Model Simulations Research Articles

Source identification of carbon monoxide over the greater Tokyo area: Tower measurement network and evaluation of global/regional model simulations at different resolutions

Because of its relatively long lifetime among short-lived climate forcers in the atmosphere, carbon monoxide (CO) is utilized as a tracer, and is expected to be simulated at coarse resolution. To better grasp the behavior of CO in the atmosphere, multi-altitude measurement is required because the main sources of CO emissions are automobiles (surface) and industry (aloft). In this work, using CO measurements obtained at remote sites and through a tower measurement network in Japan (37 m and 250 m above ground level (AGL) in an urban area, and 32 m AGL at a rural site in the greater Tokyo area), the performances of a global model (2°×2.5°) and a regional model at various resolutions (12, 4, and 1.3 km) were comprehensively evaluated. The global model successfully simulated CO at remote sites but not for high-concentration peaks at rural and urban sites, whereas the regional model increasingly improved its performance in capturing CO peaks at urban sites with resolutions up to 4 km. Therefore, we concluded that a 4 km resolution was suitable for capturing CO at urban sites, and furthermore estimated the source contributions. The regions surrounding the greater Tokyo area were dominated by the concentration from the lateral boundaries (approximately 180 ppbv), while the higher CO in central Tokyo was attributed to local sources. These local sources accounted for up to 80% of the annual average at the surface level and just 10% aloft (corresponding to the 250 m AGL site). Sensitivity simulations assessing CO sources (automobiles, industry, and others) demonstrated the important role of automobiles, while higher altitudes had more sources attributed to industry. Local sources were found to make more prominent contributions at higher concentration ranges. The appropriate modeling resolution for CO behavior can be drawn from our findings and the usefulness of simultaneous measurements at the surface level and using a tower for capturing the three-dimensional CO structure can be demonstrated as an important approach.

Cross-Shelf Exchange in Prograde Antarctic Troughs Driven by Offshore-Propagating Dense Water Eddies

Abstract This study examines the link between near-bottom outflows of dense water formed in Antarctic coastal polynyas and onshore intrusions of Circumpolar Deep Water (CDW) through prograde troughs cutting across the continental shelf. Numerical simulations show that the dense water outflow is primarily in the form of cyclonic eddies. The trough serves as a topographic guide that organizes the offshore-moving dense water eddies into a chain pattern. The offshore migration speed of the dense water eddies is similar to the velocity of the dense water offshore flow in the trough, which scaling analysis finds to be proportional to the reduced gravity of the dense water and the slope of the trough sidewalls and to be inversely proportional to the Coriolis parameter. Our model simulations indicate that, as these cyclonic dense water eddies move across the trough mouth into the deep ocean, they entrain CDW from offshore and carry CDW clockwise along their periphery into the trough. Subsequent cyclonic dense water eddies then entrain the intruding CDW further toward the coast along the trough. This process of recurring onshore entrainment of CDW by a topographically constrained chain of offshore-flowing dense water eddies is consistent with topographic hotspots of onshore intrusion of CDW around Antarctica identified by other studies. It can bring CDW from offshore to close to the coast and thus impact the heat flux into Antarctic coastal regions, affecting interactions among ocean, sea ice, and ice shelves. Significance Statement Troughs cutting across the Antarctic continental shelf are a major conduit for the transport of dense shelf water from coastal formation regions to the shelf break. This study describes a process in which clockwise-spinning eddies moving offshore in prograde troughs successively entrain filaments of relatively warm Circumpolar Deep Water from offshore across the entire shelf and into the coastal region. This eddy-induced transport provides a new understanding of the shelf edge exchange process identified in previous studies and a mechanism for further onshore intrusion of the warm Circumpolar Deep Water over parts of the Antarctic shelf. The resultant onshore heat flux could potentially bring a substantial amount of heat from offshore into the coastal region and thus affect ice–ocean interactions through melting sea ice and ice shelves.

Thermodynamic cycle performance modeling and numerical simulation of higher Mach scramjet with inlet pre-injection

This paper focuses on the thermodynamic cycle performance modeling and numerical investigation of a high Mach number scramjet with inlet pre-injection of hydrogen. The scramjet model used in this study was the Hyshot-Ⅱ engine model. Flight experiments using this model at high Mach number have been conducted previously, which provides a unique testbed for validating the computational prediction of supersonic combustion. A thermodynamic cycle performance model with inlet pre-injection was developed for operating characteristics studies of the scramjet engine. A set of three-dimensional Reynolds average Navier-Stockes (RANS) simulations of the reactive flow was performed with a 9-specie and 19-step kinetic mechanism for hydrogen combustion. The turbulence is modeled by k-ω shear stress transport (SST) turbulence model. The thermodynamic cycle performance analysis results indicate that inlet pre-injection can enhance the hydrogen mixing efficiency, and a small amount of advanced heat release can improve the overall thermal cycle efficiency. Detailed flow analysis from the three-dimensional simulation results indicates flow can be ignited in the first shock wave induced boundary layer separation (SIBLS) region. Inlet pre-injection enables the propagation of the flame toward the core flow compared to the throat injection. The inlet fuel pre-injection proved to be a promising injection method due to the higher combustion efficiency, elevated from 0.75 to 0.85, and higher specific impulse potential, increased from 1570 s to 1810 s. As the ER of inlet pre-injection increases within the examined range, the performance curve exhibits an optimal point. As the geometric compression ratio increases, the entire combustion process advances, leading to a significant increase in the performance potential of the engine. The comparative analysis of the thermodynamic cycle performance modeling and computational fluid dynamics (CFD) modeling indicates the model shows a good prediction on the performance of the scramjet engine with the inlet pre-injection, with the same trend of performance curves and an error of less than 10 % compared to the numerical simulation.

Effect of different boundary conditions on the combustion properties and fire characteristics of typical woods

To provide guidance on the bioenergy potential of wood and its fire spread numerical simulation, insights into the combustion properties and fire characteristics of one hardwood (sassafras) and two softwoods (pine and fir) were investigated. The effect of different boundary conditions are focused. The condensed phase decomposition in thermogravimetric analysis indicated that the main degradation processes for all wood combustion could be divided into two stages, and the corresponding demarcation point of conversion rate is 0.4 and 0.35 for hardwood and softwood, respectively. Three specific regions could further be established based on the activation energies variations in Stage Ⅰ. The governing reaction mechanism of sassafras wood is determined to be the diffusion model connect with order-based model, while the reaction mechanism of softwoods (pine and fir) can be considered as the reaction order (1st to three-halves) type followed by diffusion model. The gas phase flaming in FPA combustion experiments suggest that fir and pine wood have a faster charring rate and produce less CO and CO2 during pyrolysis and combustion. Sassafras requires more time to be ignited owing to its high density, which causes higher thermal inertia therefore decelerating the temperature increase on the sample surface. In addition, the dense structure of sassafras leads to an early appearance of the second HRR and MLR peaks. As hardwood has a higher density than softwood, the thermal insulation in hardwood delays char cracking on sassafras wood surface and enhances the duration between ignition and flameout. Such findings should provide guidance for wood combustion modeling and flame spread simulation in future works.

Application of Physical Modeling and Virtual Simulation Technology in Measuring the Performance of Subway Train Tracking and Operation

Application of Physical Modeling and Virtual Simulation Technology in Measuring the Performance of Subway Train Tracking and Operation

Computer Simulation for Effective Pharmaceutical Kinetics and Dynamics: A Review.

Computer-based modelling and simulation are developing as effective tools for supplementing biological data processing and interpretation. It helps to accelerate the creation of dosage forms at a lower cost and with the less human effort required to conduct the work. This paper aims to provide a comprehensive description of the different computer simulation models for various drugs along with their outcomes. The data used are taken from different sources, including review papers from Science Direct, Elsevier, NCBI, and Web of Science from 1995-2020. Keywords like - pharmacokinetic, pharmacodynamics, computer simulation, whole-cell model, and cell simulation, were used for the search process. The use of computer simulation helps speed up the creation of new dosage forms at a lower cost and less human effort required to complete the work. It is also widely used as a technique for researching the structure and dynamics of lipids and proteins found in membranes. It also facilitates both the diagnosis and prevention of illness. Conventional data analysis methods cannot assess and comprehend the huge amount, size, and complexity of data collected by in vitro, in vivo</i>, and ex vivo</i> experiments. As a result, numerous in silico</i> computational e-resources, databases, and simulation software are employed to determine pharmacokinetic (PK) and pharmacodynamic (PD) parameters for illness management. These techniques aid in the provision of multiscale representations of biological processes, beginning with proteins and genes and progressing through cells, isolated tissues and organs, and the whole organism.

Optimization of oral isavuconazole dose for population in special physiological or pathological state: a physiologically based pharmacokinetics model-informed precision dosing.

To recommend precision dosing and improve therapeutic efficacy against invasive fungal disease, a physiologically based pharmacokinetic model (PBPK) of oral isavuconazole (ISA) was established and used to explore its disposition across populations in different physiological and pathological states. Twenty-five pharmacokinetic (PK) studies of oral ISA were identified through a systematic search of PubMed. Concentration-time data were extracted using WebPlotDigitizer. Physiochemical parameters were obtained from published literature and DrugBank. Model development and simulation used the Simcyp population-based simulator, and visual predictive check and predictive error were used for the model evaluation. Probability of target attainment and the cumulative fraction of response analyses were performed for dose optimization. The developed PBPK model was successfully validated in different populations. Most predicted concentration-time points aligned with the observed data, with acceptable predictive errors for the critical parameters. We predicted the PK profiles and parameters of ISA in a population with severe hepatic impairment (HI), a population with obesity and paediatric patients aged 1 to less than 6 years old. The probability of target attainment and cumulative fraction of response analyses indicated that the population with severe HI should have half the maintenance dose. The population with obesity and population with severe HI should have a loading dose of 300 mg every 8 h for 2 days. For paediatric patients aged 1 to less than 6 years old, a weight-based dosing regimen (5.38 mg/kg) of ISA was suggested. The predicted value aligns with observations, suggesting ISA's potential predictability in PK profiles for other populations. The recommended dosing regimens increase our understanding of the use of ISA in special populations.

Diagnosing drivers of tropical precipitation biases in coupled climate model simulations

AbstractIn this study, we analyse the large-scale biases in tropical precipitation climatology of two different types of coupled ocean–atmosphere general circulation models (GCMs): the coupled model intercomparison project (CMIP) models and ICON-Sapphire, a global storm-resolving model. We employ the simple globally resolved energy balance (GREB) diagnostic precipitation model to evaluate four drivers of the precipitation biases in the simulations: surface specific humidity, surface relative humidity, tropospheric mean and variability in the vertical motion. The tropical precipitation biases in the CMIP and ICON-Sapphire simulations are surprisingly similar in their patterns and also in the elements forcing them. The results of our analysis using the GREB model show that the precipitation biases result from both biases in the sensitivity to the four forcing fields and biases in the simulated forcing fields themselves. The most significant bias for both, the CMIP and ICON-Sapphire simulations, is a too high sensitivity to the mean vertical circulation ($${\omega }_{\text{mean}}$$ ω mean ) and bias in the $${\omega }_{\text{mean}}$$ ω mean climatology itself. This also holds for specific long-standing biases, such as the double intertropical convergence zone problem. Meanwhile, biases in the climatology of specific and relative humidity play only a minor role but contribute to an overall small increase in precipitation in CMIP models that may be related to the “drizzling” bias. These results can give insights to the modelling community regarding model development, and illustrate that the GREB diagnostic precipitation model applied is a good tool for evaluating the drivers of large-scale tropical precipitation.

Cost-Effectiveness of Low-Dose Computed Tomography Screenings for Lung Cancer in High-Risk Populations: A Markov Model.

Domestic and foreign studies on lung cancer have been oriented to the medical efficacy of low-dose computed tomography (LDCT), but there is a lack of studies on the costs, value and cost-effectiveness of the treatment. There is a scarcity of conclusive evidence regarding the cost-effectiveness of LDCT within the specific context of Taiwan. This study is designed to address this gap by conducting a comprehensive analysis of the cost-effectiveness of LDCT and chest X-ray (CXR) as screening methods for lung cancer. Markov decision model simulation was used to estimate the cost-effectiveness of biennial screening with LDCT and CXR based on a health provider perspective. Inputs are based on probabilities, health status utility (quality-adjusted life years (QALYs)), costs of lung cancer screening, diagnosis, and treatment from the literatures, and expert opinion. A total of 1,000 simulations and five cycles of Markov bootstrapping simulations were performed to compare the incremental cost-utility ratio (ICUR) of these two screening strategies. Probability and one-way sensitivity analyses were also performed. The ICUR of early lung cancer screening compared LDCT to CXR is $-24,757.65/QALYs, and 100% of the probability agree to adopt it under a willingness-to-pay (WTP) threshold of the Taiwan gross domestic product (GDP) per capita ($35,513). The one-way sensitivity analysis also showed that ICUR depends heavily on recall rate. Based on the prevalence rate of 39.7 lung cancer cases per 100,000 people in 2020, it could be estimated that LDCT screening for high-risk populations could save $17,154,115. LDCT can detect more early lung cancers, reduce mortality and is cost-saving than CXR in a long-term simulation of Taiwan's healthcare system. This study provides valuable insights for healthcare decision-makers and suggests analyzing cost-effectiveness for additional variables in future research.

Instability and Mesoscale Eddy Fluxes in an Idealized 3‐Layer Beaufort Gyre

AbstractWe study the impacts of a continental slope on instability and mesoscale eddy fluxes in idealized 3‐layer numerical model simulations. The simulations are inspired by and mimic the situation in the Arctic Ocean's Beaufort Gyre, where anti‐cyclonic winds drive anti‐cyclonic currents that are guided by the continental slope. The forcing and currents are retrograde with respect to topographic Rossby waves. The focus of the analysis is on eddy potential vorticity (PV) fluxes and eddy‐mean flow interactions under the Transformed Eulerian Mean framework. Eddy lateral vorticity fluxes dominate over the continental slope where eddy form stress, that is, vertical momentum flux, is suppressed due to the topographic PV gradient. The diagnosis also shows that while eddy momentum fluxes are up‐gradient over parts of the slope, the total quasi‐geostrophic PV flux is down‐gradient everywhere. We then calculate the linearly unstable modes of the time‐mean state and find that the most unstable mode contains several key features of the observed finite‐amplitude fluxes over the slope, including down‐gradient PV fluxes. When accounting for additional unstable modes, more qualitative features of the observed eddy fluxes in the numerical model are reproduced.

Quantifying HONO Production from Nitrate Photolysis in a Polluted Atmosphere.

The photolysis of particulate nitrate (pNO3-) has been suggested to be an important source of nitrous acid (HONO) in the troposphere. However, determining the photolysis rate constant of pNO3- (jpNO3-) suffers from high uncertainty. Prior laboratory measurements of jpNO3- using aerosol filters have been complicated by the "shadow effect"─a phenomenon of light extinction within aerosol layers that potentially skews these measurements. We developed a method to correct the shadow effect on the photolysis rate constant of pNO3- for HONO production (jpNO3-→HONO) using aerosol filters with identical chemical compositions but different aerosol loadings. We applied the method to quantify jpNO3-→HONO over the North China Plain (NCP) during the winter haze period. After correcting for the shadow effect, the normalized average jpNO3-→HONO at 5 °C increased from 5.89 × 10-6 s-1 to 1.72 × 10-5 s-1. The jpNO3-→HONO decreased with increasing pH and nitrate proportions in PM2.5 and had no correlation with nitrate concentrations. A parametrization for jpNO3-→HONO was developed for model simulation of HONO production in NCP and similar environments.

Advancing Metallic Lithium Anodes: A Review of Interface Design, Electrolyte Innovation, and Performance Enhancement Strategies

Lithium (Li) metal is one of the most promising anode materials for next-generation, high-energy, Li-based batteries due to its exceptionally high specific capacity and low reduction potential. Nonetheless, intrinsic challenges such as detrimental interfacial reactions, significant volume expansion, and dendritic growth present considerable obstacles to its practical application. This review comprehensively summarizes various recent strategies for the modification and protection of metallic lithium anodes, offering insight into the latest advancements in electrode enhancement, electrolyte innovation, and interfacial design, as well as theoretical simulations related to the above. One notable trend is the optimization of electrolytes to suppress dendrite formation and enhance the stability of the electrode–electrolyte interface. This has been achieved through the development of new electrolytes with higher ionic conductivity and better compatibility with Li metal. Furthermore, significant progress has been made in the design and synthesis of novel Li metal composite anodes. These composite anodes, incorporating various additives such as polymers, ceramic particles, and carbon nanotubes, exhibit improved cycling stability and safety compared to pure Li metal. Research has used simulation computing, machine learning, and other methods to achieve electrochemical mechanics modeling and multi-field simulation in order to analyze and predict non-uniform lithium deposition processes and control factors. In-depth investigations into the electrochemical reactions, interfacial chemistry, and physical properties of these electrodes have provided valuable insights into their design and optimization. It systematically encapsulates the state-of-the-art developments in anode protection and delineates prospective trajectories for the technology’s industrial evolution. This review aims to provide a detailed overview of the latest strategies for enhancing metallic lithium anodes in lithium-ion batteries, addressing the primary challenges and suggesting future directions for industrial advancement.

Controllable cross-building multi-objective optimisation for NZEBs: A framework Utilising parametric generation and intelligent algorithms

Balancing performance and cost are crucial in optimising nearly zero-energy buildings. This study proposes a method for efficient preliminary design tailored to diverse requirements. It starts with parametric modelling and performance simulation to compile a dataset of common spatial forms in almost zero-energy office buildings. Subsequently, a multilayer perceptron surrogate model is trained on this dataset to map the relationships between design parameters and performance objectives across various spatial forms. A controllable NSGA-II constraint method enables designers to manage design variables and performance objectives independently for cross-building optimisation. The framework integrates the surrogate model with the NSGA-II algorithm under precise constraints, using TOPSIS to finalise Pareto solutions, and includes visualisation software for architects. The framework is validated in a high-performance office building in northern China by measuring data and using an equivalent model under two constraints. The results indicated that the framework reduced the energy consumption of the building by 6.6% and 14.7%, increased the thermal comfort time ratios by 2.3% and 11.4%, and lowered the initial investment costs by 19.6% and 16%, respectively, under each condition.

Research on the optimize doctor-patient matching in China

In China, patients are given the opportunity to know background information about their doctors, and patients choose their doctors to see. Due to the shortage of medical resources and the uncertainty of the quality of medical services, as well as some external factors, patients will prefer to choose well-known doctors in the hope of getting a better medical experience. In order to match the preferred healthcare resources, they may choose to lie to increase the chance of visiting the doctor. This study adopts a combination of theoretical modeling and algorithmic simulation. Through theoretical analysis, a framework model of doctor-patient matching is established. Doctors with different skills and experience, who select patients according to their conditions and their own preferences in the matching process, as well as patients with different conditions and preferences, who want to receive treatment by matching to a suitable doctor, are clarified. The Deferred Acceptance algorithm is written and operated to simulate the matching process where patients apply to doctors and doctors are screened based on their priorities. Analyze and evaluate the performance of Strategy-Proofness and Pareto Efficiency in matching by iterating the algorithm. In this case, the DA algorithm establishes a stable match between the patient and the physician despite the possibility that the patient may deceive his/her preferences. However, patient behavior may affect the efficiency and fairness of the matching process, highlighting the importance of transparency and integrity of the doctor-patient matching system.

Power supply characterization of baseload and flexible enhanced geothermal systems

We investigated the techno-economic feasibility and power supply potential of enhanced geothermal systems (EGS) across the contiguous United States using a new subsurface temperature model and detailed simulations of EGS project life cycle. Under business-as-usual scenarios and across depths of 1–7 kilometers, we estimated 82,945 GW and 0.65 GW of EGS supply capacity with lower levelized cost of electricity than conventional hydrothermal and solar photovoltaic projects, respectively. Considering the scenario of flexible geothermal dispatch via wellhead throttling and power plant bypass, these estimates climbed up to 184,112 GW and 44.66 GW, respectively. The majority of EGS supply potential was found in the Western and Southwestern regions of the United States, where California, Oregon, Nevada, Montana, and Texas had the greatest EGS capacity potential. With advanced drilling rates based on state-of-the-art implementations of recent EGS projects, we estimated an average improvement of 25.1% in the levelized cost of electricity. These findings underscored the pivotal role of flexible operations in enhancing the competitiveness and scalability of EGS as a dispatchable renewable energy source.

Drought risk in Moldova under global warming and possible crop adaptation strategies.

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence. Observed station data (1950-2020 and 1850-2020), ERA5 reanalysis data (1950-2020), and climate model simulations (period 1970-2100) are used. Crop yield data (maize, sunflower, grape), data from experimental plots (wheat), and the Enhanced Vegetation Index from Moderate Resolution Imaging Spectroradiometer satellites were also used. Results show that although the severity of meteorological droughts has decreased in the last 170 years, the impact of precipitation deficits on different crop yields has increased, concurrent with a sharp increase in temperature, which negatively affected crop yields. Annual crops are now more vulnerable to natural rainfall variability and, in years characterized by rainfall deficits, the possibility of reductions in crop yield increases due to sharp increases in temperature. Projections reveal a pessimistic outlook in the absence of adaptation, highlighting the urgency of developing new agricultural management strategies.

Novel optimized coupled rainfall model simulation based on stepwise decomposition technique

ABSTRACT Precipitation forecasting plays a pivotal role in guiding the effective management of regional water resources and providing crucial warnings for regional droughts and floods. Finding a monthly precipitation simulation model with robust fitting performance is a significant research endeavor in practical precipitation prediction. This paper introduces two modified African vulture optimization algorithms (MAVOA1 and MAVOA2). It provides hyperparameter optimization techniques for the least squares support vector machine (LSSVM), long short-term memory neural network (LSTM), and random forest (RF) models. These techniques are used to construct a monthly precipitation simulation model based on algorithmic optimization coupled with variational mode decomposition for full decomposition. The test results at five typical stations in the North China Plain reveal the following: (1) the LSSVM model demonstrates significantly better performance than the LSTM and RF models. (2) the MAVOA2-LSSVM model has the best-integrated effect: the average test fitting error is RMSE = 17.50 mm/month, MRE = 117.25%, NSE = 0.90, which shows its superiority in practical application and can significantly improve the accuracy of precipitation prediction; MAVOA2 is more suitable for machine learning models with more hyperparameters of its own, which provides a reference for hyperparameter optimization algorithms in the other fields.

Extreme precipitation events over the east coast of northeast Brazil: Synoptic study and MPAS simulation

In the present study two extreme events that occurred in the East Coast of Northeast Brazil (ENEB) during 2022 and 2023 were evaluated. These events are becoming increasingly frequent in all regions of Brazil, associated with significant material and human losses, emphasizing the significance of a deeper comprehension of these events. ERA5 global reanalysis data, GOES-16 satellite imagery and pluviometric stations were used for the analysis. Model simulations were also conducted using the Model for Prediction Across Scales (MPAS) with variable resolution (60–3 km). Both events corresponded to Easterly Wave Disturbances (EWDs) that occurred under opposite large-scale conditions of the ENSO cycle, since extreme events are becoming increasingly frequent in all regions of Brazil and could be responsible for significant material and human losses. Thus, an emphasis was given to characterize the synoptic conditions. Both analyzed cases occurred along the ENEB, specifically over the Alagoas state. The trough axis penetrating the studied area was observed on both examined dates, with a very characteristic relative vorticity of this tropical disturbance. In general, moisture convergence resulted from the high flow of moisture prevailing over the region combined with upward movements caused by the trough present at low levels, which combined with local factors in the region such as topography, contributed to the increase in rainfall over the study area in both analyzed cases. The MPAS showed excellent spatial representation when compared to station data, highlighting intense precipitation over parts of Alagoas.

Enhanced SWAT calibration through intelligent range-based parameter optimization

Hydrological models are vital tools in environmental management. Weaknesses in model robustness for hydrological parameters transfer uncertainties to the model outputs. For streamflow, the optimized parameters are the primary source of uncertainty. A reliable calibration approach that reduces prediction uncertainty in model simulations is crucial for enhancing model robustness and reliability. The optimization of parameter ranges is a key aspect of parameter calibration, yet there is a lack of literature addressing the optimization of parameter ranges in hydrological models. In this paper, we introduce a parameter calibration strategy that applies a clustering technique, specifically the Self-Organizing Map (SM), to intelligently navigate the parameter space during the calibration of the Soil and Water Assessment Tool (SWAT) model for monthly streamflow simulation in the Baishan Basin, Jilin Province, China. We selected the representative algorithm, the Sequential Uncertainty Fitting version 2 (SUFI-2), from the commonly used SWAT Calibration and Uncertainty Programs for comparison. We developed three schemes: SUFI-2, SUFI-2-Narrowing Down (SUFI-2-ND), and SM. Multiple diagnostic error metrics were used to compare simulation accuracy and prediction uncertainty. Among all schemes, SM outperformed the others in describing watershed streamflow, particularly excelling in the simulation of spring snowmelt runoff (baseflow period). Additionally, the prediction uncertainty was effectively controlled, demonstrating the SM's adaptability and reliability in the interval optimization process. This provides managers with more credible prediction results, highlighting its potential as a valuable calibration tool in hydrological modeling.

Impacts of Massive Topographies on Heat Waves in Global Drylands

AbstractLarge‐scale topographies affect global extreme weather and climate though dynamic and thermal forcing. However, the impacts of typical topographies on heat waves in different drylands globally remain unclear. In this study, we find that heat waves are mainly occurred in global drylands during 1940–2022. The frequencies and intensities of heat waves in global drylands have significantly increased after 1980s. Multiple numerical model simulations reveal that the impact range of Asian topography on heat waves in global drylands is widest, not only in East Asia, Central, and west Asia locally, but also can reach as far as North Africa and North America. While the impact ranges of topographies in Africa, Arabian Peninsula, North America, and South America on heat waves in drylands are relatively narrow, which are concentrated in localized and their surroundings. These conclusions could provide clues to understand the influence of topography on global weather and climate extremes.