Surface Model Research Articles

Central composite designs with three missing observations

In an experiment, there are many situations when some observations are missed, ignored or unavailable due to some accidents or high cost experiments. A missing observation can make the results of a response surface model quite misleading. This work therefore investigates the impact of a three missing observation of them various design points: factorial, axial and center points, on the estimation and predictive capability of the central composite design (CCD). Therefore minimaxloss CCD is formulated under a minimaxloss criterion. The minimaxloss CCD is considered to be robust to three missing observation and the investigation has been made in this article. The general formulas for the efficiency of the design when missing three observations, are presented in closed form as a function of α, where α is the value used in the CCDs' axial part. For the first time in this paper, these are calculated explicitly for CCDs from k=2 to k=7 factors and displayed in tables for practitioners to use. The corresponding graphs for the efficiencies are presented and suggestions are made for the values of α to maximize the robustness and estimability of the design for all cases.

Role of Infiltration on Land–Atmosphere Feedbacks in Central Europe: Fully Coupled WRF-Hydro Simulations Evaluated with Cosmic-Ray Neutron Soil Moisture Measurements

Abstract The skill of regional climate models partly relies on their ability to represent land–atmosphere feedbacks in a realistic manner, through coupling with a land surface model. However, these models often suffer from insufficient or erroneous information on soil hydraulic parameters. In this study, the fully coupled land–atmosphere model WRF-Hydro driven with ERA5 reanalysis is employed to reproduce the regional climate over central Europe with a horizontal resolution of 4 km for the period 2017–20. Simulated soil moisture is compared with data from cosmic-ray neutron sensors (CRNSs) at three terrestrial environmental observatories. Soil hydraulic parameters from continental and global digital soil datasets (SoilGrids and EU-SoilHydroGrids), together with Campbell and van Genuchten–Mualem retention curve equations, are used to assess the role of infiltration on modeled land–atmosphere feedbacks. The percolation parameter is calibrated to better capture observed discharge amounts in the observatories. WRF-Hydro with Campbell and SoilGrids gives the lowest mean annual temperature and mean annual precipitation differences compared to the E-OBS product from European Climate Assessment and Dataset, which is achieved by reducing soil moisture in the rootzone, increasing air temperature, and decreasing precipitation through a positive soil moisture–precipitation feedback process. WRF-Hydro with van Genuchten–Mualem and EU-SoilHydroGrids best reproduces CRNS soil moisture daily variations, despite enhanced positive biases that generate a larger proportion of convective precipitation favored over wet soils and spurious discharge peaks. This study demonstrates the importance of infiltration processes to realistically reproduce land–atmosphere feedbacks. Significance Statement The ability to correctly reproduce the feedbacks between land and atmosphere is crucial for a regional climate model. This study shows that the nature of these land–atmosphere feedbacks is partly related to the description of soil infiltration, which potentially helps to improve regional climate modeling results. An updated method to disentangle the proportion of convective precipitation being favored over wet, dry, and mixed soils is provided, which sheds new light on the soil moisture–precipitation feedback mechanism.

Morphological changes to peripheral joints supporting thumb trapezial metacarpal osteoarthritis.

The present study aims to determine if morphological differences of the scaphoid, trapezoid, and second metacarpal are associated with thumb osteoarthritis by comparing three-dimensional bone models of healthy controls and osteoarthritis patients. Fifty-eight patients with moderate to severe thumb osteoarthritis (modified Eaton≥II) and 35 healthy controls from a larger completed investigation were examined. To quantify morphological variations, a statistical shape model was created that classified binary with respect to the Eaton grading system: non-osteoarthritis or moderate/severe osteoarthritis (II-IV). Three-dimensional surface models based on computed tomography images from the scaphoid, trapezoid, and second metacarpal were used to "train" the statistical shape model and yielded features that best explain variation within the three bones: the principal components These principal components were tested for significant differences between patient and control group. Additionally, a statistical shape model entailing all three bones was created. For the second metacarpal, only a single principal component was significantly associated with osteoarthritis (p=0.035). The combined model utilizing all three bones, however, showed that with using one principal component of each of the bones, we could classify a sample as moderate/severe trapeziometacarpal osteoarthritis with an accuracy of 0.77. No individual shape components of the scaphoid or trapezoid significantly correlated to osteoarthritis. This study affirms that basilar thumb osteoarthritis is not limited to the trapeziometacarpal joint. Future studies investigating surrounding bones and joints as contributors to disease occurrence or progression will provide a more holistic insight into the prevention, diagnostic, and treatment of thumb osteoarthritis.

Lithium and cobalt recovery from spent battery cathode by acid-reductive leaching using molasses: optimisation by response surface methodology

ABSTRACT The combination of molasses and sulphuric acid is reported for the first time as a leaching agent for lithium and cobalt recovery from spent lithium-ion batteries. The acid-reductive leaching process was optimised using a design of experiment with four input variables: sulphuric acid concentration, molasses concentration, leaching temperature, and leaching time. A response surface model with a central composite design determined the number of experiments, followed by quadratic regression modelling to analyze the relationship between input parameters and leaching recovery. ANOVA assessed the statistical significance of each variable, regression modelling, and optimal leaching conditions. The analysis showed temperature significantly affected lithium recovery, while both temperature and sulphuric acid concentration influenced cobalt recovery. The optimal conditions achieved 97.76% lithium and 99% cobalt recovery (2.5 M sulphuric acid, 60 g/L molasses, 54°C, 108 min). These findings highlight an eco-friendly and efficient approach to metal recovery.

Six Sigma-Based Mathematical Optimization Framework for Flux-Switching Machines: A Roadmap for Quality, Performance, and Manufacturing Tolerances

Flux-switching wound field machines (FSWFMs) offer high torque density and independence from rare-earth materials, making them promising candidates for sustainable electric vehicles and industrial applications. However, their adoption is limited by challenges such as high torque ripple, efficiency variations, and sensitivity to manufacturing tolerances. This study presents a Design for Six Sigma (DFSS) optimization framework that integrates sensitivity analysis, response surface modeling (RSM), and multi-objective genetic algorithms to address these challenges. The optimized solution reduces torque ripple by 7.69%, improves torque output, and enhances energy efficiency. By incorporating Six Sigma principles, the framework ensures robust performance under manufacturing variations, bridging the gap between theoretical optimization and practical implementation. This scalable and efficient methodology establishes FSWFMs as viable solutions for industrial applications, revolutionizing electric machine design.

Bio-synthesised nanoparticles employment for eliminating metals from wastewater via implementation of a three level Box-Behnken technique

The objective of the present study is to optimise the removal of metals such as aluminium, zinc, and copper from industrial wastewater using green-synthesised nanoadsorbents. To achieve this, the Box–Behnken experimental design and response surface methodology will be employed. We used inductively coupled plasma mass spectrometry to analyse the metals present in the wastewater. A three-factor, three-stage Box–Behnken design was used to maximise the removal of these metals from aqueous solution. This involved response surface modelling and quadratic programming based on 17 different experimental data from a batch study. The study focused on three independent variables: pH, contact time, and adsorbent amount. The nanoadsorbents were prepared using a combination of Citrus X sinensis peel and Musa Cavendish peel extract, which served as the reducing agents, to produce a combined peel extract-silver nanoparticle product. Field emission scanning electron microscopy imaging and UV–visible spectroscopic analysis unequivocally demonstrated the presence of nanoparticles, with a surface plasmon resonance at 438 nm. The optimal values of the selected variables were determined by solving the quadratic regression model and analysing the contour plots of the reaction surface. At the experimental conditions of pH = 5, contact time = 92.5 min, and adsorbent dosage = 0.1 g/L, the recovery efficiency of Al, Cu, and Zn was significantly reduced. The optimised parameters were successfully applied to wastewater collected, and the degradation of detected metal ions was tested. The experiment demonstrated an effective reduction in these metals.

ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool

Abstract. The accurate calibration of parameters in atmospheric and Earth system models is crucial for improving their performance but remains a challenge due to their inherent complexity, which is reflected in input–output relationships often characterised by multiple interactions between the parameters, thus hindering the use of simple sensitivity analysis methods. This paper introduces the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a new tool designed with the aim of providing a simple and flexible framework to estimate the sensitivity and importance of parameters in complex numerical weather prediction models. This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods, including LASSO (see the list of abbreviations in Appendix B), support vector machine, classification and regression trees, random forest, extreme gradient boosting, Gaussian process regression, and Bayesian ridge regression. These regression algorithms are used to construct computationally inexpensive surrogate models to effectively predict the impact of input parameter variations on model output, thereby significantly reducing the computational burden of running high-fidelity models for sensitivity analysis. Moreover, the multi-method approach allows for a comparative analysis of the results. Through a detailed case study with the Weather Research and Forecasting (WRF) model coupled with the Noah-MP land surface model, ML-AMPSIT is demonstrated to efficiently predict the effects of varying the values of Noah-MP model parameters with a relatively small number of model runs by simulating a sea breeze circulation over an idealised flat domain. This paper points out how ML-AMPSIT can be an efficient tool for performing sensitivity and importance analysis for complex models, guiding the user through the different steps and allowing for a simplification and automatisation of the process.

Temperature seasonality regulates organic carbon burial in lake

Organic carbon burial (OCB) in lakes, a critical component of the global carbon cycle, surpasses that in oceans, yet its response to global warming and associated feedbacks remains poorly understood. Using a well-dated biomarker sequence from the southern Tibetan Plateau and a comprehensive analysis of Holocene total organic carbon variations in lakes across the region, here we demonstrate that lake OCB significantly declined throughout the Holocene, closely linked to changes in temperature seasonality. Process-based land surface model simulations clarified the key impact of temperature seasonality on OCB in lakes: increased seasonality in the early Holocene saw warmer summers enhancing ecosystem productivity and organic matter deposition, while cooler winters improved organic matter preservation. The Tibetan Plateau’s heightened sensitivity to climate and ecosystem dynamics amplifies these effects. With declining temperature seasonality, we predict a significant slowdown or reduction in OCB across these lake sediments, leading to carbon emissions and amplified global warming.

Hybrid approach for parametric optimization of wire-electrochemical spark micromachining of borosilicate glass

This work intends to implement a hybrid strategy for the multiobjective optimization of a wire-electrochemical spark micromachining (WECSMM) process, integrating the Taguchi technique and response surface approach. Initially, the optimum level of process variables, such as, thickness of workpiece, feed velocity of wire, concentration of electrolyte, and applied voltage, are evaluated using the characteristic loss function of Taguchi. After the process variables are optimized, the response surface model is built and refined using the central values that represent the best values. An experimental plan for borosilicate glass slicing was carried out during WECSMM, utilizing the L9 Taguchi design process. For simultaneous optimization, the two attribute criteria like surface roughness ( Ra) and material removal rate (MRR) have been chosen. When comparing the outcomes of a single strategy with the hybrid approach, the results demonstrate a significant increase in both quality aspects. As contrasted to Taguchi Methodology (TM), the couple approach was shown to increase MRR by 192% and lower Ra by 10%, respectively.

Need for judicious selection of runoff inputs in a global flood model

Abstract Numerous flood hazard assessment and risk management studies depend on hydrodynamic flood models, which require detailed inputs. However, these models face challenges when assessing flood hazards and risks at national scales due to the unavailability of input data and high computational demands. Recent advancements in global flood models (GFMs) have emerged as promising solutions. These widely adopted GFMs, capable of producing flood characteristics, require runoff input typically derived from land surface models (LSMs) or global hydrological models (GHMs), which are prone to inherit cascading uncertainties. Moreover, the utilization of a single runoff input into a GFM can produce biased and misinterpreted flood hazards due to underestimation or overestimation of GFM outputs. To highlight these implications, the present study examines GFM simulations forced with eight state-of-the-art model runoff datasets, including LSMs, GHMs, and reanalysis observations, uncovering unsafe inter-model flood depth variation (IMDV). Focusing on the flood-prone Mahanadi River Basin (MRB) of India, the study observes that IMDV surpasses the self-help range of humans (0.2 m) for 65% of the MRB region, and exceeds human and vehicle safety thresholds (2 m) for 15% of the region, based on four past flood events from the Dartmouth Flood Observatory. These regions exhibiting high IMDV overlap with densely populated areas, potentially affecting 1.66–3.65 million people. Thus, the injudicious use of runoff in GFM for flood disaster planning can lead to inaccurate flood hazard and risk assessments, significantly affecting populous regions. An alternative approach is recommended, advocating for the use of multiple simulations incorporating diverse runoff datasets. This approach would generate conservative and optimistic flood scenarios, leveraging each model’s strengths. Such comprehensive hazard scenarios would enhance flood management and decision-making for policymakers by addressing the uncertainty and providing possible impacts through risk assessments.

Physical Parameterization Sensitivity of Noah-MP for Hydrothermal Simulation Within the Active Layer on the Qinghai–Tibet Plateau

The accurate modeling of complex freeze–thaw processes and hydrothermal dynamics within the active layer is challenging. Due to the uncertainty in hydrothermal simulation, it is necessary to thoroughly investigate the parameterization schemes in land surface models. The Noah-MP was utilized in this study to conduct 23,040 ensemble experiments based on 11 physical processes, which were aimed at improving the understanding of parameterization schemes and reducing model uncertainty. Next, the impacts of uncertainty of physical processes on land surface modeling were evaluated via Natural Selection and Tukey’s test. Finally, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to identify the optimal combination of parameterization schemes for improving hydrothermal simulation. The results of Tukey’s test agreed well with those of Natural Selection for most soil layers. More importantly, Tukey’s test identified more parameterization schemes with consistent model performance for both soil temperature and moisture. Results from TOPSIS showed that the determination of optimal schemes was consistent for the simulation of soil temperature and moisture in each physical process except for frozen soil permeability (INF). Further analysis showed that scheme 2 of INF yielded better simulation results than scheme 1. The improvement of the optimal scheme combination during the frozen period was more significant than that during the thawed period.

TENet: Attention-Frequency Edge-Enhanced 3D Texture Enhancement Network

Oblique photogrammetry imagery often suffers from uneven resolution and blurred details, leading to poor surface texture quality in 3D reconstructions, particularly for building facades. To address these challenges, we propose a novel Attention-Frequency Edge-Enhanced 3D Texture Enhancement Network (TENet) and introduce a comprehensive 3D texture enhancement pipeline. This pipeline applies 2D texture super-resolution techniques to 3D models for fine-grained texture restoration, enhancing the surface texture quality. TENet leverages attention mechanisms and frequency-domain techniques to improve texture sharpness and edge accuracy. Our approach includes a Region-Resolution Adaptive Enhancement Module (RAEM) and a Frequency-Domain Edge Enhancement Mechanism (FDEEM) to enhance local details and restore critical edge features. The experimental results demonstrate that TENet outperforms existing methods, significantly improving texture quality and 3D reconstruction performance. Ablation studies confirmed the effectiveness of each component in enhancing 3D texture reconstruction. The network is validated for real-world applications, showing its ability to significantly reduce edge artifacts and restore clear, accurate textures in real-world 3D surface models.

Assessment of Pine Tree Crown Delineation Algorithms on UAV Data: From K-Means Clustering to CNN Segmentation

The crown area is a critical metric for evaluating tree growth and supporting various ecological and forestry analyses. This study compares three approaches, i.e., unsupervised clustering, region-based, and deep learning, to estimate the crown area of Pinus eldarica Medw. using UAV-acquired RGB imagery (2 cm ground sampling distance) and high-density point clouds (1.27 points/cm3). The first approach applied unsupervised clustering techniques, such as Mean-shift and K-means, to directly estimate crown areas, bypassing tree top detection. The second employed a region-based approach, using Template Matching and Local Maxima (LM) for tree top identification, followed by Marker-Controlled Watershed (MCW) and Seeded Region Growing for crown delineation. The third approach utilized a Convolutional Neural Network (CNN) that integrated Digital Surface Model layers with the Visible Atmospheric Resistance Index for enhanced segmentation. The results were compared against field measurements and manual digitization. The findings reveal that CNN and MCW with LM were the most effective, particularly for small and large trees, though performance decreased for medium-sized crowns. CNN provided the most accurate results overall, with a relative root mean square error (RRMSE) of 8.85%, a Nash–Sutcliffe Efficiency (NSE) of 0.97, and a bias score (BS) of 1.00. The CNN crown area estimates showed strong correlations (R2 = 0.83, 0.62, and 0.94 for small, medium, and large trees, respectively) with manually digitized references. This study underscores the value of advanced CNN techniques for precise crown area and shape estimation, highlighting the need for future research to refine algorithms for improved handling of crown size variability.

Physics-Based Modelling of Plate-Fin Heat Exchangers

Aluminium plate-fin heat exchangers are widely used in automotive, aerospace, and other industrial applications. Extensive research has been conducted on these coolers, yet accurate predictive tools for their thermo-hydraulic performance are still lacking, due to the wide variety of geometric parameters and working fluids involved. This work proposes an original approach based purely on physical principles and established models, combining detailed numerical models for the extended surfaces and manifolds, with global models aimed at accurately evaluating overall head losses and heat transfer rates in plate-fin heat exchangers. Extended surfaces are studied by means of computational models of unitary fin modules under fully developed flow conditions. Entrance effects are analysed through dedicated numerical models. Numerical results on extended surfaces are extended to whole heat exchangers by global models for heat transfer and head losses, based on the ε−NTU method and the Darcy–Weisbach equation, respectively. The proposed approach is presented and validated through the analysis of a case study comprising several heat exchangers featuring different geometries and working fluids. Numerically derived heat transfer rates and head losses are compared with experimental data showing maximum deviations of ±20% for most of the tested configurations, highlighting the strength of the proposed modelling methodology.

Fatigue life study of milled surfaces based on stress concentration factor

Abstract This paper adopts the method of combining theoretical modeling, experimental detection analysis and finite element simulation. The improved stress concentration factor solution equation is derived, and the fatigue life prediction model of plane milling surface is established accordingly. The fatigue life simulation analysis method based on surface stress concentration factor is used to obtain the influence law of surface morphology of typical parts on fatigue life, verify the accuracy of fatigue life prediction model, and provide an effective means for the analysis and prediction of fatigue performance of milling surface.

Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe

Abstract. Evapotranspiration (ET) and gross primary production (GPP) are critical fluxes contributing to the energy, water, and carbon exchanges between the atmosphere and the land surface. Land surface models such as the Community Land Model v5 (CLM5) quantify these fluxes, estimate the state of carbon budgets and water resources, and contribute to a better understanding of climate change's impact on ecosystems. Past studies have shown the ability of CLM5 to model ET and GPP magnitudes well but emphasized systematic underestimations and lower variability than in the observations. Here, we evaluated CLM5's predictions of water and energy fluxes using observations from eddy covariance stations from the Integrated Carbon Observation System (ICOS), remote sensing, and reanalysis data sets. We assess simulated ET and GPP from the grid scale (CLM5grid) and the plant functional type (PFT) scale (CLM5PFT). CLM5PFT exhibited a low systematic error in simulating the ET at the ICOS sites (average bias of −4.68 %), indicating that PFT-specific ET closely matches the observations' magnitude. GPP was underestimated by CLM5PFT, especially in deciduous forests (bias of −43.76 %). The results showed an underestimation of the spatiotemporal variability in the simulated ET and GPP distribution moments across PFTs for both CLM setups compared to reanalysis data and remote-sensing products. These findings provide essential insights for improving land surface models, highlighting the need to enhance the CLM5's ability to capture the spatiotemporal variability in ET and GPP simulations across PFTs.

SDUST2023GRA_MSS: the new global marine gravity anomaly model determined from mean sea surface model

The global marine gravity anomalies are primarily recovered from geoid gradients in the along-track directions obtained through satellite altimetry. However, the accuracy of the gravity model is significantly constrained by the sparse geoid gradients in the cross-track directions. To overcome the scarcity of cross-track geoid gradients, we employ a mean sea surface model to calculate geoid gradients in multiple directions, thereby recovering marine gravity anomalies. A global marine gravity anomaly model with 1-arcmin grid was determined from an optimized mean sea surface model. The accuracy of this gravity anomaly model was assessed by shipborne gravity and released marine gravity anomaly models. By the combination of geoid gradients in multiple directions, the meridian components and the prime vertical components of deflections of the vertical achieved consistent accuracy. The accuracy of the gravity anomaly model is 4.31 mGal, derived from shipborne gravity anomalies. Furthermore, the accuracy of this model has been validated across various regions, encompassing different latitudes, bathymetry, and distances from the coastline. The new gravity anomaly model slightly outperforms the released model.

Comparison of methods for determining the volume of earth masses based on the results of aerial photography from an unmanned aerial vehicle and geodetic measurements

The paper compares two modern methods of determining the volume of earth masses in the construction of buildings and structures. The first method is based on the application of satellite equipment in real-time kinematics mode. The second method is based on aerial photography using an unmanned aerial vehicle. The comparison of volumes is carried out at two different sites with embankment and excavation of soil. The effectiveness of aerial photography in this type of surveying work is shown in this article. Aerial photography allows a more accurate surface model to be built by using a dense point cloud and minimizing interpolation, and has higher effectiveness and safety in areas with complex relief.

Redistribution of dissolved inorganic nitrogen loading and transport in global rivers via surface water regulation.

Surface water (SW) regulation, including reservoir regulation and surface water use, alters the soil-river hydrological processes and then influences the dissolved inorganic nitrogen (DIN) transport from rivers to oceans. However, global response of the DIN transfer to such human activity has not been well investigated. Therefore, in this study, we have taken advantage of a recently-developed land surface model to show the effects of SW regulation on DIN loading and transport in global major rivers. Three simulations were conducted at the global scale over the period of 1991-2000. Results show that, acceleration on riverine N loading caused by the SW regulation was more noticeable. With the reservoir regulation only, removal rates of DIN in most rivers were reduced by 5%, up to 20%, while the increase of removal rate in some reaches occurred when the DIN was transferred back to soil through SW withdrawal and use. Influenced by the SW regulation, DIN flow has decreased obviously by 100-1000tNyr-1. Mean DIN flow was most significantly reduced in the Yellow River basin. Yangtze River, Mississippi River, and Nelson River suffered from the strong effect of such water regulation. The DIN exported to oceans was reduced as well. Mean reduction of 40-50% occurred for those exported to the Pacific Ocean, the Atlantic Ocean and the Indian Ocean, while slight effect for the estuaries into the Arctic Ocean. The impact on the DIN exported to the Atlantic Ocean was lasting decreasing from 50% to 40%, while the trend of the impact on that exported to the Pacific Ocean was opposite. There was no evident temporal change in the impacts on the DIN transferred to the Arctic Ocean and the Indian Ocean. The findings of this study could provide new insights for the researches on nitrogen change in rivers under the impacts of human water use.

A flux tower site attribute dataset intended for land surface modeling

Abstract. Land surface models (LSMs) require reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are widely regarded as the benchmark for LSMs. However, currently available flux tower datasets often require multiple aspects of processing to ensure data quality before application to LSMs. More importantly, these datasets frequently lack site-observed attribute data, such as fractional vegetation cover and leaf area index, which limits their utility as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, energy balance closure (EBC), and external disturbances such as irrigation and deforestation, leading to 90 high-quality sites. For these sites, we collected vegetation, soil, and topography data as well as wind speed, temperature, and humidity measurement heights from literature; regional networks; and Biological, Ancillary, Disturbance, and Metadata (BADM) files. We then compiled the final flux tower attribute dataset by filling in missing attributes with global data and classifying plant functional types (PFTs). This dataset is provided in NetCDF (Network Common Data Form) format with necessary descriptions and reference sources. Model simulations revealed substantial disparities in the output between the attribute data observed at the site and those commonly used by LSMs, underscoring the critical role of site-observed attribute data and increasing the emphasis on flux tower attribute data in the LSM community. The dataset addresses the lack of the site attribute to some extent, reduces uncertainty in LSM data source, and aids in diagnosing parameter and process deficiencies. The dataset is available at https://doi.org/10.5281/zenodo.12596218 (Shi et al., 2024).