Sensitivity Of Model Parameters Research Articles

Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer

The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

A finite control set model predictive direct speed controller for PMSM application with improved parameter robustness

Model predictive control (MPC) is an advanced control method that relies on a mathematical model containing the motor parameters in the field of Permanent Magnet Synchronous Motor (PMSM) application. However, the internal parameters of the motor and external load characteristic parameters are uncertain, and their actual values ​​may be inconsistent with the nominal values, resulting in prediction errors, which in turn affect the performance of the control algorithm. Based on the analysis of the parameter sensitivity of the motor prediction model, traditional finite control set model predictive direct speed controller (FCS-MPDSC) predicts state parameters based on a mathematical model, and there is a problem that the parameters of the motor model are uncertain, which will lead to prediction errors. In order to improve the robustness of FCS-MPDSC parameters, this paper proposes an improved FCS-MPDSC for PMSM application, which abandons the traditional PMSM control cascade structure and has the advantages of fast speed response performance, small speed error and strong parameter robustness. In order to solve the problem of load characteristic parameter uncertainty, Luenberger observer and the Model Reference Adaptive System (MRAS) algorithm are used to estimate the variation of load characteristic parameters in this paper. Moreover, a proportional-integral (PI) control term of speed error is introduced into the cost function to achieve the zero steady state error of speed and torque, which make full use of the robustness of integral action and also a faster dynamic response of direct speed control is maintained. In this paper, the HIL experimental platform is built according to the simulation. The proposed method reduces the steady-state error of the traditional method under the parameter mismatch. When the inductance parameter and the permanent magnet flux parameters deviate two times larger than nominal values, the steady-state error of the motor speed are 0.1125% and 0.8725%, respectively, demonstrating the effectiveness of the proposed method.

Parameter Calibration to Improve the Prediction of Tropical Cyclones over the Bay of Bengal Using Machine Learning–Based Multiobjective Optimization

Abstract The prediction skill of a numerical model can be enhanced by calibrating the sensitive parameters that significantly influence the model forecast. The objective of the present study is to improve the prediction of surface wind speed and precipitation by calibrating the Weather Research and Forecasting (WRF) Model parameters for the simulations of tropical cyclones over the Bay of Bengal region. Ten tropical cyclones across different intensity categories between 2011 and 2017 are selected for the calibration experiments. Eight sensitive model parameters are calibrated by minimizing the prediction error corresponding to 10-m wind speed and precipitation, using a multiobjective adaptive surrogate model-based optimization (MO-ASMO) framework. The 10-m wind speed and precipitation simulated by the default and calibrated parameter values across different aspects are compared. The results show that the calibrated parameters improved the prediction of 10-m wind speed by 17.62% and precipitation by 8.20% compared to the default parameters. The effect of calibrated parameters on other model output variables, such as cyclone track and intensities, and 500-hPa wind fields, is investigated. Eight tropical cyclones across different categories between 2011 and 2018 are selected to corroborate the performance of the calibrated parameter values for other cyclone events. The robustness of the calibrated parameters across different boundary conditions and grid resolutions is also examined. These results will have significant implications for improving the predictability of tropical cyclone characteristics, which allows us to better plan adaptation and mitigation strategies and thus help in reducing the adverse effects of tropical cyclones on society.

Optimization of Spring Constant of a Pneumatic Artificial Muscle-Spring Driven Antagonistic Structure

Pneumatic artificial muscles (PAMs) have been widely applied to robotic systems, especially assistive devices, which could benefit from PAMs’ intrinsic viscoelasticity. However, nonlinearity and hysteresis make it challenging to achieve high-accuracy control. Using pre-tensioned springs to replace one side PAMs in an antagonistic structure is one efficient way to improve control accuracy. However, in the previous study, the selection method for spring, especially the range of spring constant has not been sufficiently investigated. This study designed a method to find the optimal spring constant based on gradient descent algorithm; and verified it with prototype and simulation experiments. Results showed that the group with optimal spring constant leads to a faster response time, smaller overshoot, and lower steady-state error, which indicated that the proposed method could find optimal spring constant. Moreover, the sensitivity of PAMs dynamic model parameters to the optimal spring stiffness was investigated. This study provides insights to practical use of soft actuators.

Identification of High-Impact Uncertainty Sources for Urban Flood Models in Hillside Peri-Urban Catchments

Climate change, as well as increasing urbanization, lead to an increase in urban flooding events around the world. Accurate urban flood models are an established tool to predict flooding areas in urban as well as peri-urban catchments, to derive suitable measures to increase resilience against urban flooding. The high computational cost and complex processes of urban flooding with numerous subprocesses are the reason why many studies ignore the discussion of model uncertainties as well as model calibration and validation. In addition, the influence of steep surface (hillside) conditions on calibration parameters such as surface roughness are frequently left out of consideration. This study applies a variance-based approach to analyze the impact of three uncertainty sources on the two variables—flow and water depth—in a steep peri-urban catchment: (i) impact of DEM validation; (ii) calibration of the model parameter; (iii) differences between 1D/2D and 2D models. The results demonstrate the importance of optimizing sensitive model parameters, especially surface roughness, in steep catchments. Additional findings of this work indicate that the sewer system cannot be disregarded in the context of urban flood modeling. Further research with real heavy storm events is to be pursued to confirm the main results of this study.

Mesoscale investigation on concrete creep behaviors based on discrete element method

Studying creep behaviors of concrete materials at a mesoscale level is crucial to understanding their mechanism. Discrete element method (DEM) could provide an effective numerical tool to explain the mesoscale mechanism of concrete materials. In this study, the creep characteristics of concrete are analyzed by combining DEM and Burgers model. Firstly, a multi-phase concrete numerical model consisting of matrix, aggregate, and interfacial transition zone (ITZ) was developed. Then, the sensitivity of the Burgers model parameters was conducted. Finally, to investigate concrete creep behaviors at a mesoscale level, a series of numerical simulations were carried out. Simulation results show that Burgers model can reflect the creep behavior of concrete well. The porosity gradually decreased as the loading duration increase, and deceleration became stable at the later stage. The creep contribution of ITZ decreases with the increase of the ratio of ITZ strength to matrix strength. Force-chain strength in ITZ was greater than that in matrix, and viscoelastic sliding or squeezing occurs at Burger contact. Meanwhile, the direction distribution of force-chain strength in the matrix is regular and the main direction of the contact distribution is about 90° or 270°. In addition, the probability function of contact force is exponential, and the strength probability distribution of matrix and ITZ contact reached the peak at 0.7 f/f¯ and then decreased, and the proportion of strong contact and weak contact in matrix and ITZ was about 70% and 30%, respectively. The current study can bridge the gap between simulation and experimental research.

Complex dynamics and control analysis of an epidemic model with non-monotone incidence and saturated treatment.

In this manuscript, we consider an epidemic model having constant recruitment of susceptible individuals with non-monotone disease transmission rate and saturated-type treatment rate. Two types of disease control strategies are taken here, namely vaccination for susceptible individuals and treatment for infected individuals to minimize the impact of the disease. We study local as well as global stability analysis of the disease-free equilibrium point and also endemic equilibrium point based on the values of basic reproduction number R_0. Therefore, disease eradicates from the population if basic reproduction number less than unity and disease persists in the population if basic reproduction number greater than unity. We use center manifold theorem to study the dynamical behavior of the disease-free equilibrium point for R_0 = 1. We investigate different bifurcations such as transcritical bifurcation, backward bifurcation, saddle-node bifurcation, Hopf bifurcation and Bogdanov–Takens bifurcation of co-dimension 2. The biological significance of all types of bifurcations are described. Some numerical simulations are performed to check the reliability of our theoretical approach. Sensitivity analysis is performed to identify the influential model parameters which have most impact on the basic reproduction number of the proposed model. To control or eradicate the influence of the emerging disease, we need to control the most sensitive model parameters using necessary preventive measures. We study optimal control problem using Pontryagin’s maximum principle. Finally using efficiency analysis, we determine most effective control strategy among applied controls.

Assessment of global reanalysis precipitation for hydrological modelling in data-scarce regions: A case study of Kenya

Study region19 flood prone catchments in Kenya, Eastern Africa Study focusFlooding is a major natural hazard especially in developing countries, and the need for timely, reliable, and actionable hydrological forecasts is paramount. Hydrological modelling is essential to produce forecasts but is a challenging task, especially in poorly gauged catchments, because of the inadequate temporal and spatial coverage of hydro-meteorological observations. Open access global meteorological reanalysis datasets can fill in this gap, however they have significant errors. This study assesses the performance of four reanalysis datasets (ERA5, ERA-Interim, CFSR and JRA55) over Kenya for the period 1981–2016 on daily, monthly, seasonal, and annual timescales. We firstly evaluate the reanalysis datasets by comparing them against observations from the Climate Hazards group Infrared Precipitation with Station. Secondly, we evaluate the ability of these reanalysis datasets to simulate streamflow using GR4J model considering both model performance and parameters sensitivity and identifiability. New hydrological insights for the regionWhile ERA5 is the best performing dataset overall, performance varies by season, and catchment and therefore there are marked differences in the suitability of reanalysis products for forcing hydrological models. Overall, wetland catchments in the western regions and highlands of Kenya obtained relatively better scores compared to those in the semi-arid regions, this can inform future applications of reanalysis products for setting up hydrological models that can be used for flood forecasting, early warning, and early action in data scarce regions, such as Kenya.

Application of a novel constitutive model to evaluate the shear deformation of discontinuity

Landslides are one of the most common geological disasters with the complicated spatiotemporal processes, which can be partially reproduced in slip shear failure along the discontinuity in laboratory tests. However, the assessment and evaluation of shear behavior along the discontinuity is time-consuming and high-cost. To solve this problem, a new constitutive model is proposed to predict the shear deformation behavior of the discontinuity. In this model, the discontinuity is assumed to be composed of intact units and failed units. In the shearing process, intact units and failed units jointly bear the external loads, and intact units are gradually transformed into failed units. A damage control variable is introduced to link the mechanical response of units with the macroscopic discontinuous deformation, thereby obtaining the final expression of the new model. The model parameters can be determined directly from several standard laboratory shear tests. Subsequently, the comparison analysis among the predictive results of the new model, previous models and engineering projects is conducted. Finally, the sensitivity of model parameters is systematically discussed. A hardening index HI is proposed to characterize the shear deformation characteristics in the hardening stage, and its relationship with model parameters C and r is in a power form. The results are expected to provide theoretical guidance for landslide prediction and thus reduce the occurrence frequency of geohazards.

Influence of climate and land-use changes on the sensitivity of SWAT model parameters and water availability in a semi-arid river basin

The present study assesses the impact of climate change (CC) and land use land cover change (LULCC) on model parameter variability and alterations in streamflow and water balance components of a semi-arid river basin in western India. The Dharoi catchment in the Sabarmati River basin is a water-scarce region with India's lowest per-capita water availability. The water scarcity conditions are likely to aggravate due to changing climate and landscape modifications through human interventions. A hydrological modelling framework using a semi-distributed Soil and Water Assessment Tool (SWAT) is adopted in this study, considering two approaches: a variable-period calibration and a fixing-changing approach. Several model scenarios considered in these approaches are simulated through both single-site (SSC) and simultaneous multi-site calibration (SMSC) techniques. The variable-period calibration is adopted to assess the individual and combined influences of CC and LULCC on SWAT model parameter variability and change in water balance ratios. The fixing-changing approach evaluates the relative contribution of CC and LULCC to streamflow changes. The temporal changes in the hydroclimatic variables are analysed using non-parametric Pettitt, Mann-Whitney, Modified Mann-Kendall, and Spearman’s rho tests. The results indicated an abrupt increase in the streamflow in 2004, attributed to a significant increase in total and extreme rainfall magnitude and frequency in the catchment. The SSC and SMSC techniques yielded satisfactory and comparable performance for all model scenarios. The results showed that CC has a more significant impact on SWAT model parameter variability than LULCC. Thus, CC is the primary driver of streamflow changes in the Dharoi catchment, wherein a four-fold increase in the streamflow is noticed in the period 2005–2014 vis-à-vis 1995–2004. Thus, a transition from drier to wetter hydroclimatic conditions is evident in the Dharoi catchment, which would enhance water sufficiency in the region if appropriately managed.

Modification of Cycle Life Model for Normal Aging Trajectory Prediction of Lithium-Ion Batteries at Different Temperatures and Discharge Current Rates

Battery life is of critical importance for the reliable and economical operation of electric vehicles (EVs). Normal aging accounts for more than 80% of the battery available cycle range. Accurate and robust battery life models of normal aging are essential for battery health management systems and life evaluation before accelerated aging. Capacity recovery, test errors and accelerated aging all affect life model building during normal aging. Therefore, this paper proposes an improved life model based on wavelet transform (WT) signal processing to accurately predict the decline trend of the battery in the normal aging stage. In this paper, the capacity recovery, test noise and capacity diving in the aging trend are effectively removed by wavelet transform. We obtained an optimized life model through the analysis of the model structure and the analysis of the parameter sensitivity of the life model. The particle swarm algorithm (PSO) is employed to identify the parameters of the empirical models with the normal aging data extracted by the WT. Through verification, it is found that the modified cycle life model proposed in this paper can accurately predict the normal aging trajectory of batteries under different discharge rates and temperatures. The prediction error of the improved life model for normal aging is 1.09%.

Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: Identification of time-varying dependencies among parameters

Kinetic modeling of fermentation processes is difficult due to the use of micro-organisms that follow complex reaction mechanisms. Kinetic models are usually not perfect owing to incomplete knowledge of the system. Recently, there is a lot of interest towards data-driven modeling as the amount of data collected, stored, and utilized is growing tremendously due to the advent of super-computing power and data storage devices. Additionally, data-driven models are simple and easy to build but their utility is restricted by the amount and quality of data required. Therefore, hybrid modeling is an attractive alternative to purely data-based modeling, wherein it combines a kinetic model with a data-based model resulting in improved accuracy and robustness. In this work, a hybrid model is developed for an industry-scale fermentation process (>100,000 gallons) using a three-step process. The accuracy of the kinetic model is first improved utilizing process knowledge obtained from the literature. Sensitivity analyses are then utilized to identify sensitive parameters in the kinetic model that have considerable influence on its prediction capability. Finally, a deep neural network (DNN)-based hybrid model is developed by integrating the kinetic model with a DNN trained with time-series process data to predict sensitive and uncertain model parameters. The hybrid model is shown to be more accurate and robust than the kinetic model, providing a novel capability to capture unknown time-varying dependencies among parameters.

A quantitative microbial risk assessment for touchscreen user interfaces using an asymmetric transfer gradient transmission mode.

The ubiquitous use of public touchscreen user interfaces for commercial applications has created a credible risk for fomite-mediated disease transmission. This paper presents results from a stochastic simulation designed to assess this risk. The model incorporates a queueing network to simulate people flow and touchscreen interactions. It also describes an updated model for microbial transmission using an asymmetric gradient transfer assumption that incorporates literature reviewed empirical data concerning touch-transfer efficiency between fingers and surfaces. In addition to natural decay/die-off, pathogens are removed from the system by simulated cleaning / disinfection and personal-touching rates (e.g. face, dermal, hair and clothing). The dose response is implemented with an exponential moving average filter to model the temporal dynamics of exposure. Public touchscreens were shown to pose a considerable infection risk (∼3%) using plausible default simulation parameters. Sensitivity of key model parameters, including the rate of surface disinfection is examined and discussed. A distinctive and important advancement of this simulation was its ability to distinguish between infection risk from a primary contaminated source and that due to the re-deposition of pathogens onto secondary, initially uncontaminated touchscreens from sequential use. The simulator is easily configurable and readily adapted to more general fomite-mediated transmission modelling and may provide a valuable framework for future research.

Modelling the transmission dynamics of Lassa fever with nonlinear incidence rate and vertical transmission

In this paper, a seven-dimensional nonlinear mathematical model featuring the vertical transmission in humans, saturated incidence functions, and human population with good and poor community hygiene is formulated to describe the dynamics of Lassa fever (LF) disease transmission between the interacting human and rodent populations. The model is analysed to investigate the dynamical behaviour of its solutions using some theories of dynamical system of ordinary differential equations. The basic reproduction number, R0, of the model is established. A suitable Lyapunov function is constructed to establish the global asymptotic behaviour of the model about the Lassa fever-free equilibrium. Sensitivity analysis is carried out to gain insightful information about how R0 is influenced by the variation in the model parameters. Numerical simulations are conducted to examine the effects of the most sensitive model parameters on the population dynamics of LF. The results obtained provide epidemiological insights into the impact of key model parameters on the transmission dynamics of LF and suggest some measures to fight or guide against the disease spread in a population.

Earth system model parameter adjustment using a Green's functions approach

Abstract. We demonstrate the practicality and effectiveness of using a Green's functions estimation approach for adjusting uncertain parameters in an Earth system model (ESM). This estimation approach has previously been applied to an intermediate-complexity climate model and to individual ESM components, e.g., ocean, sea ice, or carbon cycle components. Here, the Green's functions approach is applied to a state-of-the-art ESM that comprises a global atmosphere/land configuration of the Goddard Earth Observing System (GEOS) coupled to an ocean and sea ice configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). Horizontal grid spacing is approximately 110 km for GEOS and 37–110 km for MITgcm. In addition to the reference GEOS-MITgcm simulation, we carried out a series of model sensitivity experiments, in which 20 uncertain parameters are perturbed. These “control” parameters can be used to adjust sea ice, microphysics, turbulence, radiation, and surface schemes in the coupled simulation. We defined eight observational targets: sea ice fraction, net surface shortwave radiation, downward longwave radiation, near-surface temperature, sea surface temperature, sea surface salinity, and ocean temperature and salinity at 300 m. We applied the Green's functions approach to optimize the values of the 20 control parameters so as to minimize a weighted least-squares distance between the model and the eight observational targets. The new experiment with the optimized parameters resulted in a total cost reduction of 9 % relative to a simulation that had already been adjusted using other methods. The optimized experiment attained a balanced cost reduction over most of the observational targets. We also report on results from a set of sensitivity experiments that are not used in the final optimized simulation but helped explore options and guided the optimization process. These experiments include an assessment of sensitivity to the number of control parameters and to the selection of observational targets and weights in the cost function. Based on these sensitivity experiments, we selected a specific definition for the cost function. The sensitivity experiments also revealed a decreasing overall cost as the number of control variables was increased. In summary, we recommend using the Green's functions estimation approach as an additional fine-tuning step in the model development process. The method is not a replacement for modelers' experience in choosing and adjusting sensitive model parameters. Instead, it is an additional practical and effective tool for carrying out final adjustments of uncertain ESM parameters.

Constraining organic matter composition and dynamics as a dominant driver of hypoxic blackwater risk during river Murray floodplain inundation

AbstractThe ecology of floodplain ecosystems evolved according to the historical frequency and extent of inundation. In many dryland rivers, inundation frequency has reduced due to regulation, competition for water resources and climate change. In some rivers infrastructure has been constructed to enable temporary rises in water level and more frequent floodplain inundation, delivering environmental water to ecosystems that are dependent on surface water. During such events, the release of dissolved organic carbon (DOC) from inundated plant material may result in depletion of dissolved oxygen (DO). If the rate of DO depletion exceeds re‐aeration, hypoxic conditions can occur. Models have been developed to represent these processes, however, there is a wide range of chemical and biological interactions, and hence model parameters, involved. The aim of this research was: (1) identify the dominant parameters through sensitivity analysis, (2) design and implement a field monitoring program to quantify the inherent variability in those parameters, and (3) translate that input variability into the modelled DO, ultimately to support risk management decisions when planning delivery of environmental water. The results indicated that the mass of organic matter on a floodplain had the greatest impact on the modelled DO concentrations. Based on the field monitoring results, the steady state load of organic matter, that is, when the accumulation and decay rates are equal, was estimated, and vegetation mapping used to apply the field monitoring results across the floodplain for a 206 km reach of river of the River Murray, Australia. The model results from multiple, cumulative, operations identified one particular high‐risk operational site. The results are useful to prioritize monitoring effort to focus on the most sensitive model parameters and indicate that the likelihood of hypoxic conditions can be reduced through slower rates of inundation and timing events to coincide with periods with lower water temperature. The methodology developed can be transferred to other sites where hypoxic conditions are a potential outcome associated with delivery of environmental water.

Delayed feedback between adaptive reservoir operation and environmental awareness within water supply-hydropower generation-environment nexus

In order to ensure sustainable coevolution of water supply, hydropower generation and environment (WHE), the impact of the delayed feedback between adaptive reservoir operation and environmental awareness on WHE nexus is explored in this paper through comprehensive analysis incorporating the policy, social, economic, and environmental factors. The WHE nexus states referring to water storage in reservoir, population, water use, hydropower generation, environmental awareness, and behavior response of community, have been mimicked using the WHE model. The delaying time caused by the delayed feedback can be estimated from the state processes through two indexes (i.e., response delay and recovery delay). Progressive Optimality Algorithm-Particle Swarm Optimization (POA-PSO) hybrid algorithm is adopted to search for an optimal water level process and water transfer. Moreover, the model parameter sensitivity analysis is conducted to determine the dominant model parameters affecting the time delays. Taking Jiayan reservoir as a case study, the Nash-Sutcliffe Efficiency coefficients of the WHE nexus model are above 0.5 in both calibration and validation period. The delay time between environmental awareness and decision preferences ranges from 0 to 8 years under parameter variations. The decision preference has a quicker response to the rise of the water shortage awareness (0.4 years) than the ecological awareness (5.3 years). The most sensitive parameters affecting the delay times are the response rate, decay rate, and threshold of environmental awareness. The proposed model helps us to understand the feedback among adaptive reservoir operation, environmental awareness and decision preferences, and further gives a new access to make effective and scientific long-term adaptive reservoir operation for the human society-environment coupled system.

Staring at the Abyss: a neurocognitive grounded agent-based model of collective-risk social dilemma under the threat of environmental disaster

Increasingly visible climate change consequences challenge carbon-based economies worldwide. While expert knowledge on climate change percolates through political initiatives and public awareness, its translation into large-scale policy actions appears limited. Climate change consequences unequally target regions, countries and social classes, a vital issue for social cooperation. When facing an imminent ecological collapse, in which conditions can self-interested agents gain environmental awareness and settle on a sustainable path of actions when their knowledge of the imminent collapse is bounded? This cooperation emerges from the interaction between individuals and the interaction of various cognitive processes within individuals. This article develops an agent-based model for this emergence of cooperation enriched with the Agent Zero neurocognitive grounded cognitive architecture. We investigate when agents endowed with deliberative, affective and social modules can settle on actions that safeguard their environment through numerical simulations. Our results show that cooperation on sustainable actions is the strongest when the system is at the edge of collapse. Policy measures that increase the environment’s resilience become internalized by the agents and undermine awareness of the ecological catastrophe. Depending on the cognitive channels activated, agent behaviors and reactions to specific interventions significantly vary. Our analysis suggests that taking different cognitive channels, deliberative, affective, social, and others into account, significantly impact results. The complexity of agent cognition deserves more attention to assess parameter sensitivity in social simulation models.

Evaluation of parameter sensitivity of a rainfall-runoff model over a global catchment set

ABSTRACT This paper presents an evaluation of the parameter sensitivity of a process-based model at the global scale using large-sample data. The analysis was carried out using the HYdrological Prediction of the Environment (HYPE) model, for which soil and snow parameters were evaluated using 187 river flow gauges spread worldwide. As a result, 6 out of 12 soil parameters and 7 out of 10 snow parameters were found to be sensitive. Taking advantage of the global dataset, an additional analysis was used to investigate links between catchment characteristics and parameter sensitivity. Different patterns of sensitivity were observed for different Köppen climate classes, which indicates that parameter regionalization would benefit from calibration based on climate zones. This numerical sensitivity method was compared with the judgement of a set of expert HYPE modellers to understand how numerical results compare with modellers’ experience.

A new analysis of the intraplate stress regime and stress ratio in numerically modeled mantle convection

I propose a new analysis method for determining the intraplate stress in geodynamic models using a series of numerical simulations of mantle convection in 3D spherical-shell geometry. In the present study, the intraplate stress was evaluated from numerically obtained velocity and stress fields of mantle, and quantitatively classified into nine types by analyzing the principal deviatoric stress axes and the “stress ratio,” which is a continuous parameter accounting for the stress regimes. The sensitivity of model parameters and physical conditions associated with the basic characteristics of mantle convection, such as internal heating ratio, viscosity stratification, and temperature-dependent viscosity of the mantle as well as viscoplastic rheology that causes plate-like surface motion, on the intraplate stress regimes were studied. The results demonstrated that the radial viscosity structure of the mantle interior strongly affected intraplate stress regimes, and the combination of increased viscosity in the lower mantle and the low-viscosity asthenosphere enhanced the pure strike-slip faulting regime in the stable part of plate interiors. The temporally averaged toroidal-poloidal ratio (T/P ratio) at the top surface of mantle convection with surface plate-like motion and the mantle's viscosity stratification generally ranged ~20–40%, which is comparable to the observed T/P ratio of present-day and past Earth. Under such Earth-like surface conditions, normal faulting regime with strike-slip component or strike-slip regime with normal faulting component, as well as pure strike-slip faulting regime, were broadly found in the stable parts of the plate interiors. From the definition of the stress regime in the present study, strike-slip faults on the real Earth are likely to occur where the strike-slip faulting component is dominant in the present models. The analysis method proposed herein is effective for evaluating the intraplate stress in research target regions, for which observation data is insufficient to determine the intraplate stress.