Values Of Model Parameters Research Articles

Holographic Einstein ring of a charged Rastall AdS black hole with bulk electromagnetic field* *Supported by the National Natural Science Foundation of China (11675140, 11705005, 12375043), the Innovation and Development Joint Foundation of Chongqing Natural Science Foundation (CSTB2022NSCQ-LZX0021), and the Basic Research Project of Science and Technology Committee of Chongqing (CSTB2023NSCQ-MSX0324)

We study the Einstein images of a charged Rastall AdS black hole (BH) within the fabric of AdS/CFT correspondence. Considering the holographic setup, we analyze the amplitude of the total response function for various values of model parameters. With an increase in parameter λ and temperature T, the amplitude of the response function decreases, while it increases with an increase in electric charge e and chemical potential μ. The influence of frequency ω also plays an important role in the bulk field, as it is found that decreasing ω leads to an increase in the periods of the waves, which means that the amplitude of the response function also depends on the wave source. The relation between T and the inverse of the horizon for various values of parameter λ is interpreted under fixed values of other involved parameters. These, in turn, affect the behavior of the response function and the Einstein ring, which may be used to differentiate the present study from previous ones. We construct the holographic images of the BH in bulk via a special optical system. The results show that the Einstein ring always appears with concentric stripes at the position of the north pole, and this ring transforms into a luminosity-deformed ring or bright light spot when the distant observer lies away from the north pole. Finally, we discuss the influence of the associated parameters on the Einstein ring radius, which is consistent with wave optics.

Can teleparallel f(T) models play a bridge between early and late time Universe?

ABSTRACT The ability of Big Bang Nucleosynthesis theory to accurately predict the primordial abundances of helium and deuterium, as well as the baryon content of the Universe, is considered one of the most significant achievements in modern physics. In the present study, we consider two highly motivated hybrid $f(T)$ models and constrain them using the observations from the Big Bang Nucleosynthesis era. In addition, using late-time observations of Cosmic Chronometers and Gamma-Ray Bursts, the ranges of the model parameters are confined which are in good agreement with early time bounds. Subsequently, the common ranges obtained from the analysis for early and late time are summarized. Further, we verify the intermediating epochs by investigating the profiles of cosmographic parameters using the model parameter values from the common range. From this study, we find the considered teleparallel models are viable candidates to explain the primordial-intermediating-present epochs.

Real-time dual-parameter full-waveform inversion of GPR data based on robust deep learning

SUMMARY Ground penetrating radar (GPR) is becoming an increasingly important tool for understanding the shallow electrical structures of the Earth and planets due to its adaptability to harsh detection environments, efficient data acquisition and accurate detection results. GPR full-waveform can simultaneously constrain the permittivity and resistivity of the medium, providing more comprehensive geophysical information and reducing the non-uniqueness of inversion. However, given the highly non-linear inverse problem and the massive data resulted from high temporal and spatial samplings, traditional full-waveform inversion algorithms are prohibitively costly. Inspired by Google's vision semantic segmentation system, we develop a robust deep learning-guided network that integrates geology and geophysics knowledge to support the real-time translation of zero-offset GPR data into dual-parameter electrical structures. We test our proposed network using synthetic data, which demonstrates that the algorithm can provide an accurate dual-parameter electrical model from a GPR sounding in milliseconds on a common laptop PC, exhibiting high robustness and adaptability to noise interference and extreme values of model parameters. We also apply our network to field data gathered for pollutant investigation in the United States. The resulting dual-parameter structure provides a more comprehensive and realistic depiction of subsurface electrical properties and reveals the migration and ageing of pollutants. Our algorithm's real-time and accurate advantages are expected to further unleash the potential of GPR technology and enable it to play a more significant role in earth and planetary exploration.

Efficient Cardiovascular Parameters Estimation for Fluid-Structure Simulations Using Gappy Proper Orthogonal Decomposition.

As full-scale detailed hemodynamic simulations of the entire vasculature are not feasible, numerical analysis should be focused on specific regions of the cardiovascular system, which requires the identification of lumped parameters to represent the patient behavior outside the simulated computational domain. We present a novel technique for estimating cardiovascular model parameters using gappy Proper Orthogonal Decomposition (g-POD). A POD basis is constructed with FSI simulations for different values of the lumped model parameters, and a linear operator is applied to retain information that can be compared to the available patient measurements. Then, the POD coefficients of the reconstructed solution are computed either by projecting patient measurements or by solving a minimization problem with constraints. The POD reconstruction is then used to estimate the model parameters. In the first test case, the parameter values of a 3-element Windkessel model are approximated using artificial patient measurements, obtaining a relative error of less than 4.2%. In the second case, 4 sets of 3-element Windkessel are approximated in a patient's aorta geometry, resulting in an error of less than 8% for the flow and less than 5% for the pressure. The method shows accurate results even with noisy patient data. It automatically calculates the delay between measurements and simulations and has flexibility in the types of patient measurements that can handle (at specific points, spatial or time averaged). The method is easy to implement and can be used in simulations performed in general-purpose FSI software.

Predefined-Time Zeroing Neural Networks With Independent Prior Parameter for Solving Time-Varying Plural Lyapunov Tensor Equation.

As an extension of the Lyapunov equation, the time-varying plural Lyapunov tensor equation (TV-PLTE) can carry multidimensional data, which can be solved by zeroing neural network (ZNN) models effectively. However, existing ZNN models only focus on time-varying equations in field of real number. Besides, the upper bound of the settling time depends on the value of ZNN model parameters, which is a conservative estimation for existing ZNN models. Therefore, this article proposes a novel design formula for converting the upper bound of the settling time into an independent and directly modifiable prior parameter. On this basis, we design two new ZNN models called strong predefined-time convergence ZNN (SPTC-ZNN) and fast predefined (FP)-time convergence ZNN (FPTC-ZNN) models. The SPTC-ZNN model has a nonconservative upper bound of the settling time, and the FPTC-ZNN model has excellent convergence performance. The upper bound of the settling time and robustness of the SPTC-ZNN and FPTC-ZNN models are verified by theoretical analyses. Then, the effect of noise on the upper bound of settling time is discussed. The simulation results show that the SPTC-ZNN and FPTC-ZNN models have better comprehensive performance than existing ZNN models.

A mechanohydraulic model supports a role for plasmodesmata in cotton fiber elongation.

Plant cell growth depends on turgor pressure, the cell hydrodynamic pressure, which drives expansion of the extracellular matrix (the cell wall). Turgor pressure regulation depends on several physical, chemical, and biological factors, including vacuolar invertases, which modulate osmotic pressure of the cell, aquaporins, which determine the permeability of the plasma membrane to water, cell wall remodeling factors, which determine cell wall extensibility (inverse of effective viscosity), and plasmodesmata, which are membrane-lined channels that allow free movement of water and solutes between cytoplasms of neighboring cells, like gap junctions in animals. Plasmodesmata permeability varies during plant development and experimental studies have correlated changes in the permeability of plasmodesmal channels to turgor pressure variations. Here, we study the role of plasmodesmal permeability in cotton fiber growth, a type of cell that increases in length by at least three orders of magnitude in a few weeks. We incorporated plasmodesma-dependent movement of water and solutes into a classical model of plant cell expansion. We performed a sensitivity analysis to changes in values of model parameters and found that plasmodesmal permeability is among the most important factors for building up turgor pressure and expanding cotton fibers. Moreover, we found that nonmonotonic behaviors of turgor pressure that have been reported previously in cotton fibers cannot be recovered without accounting for dynamic changes of the parameters used in the model. Altogether, our results suggest an important role for plasmodesmal permeability in the regulation of turgor pressure.

Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Estimating the Susceptible-Exposed-Infected-Recovered (SEIR) Model Parameter Values

Background: The most commonly used mathematical model for analyzing disease spread is the Susceptible-Exposed-Infected-Recovered (SEIR) model. Moreover, the dynamics of the SEIR model depend on several factors, such as the parameter values. Objective: This study aimed to compare two optimization methods, namely genetic algorithm (GA) and particle swarm optimization (PSO), in estimating the SEIR model parameter values, such as the infection, transition, recovery, and death rates. Methods: GA and PSO algorithms were compared to estimate parameter values of the SEIR model. The fitness value was calculated from the error between the actual data of cumulative positive COVID-19 cases and the numerical data of cases from the solution of the SEIR COVID-19 model. Furthermore, the numerical solution of the COVID-19 model was calculated using the fourth-order Runge-Kutta algorithm (RK-4), while the actual data were obtained from the cumulative dataset of positive COVID-19 cases in the province of Jakarta, Indonesia. Two datasets were then used to compare the success of each algorithm, namely, Dataset 1, representing the initial interval for the spread of COVID-19, and Dataset 2, representing an interval where there was a high increase in COVID-19 cases. Results: Four parameters were estimated, namely the infection rate, transition rate, recovery rate, and death rate, due to disease. In Dataset 1, the smallest error of GA method, namely 8.9%, occurred when the value of , while the numerical error of PSO was 7.5%. In Dataset 2, the smallest error of GA method, namely 31.21%, occurred when , while the numerical error of PSO was 3.46%. Conclusion: Based on the parameter estimation results for Datasets 1 and 2, PSO had better fitting results than GA. This showed PSO was more robust to the provided datasets and could better adapt to the trends of the COVID-19 epidemic. Keywords: Genetic algorithm, Particle swarm optimization, SEIR model, COVID-19, Parameter estimation.

Определение параметров реологической модели по данным лабораторных исследований

Different types of models are used to study the stress-strain behavior of asphalt concrete mixture during compaction. For modeling it is necessary to know the values of model parameters in the whole range of changes in the characteristics of the compacted material. The aim of the paper is to develop a methodology for determining model parameters based on the results of laboratory creep-recovery testing of asphalt-concrete mixture. In the paper, a model reflecting elastic, visco-elastic and visco-fluid properties of the compacted material is adopted. A differential equation representing the law of behavior of the asphalt-concrete mixture layer under load is obtained. The stages of creep-recovery testing are analytically described, namely: rapid loading with a constant load and deformation under this load; load removal and recovery. Initial conditions for the mentioned stages are determined. Taking into account the initial conditions, analytical expressions of the laws of deformation of the model under the action of a constant load and in the process of recovery are obtained. The methodology of laboratory experiments on creep-recovery is described, according to the results of which the required values of the model parameters with minimum absolute error are obtained. The developed methodology allows to determine the parameters of the model describing the stress-strain behavior of asphalt-concrete mixture of different density and temperature. Consequently, it is possible to simulate the behavior of the asphalt concrete mixture layer at the preliminary, main and final stages of compaction in order to determine the effective operating modes of compaction equipment.

Projected Public Health Impact of a Universal Rotavirus Vaccination Program in France.

In June 2022, French health authorities issued a universal recommendation for routine administration and reimbursement of rotavirus vaccines in infants. Given this recent recommendation by French health authorities, we sought to understand the public health impact of a universal rotavirus vaccination strategy compared with no vaccination. A deterministic, age-structured, nonlinear dynamic transmission model, accounting for herd immunity, was developed. We considered 3 vaccination coverage scenarios: high (95%), medium (75%) and low (55%). Model parameter values were based on published modeling and epidemiological literature. Model outcomes included rotavirus gastroenteritis (RVGE) cases and healthcare resource utilization due to RVGE (hospitalizations, general practitioner or emergency department visits), as well as the number needed to vaccinate to prevent 1 RVGE case (mild or severe) and 1 RVGE-related hospitalization. Model calibration and analyses were conducted using Mathematica 11.3. Over 5 years following implementation, RVGE cases for children under 5 years are estimated to be reduced by 84% under a high vaccination coverage scenario, by 72% under a medium vaccination coverage scenario and by 47% under a low vaccination coverage scenario. Across all scenarios, the number needed to vaccinate to avert 1 RVGE case and hospitalization varied between 1.86-2.04 and 24.15-27.44, respectively. Rotavirus vaccination with high vaccination coverage in France is expected to substantially reduce the number of RVGE cases and associated healthcare resource utilization.

Gauge fixing for sequence-function relationships.

Quantitative models of sequence-function relationships are ubiquitous in computational biology, e.g., for modeling the DNA binding of transcription factors or the fitness landscapes of proteins. Interpreting these models, however, is complicated by the fact that the values of model parameters can often be changed without affecting model predictions. Before the values of model parameters can be meaningfully interpreted, one must remove these degrees of freedom (called "gauge freedoms" in physics) by imposing additional constraints (a process called "fixing the gauge"). However, strategies for fixing the gauge of sequence-function relationships have received little attention. Here we derive an analytically tractable family of gauges for a large class of sequence-function relationships. These gauges are derived in the context of models with all-order interactions, but an important subset of these gauges can be applied to diverse types of models, including additive models, pairwise-interaction models, and models with higher-order interactions. Many commonly used gauges are special cases of gauges within this family. We demonstrate the utility of this family of gauges by showing how different choices of gauge can be used both to explore complex activity landscapes and to reveal simplified models that are approximately correct within localized regions of sequence space. The results provide practical gauge-fixing strategies and demonstrate the utility of gauge-fixing for model exploration and interpretation.

Experimental Research and Application of Small-Strain Soil Hardening Model Parameters in a Major Project in Hangzhou

According to current research, the small-strain soil hardening model (HSS model) is being used more and more widely because it can consider the small-strain characteristics of soil, making the corresponding numerical analysis results more accurate. At present, research into the test value of HSS model parameters of soil mass is mainly concentrated in Shanghai. Given the limits of the test conditions, there has been notably insufficient research into the value of small-strain parameters. Considering the regional differences of soil parameters, this paper, based on an actual project, conducts the HSS model parameter test value study on the main soil layers in the west of Hangzhou, focusing on the value study of small-strain parameters. The corresponding results are used for numerical analysis. The research shows that, compared with the Mohr–Coulomb model, the numerical analysis results using the HSS model are obviously closer to the measured data, and the prediction of the lateral deformation and its change law of the diaphragm wall is obviously more accurate. At the same time, when other HSS model parameters are unchanged, the numerical results using in situ small-strain parameters are closer to the measured results than using indoor small-strain parameters, and the corresponding deformation prediction accuracy is higher. The research results provide valuable references for determining HSS model parameters and engineering applications in the western area of Hangzhou.

Effects of Time-Dependent Adenosine Triphosphate Consumption Caused by Neuron Firing on Adenosine Triphosphate Concentrations in Synaptic Boutons Containing and Lacking a Stationary Mitochondrion.

The precise mechanism behind the supply of adenosine triphosphate (ATP) to approximately half of the presynaptic release sites in axons that lack a stationary mitochondrion is not fully understood. This paper presents a mathematical model designed to simulate the transient ATP concentration in presynaptic en passant boutons. The model is utilized to investigate how the ATP concentration responds to increased ATP demand during neuronal firing in boutons with a stationary mitochondrion and those without one. The analysis suggests that neuron firing may cause oscillations in the ATP concentrations, with peak-to-peak amplitudes ranging from 0.06% to 5% of their average values. However, this does not deplete boutons lacking a mitochondrion of ATP; for physiologically relevant values of model parameters, their concentration remains approximately 3.75 times higher than the minimum concentration required for synaptic activity. The variance in average ATP concentrations between boutons containing a stationary mitochondrion and those lacking one ranges from 0.3% to 0.8%, contingent on the distance between the boutons. The model indicates that diffusion-driven ATP transport is rapid enough to adequately supply ATP molecules to boutons lacking a stationary mitochondrion.

Thermodynamic, structural, surface and transport properties of Au-Cu melt

Thermodynamic, structural, surface and transport properties of Au-Cu liquid alloy were calculated on the basis of different theoretical modeling equations. The thermodynamic properties, such as excess Gibbs free energy of mixing, enthapy of mixing and activity were estimated and compared with the available experimental and literature data on the basis of simple theory of mixing (STM). The best fit values of model parameters were estimated using the experimental values of excess Gibbs free energy of mixing and enthalpy of mixing of the system at 1550 K. Using the same model parameters and frame, the concentration fluctuation in long wave-length limit and Warren-Cowley short range order parameter were calculated and analysed to understand the local arrangement of atoms in the liquid mixture. The surface tension and extent of surface segregation of atoms in the initial melt were computed using Butler’s model. In transport properties, the ratio of mutual to intrinsic diffusion coefficients was calculated using STM and viscosity was calculated using Kaptay equation. The system showed complete ordering tendency at its melting temperature.

Impact of energy–momentum conservation violation on the configuration of compact stars and their GW echoes

This work investigates the impacts of energy–momentum conservation violation on the configuration of strange stars constraint with gravitational wave (GW) event GW190814 as well as eight recent observations of compact objects. The GW echoes from these interesting classes of compact objects are also calculated. To describe the matter of strange stars, we have used two different equations of state (EoSs): first an ad-hoc exotic EoS, the stiffer MIT Bag model and next realistic CFL phase of quark matter EoS. We choose Rastall gravity as a simple model with energy–momentum conservation violation with a set of model parameter values. Our results show that this gravity theory permits stable solutions of strange stars and the resulting structures can foster GW echoes. We illustrate the implication of the gravity theory and found that the negative values of the Rastall parameter result in more compact stellar configurations and lower GW echo frequency. With an increase in the Rastall parameter, both the compactness of the stellar configurations and echo time decrease. It is worth mentioning here that with the chosen set of some probable strange star candidates from observational data and also in light of GW 190814, we have evaluated the radii of stellar models. Also, the GW echo frequencies associated with strange stars are found to be in the range of ≈ 9–27 kHz for both cases. From this work, it is also inferred that the assumption regarding the equivalence of Rastall’s theory to Einstein’s theory is refuted as we have noticed many deviations in the physical properties of the considered compact stars.

Mathematical analysis of a real time dynamical system: Interaction between Agricultural Variables

In this study, we are interested in the investigation and analysis of a mathematical model for the growth of normal agricultural biomass. The model is divided into four compartments as normal Agricultural assets, auxiliary Agricultural assets, Industrial assets, and Ecospherical assets. We applied the Laplace Decomposition Method to obtain approximate solutions in the form of infinite series and numerical justification was performed on the model parameter values with the aid of Maple 18 software. We studied the effect of varying the growth rate of normal agriculture on the yield of agricultural assets and mathematical analyses of the model equation to its uniqueness and stability of equilibrium are carried out. From the results, it was observed that the yield of normal Agricultural assets decreases due to the decrease in the growth rate coefficient of normal agriculture from 10 % to 90 % and the yield of normal Agricultural assets increases due to the increase in the growth rate coefficient of normal agriculture from 110 % to 190 %. We observed a loss in biodiversity when the growth rate coefficients of normal agriculture decreased from 90 %, 20 %, and 10 %, and a biodiversity gain was observed due to the increase of the growth rate coefficients of normal Agriculture from 110 % to 190 %.

Dynamical system analysis of LRS-BI Universe with [formula omitted] gravity theory

Considering the Universe as a dynamic system, the understanding of its evolution is an interesting aspect of study in cosmology. Here, we investigate the anisotropic locally rotationally symmetric (LRS) Bianchi type-I (LRS-BI) spacetime under the f(Q) gravity of symmetric teleparallel theory equivalent to the GR (STEGR) as a dynamical system and try to understand the role of anisotropy in the evolution of its various phases. For this work, we consider two models, viz., f(Q)=−(Q+2Λ) and f(Q)=−βQn to study the critical points and stability of the LRS-BI Universe. In both the models, it is found that the various phases like radiation-dominated, matter-dominated, and dark energy-dominated phases are heteroclinically connected, and there is some role of anisotropy in the evolution of these phases of the Universe. However, for both the models, there are some unphysical solutions too depending upon the values of model parameters β and n along with the anisotropic parameter α.

ADAPTATION OF THE ALGORITHM FOR DETECTING ANOMALIES IN TIME SERIES FOR NON-STATIONARY STREAMING DATA

Detection of anomalies in streaming time series data has become an important research topic due to the wide range of possible applications, including the detection of extreme weather conditions, malicious attacks on protected facilities, monitoring unauthorised gas and oil leaks, illegal pipeline connections, power cable faults, and water and other environmental pollution. Rapid detection of abnormal conditions and identifying these critical events is essential to protect lives and assets. Therefore, developing appropriate systems and detection methods is an urgent task. Anomaly detection in streaming data is challenging due to its large volume and high speed, presence of noise, and non-stationarity of the signal (or ‘concept drift’). The latter significantly complicates the identification of differences between new ‘typical’ behaviour and abnormal events. Solving this problem requires the algorithm for processing such data to learn and adapt to changing conditions. The paper proposes a modification of the algorithm for detecting anomalies in time series. This algorithm provides early detection of abnormal series in a massive collection of non-stationary streaming time series data. Anomalies are observations that are highly improbable given the previous time series values. The proposed approach is based on the primary detection of the predictive limit for the typical system behaviour using the theory of extreme values, followed by checking for the abnormality of the following series using the sliding window technique. The time series parameters are used as input data and compared by density distribution to detect any significant changes in the distribution of the characteristics. It allows the decision-making model to automatically adapt to the changing environment according to detected changes. Since anomalies are, by definition, exceptions to the typical behaviour of a system, most of the available stored data should reflect that typical behaviour of the system in question. It is unnecessary to have representative samples of all possible types of the standard behaviour of a given system for the algorithm to work well. The basic idea is to have a warm-up data set to obtain initial values for the decision model parameters. It makes it possible to determine if there is any significant difference between the last typical behaviour and the new typical behaviour. The proposed algorithm demonstrates its performance under conditions of noisy non-stationary data in several time series classes. Keywords: concept drift, extreme value theory, multivariate time series, outlier detection.

Identifying and estimating solar cell parameters using an enhanced slime mould algorithm

This study proposed an enhanced slime mould algorithm (ESMA) for identifying the solar cells’ parameters for five photovoltaic (PV) models, making two modifications to the original slime mould algorithm (SMA). The first modification was an arbitrary average position among the new individual position of slime and the best individual position of the slime mound currently found to resolve the issue of local optimum. Second, the tangent hyperbolical function of the formula p in the original SMA was replaced with an exponential function to create more variations for selecting the updated equation. The proposed ESMA was used to resolve the problem of estimating PV parameters based on the empirical current-voltage (I-V) data. Specifically, ESMA was evaluated on the parameter estimation in several photovoltaic models, i.e., the one-diode model (ODM), dual-diode model (DDM), and PV-module model (PMM). In general, ESMA outperformed the original SMA and other recent algorithms. Also, in order to provide a close approximation of the empirical I-V data of the real PV modules and cells, ESMA was able to determine the optimal parameter values for photovoltaic models.

Cosmology in non-coincident gauge formulation of f(Q,C) gravity theory

This paper is an investigation of dark energy cosmological models in non-coincident gauge formulation of non-metricity gravity with boundary term. We have considered an arbitrary function [Formula: see text], where [Formula: see text] is the non-metricity scalar, [Formula: see text] is the boundary term derived by [Formula: see text], and [Formula: see text] are the model parameters, to obtain the modified field equations from action. We have parameterized the Hubble function as [Formula: see text] with [Formula: see text] are arbitrary constants and [Formula: see text] is the scale-factor depending upon cosmic time. We have compared this Hubble function with [Formula: see text] datasets and obtained best fit values of model parameters using likelihood analysis. Using these best fit values of model parameters, we have done our result analysis and discussion with cosmological parameters viz effective equation of state parameter, energy density, energy conditions, deceleration parameter, om diagnostic analysis and age of the universe. We have found a transit phase universe model with effective EoS parameter [Formula: see text] over redshift [Formula: see text].

Coupling Reservoir Operation and Rainfall‐Runoff Processes for Streamflow Simulation in Watersheds

AbstractWe assess the overall watershed system representation via fully coupling a generic reservoir operation model with a conceptual rainfall‐runoff model. The performance of the coupled model is evaluated comprehensively by examining watershed outflow simulations, model parameter values, and a key internal flux of the watershed model (here reservoir inflow). Five published generic reservoir operation models are coupled with a watershed rainfall‐runoff model, and results are compared across the coupled models and one additional model called ResIgnore that ignores reservoir operation. Traditional loosely coupled watershed hydrologic models (where calibrated inflow is routed through reservoir operation models) are used as baselines to examine the differences in simulation performance and parameterization obtained from the fully coupled models. We find that fully coupling the Generic Data‐Driven Reservoir Operation Model (GDROM) and the Dynamically Zoned Target Release (DZTR) reservoir operation models with the rainfall‐runoff model obtains robust simulations of watershed outflow with realistic parameterization, suggesting that they can be reliably integrated into large‐scale hydrological models for simulating streamflow in heavily dammed watersheds. Our results also show that compared to ResIgnore, the fully coupled watershed models more accurately simulate the entire distribution of watershed outflow, obtain more realistic values of model parameters, and simulate reservoir inflow with higher accuracy. Finally, we note that the prediction intervals of watershed outflow obtained from the GDROM‐ and DZTR‐based fully coupled models consistently envelop observed watershed outflow across the study watersheds, indicating that GDROM and DZTR can be suitable reservoir components of large‐scale hydrology models.