Process-based Simulation Research Articles

Wmpy-power: A Python package for process-based regional hydropower simulation

wmpy-power: A Python package for process-based regional hydropower simulation

Algorithm for estimating cultivar-specific parameters in crop models for newer crop cultivars

Model parameters in mechanistic process-based crop models are broadly classified into general parameters (environmental, management, and parameters related to soil processes), species-specific parameters, and cultivar-dependent parameters. While general and species-specific parameters typically remain unchanged, identifying cultivar-dependent parameters is crucial during calibration. With the continuous development of new cultivars, updating cultivar-specific parameters in crop models is necessary. However, gathering data on the behavior of these newer cultivars and calibrating the model to accommodate them is challenging. Currently, there is no standard approach for determining cultivar-specific parameters in crop models due to the difficulty of identifying variations in the growth and development process in newer cultivars, as well as the variations in the model structure, correlations between sub-model components and between different parameters in the crop models. The present study develops a standard methodology for determining cultivar-dependent parameters based on experiments and incorporating them into process-based crop growth models. The developed methodology is demonstrated using the cotton crop and the GOSSYM, a mechanistic process-based cotton crop simulation model. Experiments are conducted on 40 major cotton cultivars grown in the USA to quantify their growth and development under the same environmental and management conditions. Cultivar-dependent parameters are derived and integrated into the GOSSYM model based on the experimental data. By establishing a standardized methodology and demonstrating its application in the context of cotton and the GOSSYM model, this study provides practical guidance for incorporating cultivar-dependent parameters into crop simulation models. Researchers and agronomists can effectively utilize this methodology to integrate new cultivars into their crop models.

The Contradictory Issue of the Impact of Antecedent Soil Moisture to Interrill Erosion in Clay Soil: A Two-Year Field Study

The impact of antecedent soil moisture content on soil erosion has been a contradictory issue in erosion research, as well as process-based soil loss estimation models. The objective of this study was to investigate the impact of antecedent soil moisture content on the loss of clay soil through two-year runoff plot experiments under natural rainfall. Volumetric soil moisture sensors were used to monitor soil moisture changes, and readings were used along with rainfall records to quantify the antecedent soil moisture conditions. The results of this study show that the impact of antecedent soil moisture on interrill erosion is conditional, and the impact only exists in erosion events with a low Rainfall–Runoff Erosivity Index. The coefficient of determination between antecedent soil moisture content and soil loss per the Rainfall–Runoff Erosivity Index (Soil Loss/EI30) varies from 0.222 to 0.758, depending on the rainfall duration and Rainfall–Runoff Erosivity. The results of this study also suggest that accumulative rainfall within 48 h (Pp48) prior to an effective erosion event is strongly correlated with Soil Loss/EI30, particularly when the duration of an effective erosion event is either 3~7 h or 10~30 h. Hence, Pp48 can be considered as a replacement for antecedent soil moisture in process-based soil loss simulation models.

Multicomponent and Surface Charge Effects on PFOS Sorption and Transport in Goethite-Coated Porous Media under Variable Hydrochemical Conditions.

Perfluorooctanesulfonate (PFOS), a toxic anionic perfluorinated surfactant, exhibits variable electrostatic adsorption mechanisms on charge-regulated minerals depending on solution hydrochemistry. This work explores the interplay of multicomponent interactions and surface charge effects on PFOS adsorption to goethite surfaces under flow-through conditions. We conducted a series of column experiments in saturated goethite-coated porous media subjected to dynamic hydrochemical conditions triggered by step changes in the electrolyte concentration of the injected solutions. Measurements of pH and PFOS breakthrough curves at the outlet allowed tracking the propagation of multicomponent reactive fronts. We performed process-based reactive transport simulations incorporating a mechanistic network of surface complexation reactions to quantitatively interpret the geochemical processes. The experimental and modeling outcomes reveal that the coupled spatio-temporal evolution of pH and electrolyte fronts, driven by the electrostatic properties of the mineral, exerts a key control on PFOS mobility by determining its adsorption and speciation reactions on goethite surfaces. These results illuminate the important influence of multicomponent transport processes and surface charge effects on PFOS mobility, emphasizing the need for mechanistic adsorption models in reactive transport simulations of ionizable PFAS compounds to determine their environmental fate and to perform accurate risk assessment.

Securing Anomaly Detection for Process-Based Time Series

Widespread innovation from artificial intelligence and machine learning (AI/ML) tools presents a lucrative opportunity for the nuclear industry to improve state-of-the-art analyses (e.g. condition monitoring, remote operation, etc.) due to increased data visibility. In recent years, the risk posed by collaborative data exchange has received increased attention due, in part, to a potential adversary’s ability to reverse-engineer intercepted data using domain knowledge and AI/ML tools. While the efficacy of typical encryption has been proven during passive communication and data storage, collaborative exchange typically requires decryption for extended analyses by a third-party, which poses an intrinsic risk due to these trustworthiness concerns. The directed infusion of data (DIOD) 1 1 A.Al Rashdan and H.Abdel-Khalik, Deceptive Infusion of Data, Non-Provisional Patent, Application No. 63/227,389, September 2022. paradigm presented in this paper discusses a novel data masking technique that relies on preserving the usable information of proprietary data while concealing its identity via reduced-order modeling. In contrast to existing state-of-the-art data masking methods, DIOD does not impose limiting assumptions, computational overheads, or induced uncertainties, thereby allowing for secure and flexible data-level security that does not alter the inferential content of the data. This paper focuses on the application of DIOD to a process-based simulation wherein a leaking reservoir with a controlled inlet pump is simulated under various experimental conditions with the goal of producing masked data that preserve the information given by anomalies. These experiments included the injection of statistically significant anomalies, subtle anomalies that occurred over an extended period, and the addition of several independent anomalous states. Each experiment showed that a classifier will identify the same anomalies whether it analyzes the original or masked data. An additional experiment also tested the case of corrupted labeling information wherein labels were arbitrarily randomized, and the loss in labeling accuracy was about the same for both datasets. Each of these experiments show that data obfuscated by DIOD may be utilized in the place of real data for a variety of condition monitoring scenarios with no loss in performance.

Close-to-nature management of tropical timber plantations is economically viable and provides biodiversity benefits

Abstract Reforestation of tropical forests is crucial to mitigate the climate crisis and restore ecosystems. However, past efforts have been criticized for establishing monoculture timber plantations with exotic tree species. Close-to-nature (CTN) practices aim to minimize negative forest management impacts on forests ecosystems by mimicking natural dynamics. So far, CTN management practices are rarely applied in tropical plantation forestry. This study evaluates the economic, carbon sequestration, and biodiversity potential of CTN management in tropical mixed-species plantations in Central America using a simulation-optimization approach. To our knowledge, this study is the first to assess the potential of tropical CTN-managed plantations on the basis of detailed process-based forest growth simulations. CTN practices such as selective harvesting, retention forestry, and shelterwood cutting of mixed-species stands were compared to even-aged mixtures and conventional monoculture practices. Results showed that CTN management was economically viable for certain species mixtures and management practices at an 8 % discount rate and had the potential to increase carbon storage and biodiversity in the modeled plantations. At current carbon prices, CTN-managed plantations may only become financially competitive with monocultures, if monocultures are excluded from carbon certification schemes that increasingly aim at co-producing non-carbon benefits like biodiversity conservation. If carbon prices increase, the sale of carbon credits could finance the transformation of monocultures to CTN-managed mixed-species stands. The competitiveness of CTN management could also be improved through performance-based biodiversity payments, such as the sale of biodiversity credits.

A method to derive nitrogen transport factors for New Zealand's agricultural lands

Risk index tools have the potential to assist farmers in making strategic decisions regarding their farm design to manage losses of nutrients. Such tools require a vulnerability framework, and these are often based on scores or rankings. These frameworks struggle to take account of interactions between elements of the physical environment. Process-based simulation models inherently take account of interactions and may be a viable alternative to score-based methods. We describe the method to populate a database of transport factors that covers the agricultural lands of New Zealand that is designed for usage as the susceptibility framework within a risk index tool. The method gives both leaching and runoff transport factors and gives values by month. The simulation model used had already been validated for simulating water and nitrogen balances and the generated spatial patterns of the transport factors was validated via expert assessment. These features allow good representation of the risks posed across a wide range of farming activities.•Use of a simulation model to quantify transport factors.•Captures the interactions between soil and weather factors in the physical environment.•Produces a country-wide database intended as a susceptibility framework for a risk index tool.

QGeoWEPP: An open-source geospatial interface to enable high-resolution watershed-based soil erosion assessment

Predicting soil erosion by water is an essential natural resource management activity. The Water Erosion Prediction Project (WEPP) model is a process-based continuous simulation tool based on erosion mechanics and channel hydraulics. Due to the extensive data requirements, preparing input parameter settings for WEPP can be time-consuming. A Geospatial Interface for WEPP (GeoWEPP) overcame this major disadvantage but had limitations regarding keeping track of operating systems and proprietary software updates. QGeoWEPP is a newly developed open-source QGIS-based GeoWEPP, offering additional novel user-based customizations of model simulations including validation data sets. QGeoWEPP provides a platform for applying WEPP at the hillslope and watershed scales that integrates the whole model application and validation process. QGeoWEPP allows applying WEPP in any study area worldwide with minimum data limitations and more spatial and temporal capabilities in areas such as soil and water conservation, land management, geohazard assessment due to land use and climate change, and many more.

Soil respiration across a variety of tree-covered urban green spaces in Helsinki, Finland

Abstract. As an increasing share of the human population is being clustered in cities, urban areas have swiftly become the epicentres of anthropogenic carbon (C) emissions. Understanding different parts of the biogenic C cycle in urban ecosystems is needed in order to assess the potential to enhance their C stocks as a cost-efficient means to balance the C emissions and mitigate climate change. Here, we conducted a field measurement campaign over three consecutive growing seasons to examine soil respiration carbon dioxide (CO2) fluxes and soil organic carbon (SOC) stocks at four measurement sites in Helsinki, representing different types of tree-covered urban green space commonly found in northern European cities. We expected to find variation in the main drivers of soil respiration – soil temperature, soil moisture, and SOC – as a result of the heterogeneity of urban landscape and that this variation would be reflected in the measured soil respiration rates. In the end, we could see fairly constant statistically significant differences between the sites in terms of soil temperature but only sporadic and seemingly momentary differences in soil moisture and soil respiration. There were also statistically significant differences in SOC stocks: the highest SOC stock was found in inactively managed deciduous urban forest and the lowest under managed streetside lawn with common linden trees. We studied the impacts of the urban heat island (UHI) effect and irrigation on heterotrophic soil respiration with process-based model simulations and found that the variation created by the UHI is relatively minor compared to the increase associated with active irrigation, especially during dry summers. We conclude that, within our study area, the observed variation in soil temperature alone was not enough to cause variation in soil respiration rates between the studied green space types, perhaps because the soil moisture conditions were uniform. Thus, irrigation could potentially be a key factor in altering the soil respiration dynamics in urban green space both within the urban area and in comparison to non-urban ecosystems.

Temperature-photoperiod interactions improve simulations of early xylem phenology: Refining the Vaganov-Shashkin growth model

Wood formation plays a crucial role in forest productivity and carbon sequestration. Changes in early xylem phenology influence wood development (xylogenesis) and tree growth, thereby affecting carbon uptake by forests. However, a deeper understanding of long-term shifts in spring growth phenology in response to climate warming is lacking. Process-based simulation models, such as the Vaganov-Shashkin (VS) growth model, could be used to simulate and trace shifts in spring growth phenology. We propose upgrading the VS model with a new function that considers the temperature-photoperiod interaction and improves the simulation of growth onset timings. To assess this refined VS model, we compared the early growing xylogenesis phases of two conifers (Pinus pinaster and Pinus sylvestris) and two ring-porous oaks (Quercus faginea and Quercus pyrenacia) coexisting in a Mediterranean continental site located in Soria, central Spain. The improved VS model successfully simulated early growing season xylogenesis during the warm-dry 2012 year, which can be considered analogous to the forecasted warmer scenarios in the 21st century. The upgraded VS model improved its ability to simulate growth onset, demonstrating the importance of considering both temperature and photoperiod. The model was then used to simulate spring phenology considering two Representative Concentration Pathways scenarios (RCP4.5 and RCP8.5) with different warming rates. An earlier, but species-specific, xylem onset was forecasted for the four tree species in response to warmer spring conditions in the late 21st century.

Coupling of microbial-explicit model and machine learning improves the prediction and turnover process simulation of soil organic carbon

Modeling soil organic carbon (SOC) is helpful for understanding its distribution and turnover processes, which can guide the implementation of effective measures for carbon (C) sequestration and enhance land productivity. Process-based simulation with high interpretability and extrapolation, and machine learning modeling with high flexibility are two common methods for investigating SOC distribution and turnover. To take advantage of both methods, we developed a hybrid model by coupling of a two-carbon pool microbial model and machine learning for SOC modeling. Here, we assessed the SOC model's predictive, mapping, and interpretability capabilities for the SOC turnover process on Ningbo region. The results indicate that the microbial model with density-dependence (β ​= ​2) and microbial biomass carbon simulation performed better in modeling the parameters of the microbial-based C cycle, such as microbial carbon use efficiency (CUE), microbial mortality rate, and assimilation rate. By integrating this optimal microbial model and random forest (RF) model, the hybrid model improved the prediction accuracy of SOC, with an increased R2 from 0.74 to 0.84, residual prediction deviation increased from 1.97 to 2.50, and reduced the root-mean-square error from 4.65 to 3.67 ​g ​kg−1 compared to the conventional RF model. As a result, the predicted SOC distribution exhibited high spatial variation and provided abundant details. Microbial CUE and potential C input, represented by net primary productivity, emerged as the primary factors driving SOC distribution in Ningbo region. Projections of SOC under the CMIP6 SSP2-4.5 scenario revealed that regional C loss in high SOC areas was mainly caused by decreased microbial CUE and C input, induced by climate change. Our findings highlight the potential of combining the microbial-explicit model and machine learning to improve SOC prediction accuracy and understand SOC feedback in a changing climate.

Calibration of Hybrid-Maize Model for Simulation of Soil Moisture and Yield in Production Corn Fields

Model calibration is essential for acceptable model performance and applications. The Hybrid-Maize model, developed at the University of Nebraska-Lincoln, is a process-based crop simulation model that simulates maize growth as a function of crop and field management and environmental conditions. In this study, we calibrated and validated the Hybrid-Maize model using soil moisture and yield data from eight commercial production fields in two years. We used a new method for the calibration and multi-parameter optimization (MPO) based on kriging with modified criteria for selecting the parameter combinations. The soil moisture-related parameter combination (SM-PC3) improved simulations of soil water dynamics, but improvement in model performance is still required. The grain yield-related parameter combination significantly improved the yield simulation. We concluded that the calibrated model is good enough for irrigation water management at the field scale. Future studies should focus on improving the model performance in simulating total soil water (TSW) dynamics at different soil depths by including more soil water processes in a more dynamic manner.

Quest to find compromised spatial and temporal resolutions for integrating remote sensing data with an agro-ecosystem model for grasslands

This paper addressed one of the main challenges in assimilating remote sensing derived variables into process-based crop model simulations, which is the inconsistent spatial and temporal resolution between information obtained from remote sensing and the outputs of process-based agroecosystem model. We proposed an applied method to reduce the number of required simulations by identifying (i) optimal points in time where additional information from remote sensing has the largest positive influence on the model performance and (ii) options to cluster 10 m grid cells to larger cells without compromising their information content. The MONICA (Model for Nitrogen and Carbon) model was applied to simulate above and below ground biomass in two grassland sites located in southern and eastern parts of Germany. The model was calibrated using LAI values obtained from Sentinel-2 and the sensitivity of output variables to two key root parameters (Root Form factor and specific root length) was evaluated. Our results showed that one or two satellite images covering the critical time periods right after cutting events significantly improved the predictions of grass yields produced by a mechanistic agroecosystem models (by up to 30 %). A larger number of images at other grass growth stages would not further improve the predictive power of the model. We also found that the sensitivity to these critical time periods was independent of model parameters. The mixed-resolution scheme (between 10 and 50 m) achieved better results compared with the high-resolution standalone state-updating method, yet it reduced computational costs by more than 50 %. In conclusion, we proposed a methodology to reduce the number of required simulations for data assimilation by aggregating data from fine to coarse resolutions. Our method was promising for applying data assimilation over large areas and benefiting more from satellite information for real-time prediction of agricultural productivity.

Intensifying rice production to reduce imports and land conversion in Africa

Africa produces around 60% of the rice the continent consumes, relying heavily on rice imports to fulfill the rest of the domestic demand. Over the past 10 years, the rice-agricultural area increased nearly 40%, while average yield remained stagnant. Here we used a process-based crop simulation modelling approach combined with local weather, soil, and management datasets to evaluate the potential to increase rice production on existing cropland area in Africa and assess cropland expansion and rice imports by year 2050 for different scenarios of yield intensification. We find that Africa can avoid further increases in rice imports, and even reduce them, through a combination of cropland expansion following the historical trend together with closure of the current exploitable yield gap by half or more. Without substantial increase in rice yields, meeting future rice demand will require larger rice imports and/or land conversion than now.

NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP)

The accurate estimation of cropland net primary productivity (NPP) remains a significant challenge. We hypothesized that incorporating prior information on NPP simulated by process-based models into normalized difference vegetation index (NDVI) data would improve the accuracy of cropland ecosystem NPP estimations. We used NDVI, MNPP (NPP of process-based model), and SNPP (statistic-based NPP) data estimated by nine process-based models and yield statistics to build a learning ensemble of the random forest model (LERFM). We used the new model to re-evaluate the cropland NPP in China from 1982 to 2010. Large spatial discrepancies among MNPPs, which indicate uncertainties in cropland NPP estimation using different methods, were observed when compared to SNPP. The LERFM model showed a slightly underestimation of only −0.37%, while the multi-model average process-based model (MMEM) strongly underestimated −15.46% of the SNPP. LERFM accurately estimated cropland NPP with a high simulation skill score. A consistent increasing trend in the LERFM and MMEM NPP during 1982–2010 and a significant positive correlation (r = 0.795, p < 0.001) between their total NPP indicate that the LERFM model can better describe spatiotemporal dynamic changes in cropland NPP. This study suggests that a learning ensemble method that combines the NDVI and process-based simulation results can effectively improve cropland NPP.

Adapting the grassland model BASGRA to simulate yield and nutritive value of whole-crop barley

Ensiling of whole-crop biomass of barley before full maturity is common practice in regions with a short growing season. The developmental stage of barley at harvest can have a large impact on yield and nutritive composition. The relationships between crop growth, environmental conditions and crop management can be described in process-based simulation models. Some models, including the Basic Grassland (BASGRA) model, have been developed to simulate the yield and nutritive value of forage grasses, and usually evaluated against metrics of relevance for whole-crop silage. The objectives of this study were to: i) modify the BASGRA model to simulate whole-crop spring barley; ii) evaluate the performance of this model against empirical data on dry matter (DM) yield and nutritive value attributes from field experiments, divided into geographical regions; and iii) evaluate DM yield, nutritive value and cutting date under current and future climate conditions for three locations in Sweden and four cutting regimes. Main model modifications included addition of a spike pool, equations for carbon (C) and nitrogen (N) allocation to the spike pool and equations for C and N translocation from vegetative plant parts to spikes. Model calibration and validation against field trial data from Sweden, including samples harvested from late anthesis stage to hard dough stage that were either pooled or divided into regions, showed better prediction accuracy, evaluated as normalised root mean squared error (RMSE), of neutral detergent fibre (NDF) (7.58–18.4%) than of DM yield (16.8–27.8%), crude protein (15.5–23.2%) or digestible organic matter in the DM (DOMD) (12.0–22.2%). Model prediction using weather data representing 1990–2020 and 2021–2040 climate conditions for three locations in Sweden (Skara, Umeå, Uppsala) showed lower DM yield, earlier harvest and slightly higher NDF concentration on average (across locations and developmental stage at cutting) when using near-future climate data rather than historical data. The model can be used to evaluate whole-crop barley performance under production conditions in Sweden or in other countries with similar climate, soils and crop management regimes.

Intelligent pest trap monitoring under uncertainty in food industry

Pests bring significant losses in both quantity and quality of products in postharvest food facilities and their uncertain activities make imprecise obtained infestation information from trap monitoring, which causes unclear estimation on detriment costs, preventing effective pest management. Trap placement, which collects insect information through determining the exact location and number of traps, will enhance the precision for obtaining insect information. However, this method involves optimizing conflicting objectives. On one hand, smaller number of traps save the monitoring cost and enlarge the monitoring efficiency. On the other hand, number of traps may be insufficient and losses the monitoring accuracy. Existing studies to enhance the monitoring accuracy via both laboratory testing and high tech devices but they rarely considered economic efficacy in practice. In this paper, we characterize the trap placement problem as a Multi-Objective Optimization Problem under Uncertainty. Feedforward Neural Networks and Particle Swarm Optimization algorithms are used to design a tailored algorithm for modeling the conflicting objectives through process-based simulation. Then, robust multi-objective optimization evolutionary algorithm (RMOA) is developed to search for the Pareto optimal solution for the least number of traps settled at optimal positions. Results show the optimal and robust performance of exact trap locations to monitor insect infestation effectively under uncertain insect locations and dynamic insect growth. Compared to the studies that were mainly utilizing general algorisms for solving agricultural problems, the proposed model has implications for effective methods of capturing the characteristics of real-world insect monitoring applications in food industry.

Comparing likely effectiveness of urban Nature-based Solutions worldwide: The example of riparian tree planting and water quality

Amongst a spectrum of benefits, Nature-based Solutions (NBS) are increasingly being advocated as improving the quality of aquatic environments in urban areas. Of these, a widely adopted measure is tree planting. Yet, because of the local complexities and spatial variability of urban hydrological response, it is difficult to predict to what extent improvements in water quality will arise. To overcome this barrier, a standardised approach to process-based model simulation of urban river quality is described (QUESTOR-YARDSTICK (QUESTOR-YS)). The approach eliminates the influence of point sources of pollution and harmonises the way in which river hydrodynamics and contributory catchment size are represented. Thereby, it focuses on differences in water quality between cities due solely to climate, river discharge and urban diffuse nutrient pollution factors. The relative sensitivity to NBS establishment between urban water bodies in different cities anywhere across the world can also potentially be quantified. The method can be readily extended to include wastewater effluents. The validity of the approach is demonstrated for a small river in Birmingham, UK; and thence demonstrated for the case of 10 km of riparian tree planting in Birmingham, Oslo (Norway) and Aarhus (Denmark). Modelling suggests that riparian tree planting can substantially improve water quality in each example city for three key indicators of water quality in sensitive summer conditions (water temperature, chlorophyll-a and dissolved oxygen). Results show the level of benefit achievable in response to a fixed amount of planting will depend on the existing level of riparian tree occupancy.

Mechanistic simulations of kelp populations in a dynamic landscape of light, temperature, and winter storms

Kelp forests are widely distributed across the coastal ocean, support high levels of biodiversity and primary productivity, and underpin a range of ecosystem services. Laminaria hyperborea is a forest-forming kelp species in the Northeast Atlantic that alters the local environment, providing biogenic structure for a diversity of associated organisms. Populations are strongly affected by light availability, temperature, and storm-related disturbance. We constructed a stage-based, two-season model of L. hyperborea populations along the coast of Great Britain and Ireland to predict biomass across a range of depths, drawing on extensive surveys and data from the literature. Population dynamics were driven by wave exposure, historic winter storm intensity, and simulated interannual variation in temperature and depth-attenuated light intensity, with density-dependent competition for light and space. High biomass was predicted in shallow depths across the domain on suitable substrate, with populations extending deeper in the north and west where light penetration was greater. Detritus production was heavily skewed across years, particularly at greater depths, with 10 % of years comprising more than 50 % of detritus on average below 10 m depth. Annual fluctuations in light and storm intensity produced opposing population oscillations with a ∼6-year period persisting for up to a decade but diminishing sharply with depth. Interannual variation in temperature had minimal impact. Biomass was most sensitive to survival and settlement rates, with negligible sensitivity to individual growth rates. This model highlights the need for an improved understanding of canopy and subcanopy mortality, particularly regarding increasingly frequent heatwaves. Estimations of kelp forest contributions to carbon sequestration should consider the high variability among years or risk underestimating the potential value of kelp forests. Process-based simulations of populations with realistic spatiotemporal environmental variability are a valuable approach to forecasting biotic responses to an increasingly extreme climate.

Estimating RQD for Rock Masses Based on a Comprehensive Approach

Rock Quality Designation (RQD) is among the widely used measures of the quality of rock masses and can be derived through Monte Carlo stochastic process-based fracture network simulations. However, repeated simulations can yield variable RQD results. Here, we introduce a four-step approach that incorporates class ratio analysis to estimate the representative RQD, which includes (1) extracting the mean and confidence interval of the RQD sample, in terms of the Confidence Neutrosophic Number Cubic Value (CNNCV), (2) employing class ratio analysis to determine the thresholds of the number of virtual boreholes and that of the number of models for a given size D, beyond which the CNNCV remains substantially unchanged, (3) accepting the CNNCV at the thresholds of the number of models as the representative RQD for the model of size D (RQD(D)) and (4) determining the representative RQD (rRQD), defined as the specific value which, once D exceeds, the RQD(D) does not change significantly. The introduced approach is illustrated with a case study of an open-pit slope in China, and it was tested for its performance. The RQD calculation results of the proposed method and the traditional single-model approach exhibit differences, which diminish with increasing model sizes. At the 95% confidence level, the stable size of the RQD determined by the proposed method is 13 m, compared to 25 m for the single-model approach. This method enhances the accuracy of representative elementary volume predictions by accounting for the diversity in the simulation results of RQDs for the same size. Overall, the introduced approach offers a reliable method for obtaining RQD estimates.