found to be associated with saturation of perturbation growth on small scales (up to about 50 km). The locations and amplitudes of upscale perturbation growth appear to be determined by the larger-scale dynamics, independent of the details of the stochastic physics. Significance Statement This study investigates how a physically based stochastic perturbation (PSP) scheme and a stochastic parameter perturbation to the microphysics parameterization (SPPMP) scheme impact forecast perturbation growth under different weather situations. The findings suggest that while the two stochastic parameterizations yield different impacts in the short term, they demonstrate overlapping effects in longer lead times despite their distinct theoretical foundations. This is due to the fact that once introduced, perturbations naturally grow in regions where the atmospheric flow exhibits moist instabilities. Consequently, sampling the two sources of uncertainty does not result in a proportional spread. This finding elucidates the notable success of ad hoc stochastic perturbation methods since forecast perturbation growth is primarily dominated by the underlying flow rather than the details of the stochastic schemes. The insights gained from this study are valuable for the community, particularly in the realm of developing ensemble systems. Moving forward, it is crucial to evaluate their impact within comprehensive ensemble systems that account for both initial and boundary condition uncertainties.

The JCSS – A major contributor to structural safety through half a century

The JCSS – A major contributor to structural safety through half a century

BIOHAZ scientific opinion on the equivalence of an alternative heat treatment process of feathers and down.

The European Commission requested EFSA to provide a scientific opinion on the equivalence between the heat treatment process of feathers and down with dry heat to a temperature of 100°C for 30 min and the treatment set up in Commission Regulation (EU) No 142/2011, in terms of inactivation of relevant pathogens. To be considered at least equivalent to the methods in the legislation, the alternative method should be able to reduce the concentration of Enterococcus faecalis or Salmonella Senftenberg by at least 5 log10 and the infectious titre of Anelloviridae and Circoviridae by at least 3 log10. An extensive literature search (ELS) was conducted to identify studies in which the log10 reduction or the D value of the indicators were determined after dry heating in matrices with low moisture/water activity. The ELS did not provide any study on the inactivation of E. faecalis by dry heating. For S. Senftenberg, there was no clear data demonstrating a 5 log10 reduction. For Anelloviridae and Circoviridae there was limited evidence and only one study reported 1 log10 reduction after 30 min at 120°C. Given the available data and sources of uncertainty, it is not possible to conclude on a 5 log10 reduction of E. faecalis using the proposed method due to lack of evidence. Similarly, a comparable reduction of S. Senftenberg cannot be concluded due to conflicting evidence. For Anelloviridae and Circoviridae, it was not possible to conclude that a 3 log10 reduction is achieved with the proposed method, as only one study on dry heat was retrieved, which did not demonstrate such a reduction. Therefore, based on data available to date, applying dry heat to feathers and down at 100°C for 30 min cannot be considered equivalent to the treatment specified in the Regulation, in terms of inactivation of relevant pathogens.

Integrating regionalisation, uncertainty, and nonstationarity in modelling extreme rainfall events in India.

Integrating regionalisation, uncertainty, and nonstationarity in modelling extreme rainfall events in India.