Calibration Parameters Research Articles

Study on the impact of bowtie filter on photon-counting CT imaging

Objective.The aim of this study was to investigate the impact of the bowtie filter on the image quality of the photon-counting detector (PCD) based CT imaging.Approach.Numerical simulations were conducted to investigate the impact of bowtie filters on image uniformity using two water phantoms, with tube potentials ranging from 60 to 140 kVp with a step of 5 kVp. Subsequently, benchtop PCD-CT imaging experiments were performed to verify the observations from the numerical simulations. Additionally, various correction methods were validated through these experiments.Main results.It was found that the use of a bowtie filter significantly alters the uniformity of PCD-CT images, depending on the size of the object and the x-ray spectrum. Two notable effects were observed: the capping effect and the flattening effect. Furthermore, it was demonstrated that the conventional beam hardening correction method could effectively mitigate such non-uniformity in PCD-CT images, provided that dedicated calibration parameters were used.Significance.It was demonstrated that the incorporation of a bowtie filter results in varied image artifacts in PCD-CT imaging under different conditions. Certain image correction methods can effectively mitigate and reduce these artifacts, thereby enhancing the overall quality of PCD-CT images.

Autonomous Landing Strategy for Micro-UAV with Mirrored Field-of-View Expansion

Positioning and autonomous landing are key technologies for implementing autonomous flight missions across various fields in unmanned aerial vehicle (UAV) systems. This research proposes a visual positioning method based on mirrored field-of-view expansion, providing a visual-based autonomous landing strategy for quadrotor micro-UAVs (MAVs). The forward-facing camera of the MAV obtains a top view through a view transformation lens while retaining the original forward view. Subsequently, the MAV camera captures the ground landing markers in real-time, and the pose of the MAV camera relative to the landing marker is obtained through a virtual-real image conversion technique and the R-PnP pose estimation algorithm. Then, using a camera-IMU external parameter calibration method, the pose transformation relationship between the UAV camera and the MAV body IMU is determined, thereby obtaining the position of the landing marker’s center point relative to the MAV’s body coordinate system. Finally, the ground station sends guidance commands to the UAV based on the position information to execute the autonomous landing task. The indoor and outdoor landing experiments with the DJI Tello MAV demonstrate that the proposed forward-facing camera mirrored field-of-view expansion method and landing marker detection and guidance algorithm successfully enable autonomous landing with an average accuracy of 0.06 m. The results show that this strategy meets the high-precision landing requirements of MAVs.

Impact of stochastic vehicle generation on traffic microsimulation model output

One of the most important characteristics of microsimulation is the ability to model the temporal and spatial nature of traffic demand. Every traffic microsimulation has a vehicle generation model that determines how and when the driver–vehicle units are introduced in the simulation. While microsimulation use has become increasingly popular, it is unclear how the vehicle generation options affect the final result. The purpose of this paper is to examine the stochastic component of the vehicle generation model and how it may affect the simulation output. Three scenarios, including the passenger car equivalent (PCE) model of the Highway Capacity Manual (HCM-7), were used for assessing stochastic volumes and their impacts on performance measures. Results indicate a statistically significant impact of the variability of stochastic volumes on the estimation of performance measures at the breakdown flow phase and synchronized flow phase for interrupted and uninterrupted flow conditions, respectively. This finding highlights the importance of reporting vehicle generation as a calibration parameter, enabling others to replicate the experiments. When utilizing microsimulations for the assessment of roadway/system performance, it is crucial for users to have a thorough understanding of demand generation.

Assessment of the Choked Flow Model of RELAP5 for Application of Inverse Quantification Methods

In the framework of deterministic safety analysis, best estimate plus uncertainty methodologies can provide a more informative detailed analysis of transients than conservative approaches. However, one important step in the application of such methodologies is derivation of uncertainty of the physical models. Probability density functions of the physical model parameters are obtained by applying inverse uncertainty quantification (IUQ) methods to experimental data. The present work deals with evaluation of the experimental database and assessment of the simulation model, which are required steps prior to the application of IUQ methods. The U.S. Nuclear Regulatory Commission system code RELAP5 has been applied for simulation of three experimental databases on choked/critical flow: Sozzi-Sutherland, Super MobyDick, and Marviken Critical Flow Tests. First, the adequacy of the experimental data to the specific target domain is carried out, to then proceed to calibration of the input model parameters by combining the use of Gaussian Process metamodels and optimization schemes. The assessment of the simulation models is performed by evaluating independently accuracy, precision, and consistency through four statistical indicators, including a novel indicator to evaluate the consistency of the results. The final results indicate that the model is capable of reproducing the selected experimental database and overall average accuracy, precision, and consistency below the 10% threshold and can be used for application of IUQ methods.

Prediction of Scour Depth for Diverse Pier Shapes Utilizing Two-Dimensional Hydraulic Engineering Center’s River Analysis System Sediment Model

Examining scouring around bridge piers is crucial for ensuring water-related infrastructure’s long-term safety and stability. Accurate forecasting models are essential for addressing scour, especially in complex water systems where traditional methods fall short. This study investigates the application of the HEC-RAS 2D sedimentation model, which has recently become available for detailed sediment analysis, to evaluate its effectiveness in predicting scoring around various pier shapes and under different water conditions. This study offers a comprehensive assessment of the model’s predictive capabilities by focusing on variables such as water velocity, shear stress, and riverbed changes. Particular attention was paid to the influence of factors like floating debris and different pier geometries on scour predictions. The results demonstrate that while the HEC-RAS 2D model generally provides accurate predictions for simpler pier shapes—achieving up to 85% precision—it shows varied performance for more complex designs and debris-influenced scenarios. Specifically, the model overpredicted scouring depths by approximately 20% for diamond-shaped piers and underpredicted by 15% for square piers in debris conditions. Elliptical piers, in contrast, experienced significantly less erosion, with scour depths up to 30% shallower compared to other shapes. This study highlights the novel application of the HEC-RAS 2D model in this context and underscores its strengths and limitations. Identified issues include difficulties in modeling water flow and debris-induced bottlenecks. This research points to the improved calibration of sediment movement parameters and the development of advanced computational techniques to enhance scour prediction accuracy in complex environments. This work contributes valuable insights for future research and practical applications in civil engineering, especially where traditional scour mitigation methods, such as apron coverings, are not feasible.

Lateral Shear Stress Calculation Model Based on Flow Velocity Field Distribution from Experimental Debris Flows

AbstractDebris flows continuously erode the channel downward and sideways during formation and development, which changes channel topography, enlarges debris flow extent, and increases the potential for downstream damage. Previous studies have focused on debris flow channel bed erosion, with relatively little research on lateral erosion, which greatly limits understanding of flow generation mechanisms and compromises calibration of engineering parameters for prevention and control. Sidewall resistance and sidewall shear stress are key to the study of lateral erosion, and the distribution of the flow field directly reflects sidewall resistance characteristics. Therefore, this study has focused on three aspects: flow field distribution, sidewall resistance, and sidewall shear stress. First, the flow velocity distribution and sidewall resistance were characterized using laboratory debris flow experiments, then a debris flow velocity distribution model was established, and a method for calculating sidewall resistance was developed based on models of flow velocity distribution and rheology. A calculation method for the sidewall shear stress of debris flow was then developed using the quantitative relationship between sidewall shear stress and sidewall resistance. Finally, the experiment was validated and supplemented through numerical simulations, enhancing the reliability and scientific validity of the research results. The study provides a theoretical basis for the calculation of the lateral erosion rate of debris flows.

Test-and-FE-based method for obtaining complete stress-strain curves of structural steels including fracture

Ductile fracture is a common limit state of frameworks of steel structures and can be predicted by incorporating models for fracture initiation and propagation in finite element (FE) analyses. However, accurate prediction of fracture relies on unique fracture parameters for a given material, requiring precise predictions of fracture strain and stress states obtained through coupon tests and accompanying FE data analysis. In recent decades, various methods have been proposed to predict ductile fracture initiation, including various design geometries of coupons, test setups and FE analyses, which may lead to inconsistent fracture strains and stress states. Hence, there is a need for proposing a standard procedure for the design, test and accompanying FE-based calibration of fracture parameters, as pursued in this paper. Given that ductile fracture initiates under significant plastic strain, a constitutive model for the full strain range is required, as also proposed in this paper. Moreover, to reduce the experimental and data analytical efforts, an empirical method is proposed that uses only a small number of coupon tests to calibrate the fracture parameters. The method encompasses three levels wherein one, two, or three coupon tests are required. All in all, the paper presents a methodology for determining the full-range true stress-strain curve and the initiation of fracture, including a new post-necking constitutive model and methods for determining the free parameters of the post-necking and fracture initiation models. While applied to steel in this paper, the proposed methodology is potentially also applicable to other metals.

Design of Exterior Orientation Parameters Variation Real-Time Monitoring System in Remote Sensing Cameras

The positional accuracy of satellite imagery is essential for remote sensing cameras. However, vibrations and temperature changes during launch and operation can alter the exterior orientation parameters of remote sensing cameras, significantly reducing image positional accuracy. To address this issue, this article proposes an exterior orientation parameter variation real-time monitoring system (EOPV-RTMS). This system employs lasers to establish a full-link active optical monitoring path, which is free from time and space constraints. By simultaneously receiving star and laser signals with the star tracker, the system monitors changes in the exterior orientation parameters of the remote sensing camera in real time. Based on the in-orbit calibration geometric model, a new theoretical model and process for the calibration of exterior orientation parameters are proposed, and the accuracy and effectiveness of the system design are verified by ground experiments. The results indicate that, under the condition of a centroid extraction error of 0.1 pixel for the star tracker, the EOPV-RTMS achieves a measurement accuracy of up to 0.6″(3σ) for a single image. Displacement variation experiments validate that the measurement error of the system deviates by at most 0.05″ from the theoretical calculation results. The proposed EOPV-RTMS provides a new design solution for improving in-orbit calibration technology and image positional accuracy.

Total Least Squares In-Field Identification for MEMS-Based Inertial Measurement Units

Inertial Measurement Units are widely used in various applications and, hardware-wise, they primarily consist of a tri-axial accelerometer and a tri-axial gyroscope. For low-end commercial employments, the low cost of the device is crucial: this makes MEMS-based sensors a popular choice in this context. However, MEMS-based transducers are prone to significant, non-uniform and environmental-condition-dependent systematic errors, that require frequent re-calibration to be eliminated. To this end, identification methods that can be performed in-field by non-expert users, without the need for high-precision or costly equipment, are of particular interest. In this paper, we propose an in-field identification procedure based on the Total Least Squares method for both tri-axial accelerometers and gyroscopes. The proposed identification model is linear and requires no prior knowledge of the parameters to be identified. It enables accelerometer calibration without the need for specific reference surface orientation relative to Earth’s gravity and allows gyroscope calibration to be performed independently of accelerometer data, without requiring the sensor’s sensitive axes to be aligned with the rotation axes during calibration. Experiments conducted on NXP sensors FXOS8700CQ and FXAS21002 demonstrated that using parameters identified by our method reduced cross-validation standard deviations by about two orders of magnitude compared to those obtained using manufacturer-provided parameters. This result indicates that our method enables the effective calibration of IMU sensor parameters, relying only on simple 3D-printed equipment and significantly improving IMU performance at minimal cost.

Calibration of discrete meta-parameters of bamboo flour based on magnitude analysis and BP neural network.

In the research and development of technology and equipment for bamboo products deep processing, such as filling, drying, and medicinal use of bamboo flour (BF), the poor compaction and fluidity of BF materials entails the need for accurate discrete element model (DEM) and BF parameters to provide a reference for the simulation of BF processing operationsand the development of related equipment. The average particle size of the 5 types of BFs ranges from 0.136 mm to 0.293 mm, and the small particle size of BF particles causes to the number of BF particles in bamboo processing equipment to reach tens of millions or even billions. When conventional methods are used for simulation, ordinary computers cannot provide the required computing power. To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. Using particle scaling theory and dimensional analysis methods, the average particle size of the BF was increased to 1 mm, and the main discrete element contact parameters of the five types of BF to be tested were used as input layers. The injection method and sidewall collapse method were used to obtain the angle of repose (AR) as the output layer. Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. The relative error between the simulated AR obtained through calibration parameters and the physical experimental values is less than 2.3%. Through BF parameter validity verification, the simulated maximum compression displacement and compression ratio after stabilization were 34.81 mm and 0.477, which were close to the actual experimental results of 34.77 mm and 0.461, respectively, verifying the accuracy of the neural network prediction model. The research results provide a reference for the simulation of BF processing operations and the development of related equipment.

A New LC-MS/MS-Based Method for the Simultaneous Detection of α-Tocopherol and Its Long-Chain Metabolites in Plasma Samples Using Stable Isotope Dilution Analysis

Background: Our study presented a novel LC-MS/MS method for the simultaneous quantification of α-tocopherol (α-TOH) and its phase II metabolites, α-13′-COOH and α-13′-OH, in human serum using deuterium-labeled internal standards (d6-α-TOH, d6-α-13′-COOH, d6-α-13′-OH). Methods: The method addresses the analytical challenge posed by the significantly different concentration ranges of α-TOH (µmol/L) and its metabolites (nmol/L). Previous methods quantified these analytes separately, which caused an increase in workflow complexity. Results: Key features include the synthesis of stable isotope-labeled standards and the use of a pentafluorophenyl-based core-shell chromatography column for baseline separation of both α-TOH and its metabolites. Additionally, solid phase extraction (SPE) with a HybridSPE® material provides a streamlined sample preparation, enhancing analyte recovery and improving sensitivity. By utilizing deuterium-labeled standards, the method compensates for matrix effects and ion suppression. This new approach achieves precise and accurate measurements with limits of detection (LOD) and quantification (LOQ), similar to previous studies. Calibration, accuracy, and precision parameters align well with the existing literature. Conclusions: Our method offers significant advantages in the simultaneous analysis of tocopherol and its metabolites despite concentration differences spanning up to three orders of magnitude. In contrast to earlier studies, which required separate sample preparations and analytical techniques for tocopherol and its metabolites, our approach streamlines this process. The use of a solid-phase extraction procedure allows for parallel sample preparation. This not only enhances efficiency but also significantly accelerates pre-analytical workflows, making the method highly suitable for large-scale studies.

Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization

In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.

Constitutive description of snow at finite strains by the modified cam‐clay model and an implicit gradient damage formulation

AbstractSnow, characterized as a unique granular and low‐density material, exhibits intricate behavior influenced by the proximity to its melting point and its three‐phase composition. This composition entails a structured ice skeleton surrounded by voids filled with air and spread with liquid water. Mechanically, snow experiences dynamic transformations, including bonding/degradation between its grains, significant inelastic deformations, and a distinct rate sensitivity. Given snow's varied structures and mechanical strengths in natural settings, a comprehensive constitutive model is necessary. Our study introduces a pioneering formulation grounded on the modified Cam‐Clay model, extended to finite strains. This formulation is further enriched by an implicit gradient damage modeling, creating a synergistic blend that offers a detailed representation of snow behavior. The versatility of the framework is emphasized through the careful calibration of damage parameters. Such calibration allows the model to adeptly capture the effects of diverse strain rates, particularly at high magnitudes, highlighting its adaptability in replicating snow's unique mechanical responses across various conditions. Upon calibration against established experimental benchmarks, the model demonstrates a suitable alignment with observed behavior, underscoring its potential as a comprehensive tool for understanding and modeling snow behavior with precision and depth.

Calibration of corn kernel simulation parameters during harvest and evaluation of its adaptability

Calibration of corn kernel simulation parameters during harvest and evaluation of its adaptability

Automated Pipeline for Multi-LiDAR Extrinsic Calibration: Experimental Evaluation and Performance Analysis

Abstract. In recent years, 3D LiDAR (Light Detection and Ranging) has become a crucial sensor in various applications such as autonomous vehicles, robotics, object detection, precision forestry, and agriculture. However, specific LiDAR sensors, such as Velodyne VLP16, exhibit some drawbacks, such as a limited field of view and sparse data density, making them inadequate for certain specific applications. Hence, this research proposes a method for calibrating two VLP16 LiDAR sensors to improve coverage and reduce blind spots. The pipeline for performing the calibration begins with mounting LiDARs correctly in a rod at a specific orientation and distance, followed by the selection of multiple sites for data collection, then performing a registration algorithm for estimating calibration parameters, and then an accuracy assessment of the calibrated point clouds. The registration algorithm used here is a modified version of ICP (Iterative Closest Point), which overcomes the need for initialization and eliminates manual intervention in installing targets or retro-reflectors. Finally, we evaluated the accuracy of the fused point cloud collected in an open environment using two calibrated Velodyne VLP16 sensors. For accuracy assessment, we used the PCA eigenvalue and RMSE value to observe how tightly point clouds are fused. As a calibration result, we got the orientation and translation parameters, which are used to achieve the common coordinate system, and accomplished calibration accuracy up to single-digit precision from all the experimental sites. Now the system of two calibrated VLP16 sensors will provide higher coverage and increased data density and might be useful for forest applications.

Design of monitoring system for material vibration screening equipment

In order to effectively reduce the failure rate of vibrating screens, a vibration monitoring system design was proposed based on the structure and working principle. The system should be able to monitor the operating state of the vibrating screen in real time, including vibration amplitude, frequency, axial displacement, etc., and be able to detect abnormal conditions in time and give warning prompts. The key hardware of the monitoring system was designed, including acceleration sensor, power module, signal filtering circuit, etc. The sensitivity of the acceleration sensor was measured using comparative method and accurately completed parameter calibration. The eccentric block was used to simulate different deviation of excitation force for system vibration signal test and calibration. The results show that under normal operating conditions, the amplitude of each frequency of the vibrating screen is less than 0.04 g. Under the condition of excitation force deviation, the amplitude of 16 Hz is obviously lower than that of other frequencies, which means that the vibrating screen is currently experiencing lateral oscillation.

Damage Evolution Mechanism of Railway Wagon Bogie Adapter 1035 Steel and Damage Parameter Calibration Based on Gursone-Tvergaarde-Needleman Model.

As a critical component of a train, the railway wagon bogie adapter has higher quality requirements. During the forging process, external loads can induce voids, cracks, and other defects in the forging, thereby reducing its service life. Hence, studying the damage behavior of the forging material, specifically AISI 1035 steel, becomes crucial. This study involved obtaining stress-strain curves for AISI 1035 steel through uniaxial tensile tests at temperatures of 900 °C, 1000 °C, and 1100 °C, with strain rates of 0.1 s-1, 1 s-1, and 10 s-1. Subsequently, SEM was used to observe samples at various deformation stages. The damage parameters, q1, q2 and q3 in the GTN model "a computational model used to analyze and simulate material damage which can effectively capture the damage behavior of materials under different loading conditions" were then calibrated using the Ramberg-Osgood model and stress-strain curve fitting. Image Pro Plus software v11.1 quantified the sample porosity as f0, fn, fc and fF. A finite element model was established to simulate the tensile behavior of the AISI 1035 steel samples. By comparing the damage parameters of f0, fn, fc and fF obtained by the finite element method and experimental method, the validity of the damage parameters obtained by the finite element inverse method could be verified.

Industrial robot calibration optimization method based on R-optimal criterion and improved IOOPS algorithm

Abstract In this paper, a novel kinematic parameter calibration method based on the R-optimal criterion and the improved iterative one-by-one pose search (IOOPS) algorithm is proposed to improve the end-effector positioning accuracy of industrial robots. First, a novel industrial robot error calibration model based on the product of exponential model and generalized error model is established, and the ideal pose transformation matrix from the robot base coordinate system to the laser tracker measurement coordinate system is obtained using the least-squares method, thereby obtaining the initial parameter vector of the robot calibration system. Second, various joint angle values at different positions within the robot’s workspace and the corresponding end-effector coordinate data, namely calibration sampling point data, are collected. Subsequently, the improved IOOPS algorithm combined with the observability index O R proposed by the R-optimal criterion is used to select the optimized calibration sampling points. Finally, the optimized sampling point data are combined with the Levenberg–Marquardt algorithm to optimize the parameters and obtain the optimal kinematic parameters. The experimental results show that the average end-effector error of the robot decreases from 0.5822 to 0.2492 mm after calibration using this method, demonstrating the effectiveness of the optimized sampling points in the calibration process achieved through the improved algorithm and the new observability index.

Development of a Virtual Chemistry Reaction Mechanism for H2/CH4 Turbulent Combustion Modelling

Abstract The identification of the combustion model is always guided by the compromise between accuracy and computational cost. While species transport models offer accuracy, their computational cost requirement can become prohibitive, especially when de-aling with higher-order hydrocarbon fuels. To mitigate this, the virtual mechanism definition aims to optimize the predictivity minimizing the amount of information to be transported. Thereby the virtual mechanism leverages fictitious species and a few step reactions whose parameters calibration is performed with a genetic algorithm. This work outlines the procedure for the derivation of a virtual reaction mechanism for the study of lean H2/CH4 fuel mixtures with 60% of H2 content (by vol.). These conditions require an adequate characterization of the virtual species differential diffusion oriented to reconstruct the flame sensitivity toward the aerodynamic stretch. After the mechanism derivation, its predictivity has been validated on a swirl-stabilized perfectly premixed turbulent test case. The artificially thickened flame model has been adopted to allow the flame front discretization on an large eddy simulation (LES) grid and to model the turbulence chemistry interaction. The numerical results show a very good agreement with the experimental optical measurements confirming the effectiveness of this approach for predicting the H2/CH4 blend.

Enhancing groundwater management with GRACE-based groundwater estimates from GLDAS-2.2: a case study of the Almonte-Marismas aquifer, Spain

AbstractThe Almonte-Marismas aquifer, southwestern Spain, is a critical ecohydrogeological system that features extensive groundwater monitoring. This study investigates the utility of gravity recovery and climate experiment (GRACE) satellite data, specifically obtained from the global land data assimilation system (GLDAS) version 2.2, for assessing groundwater storage variations in the Almonte-Marismas aquifer. The presented research emphasizes the practical application of readily available GLDAS products that do not require data preprocessing. The study validates the GLDAS-2.2-based ready-to-use groundwater storage (GWS) time series by correlating it with precipitation and piezometric information, highlighting its effectiveness in medium-scale aquifers. The results reveal a strong agreement between GLDAS-2.2-derived GWS anomalies and in-situ measurements, confirming GLDAS-2.2’s potential for assessing aquifer depletion. The study discusses the consistency of seasonal variations in groundwater levels and GLDAS-2.2 data, emphasizing their close alignment with precipitation and pumping activities. Importantly, the study introduces GLDAS-2.2-derived volumetric groundwater storage (VGWS) as a valuable calibration parameter for numerical groundwater flow models, enhancing their accuracy over time. Moreover, the analysis reveals disparities in annual recharge values between GLDAS-2.2-derived data and the soil-water mass balance. These variations suggest the importance of additional inputs to precipitation, possibly related to subsurface or lateral connections. Overall, this study contributes to the ongoing discourse on the practical applications of GLDAS-2.2-derived GWS data in groundwater management, offering insights into its effectiveness in diverse hydrogeological settings, particularly in areas that lack monitoring infrastructure.