Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes

Mitigation of sediment deposition in lined open channels is an essential issue in hydraulic engineering practice.Hence,the limiting velocity should be determined to keep the channel bottom clean from sediment deposits.Recently,sediment transport modeling using various artificial intelligence(AI) techniques has attracted the interest of many researchers.The current integrated study highlights unique insight for modeling of sediment transport in sewer and urban drainage systems.A novel methodology based on the combination of sensitivity and uncertainty analyses with a machine learning technique is proposed as a tool for selection of the best input combination for modeling process at non-deposition conditions of sediment transport.Utilizing one to seven dimensionless parameters,127 models are developed in the current study.In order to evaluate the different parameter co mbinations and select the training and te sting data,four strategies are considered.Considering the densimetric Froude number(Fr) as the dependent parameter,a model with independent parameters of volumetric sediment concentration(C_V) and relative particle size(d/R) gave the best results with a mean absolute relative error(MARE) of 0.1 and a root means square error(RMSE) of 0.67.Uncertainty analysis is applied with a machine learning technique to investigate the credibility of the proposed methods.The percentage of the observed sample data bracketed by95% predicted uncertainty bound(95 PPU) is computed to assess the uncertainty of the best models.     

UNCERTAINTY AND SENSITIVITY ANALYSES OF SEDIMENT TRANSPORT FORMULAS

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Sensitivity analysis and uncertainty assessment in water budgets simulated by the variable infiltration capacity model for Canadian subarctic watersheds

In this study, we evaluate uncertainties propagated through different climate data sets in seasonal and annual hydrological simulations over 10 subarctic watersheds of northern Manitoba, Canada, using the variable infiltration capacity (VIC) model. Further, we perform a comprehensive sensitivity and uncertainty analysis of the VIC model using a robust and state-of-the-art approach. The VIC model simulations utilize the recently developed variogram analysis of response surfaces (VARS) technique that requires in this application more than 6,000 model simulations for a 30-year (1981–2010) study period. The method seeks parameter sensitivity, identifies influential parameters, and showcases streamflow sensitivity to parameter uncertainty at seasonal and annual timescales. Results suggest that the Ensemble VIC simulations match observed streamflow closest, whereas global reanalysis products yield high flows (0.5–3.0 mm day⁻¹) against observations and an overestimation (10–60%) in seasonal and annual water balance terms. VIC parameters exhibit seasonal importance in VARS, and the choice of input data and performance metrics substantially affect sensitivity analysis. Uncertainty propagation due to input forcing selection in each water balance term (i.e., total runoff, soil moisture, and evapotranspiration) is examined separately to show both time and space dimensionality in available forcing data at seasonal and annual timescales. Reliable input forcing, the most influential model parameters, and the uncertainty envelope in streamflow prediction are presented for the VIC model. These results, along with some specific recommendations, are expected to assist the broader VIC modelling community and other users of VARS and land surface schemes, to enhance their modelling applications.     

Global sensitivity analyses for a complex hydrological model applied in an Alpine watershed

The increasing complexity of hydrological models results in a large number of parameters to be estimated. In order to better understand how these complex models work, efficient screening methods are required in order to identify the most important parameters. This is of particular importance for models that are used within an operational real‐time forecasting chain such as HQsim. The objectives of this investigation are to (i) identify the most sensitive parameters of the complex HQsim model applied in the Alpine Lech catchment and (ii) compare model parameter sensitivity rankings attained from three global sensitivity analysis techniques. The techniques presented are the (i) regional sensitivity analysis, (ii) Morris analysis and (iii) state‐dependent parameter modelling. The results indicate that parameters affecting snow melt as well as processes in the unsaturated soil zone reveal high significance in the analysed catchment. The snow melt parameters show clear temporal patterns in the sensitivity whereas most of the parameters affecting processes in the unsaturated soil zone do not vary in importance across the year. Overall, the maximum degree day factor (meltfunc_max) has been identified to play a key role within the HQsim model. Although the parameter sensitivity rankings are equivalent between methods for a number of parameters, for several key parameters differing results were obtained. An uncertainty analysis demonstrates that a parameter ranking attained from only one method is subjected to large uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.     

Predictive uncertainty of a nearshore bed evolution model

Predictions of nearshore depth evolution using process-based numerical simulation models contain inherent uncertainties owing to model structural deficiencies, measurement errors, and parameter uncertainty. This paper quantifies the parameter-induced predictive uncertainty of the cross-shore depth evolution model Unibest-TC by applying the Bayesian Generalised Likelihood Uncertainty Estimation methodology to modelling depth evolution at Egmond aan Zee (Netherlands). This methodology works with multiple sets of parameter values sampled uniformly in feasible parameter space and assigns a likelihood value to each parameter set. Acceptable simulations (i.e., based on parameter sets with a nonzero likelihood) were found for a wide range of parameter values owing to parameter interdependence and insensitivity. The 95% uncertainty prediction interval of bed levels after the 33 days prediction period was largest (0.5–1 m) near the sandbar crests that characterize the Egmond depth profile, reducing to near-zero values in the sandbar troughs and the offshore area. The prediction interval built up during storms (when sediment transport rates are largest) and remained the same or even reduced slightly during less-energetic conditions. The prediction uncertainty ranges bracket the observations near the inner-bar crest, its seaward flank, and at the seaward flank of the outer bar, suggesting that elsewhere model structural errors (and, potentially, measurement errors) dominate over parameter errors. The interdependence and the non-Gaussian marginal posterior distribution functions of the free model parameters cast doubt on the ability of commonly applied multivariate normal distribution functions to estimate parameter uncertainty.     

A two-stage sensitivity analysis for parameter identification and calibration of a physically-based distributed model in a river basin

Hydrological models demand large numbers of input parameters, which are to be optimally identified for better simulation of various hydrological processes. Identifying the most relevant parameters and their values using efficient sensitivity analysis methods helps to better understand model performance. In this study, the physically-based distributed model SHETRAN is used for hydrological simulation on the Netravathi River Basin in south India and the most important parameters are identified using the Morris screening method. Further, the influence of a particular model parameter on streamflow is quantified using local sensitivity analysis and optimal parameters are obtained for calibration of the SHETRAN model. The results demonstrate the capability of two-stage sensitivity analysis, combining qualitative and quantitative methods in the initial screening-out of insignificant model parameters, identifying parameter interactions and quantifying the contribution of each model parameter to the streamflow. The results of the sensitivity analysis simplified the calibration procedure of SHETRAN for the study area.     

MODFLOW 2000 head uncertainty,a first-order second moment method

A computationally efficient method to estimate the variance and covariance in piezometric head results computed through MODFLOW 2000 using a first-order second moment (FOSM) approach is presented. This methodology employs a first-order Taylor series expansion to combine model sensitivity with uncertainty in geologic data. MODFLOW 2000 is used to calculate both the ground water head and the sensitivity of head to changes in input data. From a limited number of samples, geologic data are extrapolated and their associated uncertainties are computed through a conditional probability calculation. Combining the spatially related sensitivity and input uncertainty produces the variance-covariance matrix, the diagonal of which is used to yield the standard deviation in MODFLOW 2000 head. The variance in piezometric head can be used for calibrating the model, estimating confidence intervals, directing exploration, and evaluating the reliability of a design. A case study illustrates the approach, where aquifer transmissivity is the spatially related uncertain geologic input data. The FOSM methodology is shown to be applicable for calculating output uncertainty for (1) spatially related input and output data, and (2) multiple input parameters (transmissivity and recharge).     

Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes

The National Weather Service (NWS) uses the SNOW17 model to forecast snow accumulation and ablation processes in snow-dominated watersheds nationwide. Successful application of the SNOW17 relies heavily on site-specific estimation of model parameters. The current study undertakes a comprehensive sensitivity and uncertainty analysis of SNOW17 model parameters using forcing and snow water equivalent (SWE) data from 12 sites with differing meteorological and geographic characteristics. The Generalized Sensitivity Analysis and the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm are utilized to explore the parameter space and assess model parametric and predictive uncertainty. Results indicate that SNOW17 parameter sensitivity and uncertainty generally varies between sites. Of the six hydroclimatic characteristics studied, only air temperature shows strong correlation with the sensitivity and uncertainty ranges of two parameters, while precipitation is highly correlated with the uncertainty of one parameter. Posterior marginal distributions of two parameters are also shown to be site-dependent in terms of distribution type. The SNOW17 prediction ensembles generated by the DREAM-derived posterior parameter sets contain most of the observed SWE. The proposed uncertainty analysis provides posterior parameter information on parameter uncertainty and distribution types that can serve as a foundation for a data assimilation framework for hydrologic models.     

Assessing titanium dioxide nanoparticles transport models by Bayesian uncertainty analysis

With the rapid growth of nanotechnology industry, nanomaterials as an emerging pollutant are gradually released into subsurface environments and become great concerns. Simulating the transport of nanomaterials in groundwater is an important approach to investigate and predict the impact of nanomaterials on subsurface environments. Currently, a number of transport models are used to simulate this process, and the outputs of these models could be inconsistent with each other due to conceptual model uncertainty. However, the performances of different models on simulating nanoparticles transport in groundwater are rarely assessed in Bayesian framework in previous researches, and these will be the primary objective of this study. A porous media column experiment is conducted to observe the transport of Titanium Dioxide Nanoparticles (nano-TiO₂). Ten typical transport models which consider different chemical reaction processes are used to simulate the transport of nano-TiO₂, and the observed nano-TiO₂ breakthrough curves data are used to calibrate these models. For each transport model, the parameter uncertainty is evaluated using Markov Chain Monte Carlo, and the DREAM_(ZS) algorithm is used to sample parameter probability space. Moreover, the Bayesian model averaging (BMA) method is used to incorporate the conceptual model uncertainty arising from different chemical reaction based transport models. The results indicate that both two-sites and nonequilibrium sorption models can well reproduce the retention of nano-TiO₂ transport in porous media. The linear equilibrium sorption isotherm, first-order degradation, and mobile-immobile models fail to describe the nano-TiO₂ retention and transport. The BMA method could instead provide more reliable estimations of the predictive uncertainty compared to that using a single model.     

Uncertainty issues of a conceptual water balance model for a semi‐arid watershed in north‐west of China

Hydrological models are useful tools for better understanding the hydrological processes and performing the hydrological prediction. However, the reliability of the prediction depends largely on its uncertainty range. This study mainly focuses on estimating model parameter uncertainty and quantifying the simulation uncertainties caused by sole model parameters and the co‐effects of model parameters and model structure in a lumped conceptual water balance model called WASMOD (Water And Snow balance MODeling system). The validity of statistical hypotheses on residuals made in the model formation is tested as well, as it is the base of parameter estimation and simulation uncertainty evaluation. The bootstrap method is employed to examine the parameter uncertainty in the selected model. The Yingluoxia watershed at the upper reaches of the Heihe River basin in north‐west of China is selected as the study area. Results show that all parameters in the model can be regarded as normally distributed based on their marginal distributions and the Kolmogorov–Smirnov test, although they appear slightly skewed for two parameters. Their uncertainty ranges are different from each other. The model residuals are tested to be independent, homoscedastic and normally distributed. Based on such valid hypotheses of model residuals, simulation uncertainties caused by co‐effects of model parameters and model structure can be evaluated effectively. It is found that the 95% and 99% confidence intervals (CIs) of simulated discharge cover 42.7% and 52.4% of the observations when only parameter uncertainty is considered, indicating that parameter uncertainty has a great effect on simulation uncertainty but still cannot be used to explain all the simulation uncertainty in this study. The 95% and 99% CIs become wider, and the percentages of observations falling inside such CIs become larger when co‐effects of parameters and model structure are considered, indicating that simultaneous consideration of both parameters and model structure uncertainties accounts sufficient contribution for model simulation uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.     

Uncertainty and sensitivity analysis of runoff and sediment yield in a small agricultural watershed with KINEROS2 / Analyse d'incertitude et de sensibilité des simulations d'écoulement et de transport solide de KINEROS2 dans un petit bassin versant agricole

Abstract

Using the Monte Carlo (MC) method, this paper derives arithmetic and geometric means and associated variances of the net capillary drive parameter, G, that appears in the Parlange infiltration model, as a function of soil texture and antecedent soil moisture content. Approximate expressions for the arithmetic and geometric statistics of G are also obtained, which compare favourably with MC generated ones. This paper also applies the MC method to evaluate parameter sensitivity and predictive uncertainty of the distributed runoff and erosion model KINEROS2 in a small experimental watershed. The MC simulations of flow and sediment related variables show that those parameters which impart the greatest uncertainty to KINEROS2 model outputs are not necessarily the most sensitive ones. Soil hydraulic conductivity and wetting front net capillary drive, followed by initial effective relative saturation, dominated uncertainties of flow and sediment discharge model outputs at the watershed outlet. Model predictive uncertainty measured by the coefficient of variation decreased with rainfall intensity, thus implying improved model reliability for larger rainfall events. The antecedent relative saturation was the most sensitive parameter in all but the peak arrival times, followed by the overland plane roughness coefficient. Among the sediment related parameters, the median particle size and hydraulic erosion parameters dominated sediment model output uncertainty and sensitivity. Effect of rain splash erosion coefficient was negligible. Comparison of medians from MC simulations and simulations by direct substitution of average parameters with observed flow rates and sediment discharges indicates that KINEROS2 can be applied to ungauged watersheds and still produce runoff and sediment yield predictions within order of magnitude of accuracy.     

Uncertainty and sensitivity analysis of a travel-time distribution-based stream water electrical conductivity model

In this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2-year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute-balance approach and (2) a water age-based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age-based model than the solute-balance model; 4. using time-variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS-TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute-balance model, the rainfall concentration C_rain and in the age-based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute-balance model. Little change in parameter sensitivity over time was found for the age-based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute-balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.     

Higher-order approximation techniques for estimating stochastic parameter of a sediment transport model

Higher-order approximation techniques for estimating stochastic parameter of the non-homogeneous Poisson (NHP) model are presented. The NHP model is characterized by a two-parameter cumulative probability distribution function (CDF) of sediment displacement. Those two parameters are the temporal and spatial intensity functions, physically representing the inverse of the average rest period and step length of sediment particles, respectively. Difficulty of estimating the parameters has, however, restricted the applications of the NHP model. The approximation techniques are proposed to address such problem. The basic idea of the method is to approximate a model involving stochastic parameters by Taylor series expansion. The expansion preserves certain higher-order terms of interest. Using the experimental (laboratory or field) data, one can determine the model parameters through a system of equations that are simplified by the approximation technique. The parameters so determined are used to predict the cumulative distribution of sediment displacement. The second-order approximation leads to a significant reduction of the CDF error (of the order of 47%) compared to the first-order approximation. Error analysis is performed to evaluate the accuracy of the first- and second-order approximations with respect to the experimental data. The higher-order approximations provide better estimations of the sediment transport and deposition that are critical factors for such environment as spawning gravel-bed.     

Higher-order approximation techniques for estimating stochastic parameter of a sediment transport model

Higher-order approximation techniques for estimating stochastic parameter of the non-homogeneous Poisson (NHP) model are presented. The NHP model is characterized by a two-parameter cumulative probability distribution function (CDF) of sediment displacement. Those two parameters are the temporal and spatial intensity functions, physically representing the inverse of the average rest period and step length of sediment particles, respectively. Difficulty of estimating the parameters has, however, restricted the applications of the NHP model. The approximation techniques are proposed to address such problem. The basic idea of the method is to approximate a model involving stochastic parameters by Taylor series expansion. The expansion preserves certain higher-order terms of interest. Using the experimental (laboratory or field) data, one can determine the model parameters through a system of equations that are simplified by the approximation technique. The parameters so determined are used to predict the cumulative distribution of sediment displacement. The second-order approximation leads to a significant reduction of the CDF error (of the order of 47%) compared to the first-order approximation. Error analysis is performed to evaluate the accuracy of the first- and second-order approximations with respect to the experimental data. The higher-order approximations provide better estimations of the sediment transport and deposition that are critical factors for such environment as spawning gravel-bed.     

The influence of parameter distribution uncertainty on hydrological and sediment modeling: a case study of SWAT model applied to the Daning watershed of the Three Gorges Reservoir Region, China

Parameter uncertainty involved in hydrological and sediment modeling often refers to the parameter dispersion and the sensitivity of the parameter. However, a limitation of the previous studies lies in that the assignment of range and specification of probability distribution for each parameter is usually difficult and subjective. Therefore, there is great uncertainty in the process of parameter calibration and model prediction. In this study, the impact of probability parameter distribution on hydrological and sediment modeling was evaluated using a semi-distributed model—the Soil and Water Assessment Tool (SWAT) and Monte Carlo method (MC)—in the Daning River watershed of the Three Gorges Reservoir Region (TGRA), China. The classic types of parameter distribution such as uniform, normal and logarithmic normal distribution were involved in this study. Based on results, parameter probability distribution showed a diverse degree of influence on the hydrological and sediment prediction, such as the sampling size, the width of 95% confidence interval (CI), the ranking of the parameter related to uncertainty, as well as the sensitivity of the parameter on model output. It can be further inferred that model parameters presented greater uncertainty in certain regions of the primitive parameter range and parameter samples densely obtained from these regions would lead to a wider 95 CI, resulting in a more doubtful prediction. This study suggested the value of the optimized value obtained by the parameter calibration process could may also be of vital importance in selecting the probability distribution function (PDF). Such cases, where parameter value corresponds to the watershed characteristic, can be used to provide a more credible distribution for both hydrological and sediment modeling.     

Sediment transport at the network scale and its link to channel morphology in the braided Vjosa River system

In this article we apply the CASCADE network-scale sediment connectivity model to the Vjosa River in Albania. The Vjosa is one of the last unimpaired braided rivers in Europe and, at the same time, a data scarce environment, which limits our ability to model how this pristine river might respond to future human disturbance. To initialize the model, we use remotely sensed data and modeled hydrology from a regional model. We perform a reach-by-reach optimization of surface grain size distribution (GSD) and bedload transport capacity to ensure equilibrium conditions throughout the network. In order to account for the various sources of uncertainty in the calculation of transport capacity, we performed a global sensitivity analysis. The modeled GSD distributions generated by the sensitivity analysis generally match the six GSDs measured at different locations within the network. The modeled bedload sediment fluxes increase systematically downstream, and annual fluxes at the outlet of the Vjosa are well within an order of magnitude of fluxes derived from previous estimates of the annual suspended sediment load. We then use the modeled sediment fluxes as input to a set of theoretically derived functions that successfully discriminate between multi-thread and single-thread channel patterns. This finding provides additional validation of the model results by showing a clear connection between modeled sediment concentrations and observed river morphology. Finally, we observe that a reduction in sediment flux of about 50% (e.g., due to dams) would likely cause existing braided reaches to shift toward single thread morphology. The proposed method is widely applicable and opens a new avenue for application of network-scale sediment models that aid in the exploration of river stability to changes in water and sediment fluxes.     

Assessing the hydrological and geomorphic behaviour of a landscape evolution model within a limits-of-acceptability uncertainty analysis framework

Landscape evolution models (LEMs) have the capability to characterize key aspects of geomorphological and hydrological processes. However, their usefulness is hindered by model equifinality and paucity of available calibration data. Estimating uncertainty in the parameter space and resultant model predictions is rarely achieved as this is computationally intensive and the uncertainties inherent in the observed data are large. Therefore, a limits-of-acceptability (LoA) uncertainty analysis approach was adopted in this study to assess the value of uncertain hydrological and geomorphic data. These were used to constrain simulations of catchment responses and to explore the parameter uncertainty in model predictions. We applied this approach to the River Derwent and Cocker catchments in the UK using a LEM CAESAR-Lisflood. Results show that the model was generally able to produce behavioural simulations within the uncertainty limits of the streamflow. Reliability metrics ranged from 24.4% to 41.2% and captured the high-magnitude low-frequency sediment events. Since different sets of behavioural simulations were found across different parts of the catchment, evaluating LEM performance, in quantifying and assessing both at-a-point behaviour and spatial catchment response, remains a challenge. Our results show that evaluating LEMs within uncertainty analyses framework while taking into account the varying quality of different observations constrains behavioural simulations and parameter distributions and is a step towards a full-ensemble uncertainty evaluation of such models. We believe that this approach will have benefits for reflecting uncertainties in flooding events where channel morphological changes are occurring and various diverse (and yet often sparse) data have been collected over such events.     

Parametric uncertainty and sensitivity analysis of hydrodynamic processes for a large shallow freshwater lake

Abstract

A parametric uncertainty and sensitivity analysis of hydrodynamic processes was conducted for a large shallow freshwater lake, Lake Taihu, China. Ten commonly used parameters in five groups were considered including: air–water interface factor, water–sediment interface factor, surrounding terrain factor, turbulent diffusion parameters and turbulent intensity parameters. Latin hypercube sampling (LHS) was used for sampling the parametric combinations, which gave predictive uncertainty results directly without using surrogate models, and the impacts of different parametric distribution functions on the results were investigated. The results showed that the different parametric distribution functions (e.g. uniform, normal, lognormal and triangular) for sampling had very little impact on the uncertainty and sensitivity analysis of the lake hydrodynamic model. The air–water interface factor (wind drag coefficient) and surrounding terrain factor (wind shelter coefficient) had the greatest influence on the spatial distribution of lake hydrodynamic processes, especially in semi-closed bays and lake regions with complex topography, accounting for about 60–70% and 20%, respectively, of the uncertainty on the results. Vertically, velocity in the surface layer was also largely influenced by the two factors, followed by velocity in the bottom layer; the middle velocity had minimal impact. Likewise, the water–sediment interface factor (i.e. bottom roughness height) ranked third, contributing about 10% to the uncertainty of the hydrodynamic processes of the lake. In contrast, turbulent diffusion parameters and turbulent intensity parameters in the lake hydrodynamic model had little effect on the uncertainty of simulated results (less than 1% contribution). The findings were sufficiently significant to reduce the parameter uncertainties and calibration workload of the hydrodynamic model in large shallow lakes.

Editor Z. W. Kundzewicz; Associate editor S. Grimaldi     

Incremental dynamic analysis with consideration of modeling uncertainties

Incremental dynamic analysis (IDA) has been extended by introducing a set of structural models in addition to the set of ground motion records which is employed in IDA analysis in order to capture record‐to‐record variability. The set of structural models reflects epistemic (modeling) uncertainties, and is determined by utilizing the latin hypercube sampling (LHS) method. The effects of both aleatory and epistemic uncertainty on seismic response parameters are therefore considered in extended IDA analysis. The proposed method has been applied to an example of the four‐storey‐reinforced concrete frame, for which pseudo‐dynamic tests were performed at the ELSA Laboratory, Ispra. The influence of epistemic uncertainty on the seismic response parameters is presented in terms of summarized IDA curves and dispersion measures. The results of extended IDA analysis are compared with the results of IDA analysis, and the sensitivity of the seismic response parameters to the input random variable using the LHS method is discussed. It is shown that epistemic uncertainty does not have significant influence on the seismic response parameters in the range far from collapse, but could have a significant influence on collapse capacity. Copyright © 2008 John Wiley & Sons, Ltd.     

青藏高原东北缘地震活动性广义帕累托模型的全域敏感性分析

基于广义帕累托分布构建地震活动性模型,因其输入参数取值难以避免不确定性,导致依据该模型所得的地震危险性估计结果具有不确定性。鉴于此,本文选取青藏高原东北缘为研究区,提出了基于全域敏感性分析的地震危险性估计的不确定性分析流程和方法。首先,利用地震活动性广义帕累托模型,进行研究区地震危险性估计;然后,选取地震记录的起始时间和震级阈值作为地震活动性模型的输入参数,采用具有全域敏感性分析功能的E-FAST方法,对上述两个参数的不确定性以及两参数之间的相互作用对地震危险性估计不确定性的影响进行定量分析。结果表明：地震危险性估计结果（不同重现期的震级重现水平、震级上限及相应的置信区间）对两个输入参数中的震级阈值更为敏感;不同重现期的地震危险性估计结果对震级阈值的敏感程度不同;对不同的重现期而言,在影响地震危险性估计结果的不确定性上,两个输入参数之间存在非线性效应,且非线性效应程度不同。本文提出的不确定性分析流程和方法,可以推广应用于基于其它类型地震活动性模型的地震危险性估计不确定性分析。