Regional Sensitivity Analysis Research Articles

Hydrological Simulation Study in Gansu Province of China Based on Flash Flood Analysis

The calibration and validation of hydrological model simulation performance and model applicability evaluation in Gansu Province is the foundation of the application of the flash flood early warning and forecasting platform in Gansu Province. It is difficult to perform the simulation for Gansu Province due to the fact that it covers a wide range, from north to south, with multiple climate types and diverse landforms. The China Flash Flood Hydrological Model (CNFF) was implemented in this study. A total of 11 model clusters and 289 distributed hydrological models were divided based on hydrology, climate, and land-use factors, among others. A spatiotemporally mixed runoff method and the Event-Specific Geomorphological Instantaneous Unit Hydrograph (GIUH) were applied based on large-scale fast parallel computation. To improve model calibration and validation efficiency, the RSA method (Regionalized Sensitivity Analysis) was used for CNFF model parameter sensitivity analysis, which could reduce the number of model parameters that need to be adjusted during the calibration period. Based on the model sensitivity analysis results, the CNFF was established in Gansu Province to simulate flood events in eight representative watersheds. The average NSE, REQ, and ET were 0.76 and 0.73, 9.1% and 12.6%, and 1.2 h and 1.7 h, respectively, in the calibration and validation period. In general, the CNFF model shows a good performance in multiple temporal and spatial scales, thus providing a scientific basis for flash flood early warning and analysis in Gansu Province.

Regional Sensitivity Analysis for Maximum Static Transfer Capability of Power Systems by Dimension-Adaptive Sparse Grid Interpolation

The maximum static transfer capability (MSTC) can be regarded as a measure of the power transmission ability of power systems in steady-state operation. These emerging uncertainty sources pose a difficult challenge for the efficient analysis and effective control of MSTC. To address this challenge, the dimension-adaptive sparse grid interpolation (DASGI) method is applied with modified boundaries of the collocation points to establish a surrogate model for the faster calculation of the MSTC based on AC optimal power flow (AC-OPF), thus accelerating its probabilistic analysis as well. Subsequently, the regional sensitivity analysis (RSA) is introduced in terms of contribution to the sample mean (CSM) and to the sample variance (CSV) to identify the critical regions of the uncertainty sources. Hence, an adjustment strategy is developed to increase the mean or decrease the variance of MSTC for secure and stable power transmission. Furthermore, the total CSM (TCSM) and total CSV (TCSV) are proposed as indicators to select the most influential random inputs as the preliminary step of RSA, which reduces the analytical burden. The effectiveness of this proposition is illustrated on the modified IEEE 118-bus system and the Polish 2383-bus power system, where the Monte Carlo simulation method, in combination with the AC-OPF-based MSTC model, provides an accuracy reference and a traditional sensitivity index provides the term for comparison.

Maximizing Regional Sensitivity Analysis indices to find sensitive model behaviors

Maximizing Regional Sensitivity Analysis indices to find sensitive model behaviors

Regional reliability sensitivity analysis based on dimension reduction technique

Reliability sensitivity analysis is crucial for efficient design optimisation based on parametric modelling. A common fact in applications is that reliability shows different degrees of sensitivity to changes in the ranges of design parameters within a design space. Therefore, a regional reliability sensitivity analysis (RRSA) is proposed to study how the reliability-based sensitivity indexes change when the local ranges of the design parameters are modified. In this study, a method combining the Bayesian formula and the sampling simulation method is utilised to obtain the derivative of the failure probability in an augmented space without the need to fit a function. Based on the idea of averaging the square of the local gradients in the entire parameter space, a covariance matrix is constructed using the average outer product of the gradient of the failure probability function (FPF) for the RRSA. As an emerging dimension-reduction technique, the active subspace method (ASM) is employed to identify the important directions of design variables to perform a reliability-based sensitivity analysis. The covariance matrix for the high-dimensional reliability analysis is estimated using the dynamic propagation sampling (DPS) method. Finally, we demonstrate the effectiveness and efficiency of the proposed RRSA through a numerical example wherein a Monte Carlo simulation (MCS) is employed for comparison.

Mathematical Problems in Engineering Joint Structure Stiffness Analysis of an Automotive Body in White with the Finite Element Method

The finite element model of the seven connection heads of an automotive body in white is established, and the stiffness calculation is carried out by taking the lower joint of the B pillar as an example. Nine stiffness values were obtained for each branch, which can be written as a 3 × 3 symmetric matrix. The front and back bending stiffness, torsional stiffness, and internal and external bending stiffness are obtained with the decoupling method. Based on this, the finite element model for unrigid and rigid joints is established, and the three stiffness ratio coefficients of each branch of the joint are calculated and evaluated. Finally, the effects of the thickness and stiffness of the sheet metal parts on the bending condition, torsion condition, first-order bending, and torsion frequency of the body in white are discussed using the regional sensitivity analysis and strain energy calculation.

Analysis of the Influence of Geomechanical Parameters and Geometry on Slope Stability in Granitic Residual Soils

Granitic residual soils are soils formed by the in situ weathering of intrusive granitic rocks and are present in different parts of the world. Due to their large presence, many civil engineering projects are carried out on and within these soils. Therefore, a correct characterization of the slopes is necessary for slope stability studies. This investigation aims to study the influence of the values of geomechanical parameters (specific weight, cohesion, and friction angle) and the geometry of a slope (height and inclination) on slope stability of residual granitic soils in dry and static conditions. To this end, an automatic system was developed for the numerical study of cases using the finite element method with limit analysis. The system allows modeling, through Monte Carlo simulation and different slope configurations. With this system, the safety factors of 5000 cases were obtained. The results of the models were processed through the SAFE toolbox, performing a Regional Sensitivity Analysis (RSA). The results of this research concluded that the order of influence of the factors were: slope angle > slope height > cohesion > friction angle > unit weight (β > H > c > ϕ > γ).

Uncertainty appraisal provides useful information for the management of a manual grape harvest

This contribution presents a novel approach to characterise uncertainty in the manual grape harvest of a winery in Tuscany (Italy). After identifying the potential sources of variability arising from randomness, weather, and management options, a model to define useful output variables is built. These output variables include the discrepancy in the harvest date of the vineyards ( harvest date discrepancy) , the discrepancy in the required workforce across harvest dates (labour discrepancy), and, finally, the potential deficit of working hours throughout the grape harvest campaign (labour deficit) . The range spanned by these variables is first assessed through a Monte Carlo uncertainty analysis wherein the model is repeated approximately 16,000 times with variable combinations of the input parameters per their probability distribution. The assessed uncertainty is then apportioned to the input parameters through a global sensitivity analysis. In turn, a regional sensitivity analysis characterises the circumstances producing a deficit of working hours, which corresponds to sufferance in the grape harvest campaign. The discussed approach could be implemented in a user-friendly decision-support tool for risk characterisation and efficient grape harvest management. • We developed a model to define sensitive variables in manual grape harvest. • We characterise their uncertainty through Monte Carlo simulations. • Output uncertainty was apportioned onto inputs with a global sensitivity analysis. • Criticalities were identified and analysed through regional sensitivity analysis. • The approach proposed could support decision making in grape harvest management.

Parameter uncertainty and sensitivity analysis of the three Gorges Reservoir and Xiangxi River EFDC model

Water quality models are decision support tools for the planning and management of the aquatic environment. However, the application of the model needs an intricate calibration process due to the various ranges of numerous parameters. To help improve the accuracy of model simulation, and reduce the workload of parameter adjustment to similar surface waters, parameter uncertainty and sensitivity analyses of the Environmental Fluid Dynamics Code (EFDC) model were carried out in this study. The EFDC model was first calibrated and simulation results agree well with the measured data. The Generalised Likelihood Uncertainty Estimation (GLUE) and Regional Sensitivity Analysis (RSA) methods are then applied to analyze the uncertainty and sensitivity of the model. Eighteen kinetic parameters related to algae and organic matter are filtered and analyzed. The results show that the sensitivities of the model to eighteen input parameters are significantly different. The modeled algae levels measured as chlorophyll-a (Chl-a) are highly sensitive to the optimal growth rate and the maximum basal metabolism rate of cyanobacteria (BMRc and PMc), with the Sensitivity Indices (SI) at 0.66 and 0.78, respectively. The inorganic nutrient levels (phosphorus, nitrate nitrogen and ammonia nitrogen) are highly sensitive to minimum respiration rates of corresponding dissolved organic matter (KDP and KDN) with SIs at 0.78, 0.56 and 0.88. Dissolved oxygen (DO) is highly sensitive to PMc and KDC, with their SIs at 0.66 and 0.85, respectively. The uncertainty interval is focused on the periods of high algae concentration. The simulated uncertainty in the surface water is higher than that in middle-layer water, and might be related to algal transport processes like settlement and horizontal transport. The results of the uncertainty and sensitivity analyses in this study support a better understand of the modeling mechanisms and provide scientific guidance for calibration in similar waterbodies.

Non-Destructive Assessment of the Elastic Properties of Low-Grade CLT Panels

The use of cross-laminated timber panels (CLT) made of low-grade structural timber has steadily increased in developing countries. These panels usually present several natural defects, which can cause a high local variation of their orthotropic elastic properties, generating future structural serviceability problems. Our work aims to estimate the local variability of the elastic properties in low-grade CLT panels by combining nondestructive transverse vibration testing, numerical simulations, and regional sensitivity analysis (RSA). Four three-layer Radiata pine CLT panels were subjected to transverse vibration tests with supports at four points. Besides, a series of numerical simulations of the panels, considering the local variability of the elastic properties of the panels in eight zones, were carried out using the finite element method. Then, RSA analysis was performed to study in which ranges of values the panels’ elastic properties generated lower differences between the measured versus simulated dynamic properties. Finally, a structural quality control indicator was proposed for the CLT panels based on keeping low the probability that the elastic properties in the central zones do not exceed minimum acceptable values. The results obtained suggest that the proposed methodology is suitable for segregating CLT panels with high concentrations of defects such as pith presence.

Global Sensitivity Analysis Applied to Train Traffic Rescheduling: A Comparative Study

The adjustment of rail traffic in the event of an electrical infrastructure disruption presents an important decision-making process for the smooth operation of the network. Railway systems are complex, and their analysis relies on expensive simulations, which makes incident management difficult. This paper proposes the use of sensitivity analysis in order to evaluate the influence of different traffic adjustment actions (e.g., spacing between trains and speed reduction) on the train supply voltage, which must never drop below the critical value prescribed by technical standards. Three global sensitivity analysis methods dedicated to black box, multivariate, nonlinear models are considered: generalized Sobol indices, energy distance-based indices, and regional sensitivity analysis. The three methods are applied to a simple traffic rescheduling test case and give similar results, but at different costs. Regional sensitivity analysis appears to be the most suitable method for the present application: it is easy to implement, rather fast, and accounts for constraints on the system output (a key feature for electrical incident management). The application of this method to a test case representative of a real rescheduling problem shows that it provides the information needed by the traffic manager to reschedule traffic in an efficient way. The same type of approach can be used for any power system optimization problem with the same characteristics.

Investigating model performance and parameter sensitivity for runoff simulation across multiple events for a large green roof

AbstractGreen roofs are a form of green infrastructure aimed at retaining or slowing the movement of precipitation as stormwater runoff to sewer systems. To determine total runoff versus retention from green roofs, researchers and practitioners alike employ hydrologic models that are calibrated to one or more observed events. However, questions still remain regarding how event size may impact parameter sensitivity, how best to constrain initial soil moisture (ISM), and whether limited observations (i.e., a single event) can be used within a calibration‐validation framework. We explored these questions by applying the storm water management model to simulate a large green roof located in Syracuse, NY. We found that model performance was very high (e.g., Nash Sutcliffe efficiency index > 0.8 and Kling‐Gupta efficiency index > 0.8) for many events. We initially compared model performance across two parameterizations of ISM. For some events, we found similar performance when ISM was varied versus set to zero; for others, varying ISM yielded higher performance as well as greater water balance closure. Within a calibration‐validation framework, we found that calibrating to larger events tended to produce moderate to high performance for other non‐calibration events. However, very small storms were notoriously difficult to simulate, regardless of calibration event size, as these events are likely fully retained on the roof. Using regional sensitivity analysis, we confirmed that only a subset of model parameters was sensitive across 16 events. Interestingly, many parameters were sensitive regardless of event size, though some parameters were more sensitive when simulating smaller events. This emphasizes that storm size likely influences parameter sensitivity. Overall, we show that while calibrating to a single event can achieve high performance, exploring simulations across multiple events can yield important insight regarding the hydrologic performance of green roofs that can be used to guide the gathering of in situ properties and observations for refining model frameworks.

REGSim: An open-source framework to estimate recharge and simulate groundwater heads

The computational complexity of distributed groundwater models poses a significant challenge in the conjunctive management of surface and groundwater resources. Here, we propose a multi-model framework to predict groundwater recharge using a simple water balance model. We compare and contrast two lumped parsimonious conceptual models of groundwater recharge, one based on P only and another, additionally including PE as a predictor. Both model variants also include an option to specify annually fixed or seasonally varying recharge factors, as well as a number of pumping scenarios. We incorporate these models within a python based open-source toolbox, REGSim (Recharge Estimation and Groundwater Simulation). The toolbox features a multi-objective evolutionary algorithm (NSGA-II), the Generalized Likelihood Uncertainty Estimation (GLUE) method, and regional sensitivity analysis (RSA). These functionalities allow the users to perform multi-objective calibration of the models, obtain confidence intervals on predicted groundwater heads, and understand the relative importance of different model parameters. REGSim is used to simulate groundwater heads for the urban agglomeration of Hyderabad, India. Using REGSim, we tested alternative conceptualizations of groundwater recharge and pumping processes in the city. We found that the intra-annual dynamics of groundwater levels are better explained by seasonally varying the recharge factors than annually fixed recharge factors. The model achieved an NSE of 0.64 and 0.70 during the validation for the formulations based on P only and using both P and PE, respectively. Furthermore, sensitivity analysis revealed that specific yield is the most influential parameter affecting the simulated groundwater head in the region.

From regional sensitivity to intra-sensitivity for parametric analysis of free-form shapes: Application to ship design

Robust Parametric Sensitivity Analysis (PSA) is a prerequisite for efficient shape optimisation via parametric modelling. A major challenge PSA has to handle is related to the fact that a parameter can be sensitive in certain local areas of the design space but become insensitive in others. Therefore, setting an applicable space for this analysis becomes a difficult task. In this paper, we introduce the concept of intra-sensitivity to identify parameters whose perturbation has a major impact on the sensitivity index of the remaining parameters. For this purpose, we firstly appeal to Active Subspace Method (ASM) and develop an ASM-based regional sensitivity analysis, which investigates parametric sensitivity in local regions of the design space and aids conducing to parameters’ intra-sensitivity. This regional analysis is applied in conjunction with a Dynamic Propagation Sampling approach, for tackling the computational complexity arising when high-dimensional problems are concerned. Once sensitive and intra-sensitive parameters are identified, then free-form features, correlated to these parameters, are evaluated using a feature saliency map built with the aid of Hausdorff distance. The so resulting methodology has been validated in the area of computer-aided ship design using two parametric modellers: the first one is a Procedural Deformation (PD) modeller which is based on T-splines and involves 24 parameters while the second one is based on Free-Form Deformation (FFD) and involves 104 parameters. The corresponding design spaces have been generated using a parent hull close to the KCS container ship and are analysed against hull’s volume of displacement and total resistance. Finally, the convergence performance of the various components of this approach is compared with state-of-the-art techniques.

Credibility distribution function based global and regional sensitivity analysis under fuzzy uncertainty

Global sensitivity analysis (GSA) is useful to recognize important inputs for assigning priority and unimportant inputs for simplifying models by exploring whole distribution ranges. Meanwhile, regional sensitivity analysis (RSA) is also studied for finding the contribution of the critical region of an input, which can be viewed as the complementary of GSA. However, there is a lack of GSA and RSA research in presence of fuzzy uncertainty. Thus, a new global sensitivity index (GSI) under the fuzzy uncertainty is devoted on the credibility distribution function (CrDF), a comprehensive distribution description under the fuzzy uncertainty. The CrDF-based GSI is defined by the fuzzy expectation of the difference between the CrDF and the conditional CrDF of the output on fixing the fuzzy input over its whole distribution range, which can quantify the contribution of the fuzzy input to the output CrDF. Then, a new fuzzy RSA technique, the contribution to this CrDF based index (shortened by CCI) plot, is also proposed, and it can assess the effect of given regions of important inputs on output CrDF. Besides, mathematical properties of the CrDF based GSI and the CCI plot are discussed, and their solution are established by use of the fuzzy simulation with the same set of samples. After the accuracy of established fuzzy simulation solution for the CrDF based GSI and the CCI plot are verified by an analytical example, other examples are used to demonstrate the reasonability and applicability of proposed CrDF based GSI and CCI plot under fuzzy uncertainty.

Global sensitivity analysis of a CaO/Ca(OH)2 thermochemical energy storage model for parametric effect analysis

Simulation models have been widely used for thermochemical energy storage to better understand its behavior and consequently to improve operational control of the device. However, incomplete knowledge of system properties leads to a significant number of uncertain parameters in the simulation models, which in turn cause uncertainties in system predictions. In this work, we perform global sensitivity analysis to identify the effect of uncertain parameters on the outputs of a thermochemical energy storage model, so that we can better understand the predictive uncertainties, proceed with targeted data acquisition or even simplify the corresponding uncertainty quantification. To get reliable sensitivity analysis results, we use both variance-based and regional sensitivity analysis since they focus on different probabilistic features of model outputs. Since the simulation model is computationally expensive, we use model reduction via the (arbitrary) polynomial chaos expansion. Then, to further confirm the results, the regional sensitivity index is also estimated based on the original model with the same given sample set. Based on the results of both sensitivity analysis methods, we can find that there are 8 unimportant parameters among 16 analyzed parameters. Thus, we can focus resources on investigating the important uncertain parameters. Also, we can ignore the uncertainty of unimportant parameters to simplify the corresponding uncertainty quantification.

Non-Destructive Assessment of the Dynamic Elasticity Modulus of Eucalyptus nitens Timber Boards.

Eucalyptus nitens is a fast-growing wood species with a relevant presence in countries like Australia and Chile. The sustainable construction goals have driven the search of structural applications for Eucalyptus nitens; however, this process has been complicated due to the defects usually presented in these timber boards. This study aims to evaluate the dynamic elasticity modulus (Exd) of Eucalyptus nitens timber boards through non-destructive vibration-based tests. Thirty-six timber boards with different levels of knots and cracks were instrumented and tested in a simply supported condition by measuring longitudinal and transverse vibrations. In the first stage, the Exd was calculated globally through simplified normative formulas. Then, in a second stage, the local variability of the Exd was estimated using operational modal analysis (OMA), finite element numerical simulations (FEM), and regional sensitivity analysis (RSA). The positive correlation found between the global static modulus of elasticity and Exd suggests that non-destructive techniques could be used as a reliable and fast alternative for the assessment of bending stiffness. Finally, the proposed method to estimate the local variability of Exdt based on the combination of OMA, FEM, and RSA techniques was useful to improve the structural selection process of timber boards for lightweight social housing floors.

Prioritization of potentially contaminated sites: A comparison between the application of a solute transport model and a risk-screening method in China

Numerous potential contaminated sites in China pose a substantial risk to human health and the local ecology. Thus, there is an urgent need to prioritize and further investigate potential contaminated sites and determine those that pose a threat in this regard. Newly developed by the Ministry of Ecology and Environment, the Risk Screening Method (RSM) scoring system is employed to assess soil and groundwater risk across China. In this study, the RSM is tested at a screening level and compared with the EPACMTP model, a solute transport model developed for the risk assessment of land disposal sites. First, a regional sensitivity analysis is conducted for EPACMTP model parameters, and those with significant sensitivity are compared with the risk indicators in the RSM. Second, 28 sites are evaluated by both prioritization methods in order to compare RSM risk scores and EPACMTP model simulations. Our results show that the RSM have similar risk assessing factors as EPACMTP model and its promising capability of prioritizing high-risk sites with very little available data. However, it does provide a conservative assessment, as risks at some sites are over-estimated, so further investigation is recommended for sites with high RSM risk scores. In addition, the initial screening should be documented by additional investigations at sites in order to prove the potential risk. The length of the period considered in the assessment has a great influence on prioritization results for heavy metals. As longer time scale will result in higher risk, its selection reflects the balance of current cost and future risk. The EPACMTP model provides a range of possible risks and can assess them within different timeframes. It is suggested to conduct further comparisons between the RSM and the solute transport models for sites from other areas, types of industries and more mobile compounds.

Structural decomposition and Regional Sensitivity Analysis of industrial consumption embedded emissions from Chinese households

Households play a vital role in producing industrial emissions through final-consumption. As a result, related literature focuses mainly on the drivers of household demand embedded industrial production (DEIP) emissions. Recent evidence on industrial emissions, however, shows that targeting industrial carbon consumers and their final demand, particularly from households, is much more effective. Unfortunately, there isn't much literature on household demand embedded industrial consumption (DEIC) emissions. The aim of this study is to develop a model that can help analyze the impact of key drivers on household DEIC emissions. The model is applied to Chinese urban and rural household DEIC emissions. Additionally, the study also employees Regional Sensitivity Analysis to rank and map the most influential factors of Chinese rural and urban DEIC emissions. Results showed that for both rural and urban households, income/capita with an average impact of 35 Mt and 111 Mt was the main driver of DEIC emissions growth. Income/capita was also the most sensitive factor for rural and urban DEIC emissions. The consumer industry's Leontief effect (technology) had the second largest positive effect on both rural and urban household DEIC emissions. However, for rural and urban households, the second most sensitive factors were different. Emission intensity for rural households and consumption tendency for urban households had the greatest negative effects on respective DEIC emissions. Finally, the article discusses results, highlighting policy implications for reducing rural and urban household DEIC emissions.

A Simple Time-Varying Sensitivity Analysis (TVSA) for Assessment of Temporal Variability of Hydrological Processes

Time-varying sensitivity analysis (TVSA) allows sensitivity in a moving window to be estimated and the time periods in which the specific components of a model can affect its performance to be identified. However, one of the disadvantages of TVSA is its high computational cost, as it estimates sensitivity in a moving window within an analyzed series, performing a series of repetitive calculations. In this article a function to implement a simple TVSA with a low computational cost using regional sensitivity analysis is presented. As an example of its application, an analysis of hydrological model results in daily, monthly, and annual time windows is carried out. The results show that the model allows the time sensitivity of a model with respect to its parameters to be detected, making it a suitable tool for the assessment of temporal variability of processes in models that include time series analysis. In addition, it is observed that the size of the moving window can influence the estimated sensitivity; therefore, analysis of different time windows is recommended.

Impact of precipitation data density and duration on simulated flow dynamics and implications for ecohydrological modelling of semi-arid catchments of Southern Africa

Understanding flow regime dynamics is important for the management and conservation of river systems and their related ecosystems under current and future pressures like climate and land use change. For this reason, the accuracy and detail with which hydrological models are able to replicate streamflow and other spatially distributed processes is critical for global and regional water management programs. One of the most critical factors for model performance is the spatial and temporal representation of precipitation data, a procedure known as regionalisation. It is therefore imperative to evaluate the sensitivity of models to data from different stations in order to identify locations that exert the strongest control on simulated streamflow. In this study, a spatially distributed rainfall/runoff model, the J2000, calibrated under near natural river flow conditions, was used to analyse the overall precipitation station impact on simulated streamflow and its flow components. For this purpose, different analysis methods including Regional Sensitivity Analysis (RSA) were used. The study was applied to the Berg River catchment, a water stressed region in the Western Cape, South Africa that was recently affected by serious drought conditions. The results show that during peak flows precipitation stations in the headwater part of the basin had the largest systematic impact, often controlling the data input for only a small proportion of the overall spatial extent of the model, but still having the largest impact on the overall modelled streamflow. In contrast, precipitation stations in lowland areas showed to be more important during low-flow periods, although they often control the data input for a larger part of the modelled catchment. The study highlights the importance of good overall data coverage for improved low-flow simulations, as well as identification of critical areas in regions responsible for peak runoff generation processes. The inclusion of systematic station weights has the potential to improve the ecohydrological representation of rainfall/runoff models in data scarce regions, as well as precipitation regionalisation approaches. The results are indicative of known microclimatic variability in semi-arid Southern Africa, highlighting the significance of catchment heterogeneity and areas of dominant sub-basins for overall runoff generation.