Adaptation analysis and fusion correction method of CMIP6 precipitation simulation data on the Qinghai-Tibetan Plateau

In order to obtain more accurate precipitation data and better simulate the precipitation on the Tibetan Plateau,the simulation capability of 14 Coupled Model Intercomparison Project Phase 6(CMIP6)models of historical precipitation(1982-2014)on the Qinghai-Tibetan Plateau was evaluated in this study.Results indicate that all models exhibit an overestimation of precipitation through the analysis of the Taylor index,temporal and spatial statistical parameters.To correct the overestimation,a fusion correction method combining the Backpropagation Neural Network Correction(BP)and Quantum Mapping(QM)correction,named BQ method,was proposed.With this method,the historical precipitation of each model was corrected in space and time,respectively.The correction results were then analyzed in time,space,and analysis of variance(ANOVA)with those corrected by the BP and QM methods,respectively.Finally,the fusion correction method results for each model were compared with the Climatic Research Unit(CRU)data for significance analysis to obtain the trends of precipitation increase and decrease for each model.The results show that the IPSL-CM6A-LR model is relatively good in simulating historical precipitation on the Qinghai-Tibetan Plateau(R=0.7,RSME=0.15)among the uncorrected data.In terms of time,the total precipitation corrected by the fusion method has the same interannual trend and the closest precipitation values to the CRU data;In terms of space,the annual average precipitation corrected by the fusion method has the smallest difference with the CRU data,and the total historical annual average precipitation is not significantly different from the CRU data,which is better than BP and QM.Therefore,the correction effect of the fusion method on the historical precipitation of each model is better than that of the QM and BP methods.The precipitation in the central and northeastern parts of the plateau shows a significant increasing trend.The correlation coefficients between monthly precipitation and site-detected precipitation for all models after BQ correction exceed 0.8.

A new monitoring index for ecological vulnerability and its application in the Yellow River Basin,China from 2000 to 2022

The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.

Integrating linear and nonlinear models to assess the built environment’s impact on older adults’behaviors in China

China is experiencing rapid population aging.The one contributing factor affecting senior citizens’lives is the disconnect between the built environment in urban and rural areas and the behavioral preferences of older adults.However,research on the relation between the built environment and the behavior of older individuals has been limited.Thus,this paper uses the most recent health tracking data on factors influencing aging in China released in 2020(China Senior Health Survey Tracking Survey).Applying traditional regression,least absolute shrinkage and selection operator regression,and two decision tree optimization models from machine learning,a comprehensive comparative study is carried out to investigate the correlation between the built environment and the physical activity,dietary habits,and social interactions of older age groups.The findings reveal that built environment variables most significantly impact physical activity,accounting for 52.525%,followed by social interaction behaviors at 50.202%and dietary intake at 47.991%.Furthermore,the authors identify population density and greenness rate as the built environment factors having considerable effects on the behavior of older adults.Thus,this study establishes a theoretical foundation for developing age-friendly community environments for older adults.

Spatiotemporal variation of ecological environment quality and extreme climate drivers on the Qinghai-Tibetan Plateau

Protecting the ecological security of the Qinghai-Tibet Plateau(QTP)is of great importance for global ecology and climate.Over the past few decades,climate extremes have posed a significant challenge to the ecological environment of the QTP.However,there are few studies that explored the effects of climate extremes on ecological environment quality of the QTP,and few researchers have made quantitative analysis.Hereby,this paper proposed the Ecological Environmental Quality Index(EEQI)for analyzing the spatial and temporal variation of ecological environment quality on the QTP from 2000 to 2020,and explored the effects of climate extremes on EEQI based on Geographically and Temporally Weighted Regression(GTWR)model.The results showed that the ecological environment quality in QTP was poor in the west,but good in the east.Between 2000 and 2020,the area of EEQI variation was large(34.61%of the total area),but the intensity of EEQI variation was relatively low and occurred mainly by a slightly increasing level(EEQI change range of 0.05-0.1).The overall ecological environment quality of the QTP exhibited spatial and temporal fluctuations,which may be attributed to climate extremes.Significant spatial heterogeneity was observed in the effects of the climate extremes on ecological environment quality.Specifically,the effects of daily temperature range(DTR),number of frost days(FD0),maximum 5-day precipitation(RX5day),and moderate precipitation days(R10)on ecological environment quality were positive in most regions.Furthermore,there were significant temporal differences in the effects of consecutive dry days(CDD),consecutive wet days(CWD),R10,and FD0 on ecological environment quality.These differences may be attributed to variances in ecological environment quality,climate extremes,and vegetation types across different regions.In conclusion,the impact of climate extremes on ecological environment quality exhibits complex patterns.These findings will assist managers in identifying changes in the ecological environment quality of the QTP and addressing the effects of climate extremes.

Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis

Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.

Machine-learning-assisted Investigation into the Relationship between the Built Environment,Behavior,and Physical Health of the Elderly in China

Due to an increasing life expectancy and decreasing fertility rate,China officially entered a stage of increased ageing in 2011[1].According to China’s seventh national census,2019,the country’s overall population was 1.44 billion,and this included eighteen per cent of people over 60(13.5%of people exceeding 65 years old).

Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word

Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.

Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.

Dynamic response of buildings under debris flow impact

This study employs the smoothed particle hydrodynamics–finite element method(SPH–FEM) coupling numerical method to investigate the impact of debris flow on reinforced concrete(RC)-frame buildings. The methodology considers the variables of debris flow depth and velocity and introduces the intensity index IDV(IDV = DV) to evaluate three different levels of debris flow impact intensity. The primary focus of this study is to investigate the dynamic response and failure mechanism of RC-frame buildings under debris flow impact, including structural failure patterns, impact force and column displacement. The results show that under a highintensity impact, a gradual collapse process of the RCframe building can be observed, and the damage mode of the frame column reflects shear failure or plastic hinge failure mechanism. First, the longitudinal infill walls are damaged owing to their low out-of-plane flexural capacity;the critical failure intensity index IDV value is approximately 7.5 m2/s. The structure cannot withstand debris flows with an intensity index IDV greater than 16 m2/s, and it is recommended that the peak impact force should not exceed 2100 k N. The impact damage ability of debris flow on buildings mostly originates from the impact force of the frontal debris flow, with the impact force of the debris flow body being approximately 42% lower than that of the debris flow head. Finally, a five-level classification system for evaluating the damage status of buildings is proposed based on the numerical simulation and investigation results of the disaster site.

Experimental study on the movement of oil spill under freeze-thaw action

Oil leakages cause environmental pollution,economic losses,and even engineering safety accidents.In cold regions,researchers urgently investigate the movement of oil spill in soils exposed to freeze-thaw cycles.In this study,a series of laboratory model experiments were carried out on the migration of oil leakage under freeze-thaw action,and the distributions of the soil temperature,unfrozen water content,and displacement were analyzed.The results showed that under freeze-thaw action,liquid water in soils migrated to the freezing front and accumulated.After the pipe cracked,oil pollutants first gathered at one side of the leak hole,and then moved around.The pipe wall temperature affected the soil temperature field,and the thermal influence range below and transverse the pipe wall(35–40 cm)was larger than that above the pipe wall(8 cm)owing to the soil surface temperature.The leaked oil's temperature would make the temperature of the surrounding soil rise.Oil would inhibit the cooling of the soils.Besides,oil migration was significantly affected by the gravity and water flow patterns.The freeze-thaw action would affect the migration of the oil,which was mainly manifested as inhibiting the diffusion and movement of oil when soils were frozen.Unfrozen water transport caused by freeze-thaw cycles would also inhibit oil migration.The research results would provide a scientific reference for understanding the relationship between the movement of oil pollutants,water,and soil temperature,and for establishing a waterheat-mass transport model in frozen soils.

Geospatial Technology Integration in Smart City Frameworks for Achieving Climate Neutrality by 2050: A Case Study of Limassol Municipality, Cyprus

This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.

Seismic fragility assessment of existing sub-standard low strength reinforced concrete structures

An analytical seismic fragility assessment framework is presented for the existing low strength reinforced concrete structures more common in the building stock of the developing countries.For realistic modelling of such substandard structures,low strength concrete stress-strain and bond-slip capacity models are included in calibrating material models.Key capacity parameters are generated stochastically to produce building population and cyclic pushover analysis is carried out to capture inelastic behaviour.Secant period values are evaluated corresponding to each displacement step on the capacity curves and used as seismic demand.A modified capacity demand diagram method is adopted for the degrading structures,which is further used to evaluate peak ground acceleration from back analysis considering each point on the capacity curve as performance point.For developing fragility curves,the mean values of peak ground acceleration are evaluated corresponding to each performance point on the series of capacity curves.A suitable probability distribution function is adopted for the secant period scatter at different mean peak ground acceleration values and probability of exceedance of limit states is evaluated.A suitable regression function is used for developing fragility curves and regression coefficients are proposed for different confidence levels.Fragility curves are presented for a low rise pre-seismic code reinforced concrete structure typical of developing countries.

Comparative Experimental and Numerical Study of Wave Loads on A Monopile Structure Using Different Turbulence Models

This study numerically and experimentally investigates the effects of wave loads on a monopile-type offshore wind turbine placed on a 1:25 slope at different water depths as well as the effect of choosing different turbulence models on the efficiency of the numerical model.The numerical model adopts a two-phase flow by solving Unsteady Reynolds-Averaged Navier−Stokes(URANS)equations using the Volume Of Fluid(VOF)method and three differentk-ωturbulence models.Typical environmental conditions from the East China Sea are studied.The wave run-up and the wave loads applied on the monopile are investigated and compared with relevant experimental data as well as with mathematical predictions based on relevant theories.The numerical model is well validated against the experimental data at model scale.The use of different turbulence models results in different predictions on the wave height but less differences on the wave period.The baseline k-ωturbulence model and Shear-Stress Transport(SST)k-ωturbulence model exhibit better performance on the prediction of hydrodynamic load,at a model-scale water depth of 0.42 m,while the laminar model provides better results for large water depths.The SST turbulence model performs better in predicting wave run-up for water depth 0.42 m,while the laminar model and standard k-ωmodel perform better at water depth 0.52 m and 0.62 m,respectively.

Receiver Power Allocation and Transmitter Power Control Analysis for Multiple-Receiver Wireless Power Transfer Systems

As different power has its own receivers,this paper analyzes and designs a multiple-receiver wireless power transfer(WPT)system systematically.The equivalent circuit model of the system is established to analyze the key parameters including transmitter power,receiver power,transmission efficiency,and each receiver power allocation.A control circuit is proposed to achieve the maximum transmission efficiency and transmitter power control and arbitrary receiver power allocation ratios for different receivers.Through the proposed control circuit,receivers with different loads can allocate appropriate power according to its power demand,the transmitter power and system efficiency do not vary with the change of the number of receivers.Finally,this control circuit is validated using a 130-kHz WPT system with three receivers whose power received is 3:10:12,and the overall system efficiency can reach as high as 55.5%.

Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake

The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems,including vegetation destruction.However,the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague,especially in the severely damaged areas(including Wenchuan,Beichuan,Mianzhu,Shifang,Qingchuan,Maoxian,Anzhou,Dujiangyan,Pingwu and Pengzhou).Here,we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition(EEMD)to analyze the time series characteristics of the Enhanced Vegetation Index(EVI),and explored the driving effects of climate,land use types,nighttime light,water system,slope,and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression(GTWR)model.The results indicated that the post-seismic vegetation recovery rate increased rapidly(acceleration>0)but slowed down after 2013.And the areas of best vegetation recovery(EVI increments>0.1)were distributed in the north of the study area,the Minjiang River Basin,and front fault and central fault of the Longmenshan Fault Zone.While the areas with the worst vegetation recovery(EVI increments<-0.1)were concentrated in the southern high-altitude areas and the Chengdu Plain.Additionally,a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery(regression coefficients>0),but the impacts weakened after the earthquake,possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature.The impact of cultivated land on vegetation recovery was mostly positive(regression coefficients>0),which may be related to the implementation of the Grain for Green Project.The nighttime light inhibited vegetation recovery(regression coefficients<0),which could be closely associated with urbanization.The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends,and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated.

A Review of Leakage and Dispersion of LNG on the Ground

Based on the analysis of the whole process of LNG spill on land,the research methods of LNG pool expansion and heavy gas diffusion are summarized and analyzed.This paper reviews the experimental and analytical work performed to data on spill of LNG.Specifically,experiments on the spill of LNG onshore,as well as experiments and numerical study on heavy gas dispersion.Pool boiling and turbulence model are described and discussed,as well as models used to predict dispersion.Although there have been significant progress in understanding the behavior of LNG spills,technical knowledge gaps to improve hazard prediction are still identified.Some of the gaps can be addressed with current modeling and testing capabilities.Finally,a discussion of the state of knowledge,and recommendations to further improvement the understanding of the behavior of LNG spills onshore.

Numerical Modeling of Natural Fracture Distributions in Shale

The production efficiency of shale gas is affected by the interaction between hydraulic and natural fractures.This study presents a simulation of natural fractures in shale reservoirs,based on a discrete fracture network(DFN)method for hydraulic fracturing engineering.Fracture properties of the model are calculated from core fracture data,according to statistical mathematical analysis.The calculation results make full use of the quantitative information of core fracture orientation,density,opening and length,which constitute the direct and extensive data of mining engineering.The reliability and applicability of the model are analyzed with regard to model size and density,a calculation method for dominant size and density being proposed.Then,finite element analysis is applied to a hydraulic fracturing numerical simulation of a shale fractured reservoir in southeastern Chongqing.The hydraulic pressure distribution,fracture propagation,acoustic emission information and in situ stress changes during fracturing are analyzed.The results show the application of fracture statistics in fracture modeling and the influence of fracture distribution on hydraulic fracturing engineering.The present analysis may provide a reference for shale gas exploitation.

Evaluating the weekly changes in terrestrial water storage estimated by two different inversion strategies in the Amazon River Basin

In this study,we estimated the weekly Gravity Recovery and Climate Experiment(GRACE)spherical harmonic(SH)solutions and regional mascon solutions using GRACE-based Geopotential Difference(GPD)data and investigated their abilities in retrieving terrestrial water storage(TWS)changes over the Amazon River Basin(ARB)from January 2003 to February 2013.The performance of the weekly GPD-SH and GPDmascon solutions was evaluated by comparing them with the weekly GFZ-SH solutions,Global Land Data Assimilation Systems(GLDAS)-NOAH hydrological model outputs,and monthly GFZ-SH,GPD-SH,and CSRmascon solutions in the spatio-temporal and spectral domains.The results demonstrate that the weekly GPD-SH and GPD-mascon present good consistency with the weekly GFZ-SH solutions and GLDAS-NOAH estimates in the spatio-temporal domains,but GPD-mascon presents stronger signal amplitudes and more spatial details.The comparison of the monthly average of weekly estimates and monthly solutions demonstrates that the weekly GPD-mascon and GFZ-SH with DDK1 filtering are close to the monthly CSRmascon and GFZ-SH solutions,respectively.However,the signal amplitudes of TWS changes from GPD-SH and GFZ-SH with 650 km Gaussian filtering are smaller than the monthly solutions,and the corresponding Root Mean Square Errors between the TWS change time series from the monthly average of weekly solutions and monthly estimates are 18.12 mm(GPD-mascon),18.81 mm(GFZ-SH-DDK1),24.93 mm(GPDSH-G650km),and 33.07 mm(GFZ-SH-G650km),respectively.Additionally,the TWS change time series derived from weekly solutions present more high-frequency time-varying information than monthly solutions.Furthermore,the 300 km Gaussian filtering can improve the signal amplitudes of TWS changes from the weekly GPD-SH solutions more than those with 650 km Gaussian filtering,but the corresponding noise level is higher.The weekly GPD-SH and GPD-mascon solutions can extend the application scopes of GRACE and provide good complements to the current GRACE monthly solutions.

Comparison of DES and URANS:Estimation of fluctuating pressure from URANS simulations in stilling basins

This study conducts a comparative analysis between detached eddy simulation(DES)and Unsteady Reynolds-averaged Navier-Stokes(URANS)models for simulating pressure fluctuations in a stilling basin,aiming to assess the URANS mode’s performance in modeling pressure fluctuation.The URANS model predicts accurately a smoother flow field and its time-average pressure,yet it underestimates the root mean square of pressure(RMSP)fluctuation,achieving approximately 70%of the results predicted by DES model on the bottom floor of the stilling basin.Compared with DES model’s results,which are in alignment with the Kolmogorov−5/3 law,the URANS model significantly overestimates low-frequency pulsations,particularly those below 0.1 Hz.We further propose a novel method for estimating the RMSP in the stilling basin using URANS model results,based on the establishment of a quantitative relationship between the RMSP,time-averaged pressure,and turbulent kinetic energy in the boundary layer.The proposed method closely aligns with DES results,showing a mere 15%error level.These findings offer vital insights for selecting appropriate turbulence models in hydraulic engineering and provide a valuable tool for engineers to estimate pressure fluctuation in stilling basins.

Quantitative Analysis of Land Degradation in Mongolia from the Perspective of Geographical Zone

Natural and anthropogenic disturbances accelerate land degradation(LD)in arid,semi-arid,and dry sub-humid areas,leading to reduced land quality and productivity,loss of biodiversity,degradation of ecosystem services,and a decline in the quality of life of local people.To address this issue,the United Nations Convention to Combat Desertification(UNCCD)has set a target for LD neutrality(LDN).However,quantifying and comparing the status of LD at global or regional scales remains challenging due to the lack of coherent quantitative methods and tools.In this study,we focused on Mongolia,a region with significant LD problems,to examine patterns of LD and changes from 2015 to 2020,accounting for regional differences.Trends.Earth was used,as recommended by the UNCCD.The main findings are as follows:(1)Overall,the degraded land area in Mongolia accounted for 12.11%of the total land area,predominantly located in the southwest desert and desert steppe,gradually spreading to the northeast steppe.(2)The areas showing improvement in the land productivity index and degradation were 17.62%and 11.79%,respectively,with the most severely degraded areas concentrated in the southern desert and desert steppe regions.(3)The areas of improvement and degradation in the land cover index were 1.80%and 0.16%,respectively,with degraded areas scattered across regions of steppe,high mountains,and mountain taiga.(4)The areas of improvement and degradation in the land organic carbon index were 1.54%and 0.22%,respectively,with degradation primarily observed in adjacent areas of mountain taiga,steppe,and desert steppe.(5)The improved area(2.999×10^(5)km^(2))of LDN are more than the degraded area(1.895×10^(5)km^(2)),indicating a positive trend toward LDN in Mongolia.