Parameter In Models Research Articles

Performance Analysis of Forest Canopy Height Model Generated from UAV and InSAR

Forest canopy height is a crucial parameter in ecosystem process modelling, yet research on generating canopy height models (CHMs) using photogrammetry method from UAV data remains limited compared to methods such as light detection and ranging (LiDAR). This study investigates the performance of accuracy and effectiveness of CHM generated from low-cost Unmanned Aerial Vehicle (UAV) via the photogrammetry method together with the well-known long-established Interferometric Synthetic Aperture Radar (InSAR). Leveraging advancements in UAV technology for three-dimensional land surface modelling, this research focuses on the Pasoh Research Station (nearby the Arboretum area) of Pasoh Forest Reserve in Negeri Sembilan, Malaysia. A series of images were captured at ten different UAV flying altitudes ranging from 120m to 500m above the mean sea level. Subsequently, CHMs were derived from both UAV and InSAR data, and compared against field measurements of tree height. The results indicate that UAV with a flying height below 200m can generate much more accurate results (with average height difference less than 10m) than InSAR (average height difference of 10.532m). It is noticeable that when comparing the InSAR-generated CHM to field measurements at lower altitudes (below 150m), the canopy height obtained from InSAR data is less accurate (more than 10m difference with field measurement) than the CHM obtained via UAV photogrammetry processing (only 5.353 m difference with field measurement). However, when UAV flying height is above 200m, InSAR data is performed more reliably. A 120m UAV flying height has a good potential to generate canopy height that is closer to the field measurement.

Flood Routing and Dam Breach Parameter Calculation on Sepaku Semoi Dam

Flood Routing and Dam Breach Parameter Calculation on Sepaku Semoi Dam

Influence of bed roughness parameter in storm surge modeling along the east coast of India

Influence of bed roughness parameter in storm surge modeling along the east coast of India

Revisiting the modelling framework for the unresolved scalar variance

The unresolved scalar variance in large-eddy simulations of turbulent flows is a fundamental physical and modelling parameter. Despite its importance, relatively few algebraic models have been developed for this important variable with the most prominent models to date being the classic scale-similarity and gradient models. In this work a new generalized modelling framework based on reconstruction has been developed, which in contrast to classic modelling approaches allows the construction of base static variance models of arbitrary accuracy. It is demonstrated that higher-order reconstructions naturally lead to base static variance models of increased accuracy, and that the classic scale-similarity and gradient models are subsets of more general and higher-order models. The classic scale-similarity assumption for developing dynamic models is also revisited, and it is demonstrated that this can essentially be reinterpreted as a two-level reconstruction approach. Based on this result, a new general methodology is proposed that allows the construction of dynamic models for any given base static model, and a corresponding general reconstruction operator, algebraic or iterative. Consequently, improved static and dynamic models for the scalar variance are developed. The newly developed models are then thoroughly tested a priori using two high-fidelity direct numerical simulation databases corresponding to two substantially different flame and flow configurations, and are shown to outperform classic algebraic models for the variance.

Experimental investigation of thawing behavior of saline soils using resistivity method

Abstract Electrical resistivity method has been widely used to study permafrost and to monitor the process of freezing-thawing. However, a thorough understanding of the mechanism of electrical response during thawing is missing. In this study, we investigated the thawing behavior of saline soils in the temperature range from roughly −10 to 15°C considering the effects of soil type and salinity. A total of nine experiments were performed with three soil types (silica sand, sandy soil, and silt) and three salinities (0.01, 0.1, and 1 S m−1). The results show that resistivity variations with temperature can be divided into three stages. In Stage I, tortuosity and unfrozen water content play major roles in the decrease of resistivity. In Stage Ⅱ, which is an isothermal or near isothermal process, resistivity still decreases slightly due to the thawing of residual ice and pore water movement. In Stage III, ionic mobility plays an important impact on decreasing resistivity. In addition, the isothermal process is found to only occur in silica sand that can be explained by latent heat effect. Exponential and linear models linking temperature with resistivity are used to fit the experimental data in Stages I and III. The fitting parameter in different models shows great correlation with soil type and salinity. Furthermore, unfrozen water content below 0°C is also estimated and uncertainty of estimation is analyzed.

A Community Information Model and Wind Environment Parametric Simulation System for Old Urban Area Microclimate Optimization: A Case Study of Dongshi Town, China

In the context of an increasingly extreme climate, Urban Heat Island (UHI) mitigation of communities through ventilation has recently attracted more attention. To explore the impact mechanisms of different morphological renovation schemes on its wind and thermal environment, this paper selected the Laozheng Community as a case study and: (1) analyzed measured data to quantitatively investigate the UHI within the community; (2) established the CIM-WTEPS system to construct community information models and to conduct wind environment parametric simulation for seven micro-renovation schemes across three levels; (3) performed correlation analyses between morphology indicators and wind environment indicators; (4) conducted the thermal environment parametric simulation of the community under different schemes. The results reveal that: (1) the Laozheng Community exhibits the Urban Heat Island Intensity (UHII) of up to 6 °C; (2) apart from the “ Hollowing “ scheme, which deteriorates the community wind environment, all other schemes optimize it, potentially increasing the average wind speed by up to 0.03m/s and in the renovated area by up to 0.42 m/s; (3) building density is highly correlated with the average wind speed and the proportion of calm wind area, with correlation coefficients of −0.916 (p < 0.01) and 0.894 (p < 0.01), respectively; (4) the adding of shading facilities can enhance the proportion of areas with lower Universal Thermal Climate Index (UTCI) without adversely affecting the optimization effects of the wind environment, achieving an maximum increase of 3.1%. This study provides a reference for optimizing the community’s microclimate through morphological micro-renovations and detailed operations, aiding designers in better controlling community morphology for in future community renewal and design planning, thereby creating a more hospitable outdoor environment.

Anisotropic thermal expansion tensor of β -Ga2O3 and its critical role in casting-grown crystal cracking

Thermal expansion tensor represents a key parameter for the numerical modeling of the crystal growth process. However, the modeling of β-Ga2O3 commonly utilizes one single thermal expansion constant that misses its anisotropic nature and temperature-dependent characteristics. Herein, we addressed this limitation by calibrating an anisotropic, temperature-dependent thermal expansion tensor using the experimental lattice parameters of β-Ga2O3 up to 1200 K. We found that COMSOL Multiphysics simulations employing the calibrated tensor yield stress distribution remarkably distinct from those relying on the commonly assumed constants. Specifically, our simulations predict a von Mises stress concentration near the crystal bottom, which explains the experimentally observed crack formation at corresponding locations. This contrasts with the simulations using the single-value thermal expansion constant, which fails to predict such stress concentration. The physical origin of crystal cracking is found to be rooted in the compressive force exerted by the iridium crucible during the cooling process. Our findings suggest that the physical anisotropy of β-Ga2O3 should be carefully considered in modeling and simulation. With the calibrated thermal expansion tensor, we provide a validated set of thermomechanical parameters for reliable β-Ga2O3 crystal growth simulations.

Modeling the effects of species associations and abiotic parameters on the abundance of mosquito species in a Mediterranean wetland

Recent outbreaks of the West Nile virus have been reported in southern Spain, a region with important wetland habitats for migratory birds. Here, we analyzed the role of species association and abiotic parameters on the abundance of seven mosquito species in the Doñana National Park, Spain. We applied the Joint Species Distribution Models (JSDM), using the Hierarchical Modeling of Species Communities approach to simultaneously model the effect of habitat type, normalized difference vegetation index, hydroperiod, distance to rivers, land surface temperature, and the abundance of seven mosquito species. We created three models with varied parameters and evaluated the effects of abiotic parameters and species-to-species associations, which served as proxies for species interactions. Our models estimated the species-specific responses of the mosquito community to abiotic parameters. A positive association was identified within the Culex community with potential biotic interactions. However, Ochlerotatus caspius, Ochlerotatus detritus, and Anopheles atroparvus had no statistically significant association with each other and with the Culex species after accounting for the effect of abiotic parameters. We also found that the potential species associations estimated at particular sites and abiotic parameters influenced the model projection for the average abundance of mosquito species. The JSDM will allow spatial projection of the abundance of each mosquito species, which is an important parameter for epidemiological models. The JSDM inference of species association is important because predation, competition, and facilitation affect the distribution and abundance of different species.

Selection of best wavelet functions and decomposition levels for coupling with soft computing methods in estimating ETo in coastal and island regions

ABSTRACT Mass transfer-based models are among the extensively applied reference evapotranspiration (ET o ) estimation approaches that have been proven to provide reliable estimates under certain climatic and geographical conditions. A major issue with such approaches is the high variations in wind speed time series, which is a governing meteorological parameter in mass transfer-based models. few studies have focused on applying wavelet transform as a robust pre-processing technique for ETo estimation. A major question with coupling wavelet transforms with soft computing methods would be the proper choice of the wavelet function, vanishing moments and the decomposition levels. The present study aims at assessing different wavelet functions coupled with boosted regression tree (BT) models, i.e. wavelet-BT (WBT), in estimating ET o values (through mass transfer parameters) in both coastal and island regions, where it is supposed that wind speed variations are affected by sea–inland interactions and so has the sharp variations (unlike ET o ). The obtained results showed that the coupled WBT model could improve the single BT model results to a great extent, and meanwhile, the Daubechies wavelet function with six vanishing moments provided the most accurate results in all the locations. Further, the best simulation outcomes with each wavelet function were obtained when the maximum possible decomposition level was used with each function.

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %–25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %–27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %–7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91–0.93, RMSE = 314.2–376.6 g C m−2 yr−1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

Robust EMPC-Based Frequency-Adaptive Grid Voltage Sensorless Control for an LCL-Filtered Grid-Connected Inverter

A robust explicit model predictive control (EMPC)-based frequency-adaptive grid voltage sensorless control is developed for a grid-connected inverter (GCI) via a linear matrix inequality (LMI) approach under the model parametric uncertainties as well as distorted and imbalanced grid voltages. In order to ensure the quality of grid currents injected into the utility grid even when the system model parameters vary, the proposed control scheme is accomplished by an enhanced prediction model rather than the conventional prediction model obtained by fixed parameters. Furthermore, an LMI-based observer is integrated with the disturbance observer to improve the reference tracking performance and to reject disturbances. The proposed observer is employed for the grid frequency-adaptive control without the need for grid voltage sensors. The proposed current controller and observer employ the LMI scheme to maintain a stable and robust operation of the GCI. The discrete-time frequency response and pole-zero map analyses are utilized to examine the system performance including the stability and robustness against parametric uncertainties. Comprehensive simulation and experimental tests as well as theoretical analyses clearly validate the robustness of the proposed control scheme under various harsh test conditions with non-ideal and unexpected grid and system parametric uncertainties.

Analysing the role of Fe (II) on flocculation of sand-clay mixtures under estuarine mixing

Estuaries are fragile environment that are most affected by climate change. One of the major consequences of climate change on estuarine processes is the enhancement in salt intrusion leading to higher salinity values. This has several implications on the estuarine sediment dynamics. Of the various factors that affect the flocculation of cohesive sediments, salinity and turbulence have been recognized as to have great significance. Many of the estuaries are contaminated with heavy metals, of which, the concentration of Iron (Fe (II)) are generally on the higher range. However, the influence of Fe (II) on the flocculation of cohesive sediments at various estuarine mixing conditions is not well known. The present study investigated the influence of Fe (II) on the flocculation of kaolin at various concentration of Fe (II), salinity and turbulence shear. The results indicated that Fe (II) and salinity have a positive influence on kaolin flocculation. The increase in turbulence shear caused an initial increase and then a decrease in floc size. In case of sand-clay mixtures, that are observed in mixed sediment estuarine environments, a reduction in the floc size was observed, which is attributed to the breakage of flocs induced by the shear of sand. Breakage coefficient, which is a measure of break-up of flocs, is generally adopted as 0.5 assuming binary breakage. The present study revealed that the breakage coefficient can take values from 0 to 1 and is a direct function of Fe (II) and salinity and an inverse function of turbulence and sand concentration. Thus, a new model for breakage coefficient with the influencing parameters has been proposed, which is an improvement of existing model that is expressed in terms of turbulence alone. Sensitivity analysis showed that the proposed model can very well predict the breakage coefficient of Fe (II) – kaolin flocs. Thus, the model can quantify the breakage coefficient of flocs in estuaries contaminated with Fe (II) that is a vital parameter for population balance models.

Self-weighted quantile regression estimation for diffusion parameter in jump-diffusion models

The self-weighted quantile regression estimator for the diffusion parameter in diffusion models with jumps is proposed. The consistency of the underlying estimator is obtained. Moreover, the better finite-sample properties are verified through the Monte-Carlo simulation study and empirical analysis.

Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients.

The objective of the present study is to evaluate the association between longitudinal and survival outcomes in the presence of competing risk events. To illustrate the application of joint modeling in clinical research, we assessed the blood oxygen saturation (SPO2) and its association with survival outcomes in coronavirus disease (COVID-19). In this prospective cohort study, we followed 300 COVID-19 patients, who were diagnosed with severe COVID-19 in the Rohani Hospital in Babol, the north of Iran from October 22, 2020 to March 5, 2021, where death was the event of interest, surviving was the competing risk event and SPO2 was the longitudinal outcome. Joint modeling analyses were compared to separate analyses for these data. The estimation of the association parameter in the joint modeling verified the association between longitudinal outcome SPO2 with survival outcome of death (Hazard Ratio (HR) = 0.33, P = 0.001) and the competing risk outcome of surviving (HR = 4.18, P < 0.001). Based on the joint modeling, longitudinal outcome (SPO2) decreased in hypertension patients (β = -0.28, P = 0.581) and increased in those with a high level of SPO2 on admission (β = 0.75, P = 0.03). Also, in the survival submodel in the joint model, the risk of death survival outcome increased in patients with diabetes comorbidity (HR = 4.38, P = 0.026). The association between longitudinal measurements of SPO2 and survival outcomes of COVID-19 confirms that SPO2 is an important indicator in this disease. Thus, the application of this joint model can provide useful clinical evidence in the different areas of medical sciences.

A GPR-based emulator for semi-numerical reionization code SCRIPT: parameter inference from 21 cm data

Semi-numerical models of reionization typically involve a large number of unknown parameters whose values are constrained by comparing with observations. Increasingly often, exploring this parameter space using semi-numerical simulations can become computationally intensive, thus necessitating the use of emulators. In this work, we present a likelihood emulator based on Gaussian Process Regression (GPR) for our semi-numerical reionization code, SCRIPT, and use it for parameter inference using mock 21 cm power spectrum data and Bayesian MCMC analysis. A unique aspect of our methodology is the utilization of coarse resolution simulations to identify high-probability regions within the parameter space, employing only a moderate amount of computational time. Samples drawn from these high-probability regions are used to construct the training set for the emulator. The subsequent MCMC using this GPR-trained emulator is found to provide parameter posteriors that agree reasonably well with those obtained using conventional MCMC. The computing time for the analysis, which includes both generation of training sets and training the emulator, is reduced by approximately an order of magnitude. This methodology is particularly advantageous in scenarios where one wants to use different parametrizations of reionization models and/or needs to start with broad prior distributions on the parameters, offering an efficient and effective means of parameter inference.

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran.

Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567, respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822, respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs.

Assessing pollinator habitat suitability considering ecosystem condition in the Hannover Region, Germany

Abstract Context Habitat suitability for pollinator species is an important indicator for pollination ecosystem service potential, i.e. for biodiversity and crop provision. Modelling habitat suitability using an expert- and process-based models such as ESTIMAP-pollination is a common and accepted approach to spatially analyse pollination service potential and to make recommendations for planning. Objectives However, the suitability as a pollinator habitat depends not only on the land use type. It is also important to consider the condition of the habitat. For this reason, ecosystem condition information was used as a parameter for ESTIMAP modelling for the first time. Ecosystem condition data was used besides the commonly (in ESTIMAP) used information from expert assessments and from land use data. Methods As parameters for ecosystem condition, the management intensity in agro ecosystems, the management of forests and the proportion of green space in urban areas were included and affected the modelled habitat suitability for wild bees. Results Not all ecosystem types of the region were equally affected by the inclusion of the ecosystem condition parameter in the model. The most affected types were agricultural areas, such as arable and horticultural biotopes, whose suitability values decreased by 25.7%. As a result, areas with low suitability account for 41% of the region and 76.6% of the agro ecosystems. In forest, shrubs and woody plants land use types, the suitability decreased respectively by 4.3 and 6%. On the other hand, urban ecosystems in the city of Hannover were characterised by relatively good habitat suitabilities, especially in the proximity of wide urban forests. In 3.4% of the agricultural land, measures to support pollinators have been established. 1.6% of these measures are located in areas with low suitability. Conclusions The results show that ecosystem condition is, in addition to land use type, an important parameter to indicate habitat suitability for pollinators. Especially for ecosystem types with varying habitat suitabilities, such as agro ecosystems, the implementation of ecosystem condition parameters is recommendable. However, the selection of suitable ecosystem condition indicators still requires further research and concise definitions.

Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions

A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature (min and max), wind speed (WS), relative humidity (RH), and solar radiation (SR) recorded at nine different weather stations in Tunisia are used as inputs to the ANN models to calculate ETo given by the FAO-56 PM (Penman–Monteith) equation. This research study proposes to: (i) compare the FAO-24 BC, Riou, and Turc equations with the universal PM equation for estimating ETo; (ii) compare the PM method with the ANN technique; (iii) determine the meteorological parameters with the greatest impact on ETo prediction; and (iv) determine how accurate the ANN technique is in estimating ETo using data from nearby weather stations and compare it to the PM method. Four statistical criteria were used to evaluate the model’s predictive quality: the determination coefficient (R2), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). It is quite evident that the Blaney–Criddle, Riou, and Turc equations underestimate or overestimate the ETo values when compared to the PM method. Values of ETo underestimation ranged from 1.9% to 66.1%, while values of overestimation varied from 0.9% to 25.0%. The comparisons revealed that the ANN technique could be adeptly utilized to model ETo using the available meteorological data. Generally, the ANN technique performs better on the estimates of ETo than the conventional equations studied. Among the meteorological parameters considered, maximum temperature was identified as the most significant climatic parameter in ETo modeling, reaching values of R and d of 0.936 and 0.983, respectively. The research showed that trained ANNs could be used to yield ETo estimates using new data from nearby stations not included in the training process, reaching high average values of R and d values of 0.992 and 0.997, respectively. Very low values of MAE (0.233 mm day−1) and RMSE (0.326 mm day−1) were also obtained.

Modeling the behavior of monoclonal antibodies on hydrophobic interaction chromatography resins

Monoclonal antibodies (mAbs) require a high level of purity for regulatory approval and safe administration. High-molecular weight (HMW) species are a common impurity associated with mAb therapies. Hydrophobic interaction chromatography (HIC) resins are often used to remove these HMW impurities. Determination of a suitable HIC resin can be a time and resource-intensive process. In this study, we modeled the chromatographic behavior of seven mAbs across 13 HIC resins using measurements of surface hydrophobicity, surface charge, and thermal stability for mAbs, and hydrophobicity and zeta-potential for HIC resins with high fit quality (adjusted R2 > 0.80). We identified zeta-potential as a novel key modeling parameter. When using these models to select a HIC resin for HMW clearance of a test mAb, we were able to achieve 60% HMW clearance and 89% recovery. These models can be used to expedite the downstream process development for mAbs in an industry setting.Graphical

Subduction plate interface shear stress associated with rapid subduction at deep slow earthquake depths: example from the Sanbagawa belt, southwestern Japan

Abstract. Maximum shear stress along an active deformation zone marking the subduction plate interface is important for understanding earthquake phenomena and is an important input parameter in subduction zone thermomechanical modeling. However, such maximum shear stress is difficult to measure directly at depths more than a few kilometers and is generally estimated by simulation using a range of input parameters with large associated uncertainties. In addition, estimated values generally represent maximum shear stress conditions over short observation timescales, which may not be directly applicable to long-timescale subduction zone modeling. Rocks originally located deep in subduction zones can record information about deformation processes, including maximum shear stress conditions, occurring in regions that cannot be directly accessed. The estimated maximum shear stress is likely to be representative of maximum shear stress experienced over geological timescales and be suitable to use in subduction zone modeling over timescales of millions to tens of millions of years. In this study, we estimated maximum shear stress along a subduction plate interface by using samples from the Sanbagawa metamorphic belt of southwestern (SW) Japan, in which slivers of mantle-wedge-derived serpentinite are widely distributed and in direct contact with metasedimentary rocks derived from the subducted oceanic plate. These areas can be related to the zone of active deformation along the subduction plate interface. To obtain estimates of maximum shear stress at the subduction interface, we focused on the microstructure of quartz-rich metamorphic rocks – quartz is the main component of the rocks we collected and its deformation stress is assumed to be roughly representative of the stress experienced by the surrounding rock and plate interface deformation zone. Maximum shear stress was calculated by applying deformation temperatures estimated by the crystallographic orientation of quartz (the quartz c-axis fabric opening-angle thermometer) and the apparent grain size of dynamically recrystallized quartz in a thin section to an appropriate piezometer. Combined with information on sample deformation depth, estimated from the P–T (pressure–temperature) path and deformation temperatures, it is suggested that there was nearly constant maximum shear stress of 15–41 MPa in the depth range of about 15–30 km, assuming plane stress conditions even when uncertainties related to the measurement direction of thin section and piezometer differences are included. The Sanbagawa belt formed in a warm subduction zone. Deep slow earthquakes are commonly observed in modern-day warm subduction zones such as SW Japan, which has a similar thermal structure to the Sanbagawa belt. In addition, deep slow earthquakes are commonly observed to be concentrated in a domain under the shallow part of the mantle wedge. Samples showed the depth conditions near the mantle wedge, suggesting that these samples were formed in a region with features similar to the deep slow earthquake domain. Estimated maximum shear stress may not only be useful for long-timescale subduction zone modeling but also represent the initial conditions from which slow earthquakes in the same domain nucleated.