Qualitative Sensitivity Analysis Research Articles

Meta-review of the barriers and facilitators to women accessing perinatal mental healthcare

Perinatal mental health (PMH) problems are common and can have an adverse impact on women and their families. However, research suggests that a substantial proportion of women with PMH problems...

Qualitative analysis of the most locally relevant runoff and erosion parameters for constructing Brazilian scenarios.

Estimating exposure is one of the most important steps in an environmental risk analysis of crop-protection products to nontarget organisms. Regulatory agencies such as the USEnvironmentalProtectionAgency(USEPA), PestManagementRegulatoryAgency(PMRA), and EuropeanFoodSafetyAuthority(EFSA) all use mathematical exposure models in their regulatory assessment process. Brazil has been discussing the adoption of the Pesticide in Water Calculator (PWC) to be applied in aquatic pesticide risk assessment. Therefore, a qualitative sensitivity analysis (Morris OAT method) was performed to understand which are the most important local parameters in the PWC to estimate environmental concentrations in surface water (EECSW ). In addition, an exercise made up of two corn scenarios in two Brazilian regions was developed (Uberlândia [UDI] and Arapoti [ARA]). Two herbicides with different soil-binding properties and modes of action were selected to estimate the EECSW . The results demonstrated that the parameters of importance were different for each site, probably the result of different soil characteristics and meteorological patterns. This outcome suggests that regulatory agencies should consider developing more than one scenario to account for different agricultural regions. For Herbicide 1, the EECSW for UDI were similar to US scenarios, whereas for ARA they were lower. For Herbicide 2, the EECSW for the UDI site was higher than most of the US scenarios, whereas at the ARA site, EECs were similar to four US scenarios and lower than the other six. Local data were used as a refinement, resulting in the decrease in the EECSW for both herbicides in the UDI site. For the ARA site, Herbicide 1 displayed a similar EECSW value, whereas for Herbicide 2, it was lower after the refinement. Overall, these results demonstrated the importance of developing local scenarios to provide more realism to estimate pesticide exposure from its agricultural use and may help regulators to determine and recommend mitigations regarding the use of crop-protection products. Integr Environ Assess Manag 2023;19:1374-1384. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Exploring spatial similarity and performance among marine protected area planning scenarios: The case of the Weddell Sea, Antarctica

The world's oceans are exposed to a variety of pressures, such as overfishing and the environmental effects of increasingly dense coastal populations. Policy and science agree that a global network of marine protected areas (MPAs) will mitigate these effects. Conservation planners face the dual challenge of planning MPAs based on complex scientific information and supporting the decision-making process through clear and transparent communication with the involved stakeholders. To this end, visual comparisons of different mapped reserve configurations are a commonly used approach, while analytical approaches that assess the efficiency of different planning scenarios and trade-offs among them are still rarely used in practice. Here, we use uni- and multivariate statistics to compare reserve configurations used in the process of designing a Weddell Sea MPA (WSMPA) in Antarctica. We show that different target level settings (low, medium, mixed) for conservation features affect the configuration of the solutions significantly. The mixed-target scenario was one of the most flexible ones in that it produced the most diverse set of solutions, providing several options for consideration. At the same time, it was also the most well balanced scenario, finding relatively cost-efficient solutions while selecting an intermediate number of planning units that were most spatially clustered. Our study complements the qualitative sensitivity analysis carried out previously (mainly visual, descriptive scenario comparisons) and will hopefully further advance the WSMPA development process under CCAMLR. Furthermore, this paper adds to the growing literature advocating the application of multivariate statistics for further thorough and systematic evaluation procedures in conservation planning. • Planning of a marine protected area (MPA) in the Weddell Sea, Antarctica. • Use multivariate statistics to investigate the performance of spatial planning scenarios. • Targets for conservation features significantly affect the configuration of MARXAN solutions. • Complements qualitative sensitivity analysis previously conducted for this MPA. • Adds support for the continued advance of this MPA proposal under CCAMLR.

Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations

Abstract. Shallow landslides pose a risk to infrastructure and residential areas. Therefore, we developed SlideforMAP, a probabilistic model that allows for a regional assessment of shallow-landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SlideforMAP uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated on observed events. For each generated landslide, SlideforMAP calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters are assigned to the generated landslides from log-normal distributions based on mean and standard deviation values representative of the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed by root proximity and root strength derived from single-tree-detection data. The ratio of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. We performed a calibration of SlideforMAP for three test areas in Switzerland with a reliable landslide inventory by randomly generating 1000 combinations of model parameters and then maximizing the area under the curve (AUC) of the receiver operation curve. The test areas are located in mountainous areas ranging from 0.5–7.5 km2 with mean slope gradients from 18–28∘. The density of inventoried historical landslides varies from 5–59 slides km−2. AUC values between 0.64 and 0.93 with the implementation of single-tree detection indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modelling of shallow-landslide probability are the soil thickness, soil cohesion and the precipitation intensity / transmissivity ratio. Furthermore, we show that the inclusion of single-tree detection improves overall model performance compared to assumptions of uniform vegetation. In conclusion, our study shows that the approach used in SlideforMAP can reproduce observed shallow-landslide occurrence at a catchment scale.

Screening analysis and unconstrained optimization of a small-scale vertical axis wind turbine

The demand for alternative and renewable energy sources has been substantially growing in recent years, mainly steered by economic and environmental inconveniences of conventional energy sources, such as oil and its derivatives. In this context, wind energy has emerged as an attractive renewable source, envisioning possibilities of developing more efficient equipment to meet the ever-growing energy demand. In this work, we coupled Computational Fluid Dynamics (CFD) with an optimization based on response surface (RS) methodologies to find an optimal design for a small-scale NACA 0021 Darrieus vertical axis wind turbine (VAWT) operating at a tip speed ratio of 2.63. For that, we investigated four geometric parameters: number of blades (N), rotor diameter (D), chord length (c), and pitch angle (β). For the numerical model, we considered a two-dimensional, incompressible, turbulent, and unsteady flow regime. A sensitivity analysis (SA) via Morris’ method was performed to identify the influence of the four geometric parameters on the turbine aerodynamic performance. Our results reveal that the pitch angle (β) contributes the most (58%) to the turbine performance. The resulting optimized turbine design increased the conversion efficiency by 40%. Additionally, we also present a detailed discussion on the flow phenomenology considering the impact of each one of the four geometric parameters on the power coefficient. Finally, the strategy adopted here, in which a qualitative sensitivity analysis combined to the response surface and unconstrained optimization, was shown to be robust and can be applied to high-dimensional and computational-expensive CFD models to reduce costs with adequate results regarding fluid flow phenomena.

Cost-Effectiveness of Early Oral Feeding Following Total Gastrectomy

BACKGROUND: Gastric cancer is a major health problem worldwide. Effectiveness of early oral feeding (EOF) following gastrectomy has been internationally evaluated, even though there is limited evidence available. In Japan, recently, a randomized controlled trial of EOF following total gastrectomy showed a decreased length of hospital stay, compared with conventional oral feeding (COF). To evaluate value for money of EOF following gastrectomy, cost-effective analysis was carried out based on this trial.
 
 METHODS: The analysis was carried out from a societal perspective as the base case. The subjects were randomly assigned to the EOF group (N=32) and the COF group (N=30). While the EOF group received a special diet (i.e., iEAT) from the postoperative day (POD) 1 to POD 3, the COF group received conventional diet (i.e., a liquid diet or rice gruel) on POD 4 and thereafter. The observation period was 3 weeks after total gastrectomy. As an effectiveness measure, quality-adjusted life days (QALDs) were estimated. Quality of life for health conditions was evaluated by using EuroQol-5Dimentions (EQ-5D)-3Levels among the subjects. Costs (e.g., nutritional support, hospitalization and treatment of adverse events) were estimated from trial data during observation. Qualitative and stochastic sensitivity analyses were performed to examine the robustness of the results. 
 
 RESULTS: The mean QALDs per patient for the EOF and the COF groups were 14.93 and 14.19, respectively. Incremental effectiveness of the EOF group to the COF group was 0.74 QALDs (p=0.359, t-test). The mean total cost per patient in the EOF and the COF groups was $3,177.7 and $3,755.4, respectively. Incremental cost was -$577.7 (p=0.03, t-test, Welch). This means EOF is dominant (cost saving). The qualitative sensitivity analysis and the stochastic sensitivity analysis showed the relative robustness of these results. 
 
 CONCLUSIONS: Economic evaluation of EOF following total gastrectomy showed that EOF was cost-saving, with non-significant increase in effectives (i.e., QALD), compared with COF. This result can contribute to strengthen the evidence for wide use of EOF.

Fish consumption is an indicator of exposure to non-dioxin like polychlorinated biphenyls in cumulative risk assessments based on a probabilistic and sensitive approach.

The health effects of non-dioxin like polychlorinated biphenyls (NDL-PCBs) include liver, immune, neurodevelopmental, and neurobehavioral effects in addition to hormone alteration. Among 209 PCB congeners, six indicator PCBs (ICES-6) account for about half of NDL-PCBs present in feed and food. The objectives of the current study were first to examine the levels of total PCBs, NDL-PCBs, and ICES-6 PCBs in 200 foods commonly consumed in Taiwan and, second, to estimate the cumulative health risks of NDL-PCB consumption. We suggest a risk management strategy that identifies foodstuffs with particularly high NDL-PCB content. In 200 food samples, which were grouped into 12 categories and 64 food types, 51 PCB congeners were detected. The concentrations of ICES-6 PCBs in all samples were lower than the maximum levels for NDL-PCBs in foodstuffs set by the European Union. PCB-153, PCB-138, and PCB-180 were the major congeners in all food, indicating that the food was contaminated in the past. ICES-6 PCBs contributed 42.2%-52.9% of total NDL-PCBs in all tested foodstuffs. The average estimated daily intake (EDI) of PCBs ranged from 1.22ng/kgbw/day to 2.89ng/kgbw/day in different age groups (95th quantile [P95]: 4.12-10.28ng/kgbw/day). The P95 EDI in 3-6-year-olds was higher than 10ng/kg/day. A qualitative sensitivity analysis in ICES-6 PCBs exposure showed that for the 3- to 6-year-old group, the highest sensitivity was how much fish they ate (37.6%) and its concentration (32.5%). The consumption of pork, eggs, fish, and other seafood accounts for over 90% of ICES-6 PCBs intake, and these foods should be highlighted in guidelines regarding NDL-PCB intake.

Dimensionality reduction and identification of valid parameter bounds for the efficient calibration of automated driving functions

The industrialization of automated driving functions according to level 3 requires an efficient test and calibration concept to deal with an increased complexity, growing customer demands, and a larger vehicle fleet offered. Therefore, a method for a complexity reduction of the calibration parameter space is presented. In the two-step approach, a qualitative sensitivity analysis is used to identify valid regions in the search space and subsequently decrease dimensionality based on the parameter-specific global influences. The reduced parameter space and sensitivity information can then serve as a starting point for an efficient calibration process on the target hardware. To examine the method’s potential, our approach is applied to the parameter space of an automated driving function. The results expose clear dependencies between parameters and driving scenarios and allow an exclusion of parameter space dimensions based on sensitivity values. The predefined search space can be narrowed down to valid regions using the parameter range identification approach. Finally, the findings are validated with a quantitative variance-based sensitivity analysis. The validation confirms that our method provides equivalent results with a comparably smaller number of system evaluations.

Assessment of Parametric Sensitivity Analysis Methods Based on A Quasi Two-Dimensional Groundwater Model

Parametric sensitivity analysis (SA) aims to select the sensitive parameters that most significantly affect the model output variables, which helps to improve model optimization efficiency by adjusting a small number of sensitive parameters instead of all adjustable parameters. The qualitative and quantitative SA methods have been commonly used to quantify the sensitive parameters of the models. However, the response surface model based quantitative SA method was rarely used. Taking the simulation of a quasi twodimensional (quasi-2D) groundwater model as an example, this study systematically assess eight SA methods divided into three categories (qualitative SA, quantitative SA, and the response surface model-based quantitative SA). The study validates the effectiveness of these methods by comparing the parameter sensitivity results, and also demonstrates the efficiency of these methods by determining the minimum sample size required. Using the minimum samples means the least number of model runs. The results show that P1 and P2 are the most sensitive parameters of the quasi-2D model for simulating groundwater table elevation. Except for local method, four global qualitative SA methods obtain reasonable parameter sensitivity rankings using 200 samples, but the parameter sensitivity scores fail. For obtaining accurate sensitivity scores, at least 2000 samples are required by the quantitative SA methods. However, for the response surface model-based quantitative SA method, 60 samples are sufficient to obtain accurate sensitivity scores, demonstrating that the method is an effective and highly efficient, and should be recommended as the primary parametric SA method, especially for the complex models with large computational demand.

Using qualitative types of risk assessments in conjunction with FRAM to strengthen the resilience of systems

In this paper, we look into how some qualitative types of risk assessments can be used in conjunction with functional resonance analysis method (FRAM) to strengthen the resilience of systems. In FRAM, variation in relation to meeting specified functions is central, but risk and uncertainty considerations are not an integral part. We suggest to add to FRAM an assessment of the modelling choices and judgements using strength of knowledge considerations and a qualitative sensitivity analysis. In this way, an improved basis for assessing and strengthening system resilience with FRAM is established. We illustrate the idea with a simple example from the oil and gas industry.

Supplementary search methods were more effective and offered better value than bibliographic database searching: A case study from public health and environmental enhancement.

We undertook a systematic review to evaluate the health benefits of environmental enhancement and conservation activities. We were concerned that a conventional process of study identification, focusing on exhaustive searches of bibliographic databases as the primary search method, would be ineffective, offering limited value. The focus of this study is comparing study identification methods. We compare (1) an approach led by searches of bibliographic databases with (2) an approach led by supplementary search methods. We retrospectively assessed the effectiveness and value of both approaches. Effectiveness was determined by comparing (1) the total number of studies identified and screened and (2) the number of includable studies uniquely identified by each approach. Value was determined by comparing included study quality and by using qualitative sensitivity analysis to explore the contribution of studies to the synthesis. The bibliographic databases approach identified 21409 studies to screen and 2 included qualitative studies were uniquely identified. Study quality was moderate, and contribution to the synthesis was minimal. The supplementary search approach identified 453 studies to screen and 9 included studies were uniquely identified. Four quantitative studies were poor quality but made a substantive contribution to the synthesis; 5 studies were qualitative: 3 studies were good quality, one was moderate quality, and 1 study was excluded from the synthesis due to poor quality. All 4 included qualitative studies made significant contributions to the synthesis. This case study found value in aligning primary methods of study identification to maximise location of relevant evidence.

Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model

Sensitivity analysis (SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting (WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters (P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters (P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10 (the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.

Effect of two-way coupling on the calculation of forest fire spread: model development

The present work addresses the problem of how wind should be taken into account in fire spread simulations. The study was based on the software system FireStation, which incorporates a surface fire spread model and a solver for the fluid flow (Navier–Stokes) equations. The standard procedure takes the wind field computed from a single simulation in the absence of fire, but this may not be the best option, especially for large fires. The two-way coupling method, however, considers the buoyancy effects caused by the fire heat release. Fire rate of spread is computed with the semi-empirical Rothermel model, which takes as input local terrain slope, fuels properties and wind speed and direction. Wind field is obtained by solving the mass, momentum and energy equations. Effects of turbulence on the mean flow field are taken into account with the k – ϵ turbulence model. The calculation procedure consists of an interchange between the fire spread model and the wind model through a dynamic interaction. The present work describes the first part of this research, presenting the underlying models and a qualitative sensitivity analysis. It is shown that the update frequency for the dynamic interaction markedly influences the total calculation time. The best strategy for updating the wind field during the fire progression is presented. The dependence of results on mesh size is also described.

Variable and differential application of nutrients to a hill country farm

Abstract Traditionally fertiliser has been aerially applied at a uniform rate to hill country, but the technology now exists to apply nutrients at a variable rate (VR) and each nutrient differentially, depending on the production potential and pasture composition of each part of the hill. A hypothetical case study of a sheep farm was modelled to show the economic benefits of VR application of phosphorus (P) and sulphur (S) and differential application of nitrogen (N), compared with application of a uniform rate of P and S. The financial analysis demonstrates that the VR strategy of less P and S to steeper slopes where there is low legume and more on easier slopes where there is more legume, costs less than the application of P and S at a uniform rate over all slopes. The cost saving could be used to apply N to steep land on both sunny and shady aspects and easy land on sunny aspects. This differential N application in late winter/early spring ensures better pasture cover for lactating ewes to improve ewe condition at weaning. When this gain in condition was maintained through to mating, lambing percentage increased in the following spring. The benefit from this increased lamb production was an increase in financial returns of $63/ha/year. A qualitative sensitivity analysis indicated that this value remains stable in response to changes in the proportion of each slope class, soil Olsen P level, the relative cost of fertiliser P and N and sheep to cattle ratio. Keywords: differential application, hill country, lamb production, nitrogen, phosphorus, aerial topdressing, variable rate

Predicting and eliminating Joule heating constraints in large dielectrophoretic IDE separators

Dielectrophoresis (DEP), a measure to manipulate motion trajectories of suspended particles, has a high potential for solving difficult particle–liquid separation problems. Applications of DEP so far have been limited to micro-channels and lab-on-a-chip devices. However, for designing DEP separators with sufficiently high throughput to reach preparative scale, an understanding of the interplay of channel geometry and electrode concept with respect to induced particle velocity is required. The objective of tailored design is a control of particle motion trajectories predominantly by DEP while avoiding electrothermal interference. It is Joule heating, which gives rise to temperature gradients in the liquid phase and, thus, induces thermal convection. In this work we demonstrate that a solution of this Joule heating problem in large scale DEP systems is a tailored ratio of electrode diameter, electrode distance, and channel height. Based on model calculations we predicted the influence of both DEP force and drag force through thermal convection on particle trajectories for a case study, a channel with rectangular cross section and an array of cylindrical interdigitated electrodes (IDE) at the bottom. These theoretical results were verified experimentally by measured velocities of polyelectrolytic resin microparticles located at the subsurface of demineralized water. This allowed for a qualitative sensitivity analysis of the impact of voltage input, particle size and medium properties on the critical design parameter. From this, design criteria were deduced for the IDE–DEP system that allow for minimizing the influence of Joule heating. The findings demonstrate that, even if high voltages are applied, Joule heating problems can be effectively suppressed in DEP system scale-up.

Stepwise sensitivity analysis from qualitative to quantitative: Application to the terrestrial hydrological modeling of a Conjunctive Surface‐Subsurface Process (CSSP) land surface model

AbstractAn uncertainty quantification framework was employed to examine the sensitivities of 24 model parameters from a newly developed Conjunctive Surface‐Subsurface Process (CSSP) land surface model (LSM). The sensitivity analysis (SA) was performed over 18 representative watersheds in the contiguous United States to examine the influence of model parameters in the simulation of terrestrial hydrological processes. Two normalized metrics, relative bias (RB) and Nash‐Sutcliffe efficiency (NSE), were adopted to assess the fit between simulated and observed streamflow discharge (SD) and evapotranspiration (ET) for a 14 year period. SA was conducted using a multiobjective two‐stage approach, in which the first stage was a qualitative SA using the Latin Hypercube‐based One‐At‐a‐Time (LH‐OAT) screening, and the second stage was a quantitative SA using the Multivariate Adaptive Regression Splines (MARS)‐based Sobol’ sensitivity indices. This approach combines the merits of qualitative and quantitative global SA methods, and is effective and efficient for understanding and simplifying large, complex system models. Ten of the 24 parameters were identified as important across different watersheds. The contribution of each parameter to the total response variance was then quantified by Sobol’ sensitivity indices. Generally, parameter interactions contribute the most to the response variance of the CSSP, and only 5 out of 24 parameters dominate model behavior. Four photosynthetic and respiratory parameters are shown to be influential to ET, whereas reference depth for saturated hydraulic conductivity is the most influential parameter for SD in most watersheds. Parameter sensitivity patterns mainly depend on hydroclimatic regime, as well as vegetation type and soil texture.

An Efficient Sensitivity Analysis Approach for Computationally Expensive Microscopic Traffic Simulation Models

Microscopic traffic simulators are useful tools for designing, evaluating and optimizing transportation systems. In order for a simulator to accurately describe reality, the corresponding traffic model must be properly calibrated. However, the calibration can be rather difficult when the model is computationally expensive and has many parameters. To overcome these difficulties, Sensitivity Analysis (SA) can be applied as an essential instrument for supporting model calibration. Through SA the practitioners can obtain valuable information about the relationship between model inputs and outputs, and hence focus on the proper set of most influential parameters for the calibration. Notice, however, that many quantitative SA techniques may also fail with computationally expensive models. To address the above issues, in this paper we developed the quasi-OTEE method, an efficient and qualitative SA approach based on the Elementary Effects (EE) method. The quasi-OTEE approach is able to screen the most influential parameters of a complex model through computing and comparing their Sensitivity Indexes. With the improved sampling strategy, this approach is much more efficient than the original EE method. The approach is validated through a numerical analysis, and a case study of a small synthetic network in Aimsun. A more detailed case study of the Zurich network in VISSIM is then provided to illustrate its application. The results demonstrate that the proposed approach is an effective SA tool for the computationally expensive microscopic traffic models, as well as other complex models in the general scientific community.

Substitutes, complements, and ripples in multicommodity flows on suspension graphs

We examine in this article when it is possible to predict, without numerical computation, the direction of change of optimal multicommodity flows on suspension graphs resulting from changes in arc‐commodity parameters. Using results of Evans (Oper Res 26 (1978), 673–679) and of Soun and Truemper (SIAM J Algebr Discrete Meth 1 (1980), 348–358), the multicommodity flow problem on a graph that is two‐isomorphic to a suspension graph is reduced to a single‐commodity flow problem on an enlarged graph, called a “rolodex graph.” Such a reduction allows us to apply results of Granot and Veinott (Math Oper Res 10 (1985), 471–497), developed for single‐commodity network‐flow problems, to derive qualitative sensitivity analysis results for multicommodity flow problems on graphs which are two‐isomorphic to suspension graphs. © 2014 Wiley Periodicals, Inc. NETWORKS, Vol. 64(2), 65–75 2014

Regional coal seam gas distribution and burial history of the Hunter Coalfield, Sydney Basin

Basin modelling has been used to improve understanding of the origin and temporal evolution of coal seam gas in the Hunter Coalfield of the Sydney Basin. Burial history models were produced based on data from seven boreholes located in the southern, eastern, central and western areas of the coalfield. Mean random vitrinite reflectance (Rv,r) data, derived from measurements of mean maximum reflectance (Rv,max), were used for calibration of the models. A qualitative sensitivity analysis of one model shows that varying the paleoheat flow has a greater influence on calculated Rv,r than varying the eroded overburden thickness.The differences between the constructed models are significant enough to provide plausible explanations for regional gas distribution in the Hunter Coalfield. Coals in the south of the coalfield appear to have the greatest potential for thermogenic gas generation. Modelling has shown that areas that have low gas contents and decreased permeability have been uplifted more, and buried less, compared with areas that have high gas contents. Burial history modelling shows noticeable variations in the extent of burial and uplift, and, consequently, in thermal maturities and potential for thermogenic gas generation; together with the assessment of other coal and gas property data, it appears that present-day gas contents may partially reflect coal ranks and adsorption capacities, with late-stage biogenic gas generation replenishing CH4 volumes that were lost following uplift during the Late Cretaceous.

A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model

Sensitivity analysis (SA) is a commonly used approach for identifying important parameters that dominate model behaviors. We use a newly developed software package, a Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), to evaluate the effectiveness and efficiency of ten widely used SA methods, including seven qualitative and three quantitative ones. All SA methods are tested using a variety of sampling techniques to screen out the most sensitive (i.e., important) parameters from the insensitive ones. The Sacramento Soil Moisture Accounting (SAC-SMA) model, which has thirteen tunable parameters, is used for illustration. The South Branch Potomac River basin near Springfield, West Virginia in the U.S. is chosen as the study area. The key findings from this study are: (1) For qualitative SA methods, Correlation Analysis (CA), Regression Analysis (RA), and Gaussian Process (GP) screening methods are shown to be not effective in this example. Morris One-At-a-Time (MOAT) screening is the most efficient, needing only 280 samples to identify the most important parameters, but it is the least robust method. Multivariate Adaptive Regression Splines (MARS), Delta Test (DT) and Sum-Of-Trees (SOT) screening methods need about 400–600 samples for the same purpose. Monte Carlo (MC), Orthogonal Array (OA) and Orthogonal Array based Latin Hypercube (OALH) are appropriate sampling techniques for them; (2) For quantitative SA methods, at least 2777 samples are needed for Fourier Amplitude Sensitivity Test (FAST) to identity parameter main effect. McKay method needs about 360 samples to evaluate the main effect, more than 1000 samples to assess the two-way interaction effect. OALH and LPτ (LPTAU) sampling techniques are more appropriate for McKay method. For the Sobol' method, the minimum samples needed are 1050 to compute the first-order and total sensitivity indices correctly. These comparisons show that qualitative SA methods are more efficient but less accurate and robust than quantitative ones.