Sources Of Uncertainty In Models Research Articles

Revised Best Estimates for the Age and Mass of the Methuselah Star HD 140283 Using MESA and Interferometry and Implications for 1D Convection

Abstract In light of recently revised observational measurements of the radius and spectroscopic parameters of the extremely old and metal-poor Gaia benchmark star HD 140283—also known as the Methuselah star due to prior suggestions that its age is in tension with the age of the universe—we present new, best estimates for the star’s mass and age from stellar modeling. These are derived using 1D stellar evolutionary tracks computed with MESA and the most up-to-date measurements from CHARA interferometry. Excluding modeling variance from the uncertainties, we report a mass of 0.809 ± 0.001 M ⊙ and an age of 12.01 ± 0.05 Gyr (1σ). When dominant sources of modeling uncertainty are taken into account, we report 0.81 ± 0.05 M ⊙ and 12 ± 0.5 Gyr, respectively. These results are consistent with recent literature, and the best-fitting age is not in conflict with the currently accepted age of the universe (13.5 Gyr).

Systematic uncertainties in models of the cosmic dawn

ABSTRACT Models of the reionization and reheating of the intergalactic medium (IGM) at redshifts z ≳ 6 continue to grow more sophisticated in anticipation of near-future 21-cm, cosmic microwave background, and galaxy survey measurements. However, there are many potential sources of systematic uncertainty in models that could bias and/or degrade upcoming constraints if left unaccounted for. In this work, we examine three commonly ignored sources of uncertainty in models for the mean reionization and thermal histories of the IGM: the underlying cosmology, halo mass function (HMF), and choice of stellar population synthesis (SPS) model. We find that cosmological uncertainties affect the Thomson scattering optical depth at the few per cent level and the amplitude of the global 21-cm signal at the ∼5–10 mK level. The differences brought about by choice of HMF and SPS models are more dramatic, comparable to the 1σ error bar on τe and an ∼20 mK effect on the global 21-cm signal amplitude. Finally, we jointly fit galaxy luminosity functions and global 21-cm signals for all HMF/SPS combinations and find that (i) doing so requires additional free parameters to compensate for modelling systematics and (ii) the spread in constraints on parameters of interest for different HMF and SPS choices, assuming 5 mK noise in the global signal, is comparable to those obtained when adopting the 'true' HMF and SPS with ≳20 mK errors. Our work highlights the need for dedicated efforts to reduce modelling uncertainties in order to enable precision inference with future data sets.

Application of an environmental multimedia modeling system for health risk assessment: Key influencing factors and uncertainties research.

In this paper, an environmental multimedia modeling system (EMMS) that combines a risk assessment with a Monte Carlo method (MCM) was used to explore the interaction between groundwater and surface water with respect to chemical exposure and risk. The EMMS-MCM simulations incorporated several key influencing factors that are inherent in traditional predictions of subsurface and surface-water interactions, including soil permeability coefficients and parameters such as hydraulic conductivity, density, recharge, layers, and depth. These influencing factors are generally associated with the largest sources of uncertainty in modeling and pose significant challenges to water management and to the optimal allocation of water resources. A case study involving benzene at the Trail Road landfill site located in the Ottawa-Carlton area of Canada is presented to illustrate the use of the EMMS-MCM approach. The model results are verified by comparisons to the results of groundwater and surface-water investigations in the landfill. The EMMS-MCM results are evaluated using a risk quotient (RQ) risk assessment method to quantify environmental risk. The EMMS-MCM simulations can be used to support hazardous field work and contribute to environmental management by predicting the possible consequences of hazardous chemical contaminations in surface waters and groundwater. The integration of the EMMS-MCM and RQ approaches represents an appropriate tool for the accurate assessment of long-term pollutant risks and environmental management of surface- and groundwater resources. Integr Environ Assess Manag 2021;17:877-886. © 2021 SETAC.

Uncertainty in runup predictions on natural beaches using XBeach nonhydrostatic

The wave time series that forces phase-resolving models is a source of model uncertainty that can propagate into wave runup predictions when the wave phase information is unknown. The effect of beach morphology on the propagation of this intrinsic uncertainty related to wave randomness (Ui) is largely unexplored. Here, we quantify the importance of uncertainty in wave runup at a dissipative (TS), intermediate (DU) and reflective (DE) beach using the phase-resolving model XBeach nonhydrostatic. Intrinsic uncertainty is evaluated with respect to the effect of free model parameters and of an evolving bed. Uncertainty contributions due to model equations and numerical methods are not assessed. The model is forced at each beach with 100 different wave time series corresponding to the same storm condition. Computed uncertainty metrics reveal that Ui is most important at TS, where extreme runup (R2%) of a single simulation can deviate up to 31% from the 100-simulation ensemble average. Intrasite differences in R2% can be attributed to the predominance of low-frequency swash (SLF), which is more sensitive to Ui than high-frequency swash and setup. Additional simulations using the suggested range of breaking and bed friction coefficients demonstrate that Ui produces more variability in SLF than the two most important model parameters, whereas the parametric range is more important for high-frequency wave height. Furthermore, morphodynamic simulations show that Ui produces a larger variability in R2% and SLF than an evolving bed under a 7-hr storm. Not surprisingly, Ui causes larger variability in morphologic than hydrodynamic predictions due to morphologic feedback. Trends can reverse from swash zone accretion to erosion (TS) by using a wave time series with differently distributed phase. Ensemble simulations are recommended for forecasts based on wave time series with randomly distributed phase.

A traceability analysis system for model evaluation on land carbon dynamics: design and applications

BackgroundAn increasing number of ecological processes have been incorporated into Earth system models. However, model evaluations usually lag behind the fast development of models, leading to a pervasive simulation uncertainty in key ecological processes, especially the terrestrial carbon (C) cycle. Traceability analysis provides a theoretical basis for tracking and quantifying the structural uncertainty of simulated C storage in models. Thus, a new tool of model evaluation based on the traceability analysis is urgently needed to efficiently diagnose the sources of inter-model variations on the terrestrial C cycle in Earth system models.MethodsA new cloud-based model evaluation platform, i.e., the online traceability analysis system for model evaluation (TraceME v1.0), was established. The TraceME was applied to analyze the uncertainties of seven models from the Coupled Model Intercomparison Project (CMIP6).ResultsThe TraceME can effectively diagnose the key sources of different land C dynamics among CMIIP6 models. For example, the analyses based on TraceME showed that the estimation of global land C storage varied about 2.4 folds across the seven CMIP6 models. Among all models, IPSL-CM6A-LR simulated the lowest land C storage, which mainly resulted from its shortest baseline C residence time. Over the historical period of 1850–2014, gross primary productivity and baseline C residence time were the major uncertainty contributors to the inter-model variation in ecosystem C storage in most land grid cells.ConclusionTraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation.

COnstraining ORographic Drag Effects (COORDE): A Model Comparison of Resolved and Parametrized Orographic Drag

AbstractThe parametrization of orographic drag processes is a major source of circulation uncertainty in models. The COnstraining ORographic Drag Effects (COORDE) project makes a coordinated effort to narrow this uncertainty by bringing together the modeling community to: explore the variety of orographic drag parametrizations employed in current operational models; assess the resolution sensitivity of resolved and parametrized orographic drag across models; and to validate the parametrized orographic drag in low‐resolution simulations using explicitly resolved orographic drag from high‐resolution simulations. Eleven models from eight major modeling centers are used to estimate resolved orographic drag from high‐resolution (km‐scale) simulations and parametrized orographic drag from low‐resolution simulations, typically used for seasonal forecasting (∼40 km) and climate projections (∼100 km). In most models, at both seasonal and climate resolutions, the total (resolved plus parametrized) orographic gravity wave drag over land is shown to be underestimated by a considerable amount (up to 50%) over the Northern and Southern Hemisphere and by more than 60% over the Middle East region, with respect to the resolved gravity wave drag estimated from km‐scale simulations. The km‐scale simulations also provide evidence that the parametrized surface stress and the parametrized low‐level orographic drag throughout the troposphere are overestimated in most models over the Middle East region, particularly at climate resolutions. Through this process‐based evaluation, COORDE provides model developers new valuable information on the current representation of orographic drag at seasonal and climate resolutions and the vertical partitioning of orographic low‐level and gravity wave drag.

Identifying the sources of structural sensitivity in partially specified biological models

Biological systems are characterised by a high degree of uncertainty and complexity, which implies that exact mathematical equations to describe biological processes cannot generally be justified. Moreover, models can exhibit sensitivity to the precise formulations of their component functions—a property known as structural sensitivity. Structural sensitivity can be revealed and quantified by considering partially specified models with uncertain functions, but this goes beyond well-established, parameter-based sensitivity analysis, and currently presents a mathematical challenge. Here we build upon previous work in this direction by addressing the crucial question of identifying the processes which act as the major sources of model uncertainty and those which are less influential. To achieve this goal, we introduce two related concepts: (1) the gradient of structural sensitivity, accounting for errors made in specifying unknown functions, and (2) the partial degree of sensitivity with respect to each function, a global measure of the uncertainty due to possible variation of the given function while the others are kept fixed. We propose an iterative framework of experiments and analysis to inform a heuristic reduction of structural sensitivity in a model. To demonstrate the framework introduced, we investigate the sources of structural sensitivity in a tritrophic food chain model.

Spatial interpolation of daily precipitation based on modified ADW method for gauge-scarce mountainous regions: A case study in the Shiyang River Basin

Precipitation is among the major drivers in hydrological modeling, and spatial rainfall distribution is essential for understanding ecohydrological processes. Inaccurate rainfall spatial surface is the main source of model uncertainty, especially where rainfall gauges are sparse in mountainous areas and, where the spatial precipitation distribution is closely related to elevation. This study proposes a daily rainfall interpolation method based on the Angular Distance Weighting (ADW) method considering the precipitation–elevation relationship and topographic effects derived from observed data. Furthermore, the method is used to generate daily rainfall in the Shiyang River basin from 1984 to 2012. Cross-validation results indicate that this method is rational, where the annual mean relative errors (MRE) are less than 30% at the majority of the stations. The rationality of the interpolated precipitation data at high altitude regions, where limited rainfall gauges exist, is also evaluated using a comparison of the observed and simulated runoff modeled by the Soil & Water Assessment Tool (SWAT). The SWAT model with interpolated rainfall could generate better-simulated discharges than that with observed rainfall data. The results demonstrate that the proposed method could provide an accurate spatial distribution of daily precipitation, which can sufficiently drive the hydrological model.

Equivalent-Frame Modeling of Two Shaking Table Tests of Masonry Buildings Accounting for Their Out-Of-Plane Response

Equivalent frame models are an effective tool for the seismic assessment of existing masonry structures. Due to their simplicity, these models can be used to perform multiple nonlinear dynamic analyses, accounting explicitly for different sources of modeling and input uncertainty. In the past, equivalent frame models have been used to effectively estimate the global response of buildings whose behavior is dominated by in-plane failure modes of piers and spandrels. The recent development of a three-dimensional macroelement formulation for modeling both the in-plane and out-of-plane response extends the use of equivalent frame models to the additional study of local out-of-plane mechanisms of a building. This work applies the newly developed formulation, implemented in OpenSees, to the modeling of two shaking table tests on a stone masonry building and on a modern mixed concrete-unreinforced masonry structure. Since the approach explicitly accounts for the quality of connections in the building (i.e., wall-to-wall and floor-to-wall connections), specific elements and material models were developed for modeling these connections in an equivalent frame idealization of the three-dimensional structure. Through comparison with the experimental results, the performance of the modeling approach is discussed, and the sensitivity of the response to the major sources of modeling uncertainty (quality of connections, damping model) is assessed. The comparisons show that these new equivalent frame models can capture the onset of out-of-plane failure for historical structures with poor floor-to-wall connections and for modern URM buildings with stiff RC slabs, where the slab can uplift from the URM wall, which leads to changing static and kinematic boundary conditions of the out-of-plane loaded wall. The results further show that 1-2\% of damping leads to good agreements with the experimental results if initial stiffness proportional Rayleigh damping is used.

Petrophysical Joint Inversion Applied to Alpine Permafrost Field Sites to Image Subsurface Ice, Water, Air, and Rock Contents

Quantification of ground ice is crucial for understanding permafrost systems and modelling their ongoing degradation. The volumetric ice content is however rarely estimated in permafrost studies, as it is particularly difficult to retrieve. Standard borehole temperature monitoring is unable to provide any ice content estimation, whereas non invasive geophysical techniques such as refraction seismic and electrical resistivity measurements can yield information to assess the subsurface ice distribution. Electrical and seismic data are hereby complementary sensitive to the phase change. A petrophysical joint inversion was recently developed to determine volumetric water, air, ice and rock contents from electrical and seismic data using a petrophysical model, but was so far only tested on synthetic data and one proof-of-concept field example. In order to evaluate its applicability on different types of permafrost materials and landforms (bedrock, rock glacier, talus slope), we apply this petrophysical joint inversion scheme to five profiles located in the northwestern Alps. The electrical mixing rule (Archie's second law) was hereby identified as a source of model uncertainty, as it applies only when the electrolytic conduction is the dominating process. We therefore investigate and compare four petrophysical models linking the electrical resistivity with the ground constituents: Archie's law, Archie's law with an additional surface conduction factor, a model considering only surface conduction, and the geometric mean model. In most cases, the three first resistivity relations yield largely comparable results, whose reliability is discussed. The geometric mean model better resolve high ice content, as it is less influenced by the ice-rock ambiguity. We perform a systematic analysis of the regularisation parameters and then evaluate our results with validation data including thaw depths and ice contents derived from borehole measurements. Geophysical surveys have generally a lower resolution than borehole data, but have the advantage of providing spatio-temporal information in 2D or 3D. The joint inversion results are in relatively good agreement with the validation data for all sites from ice-poor to ice-rich conditions, when choosing the most adequate resistivity model and porosity initial value. Additional forcing constraints (e.g. porosity range constraint) based on site knowledge can improve the model parameter estimation.

A global sensitivity analysis approach for identifying critical sources of uncertainty in non-identifiable, spatially distributed environmental models: A holistic analysis applied to SWAT for input datasets and model parameters

Abstracts Environmental models have a key role to play in understanding complex environmental phenomena in space and time. Although their inherent uncertainty and non-identifiability are being increasingly recognized with the development and application of various methods, a more holistic analysis of all sources of model uncertainty is warranted. This paper addresses sources of uncertainty from various types of input datasets and model parameters, including those related to model structure assumptions, using a Soil and Water Assessment Tool (SWAT) application for the Minjiang River watershed, China. The holistic uncertainty sources in the SWAT application are summarized, and a sensitivity analysis (SA) is applied to examine the relative importance of the uncertainty sources influencing average streamflow and the load of nitrate. The analysis reveals that uncertainties related to the stream network precision and certain SWAT parameters are the most critical factors. Furthermore, building upon our SA framework to consider uncertainty sources more holistically would provide a good starting point for subsequent SA of spatially distributed environmental models in general.

Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and Their Radiative Forcing

AbstractTropospheric aerosol radiative forcing has persisted for many years as one of the major causes of uncertainty in global climate model simulations. To sample the range of plausible aerosol and atmospheric states and perform robust statistical analyses of the radiative forcing, it is important to account for the combined effects of many sources of model uncertainty, which is rarely done due to the high computational cost. This paper describes the designs of two ensembles of the Met Office Hadley Centre Global Environment Model‐U.K. Chemistry and Aerosol global climate model and provides the first analyses of the uncertainties in aerosol radiative forcing and their causes. The first ensemble was designed to comprehensively sample uncertainty in the aerosol state, while the other samples additional uncertainties in the physical model related to clouds, humidity, and radiation, thereby allowing an analysis of uncertainty in the aerosol effective radiative forcing. Each ensemble consists of around 200 simulations of the preindustrial and present‐day atmospheres. The uncertainty in aerosol radiative forcing in our ensembles is comparable to the range of estimates from multimodel intercomparison projects. The mean aerosol effective radiative forcing is −1.45 W/m2 (credible interval of −2.07 to −0.81 W/m2), which encompasses but is more negative than the −1.17 W/m2 in the 2013 Atmospheric Chemistry and Climate Model Intercomparison Project and −0.90 W/m2 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. The ensembles can be used to reduce aerosol radiative forcing uncertainty by challenging them with multiple measurements as well as to isolate potential causes of multimodel differences.

To use or not to use synthetic stellar spectra in population synthesis models?

ABSTRACT Stellar population synthesis (SPS) models are invaluable to study star clusters and galaxies. They provide means to extract stellar masses, stellar ages, star formation histories, chemical enrichment, and dust content of galaxies from their integrated spectral energy distributions, colours, or spectra. As most models, they contain uncertainties that can hamper our ability to model and interpret observed spectra. This work aims at studying a specific source of model uncertainty: the choice of an empirical versus a synthetic stellar spectral library. Empirical libraries suffer from limited coverage of parameter space, while synthetic libraries suffer from modelling inaccuracies. Given our current inability to have both ideal stellar-parameter coverage with ideal stellar spectra, what should one favour: better coverage of the parameters (synthetic library) or better spectra on a star-by-star basis (empirical library)? To study this question, we build a synthetic stellar library mimicking the coverage of an empirical library, and SPS models with different choices of stellar library tailored to these investigations. Through the comparison of model predictions and the spectral fitting of a sample of nearby galaxies, we learned that predicted colours are more affected by the coverage effect than the choice of a synthetic versus empirical library; the effects on predicted spectral indices are multiple and defy simple conclusions; derived galaxy ages are virtually unaffected by the choice of the library, but are underestimated when SPS models with limited parameter coverage are used; metallicities are robust against limited HRD coverage, but are underestimated when using synthetic libraries.

Quantifying gravity wave forcing using scale invariance

Despite the increasing resolution, forcing on the mean circulation by resolved waves in general circulation models is not yet converging. Parameterization of the forcing remains a major source of model uncertainty. This study examines the scale invariance of zonal spectra of momentum flux and wave forcing, and shows that it can be used to quantify the forcing by unresolved waves with knowledge of the resolved ones in global models. The result reveals the leading order importance of the small-scale wave forcing, which is in general agreement with that required for obtaining the zonal mean wind climatology. It is also found that wave and mean flow interaction is important in maintaining the robust spectral structure. This method may provide a strategy to design physically consistent and scale-aware parameterization schemes for scale invariant quantities, when a model has sufficient resolution to partially resolve their spectra.

Effects of left ventricle wall thickness uncertainties on cardiac mechanics.

Computational models of the heart have reached a level of maturity that enables sophisticated patient-specific simulations and hold potential for important applications in diagnosis and therapy planning. However, such clinical use puts strict demands on the reliability and accuracy of the models and requires the sensitivity of the model predictions due to errors and uncertainty in the model inputs to be quantified. The models typically contain a large number of parameters, which are difficult to measure and therefore associated with considerable uncertainty. Additionally, patient-specific geometries are usually constructed by semi-manual processing of medical images and must be assumed to be a potential source of model uncertainty. In this paper, we assess the model accuracy by considering the impact of geometrical uncertainties, which typically occur in image-based computational geometries. An approach based on 17 AHA segments diagram is used to consider uncertainties in wall thickness and also in the material properties and fiber orientation, and we perform a comprehensive uncertainty quantification and sensitivity analysis based on polynomial chaos expansions. The quantities considered include stress, strain and global deformation parameters of the left ventricle. The results indicate that important quantities of interest may be more affected by wall thickness, and highlight the need for accurate geometry reconstructions in patient-specific cardiac mechanics models.

State-parameter estimation approach for data-driven wildland fire spread modeling: Application to the 2012 RxCADRE S5 field-scale experiment

Data assimilation is an emerging and powerful tool towards real-time flame front monitoring for wildland fire applications. The key idea is to regularly update the state and/or parameters of a fire spread model using observed firelines in order to improve a forecast on future fire locations. The merits of combining state estimation and parameter estimation through a hybrid state-parameter estimation algorithm are demonstrated through the 2012 RxCADRE S5 field-scale controlled burn experiment. For state estimation, we adopt a cost-effective Luenberger observer formulation to reconstruct a complete view of the burning state at a given time. For parameter estimation, we use an ensemble transform Kalman filter to solve the inverse modeling problem consisting of inferring more realistic wind conditions given observations of the actual burning state. The data-driven model relies on a front shape similarity measure derived from image segmentation theory to quantify position errors. We show that the hybrid approach provides an efficient framework to address all sources of model uncertainties and to select burning scenarios that are most likely to occur. Parameter estimation is a key component of the data-driven model by reducing model bias. Using the fire spread model in forecast mode is then an asset to accurately track the flame front dynamics at future lead times.

Forecasting with High‐Dimensional Panel VARs

AbstractThis paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time‐varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation‐free algorithm that relies on analytical approximations to the posterior. We use our methods to forecast inflation rates in the eurozone and show that these forecasts are superior to alternative methods for large vector autoregressions.

Modeling the Global Sowing and Harvesting Windows of Major Crops Around the Year 2000

AbstractThe lack of spatially detailed crop calendars is a significant source of uncertainty in modeling, monitoring, and forecasting crop production. In this paper, we present a rule‐based model to estimate the sowing and harvesting windows of major crops over the global land area. The model considers field workability due to snow cover and heavy rainfall in addition to crop biological requirements for heat, chilling, and moisture. Using daily weather data for the period 1996–2005 as model input, we derive calendars for maize, rice, winter and spring wheat, and soybeans around the year 2000 with a spatial resolution of 0.5° in latitude and longitude. Separate calendars for rainfed and irrigated conditions and three representative varieties (short‐, medium‐ and long‐season varieties) are estimated. The daily probabilities of sowing and harvesting derived using the model well capture the major characteristics of reported calendars. Our modeling reveals that field workability is an important determinant of sowing and harvesting dates and that multicropping patterns influence the calendars of individual crops. The case studies show that the model is capable of capturing multicropping patterns such as triple rice cropping in Bangladesh, double rice cropping in the Philippines, winter wheat‐maize rotations in France, and maize‐winter wheat‐soybean rotations in Brazil. The model outputs are particularly valuable for agricultural and hydrological applications in regions where existing crop calendars are sparse or unreliable.

Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation.

The increasing use of species distribution modeling (SDM) has raised new concerns regarding the inaccuracies, misunderstanding, and misuses of this important tool. One of those possible pitfalls − collinearity among environmental predictors − is assumed as an important source of model uncertainty, although it has not been subjected to a detailed evaluation in recent SDM studies. It is expected that collinearity will increase uncertainty in model parameters and decrease statistical power. Here we use a virtual species approach to compare models built using subsets of PCA-derived variables with models based on the original highly correlated climate variables. Moreover, we evaluated whether modelling algorithms and species data characteristics generate models with varying sensitivity to collinearity. As expected, collinearity among predictors decreases the efficiency and increases the uncertainty of species distribution models. Nevertheless, the intensity of the effect varied according to the algorithm properties: more complex procedures behaved better than simple envelope models. This may support the claim that complex models such as Maxent take advantage of existing collinearity in finding the best set of parameters. The interaction of the different factors with species characteristics (centroid and tolerance in environmental space) highlighted the importance of the so-called “idiosyncrasy in species responses” to model efficiency, but differences in prevalence may represent a better explanation. However, even models with low accuracy to predict suitability of individual cells may provide meaningful information on the estimation of range-size, a key species-trait for macroecological studies. We concluded that the use of PCA-derived variables is advised both to control the negative effects of collinearity and as a more objective solution for the problem of variable selection in studies dealing with large number of species with heterogeneous responses to environmental variables.

A dual adaptive filtering approach for nonlinear finite element model updating accounting for modeling uncertainty

Abstract This paper proposes a novel approach to deal with modeling uncertainty when updating mechanics-based nonlinear finite element (FE) models. In this framework, a dual adaptive filtering approach is adopted, where the Unscented Kalman filter (UKF) is used to estimate the unknown parameters of the nonlinear FE model and a linear Kalman filter (KF) is employed to estimate the diagonal terms of the covariance matrix of the simulation error vector based on a covariance-matching technique. Numerically simulated response data of a two-dimensional three-story three-bay steel frame structure with eight unknown material model parameters subjected to unidirectional horizontal seismic excitation is used to illustrate and validate the proposed methodology. Geometry, inertia properties, gravity loads, and damping properties are considered as sources of modeling uncertainty and different levels and combinations of them are analyzed. The results of the validation studies show that the proposed approach significantly outperforms the parameter-only estimation approach widely investigated and used in the literature. Thus, a more robust and comprehensive identification of structural damage is achieved when using the proposed approach. A different input motion is then considered to verify the prediction capabilities of the proposed methodology by using the FE model updated by the parameter estimation results obtained.